OBESITY PREVENTION
THE ROLE OF BRAIN AND
SOCIETY ON INDIVIDUAL
BEHAVIOR
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OBESITY PREVENTION
THE ROLE OF BRAIN AND
SOCIETY ON INDIVIDUAL
BEHAVIOR
Editorial Team
Laurette Dubé (Lead Editor)
Professor, James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty
of Management; McGill University, Montreal, Canada
Antoine Bechara
Department of Psychology, University of Southern California, Los Angeles, CA, USA
Alain Dagher
Montreal Neurological Institute, McGill University, Montreal, Canada
Adam Drewnowski
Epidemiology, School of Public Health and Community Medicine; Director, Center for
Public Health Nutrition, University of Washington, Washington, DC, USA
Jordan Lebel
John Molson School of Business, Concordia University, Montreal, Canada
Philip James
London School of Hygiene and Tropical Medicine,
President International Association for the Study of Obesity (IASO)
Rickey Y. Yada
Advanced Foods and Materials Network,
Networks of Centers of Excellence, University of Guelph, Ontario, Canada
Marie-Claire Laflamme-Sanders (Editorial Coordinator)
McGill World Platform for Health and Economic Convergence,
McGill University, Montreal, Canada
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10 11 12 13 14 15
10 9 8 7 6 5 4 3 2 1
Contents
List of contributors xv
2.3 Response to Conditioned Cues 17
2.4 Functional Brain Imaging of Cue Reactivity 20
2.5 Conclusion 20
References 21
Preface xix
Acknowledgments xxi
3
Introduction: On the Brain-to-Society
Model of Motivated Choice and the
Whole-of-Society Approach to Obesity
Prevention xxiii
PAWEL K. OLSZEWSKI, JOHAN ALSIÖ, HELGI B. SCHIÖTH
AND ALLEN S. LEVINE
3.1 Introduction 23
3.2 Opioids and Feeding Behavior in Rodent Models 24
3.3 Opioids and Dysregulation of Eating Patterns and
Body Weight in Human Beings 31
3.4 Conclusions and Perspectives 33
References 34
LAURETTE DUBÉ, ON BEHALF OF THE EDITORIAL TEAM
Part 1
FROM BRAIN TO BEHAVIOR
4
A. Energy is Delight: Sensory and Reward
Systems
1
Taste, Olfactory and Food-texture Processing
in the Brain and the Control of Appetite 41
EDMUND T. ROLLS
4.1 Introduction 42
4.2 Taste-Processing in the Primate Brain 42
4.3 The Representation of Flavor: Convergence of Olfactory,
Taste and Visual Inputs in the Orbitofrontal Cortex 44
4.4 The Texture of Food, Including Fat Texture 44
4.5 Imaging Studies in Humans 44
4.6 Cognitive Effects on Representations of Food 46
4.7 Synthesis 47
4.8 Implications for Understanding, Preventing, and
Treating Obesity 47
4.9 Concluding Remarks 52
References 53
The Pleasures and Pains of Brain
Regulatory Systems for Eating 5
JAAK PANKSEPP
1.1 Introduction 5
1.2 Satiety Agents versus Aversion-Inducing Agents 6
1.3 Various Methodologies to Evaluate Affective Change
in Pre-Clinical Appetite Research 7
1.4 Conditioned Taste Aversions – From Animal Models
to Human Brain Analysis? 12
1.5 Conclusion 13
References 13
2
Opioids: Culprits for Overconsumption
of Palatable Foods? 23
5
The Neurobiology of Appetite:
Hunger as Addiction 15
Cortical and Limbic Activation in Response
to Low- and High-calorie Food 57
WILLIAM D.S. KILLGORE
ALAIN DAGHER
5.1 Introduction 57
5.2 Brain Responses to Food Stimuli in Healthy
Adults 58
2.1 Introduction 15
2.2 Hunger as Addiction 16
v
vi
CONTENTS
5.3 Modulating Factors 61
5.4 Cortical and Limbic Activation to Food Images
During Adolescent Development 65
5.5 Conclusion 68
References 68
6
Reward-related Neuroadaptations Induced
by Food Restriction: Pathogenic Potential
of a Survival Mechanism 73
KENNETH D. CARR
6.1 Introduction 73
6.2 Food Restriction may Augment Neurobiological
Responses to Palatable Food in a Way that Promotes
Addictive Behavior 75
6.3 Food Restriction Enhances CNS and Behavioral
Responses to Drugs of Abuse and Dopamine
Receptor Agonists 76
6.4 Food Restriction Up-regulates D1 Dopamine
Receptor-Mediated Phosphorylation of Ionotropic
Glutamate Receptors and Signaling Proteins that
Underlie Synaptic Plasticity 77
6.5 Striatal Neuroadaptations Induced by Food
Restriction may be Secondary to Changes in
Pre-synaptic Dopamine Neuronal
Function 79
6.6 A Schema to Consider as Research
Continues 80
References 81
B. Executive Control Systems and the
Challenges They Face in the Modern
World of Plenty
7
The Neuroeconomics of Food Selection
and Purchase 89
BRIAN G. ESSEX AND DAVID H. ZALD
7.1 Introduction 89
7.2 Positive Valuations 90
7.3 Influences on Positive Valuations 90
7.4 Negative Valuations 93
7.5 Influences on Negative Valuations 95
7.6 Selection 96
7.7 Habits 100
7.8 Conclusions 101
References 101
8 Resisting Temptation: Impulse Control and
Trade-offs between Immediate Rewards
and Long-term Consequences 105
LIN XIAO, LAURETTE DUBÉ AND ANTOINE BECHARA
8.1 Introduction 105
8.2 A Neural System for Decision-Making and
Will-power: The Somatic Marker Hypothesis 106
8.3 Empirical Evidence for Deficits of Decision-making
Underlying Obesity 108
8.4 Conclusion 112
References 112
9 Hunger, Satiety, and Food Preferences:
Effects of the Brain and the Body on the
Self-Control of Eating 115
ALEXANDRA W. LOGUE
9.1
9.2
9.3
9.4
Introduction 115
The Components of Self-control 116
Physiological Influences on Self-control 117
Promoting Self-control for a Healthy Body
Weight 121
9.5 Conclusions 122
References 122
10 Associative Learning and the Control of
Food Intake 125
LOUISE THIBAULT
10.1 A Behavioral Reporting of Eating 125
10.2 Eating is a Learned Behavior 126
10.3 Forms of Learned Ingestive Response 126
10.4 Sensory-specific Anticipatory Eating 127
10.5 Diurnal Rhythms and the Learned Response
10.6 Nutrients and Cognition 130
10.7 Dietary Fats and Learning 130
10.8 Our Primitive Brain 131
References 131
130
11 Restrained Eating in a World of Plenty 135
JANET POLIVY AND C. PETER HERMAN
11.1 Introduction 135
11.2 The Effects of Having Food Cues Present 136
11.3 Response to Food Cues in Restrained and
Unrestrained Eaters 136
11.4 Food Photographs and/or Words – Indirect
Food Cues 138
vii
CONTENTS
11.5 Portion Size as Food Cue 139
11.6 The Removal of Food Cues 140
11.7 Caloric Restriction in Animals and
Humans 140
11.8 Is CR Likely to be Effective for
Humans? 141
11.9 Caloric Restriction in the Presence of
Food Cues 142
11.10 Dieting in a World of Food Cues 144
References 144
C. Biological Systems that Favor a Positive
Energy Balance and Body-weight Increase
in a World of Plenty
12 The Genetic Determinants of Ingestive
Behavior: Sensory, Energy Homeostasis
and Food Reward Aspects of Ingestive
Behavior 149
KAREN M. ENY AND AHMED EL-SOHEMY
12.1 Introduction 149
12.2 Sensory Determinants of Food
Intake 151
12.3 Energy Homeostasis Pathways and Food
Intake 152
12.4 Reward Circuits and Food Intake 155
12.5 Conclusions 156
References 157
13 Development of Human Learned Flavor
Likes and Dislikes 161
MARTIN R. YEOMANS
13.1
13.2
13.3
13.4
13.5
Introduction 161
Understanding Flavor Perception 162
Why Innate Flavor-liking is Rare 163
Flavor-preference Learning 164
Different Learning Mechanisms Interact to
Enhance Flavor-liking 168
13.6 Liking and Intake: The Role of Palatability in
Overeating 169
13.7 Acquired Liking as a Driver of
Overeating 170
13.8 Individual Differences in Learning 171
13.9 Summary 173
References 173
14 Biopsychological Factors and Body-weight
Stability 179
JEAN-PHILIPPE CHAPUT AND ANGELO TREMBLAY
14.1 Introduction 179
14.2 Is Knowledge-based Work a Potential
Determinant of the Current Obesity
Epidemic? 180
14.3 Is Short Sleep Duration a Potential
Determinant of the Current Obesity
Epidemic? 183
14.4 Weight Loss: Not Always Beneficial for the
Psychological Health 184
14.5 Physical Activity and Diet: What is the Impact on
Body-weight Stability? 186
14.6 Conclusion and Perspectives 186
References 187
15 Nutrition, Epigenomics and the
Development of Obesity: How the
Genome Learns from Experience 191
JOHN C. MATHERS
15.1 The Basics of Epigenetics and
Epigenomics 191
15.2 Epigenetic Marks During Development
and Aging 193
15.3 Nutritional Epigenomics 194
15.4 Epigenetics and Brain Function 196
15.5 An Epigenetic Basis for Developmental
Programming of Obesity? 197
15.6 Physical Activity, Epigenetic Markings and
Obesity 197
15.7 Concluding Comments 198
References 199
16 The Role of Early Life Experiences in
Flavor Perception and Delight 203
JULIE A. MENNELLA AND GARY K. BEAUCHAMP
16.1 Introduction 203
16.2 Flavor and the Ontogeny of the
Senses 205
16.3 Taste and Development 207
16.4 Learning about Food Flavors 211
16.5 Concluding Remarks 212
References 213
viii
CONTENTS
17 Implications of the Glycemic
Index in Obesity 219
JULIA M. W. WONG, ANDREA R. JOSSE, LIVIA S. A.
AUGUSTIN, NISHTA SAXENA, LAURA CHIAVAROLI,
CYRIL W. C. KENDALL AND DAVID J. A. JENKINS
17.1
17.2
17.3
17.4
19.3 Determinants of Insulin Resistance 244
19.4 Candidate Genes and Cross-population Genetic
Differences 246
19.5 Conclusion 248
References 248
Introduction 219
The concept of the Glycemic Index 220
Mechanisms of Action 221
Effects of low GI Foods on Appetite, Food Intake
and Satiety 222
17.5 GI and Obesity 224
17.6 GI and Diabetes 224
17.7 GI and Cardiovascular Disease 225
17.8 Conclusion 226
References 226
20 Neuroanatomical Correlates of
Hunger and Satiaty in Lean and Obese
Individuals 253
18
21 Neuroendocrine Stress Response
and Its Impact on Eating Behavior and
Body Weight 261
Characterizing the Homeostatic and
Hedonic Markers of the Susceptible
Phenotype 231
JOHN BLUNDELL, ELEANOR BRYANT, CLARE LAWTON,
JASON HALFORD, ERIK NASLUND, GRAHAM
FINLAYSON AND NEIL KING
18.1 The Approach 232
18.2 Susceptible and Resistant Phenotypes 232
18.3 What Would a Susceptible Phenotype Look
Like? 233
18.4 What Level of Analysis is Appropriate? 233
18.5 Appetite is Not Rocket Science – It is More
Complicated 234
18.6 Diversity, Susceptibility and Homeostasis 234
18.7 Hedonics: The Importance of Liking and
Wanting 235
18.8 Comparing Susceptible and Resistant
Phenotypes 236
18.9 Resistance to Weight Loss – The Other Side of
Susceptibility 237
18.10 Conclusions 238
References 238
19 The Carnivore Connection:
Cross-population Differences in the
Prevalence of Genes Producing Insulin
Resistance 241
STEPHEN COLAGIURI, SCOTT DICKINSON AND JENNIE
BRAND-MILLER
19.1 Background 241
19.2 The Evolution of Insulin Resistance
242
ANGELO DEL PARIGI
20.1 Physiology of Hunger and Satiety in Human Eating
Behavior 253
20.2 Functional Neuroimaging Evidence 254
References 258
BETH M. TANNENBAUM, HYMIE ANISMAN
AND ALFONSO ABIZAID
21.1 Introduction 261
21.2 Hypothalamo-pituitary-adrenal Axis 262
21.3 Stress and Food Intake: It is Not all Homeostatic or
Automatic 263
21.4 Imaging Studies in Humans 264
21.5 Peripheral Signals Regulating Energy
Balance 265
21.6 Conclusion 267
References 268
D. Integrative and Multi-level Models of
Eating and of Energy and Body-weight
Regulation
22 Eating Behavior and its Determinants:
From Gene to Environment 275
JOHN M. DE CASTRO
22.1 Introduction 275
22.2 Genes 276
22.3 The Environment 276
22.4 Genes–Environment Interactions 278
22.5 A General Model of Intake Regulation 280
22.6 Discussion 282
References 283
ix
CONTENTS
23 The Molecular Regulation of Body
Weight: The Role of Leptin, Ghrelin and
Hypocretin 287
JOHN J. MEDINA
23.1 Introduction 287
23.2 Leptin, Ghrelin and Hypocretin
23.3 Leptin Protein 288
23.4 Ghrelin Protein 290
23.5 Hypocretin Protein 291
23.6 Concluding Remarks 293
References 294
288
24 Energy Balance Regulation: Complex
Interplay between the Autonomic and
Cognitive/Limbic Brains to Control Food
Intake and Thermogenesis 299
26.3 The New Paradigm’s Contribution to Solving the
Obesity Epidemic 337
References 339
27 Resisting Temptations: How Food-Related
Control Abilities can be Strengthened
through Implementation Intentions 343
CHRISTINE STICH, PHILIP J. JOHNSON
AND BÄRBEL KNÄUPER
27.1 Introduction 343
27.2 The Motivational Nature of Food 344
27.3 Food-Related Control Abilities 346
References 350
28 The Dieter’s Dilemma: Identifying When
and How to Control Consumption 353
AYELET FISHBACH AND KRISTIAN OVE R. MYRSETH
DENIS RICHARD AND ELENA TIMOFEEVA
24.1 Introduction 299
24.2 The Regulation of Energy Balance 300
24.3 Brain Pathways Involved in the Control of Food
Intake and Thermogenesis 301
24.4 Conclusion 309
References 310
E. Individual-level Interventions to Tap into
Appropriate Brain Systems for Sustainable
Behavioral Change
25 Stealth Interventions for Obesity
Prevention and Control: Motivating
Behavior Change 319
THOMAS N. ROBINSON
25.1
25.2
25.3
25.4
Motivation for Behavior Change 319
Self-efficacy 320
Stealth Interventions 320
Social and Ideological Movements as Stealth
Interventions to Change Health Behaviors 323
25.5 Conclusion 324
References 326
26 From Diets to Healthy and Pleasurable
Everyday Eating 329
LYNE MONGEAU
26.1 The Diet Zeitgest 329
26.2 A New Weight Paradigm
335
28.1 Introduction 353
28.2 A Two-stage Model of Self-control: Identification
versus Resolution 354
28.3 Conclusions 361
References 362
29 Lifestyle Change and Maintenance in
Obesity Treatment and Prevention: A Selfdetermination Theory Perspective 365
HEATHER PATRICK, AMY A. GORIN AND
GEOFFREY C. WILLIAMS
29.1
29.2
29.3
29.4
29.5
29.6
Introduction 365
Self-determination Theory 366
Self-regulation 366
Need-supportive Contexts 367
SDT and Weight Loss 369
Potentional Limitations of Current Interventions:
An SDT Perspective 370
29.7 Directions for Future Research Based on SDT 371
References 372
30 Nutritional Genomics in Obesity
Prevention and Treatment 375
BRANDEN R. DESCHAMBAULT, MARICA
BAKOVIC AND DAVID M. MUTCH
30.1
30.2
30.3
30.4
Background 375
The Genetics of Obesity 376
Nutritional Genomics 379
The Role of Gene Polymorphisms
380
x
CONTENTS
30.5 The Role of Gene Expression 381
30.6 From Bench to Bedside: Predicting Outcome
30.7 Outlook 385
References 386
384
31 Physical Activity for Obese
Children and Adults 391
ROSS ANDERSEN AND CATHERINE SABISTON
31.1 Introduction 391
31.2 Adults and Physical Activity 392
31.3 Physical Activity and Young People 392
31.4 Linking Physical Activity and Obesity 393
31.5 The Model 394
31.6 Fit or Fat 399
31.7 Conclusion 399
References 399
Part 2
FROM SOCIETY TO BEHAVIOR:
POLICY AND ACTION
A. Economy as a Core Society System
Shaping Policy and Action that Determine
Behavior
32 Economic Growth as a Path
Toward Poverty Reduction, Better
Nutrition and Sustainable Population
Growth 407
T. N. SRINIVASAN
32.1 Introduction and a Definition of Terms 407
32.2 What is Needed to Accelerate and Sustain
Growth? 409
32.3 Country Case Study: China and India 410
32.4 The Case of Undernutrition and Obesity 413
References 416
33 The Human Agent, Behavioral
Changes and Policy Implications
34 The Four Pillars of the Industrial
Machine: Can the Wheels be Steered in a
Healthier Direction? 423
WILLIAM BERNSTEIN
34.1 Introduction 423
34.2 Malthus’ World 424
34.3 How Nations Become Wealthy 425
34.4 The Progress of Economic Development 426
34.5 Measuring Economic Development 429
34.6 The 2 Percent Productivity Cruise Control 429
34.7 The Obesity Connection 430
34.8 The Way Forward 431
References 432
35 Libertarian Paternalism: Nudging
Individuals toward Obesity
Prevention 435
LAURETTE DUBÉ
35.1 Introduction 435
35.2 Biases and Shortcomings in Human
Decision-making 436
35.3 On Libertarian Paternalism 438
35.4 Libertarian Paternalism Applied 440
35.5 Limitations and Conclusion 440
References 441
B. Needs and Challenges in Policy and
Action to Prevent Obesity
36 The Current State of the Obesity
Pandemic: How We Got Here and Where
We Are Going 445
PHILIP JAMES
417
TRANSCRIBED REMARKS BY DANIEL KAHNEMAN
33.1 The Economic and Psychological View of
Human Nature 417
33.2 Culture as an Economic Externality 418
33.3 A Psychologist’s Explanation of Behavior 418
33.4 Happiness, or the Power of Human
Adaptability 419
33.5 An Argument for Some Paternalism 420
References 421
36.1 The Current State of the Obesity
Pandemic 445
36.2 How did We Get Here? 456
36.3 The Complexity of the Problem 461
References 461
xi
CONTENTS
37 The Underweight/Overweight Paradox in
Developing Societies: Causes and Policy
Implications 463
CARLOS A. MONTEIRO, CORINNA HAWKES AND
BENJAMIN CABALLERO
37.1 Introduction 463
37.2 The Reasons Underlying the Underweight/
Overweight “Paradox” 464
37.3 Public Policies Needed to Tackle the Coexistence of
Underweight/Overweight 466
37.4 Applying the WHO Global Strategy on Diet,
Physical Activity and Health 468
37.5 Conclusion 468
References 468
38 The Drivers of Body Weight, Shape
and Health: An Indian Perspective
of Domestic and International
Influences 471
MANOJA KUMAR DAS AND NARENDRA K. ARORA
C. Policy and Action to Shift the Drivers
of Food Supply and Demand of the
Agriculture and Agri-Food Value Chains
in a Healthy Direction
40 Agriculture, Food and Health
497
KRAISID TONTISIRIN AND LALITA BHATTACHARJEE
40.1
40.2
40.3
40.4
Introduction and Context 498
Food Consumption and Nutrition Situation 498
Agriculture–Nutrition Linkages 500
Analysis of South Asian Dietary Energy Supply and
Nutrition Status 502
40.5 Dietary Transition in Asian Countries 503
40.6 The Impact of Urbanization 504
40.7 Overweight and Obesity in Asia 505
40.8 Policy Interventions 506
40.9 Conclusion and Recommendations 508
References 508
41 Changing Food Systems in
the Developing World 511
38.1 Introduction 472
38.2 Overweight and Obesity in Indian Children and
Youth 472
38.3 Trends Influencing Intake 475
38.4 Trends in Energy Expenditure 480
38.5 Cross-cutting Issues 482
38.6 Conclusions 483
References 484
41.1
41.2
41.3
41.4
39 Diets and Activity Levels of Paleolithic
versus Modern Humans: Societal
Implications for the Modern Overweight
Pandemic 487
42 Green Revolution 2.5: From Crisis to a
New Convergence Between Agriculture,
Agri-Food and Health for Healthy Eating
Worldwide 521
PETER J.H. JONES AND DYLAN MACKAY
39.1 Introduction 487
39.2 The Four Eras of Change of Human Diets 488
39.3 Contrasting Food Intake during the Paleolithic Era
versus Today 489
39.4 Energy Expenditure and Physical
Inactivity 490
39.5 The Tipping Point of Energy Imbalance 491
39.6 Insights from Paleolithic Diets to Fight the Obesity
Pandemic 491
References 492
PRABHU PINGALI
Introduction 511
Factors Driving Changes in Food Demand 512
Factors Driving Changes in Food Supply 514
Impact of Changes in Food Supply and
Demand 515
41.5 The Key Role of Institutions and Research 518
References 519
LAURETTE DUBÉ, JANET BEAUVAIS, LOUISE
FRESCO AND PATRICK WEBB
42.1 Introduction 522
42.2 Novel and Convergent Solutions for Agriculture,
Agri-Food and Health 522
42.3 An Integrated Approach to the Food and Nutrition
Value Chain 523
42.4 Challenges and Opportunities in Developing Green
Revolution 2.5 525
42.5 Conclusion 528
References 528
xii
CONTENTS
43 How High-level Consumer Research
Can Create Low-caloric, Pleasurable Food
Concepts, Products and Packages 529
HOWARD R. MOSKOWITZ AND MICHELE REISNER
43.1 Introduction 529
43.2 Where did this Systematic, RDE Approach Come
From? 530
43.3 Designing the Product and Communicating It 530
References 541
44 Reductions in Dietary Energy Density to
Moderate Children’s Energy Intake 543
BARBARA J. ROLLS AND KATHLEEN E. LEAHY
44.1
44.2
44.3
44.4
44.5
44.6
Introduction 543
What is Energy Density? 544
Why is Energy Density Important? 545
Does Energy Density Influence Energy Intake? 545
Practical Strategies to Reduce Energy Density 547
Will Reducing the Energy Density of the Diet
Benefit Every Child? 550
44.7 Future Directions in Energy Density Research 550
44.8 Conclusions 551
References 551
45 Nurturing and Preserving the
Sensory Qualities of Nature 555
TANYA L. DITSCHUN
45.1 Introduction 555
45.2 Determinants of Individual Food Choices and
Current “Healthful Eating” Trends 557
45.3 Preserving the Natural Sensory Qualities of
Food 561
References 565
46 Aligning Pleasures and Profits: Restaurants
as Healthier Lifestyle Enablers 567
JORDAN LEBEL
46.1 Introduction 567
46.2 Industry Overview 568
46.3 Food-Away-From-Home Demand Drivers
46.4 How Restaurants Compete 572
46.5 Ways Forward 573
46.6 Conclusion 577
References 577
570
47 A Study of Corporate Social
Responsibility Activities of 12 Giant
Food Companies (1980–2008) in
Promoting Healthy Food 579
SHANLING LI, DAN ZHANG AND WENQING ZHANG
47.1 Introduction 579
47.2 Literature Review 581
47.3 Data, Sample and Methodology 582
47.4 Results and Sensitivity Analysis 584
47.5 Conclusion 587
Appendix A 587
Appendix B 588
Appendix C 589
References 589
D. Policy and Action for Creating Families,
Schools, Communities and Social Networks
that Support Individual Healthy Choice
48 The Injunctive and Descriptive Norms
Governing Eating 593
ROBERT J. FISHER
48.1
48.2
48.3
48.4
Introduction 593
Injunctive versus Descriptive Eating Norms 593
Norms are Situational 595
Socialization and the Creation of Eating
Norms 595
48.5 Norm Violations 598
48.6 The Effect of Eating Norms on Health Outcomes 600
48.7 Affecting Norms through Marketing 600
48.8 Conclusion 601
References 601
49 Family Meal Patterns and Eating in
Children and Adolescents 605
NICOLE LARSON AND MARY STORY
49.1
49.2
49.3
49.4
Introduction 605
Do Family Meals Promote Good Nutrition? 606
Do Family Meals Promote Healthy Weights? 609
Do Family Meals Promote Health in Overweight
Children? 611
49.5 Do Family Meals have Other Benefits? 611
49.6 What are Strategies to Promote Family Meals? 612
49.7 What Actions can Communities Take to Promote
Family Meals? 613
xiii
CONTENTS
49.8 What Remains to be Learned about Family
Meals? 614
References 614
50 Social Influences on Eating in
Children and Adults 617
SARAH-JEANNE SALVY AND PATRICIA P. PLINER
50.1 Introduction 617
50.2 Social Influences on the Control of Intake in
Adults 618
50.3 Social Influence on Food Selection in Adults 620
50.4 Social Influences on the Control of Intake in
Children 621
50.5 Social Influences on Food Selection in Children 623
50.6 Concluding Remarks 624
References 625
51 Church- and Other Community
Interventions to Promote Healthy Lifestyles:
Tailoring to Ethnicity and Culture 629
53.3 Social Alliances as a Strategy for Corporate
Branding 663
53.4 Societal Interventions as Strategic Alliances
53.5 The Case Study Intervention 666
53.6 Discussion of the Case Study 669
53.7 Conclusion 669
References 670
663
54 Social Networks, Social Capital, and
Obesity: A Literature Review 673
SPENCER MOORE
54.1 Definition of Terms 673
54.2 Methodology 674
54.3 Two Debates 674
54.4 Social Capital and Obesity Literature
54.5 Final Considerations 683
References 684
680
55 From Society to Behavior: Neighborhood
Environment Influences 687
JOSH VAN LOON
SHIRIKI KUMANYIKA
51.1 Introduction 629
51.2 Background 630
51.3 Cultural Targeting and Tailoring in Community
Settings 632
51.4 Religious Organizations as Communities within
Communities 636
51.5 Challenges 642
51.6 Conclusion 646
References 647
55.1
55.2
55.3
55.4
Introduction 687
Identification of Neighborhoods 690
Neighborhood Boundary Definition 690
Identification and Assessment of Neighborhood
Environment Characteristics 691
55.5 Findings and Limitations 692
55.6 Conclusions and Implications 695
References 696
E. Challenges and Possibilities for Policy
and Action in Reducing the Social and
Economic Gradients in Health, Lifestyle
and Obesity
52 On Gluttony: Religious and Philosophical
Responses to the Obesity Epidemic 653
WILLIAM B. IRVINE
52.1 Introduction 653
52.2 What is Gluttony? 654
52.3 What is Wrong with Gluttony?
52.4 Conclusions 659
References 660
655
MICHAEL MARMOT AND RUTH BELL
53 Social Alliances: Moving Beyond
Corporate Social Responsibility to
Private–Public Partnerships 661
XIAOYE CHEN, KARL J. MOORE AND LISE RENAUD
53.1 Introduction 661
53.2 Partnership in Social Alliances
56 Social Determinants of Health
and Obesity 701
662
56.1
56.2
56.3
56.4
56.5
Introduction 701
The Social Gradient of Health 702
Obesity and the Social Gradient of Health
The Burden of Disease 704
The WHO Commission on the Social
Determinants of Health and a Possible
Explanatory Framework 706
56.6 Applying the Framework to Policy 707
703
xiv
CONTENTS
56.7 Targeted and Universal Policies
56.8 Conclusion 710
References 711
59.5 Improving the Global Policy Framework 751
59.6 Insights from Tobacco Control Efforts 752
59.7 Engaging the Private Sector 752
59.8 Conclusions 753
References 754
709
57 The Role of the Environment in
Socio-Economic Status and Obesity
713
GARY W. EVANS, NANCY M. WELLS AND
MICHELLE A. SCHAMBERG
60 Social Interactions and Obesity:
An Economist’s Perspective 757
KATHERINE G. CARMAN AND PETER KOOREMAN
57.1 Introduction 713
57.2 Food Consumption 714
57.3 Physical Activity 720
57.4 Summary and Conclusions
References 722
722
58 The Economics of Obesity: Why are
Poor People Fat? 727
ADAM DREWNOWSKI AND PETRA EICHELSDOERFER
58.1
58.2
58.3
58.4
58.5
Introduction 727
How do People Make Food Choices? 728
Energy-dense Foods Cost Less 729
Healthier Diets Cost More 730
The Growing Price Disparity in Food
Costs 732
58.6 Does Restricting Food Costs Lead to Energy-dense
Diets? 734
58.7 Why are Poor People Fat? 735
58.8 Approaches to Obesity Prevention 736
References 738
F. Challenges and Possibilities for a
Broad Systems Approach to Policy
and Action
59 Challenges in Making Broad Healthy
Lifestyle Plans: Revisiting the Nature of
Health Interventions 747
CATHERINE LE GALÈS
59.1
59.2
59.3
59.4
The Context of Non-communicable Diseases 747
The Current Health Policy Framework 748
The Need for Joined-up Policy-making 749
The WHO Global Strategy for Diet, Physical
Activity and Health 749
60.1
60.2
60.3
60.4
Introduction 757
The Different Guises of Social Interactions
The Literature so Far 761
Policy Interventions Related to Social
Interactions 762
60.5 Conclusions 764
References 764
758
61 A Complex Systems Approach to
Understanding and Combating the
Obesity Epidemic 767
ROSS A. HAMMOND
61.1 Introduction 767
61.2 Challenges for Study and Intervention Design 768
61.3 Complex Adaptive Systems 769
61.4 Applying a Complex Systems View to Obesity 771
61.5 Agent-based Computational Modeling 773
61.6 Conclusion 774
References 775
62 Conclusion: A Whole-of-Society
Approach to Obesity Prevention: New
Frontiers in Science, Policy and Action,
and the Emerging Models of Capitalism
and Society to Make it Possible 779
LAURETTE DUBÉ, ON BEHALF OF THE EDITORIAL TEAM
62.1 Introduction 779
62.2 New Frontiers in Science 780
62.3 New Frontiers in Policy 782
62.4 New Frontiers in Action 783
62.5 Emerging Models of Capitalism and Society
References 786
Index
787
785
List of Contributors
Jennie Brand-Miller, School of Molecular and
Microbial Biosciences, University of Sydney,
Sydney, NSW, Australia
Alfonso Abizaid, Institute of Neuroscience, Carleton
University, Ottawa, Canada
Johan Alsiö, Department of Neuroscience, Uppsala
University, Uppsala, Sweden
Eleanor Bryant, Centre for Psychology Studies,
University of Bradford, Bradford, UK
Ross Andersen, Department of Kinesiology and Physical
Education, McGill University, Montreal, Canada
Benjamin Caballero, Johns Hopkins Bloomberg
School of Public Health, Baltimore, MD, USA
Hymie Anisman, Institute of Neuroscience, Carleton
University, Ottawa, Canada
Katherine G. Carman, Department of Economics,
Tilburg University, Tilburg, The Netherlands
Narendra
K.
Arora,
International
Clinical
Epidemiology Network, New Delhi, India
Kenneth D. Carr, Departments of Psychiatry and
Pharmacology, New York University School of
Medicine, New York, NY, USA
Livia S. A. Augustin, Unilever Health Institute,
Unilever Research and Development, Vlaardingen,
The Netherlands
Jean-Philippe Chaput, Department of Social and
Preventive Medicine, Laval University, Quebec
City, Canada
Marica Bakovic, Department of Human Health
and Nutritional Sciences, University of Guelph,
Guelph, Canada
Xiaoye Chen, Marketing Department, Desautels
Faculty of Management, McGill University,
Montreal, Canada
Ruth Bell, Department of Epidemiology and Public
Health, University College London, London, UK
Laura Chiavaroli, Clinical Nutrition & Risk Factor
Modification Center, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
Gary K. Beauchamp, Monell Chemical Senses Center,
Philadelphia, PA, USA
Janet Beauvais, McGill World Platform for Health
and Economic Convergence, Desautels Faculty of
Management, McGill University, Montreal, Canada
Stephen Colagiuri, Sydney Medical School, Boden,
Institute of Obesity, Nutrition & Exercise, University
of Sydney, Sydney, New South Wales, Australia
Antoine Bechara, Brain and Creativity Institute,
Department of Psychology, University of Southern
California, Los Angeles, CA, USA
Alain Dagher, Montreal Neurological Institute,
McGill University, Montreal, Canada
William Bernstein, efficientfrontier.com, North Bend,
OR, USA
Manoja Kumar Das, International Clinical
Epidemiology Network, New Delhi, India
Lalita Bhattacharjee, National Food Policy Capacity
Strengthening Programme, Food and Agriculture
Organization of the United Nations, Bangladesh
John M. de Castro, College of Humanities and Social
Sciences, Sam Houston State University, Huntsville,
TX, USA
John Blundell, Institute of Psychological Sciences,
Faculty of Medicine and Health, University of
Leeds, Leeds, UK
Angelo Del Parigi, Senior Medical Director, External
Medical Affairs, Pfizer Inc., New York, NY, USA
xv
xvi
LIST OF CONTRIBUTORS
Branden R. Deschambault, Department of Human
Health and Nutritional Sciences, University of
Guelph, Guelph, Canada
Ross A. Hammond, Center on Social and Economic
Dynamics, Economic Studies Program, The
Brookings Institution, Washington, DC, USA
Scott Dickinson, Sydney Medical School, Boden,
Institute of Obesity, Nutrition & Exercise, University
of Sydney, Sydney, New South Wales, Australia
Corinna Hawkes, School of Public Health, University
of São Paulo, São Paulo, Brazil
Tanya L. Ditschun, Food Science and Technology
Group, Senomyx, Inc., San Diego, CA, USA
Adam Drewnowski, Center for Public Health
Nutrition, School of Public Health, University of
Washington, Seattle, WA, USA
Laurette Dubé, The McGill World Platform for
Health and Economic Convergence, Desautels
Faculty of Management, McGill University,
Montreal, Canada
Petra Eichelsdoerfer, Bastyr University Research
Institute, Bastyr University, Kenmore, WA, USA
Ahmed El-Sohemy, Department of Nutritional
Sciences, Faculty of Medicine, University of
Toronto, Toronto, Canada
Karen M. Eny, Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto,
Toronto, Canada
Brian G. Essex, Department of Psychology,
Vanderbilt University, Nashville, TN, USA
Gary W. Evans, College of Human Ecology, Cornell
University, New York, NY, USA
Graham Finlayson, Institute of
Sciences, Faculty of Medicine
University of Leeds, Leeds, UK
Psychological
and Health,
Ayelet Fishbach, Booth School of Business,
University of Chicago, Chicago, IL, USA
Robert J. Fisher, Department of Marketing,
Business Economics & Law, University of Alberta,
Edmonton, Canada
C. Peter Herman, Department of Psychology,
University of Toronto, Toronto, Canada
William B. Irvine, Department of Philosophy, Wright
State University, Dayton, OH, USA
Philip James, International Association for the Study
of Obesity, and International Obesity Task Force,
London, UK
David J. A. Jenkins, Clinical Nutrition & Risk Factor
Modification Center, and Division of Endocrinology
and Metabolism, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
Philip J. Johnson, Department of Psychology, McGill
University, Montreal, Canada
Peter J. H. Jones, Richardson Center for Functional
Foods and Nutraceuticals, Department of Food
Science, University of Manitoba, Manitoba, Canada
Andrea R. Josse, Clinical Nutrition & Risk Factor
Modification Center, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
Daniel Kahneman, Center for Health and WellBeing, Princeton University, Princeton, NJ, USA
Cyril W. C. Kendall, Clinical Nutrition & Risk Factor
Modification Center, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
William D. S. Killgore, Cognitive Neuroimaging
Laboratory, McLean Hospital, Harvard Medical
School, Belmont, MA, USA
Amsterdam,
Neil King, Institute of Health and Biomedical
Innovation, Queensland University of Technology,
Brisbane, Australia
Amy A. Gorin, Department of Psychology, University
of Connecticut, Storrs, CT, USA
Bärbel Knäuper, Department of Psychology, McGill
University, Montreal, Canada
Jason Halford, Psychology Department, University
of Liverpool, Liverpool, UK
Peter Kooreman, Department of Economics, Tilburg
University, Tilburg, The Netherlands
Louise Fresco, Universiteit
Amsterdam, The Netherlands
van
LIST OF CONTRIBUTORS
Shiriki Kumanyika, Department of Biostatistics
and Epidemiology and Department of Pediatrics
(Gastroenterology;
Section
on
Nutrition),
University of Pennsylvania School of Medicine,
Philadelphia, PA, USA
Nicole Larson, Division of Epidemiology and
Community Health, School of Public Health,
University of Minnesota, Minneapolis, MN, USA
xvii
Carlos A. Monteiro, Department of Nutrition, School
of Public Health, University of São Paulo, São
Paulo, Brazil
Karl J. Moore, Strategy and Organization
Department, Desautels Faculty of Management,
and Dept. of Neurology & Neurosurgery, McGill
University, Montreal, Canada
Clare Lawton, Institute of Psychological Sciences,
Faculty of Medicine and Health, University of
Leeds, Leeds, UK
Spencer Moore, School of Kinesiology and Health
Studies, Queen’s University; Centre de recherche
du Centre hospitalier de l’Université de Montréal,
Montreal, Canada
Kathleen E. Leahy, Department of Nutritional
Sciences, Pennsylvania State University, PA, USA
Howard R. Moskowitz, Moskowitz Jacobs Inc.,
White Plains, NY, USA
Jordan LeBel, Marketing Department, John Molson
School of Business, Concordia University, Montreal,
Canada
David M. Mutch, Department of Human Health and
Nutritional Sciences, University of Guelph, Guelph,
Canada
Catherine Le Galès, CERMES3, National Institute for
Health and Medical Research, U988, Paris, France
Kristian Ove R. Myrseth, ESMT European
School of Management and Technology, Berlin,
Germany
Allen S. Levine, Minnesota Obesity Center;
Department of Food Science and Nutrition,
University of Minnesota, Saint Paul, MN, USA
Erik Naslund, Clinical Sciences, Danderyd Hospital,
Karolinska Istitutet, Stockholm, Sweden
Shanling Li, Desautels Faculty of Management,
McGill University, Montreal, Canada
Alexandra. W. Logue, City University of New York,
New York, NY, USA
Dylan MacKay, Richardson Center for Functional
Foods and Nutraceuticals, University of Manitoba,
Manitoba, Canada
Michael Marmot, International Institute for Health and
Society, University College London, London, UK
John C. Mathers, Human Nutrition Research Centre,
Institute for Ageing and Health, Newcastle
University, Newcastle on Tyne, UK
Pawel K. Olszewski, Minnesota Obesity Center;
Department of Neuroscience, Uppsala University,
Uppsala, Sweden
Jaak Panksepp, Department of VCAPP, College of
Veterinary Medicine, Washington State University,
Pullman, WA, USA
Heather Patrick, Department of Medicine and of
Clinical and Social Psychology, University of
Rochester, Rochester, NY, USA
Prabhu Pingali, Deputy Director, Agricultural
Development, The Bill and Melinda Gates
Foundation, USA
Patricia P. Pliner, Department of Psychology,
University of Toronto at Mississauga, Canada
John J. Medina, Department of Bioengineering,
University of Washington, Seattle, WA, USA
Janet Polivy, Department of Psychology, University
of Toronto at Mississauga, Mississauga, Canada
Julie A. Mennella, Monell Chemical Senses Center,
Philadelphia, PA, USA
Michele Reisner, Moskowitz Jacobs Inc., White
Plains, NY, USA
Lyne Mongeau, Department of Social and Preventive
Medicine, University of Montreal, Montreal,
Quebec, Canada
Lise Renaud, Social and Health Communication,
Université du Québec à Montréal (UQAM),
Montreal, Canada
xviii
LIST OF CONTRIBUTORS
Denis Richard, Centre de Recherche de l’Institut
universitaire de Cardiologie et de Pneumologie de
Québec, Canada
Thomas N. Robinson, Division of General Pediatrics,
Department of Pediatrics and Stanford Prevention
Research Center, Department of Medicine, Stanford
University School of Medicine; Center for Healthy
Weight, Stanford University School of Medicine
and Lucile Packard Children’s Hospital at Stanford,
Stanford, CA, USA
Barbara J. Rolls, Department of Nutritional Sciences,
Pennsylvania State University, PA, USA
Edmund T. Rolls, Oxford Centre for Computational
Neuroscience, Oxford, UK
Catherine Sabiston, Department of Kinesiology and
Physical Education, McGill University, Montreal,
Canada
Sarah-Jeanne Salvy, Division of Behavioral Medicine,
Department of Pediatrics, University at Buffalo,
State University of New York, USA
Nishta Saxena, Clinical Nutrition & Risk Factor
Modification Center, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
Michelle A. Schamberg, Cornell University, New
York, NY, USA
Helgi B. Schiöth, Department of Neuroscience,
Uppsala University, Uppsala, Sweden
T. N. Srinivasan, Samuel C. Park Jr Professor of
Economics, Yale University, New Haven, CT, USA;
Stanford Center for International Development,
Stanford University, Stanford, CA, USA
Christine Stich, Population Health, Prevention and
Screening Unit, Cancer Care Ontario, Toronto, Canada
Mary Story, Division of Epidemiology and Community
Health, School of Public Health, University of
Minnesota, Minneapolis, MN, USA
Beth M. Tannenbaum, McConnell Brain Imaging
Centre, Montreal Neurological Institute, McGill
University, Montreal, Canada
Louise Thibault, School of Dietetics and Human
Nutrition, McGill University, Montreal, Canada
Elena Timofeeva, Centre de Recherche de l’Institut
universitaire de Cardiologie et de Pneumologie de
Québec, Canada
Kraisid Tontisirin, Institute of Nutrition, Mahidol
University, Thailand and Former Director, Food
and Nutrition Division, Food and Agriculture
Organization of the United Nations, Italy
Angelo Tremblay, Department of Social and
Preventive Medicine, Laval University, Quebec
City, Canada
Josh van Loon, School of Community and Regional
Planning, University of British Columbia, Vancouver,
Canada
Patrick Webb, Friedman School of Nutrition Science
and Policy, Tufts University, Medford, MA, USA
Nancy M. Wells, Design and Environmental
Analysis, College of Human Ecology, Cornell
University, New York, NY, USA
Geoffrey C. Williams, Departments of Medicine and
of Clinical and Social Psychology, University of
Rochester, Rochester, NY, USA
Julia M. W. Wong, Clinical Nutrition & Risk Factor
Modification Center, St Michael’s Hospital, Toronto,
Canada; Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Canada
Lin Xiao, Brain and Creativity Institute, Department
of Psychology, University of Southern California,
Los Angeles, CA, USA
Martin R Yeomans, School of Psychology, University
of Sussex, Brighton, UK
David H. Zald, Departments of Psychology, Psychiatry
and Integrative Neuroscience Program, Vanderbilt
University, Nashville, TN, USA
Dan Zhang, Desautels Faculty of Management,
McGill University, Montreal, Canada
Wenqing Zhang, Desautels Faculty of Management,
McGill University, Montreal, Canada.
Preface
Over the years, the focus of obesity prevention and treatment has shifted from genetic and
biological factors to various behavioral change
interventions, to, more recently, re-designing
physical, social and economic environments.
As the increasing prevalence of obesity and its
related chronic diseases indicate, these methods
have clearly not sufficed.
This two-part handbook provides the scientific foundations of a bolder “Brain-toSociety” approach to obesity prevention. In this
approach, biology, the individual and the environment cannot as independent factors account
for individual and collective lifestyle choices.
Rather, to stop the progression of the obesity
pandemic, we need an integrative approach
rooted in an in-depth understanding of the
pathways of the motives, antecedents, actions
and consequences within each level of influence on obesity, and at their interface. We must
develop a scientific basis that can guide the
changes in policy and action that are needed to
realign biology and the environment with what
the individual and society can sustain.
The approach taken in the handbook is crossdisciplinary, multi-level and multi-sector. It aims
at catalyzing the development of a body of scientific knowledge that can better conceive, articulate,
measure and model the interfaces of health, biological, behavioral, physical, social and economic
factors that drive individual, behavioral and
societal behaviors. This will help public health
scientists, professionals and organizations to act
more effectively as leaders in galvanizing action
and policy change to shift the dynamics underlying obesity and chronic disease prevention in a
sustainable manner. It will also inspire scientists,
professionals and organizations in food, agriculture, business, economics, politics, media, education, engineering and other non-health domains
to develop novel ways to achieve their respective objectives while simultaneously contributing
to individual and societal health. Ultimately, this
new frontier of science will transcend boundaries
across disciplines, bridge theories and data on
gene, brain, behavior and environment, and provide the basis of a bolder approach to obesity prevention and treatment.
xix
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Acknowledgments
The editors would first like to thank all the
authors for their dedication and hard work
in producing and refining these chapters. By
pushing the boundaries of their thinking and
knowledge, they lay the foundation of a bolder
approach to obesity prevention.
The editors give their warmest thanks to
Marie-Claire Laflamme-Sanders, editorial coordinator, for her brilliance and her perseverance
in bringing this book to completion.
The chapters assembled build upon a cycle
of events on obesity, hosted by the McGill World
Platform for Health and Economic Convergence
from 2005 until 2008. This cycle progressively
tapped into the “brain” and the “society” side of
obesity prevention to develop a new way of thinking of and acting upon this problem. In this effort,
we are grateful for the continued financial and
substantive support of our partner organizations,
which are committed to ensuring the health of all
individuals around the world. These are: McGill
University, the Global Alliance for the Prevention
of Obesity and Related Chronic Diseases, the
Fondation Lucie et André Chagnon, the Public
Health Agency of Canada, the Ministère de la
santé et des services sociaux, the Bill and Melinda
Gates Foundation, the Dr. Robert C. and Veronica
Atkins Foundation, the Robert Wood Johnson
Foundation, the Institut national de la santé
publique du Québec – National Collaborating
Center on Public Health, the Direction de la santé
publique de Montréal-Centre, Health Canada,
the Agence de la santé et des services sociaux
de Montréal, the Canadian Institutes of Health
Research – Institute of Nutrition, Metabolism
and Diabetes, the Canadian Institute for Health
Information, the Canadian Institutes of Health
Research – Institute of Human Development,
Child and Youth Health, the Canadian Institutes
of Health Research – Institute of Neurosciences
and Mental Health Addiction, the Heart and
Stroke Foundation of Canada, the Centre hospitalier de l’Université de Montreal, the Fond
de recherche en santé du Québec, the Réseau de
recherche en santé des populations du Québec,
the National Institute of Child Health and Human
Development, the National Cancer Institute, the
National Heart, Lung, and Blood Institute, the
National Institute of Diabetes and Digestive and
Kidney Diseases, the Office of Behavioral and
Social Sciences Research, the Health and Learning
Knowledge Center, the Ministère de l’agriculture,
des pêcheries, et de l’alimentation du Québec, the
Montreal Neurological Institute, the Advanced
Foods and Materials Network, the Canadian AgriFood Policy Institute, the International Clinical
Epidemiology Network, Advertising Standards
Canada, Agriculture and Agri-Food Canada, the
Alliance for Innovation in Agri-Food, the American
Heart Association, the Canadian Association
of Principles, the Canadian Council of Food
and Nutrition, the Canadian Obesity Network,
the Canadian Produce Marketing Association, the
Centers for Disease Control and Prevention, the
Chronic Disease Prevention Alliance of Canada,
Concerned Children’s Advertisers, the Culinary
Institute of America, Food and Consumer Products
of Canada, the International Economic Forum of
the Americas/Conférence of Montréal, the Joint
Consortium for School Health, MobilizeYouth, the
National Obesity Observatory, ParticipACTION,
the Pennington Biomedical Research Center, and
the University of Washington – Center for Public
Health Nutrition.
xxi
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Introduction: On the Brain-to-Society
Model of Motivated Choice and the
Whole-of-Society Approach to Obesity
Prevention
Laurette Dubé, on behalf of the Editorial Team
James McGill Chair, Consumer Psychology and Marketing, Desautels Faculty of Management,
McGill University, Montreal, Canada
O U T L I N E
Introduction
The Brain-to-Society Model of Motivated
Choice
xxiii
xxv
A Whole-of-Society Approach to Obesity
Prevention
xxvii
Handbook Overview
xxviii
The Choice Architecture and What it Means
for Obesity Prevention
xxvi
INTRODUCTION
The spread of childhood and adult obesity and other lifestyle-related diseases continues unabated in Canada, the USA, Europe, and
other industrialized countries around the world.
Obesity Prevention: The Role of Brain and Society on Individual Behavior
The WHO estimates that over 1 billion people
globally are overweight, and more than 400 million are obese. The number of obese is expected
to grow by 75 percent by 2015 (James, 2006). In
developing countries, such as India and China,
the increased prevalence of overnutrition occurs
xxiii
2010 Elsevier Inc.
© 2010,
xxiv
THE BtS MODEL OF MOTIVATED CHOICE
while a large proportion of the population still
suffers from undernutrition (see Chapter 37 of this
volume). This is in spite of a rich diversity of medical and behavioral individual- and communitybased interventions and wide-ranging policy
change. This pandemic poses serious challenges
not only to the health community, but also to
society as a whole: the personal, societal and
economic costs tied to it are extremely high.
It is increasingly recognized that the patterns
of food overconsumption and physical inactivity driving the obesity pandemic are rooted in
the way modern industrialized society operates. It has created environmental conditions
that overwhelm a biology evolved for dramatically different conditions. These challenges have
been compounded by the radical transformational force of globalization, which has created
a world where modern affluent economies,
developing economies and emerging markets,
and the least developed countries are all part
of the same global system, where tradition and
modernity intersect as never before (Dubé et al.,
2008a). Globalization has accelerated the spread
of ideas, information and cultural changes, and
unleashed tremendous potential for individual,
economic and social growth and developments.
It has also significantly impacted health, as economic development and industrial progress are
tied to increases in overweight and obesity rates
(Cutler et al., 2003). This poor alignment between
biology, markets and society is reflected not only
in the rise of obesity and chronic diseases, but
also in issues regarding food and nutrition security, poverty and health inequity, as well as child
development and mental health.
Our current environment presents an important challenge to human biology – one that will
only continue to grow, unless we revisit some
of the fundamental ways in which our society
operates. These include:
1. The ways in which we – as individuals,
families and communities – live, consume,
invest, and take care of our children
2. The ways in which we – as educational,
health, media and business organizations –
produce, promote, trade and provide goods
and services to individuals, families and
communities
3. The ways in which we – as trade institutions,
investment markets and governments –
maintain the present health and economic
divide that shapes the arena where
individuals, families, communities and
organizations evolve.
Cutting-edge science from both the biology
and the society sides of the equation is crucial to
this effort, as are creative thinking and sustained
commitment and action from all stakeholders around the world, at the local, national and
global levels. An unprecedented convergence of
interests, in the wake of the financial downturn,
can yield breakthrough novel and more effective pathways for individual behavioral change
as well as social and business innovation.
Challenges and opportunities lie at new frontiers
of transdisciplinary and cross-sectoral science; in
novel behavioral change interventions; in new
mind-sets and methods for organizational decision-making; in public and private investment
in business and social innovation; and in breakthrough institutional entrepreneurship for better
balanced policy, governance, and government.
Only this can pave the way toward a vision of
present and future economy and society that
biology can more sustainably withstand.
More concretely, it means that:
1. Health and public health professionals,
organizations and systems must galvanize
individual and societal action by all actors
in society. They must develop the necessary
expertise and capabilities to provide their
counterparts in education, agriculture,
business, media, urban planning, and
transportation, with guiding principles,
frameworks for action and the best available
evidence regarding the health impacts of
policies and actions.
THE BRAIN-TO-SOCIETY MODEL OF MOTIVATED CHOICE
2. Professionals, organizations and systems
from all sectors that shape the current
environment must mainstream health into
their respective everyday and strategic
activities, in a manner that is compatible
and sustainable from the perspective of their
primary sectors of activities.
3. Professionals, organizations and systems in
the whole of society must singly and jointly
engage in breakthrough and integrative
innovation in science, policy and action to
make healthy choices the default option for
individuals.
This handbook is based on the conviction that
it is possible to reap the many benefits of modern economic development worldwide, without
paying the high toll of obesity and its chronic
disease consequences.
In the rest of this Introduction, we present
the Brain-to-Society model of motivated choice
as the overarching conceptual framework of
this handbook. We then provide an overview of
the book. All in all, this collection assembles the
scientific foundations for the proposed model as
well as the multi-level and multi-sector components of the Whole-of-Society changes needed
to curb the obesity pandemic.
THE BRAIN-TO-SOCIETY MODEL
OF MOTIVATED CHOICE
The Brain-to-Society model of motivated
choice (Daniel et al., 2008; Dubé et al., 2008b) is
a broad integrative approach to understanding,
mapping, modeling, and ultimately guiding in a
more adaptive direction, the pathways by which
brain systems (considering the genetic background and psychological predispositions) and
society systems (through the familial, organizational and collective choices and policies in
health, social and economic domains that shape
environments) singly and jointly determine
xxv
individual choices in domains of motivated
adaptive behaviors. Motivated adaptive behaviors are cue-induced processes, shaped by human
evolution and tied to biological drivers, that span
a wide range of choices – for example, from food
and companionship to strategies for minimizing
physical and psychological discomfort (Kalivas
and Volkow, 2005; Dubé et al., 2008b).
At a basic level, sensory and emotional systems all have single and combined roles in
food choice and eating. These signals interact
with environmental cues in complex ways to
define the motivational or “reward” value of
food. Some of these biologically-driven processes are also involved in the less adaptive case
of addiction (Volkow and O’Brien, 2007). Yet,
on another level, humans have the capacity to
regulate behavior in a flexible and goal-directed
manner through deliberate and effortful acts
of will power and self-control. They can overcome maladaptive cue-induced impulses and
allow more adaptive choice alternatives. This
capacity is linked to executive control systems,
which include inhibition, decision-making,
goal selection and planning, and are enabled
by more recently evolved brain systems. These
are also sensitive to environmental conditions
(Diamond, 2009).
The BtS model of motivated choice views
individual choice as the outcome of the
complex and dynamic relationships between
biology and psychology, shaping choice and
behavior, in response to environmental cues
and taking into account both the immediate
context and internalized life-course information
called upon by the immediate context. These
cue-induced processes operate on different
timescales, and through a diversity of mechanisms that all together define how the brain acts
as a command center for choice and behavior.
Thus, the brain systems and society systems,
which underlie the organizational and collective choices that in turn shape environment, are
all part of the same system guiding individual
choice (Figure 1).
xxvi
THE BtS MODEL OF MOTIVATED CHOICE
Systems science
Neuroscience
Local and global society,
culture and media
Education, health and civil
society organizations
Brain-to-society model
of Inidividual choice
Brain-to-society model of
organizational & collective
choices
Valuation, decision
making and behavior
Environment cues
Society systems
Brain systems
Innovation,
entrepreneurship and
leadership
Innovation,
entrepreneurship and
leadership
Businesses as economic producers,
environment consumers and living places
Local and global economy, market and
environment
FIGURE 1. The Brain-to-Society model of motivational choice.
THE CHOICE ARCHITECTURE
AND WHAT IT MEANS FOR
OBESITY PREVENTION
The BtS model of motivated choice also
assumes that the default value of individual
choice – i.e., the easiest and most natural option –
is modulated by organizational and collective
choices, which form the choice architecture
within which individual choice occurs. The
choice architecture emerges from the single
and combined consequences of the choices
made by governments, businesses, civil society,
and community organizations that operate in
health and non-health domains at local, national
and global levels. Accounts of the biologically challenging qualities of the present choice
architecture are manifold (Wansink, 2006; Dubé
et al., 2007):
1. There is excessive reliance on information
and education in individual-level
intervention
2. Nutrition and health are not sufficiently
integrated into school, work and community
activities and environments
3. Nutrition and health have not sufficiently
penetrated innovation, value-chain and
strategic activities in agriculture, food and
other business sectors
4. The power of commercial and social
marketing and media has not been shifted
toward the promotion of healthy eating
5. Rural, industrial, economic and social
development has thus far not paid sufficient
A WHOLE-OF-SOCIETY APPROACH TO OBESITY PREVENTION
attention to the challenges imposed to
biology by environment
6. Policy changes that could lead to progress lie
outside of the health jurisdiction
7. There is a lack of convergence in policy and
action between developed and developing
countries, between health and economic
activities, and between the local, national
and global levels of decisions
8. The political will as well as the health
and development budgets devoted to the
promotion of healthy eating are insufficient.
In such a context, where the needed changes
to create a protective choice architecture lie
outside the traditional purview of health,
health and public health professionals, organizations and systems, armed with insufficient
means and limited power, cannot continue to
promote healthy lifestyles if all other social
and economic actors and individuals passively
maintain a relative status quo. Conversely,
health cannot continue to be managed from the
outside, without a sophisticated understanding of the complex mechanisms, motives and
success criteria that guide the decision-making
and action of these non-health actors. As such,
to transform the choice architecture into one
that supports healthy lifestyles, both health and
non-health actors must converge to lead effective changes.
The research program underlying the BtS
model deploys cutting-edge concepts and
methods from neuroscience and systems sciences with the latest advances in behavioral
and social sciences to better understand individual food choices in the contexts of biology
and environment. While the brain suffers from
decision-making shortcomings and self-control
challenges in the face of a plentiful environment,
it also possesses a unique capacity for selfpreservation, empathy and creativity, with the
power to foster innovation, entrepreneurship
and leadership. The approach that emerges from
xxvii
this book relies on the assumption that society
systems, just like brain systems, are more amenable to changes than previously thought. Both
systems can be changed in order to better sustain healthy lifestyle behaviors.
A WHOLE-OF-SOCIETY APPROACH
TO OBESITY PREVENTION
The BtS model of motivated choice calls for
a dramatically different approach to population
health that permeates its traditional boundaries and builds novel competencies and capacities in both health and non-health domains of
activities. The Whole-of-Society (WoS) approach
to obesity prevention reflects the fact that the
necessary changes are woven into the everyday
lives of individuals, communities, organizations,
markets and societies. It goes beyond current
“whole-of-government” approaches, which
have called for the integration of healthy public policies within all sectors that contribute to
lifestyle (education, agriculture, and industry
and trade). As comprehensive as they may be,
traditional governmental policies and programs
alone cannot reach the scale, scope and speed
of changes needed to reverse current obesity and chronic disease trends. The BtS model
is motivated by the need to go beyond crossgovernmental efforts to harness the power of
individuals themselves, communities and businesses, and of other social and economic actors.
This approach brings together recent developments in science and the best models and practices from the fields of population health and
global health, with breakthrough advances from
the key sectors that shape the environment in
which individual lifestyle choices are made:
food and agriculture, education, media, finance,
management, law, politics and economics. The
aim is to better equip the population health
and healthcare community to serve as catalysts
xxviii
THE BtS MODEL OF MOTIVATED CHOICE
and leaders in promoting policy convergence
in economic domains, and, conversely, to better
equip non-health policy-makers and strategists
to place health on their agenda. Therefore, the
WoS approach is:
1. Transdisciplinary – scientists, researchers,
decision-makers and strategists from all
fields work together to develop shared
conceptual and methodological frameworks
and strategies for policy change and
innovation, which not only integrate but
also transcend their respective disciplinary
perspectives
2. Multi-sectoral – actors from public agencies,
communities and the private sector, from all
social and economic domains that contribute
to lifestyles, are mobilized to place health on
their strategic agenda
3. Multi-level – individuals, policy-makers
and strategists whose decisions at the
community, municipal, provincial/state,
national, transnational and global levels
influence the environment in which
individual choice is made are involved.
HANDBOOK OVERVIEW
Part 1 of this two-part handbook, “From
Brain to Behavior”, provides the scientific foundations of the Brain-to-Society model of motivated choice. Part 2, “From Society to Behavior:
Policy and Action”, then lays down the foundations of a Whole-of-Society approach to population health.
In moving “From Brain to Behavior”, Section
A of Part 1 examines sensory, reward, and other
biological systems that explain how energy has
become “delight” for living species. Section B
shifts to executive control systems, which are
unique to mankind, and also addresses selfcontrol challenges in the modern world of plenty,
in particular when wired-in, non-adaptive
predispositions are culturally reinforced. The contributions in Section C move beyond brain systems driving behavior to examine more broadly
other biological systems that impact energy balance and body weight, including genetics and
epigenetics. Section D offers integrative and
multi-level perspectives on eating, energy balance
and body-weight regulation. Finally, in Section E,
existing approaches to individual behavior
changes are revisited in light of this more sophisticated understanding of the biological, motivational and rational bases of individual food
choice and its relationship to energy balance and
body weight.
In Part 2, “From Society to Behavior: Policy
and Action”, the emphasis shifts to the organizational and collective choices that shape the
environment in which individual choice is
made. Section A begins by laying out the needs
and challenges in policy and action to prevent
obesity, in both developed and developing
countries. Section B focuses on the economy as
a core agent shaping policy and action. The set
of contributions in Section C covers policy and
action to shift the drivers of food supply and
demand in a healthy direction. In Section D, we
then look to scaling up policies and actions to
create family, school, community and social networks that support healthy individual choices.
The socio-economic health gradient is examined
in Section E, and finally, in Section F, existing
broad societal approaches to obesity prevention
are analyzed and the potential of systems science is introduced.
The concluding chapter sets new frontiers
in science, policy and action, introduces the
Whole-of-Society approach to obesity prevention, and highlights the new models of capitalism and society that can support it.
References
Cutler, D. M., Glaeser, D. L., & Shapiro, J. M. (2003). Why
have Americans become more obese? Journal of Economic
Perspectives, 17(3), 93–118.
REFERENCES
Diamond, A. (2009). The interplay of biology and the environment broadly defined. Developmental Psychology,
45(1), 1–8.
Dubé, L., Kouri, D., Fafard, K., & Sipos, I. (2007). Childhood
obesity: A societal challenge in need of health public policy.
Report on Policy Implication of the Health Challenge
2007. Think Tank for Canada.
Dubé, L., Shetty, P., Webb, P., Fresco, L., McKnight, W., &
Hawkes, C. (2008a). Framing Paper. Prepared for the
Gates Foundation Workshop: From Crisis to a New
Convergence of Agriculture, Agri-Food and Health.
Held in Montreal, Quebec, November 8–9, 2008.
Dubé, L., Bechara, A., Böckenholt, U., Ansari, A., Dagher, A.,
Daniel, M., De Sarbo, W. S., Fellows, L. K., Hammond,
Ross, A., Huang, T. T.-K., Huettel, S., Kestens, Y.,
xxix
Knäuper, B., Kooreman, P., Moore, D. S., & Smidts, A.
(2008b). Towards a brain-to-society systems model of
individual choice. Marketing Letters, 19, 323–336.
James, P. (2006). Presentation offered during the 2006
Mcgill Health Challenge Think Tank. Held in Montreal,
Quebec, October 25–27, 2006.
Kalivas, P. W., & Volkow, N. D. (2005). The neural basis
of addiction: A pathology of motivation and choice.
American Journal of Psychiatry, 162(8), 1403–1413.
Volkow, N. D., & O’Brien, C. P. (2007). Issues for DSM-V:
Should obesity be included as a brain disorder?
American Journal of Psychiatry, 164(5), 708–710.
Wansink, B. (2006). Mindless eating: Why we eat more than we
think. New York, NY: Bantam Books.
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P A R T
1
FROM BRAIN TO BEHAVIOR
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A
ENERGY IS DELIGHT: SENSORY
AND REWARD SYSTEMS
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C H A P T E R
1
The Pleasures and Pains of Brain
Regulatory Systems for Eating
Jaak Panksepp
Department of VCAPP, College of Veterinary Medicine, Washington State University,
Pullman, WA, USA
O U T L I N E
1.1
Introduction
5
1.2
Satiety Agents versus Aversion-Inducing
Agents
6
1.3
Various Methodologies to Evaluate
Affective Change in Pre-Clinical
Appetite Research
1.4
1.5
12
Conclusion
13
7
1.1 INTRODUCTION
Diverse neuropeptidergic details of this circuitry have now been clarified (Horvath and
Diano, 2004; Broeberger, 2005; Konturek et al.,
2005; Gao and Horvath, 2007; Coll et al., 2008). In
brief, there are complex neuropeptide-based neural networks that are able to gauge the energy
status of the organism, and to adjust foraging
and eating behavior accordingly. This network
is constructed of hypothalamic neuropeptides,
such as hypocretin/orexin, neuropeptide Y and
agouti-related peptide, α-melanocyte-stimulating
hormone, and melanin-concentrating hormone;
All basic survival needs of the body are represented in genetically ingrained circuits concentrated in subcortical visceral regions of the brain.
Energy balance is regulated by a strict equation
(Figure 1.1) that has recently been illuminated in
great detail. For many decades, abundant evidence has indicated that medial hypothalamic
regions, concentrated especially in the arcuate
nucleus, contain major detectors for long-term
homeostatic energy balance (Panksepp, 1974).
Obesity Prevention: The Role of Brain and Society on Individual Behavior
Conditioned Taste Aversions – From
Animal Models to Human Brain
Analysis?
5
2010 Elsevier Inc.
© 2010,
6
1. BRAIN REGULATORY SYSTEMS
+
5.5 kg.
750 kcal + 20,000 kcal
CHOW
RAT
=
+
10,000 kcal + 19,750 kcal
HEAT
RAT
Daily intake error
<0.7 kcal
FIGURE 1.1 A female rat’s approximate yearly energy balance equation.
A great deal is eaten without much change in body weight. With the small increase in body weight, the daily intake error
was less than a kilocalorie. The remaining energy was dissipated as heat.
Source: Figure 9.4 of Panksepp (1998: 172), reprinted with permission of Oxford University Press.
regulatory circuits that are controlled by peripheral signals of lipid status, such as leptin; gastrointestinal hunger hormones, such as ghrelin;
as well as more direct metabolic effects on the
hypothalamus that are not as well understood.
As noted above, this knowledge has been summarized superbly many times.
Often missing from the discussion of energy
balance dynamics are the evolved psychological processes that mediate achieved/achieving
homeostasis – the nature of the feelings of hunger in the brain, and the large variety of ways
the pleasures and displeasures of taste can
promote or hinder appetite. This is in addition
to the many ways feeding behavior can be disrupted which have no relevance for the normal
mechanisms of energy balance regulation. For
instance, hunger makes sweetness taste more
pleasant, and satiety makes the same sensation
feel less pleasant (Cabanac, 1992).
This chapter will briefly focus on the latter
factors, since they need to be considered more
closely as investigators search for medicinal
agents that may help humans better regulate
their weight. It should be noted that considerable progress is being made in understanding how the brain codes taste qualities in both
animals (Berridge, 2003; Peciña et al., 2006) and
humans (Rolls, 2008; Rolls and Grabenhorst,
2009), but little of that work has yet been related
to our understanding of appetite control agents.
1.2 SATIETY AGENTS VERSUS
AVERSION-INDUCING AGENTS
Presumably, the feeling that accompanies
excessive depletion of energy leads to distressing feelings of hunger, while satisfaction of
1. FROM BRAIN TO BEHAVIOR
1.3 VARIOUS METHODOLOGIES TO EVALUATE AFFECTIVE CHANGE IN PRE-CLINICAL APPETITE RESEARCH
energy needs promotes a mood of contentment.
Of course, few investigators of animal models
of energy regulation are willing to use such psychological concepts; most restrict their discussions to measurable entities – changes in food
intake, body weight, and various body energy
distribution parameters. This is understandable,
since we have no direct way of monitoring the
psychological states of other animals. Yet this
is also shortsighted if such states do exist and
if they are of first-rate importance for how animals distribute their food-seeking and consummatory behaviors.
We would be wise to reconsider our reluctance to envision the affective controls of animal
behavior, using all the indirect methodological
approaches at our disposal. Philosophically, to
not consider such issues is tantamount to failing
to deal with the real complexities of the brain.
Practically, the failure to consider such issues
may impair our capacity to sift real satietyproducing neural pathways and neurochemical agents from the many other ways that food
intake could be disrupted. Since most pre-clinical
investigators in the field have been trained in
rigorous behavioristic approaches, where any
discussion of mental changes in animals is considered to be inappropriate, a full and open discussion of such issues is rare in the literature.
However, considering what we now know about
brain evolution and the subcortical sources of
affective processes in all mammals (Panksepp,
1998, 2005, 2008), wisdom dictates that we begin
to evaluate such issues with more intensive
methodologies. If adequate empirical approaches
for monitoring affective changes did not exist,
it would make no sense to suggest that such
issues should be considered. Yet adequate comprehensive methodologies are available, albeit
rarely used. Among the best measures are positive and negative affective states as can be measured with conditioned place preference (CPP)
and conditioned place aversion (CPA) measures
(Tzechentke, 2007). As will be noted later, there
are also other, more direct behavioral measures,
7
such as the willingness of animals to play. Such
affective measures, when used in pre-clinical
animal models, would allow us to better ferret
out those brain neurochemical pathways that
need our most focused attention for the development of optimal appetite-reducing agents.
Why do we need to consider such issues?
Any of a large variety of negative affects can
reduce feeding in animals, from anger to being
“zonked-out” by drugs, with disgust, fearfulness, separation anxiety, and stomach cramps
in between. It is important also not to forget
the negative feelings arising from a variety of
stressors, including fatigue and sickness promoting neurochemical changes in the brain
and various painful bodily feelings. If we do
not sift such appetite-reducing affects from
the normal pathways of satiety at the outset of
intensive research programs seeking new satietypromoting agents, we will be mismanaging our
budgets and the efforts of our researchers. Since
practically all agents off our pharmaceutical
shelves, in high enough doses, can reduce food
intake in animals, such affective issues need to
be considered at the front end of research programs. Unfortunately, few investigators focus
on them as fully as they deserve; usually a conditioned taste-aversion (CTA) paradigm is as
far as most are prone to go. This is a good start,
but, as will be summarized here, there are many
more subtle ways to address such issues.
1.3 VARIOUS METHODOLOGIES TO
EVALUATE AFFECTIVE CHANGE
IN PRE-CLINICAL APPETITE
RESEARCH
There are many easy ways to reduce feeding pharmacologically, but only a few of these
tell us much about the normal mechanisms of
energy regulation. As noted, fearful animals eat
less than normal. So do angry and sick ones.
There are many affective changes beside satiety
1. FROM BRAIN TO BEHAVIOR
8
1. BRAIN REGULATORY SYSTEMS
that can reduce feeding, and investigators interested in seeking satiety agents have not spent
enough time sifting those agents that produce
the normal, good feeling of satiety after a satisfying meal from all the many other affective changes that can reduce feeding. With the
increasing number of neuropeptidergic “satiety
agents” that have been discovered in the brain
(Table 1.1), we must be increasingly wary that
many of them are reducing feeding by changing
non-homeostatic affective feelings of animals
rather than a feeling of satisfaction that emerges
from no longer being hungry.
In short, establishing affective criteria whereby
one has discovered a “real” satiety agent is critical for future progress in developing medicines
that will help people to regulate body weight
optimally. When the body is out of homeostatic
TABLE 1.1 Partial list of neuropeptides and other
neuromodulators that have been found
to reduce feeding and body weight
Various amino acids
Glucagon-like peptide
AgRP
Interleukin-1
Amylin
Interleukin-6
α-MSH
Insulin (central)
Beta-endorphin
Leptin
BDNF
Norepinephrine
Bombesin
Neurotensin
CART
NPY
CCK
Oxytocin
Corticosterone
PrRP
CRH
Peptide YY
Dynorphin
Serotonin
Galanin
Tumor necrosis factor α
Galanin-like peptide
Urocortin
Abbreviations: α-MSH, α-Melanocyte Stimulating Hormone;
BDNF, Brain Derived Neutrophic Factor; CART, Cocaineand Amphetamine-Regulated Transcript CorticotropinReleasing Hormone; PrRP, Proline-Releasing Peptide.
Sources: Horvath and Diano, 2004; Broeberger, 2005;
Konturek et al., 2005; Gao and Horvath, 2007; Coll et al., 2008.
balance in terms of available energy, one feels
hunger pangs and generalized distress that can
be easily erased by a restoration of energy homeostasis. When one has eaten a satisfying meal,
the stomach is distended, blood sugar levels rise,
and one commonly feels rather sleepy. These
physiological manifestations of satiety are accompanied by a shift from negative to positive affective states that are commonly called satisfaction
or contentment. As noted, since it is admittedly
hard to peer into the minds of our experimental animals, we need to find a variety of indirect
measures that can help us gauge whether agents
that reduce objectively monitored body weight
and feeding behavior are also accompanied by
feelings of satisfaction. If agents produce less
desirable affective states, it would be good to
know about them early in any research program.
The most useful appetite control agents will
need to facilitate appropriate affective changes,
namely feelings of appetite satisfaction.
Ever since the discovery that the neuropeptides cholecystokinin and bombesin could
reduce appetite, followed soon after by the
body-fat regulator leptin, the search for neuropeptide modulators of food intake has been
a booming growth industry. The outstanding
neurobehavioral science that has been fostered
has had one enormous missing link – a meaningful
discussion of how the various agents modify affective change. Without this critical linchpin, which
will ultimately be a key to patient satisfaction
and hence long-term compliance and efficacy,
acceptable appetite control agents are unlikely
to be discovered. Hence, affective issues should
be evaluated soon after a substance is thought to
be a natural satiety-producing agent. Affective
change must be the gold standard that allows
us to sift true value from empty promissory notes
if we are to regulate appetite through a growing knowledge of feeding control and long-term
energy balance regulatory networks in the brain
(Panksepp, 1974, 1975; Panksepp et al., 1979).
To re-emphasize, it is important to note that
injecting animals with practically anything off
1. FROM BRAIN TO BEHAVIOR
1.3 VARIOUS METHODOLOGIES TO EVALUATE AFFECTIVE CHANGE IN PRE-CLINICAL APPETITE RESEARCH
the pharmaceutical shelf, at random, yields
many agents that can reduce intake. Most do not
reduce appetite normally; appetite is reduced
because the animals are feeling anything from
mildly unwell, fatigued, or simply very ill to
emotionally distraught. Few investigators carry
the discussion of their results toward such affective issues, except for the well-accepted fact that
aversion can be monitored by CTA (Conditioned
Taste Aversions); for the massive literature on
CTAs (see http://www.CTALearning.com for
an archival resource of the available literature).
For instance, Panksepp et al., (1977) provided
this service to Hoffman-LaRoche in the 1970s
for one of their prime appetite control agents,
which dissuaded them from proceeding further.
The problem with just using the CTA measure is that agents which also produce excessive
normal satiety can lead to a metabolically mediated conditioned reduction in meal size. Hence,
other measures are essential. The one proposed
early on, namely to follow the post-prandial
“satiety sequence” of post-meal grooming,
exploration, “house-keeping” activities and nap
(Antin et al., 1995), is fine as a starter, yet it is
missing a few critical keys to solving the affective issue – namely, did the “satiety agent” in
fact produce a good feeling of satiety?
Now that we have a host of peptides that
reduce feeding (Table 1.1 provides a partial list),
the above issue should be foremost in investigators’ minds. However, it is not. For instance, a
stress peptide such as corticotrophin releasing
factor (CRF), which reduces appetite, is not a
sensible candidate for clinical use in a feedingregulation clinic. It simply makes animals emotionally aroused in various negative affective
ways, including increasing signs of separation distress. It was also believed that the CRF2
receptor, reacting to urocortin, might be effective, but it has been shown only to produce emotional distress (Panksepp and Bekkedal, 1997).
So what should investigators do? Take affect
seriously. There are abundant good ways to
monitor whether investigators could realistically
9
consider their favorite peptide to be a realistic
satiety agent as opposed to an emotionally disruptive agent. The best measures would reflect
increases in behavior rather than reductions (as
with the above described “satiety sequence”,
which is largely a reduction of behavior that
also occurs when animals are simply tired).
The following half-dozen gold-standard criteria
would allow us to sift the most promising (i.e.,
real appetite control agents) from the less realistic candidates (after routine CTA studies have
been completed).
1. Hunger dramatically reduces the motivation
of young animals to indulge in roughand-tumble play. This amotivational state
is immediately reversed by a single meal
(Siviy and Panksepp, 1985). In this study, we
evaluated the capacity of CCK and bombesin
to simulate that effect. CCK had no such
capacity, while bombesin did marginally
yield some reversal. The effect, however,
was not even close to the complete reversal
of play suppression that was produced by
a single meal. We proceeded to evaluate
several other “promising” neuropeptides,
but none proved to be promising. By
fulfilling this criterion substantially, a
researcher will have identified a truly
promising neuropeptide for further study.
2. As discovered in the late 1960s, rats will
not show a hunger-induced elevation
of feeding with a rarely provided highincentive treat. Such an effect is routinely
seen when monitoring feeding with normal
maintenance chow (Figure 1.2). Thus, it
could be argued that for normal satiety, an
agent should reduce hunger-induced intake
of maintenance food much more than intake
of a rarely provided treat. Sickness would
be expected to produce more comparable
effects on each.
3. If a neuropeptide agent truly simulates a
good feeling of satiety following hunger, it
should produce a clear conditioned place
1. FROM BRAIN TO BEHAVIOR
10
1. BRAIN REGULATORY SYSTEMS
Intake as a function of level of
deprivation with two incentives
18
16
Hamburger treat
Food intake (g)
14
12
10
8
Maintenance chow
6
4
2
0
Ad Lib.
24 h.
Deprivation
Level of food deprivation
FIGURE 1.2 A summary of the effects of high-incentive
(raw hamburger) and much lower-incentive food (the rat’s
normal maintenance chow) on intake as a function of degree
of prior food deprivation.
Source: Figure 9.3 of Panksepp (1998: 173), reprinted with permission
of Oxford University Press.
preference (CPP) in hungry animals, but
not in fully satiated animals. Indeed, in
fully satiated animals, as could be insured
by gavage of part of the next meal a short
while before testing, the agent should
either produce no CPP or perhaps even a
mild conditioned place avoidance (CPA).
In this regard, it should be noted that early
on CCK produced no CPP response in
hungry animals; indeed, it generated a clear
aversion (Swerdlow et al., 1983).
4. Along the same lines, if a neuropeptide
really produces feelings of satiety, hungry
animals should work for intraventricular
administration of the peptide much more
under a state of hunger than in a state of
satiety.
5. If an animal’s set-point for regulation has
been truly shifted downward (i.e., to a leaner
body mass), then the long-term hedonic
equation should not have been shifted.
One way to monitor this is to give common
laboratory animals, such as rats, continuous
daily access to two concentrations of sugar.
Animals normally systematically shift their
intake from the more concentrated to the
less concentrated sugar solution (Figure 1.3).
Lean animals in a chronic state of hunger,
such as those with experimental type 1
diabetes, sustain their preference for the
more concentrated solution. If this were to
happen with a putative long-term appetite
control agent, then the inference should
be that the body-weight set-point has not
been shifted by the manipulation. If rats
shift away from the sweeter solution more
rapidly, the inference is that they are, in
fact, internally experiencing excess energy
repletion.
6. Finally, in line with our main thesis that the
very best way to monitor affective change in
animals is via their emotional vocalizations,
we would suggest that if one paired a
conditioned stimulus (CS) with infusion of
satiety peptides in hungry animals, then
gradually the CS would come to evoke
appetitive 50-kHZ ultrasonic vocalizations
(USVs) in anticipation of obtaining relief
from the hunger. We have already observed
this with a single 2-hour feeding period
each day (Burgdorf and Panksepp, 2000), as
well as a conditioned appetitive response
to drugs of reward (Knutson et al., 1999;
Burgdorf et al., 2001). In this case, this
maneuver does not work for repeated
short CS pairing with small bits of food
typically used in operant conditioning. This
suggests that the response has to be within
the context of ecological validity (animals
typically anticipate and take meals). If the
CS were to evoke 22-kHz aversion-indicative
USVs – a response seen with aversive
drugs (Burgdorf et al., 2001) – then it would
be highly unlikely that the peptide was
reducing food intake by evoking feelings
of satiety.
1. FROM BRAIN TO BEHAVIOR
1.3 VARIOUS METHODOLOGIES TO EVALUATE AFFECTIVE CHANGE IN PRE-CLINICAL APPETITE RESEARCH
Glucose consumed (ml)
50
11
Glucose crossover
Top: Raw data
Bottom: Relative data
Raw data
Dilute 10%
solution
40
30
20
Relative intakes of dilute
solution under various
metabolic conditions
Concentrated 35%
solution
10
Diabetic rats
Normals recovering
from obesity
80
Percent glucose intake
as dilute solution
35%
80
10%
Percent glucose taken
as dilute solution
Glucose
60
40
20
Percent preference
1
4
70
Normal
untreated
60
50
Hypothalamic
hyperphagic
Normals getting
insulin
Genetically obese
40
30
20
10
7
Days
Days
FIGURE 1.3 Summary of the patterns of sugar water consumption in animals given continuous daily access to two
solutions of different concentrations.
Animals initially take most of their sugar from the concentrated solution, but gradually shift over to the less sweet dilute
source. The right-hand graph summarizes the changes in glucose intake crossover patterns of various groups of rats with
distinct energy regulatory problems.
Source: Figure 9.10 of Panksepp (1998: 183), reprinted with permission of Oxford University Press.
The remarkable aspect of current feeding
research, with such a cornucopia of appetite
control agents, is that essentially none of these
criteria have been studied. If it were demonstrated that a presumptive “satiety peptide”
fulfilled all of these criteria, then this could be
the recipe for reducing feeding with the desired
positive affective consequences. Without at least
fulfilling some of these criteria, claims simply
from reductions of amount of food consumed
are premature, and reflect hubris rather than
sound affective neuroscientific thinking.
Such tests are not often conducted because
they are more difficult than the mere measurement of food intake. A more troublesome
reason for neglect is that the above analysis
also requires investigators to openly consider
a variety of affects as real functional properties of mammalian brains (Panksepp, 1998,
2005). Affects are real brain functions that
allow animals to anticipate life-sustaining
and life-detracting events. There has been one
affective measure, conditioned taste aversion
(CTA), that has been superbly developed, and it
would be worthwhile providing an overview
of this work as a trail-marker for what needs
to be done with some of the other measures
described above. The CTA procedure is now
developed to a point where it could be used
as one of the most rigorous ways to study
appetite-related adverse affective changes in the
human brain.
1. FROM BRAIN TO BEHAVIOR
12
1. BRAIN REGULATORY SYSTEMS
1.4 CONDITIONED TASTE
AVERSIONS – FROM ANIMAL
MODELS TO HUMAN BRAIN
ANALYSIS?
In general, it is difficult to bring human emotional feelings under tight experimental control in research settings. This is not the case for
other powerful affects, such as homeostatic feelings (e.g., hunger and thirst, which can easily be
evoked hormonally). Likewise, certain sensory
affects, such as nausea, can be modeled in animals using the straightforward CTA procedure,
for which there is now a massive database (Riley
and Freeman, 2004) – a rich pre-clinical animal
literature (for recent reviews, see Sandner, 2004;
Sewards, 2004; Mediavilla et al., 2005), including
a fairly precise understanding of the underlying
neuroanatomies in other mammals (Yamamoto
et al., 1994; Reilly, 1999; Jiménez and Tapia, 2004;
Reilly and Bornovalova, 2005; de la Torre-Vacas
and Agüero-Zapata, 2006; Ramírez-Lugo et al.,
2007). The CTA measure and the associated negative affects have widespread implications for
human nutritional habits (Gietzen and Magrum,
2001; Scalera, 2002).
This model has immediate implications for
medical treatments and development of new
and more precise therapeutics. CTA is a highly
replicable and simple learning paradigm where
novel tastes that are not intrinsically nauseating
can be imbued with that aversive affect through
simple classical conditioning principles (i.e., the
pairing of a new taste with a nausea-producing
manipulation). Lithium chloride is most commonly used, even though there are now many
more precise brain manipulations, such as
stimulation of 5HT3 and Substance P receptors
(see below).
Indeed, the human brain consequences of
such conditioning could be evaluated with
human brain-imaging. The conditioning could
be done off-line (i.e., outside the scanner), which
prevents people from being confronted with
unconditional nausea-promoting stimuli in the
scanner. Such procedures may be also useful for
delineating the circuitry for the associated fixed
action patterns, such as gaping in rats (Limebeer
et al., 2006).
Beside the ability to control this powerful
affect experimentally with a large number of
distinct manipulations, the CTA paradigm provides a variety of controls that would be desirable to pursue both raw affective as well as
learned-cognitive interactions (see, for example, Welzl et al., 2001; Hall and Symonds, 2006)
that reflect true life experiences but can also be
submitted to tight experimental control. Indeed,
there are two distinct types of taste conditioning
that transpire (Parker, 2003), one related to nausea (aversion) and one related to fear (avoidance), which can allow investigators to study
two very distinct affects under almost identical
conditions.
A great strength of this model is the abundance of neurochemical manipulations currently
available to directly modify specific neurochemical aspects of the underlying affect-generating
circuitry. This has arisen largely because of the
medical importance of controlling nausea and
malaise following radiation and chemotherapies for cancers. Among the most commonly
used anti-nausea agents are prochlorperazine,
ondansetron and aprepitant. Their mechanisms
of action are distinct and well-characterized,
pharmacologically, neurochemically and functionally – especially for the latter two agents.
Ondansetron is a specific serotonin 5-HT3
receptor antagonist, and aprepitant selectively
blocks the NK1 tachykinin (i.e., Substance
P) receptor. The former generally has a more
restricted therapeutic profile (McAllister and
Pratt, 1998). Although ondansetron can reverse
classic lithium chloride-induced CTAs (Balleine
et al., 1995) as well as aversions induced by
imbalanced amino acid diets (Terry-Nathan
et al., 1995), many other nausea-provoking emetics are not effectively reversed by ondansetron
(Rudd et al., 1998).
1. FROM BRAIN TO BEHAVIOR
REFERENCES
The large number of neurochemical manipulations for generating nausea, from apomorphine
to 5-HT3 and Substance P receptor agonists
(Landauer et al., 1995; Ciccocioppo et al., 1998)
provides an armamentarium of convergent
manipulations for actually taking the analysis of
this affect to a fine circuit level in both animals
and humans. At present, there is to our knowledge not a single brain-imaging study that has
sought to study this as a model system – one that
has all the desired advantages for a thorough
scientific analysis, and perhaps none of the disadvantages of weak and ephemeral affects that
are commonly used in human brain imaging
of affective processes. The disadvantage is that
these are experiments that one would not want
to impose on non-medically sophisticated volunteer subjects. This may also be a blessing
for obtaining the highest quality data from professionally qualified individuals.
1.5 CONCLUSION
Affective changes in energy regulatory studies have been neglected because it is widely
assumed that qualities of animal minds cannot
be systematically studied. That is wrong. Affects
are ancient solutions for living, and primaryprocess variants appear to be a shared heritage
in all mammals. Thus, we are finally in a position empirically to evaluate such issues in animals. Once we begin to do this with the wide
array of objective measures that are available,
we may be able to identify useful appetite regulating agents more readily than if we just continue traditional behavior-only analyses.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
2
The Neurobiology of Appetite:
Hunger as Addiction
Alain Dagher
Montreal Neurological Institute, McGill University, Montreal, Canada
O U T L I N E
2.1
Introduction
15
2.2
Hunger as Addiction
16
2.3
Response to Conditioned Cues
17
2.1 INTRODUCTION
Obesity is caused by the consumption of
excess calories. As such, it can be viewed as a
failure of homeostatic systems that control body
weight or, more appropriately, energy balance.
The obese individual consumes excess calories in a non-homeostatic manner, as a result of
excessive motivation or drive. A similar model
has been proposed to explain drug addiction,
in which hedonic homoestatic systems are dysregulated (Koob, 2008). There is considerable
overlap between brain systems and neurotransmitters implicated in drug addiction and those
known to control feeding behavior.
Another way of looking at excess consumption of calories is that it is driven by pleasure,
Obesity Prevention: The Role of Brain and Society on Individual Behavior
2.4
2.5
Functional Brain Imaging of
Cue Reactivity
20
Conclusion
20
or the anticipation of the palatable qualities
of food. It has been suggested that there are
two parallel systems for driving food intake: a
homeostatic one that responds to energy signals
from the body, transmitted mainly via the circulation and the vagus nerve, and acting via the
hypothalamus; and a hedonic one in which food
cues (odors, thoughts, the sight of food) have
the ability to stimulate appetite in the absence
of metabolic need (Figure 2.1). Note, however,
that the addiction model provides a slightly different explanation of the effect of food cues, by
viewing them as conditioned stimuli predictive
of reward. In this chapter, we review the evidence linking drug addiction and obesity, and
a recent study that suggests homeostatic signals
interact with hedonic and incentive signals to
trigger food intake.
15
2010 Elsevier Inc.
© 2010,
16
2. NEUROBIOLOGY OF APPETITE
Homeostatic
system
Blood nutrients
Hedonic
system
Leptin
Sensory cues
Food thoughts
Vagus n.
Gut peptides
Hypothalamus
Cerebral cortex
Feeding
Feeding
FIGURE 2.1 A model of feeding behavior.
According to this model, there are two kinds of eating: that triggered by energy deficit; and that triggered by hedonic
factors, such as the anticipation of a delicious meal.
2.2 HUNGER AS ADDICTION
Donald Hebb suggested that hunger could
be viewed as an addiction (Hebb, 1949). He
proposed that hunger was a learned behavior,
in which food is initially reinforcing because it
reverses unpleasant sensations caused by lack
of nutrients or stomach contractions. With time
the behavior becomes organized and food cues,
such as the sight and smell of food, become
conditioned and develop the ability to induce
craving, approach and consumption, much as
drug-associated cues do. Conditioned cues are
known to exert their incentive properties in part
via the mesolimbic dopamine system (Phillips
et al., 2003). Initially, unexpected food rewards
trigger dopamine release; however, with time,
conditioned cues paired with reward eventually promote dopamine release (Schultz, 2006).
Drugs of abuse also promote dopamine release,
as do their conditioned cues. The link between
dopamine and feeding was first established
when a dopamine receptor blockade attenuated the reinforcing effects of food, as it did for
stimulant drugs and electrical brain stimulation,
leading to the conclusion that addictive drugs
act on the brain circuitry that controls feeding
(Wise et al., 1978). The addiction model, however, leads to the paradox of ascribing energy
intake, the most basic survival behavior, to
aberrant function of neurobiological systems.
Nonetheless, evidence of parallels between
addiction and feeding behavior continue to
accumulate at the neurobiological and behavioral levels (Grigson, 2002), and we therefore have
much to learn about obesity from the neuroscience of addiction (Volkow and Wise, 2005).
Further support for the addiction model comes
from recent findings in two domains: obesity
genes and circulating energy balance hormones.
Many obesity genes appear to act on reward circuitry (Farooqi and O’Rahilly, 2007). The FTO
gene is expressed throughout the body and brain,
but is particularly abundant in feeding-related
areas in the hypothalamus, including the arcuate
nucleus (Fredriksson et al., 2008). The arcuate has
a direct projection to the lateral hypothalamic area
(LHA), a structure long implicated in reward.
Indeed, the LHA, through its outputs to the striatum and brainstem autonomic and motor nuclei,
is the main hypothalamic output nucleus for the
control of feeding behavior. It is also one of the
sites where electrical brain stimulation is most
rewarding (Wise, 2002). The product of the MC4-R
gene, which has the strongest association with
1. FROM BRAIN TO BEHAVIOR
2.3 RESPONSE TO CONDITIONED CUES
obesity, is also abundantly expressed in the arcuate nucleus and LHA (Adan et al., 2006). LHA neurons contain the neurotransmitter orexin, named
because of its role in controlling feeding behavior,
but which has also recently been implicated in
drug addiction (Borgland et al., 2006). Indeed, in
animals conditioned to associate a certain environment with food or drugs such as morphine or
cocaine, orexin neurons in the LHA play a critical role in the expression of the preference and
in its reinstatement after extinction (Harris et al.,
2005). This finding further supports the idea of
overlapping brain systems that mediate drug
addiction and feeding. More compellingly, perhaps, an allele of the Taq1A polymorphism (the
A1 allele) has been associated with both addiction
and obesity (Barnard et al., 2009). This polymorphism appears to regulate dopamine D2 receptor
expression, suggesting that it plays a role in the
function of the reward system.
Feeding is controlled in part by peripheral signals that convey information about the
energy state of the individual. Four such metabolic signals have been shown to act on brain
reward centers, indirectly via the hypothalamus,
but also through direct effects on the mesolimbic
dopamine system. Leptin, ghrelin and insulin all
modulate food intake and act directly, though
not exclusively, on dopamine neurons (Figlewicz
et al., 2003; Abizaid et al., 2006; Fulton et al.,
2006). Functional magnetic resonance imaging studies in humans have confirmed that the
appetite stimulating hormone ghrelin enhances
the response of the reward system to food cues
(Malik et al., 2008) while the anorexigenic peptides PYY and leptin also act on reward-related
brain areas (Batterham et al., 2007; Farooqi et al.,
2007), presumably in an inhibitory fashion. One
often mentioned difference between food and
drugs is that drugs of abuse act directly on the
brain, whereas the effects of food are indirect,
since they must be digested before their components can enter the bloodstream. For example, after a puff of a cigarette, nicotine enters the
brain within seconds, where it directly increases
17
dopamine concentration by acting on dopamine
neurons. Note, however, that there are multiple
parallel neuronal and humoral avenues of communication between the gut and brain (Berthoud
and Morrison, 2008). Appetite regulating peptides such as ghrelin (Abizaid et al., 2006) and
PYY (Batterham et al., 2007), whose secretion is
directly influenced by gut contents, can act on
the dopamine system, providing a mechanism
for food to act on reward systems almost as rapidly as some abused drugs. Also, ingested nutrients such as glucose enter the bloodstream and
cross the blood–brain barrier. Indeed, experimental evidence suggests that sucrose has addictive properties very similar to those of cocaine
and amphetamine (Avena et al., 2008).
Finally, another similarity between feeding
and addiction is the important role of stress
in both behaviors. Stress is a major cause of
relapse amongst abstinent drug users, and also
a significant cause of failure in dieters (Adam
and Epel, 2007). During stressful periods, most
individuals increase their caloric intake (in particular, of saturated fats and carbohydrates). The
brain areas that make up the appetitive network
depicted in Figure 2.2 are all stress-sensitive.
2.3 RESPONSE TO CONDITIONED
CUES
Previously neutral cues paired repeatedly
with rewards acquire incentive properties. This
phenomenon depends on the neural systems
depicted in Figure 2.2. Four interconnected
structures (shown in gray), the amygdala/hippocampus, insula, orbitofrontal cortex (OFC)
and striatum are central elements in the control
of appetitive behavior. Although each structure depicted in this figure has a different
role, the network as a whole is involved in learning about rewards (foods and drugs), allocating
attention and effort towards them, assigning
incentive value to stimuli in the environment
1. FROM BRAIN TO BEHAVIOR
18
2. NEUROBIOLOGY OF APPETITE
PFC
ACC
Sensation
(taste, olfaction, vision)
Interoception
(hunger, nausea)
Insula
Amygdala
hippocampus
OFC
Striatum
VTA (DA)
Hypothalamus
FIGURE 2.2 The appetitive network.
Brain regions involved in assigning incentive value to food- and drug-related stimuli and actions.
Abbreviations: PC, prefrontal cortex; ACC, anterior cingulate cortex; OFC, orbitofrontal cortex; VTA, ventral tegmental area;
DA, dopamine.
(e.g., conditioned cues) and integrating homeostatic information with information about the
outside world (such as the availability of food).
Homeostatic information is conveyed to the
brain by circulating nutrients and hormones
such as leptin, PYY and ghrelin acting primarily on the hypothalamus, and by the vagus
nerve. The amygdala, insula, OFC and striatum all respond to conditioned stimuli predictive of reward (food- or drug-associated cues),
as assessed in animals using electrical recordings, and in humans using functional magnetic
resonance imaging (fMRI). Moreover, lesions of
this network impair feeding or drug-seeking.
For example, lesions of the amygdala or OFC
(or disconnection of the two structures) abolish
a behavior known as sensory specific satiety, in
which a cue associated with a food fed to satiety loses its incentive properties (Holland and
Gallagher, 2004). More generally, this network
assigns incentive value to food (or drug) cues,
and to the associated actions that lead to the consumption of the food (or drug). Lesions of any
of these four regions impair feeding behavior in
some way. Cognitive influences on appetite are
mediated by the prefrontal cortex, which exerts
modulatory control over appetitive regions.
A key component of the reward system is the
ensemble of dopamine neurons that originate
in the midbrain (especially the ventral tegmental area) and project to the striatum, amygdala,
OFC, insula and prefrontal cortex. Dopamine
has long been implicated in addiction, as it is
released by all drugs of abuse as well as food
and food cues (Di Chiara and Imperato, 1988),
and dopamine blockade abolishes responding
for food or drugs (Wise and Rompre, 1989).
The insula has an important role in the multimodal processing of food information. The
anterior insula is the first cortical relay of information from taste receptors in the oral cavity, but
neurons there also respond to other properties
of foods, such as texture, temperature, and olfactory and visual properties. The insula is also the
sensory cortex for visceral information from the
gut (Craig, 2002). Insula activity is modulated by
cognitive and emotional factors, including hunger and attention, and by gut peptides such as
1. FROM BRAIN TO BEHAVIOR
2.3 RESPONSE TO CONDITIONED CUES
ghrelin (Malik et al., 2008). The multimodal sensory features of foods in the mouth are encoded
by neuronal ensembles within the insula, and
this activity is modulated by hunger and satiety (de Araujo et al., 2006). The insula plays a
crucial role in learning about the nutritional
effects of ingested foods (de Araujo et al., 2008)
and therefore aids in the ability of food cues to
become conditioned. Insular lesions disrupt this
phenomenon; animals with insula lesions fail to
attribute incentive value to calorie-rich foods,
for example (Balleine and Dickinson, 2000).
Interestingly, cigarette smokers who developed
insula damage (e.g., from stroke) found it easy
to quit smoking (Naqvi et al., 2007).
The ability of conditioned cues stimuli to
trigger incentive states is a feature of both drug
addiction and eating. Abstinent drug addicts
report that drug cues or thoughts cause them
to crave the drug, and everyone knows the
feeling of seeing a dessert tray at the end of a
meal, or walking past a bakery. The ability of
cues to trigger appetite may be a component
of the obesogenic environment, which bombards us with foods, odors, advertising, brand
names and logos. Cue reactivity has been extensively studied in animals, and is starting to be
used as a paradigm in human functional neuroimaging. A neutral stimulus or environment
paired repeatedly with food or drug acquires
the ability to trigger consumption of the food
or drug. Kelley and colleagues have mapped
gene expression changes in animals exposed to
an environment previously paired with rewards
(Kelley et al., 2005). For both food and addictive
drugs, the paired environments caused activation in the prefrontal cortex, anterior cingulate
cortex, insula, striatum and amygdala. A similar
phenomenon is cue-potentiated feeding, where
a conditioned cue triggers feeding, even in
sated animals. This is thought to reflect craving
rather than a non-specific increase in appetite,
since it is only the food that was paired with
the conditioned stimulus that is consumed (by
analogy with drug cues, which do not cause a
19
non-specific incentive state, but a specific craving for the drug itself). A similar phenomenon
is described in humans, where food odors or
thoughts only cause increased consumption of
the target food (Fedoroff et al., 2003). Petrovich,
Gallagher and Holland have, in a series of animal experiments, delineated the neural network
for cue-potentiated feeding (see, for example,
Petrovich and Gallagher, 2007). Key components include the basolateral amygdala, LHA,
and medial prefrontal cortex including the OFC.
In particular, a projection from the basolateral
amygdala to LHA is crucial, since disconnecting
these regions abolishes cue-potentiated feeding.
Conditioned cues do more than inform an
individual about available rewards; such cues
also energize individuals by creating an incentive
state, motivating them to approach and consume
food or other rewards with great vigor – a phenomenon that appears to be mediated in large
part by dopamine (Phillips et al., 2003). It appears
that certain individuals react to appetitive
signals with greater drive, and this may be a
risk factor for developing addiction and obesity.
Although considerable evidence exists for this
model with respect to drug addiction, evidence
is less abundant in the field of obesity. Some
measures of reward sensitivity appear to predict
appetitive behavior and obesity (Franken and
Muris, 2005; Davis and Fox, 2008). Moreover,
the personality variable of impulsivity, defined
as a tendency to act without due consideration
of long-term consequences, has been shown to
confer vulnerability to drug addiction in humans
(Verdejo-Garcia et al., 2008) and animals (Belin
et al., 2008). Impulsivity may be in part a consequence of enhanced reward drive, where immediately available rewards acquire great saliency
and incentive properties, and in our obesogenic
environment it may promote excess calorie
intake and obesity (Davis, 2009). Interestingly,
humans who develop drug addiction also display an enhanced preference for sweet foods
(Grigson, 2002), suggesting a common substrate
for both.
1. FROM BRAIN TO BEHAVIOR
20
2. NEUROBIOLOGY OF APPETITE
2.4 FUNCTIONAL BRAIN IMAGING
OF CUE REACTIVITY
Functional magnetic resonance imaging
measures changes in regional cerebral blood
flow, which indicates changes in synaptic activity. Conditioned cues were first combined with
fMRI in drug addicts. Cigarette cues (pictures,
videos, stories) typically activate areas of the
appetitive network (Figure 2.2), namely the
prefrontal cortex, amygdala, insula, striatum
and OFC (Due et al., 2002; Wilson et al., 2004;
McBride et al., 2006). Interestingly, very similar
areas are activated by food cues (LaBar et al.,
2001; Small et al., 2001, 2005; Arana et al., 2003;
Simmons et al., 2005; Malik et al., 2008; Stice et al.,
2008), including pictures, odors and tastes.
Thus, as in animals (Kelley et al., 2005), conditioned cues activate an appetitive network,
whether the cues are food- or drug-related.
The fMRI response in these regions appears to
be a measure of the appetitive impact of the cues.
For example, the appetite stimulating peptide
ghrelin enhanced the brain response to food pictures in the amygdala, insula, OFC and striatum
(Malik et al., 2008). The response in amygdala and
OFC correlated with subjective hunger.
Interestingly, a personality measure of reward
sensitivity also predicted the appetitive impact
of food cues in humans (Beaver et al., 2006).
The fMRI response to appetizing versus bland
food pictures in the amygdala, OFC and ventral
striatum was proportional to the score on the
Behavioral Activation Scale. This measure also
predicts weight gain and obesity (Franken and
Muris, 2005).
The foregoing suggests that fMRI responses to
food or food cues could be a biomarker of vulnerability to obesity. Recent human brain-imaging
studies appear to support this idea. Stice and colleagues have suggested that an enhanced insula
response to the anticipation of food is predictive
of obesity (Stice et al., 2009). Conversely, obesityprone individuals may display reduced activation
to actual food consumption in the striatum (Stice
et al., 2008). Interestingly, the relationship between
BMI and the fMRI signal was modulated by the
Taq1A polymorphism.
2.5 CONCLUSION
Obesity has been described as a neurobehavioral disorder caused by an interaction between
a vulnerable brain and an obesogenic environment (O’Rahilly and Farooqi, 2008). Conditioned
cues such as the sight and smell of food, or food
advertising, have the ability to trigger an incentive state that is very similar to phenomena seen
in drug addicts. The neural structures activated by
conditioned cues, whether drug- or food-related,
appear to overlap considerably. On the other
hand, hormonal energy balance signals, such as
ghrelin (Malik et al., 2008), also appear to act on
the appetitive network, to increase feeding. This
suggests an alternate model where there are not
two brain systems for feeding (Figure 2.1), but a
single appetitive system that can be modulated by
both homeostatic and hedonic signals (Figure 2.3).
Homeostatic
signals
Hedonic
signals
Vagus n.
Sensory cues
Leptin
Food thoughts
Blood nutrients
Appetitive
system
Gut peptides
Arousal
Attention
Motivation
Feeding
FIGURE 2.3 A new model of feeding behavior.
Here, energy balance signals and conditioned cues act on the
same brain systems to promote food intake. The shaded structure represents the appetitive system depicted in Figure 2.2.
Not shown are cognitive factors, such as restraint (e.g., in dieters), that can down-regulate responses in this system.
1. FROM BRAIN TO BEHAVIOR
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
3
Opioids: Culprits for Overconsumption
of Palatable Foods?
Pawel K. Olszewski1,2, Johan Alsiö2, Helgi B. Schiöth2
and Allen S. Levine1
1
Minnesota Obesity Center, Department of Food Science and Nutrition, University of Minnesota,
Saint Paul, MN, USA
2
Department of Neuroscience, Uppsala University, Uppsala, Sweden
O U T L I N E
3.1
Introduction
3.2
Opioids and Feeding Behavior
in Rodent Models
3.2.1 Opioids Promote the Intake
of Palatable Foods
3.2.2 Opioids Within the Central
Feeding-related Reward Network
3.2.3 Palatability of Ingested Tastants
Affects Endogenous Opioid Tone
3.2.4 Opioids: in Search of Palatability or a
Specific Macronutrient?
23
24
24
25
27
3.4
29
Opioids and Dysregulation of Eating
Patterns and Body Weight in Human
Beings
3.3.1 Excessive Eating and Body Weight
3.3.2 Underweight Individuals
31
31
33
Conclusions and Perspectives
33
28
3.1 INTRODUCTION
A traditional dietary questionnaire probes
eating habits of an individual, and it routinely
assesses what foods are most palatable and
Obesity Prevention: The Role of Brain and Society on Individual Behavior
3.3
3.2.5 Opioids: Feeding for Calories or for
Pleasure?
what amounts of such tastants are ingested
with regularity. Preferred diet profiles differ a
lot between people, yet one distinctive characteristic emerges, and that is that fat and/or
sugar constitute a significant percentage of the
favored foods. Importantly, palatability serves
23
© 2010,
2010 Elsevier Inc.
24
3. OPIOIDS AND OVERCONSUMPTION
as a key factor promoting excessive intake of
calories, which consequently leads to excess
weight and obesity.
Among endogenous substances involved in
food-intake regulation, opioid peptides are perhaps the prime suspects driving excess intake
of palatable food. This notion is based on the
effects of opioid agents on the consumption of
preferred tastants, especially those rich in fat
and sugar. Here, we discuss the role of opioids
in overeating palatable foods as elucidated in
animal studies, and present applicability of the
basic research findings to understanding and
treating obesity in humans.
3.2 OPIOIDS AND FEEDING
BEHAVIOR IN RODENT MODELS
3.2.1 Opioids promote the intake
of palatable foods
Although opioid receptor antagonists reduce
food intake in general, their effects are more
pronounced when palatable foods are provided. Chronic naltrexone infusions via osmotic
minipumps suppressed food intake and bodyweight gain more efficiently in animals with free
access to a sucrose solution than in chow controls (Marks-Kaufman et al., 1984). Naltrexone
decreased intake of both sweet and oily mash
in non-deprived rats (Kirkham et al., 1987).
Furthermore, naloxone reduced the intake of
sweet chow more than of standard chow in
ad libitum- and schedule-fed animals, as well as
after deprivation and chronic restriction (Levine
et al., 1995). In fact, when rats were schedulefed and received sweetened chow for just
20 minutes per day, naloxone decreased consumption of the palatable diet, but only to the
level of the minimum daily calorie requirement.
In that experiment, only high doses were effective in lowering the intake of standard pellets.
Naloxone also reduced the intake of sucrose
and polycose in food-restricted rats, but not the
intake of a cornstarch diet (Weldon et al., 1996).
Anorexigenic effects of naloxone were most
pronounced when chocolate chip cookies were
presented, while naloxone did not modify the
intake of high-fiber bland food (Giraudo et al.,
1993). These results show that rats eating sugar
diets are more sensitive to naloxone than rats
fed plain food, and the ability to fulfill energy
needs is not impaired by the antagonism of opioid receptors.
The relationship between the opioid system and palatability was supported by studies
employing non-caloric palatable substances.
Opioid-receptor deficient mice displayed lower
saccharin preference than wild-type mice
(Yirmiya et al., 1988). The effects of naloxone on
the consumption of artificially sweetened solutions are similar to the effects on sucrose intake.
Naloxone reduced the intake of saccharin solutions at lower doses than it reduced the consumption of NaCl or water (Turkish and Cooper,
1983). Naloxone pre-treatment reduced saccharin
intake in non-deprived animals, blocked normal
acquisition of saccharin preference (Lynch,
1986), and suppressed imbibition of fluid with
saccharin, sucrose, NaCl or HCl, but it did not
influence the intake of aversive quinine (Levine
et al., 1982).
Similar conclusions were reached in experiments utilizing sham-feeding and sham-drinking;
in this paradigm, the tastant consumed by
rodents is immediately removed from the stomach via a gastric fistula, preventing post-ingestive
or post-absorptive feedback. In sham-drinking,
naloxone reduced sucrose intake in non-deprived
and water-deprived rats (Rockwood and Reid,
1982). Furthermore, while sucrose sham-drinking
increases proportionally to sugar concentration
(Kirkham and Cooper, 1988a), naloxone shifts the
concentration-intake curve – that is, it reduces the
intake of 10% sucrose to the amounts consumed
by saline-injected animals drinking a less preferred 5% sucrose solution (Kirkham and Cooper,
1988a, 1988b). Importantly, naloxone does not
1. FROM BRAIN TO BEHAVIOR
3.2 OPIOIDS AND FEEDING BEHAVIOR IN RODENT MODELS
influence the ability to discriminate between
sucrose concentrations, indicating that processing of taste (i.e., “interpreting” flavor on a relative reward scale) is affected by opioid signaling,
not the ability to discriminate a given taste
(O’Hare et al., 1997). These results suggest that
the palatability of solutions – that is, their orosensory reinforcing value – is reduced by naloxone.
Consequently, the acquisition and expression
of preference for the orange odor, induced by
concomitant intraoral infusions of sucrose, was
blocked by systemic naltrexone applied prior to
pairing the exposure to this odor with sucrose
(Shide and Blass, 1991). In contrast, flavor preferences induced by intragastric infusions of sucrose
were not affected by naltrexone (Azzara et al.,
2000).
In operant experiments, the progressive ratio
schedule measures motivation for a reinforcer; the effort needed for successive reward
increases exponentially over sessions. When
responding for sucrose, motivation increases
with the concentration of sugar (Sclafani and
Ackroff, 2003); naloxone, however, reduces this
motivation (Cleary et al., 1996). A genetic deletion of the mu opioid receptor (MOR) leads
to a decreased motivation to consume food
(Papaleo et al., 2007). In operant behavior studies, this diminished motivation has been associated with the consumption of palatable and
bland diets, which suggests that the MOR supports a drive to ingest foods regardless of their
attractiveness. In addition, the MOR-null animals display a decreased feeding anticipatory
activity (Kas et al., 2004). It does not negate the
involvement of this receptor in hedonics of
feeding, because any diet – with the exception
of aversive tastants – is associated with pleasure
and a positive “incentive” of feeding. Tabarin
et al. (2005) reported that mice lacking the MOR
were resistant to diet-induced obesity, and they
did not exhibit impaired glucose tolerance. It
was particularly the case when high-fat food
was offered. Resistance to obesity did not stem
only from a diminished propensity to overeat,
25
but was also linked with a higher expression of
mitochondrial enzymes involved in fatty acid
oxidation in skeletal muscles.
3.2.2 Opioids within the central
feeding-related reward network
Initial studies elucidating the role of opioids
in feeding focused on peripheral injections.
However, a crucial question was later asked as
to whether the action is mediated via peripheral
and/or central receptors.
Early experiments compared the effects of
regular versus quarternary naltrexone on food
intake. The latter, a charged molecule which
does not cross the blood–brain barrier, has no
effect on feeding, while the uncharged naltrexone reduces food intake (Carr and Simon, 1983;
Marks-Kaufman et al., 1985). Hence, the targets
for opioid agents are present at the central level.
Subsequent studies showed that intracerebroventricular and site-specific microinjections of
opioid ligands affected consummatory behavior (Levine and Billington, 2004; Olszewski and
Levine, 2007).
Importantly, the reward network seems to
have a close relationship with opioid mechanisms driving intake of palatable foods. The
nucleus accumbens (NAcc) is one of the major
players mediating hedonics of food. Peripheral
morphine increases the intake of sucrose and
saccharin, and this effect can be reversed by
naloxone. This was observed also following
morphine injections into the NAcc and ventromedial striatum, but not into the caudate putamen (Evans and Vaccarino, 1990; Bakshi and
Kelley, 1993a). In addition, systemic morphine
induced Fos immunoreactivity, which serves as
the marker of neuronal activation, in the NAcc
(Bontempi and Sharp, 1997); it suggests that neurons in this site respond to opiate stimulation.
Although all types of opioid receptors are
present in the NAcc, the MOR appears to be of
greatest importance for food intake; the delta
1. FROM BRAIN TO BEHAVIOR
26
3. OPIOIDS AND OVERCONSUMPTION
receptor (DOR) plays a relatively limited role,
while activation of the kappa receptor (KOR) has
rarely been shown to stimulate feeding. NAcc
injections of agonists, such as morphine, [DAla2, D-Leu5]-enkephalin and beta-endorphin,
elevated chow intake in rats by acting at the
MOR and DOR. KOR stimulation had less or
no effect; dynorphin produced an increase in
food intake only at a very high dose, whereas
the selective KOR agonist, U50488, was inefficient (Majeed et al., 1986). Consumption of
sucrose was increased by NAcc morphine, [DAla2, N-MePhe4, Gly-ol]-enkephalin (DAMGO)
and [D-Pen2,5]-enkephalin, but not dynorphin
or U50488 (Zhang and Kelley, 1997). DAMGO
produced robust increases in food intake when
injected into the NAcc (Bakshi and Kelley,
1993b). A smaller effect was seen with the DOR
stimulation, while KOR agonists had no influence (Bakshi and Kelley, 1993b). The selective
blockade of the MOR by administration of a
non-reversible antagonist, beta-FNA, decreased
consumption of sucrose by 40 percent during
4-h tests conducted 2, 3 and 4 days after infusion (Ward et al., 2006). In the operant behavior setting, NAcc DAMGO increased correct
lever presses for sucrose and resulted in higher
break-points on the progressive ratio schedule
(Zhang et al., 2003). DAMGO also increased the
intake of high-fat and high-carbohydrate diets
in non-deprived rats, and elevated consumption
of a high-fat diet in deprived rats (Zhang et al.,
1998). The feeding effects of morphine at the
NAcc are subject to sensitization: with repeated
injections, consumption is further increased
(Bakshi and Kelley, 1994). Such sensitization
may implicate cues (environmental or procedural) that over time become associated with
hyperphagia and, through conditioning, induce
feeding on their own (Wardle, 1990). The effect
of morphine may stem from the compound’s
acute stimulation of feeding as well as from
facilitation of conditioning.
The ventral tegmental area (VTA) is another
component of the reward network that mediates
orexigenic action of opioids. Morphine injected
in the VTA increases food intake, and this effect
is reversible by naloxone (Mucha and Iversen,
1986). [D-Ala, Met]-enkephalin (DALA) infused
into the VTA induces feeding in deprived and
non-deprived rats (Cador et al., 1986). Not only is
the amount of food eaten increased; so too is the
time spent eating (Hamilton and Bozarth, 1988).
VTA morphine increased the speed of consumption upon refeeding, but it did not affect the
latency to begin a meal (Noel and Wise, 1993).
Deprivation-induced feeding was accelerated by
VTA injections of DAMGO or [D-Pen2,D-Pen5]enkephalin (Noel and Wise, 1995). VTA DAMGO
affected latency to feed and the number of active
feeding episodes, and facilitated food-related
behaviors (including those that did not result
in ingesting calories); however, the magnitude
of the observed effects was dependent on the
context – for example, the presence of the animal in or outside the cage (Badiani et al., 1995).
Finally, operant behavior experiments showed
that the VTA MOR was the major mediator of
reward (Devine and Wise, 1994).
Since opiates are associated with the intake
of palatable foods, endogenous opioids have
been proposed to mediate hedonics of eating.
In humans, experiments assessing the hedonic
impact of food are performed with the aid of
visual analog scale ratings. To measure hedonic
impact in animals, the taste reactivity test was
developed (Grill and Norgren, 1978); this can be
used to assess the affective influence of tastants
(Berridge, 2000). The test is based on stereotypic behaviors: positive (hedonic) and negative (aversive) reactions to palatable and bitter
tastes, respectively. These responses are homologous across species, and they include both
positive reactions (such as rapid tongue protrusions) and negative ones, including gaping and
head shakes (Steiner et al., 2001). The magnitude
of these responses is used to define the hedonic
aspects of a tastant (Grill and Norgren, 1978).
It was hypothesized that if opioids affect the
hedonic processing of a gustatory stimulus,
1. FROM BRAIN TO BEHAVIOR
3.2 OPIOIDS AND FEEDING BEHAVIOR IN RODENT MODELS
these behaviors would be altered by opioid
manipulations. Morphine increased the hedonic
response to sucrose and attenuated aversive
reactions to quinine, while naltrexone decreased
the number of hedonic reactions to sucrose
(Parker et al., 1992; Doyle et al., 1993; Clarke
and Parker, 1995; Pecina and Berridge, 1995;
Rideout and Parker, 1996). Changes in hedonic
responses were coupled to concomitant effects
on food intake, showing that the higher the perceived palatability, the greater the amount of
consumed food. Subregions of the NAcc and
ventral pallidum have been dubbed “hedonic
hot spots” likely to be implicated in this palatability-induced hedonic state. Opioid activity
within these regions contributes to reward
motivation: the notion that discrete sites mediate hedonic reactions to opioids has been
assessed by the taste reactivity test (Pecina and
Berridge, 2000, 2005; Smith and Berridge, 2005).
DAMGO elicits hedonic reactions only when
administered in a small rostrodorsal subregion
of the medial NAcc shell, while food intake is
stimulated regardless of injection site in the
medial shell area (Pecina and Berridge, 2005).
Subdivisions of the ventral pallidum are even
more distinct: DAMGO in the posterior part
stimulates hedonic reactions and eating, while
anterior infusions reduce the hedonic aspect of
feeding and inhibit consumption (Smith and
Berridge, 2005). These studies corroborate the
involvement of opioid signaling within reward
sites in hedonic reactions to palatable food.
3.2.3 Palatability of ingested tastants
affects endogenous opioid tone
Numerous studies have indicated that the
opioid system’s activity is affected by palatable
foods. Initial observations revealed that consumption of such tastants modified certain behaviors
or perception of stimuli in a similar way to that
of administration of opioid agonists. For example
sucrose relieves the sensation of pain or increases
27
the thresholds for pain reactions – for example,
in the hot-plate and tail-flick tests (Anseloni et al.,
2005; Segato et al., 2005). This effect was dependent on the concentration rather than the total
amount of the ingested macronutrient (hence, it
paralleled the palatability scale).
Rats provided with intermittent access to
25% glucose or 10% sucrose for 12 hours per
day increased their daily sugar intake over time
and doubled it within 10 days (Colantuoni et al.,
2001). Interestingly, when deprived of sugar the
animals displayed a range of behaviors (anxietylike behavior in the plus-maze test, teeth chattering, forepaw tremor, head shakes) typical for
rats undergoing opiate withdrawal (Colantuoni
et al., 2002; Avena et al., 2008a, 2008b). In line
with those findings, a similar array of behaviors
was induced through the blockade of opioid
signaling with naloxone in rats given access to
sugar. The data indicate that intermittent access
to sugar produces a state analogous to opioid
dependence (Avena et al., 2005).
Thirty days of intermittent sugar intake led to
the elevation of MOR1 binding in the NAcc shell,
cingulate cortex, hippocampus and locus coeruleus (Colantuoni et al., 2001). It is noteworthy that
a similar pattern of MOR binding was detected
as a result of opiate infusions (Vigano et al.,
2003). In addition, enkephalin expression in the
striatum and the NAcc was decreased in animals
subjected to intermittent sucrose access (Spangler
et al., 2004) or with limited daily access to a highfat/high-sugar diet (Kelley et al., 2003); analogous
effects have been induced by repeated morphine
injections. Thus, the influence of intermittent
sucrose access is equivalent to the development
of opiate dependence also at the neurochemical
level. In addition, it has been hypothesized that
down-regulation of enkephalin gene expression
causes a compensatory increase in MOR presentation (Avena et al., 2008b).
Pomonis and collaborators studied neuronal activation in rats given access to sucrose
for 3 weeks. Sugar consumption induced an
increase in Fos-IR in areas associated with
1. FROM BRAIN TO BEHAVIOR
28
3. OPIOIDS AND OVERCONSUMPTION
opioid-mediated reward, including the NAcc,
bed nucleus of the stria terminalis and the
amygdala (Pomonis et al., 2000). Sucrose shamdrinking for 10 days produced a similar pattern
of c-Fos expression in reward sites (Mungarndee
et al., 2008). In rats consuming sugar for several weeks, naltrexone injection not only precipitates withdrawal-like symptoms, but also
changes Fos IR in the reward circuitry that contains opioid receptors and responds to sucrose
intake (Pomonis et al., 2000). It should be noted
that a change in opioid tone within one site is
capable of affecting the activity of other areas,
which illustrates the complexity of the neural
network governing feeding reward. For example, injecting an orexigenic dose of DAMGO in
the amygdala generates changes in c-Fos in the
NAcc shell (Levine et al., 2004). Opioid-driven
changes in neuronal activity have also been
shown within the amygdala–hypothalamic paraventricular nucleus (PVN) pathway (Pomonis
et al., 1997).
Molecular studies have also provided a link
between palatability and opioids. Intake of a
high-fat/high-sucrose diet increased expression
of dynorphin in the arcuate nucleus (ARC) of
rats, simultaneously increasing dynorphin peptide levels in the PVN (Welch et al., 1996). There
were no effects on the mRNA levels of enkephalin or proopiomelanocortin (POMC) in the ARC,
or PVN protein levels of met-enkephalin or betaendorphin. When the intake of the palatable
diet was matched through pair-feeding to the
caloric intake of control animals maintained on
a standard diet, no change in dynorphin levels
was detected; this indicates that the diet per se
does not affect dynorphin, but that such effects
are seen only when palatable food is ingested in
excessive amounts. Interestingly, restricted access
to palatable food decreased the ARC expression
of enkephalin and POMC. These effects indicate
that limiting access to palatable food is similar
to food restriction with regard to effects on opioid signaling (see, for example, Kim et al., 1996).
Accordingly, rats switched from palatable food
to a standard diet had decreased mRNA levels
of POMC in the ARC, and decreased dynorphin
expression in the PVN, ARC and lateral hypothalamus (LH) (Levin and Dunn-Meynell, 2002).
Restricted access to preferred food also affects
the striatal regions: rats with a 3-h daily access
to a palatable diet showed reduced expression
of enkephalin in the NAcc shell and dorsal striatum (Kelley et al., 2003); acute exposure had no
effect.
Overall, palatable diets, regardless of whether
high in fat or sugar, promote increased activity
within opioidergic circuits. Together with the
data showing that opioids acting in these same
central areas promote intake of palatable foods,
feeding reward-driven increase in opioid tone
may function as positive feedback, propelling
consummatory behavior even further.
3.2.4 Opioids: in search of palatability
or a specific macronutrient?
The conclusion that opioids stimulate consumption of palatable tastants was based on
the outcome of experiments utilizing diets rich
in fat and sugar. Hence, a concern was raised
that opioids may not be involved in palatability- but in macronutrient-driven feeding.
In fact, neuropeptides affect consumption of
individual macronutrients in a different manner. For example, neuropeptide Y (NPY) preferentially stimulates carbohydrate intake, while
ghrelin enhances fat ingestion (Shimbara et al.,
2004). Oxytocin seems implicated in sucrose
consumption: oxytocin knockout mice overeat
sucrose but not lipid emulsions (Miedlar et al.,
2007; Sclafani et al., 2007). The orexigenic effects
of galanin are stronger when high-fat diets are
offered (Tempel et al., 1988; Odorizzi et al., 1999).
The initial studies indicated that morphine
increased preference for dietary fat in rats
(Marks-Kaufman, 1982; Marks-Kaufman and
Kanarek, 1980, 1990), whereas naloxone (MarksKaufman and Kanarek, 1981) and naltrexone
1. FROM BRAIN TO BEHAVIOR
3.2. OPIOIDS AND FEEDING BEHAVIOR IN RODENT MODELS
(Marks-Kaufman et al., 1985) decreased fat preference. In addition, KOR agonists increased
intake of a high-fat diet to a greater extent than
carbohydrate (Romsos et al., 1987). Other studies linked opioids with elevated preferences
toward protein and fat (Bhakthavatsalam and
Leibowitz, 1986; Shor-Posner et al., 1986).
Subsequently, preference studies confirmed
that – just as in humans – food preferences vary
between individuals in any strain of outbred
rodents, and that accounting for such variations provides more accurate results. Correcting
for baseline preference allowed investigators
to show that morphine selectively increases
intake of the macronutrient a given animal
prefers: carbohydrate-preferring rats increased
carbohydrate intake, while fat consumption
was increased in fat-preferrers (Gosnell et al.,
1990). It supported the conclusion that since
opioids specifically increase intake of palatable
food, the individual animal treated with opioid
agonists is driven to consume the most palatable tastant. Accordingly, naloxone reduced the
intake of only the preferred diet in animals with
a simultaneous access to high-carbohydrate and
high-fat food (Glass et al., 1996). It should be
noted that conflicting data show that even after
correcting for baseline preference, morphine
increases primarily fat intake (Welch et al., 1994);
this may still be a matter of palatability, since
the preference for lipids was increased by morphine only when these lipids were preferred by
the animals at baseline (Glass et al., 1999a). Fat
preference is thus affected only when the fat
diet is preferred or palatable. These findings are
in line with the role of opioids in palatability,
and they do not provide a link between macronutrients and opioid activity.
3.2.5 Opioids: feeding for calories
or for pleasure?
Another dilemma was whether opioids’ effect
on the intake of palatable diets was associated
29
with reward, or with a drive to ingest calories.
This uncertainty was based on the fact that opioid antagonists suppressed the consumption of
diets whose hedonic value was very limited,
such as standard laboratory chow. In addition,
these antagonists were also capable of diminishing orexigenic properties of peptides involved
in the development of hunger, such as NPY or
Agouti-related protein (AgRP); in those studies, regular chow pellets were offered (Pomonis
et al., 1997; Olszewski et al., 2001).
In order to evaluate the possible link between
opioids and hunger, opioid gene expression and
peptide levels were assessed during food restriction and food deprivation. Hunger-signaling
genes and molecules such as NPY (Brady
et al., 1990; Bi et al., 2003; de Rijke et al., 2005;
Johansson et al., 2008) and AgRP (de Rijke et al.,
2005) are typically up-regulated during catabolic
states, while anorectic agents such as cocaineand amphetamine-regulated transcript (CART)
(de Rijke et al., 2005) are down-regulated. Gene
expression levels of dynorphin, enkephalin and
POMC were decreased following different levels of food restriction in a “dose (thus, hunger-)dependent” manner (Kim et al., 1996). The overall hypothalamic expression of dynorphin was
also reduced by chronic food restriction and
by acute deprivation (Johansson et al., 2008).
Food deprivation lasting 48 hours was associated with decreased dynorphin and POMC
expression, whereas 24-h deprivation decreased
only POMC mRNA levels; enkephalin was not
affected (Kim et al., 1996). Also, ARC POMC
expression was lower upon deprivation (Brady
et al., 1990; Bi et al., 2003; Johansson et al., 2008),
although one should not forget that POMC
gives rise to several molecules of orexigenic
(beta-endorphin) as well as anorexigenic (alphamelanocyte stimulating hormone; α-MSH)
properties, which can make the POMC expression analysis difficult. Overall, deprivation and
restriction studies have shown that opioid gene
expression tends to be opposite to changes in
the expression profile of hunger-related genes.
1. FROM BRAIN TO BEHAVIOR
30
3. OPIOIDS AND OVERCONSUMPTION
It suggests that opioids do not play a major role
in stimulating consummatory activity per se
even under the conditions of depleted energy
stores of the organism (Haberny and Carr, 2005).
In fact, a diminished activity of genes coding
opioids in low-energy states seems to reflect a
low level of hedonic stimulation in underfed
animals.
The aforementioned data were also in concert with the hypothesis that opioids affected
maintenance of meals, rather than initiation of
feeding. Using a two-phase operant technique,
Rudski and colleagues distinguished initiation
and maintenance of meals; the animals had to
press a lever 80 times to receive the first food
pellet (initiation), but 3 times to receive each
consecutive pellet (maintenance) (Rudski et al.,
1994). Naloxone did not modify the time to
receive the first pellet, but reduced the total
number of pellets consumed. Hence, initiation
of the meal was not affected, but maintenance
of the meal was disrupted. Similarly, Kirkham
and Blundell used a runway test to monitor
both food motivation and food consumption
(Kirkham and Blundell, 1986). Naloxone and
naltrexone reduced food intake, while having no
effect on running speed or latency to reach the
food. Naloxone reduced sucrose consumption at
a dose which had no effect on conditioned place
preference for sucrose (Agmo et al., 1995). This
finding also indicates that opioids are implicated in the rewarding effect of sucrose, rather
than the reinforcing effects. Although naloxone
reduced break-points for both sucrose and grain
pellets on an operant progressive ratio schedule,
the effects of the compound were stronger with
free access to food (Glass et al., 1999b).
One plausible role for opioids in the regulation of calorie intake is through interference
with satiety signals, which occur in response
to a plethora of cues indicating that a sufficient amount of food has been consumed. They
include stomach distension and changes in
gastrointestinal tract peristalsis, and increased
blood levels of salt, glucose and nutrients
(Olszewski and Levine, 2007). Neuroactive
agents released due to the presence of these
cues – such as α-MSH, cholecystokinin, oxytocin and insulin – support termination of consummatory activity. It is noteworthy that these
feeding inhibitory mechanisms do not seem to
act with similar efficiency when palatable versus bland diets are offered. When “rewarding”
ingestants are available, food intake is increased
in terms of both calories and volume. For example, short-term food-deprived rats given cornstarch eat 50 percent less than animals presented
with a diet high in sugar (Levine and Billington,
1989). Even rats fed for only 2 hours per day eat
circa 30 percent more when sucrose is added
to the diet, despite the fact that the amount of
consumed sweet food approaches the volume
capacity of the stomach.
As mentioned above, opioid release seems
to coincide with the consumption of preferred
tastants; hence, the relationship between opioids and satiety has been studied in the quest
to elucidate mechanisms responsible for overconsumption of palatable foods. Two such systems, oxytocin and α-MSH, appear to be affected
by the activity of the opioidergic pathways.
Naltrexone at anorexigenic doses induced Fos
IR in ARC α-MSH neurons (Olszewski et al.,
2001). Conversely, an opioid-like hypherphagic
peptide, nociceptin/orphanin FQ, decreased
the percentage of Fos-IR α-MSH cells at refeeding (Bomberg et al., 2006). α-MSH levels were
lower in the medial basal hypothalami of rats
equipped with peripheral morphine pumps compared to controls (Wardlaw et al., 1996). Chronic
morphine infusions led to a time-dependent
down-regulation of the melanocortin-4 receptor
(MC4R) gene expression in the striatum (Alvaro
et al., 1996). Morphine also reduced the expression of MC4R mRNA in the NAcc (Alvaro
et al., 1996). The link between oxytocin and opioids in feeding has been less extensively studied. Butorphanol tartrate, an opioid agonist,
increases consumption of high-sugar food. The
effective dose of butorphanol decreased sucrose
1. FROM BRAIN TO BEHAVIOR
3.3 OPIOIDS AND DYSREGULATION OF EATING PATTERNS AND BODY WEIGHT IN HUMAN BEINGS
intake-driven activity of PVN oxytocin neurons
(Olszewski and Levine, 2007). Interestingly, aside
from affecting satiety, administration of opiates
alleviates aversive consequences of toxin-tainted
foods; therefore, activity of the opioid system
prevents termination of feeding even under the
conditions of jeopardized homeostasis. In line
with these findings, naloxone potentiated aversion-related hypophagia (Flanagan et al., 1988).
It should be noted that oxytocin is a mediator of aversive responsiveness: opioids diminished activity of the oxytocin system, whereas
naloxone stimulated oxytocin release in toxintreated rats.
Overall, the relationship between opioids
and energy consumption seems to be based on
facilitating excessive intake of palatable food.
Opioids, the release of which is stimulated by
eating preferred foods, maintain consummatory
behavior by silencing mechanisms that signal
satiety. These peptides also contribute to “ignoring” cues that suggest a danger to the homeostasis brought on by ingesting “risky” foods.
3.3 OPIOIDS AND
DYSREGULATION OF EATING
PATTERNS AND BODY WEIGHT
IN HUMAN BEINGS
One of the key issues in studies utilizing animals is applicability of the outcome of these
studies to human conditions. Data obtained in
basic research experiments pertaining to opioids and overconsumption of palatable foods
seem of particular importance, considering the
growing number of overweight and obese individuals in the industrialized world. Hence, the
discovery of the relationship between opioids
and feeding reward in animals led to attempts
to confirm it in humans, and subsequently to
administer opioid ligands in people in order to
alter eating patterns and, in consequence, modify body weight.
31
3.3.1 Excessive eating and body weight
A study performed on morbidly obese
patients revealed a significant positive correlation between beta-endorphin and body weight,
and with a degree of body-weight increase
(Karayiannakis et al., 1998a). A higher plasma
beta-endorphin level has been found in female
subjects exhibiting moderate obesity (BMI:
31–39) compared to lean women (Baranowska
et al., 2000). Interestingly, some cases of pharmacotherapy-induced decreases in body weight
in obese people lead to a decline in the betaendorphin profile (Baranowska et al., 2005).
In non-diabetic obese women, higher plasma
concentrations of this opioid correlate with
lower insulin sensitivity (Percheron et al., 1998).
Increased opioid activity is associated with the
abdominal-type body fat distribution in obese
women (Pasquali et al., 1993). Bulimia is characterized by the dysregulation of beta-endorphin
signaling; changes in plasma levels of this peptide are mitigated by body weight (Fullerton
et al., 1986; Waller et al., 1986). Also, craving for
sweet tastants has been often associated with
addiction to opiates, while opiate withdrawal
can be relieved by consumption of sweets
(Morabia et al., 1989; Willenbring et al., 1989).
As early as in 1971, Zaks and co-workers
reported a case of a detoxified drug addict
treated daily with 1500 mg oral naloxone who
experienced reduced appetite in response to this
antagonist (Zaks et al., 1971). The use of lower
doses of naltrexone – which is more potent than
naloxone and has a half-life of about 4–10 hours –
in similar patients for 14–42 days caused a
decrease in body weight (Sternbach et al., 1982).
Kyriakides and colleagues showed that naloxone
reduced hyperphagia in two of three studied
obese Prader-Willi patients (Kyriakides et al.,
1980); however, subsequent studies produced
conflicting results (Zlotkin et al., 1986; Zipf and
Berntson, 1987; Benjamin and Buot-Smith, 1993).
The first study on opioids and overweight
in people without any underlying pathology
1. FROM BRAIN TO BEHAVIOR
32
3. OPIOIDS AND OVERCONSUMPTION
was performed by Theodore Schwartz, who
used himself as the subject (Schwartz, 1981).
He was 120 percent of his ideal body weight.
Over 48 days, which included 39 days of selfadministering various doses (up to 9.6 mg per
day orally) of naloxone, a weight loss of 9 kg was
achieved. There was no evident effect on hunger
or eating stimulated by conditioning or social
interactions; instead, naloxone seemed helpful in
making a decision to discontinue consumption.
Trenchard and Silverstone (1983) conducted
a follow-up study employing double-blind conditions in 12 volunteers given 0.8 and 1.6 mg
naloxone intravenously (i.v.). The antagonist
dose-dependently suppressed consumption,
and the maximum effect was observed 2.5 h
post-injection. No changes in subjective hunger
ratings were noted. Cohen and colleagues studied a group of adults whose BMI did not exceed
normal values by more than 20 percent, and
found that i.v. naloxone reduced energy intake
from 1918 kcal to 1372 kcal per day, including
reducing intake of dietary fat by 30 percent
(Cohen et al., 1985).
To date, many studies have examined the
effect of opioid receptor antagonism on eating.
These trials have utilized naltrexone, naloxone
and nalmefene. In the majority of experiments,
a significant decrease in food consumption was
achieved in subjects with normal and elevated
body weight (Atkinson, 1982; Thompson et al.,
1982; Trenchard and Silverstone, 1983; Cohen
et al., 1985; Malcolm et al., 1985; Wolkowitz et al.,
1985, 1988; Fantino et al., 1986; Spiegel et al.,
1987; Melchior et al., 1989; Yeomans et al., 1990;
Drewnowski et al., 1992; Yeomans and Gray,
1996). The reduction in ingested energy oscillated around 20 percent, and did not seem
affected by the body weight. Hunger at the
onset of consumption did not differ between
drug- and placebo-treated normal patients.
Interestingly, some reports indicate that opioid
antagonists may be effective at reducing hunger
only in obese people; this is still under debate,
as the results are inconclusive (Atkinson, 1982;
Malcolm et al., 1985; Wolkowitz et al., 1985, 1988;
Spiegel et al., 1987; Drewnowski et al., 1992).
Human experiments confirm the validity of
the opioid-eating reward hypothesis. Naloxone
was more effective at decreasing the intake
of preferred food than of a less attractive diet.
Antagonists did not affect perception of various flavors, yet they reduced pleasantness of
tastants containing sugar and fat (Yeomans,
2000). This diminished rewarding value of a
meal led to a decrease in calorie consumption.
Vehicle-treated subjects displayed a gradual
increase in appetite during the initial stages
of the meal, once the palatable nature of the
tastants was discovered. Subjects belonging to
the naloxone group did not exhibit this increase
in appetite (Yeomans and Gray, 1997).
Aside from their effects on consumption, opioid ligands have been linked to other aspects of
energy metabolism. Naloxone inhibits the insulin and C-peptide response to an oral glucose
load in obese but not in lean subjects (Giugliano
et al., 1987). Metabolic and hormonal effects of
beta-endorphin differ in obese versus normalweight individuals: the infusion of the opioid
increased glucose, insulin and C-peptide levels and suppressed circulating free fatty acid
concentration in overweight subjects, whereas
in lean subjects an opposite effect was seen in
relation to the free fatty acids (Giugliano et al.,
1992).
Undergoing a successful dieting program,
which causes a return to normal BMI values,
does not alter metabolic responses to betaendorphin compared to obese controls (Giugliano et al., 1991). Patients subjected to vertical
banded gastroplasty which resulted in a weight
loss retained the altered beta-endorphin and
insulin responses compared to lean controls,
although these values were slightly lower than
the pre-operative measurements (Karayiannakis
et al., 1998b). It suggests that malfunctioning of
this opioid system may predispose individuals
to abnormal weight gain. This notion is supported by the fact that first-degree relatives of
1. FROM BRAIN TO BEHAVIOR
3.4 CONCLUSIONS AND PERSPECTIVES
obese people present a similarly altered metabolic response to elevated beta-endorphin levels
(Cozzolino et al., 1996).
While the benefits stemming from the use of
opioid antagonists in obesity still remain controversial, these ligands have shown promise in
the alleviation of symptoms of the binge-eating
disorder characterized by overconsumption of
palatable foods. Drewnowski and colleagues
tested the effect of i.v. naloxone on the consumption of snacks in obese binge eaters and
normal-weight controls (Drewnowski et al.,
1992). The opioid receptor antagonist was
effective at reducing taste preferences in both
groups, but it suppressed caloric intake only
in overweight binge eaters, which supports
the view that opioids regulate reward- rather
than energy-driven eating. These results were
corroborated by the study which showed that
naloxone decreased hedonic responses to a variety of palatable high-fat and -sugar tastants in
binge eaters and healthy controls, but reduced
calorie intake only in individuals with the eating
disorder (Drewnowski et al., 1995). It has been
suggested that antagonism of opioid receptors
suppresses episodic overconsumption; hence,
the use of opioid ligands may be feasible only as
a pre-meal and “acute” treatment (Drewnowski,
1995).
3.3.2 Underweight individuals
Malfunctioning of opioid pathways may contribute to the development of disorders resulting
in self-starvation, such as anorexia nervosa and
malnutrition related to aging. Beta-endorphin
levels in the cerebrospinal fluid (CSF) are
reduced in the elderly experiencing idiopathic
senile anorexia (Martinez et al., 1993). Subjects
at an advanced age display a trend indicating a
higher appetite-related sensitivity to naloxone
(MacIntosh et al., 2001).
Females suffering from anorexia exhibit
decreased CSF beta-endorphin; a successful
33
recovery is associated with returning betaendorphin levels to normal values. Anorexia
patients exhibit a decline in plasma betaendorphin in response to a gastric load of just
300 kcal; a similar change is seen following a
load of 700 kcal (Rigaud et al., 2007). In addition,
morning levels of this peptide are also lower in
the affected females compared to healthy controls (Baranowska et al., 2000).
These findings support the hypothesis that
an impaired opioid tone may serve as a key
element in the development of the condition
dubbed the reward-deficiency syndrome. Drug
and alcohol abuse, gambling, compulsive sexual activity and undertaking risky sports are
associated with it. It is characterized by general
or specific anhedonia in response to the normal level of stimulation, and it includes a lack
of pleasure derived from consumption of regular food at typical quantities and a necessity
to ingest a greater amount of palatable food to
perceive eating activity as rewarding (Comings
and Blum, 2000).
There have been attempts to use opioid
receptor agonists in the eating disorder context.
Intravenous butorphanol in women of normal
body weight slightly increased the pleasantness
of a subsequent meal, yet it did not translate
into a higher caloric intake or elevated hunger
levels (Drewnowski et al., 1992). However, the
possibility that the consummatory response
to opioid stimulation in underweight patients
might be different cannot be excluded.
3.4 CONCLUSIONS AND
PERSPECTIVES
Opioid peptides drive consumption of palatable foods. Simultaneously, ingestion of such
diets enhances the activity of the central reward
circuitry, which incorporates endogenous opioids. These peptides suppress brain mechanisms
responsible for termination of eating behavior;
1. FROM BRAIN TO BEHAVIOR
34
3. OPIOIDS AND OVERCONSUMPTION
hence, they allow more calories to be ingested
when preferred tastants are available. Altogether,
this creates the self-propelling machinery of
overconsumption of palatable foods, in which
excessive intake of attractive ingestants activates opioid circuitry, which in turn causes further increases in consumption. Although opioid
receptor ligands have not been used extensively
in obesity-related clinical trials, primarily due to
these drugs’ toxicity and side effects, the opioid
system should be considered at least as a viable
target for prospective therapies aimed at decreasing food intake and, in consequence, combating
overweight and obesity. Importantly, interactions with other components of the reward circuitry, such as dopamine, should be utilized in
the development of advanced pharmacological
interventions based on affecting several neural
systems. This is particularly important in light
of the recent findings showing the interdependent manner in which opioids and dopamine
facilitate consummatory behavior. In addition,
opioid gene expression and peptide profile can
be used as a potential diagnostic marker in individuals predisposed to develop obesity or eating
disorders, hence allowing preventative measures to be undertaken and certain therapeutic
approaches to be initiated prior to the onset of
the condition, or at its early stage.
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C H A P T E R
4
Taste, Olfactory and Food-texture
Processing in the Brain and the Control
of Appetite
Edmund T. Rolls
Oxford Centre for Computational Neuroscience, Oxford, UK
O U T L I N E
4.1
Introduction
4.2
Taste-Processing in the Primate Brain 42
4.2.1 Pathways
42
4.2.2 The Primary Taste Cortex
42
4.2.3 The Secondary Taste Cortex
42
4.2.4 The Pleasantness of the Taste of Food,
Sensory-Specific Satiety, and the Effects
of Variety on Food Intake
43
The Representation of Flavor:
Convergence of Olfactory, Taste and Visual
Inputs in the Orbitofrontal Cortex
44
The Texture of Food, Including Fat
Texture
44
Imaging Studies in Humans
44
4.5.1 Taste
44
4.5.2 Odor
45
4.5.3 Olfactory–Taste Convergence to Represent
Flavor and the Influence of Satiety
46
4.5.4 Oral Viscosity and Fat Texture
46
4.5.5 The Sight of Food
46
4.3
4.4
4.5
4.6
4.7
42
4.8
Implications for Understanding,
Preventing and Treating Obesity
47
4.8.1 Brain Processing of the Sensory
Properties and Pleasantness of Food 49
4.8.2 Genetic Factors
49
4.8.3 Endocrine Factors and their
Interaction with Brain Systems
50
4.8.4 Food Palatability
50
4.8.5 Sensory-specific Satiety and
the Effects of Variety on Food Intake 51
4.8.6 Fixed Meal-times and the
Availability of Food
51
4.8.7 Food Saliency and Portion Size
51
4.8.8 Energy Density of Food
51
4.8.9 Eating Rate
51
4.8.10 Stress
52
4.8.11 Food Craving
52
4.8.12 Energy Output
52
4.8.13 Cognitive Factors
52
4.8.14 The Psychology of Compliance with
Information about Risk Factors
for Obesity
52
Concluding Remarks
Cognitive Effects on
Representations of Food
46
4.9
Synthesis
47
Acknowledgments
Obesity Prevention: The Role of Brain and Society on Individual Behavior
41
52
53
© 2010,
2010 Elsevier Inc.
42
4. FOOD, TASTE, SMELL AND TEXTURE
4.1 INTRODUCTION
The aims of this chapter are to describe the
rules of the cortical processing of taste and
smell, how the pleasantness or affective value
of taste and smell are represented in the brain,
how cognitive factors modulate these affective
representations, and how these affective representations play an important role in the control
of appetite, food intake and obesity. To make the
results relevant to understanding the control of
human food intake, complementary evidence is
provided by neurophysiological studies in nonhuman primates, and by functional neuroimaging studies in humans. A broad perspective of
the brain processing involved in emotion and in
hedonic aspects of the control of food intake is
provided by Rolls (2005a).
4.2 TASTE-PROCESSING IN THE
PRIMATE BRAIN
4.2.1 Pathways
A diagram of the taste and related olfactory,
somatosensory and visual pathways in primates
is shown in Figure 4.1. The multimodal convergence that enables single neurons to respond to
different combinations of taste, olfactory, texture, temperature and visual inputs to represent
different flavors produced by often new combinations of sensory inputs is a theme of recent
research that will be described.
4.2.2 The primary taste cortex
The primary taste cortex in the primate anterior
insula and adjoining frontal operculum contains
not only taste neurons tuned to sweet, salt, bitter,
sour (Scott et al., 1986; Yaxley et al., 1990; Rolls and
Scott, 2003) and umami as exemplified by monosodium glutamate (Baylis and Rolls, 1991; Rolls
et al., 1996a), but also other neurons that encode
oral somatosensory stimuli, including viscosity,
fat texture, temperature and capsaicin (Verhagen
et al., 2004). Some neurons in the primary taste
cortex respond to particular combinations of
taste and oral texture stimuli, but do not respond
to olfactory stimuli or visual stimuli (Verhagen
et al., 2004). Neurons in the primary taste cortex
do not represent the reward value of taste – that
is, the appetite for a food – in that their firing is
not decreased to zero by feeding the taste to satiety (Rolls et al., 1988; Yaxley et al., 1988).
4.2.3 The secondary taste cortex
A secondary cortical taste area in primates
was discovered by Rolls, Yaxley and Sienkiewicz
in the caudolateral orbitofrontal cortex, extending several millimeters in front of the primary
taste cortex (Rolls et al., 1990). Neurons in this
region respond not only to each of the four classical prototypical tastes sweet, salt, bitter and
sour (Rolls, 1997; Rolls and Scott, 2003); many
also respond best to umami tastants such as
glutamate (which is present in many natural
foods, such as tomatoes, mushrooms and milk)
(Baylis and Rolls, 1991), and inosine monophosphate (which is present in meat and some fish,
such as tuna) (Rolls et al., 1996a). This evidence,
taken together with the identification of glutamate taste receptors (Zhao et al., 2003; Maruyama
et al., 2006), leads to the view that there are five
prototypical types of taste information channels,
with umami contributing, often in combination
with corresponding olfactory inputs (Rolls et al.,
1998; McCabe and Rolls, 2007; Rolls, 2009), to
the flavor of protein. In addition, other neurons
respond to water and some to somatosensory
stimuli, including astringency as exemplified by
tannic acid (Critchley and Rolls, 1996a), and capsaicin (Rolls et al., 2003a; Kadohisa et al., 2004).
Taste responses are found in a large mediolateral
extent of the orbitofrontal cortex (Pritchard et al.,
2005; Rolls, 2008; Rolls and Grabenhorst, 2008).
1. FROM BRAIN TO BEHAVIOR
43
4.2 TASTE-PROCESSING IN THE PRIMATE BRAIN
VISION
V1
V2
V4
Inferior temporal
visual cortex
Cingulate cortex
Behavior
Amygdala
Striatum
Behavior
TASTE
Taste
receptors
Nucleus of the
Thalamus
solitary tract VPMpc nucleus
Lateral
hypothalamus
Frontal operculum/Insula
(Primary taste cortex)
Gate
Orbitofrontal
cortex
Gate
function
Autonomic
responses
Hunger neuron controlled
by e.g., glucose utilization,
stomach distension or body
weight
OLFACTION
Olfactory
bulb
Olfactory (Pyriform)
cortex
Insula
TOUCH
Thalamus VPL
Primary somatosensory cortex (1.2.3)
FIGURE 4.1
Schematic diagram of the taste and olfactory pathways in primates including humans showing how they
converge with each other and with visual pathways.
Hunger modulates the responsiveness of the representations in the orbitofrontal cortex of the taste, smell, texture and sight
of food (indicated by the gate function), and the orbitofrontal cortex is where the palatability and pleasantness of food is
represented.
VPMpc, ventralposteromedial thalamic nucleus; V1, V2, V4, visual cortical areas.
4.2.4 The pleasantness of the taste of
food, sensory-specific satiety, and the
effects of variety on food intake
The modulation of the reward value of a sensory stimulus such as the taste of food by motivational state – for example, hunger – is one
important way in which motivational behavior
is controlled (Rolls, 2005a, 2007). The subjective
correlate of this modulation is that food tastes
pleasant when hungry, and tastes hedonically
neutral when it has been eaten to satiety. The discovery of sensory-specific satiety was revealed
by the selective reduction in the responses of
lateral hypothalamic neurons to a food eaten to
satiety (Rolls, 1981; Rolls et al., 1986). It has been
shown that this is implemented in a region that
projects to the hypothalamus, the orbitofrontal
1. FROM BRAIN TO BEHAVIOR
44
4. FOOD, TASTE, SMELL AND TEXTURE
cortex (secondary taste), for the taste, odor, sight
and texture of food (Rolls et al., 1989; Critchley
and Rolls, 1996b; Rolls et al., 1999).
This evidence shows that the reduced acceptance of food that occurs when food is eaten to
satiety, the reduction in the pleasantness of its
taste and flavor, and the effects of variety to
increase food intake (Cabanac, 1971; Rolls and
Rolls, 1977, 1982, 1997; Rolls et al., 1981a, 1981b,
1982, 1983a, 1983b, 1984; Rolls and Hetherington,
1989; Hetherington, 2007) are produced in the
orbitofrontal cortex, but not at earlier stages of
processing where the responses reflect factors
such as the intensity of the taste, which is little
affected by satiety (Rolls et al., 1983c; Rolls and
Grabenhorst, 2008). In addition to providing
an implementation of sensory-specific satiety
(probably by habituation of the synaptic afferents to orbitofrontal neurons with a time-course
of the order of length of a meal course), it is
likely that visceral and other satiety-related signals reach the orbitofrontal cortex (as indicated
in Figure 4.1) (from the nucleus of the solitary
tract, via thalamic and possibly hypothalamic
nuclei), and there modulate the representation
of food, resulting in an output that reflects the
reward (or appetitive) value of each food (Rolls,
2005a).
4.3 THE REPRESENTATION OF
FLAVOR: CONVERGENCE OF
OLFACTORY, TASTE AND VISUAL
INPUTS IN THE ORBITOFRONTAL
CORTEX
Taste and olfactory pathways are brought
together in the orbitofrontal cortex, where flavor
is formed by learned associations at the neuronal
level between these inputs (see Figure 4.1) (Thorpe
et al., 1983; Rolls and Baylis, 1994; Critchley
and Rolls, 1996c; Rolls, 1996; Rolls et al., 1996b;
Verhagen et al., 2004). The visual and olfactory as
well as the taste inputs represent the reward value
of the food, as shown by sensory-specific satiety
effects (Critchley and Rolls, 1996b).
4.4 THE TEXTURE OF FOOD,
INCLUDING FAT TEXTURE
Some orbitofrontal cortex neurons have oral
texture-related responses that encode parametrically the viscosity of food in the mouth
(shown using a methyl cellulose series in the
range 1–10,000 centiPoise). Others independently encode the particulate quality of food in
the mouth, produced quantitatively, for example, by adding 20- to 100-μm microspheres to
methyl cellulose (Rolls et al., 2003a). Others,
finally, encode the oral texture of fat (Rolls
et al., 1999; Verhagen et al., 2003), as illustrated
in Figure 4.2. In addition, some neurons in the
orbitofrontal cortex reflect the temperature of
substances in the mouth (Kadohisa et al., 2004,
2005). This temperature information is represented independently of other sensory inputs
by some neurons, and in combination with taste
or texture by other neurons.
4.5 IMAGING STUDIES IN
HUMANS
4.5.1 Taste
In humans, it has been shown in neuroimaging studies using functional magnetic resonance
imaging (fMRI) that taste activates an area of
the anterior insula/frontal operculum, which
is probably the primary taste cortex, and part
of the orbitofrontal cortex, which is probably
the secondary taste cortex (Francis et al., 1999;
O’Doherty et al., 2001; de Araujo et al., 2003a).
Within individual subjects, separate areas of the
orbitofrontal cortex are activated by sweet (pleasant) and by salt (unpleasant) tastes (O’Doherty
et al., 2001).
1. FROM BRAIN TO BEHAVIOR
45
4.5 IMAGING STUDIES IN HUMANS
Fat responsive neurons respond independently of viscosity e.g.
Firing rate (spikes/sec;
mean+/–sem)
20
bk265
15
vegetable oil
55
safflower oil 50
10
mineral oil
280
25
40
5
silicone oil
coconut oil
CMC series
0
1
10
100
1000
10,000
Viscosity (cP)
FIGURE 4.2 A neuron in the primate orbitofrontal cortex responding to the texture of fat in the mouth independently of
viscosity.
The cell (bk265) increased its firing rate to a range of fats and oils (the viscosity of which is shown in centiPoise). The information
that reaches this type of neuron is independent of a viscosity-sensing channel, in that the neuron did not respond to the methyl
cellulose (CMC) viscosity series. The neuron responded to the texture rather than the chemical structure of the fat in that it also
responded to silicone oil (Si(CH3)2O)n) and paraffin (mineral) oil (hydrocarbon). Some of these neurons have taste inputs.
Source: Adapted from Verhagen and colleagues (2003).
We also found activation of the human amygdala by the taste of glucose (Francis et al., 1999).
Extending this study, O’Doherty and colleagues
(2001) showed that the human amygdala was as
much activated by the affectively pleasant taste
of glucose as by the affectively negative taste of
salt, and thus provided evidence that the human
amygdala is not especially involved in processing
aversive as compared to rewarding stimuli. Zald
et al. (1998) had shown earlier that the amygdala,
as well as the orbitofrontal cortex, responds to
aversive (saline) taste stimuli.
Umami taste stimuli activate the insular
(primary), orbitofrontal (secondary) and anterior cingulate (tertiary; Rolls, 2008) taste cortical areas (de Araujo et al., 2003b). When the
nucleotide 0.005-M inosine 5-monophosphate
(IMP) was added to MSG (0.05 M), the BOLD
(blood oxygenation-level dependent) signal
in an anterior part of the orbitofrontal cortex
showed supralinear additivity. This may reflect
the subjective enhancement of umami taste that
has been described when IMP is added to MSG
(Rolls, 2009). Overall, these results illustrate that
the responses of the brain can reflect inputs produced by particular combinations of sensory
stimuli with supralinear activations. The combination of sensory stimuli may be especially
represented in particular brain regions, and may
help to make the food pleasant.
4.5.2 Odor
In humans, in addition to activation of the
pyriform (olfactory) cortex (Zald and Pardo, 1997;
Sobel et al., 2000; Poellinger et al., 2001), there is
strong and consistent activation of the orbitofrontal cortex by olfactory stimuli (Zatorre et al.,
1992; Francis et al., 1999). This region appears
to represent the pleasantness of odor, as shown
by a sensory-specific satiety experiment with
banana versus vanilla odor (O’Doherty et al.,
2000). Further, pleasant odors tend to activate the
medial, and unpleasant odors the more lateral,
orbitofrontal cortex (Rolls et al., 2003b), adding
to the evidence that there is a hedonic map in the
1. FROM BRAIN TO BEHAVIOR
46
4. FOOD, TASTE, SMELL AND TEXTURE
orbitofrontal cortex and in the anterior cingulate
cortex, which receives inputs from the orbitofrontal cortex (Rolls and Grabenhorst, 2008).
4.5.3 Olfactory–taste convergence to
represent flavor and the influence of satiety
Supra-additive effects indicating convergence
and interactions were found for taste (sucrose)
and odor (strawberry) in the orbitofrontal and
anterior cingulate cortex. Activations in these
regions were correlated with the pleasantness
ratings given by the participants (de Araujo
et al., 2003c; Small et al., 2004; Small and Prescott,
2005). These results provide evidence on the
neural substrate for the convergence of taste and
olfactory stimuli to produce flavor in humans,
and on where the pleasantness of flavor is represented in the human brain.
McCabe and Rolls (2007) have shown that
the convergence of taste and olfactory information appears to be important for the pleasantness
of umami. They showed that when glutamate is
given in combination with a consonant savory
odor (vegetable), the resulting flavor can be much
more pleasant than the glutamate taste or vegetable odor alone. This reflected activations in the
pregenual cingulate cortex and medial orbitofrontal cortex. Certain sensory combinations,
therefore, can produce very pleasant food stimuli,
which may be important in driving food intake.
To assess how satiety influences the brain activations to a whole food which produces taste,
olfactory and texture stimulation, we measured
brain activation by whole foods before and after
the food is eaten to satiety (de Araujo et al., 2003b).
The foods eaten to satiety were either chocolate
milk or tomato juice. A decrease in activation by
the food eaten to satiety relative to the other food
was found in the orbitofrontal cortex (Kringelbach
et al., 2003) but not in the primary taste cortex.
This study provided evidence that the pleasantness of the flavor of food and sensory-specific
satiety are represented in the orbitofrontal cortex.
4.5.4 Oral viscosity and fat texture
The viscosity of food in the mouth is represented in the human primary taste cortex (in the
anterior insula), and also in a mid-insular area that
is not taste cortex but which represents oral somatosensory stimuli (de Araujo and Rolls, 2004).
Oral viscosity is also represented in the human
orbitofrontal and perigenual cingulate cortices. It
is notable that the pregenual cingulate cortex, an
area in which many pleasant stimuli are represented, is strongly activated by the texture of fat
in the mouth and by oral sucrose (de Araujo and
Rolls, 2004). The pleasantness of fat texture may
be represented in the orbitofrontal and anterior
cingulate cortex, for activations in these regions
are correlated with the subjective pleasantness of
fat (Grabenhorst et al., 2009).
4.5.5 The sight of food
O’Doherty et al. (2002) showed that visual
stimuli associated with the taste of glucose
activated the orbitofrontal cortex and some
connected areas, consistent with the primate neurophysiology. Simmons, Martin and
Barsalou found that showing pictures of foods,
compared to pictures of locations, can also activate the orbitofrontal cortex (Simmons et al.,
2005). Similarly, the orbitofrontal cortex and
connected areas were also found to be activated after presentation of food stimuli to fooddeprived subjects (Wang et al., 2004).
4.6 COGNITIVE EFFECTS ON
REPRESENTATIONS OF FOOD
To what extent does cognition influence the
hedonics of food-related stimuli, and how far
down into the sensory system does cognitive
influence reach? To address this, we performed
an fMRI investigation in which the delivery of a
standard test odor (isovaleric acid combined with
1. FROM BRAIN TO BEHAVIOR
4.8 IMPLICATIONS FOR UNDERSTANDING, PREVENTING AND TREATING OBESITY
Cheddar cheese flavor, presented orthonasally
using an olfactometer) was paired with a descriptor word on a screen, which on different trials was
“Cheddar Cheese” or “Body Odor”. Participants
rated the affective value of the test odor as significantly more pleasant when labeled “Cheddar
Cheese” than when labeled “Body Odor”. These
effects reflected activations in the medial orbitofrontal cortex (OFC)/rostral anterior cingulate
cortex (ACC) that had correlations with the
pleasantness ratings (de Araujo et al., 2005) (see
Figure 4.3).
The implication is that cognitive factors can
have profound effects on our responses to the
hedonic and sensory properties of food: these
effects are manifest quite far down into sensory processing, so that hedonic representations
of odors are affected (de Araujos et al., 2005).
Similar cognitive effects and mechanisms have
now been found for the taste and flavor of food
(Grabenhorst et al., 2008). In addition, it has been
found that with taste, flavor and olfactory foodrelated stimuli, attention to pleasantness modulates representations in the orbitofrontal cortex,
whereas attention to intensity modulates activations in areas such as the primary taste cortex
(Grabenhorst and Rolls, 2008; Rolls et al., 2008).
4.7 SYNTHESIS
These investigations show that representations
of the reward/hedonic value and pleasantness
of sensory, including food-related, stimuli in the
brain are formed separately from representations
of what the stimuli are. The pleasantness/reward
value is represented in areas such as the orbitofrontal cortex and pregenual cingulate cortex. It
is here that satiety signals modulate the representations of food to make them implement reward
so that they only occur when hunger is present.
The satiety signals that help in this modulation may reach the orbitofrontal cortex from the
hypothalamus. In turn, the orbitofrontal cortex
47
projects to the hypothalamus, where neurons are
found that respond to the sight, smell and taste
of food if hunger is present (Rolls, 2007; Rolls and
Grabenhorst, 2008). We have seen above some of
the principles that help to make the food pleasant, including particular combinations of taste,
olfactory, texture, visual and cognitive inputs.
Below is developed a hypothesis that obesity is
associated with overstimulation of these reward
systems by very rewarding combinations of taste,
odor, texture, visual and cognitive inputs.
4.8 IMPLICATIONS FOR
UNDERSTANDING, PREVENTING
AND TREATING OBESITY
Understanding the mechanisms that control
appetite is becoming an increasingly important
issue, given the growing incidence of obesity
(a three-fold increase in the UK since 1980 to a
figure of 20 percent, as defined by a BMI 30)
and its association with major health risks (with
1000 deaths each week in the UK attributable
to obesity). It is important to understand and
thereby be able to minimize and treat obesity, because many diseases are associated with
a body weight that is much above normal.
These diseases include diabetes, hypertension,
cardiovascular disease, hypercholesterolemia
and gall bladder disease; in addition, obesity is
associated with some deficits in reproductive
function (e.g., ovulatory failure) and an excess
mortality from certain types of cancer (Garrow,
1988; Barsh and Schwartz, 2002; Cummings
and Schwartz, 2003; Schwartz and Porte, 2005).
There are many factors that can cause or contribute to obesity in humans (Brownell and
Fairburn, 1995; Morton et al., 2006; O’Rahilly
and Farooqi, 2006) that are investigated with
approaches within or related to neuroscience
and psychology (Rolls, 2005a, 2005b, 2006, 2007).
Rapid progress is being made in understanding
these, with the aim of leading to better ways
1. FROM BRAIN TO BEHAVIOR
48
4. FOOD, TASTE, SMELL AND TEXTURE
a
b
R
Y = 42
X = 13
z
0
c
2
4
d
Y=0
Y = 15
e
f
0.7
BOLD (%change)
0.4
0.0
–0.4
16
8
PST (sec)
0 –2
–1
0
1
2
Pleasantness
ratings
0.5
0.2
0
16
2
1
8
PST (sec)
0
0
–1
–2
Pleasantness
ratings
FIGURE 4.3 Cognitive influences on olfactory representations in the human brain.
Group (random) effects analysis showing the brain regions where the BOLD signal was correlated with pleasantness ratings
given to the test odor. The pleasantness ratings were being modulated by the word labels. (a) Activations in the rostral anterior cingulate cortex, in the region adjoining the medial OFC, shown in a sagittal slice. (b) The same activation shown coronally. (c) Bilateral activations in the amygdala. (d) These activations extended anteriorly to the primary olfactory cortex. The
image was thresheld at P 0.0001 uncorrected in order to show the extent of the activation. (e) Parametric plots of the data
averaged across all subjects showing that the percentage BOLD change (fitted) correlates with the pleasantness ratings in
the region shown in (a) and (b). The parametric plots were very similar for the primary olfactory region shown in (d). PST,
post-stimulus time(s). (f) Parametric plots for the amygdala region shown in (c).
Source: Adapted from DeAraujo et al., 2005.
1. FROM BRAIN TO BEHAVIOR
4.8 IMPLICATIONS FOR UNDERSTANDING, PREVENTING AND TREATING OBESITY
to minimize and treat obesity. These factors
include the following:
4.8.1 Brain processing of the sensory
properties and pleasantness of food
The way in which the sensory factors produced by the taste, smell, texture and sight of
food interact in the brain with satiety signals
(such as gastric distension and satiety-related
hormones) to determine the pleasantness and
palatability of food, and therefore whether and
how much food will be eaten, is described above
and shown in Figures 4.1 and 4.4. The concept is
that convergence of sensory inputs occurs in the
orbitofrontal cortex and builds a representation
of food flavor. The orbitofrontal cortex is where
the pleasantness and palatability of food are
represented, as demonstrated by the discoveries
that these representations of food are only activated if hunger is present, and correlate with the
subjective pleasantness of the food flavor (Rolls,
2005a, 2005b, 2006, 2007; Rolls and Grabenhorst,
2008). The orbitofrontal cortex representation of
whether food is pleasant (given any satiety signals present) then drives brain areas such as the
striatum and cingulate cortex that then lead to
eating behavior.
In the context of the obesity crisis, the past 30
years have seen a dramatic increase of the sensory
stimulation produced by the taste, smell, texture
and appearance of food, as well as its availability. Conversely, the satiety signals produced by
stomach distension, satiety hormones, etc., have
remained essentially unchanged. The effect on
the brain’s control system for appetite (shown
in Figures 4.1 and 4.4) is to lead to a net average
increase in the reward value and palatability of
food which overrides the satiety signals, contributes to the tendency to be over-stimulated by
food, and therefore leads to overeating.
In this scenario, it is important to better
understand the rules used by the brain to produce the representation of the pleasantness of
49
food and how the system is modulated by eating and satiety. This understanding, and how
the sensory factors can be designed and controlled so as not to override satiety signals, are
important research areas in the understanding,
prevention and treatment of obesity. Advances
in understanding the receptors that encode the
taste and olfactory properties of food (Buck,
2000; Zhao et al., 2003), and the processing in
the brain of these properties (Rolls, 2004, 2005a,
2005b), are also important in providing the
potential to produce highly palatable food that
is at the same time nutritious and healthy.
An important aspect of this hypothesis is that
different humans may have reward systems that
are strongly driven by the sensory and cognitive
factors that make food highly palatable. In a test
of this, we showed that activation to the sight
and flavor of chocolate in the orbitofrontal and
pregenual cingulate cortex was much higher in
chocolate cravers than non-cravers (Rolls and
McCabe, 2007). The concept that individual differences in responsiveness to food reward are
reflected in brain activations in regions related
to the control food intake (Beaver et al., 2006;
Rolls and McCabe, 2007; Lowe et al., 2009; Van
den Eynde and Treasure, 2009) may provide a
way for understanding and helping to control
food intake.
4.8.2 Genetic factors
Genetic factors are of some importance, with
some of the variance in weight and resting metabolic rate in a population of humans attributable to inheritance (Morton et al., 2006; O’Rahilly
and Farooqi, 2006, 2008). However the “obesity
epidemic” that has occurred since the 1990s cannot be solely attributed to genetic changes, for
which the timescale is far too short. Factors such
as the increased palatability, variety and availability of food (as well as less exercise), crucial
drivers of food intake, and the amount of food
that is eaten (Rolls, 2005a, 2005b, 2006, 2007) are
1. FROM BRAIN TO BEHAVIOR
50
4. FOOD, TASTE, SMELL AND TEXTURE
Obesity: sensory factors that make food increasingly palatable
may override existing satiety signals
Cognitive factors:
Conscious rational control
Beliefs about the food
Advertising
Sensory factors:
Taste
Smell
Texture
Sight
Brain mechanisms:
Sensory factors modulated
by satiety signals
produce reward value
and appetite
Effects of:
Variety
Sensory-specific satiety
Palatability
Food concentration
Portion size
Ready availability
Eating:
Autonomic,
and endocrine
effects
Satiety / hunger signals:
Adipose signals
Gut hormones
Gastric distension
FIGURE 4.4 Obesity: sensory factors that make food increasingly palatable may override existing satiety signals.
Schematic diagram to show how sensory factors interact in the orbitofrontal cortex with satiety signals to produce the hedonic,
rewarding value of food, which leads to appetite and eating. Cognitive factors directly modulate this system in the brain.
more likely to be responsible for the upsurge in
the incidence of obesity.
4.8.3 Endocrine factors and their
interaction with brain systems
A small proportion of cases of obesity can
be related to gene-related dysfunctions of the
peptide systems in the hypothalamus, with, for
example, 4 percent of obese people having deficient (MC4) receptors for melanocyte stimulating hormone (Morton et al., 2006; O’Rahilly
and Farooqi, 2006). Cases of obesity that can be
related to changes in the leptin hormone satiety
system are very rare (O’Rahilly and Farooqi,
2006; Farooqi and O’Rahilly, 2009). Further,
obese people generally have high levels of leptin, so leptin production is not the problem.
Instead, leptin resistance (i.e., insensitivity) may
be somewhat related to obesity, with the resistance perhaps related in part to smaller effects of
leptin on arcuate nucleus NPY/AGRP neurons
(Munzberg and Myers, 2005).
4.8.4 Food palatability
A factor in obesity is food palatability, which,
with modern methods of food production, can
now be greater than would have been the case during the evolution of our feeding control systems.
1. FROM BRAIN TO BEHAVIOR
4.8 IMPLICATIONS FOR UNDERSTANDING, PREVENTING AND TREATING OBESITY
These brain systems evolved so that internal
signals from, for example, gastric distension
and glucose utilization could act to decrease the
pleasantness of the sensory sensations produced
by feeding sufficiently by the end of a meal to
stop further eating (Rolls, 2004, 2005a, 2005b).
However, the greater palatability of modern food
may mean that this balance is altered, so that
there is a tendency for the greater palatability of
food to be insufficiently decreased by a standard
amount of food eaten (see Figure 4.4).
4.8.5 Sensory-specific satiety and the
effects of variety on food intake
Sensory-specific satiety is the decrease in the
appetite for a particular food as it is eaten in a
meal, without a decrease in the appetite for different foods (Rolls, 2004, 2005a, 2005b), as shown
above. It is an important factor influencing how
much of each food is eaten in a meal. Its evolutionary significance may be to encourage eating
of a range of different foods, and thus obtaining a
range of nutrients. As a result of sensory-specific
satiety, if a wide variety of foods is available,
overeating in a meal can occur. Given that it is
now possible to make available a wide range of
food flavors, textures and appearances, and that
such foods are readily available, this variety effect
may be a factor in promoting excess food intake.
4.8.6 Fixed meal-times and the
availability of food
Another factor that could contribute to obesity is fixed meal-times, in that the normal control
of food intake by alterations in inter-meal interval is not readily available in humans. Therefore,
food may be eaten at a meal-time even if hunger
is not present (Rolls, 2005a). Even more than this,
because of the high and easy availability of food
(in the home and workplace) and stimulation by
advertising, there is a tendency to start eating
51
again when satiety signals after a previous meal
have decreased only a little, and the consequence
is that the system again becomes overloaded.
4.8.7 Food saliency and portion size
Making food salient, for example by placing it
on display, may increase food selection, particularly in the obese (Schachter, 1971; Rodin, 1976).
Portion size is a factor, since more is eaten if a
large portion of food is presented (Kral and Rolls,
2004). Whether it can lead to obesity has not yet
been demonstrated. The driving effects of visual
and other stimuli (including the effects of advertising) on the brain systems that are activated by
food reward may be different in different individuals, and contribute to obesity.
4.8.8 Energy density of food
Although the gastric emptying rate is slower
for high energy-density foods, this does not fully
compensate for the energy density of the food
(Hunt and Stubbs, 1975; Hunt, 1980). The implication is that eating energy-dense foods (e.g., highfat foods) may not allow gastric distension to
contribute sufficiently to satiety. Because of this,
the energy density of foods may be an important
factor that influences how much energy is consumed in a meal (Kral and Rolls, 2004). Indeed,
it is thought that obese people tend to eat foods
with high energy-density, and to visit restaurants
with high-energy density (e.g., high-fat) foods. It
is also a matter of clinical experience that gastric
emptying is faster in obese than in normal-weight
individuals, meaning that gastric distension may
play a less effective role in contributing to satiety
in the obese.
4.8.9 Eating rate
A factor related to the effects described above
is the eating rate, which is typically fast in the
1. FROM BRAIN TO BEHAVIOR
52
4. FOOD, TASTE, SMELL AND TEXTURE
obese (Otsuka et al., 2006) and may provide
insufficient time for the full effect of satiety signals as food reaches the intestine to operate.
4.8.10 Stress
Another potential factor in obesity is stress,
which can induce eating and contribute to obesity. In a rat model of this, mild stress in the presence of food can lead to overeating and obesity
(Torres and Nowson, 2007). This overeating is
reduced by anti-anxiety drugs.
4.8.11 Food craving
Binge eating has some parallels to addiction.
In one rodent model of binge eating, access to
sucrose for several hours each day can lead to
binge-like consumption of the sucrose over a
period of days (Colantuoni et al., 2002; Avena
and Hoebel, 2003a, 2003b; Spangler et al., 2004).
The binge eating is associated with the release
of dopamine. In this model, binge eating resembles an addictive process, in that after binge
eating has become a habit, sucrose withdrawal
decreases dopamine release in the ventral striatum (a part of the brain involved in addiction
to drugs such as amphetamine), altered binding of dopamine to its receptors in the ventral
striatum is produced, and signs of withdrawal
from an addiction occur. In withdrawal, the
animals are also hypersensitive to the effects
of amphetamine. Another rat model is being
used to investigate the binge eating of fat, and
whether the reinforcing cues associated with it
can be reduced by the GABA-B receptor agonist
baclofen (Corwin and Buda-Levin, 2004).
4.8.12 Energy output
If energy intake is greater than energy output,
body weight increases. Energy output is thus an
important factor in the equation. A lack of exercise
tends to limit energy output, and thus contributes
to obesity. It should be noted, though, that obese
people do not generally suffer from a very low
metabolic rate: in fact, as a population, in line
with their elevated body weight, obese people
have higher metabolic rates than normal-weight
humans (Garrow, 1988).
4.8.13 Cognitive factors
As shown above, cognitive factors, such as
preconceptions about the nature of a particular
food or odor, can reach down into the olfactory
system in the orbitofrontal cortex which controls the palatability of food to influence how
pleasant an olfactory stimulus is (de Araujo
et al., 2005). This has implications for further
ways in which food intake can be controlled,
and needs more investigation.
4.8.14 The psychology of compliance
with information about risk factors
for obesity
It is important to develop better ways to
provide information that will be effective in
the long term in decreasing food intake while
maintaining a healthy diet, and in promoting an
increase in energy expenditure by, for example,
encouraging exercise.
4.9 CONCLUDING REMARKS
Recent advances are showing how the reward
value of food is represented in the brain as a
combination of taste, oral texture, olfactory and
visual attributes of food; and how this reward
representation which drives appetite and food
intake is modulated by internal satiety signals,
by sensory-specific satiety, by cognition and by
attention.
1. FROM BRAIN TO BEHAVIOR
REFERENCES
In this context, it is argued that the factors
that contribute to driving people towards obesity include the greater stimulation in the past 30
years of the brain by sensory stimuli that make
food palatable and pleasant, relative to internal
satiety signals, which have remained unchanged
in this short time. In this situation, it is important
to understand much better the rules used by the
brain to produce the representation of the pleasantness of food, and how the system is modulated by eating and satiety. This understanding,
and how the sensory factors can be designed and
controlled so as not to override satiety signals,
are important research areas in the understanding, prevention and treatment of obesity.
In this context, it may be important to better
understand individual differences in the sensitivity of this food reward system.
The factors that contribute to overstimulating
the brain’s food reward systems relative to satiety signals include food palatability and appearance, sensory-specific satiety, food variety, food
availability, the effects of visual stimulation and
advertising, the energy density and nutritional
content of food, portion size, and cognitive
states. All these factors may need to be taken
into account in the prevention of obesity.
ACKNOWLEDGMENTS
This research was supported by the Medical
Research Council. The participation of many
colleagues in the studies cited is sincerely
acknowledged.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
5
Cortical and Limbic Activation
in Response to Low- and
High-calorie Food
William D.S. Killgore
Cognitive Neuroimaging Laboratory, McLean Hospital,
Harvard Medical School, Belmont, MA, USA
O U T L I N E
5.1
Introduction
57
5.2
Brain Responses to Food Stimuli
in Healthy Adults
58
Modulating Factors
5.3.1 Body Mass
61
61
5.3
5.1 INTRODUCTION
Despite the marvelous advances of modern
medicine and the undeniable evidence linking
obesity to poor health, disease, and reduced life
span, the industrialized world’s population is
increasingly overweight/obese. In particular, in
the United States obesity has reached epidemic
proportions: some estimates suggest that nearly
two-thirds of adults are overweight, and a
Obesity Prevention: The Role of Brain and Society on Individual Behavior
5.3.2 Mood
63
5.4
Cortical and Limbic Activation to Food
Images During Adolescent Development 65
5.5
Conclusion
68
quarter or more of American adults are obese
(Flegal et al., 2002; Hill and Wyatt, 2005).
Inadequate education and lack of awareness
cannot alone account for the increasing weight
of Americans: Americans are reasonably welleducated in matters pertaining to health and
medical issues, and popular media, including
television and magazines, inundate the average person with messages about weight loss,
nutrition and health on a daily basis. Thus,
57
The Contents of this Chapter are in the Public Domain
58
5. CORTICAL - LIMBIC ACTIVATION
despite widespread awareness of the problem
and a general comprehension of and appreciation for the importance of maintaining a healthy
diet and exercise routine, a majority of individuals continue to have difficulty maintaining their caloric intake within a healthy range.
Clearly, other factors must also be at work. For
this reason, researchers have begun studying
the various behavioral, neurobiological and
genetic factors that may contribute to the types
of food choices individuals make (Karhunen
et al., 2000).
The complex decisions about what and
how much to eat are ultimately guided by the
interplay of multiple neural systems within
the brain. Recent advances in neuroimaging
technologies have made it possible to observe
directly the responses of the human brain to
a variety of stimuli and cues associated with
food, hunger, taste, smell and other influences
on eating-related behavior. Techniques such as
positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have
proven useful in mapping the regions of the
human brain that are activated in response to
food-related stimuli. They are providing important clues that may help explain why many people may find it difficult to resist certain foods,
particularly those that are most implicated in
weight-related problems. While the neuroimaging literature on brain responses to food
and eating related stimuli has expanded rapidly in recent years (see, for example, Simmons
et al., 2005; Beaver et al., 2006; Geliebter et al.,
2006; Santel et al., 2006; Verhagen and Engelen,
2006), the present chapter will focus primarily on a circumscribed set of fMRI studies that
have explored how the brain responds to visual
images of foods that differ in their caloric content. This review is not meant to be exhaustive,
but rather highlights some of the specific neural
systems that may be particularly relevant to the
initial responses people have when first seeing
something edible.
5.2 BRAIN RESPONSES TO FOOD
STIMULI IN HEALTHY ADULTS
Because food is so essential to life sustenance,
it makes sense for the brain to possess specialized systems for identifying and responding
to potential sources of nutrients and energy.
Studies with primates have shown that there are
neurons in the brain that fire only in response to
visual presentations of food (Rolls, 1994). These
populations of neurons are found primarily
in the brain systems important for motivation
and emotion, including the orbitofrontal cortex, the hypothalamus and the amygdala. These
brain structures are key nodes for identifying
stimuli or environmental contingencies that are
likely to affect the survival or wellbeing of an
organism, and therefore evoke corresponding
emotional states and behavioral responses. In
contrast to the large body of knowledge on primates (Rolls, 1994, 1999, 2000), relatively little
research has examined the neural responses of
healthy humans to visual presentations of food
stimuli. The primary body of evidence comes
from neuroimaging techniques. Some early
studies using neuroimaging techniques such
as single photon emission computed tomography (SPECT) and PET suggested that visually
presented food stimuli only produced minor
changes in regional cerebral blood flow (rCBF)
in normal-weight women (Karhunen et al.,
1997, 1999, 2000; Gordon et al., 2000). However,
greater changes were observed in women with
eating or appetite-related problems such as
obesity or binge eating, particularly within
frontal, prefrontal, temporal and parietal cortices (Karhunen et al., 1997, 2000). This suggested
that cortical responses to visual presentations of
food might be moderated by the motivational
status of the individual, with these overweight
individuals’ brain regions responding more to
food stimuli compared to individuals of normal weight. LaBar and colleagues used fMRI
1. FROM BRAIN TO BEHAVIOR
59
5.2 BRAIN RESPONSES TO FOOD STIMULI IN HEALTHY ADULTS
to study responses to food stimuli when participants were hungry and when satiated (LaBar
et al., 2001). They found that the amygdala was
more responsive to food stimuli when a person
is hungry relative to when satiated, suggesting
that motivational status can significantly affect
the responses of critical emotion-related brain
structures to the same food stimuli. Although
hunger is clearly the most salient motivator guiding human appetitive behavior, there
are numerous other factors that can influence
whether a person will eat, and what foods they
will choose. In particular, the motivation to eat
can be influenced by qualities intrinsic to the
food itself, such as its flavor, texture and potential energy value – attributes which often correlate with higher caloric and fat content.
Our research team, under the direction of
Deborah Yurgelun-Todd, at McLean Hospital,
was interested in whether there were specific
neural systems in the brain that respond differently to visual images of foods according to their
energy value (i.e., caloric content). To examine
this question, we conducted a neuroimaging
study in which healthy young women underwent fMRI scanning while viewing color photographs of food stimuli (Killgore et al., 2003). Two
categories of foods were shown: (1) high-calorie
foods such as cakes, cookies, ice cream, hot
dogs, hamburgers and spaghetti dinners; and
(2) low-calorie foods such as green leafy salads,
raw vegetables, fruits, and whole-grain cereals.
A third condition also presented subjects with
non-edible food-related utensils such as forks,
spoons, plates and cups, with no food present in
the image. Some of the main findings from that
study are summarized in Figure 5.1, with white
areas indicating regions that were significantly
activated by high-calorie foods, gray areas by
low-calorie foods, and black areas by non-edible
dining-related utensils. Most importantly,
these three types of stimuli activated very different regions of the cortex and limbic system.
Overall, when we looked for regions that were
Front
Back
Right hemisphere
Left hemisphere
FIGURE
5.1 Regions of significant activation in
response to images of high-calorie foods (white), low-calorie
foods (gray) and non-edible dining-related utensils (black)
in healthy adult females.
High-calorie foods activated large regions of dorsolateral and
dorsomedial prefrontal cortex, while low-calorie foods activated only a few discrete regions of cortex, particularly in the
temporal lobe. Non-edible utensils activated regions typically
involved in visual processing of common objects and tools.
All regions are significant at P 0.0005 (uncorrected), k 20.
Source: Adapted from Killgore et al. (2003).
activated in common for both categories of food,
we found that all foods shared activity within
many limbic system structures, including the
amygdala, hippocampus, posterior cingulate
gyrus, calcarine cortex and medial prefrontal
cortex – regions heavily involved in primitive emotional and motivational behavior (see
Figure 5.2). When the food categories were considered separately, however, there were notable
differences between the brain activity patterns
of high- and low-calorie foods. The presentation
of high-calorie foods, which were rated as most
appealing by volunteers, clearly activated a distributed network of cerebral structures involved
in emotion, self-reflection, inhibition, response
selection and behavioral regulation, including
the medial and dorsolateral prefrontal cortex,
1. FROM BRAIN TO BEHAVIOR
60
5. CORTICAL - LIMBIC ACTIVATION
Medial
prefrontal
cortex
Calcarine
cortex
FIGURE 5.2 Regions of common activation
(i.e., conjunction analysis) for both high- and
low-calorie foods in healthy adult females).
Significant common activation was observed in
limbic regions, including the amygdala, hippocampus and parahippocampal gyrus (upper left,
upper right and lower left panels), the medial
prefrontal cortex (upper right panel), and calcarine cortex (upper right and lower left panels).
All regions are significant at P 0.005 (uncorrected), k 20.
Source: Adapted from Killgore et al. (2003).
Amygdala/
hippocampus
Amygdala/
hippocampus
SPM {t}
3.0
2.5
Calcarine
cortex
2.0
1.5
1.0
0.5
0
middle temporal gyri, amygdala, parahippocampal gyrus, thalamus, hypothalamus and cerebellum (see Figure 5.3). The activity within the
prefrontal cortex was particularly interesting, as
the medial aspects show heightened activation
during evaluative judgments such as deciding
whether an object is liked or disliked (Zysset
et al., 2002), self-reflective thought processes
(Lane et al., 1997; Gusnard et al., 2001; Johnson
et al., 2002) and conflict resolution (Ridderinkhof
et al., 2004; Etkin et al., 2006). This suggests that
appealing high-calorie foods may activate prefrontal regions important for evaluating and
relating stimuli to one’s own preferences and
self-perceptions. The strong activity within
the dorsolateral regions is also intriguing, as
this area is often associated with inhibitory
suppression and control of behavior (Liddle
et al., 2001; Pliszka et al., 2006; Rubia et al., 2006).
Perhaps, for most normal-weight eaters, visual
perception of high-calorie foods initiates a complex series of processes involving evaluative,
self-reflective and inhibitory activities within
the prefrontal cortex (Del Parigi et al., 2002).
In contrast to the robust findings for visual
presentations of high-calorie foods, we found
that low-calorie foods were associated with considerably less brain activation overall, which
was localized to a few small regions including
the middle and superior temporal gyrus, the
somatosensory cortex and the medial orbitofrontal cortex (Killgore et al., 2003). It is interesting to note that presentation of low-calorie
foods produced very little additional activation
above and beyond that produced by the “resting” control condition, which involved viewing
similarly colorful and visually complex images
of non-edible shrubs, trees, rocks and flowers.
We interpreted these findings to suggest that
visual perception of low-calorie foods did little
to arouse the self-evaluative, conflict monitoring or inhibitory regions of the prefrontal cortex,
while alternatively activating small regions of a
visceral/gustatory/sensory processing system.
1. FROM BRAIN TO BEHAVIOR
61
5.3 MODULATING FACTORS
High calorie
Low calorie
Medial
prefrontal
cortex
Medial
orbitofrontal
cortex
SPM {t}
6
4
5
3
4
3
2
2
1
1
Thalamus
Amygdala/
parahippocampal
gyrus
0
0
Hippocampus
FIGURE 5.3 Regions of activation specific to images of high- and low-calorie foods.
High-calorie food images (left panel) significantly activated large regions of the dorsomedial prefrontal cortex, thalamus and
limbic structures, including the amygdala and parahippocampal gyrus (P 0.0005, k 20). Low-calorie food images (right
panel) produced limited activation of a few small regions, including the medial orbitofrontal cortex, hippocampus and superior temporal gyrus (P 0.005, k 20).
Source: Adapted from Killgore et al. (2003).
Finally, as expected, non-edible utensils simply
activated visual object processing regions within
the occipito-temporal junction that are known
to activate in response to common objects (GrillSpector et al., 2001) and tools (Martin et al.,
1996; Tranel et al., 1997), but not in the evaluative, self-referential and inhibitory regions of
the prefrontal cortex seen for high-calorie foods.
In conclusion, the brain responds very differently to foods that promise high energy and
high palatability relative to those that may be
healthier but less tantalizing choices. If “energy
is delight”, then this is clearly evident in cortical
responses to high-calorie foods.
enticing images of carrot sticks, celery stalks and
whole-grain cereals, there are several factors that
may modulate these effects. For instance, it is possible that individual differences lead some people to have a more pronounced brain response
to particular classes of foods than others. Such
individual differences in brain responses could
conceivably lead to a pattern of food choices
that over a lifetime could significantly affect
health and weight status. Two factors that we
believed might be particularly relevant to issues
of food choices included body mass and mood
state.
5.3.1 Body mass
5.3 MODULATING FACTORS
While
viewing
burgers,
activates
the previous findings suggested that
high-calorie foods such as cheeseFrench fries and creamy milkshakes
the prefrontal cortex more than less
An obvious question is to ask whether people with weight problems respond differently
to images of food than do normal-weight individuals. Differences in brain activity between
normal-weight and obese individuals in response
to images of food might provide clues about the
1. FROM BRAIN TO BEHAVIOR
62
5. CORTICAL - LIMBIC ACTIVATION
underlying neurobiology of weight gain and
obesity. Several studies have directly contrasted
brain activity of lean and obese women during
visual exposure to food (Karhunen et al., 1997,
1999, 2000; Geliebter et al., 2006). Generally, these
studies have shown that, compared to women of
normal weight, several cortical regions of obese
women responded more to images of food, with
frontal and prefrontal regions showing prominent activity in obese women with a history of
binge eating (Karhunen et al., 2000), particularly
for food stimuli typically involved in binge eating (Geliebter et al., 2006).
We were interested to see whether body
mass index (BMI) might be related to patterns of brain activity even among individuals
within the normal weight spectrum (Killgore
and Yurgelun-Todd, 2005a). If present, such
patterns of responsiveness might provide biological markers for the predisposition toward
eventual weight gain, or provide clues as to
why some people eventually gain more weight
than others. To examine this possibility, we
again looked at the brain activity of a sample
of normal-weight women described in the previous section. We focused on the orbitofrontal cortex, a region of the brain important for
appetite-related responses, behavioral inhibition, and learning reward and punishment contingencies. This part of the cortex is critical for
the ability to alter learned patterns of behavior
in favor of new ones when the old behaviors no
longer prove rewarding (Rolls, 2000). We asked
whether differences in orbitofrontal activity in
response to high-calorie and low-calorie foods
might be related to subtle differences in body
mass in these normal-weight women (Killgore
and Yurgelun-Todd, 2005a).
The results for that study are summarized
in Figure 5.4. As expected, activity within the
orbitofrontal cortex and anterior cingulate
gyrus was significantly correlated with the
women’s BMI scores, but the specific regions of
correlated activity differed for high- and lowcalorie foods. When high-calorie food images
were presented, the leanest participants showed
the greatest activity in these regions, while
those with higher BMI scores tended to have
less activity in these inhibitory and emotion
processing regions (Killgore and Yurgelun-Todd,
2005a). Low-calorie food images evoked a similar negative relationship between BMI and brain
activity, but only in a small region located at the
juncture of the right inferior orbitofrontal cortex
and the superior temporal pole (see Figure 5.4).
These negative relationships were not observed
when participants viewed images of diningrelated utensils. Thus, even within the normal
weight range, individuals with the greatest
body mass tended to show the least activity
within the orbitofrontal and anterior cingulate
regions of the brain in response to images of
highly rewarding foods.
The findings suggest that the magnitude of
responsiveness of the orbitofrontal cortex to
food is correlated with body mass. The orbitofrontal cortex is important for representing
the punishment or reward value of primary
reinforcers, and for ascribing reward value to
stimuli. It also plays a critical role in the ability
to adapt to complex and changing environments
by reversing previously learned stimulusreinforcement associations in response to changing reinforcement contingencies (Rolls, 2000).
The orbitofrontal cortex allows the organism
to adapt flexibly to new environments and to
modify behavior as necessary (Rolls, 1984), and
is important for behavioral inhibition (Bokura
et al., 2001; Altshuler et al., 2005). Even for
women within the normal range of weight, our
findings showed that individuals with larger
body mass were likely to have significantly less
activity in response to images of food within
brain regions that are vital for the ability to
inhibit or change behavior as appropriate to meet
long-term goals. Although these results were
correlational in nature and limited by small
sample size, they raise the speculative possibility that subtle differences in the responsiveness of the orbitofrontal cortex to high-calorie
1. FROM BRAIN TO BEHAVIOR
63
5.3 MODULATING FACTORS
Adjusted fMRI response
Body mass index
R
A
L
0.00
–0.20
R2= 0.57
–0.40
18
20
22
24
26
28
30
Body mass index (kg/m2)
Adjusted fMRI response
P
0.20
0.20
0.00
–0.20
R2= 0.75
–0.40
18
20
22
24
26
28
30
Body mass index (kg/m2)
FIGURE 5.4 Body mass index (BMI) was significantly correlated with activity in the orbitofrontal cortex during visual
presentations of food images.
The image on the left shows the ventral surface of the brain with regions of significant activity superimposed. During presentations of low-calorie foods (white voxels), BMI was negatively related to activity in a small region of the right posterior
orbitofrontal cortex. The top graph shows the scatterplot for the maximally correlated voxel for the low-calorie food condition. During presentations of high-calorie foods (black voxels), BMI was negatively correlated with activity within the anterior cingulate gyrus and orbitofrontal cortex. The bottom graph shows the scatterplot for the maximally correlated voxel for
the high-calorie food condition. All regions are significant at P 0.005, k 10. A, anterior; P, posterior; R, right; L, left.
Source: Adapted from Killgore and Yurgelun-Todd (2005a).
foods could affect long-term preferences for certain foods, and the choices people make regarding the short-term rewards of high-calorie foods
versus the long-term health benefits of lowercalorie foods. Even a slight difference in such
choices beginning early in life could potentially
affect long-term health and weight status if such
patterns persist for years or decades. Clearly,
more research will be needed to evaluate these
possibilities adequately.
5.3.2 Mood
Another factor that may affect human dietary choice is a person’s emotional state (Macht,
1999). Many people find that they have cravings
for certain types of foods when they are experiencing an emotionally stressful situation or
a period of “the blues,” leading to the phenomenon known as “emotional eating” (Hill
et al., 1991). Some individuals are carbohydrate
cravers, who tend to experience these cravings
more intensely when experiencing strong negative moods (Christensen and Pettijohn, 2001).
Depression and other severe mood disturbances
are often accompanied by changes in appetite
and carbohydrate cravings (Fernstrom et al.,
1987; Krauchi et al., 1990; Wardle, 1990; Kazes
et al., 1993; Christensen and Somers, 1994).
It is well known that mood states, particularly extreme negative states such as clinical
depression, can alter the normal functioning
of the prefrontal cortex, anterior cingulate, and
1. FROM BRAIN TO BEHAVIOR
64
5. CORTICAL - LIMBIC ACTIVATION
limbic system (Baxter et al., 1989; Drevets et al.,
1997; Mayberg et al., 1999). Mood-related alterations in the functioning of the prefrontal cortex
and associated systems might also change the
way these systems evaluate the motivational
value of food, as well as the ability to inhibit
behavior associated with cravings. As discussed
earlier, several of these same prefrontal and limbic regions, the orbitofrontal and insular cortices
in particular, appear to be critically involved in
processing food-related stimuli and guiding the
dietary choices that humans make (Wang et al.,
2004). First, evidence suggests that the orbitofrontal cortex plays an important role in appetitive behavior through an interaction between its
medial and lateral aspects. Functionally, greater
activity in the medial aspect of the orbitofrontal
cortex has been associated with increased hunger and enhanced motivation to eat (Tataranni
et al., 1999; Morris and Dolan, 2001; Small et al.,
2001). However, once a person has eaten to satiety and no longer finds the taste of food to be
pleasurable these same medial regions of the
orbitofrontal cortex show reduced blood-flow
activity, whereas regions of the lateral orbitofrontal cortex show increased activation (Gautier
et al., 2001; Small et al., 2001). Together, these
findings suggest that activity within the medial
orbitofrontal cortex may be associated with
increased appetite and food-seeking, whereas
the lateral regions of the orbitofrontal cortex
may act to inhibit eating once the individual is
satiated. A second important region for appetitive behavior appears to be the insula, a region
of cortex that is wrapped within the inner folds
of the lateral fissure. The insula may function as
primary gustatory cortex in humans (Faurion
et al., 1998; Pritchard et al., 1999; Del Parigi
et al., 2005), and is activated directly when subjects taste salty or sweet flavors (Kobayakawa
et al., 1996; Murayama et al., 1996). The insula
also shows increased activity in response to
the smell of food (O’Doherty et al., 2000), and
is believed to monitor the ongoing status of
internal somatic states (Reiman, 1997; Reiman
et al., 1997; Craig, 2003; Critchley et al., 2004).
Moreover, the insula shows heightened activation during hunger (Tataranni et al., 1999; Small
et al., 2001) and decreased activation following
satiation (Gautier et al., 2000, 2001; Small et al.,
2001; Del Parigi et al., 2002), suggesting that it
is directly related to the desire to seek out or
abstain from food.
Because activity in these two regions can be
affected by hunger as well as mood state, we
hypothesized that state affect might modulate
appetite via its influence on brain activity in the
orbitofrontal and insular cortices (Killgore and
Yurgelun-Todd, 2006). To test this possibility, we
correlated the brain activity within the orbitofrontal and insular cortices during high- and
low-calorie food perception with self-reported
mood state on a well-established scale known
as the Positive and Negative Affect Schedule
(PANAS) (Watson et al., 1988). This scale provides scores on two independent dimensions
of mood state, known as positive affect (PA)
and negative affect (NA). PA is a state of feeling
enthusiastic, alert and active, while NA is a state
of heightened unpleasant feelings and subjective distress (Watson and Tellegen, 1985).
As shown in Figure 5.5, we found that PA and
NA were linearly-related to fMRI signal intensity in specific regions of the orbitofrontal and
insular cortices. Moreover, the pattern of activation suggested an interaction between mood
state and the calorie content of the food images.
People with the highest scores on PA showed
greater activity in the medial orbitofrontal cortex and posterior insula (i.e., the “start eating”
or “hunger” regions) in response to healthy
low-calorie food images. When presented with
less healthy high-calorie food images, PA scores
correlated with greater activity in the lateral
regions of the orbitofrontal cortex (i.e., the “stop
eating” or “satiation” regions). In contrast, for
those with higher scores on NA, perception of
high-calorie foods was associated with greater
activity in the medial orbitofrontal and posterior
insular cortex (i.e., the “start eating” or “hunger”
1. FROM BRAIN TO BEHAVIOR
5.4 CORTICAL AND LIMBIC ACTIVATION TO FOOD IMAGES DURING ADOLESCENT DEVELOPMENT
Positive affect (PA)
High-calorie foods
Lateral prefrontal cortex
“Satiation” regions
P
A
Low-calorie foods
Medial prefrontal cortex
“Hunger” regions
L
Negative affect (NA)
Low-calorie foods
Lateral prefrontal cortex
“Satiation” regions
P
A
High-calorie foods
Medial prefrontal cortex
“Hunger” regions
L
FIGURE 5.5 Positive affect (PA) and negative affect
(NA) correlated significantly with activity in discrete
regions of the orbitofrontal cortex (OFC) during presentation of high-calorie (black) and low-calorie (white) food
images.
Higher scores on PA (top) correlated with greater activity in
lateral OFC during high-calorie food presentations (black)
and with activity in the medial OFC during low-calorie food
presentations (white). In contrast, higher scores on NA (bottom) correlated with activity in the lateral OFC for low-calorie
food presentations (white) and with activity in the medial
OFC for high-calorie food images (black). Areas showing
positive correlations with affect scores for both types of
foods are shown in medium gray. All regions are significant
at P 0.05, k 10. A, anterior; P, posterior; L, left.
Source: Adapted from Killgore and Yurgelun-Todd (2006).
regions), whereas for healthier low-calorie food
images high NA scores were related to greater
activity in the lateral orbitofrontal cortex (i.e.,
the “stop eating” or “satiation” regions).
In short, the findings from that analysis suggest that mood state may affect appetite via the
relative changes in brain activity between lateral and medial orbitofrontal cortices. These
findings suggest that a good or energetically
positive mood (i.e., high PA) is associated with
a pattern of activity in brain regions that might
65
increase the desire to consume healthier lowcalorie foods and reduce the desire to consume
unhealthy high-calorie/high-fat foods. Similarly,
a bad or subjectively distressed mood (i.e., high
NA), was associated with patterns of brain activity that would lead to a greater desire to consume less healthy high-calorie/high-fat foods
and lower the preference for healthier food
choices. Although these findings are consistent
with the reported cravings for unhealthy foods
during negative mood states (Hill et al., 1991;
Christensen and Pettijohn, 2001), they remain
speculative, as the data are correlational in nature
and we did not collect any ratings of cravings
or preferences for the types of foods presented
in the study. Recent evidence also suggests that
there are individual differences in reward sensitivity that may contribute to the responsiveness
of prefrontal and subcortical regions to highly
rewarding foods (Beaver et al., 2006).
5.4 CORTICAL AND LIMBIC
ACTIVATION TO FOOD
IMAGES DURING ADOLESCENT
DEVELOPMENT
Although long-term patterns of food consumption in adulthood can have dramatic
consequences on health status, problems with
overweight and obesity are increasingly common among children and adolescents (Flegal
et al., 2002). Because eating and exercising patterns, established early in life, are likely to guide
subsequent lifestyle choices and health-related
behaviors, it is important to understand how
the brains of children respond to images of food
in ways that are similar to and different from
those of adults. We know that the brain continues to develop throughout adolescence and into
early adulthood, and that these changes affect
how children process information (Casey et al.,
2000; Killgore et al., 2001; Yurgelun-Todd et al.,
2002, 2003; Yurgelun-Todd and Killgore, 2006).
1. FROM BRAIN TO BEHAVIOR
66
5. CORTICAL - LIMBIC ACTIVATION
Because the regions of the brain most critical to
the ability to form good judgments, delay gratification and inhibit inappropriate behavior are
also the same ones that develop latest during
adolescence (Casey et al., 2000; Giedd et al., 1999;
Giedd, 2004), it may be more difficult for children to effectively regulate their dietary needs.
For example, it has been shown that by about
7 years of age healthy children generally exhibit
some understanding of when it is appropriate
to inhibit certain responses, but their ability to
successfully inhibit such responses may not be
fully developed until years later (Dowsett and
Livesey, 2000). These inhibitory capacities are
believed to be mediated predominantly by the
prefrontal cortex (Watanabe et al., 2002), a region
that develops rapidly during the early adolescent years (Giedd et al., 1999; Kanemura et al.,
2003), although the dorsolateral regions, which
are particularly important for inhibition and
judgment, may not reach full maturity until the
early twenties (Giedd, 2004; Lenroot and Giedd,
2006). Functional neuroimaging studies suggest
that the prefrontal cortex becomes progressively
more active during self-control (Marsh et al.,
2006) and emotional tasks as children mature
(Killgore et al., 2001; Killgore and YurgelunTodd, 2004; Yurgelun-Todd and Killgore, 2006),
and this activity correlates with the ability to
inhibit behavior (Rubia et al., 2000).
As described earlier, many of these same prefrontal regions are highly active when adults
view images of high-calorie foods but not when
viewing visually similar low-calorie foods, suggesting that the perception of highly appealing
yet unhealthy foods stimulates much activity
in brain regions important for inhibition and
self-control (Killgore et al., 2003). Therefore, in a
subsequent study, we decided to study the brain
activation of children and adolescents as they
viewed these same sets of food images and compare the results to our adult sample (Killgore
and Yurgelun-Todd, 2005b). In that study, we
presented the food images to eight healthy
female children and adolescents ranging in age
from 9 to 15 years old as they underwent fMRI
scanning. To gain a better understanding of the
development of cortical and limbic responses to
food, the data from the children and adolescents
were compared statistically to the data from the
adults reported earlier.
Several important findings emerged. First, as
with the adults in the previous study, the adolescent children showed significant activation of
limbic regions, especially the hippocampus and
parahippocampal gyri, in response to images of
food, regardless of caloric content (see Figure
5.6a). Secondly, the children showed significantly different cortical responses to the highand low-calorie foods compared to adults (see
Figure 5.6b). Whereas the adults showed large
regions of activity within the dorsolateral and
medial prefrontal cortex, the adolescent sample
did not. A direct statistical comparison between
the adolescents and adults showed greater activity in the medial prefrontal cortex of adults during the perception of high-calorie foods. The fact
that the adult sample showed significant activation within inhibitory and self-reflective prefrontal regions whereas adolescents failed to show
such activation suggests that adolescent brain
development may not have reached the level of
maturity necessary to consistently engage these
important self-regulatory regions when viewing
appetizing but unhealthy high-calorie foods. The
adolescents, in contrast, tended to respond to
food images with greater activation than adults
in posterior brain regions that are generally
associated with visual processing of objects such
as tools and other inanimate objects. Finally,
within the adolescent sample, we correlated the
high-calorie brain responses with age to identify
regions that might show developmental changes
in brain activity. Interestingly, we found that
activity within the orbitofrontal cortex increased
with age in response to high-calorie but not lowcalorie foods (see Figure 5.6c). Together, these
findings suggest that subcortical and limbic
1. FROM BRAIN TO BEHAVIOR
5.4 CORTICAL AND LIMBIC ACTIVATION TO FOOD IMAGES DURING ADOLESCENT DEVELOPMENT
(a) Adolescent High–low
calorie conjunction
Hippocampus
(b) Adults vs
adolescents
(high calorie)
Dorsal medial
prefrontal cortex
Anterior cingulate
(c) Correlation
with age
(high calorie)
Medial orbitofrontal
cortex
FIGURE 5.6 Developmental findings for responses of
healthy adolescent children to images of food.
(a) Regions of common activation (i.e., conjunction analysis) for both high- and low-calorie foods in healthy adolescents. Similar to the findings reported previously for
adults, significant common activation was observed
in the hippocampus for high- and low-calorie foods,
P 0.005 (uncorrected), k 20. (b) When viewing images
of high-calorie foods, adults show significantly greater
activation of the dorsomedial prefrontal cortex, anterior cingulate, precuneus and posterior cingulate gyrus
than adolescents, while adolescents show greater activation in posterior visual processing regions than adults,
P 0.005 (uncorrected), k 20. (c) A significant positive
correlation (r 0.97) was observed between adolescent
age and functional activity in the medial orbitofrontal cortex during visual perception of high-calorie food images,
P 0.01, k 10.
Source: Adapted from Killgore and Yurgelun-Todd (2005b).
67
responses to rewarding food stimuli remain relatively constant over development, without much
difference between children and adults, whereas
regions of the brain that are particularly important for self-reflection, inhibition and modification of behavior show progressive age-related
increases in functional activation. This may be
due to experience, neural development through
adolescence or, most likely, some combination
of the two. The findings are in accord with an
emerging neurodevelopmental model that suggests that acquisition of the greater self-control
and inhibition capacities that normally emerge
with adolescent development occurs in conjunction with progressive structural and functional changes within the prefrontal cortex and
its interactions with subcortical structures (Luna
et al., 2001; Fuster, 2002; Gomez-Perez et al., 2003;
Marsh et al., 2006).
While the behavioral correlates of these patterns of activation have yet to be determined,
the developmental neuroimaging findings are
important because they suggest that (1) some
of the most primitive emotional regions of the
brain, such as the limbic system, show very consistent activation among children and adults
when presented with images of enticing highcalorie foods, but that (2) adult brains may
respond to such foods with greater activation of
inhibitory, self-monitoring and self-regulatory
regions of the prefrontal cortex, perhaps allowing mature individuals greater voluntary control
over their dietary intake. While speculative, this
difference in frontal activity may partly explain
why children, even if they have been educated
about proper nutrition, may find it difficult (or at
times impossible) to resist the temptations presented by the vast cornucopia of easily accessible high-calorie junk foods. At present, caution is
warranted in extrapolating these imaging data to
actual behavior, but future studies will undoubtedly clarify the extent to which differences in
childhood and adult brain responses may contribute to food choices and health outcomes.
1. FROM BRAIN TO BEHAVIOR
68
5. CORTICAL - LIMBIC ACTIVATION
5.5 CONCLUSION
From this limited review, it is clear that the
human brain responds to visual images of
food differently according their energy value
and immediate reward potential. For adults
and children alike, several of the more primitive regions of the brain involved in motivation, emotion and memory appear to share
a common activation to both high-and lowcalorie foods. Cortical regions, on the other
hand, responded differently depending on the
caloric content of the foods. When confronted
with images of high-calorie food images such
as Big Macs and French fries, the adult brain
appears to show widespread cortical activation, particularly in prefrontal regions. Similar
responses appear to be virtually absent for
images of healthier low-calorie foods such as
salads and whole-grain cereals. Because the
activated regions are frequently implicated in
tasks of judgment, decision-making, inhibition, self-reflection and behavioral control, one
interpretation of these data is that high-calorie
foods arouse considerable approach-avoidance
conflict in healthy adults, but much less so in
children and adolescents because of the immature development of their prefrontal inhibitory
systems. The lack of arousal of such conflict
and inhibition may account for some of the difficulties children have in moderating unhealthy
food choices, and may help explain why the epidemic of obesity is growing so rapidly among
young people. Similarly, the findings suggest
that the responsiveness of these prefrontal systems to images of food may also be modulated
by other individual factors, with higher body
mass and negative mood states associated with
patterns of brain activity that are similar to
those associated with reduced inhibitory control
and greater appetite in some studies. Clearly,
these data are very preliminary and generate
more new questions than they answer. Future
research that links food-related brain responses
with actual eating behavior will be necessary
to clarify the full implications of these findings.
The emerging picture, however, suggests that
some mostly subcortical and limbic aspects of
our brains are hard-wired to respond to images
of food in general, while with maturation other
cortical regions appear to develop particularly
strong responses to foods that have been associated with energy and pleasure.
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C H A P T E R
6
Reward-related Neuroadaptations
Induced by Food Restriction: Pathogenic
Potential of a Survival Mechanism
Kenneth D. Carr
Departments of Psychiatry and Pharmacology, New York University School of Medicine,
New York, NY, USA
O U T L I N E
6.1
Introduction
6.2
Food Restriction may Augment Neurobiological Responses to Palatable Food in
a way that Promotes Addictive Behavior 75
6.3
6.4
73
Food Restriction Enhances CNS and
Behavioral Responses to Drugs of
Abuse and Dopamine Receptor Agonists 76
Food Restriction Up-regulates
D1 Dopamine Receptor-mediated
Phosphorylation of Ionotropic
6.5
6.6
Striatal Neuroadaptations Induced by
Food Restriction may be Secondary
to Changes in Pre-synaptic Dopamine
Neuronal Function
79
A Schema to Consider as Research
Continues
80
Acknowledgments
6.1 INTRODUCTION
The increasing prevalence of obesity in Westernized societies has been partly attributed to the
increasing abundance of inexpensive, energydense, highly palatable foods (Centers for Disease
Obesity Prevention: The Role of Brain and Society on Individual Behavior
Glutamate Receptors and Signaling Proteins
that Underlie Synaptic Plasticity
77
81
Control and Prevention (CDC), 2004; Cordain
et al., 2005). It is not entirely understood why
homeostatic regulatory systems are superseded
in the “obesogenic” environment; it has been
pointed out, however, that human evolution in
an ecology of scarcity will have favored rigorous
73
2010 Elsevier Inc.
© 2010,
74
6. FOOD RESTRICTION AND REWARD
homeostatic controls selected to defend against
starvation but not obesity (Chakravarthy and
Booth, 2004; Schwartz and Niswender, 2004;
Polivy and Herman, 2006). An additional significant factor may be the recent introduction of
refined sugars to the human diet (Cordain et al.,
2005), which are proposed to generate a supranormal reward signal in brain (Lenoir et al., 2007),
not unlike that linked to drugs of abuse, and
which can potentially lead to addictive behavior.
The plausibility of this suggestion is highlighted
by the recent pre-clinical finding that rats trained
in a two-lever task invariably self-administered
oral sucrose solution rather than concurrently
available intravenous cocaine (Lenoir et al., 2007).
Sucrose, by way of orosensory (Yu et al., 2000;
Smith, 2004) and post-ingestive (de Araujo et al.,
2008) signaling, leads to increased extracellular
dopamine (DA) concentrations in the nucleus
accumbens (Hajnal et al., 2004; Norgren et al.,
2006) – an effect shared with virtually all drugs
of abuse, and key to their rewarding and addictive properties (Pontieri et al., 1995; Bassareo and
Di Chiara, 1999a). Nucleus accumbens dopamine
concentrations also increase in response to cues
signaling cocaine or sucrose availability, and
coincide with the initiation of a response to
obtain these rewards (Carelli, 2004). Commercial
formulations of high-fat and high-sugar “snack”
foods possess similarly potent incentive properties (Jarosz et al., 2006), and high-fat corn oil
induces nucleus accumbens dopamine release
similar to that observed during sucrose intake
(Liang et al., 2006).
While addiction has been conceptualized as
a process through which drugs usurp and produce synaptic plasticity in dopaminergic and
related neuronal circuits that normally mediate adaptive goal-directed behaviors such as
food-seeking and procurement (Kelley and
Berridge, 2002; Cardinal and Everitt, 2004; Di
Chiara, 2005; Volkow and Wise, 2005), food
itself, under some conditions, may commandeer
this circuitry in a way that sustains maladaptive
behavior. The proposed phenomenon of sugar
“addiction” is supported by animal models in
which (1) intermittent access to sucrose produces behavioral cross-sensitization to psychostimulant drugs of abuse (Avena and Hoebel,
2003; Gosnell, 2005); (2) alternating 12-hour periods of sucrose access and food deprivation lead
to binge-like intake of sucrose followed by withdrawal signs when access is terminated (Avena
et al., 2008); and (3) a period of abstinence from
sucrose is followed by powerful cue-induced
reinstatement of sucrose-seeking similar to that
observed in animal subjects abstaining from
cocaine (Grimm et al., 2005). Several studies
have assessed whether neuroadaptations that
accompany cocaine addiction are present in rats
with a history of sucrose self-administration and
seeking. To date, neuroplastic changes induced
by cocaine – including molecular and structural
changes in the cell body and terminal regions of
the mesoaccumbens dopamine pathway – have
not been observed in rodents self-administering
or withdrawn from sucrose (Robinson et al.,
2001; Lu et al., 2003; Jones et al., 2007; Chen
et al., 2008). An exception may be the bingepromoting protocol in which 12-hour periods
of food deprivation are alternated with 12-hour
periods of sucrose access (Avena et al., 2008).
At sacrifice, these animals displayed decreased
D2 dopamine receptor binding in the terminal
field of the nigrostriatal pathway (i.e., caudateputamen) – a finding also obtained in neuroimaging studies of human cocaine addicts
and obese subjects (Wang et al., 2004) – and
increased D1 DA receptor binding in the
nucleus accumbens. Interestingly, increased D1
DA receptor function in the nucleus accumbens
appears to be a fundamental underpinning of
the enhanced rewarding, cell signaling and transcriptional responses of food-restricted rats to
drug challenge (Carr, 2007). As will be outlined
below, food restriction produces reward-related
neuroadaptations that likely promote foraging, food acquisition and ingestive behavior in
1. FROM BRAIN TO BEHAVIOR
6.2 FOOD RESTRICTION MAY AUGMENT NEUROBIOLOGICAL RESPONSES TO PALATABLE FOOD
the energy-deficient subject. However, during
self-imposed food restriction, induction of these
neuroadaptations in an obesogenic environment
may contribute to the ultimate failure of many
weight-loss diets, the emergence of binge eating
in chronic dieters, and the high co-morbidity of
eating disorders and substance abuse.
6.2 FOOD RESTRICTION MAY
AUGMENT NEUROBIOLOGICAL
RESPONSES TO PALATABLE FOOD
IN A WAY THAT PROMOTES
ADDICTIVE BEHAVIOR
Self-imposed calorie restriction is a frequent
response to the fear or attainment of overweight
and obesity, yet the majority of diets fail (Polivy
et al., 2008). Restrained eating oftentimes leads
to loss of control, poor food choices, a disposition to binge, and the regain or surpassing of
baseline body weight (Vitousek, 2004; Vitousek
et al., 2004a; Polivy and Herman, 2006; Polivy
et al., 2008). In fact, it has been suggested that
dieting contributes to the obesity epidemic
(Polivy and Herman, 2006). In the context of
abundant palatable foods and associated cues,
a common response to dietary restraint is binge
eating, as observed in the Minnesota Study
of calorie restriction among World War II conscientious objectors (Keys et al., 1950), studies
of chronic dieters (Polivy and Herman, 2006)
and protocols of food restriction or dieting in
non-clinical populations (Laessle et al., 1996;
Stice et al., 2000). These observations are not
surprising, given the enhanced motivationalaffective responses to food and cues that have
been observed in food-restricted subjects in the
laboratory. Cabanac and LaFrance (1991) demonstrated in human and rodent subjects that
several weeks of food restriction with weight
loss prevents negative alliesthesia – i.e., the phenomenon whereby a preload of glucose causes
75
a previously attractive sweet taste to become
unpleasant. Another, though neurochemical,
example of a persistent positive response in
food-deprived relative to free-feeding subjects
is dopamine release in the nucleus accumbens
during ingestion of palatable food. Normally, an
important difference between the dopaminergic
response to food and abused drugs, believed to
contribute to the addictive properties of the latter, is that contact with palatable food releases
dopamine in the nucleus accumbens shell
subdivision only when it is novel (as a learning signal), while drugs of abuse repeatedly
increase extracellular dopamine in this region
(Pontieri et al., 1995; Bassareo and Di Chiara,
1999a). When subjects are food-deprived, palatable food retains its ability to release dopamine
in the nucleus accumbens shell despite the
subject’s familiarity with it (Bassareo and Di
Chiara, 1999b), thus rendering food more
“drug-like” in this regard. In another example,
when restrained eaters were exposed to food
odors for 10–12 minutes prior to an opportunity
to consume, craving and intake were strongly
increased, while the same pre-exposure either
had no effect or actually decreased intake in
control subjects (Jansen and van den Hout, 1991;
Fedoroff et al., 1997, 2003). In a rodent neurochemical parallel to this observation, anticipation of access to highly palatable food was
accompanied by nucleus accumbens dopamine
release in food-deprived rats but not ad libitumfed rats, despite the fact that both groups consumed the food (Wilson et al., 1995).
Polivy and Herman (2006) have discussed
evolutionary explanations of the up-regulated
incentive response, proclivity to binge, and low
success rate among voluntary dieters. They
point out that “gorging”, whenever possible,
confers selective advantage upon a species that
evolved to survive alternating cycles of scarcity
and abundance. Consequently, in the physiological context of dieting or significant dieting
history, the prepotent response to abundance is
1. FROM BRAIN TO BEHAVIOR
76
6. FOOD RESTRICTION AND REWARD
self-indulgence. Binge eating is common in the
obese population, and is considered a contributing factor to the obese condition (Yanovski,
1993). It is estimated that up to 30 percent of
obese patients display binge-eating disorder
(Yanovski, 1993; Striegel-Moore and Franko,
2003) and, importantly, among obese individuals
who binge eat there is a greater prevalence of
cyclic dieting than among those who do not
(Howard and Porzelius, 1999). In two welldeveloped pre-clinical models of binge-eating
disorder, repeated cycles of food restriction or
deprivation combined with periodic access to
highly palatable food are necessary conditions
for the emergence of binge-eating behavior
(Hagan and Moss, 1997; Hagan et al., 2003;
Avena et al., 2008). A variant form of maladaptive reward-directed behavior accompanying
food restriction is the enhancement of drugseeking and -taking in animal models (Carroll
and Meisch, 1984), and the association between
dietary restraint and substance abuse in clinical
and non-clinical populations (Herzog et al., 1992;
Krahn et al., 1992; Wilson, 1993; Wiederman and
Pryor, 1996; Corwin, 2006). Our laboratory has
investigated CNS and behavioral responsiveness of food-restricted animals to drugs of abuse
and other pharmacological probes. It is expected
that some of the findings obtained will be applicable to understanding the enhanced incentive
effects of food and cues among restrained eaters,
and neurobiological factors contributing to the
genesis of binge eating.
6.3 FOOD RESTRICTION
ENHANCES CNS AND
BEHAVIORAL RESPONSES TO
DRUGS OF ABUSE AND DOPAMINE
RECEPTOR AGONISTS
Initial studies of the laboratory were aimed
at assessing whether the enhancement of drug
self-administration behavior by food restriction
(Carroll et al., 1979; Carroll and Meisch, 1984)
reflects an increase in drug-reward magnitude.
To conduct these studies, mature male rats were
food-restricted until body weight decreased by
20 percent; testing occurred during the ensuing
1–3 weeks during which body weight was
clamped at this value by titrating daily food
allotment. A curve-shift method of electrical
brain stimulation reward testing was used in
conjunction with passive drug administration
to quantify the reward-potentiating effects of
drugs, as reflected in the leftward shift produced in the curve that relates animals’ rate of
lever-pressing to the pulse frequency of contingent brain stimulation. This approach provides
a measure of reward-related drug effects that
is not conflated with the effects of food restriction on learning and performance capacity, or
the possible negative reinforcing effects of drugs
in the hungry subject. Consequently, effects of
d-amphetamine, phencyclidine, cocaine and
several other drugs were shown to be greater in
food-restricted than ad libitum-fed rats (Cabeza
de Vaca and Carr, 1998; Carr et al., 2000). To
assess whether these enhanced drug effects
were more likely a result of increased CNS sensitivity than changes in drug pharmacokinetics
and bioavailability, intraperitoneal and intracerebroventricular (i.c.v.) routes of administration were compared. Consistent and pronounced
differences between feeding groups in response
to i.c.v. drug administration supported the
involvement of a sensitized neural substrate.
As follow-up to the behavioral studies, immunostaining for the protein product of the immediate
early gene, c-fos, revealed that d-amphetamine
and the D1 dopamine receptor agonist SKF82958 produced greater cellular activating effects
in several dopamine terminal areas of foodrestricted relative to ad libitum-fed rat brain,
including the dorsal and ventral striatum – i.e.,
caudate-putamen and nucleus accumbens (Carr
and Kutchukhidze, 2000; Carr et al., 2003).
Further, the D1 agonist was shown to induce
1. FROM BRAIN TO BEHAVIOR
6.4 FOOD RESTRICTION UP-REGULATES D1 DOPAMINE RECEPTOR-MEDIATED PHOSPHORYLATION
greater activation of the ERK 1/2 MAP kinase
signaling cascade, the downstream nuclear
transcription factor CREB, and neuropeptide
gene expression (preprodynorphin and preprotachykinin) in the nucleus accumbens (Haberny
et al., 2004; Haberny and Carr, 2005a, 2005b).
These findings, and the abundant literature
identifying the nucleus accumbens as a locus in
which psychostimulants exert positive reinforcing effects and enhance responding for non-drug
reinforcers (e.g., Hoebel et al., 1983; Pontieri et al.,
1995; Carlezon et al., 1995; Carlezon and Wise,
1996; McBride et al., 1999; Parkinson et al., 1999;
Wyvell and Berridge, 2000; Rodd-Henricks
et al., 2002), suggested that, among the neuroadaptations associated with food restriction,
increased D1 dopamine receptor function may
be critically involved in the enhanced behavioral responsiveness to drugs of abuse. Support
for this hypothesis was obtained in a study
demonstrating that direct microinjections of
d-amphetamine or SKF-82958 into the nucleus
accumbens produced reward-potentiating and
locomotor-activating effects that were markedly
greater in food-restricted than ad libitum-fed
subjects (Carr et al., 2009a). These findings
almost certainly have implications for fooddirected behavior in as much as (1) persistent
elevation of extracellular dopamine levels in the
nucleus accumbens via dopamine transporter
knockdown increases food intake and incentive motivation to obtain a sweet food reward
(Pecina et al., 2003); (2) amphetamine microinjected into the nucleus accumbens increases
the incentive salience of sucrose reward (Wyvell
and Berridge, 2000) and sucrose consumption
(Sills and Vaccarino, 1996); (3) inactivation of
dopamine input to the nucleus accumbens blocks
local neuronal responses to a sucrose-paired
cue and abolishes sucrose-directed behavior
(Yun et al., 2004); and (4) a D1 dopamine receptor antagonist microinjected into the nucleus
accumbens decreases sham drinking of sucrose
solution (Smith, 2004; see, however, Hajnal and
Norgren, 2001).
77
6.4 FOOD RESTRICTION
UP-REGULATES D1 DOPAMINE
RECEPTOR-MEDIATED
PHOSPHORYLATION OF
IONOTROPIC GLUTAMATE
RECEPTORS AND SIGNALING
PROTEINS THAT UNDERLIE
SYNAPTIC PLASTICITY
The dopamine innervation of striatum is convergent with glutamate inputs from amygdala,
hippocampus and prefrontal cortex (Groenewegen
et al., 1999; Kalivas et al., 2005), and the integration of dopamine- and glutamate-coded signals
is fundamentally involved in the regulation of
accumbens neuronal activity (e.g., Surmeier et al.,
2007), appetitive goal-directed behavior, and
reward-related learning (Berke and Hyman, 2000;
Kelley, 2004; Malenka and Bear, 2004; Dalley et al.,
2005; Hyman et al., 2006). Acquisition of instrumental and Pavlovian food-directed responses
is dependent upon coincident activation of D1
dopamine and NMDA glutamate receptors in
the nucleus accumbens (Kelley, 2004; Dalley
et al., 2005), and this mechanism appears to
be up-regulated by food restriction. Upon D1
dopamine receptor stimulation, phosphorylation
of the NMDA receptor NR1 subunit in the nucleus
accumbens is greater in food-restricted than ad
libitum-fed rats (Haberny and Carr, 2005a). NR1
phosphorylation increases NMDA receptor function (cation channel conductance) and recruits
NMDA receptor-linked signal transduction pathways, including ERK 1/2 and CaMK II (Leonard
et al., 1999; Vanhoutte et al., 1999; Dudman et al.,
2003). Thus, we observed that D1 agonist administration induced an enhanced activation of ERK
1/2, CaMK II and CREB in food-restricted rats,
and these effects were blocked by pre-treatment
with the NMDA receptor antagonist MK-801.
Among the ionotropic glutamate receptor types that are co-expressed with dopamine
receptors in striatal neurons (Bernard et al., 1997;
1. FROM BRAIN TO BEHAVIOR
78
6. FOOD RESTRICTION AND REWARD
Wang et al., 2006), AMPA receptors mediate fast
excitatory synaptic transmission (Barry and Ziff,
2002). The GluR2-lacking GluR1 subtype of this
receptor undergoes activity-dependent trafficking, is Ca2 permeable, and its insertion and
removal from the neuronal membrane underlie
changes in synaptic strength (Barry and Ziff,
2002). Phosphorylation of GluR1 on Ser845 by
the D1 dopamine receptor → cAMP and/or
NMDA receptor → cGMP pathways enhances
AMPA currents and facilitates rapid insertion
into the post-synapse (Roche et al., 1996; Snyder
et al., 2000; Man et al., 2007; Serulle et al., 2007).
A single injection of cocaine or amphetamine,
or a brief bout of sugar consumption, all lead
to phosphorylation of GluR1 on Ser845 in a
D1 receptor-dependent manner (Snyder et al.,
2000; Rauggi et al., 2005). Recently, we observed
that a brief intake of sucrose solution rapidly
increased GluR1 protein levels in the synaptosomal fraction of the nucleus accumbens, suggesting increased trafficking to the synaptic
membrane (Tukey et al., 2007). Moreover, phosphorylation of GluR1 on Ser845 in response
to both D1 receptor stimulation and intake of
sucrose solution is greater in food-restricted
than ad libitum-fed subjects (Carr et al., 2010).
Considering that increased surface expression of GluR1 in the nucleus accumbens has
been identified as an enduring consequence of
chronic cocaine that is necessary for behavioral sensitization, “craving” and vulnerability
to relapse (Boudreau and Wolf, 2005; Conrad
et al., 2008), the phosphorylation and trafficking of GluR1 during sucrose intake, and the
up-regulation of D1 receptor-mediated phosphorylation of GluR1 by chronic food restriction, suggest a mechanism that may play a
role in the facilitatory effect of food restriction
on both adaptive and maladaptive forms of
reward-directed behavior. Confirming a role
of GluR1 phosphorylation in the invigoration
of food-directed behavior, it was recently demonstrated that a conditioned stimulus associated with a sweet solution loses its ability to
increase instrumental responding for sucrose
reward during extinction when food-restricted
mice have mutations on the Ser831 and Ser845
phosphorylation sites of the GluR1 receptor
(Crombag et al., 2008).
The up-regulation of striatal ERK 1/2 MAP
kinase signaling by food restriction may represent a mechanism, secondary to increased phosphorylation of NR1 and GluR1, that mediates
enhanced reward-related learning under conditions of food scarcity, with the potential to be
subverted by drugs and highly palatable foods
during dietary restraint. Drugs of abuse activate
ERK throughout the striatum in a D1 dopamine
receptor-dependent manner (Valjent et al.,
2004). The ERK cascade activates downstream
transcription factors (CREB, Elk-1) and gene
expression (Thomas and Huganir, 2004), and
mediates synaptic plasticity, learning and memory (Sweatt, 2001; Thomas and Huganir, 2004).
Importantly, several enduring addiction-related
behavioral changes induced by drugs of abuse,
including conditioned place preference (Valjent
et al., 2000, 2001; Salzmann et al., 2003; Gerdjikov
et al., 2004; Miller and Marshall, 2005), locomotor sensitization (Valjent et al., 2006) and cueinduced reinstatement of drug-seeking (Lu et al.,
2005), are dependent upon ERK signaling. In this
laboratory, it has been determined that ERK signaling induced by D1 dopamine receptor stimulation, though markedly greater in the nucleus
accumbens and caudate-putamen of foodrestricted than ad libitum-fed subjects, does not
contribute to the unlearned rewarding or motoractivating effects of drugs (Carr et al., 2009b).
However, the up-regulated ERK signaling was
shown to be necessary for the increased activation of the nuclear transcription factor CREB
(Haberny and Carr, 2005a), and the immediateearly gene c-fos (Carr et al., 2009b). It therefore
seems reasonable to hypothesize that the functional consequences of increased ERK signaling
will be evident in behavioral processes that are
dependent on network strengthening, such as
drug-mediated associative learning, which is
1. FROM BRAIN TO BEHAVIOR
6.5 STRIATAL NEUROADAPTATIONS INDUCED BY FOOD RESTRICTION MAY BE SECONDARY TO CHANGES
known to be increased by food restriction (Bell
et al., 1997; Cabib et al., 2000). Given that drugs
of abuse act on brain reward circuitry as proxies for natural rewards, up-regulation of a cell
signaling cascade that has been implicated in
drug addiction-related processes suggests that
the natural function of this neuroadaptation
is to facilitate synaptic plasticity and associative learning that promote food acquisition and
ingestive behavior. This expectation is supported
by a recent study in which training of foodrestricted rats to associate a cue with sucrose
delivery was accompanied by activation of ERK
in the nucleus accumbens (Shiflett et al., 2008).
Moreover, the ability of this cue to subsequently
invigorate instrumental responding for sucrose
pellets was blocked by pharmacological inhibition of nucleus accumbens ERK signaling. Thus,
the learned incentive effects of cues associated
with drugs of abuse and palatable food, as well
as their ability to potentiate reward-directed
instrumental behavior, appear to be reliant upon
nucleus accumbens ERK signaling – a mechanism that is up-regulated by food restriction. As
a result, dieting in the context of abundance may
have pathogenic potential in the sense that neuroadaptations increase vulnerability to incentive
properties of food and cues, and consequent episodes of binge intake will have increased capacity to produce synaptic plasticity and engender
enduring augmentation of those incentive effects
and their control over behavior.
6.5 STRIATAL
NEUROADAPTATIONS INDUCED
BY FOOD RESTRICTION MAY BE
SECONDARY TO CHANGES IN
PRE-SYNAPTIC DOPAMINE
NEURONAL FUNCTION
The increases in striatal dopamine and glutamate receptor function described above may be
secondary, and compensatory, to a decrease in
79
basal dopamine neuronal activity. Pothos and
colleagues (1995a, 1995b) observed that basal
extracellular dopamine concentrations in the
nucleus accumbens of food-restricted rats are
substantially lower than in ad libitum-fed subjects. Consistent with this finding, our laboratory obtained evidence of a decreased rate of
striatal tyrosine hydroxylation (Pan et al., 2006) –
suggestive of decreased dopamine synthesis
and utilization – and, in a recent preliminary
study, decreased membrane excitability of presumed ventral tegmental area (VTA) dopamine
neurons in midbrain slice preparations from
food-restricted subjects. Food restriction was
also found to decrease striatal dopamine transporter function (i.e., Vmax; Zhen et al., 2006)
which, according to the present schema, could
be a pre-synaptic compensatory response to a
persistent decrease in basal dopamine release.
A dampening of basal dopaminergic activity, as suggested above, may conserve energy
by decreasing spontaneous motor activity – as
is known to be the case in food-restricted animals when in familiar environments devoid of
food and related cues (Duffy et al., 1990; Hart
and Turturro, 1998; Vitousek et al., 2004b). On
the other hand, the potentiated response to
food and cues – proposed to be based partly
in striatal neuroadaptations – may begin with
a positive gating of food-related signals to the
mesoaccumbens dopamine pathway (Tobler
et al., 2005; Yamamoto, 2006). Signals relating to
less urgent biological drives, such as reproductive behavior, may be inhibited upstream of the
dopamine pathway, as in the suppression of
estrus (Campbell et al., 1977; Jones and Wade,
2002) and circulating testosterone (Sirotkin et al.,
2008) by food restriction in female and male
rats, respectively. Using immediate-early gene
expression (c-fos) as a marker of neural activity, our laboratory observed that contact with
a small palatable meal or environmental cues
associated with that meal activated neurons in
the dopamine cell body-rich ventral tegmental
area (Park and Carr, 1998). More recently, we
1. FROM BRAIN TO BEHAVIOR
80
6. FOOD RESTRICTION AND REWARD
observed that transfer of rats to a fresh cage
containing a morsel of lab chow activated VTA
neurons in food-restricted but not ad libitumfed subjects, suggesting along with foregoing
examples that food restriction may positively
gate access of food-related signals to the mesoaccumbens dopamine pathway. A reordering
of the hierarchy of survival needs during food
restriction, in conjunction with episodic binges
on supranormally rewarding food, may contribute to a narrowing of the behavioral repertoire
of subjects by weakening accumbal neuronal
ensembles (Pennartz et al., 1994) dedicated to
competing forms of goal-directed behavior and
strengthening those dedicated to food-directed
behavior, paralleling a process that is hypothesized to occur during development of drug
addiction (Kalivas and Hu, 2006).
Studies of mRNA and protein levels of tyrosine hydroxylase, the rate-limiting enzyme in
dopamine synthesis, have not yielded simple
results that are consistent with the hypothesis of
decreased dopamine neuronal activity in foodrestricted subjects. Although we have observed
a decreased rate of tyrosine hydroxylation in the
nucleus accumbens of food-restricted subjects –
suggestive of decreased dopamine synthesis and
utilization – food-restricted subjects displayed
elevated tyrosine hydroxylase protein levels (Pan
et al., 2006). The two findings are difficult to reconcile unless food restriction slows tyrosine hydroxylase degradation or decreases concentrations of
co-factor tetrahydrobiopterin. Measurement of
tyrosine hydroxylase mRNA in VTA has not
helped to clarify the situation. Lindblom and
colleagues (2006) have observed that 12 days of
food restriction leading to 8 percent body-weight
loss in adolescent rats increased levels of tyrosine
hydroxylase mRNA in the VTA. This result is
suggestive of increased dopamine synthesis and
utilization. Yet, in the protocol of our laboratory,
which involves at least 21 days of food restriction
and a 20 percent body-weight loss in mature rats,
no changes in VTA tyrosine hydroxylase mRNA
levels were detected (Pan et al., 2006). The changes
in dopamine neuronal function during food restriction may be complex and not reflected in measures of tyrosine hydroxylase levels and synthetic
activity, particularly when they are measured at
a single moment in time. What does seem likely,
based on existing evidence, is that mesoaccumbens
dopamine activity during food restriction alternates between two extremes, with hypoactivity
prevailing when the prospect of food acquisition is
nil and hyperactivity when food is anticipated, procured or ingested. The periods of low dopamine
neuronal activity may lead to increased accumulation of cytoplasmic dopamine, and thereby
increase the amount of dopamine released per
storage vesicle in exocytosis (Pothos, 2002). Such
an increase in quantal size could underlie the elevated extracellular dopamine concentrations seen
in the nucleus accumbens of food-restricted rats
injected with cocaine or amphetamine (Pothos
et al., 1995a; Rouge-Pont et al., 1995; Cadoni et al.,
2003). However, the high extracellular dopamine
concentrations that occur during phasic release
(e.g., in response to palatable food or psychostimulant challenge) may in turn trigger exceptional
dopamine conservation responses in the foodrestricted subject. We have observed that following
administration of the dopamine-releasing psychostimulant d-amphetamine, only food-restricted
subjects show evidence of feedback inhibition
of dopamine synthesis in nucleus accumbens,
as inferred from decreased phosphorylation of
tyrosine hydroxylase at Ser40 (a requirement for
tyrosine hydroxylase activation and consequent
dopamine synthesis) (Pan et al., 2006).
6.6 A SCHEMA TO CONSIDER AS
RESEARCH CONTINUES
While the pre-clinical research is still in an
early stage, evidence exists to support a schema
in which an increased dopamine signal-to-noise
ratio characterizes the episodic anticipation and
ingestion of palatable food, which in turn exploits
1. FROM BRAIN TO BEHAVIOR
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repeated episodes of palatable food bingeing during dietary restraint, and perhaps across multiple
episodes of dietary restraint, may lay the neurobiological foundation for a persistent proclivity
to binge, paralleling processes that are believed
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Recurrent pathogenic events and associated
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a role in the development and progression of
numerous behavioral disorders, including affective illness, panic disorder, functional psychoses
and drug addiction (Klein and Gorman, 1987;
Hyman and Malenka, 2001; Post, 2004; Kalivas,
2005). Though a variety of behavioral and neurobiological effects of a single episode of food
restriction have been described above, little is
known about their persistence or the effects of
cyclic food restriction on brain reward mechanisms and behavioral sensitivity to drugs or
sucrose. Considering the frequency with which
individuals in Western societies cycle between
dieting and relapse to unrestrained eating and
weight gain, and the documented contribution
of dieting history to drug abuse and binge eating,
coordinated investigation of synaptic plasticity
and behavioral sequelae of drug or palatable food
exposure across multiple episodes of food restriction may provide additional insights into the
underpinnings of maladaptive eating behavior
that contribute to obesity.
ACKNOWLEDGMENTS
Research conducted in the author’s laboratory and discussed in this chapter is supported
by grant DA03956 from NIDA/NIH.
81
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1. FROM BRAIN TO BEHAVIOR
B
EXECUTIVE CONTROL SYSTEMS
AND THE CHALLENGES THEY
FACE IN THE MODERN WORLD OF
PLENTY
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C H A P T E R
7
The Neuroeconomics of Food Selection
and Purchase
Brian G.Essex and David H.Zald
Department of Psychology, Vanderbilt University, Nashville, TN, USA
O U T L I N E
7.1
Introduction
89
7.2
Positive Valuations
90
7.3
Influences on Positive Valuations
7.3.1 Temporal Discounting
7.3.2 Satiety
7.3.3 Recent Availability
7.3.4 Relative Reward
90
90
92
93
93
Negative Valuations
93
7.4
7.1 INTRODUCTION
From a behavioral economics perspective, a
food purchase (or any other type of purchase)
can be broken down into processes involved
in appraising the value and costs of available
items, and selecting between items with competing valuations and associated costs. On the
surface, this is a simple formulation that can
Obesity Prevention: The Role of Brain and Society on Individual Behavior
7.4.1 Effort
94
7.5
Influences on Negative Valuations
95
7.6
Selection
7.6.1 Positive vs Positive Decisions
7.6.2 Integration of Positive and Negative
96
96
99
7.7
Habits
100
7.8
Conclusions
101
be used to understand factors influencing food
choice. This formulation is also amenable to
examination at the neural level. In the past decade, electrophysiological studies in non-human
primates and functional neuroimaging studies in humans have made significant inroads
into elucidating the neural substrates involved
in valuation and selection processes. In this
chapter, we review the key neural substrates
89
© 2010,
2010 Elsevier Inc.
90
7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
involved in positive valuation, negative valuation (i.e., costs, potential losses) and selection.
An understanding of factors that influence these
three processes helps reveal some of the difficulties (and perhaps some solutions) in directing
individuals towards healthier food purchases.
We particularly focus on processing within the
orbitofrontal cortex (OFC), nucleus accumbens,
anterior cingulate and insula, as these regions
appear critical for aspects of positive and negative valuation, and selection.
7.2 POSITIVE VALUATIONS
Individuals ultimately decide to purchase particular foods because they want them and value
them positively. The positive valuation may
incorporate several different features, including
the expected pleasant perceptual experiences of
flavor, temperature and texture, as well as more
abstract valuations such as the food’s long-term
health benefits. Ultimately, these different valuation components are integrated to provide a net
valuation of the food. This integration is likely
to be dynamic, with the relative weighting of
the different components changing over time
depending upon the person’s current internal
state and long-term goals. Changes in the relative weighting of different valuation components may be particularly critical in cases where
there is disagreement between the different valuation components. For instance, some foods,
such as ice cream and French fries, may be valued positively in the short run because they have
a pleasant flavor and texture. However, these
same foods may not be valued positively in
terms of nutritional value and long-term cardiovascular health. In contrast, foods such as carrots and broccoli are likely to be valued less
positively in the short run but more so in the
long run, because they provoke weaker expectations of a positive flavor perception but provide
longer-term health benefits.
Recent research has elucidated key neural
systems involved in positive valuations of items
that will be attained in the near future. These
neural systems traverse midbrain, striatal and
cortical levels of processing, and are marked by
the presence of cells (or cell populations) whose
activity scales with the magnitude of the reward
(either in an absolute sense, or more often relative to a range of potentially available rewards).
Evidence of scaled responses has been observed
in studies on monkeys that vary either the type
or the amount of food reward at the level of
the dopamine (DA) midbrain, the striatum and
the OFC (Tremblay and Schultz, 1999; PadoaSchioppa and Assad, 2006). Functional neuroimaging studies in humans have confirmed
the sensitivity of these regions to rewards, and,
depending upon the specific paradigm and
region, the responses appear sensitive to the
degree of pleasantness or the magnitude of the
anticipated or received reward (Anderson et al.,
2003; Small et al., 2003; Knutson et al., 2005).
7.3 INFLUENCES ON POSITIVE
VALUATIONS
7.3.1 Temporal discounting
Individuals tend to discount the future valuation of money when making choices. The
declining positive valuation of future reward
is well described by a hyperbolic discounting function (Rachlin et al., 1991; Kirby and
Marakovic, 1995). This type of discounting
function implies that as one moves farther away
from the present, differences in time-points provide progressively less impact on valuation.
Critically, it also indicates that there will be a
substantial decrement in value associated with a
delayed reward relative to an immediate reward
(see Figure 7.1). Applied to decisions involving
foods, this implies that individuals will give a
much greater valuation to immediate benefits
1. FROM BRAIN TO BEHAVIOR
7.3 INFLUENCES ON POSITIVE VALUATIONS
B
Value
A
t2
t1
tA
tB
Time
FIGURE 7.1 Subjective value of rewards increases as the
time of reward nears.
At time t1, the reward with the larger positive objective
value (B) is preferred. At time t2, however, the reward with
the smaller positive objective value (A) is preferred. The
benefits of reward A are obtained at time tA and the benefits
of reward B are obtained at time tB. The objective valuation of each reward is indicated by the height of the reward
curve at the time point of reward receipt (e.g., at time tA
for reward A); heights of the curves at times before this are
subjective values. This model can help explain why foods
with greater but more distant benefits are chosen when food
consumption will be in the distant future, but rewards with
smaller but immediate benefits are chosen when food consumption will be in the near future.
Source: Adapted from Sozou, 1998.
than to benefits obtained in the future, leading
individuals to ascribe little weight to long-term
health benefits. In other words, in the face of an
immediate perceptual reward, long-term benefits related to health will often fail to receive a
valuation that can compete with the immediate
perceptual components of reward. However,
since the discount rate between two future timepoints is smaller than that between the present
time-point and a future time-point, long-term
health benefits may be better able to compete
with the perceptual expectations of food when
91
the decision is being made about what to eat at
a future time-point. In other words, long-term
meal planning may provide an advantage when
attempting to emphasize health benefits over
the perceptual experience associated with different foods.
While long-term planning may be beneficial
for promoting healthy eating, temporal discounting also helps to explain why individuals often
fail to stick to their commitments to eat a healthy
diet. Take, for example, a New Year’s resolution
to stop frequenting a favorite fast-food establishment in order to establish a healthier diet. When
deciding to forgo French fries in the next year in
favor of healthier choices, an individual does not
have to forgo any immediate benefits obtained
from appealing fried foods, since all benefits are
delayed (i.e., in the future). In such cases, he or
she may decide to eat healthy foods, since the
delayed (or long-run) discounted benefit of eating healthy foods is likely to be valued more
highly in relation to the delayed discounted benefits of the less healthy French fries. However,
when an individual is actually confronted with
a decision to eat at a fast food restaurant versus
buying food at a health food store in the present
moment, it is harder to choose the healthy choice
since the immediate non-discounted benefits
from the French fries will be valued more highly
than the delayed discounted benefits from the
healthier food.
There is increasing evidence that specific
neural circuits may influence the slope of the
temporal discounting functions, leading to
more or less weighting of immediate over
long-term rewards. Lesions of the basolateral
amygdala and nucleus accumbens in rodents
caused increased selection of smaller immediate
rewards over larger delayed rewards (Cardinal
et al., 2001; Winstanley et al., 2004). Conversely,
the OFC may be involved in valuing immediate
benefits more than larger future benefits, since
lesions of this area in rodents lead to increased
choosing of larger delayed over smaller immediate rewards (Winstanley et al., 2004) and more
1. FROM BRAIN TO BEHAVIOR
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7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
neurons in this region respond to rewards delivered after a short delay than after a long delay
(Roesch et al., 2006).
7.3.2 Satiety
Whereas temporal discounting reflects a
decline in value for future rewards, satiety and
habituation cause declines in current valuations. Specifically, satiety produces a decline in
the valuation of food rewards. This can occur
at a global level, where any food is devalued,
or at the sensory specific level, where a specific
food is devalued. Many OFC cells that show
activity in response to foods when an animal is
hungry become unresponsive or show substantially reduced responsiveness to food when the
animal is sated (Scott et al., 1995; Pritchard et al.,
2008), with a more medial OFC area showing more general declines in responsiveness,
and a more lateral area showing more selective
declines. Selective satiety effects have also been
observed when the foods are presented visually
or as a smell only (Critchley and Rolls, 1996).
In their natural environment, animals do not
purchase food rewards. However, valuation can
be expressed in terms of an animal’s willingness to perform operant responses to obtain a
reward. In normal animals, the readiness to perform operant responses (e.g., voluntary leverpresses that have been previously reinforced)
for a specific food reward inversely relates to
the animal’s level of satiety (Baxter et al., 2000;
Izquierdo et al., 2004). However, despite the
OFC’s role in selective satiety, OFC lesions do
not completely abolish satiety effects. In formal
tests, monkeys with OFC lesions show a normal decrement in responses following selective satiation procedures (Baxter et al., 2000;
Izquierdo et al., 2004). At the same time, damage to areas of the ventral frontal lobe (including
the OFC) often result in a discontrol of eating.
For instance, Bachevalier and Mishkin (1986)
describe monkeys with hyperorality following
ventromedial lesions who would “grab objects,
which they voraciously ate or destroyed”.
Similarly, a number of case reports have noted
the presence of voracious appetites in humans
following OFC lesions or degeneration (Erb et al.,
1989; Kirschbaum, 1951). More recent studies of dementia of the frontal-lobe type have
examined correlations between atrophy and
ratings of hyperphagia or “sweet tooth”, and
have observed specific associations with OFC
atrophy (Ikeda et al., 2002; Whitwell et al., 2007;
Woolley et al., 2007). It remains to be seen, however, if these symptoms reflect a lack of satiety
or more general disinhibition. The application
of behavioral procedures similar to those used
in animal studies may be needed to address this
question directly.
The effects of satiety are apparent at multiple levels of processing, including both cortical and subcortical areas involved in value.
For instance, food satiety attenuates operant
responses for intracranial self-stimulation both
in the OFC and the lateral hypothalamus (Mora
et al., 1979). Critically, the devaluation of stimuli
based on satiety is also represented in the DA
system, as food-associated DA efflux in both the
nucleus accumbens and medial frontal cortex of
rodents shows sensory-specific declines when
rodents become sated on a specific food (Ahn
and Phillips, 1999).
Satiety may provide a particularly useful tool
for reducing food intake, either at the specific or
at the general level. This may be accomplished
in several ways. At the most invasive level,
bariatric surgery, vagal nerve stimulation or
hormonal manipulations can increase signals of
satiety (Sobocki et al., 2005; Chaudhri et al., 2008;
Xanthakos, 2008). However, less invasive techniques may also increase satiety. For instance,
slowing down the consumption of food may
allow temporally lagged perceptions of satiety to
impact reward valuation mechanisms prior to
the conclusion of the meal. Similarly, asking
individuals to slow down and concentrate on
the perception of every bite may allow enough
1. FROM BRAIN TO BEHAVIOR
7.4 NEGATIVE VALUATIONS
sensory exposure to induce sensory-specific
satiety that might not develop if the person ate
at a normal rate due to decreased exposure time
(Proulx, 2008).
7.3.3 Recent availability
While satiety affects valuations in the short
term, there are more lingering effects of recent
exposure that may last for days, weeks or even
months. These have generally not been studied
at the neural level, but almost certainly affect
valuation, with items that one is frequently
exposed to being generally devalued and items
that are more rarely available gaining heightened valuation. Such heightened valuation
occurs with items that are only seasonally available. The modern global distribution of many
foods has removed the seasonal nature of some
fruits and vegetables, but some retain clear
seasons. Other items that are associated with
specific holidays, or which are only marketed
at certain times of the year (such as Girl Scout
cookies in the United States) provide examples
in which limited availability heightens the valuation when they first become available again.
Most diets do not specifically incorporate seasonal or rarity features, but attention to such
details could aid in increasing the valuation of
certain foods.
93
alternatively an ability to produce relative valuations, such that food valuations are made relative to the other available foods. Although some
food-sensitive neurons in regions such as the
OFC have been found to have responses that
are invariant in the presence of other potential
foods (Padoa-Schioppa and Assad, 2008), studies examining reward responses at the level of
the DA midbrain, striatum and OFC have more
frequently observed evidence of responses that
are consistent with relative rather than absolute coding of rewards (Tremblay and Schultz,
1999; Bayer and Glimcher, 2005; Nieuwenhuis
et al., 2005; Tobler et al., 2005; Elliott et al., 2008).
Such responses are not limited to the actual
receipt of the reward, but also to the expectation of the reward. This is important, given
that food purchases are usually made based
on the expectation of reward rather than on an
actual sampling of the currently available foods.
Additionally, such data suggest that during
selection of rewards there may be an anchoring
or rescaling based on other currently available
rewards. For instance, in a study of DA neuron
firing, Tobler and colleagues (2005) demonstrated that DA firing was adaptively rescaled
depending upon the range of possible rewards
in the current context. Given such adaptive coding, promotion of healthy food selection must
be considered in relation to other available food
options, since the other food options will dramatically influence the scale range involved in
the valuation process.
7.3.4 Relative reward
In many cases, potential rewards do not
occur in isolation but amongst a set of other
potential rewards. Indeed, any trip to a modern grocery store or a shopping mall food court
makes apparent the abundance of potential
food rewards that could be obtained. In order to
select among a large group of positively valued
foods, we need either to have very precise absolute valuations of each food, with many gradations in order to produce a clear hierarchy, or
7.4 NEGATIVE VALUATIONS
When deciding whether or not to purchase
particular foods, individuals will also weigh
costs associated with a purchase. Every food
purchase is associated with an immediate cost –
the money that must be given up to receive it.
The time and effort necessary to obtain or prepare the food may also be tallied as an immediate
1. FROM BRAIN TO BEHAVIOR
94
7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
or short-term cost. When monkeys make choices
to receive a juice reward, neurons in the anterior cingulate cortex, OFC and lateral prefrontal
cortex are responsive to the amount of effort
(number of lever-presses) that will be required
to obtain it (Kennerley et al., 2009). Unhealthy
foods are also associated with long-term
costs, such as potential weight gain and other
health problems (e.g., high blood pressure and
cholesterol).
fMRI research with humans has shown that
many of the regions involved in the anticipation of reward (i.e., monetary gain) are also
involved in the anticipation of aversive events
(i.e., monetary loss). Anticipation of potential
monetary gains and losses on the Monetary
Incentive Delay Task are both consistently associated more with activation than anticipation of
a neutral outcome in the caudate, insula, thalamus and ventral striatum (Knutson et al., 2001,
2003, 2008; Bjork et al., 2004; Juckel et al., 2006;
Samanez-Larkin et al., 2007; Wrase et al., 2007a,
2007b; Dillon et al., 2008). In these studies,
the anticipation of losses and gains occurs in
the context of preparing to make a speeded
response in order to gain money or avoid losing money. Thus, this may not represent a passive tallying of value, as much as the extent to
which brain regions necessary to make appropriate goal-oriented responses are modulated
by the value of stimuli. As such, the common
ability of both positive and negative valuations
to motivate responses appears to be executed
through a shared neural circuitry. Interestingly,
of the above-mentioned areas showing modulation based on anticipated gain and loss, only the
ventral striatum shows a truly preferential pattern of modulations of positive more than negative valuations. This may provide a critical bias
that may make it harder for negative valuation
associated with health consequences to compete
with positive valuations of foods.
The neuroeconomics literature equates negative valuation with monetary loss, but in any
ecologically valid system these valuations must
also include physical harm, pain, and other aversive events. Although multiple brain regions
respond to aversive sensations, recent data have
suggested that the insula plays a particularly
important role in response to negative valuations
across different modalities. Both the anticipation of painful heat and the anticipation of painful electrical stimulation that cannot be avoided
are associated with increased activation in the
anterior insula in humans (Ploghaus et al., 1999;
Jensen et al., 2003). Since Jensen and colleagues
(2003) also observed increased activity in the
anterior insula following anticipation of painful
electrical shock that could be avoided, this region
appears to be important regardless of whether a
response is required.
7.4.1 Effort
One aspect related to food choice is how much
work or effort may be required to obtain and
prepare different foods. Research reveals that
both the ventral striatum and anterior cingulate cortex are particularly important for these
processes. When placed in a T-maze, rats usually
prefer to engage in an effortful response (i.e.,
scale a barrier) to receive a large reward than
to engage in a less effortful response to receive
a smaller reward. However, rats with depleted
levels of DA in the nucleus accumbens decrease
their choice of the high-effort, high-reward arm
of the T-maze and increase their choice of the
low-effort, low-reward arm (Salamone et al.,
1994; Cousins et al., 1996). Similarly, rats with
lesions to the anterior cingulate cortex, those
with severed connections between the anterior
cingulate cortex and the basolateral amygdala,
and those treated with a D1 dopamine receptor
antagonist in the anterior cingulate cortex do
not choose the high-effort, high-reward option
in the T-maze more frequently than the loweffort, low-reward option (Walton et al., 2003;
Schweimer and Hauber, 2005, 2006; Floresco
and Ghods-Sharifi, 2007). These effects do not
1. FROM BRAIN TO BEHAVIOR
7.5 INFLUENCES ON NEGATIVE VALUATIONS
appear to reflect an inability to perform effortful
motor responses or a preference for low reward,
since rats with lesions to the medial frontal cortex, including the cingulate cortex, will more
frequently choose the high-reward option if
both options require a high level of effort (i.e.,
scaling a barrier) (Walton et al., 2002). Likewise,
rats with depleted levels of DA in the nucleus
accumbens do not choose to scale the barrier for reward any less than control rats if the
other arm of the T-maze does not contain any
reward (Cousins et al., 1996). DA-depleted rats
also choose the high-reward option as much as
control rats if neither arm of the T-maze contains a barrier (Salamone et al., 1994). These data
indicate that DA depletions do not prevent the
selection of appropriate actions when an individual just has to choose between no effort and
effort, or no reward and reward. Rather, DA in
the cingulate and accumbens impacts the integration of the negative valuations associated with
effort with the positive valuations associated
with obtaining a food reward, such that relative
weighting of the positive and negative valuations is shifted in the positive direction.
7.5 INFLUENCES ON NEGATIVE
VALUATIONS
Like positive valuations, valuations of immediate (and short-term) negative value are likely
to be computed more automatically than valuations of long-term negative value. Similarly, a
variant of temporal discounting will apply, such
that potential future aversive events will be
viewed as less aversive than potential immediate aversive events. Temporal discount rates
for monetary losses appear lower than those for
monetary gains (Frederick et al., 2004), indicating that future aversive events will likely be less
strongly impacted by discounting than future
positive events. Nevertheless, the presence of
any discounting may still weaken the impact of
95
knowledge of future health consequences relative to the strong valuation of an immediately
available food. Interestingly, some individuals
actually prefer to suffer a loss immediately than
delay it until the future (Yates and Watts, 1975;
MacKeigan et al., 1993; Redelmeier and Heller,
1993), indicating that there may be a negative
discount rate for some future aversive events
(i.e., over time the valuation is augmented
rather than decreased). This may arise because
the emotional effects of waiting for a negative
event may themselves be negative, thus providing an additional contribution to the negative
valuation. There may be significant advantages
to a negative discount rate for aversive events.
Specifically, the presence of such an effect may
help explain why some individuals prefer to
incur present costs, such as the time, effort
or financial costs associated with preparing
healthy foods, in order to avoid more delayed
costs, such as the health problems resulting
from unhealthy eating habits.
The issue of risk and ambiguity will also
impact negative expected value. In behavioral
economics, risk refers to the known probability of a negative event; as the probability of the
negative event increases, the level of risk also
increases. Ambiguity refers to the degree of
uncertainty about the probability of the event.
A situation is completely ambiguous if an individual has no knowledge of the probabilities of
potential events, and it is partially ambiguous if
an individual has a subjective idea of what the
probabilities might be but does not know the
objective probabilities. In considering risk, economists often examine expected value rather than
a simple estimation of value, in that expected
value takes into account both the probability of
the risk and the value. For instance, if you flip a
coin (50 percent probability of heads), with heads
resulting in a $1.00 loss and tails resulting in no
change, the expected value is 50 cents (because
the average change is predicted to be a loss of
50 cents). An important implication of expected
value is that the risk of developing a health
1. FROM BRAIN TO BEHAVIOR
96
7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
problem from eating a food will influence the
level of negative valuation in proportion to the
perceived risk. However, when faced with
the decision to select a food, a person is usually in a partially ambiguous situation because
he or she is generally unable to provide a specific estimate of the risk of an adverse outcome
from any given consumption of a food. In other
words, negative valuation will be impacted
by the uncertainty of risks. Whereas the person knows with 100 percent probability that he
or she will lose money in buying the food, and
often with very high probability that the food
will be enjoyable, the expected negative valuation associated with health issues is often low,
because the impact of any single food purchase
on long-term health is perceived to be low and
knowledge of the actual risk uncertain. The risk
associated with repeated purchases of unhealthy
foods will be more consequential, but (1) food
selections are often made one at a time; and (2)
even taken with respect to repeated purchases,
temporal discounting and ambiguity regarding
future outcomes will usually lower the strength
and impact of the negative valuations.
7.6 SELECTION
When purchasing a food, an individual is
often confronted with many possible choices.
Each potential choice has certain aspects that
are valued positively and others that are valued negatively. To make a good choice, a person
must somehow weigh the costs and benefits of
each choice and then select the item that has
the highest overall valuation. Like the valuation process, integration of values can be done
automatically or more deliberately (e.g., rational
weighing of costs and benefits). Research shows
that individuals are more likely to choose an
unhealthy food (i.e., chocolate cake) over a
healthy food (i.e., fruit salad) under conditions
of high cognitive load, suggesting that optimal
long-term valuations may require deliberate processing (Shiv and Fedorikhin, 1999).
Different neural systems may be involved in
different aspects of the integration process.
To date, studies examining these integrative
processes have primarily utilized explicit decision-making paradigms, in which a person has
to select between potential rewarding stimuli.
These types of studies can be divided into two
categories: (1) those in which the selection process is strictly between two or more positively
valued stimuli, with no explicit consideration of
negative valuations, and (2) those that include
an element of negative valuation, specifically
monetary cost or effort.
7.6.1 Positive vs positive decisions
There exist numerous human neuroimaging studies in which individuals have to choose
between two or more stimuli, but typically
such studies examine perceptual decisions or
learning rather than the selection of one stimulus over another based on inherently differential reward value. As we have already seen, a
number of brain regions show responses that
scale with reward value. Such regions are natural candidates for the decision-making process of selecting between different rewards.
However, the presence of valuation-linked
responses also causes an empirical confound in
that, unless properly accounted for, activations
in a task might reflect the general process of
valuation rather than specifically assaying the
selection process. Although several of the studies described below attempt to assess the selection process, the extent to which they reflect
selection vs valuation often remains unclear.
One of the most directly pertinent reports
regarding food selection is a study by Arana
and colleagues (2003), who asked participants to
imagine that they were in a restaurant looking
at different menus while measuring brain activity with PET. All of the menus were individually
1. FROM BRAIN TO BEHAVIOR
7.6 SELECTION
tailored according to the subject’s preferences.
In some trials subjects simply viewed the menu,
while in others they were required to select items.
The authors found that the rostral gyrus rectus
(anterior-medial OFC) showed a greater response
to high-incentive menus, and responded more
when a choice was required. Although using a
more restricted set of stimuli, Paulus and Frank
(2003) reported both a medial frontopolar and an
anterior cingulate activation when participants
had to select their preference between two different soft-drink brands, relative to a perceptual
judgment between the stimuli.
Somewhat more complex experimental
designs have analyzed preference for items other
than food. For example, Kim and colleagues
(2007) asked people to make preference decisions
about faces vs making a perceptual judgment
about the faces. Activations in the preference
condition localized to the right ventral striatum
and left medial OFC, with ventral striatal activations occurring earlier than OFC activations. The
authors suggest that the ventral striatal activation provides a signal that the OFC uses when
making an explicit choice.
As noted above, a question can be raised as
to whether these studies capture the selection
process or a valuation process, since a simple liking or pleasantness assessment without
choice is rarely used as a control condition. In
other words, if we wish to understand selection
without it being confounded by the valuation
process on its own, the ideal contrast for a selection condition is one that explicitly involves
valuations of single items without requiring the
participant to draw contrasts, or make an actual
selection. Such studies are missing from the
literature. An alternative approach is to avoid
the issue of separate conditions, and instead
vary the difficulty of making the selection. This
type of design is based on the premise that a
harder selection process will require a greater
engagement of areas involved in selection, and
since both an easy and a hard decision still
require valuation, the study avoids many of the
97
limitations of contrasting a preference selection
with another perceptual task.
In their menu selection study, Arana and colleagues (2003) observed that the lateral OFC
responded more when subjects had to choose
between stimuli with similar prior pleasantness ratings. The authors suggest that this lateral OFC engagement arises because of a need
to suppress responses to alternative desirable
items. However, it is possible that lateral OFC
areas become preferentially involved during hard decisions because a more fine-tuned
valuation is necessary. If activations do indeed
reflect the difficulty of the decision, they should
vary parametrically in an inverse manner to
the difference between the independent valuation of food stimuli. To date, no studies have
taken this approach with food; however, Blair
and colleagues (2006) examined the effect of
reward distance when selecting objects that
had been arbitrarily given different reward values (points). As in other selection tasks, ventromedial prefrontal activity was associated with
decisions for rewarding stimuli, with increasing
activations as the value went up. In contrast, the
dorsal anterior cingulate appeared more responsive as the decision became more difficult (i.e.,
the closer the point value of the individual stimuli). This pattern of activity is consistent with a
well-recognized role of the cingulate in conflict
monitoring (Botvinick et al., 1999). However, it
is important to note that the conflict here is not
simply reflective of a need to control or inhibit
motor responses, as is the case in many conflictmonitoring situations. Rather, the activity
appears to be associated with conflict during the
decision process (see also Pochon et al., 2008).
Lesion studies also support the involvement of OFC/ventromedial regions in selecting
between different food rewards. Monkeys and
humans with OFC impairment show alterations in food selection (Baylis and Gaffan, 1991;
Ikeda et al., 2002). In monkeys, there may be
an important distinction between novel versus
familiar items. Baylis and Gaffan (1991) studied
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98
7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
responses primarily to novel foods, observing that monkeys with OFC lesions had more
erratic establishment of preferences, and a willingness to eat foods that would normally be
avoided (raw meat). In contrast, monkeys with
OFC lesions have been observed to show stable food preferences in selecting among palatable foods that they were already familiar with
(Izquierdo et al., 2004), indicating that, at a minimum, some form of preferential food selection
is maintained. This suggests that relatively stable selection preferences can occur even in the
absence of the OFC, when valuations and selection preferences are already established. This
suggests that the OFC may play a rather specific
role in situations where new valuations or new
selection contrasts must be made. It may be similarly critical when valuations are dynamic. For
instance, monkeys with OFC lesions fail to alter
their selection when an item should have been
devalued based on selective satiation (Baxter
et al., 2000; Izquierdo et al., 2004). Because the
monkeys in these studies still show evidence of
satiation, Izquierdo and colleagues suggest this
reflects an inability to use information about
the devaluation of the items in making their
selection, rather than a failure of the satiation
process.
Fellows and Farah (2007) examined the stability of preference judgments by examining
the transitivity of preference ratings. That is, if
a person has a stable preference hierarchy, such
that they prefer item a over item b, and item b
over item c, then they should also prefer item
a over item c. A failure to show this transitivity suggests a core problem in establishing or
accessing a preference hierarchy. To test for
such errors, Fellows and Farah asked patients
with ventromedial frontal damage and controls
to select their preferences from various pairings of items from three categories of stimuli:
food, famous people, and colors. The patients
with ventromedial prefrontal damage showed
substantially more errors of transitivity than
control subjects. The patients clearly are able to
make selections, but appear unable to utilize a
consistent scaling of item valuations when making their selections. Fellows and Farah suggest
that this may lead to the appearance of capricious decision-making. Studies in both monkeys and humans with OFC impairment have
been observed to show alterations in food selection (Baylis and Gaffan, 1991; Ikeda et al., 2002).
However, it is unclear whether these changes
reflect actual disruption of the evaluation of the
relative value of foods, or are simply a consequence of perceptual deficits or impairments in
associative learning. Recent data indicate that
monkeys with OFC lesions show stable food
preferences in selecting among palatable foods
that they are familiar with (Izquierdo et al.,
2004), indicating that, at a minimum, some form
of preferential food selection is maintained,
even though such animals may lose the ability
to adaptively use information about food preferences to guide behavioral choices, especially
when the value of one of the foods changes
(Baxter et al., 2000).
In summary, the neuroimaging and lesion
data highlight the involvement of the OFC and
anterior cingulate in explicit tasks involving
the selection of competing rewarding stimuli.
Studies of individuals with OFC lesions (particularly those including the medial OFC) support
a role for this area in food selection processes,
but suggest that it is not essential for selections
per se. Rather, the OFC may be critical for making consistent decisions, especially in the face
of changing valuations or potential comparisons. The anterior cingulate and the lateral OFC
also appear to contribute to the selection process, and to be particularly important when the
choices are difficult and there is a need to reject
an otherwise positive choice or resolve the relative valuation of two positively valued items,
although the exact contribution (comparison
of valuations, selection, inhibition of alternate
items, conflict in making a selection, monitoring) remains to be detailed. The OFC’s contribution to these processes in regards to feeding
1. FROM BRAIN TO BEHAVIOR
7.6 SELECTION
is particularly notable in that this region is
known to possess sensory representations of
food, and alterations in food selection occur following OFC lesions. In contrast, the cingulate’s
contribution during difficult decisions appears
consistent with its role in broader executive
functions in situations with conflicting potential
responses.
7.6.2 Integration of positive and negative
As already noted, we must make a distinction
between situations in which individuals simply
choose between different rewards vs when they
have to also incorporate negative valuations
into the decision process. A recent fMRI study
by Knutson and colleagues (2007) is consistent with the conjecture that different regions of
the brain compute positive and negative valuations of purchases. Subjects in this study performed a SHOP (“Save Holdings Or Purchase”)
task in which they could purchase various products. For each trial, they first saw a picture of a
labeled product (product period), then saw the
price of the product (price period), and finally
chose whether or not they would purchase the
product. Following each of the two scanning
sessions, the result of one random purchase
decision was counted for real, and the subject
was shipped the product and charged the price
listed during the task if they had chosen to purchase it. Knutson and colleagues observed that
activations in the nucleus accumbens (ventral
striatum) during the product period and in the
medial prefrontal cortex during the price period
increased the probability that an individual
decided to purchase a product, while activations
in the insula during the price period decreased
the probability. These data appear consistent
with a parcellation of valuations, such that the
nucleus accumbens and medial prefrontal cortex activity are associated with positive valuations of purchases, while the insula activity is
associated with negative valuations.
99
However, it should be noted that other variables (e.g., self-report variables, task variables)
also predicted individuals’ choices in the study
by Knutson and colleagues, and although activity
in brain regions added to the predictions beyond
what was predicted from these other variables,
the prediction rate of the brain variables alone
was only 60 percent. While this may suggest
that activity in these brain regions does not accurately predict an individual’s selection, newer
prediction methods have led to stronger predictions. Applying a different analytic approach to
prediction on the data from the study of neural predictors of purchases, Grosenick and colleagues (2008) were able to predict subsequent
choices from hemodynamic data alone at a prediction rate as high as 67 percent.
While the work by Knutson and colleagues
demonstrates a predictive relationship between
activity in areas with positive and negative valuations and purchasing, it does not address how
these valuations are actually integrated during
a purchase decision. This question has yet to be
fully answered, but there is a small set of candidate regions. Montague and Berns (2002) have
proposed that a circuit containing the OFC and
striatum, both of which are innervated by DA
neurons, generates a common internal currency
(a common valuation scale) for different rewards
and aversive events and their predictors. This
hypothesis rests on evidence that in both
regions neurons can be found with responses
to positive and negative stimuli, suggesting a
potential for integration of both positive and
negative valuations. Kennerley and colleagues
(2009) further indicate that some OFC cells
respond to a combination of both reward value
and the amount of effort that will be needed
to obtain the reward. However, the OFC’s role
in coding costs is relatively abstract in that the
OFC (at least in primates) does not appear to
process much information about the specific
motor actions necessary to obtain a reward
(Wallis and Miller, 2003; Kennerley et al., 2009).
Consistent with this interpretation, lesions of
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100
7. THE NEUROECONOMICS OF FOOD SELECTION AND PURCHASE
the OFC in monkeys lead to deficits in stimulus
selection but not action selection for rewards
(Rudebeck et al., 2008).
In contrast, the anterior cingulate cortex
appears to integrate valuations with information
about the effort or responses needed to obtain
the reward. Monkeys with lesions of the anterior
cingulate cortex have deficits in action selection
but not stimulus selection for rewards, a pattern
of deficits opposite to that of monkeys with OFC
lesions (Rudebeck et al., 2008). Value integration
in the anterior cingulate cortex is elegantly demonstrated in the recent study by Kennerley and
colleagues (2009), described earlier. Kennerley
determined the percentage of cells in the anterior
cingulate, OFC and lateral prefrontal cortex that
were responsive to the probability of obtaining
juice, the reward magnitude (amount of juice)
and the cost (number of lever-presses) in two
monkeys. Significantly more neurons in the anterior cingulate cortex responded to all three variables than did neurons in the other two regions.
These findings support the idea that both the cingulate and the OFC are involved in integrating
different aspects of positive and negative value,
but suggest that the anterior cingulate neurons
code a more complete set of variables impacting expected value and cost. To date, no studies that we are aware of have attempted to look
at the integration of different aspects of costs.
For instance, we would argue that three distinct types of costs may come into play for food
purchases: one involving the monetary costs,
one involving the amount of effort necessary to
obtain or prepare the food, and one involving
expected long-term health risk. Given the multiplexed nature of anterior cingulate coding, it is
a leading candidate for this type of integration.
However, this hypothesis is as yet untested.
7.7 HABITS
A criticism of the above analysis may be raised,
in that the process of valuation and selection
ignores the habit-based nature that often directs
the selection of food. Specifically, selection of
the type of food, brand of food, or even which
stores or restaurants to frequent, may arise
not through an elaborate decision process but
through a relatively automatic habit-based decision process. We often buy the same type or
brand of food items without processing what
alternatives are available. In such cases, there
may be little in the way of a true selection process. However, even in the face of habit-based
purchasing there is typically variability from
day-to-day and week-to-week in what we
choose to eat, because processes of long-term
habituation limit the appeal of repeatedly eating the same thing at every meal. Stated another
way, our natural need for variety in our diet
(which promotes balanced nutritional intake)
works in opposition to habit-based decisionmaking. The key question, then, is how do
habit-based and more active selection processes
interact? There are several possibilities. One
possibility may function like a logistic decision
tree, in which as long as the available items
are not currently valued at too low a level, no
active selection process is utilized. However,
this simple decision rule would mean that individuals would turn down more valued options
simply because a more familiar and acceptable
item was present. A more likely decision tree
would therefore require some element of evaluation of the reward magnitude of available food
rewards, with the habit system being used when
there is not a major discrepancy between available rewards (thus allowing high-value stimuli
to trump habit). Both of these models rely on
the idea that there is a dichotomy between a
habit-based system and an active selection process, with some rule determining which system
is utilized. However, it is possible that we do
not need a two-system model to explain purchase behavior. Rather, the appearance of habitlike responses may be an artifact of the role of
familiarity in valuation. Familiarity provides
confidence that a food will induce pleasant
1. FROM BRAIN TO BEHAVIOR
REFERENCES
sensations, and hence increases the expected
value of the food item over other items for which
there is lower confidence (and hence lower
expected value). This familiarity would work
in opposition to the devaluing habituation process, although both processes may have very
different temporal properties, with habituation
being strongest at short intervals since the last
exposures, and familiarity’s positive influence
acting over longer time intervals. This approach
views habit-like food purchases not so much as
an alternative to active selection, but rather as
an expression of the strength of familiarity in
the valuation process. These two formulations
have different implications when attempting
to change purchase behavior, in that if the purchase is related to habits there is a need to stop
the automaticity of the purchasing behavior and
potentially establish new habits. In contrast, if
familiarity is driving the selection by enhancing the expected value of the foods, the emphasis should be placed on familiarizing the person
with new, healthier options that are pleasant
enough to provide a competitive alternative to
the less healthy options.
7.8 CONCLUSIONS
In this chapter, we have emphasized the
processes of valuation, factors that influence
valuation, and mechanisms for integrating different valuation components.
Based on the emerging field of neuroeconomics, we have argued that there are several
specific brain circuits that are involved in the
process of positive valuation, negative valuation and choice behaviors based on these valuations. As a relatively young field, much remains
to be elucidated about precisely how these
brain areas accomplish their tasks. However,
as our understanding of the specific biases
within these systems becomes clearer it may be
increasingly possible to utilize these insights in
101
building appropriate strategies to address problematic eating behaviors.
From the behavioral economics perspective,
public policy programs aimed at helping people make healthier food choices should focus
on understanding and altering how individuals perceive the positive and negative valuation of different food options. If people believe
that there is a cost for choosing the healthier
items, either the perception of cost or the actual
cost should be addressed. For instance, if people perceive that unhealthy foods are easier to
obtain, encouraging the development of convenient healthy foods (such as drive-through
“fast health food” restaurants) would help to
alleviate this negative valuation of the healthy
items. Similarly, information about risks and
benefits of different foods must attend to issues
of ambiguity, uncertainty, and short-term versus long-term effects. The move towards having
published dietary information on food labels
and in restaurants is a good first step, but these
data often provide almost no information about
actual risks or benefits, especially short-term
risks or benefits, which may greatly limit their
impact. At the individual level, programs aimed
at establishing healthier eating behaviors may
also benefit from attending to temporal discounting factors in that forcing earlier decisions
about where or what to eat will lead to greater
relative weighting of the long-term benefits of
healthy foods than when immediate decisions
about food selection are required. None of these
suggestions represent “silver bullets” for curing the public health problems associated with
obesity; however, taken together they may provide enough of a bias on eating decisions to aid
attempts to foster healthy eating behaviors.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
8
Resisting Temptation: Impulse Control
and Trade-offs between Immediate
Rewards and Long-term Consequences
Lin Xiao1, Laurette Dubé 2 and Antoine Bechara3
1
Brain and Creativity Institute, Department of Psychology, University of Southern California,
Los Angeles, CA, USA
2
Faculty of Management, McGill University, Montreal, Canada
3
Department of Psychiatry and Faculty of Management, McGill University, Montreal, Canada
O U T L I N E
8.1
Introduction
8.2
A Neural System for Decision-making
and Will-power: The Somatic Marker
Hypothesis
106
8.3
Empirical Evidence for Deficits of
Decision-making Underlying Obesity
105
8.3.2
8.4
108
8.1 INTRODUCTION
Food is a primary reward, with high-calorie
foods typically being highly rewarding. The overconsumption of appetizing high-calorie food
has contributed to the dramatic increase in
obesity in modern society. More and more
Obesity Prevention: The Role of Brain and Society on Individual Behavior
8.3.1
Hypersensitivity to Reward of
Food and Food-related Cues
Hypoactivity in the Reflective
System
Conclusion
Acknowledgments
108
110
112
112
evidence show that there are similarities
between drug addiction and obesity (Kelley and
Berridge, 2002; Rolls, 2007a; Trinko et al., 2007;
Volkow et al., 2008a). Indeed, obese individuals
demonstrate the loss of control and compulsive eating that drug addicts demonstrate with
respect to drugs. Moreover, both drug addicts
105
© 2010,
2010 Elsevier Inc.
106
8. RESISTING TEMPTATION: IMMEDIATE REWARDS AND LONG-TERM CONSEQUENCES
and obese individuals tend towards the immediate gratification behaviors, such as taking
drugs or consuming foods, and they neglect
the negative future consequences of such
actions. Therefore, we argue that dysfunction in
decision-making, one of the core characteristics
underlying drug addiction, also contributes to
overeating and obesity. Specifically, we propose
that the neurobiological mechanisms underlying obesity might result from the imbalance of
two separate but interacting processes. One is
related to hyperactivity in the impulsive mesolimbic systems, thereby resulting in exaggerated processing of the incentive values of food
and food-related stimuli. The other is related to
hypoactivity of the reflective prefrontal cortical
(PFC) system, critical in inhibitory control over
behavior associated with immediate reward.
An imbalance in the interactions of these two
systems leads to a loss of willpower in resisting
drugs and food.
8.2 A NEURAL SYSTEM FOR
DECISION-MAKING AND WILLPOWER: THE SOMATIC MARKER
HYPOTHESIS
The somatic marker hypothesis is a systemslevel neuroanatomical and cognitive framework
for decision-making and for choosing according
to long-term, rather than short-term, outcomes
(Damasio, 1994). The key idea of this hypothesis
is that the process of decision-making depends in
many important ways on neural substrates that
regulate homeostasis, emotion and feeling. The
term “somatic” refers to the collection of bodyand brain-related responses that hallmark affective and emotional responses. Somatic states
can be induced by both primary inducers and
secondary inducers.
Primary inducers are innate or learned stimuli
that cause pleasurable or aversive states. Once
present in the immediate environment, they
automatically and obligatorily elicit a somatic
response. Food in the immediate environment is
an example of a primary inducer. After a somatic
state has been triggered by a primary inducer
and experienced at least once, a pattern for this
somatic state is formed. The subsequent presentation of a stimulus will evoke memories about
a specific primary inducer. The entities generated by the recall of a personal or hypothetical
emotional event (i.e., “thoughts” and “memories” of food taken) are called secondary inducers.
Secondary inducers are presumed to reactivate
the pattern of somatic state belonging to specific primary inducers. For example, recalling
or imagining the experience of food taken reactivates the pattern of somatic state belonging to
actual previous encounters of that food.
Both the amygdala and ventromedial prefrontal cortex/orbital prefrontal cortex (VMPC/
OFC) are critical for triggering somatic states
(Figure 8.1). However, their specific roles most
likely differ. The amygdala is a critical substrate in the neural system necessary for triggering somatic states from primary inducers.
According to the somatic marker framework,
the amygdala links the feature of the stimulus
to its affective/emotional attributes. The responses triggered through the amygdala are
short-lived and habituate very quickly. The
affective/emotional response is evoked through
visceral motor structures such as the hypothalamus and autonomic brainstem nuclei that produce changes in internal milieu and visceral
structures, as well as through behavior-related
structures such as the striatum, periaqueductal gray (PAG) and other brainstem nuclei that
produce changes in facial expression and specific approach or withdrawal behaviors. The
primary inducers can be processed subliminally
via the thalamus or explicitly via early sensory
and high-order association cortices such as the
insular and somatosensory cortex (Damasio,
1994; Bechara, 2004) (see Figure 8.1). Given the
automatic and fast properties of this system for
processing the affective/emotional attributes of
1. FROM BRAIN TO BEHAVIOR
8.2 A NEURAL SYSTEM FOR DECISION-MAKING AND WILL-POWER
DLPC
AC
Striatum
Insula
DA
VMPC
A
Hyp
5–HT
FIGURE 8.1 A schematic diagram illustrating key structures belonging to the impulsive and the reflective systems.
These include regions involved in (1) representing patterns
of affective states (e.g., the insular and somatosensory cortices); (2) triggering of affective states (e.g., amygdala (A) and
VMPC); (3) memory, impulse and attention control (e.g.,
lateral orbitofrontal, inferior frontal gyrus and dorsolateral
prefrontal cortex (DLPC), hippocampus (Hip) and anterior
cingulated (AC); and (4) behavioral actions (e.g., striatum and supplementary motor area). 5-HT, serotonin; DA,
dopamine.
a stimulus, we have referred to it as the “impulsive” system, in which the amygdala is a key
neural substrate.
By contrast, the VMPC/OFC is a critical substrate in the neural system necessary for triggering somatic states from secondary inducers,
although it can be involved in the emotions triggered by some primary inducers as well. Unlike
the amygdala response, which is sudden and
habituates quickly, the VMPC/OFC response is
deliberate, slow, and lasts for a long time. The
VMPC/OFC is a key structure in the reflective
system, and dependent on the integrity of three
sets of neural systems: the first is critical for
working memory and its executive processes
(inhibition, planning, cognitive flexibility), in
which the dorsolateral prefrontal cortex (DLPC)
is a critical neural substrate; the second is
critical for processing emotions related to the
107
non-conscious (e.g., in the brainstem) or conscious (e.g., in the insular/somatosensory cortex); and the third is critical for executing the
emotional response, in which the anterior cingulate/supplementary motor area (SMA) are
key structures (see Figure 8.1). The VMPC/
OFC serves the role of coupling these systems
together. Damage or dysfunction of any of these
systems, including the DLPC and anterior cingulate/SMA, can indirectly alter the normal
function of the VMPC/OFC. Given the cognitive
and slow nature of this system for processing the
affective/emotional attributes of a stimulus, we
have referred to it as the “reflective” system, in
which the VMPC/OFC is a key neural substrate.
When confronted with a choice, both the
impulsive and reflective systems (or both primary and secondary induction) may be stimulated at the same time. The decisions are the
product of a complex cognitive process subserved by these two separate, but interacting,
neural systems: (1) an impulsive, amygdaladependent, neural system for signaling the pain
or pleasure of the immediate prospects of an
option; and (2) a reflective, prefrontal-dependent,
neural system for signaling the pain or pleasure
of the future prospects of an option (Bechara,
2005). While the amygdala is engaged in emotional situations requiring a rapid response (i.e.,
“low-order” emotional reactions arising from
relatively automatic processes), the VMPC/OFC
is engaged in emotional situations driven by
thoughts and reflection. Once this initial amygdala emotional response is over, “high-order”
emotional reactions begin to arise from relatively more controlled, higher-order processes
involved in thinking, reasoning and consciousness. The final decision is determined by the
relative strengths of the pain or pleasure signals
associated with immediate or future prospects:
when the immediate prospect is unpleasant but
the future is more pleasant, then the positive
signal of future prospects forms the basis for
enduring the unpleasantness of immediate prospect. This also occurs when the future prospect
1. FROM BRAIN TO BEHAVIOR
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8. RESISTING TEMPTATION: IMMEDIATE REWARDS AND LONG-TERM CONSEQUENCES
is even more pleasant than the immediate one.
Otherwise, immediate prospects predominate,
and decisions shift towards short-term horizons. Therefore, there are at least two underlying types of dysfunction where this overall
signal turns in favor of immediate outcomes: (1)
hyperactivity in the amygdala or impulsive system, which exaggerates the rewarding impact
of available incentives such as food; and (2)
hypoactivity in the prefrontal cortex or reflective system, which forecasts the long-term consequences of a given action. Obese individuals
may be afflicted with either one or both of those
dysfunctions. We will review the evidence supporting this notion in the next section.
8.3 EMPIRICAL EVIDENCE FOR
DEFICITS OF DECISION-MAKING
UNDERLYING OBESITY
8.3.1 Hypersensitivity to reward of food
and food-related cues
The hedonic effects of food are central to
understanding food intake (Rolls, 2007b, 2007c).
The most clearly established commonality of
the mechanisms of food and drug intake is that
they both exert their reinforcing effects partly by
increasing dopamine (DA) in the brain reward
circuitry including the ventral striatum, amygdala, midbrain and VMPC/OFC (Kelley et al.,
2005). Animal studies also show that direct
pharmacological activation of the ventral striatum amygdalo-hypothalamic circuit produces
hyperphagia and increases preferentially the
intake of foods high in fat and sugar, even in
animals fed beyond apparent satiety (Petrovich
et al., 2002; Kelley, 2004).
Several lines of evidence suggest that food
may induce greater incentive value in obese individuals compared to normal controls. Behavioral
studies show that overweight children indicate food (pizza and snack food) as more
reinforcing, and consumed more energy than
their leaner peers. The relative reinforcing value
of food versus two non-food alternatives (time
spent playing a hand-held video game, or time
spent reading magazines or completing word
searches or mazes) is also higher in overweight
children and lower in non-overweight children
(Temple et al., 2008). Eating food is also found
to be more reinforcing than selected alternative activities for obese in comparison to nonobese young women (Epstein et al., 1996). The
results of functional magnetic resonance imaging (fMRI) studies corroborate these behavioral
data. One recent fMRI study reports that compared to lean adolescent girls, obese girls show
greater activation in the gustatory cortex (anterior and mid-insular frontal operculum) and in
somatosensory regions (parietal operculum and
Rolandic operculum) in response to the anticipated intake of chocolate milkshake (versus a
tasteless solution) and to actual consumption
of milkshake (versus a tasteless solution) (Stice
et al., 2008). Interestingly, one previous study
shows that even during non-stimulation conditions (resting state), morbidly obese individuals
had significantly greater glucose metabolism
in the vicinity of the post-central gyrus in the
left and right parietal cortex (Brodmann’s area
1). Such enhanced activation is consistent with
an enhanced sensitivity to food palatability in
obese subjects (Wang et al., 2002). This area of
the parietal cortex is where the somato-sensory
maps of the mouth, lips and tongue are located,
and is involved with taste perception (Urasaki
et al., 1994).
Indeed, it has also been suggested that the
insular and somatosensory (SII, SI) cortex plays
a key role in translating the raw physiological signals that are the hallmark of a somatic
state into what one subjectively experiences as
a feeling of desire or anticipation, or an urge
(Damasio, 1994; Bechara and Damasio, 2005).
Evidence shows that the insula is implicated
in food craving (Pelchat et al., 2004). Recent
evidence also shows that strokes that damage
1. FROM BRAIN TO BEHAVIOR
8.3 EMPIRICAL EVIDENCE FOR DEFICITS OF DECISION-MAKING UNDERLYING OBESITY
the insula tend literally to wipe out the urge
to smoke in individuals previously addicted to
cigarette smoking (Naqvi et al., 2007). Similarly,
other studies report changes in activity in the
insular and somatosensory cortex in association
with “high” or euphoric experience of acute
doses of drugs (Verdejo-Garcia and Bechara,
2009). Taken together, these studies suggest
that enhanced sensitivity in regions involved in
the sensory processing of food may make food
more rewarding and generate greater craving
for high-fat, high-sugar food, and thus contribute to excess food consumption in these obese
individuals. One proposed mechanism for how
this may take place is that activation of interoceptive representations through the insula can,
on the one hand, sensitize the impulsive system
by increasing the desire, urge, or motivation to
seek the rewarding food item (this action also
includes engagement of the nucleus accumbens
and associated mesolimbic dopamine system). On the other hand, insula activation may
impact the prefrontal cortex functions, so that it
can subvert attention, reasoning, planning and
decision-making processes to formulate plans
for action to seek and procure food. Put differently, these interoceptive representations have
the capacity to “hijack” the cognitive resources
necessary for exerting inhibitory control to resist
calorie-rich food items (Naqvi and Bechara,
2009).
This neural formulation can explain many
of the neuroimaging results associated with
brain activities induced by food-related items.
Evidence suggests that in a normal brain, primary and secondary inducer processing can
be elicited by the same stimulus and at the
same time. For instance, looking at a picture
of palatable food (chocolate or ice cream) may
quickly and automatically trigger an emotional
response (serving as a primary inducer), but at
the same time it may generate thoughts (e.g.,
picturing oneself eating this food) that operate as a secondary inducer. Consistent with
this notion, some neuroimaging studies show
109
that food versus non-food pictures activate the
amygdala (LaBar et al., 2001), the ventral stratum (Beaver et al., 2006), the insula (Wang et al.,
2004; Porubska et al., 2006) and the orbitofrontal
cortex (Holsen et al., 2005; Simmons et al., 2005)
in healthy individuals. Moreover, the increased
activity induced by food presentation in the
caudate, insula and right OFC is significantly
correlated with self-reports of hunger and desire
for food in normal-weight subjects (Pelchat
et al., 2004; Wang et al., 2004).
Behavioral studies indicate that obese individuals are hyper-responsive to food cues in a
wide range of assessments (Braet and Crombez,
2003; Halford et al., 2004). Current models of
addiction have proposed that drug-related cues
may trigger drug-seeking behaviors by eliciting hyperactivity in a brain network of reward
areas (Robinson and Berridge, 2003; Volkow
et al., 2003). Recent studies suggest that the same
network of structures showing exaggerated
responsiveness to drug cues in addiction is
also hyper-reactive to visual food cues in obese
individuals. For example, compared to normalweight controls, obese women exhibit greater
activation in response to pictures of high-calorie
foods in the medial and lateral OFC, amygdala,
nucleus accumbens/ventral striatum, insula,
anterior cingulate cortex, ventral pallidum,
caudate, putamen and hippocampus (Stoeckel
et al., 2008). In another fMRI study, relative to
controls, obese women also show enhanced
activity in the caudate, putamen, anterior insula,
hippocampus and parietal lobule when they
viewed high-calorie foods (Rothemund et al.,
2007). The observed enhanced responsiveness
of these regions could contribute to exaggerated appetitive motivation in obese individuals
in response to food cues. Moreover, enactment
of secondary inducers (recalling or imagining
the experience of eating), which activates the
VMPC/OFC and cingulate cortex, may produce
an increase of the craving sensation and possibly a decrease in inhibitory control in obese
individuals.
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8. RESISTING TEMPTATION: IMMEDIATE REWARDS AND LONG-TERM CONSEQUENCES
Other studies have investigated the relationship between some personality traits, such as
reward sensitivity, and overeating. Reward sensitivity and other constructs, such as behavioral
activity and novelty/sensation, are conceptualized as a biologically-based personality trait
regulated by the meso-cortico-limbic dopamine
system (Cohen et al., 2005; Evans et al., 2006).
Behavioral research in both healthy and overweight populations has shown that a personality trait of reward drive and related constructs
predicts food craving, overeating, and relative
body weight (Davis et al., 2002, 2004a; Bulik
et al., 2003). Recent studies also show that sensitivity to reward positively predicts the tendency to overeat beyond caloric need and in the
absence of hunger. It also predicts a heightened
preference for foods high in fat and sugar. These
two behaviors, in turn, predict a higher body
mass index (BMI) (Franken and Muris, 2005;
Davis et al., 2007). Using fMRI, Beaver and colleagues (2006) reported that individual variation
in trait reward sensitivity is highly correlated
with activation to images of highly palatable,
appetizing foods (e.g., chocolate, ice cream) in a
fronto-striatal-amygdala-midbrain network in
healthy volunteers (Beaver et al., 2006). One
recent study has provided the first evidence for
a link between neural activity and reward sensitivity in patients with a binge-eating disorder
(Schienle et al., 2008). It shows that the bingeeating disorder patients report enhanced reward
sensitivity and display stronger medial OFC
responses while viewing high-calorie food pictures than other groups, and, as in the bingeeating disorder patients, medial OFC activity was
positively correlated with self-reported reward
responsiveness (Schienle et al., 2008). Taken
together, these studies suggest that sensitivity to
reward may underlie individual differences in
preference for highly palatable and high-calorie
food, thereby providing one with a behavioral
predisposition to obesity (Davis et al., 2004a).
However, some studies suggest that obese
individuals may experience less food reward
and may use food to increase DA stimulation
to a more desirable level. Wang and colleagues
(2001) found that obese individuals had a significant reduction in DA D2 receptor availability in the striatum relative to lean individuals
(Wang et al., 2001). The Taq1A allele (thought
to be linked with lower receptor levels) is also
more prevalent in obese individuals compared
to normal controls (Noble et al., 1994). However,
these studies generally used morbidly obese
subjects, typically recruited from obesity treatment clinics – for example, in the study by
Wang and colleagues (2001), the obese adults all
had a BMI of over 40 (Class III obesity). In other
studies, though, which show that sensitivity
to reward positively predicts food craving and
body weight, most of the samples were normalweight people (Franken and Muris, 2005;
Beaver et al., 2006; Davis et al., 2007). Therefore,
it is possible that high sensitivity to reward may
foster the overeating of high-fat and high-sugar
foods, which leads to down-regulation of D2
receptors to compensate for its overstimulation
(Davis et al., 2004a). Interestingly, one recent
study found that binge-eating disorder patients
and obese subjects reported greater reward sensitivity than normal-weight controls, but only
among those carrying the Taq1A allele with low
DA D2 receptor (Davis et al., 2008). The authors
suggest that one explanation for their findings
could be that there is another genetic variant that interacts with the A1 allele to produce
higher dopamine activity in the binge-eating
disorder patients and obese participants (Davis
et al., 2008).
8.3.2 Hypoactivity in the reflective
system
A critical neural region in the reflective
system is the VMPC/OFC, but other neural
components, including the DLPC and cingulate cortex outlined earlier, are also important.
Several recent studies suggest that cognitive or
1. FROM BRAIN TO BEHAVIOR
8.3 EMPIRICAL EVIDENCE FOR DEFICITS OF DECISION-MAKING UNDERLYING OBESITY
regulatory control of food intake is mediated by
brain regions that we have hypothesized to be
components of the so-called “reflective system”.
For example, one study shows that word-level
cognitive labels can change the subjective ratings
of the affective value of the taste and flavor of a
food when the taste or flavor stimulus is identical; this cognitive modulation is expressed in the
OFC and anterior cingulate cortex (Grabenhorst
et al., 2008). Evidence also shows that lean individuals preferentially increased neuronal activity
in the prefrontal cortex to inhibit food consumption due to satiation (Del Parigi et al., 2002).
Moreover, a recent study examined the responsiveness of the brain to images of food that differed in caloric content among normal-weight
adolescent females. These authors found significant age-related increases in the activation of the
OFC in response to high-calorie food images,
but not to low-calorie images, suggesting a progressive engagement of reward evaluation and
response inhibition in reaction to fattening and
unhealthy food images during development
(Killgore and Yurgelun-Todd, 2005).
As has been proposed with regard to addiction, abnormalities in the reflective system
involved in inhibitory control may also contribute
to obesity. Using neuropsychological measures,
one study shows that obese individuals make
less advantageous choices in the Iowa Gambling
Task – a paradigm that relies on the integrity
of the VMPC/OFC for execution (Davis et al.,
2004b). Decrements in other higher brain functions, including memory, abstract reasoning and
attention, are also associated with an increased
body weight in adults (Gunstad et al., 2006, 2007).
One recent study further demonstrates that this
association may exist as early as in childhood:
overweight children and children at risk of overweight have decreased visuospatial organization
and general mental ability compared to normalweight children (Li et al., 2008).
In addition to these behavioral studies, other
lines of research also provide evidence for
impairment in the reflective system leading to
111
uncontrolled eating behaviors. Two case studies report that damage to the right frontal cortex
can result in the “Gourmand syndrome”, characterized by the passion for eating and a specific
preference for fine food (Regard and Landis,
1997; Uher and Treasure, 2005). Indeed, AlonsoAlonso and Pascual-Leone (2007) proposed that
hypoactivity in the right frontal cortex of obese
individuals can lead to a general disregard for
the long-term adverse consequences of behavioral choices, such as increased risk-taking and
excessive food intake. In fMRI studies, obese
men and women also have less activation of
the left DLPC in response to a mean than do
their lean counterparts (Le et al., 2006, 2007).
Moreover, obese women showed the decreased
left DLPC response to a mean compared to formerly obese women who successfully achieved
weight loss by diet and exercise and maintained their weight loss for more than 3 months
before the study (Le et al., 2007). These studies
are consistent with other reports which show
that the dorsal prefrontal cortex is particularly
activated in successful weight-loss maintainers
in response to meal consumption (Del Parigi
et al., 2007), and stimulation of the left DLPC by
using repetitive transcranial magnetic stimulation inhibited the development of food cravings
(Uher et al., 2005).
Although the mechanisms by which low D2
receptor availability would increase the risk of
overeating are poorly understood, one recent
study shows that low dopamine striatal D2
receptors are positively associated with metabolism in the prefrontal cortex, including the
DLPC, medial OFC and anterior cingulate gyrus
(Volkow et al., 2008b). These results suggest that
decreased D2 receptors in obese subjects contribute to overeating in part through deregulation of
prefrontal regions implicated in inhibitory control, emotion regulation and decision-making
(Volkow et al., 2008b). Moreover, it appears that
reduced D2 receptor density is associated with
reduced capacity to learn negative characteristics of a stimulus from negative feedback. In a
1. FROM BRAIN TO BEHAVIOR
112
8. RESISTING TEMPTATION: IMMEDIATE REWARDS AND LONG-TERM CONSEQUENCES
probabilistic learning task, individuals with the
Taq1A allele associated with lower D2 receptor
showed lower activity in the posterior medial
frontal cortex (pMFC), involved in feedback
monitoring, in response to negative feedback,
than others did. This parallels the behavioral
data that these A1-allele carriers are less efficient
at avoiding actions with negative consequences
(Klein et al., 2007). Taken together, these studies
suggest that obese individuals have a downregulated reflective system. As a result, they are
less likely to succeed in inhibiting the proponent
responses, such as an intense desire to eat highcalorie food, and are also insensitive to future
adverse consequences (e.g., gaining weight, diabetes) of their over-eating behaviors.
affective signals through the amygdala will
then modulate, bias or even “hijack” the topdown cognitive mechanisms needed for the
normal operation of the reflective system. This
is why, from the perspective of someone who
is dieting and has lost the willpower to resist
a tempting food, the decision to eat that food
becomes very reasonable and logical at the time
of consumption.
ACKNOWLEDGMENTS
The studies described in this chapter were
supported by NIDA grant R01 DA023051.
References
8.4 CONCLUSION
Modern societies, where widely available,
highly palatable and energy-dense food coexists
with a continuous flow of food-related promotion and advertising through mass media, challenge individuals’ ability to inhibit desire, resist
temptation and make advantageous decisions
(Wardle, 2007). Here, we propose that addiction
to anything, even food, is a condition in which
the person becomes unable to choose according
to long-term outcomes, which requires that the
pain/pleasure signals triggered by the reflective
system dominate those from the impulsive system. Two broad types of conditions could alter
this relationship and lead to loss of willpower:
(1) a dysfunctional reflective system that has lost
its ability to process and trigger affective signals, which forecast the affect/emotion of future
prospects; and (2) a hyperactive impulsive system that exaggerates the affective signals from
immediate prospects. This increased strength
of the impulsive system can alter the balance of
power in favor of an overall affective state congruent with that of the amygdala. The triggering
of these bottom-up, automatic and involuntary
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
9
Hunger, Satiety, and Food Preferences:
Effects of the Brain and the Body on the
Self-control of Eating
Alexandra W. Logue
City University of New York, New York, NY, USA
O U T L I N E
9.1
Introduction
115
9.2
The Components of Self-Control
116
9.3
Physiological Influences on
Self-Control
9.3.1 Preferences for Salty, Sweet, and
other Calorically Dense Food
9.3.2 Food Cues and Physiological
Responses
117
117
118
9.1 INTRODUCTION
Many individuals in developed countries
must constantly choose between an energy
intake that is not so pleasurable in the short
term but is healthy in the long term (selfcontrol), and energy intake that is pleasurable
in the short term but is unhealthy in the long
term (impulsiveness) (Logue, 1988, 1995). They
Obesity Prevention: The Role of Brain and Society on Individual Behavior
9.3.3 Adipose Cells and the Set Point
9.3.4 Metabolic Rate and Energy
Expenditure
9.3.5 The Hypothalamus
9.3.6 Genetic Contributions
119
119
120
120
9.4
Promoting Self-Control for a Healthy
Body Weight
121
9.5
Conclusions
122
must choose between eating full-fat ice cream
and sitting on the couch now, versus having
clear arteries and normal insulin levels 30 years
from now. In what ways do our bodies influence these choices? The present chapter will
describe some of the ways in which our physiology affects the self-control of eating.
The influence of our physiology on impulsive eating has much more than a theoretical
115
© 2010,
2010 Elsevier Inc.
116
9. PHYSIOLOGY, SELF-CONTROL AND EATING
effect. Body fatness is generally measured using
the body mass index, or BMI (which is weight
measured in kilograms divided by the square of
height measured in meters). Using a definition
of overweight as a BMI between 25.0 and 29.9,
and of obese as a BMI of 30.0 or more, the percentage of adults aged 20–74 years in the United
States classified as overweight or obese rose
from 47 percent in 1976–1980 to over 66 percent
in 2003–2004. By 2007, only Colorado had an
obesity rate lower than 20 percent. In 30 states,
over 30 percent of the population was obese
(Centers for Disease Control and Prevention,
n.d.). Such changes are not limited to the
United States. What is eaten and how much, as
well as activity levels, are rapidly changing in
Asia, Africa, the Middle East and Latin America,
resulting in rapidly rising obesity rates (Popkin,
2004). Energy expenditure has decreased due to
increased use of elevators, washing machines,
power lawn mowers, remote controls, cars, etc.,
and energy intake has increased as high-calorie
foods (ice cream, fast food, soda, chips, etc.)
have become easier and cheaper to purchase.
The resulting growing obesity rates have been
described as an epidemic (Abelson and Kennedy,
2004), with terrible medical consequences, such
as cardiovascular disease, diabetes, and cancer
(Calle et al., 2003; Hill et al., 2003).
This epidemic can be explained based
on what we know about human evolution.
Humans evolved in an environment in which
food was scarce, physical activity was generally
necessary in order to survive, and the foods that
were available tended to be low in fat, sugar,
and salt, and often high in fiber. Given that
insufficient food results in weakness and then
death, it was adaptive for evolving humans,
similar to other animals, to eat as much as possible, and for the food consumed to be conserved within the body as much as possible. It
was also adaptive to discount delayed food and
other delayed reinforcers. Delayed reinforcers
are uncertain reinforcers: events during a delay,
such as death due to starvation while waiting for food, can prevent someone from ever
receiving the delayed reinforcer. Our bodies
evolved to behave in exactly these ways – eating
as much as possible, conserving the energy consumed within the body as much as possible,
and discounting delayed reinforcers. However,
what helped us in the environment in which
we evolved does not help us now. Our evolved
physiological mechanisms are maladaptive for
the world in which we live today (Logue, 2004).
There is a vast research literature on topics
related to how our bodies influence our choices
between healthy and non-healthy energy intake.
Therefore, this chapter will touch only on some
of the most prominent topics.
9.2 THE COMPONENTS OF
SELF-CONTROL
In comprehending the effects of different factors on self-control, it is helpful first to understand self-control’s constituent components,
reinforcer amount and reinforcer delay. Choices
of certain combinations of relatively smaller
and larger reinforcer amounts, and of relatively
smaller and larger reinforcer delays, constitute
self-control and impulsiveness.
Much research indicates that reinforcer
amounts are valued less – are discounted –
if they are delayed. Further, that discounting occurs according to a hyperbolic function
(Figure 9.1a). It then follows that, under certain
conditions, if someone is choosing between two
reinforcers of different amounts and different
delays, the value functions for the two reinforcers will cross (Figure 9.1b). Under these circumstances, during the time period from x to y the
larger, more delayed reinforcer is worth more
and the individual will demonstrate self-control,
and during the time period from y to z the
smaller, less delayed reinforcer is valued more
1. FROM BRAIN TO BEHAVIOR
117
Reinforcer value
Reinforcer value
9.3 PHYSIOLOGICAL INFLUENCES ON SELF-CONTROL
Now
(a)
z
y
x
Now
Later
(b)
Time of choice
Later
Time of choice
Much later
FIGURE 9.1 Reinforcer value as a function of time.
(a) One reinforcer. The vertical bar shows the value of the reinforcer at the time it is received in the future; that value
decreases as the current time is approached. (b) A larger, more delayed reinforcer, and a smaller, less delayed reinforcer. The
letters x and y denote the time period during which choices will result in self-control, and the letters y and z denote the time
period during which choices will result in impulsiveness.
and the individual will demonstrate impulsiveness (Logue, 1988, 1995). Defined in this way, it
becomes clear that self-control can be increased
or decreased by changing the perceived relative
sizes of the reinforcers.
9.3 PHYSIOLOGICAL INFLUENCES
ON SELF-CONTROL
The physiological factors that influence selfcontrol for food affect both preferences for certain types of foods as well as the total amount
consumed. These behaviors are not independent, because people eat more of highly preferred foods and thus are likely to overconsume
highly preferred foods to an unhealthy degree.
9.3.1 Preferences for salty, sweet, and
other calorically dense food
Humans, as well as many other species, are
genetically predisposed to prefer salty foods,
sweet foods, and other calorically dense food.
At birth humans do not show a preference for
salt, apparently because the taste mechanism
for salt is not yet mature. However, after about
4 months of age, babies and older humans show
a preference for salty tastes (Bartoshuk and
Beauchamp, 1994). The preference for sweet is
stronger than for any other taste, and is present
at birth (Pfaffmann, 1977; Maone et al., 1990).
People also learn, with experience, to prefer
foods that are calorically dense, such as high-fat
foods (Logue, 2004).
These preferences were adaptive during
human evolution. Our bodies need salt for a
variety of physiological functions, but salt can
be difficult to obtain in the natural environment. Therefore, it would have been adaptive for humans to prefer salt whenever it was
tasted. Similarly, in nature the taste of sweet is
usually associated with ripe fruit. Ripe fruit not
only contains essential vitamins, but also tends
to have significant numbers of calories – two
food characteristics that were difficult to obtain
in the environment in which our ancestors
evolved, but that were critical to their survival.
Thus, once again, it would have been adaptive
1. FROM BRAIN TO BEHAVIOR
118
9. PHYSIOLOGY, SELF-CONTROL AND EATING
for humans to prefer sweet foods whenever
they were encountered (Logue, 2004).
In our current environment not only are
foods with these characteristics much more
easily available, but manufacturers have also
learned how to generate maximally preferred
foods by combining several preferred characteristics in one food – such as honey-roasted
peanuts, which are not only sweet but also
salty and high in fat. The result is that in a
choice between a smaller, less delayed reinforcer (unneeded, immediate food) and a larger,
more delayed reinforcer (future health), the
smaller, less delayed reinforcers (the foods) are
not so small; the difference in amount between
the smaller, less delayed reinforcers and the
larger, more delayed reinforcers is not so great.
Therefore self-control, choice of the larger, more
delayed reinforcer of good health, is less likely
to occur. In other words, the characteristics of
the food available can affect whether or not we
overconsume and are impulsive (Forzano and
Logue, 1995, Forzano et al., 1997).
9.3.2 Food cues and physiological
responses
The effects of food preference and food type
on self-control for food can be observed simply by examining food choice. However, how
strongly food preferences influence self-control
is a function of the differing strength of the
physiological responses to certain foods.
One example of such a physiological response
is the pancreas’s release of insulin. Tasting food,
smelling food, or even just thinking about food
can cause this release. The insulin lowers blood
sugar, and can thus induce hunger. In addition,
the presence of insulin can also make it more
likely that what is eaten will be stored as fat.
Just as the salivation reflex can be conditioned
to occur to the sound of a bell, so too can insulin
release be conditioned to occur in response to
the stimuli associated with food consumption.
Different foods can cause different amounts of
insulin to be released (Powley, 1977; Vasselli,
1985; Tordoff and Friedman, 1989; Le Magnen,
1992). Foods that result in higher levels of blood
glucose (i.e., foods that are characterized as
having a high glycemic index) will also result in
larger amounts of insulin being released. Such
foods (white bread, as opposed to whole-wheat
bread, for instance) are less satiating than are
foods that result in less glucose being released
(Holt et al., 1992; Lavin and Read, 1995). To further complicate this picture, some people are
physiologically more responsive than others to
food cues (Rodin, 1981). For all of these reasons,
food cues can result in physiological changes
that make it difficult to eat moderately, and for
some people eating moderately is extremely
difficult.
Insulin is just one of many substances that
our bodies release in response to food stimuli.
Additional examples are cholecystokinin (CCK)
and glucagon. These substances, similar to insulin, aid digestion. CCK is produced in the small
intestine, and glucagon, again similar to insulin, is produced in the pancreas. Experiments
suggest that increased levels of both CCK and
glucagon tend to decrease feeding – these substances may be physiological indicators of satiety (Logue, 2004). They help to ensure that,
once eating has begun, it does not continue
indefinitely.
Food manufacturers, grocery stores, and restaurateurs appear to take deliberate steps to
increase the amount people eat through amplifying the food cues that cause appetite-inducing
physiological responses. In addition to combining many inherently appealing characteristics
in one food (such as honey-roasted peanuts),
the food industry uses advertising that contains many appealing, mouth-watering stimuli,
it supersizes portions, and it parades in front
of us a huge variety of foods (Lieberman, 2006;
Wansink, 2006). Similar to many other species, if humans are given the choice between
novel foods, familiar foods eaten recently, and
1. FROM BRAIN TO BEHAVIOR
9.3 PHYSIOLOGICAL INFLUENCES ON SELF-CONTROL
familiar foods not eaten recently, they will tend
to choose familiar foods not eaten recently.
Therefore, humans will consume more when
they are presented with varying sets of familiar foods then if they are presented with the
same set of foods repeatedly (Logue, 2004).
The food industry plays upon our physiological responses to food in order to make us more
likely not only to choose certain foods, but also
to eat larger amounts of those foods.
9.3.3 Adipose cells and the set point
Fat is stored in the body in adipose cells.
When these cells are full, people are less hungry,
and vice versa (Sjöström, 1978, 1980). Thus, these
cells contribute to the regulation of the body’s
set point, the particular weight at which a body
is approximately maintained, and to the regulation of how much is eaten.
Heredity contributes to the number and distribution of adipose cells. When 12 pairs of
identical adult male twins were overfed under
highly controlled conditions, although individuals had gained between 10 and 29 pounds,
the amounts of weight gained by the members
of a twin pair were much more similar than the
amounts gained by unrelated men. Similarly,
the amounts of body fat and the locations of that
body fat were more similar for the members of
a twin pair than for unrelated men (Bouchard
et al., 1990).
Although the number of adipose cells can
increase when someone gains weight, the number
of adipose cells can never decrease (Sjöström,
1978, 1980). When someone loses weight that
person’s total amount of body fat decreases. This
decrease occurs by means of a reduction in the
amount of fat stored in the adipose cells, not by a
decrease in the number of cells. Thus, an individual whose weight is below his or her highest lifetime weight will always be hungry. Once again,
such physiological characteristics were adaptive for the food-scarce environments in which
119
humans evolved, but not for the environment in
which most of us live now.
9.3.4 Metabolic rate and energy
expenditure
Body weight is a function not only of the
amount and type of food consumed but also
of the body’s energy expenditure. Total energy
expenditure consists of three components: (1)
basal metabolic rate – the energy expended
for basic metabolic functions such as respiration and circulation, (2) the energy used by the
body for voluntary and non-voluntary physical
activity such as walking and fidgeting, and (3)
the energy used following eating (Jéquier, 1987;
Ravussin and Danforth, 1999). Different people
have different metabolic rates, and there are
many reasons for these individual differences.
One is that fat supports a lower metabolic rate
than muscle or bone (Jéquier, 1987). Therefore,
two people can weigh the same, but the one
with a higher percentage of body fat will have
a lower metabolic rate. Metabolic rate is also
affected by the amount eaten. If someone’s
food intake decreases so that weight is lost,
that person’s metabolic rate decreases, possibly for months after the level of food intake has
returned to normal (Keesey and Corbett, 1984;
Steen et al., 1988; Elliot et al., 1989). Metabolic
rate can also be increased by certain types of
exercise, such as a strenuous game of football,
and that increase can last for hours beyond
when the exercise ends (Edwards et al., 1935;
Poehlman and Horton, 1989). However, there
are individual differences in the degree to which
exercise affects weight. In addition, the energy
used following eating varies according to the
amount and type of food eaten – more energy is
used if more is eaten, and more energy is used
following a high-carbohydrate than a high-fat
meal (Keesey and Corbett, 1984; Jéquier, 1987).
There appear to be links between adipose
tissue, energy usage, and the body-weight set
1. FROM BRAIN TO BEHAVIOR
120
9. PHYSIOLOGY, SELF-CONTROL AND EATING
point. If rats’ weights are increased by continued free access to a high-fat diet, their adipose
cells increase in number, and eventually the
increased energy usage seen after eating disappears; a new set point has been reached (Keesey
and Corbett, 1984).
The links between adipose tissue, energy
usage and the body-weight set point may be
related to the level of leptin in the body. Leptin
is a hormone produced by adipose cells; the
greater the amount of adipose tissue the more
leptin, and vice versa. When body weight
decreases and adipose tissue shrinks, both the
energy expended by the body and the amount
of leptin in the body decrease. However, if
someone who has lost weight is given injections of leptin sufficient to raise his or her leptin
levels to those prior to the weight loss, energy
expenditure can be maintained at pre-weightloss levels (Rosenbaum et al., 2005).
9.3.5 The hypothalamus
Several brain structures have been found to
affect how much and what is eaten. The structure
that has been most extensively investigated, and
that has been found to have a number of effects
on eating, is the hypothalamus. Data collected
in the 1940s and 1950s indicated that the ventromedial hypothalamus is important in satiation,
and that the lateral hypothalamus is important in hunger (Logue, 2004). Subsequently, the
hypothalamus was seen to be a major integrator of information regarding food consumption
and energy storage and availability, both when
that information is obtained from different brain
structures and when it is obtained from elsewhere in the body. The hypothalamus integrates
information from central (brain) areas, as well as
from peripheral areas such as the gastrointestinal
tract. The hypothalamus also plays a critical role
in influencing the body’s responses that maintain homeostasis and the body-weight set point
based on all of the collected information (Stellar
and Stellar, 1985; Badman and Flier, 2005).
Recent research indicates that there may be a
very long-lasting effect of leptin on the set point
maintained by the hypothalamus. It appears
that leptin actually affects the plasticity of synapses in the hypothalamus, and guides axon
growth and location during hypothalamus
development. Mice that are deficient in leptin
develop with abnormally low numbers of certain kinds of excitatory and inhibitory synapses
in the hypothalamus, and lack certain neural
projection pathways. These brain structures are
all related to feeding. Treatment with exogenous
leptin can, under certain conditions, reverse
these effects (Bouret and Draper, 2004; Elmquist
and Flier, 2004; Pinto et al., 2004).
These findings with leptin could help to
explain the effects of food deprivation during
development on later obesity. It is well known
that food deprivation early in a woman’s pregnancy results in an increased probability of
obesity in the offspring. This was demonstrated
in men who had been conceived but not yet
born at the time of the Dutch famine of 1944–
1945 (Ravelli et al., 1976). Not only are such
offspring more likely to be obese, but they also
show various physiological abnormalities such
as lower fat oxidation (Sawaya et al., 2004).
9.3.6 Genetic contributions
Some genetic contributions to the type
and quantity of food eaten have already been
described. In recent years, researchers have been
successful at identifying a number of the specific
genes that contribute to obesity. Such identifications have included a single rare gene whose presence results in morbid obesity, as well as a genetic
variant associated with obesity and present in
about 10 percent of the European-American and
African-American populations. It is believed that
these genes result in obesity by means of, in the
first case, inhibiting the synthesis of fatty acids,
and in the second case, by promoting overeating
(Farooqi et al., 2003; Herbert et al., 2006).
1. FROM BRAIN TO BEHAVIOR
9.4 PROMOTING SELF-CONTROL FOR A HEALTHY BODY WEIGHT
At the same time that research has been
increasingly successful at identifying the genes
that contribute to obesity, recent discoveries
have also highlighted the dramatic influence on
obesity of interactions between the genes and the
environment. Certain environments can affect
a fetus’s genes, and these effects can be passed
on to the next generation. Such effects have
been found in situations, described previously,
in which food deprivation during pregnancy is
associated with obesity in adulthood. This food
deprivation can directly affect the fetus’s DNA,
suppressing the expression of certain genes, perhaps permanently. Individuals who as fetuses
suffered food deprivation are more likely to
accumulate fat and to gain weight, as well as to
suffer from type 2 diabetes and cardiovascular
disease. Because the gene expression suppression can be permanent, it can be passed on to
future generations. Such effects are now thought
to explain the sudden huge increases in obesity rates in developing countries – countries
in which, not too long ago, pregnant women
often did not have enough to eat, but who now
have more food available as well as less need to
expend energy (Marabou, 2006).
9.4 PROMOTING SELF-CONTROL
FOR A HEALTHY BODY WEIGHT
This chapter has outlined an extensive set of
physiological characteristics that contribute to
people overeating, acquiring body fat, and retaining body fat. When self-control is defined as eating and exercising so as to maintain a healthy
weight, as opposed to an excessive, unhealthy
weight, it is clear that the physiological characteristics described above make self-control extremely
difficult to achieve. However, self-control is not
impossible. Even though human physiology
makes it likely that we will overeat and gain
weight, this does not mean that these outcomes
are inevitable. Just a few decades ago, when
121
we had the same physiology that we have now,
overweight and obesity were much less frequent
in the United States. Despite humans’ physiological predispositions, the environment can have
a strong influence on weight. Manipulating the
environment to help control weight is an example
of self-control. More specifically, the environment
can be manipulated and self-control increased
by the use of pre-commitment devices. These are
actions that we take in the time period between
x and y in Figure 9.1b to ensure that we cannot
make the impulsive choice that would occur in
the time period between y and z. Specific examples of pre-commitment devices that can help
control weight include parking the car further
away from the office to increase walking, keeping
only healthy food in the house, and making pacts
with friends to go bowling every Saturday. Precommitment devices can also be used to increase
satiation and decrease calorie consumption by
guaranteeing the presence at meals of only noncaloric beverages, small portion sizes, and highfiber breads and cereals (Ludwig et al., 1999;
DellaValle et al., 2005; Kral, 2006).
These self-control strategies may be useful in helping people to maintain their weight.
However, if someone’s weight is sufficiently
large as to cause significant health problems,
behavioral self-control strategies may be insufficient in achieving a long-lasting weight loss.
As described earlier in this chapter, physiological factors such as metabolic rate and adipose
cells may make it extremely difficult for some
individuals to lose weight permanently unless
they are prepared to eat fewer calories and be
hungry, perhaps for the rest of their life. There
is some evidence that large amounts of exercise,
at least an hour of aerobic exercise per day, may
assist such a person in maintaining a weight loss
(Jakicic and Otto, 2006). However, maintaining
this level of exercise over perhaps decades is
not easy for today’s Americans. An alternative
in such cases, although it is not without risks,
is surgery to bypass parts of the gastrointestinal
tract. Such operations decrease how much food
1. FROM BRAIN TO BEHAVIOR
122
9. PHYSIOLOGY, SELF-CONTROL AND EATING
is absorbed from the gastrointestinal tract and
help the patient to feel fuller faster. Although
generally safe, such operations are not without surgical and post-surgical risk, and should
therefore only be entertained for people who are
at least 100 pounds overweight and for whom
other weight-loss strategies have been unsuccessful (Bray and Gray, 1988; Kral, 1995).
9.5 CONCLUSIONS
Although the research described in this chapter may seem quite daunting – outlining the
great many physiological factors that contribute to overeating and weight gain in our current
environment – the situation is not hopeless. No
longer than a couple of decades ago, Americans
were less obese and less likely to suffer from diseases caused or exacerbated by being overweight.
Therefore, it is not impossible to change our current environment to promote healthier weights.
Already, some schools and communities are regulating the types of foods available and the amount
of exercise required (Winderman, 2004). Such
actions are society’s versions of pre-commitment
devices to ensure that students and citizens have
healthy lifestyles. It is possible to create healthpromoting environments. The question becomes
one of how much regulation is appropriate in our
society in order to promote health. Armed with
the information in this chapter, it is possible for
us to escape our long-term unhealthy fate, and to
avoid eating excessive amounts of highly caloric
food. There are strategies that we can follow to
ameliorate the effects of our evolutionary history
and resulting physiology.
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C H A P T E R
10
Associative Learning and the Control of
Food Intake
Louise Thibault
School of Dietetics and Human Nutrition, McGill University, Montreal, Canada
O U T L I N E
10.1
A Behavioral Reporting of Eating
125
10.6
Nutrients and Cognition
130
10.2
Eating is a Learned Behavior
126
10.7
Dietary Fats and Learning
130
10.3
Forms of Learned Ingestive Response 126
10.8
Our Primitive Brain
131
10.4
Sensory-specific Anticipatory Eating 127
10.5
Diurnal Rhythms and the Learned
Response
130
10.1 A BEHAVIORAL REPORTING
OF EATING
Food intake from laboratory animals or
human participants is generally reported as
ingested weight (g), energy (kcal or kJ) or percent of total energy (%E) of carbohydrate, protein and fat. Such gravimetric estimates of caloric
and macronutrient intake measures are often
used as dependent variables in the elucidation
of physiological events controlling eating and
Obesity Prevention: The Role of Brain and Society on Individual Behavior
drinking in experiments designed to provide
evidence of the mechanisms involved. However,
a measured intake of a food or a drink is not
ingestive behavior, and tells us nothing about
the neural and mental processes controlling the
behavior of eating and drinking, let alone hunger and satiety.
It was postulated that caloric and nutrientspecific control of intake are regulatory functions
and not observable mechanisms (Booth et al.,
1970; Booth, 1972a, 1972b; Peck, 1976; Pudel, 1976;
Rolls, 1976; Wooley et al., 1976), and the existence
125
© 2010,
2010 Elsevier Inc.
126
10. LEARNING AND FOOD INTAKE
of regulation has to be established by observing
compensation to a challenge. The compensation
of intake to such a challenge could then be used
to work out the mechanisms involved at the
behavioral level – for example, the input/output
transformations in ingestion. Such a behavioral
understanding of eating would require the observation of mental processes underlying immediate
choices to accept or reject food or foods offered
at a meal or over a fixed period. There is no recognized mechanism for the ingestion of a given
food being controlled by its macronutrient content or even energy content without having some
unlearned or learned (pre-ingestional) cues that
predict post-ingestional effects specific to these.
In this regard, reporting intake in units of volume rather than in energy units is more appropriate. Choices of whether or not to ingest foods
are learned behaviors. Associate conditioning
through sensory attributes from foods and drinks
and their post-ingestional effects directly controls
these choices. This behavioral understanding of
eating and drinking is important in research on
food intake.
less when sensory cues predicted lower energy
supply (conditioned aversion). However, it was
reported later that some of the food’s energy
substrates may be metabolized before the end of
the meal (within 1–2 minutes), which might also
be predictive of subsequent absorption (Pilcher
et al., 1974).
The control of food intake based on the sensory properties of foods, such as flavors or textures, has been tested. It has been demonstrated
that choices of foods and drinks are achieved
by learning relative preferences/aversions for
foods and drinks and appetite/satiety for nutrients, regardless of the source of energy (Booth,
1985; Booth and Thibault, 2000). Learning is a
change in the organization of an individual’s
behavior so that performance represents an
adaptation to the external and internal environments (Booth, 1987). When sensory cues
from foods (conditioned stimuli) are paired
with prompt nutritional after-effects of eating
(unconditioned stimuli), conditioned responses
of food choice and intake can be induced
(Le Magnen, 1999a).
10.2 EATING IS A LEARNED
BEHAVIOR
10.3 FORMS OF LEARNED
INGESTIVE RESPONSE
Since 1955, it has been claimed that the control of dietary intake according to the foods’
energy content is not immediate but rather
an indirect response (Le Magnen, 1955). Le
Magnen’s argument was based on the fact that
there is not enough time before the end of a
meal for energy to metabolize following the
absorption of the digested macronutrients.
Therefore, the post-ingestive effects of macronutrients must be predicted through receptors in
the oronasal cavity and the stomach. He indeed
showed that laboratory rats could learn to eat
greater amounts of foods when added sensory
cues were predictive of energy supply to the
tissue (conditioned preference), or learn to eat
In learned satiety, laboratory rats acquire
the ability to predict the energy-related effects
of eating a specific food from its oral sensory
attributes and its distension of the stomach. This
results in learned satiety: the rat responds to
this configuration of stimuli, ends the meal, and
so controls the amount eaten on that occasion
(Booth, 1972c; Booth and Davis, 1973). Learned
satiety pertaining to meal size (conditioned satiety) has been shown in human subjects (Booth
et al., 1976), in rats (Booth, 1972c; Booth and
Davis, 1973) and monkeys (Booth and Grinker,
1993). In contrast, learned appetite occurs in
the state of nutritional deficiency. It pairs sensory cues from food and visceral state with the
1. FROM BRAIN TO BEHAVIOR
10.4 SENSORY-SPECIFIC ANTICIPATORY EATING
post-absorptive effects of the lacking nutrient,
which leads to a learned appetite for the nutrient (Gibson et al., 1995).
Both these forms of learned ingestive response are directly controlled in part by the physiological negative-feedback signals of depletion
or repletion. For example, rats given a choice
between a protein-free (but complete) diet and
a diluted protein diet increased their intake of
the diluted one. When given a choice between
a protein-free and a concentrated protein diet,
rats decreased their intake of the concentrated
one (Booth, 1974a). When diets were flavored,
protein-deprived rats acquired a preference
for the flavor of the protein-rich diet (Booth,
1974b). This protein-conditioned sensory preference depends upon an internal state set up by a
recent protein deficit. The flavor preference was
not expressed after gastric administration of
hydrolyzed protein, but reappeared after an equicaloric dose of carbohydrate (Gibson and Booth,
1986; Baker et al., 1987).
Classical conditioning and instrumental conditioning are two types of associative control of
eating, in which presented relations between
responses and/or stimuli result in a persisting
change in behavior. In classical conditioning,
first identified by Ivan Petrovich Pavlov (1927),
a relatively neutral stimulus (CS) is paired
with a stimulus of significant biological and
behavioral change, such as food (US). Classical
conditioning occurs when associations are synthesized between two neutral stimuli, meaning that the predictive relation between the CS
and the US leads to a conditioned response (CR)
to the CS. Most of our likes and dislikes are
learned through classical conditioning (Rozin
and Millman, 1987). In discriminative instrumental eating, the divergent sensory characteristics of the food serve as discriminative stimuli
for greater or lesser reinforcement (from subsequent lack of food) of eating as an operant
127
response. Instrumental learning results from
the association between action and consequence
(outcome) (Dickinson and Balleine, 1994).
10.4 SENSORY-SPECIFIC
ANTICIPATORY EATING
Le Magnen (1999b) applied the term “anticipatory satiety” to the process by which a rat
learns to eat a smaller meal when it is followed
by a short fast; conversely, it eats more of a differently flavored meal when it is followed by a
long fast. It is also conceivable that the animal
acquires an “anticipatory hunger” for the food
preceding a long fast, with the increased intake
overcoming the effects of prolonged deprivation. This contrasts with conditioned satiety
(Le Magnen, 1955), which has been defined
as learning to eat less as a result of immediate
effects of the food (Booth and Davis, 1973). The
difference is whether the lack of energy results
from food deprivation (reinforcing anticipatory
hunger) or from the consumption of foods that
are calorically diluted (reinforcing conditioned
appetite).
This experimental approach has been reexamined in only a few studies (White et al.,
2001; Thibault and Booth, 2006; Jarvandi et al.,
2007, 2009a, 2009b). Each of these studies showed
that rats could learn to eat more of a specifically
textured/smelling food when it was followed
by a long fast. This was compared to a test food
paired with a subsequent short fast.
Improving upon Le Magnen’s design of cycles
of training and testing1, White and colleagues
(2001) evaluated the impact of the texture of
high-fat test foods on the amounts eaten before a
short fast (3 hours) and a long fast (12.5 hours).
Le Magnen (1967) used different textures and
odors as cues to be conditioned by different
1
At the start of dark phase, maintenance food is removed for 3 hours and then test food is presented for 90 minutes.
Subsequently, food deprivation of a duration specified to a distinctive sensory cue in the test food is induced.
1. FROM BRAIN TO BEHAVIOR
128
10. LEARNING AND FOOD INTAKE
eating after-effects. However, odorants, tastants
and colors (for visual species) are easier to control than textural attributes (Thibault and Booth,
1999). Most work on learned preferences has
indeed used flavoring as a conditioned or discriminative stimulus. Nevertheless, the tactile
sense is as important in the liking of food and
drink; research should be undertaken to examine mouthfeel as a cue. Booth and Baker (1990)
showed that the size of food crumbs could serve
as a cue in conditioned appetites. White and
colleagues (2001) built upon this and prepared
high- and low-fat diets as either a coarse powder or as small pellets. They also explored the
possibility that learning an anticipatory satiety
response depended upon the energy density of
the test meals. Sufficient caloric intake may be
crucial in preventing hunger; thus, this study
better tested learning in relation with a highfat diet. Poppitt and Prentice (1996) too argued
that diets high in energy density contribute to
obesity. The limited experimental work on this
issue points, however, to some ways in which
concentrated energy can also moderate intake
(Booth and Davis, 1973; Gibson and Booth,
2000). White and colleagues (2001) found a
learned texture-cued increase in intake (from
either a high- or low-fat diet) before a long fast
relative to a shorter fast in rats that had the largest intakes in the first days of training. It was
concluded that anticipatory hunger/satiety is
observed only if enough calories are consumed
before the short fast to prevent hunger from
returning before the next access to food. That is,
anticipatory hunger/satiety is an instrumental
response that avoids some or all of the physiological effects of food deprivation.
The effect of the macronutrient composition
of meals on anticipatory eating was also tested,
with flavor as the cuing stimulus, where the
energy nutrient was entirely either carbohydrate
or protein. The long fast was of 10 hours instead
of 12.5 hours (Thibault and Booth, 2006). Both
Le Magnen (1999a) and White and colleagues
(2001) used conventional diets containing both
carbohydrate and protein. Nevertheless, there is
considerable evidence in humans that the protein content of a meal is important in slowing
the rise of hunger that leads to the next meal.
We then tested a carbohydrate-free protein diet
and a protein-free carbohydrate diet to see if protein was more effective in negatively reinforcing
the acquisition of anticipatory hunger in rats.
It has been suggested that glucose circulating
to the brain is capable of enhancing memory in
rats and humans (Gold, 1986; McNay et al., 2000).
The hypothesis that glucose may improve memory is a basis for predicting that the carbohydrate
diet would support learning better than the protein diet in the difficult task of anticipating a
deficit in the supply of energy to tissues after a
delay of some hours. Carbohydrate meals were
therefore also compared to protein meals, to see
if the diet yielding abundant glucose enhanced
the memory of the different consequences of the
deprivation periods which followed various flavors and thereby facilitated the learning of anticipatory hunger. Both the carbohydrate-diet and
the protein-diet groups switched from an initial
greater intake of test food having an odor predictive of the short fast (conditioned preference) to
a larger meal of the identical diet with a long-fast
odor (anticipatory eating). However, the learned
response declined in the last cycles of training. It
was suggested that this phenomenon arose from
the extra food’s effect on the negative reinforcement from physiological effects of lack of food.
Indeed, the learned increase in intake before a
long fast removes its own reinforcer (Booth and
Davis, 1973).
The phenomenon was also tested in rats of
different genders (Thibault and Booth, 2006).
Nance and colleagues (1976) suggested that
male rats are similar to animals with ventromedial hypothalamus lesions, and adjust to
post-ingestional effects of food slower than
female rats do. It is possible that male rats may
not learn anticipatory hunger as well as female
rats, or may show some differences in temporal pattern of instrumental acquisition, its
1. FROM BRAIN TO BEHAVIOR
129
10.4. SENSORY-SPECIFIC ANTICIPATORY EATING
self-extinction and/or counteractive preference
conditioning. Since female rats have a lower
body weight and thus require less energy than
male rats, they may be better able to ingest sufficient amounts of food to avoid hunger during a
short postprandial fast. In addition, differences
between sexes, at least in rats, may be involved
in the regulation of body weight and control
of food intake – for example, through sex hormones. In this experiment, diets were composed
of a combination of carbohydrate and protein
(Le Magnen, 1999a; White et al., 2001). If carbohydrate and protein individually have different effects, then the question is posed as to the
extent to which either of these effects survive in
a combination. A strong and rapid learning of
anticipatory hunger was observed in male rats,
which may relate to their greater size, in comparison to female rats. Large rats may be more
responsive to the reinforcing and conditioning
stimuli than the modest-sized females. In addition, large rats’ greater intake at experimental
meals may generate more effective stimulation
of the flavor cues and/or by the physiological
consequences of the period of food deprivation.
Jarvandi and colleagues (2007) examined
anticipatory hunger/satiety in rats given a
choice of nutrients before the fasts. Both White
and colleagues (2001) and Thibault and Booth
(2006) used a single food (i.e., animals could
not change the composition of their test food).
Allowing the subject to select its own food permits it to optimize the proportions of the differing components. A greater number of food
containers encourages greater intake (Tordoff,
2002), which could be important for rats to
discriminate a lack of hunger throughout the
shorter fast from the hunger that develops during the longer fast (White et al., 2001). Moreover,
previous experiments on the roles of macronutrients in anticipatory eating showed that
rats learn to avoid hunger by consuming either
protein-rich or carbohydrate-rich food (Thibault
and Booth, 2006). Therefore, Thibault and Booth
(2006) looked to see if a simultaneous choice
between two test foods, one protein-rich and
the other carbohydrate-rich, affected anticipatory eating relative to access to a single balanced
food. It was found that anticipatory hunger
is learned when a choice is given between
protein-rich and carbohydrate-rich foods, as
well as on a single food. Also, anticipatory
hunger extinguished itself, which again indicates that such learning improves on negativefeedback homeostasis with a feed-forward
“hyper-homeostatic” mechanism.
In a recent study, Jarvandi and colleagues
(2009a) used a novel conditioning paradigm in
rats and submitted them only to a long fast. They
then examined whether the restoration of hunger
as a result of extinction causes the re-learning of
deficit-avoidance eating by continuing cycles
beyond the expected extinction of the learned
response. The results confirm previous observations, and are consistent with deficit-avoidance
being acquired and partly self-extinguishing.
The learned extra intake of food is instrumental
to preventing the return of hunger (the negative reinforcer), whose removal extinguished the
learned response (Jarvandi, 2008; Figure 10.1).
This extinction (self-extinction) of the learned
extra intake results in the return of hunger which
as a negative reinforcement should emit new
learned response (relearning). As expected, when
training is continued, the resulting return of hunger induced re-learning of anticipatory eating.
Because of the absence of any contrast with trials
followed by short fasts, these findings provided
Anticipatory eating is a hunger-reinforced instrumental behaviour.
<Acquisition of learning, self-extinction, re-learning>
Lack of food
Hunger
Negative reinforcement
Extra intake
Restoring hunger
Re-learning
Self-extinction of
extra eating
FIGURE 10.1 Evidence for instrumental behavior.
Source: Jarvandi (2008).
1. FROM BRAIN TO BEHAVIOR
130
10. LEARNING AND FOOD INTAKE
robust evidence that eating in rats can be controlled by instrumental learning reinforced by hunger. The use of this novel conditioning paradigm
warrants more attention.
of anticipatory learning. Indeed, the parametric limits on anticipatory eating remain to be
defined, with their potential implications about
mechanisms.
10.5 DIURNAL RHYTHMS AND
THE LEARNED RESPONSE
10.6 NUTRIENTS AND
COGNITION
Meal patterns in rats have a strong nycthemeral rhythm (Selmaoui and Thibault, 2006), and
the endogenous rhythm of melatonin is responsible for the increased intake of carbohydrates
at the beginning of the activity period (Angers
et al., 2003). The diurnal rhythms of intake and
secretion of hormones can be synchronized to
the availability of food (Woods et al., 2000). In
rats fed ad libitum, the 24-hour rhythm of intake
was statistically reliable from day to day for
water, carbohydrate and protein; however, the
rhythm was not predictable for fat (Selmaoui
et al., 2004). This raises questions about the specific influence of fatty acids. The intake of carbohydrate and protein is more precisely controlled
as a result of the effects of glucose and amino
acids, respectively. The lack of control in regards
to fat could account for the over-consumption of
certain high-fat foods; the underlying behavioral mechanisms are worth investigating further.
There are also no data available regarding
the learned control of intake at different stages
of the nycthemeral cycle. Sensory-specific
anticipatory eating studies have thus far measured intake of test food over a fixed period. The
effects of anticipatory learning on the sizes of
the first and any subsequent meals and/or on
the interval(s) between them are not known.
The effects of anticipatory eating in meal size
adjustment and/or in frequency of intake during an access period before deprivation could
be measured by recording intakes with a computerized system. Testing the effect of circadian
time in the light/dark cycle is also important,
and would account for the metabolic conditions
Increasing attention has been focused in
recent years on the effects of energy nutrients on
cognition. Administering glucose solutions has
been shown to facilitate cognitive performance
such as memory in several studies. The mechanisms underlying this have yet to be elucidated.
The administered glucose has both peripheral
and central effects (Booth, 1979). Unlike glucose,
fructose does not cross the blood–brain barrier and is metabolized only by the peripheral
organs (mainly the liver). Therefore, a similar
memory-enhancing effect with fructose in some
cognitive tasks implies peripheral mechanisms.
Hence, the systemic roles of dietary carbohydrate and glucogenic amino acids in the memory for the flavor-hunger contingency should be
investigated.
10.7 DIETARY FATS AND
LEARNING
Another issue pertains to the role dietary lipids may have in the central nervous system as
it relates to the learning of anticipatory eating.
A diet rich in saturated fats impairs the performance of rats in learning tasks (Greenwood
and Winocur, 1996). In addition, (fat-rich) dietinduced obese (DIO) rats are more prone to disruptive effects of dietary fats on brain processes
involved in motivation (Chambers et al., 2006).
Lindqvist and colleagues (2006) reported that
male rats fed a high-fat diet had fewer nerve cells
in the hippocampus, even before they developed
1. FROM BRAIN TO BEHAVIOR
REFERENCES
obesity. Feeding with fat, independently of its
obesity-inducing effect, can impair working
memory (Granholm et al., 2004).
In a preliminary study of the role of high-fat
maintenance diet in learned anticipatory eating,
Jarvanti and colleagues maintained eight rats
on a high-fat diet for 28 days and then evaluated their anticipatory eating of flavored chow.
Intake of fat attenuated the overall pattern of
learning eating in the animals, independently
of body weight gain (Jarvandi et al., 2009b).
Comparing the strength of learned response
from previous experiments conducted in animals maintained on standard laboratory chow
showed a weaker learned response of rats
fed a high-fat diet (Jarvandi et al., 2009b). To
the author’s knowledge, this is the first report
regarding the effect of high-fat feeding on anticipatory eating. Measuring the extent to which
learned anticipatory eating is weakened following chronic intake of fat, replication of these
results using dietary fats with different fatty
acid profiles would be important. Mechanisms
such as reduced or reversed intake-motivating
effect of the longer food deprivation by the
high-fat maintenance, satiating effects of fats in
meals, and altered metabolic response of highfat feeding to fasting through fat oxidation
should be investigated.
10.8 OUR PRIMITIVE BRAIN
Humans clearly can evaluate the delay
between eating opportunities, and so can deliberately adjust their intake accordingly in order to
manage hunger (Dibsdall et al., 1996). However,
we may share with rats a more primitive capacity to acquire anticipatory hunger and satiety,
according to the circumstances in which we
experience hunger. We tend to eat meals according to social prescriptions, rather than when we
choose. To prevent obesity, we ought to eat just
enough to carry us through to the next meal.
131
Even the tiny brain of a laboratory rat enables it
to eat more of a food that prevents hunger, and
to eat less if it is presented with a food before it
is hungry again. It is therefore likely that, deep
inside our brains, there are automatic mechanisms that help us (maybe unconsciously) to eat
more if we tend to be hungry after a meal, or to
eat less when there is more than enough to carry
us through.
The literature on learned appetite and satiety supports the concept that short-term intake
is influenced by learning, addressing both current and future physiological requirements.
Cognitive processes contribute to the control of
food intake, as evidenced both in people and in
laboratory rats. The improved understanding
of the behavioral mechanisms of this underresearched type of learning is highly significant
in approaching the development of diet-induced
obesity. Poor performance of a basic regulatory
mechanism with a high-fat diet could prove to
be an extremely important phenomenon.
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C H A P T E R
11
Restrained Eating in a World of Plenty
Janet Polivy1 and C. Peter Herman2
1
Department of Psychology, University of Toronto at Mississauga, Mississuaga, Canada
2
Department of Psychology, University of Toronto, Toronto, Ontario, Canada
O U T L I N E
11.1
Introduction
135
11.2
The Effects of Having Food Cues
Present
136
11.3
11.4
11.5
11.6
The Removal of Food Cues
140
11.7
Caloric Restriction in Animals
and Humans
140
Is CR Likely to be Effective for
Humans?
141
Caloric Restriction in the Presence
of Food Cues
142
Response to Food Cues in Restrained
and Unrestrained Eaters
136
11.8
Food Photographs and/or
Words – Indirect Food Cues
11.9
138
Portion Size as Food Cue
139
11.1 INTRODUCTION
Western society now has the highest rates
of obesity in history, at the same time as it has
the highest rates of restrictive eating disorders.
Dieting and body dissatisfaction have become
the norm for young females (see, for example,
Vartanian et al., 2005) as attitudes toward the
overweight and obese have become less tolerant and accepting (Puhl and Brownell, 2001).
Motivation to restrict eating is supported not
only by society’s preference for thin physiques,
Obesity Prevention: The Role of Brain and Society on Individual Behavior
11.10 Dieting in a World of Food Cues
144
but also by experimental evidence derived from
animal studies (Pinel et al., 2000) that suggest
that food restriction extends life and improves
health. This evidence may apply to humans as
well (see, for example, Walford, 2000; Delaney
and Walford, 2005). As dieting has become
almost ubiquitous among young women, the
food environment has become “toxic” (Brownell
and Horgen, 2004) in promoting overeating
and obesity. Portion sizes in the Unites States
have become huge, healthy food is more expensive than unhealthy, high-fat and high-sugar
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© 2010,
2010 Elsevier Inc.
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11. RESTRAINED EATING IN A WORLD OF PLENTY
counterparts, and food and food cues are everywhere. How, then, does this environment of
plenty affect those who are trying to restrain
their eating (i.e., diet)?
The present chapter will discuss the ubiquity
of food cues in our environment, and review the
literature on people’s responses to them. Such
cues make it particularly difficult for humans to
reduce, let alone control, their food intake and,
particularly, to restrict their intake to the levels
required for life-extension. We will discuss differences between animals in caloric-restriction
experiments in laboratories, on the one hand,
and free-living humans in our society, on the
other. We will argue that the super-abundance of
food cues in our society makes it particularly difficult for people to restrain their eating and diet
successfully or achieve real caloric restriction.
11.2 THE EFFECTS OF HAVING
FOOD CUES PRESENT
In nature, food cues customarily indicate what
one can (and should) eat, and also specify what
can be eaten (Weingarten, 1985). Thus, the presence of food cues may stimulate a desire to eat,
specify what should be eaten, and increase the
amount consumed (Woods, 1991; Weingarten,
1985). Exposure to palatable foods increases
self-reported appetite, and consuming a little of
the food increases the desire to eat it (Yeomans
et al., 2004). Cornell and colleagues (1989) fed
male subjects until they were sated, and then
gave them a taste of ice cream or pizza, or nothing. All participants were then given ice cream
and pizza to eat ad libitum. Those who had
tasted ice cream ate more ice cream, and those
who had tasted pizza ate more pizza, leading to
the conclusion that specific food cues increase
desire for the same food rather than stimulate a
broader appetite for food in general.
Fedoroff and colleagues (1997) exposed
participants to the smell of pizza baking, and
observed increased pizza craving and pizza consumption for both restrained and unrestrained
eaters (although this effect was particularly
strong in restrained eaters, as will be discussed
later). Fedoroff and colleagues (2003) replicated
and extended these findings: participants sat in
a room with an oven baking either cookies or
pizza, and were then offered one of these. As
in the study by Cornell and colleagues (1989),
those who had smelled cookies ate more cookies
but not pizza, whereas those who had smelled
pizza ate more pizza but not cookies. Thus,
the food cues elicited a food-specific response,
increasing consumption only of the food to
which participants had olfactory pre-exposure.
Painter and colleagues (2002) placed containers of 30 chocolate candy kisses in offices
every day for 15 consecutive working days. The
candy containers were positioned on the desk
for 5 days (making the candies both visible and
convenient), in a desk drawer for 5 days (so that
they were convenient but not visible) and on a
shelf 2 meters away from the desk for 5 days (so
they were visible but not convenient). When the
container of candies was visible and convenient,
more candies were eaten; when the container
was inconveniently placed on the shelf, fewer
candies were eaten. Having food both visually
salient and easily attainable increased intake.
11.3 RESPONSE TO FOOD
CUES IN RESTRAINED AND
UNRESTRAINED EATERS
Several studies have focused on how the presence of food cues influences restrained and
unrestrained eaters (chronic dieters and nondieters). Restrained eaters attempt to control
their food intake in order to lose weight. They
are also susceptible to disinhibition of this restraint, so that they often overeat when their
inhibitions are violated or even merely threatened (for reviews, see, for example, Polivy,
1. FROM BRAIN TO BEHAVIOR
11.3 RESPONSE TO FOOD CUES IN RESTRAINED AND UNRESTRAINED EATERS
1996; Herman and Polivy, 2004). Legoff and
Spiegelman (1987) found increased salivation
in response to attractive food cues in restrained
eaters, but not in unrestrained ones. Rogers and
Hill (1989) showed that the sight and smell of
attractive foods increased hunger, salivation and
eating in restrained eaters more than in unrestrained eaters exposed to the cues, restrained
eaters not exposed to food cues, and restrained
eaters exposed to unattractive, non-preferred
foods. However, the changes in hunger ratings
and/or salivation did not predict the amount
of food eaten by individual participants: the
amount eaten following exposure to food cues
is thus not mediated by hunger. It appears that
the mere sight and smell of palatable food,
however, can overwhelm dieters’ motivation to
restrain their eating.
Restrained eaters ate more than unrestrained
eaters after merely smelling attractive foods
(Jansen and Van den Hout, 1991) and after
smelling and thinking about palatable foods
(Fedoroff et al., 1997, 2003). Fedoroff and colleagues (1997) had restrained and unrestrained
eaters think about pizza while they smelled
pizza baking, and then allowed them to eat
pizza ad libitum. Pizza intake was higher in all
food-cues conditions, but particularly so for
restrained eaters. In addition, restrained and
unrestrained eaters rated several foods, including pizza, similarly before they were exposed to
the food cues. Following exposure, restrained
eaters reported more cravings, liking and desire
for the cued food than did unrestrained eaters (and more than did restrained eaters not
exposed to food cues). Pre-exposure to the food
cues induced a desire to eat in restrained eaters,
but not in unrestrained eaters.
Fedoroff and colleagues (2003) replicated
and extended their 1997 study in order to
determine whether the increased intake they
had observed reflects a general desire to eat or
a specific desire or craving for the particular
cued food. Participants were assigned to one of
three conditions: pizza cues (the smell of pizza
137
baking while they wrote down their thoughts
about pizza), cookie cues (the smell of chocolatechip cookies baking while they wrote down
their thoughts about cookies) or no cue (no food
baking while they wrote down their current
thoughts). After pre-exposure (or not) to food
cues, participants were presented with either
pizza or cookies to eat ad libitum. Restrained eaters were again more responsive to food cues, eating more after exposure to them. However, as
reported above, restrained eaters ate more only of
the particular food to which they had previously
been exposed, demonstrating the specificity of
the response to food cues. Additionally, reports
of craving, liking and desire to eat the foods were
elevated only for the cued food. Restrained eaters
presented with the non-cued food ate the same
amount as did restrained eaters who had not
been exposed to any food cues. Food cues thus
appear to have a greater effect on restrained eaters than on unrestrained eaters for both the desire
to eat a food and how much is actually eaten.
Lowe and Butryn (2007) attribute increased
motivation to eat palatable, attractive foods
to the presence of attractive food in the environment, especially in chronic dieters. As in
Weingarten’s (1985) two-factor theory of hunger
and appetite, Lowe and Butryn’s (2007) hypothesis suggests that the mere presence of food
may stimulate psychological hunger or appetite
in dieters.
Restrained eaters have a tendency to binge
eat in a variety of situations when their diets
become irrelevant or less important to them (for
a review, see Polivy, 1996). Propensity for bingeeating behavior rather than restrained eating per
se was studied in two experiments that examined psychophysiological responses to the presence of food (Karhunen et al., 1997; Vogele and
Florin, 1997). In one study (Vogele and Florin,
1997), 30 female binge eaters and 30 non-bingers
were assessed on measures of heart rate, blood
pressure, electrodermal activity, and respiration rate at baseline, during exposure to their
favorite (binge) food(s) and while eating the
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11. RESTRAINED EATING IN A WORLD OF PLENTY
food(s). Self-reported restrained eating, nervousness, distress, desire to binge and hunger
were also measured. Psychophysiological
arousal was elevated in binge eaters during the
food-exposure period. Heart rate during foodcue exposure predicted the amount of food
eaten by all types of participants, but particularly by binge eaters and normal restrained eaters. Karhunen and colleagues (1997) measured
cephalic-phase responses repeatedly in 11 obese
female binge eaters and 10 obese non-bingers
during a baseline phase, followed by periods of
anticipation of food, food exposure, and ad libitum eating. They examined indices of cephalicphase responses such as serum insulin, free fatty
acids, plasma glucose and salivation, as well as
hunger and the desire to eat. There were no differences between groups in the amount of food
eaten or in cephalic-phase responses, but binge
eaters exhibited a greater desire to eat following exposure to food cues than did non-bingers,
indicating greater subjective reactivity to food in
binge eaters, despite a lack of increased physical
responsiveness.
11.4 FOOD PHOTOGRAPHS
AND/OR WORDS – INDIRECT
FOOD CUES
It is not terribly surprising that prominent,
sensory food cues (e.g., looking at and smelling
food) influence hunger and eating, but recent
research indicates that more remote, abstract,
and indirect food cues also appear to influence
appetite and eating. Female students who studied lists of food-related words (high- and lowcalorie foods) reported thoughts about eating,
particularly if they were restrained eaters (Boon
et al., 1998). Restrained eaters reported more
thoughts about eating control, weight and body
shape after reading such lists or after an actual
eating episode. In fact, these dieters claimed
that they ate somewhat less when thinking
about diet-related matters, but only if they were
currently dieting; restrained eaters not currently
dieting actually reported eating more when they
experienced diet-related thoughts. Unrestrained
eaters, on the other hand, were not affected by
their thoughts either way.
Similarly, Fishbach and colleagues (2003)
showed that priming dieters with tempting
food words made their diet goals more salient.
Other remote food cues such as a magazine
about chocolate or candy bars and other fattening foods that were inaccessible to the participants – the foods were presumably there for a
later meeting in the same room – also increased
dieters’ resistance to temptation; they were more
likely to select a non-fattening food reward (an
apple) than a fattening candy bar.
Oakes and Slotterback (2000, 2001) assessed
reactions to a list of foods to be rated for nutritional value (i.e., verbal food cues). Even such
indirect, remote food cues led to lower fullness
ratings and increased ratings of general hunger,
desire to eat, and desire to eat a greater number
of specific foods. This effect was stronger for
non-dieters than for restrained eaters. On the
other hand, Braet and Crombez (2003) found that
obese children, like restrained eaters, were hyperresponsive to food words compared to non-food
words and to non-obese children. These authors
speculated that hyper-reactivity to food-related
stimuli might initiate or maintain excessive eating which causes these children’s obesity.
When restrained and unrestrained eaters were
asked to imagine eating chocolate cake or drinking water while performing a simple reactiontime task, the reaction-time performance of
the restrained eaters was slower while they
imagined eating the cake, relative to imagining
drinking the water. The reaction times of unrestrained eaters were not affected by cue exposure, suggesting again that restrained eaters are
more reactive to food cues (Higgs, 2007).
The evidence thus indicates that subtle, indirect food cues affect hunger and eating, especially in chronic dieters. Schachter’s (1971)
1. FROM BRAIN TO BEHAVIOR
11.5 PORTION SIZE AS FOOD CUE
externality theory of obese/normal differences
in behavior suggested that differences in responsiveness to food cues are based on body weight.
Rodin (1981) argued that people who are more
externally responsive are particularly sensitive
to food cues, and thus have to exercise restraint
to avoid eating and gaining weight whenever
attractive food cues are present. Herman and
Polivy (1980) extended the externality theory to
include restrained and unrestrained eaters, positing that obese people and chronic dieters are
more responsive to salient external food cues
than are non-obese individuals, possibly because
of their focus on dieting and weight control.
They argued that the correlation between external responsiveness and restraint could reflect
the effects of restraining one’s intake rather than
being the cause of the restraint. Whichever way
the causal connection works, the findings we
have just reviewed support the hypothesis that
restrained eaters should be more responsive to
salient, external food cues than are unrestrained
eaters.
11.5 PORTION SIZE AS
FOOD CUE
Portion sizes in North America have grown
larger in recent years (Young and Nestle, 2002;
Nielsen and Popkin, 2003). Increased amounts of
food mean increased food cues present during an
eating episode, making it more difficult for people to restrict their eating because people tend to
eat all of the food served to them (Herman, 2005).
Experimental manipulations of portion size (see,
for example, DiLiberti et al., 2004; Levitsky and
Youn, 2004; Wansink et al., 2005) indicate that
larger portions lead to increased eating.
DiLiberti and colleagues (2004) manipulated
the sizes of pasta portions served in a cafeteria, presenting a standard portion of 248 grams
and a large portion of 377 grams (for the same
price). The patrons who purchased the meal
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(which was surreptitiously weighed before
and after the person ate it) answered questions about their perceptions of how appropriate the meal size was and how much they ate
compared to their usual intake. The larger meal
increased consumption of the pasta by 172 calories, which was 43 percent more than the intake
of customers served the regular-sized portion.
Regardless of meal size, perceived appropriateness was the same for both groups. Levitsky
and Young (2004) gave students a normal portion size, 25 percent more or 50 percent more
than they ate at a baseline buffet lunch, and
found that the more they were served, the more
they ate (of each of the four foods that comprised the lunch meal). Similarly, Wansink and
colleagues (2005) manipulated soup portions
by fashioning bowls of soup that refilled themselves imperceptibly while the participants were
eating. Participants given the self-refilling bowls
ate 73 percent more soup than did students who
ate from normal bowls of the same size. Despite
their increased intake, these participants felt no
more sated, and believed that they had eaten
the same amount as did those eating from the
normal bowls. Wansink and Kim (2005) served
popcorn in large versus medium-sized packages; the large portions increased consumption
by 45.3 percent. Using stale and bad-tasting
popcorn produced the same pattern; large portions increased consumption by 33.6 percent
more than did medium-sized portions.
The effects of portion sizes on consumption
may reflect what has been called “unit bias”
(Geier et al., 2006). People appear to believe that
a unit of food represents the appropriate portion of that food, so if the unit is larger, people think that it is appropriate to eat more. For
example, when serving themselves M&Ms with
a large spoon, people take more than if using a
small spoon; they will eat greater amounts of a
large Tootsie Roll than of a small one. The unit
bias may be a cultural norm, learned in childhood (Rolls et al., 2000). Regardless of the source
of the bias, it appears that whatever the portion
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they are given, people see it as the appropriate
amount to eat. So if the portion is larger, the
perception of what is appropriate to eat is also
larger. The size of the portion is thus a food cue
telling the individual how much to eat.
11.6 THE REMOVAL OF
FOOD CUES
The presence of food cues of various kinds
clearly has a major influence on hunger and
eating behaviors. Limiting or removing food
cues should also affect eating. When only a
limited range of foods is available over an
extended period of time, liking for a given
food decreases over repeated presentation of it
(Siegel and Pilgrim, 1958); this effect is referred
to as “monotony”. Raynor and colleagues (2006)
allowed overweight adults access to only one
snack food for 8 weeks, inducing monotony and
lowering liking ratings for the initially wellliked snack. The participants also lost weight
over the 8 weeks. Monotony thus acts in a manner opposite to the presence of abundant food
cues, which stimulate hunger and increased eating. A large literature demonstrates that when
only limited types of foods are available and
food cues are thus restricted, eating declines
(either in the short-term, in a single meal due to
sensory-specific satiety, whereby the palatability
of the food decreases as the food is being consumed, or over the long-term, due to the effects
of monotony; see review by Raynor and Epstein,
2001). One example of the effects of limiting
food cues involves US soldiers who received a
repetitious diet of prepared food rations. The
soldiers lost more than 10 pounds in a single
month because they ate so little (Hirsch, 1995).
When food is forbidden, avoided voluntarily
or simply not available, this reduces or eliminates food cues in a different manner and leads
to increased thoughts of food (see, for example,
Keys et al., 1950; Mann and Ward, 2001). When
people are undergoing starvation and food cues
are drastically limited, not only do hunger and
the urge to eat increase, but thoughts of food
and even fantasies about food (producing imagined food cues) also increase (Keys et al., 1950).
The desire to eat a forbidden food also increases,
although increased consumption of the forbidden foods does not necessarily occur (Karhunen
et al., 1997; Mann and Ward, 2001). It has been
argued, however, that when the prohibited food
is eaten, disinhibition often occurs and leads
to increased consumption (see, for example,
Polivy, 1998; Herman and Polivy, 2004). In fact,
dieters who restrict their intake of particular
favored foods appear to be especially likely to
overeat when their diets are broken (Polivy and
Herman, 1985, 1987).
A relative absence of food cues thus has an
effect opposite to the presence of abundant food
cues, reducing rather than increasing consumption. It has been proposed that the development
of sensory-specific satiety and monotony effects
may even be an evolutionary adaptation to
allow humans to stretch out meager food supplies during periodic food shortages (Polivy and
Herman, 2006). On the other hand, when the
reduction in food cues is a voluntary restriction
(as in dieting) or when the restriction results in
real hunger or malnutrition, when food becomes
available there is often a rebound and food is
overeaten (Polivy and Herman, 1985, 1987).
11.7 CALORIC RESTRICTION IN
ANIMALS AND HUMANS
A growing body of research suggests that
underfeeding, or caloric restriction (CR), leads
to better health and greatly increases longevity
in calorically-restricted animals (for a review,
see Pinel et al., 2000). Animals whose caloric
intake is severely restricted (i.e., consuming only
60–70 percent of ad libitum [AL] intake) exhibit a
variety of physiological benefits. In addition to
1. FROM BRAIN TO BEHAVIOR
11.8 IS CR LIKELY TO BE EFFECTIVE FOR HUMANS?
greater longevity and improved general health,
these animals have been shown to have delayed
onset of disorders such as cancer, heart disease
and diabetes (Pinel et al., 2000), and slowed agerelated declines in cognitive functioning (Patel
and Finch, 2002). Such advantages of CR have
been demonstrated in animals from earthworms
to rodents to primates (monkeys) (Pinel et al.,
2000). The many benefits of CR seen in animal studies have encouraged speculation that
longer, healthier lives are possible for humans
if they too severely restrict their food intake
(see, for example, Walford, 2000; Delaney and
Walford, 2005).
Despite the promised benefits of CR, some
researchers have pointed out that it is difficult
to maintain a diet as spartan as is required for
CR, and have concluded that “for most people,
quality of life seems to be preferred to quantity
of life” (Olshansky et al., 2002: 9). One question
is whether physiological outcomes of practicing CR are truly superior to those of healthy
controls who do not restrict their food intake.
Unfortunately, such research on human CR is
limited. Although the research that has been
done does suggest that CR can have physiological benefits (see, for example, Fontana
et al., 2004; Meyer et al., 2006), these studies are
short-term, and are constrained by self-selection
biases with respect to the CR participants (i.e.,
people who choose to undertake CR may be
healthier to begin with). The main claim for CR,
that it greatly increases longevity, has yet to be
demonstrated in humans (Polivy et al., 2008).
11.8 IS CR LIKELY TO BE
EFFECTIVE FOR HUMANS?
Several researchers have raised questions
about the likelihood that CR will prove to be
beneficial for humans, especially for humans living outside of a laboratory environment (see, for
example, Vitousek, 2004; Le Bourg and Rattan,
141
2006). In a thorough review of the literature on
CR, Vitousek and colleagues (2004a) observed
that animals subjected to CR suffer some
impairment in physical functions. For example,
growth, reproductive development, and resistance to some stressors all show evidence of
problems in response to CR. In addition, other
deleterious effects of CR include cold intolerance, higher levels of stress hormones, lower
levels of sex hormones, and postural hypotension. There are also psychological side effects,
such as (not surprisingly) hunger, accompanied
by obsessive thoughts about food and eating,
emotionality/irritability, social withdrawal, and
a loss of interest in sex. For those conversant
with the literature on eating, these side effects
may sound familiar, as they closely resemble
the problems reported by Keys and colleagues
in the famous World War II Minnesota human
starvation experiment (Keys et al., 1950), which
was effectively a CR experiment, performed
on a group of conscientious objectors. The participants were asked to lose 25 percent of their
body weight so that the effects of caloric deficits, such as those being experienced in wartorn Europe and Asia, could be studied. The
participants had great difficulty achieving the
desired weight loss – the experimenters ultimately had to accept a loss of only 24 percent
of the initial weight – and exhibited many of the
same negative symptoms described above with
respect to current CR experiments. Moreover,
when weight was restored and food was again
freely available, these participants exhibited
the behavioral symptom of binge eating. As
Vitousek and colleagues (2004a) point out, people experiencing so many disturbing symptoms
would normally be seen as unhealthy. This
point is not mentioned in CR research, however, because the outcomes of interest in CR
studies are only longevity and freedom from
disease. The fact that CR animals excel on these
two main outcomes allows proponents to see
CR as advantageous and to ignore the negative
(side) effects. Laboratory animals are not in a
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11. RESTRAINED EATING IN A WORLD OF PLENTY
position to complain, but humans may prove to
be more vocal in their objections. Vitousek and
colleagues object to CR researchers’ focus on
one set of benefits while ignoring a large set of
negative effects (Vitousek et al., 2004a).
Another problem pointed out by Vitousek
and colleagues (2004a) in regard to CR in animals is that those undergoing food restriction live in cages with no other food available,
leaving them no opportunity to abandon their
restriction and eat normally, let alone overeat.
Moreover, these animals are protected from the
stresses of daily life; they do not need to find
food or shelter, are not exposed to germs or
variations in temperature or other meteorological conditions, and avoid all of the stresses of
social and family life. It is unlikely that humans
undergoing CR would be able to enjoy such an
ability to focus only on their diets. Other work
by Vitousek and colleagues (2004b) criticizes CR
advocates for failing to even examine behavioral and psychological effects of CR while advocating this treatment for use in humans, whose
psychological distress will be more difficult to
ignore.
As with most natural phenomena, there are
individual differences in response to CR. Not
all animals tolerate CR equally well, especially
among primates. Some animals become seriously ill on the same restriction that is beneficial for others. Vitousek and colleagues (2004a,
2004b) draw a parallel between CR animals and
patients suffering from anorexia nervosa (AN);
as with primates undergoing CR, some AN
patients tolerate the caloric deficit (inherent in
their disorder) better than do others. Those less
able to tolerate severe caloric restriction may be
the patients who subsequently become bulimic,
reminiscent of the starved Minnesota conscientious objectors (see, for example, Bulik et al.,
2005) or starved prisoners of war in World War
II (Polivy et al., 1994) who began binge eating
once food was freely available. Other negative
side effects of CR have been reported in humans.
Some people who attempt CR on their own
develop serious cardiac irregularities, as has also
been observed in some AN patients (Vitousek
et al., 2004a). The health effects of CR in humans
are thus not uniformly positive at all.
It must also be remembered that CR was
developed in the rarified and protected environment of the animal laboratory; applying
the same restrictions to humans requires careful consideration of the very different environment of free-living people. The stresses of daily
life are likely to be magnified when added to
the stress of CR, which would probably lead to
serious negative effects in humans. This concern may be irrelevant, however, as the evidence suggests that very few humans will be
able to maintain CR for long enough periods
to do much damage to themselves (Vitousek
et al., 2004a). Not even the most ardent advocate
of CR, Roy Walford, was able to sustain CR. He
and seven of his colleagues in the Biosphere II
project were forced drastically to reduce their
intake because their rations were inadequate,
leading them all to undergo CR for 2 years.
Although these researchers believed they had
benefited from their restriction and intended to
continue to maintain a restricted diet, all eight
quickly regained the weight they had lost while
in the Biosphere as soon as the project terminated (Vitousek et al., 2004a).
11.9 CALORIC RESTRICTION IN
THE PRESENCE OF FOOD CUES
Living in a biosphere (like an animal living
in a laboratory) may allow humans to eat minimal amounts while minimizing discomfort.
Most humans, however, do not live in a biosphere. Our environment is filled with attractive
food cues in all areas of our lives. Store windows, food carts on the street, coffee and pastries in the lobby of every building, television
and other media advertisements – food cues
are ubiquitous in developed societies. Unlike
1. FROM BRAIN TO BEHAVIOR
11.9 CALORIC RESTRICTION IN THE PRESENCE OF FOOD CUES
CR animals in the laboratory, who experience
almost no food cues at all in their foodless cages
and are housed with equally deprived fellow
animals, humans are virtually unable to escape
food cues. As we have seen, food cues produce
increased food consumption in animals (see, for
example, Woods, 1991) and humans (Fedoroff
et al., 1997, 2003), even when they are not food
deprived and have already eaten (see, for example, Cornell et al., 1989). A study of two severely
amnesic patients who were unable to remember events for more than a minute (Rozin et al.,
1998) demonstrated quite graphically that food
cues are more powerful than internal signals of
satiety. These patients ate a normal lunch, and
after everything was cleared away were given
a second lunch 10–30 minutes later. They proceeded to eat the second meal, and after another
10–30 minutes were happy to begin eating a
third meal (until it was taken away out of fear
that it might make them ill). The presence of
food cues induces eating (even after a full meal
or meals), especially if one does not remember
having already eaten. A weaker version of this
effect has been demonstrated by Higgs, who
has shown that people reminded of their lunch
eaten a couple of hours earlier eat less of a snack
than do people who have not been reminded
of their recent lunch (Higgs, 2002). Most people do not eat multiple full meals one after
another, despite the ever-present food cues, but
humans today are eating more than they ever
did (Brownell and Horgen, 2004). Thus, caloric
restriction is a much greater challenge for a freeliving humans living in a world of ever-present
food cues than it is for animals undergoing a CR
regimen in a food-free laboratory environment
(Polivy et al., 2008).
Even for animals, the presence of food cues
adds to the stress and difficulty of CR. We deliberately manipulated the presence of food cues
during a 14-week CR study of laboratory rats
(Coelho et al., 2009) to determine whether the
presence or absence of food cues affected physiological and behavioral responses to CR. Two
143
groups of rats (CR and ad libitum-fed) were
tested; half of each group was exposed to attractive, inaccessible food cues, i.e. Fruit Loops
cereal that could be seen and smelled (but not
reached or eaten) in wire-mesh baskets attached
to the top of their cages. The food-cue-exposed
rats were found to have higher levels of corticosterone (a stress hormone), increased food consumption over 24 hours during the re-feeding
period, and weighed more after the ad libitum feeding period than did the non-cued rats.
During the deprivation period, however, the
CR cued rats weighed less than the non-cued
CR rats did, possibly reflecting their greater
stress in response to the presence of food cues.
These animals went on to gain weight much
more rapidly after the deprivation period, when
food was again available on an ad libitum basis,
and soon weighed as much as (or slightly more
than) their non-cued peers. The presence of
food cues during caloric restriction is thus an
added stressor. In addition, it appears that those
who try CR and then give up are likely to overeat and gain weight once they are re-exposed to
food cues, which, in our society, is pretty much
inevitable.
The effect of food deprivation on responses
to food cues has been investigated in humans
as well as rats. College students in a series of
two studies were deprived of food for 0, 6 or
24 hours, and were shown emotion-inducing
or food-related pictures (Drobes et al., 2001).
Although the emotional pictures had no effect
on the students, food deprivation influenced
both self-reports and physiological reactions in
both studies. Heart-rate responses to food pictures were elevated in acutely food-deprived
and chronically binge-eating students, who
also rated the pictures as pleasanter than did
controls and habitual food-restricting students.
These results were seen as indicative of heightened appetitive motivation in response to food
cues in deprived and binge-eating participants.
A review of the research on food cravings and
“food addictions” came to a similar conclusion;
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11. RESTRAINED EATING IN A WORLD OF PLENTY
attempts to restrict consumption of preferred
foods produce increased desire to eat the food
when it is present and available for eating
(Rogers and Smit, 2000).
11.10 DIETING IN A WORLD OF
FOOD CUES
As we have seen, chronic dieters or restrained
eaters are more susceptible than are non-dieters
to increased appetite and overeating when
confronted with food cues (Polivy et al., 2008).
Even though restrained eaters are not attempting the prodigious caloric restriction advocated
for increased longevity, they are still trying to
eat less than they would like, and to avoid palatable but high-calorie foods that would make
it harder for them to lose weight (Herman and
Polivy, 1980). The literature over the past three
decades indicates that restraining one’s eating in
order to diet and lose weight is a difficult enterprise. Stunkard’s famous conclusion that “most
obese persons will not enter treatment for obesity. Of those who enter treatment, most will
not lose much weight and of those who do lose
weight, most will regain it” (Stunkard, 1975: 196)
is as apt today as it was over 30 years ago,
except that many obese, overweight and even
normal-weight individuals try repeatedly to lose
weight, but are unable to achieve lasting success
(Polivy and Herman, 2002). Even when people
are successful at losing weight, the long-term
outcome for the vast majority is that they regain
the weight that they lost (Wilson, 2002). In fact,
the literature on restrained eating makes it
clear that restrained eaters are particularly susceptible to the lure of attractive food cues, which
not only impairs their ability to diet successfully, but also actually contributes to their tendency to overeat (for a review, see Polivy, 1996).
It is not readily apparent whether those who
decide to diet are inherently more vulnerable
to the temptation posed by attractive food cues
(Rodin, 1981), or whether dieting makes them
more receptive to such cues (Heatherton and
Polivy, 1992). Attempting to restrain one’s intake
in the face of ever-present reminders of the food
that one is sacrificing clearly contributes to the
well-documented difficulty of dieting. Trying
to restrain one’s eating in the presence of ubiquitous attractive food cues makes dieting not
only difficult but stressful; the attractive food
cues surrounding us make caloric restriction
both impossible and unbearable. It is thus not
surprising that in a society of plentiful food,
dieters are generally unable to lose weight and
obesity is on the rise. As Brownell has pointed
out (Brownell and Horgen, 2004), the superabundance of food in our society has created a
“toxic environment” that encourages overeating and overweight, not caloric restriction
and weight loss. Thus, although many people
acknowledge the need for moderation, environmental conditions conduce toward excess.
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1. FROM BRAIN TO BEHAVIOR
C
BIOLOGICAL SYSTEMS THAT
FAVOR A POSITIVE ENERGY
BALANCE AND BODY-WEIGHT
INCREASE IN A
WORLD OF PLENTY
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C H A P T E R
12
The Genetic Determinants of Ingestive
Behavior: Sensory, Energy Homeostasis
and Food Reward Aspects of Ingestive
Behavior
Karen M. Eny and Ahmed El-Sohemy
Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
O U T L I N E
12.1
Introduction
149
12.4
Reward Circuits and Food Intake
155
12.2
Sensory Determinants of Food Intake 151
12.5
Conclusions
156
12.3
Energy Homeostasis Pathways and
Food Intake
Acknowledgments
12.1 INTRODUCTION
The prevalent obesogenic environment has
been blamed in fostering the spread of the obesity epidemic over the past three decades (Hill
et al., 2003). However, not all individuals respond physiologically and behaviorally the same
way to the over-abundant food supply and sedentary lifestyle (Speakman, 2004). Indeed, obesity is a polygenic disorder, which results from
imbalances between energy input and energy
Obesity Prevention: The Role of Brain and Society on Individual Behavior
157
152
expenditure (Speakman, 2004). Although environmental factors may affect either side of the
energy balance equation, understanding the
genetic determinants of food intake may help in
developing appropriate prevention and treatment strategies to address this growing problem.
Early studies examining the genetic contribution to food intake phenotypes such as total
energy and macronutrient intake, macronutrient
selection and meal patterns, to more recent studies examining food neophobia, have measured
149
© 2010,
2010 Elsevier Inc.
150
12. GENETIC DETERMINANTS OF INGESTIVE BEHAVIOR
heritability using family units or the comparison
of monozygotic to dizygotic twins (Wade et al.,
1981; Heller et al., 1988; Perusse et al., 1988; de
Castro, 1993; Knaapila et al., 2007). Overall, the
genetic component contributed between 11 and
70 percent of the variance, with this range of
results possibly due to differences in the phenotype that was measured and how it was measured, as well as how shared environment was
accounted for across studies (Wade et al., 1981;
Heller et al., 1988; Perusse et al., 1988; de Castro,
1993; Rankinen and Bouchard, 2006). An alternative method to measuring the genetic component of food intake is by using the candidate
gene approach. Candidate genes are selected
based on knowledge of underlying mechanisms
related to food intake, which can occur at multiple points along the entire food intake process,
even before food is consumed. Genetic variations such as single nucleotide polymorphisms
(SNPs) or copy number variants (CNVs) can
be examined to determine the role of the gene
in various food intake phenotypes (Kowalski,
2004; Martinez-Hernandez et al., 2007). There
has been considerable progress in examining
genes involved in appetite regulation pathways,
predisposing individuals to both polygenic
obesity as well as severe early-onset monogenic and Mendelian forms of obesity (Loos
and Bouchard, 2003; Rankinen et al., 2006; Cecil
et al., 2007; Martinez-Hernandez et al., 2007), in
addition to studies examining individuals with
eating disorders such as anorexia nervosa and
bulimia (Bulik et al., 2007). These studies, which
examined obesity and anorexia as outcome variables, have shed light on genetic variants likely
involved in affecting ingestive behaviors.
The present review will focus on the recent
discoveries of genes associated with ingestive
behavior phenotypes as well as other candidate genes to examine in the future. It will also
underline the methodological considerations
needed to progress our current understanding
of how common genetic variations affect food
intake.
Given that ingestive behavior is a product of
both environmental and genetic interactions,
examining genetic determinants of food intake
should account for all stages of food intake, from
the pre-consummatory stage to termination and
satiety, and consider both the external environmental and internal biological signals contributing to food intake (Berthoud, 2002, 2004). Watts
proposed a comprehensive model which breaks
down ingestive behavior into several stages: initiation, procurement, consummatory, termination and satiation (Watts, 2000; Berthoud, 2002).
First, the “initiation” phase can result from external factors such as the sight and smell of food
when it is directly available. Alternatively, internal factors can stimulate food intake, which can
be signals associated with the incentive value
of a food or other energy homeostatic factors
regulating food intake. Next, in the “procurement” phase, reward systems including learning and memory processes direct the individual
to acquire the food. This phase is likely not as
prominent in today’s over-abundant environment, characterized by ubiquitous fast-food outlets and high consumer demand for ready-to-eat
foods. The “consummatory” phase spans from
the cephalic to the gastrointestinal stages of evaluating the sensory properties of the food as well
as sensing the ingested food, which together
form memories of either reward or aversion.
Finally, ingestive behavior ends with “termination”, where circulating nutrients and hormones
continue to be sensed in the absorptive and
post-absorptive states. Termination lasts as long
as satiety signals prevail over other competing
external factors that will initiate the next cycle
of food intake (Watts, 2000; Berthoud, 2002).
Accordingly, the biological determinants of
ingestive behavior can be categorized into sensory, energy homeostatic and reward aspects
of food intake. It is therefore important to
measure ingestive behavior phenotypes using
different tools and study designs that can complement traditional dietary intake methods. In
addition to measuring total energy intake and
1. FROM BRAIN TO BEHAVIOR
12.2 SENSORY DETERMINANTS OF FOOD INTAKE
the breakdown of macronutrients consumed
using dietary records, recalls or questionnaires, food-intake behavior inventory questionnaires and food-preference checklists can
be used to measure behaviors and food preferences. Furthermore, acute studies that measure
preference and those that measure food intake
initiation versus termination are important for
determining genetic factors involved in influencing ingestive behaviors on the level of a single meal or snack. A number of studies, which
used different methodological approaches, have
identified genetic variants involved in sensory
perception, energy homeostasis and reward circuits as they affect ingestive behaviors. These
studies will be reviewed below in order to offer
examples of strategies to be used to help identify new candidate genes affecting food intake.
12.2 SENSORY DETERMINANTS
OF FOOD INTAKE
Sensory factors including sense of smell and
taste lie at the interface between the biological
and environmental determinants of food intake.
They can therefore play an important role in
initiating food intake as well as influencing the
reward circuits involved in learning and memory which can drive the procurement phase of
ingestive behavior (Berthoud, 2002). It has been
determined that sensory receptor genes, in addition to immune response genes, are significantly
over-represented among the genes shown to
vary in copy numbers, with olfactory receptor genes particularly variable in copy numbers among the sensory genes (Nozawa et al.,
2007). The mean difference in copy numbers of
olfactory receptor genes between two individuals was 10.9, with the most extreme difference
between two individuals being of 49 more genes
(Nozawa et al., 2007). Thus, differences in copy
numbers among olfactory receptor genes may
explain individual variations in perception of
151
smell. In addition to CNVs, genetic variations
resulting from SNPs may also contribute to interindividual differences in olfaction and, therefore, dietary behaviors. A recent study examined
a common variation in two positions in the
human odorant receptor gene OR7D4, resulting in amino acid changes, R88W and T133M,
which are in complete linkage disequilibrium
(Keller et al., 2007). In comparison to individuals
homozygous for the major allele (RT/RT), those
heterozygous for the variants (RT/WM) rated
androstenone, an odorous steroid compound,
to be less intense (Keller et al., 2007). In addition,
when subjects were exposed to four different
odorants, the heterozygous individuals were 42
percent less likely to describe androstenone as
“sickening” from a list of 146 semantic descriptors in comparison to the RT/RT homozygous
individuals. Individuals who were heterozygous
for the variant were also more likely to describe
the smell of vanillin as “honey”, “sweet” and
“vanilla” from the list of 146 semantic descriptors (Keller et al., 2007). Another olfactory receptor, OR13G1, has been associated with risk
of myocardial infarction (MI), and has been
hypothesized to predispose individuals to MI by
affecting food preferences (Shiffman et al., 2005).
Future studies such as the one by Keller and colleagues will be important to continue to identify the odorants that stimulate each of the 437
putative human odorant receptors (Zhang et al.,
2007). Subsequent studies can then examine how
variants in olfactory receptor genes involved in
detecting palatable food-related aromas affect
food preferences and intake.
Early studies involving humans have documented wide individual differences in taste
perception between individuals (Blakeslee and
Salmon, 1935). Taste perception, which is influenced by both genes and environment, may be
the most important determinant shaping food
preferences (Glanz et al., 1998; El-Sohemy et al.,
2007; Garcia-Bailo et al., 2009). Facial responses
from newborns in response to sweet and
unpleasantly salty solutions (Berridge, 1996)
1. FROM BRAIN TO BEHAVIOR
152
12. GENETIC DETERMINANTS OF INGESTIVE BEHAVIOR
suggest an innate genetic contribution to taste
perception. As individuals age, other competing
factors such as diminished taste acuity, and environmental factors such as socio-cultural influences, may contribute to overall taste perception
and, therefore, food preferences (Mennella et al.,
2005; Navarro-Allende et al., 2008). Thus, examining differences in taste perception may be
important in understanding obesity risk in children and young adults.
Over the past decade there have been considerable advances in identifying putative taste
receptors involved in detecting the five traditional taste modalities; bitter, sweet, sour, salty
and umami (Garcia-Bailo et al., 2009). In addition, evidence from CD36 knockout mice suggests a sixth modality for “fat taste” (Laugerette
et al., 2005). Examining genetic variation in taste
receptors may identify individuals predisposed
to obesity because of differences in food preferences. Thus far in humans, genetic variations
in bitter taste have been the most extensively
studied. Bitter taste receptors are encoded by
the family of T2R taste receptors, consisting
of approximately 25 members (Behrens and
Meyerhof, 2006). The TAS2R38 gene is characterized by three SNPs (A49P, V262A, and
I296V) which make up two common haplotypes, AVI and PAV, and TAS2R38 detects two
bitter compounds called phenylthiocarbamide
(PTC) and 6-n-propylthiouracil (PROP) (Kim
et al., 2003; Wooding et al., 2004; Bufe et al., 2005;
Drayna, 2005; El-Sohemy et al., 2007; Mennella
et al., 2005). Carriers of the PAV haplotype have
been classified as “tasters”, since they have a
higher sensitivity to PTC or PROP in comparison to individuals homozygous for AVI (Kim
et al., 2003; Wooding et al., 2004; Bufe et al., 2005;
Drayna, 2005; El-Sohemy et al., 2007; Mennella
et al., 2005). A study involving children aged
5–10 years reported that carriers of the PAV
haplotype preferred sweeter-tasting foods as
measured both by a forced-choice, paired comparison of sucrose solutions and by asking
participants about their favorite cereals and
beverages (Mennella et al., 2005). However,
there were no associations found between genotype and sweet food preferences among the
mothers of the children (Mennella et al., 2005).
The discrepancy between the parents and offspring may be due to diminishing taste acuity
with increasing age, as well as other competing
cultural or environmental influences overriding
taste preferences (Mennella et al., 2005). Like the
olfactory receptor gene OR13G1, TAS2R50 was
also associated with risk of MI, which was also
hypothesized to be due to differences in dietary
preferences (Shiffman et al., 2005). Future studies investigating genetic variations in the T1R
family encoding sweet and umami taste receptors and CD36 may help identify individuals at
risk of developing poor dietary food preferences
and a predisposition to obesity.
12.3 ENERGY HOMEOSTASIS
PATHWAYS AND FOOD INTAKE
Multiple hormonal, metabolic and neural
inputs converge in the hypothalamus and brainstem to regulate the energy homeostasis pathways which control several phases of ingestive
behavior (Schwartz et al., 2000; Berthoud, 2002;
Morton et al., 2006). Both short-term and longterm signals are involved in orchestrating
ingestive behaviors, with gastrointestinal hormones largely regulating food intake acutely,
whereas insulin and leptin, which reflect adipose tissue stores, provide long-term regulation (Woods et al., 1998). Several gastrointestinal
hormones have been identified over the past
40 years (Chaudhri et al., 2006). Polymorphisms
in the genes that encode each of these are good
candidates that may impact food intake and that
have yet to be examined. Recently, the European
Prospective Investigation into Cancer and
Nutrition (EPIC) study identified four genetic
variants in cholecystokinin (CCK), a gastrointestinal hormone signaling satiety, to be associated
1. FROM BRAIN TO BEHAVIOR
12.3 ENERGY HOMEOSTASIS PATHWAYS AND FOOD INTAKE
with extreme meal size in a population of Dutch
women that compared obese women, classified
as extreme meal-size consumers, to randomly
selected controls (de Krom et al., 2007). Leptin,
which is also involved in reducing food intake,
was also examined in this cohort of women.
Variants in the leptin and leptin receptor genes
were associated with extreme snacking (de
Krom et al., 2007). Previous studies have been
mainly successful at identifying rare variations in genes involved in the leptin pathway,
associated with monogenic forms of obesity
(Montague et al., 1997; Ravussin and Bouchard,
2000; Loos and Bouchard, 2003), while those
examining polygenic forms of obesity as an outcome variable have been equivocal (Paracchini
et al., 2005). Although a more recent study that
also examined the association between leptin,
leptin receptor genes, and extreme snacking and
meal size reported no association, the definition
used to classify individuals as extreme consumers was not clear (Bienertova-Vasku et al., 2008).
The use of an extreme discordant phenotype
approach in the EPIC study, which defined
extreme meal size and snacking by examining the top fifth percentile of subjects displaying each phenotype, was effective as it had the
power to detect common genetic variations
underlying these extreme phenotypes (Nebert,
2000; de Krom et al., 2007). Thus, the extreme
discordant phenotype approach offers a preliminary step in identifying genetic variants that
may contribute to risk of a more complex phenotype such as polygenic obesity (Nebert, 2000;
de Krom et al., 2007).
Examining genetic variations in transcription
factors regulating the expression of the hormones
involved in regulating food intake are also
important candidates to consider. A genetic variant resulting in a C to T substitution (C1431T)
in the peroxisome proliferator-activated receptor-γ (PPAR-γ), which is involved in regulating
leptin gene transcription, was associated with
poor caloric compensation at lunch, 90 minutes after exposure to three calorically different
153
mid-morning snacks, among children aged
between 4 and 10 years who were carriers of the
T allele (Cecil et al., 2007). Thus, using a pre-load
study design approach offers a valuable way to
quantify food intake regulation phenotypes in
young children, which may be a more effective
way of measuring food intake compared to other
methods such as dietary recall or questionnaires
in children. Similar to leptin, insulin signaling in
the brain decreases food intake (Schwartz et al.,
2000). Among Dutch women from the EPIC
study which examined a variation in the TUB
gene, a downstream transcription factor and/or
an adaptor molecule involved in insulin signaling in the hypothalamus was associated with
a lower consumption of energy from fat and a
higher consumption of energy from mono- and
disaccharides (van Vliet-Ostaptchouk et al.,
2008). Glycemic load was also higher among
individuals with the minor allele at the same
locus as well as at a second locus, which are both
in non-coding regions of the gene (van VlietOstaptchouk et al., 2008).
In addition to hormonal signaling in the
brain, nutrient sensing pathways may play a
role in regulating food intake (Cota et al., 2007).
A genetic variation in the glucose transporter
type 2 (GLUT2) was found to be associated with
a higher consumption of sugars in a cohort of
obese individuals with early type 2 diabetes, as
well as in a lean, diabetes-free cohort of young
adults (Eny et al., 2008). This observation was
reproduced both within the first cohort and
between the two cohorts using two sets of 3-day
food records completed 2 weeks apart in the
first cohort, and a 1-month food frequency questionnaire (FFQ) in the second cohort. Since no
other macronutrients were consumed in higher
amounts, results from this study suggested that
GLUT2 is involved in glucose sensing to affect
habitual sugar consumption (Eny et al., 2008).
In another study, the 1291C G polymorphism in the α2a-adrenoreceptor (ADRA2A)
gene, which is known to affect fasting glucose
levels and insulin secretion, was associated with
1. FROM BRAIN TO BEHAVIOR
154
12. GENETIC DETERMINANTS OF INGESTIVE BEHAVIOR
consumption of sweet food and sour milk products among children in grades 3 and 9 in Estonia
(Maestu et al., 2007). Furthermore, children
homozygous for the G allele had lower fasting glucose concentrations (Maestu et al., 2007).
Therefore, the higher consumption of sweet
food products observed among those with the
GG genotype may be in response to sensing low
fasting blood glucose. There has been growing
evidence from animal models that fatty-acid
sensing also plays a role in energy homeostasis
(He et al., 2006). The carnitine palmitoyltransferase I (CPT1) gene is an interesting candidate
to determine whether differences in dietary fat
consumption exist, since CPT1 is the rate limiting enzyme for the entry of long-chain fatty
acyl-CoAs in the mitochondria (He et al., 2006).
Genetic variants of CPT1 have previously been
implicated in modifying indices of obesity in
response to dietary fat consumption (Robitaille
et al., 2007). However, it is not known whether
variants of this gene affect fat intake.
Specialized neurons in the brain respond to
the multiple inputs from hormones, nutrients
and nerves. This results in either increased or
decreased expression of neuropeptides affecting
food intake (Schwartz et al., 2000). In animals,
neuropeptide Y (NPY) acts as a potent inducer
of food intake (Stanley and Leibowitz, 1985),
but the effect in humans is unclear. A genetic
variant in prepro-NPY was not associated with
increased food intake in children aged between
1 and 9 years, as measured by parents and daycare staff recording consumption for 4 days
twice per year, yet was associated with increased
fasting triglycerides in boys aged 5, 7 and 9
years (Karvonen et al., 2006). Thus, the Leu7Pro
variant examined (Karvonen et al., 2006) may
represent a functional locus to be examined in
future studies measuring actual food intake
with a pre-load study approach in children as
described previously (Cecil et al., 2007).
Like NPY, the agouti-related protein (AGRP),
which is another orexigenic neuropeptide, carries
two ethnic-specific polymorphisms, one found
only in Caucasians (Ala67Thr) and one found
only among African-Americans (38C T) (Loos
et al., 2005). The Ala67Thr polymorphism was
associated with consuming a diet that was low
in fat and high in carbohydrates as a percentage of total energy intake in Caucasians (Loos
et al., 2005). Among African-Americans, the
38C T polymorphism was observed to be
associated with a lower percent of energy from
protein consumed (Loos et al., 2005). The observation for differences in percentage of energy
from macronutrients suggests that AGRP affects
macronutrient selection preference rather than
absolute intake, which is hypothesized to be
mediated by the interaction of AGRP with the
opioid system (Loos et al., 2005). It is possible
that the discrepancy in macronutrient selection
observed between the ethnic-specific genotypes
may be due to other cultural or genetic differences in taste preference between the two ethnicities (Loos et al., 2005).
Thus far, there has been extensive research
investigating the role of rare variants of the anorexigenic neuropeptide pro-opiomelanocortin
(POMC) and its related melanocortin receptor genes and risk of severe obesity (Loos and
Bouchard, 2003; Adan et al., 2006; Oswal and
Yeo, 2007). Using a genome-wide linkage mapping approach was useful in detecting a gene
locus associated with dietary intake using an
FFQ among 816 participants of the San Antonio
Heart Study (Cai et al., 2004). The locus identified was found on chromosome 2p22, which
harbors the POMC gene, and was associated
with total calories, protein, total fat, saturated
fat, polyunsaturated fat and monounsaturated
fat intake, with saturated fat having the highest
linkage score (Cai et al., 2004). Yet, upon genotyping for two variants in the POMC gene, no
association was reported with saturated fat
intake, which was the only macronutrient examined in this follow-up analysis (Cai et al., 2004).
Similarly, a genome-wide linkage study involving Hispanic children aged between 4 and 19
years identified a marker on chromosome 18 to
1. FROM BRAIN TO BEHAVIOR
12.4 REWARD CIRCUITS AND FOOD INTAKE
be associated with time participating in physical
activity, carbohydrate intake, and percentage of
energy from carbohydrate intake, as measured
using two multiple-pass 24-hour recalls administered by dietitians and assisted by mothers
when children were under 7 (Cai et al., 2006).
The region identified on chromosome 18 harbors the melanocortin 4 receptor (MC4R) gene
as well as another positional candidate gene,
gastrin-releasing peptide, which is released
by the gastrointestinal tract and also inhibits
food intake by signaling in the brain (Cai et al.,
2006). Consistent with Cai et al. (2006), a recent
study examining the most common variant in
the MC4R gene (V103I) reported that individuals carrying the 103I allele were more likely to
be high carbohydrate consumers (P 0.06) as
measured using a short qualitative FFQ among
7888 adults (Heid et al., 2008). This higher carbohydrate consumption was examined as a potential factor in mediating the protective effect of
this polymorphism on features of the metabolic
syndrome, because the same variant was associated with higher HDL-C, and lower waist circumference and HbA1c (Heid et al., 2008).
12.4 REWARD CIRCUITS
AND FOOD INTAKE
Given that the drive to eat has been described
to be one of the most powerful urges of human
behavior (Del Parigi et al., 2003), coupled with the
ubiquitous exposure to palatable foods, neural
circuits involved in food reward and addiction
have been proposed to possibly override energy
homeostatic controls of food intake (ErlansonAlbertsson, 2005; Palmiter, 2007). Dopamine,
serotonin and opiates have all been implicated in
mediating the rewarding effect of palatable food
(Erlanson-Albertsson, 2005). A Taq1 A1 variation
which resides in a gene downstream from the 3
end of the dopamine D2 receptor (DRD2*A1) has
been widely examined as a marker for genetic
155
variation in the DRD2 gene as it relates to addictive behaviors (Neville et al., 2004), including
studies investigating food reward. The DRD2*A1
was associated with food reinforcement among
smokers undergoing smoking cessation treated
with placebo versus those treated with bupropion, a dopamine reuptake inhibitor (Lerman
et al., 2004). Food reinforcement was assessed
by having each subject choose either 100 g of
their favorite snack food over a $1 alternative
as a reward for completing a task, consisting
of pushing a button 20 times, with increasing
difficulty as the subject progressed (Lerman
et al., 2004). A similar association was observed
between DRD2 genotype and food reinforcement
behavior among non-smoking obese subjects
(Epstein et al., 2007). Furthermore, acute food
intake was found to be higher among carriers of
the DRD2*A1 allele who were classified as being
high in food reinforcement as measured over a
taste-test panel period in comparison to those
without the DRD2*A1 allele or those low in food
reinforcement with or without the DRD2*A1
allele (Epstein et al., 2004, 2007).
The serotonergic pathway has also been
examined in gene association studies relating to ingestive behaviors, and is thought to
act as a satiety signal involved in food reward
(Erlanson-Albertsson, 2005). Among overweight
individuals a 1438G A polymorphism in
the serotonin receptor 5-HT2A (5-hydroxytryptamine) was associated with lower energy
intake (Aubert et al., 2000). The same variant
was associated with a lower intake of energy,
total fat, monounsaturated fat, saturated fat
and percent of energy from fat among children
and adolescents aged 10–20 years (Herbeth
et al., 2005). Diet was assessed using 3-day food
records, which were checked and completed by
a dietitian using photographs with three different portion sizes (Herbeth et al., 2005). Finally,
a third study involving elderly individuals
reported that the T102C polymorphism in the
5-HT2A receptor gene was associated with a
higher consumption of all essential amino acids
1. FROM BRAIN TO BEHAVIOR
156
12. GENETIC DETERMINANTS OF INGESTIVE BEHAVIOR
and beef among individuals with the TT genotype (Prado-Lima et al., 2006). Since the essential amino acid tryptophan acts as a precursor
for serotonin production (Young, 1996), this
study suggests that TT individuals may have
lower receptor activity and, therefore, signal
for greater serotonin production, increasing
the demand for tryptophan in the body. These
observations correspond with the studies associated with lower energy intake among individuals homozygous for the minor allele for
the 1438G A variant (Aubert et al., 2000;
Herbeth et al., 2005), which is in linkage with
the T102C variant (Prado-Lima et al., 2006).
12.5 CONCLUSIONS
Using the candidate gene approach has been
useful for identifying genes involved in ingestive behaviors. Genome-wide linkage scans
among family pedigrees offer another useful
strategy, as shown by studies that have identified POMC, melanocortin 4 receptor and gastrinreleasing peptide genes as being associated
with dietary intake (Cai et al., 2004, 2006). This
approach has the ability to identify new genetic
loci involved in food intake phenotypes such as
eating behaviors, or energy and nutrient intake
or preference (Steinle et al., 2002; Bouchard et al.,
2004; Collaku et al., 2004; Keskitalo et al., 2007).
Recently, frequent use of sweet foods has been
mapped to chromosome 16, which is currently
not known to harbor any genes related to sugar
consumption, yet contains three genes that
have an unidentified function (Keskitalo et al.,
2007). Genome-wide linkage studies as well as
genome-wide association studies (GWAS) are
beneficial in that no prior information regarding gene function is required, and therefore
they can be used in conjunction with the candidate gene approach to identify new gene targets
(Comuzzie, 2004; Kowalski, 2004; Hirschhorn
and Daly, 2005; Martinez-Hernandez et al., 2007).
For example, a variant in the apolipoprotein
A-II (APOA2) gene, an HDL-related protein,
was reported to be associated with total energy,
total fat and protein intake (Corella et al., 2007).
Correspondingly, an earlier genome-wide linkage study identified a strong linkage for dietary
energy and fat intake on chromosome 1p21.2,
which places APOA2 as a potential candidate
gene, although it was not mentioned in that
study (Collaku et al., 2004). The fat mass and
obesity-associated (FTO) gene was also first discovered by GWAS (Frayling et al., 2007), and was
subsequently associated with increased energy
intake among children in two populations (Cecil
et al., 2008; Timpson et al., 2008). Future research
should consider utilizing the GWAS approach,
because, unlike genome-wide linkage studies,
families are not required and GWAS technology
offers finer mapping in order to identify potential causal genes within the locus identified
(Hirschhorn and Daly, 2005).
Genetic variants not only shift the preference
for tastes of food that are calorie-rich, but also
lead to increased consumption. Therefore, in
order to capture a comprehensive perspective of
ingestive behavior, future studies could examine
interactions between genes involved in sensory
aspects of food intake, energy homeostatic pathways and reward circuits driving food intake,
since each of these pathways might not function
in isolation. Indeed, the melanocortin 4 receptor, which is involved in energy homeostasis, is
also expressed in regions of the brain involved
in food reward (Huang et al., 2003). Discovering
genetic variants involved in ingestive behaviors
will ultimately help clinicians identify individuals predisposed to certain food intake phenotypes that may increase the risk of obesity or
other food intake related behaviors (Figure
12.1). Several studies have examined genetic
variants that modify an individual’s response
to a behavioral intervention of diet and/or exercise (Fogelholm et al., 1998; Shiwaku et al., 2003;
Masuo et al., 2005). Thus, once a genetic variant has been established to be associated with
1. FROM BRAIN TO BEHAVIOR
157
REFERENCES
Sensory
perception
Energy
homeostasis
Food
reward circutis
Ingestive
behaviors
Energy
expenditu
re
Food
intake
Obesity risk
FIGURE 12.1
Genetic variation in sensory perception, energy homeostasis and food reward circuit pathways may
influence ingestive behaviors, favoring increased food intake. An imbalance between energy expenditure and energy intake
results in weight gain and risk of obesity.
a particular ingestive behavior, research efforts
should aim to determine what foods or dietary
pattern help the individual control their predisposed ingestive behaviors. Ultimately, identifying genetic variants involved in food intake has
the potential to assist clinicians in understanding
an individual’s behavior from a biological perspective, and help plan an appropriate strategy
to prevent or treat obesity in the future as we
move towards a more personalized medicine.
ACKNOWLEDGMENTS
This work was supported by the Advanced
Foods and Materials Network (AFMNet). Karen
Eny is a recipient of a Natural Sciences and
Engineering Research Council of Canada Julie
Payette Research Scholarship and a Canadian
Institutes of Health Research Training grant.
Ahmed El-Sohemy holds a Canada Research
Chair in Nutrigenomics.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
13
Development of Human Learned Flavor
Likes and Dislikes
Martin R. Yeomans
School of Psychology, University of Sussex, Brighton, UK
O U T L I N E
13.1 Introduction
161
13.2 Understanding Flavor Perception
162
13.3 Why Innate Flavor-liking is Rare
163
13.4 Flavor-preference Learning
13.4.1 Mere Exposure and the
Importance of Familiarity
13.4.2 Flavor–Consequence Learning
13.4.3 Flavor–Flavor Models of
Evaluative Conditioning
13.4.4 Social Acquisition of
Flavor-liking
164
164
164
166
168
13.6 Liking and Intake: The Role of
Palatability in Overeating
169
13.7 Acquired Liking as a Driver of
Overeating
170
13.8 Individual Differences in
Learning
171
13.9 Summary
173
167
13.1 INTRODUCTION
The human diet is extremely varied, and
humans have the ability to recognize valuable
sources of nutrition while avoiding items which
are poisonous. Critical to this ability is an appetite
control system that facilitates the development of
liking for the flavor of foods which provide nutritional or other benefits, ambivalence to items with
Obesity Prevention: The Role of Brain and Society on Individual Behavior
13.5 Different Learning Mechanisms
Interact to Enhance Flavor-liking
little or no benefit, and dislike of items which are
harmful. These acquired likes and dislikes guide
food choice, and in part determine the amount
we consume. As will be detailed in this chapter,
since it is clear that we rapidly acquire a liking
for energy-dense foods, the system which underlies flavor preference development may also contribute to overconsumption and consequent risk
of obesity. This chapter therefore explores current
161
© 2010,
2010 Elsevier Inc.
162
13. ACQUIRED FLAVOR LIKES AND DISLIKES
theories of how we acquire flavor likes and dislikes, examines the impact of these changes on
eating behavior, and considers how individual
differences in the ability to acquire such preferences may be a risk factor for development of
obesity.
13.2 UNDERSTANDING FLAVOR
PERCEPTION
In order to understand how we acquire liking
for flavors, it is first necessary to outline current
theories of how our experience of flavor arises.
Unlike primary senses such as taste, smell, hearing and vision, flavor is a higher-level construct
arising from the integration of multiple sensory inputs relating to the experience of food
or drink in the mouth. There is now increasing
understanding of how this integration occurs
both at the phenomenological and the neural
level (for recent reviews, see Small and Prescott,
2005; Rolls, 2006; Auvray and Spence, 2008).
In brief, primary sensory characteristics are
sensed by peripheral sensors, for example by
taste receptors on the tongue and buccal cavity which are tuned to detect five taste qualities
(Chandrashekar et al., 2006), and food-related
odors stimuli by the olfactory bulb. These primary sensory qualities activate distinct areas of
the cortex (primary cortex for taste, smell, etc.).
These separate sensory qualities are then integrated in different areas of the cortex, which can
be thought of as secondary taste/smell cortex,
including the orbitofrontal cortex. Critical to the
current discussion is the observation that the
palatability of this multi-sensory flavor percept
appears also to be encoded in areas of the orbitofrontal cortex (O’Doherty et al., 2001). However,
we are far from a full understanding of the neural processes underlying flavor perception: for
example, how the brain knows that these separate sensory inputs all relate to the same external food or drink stimulus remains unclear
(Small, 2008), and we do not know how the
types of flavor learning described in the main
body of this discussion modify these neural
responses. However, the general acceptance that
flavor involves complex multi-sensory integration in the brain is crucial to understanding
how flavor-liking may be acquired.
An important issue is how the process of
integration of sensory qualities into our perception of flavor and the processes underlying the
hedonic experience of that flavor are related.
In theory, three different possible relationships
may exist. The first is that the processing which
determines flavor perception also determines
flavor-liking. However, although there have
been many studies published which appear to
assume that this is the case, there is a wealth
of evidence that these processes are separate.
For example, it is possible to pharmacologically modify liking for food flavors by blockade
of opioid receptors in the brain, yet this blockade has no impact on either the ability to sense
the flavor or indeed to alter flavor quality (see
Yeomans and Gray, 2002). More importantly, in
relation to this discussion, there is a wealth of
evidence, reviewed later, that experience can
modify flavor-liking without altering flavor
perception. How, then, do the separate processes underlying liking and flavor relate to
each other? The second possible relationship
would be one where these represent neuronally
distinct processes, and in non-human animals
this is supported by evidence of distinct sensory
and hedonic pathways in the brain (for review,
see Sewards, 2004). However, in humans this
seems a less plausible model, as the areas in
the brain that appear critical to flavor-liking are
downstream of those areas involved in flavor
perception (see Rolls, 2006). This suggests that
the most plausible model is one where the brain
integrates signals relating to the sensory quality of an ingested food or drink and determines
whether this is a liked or disliked experience –
a decision that has major effects on behavior
through rapid rejection of disliked items but
1. FROM BRAIN TO BEHAVIOR
13.3 WHY INNATE FLAVOR-LIKING IS RARE
acceptance and enjoyment in ingesting liked
items. The question that then follows is: why
are some flavors liked and others disliked?
13.3 WHY INNATE FLAVOR-LIKING
IS RARE
If the human diet were restricted to a limited set of foods, then there would be scope
for the evolution of clearly defined genetically pre-determined flavor likes and dislikes.
However, a feature of the adaptive success of
humans is our ability to exploit an extraordinarily wide range of possible sources of nutrition, with minimal evidence for genetic flavor
preferences. Indeed, genetically pre-determined
flavor preferences are extremely rare across all
species. The example of a genetic flavor preference in some populations of garter snakes
for their most common prey, the banana slug
(Arnold, 1977), illustrates why such preferences
are rare. Coastal garter snakes eat banana slugs,
but mountain garter snakes avoid them. Newly
hatched snakes from coastal parents showed
clear appetitive responses when presented with
banana-slug odor, whereas naive offspring of
mountain snakes showed little response. The
rarity of this genetic preference can be attributed to two features of garter-snake biology: a
limited diet, and a distinct flavor component of
their primary prey. In contrast, the human diet
is highly varied, and although many foods have
distinctive flavors, the complexity and variety of
both the sensory stimuli and source of nutrition
offers little opportunity for innate flavor-liking
to have evolved in the way that it has in garter
snakes.
A real difficulty in assessing innate components to human flavor likes and dislikes is the
problem of providing unequivocal evidence that
flavor-liking is unlearned, particularly now that
there is clear evidence that flavor exposure in
utero can impact on later food-liking (Mennella
163
et al., 2004). The most widely cited approach has
been to examine responses of newborn babies to
elements of widely liked and disliked flavors,
particularly primary tastants. For example, the
application of sweet tastants to the tongues of
newborn babies (Desor et al., 1973; Steiner, 1979;
Berridge, 2000) found clear acceptance of sweet
tastes, with positive facial responses consistent
with flavor-liking, whereas the responses to bitter tastants were characteristic of a strong aversive response. A counter-argument might be that
such sweet liking was acquired in utero, although
this is countered by findings of acceptance of
sweet tastes by premature babies (Maone et al.,
1990). Liking for sweet tastes has been explained
in terms of the reliable relationship in nature
between a sweet taste and safe, nutritious foods
rich in sugars (Hladik et al., 2002). Mammals
have specific sweet-taste receptors (Matsunami
et al., 2000), and the molecular structure of the
vertebrate sweet-taste receptor has been conserved across species from fishes to humans,
with only rare examples of sweet-insensitive
species such as chickens (Shi and Zhang, 2006).
Further evidence that sweet-taste preferences are
genetically predetermined comes from breeding
studies that have successfully reared separate
lines of sweet-preferring and sweet-disliking
rats (Bachmanov et al., 2002).
The other reliable evidence for an innate component of hedonic evaluation of flavors is a dislike for bitter tastes, which has been interpreted
as an innate avoidance of items that have the
potential to be poisonous (Fischer et al., 2005;
Behrens and Meyerhof, 2006), since most poisons have bitter tastes. There have been striking
advancements in our understanding of bittertaste perception, with the identification of 25
different bitter-taste receptor genes to date in
humans (Behrens and Meyerhof, 2006), which is
consistent with the idea that bitter-taste aversion
relates to avoidance of poisonous molecules.
However, these initial aversive responses can
be reversed if ingestion of bitter-tasting items
fails to lead to illness, or leads to a positive
1. FROM BRAIN TO BEHAVIOR
164
13. ACQUIRED FLAVOR LIKES AND DISLIKES
experience such as the effects of alcohol or caffeine, as discussed later.
It is striking how little genetics predisposes
humans to like or dislike food flavors, but it
appears that evolution has favored the development of complex learning systems which
allow us to assess potential foods for the nutritional content, and rapidly to acquire liking for
potential foods which deliver useful nutrients
and a profound dislike for flavors which lead
to illness. The next set of questions thus relates
to the nature of the learning processes that are
involved in these processes.
13.4 FLAVOR-PREFERENCE
LEARNING
Among the myriad potential explanations for
how flavor likes and dislikes may be acquired,
four theoretical approaches to our understanding of flavor learning have been widely supported and are the focus of this review.
13.4.1 Mere exposure and the
importance of familiarity
One of the earliest learning concepts to be
discussed in relation to acquired flavor-liking
in humans was mere exposure (Zajonc, 1968),
which essentially argues that repeated exposure to any stimulus in any modality results in
increased liking through familiarity. Although
specific studies examining how mere exposure
alone may lead to flavor-liking have been limited in number and scope (Pliner, 1982; Crandall,
1984; Stevenson and Yeomans, 1995), the mere
exposure concept remains a useful description
of familiarity effects. Attempts to explain how
mere exposure works make reference to reduced
neophobia, or other explanations such as opponent-process affective responses (Solomon and
Corbit, 1974). However, although overcoming
neophobia may be an important element in trying to direct food preferences – for example, in
children faced with unfamiliar flavors (Birch
and Marlin, 1982; Pliner, 1982) – this concept
does not offer any explanation for why the flavors of certain classes of foods, most notably
those high in fat and sugar, are usually the most
liked items in the human diet.
13.4.2 Flavor–consequence learning
A seminal discovery in our understanding of
how humans and other animals may develop
flavor likes and dislikes was the observation
that pairing of a novel flavor with subsequent
gastric illness leads to a profound and enduring
aversive reaction to the flavor. This phenomenon
was initially labeled conditioned taste aversion
(CTA), but with increasing evidence that this
learning could be supported by non-gustatory
flavor components (Capaldi et al., 2004) it is
now better characterized as conditioned flavor
aversion (CFA). Once CFA had been discovered, researchers speculated that just as a flavor
which predicted illness would become an aversive (disliked) stimulus, so a flavor that reliably
predicted a safe source of nutrition should lead
to conditioned flavor-liking (Rozin and Kalat,
1971; Booth, 1985), with the general learning
process underlying these types of changes classified as flavor-consequence learning (FCL).
The ideas behind FCL are heavily influenced
by broader concepts in associative learning,
with the primary association being between the
perceived flavor of the ingested food or drink
(acting as the Pavlovian conditioned stimulus, CS) and the post-ingestive effects of the
food or drink (the Pavlovian unconditioned
stimulus, US). How flavor and consequence
come to be associated in FCL is summarized in
Figure 13.1a. Critical to this chapter is consideration of whether such learning could explain our
avidity for energy-dense food, since it is wellknown that average ratings of liking for flavors
1. FROM BRAIN TO BEHAVIOR
165
13.4 FLAVOR-PREFERENCE LEARNING
(a) Flavor-consequence learning
Conditioned stimulus
(CS)
Flavor
Repeat
Pairing
Unconditioned stimulus
(US)
Post-ingestive effect of
food/drink
(b) Flavor-flavor learning
Conditioned stimulus
(CS)
Novel flavor
component
Repeat
pairing
Unconditioned stimulus
(US)
Liked/disliked flavor
component
Acquired
response
Normal
response
Acquired
response
Normal
Response
Conditioned Response
(CR)
Altered flavor liking
Unconditioned Response (UR)
Hedonically-significant
consequence (e.g. illness,
satiation, drug effects)
Conditioned Response
(CR)
Altered flavor liking
Unconditioned Response (UR)
Positive or negative
affective reaction
FIGURE 13.1 The associative substructure underlying (a) flavor–consequence learning and (b) flavor–flavor evaluative
conditioning.
Source: Adapted from Yeomans (2006), with permission.
correlates positively with energy density (Holt
et al., 1995; Drewnowski, 1998; Yeomans et al.,
2004a), consistent with predictions from FCL.
Once research had established the robustness of
CFA, studies explored whether consumption of
energy-containing foods could come to enhance
flavor-liking in humans and preference in animals (Sclafani, 1999; for reviews, see Capaldi,
1992; Gibson and Brunstrom, 2007). There is
now a large body of evidence that exposure of
animals and humans to novel flavors paired
with consumption, or gastric delivery, of energy
in the form of fat, carbohydrate or protein
leads to enhanced preference for the nutrientassociated flavors. In animals, the evidence
for these types of associations is particularly
strong, with clear evidence of acquired flavor
preferences for flavor CS paired with sucrose
(Fedorchak and Bolles, 1987; Capaldi et al.,
1994; Harris et al., 2000; Sclafani, 2002), glucose
(Myers and Sclafani, 2001a), starch (Elizalde
and Sclafani, 1988; Sclafani and Nissenbaum,
1988; Ramirez, 1994), fats (Lucas and Sclafani,
1989), protein (Delamater et al., 2006) and alcohol (Ackroff and Sclafani, 2001, 2002, 2003). The
most convincing studies, based on the extensive
work of Sclafani’s group, pairs consumption of
one of two non-nutritive flavored drinks with
intra-gastric nutrient infusion, and the second
infusion with water, resulting in a profound
and enduring preference for the nutrient-paired
flavor (see, for example, Elizalde and Sclafani,
1990; Azzara and Sclafani, 1998; Myers and
Sclafani, 2001a, 2001b; Sclafani, 2002; Yiin et al.,
2005; Ackroff and Sclafani, 2006).
In humans, an increased interest in the importance of flavor-liking as a cause of overeating
has resulted in many studies that have shown
clear increases in liking for novel flavors which
have been associated with ingestion of nutrients in humans (Appleton et al., 2006; Brunstrom
and Mitchell, 2007; Mobini et al., 2007; Yeomans
et al., 2005a, 2008a, 2009a), adding to a small but
important older literature (Booth et al., 1982;
Birch et al., 1990; Kern et al., 1993). The increased
preference by children for the flavor of yoghurt
that has been consumed in a high-fat version relative to a second flavor always experienced as a
low-fat, low-energy version illustrates the effects
of FCL with nutrient reinforcers (Figure 13.2).
Note that not only do children come to prefer the
high-fat flavor in spite of their ignorance of the
difference in nutritional content, but also their
expression of this liking is stronger when hungry than when satiated, an observation consistent with expression of acquired liking induced
through FCL with sucrose as reinforcer in adults
(Mobini et al., 2007). As with the animal studies,
1. FROM BRAIN TO BEHAVIOR
166
13. ACQUIRED FLAVOR LIKES AND DISLIKES
Preference ranking more
preferred >
5
4
3
2
6 weeks
conditioning
1
Hungry
Pre-treatment
Full
Hungry
Post-treatment
Hungry
Delayed
FIGURE 13.2 Changes in preference for the flavors of
yoghurts consumed in low-fat (striped column) or high-fat
(solid column) form by children.
Source: Adapted from Kern et al. (1993), with permission.
acquired liking is not restricted to one source of
nutrients, with studies to date showing changes
both with carbohydrate and fat as energy
sources. These data fit well with the broad observation that energy-dense foods (that is, foods
rich in the major macronutrients) are generally
the most liked (Stubbs and Whybrow, 2004).
Further evidence of the broad significance of
FCL as an explanation for acquired flavor-liking
can be seen in the strength of acquired liking
for the flavor of drinks that contain substances
with psychoactive consequences, such as alcohol and caffeine – preferences that counteract
the normal aversive reaction to the bitter taste
of caffeine and alcohol. In humans, a wealth
of research has shown clear and enduring
increases in liking for the flavor of drinks that
have been paired with ingestion of caffeine (see,
for example, Rogers et al., 1995; Yeomans et al.,
1998, 2005b; Tinley et al., 2003; Dack and Reed,
2008), showing that the effects of FCL extend
beyond an ability to acquire liking for the flavors of nutrient-dense foods.
13.4.3 Flavor–flavor models of
evaluative conditioning
Evaluative conditioning (EC) involves transfer of affective value from a known liked or
disliked stimulus to a second, novel stimulus
(Field and Davey, 1999; De Houwer et al.,
2001). In the case of flavor-based learning, such
changes in liking are usually interpreted within
an associative learning framework based on the
principles of Pavlovian conditioning, where
repeated pairing of a previously hedonically
neutral flavor or flavor component (interpreted
as a Pavlovian CS) with a second flavor or flavor
element that is already liked or disliked (interpreted as the UCS) results in transfer of liking
to the previously neutral flavor CS (see Figure
13.1b). There are now many published examples
of both acquired flavor-disliking (Baeyens et al.,
1990; Dickinson and Brown, 2007; Wardle et al.,
2007) and -liking (Zellner et al., 1983; Yeomans
et al., 2006; Brunstrom and Fletcher, 2008) based
on laboratory-based studies of flavor–flavor
associations in humans.
In terms of understanding the nature of flavor–
flavor learning, one variation of flavor–flavor
learning, where the CS is a food-related odor
and the US is a taste (Stevenson et al., 1995, 1998,
2000a; Stevenson, 2003; Stevenson and Boakes,
2004), has proved particularly valuable since it
helps define the different flavor elements more
clearly than do studies that use more mixed flavors as CS. The typical design of these olfactory
conditioning studies is relatively simple: odors
are first presented orthonasally (i.e., sniffed) on
their own, and evaluations of various sensory
characteristics, including those using gustatory
descriptors (e.g., sweetness, sourness, saltiness
etc), along with hedonic ratings, are made. The
odor is then experienced repeatedly paired
with a taste stimulus (e.g., 10% sucrose to give
a sweet US) in a number of disguised training
trials. Finally, the odor is re-evaluated orthonasally. The consistent finding was that ratings
of the degree to which the odor possessed the
sensory dimension related to the trained US
increased. For example, when an odor was
paired with sucrose, the rated sweetness of the
odor post-training was consistently higher than
it was before training started (Stevenson et al.,
1. FROM BRAIN TO BEHAVIOR
167
13.4 FLAVOR-PREFERENCE LEARNING
13.4.4 Social acquisition of flavor-liking
Social learning may contribute to acquisition
of flavor-liking in two different ways. Social
facilitation refers to modified behavior due to
Change in rated pleasantness
Pleasantness
50.00
40.00
30.00
20.00
10.00
0.00
–10.00
–20.00
Sucrose
Water
US during training
(a)
Sweetness
Change in odor sweetness
1995, 1998), even though the sucrose was not
present when odors were rated orthonasally. EC
would predict that odors paired with sweet tastes
would become liked, but whereas some studies
report increased liking for sweet-paired odors
(Yeomans and Mobini, 2006; Yeomans et al., 2006,
2007), many of the earlier studies failed to find
these effects (Stevenson et al., 1995, 1998, 2000a,
2000b). However, increased liking would only
be predicted if the individual under test actually rated the sweet US as pleasant, and since
there are individual differences in rated evaluation of sweet tastes (Looy et al., 1992; Looy and
Weingarten, 1992), a simple explanation for the
variability in these findings is that those studies
that failed to find increased liking did not have
sufficient sweet-likers to support this change –
a suggestion supported by clear findings of
increased liking when participants are preselected to be sweet-likers (Yeomans and Mobini,
2006; Yeomans et al., 2006, 2008a, 2009b, 2009c).
This is illustrated in Figure 13.3, where changes
in rated pleasantness and sweetness are shown
in relation to classification of sweet-liking. Note
that while in Figure 13.3b acquired sweetness
was seen regardless of liker status, changes in
odor pleasantness (Figure 13.3a) depended critically on hedonic evaluation of the 10% sucrose
solution used during odor–taste pairing.
Overall, development of a dislike for flavor components consistently paired with an
aversive flavor US appears more robust than
does acquired liking for a flavor paired with
a second liked flavor element. Flavor–flavor
learning, therefore, appears an important element of human flavor-preference development,
although, as with FCL, more research is needed
to determine the full scope and importance of
flavor–flavor associations.
50.00
40.00
30.00
20.00
10.00
0.00
–10.00
–20.00
(b)
Sucrose
Water
US during training
FIGURE 13.3 Changes in the rated (a) pleasantness
and (b) sweetness of odors rated orthonasally following
repeated retronasal exposure to the same odor paired with
10% sucrose either by sweet-likers (solid bars) or by sweetdislikers (open bars).
Source: Adapted from Yeomans et al. (2006), with permission.
the mere presence of others (Guerin, 1993). In
the context of food, social facilitation has been
shown to influence eating in ways that may
influence flavor preference development. People
reliably consume more when in groups than
alone (De Castro, 1990; De Castro et al., 1990;
Redd and De Castro, 1992). This social facilitation of meal-size may lead to acquired preference if the increased intake includes novel items,
where exposure alone may enhance liking, perhaps reinforced further by the post-ingestive
effects of the meal. Direct evidence for social
facilitation of food preferences has been reported
in species other than humans, such as capuchin
monkeys (Visalberghi and Addessi, 2000). The
1. FROM BRAIN TO BEHAVIOR
168
13. ACQUIRED FLAVOR LIKES AND DISLIKES
acquisition of liking for the burning sensation
of spicy food by Mexican children could be
interpreted as evidence of social facilitation
effects: Mexican children are exposed to these
foods in the context of meals where they are
consumed by adults (Rozin and Schiller, 1980;
Rozin, 1982), and the mere presence of others
may reduce neophobia and so promote acceptance and later liking. Many other studies report
increased acceptance of, and reduced neophobia towards, unfamiliar foods by children when
exposed to these foods in a social context (Birch,
1980). A more recent study confirms the role
of social facilitation: children showed neophobic responses to unfamiliar foods which were
reduced by the presence of an adult consuming
that food (Addessi et al., 2005).
A more powerful social influence on flavorliking acquisition may be through social modeling (also referred to as observational learning
or social imitation). Here, the observation by
one individual of a second individual who
is consuming and enjoying a food may lead
to increased liking for the same food by the
observer. Thus, children showed increased
acceptance of an unfamiliar food when an adult
was eating that food than when an adult merely
offered the food to them (Harper and Sanders,
1975). Similarly, the presence of an enthusiastic teacher who modeled food acceptance was
highly effective in encouraging repeated consumption and increased acceptance of novel
foods by children (Hendy and Raudenbush,
2000). Combining observation of a peer consuming a food with positive social reinforcers has
also proved an effective method of enhancing
children’s preferences for less preferred foods,
such as vegetables (Horne et al., 1995, 2004).
Enhanced intake of foods through social modeling by peers may be particularly influential on
development of food likes in children (Horne
et al., 2004; Romero et al., 2009).
Overall, social learning is clearly an important element in flavor preference development,
which seems to operate primarily by reducing
neophobia and so allowing more direct flavorlearning (FFL and FCL) to occur.
13.5 DIFFERENT LEARNING
MECHANISMS INTERACT TO
ENHANCE FLAVOR-LIKING
Although experimental studies have been
able to establish multiple mechanisms through
which flavor-liking may be acquired, the typical experimental study examines one putative
mechanism while ensuring that as many alternative influences as possible are controlled for.
Thus, for example, studies examining effects
of multiple exposures of a flavor paired with
ingestion of some form of nutrient typically
run control groups exposed to the flavor alone
(Kern et al., 1993). However, in real-life it is clear
that flavor-liking for foods is likely to develop
through multiple mechanisms at the same time.
Consider, as an illustrative example, how liking
for the flavor of chocolate might be acquired.
Most chocolates consumed in Western society are sweetened, and our innate tendency to
like sweet tastes should predispose us to find
chocolate to be acceptable on first exposure.
Our first exposure will confirm that the food is
not poisonous, leading to reduced neophobia
for the food through learned safety. It is also
likely that our first exposure to chocolate will
occur in the presence of others, and observation
that other people are consuming it will further
help reduce neophobic reactions through social
facilitation. Also, if we observe pleasurable
responses to consuming chocolate by people
who we trust, this in turn may enhance liking
through social modeling. The pairing of unique
chocolate flavor elements with sweetness would
be predicted to enhance liking through flavor–
flavor associations, and, once ingested, the highfat and -sugar content of chocolate, along with
small amounts of caffeine, should all promote
flavor-liking through FCL. Thus, in the case of
1. FROM BRAIN TO BEHAVIOR
13.6 LIKING AND INTAKE: THE ROLE OF PALATABILITY IN OVEREATING
liking for foods like chocolate, which has been
reported as the food most often named as a
craved item (Hetherington and MacDiarmid,
1993; Gibson and Desmond, 1999; Parker et al.,
2006), we can see plausible influences of all
the major learning elements described in this
chapter working together to generate a strong
acquired like.
Several experimental studies have confirmed
how learning mechanisms interact to modify
flavor-liking. For example, when a novel flavor was paired with sweetness (FFL), energy
(FCL), or sweetness and energy (FFL and FCL),
the largest increase in liking was seen where the
opportunity for both associations was present,
with smaller increases with either FFL or FCL
alone (Yeomans et al., 2008a). Likewise, the
increased liking for a drink flavor by association
with caffeine consumption was enhanced when
training was in a sweet context (where a flavor–
sweet association could add to the flavor–
caffeine association: Figure 13.4), but was
retarded when caffeine was consumed in a bitter context (Yeomans et al., 2007). Thus, FFL and
Change in flavor pleasantness
40
30
20
10
0
–10
–20
–30
–40
Water
Aspartame
Quinine
Trained drink flavor
FIGURE 13.4 Rated pleasantness of drink flavors before
and after repeated pairing with caffeine (hashed bar) or placebo (open bar), and with added sweetness (aspartame), bitterness (quinine) or no added flavoring (water).
Source: Adapted from Yeomans et al. (2007), with permission.
169
FCL appear to have additive effects on acquisition of flavor-liking. However, social effects
seem to interact with FCL to generate liking
(Jansen and Tenney, 2001), since the effectiveness of a social model in enhancing food preferences in children was greater when the ingested
food was high energy than low energy, implying a synergistic effect between social reinforcement and post-ingestive effects.
Overall, these studies provide clear predictions about the situations where flavor-liking
will develop, and these models are consistent
with actual observations of flavor preferences.
The critical question now is how these acquired
likes may modify food intake and so be a risk factor for overeating and consequent weight gain.
13.6 LIKING AND INTAKE:
THE ROLE OF PALATABILITY
IN OVEREATING
Why does understanding the basis of flavorliking matter to obesity? The answer lies in the
role of flavor hedonics as a driver of short-term
food intake. Many studies in humans and other
animals have established a clear relationship
between hedonic evaluation of a food and consequent intake (Nasser, 2001; Sorensen et al., 2003;
Yeomans et al., 2004a; Westerterp, 2006). Since it
is harder to evaluate hedonic evaluation in nonhuman animals, this discussion concentrates on
the human literature. The simplest studies take
the same food and modify its flavor, either by
adding a disliked component (or note in sensory terms) or by adding liked flavor elements.
The outcome is very clear: a change in liking
produces a predictable change in intake, with a
linear relationship between hedonic evaluation
and overall consumption (Yeomans et al., 2004a).
In relation to short-term overconsumption, this
implies that liking drives overeating and so may
be a significant risk factor for development of
obesity. Indeed, many people have suggested
1. FROM BRAIN TO BEHAVIOR
170
13. ACQUIRED FLAVOR LIKES AND DISLIKES
that the availability of energy-dense palatable
food has been a major environmental component which has fostered the rapid increase in
obesity (Wansink, 2004; Ulijaszek, 2007). It is also
notable that intake does not decrease reliably as
energy density decreases: it appears that, in the
short-term, it is the volume of food that is regulated, leaving a risk of passive overeating as the
energy density of our diet increases (Westerterp,
2006). As energy density is also enhanced with
greater liking and so may actively drive overconsumption, it is easy to see how the combined
active and passive overconsumption of energydense food greatly increases the risk of obesity.
Until recently, what was not clearly known was
whether this active overconsumption was also
seen for acquired flavor likes.
In terms of the mechanism through which
palatability drives short-term intake, our understanding has increased at both the phenomenological and biological levels of explanation.
Thus, there is clear evidence that increased
flavor-liking leads to short-term increases in
desire to eat (the experience of hunger). This
appetizer effect (Yeomans, 1996; Figure 13.5)
Very
100
Bland
Palatable
Strong
Rated hunger
80
60
40
20
Not at all 0
0
200
400
600
800
1000
Food intake (g)
FIGURE 13.5 Effects of manipulated palatability on the
experience of appetite during a meal.
Source: Adapted from Yeomans (1996), with permission.
offers a behavioral description of how evaluation of sensory quality modulates internal
appetitive state and so alters short-term intake.
Pharmacological studies have offered some
insights into the biological basis of how liking
enhances appetite. For example, blockade of
opioid receptors both reduces food pleasantness
(Drewnowski et al., 1989; Yeomans et al., 1990)
and abolishes the appetizer effect (Yeomans and
Gray, 1997). However, all of these types of study
rely on contrasts between foods varying in
immediate palatability, without consideration of
whether this is a consequence of learned liking.
13.7 ACQUIRED LIKING AS A
DRIVER OF OVEREATING
The previous discussion clearly shows that
liking drives short-term intake, but was based
on analyses of either manipulation or variation
in liking on intake. Since most liking for flavors
is acquired, one interpretation of these findings
is that acquired liking then is a driver of intake.
Two recent studies in our laboratory suggest
this is the case. First (Yeomans et al., 2008a), liking and voluntary intake of a highly novel food
(a fruit sorbet) was tested before and after the
same flavor was associated with energy (provided by the non-sweet carbohydrate maltodextrin), sweetness (aspartame), or energy and
sweetness (sucrose). Exposure to the same flavor
paired with sucrose (i.e., where both flavor–
sweetness and flavor–energy associations could
be made) resulted in a large increase in liking for the flavor in the sorbet context and an
increase in voluntary intake (Figure 13.6). In
a different learning model, people evaluated
and consumed a low-energy soup on separate
days before and after repeated experience of the
same soup either unaltered or with its flavor
enhanced by monosodium glutamate (MSG:
Yeomans et al., 2008b). As predicted by evaluative condition (EC), the greater liking for the
1. FROM BRAIN TO BEHAVIOR
171
13.8 INDIVIDUAL DIFFERENCES IN LEARNING
Intake
50
40
30
20
10
SUC MALT ASP
(a)
Condition
40
30
20
10
0
EXP CONT
50
Change in rated sweetness
60
0
Pleasant
50
Change in rated pleasantness
Change in sorber intake (g)
70
SUC MALT ASP
(b)
Condition
40
30
20
10
0
EXP CONT
(c)
Sweet
SUC MALT ASP
EXP CONT
Condition
FIGURE 13.6
Changes in (a) intake, (b) liking and (c) sweetness of a novel-flavored sorbet after experiencing the same
flavor paired with ingestion of sweetness and energy (sucrose: SUC), energy alone (maltodextrin: MALT), sweetness alone
(aspartame: ASP) or unaltered (exposure control: EXP), along with an unexposed control condition.
Source: Adapted from Yeomans et al. (2008a), with permission.
MSG-enhanced version during the training sessions transferred to the soup alone, resulting in
increased liking, an enhanced appetizer effect,
and greater intake at post-training. Both these
examples provide unequivocal confirmation
that acquired liking can act as a driver of shortterm intake.
13.8 INDIVIDUAL DIFFERENCES
IN LEARNING
An important observation in relation to
the recent increases in the incidence of obesity is that there are large phenotypic variations
in whether individuals who are exposed to
the modern, obesogenic environment become
obese, with a significant proportion remaining
lean. Thus, some people are susceptible to gaining significant weight in a weight-promoting
environment, but others are resistant to weight
gain (Blundell and Cooling, 2000; Blundell et al.,
2005; Carnell and Wardle, 2008). There are
myriad factors that may contribute to this variability, many of which are reviewed elsewhere
in this book. In the present context, it is notable that the idea that over-responsiveness to
hedonic cues has been reported in obese participants (Nisbett, 1968; Price and Grinker,
1973; Rissanen et al., 2002), and has been cited
as a factor underlying overeating (Drewnowski
et al., 1985; Nasser, 2001; Sorensen et al., 2003;
de Graaf, 2005). Moreover, it has recently been
argued that in terms of external food cues driving short-term intake, a distinction can be made
between normative cues such as portion size,
and sensory cues such as palatability, with most
people sensitive to normative cues but the obese
over-responsive to sensory cues (Herman and
Polivy, 2008). Thus, acquired flavor likes may be
significant contributors to overeating and consequent obesity.
Since the major argument of this chapter is
that acquired flavor-liking may be a significant
driver of short-term overconsumption, one possible source of individual differences in response
to food cues may relate to the extent to which
individuals learn flavor-based associations,
either through FCL or FFL. To date, no studies
have specifically contrasted these learning mechanisms between obese and normal populations;
1. FROM BRAIN TO BEHAVIOR
172
13. ACQUIRED FLAVOR LIKES AND DISLIKES
however, a number of studies have started to
identify significant differences in ability to learn
through both FFL and FCL in subsets of normalweight individuals, some of which may contribute to weight gain and later lead to increased
body weight.
The first studies to report individual differences in flavor-based learning did so in relation to dietary restraint (Brunstrom et al., 2001,
2005) – the tendency to self-restrict food intake
in order to control body-weight. People who
score high on measures of restraint do so
because they are either trying to lose weight,
or are aware that their unrestrained behavior
places them at risk of gaining weight. Thus, in
the absence of studies with obese patients, studies with restrained eaters might identify deficits
in flavor-based learning that may be relevant
to our understanding of obesity. Crucially,
these studies reported that women who scored
higher on a questionnaire measure of dietary
restraint (restrained eaters) were insensitive
to FFL (Brunstrom et al., 2001, 2005) and FCL
(Brunstrom and Mitchell, 2007). In the original FFL study (Brunstrom et al., 2001), women
were presented with a novel drink flavor (CS)
followed by consumption of small sweets, with
different contingencies between different flavors
and the frequency with which sweet reinforcers were presented. Subsequent liking for the
drink flavors increased as a function of the contingent relationship with sweet presentation in
unrestrained eaters, but did not differ between
restrained eaters. A second series of studies
(Brunstrom et al., 2005) extended these findings.
It was found that restrained women tended to
experience increased liking for flavors that were
least frequently paired with delivery of sweets,
in contrast to unrestrained women who showed
strongest liking for the flavors most frequently
paired with sweet delivery. These effects were
replicated in a further study where the CS consisted of pictures rather than flavors (Brunstrom
et al., 2005). Finally, the most recent study
examined acquired liking for flavors through
association with energy in a test of FNL, and
again found impaired learning in restrained but
not unrestrained participants (Brunstrom and
Mitchell, 2007).
Alongside restraint, a second measure also
seems to identify significant individual differences in response to foods. The Three Factor
Eating Questionnaire disinhibition scale (TFEQ-D)
has been shown to reliably measure a number
of aspects of eating that may increase the risk
of becoming obese (Bryant et al., 2008). In relation to appetite, high scores on the TFEQ-D have
been shown to be a better predictor than restraint
of eating in response to stress (Oliver and Huon,
2001; Haynes et al., 2003), and to be associated
with a heightened appetite response to palatability (Yeomans et al., 2004b) and greater selection
of high-fat and sweetened foods (Lahteenmaki
and Tuorila, 1995; Contento et al., 2005; Bryant
et al., 2006). Many studies also report a positive
association between TFEQ-D scores and BMI
(Williamson et al., 1995; Provencher et al., 2003,
2004; Bellisle et al., 2004; Hays and Roberts, 2008).
In relation to flavor-based learning, we recently
reported that scores on the TFEQ-D were found
to predict the extent to which women acquired
liking for a flavor paired with sweetness in an
FFL paradigm (Yeomans et al., 2009a: Figure
13.7). Notably, in that study, it was not an ability
to acquire flavor-liking per se that was impaired,
since all women acquired a dislike for a flavor
paired with an unpleasant taste (the bitter taste
of quinine). What the study indicated was that
women scoring high on the TFEQ-D showed a
greater increase in liking for the sucrose-paired
flavor despite no differences in actual liking
for sucrose between groups. Thus, the TFEQ-D
appears to measure some aspect of overexpression of hedonic response, which in turn appears
to be a risk factor for overeating.
Although research into individual differences
in the tendency to acquire flavor-liking is still at
an early stage, the outcome of the few studies
reported to date does suggest that differences in
the way individuals acquire flavor-liking may
1. FROM BRAIN TO BEHAVIOR
REFERENCES
Change in rated pleasantness
20
15
Sucrose
Quinine
10
5
0
–5
–10
–15
–20
Low
High
Disinhibition
FIGURE 13.7 Changes in pleasantness of a novel odor
paired either with a pleasant sweet taste (sucrose: open bars)
or with an unpleasant bitter taste (quinine: solid bars) by
sweet-liking women who scored either high or low on the
disinhibition scale of the Three Factor Eating Questionnaire.
Source: Adapted from Yeomans et al. (2009a), with permission.
make people more or less at risk of overeating,
and so becoming obese. Future studies are needed
to confirm these findings in obese groups.
13.9 SUMMARY
Multiple learning mechanisms operate together
to allow humans to identify safe and nutritious
foods from the huge variety of potential food
items in our environment. Social factors are likely
to be key to our initial exposure to foods, and such
exposure helps us rapidly to learn what is safe.
Innate and previously learned flavor preferences
direct our liking for associated novel flavors, and
once ingested, the experience of nutrient and other
effects of food constituents becomes associated
with the flavors, leading to powerful acquired liking for energy-dense foods. Liking itself, including acquired flavor likes, is a short-term driver
of food intake. In environments where food
was scarce, this would have meant that the
consumer took maximum advantage of rare but
173
highly nutritious food sources. However, in the
modern world, where such foods are abundant,
the ability of acquired likes to drive intake is a
risk factor for overeating and obesity. What
remains less clear is the extent to which individual variation in response to the sensory quality
of foods may help explain phenotypic variation
in the tendency to become obese. Emerging evidence that women who are prone to overeat also
show heightened responses in acquiring flavor
likes, in addition to a large body of literature
suggesting that obese individuals over-respond to
hedonic food cues, suggests that liking may be a
key factor in explaining individual differences in
obesity risk.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
14
Biopsychological Factors and Body
Weight Stability
Jean-Philippe Chaput and Angelo Tremblay
Department of Social and Preventive Medicine,
Laval University, Quebec City, Canada
O U T L I N E
14.1
Introduction
179
14.2
Is Knowledge-based Work a
Potential Determinant of the
Current Obesity Epidemic?
180
14.3
Is Short Sleep Duration a
Potential Determinant of the
Current Obesity Epidemic?
183
14.1 INTRODUCTION
The maintenance of an adequate body weight
is a major determinant of the survival of higher
organisms, including mammals. Body-weight
and body-composition stability over long periods
of time require that energy intake matches energy
expenditure. In human adults, there are mechanisms partly influenced by heredity that balance
Obesity Prevention: The Role of Brain and Society on Individual Behavior
14.4
Weight Loss: Not Always Beneficial
for the Psychological Health
184
14.5
Physical Activity and Diet:
What is the Impact on Bodyweight Stability?
186
Conclusion and Perspectives
186
14.6
energy intake and expenditure. Body-weight
regulation requires the maintenance of not only
energy balance but also nutrient balance – i.e.,
the mixture of fuel oxidized must be adjusted to
match the composition of fuel mix ingested (Flatt,
1987). Because protein and carbohydrate reserves
stored in adults vary relatively little, body-weight
regulation mainly concerns adipose tissue mass.
The chronic imbalance between energy intake
179
© 2010,
2010 Elsevier Inc.
180
14. BIOPSYCHOLOGICAL FACTORS
and expenditure results in changes in the adipose tissue mass. Therefore, body-weight regulation implies that adipose tissue mass is “sensed”
and leads to appropriate responses in individuals
who maintain body weight and body composition constant during prolonged periods of time.
A variety of factors determine body-weight balance and regulation, and the size of the adipose
tissue mass is not subjected to a strict set point.
Many individuals, whether lean or obese,
maintain their body weight within small limits
during long periods of time. If energy intake
exceeds expenditure by 1 percent daily for
1 year, the result would be approximately
9000 kcal stored, or 1.15 kg of body weight
(Rosenbaum et al., 1997). The mean weight gain
of the average American between 25 and 55
years of age is about 9 kg, which represents a
mean excess of circa 0.3 percent ingested calories over energy expenditure (Rosenbaum et al.,
1997). The high precision of energy balance
maintenance is achieved by several regulatory
loops. Many pathways participate in homeostatic responses that tend to maintain adequate
fuel storage. The combined responses that control energy intake and expenditure to maintain
energy homeostasis have conferred a survival
advantage to humans.
Food is increasingly available, and advances
in technology and transportation have reduced
the need for physical activity. These two environmental changes challenge body-weight
regulation, and contribute to the increasing
prevalence of obesity worldwide. Beyond the
“Big Two” factors (physical inactivity and poor
diet), recent research has emphasized the potential roles of additional environmental factors in
contributing to the obesity epidemic (Keith et al.,
2006), including:
●
●
●
●
sleep debt
endocrine disruptors
reduction in variability in ambient
temperature
decreased smoking
●
●
●
●
●
●
pharmaceutical iatrogenesis
changes in distribution of ethnicity and age
increasing gravida age
intrauterine and intergenerational effects
assortative mating and floor effects
body mass index-associated reproductive
fitness.
The list is not exhaustive.
Public health practitioners and clinicians
need to take these into account when looking
at anti-obesity policies and actions. In spite of a
growing number of works in this field, the obesity crisis rages on. This suggests that the obesity
problem is multifaceted, and requires a combination of therapies in order to be managed.
This chapter focuses on two phenomena
characterizing our modern society and that
challenge body-weight stability: (1) the increase
of knowledge-based work (KBW) in daily labor
as well as in leisure time; and (2) the reduction
of sleeping time. In addition, we discuss the
psychological impact of dieting and weight loss,
which may impede the success of diet/physical
activity clinical interventions. Finally, integrative comments and novel insights are provided.
14.2 IS KNOWLEDGE-BASED WORK
A POTENTIAL DETERMINANT
OF THE CURRENT OBESITY
EPIDEMIC?
Technological changes have brought about a
progressive shift away from physically demanding tasks to knowledge-based work (KBW),
which solicits an enhanced cognitive demand
(Mitter, 1999). This modern transition has also
redefined the notion of “fatigue at work”, which
is now more of a psychosomatic nature (such as
a burnout) than physical exhaustion (Iacovides
et al., 2003).
From a physiological standpoint, KBW represents a type of activity that relies on the brain,
1. FROM BRAIN TO BEHAVIOR
14.2 IS KNOWLEDGE-BASED WORK A POTENTIAL DETERMINANT OF THE CURRENT OBESITY EPIDEMIC?
Energy expenditure (kJ/45 min)
Chaput and Tremblay (2007) undertook an
interventional study with female students to
evaluate the impact of KBW on feeding behavior and spontaneous energy intake, using a
crossover design. They used a two-session protocol including an ad libitum buffet which was
preceded by either a 45-minute cognitive task
(reading a document and writing a 350-word
summary on a computer) or a 45-minute resting period (in the sitting position). As shown
in Figure 14.1, the mean energy expenditure of
the two conditions was comparable (13 kJ difference), whereas the mean ad libitum energy intake
in the KBW group task exceeded that in the resting group by 959 kJ (P 0.01). Furthermore, the
300
270
240
210
180
150
120
90
60
30
0
Δ = 13 kJ
Control
KBW
Δ = 959 kJ
*
6000
Energy intake (kJ)
which essentially utilizes glucose for its energy
metabolism. Physical activity solicits skeletal muscle metabolism, which, to a significant
extent, relies on fat metabolism. In addition,
tasks requiring a significant cognitive demand
are more likely to be confounded with neurogenic stress, which is known to promote a positive energy balance (Akana et al., 1994; Pijlman
et al., 2003).
In humans, many observations support the
idea that an increase in KBW and/or stress
promotes excess energy intake. Indeed, it was
found, for instance, that the increased workload
associated to the preparation of an NIH grant
application was associated with a high energy
intake and percent energy from fat compared to
a lower workload period (McCann et al., 1990).
Also, Wardle and colleagues (2000) found that
high workload periods in a department store – 47
hours of work over 7 days, with a high level of
perceived stress – were related to higher energy,
saturated fat and sugar intakes compared to low
workload periods (32 hours of work per week).
Other studies have shown that overtime hours
are positively correlated with 3-year changes in
body mass index (BMI) and waist circumference
(Nakamura et al., 1998). Moreover, the excess
weight gain in spouse caregivers of individuals with Alzheimer’s disease was also associated with increased energy intake compared
to spouses in the control group (Vitaliano et al.,
1996). The impact of stress on spontaneous feeding has been studied under well-standardized
laboratory conditions. Macht (1996) demonstrated
that subjective hunger motivation was potentiated by emotional stress when energy intake was
low in the preceding hours. Epel and colleagues
(2001) observed that stress-induced cortisol reactivity was associated with increased energy intake
after a first stress session. This is consistent with
Wallis and Hetherington (2004), who reported
that chocolate consumption increased by 15 percent after a cognitive task (Stroop Test) as compared to a control session.
181
5000
4000
3000
2000
Control
KBW
FIGURE 14.1 Energy expenditure of rest (control) and
knowledge-based work (KBW) and spontaneous energy
intake in a buffet-type meal offered after the completion of
each task.
Data are expressed as mean standard error of the mean
(SEM); *significantly different from control value (P 0.01).
Source: Adapted from Chaput and Tremblay (2007).
1. FROM BRAIN TO BEHAVIOR
182
14. BIOPSYCHOLOGICAL FACTORS
subjects did not compensate for the ad libitum
buffet by eating less during the rest of the day.
This suggests a net caloric surplus. Dallman
and colleagues (2003) have suggested that the
overconsumption of food may be perceived as
a reaction, whereby eating serves as a consolation and/or compensation for emotional stress.
According to these authors, people eat “comfort
food” in an attempt to reduce the activity of the
stress-response network.
Beyond this interpretation, other data suggest that KBW can be viewed as its own specific
entity, producing certain physiological effects
that promote a positive energy balance, independently of the emotional stress with which
it is occasionally paired. In this regard, KBW
produced plasma glucose and insulin instability (defined as the sum of absolute changes
between each time of blood collection at every
15 minutes) 2.2 and 8.3 times greater, respectively compared to the resting activity (Tremblay
and Therrien, 2006). Furthermore, Chaput and
colleagues (2008a) recently reported in another
experimental study that cognitive work acutely
induced an increase in spontaneous energy
intake and promoted increased fluctuations in
plasma glucose and insulin levels. According
to the glucostatic theory of appetite control1,
energy intake may be triggered with the goal
of restoring glucose homeostasis (Mayer, 1953;
Chaput and Tremblay, 2009a). Interestingly,
Chaput and Tremblay (2009b) also observed
that mental work solicited by computer-related
activities produced an increase in cortisol levels,
which was related to a compensatory increase
in caloric intake. This observation is in line with
the results from Epel and colleagues (2001), who
found that high cortisol reactors (defined as the
increase from baseline to stress levels of salivary
cortisol) consumed significantly more calories
and more high-fat, sweet foods on the stress
day compared with low reactors, but consumed
similar amounts on the control day.
Thus, computer-related activities represent a
particular type of sedentary activities that are
stressful and biologically demanding. According
to Tremblay and colleagues (2009), this type of
activity cannot be in any way considered a restful activity, and deserves to be counterbalanced
by an adequate physical activity regimen. As
opposed to KBW, physical exercise enhances the
accuracy and cell sensitivity to numerous hormones and substrates (Tremblay and Therrien,
2006). Consequently, the progressive shift from
physically demanding tasks to KBW, which
necessitates cognitive demand, has changed the
biological requirements of the human organism.
It is therefore noteworthy to focus on the impact
of cognitive tasks and their potential effect on
the control of food intake.
Taken together, these observations suggest
that activities requiring significant cognitive
demand favor overconsumption of foods and
body-weight gain. Moreover, acute effects of
KBW suggest that this work modality might
promote a greater positive energy balance in
comparison to what would be expected from
a sedentary activity. This adds a new component to sedentary lifestyles, made more harmful
when one is subjected to mental stress. It also
raises an additional obstacle in the fight against
obesity, in that KBW is now the modern way of
working. The orexigenic effect of mental work
implies, too, that modern societies might be in
a conflictual state, as KBW could significantly
handicap the ability to spontaneously match
energy intake and expenditure, and thus promote weight gain.
1
More than 50 years ago, Jean Mayer proposed that changes in blood glucose concentrations or arteriovenous glucose differences are detected by glucoreceptors that affect energy intake. According to this theory, an increase in
blood glucose concentrations results in increased feelings of satiety, whereas a drop in blood glucose concentrations
has the opposite effect.
1. FROM BRAIN TO BEHAVIOR
14.3 IS SHORT SLEEP DURATION A POTENTIAL DETERMINANT OF THE CURRENT OBESITY EPIDEMIC?
14.3 IS SHORT SLEEP DURATION
A POTENTIAL DETERMINANT
OF THE CURRENT OBESITY
EPIDEMIC?
Reduced sleeping time has become a widespread phenomenon driven by the demands
and opportunities of the modern “24-hour”
society. Not surprisingly, reports of fatigue and
tiredness are more frequent today than a few
decades ago (Bliwise, 1996). Over the course
of the second half of the twentieth century,
the dramatic increase in the incidence of obesity appears to have paralleled the progressive
decrease in the duration of self-reported sleep
(Flegal et al., 1998; Van Cauter et al., 2005).
Consequently, many researchers have suggested that our “cavalier attitude” toward sleep
could be partly responsible for our expanding waistlines. Indeed, a good night’s sleep, an
activity that should ideally occupy about onethird of our lives, is an integral part of a “good
health package”. It is therefore relevant to ask
whether the current emphasis on poor diet and
lack of exercise omits the importance of sleep
in the battle against obesity, thereby hindering
individuals’ ability to maintain a healthy body
weight.
Chaput and colleagues (2006a) reported a
dose–response relationship between short sleeping hours and childhood overweight/obesity.
The risk for overweight/obesity in children
reporting sleeping 8–10 hours per night was
3.45 times greater than for those who reported
12–13 hours per night. As seen in Figure 14.2,
short sleep duration was the most important
determinant of the potential risk to overweight/
obesity in children. Other studies examined
the sleep–body weight association in children,
and the conclusions were concordant with the
Chaput and colleagues (2006a) findings (Gupta
et al., 2002; Sekine et al., 2002; Von Kries et al.,
2002; Reilly et al., 2005).
In adults, short sleep duration also predicted
an increased risk of being overweight or obese
(Hasler et al., 2004; Spiegel et al., 2004; Taheri et al.,
2004; Gangwisch et al., 2005; Vorona et al., 2005;
Chaput et al., 2007, 2008b). Importantly, it was
shown that the neuroendocrine control of appetite
Low total family income
Physical inactivity
Low parental educational level
Long hours of TV watching
Parental obesity
Short sleep duration
4
3.5
Odds ratio
3
2.5
2
1.5
1
0.5
0
FIGURE 14.2
183
Relationship between potential risk factors and childhood overweight/obesity.
Source: Adapted from Chaput et al. (2006a).
1. FROM BRAIN TO BEHAVIOR
184
14. BIOPSYCHOLOGICAL FACTORS
was affected as plasma levels of the anorexigenic
hormone, leptin, were decreased. Levels of the
orexigenic hormone, ghrelin, increased (Spiegel
et al., 2004; Taheri et al., 2004). Hence, these neuroendocrine changes were associated with increased
hunger and appetite, which may lead to overeating and weight gain.
Other large-scale studies also showed that
both short and long sleeping durations are
independently linked to an increased risk
of coronary events, symptomatic diabetes
and mortality (Ayas et al., 2003a, 2003b; Patel
et al., 2004; Tamakoshi and Ohno, 2004). In these
studies, the cut-off point of minimal mortality
and related events was at 7 hours of sleep daily.
Thus, there may be an “optimal sleeping time”
for the prevention of common diseases and
premature death. However, the mechanisms
behind these associations are not fully understood, and the effects of long sleep duration
on body weight and/or other health outcomes
appear to be different from those associated
with shorter sleep duration. Besides decreased
leptin and increased ghrelin levels, physiological data in adults suggest that short-term partial sleep restriction leads to striking alterations
in metabolic and endocrine functions, including decreased glucose tolerance, insulin resistance, increased sympathetic tone, elevated
cortisol concentrations, and elevated levels of
pro-inflammatory cytokines (Spiegel et al., 1999;
Vgontzas et al., 2000; Taheri et al., 2004). Thus,
one could speculate that a chronic lack of sleep
represents a stress factor stimulating appetite,
promoting weight gain and impairing glycemic
regulation, with a subsequently increased risk
of impaired glucose tolerance and, eventually,
type II diabetes.
However, a good night’s sleep is different for each individual, and is subject to a
broad range of potential confounding variables. Consequently, many experts doubt that
more sleep, natural or drug-induced, can be the
answer to successful weight loss. Once a person is overweight, poor sleep and uncontrolled
appetite could become part of a vicious cycle;
obesity might make it hard to sleep, and poor
sleep might make it harder to lose weight.
Instead, researchers have focused on identifying individuals with “high-risk” sleeping
patterns, in order to prevent weight-related
problems before they arise. An early warning
sign, such as altered leptin concentration, might
alert physicians that the body is suffering more
than is immediately obvious. In addition, it may
be useful to identify children who do not sleep
enough and to encourage parents to change
these sleeping habits.
Future research needs to examine the effect
of short sleeping duration on appetite, food
intake and obesity. These studies should use
an interventional study design to establish the
cause-and-effect relationship behind sleep duration and obesity. They should also examine the
effects of restricted sleep on both sides of the
energy balance equation, with the use of objective measures for sleep duration and quality. It
may thus be demonstrated that the rise of obesity in many societies around the world is partly
linked to sleep deprivation. Future studies can
also examine whether increasing sleep to 7 or
8 hours per night can help individuals lose
weight or prevent weight gain. This may prove
to be a pleasurable way to control obesity.
14.4 WEIGHT LOSS: NOT
ALWAYS BENEFICIAL FOR THE
PSYCHOLOGICAL HEALTH
From a psychosocial perspective, overweight
and obesity adversely affect the quality of life.
They carry a social stigma that may contribute to
higher rates of anxiety, depression and low selfesteem (Puhl and Brownell, 2001; Kottke et al.,
2003; McElroy et al., 2004). Depression may contribute to weight gain and obesity and, vice versa,
obesity may contribute to depression (Wyatt
et al., 2006). From a weight-loss standpoint, it
1. FROM BRAIN TO BEHAVIOR
14.4 WEIGHT LOSS: NOT ALWAYS BENEFICIAL FOR THE PSYCHOLOGICAL HEALTH
is realistic to say that the association between
obesity and metabolic complications generally constitutes the main argument in justifying
weight-reducing programs: the aim is to improve
the metabolic risk profile of obese individuals.
However, benefits to physical health should not
be associated with detrimental effects on mental
health or psychological wellbeing. In this regard,
the psychological effects that accompany weight
loss in obese individuals are of high importance
in order to understand the psychological barriers to weight loss, and the optimal management
of obesity.
The majority of the evidence in this field of
research shows the beneficial impact of weight
reduction on mental wellbeing and health-related
quality of life (Rippe et al., 1998; Fine et al.,
1999; Fontaine et al., 1999; Kaukua et al., 2002;
Karlsson et al., 2003). However, they fail to mention the possible psychological costs associated
with weight loss, reflected by a destabilization of body homeostasis. Such negative psychological costs require a cautionary approach
to weight reduction. As shown in Figure 14.3,
Chaput and Tremblay found that depression
symptoms increased significantly after a weight
Clinical threshold of depression
BDI scores (arbitrary units)
16
14
12
**
10
8
*
6
4
2
0
Baseline
–5 kg
–10 kg
Plateau
FIGURE 14.3 Clinical threshold of depression. Evolution of depression symptoms, measured with the Beck
Depression Inventory (BDI), over the course of a progressive body-weight loss program that consisted of a supervised diet and exercise clinical intervention.
*significantly different from baseline mean score (P 0.05);
**P 0.01.
Source: Adapted from Chaput et al. (2005; 2006b).
185
loss of 10 kg (Chaput et al., 2005; dynamic
weight-loss phase). These symptoms were more
pronounced at the static weight-loss phase, the
plateau (Chaput et al., 2006b). Furthermore, the
increase in the symptoms of depression was
associated with an increased restraint of eating
(Chaput et al., 2005, 2006b). This psychobiological phenomenon was observed concomitantly
with a significant decrease in resting energy
expenditure, and a significant increase in hunger and a desire to eat (Chaput et al., 2006b).
In another recent study, Chaput and colleagues (2008c) linked the increase in depression symptoms with glucose homeostasis and
thyroid function. Specifically, the significant
increase in depression symptoms observed
after an average loss of 11.2 percent of initial
body weight induced via energy restriction
(700 kcal/day) and an aerobic exercise program was shown to be highly associated with
hypoglycemia at the end of an oral glucose challenge, and with a decrease in total triiodothyronine (T3) and free thyroxine (fT4) levels. Such
results are not surprising, as glucose is the main
substrate of the brain and thyroid function is
related to metabolism, which represent effects
that may influence mood and wellbeing as well
as the perception of “body energy”. This suggests that weight loss up to a certain level has
the potential to destabilize body homeostasis
and induce a psychobiological vulnerability
favoring weight regain.
For health professionals, these observations indicate that body-weight management
should maintain a reasonable balance between
the health benefits associated with weight loss
and the potential negative consequences for
the control of energy intake and expenditure.
Furthermore, various psychological and physiological adaptations make body-weight maintenance following weight loss difficult, and render
the individual vulnerable to weight regain.
In this context, patients may need to accept a
more modest weight-loss outcome (Foster et al.,
1997).
1. FROM BRAIN TO BEHAVIOR
186
14. BIOPSYCHOLOGICAL FACTORS
14.5 PHYSICAL ACTIVITY AND
DIET: WHAT IS THE IMPACT ON
BODY-WEIGHT STABILITY?
An individual looking to lose weight through
a healthy diet and regular physical activity usually asks how much weight can be feasibly lost.
There is no straightforward answer to this question, as it depends on numerous factors. From
a physiological standpoint, the most realistic
answer compares the loss of the regulatory
impact on fat balance that occurs with weight
loss with the gain in the regulatory impact on
fat balance that can be promoted by a healthy
lifestyle. This assumption means that the fat
compartment contains beneficial molecules
that aim to fight against a further weight gain.
Indeed, fat gain facilitates the maintenance of
body homeostasis because of an increased hormonal gradient which favors the regulation of
energy balance. An increase in plasma free-fatty
acids, fat oxidation, sympathetic nervous system
activity, insulinemia at euglycemia, and leptinemia are all adaptations that contribute to promote body-weight stability over time (Tremblay
and Doucet, 2000; Chaput and Tremblay, 2009a).
Simply put, the increase in body fatness is
accompanied by neuroendocrine adaptations
that favor an increase in energy expenditure
and a decrease in energy intake. Accordingly,
if one wants to maintain a reduced-obese state,
the stimulating effects of a healthy lifestyle on
the regulatory processes should, theoretically,
be equivalent to what is lost with body mass
reduction. Up to now, we have not been able
to promote weight losses exceeding 10–12 percent of the initial body weight without inducing
metabolic and behavioral changes compromising the ability to maintain subsequent long-term
weight stability. It is thus likely that individuals
cannot continue to lose weight without more
demanding activity and diet changes than those
displayed at the end of the program, when the
plateauing occurred.
The failure to adhere to healthy lifestyle
habits following weight loss leaves the patient
with two possible strategic choices in regards
to maintaining subsequent body-weight stability. The first is a self-imposed energy restriction, irrespective of the hunger sensations that
may be perceived. This option may be counterproductive in the long term; Drapeau and colleagues (2003) observed greater weight gain
over time in women that displayed restraint
behaviors. The second scenario is to simply not
adhere to a healthy lifestyle, the consequence
of which will be weight regain as the body will
attempt to re-equilibrate the energy and fat
balance.
Consequently, the reduced-obese individual
wishing to maintain the new morphological
status is left with very few alternatives. In fact,
the only real and valid option is to improve
body functionality by healthy activity and diet
habits, and thus to compensate for the loss of
physiological impact of the decrease in body
fat. However, even if a person displays an
exemplary discipline in the implementation
of a healthy lifestyle, the resulting beneficial
impact is not limitless. Body-weight management imposes systematically a balance between
the expectations of an individual and what his
or her biology can tolerate in terms of lifestyle
changes. In some cases, the management of this
balance may be complicated by the increased
practice of KBW and/or short sleep duration.
14.6 CONCLUSION AND
PERSPECTIVES
The modern world demands less energy,
and is characterized by an improved quality of
life. Modernity has thereby provided numerous products and services contributing to the
comfort and wellbeing of people. Beside the
obvious positive changes related to the health
status and life expectancy of individuals, it has
1. FROM BRAIN TO BEHAVIOR
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decreased sleeping time and increased KBW
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chapter, this new reality can partly explain the
current obesity epidemic, and also mitigates the
potential outcomes of a diet–physical activity
weight-reducing program. In this context, an
increased level of body fat might be necessary
to maintain body-weight stability.
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Wyatt, S. B., Winters, K. P., & Dubbert, P. M. (2006).
Overweight and obesity: Prevalence, consequences, and
causes of a growing public health problem. American
Journal of the Medical Sciences, 331, 166–174.
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C H A P T E R
15
Nutrition, Epigenomics and the
Development of Obesity: How the
Genome Learns from Experience
John C. Mathers
Human Nutrition Research Centre, Institute for Ageing and Health,
Newcastle University, Newcastle on Tyne, UK
O U T L I N E
15.1 The Basics of Epigenetics and
Epigenomics
191
15.5 An Epigenetic Basis for Developmental
Programming of Obesity?
197
15.2 Epigenetic Marks During Development
and Aging
193
15.6 Physical Activity, Epigenetic Markings
and Obesity
197
15.3 Nutritional Epigenomics
194
15.7 Concluding Comments
198
15.4 Epigenetics and Brain Function
196
Acknowledgments
199
15.1 THE BASICS OF EPIGENETICS
AND EPIGENOMICS
The human DNA sequence defines biological capacity in that it determines the genes, and
the functionality of those genes, which can be
expressed by the individual. However, although
all nucleated cells in a person contain exactly the
same genomic sequences, the diversity of structure
Obesity Prevention: The Role of Brain and Society on Individual Behavior
and function in different cells and tissues is manifested by the expression of characteristically
different consortia of genes. This cellular differentiation is programmed, at least in part, by epigenetic mechanisms that regulate the expression of
genes over long periods of time. Epigenetics is the
science of chromatin modifications responsible
for such altered regulation of gene expression
occurring in the absence of changes in the primary DNA sequence. In other words, epigenetics
191
© 2010,
2010 Elsevier Inc.
192
15. NUTRITION, EPIGENOMICS AND OBESITY
is a mechanism which facilitates phenotypic plasticity in the context of a fixed genotype. Chromatin
can be considered as “smart packaging” which,
in addition to helping package the 2 m of DNA
into the nucleus (diameter 20 μm), carries a
sophisticated pattern of marks that regulate chromatin structure, DNA accessibility and transcription of specific sequences.
The best understood epigenetic mark is the
covalent addition of a methyl group at the 5
position in a cytosine residue when this precedes
a guanine residue – a so-called CpG dinucleotide. The prevalence of the CpG dinucleotide in
the human genome is much less than would be
expected, and these dinucleotides tend to cluster
in DNA domains known as CpG islands which
are characterized by high G C and CpG contents (Bird, 2002). About 50 percent of human
genes have CpG islands (CGI) in their promoter
regions, sometimes extending into the first exon.
The cytosines in these regulatory regions of
genes are usually unmethylated, in contrast with
cytosine residues elsewhere in the genome
which are heavily methylated. Indeed, mammalian genomes are dominated by methylated
DNA, with unmethylated domains (largely
CGI) accounting for only 1–2 percent of the total
(Suzuki and Bird, 2008). This divergent methylation landscape reflects the functionality of the
individual DNA sequences with unmethylated
promoters allowing transcription of the associated
gene, whereas methylated regions are transcriptionally silent. At its simplest, DNA methylation
acts as a transcriptional switch which is in the
“on” position when the CpG island is unmethylated and signals “off” when methylated.
Within the nucleus, DNA is packaged by
sophisticated wrapping around an octet of globular proteins known as histones i.e. two copies
of each histone H2A, H2B, H3 and H4. These
histones host further epigenetic marks in the
form of post-translational chemical modification of amino acid residues, including acetylation and ubiquitination of lysine residues,
phosphorylation of serines, and methylation of
lysine and arginines (Berger, 2007). In all, there
are more than 100 distinct post-translational
modifications of histones (Kouzarides, 2007).
Individual histone modifications and patterns of
modifications, described as histone decoration,
constitute a histone code (Jenuwein and Allis,
2001) which, in conjunction with DNA methylation status, regulates the expression of associated genes (Bernstein et al., 2007). Although
there is some dispute about how inclusive the
term “epigenetics” should be, many in the field
consider that the density of nucleosome packing
along DNA, the presence of proteins that recognize methylated DNA or modified histones, and
higher-level topological organization of these
elements into complex structures (Berger, 2007)
contribute to the complexity of epigenetic information (Feinberg, 2008). The term “epigenome”
describes the totality of epigenetic marks in a
given cell under specified conditions, and “epigenomics” is the science (and technology for the
study) of genome-wide epigenetic marks.
If epigenetic marks are important in defining
over long time periods the complement of genes
that characterize specific cell types, then it is
evident that there must be mechanisms for sustaining patterns of epigenetic information across
cell generations. For example, when a hepatocyte divides, its daughters “need to know” that
they are liver cells rather than kidney or bone
cells; the tissue of origin is remembered (LaddAcosta et al., 2007). Indeed, in mitotic tissues,
this hypothesis would predict that epigenetic
features characteristic of individual stem cells
would be recapitulated in the progeny of those
stem cells. This prediction holds, and the phenomenon is best exemplified in the intestinal
mucosa, where the patterns of DNA methylation
differ between individual crypts. This reflects
the diversity of methylation patterns in the stem
cells populating those crypts (Kim and Shibata,
2004). The molecular mechanism responsible
for “memorization” of DNA methylation marks
1. FROM BRAIN TO BEHAVIOR
15.2 EPIGENETIC MARKS DURING DEVELOPMENT AND AGING
through mitosis is well understood. During
semi-conservative replication, DNA methylation
transferase 1 (DNMT1) uses the parental strand
as a template to methylate the daughter strand,
with S-adenosyl methionine (SAM) acting as
the methyl donor. In contrast, the molecular
mechanisms for memorization of histone modifications remain obscure. No enzyme has been
identified that recognizes chromatin modifications in the parental cell and reproduces them in
the daughter cells (Feinberg, 2008). However, it
appears that histones segregate randomly during mitosis so that each daughter cell acquires
some of the marked proteins, which then spread
to the newly deposited histones (Hatchwell and
Greally, 2007).
15.2 EPIGENETIC MARKS DURING
DEVELOPMENT AND AGING
Each individual’s DNA sequence is fixed at
conception, but their epigenetic state, as indicated by DNA methylation patterns, changes
throughout the life-course. The most dramatic of these changes occur very early after a
highly methylated sperm fuses with a relatively
unmethylated egg. In the first few cell divisions,
the new individual undergoes genome-wide
demethylation, which erases parental methylation marks for all genomic sequences with the
exception of imprinted genes (Reik, 2007) – that
is, genes which are expressed in a parent-oforigin-specific manner. Between the morula and
the blastocyst stages there is genome-wide de
novo methylation, with tissue-specific methylation patterns emerging later in embryonic
development (Reik, 2007; Feinberg, 2008). In
embryonic stem (ES) cells, there appears to be a
novel chromatin-based mechanism for maintaining pluripotency through which expression of
developmentally-important transcription factors
193
is regulated epigenetically by “bivalent domains”,
which silence these genes in ES cells but keep
them poised for activation (Bernstein et al., 2006).
When compared with the tsunami-like
remodeling of the epigenetic landscape seen in
early embryonic life, DNA methylation patterns
(and, by inference, other epigenetic marks) are
relatively stable following birth. However, there
is substantial evidence that these epigenetic patterns continue to evolve over the life-course. A
good illustration of this evolution is provided
by Fraga’s study of monozygotic twins, which
found that members of twin pairs were epigenetically indistinguishable when young but epigenetic portraits (DNA methylation patterns
and histone acetylation) diverged with age
(Fraga et al., 2005). These epigenetic differences
in older twin pairs were reflected in greater differences in gene expression (Fraga et al., 2005),
suggesting that the greater epigenetic heterogeneity may have functional consequences.
Epigenetics is emerging as an important field
for those studying the biology of aging and agerelated diseases because of the potential functional
consequences of the changes in epigenetic marks
that accumulate with age (Fraga and Esteller,
2007). Studies of aging cells in culture, of animal
models, and of older humans indicate that, in
general, genomic DNA becomes progressively
demethylated with age. In contrast, some genes
(for example, some tumor suppressor genes and
other DNA defense genes) become silenced by
promoter methylation (Fraga and Esteller, 2007).
Until recently, the understanding of aging’s effects
in humans was handicapped by the restriction of
cross-sectional studies which cannot provide information on intra-individual changes in epigenetic
marks over time. A study of an Icelandic cohort in
whom DNA was collected 11 years apart, and that
of a Utah (USA) cohort sampled 16 years apart
(Bjornsson et al., 2008), has changed this landscape. This study showed that genome-wide DNA
methylation changed in a substantial proportion of
each cohort, with individuals showing both gains
1. FROM BRAIN TO BEHAVIOR
194
15. NUTRITION, EPIGENOMICS AND OBESITY
and losses of cytosine methylation (Bjornsson et al.,
2008). In contrast, previous studies found little evidence of age-related changes in methylation of the
human IGF2/H19 locus (Heijmans et al., 2007) and
of human chromosomes 6, 20 and 22 (Eckhardt
et al., 2006). However, both of these studies were
cross-sectional and, because some individuals gain
DNA methylation whilst others become relatively
hypomethylated with age, the process of averaging degrees of methylation for each age group is
likely to obscure individual age effects (Bjornsson
et al., 2008).
Further, studies of epigenetic markings at an
individual cell level may provide novel insights
into the development of age-related frailty.
Changes in DNA methylation over the lifecourse may not occur equally in all cells within
a tissue – that is, aging may increase the extent
of epigenetic mosaicism within a tissue. Since
copying of DNA methylation patterns across
cell generations is much less well-policed than
is the primary sequence, methylation patterns
may drift over time, leading to greater intercellular divergence in methylation patterns
within a tissue with age. By expanding HMEC
cells from 1 to 106 followed by bisulfite sequencing, Ushijima and colleagues (2003) quantified
epigenetic error rates for a panel of genes and
reported a mean of 0.1 percent “errors” per site
per cell generation. This increased heterogeneity
in epigenetic markings with time may contribute to the greater cell-to-cell variation in gene
expression that is observed in cardiomyocytes
of older mice (Bahar et al., 2006). The observation that this greater cell-to-cell variation
appeared to be random (i.e., differed between
genes within a cell) (Bahar et al., 2006) is consistent with the mechanism of epigenetic drift
over time. Increased cell-to-cell diversity in epigenetic marking with age may have important
functional consequences and, at a tissue level,
may explain some of the reduction in speed
and magnitude of response to stimuli (loss of
homeostasis) that characterizes aging and the
development of frailty (Figure 15.1).
Young
Large,
unified
response
Old
Reduced,
variable
response
FIGURE 15.1 Conceptual functional consequences of
increased inter-cellular heterogeneity in promoter methylation and subsequent silencing of a gene age in a given tissue.
䊉, unmethylated gene, expression; 䊊, methylated gene, no
expression.
Source: Mathers and Ford (2009).
15.3 NUTRITIONAL EPIGENOMICS
There is indisputable evidence that nutritional
exposures contribute to phenotypic plasticity
and, indeed, that exposures early in life can have
profound effects on health decades later. As
mechanisms that play a significant role in orchestrating the complex interplay between nutrition
(and other lifestyle exposures) and the genome
that determines individual phenotype, epigenetic processes are strong candidates. In other
words, it is proposed that epigenetic markings
(1) allow phenotypic plasticity in a fixed genotype, and (2) connect environmental exposures
with gene expression and function (Feinberg,
2007). To help focus research attention on the key
processes likely to be involved in linking environmental (nutritional) exposure with altered
phenotypes, we developed the simple conceptual model of the “4Rs of epigenomics” (Figure
15.2; Mathers and McKay, 2009). This model proposes that nutritional exposures are “Received”
and “Recorded” by epigenetic mechanisms, and
that the environmentally determined epigenetic
marks are “Remembered” across succeeding cell
generations. Sometime later, the consequences of
earlier environmental exposures are “Revealed”
1. FROM BRAIN TO BEHAVIOR
15.3 NUTRITIONAL EPIGENOMICS
Environment
(diet)
Receive
and
Record
Remember
Reveal
Time
FIGURE 15.2
The four Rs of epigenomics.
Conceptual model of the key processes through which
altered epigenomics markings as a result of nutritional exposures are Received, Recorded, Remembered and Revealed.
Source: Mathers (2008), reproduced with permission from Cambridge
University Press.
as altered gene expression, which translates into
changes in cellular and tissue function (Mathers
and McKay, 2009).
There is only fragmentary understanding
of the mechanisms through which nutritional
exposures are received and recorded as novel
epigenetic marks (the first two “Rs”), yet the
list of food components which modulate DNA
methylation and histone decoration is expanding (for reviews, see Arasaradnam et al., 2008;
Mathers and Ford, 2009). In many cases, the
functional consequences of the altered epigenetic marks are not known and the field is ripe
for the systematic study of the relationships
between specific epigenetic marks and transcriptional responses (the fourth “R”). The impact
of maternal nutrition on epigenetic markings,
gene expression and phenotype is probably
best exemplified by studies in the viable yellow agouti (Avy) mouse (Waterland and Jirtle,
2003). The offspring of mouse dams fed a diet
enriched with methyl donors (folate, vitamin
B12, choline and betaine) during pregnancy are
more likely to have mottled or pseudo-agouti
coats (rather than yellow coats) and a reduced
risk of being obese (Waterland and Jirtle, 2003).
The molecular mechanism for these effects
195
appears to involve greater methylation of the
cryptic promoter in the proximal end of the Avy
intracisternal A-particle (Waterland and Jirtle,
2003). Intriguingly, similar effects are seen when
the diet of the mouse dam is supplemented with
genistein, which is not a methyl donor. This epigenetic remodeling does not seem to be driven
by availability of methyl groups (Dolinoy
et al., 2006).
The rapid increase in obesity prevalence
in the past few decades is consistent with a
hypothesis of transgenerational amplification
of adiposity, which might be mediated by the
effects of maternal adiposity on birth weight
and subsequent adult adiposity (Lawlor et al.,
2007). Recent data suggest that maternal
obesity in Avy mice induces transgenerational
amplification of obesity. This adverse effect,
however, can be ameliorated by supplementing the dams with dietary methyl donors
(Waterland et al., 2008). Importantly, the effects
in these mice were independent of epigenetic
changes at the Avy locus (Waterland et al., 2008).
The search for epigenetic mechanisms will
need to be widened to include, for example,
genes in pathways regulating food intake and/
or energy expenditure. These results also provide proof of concept that the putative cycle
of transgenerational amplification of obesity
might be broken by readily implemented nutritional interventions. The mandatory fortification of staple foods with folic acid (one of the
methyl donors used in the mouse studies) in
the US, Canada and elsewhere has resulted
in significant increases in folate status of the
whole population, including women of childbearing age (Pfeiffer et al., 2005). This “natural”
experiment provides an opportunity to test
the hypothesis that maternal methyl donor
supplementation per se is effective in reducing
the risk of obesity in the offspring by examining the relationships between maternal and
offspring adiposity before and since the widespread fortification of baked goods with folic
acid in 1996.
1. FROM BRAIN TO BEHAVIOR
196
15. NUTRITION, EPIGENOMICS AND OBESITY
15.4 EPIGENETICS AND
BRAIN FUNCTION
Investigation of epigenetically-mediated
mechanisms in the brain is in its early stages,
but it is already apparent that epigenetic marks
are important for brain structure and function. For example, Rett syndrome (RTT), the
single gene disorder caused by mutations in
the gene encoding methyl-CpG-binding protein 2 (MeCP2 – located at chromosome Xq28),
presents a progressive loss of developmental milestones associated with aberrant gene
expression (Feinberg, 2008). In the healthy state,
MeCP2 selectively binds CpG dinucleotides and
mediates transcriptional repression through
interaction with histone deacetylase and the corepressor SIN3A. The loss of this repression is
the mechanism underlying the pathogenesis of
RTT (Amir et al., 1999). Recent analysis of DNA
methylation signatures in the human brain
has shown that different brain regions (cerebellum, cerebral cortex and pons) are distinguished by characteristically different patterns
of DNA methylation (Ladd-Acosta et al., 2007).
Differences between brain regions within individuals were much greater than those between
individuals due to potential confounders including age, sex, post-mortem interval or cause of
death. These authors suggested that epigenetic
signatures may, in part, determine brain functional programs (Ladd-Acosta et al., 2007). To
date, there has been little research on the effects
of altered supply of specific nutrients on brain
epigenetic marks. However, a recent publication
reported that long-term feeding of a diet low
in methyl donors caused genomic DNA hypermethylation in the rat cortex which was associated with reduced expression of DNMT1 and
increased expression of the de novo DNA methyl
transferase DNMT3A (Pogribny et al., 2008).
There is substantial proof of principle that
environmental factors program gene expression
in the brain, that this occurs through epigenetic
mechanisms, and that the sequelae are both
long-lasting and important for health. In a rat
model, high-quality maternal care characterized
by licking, grooming and arched back nursing in
the first week of life (“good” mothers) produces
offspring with reduced fearfulness and more
modest hypothalamo-pituitary-adrenal (HPA)
responses to stress (Weaver et al., 2004). In this
model, whole genome transcriptomic analysis of hippocampal tissue revealed more than
900 genes that were differentially expressed
between the adult offspring of “good” and
“poor” mothers (Weaver et al., 2006). Maternal
care was associated with alterations in the pattern of methylation of the glucocorticoid receptor (GR – also designated NR3C1, for nuclear
receptor sub-family 3, group C, member 1) gene
and altered histone acetylation within the hippocampus which became apparent within the
first week of life and persisted into adulthood
(Weaver et al., 2004). Importantly, these aberrant
epigenetic marks could be reversed by crossfostering. In addition, central infusion of trichostatin A (a histone deacetylase inhibitor) ablated
the effects of maternal care on histone acetylation,
DNA methylation, GR expression, and HPA
responses to stress (Weaver et al., 2004). These
findings support the hypothesis that epigenetic
processes in the brain provide a mechanism
through which maternal care influences longterm responses to stress in the offspring (Weaver
et al., 2004). Interestingly, “good” maternal
care resulted in demethylation of very specific
CpG sites corresponding with the nerve growth
factor-inducible protein A (NGFI-A) transcription factor response element in exon 17 of the
GR promoter (Weaver et al., 2004).
A recent study in mother–infant pairs provides support for the hypothesis of environmental “programming” of the HPA axis by
maternal factors in humans (Oberlander et al.,
2008). Methylation of specific CpG residues in
the potential NGIF-A consensus binding site
within exon 17 of the glucocorticoid receptor
gene (NR3C1) in neonatal cord (venous) blood
1. FROM BRAIN TO BEHAVIOR
15.6 PHYSICAL ACTIVITY, EPIGENETIC MARKINGS AND OBESITY
mononuclear cells correlated with exposure to
maternal depression in the third trimester of
pregnancy (Oberlander et al., 2008). Importantly,
this increased methylation correlated positively with HPA stress reactivity assessed as
the change in salivary cortisol concentration in
response to a non-noxious stressor (Oberlander
et al., 2008).
Given the lower risk of childhood (Arenz
et al., 2004) and perhaps adult (Owen et al., 2005)
obesity among those who have been breastfed,
it is tempting to speculate that the nature of
maternal care in the early post-natal period may
have profound effects on adult health through
altered programming of behaviors mediated by
epigenetic mechanisms in the brain.
15.5 AN EPIGENETIC BASIS
FOR DEVELOPMENTAL
PROGRAMMING OF OBESITY?
There is now strong evidence from both observational studies in humans and experiments in
animal models that nutritional insults during
intrauterine and early post-natal development
enhance the risk of increased adiposity later in
life. Intriguingly, both maternal under-nutrition
(leading to low birth weight) and maternal obesity (associated with greater birth weight and
adiposity) increase risk of childhood and adult
obesity (Taylor and Poston, 2007). Whether similar molecular and cellular mechanisms underlie
the phenotypic convergence resulting from these
two contrasting adverse nutritional exposures
remains to be discovered, but it seems likely that
both cause hypothalamic “malprogramming”
(Plagemann, 2005). The adipokine leptin appears
to be the dominant factor, providing the brain
with long-term information about the status of
energy reserves in adipose tissue by binding to
the leptin receptor in the hypothalamus and activating the JAK–STAT and other signal transduction pathways (Badman and Flier, 2005). There
197
is growing evidence that leptin concentrations
in the early post-natal period may play a central
role in hypothalamic programming (reviewed by
Taylor and Poston, 2007). For example, oral dosing with physiological amounts of leptin during
the suckling period in rats resulted in reduced
body fat content in adulthood, and altered
hypothalamic expression of a number of genes
involved in leptin signaling (Pico et al., 2007). Of
particular interest was the lower expression of
suppressor of cytokine signaling 3 (SOCS3), an
important mediator of leptin resistance, which
may produce enhanced sensitivity to leptin in
the regulation of food intake (Pico et al., 2007).
Since leptin is present in human breast milk but
not in infant formula, it is possible that leptin
supply during breast-feeding may contribute
to the “protection” against obesity (Pico et al.,
2007) seen among those who have been breastfed (Arenz et al., 2004). The mechanism through
which leptin (or other exposures) alters SOCS3
expression remains to be discovered. However,
this may involve an epigenetic mechanism, since
the SOCS3 gene contains a large CpG island
extending from the promoter region into exon
2, and aberrant methylation is associated with
altered expression of the gene and disruption of
JAK–STAT signaling (Niwa et al., 2005). Given
the centrality of the hypothalamus in the control
of food intake, there is an a priori case that epigenetic dysregulation of expression of appetite
regulatory genes and/or of associated receptors
and signaling cascades may play an important
role in the programming of obesity.
15.6 PHYSICAL ACTIVITY,
EPIGENETIC MARKINGS AND
OBESITY
In contrast with the expanding body of evidence that dietary factors have wide-ranging
effects on epigenetic marks and, in so doing, may
modulate risk of obesity, very little is known
1. FROM BRAIN TO BEHAVIOR
198
15. NUTRITION, EPIGENOMICS AND OBESITY
about the impact of physical activity on the epigenome or, conversely, about how epigenetically
altered regulation of gene expression might influence willingness to undertake (or capacity for)
physical activity. However, in the cancer field,
there is epidemiological evidence for associations between physical activity and gene methylation. In a study of promoter methylation of a
panel of six genes in colonic tumors, the number
of methylated CpG islands increased with age
but, perhaps surprisingly, fewer were methylated
in those with higher BMI (Slattery et al., 2007).
This study found no relationship between level
of physical activity and number of methylated
genes, but there was evidence that those reporting high physical activity had a lower risk of both
CIMP-low and CIMP-high tumors (CIMP CpG
island methylator phenotype) (Slattery et al.,
2007). The widespread genomic derangements
in tumors make it difficult to ascribe causality to
such observations, and studies of non-tumor tissue can be more informative. The likelihood of
promoter hypermethylation of the tumor suppressor gene APC in non-malignant breast tissue
is inversely related to recent and lifetime measures of physical activity (Coyle et al., 2007). Given
that physical activity appears to lower the risk of
breast cancer, and that the loss of function of APC
(by promoter methylation or mutation) is mechanistically important in tumor development, these
data support the hypothesis that physical activity
might be protective through reducing the likelihood that aberrant epigenetic marking will disable key defense genes.
The mechanism(s) through which physical
activity appears to impact on epigenetic markings are not understood, but inflammation is
potentially a critical mediator. There is good evidence that ulcerative colitis (a common type of
inflammatory bowel disease) is associated with
higher methylation of several genes in the human
colonic mucosa (Issa et al., 2001), and chronic gastric inflammation is accompanied by increased
methylation of several genes (Kang et al., 2003).
More recently, global DNA hypermethylation in
peripheral blood leukocytes was correlated with
chronic systemic inflammation (based on circulating concentration of C-reactive protein) and
shown to be associated significantly with both allcause and cardiovascular disease mortality even
after adjustment for age, inflammation, and other
risk factors (Stenvinkel et al., 2007). Since lack of
physical activity and obesity are each associated
with a chronic inflammatory state (Handschin
and Spiegelman, 2008) it is reasonable to suppose
that both may have effects on epigenetic marks,
and disentangling cause from consequence
will be a considerable challenge. Currently,
there is major interest in the role of the powerful transcriptional co-activator PGC1α (peroxisome-proliferator-activated receptor γ (PPARγ)
co-activator 1α) as the master down-regulator of
inflammation in response to exercise (Handschin
and Spiegelman, 2008), and it will be important to
discover whether expression of the gene encoding PGC1α is epigenetically regulated.
15.7 CONCLUDING COMMENTS
The topology of the epigenomic landscape
provides a sophisticated and long-lasting set of
signals for regulating gene expression in a given
cell under particular circumstances, and across
cell generations. However, these epigenetic
marks are plastic and respond to environmental
exposures, including diet. It is therefore probable that epigenetic processes are a major mechanism through which nutrition modulates health
throughout the life-course. Technologies for characterizing the epigenomics landscape are readily
available (Esteller, 2007) and developments in
this area are expected to accelerate. Epigenetics
has been identified by the National Institutes
of Health as an emerging frontier of science
( http://nihroadmap.nih.gov/epigenomics/ ).
In contrast with the rapid advances in understanding of the role of epigenetics in the etiology
of cancers (Esteller, 2008), there has been little
1. FROM BRAIN TO BEHAVIOR
REFERENCES
research on epigenetic mechanisms in the development of obesity, and the field is open for novel
investigations of, for example, how expression of
the genes responsible for regulating food intake
and energy expenditure are controlled epigenetically. Small differences in expression sustained
over long periods of time would be expected to
have profound effects on energy balance and,
therefore, risk of obesity. The tools, including
bioinformatics approaches (McKay et al., 2008),
necessary to support research on nutritional epigenomics and obesity are there to be used.
ACKNOWLEDGMENTS
Nutritional epigenomics research in my laboratory is funded by the BBSRC and EPSRC
through the Centre for Integrated Systems
Biology of Ageing and Nutrition (CISBAN)
(BB/C008200/1), by the BBSRC (grant no.
BH081097) and by NuGO “The European
Nutrigenomics Organisation; linking genomics,
nutrition and health research” (NuGO; CT2004-505944), which is a Network of Excellence
funded by the European Commission’s Research
Directorate General under Priority Thematic
Area 5, Food Quality and Safety Priority, of the
Sixth Framework Programme for Research and
Technological Development. Further information about NuGO and its activities can be found
at http://www.nugo.org.
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C H A P T E R
16
The Role of Early Life Experiences in
Flavor Perception and Delight
Julie A. Mennella and Gary K. Beauchamp
Monell Chemical Senses Center, Philadelphia, PA, USA
O U T L I N E
16.1 Introduction
203
16.2 Flavor and the Ontogeny of the
Senses
16.2.1 Taste
16.2.2 Olfaction
16.2.3 Chemical Irritation
16.2.4 Ontogeny of the Flavor Senses
205
205
206
206
206
16.1 INTRODUCTION
Food is much more than a source of calories,
since its flavor can signal nutrient sources, provide pleasure (or pain) and, through experience,
be identified with one’s family, community and
culture. The pleasure experienced upon ingestion of a food is a complex process mediated by
the chemical senses (taste and smell and irritant
Obesity Prevention: The Role of Brain and Society on Individual Behavior
16.3 Taste and Development
16.3.1 Sweet Taste
16.3.2 Salt Taste
16.3.3 Bitter Taste
207
207
208
209
16.4 Learning about Food Flavors
211
16.5 Concluding Remarks
212
Acknowledgments
213
properties of foods) in the periphery and then
multiple brain substrates, which are remarkably well conserved phylogenetically (Berridge
and Kringelbach, 2008). The degree to which
the chemicals that stimulate these flavor senses
are liked or disliked is determined by innate or
inborn factors, learning and experience, and
the interactions among these. In essence, these
senses, which are already well-developed at birth
(for review, see Ganchrow and Mennella, 2003),
203
© 2010,
2010 Elsevier Inc.
204
16. EARLY FLAVOR EXPERIENCES
function as gatekeepers throughout one’s life.
They control one of the most important decisions
an animal makes – whether to reject a foreign
substance or take it into the body. Furthermore,
these senses function to inform the gastrointestinal system about the quality and quantity of the
impending rush of nutrients.
Although the modernization and industrialization of the food supply has produced many
benefits, unanticipated consequences from
eating diets rich in sugars, salt and fats have
become increasingly commonplace (Gidding
et al., 2009). Excessive intake of foods containing high amounts of salt or sugars (and consequently foods that taste salty and sweet)
causes or exacerbates a number of illnesses. For
example, high intake of salt has been linked to
hypertension in some individuals, and there
is a broad, but not universal, agreement that
decreasing salt intake on a population-wide
basis could save many lives (Hooper et al.,
2004). Similarly, excessive intake of refined sugars has been linked to the metabolic syndrome
and, perhaps less persuasively, to obesity (Reed
and McDaniel, 2006). Thus, it is recommended
that both adults and children limit the amount
of salt and simple sugars; minimize excessive
intakes of energy, saturated fat, trans fat and
cholesterol; and favor diets rich in vegetables
and fruits, whole grains, low- and non-fat dairy
products, legumes, fish and lean meat (Gidding
et al., 2005; Lichtenstein et al., 2006).
Despite such recommendations, neither
adults nor their children are complying. The
2004 Feeding Infants and Toddlers Study in
the US alarmingly revealed that while toddlers were more likely to be eating fruits than
vegetables, one in four did not even consume
one vegetable on a given day (Briefel et al.,
2006; Mennella et al., 2006). Instead, they, like
older children (Siega-Riz et al., 1998; Mannino
et al., 2004; Nicklas et al., 2004; Schmidt et al.,
2005), were more likely to eat fatty foods such
as French fries, sweet- and salty-tasting snacks
and sweet beverages, and less likely to eat
bitter-tasting vegetables (Briefel et al., 2006;
Mennella et al., 2006). None of the top five vegetables consumed by toddlers was a dark green
vegetable (Mennella et al., 2006). Not only is the
consumption of fruits and vegetables generally
low in pediatric populations (Briefel et al., 2006;
Mennella et al., 2006), but acceptance of these
foods is difficult to enhance beyond toddlerhood (Wardle et al., 2003a, 2003b). Moreover,
despite participation in high-quality dietary
intervention programs, snacks, desserts and
pizza continue to contribute heavily in the
diets of elementary school students (Van Horn
et al., 2005). One reason for why it is difficult to
alter children’s dietary intake is the remarkably
potent rewarding properties of the flavors of
foods.
This chapter will focus on the biological
imperatives that shape food and flavor likes
and dislikes, and will take a developmental
approach since, although some changes in preference occur during adolescence, many food
preferences are firmly in place by the time a
child reaches the age of 3 years (Resnicow et al.,
1998; Skinner et al., 2002a; 2002b; Cooke et al.,
2004; Nicklaus et al., 2004, 2005). Because the
senses of taste and smell are the major determinants of whether young children will accept a
food (that is, they eat only what they like (Birch,
1998)), these senses take on even greater significance in understanding the bases for food
choices in children than they do for adults.
In what follows, it will be argued that the
type of foods preferred or rejected by children reflects their basic biology. We focus on
the ontogeny of sweet, salty and bitter tastes
because these tastes have been most extensively studied, are directly involved in choices
of specific foods of concern (for example, sweet
and salty snacks, green vegetables), and exhibit
age-related changes in function. Although flavors associated with fats and fatty acids may also
be detected, in part, by the sense of taste, there is
insufficient evidence to review the ontogeny of
fat taste. However, given children’s preference
1. FROM BRAIN TO BEHAVIOR
16.2 FLAVOR AND THE ONTOGENY OF THE SENSES
(Fisher and Birch, 1995) and the rewarding properties of fats (see, for example, Johnson et al.,
1991), this is certainly a research topic worthy of
further investigation. The inherent plasticity of
the chemical senses and how, as a consequence
of post-natal maturation and early life experiences, developmental processes act to ensure
that a child is not restricted to a narrow range
of foodstuffs by virtue of few preferences and
strong aversions for foods will be discussed.
First, though, the chapter begins by providing a
basic understanding of taste, smell and chemical irritation, the differences between them, and
how they interact to produce the overall impression of a food which we define as its flavor.
16.2 FLAVOR AND THE
ONTOGENY OF THE SENSES
The perceptions arising from the senses of
taste, smell and chemical irritation combine
in the oral cavity to determine flavor. These
perceptions are often confused and misappropriated (Rozin, 1982), with such olfactory sensations as vanilla, fishy, chocolate and coffee being
erroneously attributed to the taste system per se
when, in fact, much of the sensory input is due
to retronasal olfaction (see below).
16.2.1 Taste
The taste system is attuned to a small number
of perceptual classes of experience, the so-called
basic tastes, each of which specifies crucial information about nutrients or dangerous substances.
This small number of primary taste qualities
(e.g., sweet, salty, bitter, sour and savory or
umami) is detected by specialized receptors on
the tongue, other parts of the oral cavity and
even in the gastrointestinal system (Bachmanov
and Beauchamp, 2007; Egan and Margolskee,
2008). These basic tastes either stimulate intake
(sweet, salty and savory) or inhibit it (bitter and
205
perhaps sour) when ingested within a generally
restricted range of concentrations.
Major progress has been made in identifying the initial events in taste recognition (for
more extensive reviews, see Chandrashekar
et al., 2006; Kim et al., 2006; Bachmanov and
Beauchamp, 2007; Lumpkin and Caterina, 2007;
Katz et al., 2008). It appears that two different
strategies have evolved to detect taste molecules.
For salty and sour tastes, it is widely believed
that ion channels serve as receptors. Here,
H (sour) and Na (salty) ions interact with
channels in the taste cell membrane. The cell is
then activated, and sends an electrical message
to the brain. However, for both of these taste
qualities the molecular identity of the receptors
and their exact mechanisms are still unknown.
For sweet, umami and bitter tastes, G-proteincoupled receptors (GPCRs) appear to play the
most prominent roles. These GPCRs bind taste
molecules in a sort of lock-and-key mechanism, thereby activating the taste cell to send an
electrical message to the brain. For sweet and
umami, a family of three GPCRs, named T1R1,
T1R2 and T1R3, act in pairs (T1R1 T1R3 for
umami and T1R2 T1R3 for sweet) to detect
molecules imparting these taste qualities.
Other GPCRs may also be involved. A substantially larger family of GPCRs, the T2Rs (n ⬃ 25),
constitutes the bitter receptors.
From an evolutionary perspective, these taste
qualities likely evolved to detect and reject that
which is harmful and to seek out and ingest
that which is beneficial. It has been hypothesized that the small number of taste qualities
evolved because of the functional importance of
the primary stimuli (e.g., sugars, sodium chloride, amino acids and protein, organic acids, bitter toxins) in nutrient selection, especially in
children. Preference for salty and sweet tastes is
thought to have evolved to attract us to minerals
and to energy-producing sugars and vitamins,
respectively. Rejection of bitter-tasting and irritating substances evolved to protect the animal
from being poisoned and the plant producing
1. FROM BRAIN TO BEHAVIOR
206
16. EARLY FLAVOR EXPERIENCES
these chemicals from being eaten (Jacobs et al.,
1978; Glendinning, 1994). However, while bitter tastes are innately disliked, with experience
people may come to like certain foods that are
bitter, particularly some vegetables, and foods
and beverages with pharmacologically active
bitter compounds, such as caffeine or ethanol.
16.2.2 Olfaction
The organization of the olfactory system
reflects the need to recognize a wide range
of odors and to discriminate one odor from
another. In fact, the olfactory receptors are
encoded by the largest mammalian superfamily of genes (Buck and Axel, 1991). In contrast to the taste system, there are thousands of
diverse odor qualities. Volatile molecules (odorants) bind to olfactory receptors located on a
relatively small patch of tissue high in the nasal
cavity. Odor molecules can reach these receptors by entering the nostrils during inhalation
(orthonasal route) or traveling from the back
of the oral cavity toward the roof of the nasal
pharynx (retronasal route). It is this retronasal
stimulation arising from the molecules of foodstuffs that leads to many of the flavor sensations
we experience during eating.
Although there is some evidence that certain odors may be innately biased in a positive or negative direction (Khan et al., 2007),
individual experiences largely determine how
much a person likes or dislikes the odor component of a food or beverage flavor. Through
experiences, odors acquire personal significance
(Epple and Herz, 1999; Mennella and Forestell,
2008; Mennella and Garcia, 2000). Memories
evoked by odors are more emotionally charged
and resistant to change than those evoked by
other sensory stimuli (Herz and Cupchik, 1995;
Epple and Herz, 1999). The unique processing
of olfactory information (Cahill et al., 1995) and
the olfactory system’s immediate access to the
neurological substrates underlying non-verbal
aspects of emotion and memory (Royet and
Plailly, 2004) help explain the large emotional
component of food aromas. This, coupled with
the recent finding that the most salient memories formed during the first decade of life will
likely be olfactory in nature (Willander and
Larsson, 2006), explains how food aromas can
trigger memories of childhood, and why flavors
and food aromas experienced during childhood
remain preferred and can, to some extent, provide comfort.
16.2.3 Chemical irritation
Sensations resulting from chemicals stimulating receptors and free nerve endings of
the trigeminal and vagal nerves lead to oral,
nasal and pharyngeal sensations such as pain,
heat, coolness, tingling, tickle and itch. Recent
research has shown that a family of transient
receptor potential (TRP) channels is involved
in detecting many of these chemicals (Bautista
et al., 2006; Liman, 2007). These channels also
respond to actual heat and cooling. While “irritating” sensations are critical in food and flavor
acceptability, and most likely have a huge
impact on acceptance by children, there is virtually no research on their ontogeny. Thus, the
remainder of this chapter focuses only on taste
and smell.
16.2.4 Ontogeny of the flavor senses
Both taste and olfactory systems are welldeveloped and functioning before birth (for
review, see Ganchrow and Mennella, 2003). The
anatomical substrates mediating the detection
of taste stimuli make their first appearances at
around the seventh or eighth week of gestation, and by the thirteenth to fifteenth weeks,
the taste bud in which taste receptor cells arise
begins to morphologically resemble the adult
bud, except for the cornification overlying the
papilla (Bradley and Stern, 1967; Bradley and
1. FROM BRAIN TO BEHAVIOR
16.3 TASTE AND DEVELOPMENT
Mistretta, 1975). Taste receptor cells are capable
of conveying gustatory information to the central nervous system by the last trimester of
pregnancy. This information is available to systems organizing sucking, facial expressions, and
other affective behaviors.
With regard to olfaction, the olfactory bulbs
and receptor cells needed to detect olfactory
stimuli have attained adult-like morphology
by the eleventh week of gestation (Humphrey,
1940; Pyatkina, 1982). Olfactory marker protein, a biochemical correlate of olfactory receptor functioning, has been identified in the
olfactory epithelium of human fetuses at 28
weeks of gestation (Chuah and Zheng, 1987).
Because the external nares (nostrils) are opening between the sixteenth and twenty-fourth
gestational weeks, there is a subsequent continual movement of amniotic fluid through the
nasal passages such that, by the last trimester
of pregnancy, the fetus inhales more than twice
the volume of amniotic fluid it swallows. The
chemical composition of this fluid, and hence
its flavor, changes constantly, in part because of
the passage of food flavors from the maternal
diet (Hepper, 1988; Mennella et al., 1995; Schaal
et al., 2000). Even in air-breathing organisms,
volatile molecules must penetrate the aqueous
mucus layer covering the olfactory epithelium
to reach receptor sites on the cilia. Thus, there
is no fundamental distinction between olfactory
detection of airborne versus waterborne stimuli
during fetal life.
16.3 TASTE AND DEVELOPMENT
From the perspective of taste development,
what children like to eat (e.g., sweet cereals, desserts, salty snacks) and do not like to eat (e.g.,
green vegetables) is not surprising. Children are
programmed, through the sense of taste, to like
foods and beverages that taste sweet or salty,
and to dislike bitter ones (Cowart et al., 2004).
207
16.3.1 Sweet taste
Intense liking for sweet taste is evident
early in ontogeny. Within the first few hours
of life, consistent, quality-specific facial expressions such as smiling and relaxation of facial
muscles are elicited when infants taste sweettasting solutions (Steiner, 1977; Rosenstein and
Oster, 1988). This suggests that the liking for
sweet reflects basic human biology, and is not
solely a product of modern-day technology and
advertising.
For infants and children around the world,
the general rule seems to be the sweeter the better (for review, see Liem and Mennella, 2002;
Mennella, 2008). Preferences for sweets remain
heightened throughout childhood (Beauchamp
and Moran, 1984; Mennella et al., 2005; Pepino
and Mennella, 2005a) and early adolescence
(Desor et al., 1975), but then decline to adult
levels during late adolescence (Desor and
Beauchamp, 1987). In a cross-sectional study
that measured sweet preference in more than
750 participants, 50 percent of the children and
adolescents, but only 25 percent of the adults,
selected a 0.60-M sucrose concentration as their
favorite solution. To put this in perspective, a
0.60-M sucrose concentration is equivalent to
approximately 12 spoonfuls of sugar in 230 ml
of water (an 8-ounce glass), whereas a typical cola is about half of this sucrose (or sucrose
equivalent) concentration.
Making foods, beverages and even medications
taste sweet can increase both liking and acceptance by children (Filer, 1978; Beauchamp and
Moran, 1984; Sullivan and Birch, 1990). This strong
preference for sweet tastes may have an ecological
basis. At birth, a sweet-liking may help to ensure
the acceptance of sweet-tasting mother’s milk.
As children begin to eat solid foods, their sweet
preference attracts them to foods, such as fruits,
that are associated with energy-producing sugars,
minerals and vitamins. Although strong evidence
is lacking, it has been suggested that such preferences evolved to solve a basic nutritional problem
1. FROM BRAIN TO BEHAVIOR
208
16. EARLY FLAVOR EXPERIENCES
of attracting children to sources of high energy
during periods of maximal growth (Simmen and
Hladik, 1998; Drewnowski, 2000; Coldwell et al.,
2009).
Although the liking for sweet-tasting substances is inborn, the degree to which early
experiences alter or modulate sweet preferences
later in life is largely unknown. Longitudinal
studies revealed that babies who were routinely
fed sweetened water during the first months of
life exhibited a greater preference for sweetened
water when tested at 6 months (Beauchamp and
Moran, 1982) and then again at 2 years of age
(Beauchamp and Moran, 1984) when compared
to those who had little or no experience with
sweetened water. Similarly, a more recent crosssectional study on 6- to 10-year-old children
revealed that such feeding practices may have
longer-term effects on the preference for sweetened water than previously realized (Pepino
and Mennella, 2005a).
However, there are no compelling data suggesting that repeated exposure to sugar water
results in a generalized heightened hedonic response to sweetness (Beauchamp and Moran,
1984). Rather, the context in which the taste experience occurs is an important factor. Through
familiarization, children develop a sense of what
should, or should not, taste sweet (Beauchamp
and Cowart, 1985). The cultures in which children live and their early-life experiences enable
them to develop a sense of how foods should
taste.
If the goal is to limit consumption of sweet
foods and beverages, children’s preferences
for sweetness may not be the only barrier.
Sweet-liking may also have its roots in how
sweets make children feel. A small amount of
a sweet solution placed on the tongue of a crying newborn can blunt expressions of pain and
calm both preterm and full-term infants who
have been subjected to painful events such as
heel stick or circumcision, presumably via the
involvement of the endogenous opioid system
(Blass and Hoffmeyer, 1991; Barr et al., 1999).
Afferent signals from the mouth, rather than
gastric or metabolic changes, appear to be
responsible for the analgesic properties of
sugars (Barr et al., 1999; Ramenghi et al., 1999;
Bucher et al., 2000).
The ability of sweets to reduce pain continues during childhood (Miller et al., 1994; Pepino
and Mennella, 2005b), and the more children
like sucrose, the better it works in increasing pain tolerance during the cold pressor test
(Pepino and Mennella, 2005b). Thus, it is important to realize that trying to limit consumption of sweet-tasting foods and beverages may
be more difficult for some children or certain
ethnic groups (Desor et al., 1975; Bacon et al.,
1994; Pepino and Mennella, 2005a) because of
individual differences in the inherent hedonic
value of sweet tastes and how sweets make a
person feel.
16.3.2 Salt taste
Children’s avidity for salt is more complex
and less well understood than that for sweets.
A liking for salt water relative to plain water is
not evident at birth (Steiner, 1977; Rosenstein
and Oster, 1988). Young infants (2–4 months of
age) did not detect and differentiate salt solutions from plain water. Rather, the ability to
detect salty tastes appears to develop later; it is
in most children around 4–5 months of age that
a preference begins to be observed (Beauchamp
et al., 1986). Moreover, to a greater extent than
that observed for sweet taste, the degree of
avidity for salt seems to be affected by individual experiences, beginning in utero (Crystal
and Bernstein, 1995, 1998; Stein et al., 2006). For
example, severe maternal emesis can have an
enduring influence on an offspring’s response to
salty tastes (Crystal and Bernstein, 1995; Leshem,
1999). Similarly, several behavioral measures
related to salty taste preference have been found
to be inversely related to birth weight over the
first 4 years of life (Stein et al., 2006). Because it
1. FROM BRAIN TO BEHAVIOR
16.3 TASTE AND DEVELOPMENT
is generally accepted that excess salt intake can
lead to or exacerbate hypertension, we speculate
that one mechanism predisposing to high salt
intake is the heightened preferences caused by
in utero events common to lower birth-weight
babies, although the mechanisms underlying
this effect of body weight are not known.
Like sweet tastes, children prefer substantially
higher levels of salt than do adults, and adding salt to many foods can drive consumption
(Beauchamp and Moran, 1984; Beauchamp et al.,
1994). Factors responsible for this age-related
difference are not known. Nevertheless, we do
know that salt-liking and preference in infants
and young children are regulated to some extent
by prior dietary exposure. For example, bottlefed infants exhibit higher salt preferences than
do breastfed infants (Beauchamp and Stein,
2008), perhaps due to the greater amounts of
sodium in formula relative to breast milk. Other
evidence indicates that infants who are fed
starchy foods (that likely also contain substantial amounts of salt) early in life have elevated
salt preferences compared to infants whose early
supplemental feedings do not contain these
high-salt foods (Beauchamp and Stein, 2008).
The findings relating preference for salty
taste with amount of exposure were correlational, and hence do not prove cause and
effect. However, studies on adults revealed that
the experimental manipulation of salt intake
can alter salt-taste perception and preference
(Bertino et al., 1982; Beauchamp et al., 1990).
When total salt intake is reduced over a substantial period of time, adults prefer lower levels of
salt and perceive a given level of salt as being
more intense. This taste change, which takes
2 to 3 months, can be rapidly reversed when
individuals are returned to their typical dietary
salt level (Beauchamp et al., 1990).
In conclusion, salty taste preferences begin to
be observed at about 4 months of age, and are
apparently more plastic than are sweet preferences. Nevertheless, our knowledge of how
early exposures impact later preferences and
209
intake remains incomplete. It will be important
to determine whether early exposure to lowersalt foods can help protect the developing child
from excess intake later in life.
16.3.3 Bitter taste
A rejection of bitter compounds is common across many phyla, and is thought to
reflect the need to avoid consuming toxic compounds. There are, however, many species differences in sensitivities to bitter compounds and
the number of different bitter receptors that
are expressed (Go, 2006); these differences
are thought to reflect differences in ecological
niches and food choices. It is generally assumed
that the existence of multiple bitter receptors (there are approximately 25 in humans;
Chandrashekar et al., 2000; Mueller et al., 2005)
reflects the wide structural variability of bitter
compounds, which in turn reflects the evolution of protective compounds by plant species.
Plants do not “want” to be eaten, and animals
do not “want” to be poisoned. Thus, a strong
rejection of bitterness by children is evolutionarily prudent: children may be at particular risk
from the ingestion of toxic, bitter compounds.
Rejection of bitter tastes is evident early in
life, although there seem to be differences based
on the bitter compound tested. For example,
while human infants respond with highly negative facial expressions to concentrated quinine,
significant rejection of urea does not occur until
a few weeks after birth (Kajuira et al., 1992).
A different developmental timetable for rejecting different bitter compounds may reflect the
multiple controls of bitterness sensation that
develop at different rates (Margolskee, 2002).
Moreover, the 25 different bitter receptors, each
likely responsive to one or several structurallyrelated bitter compounds, could be expressed at
different times during development.
One of the predominant flavor characteristics
of the prototypical healthy foods – vegetables – is
1. FROM BRAIN TO BEHAVIOR
210
16. EARLY FLAVOR EXPERIENCES
their bitterness. Indeed, many of the apparent health-related benefits of consuming vegetables come precisely from bitter ingredients
such as glucosinolates, which at low levels are
healthful but at higher levels can be harmful.
However, there is a great deal of individual differences in how sensitive people are to specific
bitter compounds. The classic example of genetic
differences in taste sensitivity is for phenylthiocarbamide (PTC) and the related chemical 6-npropylthiouracil (PROP). Some people can detect
these compounds at low concentrations, whereas
others need much higher concentrations, or
cannot detect them at all (Kim et al., 2003; Bufe
et al., 2005; Hayes et al., 2008). The gene TAS2R38,
variants which accounts for the majority of this
taste polymorphism, codes for one member of
the family of taste receptors that respond to bitter stimuli. Recently, it was discovered that variation in this bitter receptor specifically regulates
adults’ bitterness perception of cruciferous vegetables known to contain PTC-like glucosinolates
(e.g., turnips, broccoli, mustard greens) (Sandell
and Breslin, 2006).
Children are not only more likely to experience a strong bitter taste from PTC and PROP,
but are also more sensitive to it, detecting it at
lower concentrations than adults (Blakeslee,
1932; Karam and Freire-Maia, 1967; Anliker
et al., 1991; Mennella et al., 2005). This agerelated change in sensitivity for PROP was
recently shown to be affected by sequence diversity in the bitter taste receptor TAS2R38 gene.
Children who were heterozygous for the common form of this receptor were more sensitive
to the bitterness of PROP than were adults with
this same form (Mennella et al., 2005). Like sweet
and salt preference, the timing of the shift from
child-like to adult-like PROP perception occurs
during adolescence (Mennella et al., 2010). The
age-related change in bitter perception is likely
to have a broad impact because of the high allele
frequencies of the taster and non-taster haplotypes in the human population.
One effective strategy in reducing the bitterness of certain foods, and thereby increasing
their acceptability, is to add salt. This may partly
explain the ubiquitous use of salt in cooking evident in many cultures. Sodium salts, particularly
sodium chloride (i.e., table salt), impart a desirably salty taste to foods (Kemp and Beauchamp,
1994). One mechanism underlying this increase
in palatability may be the suppressing activity
of sodium on bitter taste by a mechanism that is
still obscure. There are substantial compoundspecific differences in the effectiveness of salt in
inhibiting bitterness, presumably reflecting the
wide array of bitter compounds and the multiple
receptor-transductive pathways for bitterness.
Salt also enhances the intensity of sweetness, presumably by blocking bitterness and
thereby releasing sweetness from suppression
(Breslin and Beauchamp, 1997). Furthermore, like
adults (Kroeze and Bartoshuk, 1985; Breslin and
Beauchamp, 1995; Keast and Breslin, 2002), the
perceived bitterness of some bitter compounds is
reduced when such compounds are mixed with
sodium salts in children (Mennella et al., 2003).
Perhaps a little salt may go a long way in getting
children to accept the taste of bitter vegetables.
Childhood may represent a time of heightened bitter-sensitivity. As will be discussed in
the next section, children’s acceptance of bittertasting foods such as leafy green vegetables
can be facilitated with early and repeated exposure (Gerrish and Mennella, 2001; Forestell and
Mennella, 2007; Mennella et al., 2008). However,
it may be harder to ensure that children who
are particularly sensitive to compounds in bitter vegetables are exposed to these, in comparison with bitter-insensitive children. An absence
of early exposure to bitterness may, in turn,
affect the development of their taste system. In
rodents, early taste deprivation remodels the
central nervous system (Mangold and Hill, 2007),
and experience with bitterness during early life
changes bitter taste preferences in adulthood
(Harder et al., 1989).
1. FROM BRAIN TO BEHAVIOR
16.4 LEARNING ABOUT FOOD FLAVORS
16.4 LEARNING ABOUT FOOD
FLAVORS
The flavor of food is comprised of much more
than the basic tastes of sweetness, sourness, bitterness, saltiness and umami or savoriness. The
contribution to the overall flavor of the volatile
odors of foods, perceived retronasally, is crucial
for identifying foods. During the past two decades, a growing body of data has suggested that
early experiences with these food volatiles serve
as the foundation for lifelong habits. That is, in
contrast to taste preferences, preferences for
volatile flavor compounds detected by the sense
of smell retronasally are generally more highly
influenced by experiences, with those occurring
early in life being particularly salient (Bartoshuk
and Beauchamp, 1994).
The sensory environment in which fetuses
live, the amniotic sac, changes as a function of the
mother’s food choices, since dietary flavors are
transmitted and flavor amniotic fluid (Hepper,
1988; Mennella et al., 1995; Schaal et al., 2000).
Prenatal experiences with food flavors, which are
transmitted from the mother’s diet to the amniotic
fluid, lead to greater acceptance and enjoyment of
these foods during weaning. This flavor-learning
continues when infants are breast-fed, since
human milk is composed of volatile flavors which
directly reflect the foods, spices and beverages
ingested or inhaled (e.g., tobacco) by the mother
(Mennella and Beauchamp, 1991, 1993, 1996). In
common with other mammals (for review, see
Mennella, 2007), early exposure leads to greater
liking and acceptance. For example, infants whose
mothers ate more fruits and vegetables during
pregnancy and lactation were more accepting of
these foods during weaning (Mennella et al., 2001;
Forestell and Mennella, 2007).
That amniotic fluid and breast milk share a
commonality in flavor profiles with the foods
eaten by the mother suggests that breast milk may
“bridge” the experiences with volatile flavors
211
in utero to those with solid foods. Moreover, the
sweetness and textural properties of human
milk, such as viscosity and mouth-coating, vary
from mother to mother, thus suggesting that
breast-feeding, unlike formula feeding, provides
the infant with the potential for a rich source of
other variations in chemosensory experiences.
The types and intensity of flavors experienced in
breast milk may be unique for each infant, and
serve to identify the culture to which the child is
born and raised. In other words, the flavor principles of the child’s culture are experienced prior
to their first taste of solid foods.
When infants are exposed to a flavor in the
amniotic fluid or breast milk and are tested
sometime later, the exposed infants accept the
flavor more than infants without such experience (Mennella et al., 2001). This pattern makes
evolutionary sense, since the foods that a
woman eats when she is pregnant and nursing are precisely the ones that her infant should
prefer. All else being equal, these are the flavors
that are associated with nutritious foods, or
at least foods she has access to, and hence the
foods to which the infant will have the earliest
exposure. In a recent study, it was shown that
breast-feeding conferred an advantage when
infants first tasted a food, but only if their mothers regularly eat similar tasting foods (Forestell
and Mennella, 2007). If their mothers eat fruits
and vegetables, breast-fed infants will learn
about these dietary choices by experiencing the
flavors in their mother’s milk, thus highlighting the importance of a varied diet for both
pregnant and lactating women (Forestell and
Mennella, 2007). These varied sensory experiences with food flavors may help explain why
children who were breastfed were found to be
less “picky” (Galloway et al., 2003) and more
willing to try new foods (Sullivan and Birch,
1994; Mennella and Beauchamp, 1996), which
in turn contributes to greater fruit and vegetable
consumption in childhood (Skinner et al., 2002a;
Cooke et al., 2004; Nicklaus et al., 2005). Formula
1. FROM BRAIN TO BEHAVIOR
212
16. EARLY FLAVOR EXPERIENCES
feeding, quite a new innovation in human infant
eating practices, differs from breast milk in that
it lacks sensory variety and does not reflect the
foods the mother consumes. There is not enough
known about how this lack of flavor experience
impacts later food choices, but it is reasonable
to hypothesize that formula-fed children are at
a nutritional disadvantage. Nevertheless, recent
research revealed that once infants, regardless
if they are breast- or formula-fed, are weaned
to solid foods, acceptance can be facilitated by
different types of early dietary experience. One
type of experience entailed repeated dietary
exposure to a particular vegetable or fruit for at
least 8–9 days (Sullivan and Birch, 1994; Gerrish
and Mennella, 2001; Forestell and Mennella,
2007; Mennella et al., 2008). Like children (Birch
and Marlin, 1982), infants ate significantly more
of the fruit or vegetable to which they were
repeatedly exposed. Merely looking at the food
does not appear to be sufficient, since children
have to experience the flavor of the food to learn
to like it (Birch et al., 1987).
Another type of dietary experience does not
require actual exposure to the target fruit or
vegetable, but rather experience with a variety of flavors. Infants who were repeatedly
exposed to a different starchy vegetable each
day ate as many carrots after the exposure as
did infants who were repeatedly exposed to
carrots (Gerrish and Mennella, 2001). Similarly,
repeated dietary experience with a variety of
fruits enhanced acceptance of a novel fruit, but
had no effect on the infants’ acceptance of green
vegetables (Mennella et al., 2008). Because rejection of bitter taste is largely innate (Kajuira et al.,
1992), infants may need actual experience with
bitter taste, more exposures, or a different type
of variety experience to enhance acceptance of
green vegetables. Additional experimental studies, as well as randomized nutrition interventions that focus on maternal dietary habits and
infantile dietary experiences, are needed to better understand how liking for the taste of foods
develops (Lucas, 1998).
16.5 CONCLUDING REMARKS
The child’s basic biology, a consequence of a
long evolutionary history, does not predispose
the child to favor low-sugar, low-sodium and
vegetable-rich diets. The sensory and biological considerations reviewed herein shed light
on why it is difficult to make lifestyle changes
in young children, and why it is difficult for
children to eat nutritious foods when these
foods do not taste good to them. We cannot easily change the basic ingrained biology of liking
sweets and avoiding bitterness.
If this is the bad news, the good news arises
from our growing knowledge of how, beginning very early in life, sensory experience can
shape and modify flavor and food preferences.
In other words, what we can do is modulate
children’s flavor preferences by providing early
exposure, starting in utero, to a wide variety
of healthy flavors, and moderating exposure
to salt. To this end, the pregnant and nursing mother should widen her food choices to
include as many flavorful and healthy foods as
possible. Infants of women who do not breastfeed should be exposed repeatedly to a variety of foods, particularly fruits and vegetables,
from an early age. Further, mothers should
be encouraged to focus on their infants’ willingness to eat the food, and not just the facial
expressions made during feeding. They should
also be made aware that, with repeated dietary
exposure, it may take longer to observe changes
in facial expressions than intake (Forestell and
Mennella, 2007). Also, infant formula manufacturers should be encouraged to provide
lower-salt infant formula that contains flavors
of the foods that children will be weaned to
(e.g., fruits, vegetables). These experiences,
combined with provision of infants and children with nutritious foods and flavor variety as
well, should maximize the chance that they will
select and enjoy a more healthy diet. Moreover,
many of these preferences may last throughout
1. FROM BRAIN TO BEHAVIOR
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The best predictor of what children eat
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flavor-related memories initiated early in life
together play a role in the strong emotional
component of food habits. An appreciation of
the complexity of early feeding, and a greater
understanding of the cultural and biological
mechanisms underlying the development of
food preferences, will aid in our development
of evidence-based strategies and programs to
improve the diets of our children.
ACKNOWLEDGMENTS
The preparation of this manuscript and much
of the research described herein was supported
by NIH Grant HD37119 from the National
Institutes of Health, USA. We thank Dr Allison
Ventura Rubenstein for helpful comments on
the manuscript.
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1. FROM BRAIN TO BEHAVIOR
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C H A P T E R
17
Implications of the Glycemic
Index in Obesity
Julia M.W. Wong1,2, Andrea R. Josse1,2, Livia Augustin3,
Nishta Saxena1,2, Laura Chiavaroli1,2, Cyril W.C. Kendall1,2
and David J.A. Jenkins1,2,4
1
Clinical Nutrition & Risk Factor Modification Center, St. Michael’s Hospital, Toronto, Canada
2
Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada
3
Unilever Health Institute, Unilever Research and Development, Vlaardingen, The Netherlands
4
Department of Medicine, Division of Endocrinology and Metabolism, St Michael’s Hospital,
Toronto, Canada
O U T L I N E
17.1 Introduction
219
17.5 GI and Obesity
224
17.2 The Concept of the Glycemic Index
220
17.6 GI and Diabetes
224
17.3 Mechanisms of Action
221
17.7 GI and Cardiovascular Disease
225
17.8 Conclusion
226
17.4 Effects of Low GI Foods on Appetite,
Food Intake and Satiety
222
17.1 INTRODUCTION
The growing prevalence of obesity in adults
and children is an important public health concern, as these individuals are at greater risk of
developing chronic diseases such as coronary
Obesity Prevention: The Role of Brain and Society on Individual Behavior
heart disease (CHD) and diabetes. Nutritional
strategies to combat this growing concern have
never been more important. The current recommendation of high-carbohydrate diets to help
manage weight (Klein et al., 2004) has recently
been challenged as the number of people who
are classified as overweight (body mass index
219
© 2010,
2010 Elsevier Inc.
220
17. GLYCEMIC INDEX AND OBESITY
(BMI) 25 kg/m2) or obese (BMI 30 kg/m2)
(WHO, 2000) continues to rise. Alternative
dietary approaches have emerged which vary
in their total calories, macronutrient (carbohydrate, fat, and protein) content, energy density
and glycemic index, as well as portion control
(Klein et al., 2004). However, at the center of
this debate are the metabolic effects of carbohydrates: these diets focus on decreasing the total
carbohydrate content and increasing the intake
of protein. However, the nature of the carbohydrate may be important – that is, slow-release
carbohydrates or low glycemic-index (GI) foods
versus fast-release carbohydrates or high GI
foods. Evidence suggests that there are metabolic advantages to increasing low GI food consumption, and that their consumption should
be advised over that of high GI foods.
foods based on the rate of carbohydrate absorption as determined by its postprandial glycemic
response compared to a reference standard
(Jenkins et al., 1981, 1984). Thus, the GI differentiates carbohydrate-rich foods that result in a
lower postprandial blood glucose rise (i.e., low
GI foods) from those that produce a larger postprandial blood glucose rise (i.e., high GI foods).
As a result, the GI is considered a specific property of the food itself and differs from the term
“glycemic response”, which is an individual’s
change in blood glucose after ingestion of the
food (Wolever, 2006).
Many starchy staples of traditional cultures
have a lower GI, including pasta, some wholegrain breads, cracked wheat or barley, some rices,
dried peas, beans and lentils (Jenkins et al., 1980,
1986; Thorne et al., 1983) (Tables 17.1 and 17.2).
In cultures such as the Pima Indians and the
17.2 THE CONCEPT OF THE
GLYCEMIC INDEX
It was traditionally believed that postprandial blood glucose responses were determined
by the carbohydrate chain length, often referred
to as simple or complex carbohydrates. Over
time, increasing experimental evidence has
questioned this classification and given rise to
the concept of the GI. It suggests, as an extension to the dietary fiber hypothesis first proposed by Burkitt and Trowell (Burkitt and
Trowell, 1977), that certain carbohydrates, by
virtue of their rate of digestibility and absorption, may provide a strategy to prevent and
manage chronic diseases such as diabetes and
CHD (Jenkins and Jenkins, 1995). The GI is
defined as the incremental area under the blood
glucose response curve (IAUC) elicited by a
50-g available carbohydrate portion of a food,
expressed as a percentage of the response after
the consumption of 50 g of anhydrous glucose
or white bread (Wolever, 2006). In other words,
it is a numerical classification of carbohydrate
TABLE 17.1 Glycemic indices of some traditional
and contemporary foods
Food
GI*
Traditional foods
Pasta (spaghetti)
60
Pumpernickel bread
58
Cracked wheat
68
Pearled barley
36
Parboiled rice
68
Beans
39–55
Lentils
36–42
Chickpeas
39
Contemporary foods
White bread
101
White bagels
103
Instant mashed potatoes
122
Glutinous white rice
132
Corn flakes
116
*
GI values are based on white bread as the reference food,
which has a glycemic index of 100.
Source: Adapted from Foster-Powell et al. (2002).
1. FROM BRAIN TO BEHAVIOR
221
17.3 MECHANISMS OF ACTION
TABLE 17.2
Classification of foods based on
GI values*†
Low GI (78 or less)
Medium GI
(79–99)
High GI (100
or more)
Grains:
Grains:
Grains:
Barley
Wheat roti
White bread
Pasta/noodles
Brown rice
White rice
Rice noodles
Fruits & vegetables:
Fruits &
vegetables:
Fruits &
vegetables:
Strawberries, raw
Pineapple, raw
Banana, ripe
Orange, raw
Grapes, raw
Watermelon
Peaches, raw and
canned in natural juice
Pumpkin,
boiled
Potato, baking
(Russet)
Carrots, boiled
Potato, new/
white
Extensive research has led to the compilation
of data into comprehensive international GI food
tables, which have greatly facilitated research and
clinical applications of this concept (Foster-Powell
et al., 2002; Atkinson et al., 2008). Furthermore, the
concept of glycemic load (GL) has been developed to assess the total glycemic impact of the
diet. The GL is the product of the dietary GI and
the amount of available dietary carbohydrate in a
food or diet (Salmeron et al., 1997a).
17.3 MECHANISMS OF ACTION
It has been suggested that the metabolic
effects of low GI foods relate to their rate of
absorption in the gut (Figure 17.1). Low GI
foods are absorbed at slower rates, which in
turn results in a lower rise in postprandial blood
Glucose
Australian Aborigines, the relatively recent shift
from traditional low GI foods to high GI foods
may partially explain the increasing rates of diabetes among these populations (Thorburn et al.,
1987; O’Dea, 1991; Boyce and Swinburn, 1993).
The GI of traditional common starch foods may
also have been affected by recent changes in
food processing and manufacturing that reflect a
changing consumer demand (Bjorck et al., 2000).
Sweet potato, boiled
Sweet corn
Other:
Other:
Other:
Legumes
Popcorn
Rice cakes
Chickpeas
Potato crisps
Soda crackers
(a)
Time
Glucose
Kidney beans
Lentils
Soya beans
Milk, skim & full fat
Yogurt
*
GI values are based on white bread as the reference food,
which has a glycemic index of 100.
†
Canadian values where available.
Conversion: 70/100 to glucose scale
Source: Adapted from Foster-Powell et al. (2002) and
Atkinson et al. (2008).
(b)
Time
FIGURE 17.1
Hypothetical effect of feeding diets with
a (a) low or (b) high glycemic index on gastrointestinal glucose absorption and postprandial blood glucose.
Source: Reproduced from Jenkins et al. (2002), with permission.
1. FROM BRAIN TO BEHAVIOR
222
17. GLYCEMIC INDEX AND OBESITY
glucose and insulin levels. Many factors may
influence the rate of carbohydrate absorption of
a food and, therefore, its GI value. These include
the rate of digestion (Jenkins et al., 1981; Englyst
et al., 1999) and transit time (Englyst et al., 1992),
food form (physical form, particle size), type of
preparation (cooking method and processing)
(Haber et al., 1977; O’Dea et al., 1980; Jenkins
et al., 1982a; Sheard et al., 2004), ripeness (Englyst
and Cummings, 1986), nature of the starch (predominance of amylose or amylopectin) (Wursch
et al., 1986; Sheard et al., 2004), monosaccharide
components, presence of antinutrients such
as α-amylase inhibitors (Isaksson et al., 1982;
Yoon et al., 1983), and the amount and type of
fiber, fat and protein content (Thorne et al., 1983;
Krezowski et al., 1986).
The metabolic effect of a reduced rate of
absorption has been demonstrated in studies of healthy individuals as well as of people
with type 2 diabetes, and when carbohydrates
are ingested slowly over a prolonged period
of time. For example, when a glucose solution
was sipped at an even rate over 180 minutes
in comparison to the same amount of glucose
taken as a bolus, a marked decrease in insulin
secretion and lower serum free fatty acid (FFA)
levels were observed (Jenkins et al., 1990). This
improvement was also observed after consuming low GI foods. This may be due in part to a
sustained tissue insulinization, a suppressed FFA
release and the absence of counter-regulatory
endocrine responses (Wolever et al., 1988;
Jenkins et al., 1990; Ludwig et al., 1999), hence
resulting in minimal hormonal fluctuations.
Over time, glucose is removed from circulation
at a faster rate and blood glucose concentrations return toward baseline despite continued
glucose absorption from the gut. This results in
an improved postprandial peak and incremental area under the glucose curve. Other studies
have demonstrated an improved “second meal”
effect, such that an intravenous glucose tolerance test shows a more rapid uptake of glucose
(increased KG) after sipping than after the bolus
drink (Jenkins et al., 1990). The improved postprandial glycemia of the second meal may be
related to the prolonged suppression of FFA levels (Jenkins et al., 1982b).
17.4 EFFECTS OF LOW GI FOODS
ON APPETITE, FOOD INTAKE
AND SATIETY
It has been proposed that low GI foods have
properties that may make them potentially beneficial for weight control. These include the ability to promote satiety and delay hunger, reduce
fluctuations in glycemia and insulinemia, promote higher rates of fatty acid oxidation, and
minimize the decline in metabolic rate during
energy restriction (McMillan-Price and BrandMiller, 2006). However, the reverse has also
been observed in acute studies. High, not low,
GI foods have been associated with satiety and
reduced food intake (Anderson and Woodend,
2003a). This is observed in studies where subjects
are given various preloads and short-term (e.g.,
1–2 hours) food intake is measured after consumption of the preload (Holt et al., 1995; Woodend and
Anderson, 2001; Anderson et al., 2002). Satiety
may be increased in the short term with the rapid
increase in blood glucose after the intake of high
GI foods, whereas the intake of low GI foods
may be more effective in sustaining satiety in the
long-term (Anderson and Woodend, 2003b; van
Amelsvoort and Weststrate, 1992).
Over 50 years ago, the glucostatic theory
first suggested a link between blood glucose
concentrations and appetite sensations. More
specifically, high blood glucose utilization was
considered to signal satiety and the termination
of feeding, whereas low blood glucose utilization was believed to trigger the onset of feeding
(Mayer, 1955). This theory continues to generate
interest, as seen by a growing number of studies still exploring this concept. Proponents of the
theory agree that meal initiation is dependent on
1. FROM BRAIN TO BEHAVIOR
17.4 EFFECTS OF LOW GI FOODS ON APPETITE, FOOD INTAKE AND SATIETY
the transient declines in blood glucose (i.e., patterns in blood glucose) (Campfield and Smith,
2003), whereas opponents support the theory that
low GI foods are more satiating because of their
lower rate of digestion and absorption from the
gut, by modulating the appetite-controlling gut
hormones, and not just to postprandial glycemia
alone (Holt et al., 1992; Jenkins et al., 1982c).
Reviews of short-term studies of GI and appetite generally demonstrate that increased satiety,
delayed return of hunger or decreased ad libitum food intake after the consumption of low
compared with high GI foods, as measured by
visual analog scales or subsequent meal intakes
(Ludwig, 2000; Roberts, 2000; Ebbeling and
Ludwig, 2001). However, others have found no
consistent association between GI, appetite and
food intake (Raben, 2002), and it has also been
reported that, acutely, high GI foods are more
satiating (Anderson and Woodend, 2003a). It is
worth noting that a number of these studies did
not completely control for differences in the test
diets; differences in variables such as energy density, macronutrient content or palatability may or
may not have affected the results (Roberts, 2000).
In another study, the effect of high, medium or
low GI breakfast meals on subsequent ad libitum meal intake was investigated in obese teenage boys (Ludwig et al., 1999). It was observed
that voluntary energy intake was significantly
reduced by 53 percent and 81 percent after the
medium and low GI meals respectively, compared to the high GI meal. These results suggested that low GI meals had a greater effect on
satiety and subsequent food intake compared to
an isocaloric high GI meal. High GI foods tend
to increase the rate of carbohydrate absorption, cause large blood glucose and hormonal
(insulin/glucagon) fluctuations and, together
with reduced satiety, promote excess food intake
over time (Haber et al., 1977; Ludwig et al., 1999).
Many studies looking at appetite, satiety and
food intake were conducted in the short term,
and may not be indicative of what might occur
in the long term. Further studies will need to be
223
conducted to determine whether these effects are
observed in the long term.
The gastrointestinal tract releases a number of
regulatory peptide hormones that influence certain physiological processes, including gut motility and short-term feelings of hunger and satiety.
These gut hormones include ghrelin, peptide
YY, cholecystokinin (CCK), pancreatic polypeptide, amylin, glucose-dependent insulinotropic
polypeptide (GIP), glucagon-like peptide-1
(GLP-1), oxyntomodulin, gastrin and secretin
(Murphy and Bloom, 2006). Two specific hormones, GIP and GLP-1, have important effects
on insulin action (Drucker, 2007), which may be
relevant to the inclusion of low GI foods for the
treatment of hyperinsulinemic conditions (e.g.
metabolic syndrome, first stages of type 2 diabetes) and in diabetes prevention. There is some
evidence indicating that slowly absorbed carbohydrates induce a lower acute response in GIP
and GLP-1 (Jenkins et al., 1982b, 1990; Juntunen
et al., 2002), but this does not appear to affect
pancreatic polypeptide responses (Jenkins et al.,
1990). The GI of a meal has also been found to be
inversely associated with the perception of satiety and CCK levels, a hormone involved in appetite suppression (Holt et al., 1992). Furthermore,
the addition of viscous fiber in the form of beans
(Bourdon et al., 2001) or barley (Bourdon et al.,
1999) has been shown to increase postprandial
CCK responses. This suggests a possible role for
the gastric volume and the bulkiness of food in
the maintenance of appetite suppression.
In clinical trials, dietary adherence is often an
important consideration when relating findings
to everyday practices. There are many factors that
affect dietary adherence, including taste – also
known as palatability (Lloyd et al., 1995; Glanz
et al., 1998; Brekke et al., 2004). Many studies
assessing the palatability of low GI diets in comparison to high GI diets have shown that they
are equally acceptable (Jimenez-Cruz et al., 2003,
2004; Ebbeling et al., 2007; Jenkins et al., 2008;
Wolever et al., 2008). Furthermore, not only is there
a lack of studies demonstrating poor acceptability
1. FROM BRAIN TO BEHAVIOR
224
17. GLYCEMIC INDEX AND OBESITY
of low GI diets, but some studies also show they
are the preferred diet choice (Gilbertson et al.,
2001; Barnard et al., 2009). For example, the study
by Gilbertson and colleagues (2001) looked at
glycemic control in children with type 1 diabetes
given dietary advice on following either a low
GI diet or a carbohydrate-exchange diet. A clear
preference for the low GI dietary regimen by
both the children and their parents who experienced the two types of dietary advice was demonstrated by quality of life questionnaires, and
was the choice of diet continued after completion
of the study (Gilbertson et al., 2001).
17.5 GI AND OBESITY
Low GI diets may play a potential role in
body-weight regulation in that the type of carbohydrate may be more important than the total
amount. This is supported by a number of population studies. In a seasonal variation of blood
cholesterol study of 572 individuals, the GI
was associated with a higher body mass index,
thereby suggesting that the type of carbohydrate
is important in determining its effect on body
weight (Ma et al., 2005). Similarly, the EURODIAB
Complications Study of over 3000 individuals
with type 1 diabetes found that a lower GI diet
was associated with lower levels of waist-to-hip
ratio and waist circumference (Toeller et al., 2001).
Several studies have looked at the effects of
low GI weight-loss diets on body weight or composition, compared to a high GI diet. Slabber and
colleagues (1994) compared two energy-restricted
diets of either high or low GI in healthy obese
females for 12 weeks in a parallel study (n 30),
followed by some subjects crossing over to the
alternate treatment for another 12 weeks (n 16)
after a washout period. Both diets resulted in a
significant reduction in weight after the parallel
study (9.3 kg low GI vs 7.4 kg high GI), but after
the crossover study, the low GI diet resulted in
a greater reduction in body weight than did the
high GI diet (7.4 kg v. 4.5 kg respectively, P 0.04)
(Slabber et al., 1994). Bouché and colleagues
(2002) looked at 11 healthy men who were randomized into a 5-week low or high GI diet in a
crossover design. Body weight remained comparable between the two diets after the intervention periods. However, the low GI diet resulted
in a greater reduction in body fat mass (⬃700 g
reduction) and an increased lean body mass as
measured by dual-energy X-ray absorptiometry (DEXA). The reduction in body fat mass
was mainly attributable to a decline in trunk fat
(Bouché et al., 2002). Similarly, a study of 14 obese
adolescents who received an energy-restricted
low GI and low GL diet for 6 months followed by
a 6-month follow-up demonstrated a significant
reduction in both body weight (at 12 months) and
fat mass (at 6 and 12 months), as measured by
DEXA, as compared to the energy-restricted lowfat diet group (Ebbeling et al., 2003). Despite these
positive findings of the effects of low GI diets on
body weight and composition, some studies have
shown no benefit (Frost et al., 2004; Sloth et al.,
2004; Ebbeling et al., 2005, 2007).
At this time, there is no consensus as to the
effect of low GI diets on body weight or composition. However, low GI diets may still reduce
risk factors for CHD and diabetes, which are
often present in those who are overweight or
obese (Grundy et al., 2004). This issue needs to
be addressed in long-term studies with large
sample sizes and well-controlled dietary interventions where only the GI differs. Care should
be taken to ensure that the intervention diets
are matched in palatability, energy density, fiber
content and macronutrient composition (Sloth
and Astrup, 2006).
17.6 GI AND DIABETES
Several studies have looked at dietary GI in
relation to the risk and management of type 2
diabetes. Large prospective cohort studies investigating the association between GI and the risk
of type 2 diabetes have found a positive relation,
1. FROM BRAIN TO BEHAVIOR
17.7 GI AND CARDIOVASCULAR DISEASE
where higher dietary GI resulted in increased
diabetes risk (Salmeron et al., 1997a, 1997b;
Hodge et al., 2004; Schulze et al., 2004). However,
this was not observed in the Iowa Women’s
Health Study and the Atherosclerosis Risk in
Communities (ARIC) Study (Meyer et al., 2000;
Stevens et al., 2002). The first found no association between GI and GL with type 2 diabetes; this
is possibly because of the inclusion of an elderly
cohort, which could have introduced a selection
bias (Meyer et al., 2000). The ARIC Study also
observed no association; this may relate to the
dietary assessment tool used, which was not
specifically designed to assess GI (Stevens et al.,
2002). Two recent meta-analyses summarizing the
effects of low GI diets on risk factors for diabetes
and CHD demonstrated a significant reduction
in fructosamine and hemoglobin A1c (HbA1c)
in those receiving low GI diets (Kelly et al., 2004;
Opperman et al., 2004), but no significant changes
in blood glucose or insulin (Kelly et al., 2004).
One meta-analysis of 14 randomized controlled trials comparing low GI diets to conventional or high GI diets and glycemic control in
individuals with diabetes found that the low
GI diets were able to reduce glycated proteins
by 7.4 percent and HbA1c by 0.43 percent compared to high GI diets (Brand-Miller et al., 2003).
The studies included in this meta-analysis
were either of a randomized crossover or a parallel design, of 12 days to 12 months in duration (mean: 10 weeks), and comprised a total
of 356 subjects. Subsequent studies have been
consistent with the results of this meta-analysis
(Jimenez-Cruz et al., 2003; Rizkalla et al., 2004),
though there is an indication that larger and
longer low GI studies have not found the benefit in glycosylated protein (Wolever et al., 2008).
In addition to the positive effects of low GI
foods on the treatment of diabetes, drug therapies that reduce the rate of glucose absorption
have also been shown to be effective in the control of diabetes and its complications. Use of acarbose, an α-glucosidase enzyme inhibitor which
converts the diet into a low GI diet, at a dosage
225
of 100 mg three times daily in subjects with type
2 diabetes in the UK Prospective Diabetes Study
(UKPDS), resulted in significantly lower HbA1c
compared to placebo at 3 years (Holman et al.,
1999). This improvement in glycemic control was
comparable to that achieved by monotherapy
with a sulfonylurea, metformin or insulin. In the
STOP-NIDDM trial, subjects with impaired glucose tolerance were randomized to receive either
100 mg of acarbose three times daily or a placebo
(Chiasson et al., 2002). For those on acarbose,
there was a 25 percent reduction in the risk of
progression of diabetes and a significant increase
in reversion of impaired glucose tolerance to
normal glucose tolerance.
17.7 GI AND CARDIOVASCULAR
DISEASE
Epidemiological and clinical studies assessing
the role of GI on the development of cardiovascular disease (CVD) have shown that low GI diets
are associated with reduced CVD risk, possibly
suggesting a protective role. The Nurses’ Health
Study of over 75,000 women demonstrated a
direct positive relation between fatal and non-fatal
myocardial infarction, and GI and GL. The association of dietary GI and GL with CHD risk was
more prominent in those with a BMI 23 kg/m2,
suggesting that dietary GI may be more important in those with a greater BMI who may also
have a greater degree of insulin resistance (Liu
et al., 2000). Similarly, a high carbohydrate intake
or, more specifically, a high GI diet tended to be
positively associated with atherosclerotic progression in postmenopausal women (Mozaffarian et
al., 2004). However, the Zutphen Study of older
men (van Dam et al., 2000) observed no significant association of GI or GL with CHD, possibly
due to the smaller sample size and the age of the
cohort at baseline. Drug therapies that reduce
the rate of glucose absorption have also been
shown to be effective in reducing the risk of CVD.
1. FROM BRAIN TO BEHAVIOR
226
17. GLYCEMIC INDEX AND OBESITY
The STOP-NIDDM trial demonstrated that
decreasing postprandial hyperglycemia with
acarbose was associated with a 49 percent relative risk reduction in the development of cardiovascular events and a 2.5 percent absolute risk
reduction in subjects with impaired glucose tolerance. Furthermore, acarbose was associated with
a 34 percent relative risk reduction in new cases
of hypertension and a 5.3 percent absolute risk
reduction (Chiasson et al., 2003).
Numerous studies have explored the effect of
low GI diets on CHD risk factors. Epidemiological studies have shown that a low GI dietary pattern is associated with lower serum triglycerides
and/or higher serum HDL cholesterol levels,
suggesting that low GI diets may help preserve
HDL-C (Frost et al., 1999; Ford and Liu, 2001;
Liu et al., 2001; Slyper et al., 2005). Furthermore,
in the Women’s Health Study, GI was positively
associated with C-reactive protein (CRP) (Liu
et al., 2002). The effects of low GI diets in clinical trials on major risk factors for CVD have
been summarized in recent meta-analyses (Kelly
et al., 2004; Opperman et al., 2004); 15 or 16 clinical trials were included in each analysis, and
these trials varied in terms of subjects’ disease
classification (healthy, with CHD, or with type
1 or 2 diabetes). It was found that low GI diets
resulted in no change in HDL-C, triglycerides
and LDL-C compared to high GI diets. However,
improvements in total cholesterol were observed
(Kelly et al., 2004; Opperman et al., 2004), with
greater reductions in those with a higher baseline level (Opperman et al., 2004). Interestingly,
the observed improvement in HDL-C in epidemiological studies is not consistent with the
clinical trials. Nonetheless, despite the appearance of only a weak effect of low GI diets on
CHD risk factors, it was concluded that the studies conducted to date were short term, of poor
quality, and small in sample size. Therefore,
there is a need for more well-designed randomized controlled trials of adequate power and
duration to assess the effect of low GI diets on
CHD (Kelly et al., 2004). Other clinical trials have
started to investigate new and emerging risk factors for CHD. Plasminogen activator inhibitor-1
(PAI-1) levels were reduced on a low GI diet in
subjects with type 2 diabetes (Jarvi et al., 1999;
Rizkalla et al., 2004). A low GL diet compared to
a low-fat diet during weight loss found marked
improvements in heart disease risk factors such
as insulin resistance, TG levels, CRP and blood
pressure while on the low-GL diet (Pereira et al.,
2004).
17.8 CONCLUSION
The habitual consumption of low GI foods in
the context of a high-carbohydrate diet may help
to reduce the risk of obesity, type 2 diabetes and
heart disease. Drastic dietary changes may result
in short-term health benefits, but long-term compliance is often an issue. Despite continuing
controversy, the concept of the GI may still have
great clinical implications if it can be easily incorporated into dietary and lifestyle modification
strategies to help in the selection of better quality
starchy foods. Moreover, if lower GI foods were
to contribute to greater satiation, reduced postprandial glycemia and/or insulinemia, bodyweight reduction or change in composition, these
attributes may help to reduce the risk of CHD
and diabetes. More long-term efficacy and effectiveness studies are required to better determine
the potential health benefits of low GI diets.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
18
Characterizing the Homeostatic and
Hedonic Markers of the Susceptible
Phenotype
John Blundell1, Eleanor Bryant2, Clare Lawton1, Jason
Halford3, Erik Naslund4, Graham Finlayson1
and Neil King5
1
Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK
2
Centre for Psychology Studies, University of Bradford, Bradford, UK
3
Psychology Department, University of Liverpool, Liverpool, UK
4
Clinical Sciences, Danderyd Hospital, Karolinska Istitutet, Stockholm, Sweden
5
Institute of Health and Innovation, Queensland University of Technology, Brisbane, Australia
O U T L I N E
18.1 The Approach
232
18.2 Susceptible and Resistant Phenotypes 232
18.3 What Would a Susceptible
Phenotype Look Like?
18.4 What Level of Analysis is
Appropriate?
18.5 Appetite is Not Rocket Science –
It is More Complicated
Obesity Prevention: The Role of Brain and Society on Individual Behavior
233
233
234
18.6 Diversity, Susceptibility and
Homeostasis
234
18.7 Hedonics: The Importance of
Liking and Wanting
235
18.8 Comparing Susceptible and
Resistant Phenotypes
236
18.9 Resistance to Weight Loss – The
Other Side of Susceptibility
237
18.10 Conclusions
238
231
© 2010,
2010 Elsevier Inc.
232
18. CHARACTERIZING THE SUSCEPTIBLE PHENOTYPE
18.1 THE APPROACH
Recently, after 50 years of concentrated
research on the mechanisms underlying energy
homeostasis, there has developed increased
interest in the potency of non-homeostatic influences on appetite control and body-weight
regulation. This is often expressed as the relationship between hedonic and homeostatic
processes (see, for example, Saper et al., 2002;
Berthoud, 2004, 2006; Blundell and Finlayson,
2004). To express the force of non-regulatory
eating, the term “hedonic hunger” has been
proposed (Lowe and Butryn, 2007). These conceptualizations have shifted the understanding of poor weight control towards the idea
that hedonic processes overcome homeostatic
regulation. Indeed, for some time it has been
recognized that the homeostatic system operates asymmetrically, easily permitting overconsumption but more strongly defending against
under-eating and weight decrease. The system
therefore appears to be “permissive” of weight
gain, and the term “passive obesity” has been
used in a recent influential report to convey
this idea (Foresight Report, 2007). This, in turn,
reflects the concept of “passive overeating”
(Blundell et al., 1996).
The first stage in understanding susceptibility and resistance is to decide what questions to
ask. Inevitably, susceptibility and resistance will
not be uni-dimensional constructs that apply
universally and can be categorically defined.
Different clusters of susceptibility (individuals
sharing patterns of physiology and behavior)
can be envisaged (and demonstrated). Therefore,
susceptibility will exist in several forms, or subtypes. These subtypes – or phenotypes – can
be an appropriate target for research. They
define a construct between the truly universal or nomothetic approach, and the truly individual or idiographic approach (Allport, 1937).
Different susceptible phenotypes can exist in
parallel, and “obesogenic” environments exploit
this susceptibility. This chapter will describe an
approach to studying susceptibility to weight
gain (and its partner construct, resistance to
weight loss).
18.2 SUSCEPTIBLE AND
RESISTANT PHENOTYPES
An obesogenic environment clearly encourages weight gain and obesity. However, not all
people living in an obesigenic culture become
obese: some remain of normal weight, or lean.
Considerable individual differences exist in the
capacity of people to succumb to weight gain
or to resist it. This implies the existence of a
spectrum of proneness or vulnerability within
a population (for model, see Ravussin and
Kozak, 2004). Along this spectrum it is possible to identify clusters of individuals who are
susceptible and clusters who are resistant; we
have called these contrasting groups “phenotypes” because they can be defined according
to particular markers. Characterizing the ways
in which these phenotypes differ can shed light
on the particular biological and behavioral features, and their responsiveness to the environment, that encourage weight gain. Obesity is a
heterogeneous entity. There is a need to differentiate between groups of obese individuals by
assessing what risk factors predispose them to
becoming obese and, in some cases, what characteristics prevent them from losing weight.
Could the identification of a susceptible
phenotype help in the prevention of obesity?
The first stage concerns how to detect a susceptible phenotype. By definition, a susceptible
person is gaining weight or has already become
obese. It is more difficult to detect a person in
the process of weight gain than it is to identify,
for instance, someone who has already attained
a BMI of 35. However, the stratification of BMI
1. FROM BRAIN TO BEHAVIOR
18.4 WHAT LEVEL OF ANALYSIS IS APPROPRIATE
that is commonly recognized (normal, 20–24.9;
overweight, 25–29.9; obese, 30; super obese,
40) should not be regarded as definitive. This
handy classification, now a rule of thumb, is
based solely on the accumulation of risk factors
and morbidity; it has no grounding in etiology,
and nothing to say about causation. Therefore,
a susceptible phenotype can exist at any BMI,
and reflects the capacity of a person to persistently gain weight even after accumulating a significant amount of fat. However, the easiest first
step in characterizing susceptibility would be
to study someone with a high BMI. Such identifying features could, though, be a consequence
of obesity as well as a cause. Therefore, longitudinal studies of weight-gaining individuals
are necessary. Studies already carried out have
identified predictors (moderators) of weight
gain (see, for example, Hays et al., 2002; Dykes
et al., 2004). In order to be of use in the prevention of obesity, it is necessary to identify markers of susceptibility in a lean or normal-weight
person; at this stage, a strategy to oppose or offset the susceptibility features could be initiated.
Such markers can be identified.
18.3 WHAT WOULD A
SUSCEPTIBLE PHENOTYPE
LOOK LIKE?
The idea of susceptibility implies a set of biobehavioral processes that favor the achievement
of a positive energy balance. In simple terms,
this means the promotion of overconsumption
together with a sedentary lifestyle, which conjointly would lead to an increased accretion of
energy. Although these features often coexist,
the weight of evidence suggests that increased
energy intake is more pernicious and of greater
potency. Therefore, the major features of a susceptible phenotype relate to an excessive food intake
233
in an obesogenic environment. The phenotypic
expression of this feeding could take the form of
extended eating periods (defective satiation leading to large meals), frequent initiation or rapid
re-initiation of eating episodes (weak postprandial satiety), binge-eating episodes (features of
weak satiation and satiety), heightened sensitivity to the pleasurable aspects of eating, tendency
to seek high-energy dense foods, etc. A susceptible phenotype may not show all of these features.
Just as there are multiple routes to weight gain
and obesity, there will be subgroups of susceptible phenotypes (Blundell and Cooling, 2000). The
common element among susceptible individuals
is the expression of a poorly restrained willingness to eat.
18.4 WHAT LEVEL OF ANALYSIS
IS APPROPRIATE?
It should be clear that the ideology of psychobiology contains the belief that susceptibility incorporates a genetic component. Therefore,
the susceptible phenotype will be associated
with specific polymorphic markers of particular
genes related to weight gain or obesity itself (see
Bouchard, 2008). At one level, therefore, there will
be a genetic analysis of susceptibility. This genetic
“explanation”, however, may be distant from the
observed expression of susceptibility that can
be studied in research units or be managed in
clinical or public health settings. Psychobiology
implies a two-way bridge between physiology
and the environment, with the bridge reflecting
behavior itself. Therefore, one way of defining
susceptibility (and, by implication, resistance)
is through the architecture of behavior and the
proximal processes that influence this behavior.
This approach gives susceptibility a form that is
an accessible target for physiological (pharmacological), behavioral and public health approaches
to dealing with the obesity epidemic.
1. FROM BRAIN TO BEHAVIOR
234
18. CHARACTERIZING THE SUSCEPTIBLE PHENOTYPE
18.5 APPETITE IS NOT
ROCKET SCIENCE – IT IS MORE
COMPLICATED
It is often remarked that appetite control
is not rocket science; indeed, it is much more
complicated than that. The reason for believing this apparent absurdity concerns predictability. Whereas physical science embodies
sufficient predictability to enable a rocket to be
sent to a distant planet, most of us cannot predict what we are going to eat for the next meal
(or how much). In an obesogenic environment,
the act of eating is unpredictable because the
environment contains a huge range of possibilities creating a tapestry of eating opportunities,
requiring choice. This is part of the legacy of
humans being omnivores. For omnivores, food
choice is not an option, it is an obligation – and
it extends the range of edible foods beyond the
limits of optimal nutrition.
Coupled with this lack of predictability
is the tremendous diversity in the expressed
forms of eating behavior. This is apparent when
comparing dietary profiles and patterns of eating among different geographical and cultural
regions, yet there is also equal diversity within
ethnic or social groups. Consequently, the eating behavior of humans is characterized by
huge individual variability; there is no universal, normal pattern, nor is there any unique
pathological pattern. An attribute of eating
that can be predicted with some certainty is
that it will be enjoyable. Although there are
exceptions to this rule, food is a common and
potent source of pleasure. This is made possible
because of the links between sensory receptors
(mainly sight, taste and smell) and the neural pathways mediating liking and wanting.
Independent of the dietary profile and the topographical pattern, eating normally generates a
hedonic response that can be extremely potent.
One feature of the susceptible phenotype is the
high hedonic response, especially to fatty and
sweet foods. There are two reasons why this
form of overconsumption is difficult to manage. The first is the likelihood that the selection of foods with these properties (fattiness
and sweetness) connotes a biologically useful,
energy-yielding capacity which humans would
be genetically predisposed to find attractive.
The second is the unwillingness of humans
to relinquish a potent source of pleasure. The
importance of such food choices in susceptible
people is therefore both subconscious (mediated via implicit predispositions) and conscious (perceived loss of rewarding stimulus)
(Finlayson et al., 2008). An important demonstration (by means of visual evoked potentials) has been that the brain has the capacity
to detect and recognize the fat content of foods
within 150–200 ms of exposure to visual images
of foods (Toepel et al., 2008).
18.6 DIVERSITY, SUSCEPTIBILITY
AND HOMEOSTASIS
Homeostasis is an inherent property of a
biologically regulated system. One of the reasons why humans (as omnivores) are successful is because whatever the profile of foods
consumed (from the huge range available), the
biological system can adapt. Therefore, greatly
divergent patterns of eating are biologically
viable – the physiological and biochemical
processes operate to maintain the system. This
means that behavioral adaptation (to dietary
possibilities) is not always necessary. Thus,
behavioral regulation of food choice, although
feasible, is not an imperative. However, behavioral regulation of internal states is clearly an
adaptive strategy that serves a homeostatic purpose, since behavior can be initiated and terminated to optimize biological requirements. In
the control of appetite, this motivated behavior
takes the form of an increase in drive (hunger)
in response to signals of need (low glycogen
1. FROM BRAIN TO BEHAVIOR
18.7 HEDONICS: THE IMPORTANCE OF LIKING AND WANTING
levels or an empty stomach), and a positive
inhibition of eating (satiation) and the maintenance of inhibition (satiety) in response to
signals of repletion. In a closed or tightly controlled situation the operation of this biobehavioral system can be demonstrated. However,
in some situations (see, for example, Levitsky,
2005), eating appears to be unregulated by
internal signals and dominated by environmental stimuli (such as portion size). Data of this
type are often used to argue that the biological
regulation of eating is irrelevant to the obesity
epidemic. There are many examples of eating
being initiated in the absence of a drive (hunger), and of eating persisting in the presence of
inhibitory satiety signals. Therefore, although
the homeostatic system displays regulatory
properties, these mediating processes can be
readily over-ridden. Susceptibility can involve a
pattern of eating that operates with weak influence of homeostatic regulation.
It has been authoritatively stated that “a wellknown response in nutrition research and practice is the dramatic variability in inter-individual
response to any type of dietary intervention”
(Ordovas, 2008: S40). The difference between
susceptible and resistant individuals reflects the
spectrum of this inter-individual responsiveness. In research, it is clearly possible to work
with the variance itself. Sectioning the variance
and working with subunits (phenotypes) is a
manageable and transparent approach (Blundell
and Cooling, 1999).
Therefore, on theoretical grounds, susceptibility to weight gain is likely to involve weak
homeostatic regulation (that would permit a
ready initiation of eating and a weak inhibition)
and a potent hedonic influence (strong attraction to energy-dense foods and a disproportionately strong liking and wanting for specific
foods). These attributes would be expressed
through enduring traits (reflecting biologically
based predispositions) and through episodically
oscillating states (such as hunger sensations)
(see, for example, Blundell et al., 2008).
235
18.7 HEDONICS: THE
IMPORTANCE OF LIKING
AND WANTING
Just as homeostatic processes can be analyzed
to reveal components such as orexigenic drive
(hunger), satiation and satiety, hedonic processes also have a structure that can be dissected.
The fundamental processes of “liking versus
wanting” inform current theory and research
(Berridge, 1996). Liking and wanting have the
logical status of hypothetical constructs that
mediate between a neuropsychological process
and a directive behavior. These processes can be
investigated in humans (Finlayson et al., 2007a),
and have a dominant role to play in food preferences. Consequently, they also play a key role in
susceptibility to overeating.
Many people would assume that liking and
wanting are identical phenomena, both of which
signify a positive attraction to food. In behavioral terms, we assume that a change in liking will
lead to proportional adjustments in wanting
and, likewise, differences in wanting will predict changes in liking. This would be the natural
view of a layperson. However, there are strong
grounds for recognizing that liking and wanting
can be clearly dissociated, and constitute distinct identities. This means that they have much
greater resolving potential for understanding
the role of hedonics on eating and, therefore, on
overconsumption. Thus, the importance of liking versus wanting reflects the functional significance of these two distinguishable processes,
operating within the hedonic domain, for overconsumption and weight regulation in humans.
A reasonable proposal is that wanting rather
than liking may be the crucial process in maintaining an obese state. For this to be confirmed,
it is necessary that wanting and liking can
be dissociated. This is clearly shown in the
parallel field of research on chronic drug
abusers which shows that repeated drugtaking behavior and strong motivation to obtain
1. FROM BRAIN TO BEHAVIOR
18. CHARACTERIZING THE SUSCEPTIBLE PHENOTYPE
18.8 COMPARING SUSCEPTIBLE
AND RESISTANT PHENOTYPES
Variability can begin with preferences for,
and selection of, particular foods in the habitual diet. In an environment that contains a surfeit of all types of foods, people “choose” to eat
quite diverse ranges of foods within a single
culture (of course, there are major intercultural
differences). For example, high-fat and lowfat phenotypes have been identified (Cooling
and Blundell, 2000). Habitual low-fat diets
appear to confer a resistance to weight gain
(Macdiarmid et al., 1996), a characteristic also
shown by successful weight-losers (Klem et al.,
1997), although one must note that a low-fat,
high-carbohydrate diet may not be beneficial
for all (particularly for some obese, highly sedentary people). However, clear variability can
be demonstrated in the response to a high-fat
diet. Although a high fat intake is a potent risk
factor for weight gain, the relationship between
the preference for high-fat foods and weight
gain is not a biological inevitability (Blundell
and Macdiarmid, 1997). Some people habitually
consuming a high-fat diet are obese (susceptible)
whilst others are lean (resistant) (Figure 18.1).
High-Fat Group
30
20
10
38
36
34
32
30
28
26
24
22
20
18
0
16
a “fix” (wanting) can occur in the absence of any
pleasant sensations (liking) during ingestion
(Lamb et al., 1991). Moreover, food-liking is often
a rather stable characteristic within an individual,
and appears relatively uninfluenced by increasing
weight status (Cox et al., 1999). The implication
is that liking may be important in establishing
the motivational properties of food, but once
these are formed it is the up-regulation of wanting in an obesogenic environment – insensitivity to homoeostatic signals but over-reactivity to
external cues – that promotes overconsumption by influencing what, and how much, is
eaten from moment to moment (Finlayson et al.,
2007b).
number of subjects
236
BMI (Kg/m2)
FIGURE 18.1 Frequency distribution of BMI for highfat consumers, defined by the absolute amount of fat consumed after the database had been cleaned by the exclusion
of implausible reports. The distribution for low-fat consumers also shows wide variation in BMI, but only one person
reached a BMI of 30.
Source: Adapted from Macdiarmid et al. (1996).
This situation in humans is a parallel of the
phenomenon seen in rats habitually exposed to a
potentially weight-inducing, high-fat diet (Levin
et al., 1989). Susceptible individuals show a cluster of characteristics when appetite regulation is
challenged. There is a relatively weak suppression of hunger in response to consumed high-fat
foods, suggesting weak, fat-induced satiety signaling (Blundell et al., 2005). This may involve
CCK, PYY or some other gut peptide. A weak
satiation response leads to larger meals. These
factors suggest variable strength (impairment)
of homeostatic signaling systems. Other studies indicate a weak compensation to high-fat
loads related to insulin resistance (Speechly and
Buffenstein, 2000) and poor compensation to
enforced overconsumption (Cornier et al., 2004).
Evidence also points to a differential responsivity in the hedonic processes influencing eating (Blundell and Finlayson, 2004). There is
a preference for high energy-dense over low
energy-dense foods (Westerterp-Plantenga et al.,
1998), and an increased wanting for high-fat
foods under postprandial satiation conditions
(Le Noury et al., 2004). Long-standing evidence
points to a link between adiposity and sensory
preference for fat (Mela and Sacchetti, 1991).
Susceptible high-fat phenotypes also report
1. FROM BRAIN TO BEHAVIOR
18.9 RESISTANCE TO WEIGHT LOSS – THE OTHER SIDE OF SUSCEPTIBILITY
more dramatic hedonic responsivity to foods
than do lean people (Blundell et al. 2005). A high
binge-eating score on the Binge Eating Scale
(BES) (Gormally et al., 1982) is also a feature
of susceptibility and, even in normal-weight
women, is associated with an increased liking
for all foods and an increased wanting for sweet,
high-fat foods (Blundell and Finlayson, 2008).
Susceptible and resistant individuals also differ
in the strength of certain traits measured by psychometric tests. Men defined as susceptible on a
habitual high-fat diet score much higher on traits
of Disinhibition and Hunger (but not Restraint)
on the Three Factor Eating Questionnaire (TFEQ)
than do resistant (same age) men. Such individuals can be regarded as opportunistic eaters who
are often in a state of high readiness to eat, and
also are likely to be easily provoked into eating by environmental triggers (for a review, see
Bryant et al., 2007). The trait of Disinhibition is
also associated with weight gain or obesity in
large-scale surveys (Hays et al., 2002; Dykes et al.,
2004) and smaller intervention studies (Lawson
et al., 1995). There is, moreover, presumptive evidence that this trait has a genetic basis (Steinle et
al., 2002; Bouchard et al., 2004) and may be linked
to the GAD-2 gene. Other evidence suggests that
the Disinhibition trait is associated with fasting levels of leptin and adiponectin which may
influence the tonic control of appetite (Blundell
et al., 2008). Consequently, individuals susceptible to weight gain appear to display a portfolio
of risk factors which, acting together, make such
people extremely vulnerable in the obesogenic
environment.
18.9 RESISTANCE TO WEIGHT
LOSS – THE OTHER SIDE OF
SUSCEPTIBILITY
It may be claimed that the rate of increase
in the prevalence of obesity is driven by three
intrinsic features: the susceptibility of people to
237
gain weight, the failure of people to maintain
weight loss, and the resistance to lose weight.
Therefore, the natural increase in weight gain
combined with the failure (of those already
obese) to lose or maintain weight will contribute to the overall increase in obesity. The resistance to lose weight is different from the failure
to maintain lost weight, which reflects a susceptibility to weight (re)gain. When groups of
people are subjected to weight-loss regimes, the
physiological system generates automatic compensatory metabolic processes to adjust for the
energy deficit (Rosenbaum et al., 2005). The system can also make behavioral adjustments by
up-regulating the orexigenic drive and increasing energy intake (Heini et al., 1998). This can
be demonstrated very clearly by the response to
imposed and supervised exercise regimes (King
et al., 2008). In a scientifically controlled study,
obese people participating in a fully supervised
12-week program of exercise showed an average weight loss of 3.3 kg. However, the most
remarkable effect was the diversity of individual responses, which ranged from a loss of
14 kg to a weight gain of 2 kg, despite individuals achieving similar levels of exercise-induced
energy expenditure (Figure 18.2).
This type of diversity in response to imposed
exercise was noted many years ago (see, for
example, Bouchard, 1994; Bouchard and
Rankinen, 2001) but apparently overlooked by
most researchers. It follows that any interpretation based on the average weight-loss
response would obliterate the true response of
the individuals doing the exercise. “This kind
of variation is an example of normal biological
diversity … and is beyond measurement error
and day-to-day variation” (Rankinen and
Bouchard, 2008: S47). It reflects the degree of
individual variation in physiologically and
behavioral adaptive processes. In the investigation by King and colleagues (2008), the design
of the study permitted the source of variability
in the compensatory response to be identified
and measured. In those individuals who were
1. FROM BRAIN TO BEHAVIOR
238
18. CHARACTERIZING THE SUSCEPTIBLE PHENOTYPE
4
BW
FM
2
Change in BW and FM (kg)
0
–2
–4
–6
–8
–10
–12
–14
–16
FIGURE 18.2 Individual changes in body weight and body fat at the end of the mandatory exercise program.
BW, body weight; FM, fat mass.
Source : Adapted from King et al. (2008).
“resistant” to the theoretical weight loss, the
exercise increased the orexigenic drive, reflected
in a persistently high hunger, accompanied by
an increased food intake, a preference for high
energy-dense fatty foods and a relative aversion
for low energy-dense foods (fruit and vegetables) (Caudwell et al., 2009). In turn, these
food preferences represent an altered hedonic
response to exercise (Finlayson et al., 2009). The
characteristics of this “resistant” phenotype are
still under investigation, but clearly illustrate
the diversity of the human psychobiological
response. Inter-individual variability is a dominant feature of both nutritional and exercisebased interventions.
is readily apparent. Investigation of the spectrum reveals clusters of individuals who can
be termed susceptible phenotypes, and clusters that are resistant. Scientific comparison
between these contrasting phenotypes is a legitimate and powerful approach that can throw
light on the way in which bio-social processes
influence individual behavior. The susceptible phenotype is a suitable target for scientific
study and for management of clinical and public health programs, and early identification of
a susceptible phenotype in children (see, for
example, Carnell and Wardle, 2008) would be
very valuable.
References
18.10 CONCLUSIONS
The heterogeneity of the human response
to interventions that impact on energy balance
and weight regulation is a demonstrable fact.
The existence of a spectrum of susceptibility
Allport, G. W. (1937). Personality: A psychological interpretation. New York, NY: Holt Rinehart & Winston.
Berridge, K. C. (1996). Food reward: Brain substrates of
wanting and liking. Neuroscience and Biobehavioral
Reviews, 20, 1–25.
Berthoud, H. R. (2004). Neural control of appetite: Crosstalk between homeostatic and non-homeostatic systems.
Appetite, 43, 315–317.
1. FROM BRAIN TO BEHAVIOR
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
19
The Carnivore Connection: Crosspopulation Differences in the Prevalence
of Genes Producing Insulin Resistance
Stephen Colagiuri1, Scott Dickinson1 and Jennie Brand-Miller2
1
Sydney Medical School, Boden, Institute of Obesity, Nutrition & Exercise, and
School of Molecular and Microbial Biosciences, University of Sydney, NSW, Australia
2
O U T L I N E
19.1 Background
241
19.2 The Evolution of Insulin Resistance
242
19.3 Determinants of Insulin Resistance
19.3.1 Physiological Determinants
19.3.2 Pathological Determinants
244
244
245
19.1 BACKGROUND
Although insulin has a number of metabolic
effects, insulin resistance is usually defined as
a state in which physiological levels of insulin
have a decreased biological action on plasma
glucose. Glucose uptake by skeletal muscle
and adipose tissue, and suppression of hepatic
glucose production, are affected. To maintain
normoglycemia in the insulin-resistant state,
Obesity Prevention: The Role of Brain and Society on Individual Behavior
19.3.3 Genetic Determinants
246
19.4 Candidate Genes and Cross-population
Genetic Differences
246
19.5 Conclusion
248
excessive compensatory increases in insulin
are required which may eventually lead to a
decline in and exhaustion of insulin-producing
pancreatic beta cells, the development of glucose intolerance, and, finally, type 2 diabetes
(Polonsky, 1999). Insulin resistance is associated
with a constellation of traits other than glucose
intolerance, including visceral obesity, dyslipidemia, hypertension, and a prothrombotic state
(Reaven, 1988). Epidemiological studies consistently show an independent association between
241
© 2010,
2010 Elsevier Inc.
242
19. THE CARNIVORE CONNECTION
insulin resistance and risk of cardiovascular
disease (McFarlane et al., 2001). The term insulin
resistance is often used interchangeably with
decreased insulin sensitivity or reduced insulin
action.
There are wide differences in the ability of
insulin to mediate glucose disposal among individuals. Insulin sensitivity is a continuous variable, and distinguishing normal and abnormal
insulin sensitivity in individuals is difficult
because there is no uniform quantitative definition of what constitutes insulin resistance.
Individuals are considered insulin resistant if
they have normal glucose tolerance but lie in
the most insulin-resistant quartile of a given
population (Reaven et al., 1993). In individuals
with normal glucose tolerance, insulin resistance can vary by four- to ten-fold, with some
measures of resistance not differing substantially from those of people with impaired glucose tolerance (IGT) or type 2 diabetes (Reaven
et al., 1989; Clausen et al., 1996). However, those
subjects in the most insulin-resistant quartiles
are generally significantly more obese and less
glucose tolerant compared with more insulinsensitive individuals (Clausen et al., 1996). Also,
those with insulin resistance but normal glucose tolerance are hyperinsulinemic compared
with insulin sensitive controls, allowing insulinresistant individuals to overcome the defect in
the short term.
Quantitative comparisons of insulin resistance across racial/ethnic groups are difficult
because of the need to take into account the
effects of age, gender, weight, physical fitness,
and glucose tolerance. However, some data
support real differences. Comparative data
are available for Mexican Americans (Haffner
et al., 1992) and Australian Aborigines (Proietto
et al., 1992) consistent with the view that insulin resistance is more common in individuals
without diabetes in these populations. AfricanAmericans maintain glycemia following a carbohydrate load by producing a much larger
insulin response, two- to three-fold greater than
that seen in matched European Caucasians
(Osei and Shuster, 1994). African-Americans
also have lower adiponectin levels than
Caucasians, which is associated with insulin
resistance (Osei et al., 2005). Asian Indians are
more insulin resistant than matched European
Caucasians, as demonstrated by reduced rates
of glucose disposal adjusting for confounding
variables (Chandalia et al., 1999). Dickinson and
colleagues (2002) studied 60 lean, healthy young
adults from five racial/ethnic groups (European
Caucasians, Chinese, Southeast Asians, Asian
Indians and Arabic Caucasians) and assessed
glucose and insulin responses following a 75-g
carbohydrate meal, and insulin sensitivity by
HOMA or the hyperinsulinemic euglycemic
clamp technique. While mean fasting glucose
concentrations were similar among the groups,
Southeast Asian and Chinese subjects showed
markedly higher postprandial glycemia than
did European Caucasians, with 1.5- to 2.0-fold
higher mean incremental areas under the glucose curve. The groups also differed significantly in insulin sensitivity, with European
Caucasians being the most sensitive, whereas
Southeast Asians were the most resistant. The
results were not explained by differences in sex,
age, BMI or birth weight.
The variation in insulin resistance across
ethnic groups could be due to genetic and/or
biochemical differences, and in the absence of
definitive data it is not currently possible to
separate these influences.
19.2 THE EVOLUTION OF INSULIN
RESISTANCE
Several theories have been proposed to
explain the current high prevalence and population differences in insulin resistance and
the associated type 2 diabetes, both of which
increase in populations transitioning from a
traditional lifestyle. The “thrifty genotype”
1. FROM BRAIN TO BEHAVIOR
19.2 THE EVOLUTION OF INSULIN RESISTANCE
hypothesis was first proposed by Neel (1962),
and postulates the presence of genetic traits that
had survival value for hunter-gatherers, allowing the ability to go without food for extended
periods of time. A “thrifty” metabolism made
for efficient storage of fat during times of plenty,
providing an energy buffer during scarcity.
Such a gene(s) would now be detrimental, with
the abundant food supply and lack of physical
activity predisposing to obesity.
Subsequently, it was proposed that insulin
resistance was the phenotypic expression of the
thrifty genotype (O’Dea, 1991). In the presence
of modern-day constant food supply, insulin
resistance would result in hyperinsulinemia and
eventual diabetes. Diamond (1992) described
it as the “collision of our old hunter-gatherer
genes with our new twentieth-century life
style”. The “not-so-thrifty genotype” was suggested by Reaven (1998), who hypothesized
that the purpose of insulin resistance was not
to increase fat storage, as Neel suggested, but
to spare the proteolysis of muscle tissue during
periods of famine.
Hales and colleagues (1991) proposed a
“thrifty phenotype” to explain metabolic adaptations to allow survival of a malnourished
fetus. The hypothesis, based on anthropometric records of infants, associates poor early fetal
and infant growth with insulin resistance and
the later development of type 2 diabetes and
other metabolic abnormalities. Subsequently, it
has been suggested that low birth weight may
be genetically determined (Poulsen et al., 1997).
Hattersley and Tooke (1999) proposed the “fetal
insulin” hypothesis, which argues against the
thrifty phenotype and in favor of genetically
determined insulin resistance resulting in low
insulin-mediated fetal growth in utero as well
as insulin resistance later in adult life. They
propose that low birth weight, hypertension,
IGT and eventual diabetes are the phenotypic
expression of the insulin-resistant genotype.
Brand-Miller and Colagiuri developed the
“carnivore connection” hypothesis to explain
243
the evolution of genes predisposing to insulin
resistance (Miller and Colagiuri, 1994; Colagiuri
and Brand-Miller, 2002), and proposed a critical role for the quantity and quality of dietary carbohydrate in the evolution of insulin
resistance and hyperinsulinemia. Insulin resistance offered survival and reproductive advantages during the Ice Age, which dominated
the last two million years of human evolution
and which was characterized by low-carbohydrate, high-protein diets (Richards et al., 2000).
While carbohydrate was scarce, compensatory hyperinsulinemia would not have been
needed to maintain normal glucose tolerance.
Dietary carbohydrate increased beginning
about 10,000 years ago, following the end of
the last Ice Age and the development of agriculture. Traditional carbohydrate foods have
a low glycemic index (GI) and produce only
modest postprandial increases in plasma insulin. However, beginning with the Industrial
Revolution, there is now a constant supply of
highly refined high GI carbohydrate in modern diets, resulting in excessive postprandial
hyperinsulinemia, exposing the disadvantages
of the insulin resistance genotype and predisposing to type 2 diabetes and other metabolic
abnormalities. The situation has been further
aggravated over the past 60 years by the explosion in the range of available convenience and
“fast foods”, which expose most populations to
caloric intakes far in excess of energy requirements. This overconsumption has been responsible for the increased prevalence of obesity
in Western and developing societies, and an
important factor in determining the prevalence
of insulin resistance in any population. The carnivore connection also offers an explanation
for the relative insulin sensitivity of European
Caucasians.
These theories are based on the assumption
of the advantage of insulin resistance for reproduction and survival during periods of famine
thought to have been common through human
evolution. Extensive evidence now shows that
1. FROM BRAIN TO BEHAVIOR
244
19. THE CARNIVORE CONNECTION
starvation was in fact not common in prehistoric
hominids or among modern hunter-gathers
(Cordain et al., 1999). However, evidence
supports natural selection to a mixture of
famines and seasonal food shortage in the postagricultural era mediated through fertility,
rather than viability selection (Prentice et al.,
2008). Speakman (2008) challenges this view,
proposing instead the “drifty gene” hypothesis
which puts forward possible scenarios based on
random unselected genetic drift.
The relative contribution of environmental and genetic influences to insulin sensitivity
remains unclear. While the molecular basis of
these theories remains unknown, the relative
roles played by genetic and environmental factors will continue to be the subject of intense
debate.
19.3 DETERMINANTS OF INSULIN
RESISTANCE
Insulin action is influenced by physiological,
pathological and genetic factors (Figure 19.1).
19.3.1 Physiological determinants
Insulin resistance increases with age. This
trend, however, diminishes when adjustments are made for the effect of BMI, body
composition, and physical activity (Ferrannini
et al., 1996; Basu et al., 2003). Physical activity
increases insulin sensitivity, an effect that can be
demonstrated after 4–6 weeks of intensive training (Koivisto et al., 1986). Diet also influences
insulin action. Epidemiological studies suggest
an association between high saturated fat intake
and reduced insulin sensitivity in humans
(Marshall et al., 1997; Mayer-Davis et al., 1997)
while animal studies demonstrate that diets
high in fat, particularly saturated fat, lead to
insulin resistance (Lee et al., 2006). A study in
women with advanced CVD awaiting bypass
surgery (Frost et al., 1998) showed an improvement in glucose tolerance and insulin sensitivity after 4 weeks on a low GI diet (compared
with a high GI diet). In overweight, middleaged men, Brynes and colleagues (2003) demonstrated that HOMA-insulin resistance increased
significantly on a high GI diet compared with a
macronutrient-matched low GI diet.
Evolutionary environment
- food availability and type
- reproduction and fertility
Genetic selection
Genes
Physiologic determinants
- age
- diet
- physical activity
- pregnancy
Genetic drift
Pathologic determinants
- overweight/obesity
Insulin action
FIGURE 19.1 Determinants of insulin action.
1. FROM BRAIN TO BEHAVIOR
19.3 DETERMINANTS OF INSULIN RESISTANCE
Insulin sensitivity decreases during pregnancy
(Reece et al., 1994) and may result in gestational
diabetes, a common and increasing problem,
especially in women of non-European background (Kaaja and Greer, 2005).
19.3.2 Pathological Determinants
Regardless of glucose tolerance, body weight
has a major influence on insulin sensitivity.
An increase in body weight of 35–40 percent
above the normal range results in an insulin
sensitivity decline of 30–40 percent (DeFronzo
and Ferrannini, 1991). The worldwide increasing rates of overweight and obesity are a major
determinant of individual and population insulin resistance. Insulin resistance, in the context
of obesity, is the most common risk factor for
type 2 diabetes and metabolic abnormalities
(Eckel et al., 2005). There is a strong association between abdominal adiposity and insulin
resistance (Abate et al., 1995; Cnop et al., 2002;
Wagenknecht et al., 2002) for any level of total
body fat; the subgroup of individuals with
excess intra-abdominal fat has a substantially greater risk of having insulin resistance
(Despres and Lemieux, 2006).
Several mechanisms may result in obesityrelated insulin resistance and have provided a
focus for the search for the genetic determinants
of insulin resistance. Impaired non-esterified fatty
acid (NEFA) metabolism is an important contributor to insulin resistance in the viscerally obese
(Despres et al., 1990; Pouliot et al., 1992; Chan et al.,
1994; Folsom et al., 2000; Hayashi et al., 2008).
Adipose tissue not only stores and mobilizes
lipids, but also releases a number of cytokines
and pro-inflammatory molecules. The macrophage infiltration in adipose tissue in the obese is
likely to play a role in the inflammatory profile
characteristic of people with abdominal obesity
(Weisberg et al., 2003), and may be responsible
for obesity-related insulin resistance (Wellen and
Hotamisligil, 2005). Other possible mechanisms
245
include endoplasmic reticulum stress (Ozcan
et al., 2004) leading to up-regulation of JNK,
which in turn suppresses insulin action through
inhibition of the insulin receptor substrate-1 and
its associated downstream signaling pathways.
Insulin-resistant subjects have elevated levels
of lipid in their skeletal muscle cells compared
with matched insulin-sensitive subjects. Falholt
and colleagues (1988) observed a relationship
between insulin resistance and intramyocellular lipid (IMCL) independent of overweight or
obesity. Storlien and colleagues (1991) fed rats
a high-fat diet and found that mean muscle triglyceride accumulation was inversely correlated
with insulin sensitivity, suggesting involvement
of the intracellular glucose–fatty acid cycle. In
Pima Indians without diabetes, skeletal muscle
triglycerides were inversely correlated with insulin sensitivity even after controlling for all other
measures of fat (Pan et al., 1997). With the availability of magnetic resonance spectroscopy to
measure IMCL, more data have emerged showing significant associations between skeletal
muscle triglycerides and insulin resistance (Jacob
et al., 1999; Krssak et al., 1999; Perseghin et al.,
1999; Virkamaki et al., 2001). However, this relationship was not observed in a group of South
Asian subjects, but was present in a European
Caucasian control group (Forouhi et al., 1999).
Shulman proposed a unifying hypothesis for
a number of forms of human insulin resistance.
He suggested that an accumulation of intracellular fatty acid metabolites in muscle or liver,
whether by increased caloric intake or by a failure of mitochondrial fatty acid oxidation, could
produce an insulin-resistant state (Shulman,
2000) through activation of a serine/threonine
kinase cascade, downstream activation of IB
kinase-β (IKK-β) and c-JUN NH2-terminal protein kinase (JNK-1), phosphorylation at serine
sites on insulin receptor substrate-1 (IRS-1) and
decreased activation of glucose transport due
to the inability of serine-phosphorylated forms
of IRS-1 to associate with phosphatidylinositol
3-kinase (PI3K) (Shulman, 2004).
1. FROM BRAIN TO BEHAVIOR
246
19. THE CARNIVORE CONNECTION
19.3.3 Genetic Determinants
Since insulin sensitivity varies between individuals and populations even when standardized for confounders, it is likely that there is a
contributing genetic component. This is supported by familial clustering (Lillioja et al.,
1987), twin studies (Newman et al., 1987; Mayer
et al., 1996; Narkiewicz et al., 1997) and other
lines of evidence (Rewers and Hamman, 1995;
Abate et al., 1996; Rique et al., 2000; Malecki
and Klupa, 2005). However, the precise genetic
determinants of the more common form of
insulin resistance remain unclear, and it is likely
that multiple genes in various combinations are
responsible.
19.4 CANDIDATE GENES AND
CROSS-POPULATION GENETIC
DIFFERENCES
There are several possibilities where genetic
variation and candidate genes could play a
role, including the insulin receptor, cellular signaling and glucose metabolism. Studies have
highlighted some population-specific genes
associated with insulin action.
Pima Indians without diabetes show a familial aggregation of insulin sensitivity suggestive of a single gene with a co-dominant mode
of inheritance (Bogardus et al., 1989) linked to
chromosomal markers on 4q (Prochazka et al.,
1993). This region is also linked to insulin resistance and 2-h plasma insulin concentrations
in Mexican Americans (Prochazka et al., 1993;
Mitchell et al., 1995). The FABP2 (a protein that
binds saturated and unsaturated long-chain
fatty acids) is linked to this chromosomal region
and is expressed in the epithelial cells of the
small intestine, and likely plays a role in the
absorption of fatty acids. A missense mutation
of FABP2 (Ala54Thr) has been identified, and
has an allele frequency of 0.29 in Pima Indians,
0.34 in Japanese, 0.31 in Caucasians, 0.28 in
Finns, and 0.14 in indigenous Canadians. In
Pima Indians, the Ala54Thr variant was associated with both reduced insulin sensitivity and
elevated fasting insulin levels (Baier et al., 1995).
The β3-adrenergic gene is mainly expressed in
visceral adipose tissue, where it plays an important role in lipid metabolism (Walston et al., 1995).
A missense mutation in the gene (Trp64Arg)
is associated with early onset of type 2
diabetes, overweight (visceral fat accumulation), and insulin resistance (Sakane et al., 1997).
Pima Indians homozygous for Trp64Arg have
a higher predisposition to early onset of type 2
diabetes, a higher BMI and lower resting metabolic rate (Walston et al., 1995). Finns, heterozygous for this mutation, show earlier onset of
type 2 diabetes and decreased glucose disposal
rates (Widen et al., 1995).
PPARy has two isoforms determined by differential splicing of the gene on chromosome
3p25. PPARy1 is present in most tissues, whereas
PPARy2 is predominantly expressed in adipose
tissue. While both positive (Deeb et al., 1998) and
negative associations (Meirhaeghe et al., 2000)
between the gene and insulin resistance have
been reported, recent studies show that substitution of proline to alanine at position 12 in the
y2-specific exon (Pro12Ala) is associated with
significantly less insulin resistance (Ek et al., 2001;
Gonzalez-Sanchez et al., 2002; Helwig et al., 2007).
Insulin resistance has been linked with the
ectoenzyme plasma cell membrane glycoprotein-1 differentiation antigen (PC-1) in humans
where levels of PC-1 are elevated two- to threefold in key tissues (Frittitta et al., 1996, 1997).
PC-1 binds to the insulin receptor but does not
block the ability of insulin to bind the receptor;
instead, it interferes with insulin-induced autophosphorylation of the receptor and tyrosine
kinase activation (Maddux and Goldfine, 2000).
Abate and colleagues (2003) reported that the
PC-1 K121Q polymorphism was associated with
insulin resistance in Asian Indians compared
with Caucasians.
1. FROM BRAIN TO BEHAVIOR
19.4 CANDIDATE GENES AND CROSS-POPULATION GENETIC DIFFERENCES
The insulin receptor substrate-1 gene (encoded
on chromosome 2q36 as a single exon) plays a
role in determining insulin resistance. Several
IRS-1 gene mutations have been identified in
humans, but only G971R and Gly972R appear
to have an association with obese subjects with
type 2 diabetes (Clausen et al., 1995; Baroni et al.,
2004).
Horikawa and colleagues (2000) identified a
susceptibility gene for type 2 diabetes in Mexican
Americans on chromosome 2q in association with
the gene encoding cysteine protease calpain-10.
Although the exact role of calpain-10 in insulin
resistance remains controversial, it appears to
affect glucose uptake pathways in skeletal muscle (Otani et al., 2004) and adipose tissue (Paul
et al., 2003).
Adiponectin is encoded by the gene
ADIPOQ and is involved in glucose and lipid
metabolism; it is decreased in insulin-resistant
states (Yamauchi et al., 2001; Bajaj et al., 2004).
Adiponectin acts through its receptors ADIPOR1
and ADIPOR2, with ADIPOR2 being the main
isoform for the insulin-sensitizing effects in
human skeletal muscle (Civitarese et al., 2004).
Polymorphisms in the ADIPOQ gene have
been studied in various populations, including
Caucasians and Japanese, and suggest that gene
variation predisposes to insulin resistance (Gu
et al., 2004; Nakatani et al., 2005). The conversion from pre-diabetes to type 2 diabetes in the
STOP-NIDDM trial was predicted by SNP 45
and SNP 276 polymorphisms of the ADIPOQ
gene (Zacharova et al., 2005). Damcott and colleagues (2005) found an association between
ADIPOR1 and ADIPOR2 variants and type 2
diabetes in an Old Order Amish population,
while Stefan and colleagues (2005) showed that
a variation in ADIPOR1 may affect insulin sensitivity in Europeans.
Studies from Canada suggest that insulin resistance may be a significant inherited
trait contributing to the onset of type 2 diabetes (Hegele et al., 2003). Linkage of type 2 diabetes to chromosome 20q12-q13.1 has been
247
reported (Klupa et al., 2000; Permutt et al., 2001).
Localized in this region is the transcription factor hepatic nuclear factor (HNF)-4, which plays
a critical role in the regulation and expression
of genes essential to the normal functioning of
the liver, pancreas and gut (Stoffel and Duncan,
1997). Hegele and colleagues (1999) identified
a population-specific HNF-1 G319S mutation
among a group of Oji-Cree Indians in Northern
Canada which conferred susceptibility to type 2
diabetes. Individuals carrying this mutant gene
had significantly higher post-challenge plasma
glucose levels and fasting hyperinsulinemia
suggestive of insulin resistance.
Other studies suggest a major locus on chromosome 6 (near marker D6S403) strongly influencing plasma insulin concentrations and insulin
resistance in Mexican Americans (Duggirala
et al., 2001); two diabetes susceptibility loci on
chromosome 6q associated with insulin resistance and insulin secretion in Finns (Watanabe
et al., 2000; Shtir et al., 2007) and, in the same
region, diabetes in both Pima Indians (Hanson
et al., 1998) and Japanese (Iwasaki et al., 1999).
Insulin levels and body fat in the Quebec Family
Study were linked to a region on chromosome
1p32-22 (Chagnon et al., 2000). In a study involving 2684 Asian Indians from the UK (Chambers
et al., 2008), a genome-wide association study
found a significant association between four
SNPs in the MC4R gene and insulin resistance
(HOMA-IR) with the association with the SNP
rs12970134 persisting after adjusting for waist
circumference, BMI and body mass.
Several syndromes of insulin resistance
based on single mutations of genes have been
described. Over 50 mutations in the insulin
receptor gene (located on chromosome 19p13.213.3) have been reported and are associated
with severe insulin resistance and hyperinsulinemia (Taylor et al., 1992). These syndromes
result in severe outcomes, including intrauterine growth retardation, fasting hypoglycemia
and death, within the first year of life (Mercado
et al., 2002).
1. FROM BRAIN TO BEHAVIOR
248
19. THE CARNIVORE CONNECTION
19.5 CONCLUSION
Insulin resistance is common, and is implicated in a number of metabolic abnormalities,
particularly the development of type 2 diabetes. Differences in insulin resistance are apparent across populations, and likely contribute to
the differences in prevalence of diabetes and
other metabolic abnormalities. While insulin
action is influenced by many factors, including age, diet, physical activity and especially
body weight, ethnic/racial differences also
exist, implying underlying genetic variations.
Various theories have been proposed to explain
the evolution of variations in insulin resistance
and the interaction with the modern-day environment. The carnivore connection hypothesis is based on the evolutionary changes in
the quantity and quality (GI) of dietary carbohydrate and the advantages of insulin resistance for reproduction and during times when
dietary carbohydrate, rather than energy, was
scarce. These theories will remain speculative,
however, until progress is made in identifying the specific molecular and genetic basis for
population and individual differences in insulin
action.
Although our genetic make-up may exacerbate the impact of our current lifestyle, finding individual and societal solutions to combat
these evolutionary changes is proving challenging. Without a major global catastrophe we are
unlikely to be able to turn back the clock, and
neither would many want to, considering the
benefits of modernization. Increasing physical activity is arguably the most amenable way
of increasing an individual’s insulin sensitivity, especially when coupled with appropriate dietary changes. Increased attention to
urban design and providing individuals with
the opportunity to exercise is fundamental.
However, effective and sustainable strategies to
address the excessive and inappropriate energy
intake are more problematic.
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1. FROM BRAIN TO BEHAVIOR
C H A P T E R
20
Neuroanatomical Correlates of Hunger
and Satiaty in Lean and Obese
Individuals
Angelo Del Parigi
Senior Medical Director, Medical Affairs, Pfizer Inc., New York, NY, USA
O U T L I N E
20.1 Physiology of Hunger and Satiety
in Human Eating Behavior
253
Eating behavior in humans is not a stereotypical behavior driven only by the need to compensate for acute changes in energy status. It is
clear that emotional, cognitive and cultural factors play a major role in the initiation and termination of an eating episode. To put it simply,
a negative energy balance is sufficient but not
necessary to initiate eating. However, homeostatic, hedonic and cognitive controls of eating
behavior are intimately intertwined. Their separation as discrete neurophysiological processes
is, in fact, supported by theoretical principles
rather than by empirical evidence (Berthoud
and Morrison, 2008).
Obesity Prevention: The Role of Brain and Society on Individual Behavior
20.2 Functional Neuroimaging
Evidence
254
20.1 PHYSIOLOGY OF HUNGER
AND SATIETY IN HUMAN EATING
BEHAVIOR
Hunger and satiety are at the crossroads of
this complex interplay between metabolic and
non-metabolic factors regulating human eating
behavior. In fact, energy balance is continuously
monitored by the brain through multiple endocrine and neural mechanisms, which include
long- and short-term signals of changes in energy
stores, and changes in energy currency, respectively. On this dynamic background which steers
253
© 2010,
2010 Elsevier Inc.
254
20. BRAIN DOMAINS INVOLVED IN HUMAN EATING BEHAVIOR
the individual toward the decision to start eating
or not, or to stop eating or not, the information
from the external environment, either sensorial
(such as the sight, smell or taste of food) or social
(such as the availability of a scheduled “break”
for lunch) may in fact act as triggering factors. If
we add additional layers of complexity that pertain to cultural, psychological and environmental
constraints related to the logistics of food consumption, and/or to the projected body image,
and/or to the choice of specific foods (for example, inducted by a commercial or by religious
beliefs), we encompass the multitude of factors
which define the rhythm and the gears of human
eating behavior.
Although a simple model of meal control
might suggest that feeding terminates when
a sufficient quantity of nutrients has been
ingested to meet individual nutritional needs, it
is clear that the normal rate of eating indicates
that meal termination occurs too early to reflect
absorption of the ingested nutrients. The satiety response can actually be resolved in different chronological phases (the so-called “satiety
cascade”) characterized by different underlying
phenomena, mainly sensorial and cognitive,
leading to the actual termination of a meal (i.e.,
satiation), or post-ingestive and post-absorptive
phases, supporting the duration of fasting
intervals between meals – that is, the properly
defined satiety (Blundell and Tremblay, 1995).
As such, satiation defines the discrete transition from eating to fast, while satiety characterizes the period of fast that follows. During
this interval, as satiety declines, the subjective
feeling of the drive to eat reaches the threshold of conscious appreciation, which is what is
generally defined as hunger. In fact, operationally, satiety can be defined as the state of hunger suppression. Although fasting is a common
denominator of both satiety and hunger, satiety is associated with a feeling of comfort and
low desire for food, whereas hunger is associated with discomfort and high desire for food.
Furthermore, in normal conditions, while satiety
is mainly a digestive-metabolically driven process, where the gastrointestinal processing of the
alimentary bolus and consequent absorption of
nutrients and elicited hormonal responses are
the leading factors, hunger can also be triggered
by externally or internally generated cues, such
as the sight or smell of food or the desire for
prompt gratification – for example, in stressful
life conditions.
Consistent with the view of protracted overeating, or eating in excess of metabolic needs, as the
leading contributing factor to weight gain and
obesity, dysregulation of hunger and/or satiety
appears to be a plausible working hypothesis for
the understanding of the pathophysiology of obesity. In fact, the search for the biological underpinnings of a positive energy imbalance and weight
gain has been intensely focusing on the molecular signatures of hyperphagia or overeating in
animals and in humans. It is beyond the scope
of this chapter to review the evidence accrued on
the molecular pathways associated with weight
gain. Suffice it to say that many catabolic and anabolic signals have been identified and tested in
rodent models of obesity, and that overwhelming
evidence supports the notion that weight gain
and obesity are associated with neurofunctional
aberrations (Bray, 2004). However, the translation
of this experimental evidence to common forms
of human obesity has been disappointing. Part of
this loss in translation is likely due to the limitations of access to the brain for scientific experiments in humans.
20.2 FUNCTIONAL
NEUROIMAGING EVIDENCE
One of the few available options for a noninvasive exploration of the in vivo biology of
the human brain is offered by functional neuroimaging, which, depending on the technique of
choice, measures different proxies for changes
in local neural activity or receptor binding,
1. FROM BRAIN TO BEHAVIOR
20.2 FUNCTIONAL NEUROIMAGING EVIDENCE
ultimately allowing for the identification of
regional brain responses to specific stimulations.
As such, functional neuroimaging has also been
used for the investigation of brain responses to
food-related stimuli in the attempt to identify
neurobiological patterns associated with different states of eating behavior and different metabolic conditions, including obesity and the risk
for obesity.
In this context, hunger and satiety have been
investigated to identify possible neurofunctional markers of these conditions in obese and
normal-weight individuals, and assess their
importance to the pathophysiology of obesity. From a theoretical/behavioral standpoint,
such an approach could contribute to answering the atavistic and always stimulating question: is overeating (and the consequent weight
gain) induced by enhanced feelings of hunger,
or by a weakened satiety response, or by both?
From a methodological/experimental point of
view, such an approach would rather test the
question: are there neurofunctional markers of
overeating in obese individuals expressed at a
scale that can be investigated with functional
neuroimaging?
Within this theoretical and methodological
framework, a pioneering neuroimaging program
was designed and implemented at the National
Institute for Diabetes, Digestive and Kidney
Diseases (NIDDK) branch in Phoenix, Arizona,
USA, using positron emission tomography (PET)
and 15O-water to measure changes in regional
cerebral blood flow (rCBF), a marker of local
neural activity. This technique works by measuring the effect of the intravenous administration
of a dose of 15O-water, which is a tracer conveyed
and distributed to tissues throughout the body
by the arterial blood flow. This tracer rapidly diffuses through the blood–brain barrier, making it
suitable for the measurement of rCBF. The spatial resolution of this technique is limited by the
precision of the localization of the positron emitting nucleus (1- to 6-mm radius). On the other
hand, the short half-life of 15O (122.24 seconds)
255
makes it possible to acquire multiple images during a single scanning session, allowing for each
person to serve as his or her own control, which
eliminates a series of confounders. For example,
the assessment of a change in local neural activity in response to a stimulus is implemented by
subtracting the map of rCBF associated with
the application of the stimulus from the map
of rCBF associated with the baseline condition.
Automated algorithms transpose each individual PET subtraction map onto a standardized
stereotactical space, perform group data analysis,
and generate statistical parametric maps of statedependent changes in rCBF (Acton and Friston,
1998). When superimposed onto an MRI scan of
the same subject, these maps allow precise identification of the neuroanatomical location of the
change in neuronal activity subject by subject.
We have used PET and 15O-water to study
the brain responses to hunger (after a 36-hour
fast) and to early satiety (in response to a liquid
meal providing 50 percent of the resting energy
requirements) in normal weight, obese and postobese men and women (Del Parigi et al., 2002a,
2004, 2005; Gautier et al., 2000, 2001; Tataranni
et al., 1999; Tataranni and Del Parigi, 2003).
Subjects were admitted to the clinical research
unit of the NIDDK in Phoenix for approximately 1 week. On admission, all subjects were
placed on a weight-maintaining diet (50 percent
carbohydrate, 30 percent fat, 20 percent protein). Body composition was assessed by dual
energy X-ray absorptiometry (DPX-l, Lunar,
Madison, WI), and resting energy expenditure,
after a 12-hour overnight fast, was measured
for 45 minutes by using a ventilated hood system (DeltaTrac, SensorMedics, Yorba Linda,
CA). Extreme abnormalities in eating behavior
were excluded by using the Three-Factor Eating
Questionnaire (Stunkard and Messick, 1985)
which estimates three major dimensions of eating behavior – dietary restraint, a measure of
cognitive control over eating behavior; disinhibition, a measure of susceptibility to sensory
and emotional cues; and hunger, a measure of
1. FROM BRAIN TO BEHAVIOR
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20. BRAIN DOMAINS INVOLVED IN HUMAN EATING BEHAVIOR
sensitivity to physiological cues. The imaging session took place after a 36-hour fast,
during which only water and non-caloric, noncaffeinated beverages were provided.
First, we obtained a structural MRI of the
head, to rule out gross abnormalities and provide anatomical information for the precise
localization of the functional findings. Soon
afterwards, the functional session began. A
PET transmission scan using a 68Germanium/
68
Gallium ring source was performed to correct
subsequent emission images for radiation attenuation. Next, the preparation of the subject for
the functional imaging session continued with
the insertion of a plastic extension tube into the
mouth to the middle of the tongue, while the
subject was lying supine on the PET table. This
apparatus was then connected to a peristaltic
pump (IMED 980, Imed, San Diego, CA), set
to deliver, over a period of 25 minutes, a liquid
formula meal (Ensure-Plus 1.5 kcal/ml, RossAbbott Laboratories, Columbus, OH) providing 50 percent of the previously measured daily
resting energy expenditure. Two 1-minute PET
scans were performed right before starting the
administration of the liquid meal (i.e., with the
subject hungry) and two PET scans were collected right after the administration of the liquid
meal (i.e., with the subject sated), with intervals
of 10 minutes between scans. For each scan, a
50-mCi intravenous bolus of 15O-labeled water
was injected. To eliminate possible confounding
factors such as tactile stimulation of the tongue
and motor neuron activity, swallowing was consistently induced by administering 2 ml of water
before each of the four PET scans. During each
scan, subjects rested still in the supine position,
the head immobilized in a custom-made solidified foam helmet, and were asked to keep their
eyes closed and pointing forward. Subjective
ratings of hunger and satiety were recorded
after each PET scan, using a 100-mm visual analog scale (Lawton et al., 1993). Blood samples
were also collected immediately after each scan
for the measurement of plasma glucose, free
fatty acids, insulin and leptin concentrations. To
familiarize each subject with the experimental
setting and minimize the risk of learning-related
artifacts, the feeding procedure was practiced
on the research ward before PET scanning.
PET images were reconstructed with an inplane resolution of 10 mm full width at halfmaximum (FWHM), and a slice thickness of
5 mm FWHM.
We used this approach to seek the answer to
three main experimental questions:
1. Can the functional neuroanatomical
correlates of hunger and satiety be imaged in
humans?
2. Are there selective differences in the brain
responses to meal consumption between
obese and normal-weight individuals?
3. What is the pathophysiological relevance, if
any, of these differences?
In regard to the first question, our results demonstrated that the administration of a satiating
meal to hungry individuals was associated with
increased neural activity in the prefrontal cortex (generally involved in the top-down control
of behavior, especially inhibiting inappropriate
response tendencies) and decreased neural activity in several limbic and paralimbic areas (regions
involved in a wide array of functions spanning
metabolic, affective and motivational processes),
and cerebellum. Among the limbic/paralimbic
areas, we observed decreased activity in response
to the meal in the insular cortex (a visceral sensory area also involved in processing food craving (Pelchat et al., 2004a)), the anterior cingulate
(selectively involved in response to noxious
stimuli (Craig et al., 1996)) and the orbitofrontal
cortex (involved in cross-sensorial processing).
Some of these findings were also replicated in a
study of the changes in brain activity related to
eating solid food (Small et al., 2001).
Taken together, these findings not only
demonstrated the feasibility of a neuroimaging study applied to obesity-related questions,
but also showed that hunger and early satiety
1. FROM BRAIN TO BEHAVIOR
20.2 FUNCTIONAL NEUROIMAGING EVIDENCE
are associated with specific regional brain
responses. These regional responses clustered
in a hunger-related domain, encompassing
areas involved in responses to emotional cues
and sensory information as well as to metabolic changes. Several of these areas were also
implicated by other neuroimaging studies in
responses to other forms of urges, such as thirst
(Denton et al., 1999) and hunger for air (Brannan
et al., 2001). Conversely, early satiety appeared
to be associated with the activation of only one
brain region, the prefrontal cortex, an area that
reached the greatest phylogenetic expansion in
humans and functionally presides over cognitive processing of information, including topdown control over behavioral responses. In
light of these results and the evidence that all
these major regional brain domains are reciprocally interconnected, we postulated that the
prefrontal cortex, through efferent inhibitory
projections to the limbic and paralimbic areas,
exerts inhibiting effects on eating by suppressing the hunger-related activation of these brain
areas. As an aside, the presence of a distributed,
and possibly redundant, network of brain areas
activated by hunger seems to support the common notion that the control of energy balance is
inherently biased, favoring anabolic processes
such as food intake (Schwartz et al., 2003).
In regard to the second question, we observed
that obese individuals respond to hunger and
early satiety with greater changes in some of the
limbic/paralimbic areas and in the prefrontal
cortex, respectively, compared to normal-weight
individuals. Specifically, in obese compared
to normal-weight individuals, we observed
that limbic and paralimbic areas, including the
orbitofrontal cortex, insula and hippocampus,
showed a greater activity in response to hunger, whereas dorsal and ventral prefrontal areas
showed a greater activity in response to satiety. These differences were generally consistent in men and women (Del Parigi et al., 2002b).
However, in men only, the hunger response
in the hypothalamus was attenuated in obese
257
compared to normal-weight individuals (Gautier
et al., 2000). A similar observation was reported
in lean and obese individuals in response to the
ingestion of a glucose solution (Matsuda et al.,
1999). We also found significant associations
between postprandial changes in plasma insulin, glucose and FFA, and postprandial changes
in neural activity in several brain regions, which
suggests that hormones and metabolites might
contribute to the generation of postprandial neural responses. In some instances, the correlations
between postprandial changes in hormones/
metabolites and neural activity were in opposite
directions in obese and normal-weight individuals (Del Parigi et al., 2002a).
To answer the third and most challenging
question, we recruited post-obese individuals
who had successfully achieved and maintained
a normal body weight by lifestyle changes
despite a past of morbid obesity (BMI 35)
(Del Parigi et al., 2004). Anthropometrically,
these individuals showed a normal-weight
phenotype, not different from the normalweight group previously studied, while their
past as formerly obese individuals indicated a
high susceptibility to weight gain, constantly
counteracted by an intense physical activity regimen and actively controlled dietary
intake. Although in a cross-sectional fashion,
we planned the study of these formerly obese
individuals in order to explore functional similarities in the brain responses to hunger and
satiety between a group of obese-prone and
a group of currently obese individuals to be
interpreted as putative markers for neurofunctional signatures of predisposition to weight
gain and obesity. Similarities between postobese and obese were actually observed only in
the posterior hippocampus, which exhibited a
similar decrease of neural activity in the obese
and post-obese groups, whereas in the normalweight group the regional activity increased
(Del Parigi et al., 2004). The hippocampus is
a brain region implicated in many cognitive
phenomena, chiefly related to mnemonic and
1. FROM BRAIN TO BEHAVIOR
258
20. BRAIN DOMAINS INVOLVED IN HUMAN EATING BEHAVIOR
learning processes, but it is also rich in receptors
of metabolic signals and has been associated
with food craving by another neuroimaging
study which reported a response in an overlapping hippocampal area (Pelchat et al., 2004b).
While suggestive of putative neurofunctional
markers of increased risk for weight gain and
obesity, this finding still awaits testing in properly designed longitudinal studies in individuals at high risk for obesity before and after
gaining weight. Until then, the assessment of the
pathophysiological relevance of the neurofunctional differences observed between obese and
normal-weight individuals remains exploratory.
In conclusion, we believe that the use of functional neuroimaging in the search for the neurofunctional underpinnings of eating behavioral
differences between obese and normal weight
individuals has proven to be both feasible and
useful. The study of the neurofunctional correlates of hunger and satiety is one of the viable
experimental settings in pursuing a better understanding of the neurobiology of eating behavior
and its aberrations. We have reported a series
of exploratory findings that have been partially
confirmed in independent studies. These observations have generated hypotheses that are
amenable to proper testing in longitudinal studies, where the pathophysiological importance of
obesity-related neural abnormalities can be determined and offer the rationale for new investigational targets for the pharmacotherapy of obesity.
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C H A P T E R
21
Neuroendocrine Stress Response and Its
Impact on Eating Behavior and
Body Weight
Beth M. Tannenbaum1, Hymie Anisman2 and Alfonso Abizaid2
1
McConnell Brain Imaging Centre, Montreal Neurological Institute,
McGill University, Montreal, Canada
2
Institute of Neuroscience, Carleton University, Ottawa, Canada
O U T L I N E
21.1 Introduction
261
21.3 Stress and Food Intake: It is not all
Homeostatic or Automatic
263
21.5 Peripheral Signals Regulating Energy
Balance
21.5.1 Leptin
21.5.2 Insulin
21.5.3 Ghrelin
21.4 Imaging Studies in Humans
264
21.6 Conclusion
21.2 Hypothalamo-Pituitary-Adrenal Axis 262
21.1 INTRODUCTION
There is considerable overlap between the
physiological systems that regulate food intake
and those that mediate stress responses, and
stressful events may influence food ingestion.
Considering that the ability of an organism to
mount an effective defensive response is highly
dependent on available energy stores (i.e., the
Obesity Prevention: The Role of Brain and Society on Individual Behavior
265
265
266
266
267
mobilization and utilization of blood glucose), it is
not surprising that overlapping mechanisms exist
among stress and consummatory systems. That
said, under certain conditions it may be adaptive
for processes that stimulate defensive behaviors
to inhibit those relating to ingestive processes –
for example, it would clearly be counterproductive for an organism facing a threat from
predators to engage in a search for food.
261
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2010 Elsevier Inc.
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21. STRESS AND EATING BEHAVIOR
Under non-stressful conditions, individuals
have the opportunity to adopt healthy eating
practices and maintain a healthy body weight.
However, under stressor conditions, particularly
if these are chronic and unremitting, the wear
and tear on biological and behavioral coping
methods may be excessive (McEwen, 2007), and
shifts may occur to food consumption patterns
in regard to both quality and quantity. In order
to maintain “allostasis”, the processes by which
homeostasis is maintained in the face of stressors, numerous short-term adaptive coping strategies can be employed, although some of these
may have negative long-term repercussions. In
rodents, stressors typically result in reduced food
intake. However, in humans there are marked
individual differences: some individuals reduce
their consumption, whereas others increase
ingestion (particularly carbohydrate snacks).
Some of these individuals may employ eating
as a coping method, even if it is an ineffective or
counterproductive one. Indeed, this coping strategy may involve a shift from the consumption of
healthy foods to the overconsumption of “comfort foods” that are typically high in calories, fat
and sugar, and low in nutritional value.
As individuals turn to “comfort foods” to
alleviate stress, the continued failure to cope
with stressors may promote the development
of obesity (Laitinen et al., 2002). This drive for
comfort is associated with a shift from the
homeostatic/allostatic system (dependent on
energy stores and nutritional status) to the nonhomeostatic or reward-seeking system (involved with the motivational aspects of eating).
The “homeostatic” and “non-homeostatic” controls on food intake and energy expenditure
are achieved through coordination between the
hypothalamus, the brainstem and various limbic areas. However, if pleasure is experienced
after the consumption of high-sugar/high-fat
foods, the hedonic response might be capable of
over-riding homeostasis/allostasis, and result
in an elevated appetite and a drive to overeat
“pleasurable” calories (McEwen, 2007).
The present chapter addresses some of the
current research findings in both animal and
human populations that have elucidated how
and why food consumption patterns can be
altered under stressor conditions. It is suggested
that cortisol (or corticosterone in rodents) and
several metabolic hormones, released under
stress and anxiety conditions, are linked to
changes in metabolic function. Moreover,
through repeated experiences, individuals may
learn that eating high-caloric foods can reduce
some of the unpleasant effects of stress and
thus, with further stressor encounters, individuals may “self-medicate” through eating “comfort foods”.
21.2 HYPOTHALAMO-PITUITARYADRENAL AXIS
The activation of the hypothalamo-pituitaryadrenal (HPA) axis is comprised of a network of
regions that span both the central and peripheral nervous systems. In response to stressors, corticotrophin releasing hormone (CRH)
is released from cells in the paraventricular
nucleus (PVN) of the hypothalamus. CRH acts
on the anterior pituitary corticotrophs to stimulate the synthesis and release of adrenocorticotropic hormone (ACTH), which then acts on the
adrenal cortex to stimulate the release of cortisol
(humans) or corticosterone (animals). Cortisol
regulates its own levels via a series of negative-feedback loops at both brain and pituitary
sites. Excessive and/or chronic stressors, possibly through actions on HPA functioning, can
adversely impact a variety of physiologic functions and behavioral outputs, such as growth,
reproduction, glucose metabolism (insulin resistance and type 2 diabetes), immunocompetence,
deposition of body fat, atherosclerosis, hippocampal atrophy, and depression (Kyrou et al.,
2006). This makes negative-feedback efficacy
essential, but under conditions of chronic stress
1. FROM BRAIN TO BEHAVIOR
21.3 STRESS AND FOOD INTAKE: IT IS NOT ALL HOMEOSTATIC OR AUTOMATIC
the ability of the axis to “shut-off” or dampen
circulating cortisol levels is often compromised,
and the diurnal rhythms associated with stressors may be disturbed (Michaud et al., 2008).
In addition to the hypothalamus, the amygdala plays a fundamental role in mounting
effective stress responses. It contains many of
the peptides implicated in stress and anxiety
regulation, such as CRH (Behan et al., 1996).
However, in contrast to the hypothalamus, corticosterone stimulates CRH release in the amygdala, which then affects anxiety/fear responses
(Makino et al., 1994) as well as the regulation of
food intake and appraisal. Like the amygdala,
medial prefrontal cortical regions (mPFC) (i.e.,
the anterior cingulate gyrus, the subcallosal
gyrus and the orbitofrontal cortex), appear to
be involved in the memory of the emotional
valence of stimuli, and thus play an active role
in the inhibition of fear responses mediated by
the amygdala (Pignatti et al., 2006; Petrovich
et al., 2007). Therefore, the response to stress
is highly complex and recruits varied brain
regions that serve and support both the endocrine and cognitive aspects of the axis.
21.3 STRESS AND FOOD INTAKE:
IT IS NOT ALL HOMEOSTATIC OR
AUTOMATIC
Animals exposed to repeated stressors typically eat less, and therefore weight gain is limited. Yet, under some conditions, they also
show increased consumption of palatable foods
and liquids, accompanied by reduced HPA
axis activity (Pecoraro et al., 2004; la Fleur et al.,
2005). As indicated earlier, intake of “comfort
foods” reduces HPA axis activity and promotes
the activation of brain circuits implicated in
reward-seeking behaviors (Dallman et al., 2003).
In line with the view that “comfort foods” have
positive effects, when provided to chronically
stressed rats they may negate stress-induced
263
abnormalities of cortisol and dopamine (DA)
functioning (Dallman et al., 2006). In effect, it
may be that the inhibition of neuroendocrine
responses to stressors in rats eating “comfort
foods” is explained by the interplay between the
negative effects of chronic stressors and the positive effects of “comfort foods” on inputs to the
ventral tegmental area (VTA) nucleus accumbens
reward network – brain sites critically involved
in dopamine DA regulation, the primary neurochemical implicated in responses to reward
and addiction. As such, DA modulation may be
responsible for regulating the reward or reinforcement necessary to enhance feeding as seen in
obesity (Berridge, 1996). These reward-related
areas are also activated in response to drugs
of abuse (McQuade et al., 2004), and it is conceivable that the underlying brain mechanisms associated with stressor-provoked eating are similar
to those that ultimately result in the compulsive
drug consumption seen in addiction (Volkow
and O’Brien, 2007).
Data from human studies support the idea
that stressors can enhance caloric intake as a
means to cope with stressful events (Anisman
et al., 2008). Daily hassles were associated with
increased consumption of high-fat/high-sugar
snacks, and with a reduction in the frequency
of main meals and the consumption of vegetables. Interestingly, psychosocial stressors elicited hyperphagic responses in subjects, whereas
physical stressors caused hypophagic responses
(O’Connor et al., 2008). There appear to be premorbid features that predict the impact of stressors on eating and weight gain. Specifically, it
was reported that among students followed
over a 12-week stressful period, dietary restraint
decreased and their body mass index (BMI)
increased (Roberts et al., 2007). Further, those
with the highest dietary restraint scores were
those with the highest initial BMI and lowest
daily salivary cortisol secretion. This is consistent with evidence that restrained eaters struggle to control food intake and weight, as well
as with the predictions of the dietary restraint
1. FROM BRAIN TO BEHAVIOR
264
21. STRESS AND EATING BEHAVIOR
model; namely, that reliance on cognitive control over eating, rather than physiological cues,
renders dieters vulnerable to uncontrolled eating. Paralleling such findings, healthy medical students who had higher urinary cortisol
and insulin during academic exams had identified themselves as stress eaters and showed
greater weight gain than non-stress eaters during a stressful academic period (Epel et al.,
2004). Others showed that laboratory stressors
(unsolvable anagrams, Trier Social Stress Test)
were associated with increased caloric intake
(and, in particular, fat) (Zellner et al., 2006),
especially in those who showed substantial cortisol responses to the stressor (Epel et al., 2001).
21.4 IMAGING STUDIES IN
HUMANS
Brain imaging studies have been instrumental in elucidating the functional network
that controls appetite, identifying the specific
brain regions differentially involved in hunger and satiety in humans (Tataranni et al.,
1999). For example, hunger was associated
with increased regional cerebral blood flow
(rCBF) in the hypothalamus, the anterior cingulate cortex, and the insular and orbitofrontal cortices (IC and OFC), whereas satiety was
associated with changes in the prefrontal cortex
(PFC) (Tataranni et al., 1999). It has been postulated that the PFC has inhibitory effects on the
hypothalamic regions that regulate hunger in
humans, thus promoting meal termination. In
effect, there is decreased activity of the ‘‘hunger ’’ areas when the individual is satiated. Since
the PFC is known to have inhibitory projections
to these areas, termination might be provoked
by the “anorexigenic” PFC down-regulating
neuronal activity in the orexigenic CNS regions.
PET analyses indicate that gastric distension
(as a mechanic visceral stimulus to simulate satiety) provokes activation of the inferior frontal
gyrus (a component of the PFC) (Le et al., 2006).
This suggests that this region plays a pivotal
role as a convergence zone for processing foodrelated/visceral stimuli, and for the coordination of states of appetite and satiety. In addition,
it appeared that the amygdala was involved in
the coordination of appetitive behaviors (Baxter
and Murray, 2002; Cardinal et al., 2002; Holland
and Gallagher, 2004). Specifically, it is thought
that the amygdala, through interactions with
the OFC, signals the hedonic value of a stimulus or object (Holland and Gallagher, 2004).
By interacting with posterior visual areas, this
region may be important in defining the salience of biologically relevant stimuli (LaBar
et al., 2001).
The processing of hunger and satiety cues
appears to be contingent on the inherent reward
value of the food, the individual’s motivational
state, and other factors that could influence
motivational processes (such as stressor experiences). In this regard, it was found that when
students were highly motivated to eat chocolate
(and rated the chocolate as being highly pleasant), rCBF increased in the medial OFC and IC.
Conversely, rCBF in the PFC and lateral OFC
increased with satiety as the chocolate became
less pleasant (Small et al., 2001). Killgore and colleagues (2003) likewise tested whether images
of high-caloric foods would have greater motivational salience in the amygdala and PFC
relative to images of low-caloric foods, as measured by fMRI (Killgore et al., 2003). They found
activation of the amygdala irrespective of the
caloric content of the food image, but significant
activation in the PFC following the presentation of high-caloric foods. Given these data and
those supporting the inhibitory role of the PFC
on food intake (Del Parigi et al., 2007), it is likely
that the inhibition of food reward is probably a
goal of this prefrontal-orbitofrontal loop. Thus,
in addition to the differential recruitment of
brain areas in conditions of satiety and hunger,
the neural activity in these areas can be modulated by the incentive value of food stimuli
1. FROM BRAIN TO BEHAVIOR
21.5 PERIPHERAL SIGNALS REGULATING ENERGY BALANCE
(i.e., the inherent reward value) as well as differences in motivational state.
Several studies have shown that there is
overlap between brain regions associated with
food intake and in anticipation of or in response
to stressful stimuli. For example, anticipation
of public speaking was associated with activation in the hippocampus (HC)/amygdala
(Tillfors et al., 2001). Subjects asked to solve difficult mathematical problems showed increased
activation of the ventral right prefrontal cortex
(rPFC) and insula/putamen, as measured by
perfusion MRI (Wang et al., 2007). rPFC activation persisted well beyond the termination
of the stressor task, suggesting a heightened
state of vigilance or emotional arousal (Wang
et al., 2005). Based on several PET studies, it also
appears that subcortical DA was increased in
response to physiological (Adler et al., 2000) and
psychological (Pruessner et al., 2004) stressors.
For instance, Pruessner and colleagues reported
that a mental arithmetic stressor produced
brain activations involving the occipital, parietal and motor cortex. The most profound effect
of the stressor, however, seemed to involve a
deactivation across a network of limbic structures, including the hippocampus, amygdala,
insula, hypothalamus, ventral striatum, medioorbitofrontal cortex and posterior cingulate
cortices (Pruessner et al., 2008). Interestingly,
individuals who reacted to the stressor with
a significant increase in circulating cortisol
showed the greatest deactivation in the aforementioned brain regions (Pruessner et al., 2008).
These results suggest that this set of limbic system structures shows high activity during nonstressful states, serving as a threat-detecting
system. The system constantly scans the environment for signs of incoming danger or threat.
Once such a condition is met, the activity of this
system is actively curtailed to initiate the alarm
response consisting of hormonal and physiological responses of the HPA and other axes to
allow adaptation of the organism in response
to the threat. As such, one mechanism for acute
265
stress-induced changes in food consumption
may be a redirecting of resources normally
available for feeding processes towards basic
survival needs. However, under conditions of
chronic stress, continuously elevated adrenal
glucocorticoids, through a compromised alarm
system, may be associated with a shift towards
hedonic feeding patterns.
21.5 PERIPHERAL SIGNALS
REGULATING ENERGY BALANCE
The brain regions discussed here appear to
be fundamental in integrating both internal and
external cues, promoting appropriate physiological and behavioral responses to maintain
homeostasis. Some of these brain regions are
also influenced by peripheral hormones which
are fundamental in determining consumption
and satiety.
21.5.1 Leptin
The discovery of leptin, the protein encoded
by the Ob gene, could be considered among the
most important research findings in the field
of energy balance. Produced primarily by adipocytes, leptin is secreted into the circulation
and targets the brain and peripheral organs to
ultimately decrease food intake, increase energy
expenditure and reduce adiposity (Zhang et al.,
1994; Campfield et al., 1995; Halaas et al., 1995).
Mutation of this gene or the gene encoding the
leptin receptor results in morbid obesity, insulin resistance and infertility (Zhang et al., 1994;
Chen et al., 1996). In addition to being a metabolic hormone, leptin also acts as a modulator
of the HPA axis and might influence the effects
of stressors on systems other than those regulating energy homeostasis (Lu et al., 2006). Indeed,
leptin targets critical brain regions responsible
for the regulation of the HPA axis, such as the
1. FROM BRAIN TO BEHAVIOR
266
21. STRESS AND EATING BEHAVIOR
hippocampus, the brainstem and the hypothalamic PVN (Hakansson et al., 1998; Hosoi et al.,
2002). Leptin receptors are also found in regions
where corticosterone and CRH may affect food
intake and energy balance. For example, leptin
targets the raphe nuclei and the VTA in the midbrain, where it modulates the activity and release
of 5-HT and DA, respectively (Finn et al., 2001;
Fernandez-Galaz et al., 2002; Clark et al., 2006;
Fulton et al., 2006; Hommel et al., 2006). The presence of receptors in these regions also suggests
that leptin may directly influence reward-seeking
behaviors, and affective tone, related to feeding.
Support for this idea comes from studies where
leptin promoted anhedonia reflected by an increase in the reward threshold (i.e., reduced value
of the reward) among rats responding to rewarding electrical stimulation from the lateral hypothalamus (Fulton et al., 2000).
Like several other hormones, leptin is influenced by stressors (Konishi et al., 2006). It has
been suggested that, through actions on reward
mechanisms, it might contribute to stressrelated pathology such as depression (Anisman
et al., 2008). In fact, in rodents the depressivelike behavioral disturbances introduced by a
chronic stressor could be antagonized by systemic or intrahippocampal (but not hypothalamic) leptin administration (Lu et al., 2006).
However, the data concerning leptin variations
in relation to depression in humans have been
inconsistent (Deuschle et al., 1996; Kraus et al.,
2001; Atmaca et al., 2002; Westling et al., 2004;
Kauffman et al., 2005; Eikelis et al., 2006; Jow
et al., 2006; Otsuka et al., 2006; Pasco et al., 2008),
and the source for these inconsistencies is uncertain. However, these may have been related to
variability of the features of depression across
individuals. While some display typical features
(e.g., reduced eating and sleeping), in atypical
depression, symptoms may be comprised of
reverse neurovegetative features (e.g., increased
eating, sleeping). More research is needed to
obtain a clearer picture of the specific role of leptin on depressive disorders. Given the relation
between leptin, CRH and glucocorticoid processes, it can be suggested that leptin contributes to the different stressor-provoked changes
of ingestion evident in depressive illness.
21.5.2 Insulin
The role of insulin in the regulation of energy
balance is well established, and it is suggested
that its interactions with glucocorticoids, leptin, ghrelin and cytokines play a critical role
in the development of obesity and metabolic
anomalies seen after continuous exposure to
stressful events (Landsberg, 2001). Like leptin,
insulin secretion from the pancreas is increased
in animals exposed to stressors (Black, 2006;
Innes et al., 2007). Acutely, insulin increases glucose utilization in the periphery and targets the
hypothalamic ARC to reduce NPY synthesis and
ultimately decrease food intake (Woods et al.,
1996). Nevertheless, chronic stressor exposure
leads to insulin insensitivity through a number of
mechanisms that may be mediated by elevated
glucocorticoid action (Black, 2006). One reason
for this is that insulin resistance is ameliorated
by adrenalectomy (Saito and Bray, 1984; Duclos
et al., 2005). Interestingly, insulin depletion also
prevents some of the obesogenic effects of glucocorticoids, particularly those where stressors may
promote increased consumption of high-caloric
foods (la Fleur et al., 2004). Thus, in the absence
of insulin, animals with elevated corticosterone
levels may not experience the soothing effects of
“comfort foods” (la Fleur et al., 2004). Given that
dopamine cells in the VTA express insulin receptors (Figlewicz et al., 2003), it is possible that
insulin targets midbrain dopamine cells to sensitize them to the action of glucocorticoids, thereby
enhancing food-seeking behaviors.
21.5.3 Ghrelin
Ghrelin is a stomach-derived peptide that has
generated considerable attention because, unlike
1. FROM BRAIN TO BEHAVIOR
267
21.6 CONCLUSION
other peripheral signals, ghrelin stimulates food
intake, increases adiposity and decreases metabolic rate (Kojima et al., 1999; Tschop et al., 2000).
The effects of ghrelin, like those of glucocorticoids, leptin and the other hormones discussed
to this point, occur both centrally and peripherally (Kojima and Kangawa, 2005). Within the
hypothalamus, ghrelin receptors are concentrated in the VMH and ARC, where they target
primarily NPY/AGRP neurons to modulate its
orexigenic properties (Nakazato et al., 2001). Ghrelin also directly stimulates orexin cells in the LH
as well as cells in the PVN, VMH, DMH and
SCN, suggesting a large degree of complexity in
its effects within the hypothalamus (Guan et al.,
1997; Toshinai et al., 2003; Zigman et al., 2006).
In addition to its hypothalamic actions,
ghrelin also targets extrahypothalamic structures that overlap with targets of glucocorticoid action, including the hippocampus, the
brainstem and the midbrain VTA, the substantia nigra and the raphe nuclei (Guan et al.,
1997; Abizaid et al., 2006; Zigman et al., 2006).
Microinfusion of ghrelin into the VTA leads to
increased food intake, whereas microinfusion of
antagonists decreased compensatory food intake
after a fast (Guan et al., 1997; Carlini et al., 2004;
Naleid et al., 2005; Abizaid et al., 2006; Zigman
et al., 2006). Interestingly, ghrelin injections
increase food-related imagery and stimulate the
activity of reward pathways in human subjects,
including the PFC and amygdala. This further
implicates ghrelin in appetitive responses to
incentive cues (Schmid et al., 2005; Malik et al.,
2008). Peripherally, ghrelin targets the pituitary to enhance the release of growth hormones
and stress hormones such as ACTH and prolactin (Arvat et al., 2001; Stevanovic et al., 2007).
Ghrelin also stimulates the release of corticosterone, an effect mediated by the increases in
the release of ACTH (Stevanovic et al., 2007). It
is notable that although adrenalectomy reduces
food intake and body weight, the orexigenic
effects of ghrelin are not affected by this manipulation, supporting the idea that ghrelin does
not promote food intake through the stimulation of corticosterone secretion (Proulx et al.,
2005). In this regard, ghrelin also stimulates the
proliferation of adipocytes, which might underlie the obesogenic effects of this hormone (Kim
et al., 2004; Zwirska-Korczala et al., 2007).
There is evidence that ghrelin levels fluctuate in response to acute and chronic stressors
(Kristenssson et al., 2006; Ochi et al., 2008), but
little is known about the potential role of ghrelin in the metabolic alterations that follow continuous exposure to stressors. In humans, an
acute stressor (the Trier Social Stress Test; TSST)
increases plasma ghrelin and cortisol levels,
although the post-stress increase in the urge
to eat found to occur in some individuals was
unrelated to acute changes in plasma ghrelin
levels (Rouach et al., 2007). Nevertheless, little is
known about ghrelin responses to chronic stressors and their possible interactions. There are
several potential mechanisms by which ghrelin and corticosterone might influence metabolic processes. These include the interaction
of stressor-induced corticosterone and ghrelin
action on the melanocortin system to modulate
sympathetic outflow; VTA and substantia nigra
functioning to regulate motivational aspects
of feeding; and actions at the hippocampus to
regulate feedback mechanisms that keep HPA
activity in check.
21.6 CONCLUSION
The obesity epidemic is often viewed as the
outcome of an inherited genetic predisposition
to store energy in the form of adipose tissue
in combination with sedentary lifestyles. The
current review offers stress as a possible factor in the generation of obesity and metabolic
syndrome. Here, we have reviewed evidence
that acute activation of the HPA axis affects
brain and peripheral organs to affect appetite.
Continued stimulation of this system results in
1. FROM BRAIN TO BEHAVIOR
268
21. STRESS AND EATING BEHAVIOR
severe energetic dysregulation leading to obesity, insulin resistance, cardiovascular disease
and early death. There are numerous mechanisms that engender these pathological conditions, including effects on brain regions that
regulate metabolism, autonomic function, and
behavioral processes that include motivational,
cognitive and affective behaviors. Particular
emphasis has been placed upon regulatory and
autonomic processes. However, a therapeutic
agent might be developed on the basis of an
understanding of processes linking stress and
the soothing effects of high-caloric foods, as
well as a better understanding regarding the
contribution of corticosterone in relation to the
feeding, metabolic and rewarding effects of leptin, insulin, dopamine and ghrelin.
It could be speculated that behavioral and
cognitive-based therapies aimed at reducing
stress, as well as modifying behavior via programs that diminish the incentive value of
high-calorie diets while enhancing the incentive value of physical activity as a means of
reducing stress, may prove to be effective clinical tools to reduce obesity. It has been shown
that physical activity is associated with the
release of so-called “feel-good” endorphins in
frontolimbic brain structures that may mediate some of the therapeutically beneficial consequences of exercise on depression, stress and
anxiety in patients. As such, interventions introducing physical activity into one’s daily life
could serve to promote the same “reward” as
high-calorie foods, but without the detrimental
health consequences in the long term. In time,
the observed health benefits of increased physical activity may serve as motivation to adopt
more balanced and nutritious eating patterns as
an adjunct to an overall healthier lifestyle. Thus,
in order to reduce the physical, mental and economic costs of the current obesity epidemic, it
is imperative that preventative strategies that
involve a remodeling of the notion of “eating
for pleasure” are introduced early on and promoted throughout the lifespan.
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