RISK ASSOCIATIONS BETWEEN PERCEIVED STRESS, ALLOSTATIC LOAD
AND INSULIN RESISTANCE AMONG NONDIABETIC AFRICAN AMERICAN
WOMEN IN THE JACKSON HEART STUDY
by
ALETHEA N. HILL
SUSAN APPEL, COMMITTEE CHAIR
LINDA MONEYHAM
ERICA R. PRYOR
FRANCES HENDERSON
ERROL CROOK
A DISSERTATION
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Nursing
BIRMINGHAM, ALABAMA
Copyrighted by
Alethea N. Hill
2011
RISK ASSOCIATIONS BETWEEN PERCEIVED STRESS, ALLOSTATIC LOAD
AND INSULIN RESISTANCE AMONG NONDIABETIC AFRICAN AMERICAN
WOMEN IN THE JACKSON HEART STUDY
.
ALETHEA HILL
SUSAN APPEL, COMMITTEE CHAIR
UNIVERSITY OF ALABAMA AT BIRMINGHAM
SCHOOL OF NURSING
ABSTRACT
Objectives: The purpose of this study was to evaluate the associations between perceived
stress, allostatic load, and insulin resistance measured by HOMAIR among nondiabetic
African American women of the Jackson Heart Study.
Methods: The investigative team examined the cross-sectional associations (2000-2004)
of perceived stress, summary indices of allostatic load, and insulin resistance in 2,245
nondiabetic African American women, stratified by glycemic profile, participating in the
Jackson Heart Study. Measurements include physiological markers, anthropometric
measures, and (3) stress instruments: Weekly Stress Inventory (WSI), Global Stress
(STS), and JHS Discrimination Questionnaire (DIS). t-tests, χ2 test, bivariate
correlations, and forward hierarchical linear regression were used to analyze the data.
Results: Increased allostatic load scores were associated with insulin resistance among
both, euglycemic and prediabetic women of the JHS. Among the prediabetic group, the
only consistent associations were allostatic load scores with no contribution from the set
iii
of covariates or perceived stress measures. In the euglycemic group, associations with
insulin resistance persisted with the set of covariates in the model, specifically age and
BMI, and after adjusting for perceived stress.
Of the perceived stress instruments,
Everyday Discrimination was the only measure associated with insulin resistance among
the euglycemic group. Although there was some evidence among the euglycemic group
that physical activity was inversely associated with insulin resistance, after adjusting for
allostatic load scores, the association was not maintained.
Discussion: Findings support the hypothesis that the persistent day-to-day stress of
discrimination can lead to an insulin resistant state and be detrimental to the health of
African Americans by leading to cumulative “wear and tear” on multiple body systems.
Other types of stress, such as general and acute stressors in this study, were not found to
contribute to increased allostatic load scores or insulin resistance.
DEDICATION
I would like to dedicate this work to my daughter, parents, two brothers, Aunt
Deborah and Uncle Darryl. They supported me through this journey with unconditional
love and encouragement that allowed me to make it to this day. My daughter Alyssa
inspired me to persevere through the tough times so that I may stand as a role model for
her. My hope is that she understands the pursuit and completion of this degree
exemplifies the magnitude of my commitment to her future.
The love and support of my parents is unmatched and immeasurable. It reflects
their love for me, belief in diligence and the pursuit of academic excellence. Because of
their strong value system during the instrumental years of my life as a child, I was
prepared for a moment such as this, embracing the challenge with my feet planted and
shoulders squared.
My brothers, James and Brian, were pillars of strength, encouragement, laughter,
and solace through it all, I respect, appreciate, and love them dearly for that. My closest
friends, Chanda Hosey, Telisha Abney, and Jacqueline Durgin have loved and supported
me near and far, providing me sisterly love helping me sustain the simultaneous
challenges of life’s circumstances and doctoral preparation. Finally, I am sincerely
thankful to the Morrissette family for sharing such a loving family with me and Alyssa at
a time when we needed it most. Without the love of Jesus Christ and the people He
strategically placed in my life, this moment would not be possible.
iii
ACKNOWLEDGEMENTS
I am sincerely grateful to my committee chair, Dr. Susan Appel, whose
encouragement, guidance and support from the initial development of my ideas until now
enabled me to develop and grow as a doctoral student. She has grown to be someone that
I admire and respect a great deal. Her experience and accomplishments within the
profession of nursing motivate and provoke me to become an impactful nurse researcher.
It is a pleasure to thank other committee members at the University of Alabama at
Birmingham who helped me write my dissertation successfully. Dr. Pryor played a key
role in my development as a doctoral student from inception. Without her patience,
guidance, and mentorship, I would not have made it through the first two years of the
doctoral program. Her ability to extract the best out of me made all of the difference. Dr.
Moneyham has poured a wealth of knowledge and inspiration into me within a limited
amount of time. Her knowledge and contributions to nursing are indisputable.
I am truly indebted and thankful to the principal investigator of the Jackson Heart
Study (JHS), Dr. Herman Taylor, for allowing me the opportunity to engage in the largest
single-site, prospective, epidemiologic investigation to focus on traditional and nontraditional risk factors essential to cardiovascular disease development and progression in
African Americans. This dissertation would not have been possible without the gift of
entry to the JHS provided by Dr. Errol Crook. Through his encouragement and guidance,
I was able to benefit from this experience.
iv
I would be remiss if I did not extend a sincere expression of thanks to many of the
JHS investigators and Jackson State University faculty for their guidance, such as Dr.
Daniel Sarpong, Dr. Frances Henderson, Dr. Mario Sims, Dr. Demarc Hickson, Dr.
Marino Bruce, Dr. Jiankang Liu, Dr. Nimr Fahmy, Gregory Wilson, Brenda Campbell
and Darlene Keyes.
I owe sincere and earnest thankfulness to the leadership at the University of South
Alabama, College of Nursing; specifically, Dr. Ronald Franks, Dr. Debra Davis and Dr.
Michael Jacobs for their support as I pursued doctoral preparation. I am obliged to many
of my colleagues, who supported me, with heartfelt thanks to Dr. Barbara Broome,
Dr. Bridget Robinson, and Dr. Hattie Myles as colleagues and friends. Finally, I would
like to thank my fellow graduate students, Tonya Moore and Tracy Shamburger, who
provided me invaluable support throughout this process.
v
TABLE OF CONTENTS
Page
ABSTRACT ....................................................................................................................... iii
DEDICATION .....................................................................................................................v
ACKNOWLEDGEMENTS ............................................................................................... vi
LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES .............................................................................................................x
LIST OF ABBREVIATIONS ............................................................................................ xi
CHAPTER 1: INTRODUCTION .......................................................................................1
Statement of the Problem ...........................................................................................3
Purposes of the Study.................................................................................................5
Research Questions ....................................................................................................6
Significance of the Study ...........................................................................................7
Conceptual Framework ..............................................................................................7
Definition of Terms..................................................................................................12
Assumptions.............................................................................................................13
CHAPTER 2: REVIEW OF LITERATURE ....................................................................15
Stress ........................................................................................................................16
Allostatic Load .........................................................................................................23
Insulin Resistance ....................................................................................................26
vii
TABLE OF CONTENTS (Continued)
Page
CHAPTER 3: METHODOLOGY ....................................................................................31
Research Design.....................................................................................................31
Protection of Human Subjects ...............................................................................32
Setting ....................................................................................................................32
Population and Sample ..........................................................................................33
Instruments .............................................................................................................33
Data Collection Procedure .....................................................................................41
Data Management ..................................................................................................42
Data Analysis .........................................................................................................43
CHAPTER 4: FINDINGS.................................................................................................48
Description of Cohort ...........................................................................................49
Description of Analyzed Sample Stratified by Glycemic Profile………..52
Research Questions
Research Question 1 ..................................................................................56
Research Question 2 ..................................................................................58
Research Question 3 ..................................................................................60
Summary of Findings ............................................................................................64
CHAPTER 5: DISCUSSION, LIMITATIONS, IMPLICATIONS..................................67
Discussion of Findings Related to Conceptual Framework ...................................67
Discussion of Findings Related to Literature Review ...........................................70
Study Strengths and Limitations ............................................................................76
Implications for Nursing Research ........................................................................82
Implications for Nursing Education .......................................................................83
Implications for Nursing Practice ..........................................................................83
LIST OF REFERENCES ...................................................................................................93
APPENDIX ......................................................................................................................121
A INSTITUTION REVIEW BOARD APPROVAL ...................................................122
B JACKSON HEART STUDY MANUSCRIPT APPROVAL ...................................123
C
PERMISSION TO USE FIGURES...........................................................................126
viii
LIST OF TABLES
Table
Page
1
Operationalization of Allostatic Load in Research Studies ........................................86
2
History and Description of the Emerging Definition of Insulin Resistance ...............88
3
HOMAIR and Insulin Sensitivity.................................................................................89
4
HOMAIR and CVD Risk Prediction ............................................................................91
5
Demographics of Cohort:
Age, BMI, Physical Activity .......................................................................................50
6
Demographics of Cohort:
Education, Marital Status, Income, Smoking .............................................................51
7
Demographics of Study Sample:
Age, BMI, Physical Activity .......................................................................................52
8
Demographics of Study Sample:
Education, Marital Status, Income, Smoking .............................................................53
9
Comparison of Mean and Standard Deviation for Allostatic Load Factors
.....................................................................................................................................55
10 Concurrent use of Medications by Glycemic Profile ..................................................56
11 Bivariate Analysis:
Perceived Stress Measures and Insulin Resistance of Euglycemic and Prediabetic
African American Women of the JHS
.....................................................................................................................................57
12 Bivariate Analysis:
Allostatic Load Factors and HOMAIR of Euglycemic and Prediabetic
African American Women of the JHS ........................................................................59
13 Forward Hierarchical Regression Analysis of Prediabetic Group ..............................61
14 Forward Hierarchical Regression Analysis of Euglycemic Group .............................64
ix
LIST OF FIGURES
Figure
Page
1
Environmental and Genetic Factors on the HPA axis ................................................10
2
Hypothesized Progression of Insulin Resistance ........................................................11
3
Hypothesized Conceptual Framework for Study .........................................................12
4
Allostatic Load Factors ................................................................................................26
5
Forward Hierarchical Linear Regression of Study Variables ......................................46
x
LIST OF ABBREVIATIONS
A1C
Glycosylated Hemoglobin A1C
AA
African American
AAW
African American Women
BMI
Body Mass Index
CVD
Cardiovascular Disease
DIS
Discrimination Form
HOMAIR
Homeostatic Model Assessment of Insulin Resistance
HPA
Hypothalamic-Pituitary-Adrenocortical Axis
JHS
Jackson Heart Study
STS
Global Stress Scale
WSI
Weekly Stress Inventory
xi
1
CHAPTER 1
INTRODUCTION
It is common knowledge that stress can have deleterious effects on health.
Animal models have demonstrated that excessive exposure to stress generates negative
stress emotions and strains hormone reactivity, impairs cognitive functioning, accelerates
the aging process, contributes to abdominal adiposity, and increases risk for
cardiovascular disease (CVD) (Shiveley & Clarkson, 1988; Bodkin & Metzger, 1989;
Shiveley, Laber-Laird, & Anton, 1997; Kaufman et al., 2007). Adaptation to stressful
life experiences is supported by complex regulatory systems in the body that are
challenged by internal and external stressors. Such stress factors include socioeconomic,
psychological, neuroendocrine, metabolic and physical environments. Researchers have
hypothesized that social and physical determinants of health, such as gender, age, or
race/ethnicity, impact how an individual perceives stress (Brunner, 1997). The influence
of childhood social disadvantages and adult life experiences play a vital role in how an
individual appraises a stressful event. Further, these experiences affect how an individual
handles new situations and relationships (Brunner).
The body’s reaction to these stressors, real or perceived, activates a myriad of
physiological, behavioral and peripheral adaptive responses (Evangelina, Constantine, &
Chrousos, 2005). One of the primary adaptive responses to stress is mediated by the
hypothalamic-pituitary-adrenocortical (HPA) axis. Disharmony within the HPA axis
2
caused by exposure to repetitive or chronic environmental stress, and has been
hypothesized to contribute to the development of a number of stress-related illnesses,
including insulin resistance (Vanltallie, 2002).
Insulin resistance is defined as the consequence of β-cell dysfunction and
impaired insulin-stimulated glucose metabolism (Groop, 1999). When β-cells of the
pancreas continually attempt to compensate for the increased glucose production the
insulin receptor sites at the peripheral tissue level have become less sensitive, and blood
glucose levels rise. Consequently, the propensity to experience metabolic imbalance and
vascular injury increases exponentially (Groop, 1999; Fonesca, 2007; Li et al., 2006).
The trajectory toward an insulin resistant state in nondiabetic individuals entails
dyslipidemia, inflammation, and dysregulation of the pancreatic β cells three to five years
prior to obvious hyperglycemia. The momentum at which this dysregulation occurs is
enhanced by maladaptive physiological responses across multiple systems and exhausted
compensatory mechanisms. Evidence from studies by Groop (1999) and Li et al. (2006)
indicate that insulin resistance is the antecedent, or causal pathway, to other underlying
maladaptive physiological responses, that contribute to health disparities, such as obesity.
Studies by Fonseca (2003), Haffner (1999), Osei et al. (2004), Rewers et al. (2004) and
Resnick et al. (2003) all found links between insulin resistance and metabolic Syndrome,
type 2 diabetes, hypertension, and CVD. These conditions carry tremendous economic
burden with associated long-term injury, disability, morbidity, and mortality throughout
the United States (U.S.).
Allostatic load is a biophysiological risk assessment of parameters depicting
dysregulation across systems, such as the HPA axis, sympathetic nervous, cardiovascular,
3
and metabolic processes. More specifically, the incessant activation of the HPA axis
facilitated by situational stressors or events promotes the allostatic load sequelae across
these systems. Exposure to both acute and chronic stressors (Krieger, 2001; Clark,
Anderson, Clark, & Williams, 1999; Sims, Wyatt, Gutierrez, Taylor & Williams, 2009),
such as those associated with racism and discrimination, have been hypothesized to be
detrimental health determinants of not only insulin resistance (Crimmins et al., 2003;
McEwen, 2004; Räkkönen et al., 1996), but also with low birth weight, infant mortality,
depression, mental illness, type 2 diabetes, hypertension, stroke, and CVD among African
American (AA) populations (Wyatt et al., 2003; Clark & Adams, 2004; Warren-Findlow,
2006; Mays, Cochran, & Barnes, 2007).
Statement of the Problem
Epidemiologic research has shown that insulin resistance is highly prevalent
among African American women (AAW) in the U.S. (Haffner, et al. 1996; Torréns et al.,
2004). Likewise, perceived stress, defined as major and minor life stressors, as well as
discrimination, have been shown to have multidimensional effects on negative health
outcomes, such as insulin resistance, in AA populations (Tull et al., 2005; Innes, Vincent
& Taylor, 2007a, 2007b; Dorner et al., 2009; Geronimous et al., 2010). The
interrelationships among these variables antecede the development of clinical endpoints
such as type 2 diabetes has not been extensively studied in AAW. Multiple research
studies indicate that AAW experience insulin resistance almost 2-3 times the rate of
Caucasian women (Albu et al., 2005; Osei & Schuster, 1994; Haffner et al., 1996;
Haffner et al., 1997; Lovejoy, de la Bretonne, Klemperer, & Tulley, 1996; Lovejoy,
4
Smith, & Rood, 2001; Marcus, Murphy, Pi-Sunyer, & Albu, 1999; Ryan, Nicklas, &
Berman, 2002; Goran, Bergman, Cruz & Watanbe, 2002; Kasim-Karakas, 2000).
Consequently, insulin resistance is implicated in many of the health disparities that AAW
experience. The age range experiencing the highest rates of insulin resistance is 35-65
years, and the trajectory from an insulin resistant state to type 2 diabetes can be as little as
six years in the absence of obesity. With the increased prevalence of obesity among AA,
the time from insulin resistance to type 2 diabetes may be less than six years (Carnethon,
Palaniappan, Burchfiel, Brancati, & Fortmann, 2002; Li et al., 2006; Osei, Rhinesmith,
Gaillard, & Schuster, 2004). The stressful life experiences of AAW may further
contribute to the development of diseased states, premature death, and loss of quality of
life for AAW and their families.
How AAW differ specifically in their transition or pathway to insulin resistance is
unknown. Currently, the risk assessment used in clinical practice accounts for indirect
measures of insulin resistance, such waist circumference, blood pressure, fasting blood
glucose level, low density lipoproteins, and high density lipoproteins. Moreover, the
parameters which are presently used are not race-gender specific; they were established
primarily among homogenous cohorts of Caucasian men and women, without estimation
of AA physiological expressions of the same disease. The implications of insufficient
distinguishable, race-gender specific studies that distinctively account for AAW
populations and those stressors most relevant to disease development may mean that
current biophysiological risk assessments will underestimate the presence of disease for
this population. Therefore, a multisystem biophysiological risk assessment relevant to the
cumulative effects of exposure and perceptions of stress in AAW might explain how
5
nondiabetic individuals express insulin resistance over time. Highlighting intercessory
time points for the elimination of preventable disease identifies a way to counter the
disparaging rates of injury, disability, morbidity, and mortality currently experienced by
AAs.
Although the measurement of Homeostatic Model Assessment of insulin
resistance (HOMAIR) is a reliable, cost-effective method to evaluate insulin resistance,
the thresholds have been found to be inconsistent, inconclusive, and to vary among
populations, such as AAs, Caucasians, Japanese, and Brazilians (Ausk et al., 2010;
Barzilay et al., 2007; Nakai et al., 2002). It is critical to discover whether exposure to
stress over the course of an AAW’s life serves as a catalyst to changes in allostatic load
and onset of insulin resistance that contribute to negative health outcomes and premature
death (Geronimus, 2001; Geronimus et al., 2006; Geronimus et al., 2010). Investigating
clinically relevant measures in this high-risk population specific to cardiovascular disease
risk factors will expand the science and make a significant contribution toward the
resolution of insulin resistance associated health disparities for AAW.
Purpose of the Study
This study examined the contributing effect of perceived stress, allostatic load,
and insulin resistance across multiple systems for associations among nondiabetic
(defined as fasting blood glucose 70-125 mg/dL) AAW in the Jackson Heart Study (JHS).
The perceived stress construct encompassed acute and chronic stress measurements,
including acute and chronic perceived discrimination as a stressor. Allostatic load was
6
operationalized as a multisystem biophysical risk assessment of objective clinical
parameters hypothesized to manifest in the presence of disease due to exposure to stress.
The purpose of the study was to compare nondiabetic AAW of the JHS by
delineating them into euglycemic (70-99 mg/dL) and prediabetes (100-125 mg/dL)
classifications at baseline (2000-2004) in order to examine: 1.) the association between
perceived stress and insulin resistance (defined as HOMAIR); 2.) the association between
each allostatic load factor and insulin resistance; and 3.) the extent to which individual
factors, perceived stress and allostatic load factors are predictors of insulin resistance.
Research Questions
The research questions of this study were as follows:
1. What are the associations between perceived stress and insulin resistance
(HOMAIR) among non-diabetics in the JHS, stratified by glycemic profiles?
2. What are the associations between individual allostatic load factors, as well as
the summed allostatic load score, and insulin resistance (HOMAIR) among
non-diabetics in the JHS, stratified by glycemic profiles?
3. After adjustment for age, education, marital status, smoking, and physical
activity, to what extent do perceived stress and allostatic load factors predict
insulin resistance (defined as HOMAIR) among nondiabetic AAW in the JHS,
when stratified by glycemic profile?
7
Significance of the Study
There is a paucity of highly sampled race-gender specific population based
studies that explore the cumulative interplay of perceived stress, allostatic load, and
insulin resistance in this population. The Jackson Heart Study (JHS) is designed to
address such issues, presenting an opportunity to evaluate these relationships in an AA
population-based cohort. By measuring HOMAIR as a health outcome and an indicator of
early insulin resistance, the investigative team was able to explore the associations among
perceived stress, allostatic load and insulin resistance in nondiabetic AAW of the JHS a
over four period. These distinctions are key elements to elucidate if health equity is to be
achieved and maintained for AAW.
Conceptual Framework
Allostatic Load
Stress is a term that is ambiguous and subjective, relative to context, setting,
perception, and the individual. Whether it is the cumulative effects of daily stressors or a
major event, stress has been implicated as a precursor to many chronic illnesses. Mindbody medicine, also noted as psychoneuroimmunology or psychoneuroendocrinology,
studies the role of the mind and body interacting with hormones in a multidirectional
fashion impacting health outcomes (Pacak & Palkovits, 2001; Peyrot, McMurray, &
Kruger, 1999; Vitetta, Anton, Cortizo, & Sali, 2005). Neurohormonal models posit that
stress potentially affects glycemic levels secondary to increased stress hormone release
(Baum & Posluszny, 1999; McEwen, 1999, 2005; Peyrot et al., 1999). As the healthy
individual ages, insulin sensitivity decreases while insulin resistance increases,
8
facilitating the transition from a prediabetic to diabetic state (Burén & Eriksson, 2005;
Peyrot et al., 1999; Rosmond, 2003).
Stress, stressors, and how an individual perceives stress can have an impact on
health status and outcomes if the body is unable to handle the physiological challenges
encountered. Cumulative effects of prolonged exposure to psychosocial, environmental,
or socio-economic stressors often manifest in disease due to excessive physical and
mental taxation (Pacák & Paklovits, 2001; Vitetta et al., 2005). The temporal sequencing,
frequency, order, or extent to which these events have to occur prior to disease
development remains unclear. However, McEwen developed a framework to study and
describe these effects since the 1990s focusing on “allostasis” and “allostatic load.”
McEwen discusses allostastis and allostatic load (McEwen, 1998a, 1998b, 1999, 2004,
2005) as the net effects on the body exposed to constant, persistent, high stress over time
once the body has surpassed its ability to effectively respond to such taxation, and the
development of stress-related illnesses. He defines allostasis as an individual’s ability to
obtain stability in a continually changing environment. Allostatic load, on the other hand,
is defined as prolonged or repeated insults to the balancing systems that manage
allostasis, resulting in disease development. The framework provides an explanation of
the interplay between stressful experiences, behavioral and physiological responses, and
health outcomes. There are levels of health across all ranges of age, income, education,
social status, working and living environments that are relevant to behaviors and
experiences in life with stress. McEwen’s framework offers plausible explanations by
attempting to account for predisposing, mediating and moderating linkages within this
framework.
9
Allostatic load provides an explanation of the effects of stress and repetition that
follow a predictable pattern in the physical body. First, an attempt to gain stability
through change, such as increased glucocorticoid secretion in response to a stressful
event, is necessary to maintain homeostasis based on preset parameters vital to the
function of life. In pursuit of this stability, allostasis is achieved. Once the body can no
longer sustain or counter the variation occurring, the body’s thermostat is broken and
allostatic load increases. This leads to maladaptive physiological responses that are no
longer able to appropriately upregulate or downregulate to restore an appropriate stress
response; hence stress hormones, such as cortisol, catecholamines, and
dehydroepiandrosesterone sulfate (DHEA-S), remain at excessively high levels
throughout the body for prolonged periods without appropriate means to wash or
eradicate them from immediate access in the circulatory system.
In time, extensive effects on organs within the body become apparent and
manifest in insulin resistant processes that are proposed to transition into disparities, such
as type 2 diabetes, metabolic syndrome, CVD, or coronary heart disease (McEwen,
1998a, 1999, 2004, 2005). The persistent hyperactivation of the HPA axis due to
situational stressors or events, like environmental, psychosocial, or socio-economic
imbalances, are plausible influential triggers increasing formation of endogenous cortisol
and catecholamines. The synergistic effects of hypercortisolemia and increased
catecholamines have been stated to have counterregulatory effects on insulin in the body
(Burén & Eriksson, 2005; Vitetta et al., 2005). Circulating antagonists of insulin, such as
cortisol, glucagon, catecholamines, and growth hormones, are increased during this time
and serve as additional facilitators of an insulin resistant state (Reynolds & Walker,
10
2003). Insulin resistance further contributes to the lack of glycemic control because of
defective insulin target cells and neuroendocrine disequilibrium (see Figure-1). In
addition, hypercortisolemia induces disruption in feedback mechanisms, potentiating
excessive storage and centralization of fat or visceral obesity (Burén & Eriksson, 2005;
Rosmond, 2003).
Figure-1: HPA axis
Figure 1- Diagram depicting the impact of environmental and genetic factors on the HPA axis
and development of visceral obesity. From “Stress induced disturbances of the HPA axis: A
pathway to Type 2 diabetes?” by R. Rosmond, 2003. Medical Science Monitor, 9 (2), RA35-9.
Copyright 2003 by Medical Science Monitor. Adapted with permission of the author (see
Appendix C).
The framework for this study makes the assumption that individual factors such as
age, education, income, physical activity, smoking, and marital status account for
associations with an individual’s perceived stress level which can further influence
physiological responses, such as accumulation and centralization of visceral fat, exhibited
by increased waist circumference, hyperglycemia, hyperinsulinemia, dyslipidemia, and
11
hypertension measured quantitatively by allostatic load scores. The cumulative effect of
these factors has been identified as a likely path of causal relationships between an
individual’s progression from a healthy state, to prediabetes, on to a diabetic state over
time (see Figure-2) (Burén & Eriksson, 2005).
Figure-2: Progression of Insulin Resistance in African American Women
Figure 2- Hypothesized pathway for the progression of insulin resistance toward the development
of Type 2 diabetes, Coronary Heart Disease, Stroke, and Cardiovascular Disease in African
American Women. From “Is insulin resistance caused by defects insulin’s target cells or by a
stressed mind?” by J. Burén and J. Eriksson, 2005, Diabetes Metabolism Research and Reviews,
21, 487-494. Copyright 2005 by John Wiley & Sons, Ltd. Adapted with permission of the author
(see Appendix C).
As an individual without a diagnosis of diabetes transitions over time, insulin
sensitivity decreases and HOMAIR increases (Burén & Eriksson, 2005; Peyrot et al.,
1999; Rosmond, 2003; Vitetta et al., 2005). The current study evaluated associations
12
between individual factors, perceived stress, allostatic load and how nondiabetic AAW of
the JHS transition toward insulin resistance, as measured by HOMAIR.
Figure 3: Hypothesized Conceptual Framework
Definition of Terms
The definitions used in the study were as follows:
1. Nondiabetic is defined as participants with no history of type 2 diabetes or a
fasting blood glucose > 126 mg/dL.
2. Euglycemic is defined as those participants with a fasting plasma glucose ranging
from 65-99 mg/dL.
3. Prediabetes is defined as those participants with a fasting plasma glucose ranging
from 100-125 mg/dL.
4. Perceived stress construct is defined as both acute (weekly) and chronic (ranging
from 12 months) stress. Acute stress was defined as the score on the Weekly
Stress Inventory, an 87-item scale comprised of eight subscales. Chronic stress
13
was defined as the score on the Global Perceived Stress Scale, an eight item
measure. Perceived discrimination, as an additional stress measure, encompasses
an acute (lifetime) and chronic perceived discrimination (daily) as a stressor.
5. Allostatic load is defined as: fasting insulin, serum cortisol, Hemoglobin A1C
(A1C), waist circumference (WC), systolic blood pressure (SBP), diastolic blood
pressure (DBP), high-density lipoprotein (HDL), triglycerides (TG), and lowdensity lipoprotein (LDL), and/or use of antihypertensive, antidiabetic, or fibrate
medication in the past two weeks as stated in the Medication Survey Form of the
JHS. Of these 12 factors, participants were selected by identified high-risk
thresholds. For each one exceeding the parameters identified, the allostatic load
factor was scored as ‘1’, except HDL where the parameter was set according to
the lowest quartile.
6. Insulin Resistance was measured by Homeostatic Model Assessment of insulin
resistance (HOMAIR). The following formula was used to calculate HOMAIR
(mmol/L x μU/mL) = fasting glucose (mmol/L) x fasting insulin (μU/mL)/22.5.
Assumptions
For the purpose of this study, the following assumptions were made:
1. Repeated encounters with stressors for prolonged periods to dysregulation of
compensatory mechanisms that result in insulin resistance.
2. Discrimination is a source of acute and chronic stress for minority populations
and is associated with negative health outcomes.
14
3. Insulin resistance is the antecedent to type 2 diabetes and cardiovascular
disease; therefore, prevention of an insulin resistant state will prevent an
individual from experiencing the deleterious effects of these disease
processes.
15
CHAPTER 2
REVIEW OF LITERATURE
The purpose of this chapter is to review the research literature related to potential
covariates along pathways related to perceived stress and insulin resistance as a
physiological response among nondiabetic AAW of the JHS. More specifically,
subsections provide a discussion of potential factors hypothesized to influence the
trajectory toward an insulin resistant state among nondiabetic AAW. This section will
conclude with a summarization on the review of research literature.
Review Methods
The literature search included longitudinal and cross-sectional studies published
in English in the following data bases: Cumulative Index in Nursing and Allied Health
Literature, Medline, PubMed, PsychINFO, Cochrane Database of Systematic Reviews,
Scholar Google, and DynaMed. The time frame was not limited to ensure seminal or
primary works by stress theorists were retrievable. The following keywords were
searched: stress, perceived stress, allostatic load, insulin resistance, HOMAIR, African
American women, and black women.
The current literature available related to all three concepts, perceived stress,
allostatic load, and insulin resistance is abundant, with varying levels of agreement on the
physiological affects of perceived or psychological stress on health outcomes such as
16
insulin resistance. However, upon stratifying by race and gender, the available literature
related to AAW reduced drastically when compared to a literature search with minimal
limitations. These findings demonstrate that while significant strides have been made to
include women and minorities in research studies, there is still disparate representation
specific to these populations.
Stress
W.B. Cannon (1929) introduced the term “homeostasis” to describe the body’s
ability to recognize, maintain and correct varying physiologic demands, which later
included psychosocial threats (Goldstein & Kopin, 2007). Hans Selye was inspired by
Cannon, whose work focused on the “fight or flight” experience (Cannon 1935; Neylan,
1998; Pacák & Paklovits, 2001). Selye (1950) was the first to propose that living
organisms experience non-specific responses to stress and their “adaptation energy” or
adaptability is contingent upon the participation of vital organs and body systems. This
adaptation is due to general adaptation syndrome or GAS. GAS presents in three phases:
a) alarm reaction, b) stage of resistance, and c) stage of exhaustion (Selye, 1950).
Essentially, the stage of resistance serves to offset the manifestations that develop
in the alarm reaction, only for the manifestations to resurface in the stage of exhaustion if
balance is not achieved (Selye, 1950; McEwen, 2005). Hence, adrenalomegaly, decreased
inflammatory response, immunosuppression, and thymic and lymph node atrophy
promote disease formation and progression (Lázár, 1950). Sterling and Eyer (1988)
introduced the concept of “allostasis” or the body’s ability to experience variability in
17
response to stress. Chrousos and Gold (1992) further explained stress-related illness as a
state of maladaptiveness when perceived stress exceeds normal thresholds.
In light of Selye’s pioneering work related to stress, there are others that either
propose modifications or respectfully disagree with the non-specific attribute of stress
response as he postulates. Bruce McEwen’s (McEwen, 1998, 2003) perspective of stress,
allostasis, and allostatic load model was developed, in part, from Selye’s stress model.
McEwen has had mixed views on some aspects of Seyle’s seminal work redefining what
it means to be “stressed out” by stating: a) Selye’s GAS is not in response to all forms of
stress, b) HPA axis and noradrenergic and adrenergic paths possess distinctly different
stressor-specific paths, c) “fight or flight” experiences are gender specific, and d) the
exhaustion stage requires redefining, acknowledging the dual effects, protective and
negating, of stress (McEwen, 2005). Further discussion of findings in the literature
relevant to allostatic load will follow in another section.
How does one distinguish between acute versus chronic stress? Could it be that
acute stress responses transition to chronic when influenced by repeated encounters with
the stressor? Or, is it that chronically stressed individuals somehow adapt to their lives
being in this state, readjusting internal physiological responses so that a heightened state
remains constant in preparation for old and new stressors? This type of dissonance in an
individual’s perception of stress eventually weakens the neuroendocrine hormonal
feedback system resulting in maladaptive responses (Polito & Annane, 2008).
The acknowledgement of stress begins in the brain and is accentuated by how an
individual perceives the encounter which serves as a catalyst for adaptive neurological,
cardiovascular, autonomic, immune, and metabolic responses manifesting into behavioral
18
and physiologic responses in the human body. The pivotal moment between stress, brain,
and body is when internal thresholds have been exceeded and dysregulation occurs within
the sympathetic nervous system (SNS) and HPA axis. The dual effects of stress,
protective and damaging, result in nonlinear compensatory associations yielding an “ebb
and flow” effect of mediator release in response to the stressor (McEwen & Seeman,
1999; Burén & Eriksson, 2005).
The SNS experiences an upregulation of norpinephrine and epinephrine elicited
by stressors inciting emotions of fear, anger, hypervigilance, or anxiety. The “fight or
flight” effect stimulates both, α-adrenergic and β-adrenergic receptors which mimics
sympathetic stimulation (McCance, Forshee, & Shelby, 2006). An increased level of
norepinephrine raises blood pressure and constricts smooth muscle. Epinephrine
increases heart rate, myocardial contractility, and venous return, in turn raising blood
pressure and causing decreased glucose uptake in the muscles accompanied by transient
hyperglycemia during the encounter. Concurrent stimulation of the hypothalamic
corticotrophin hormone release prompting pituitary gland releases of growth hormones,
vasopressin, and ACTH. The adrenal glands release glucocorticoids, or cortisol, which
directly impacts gluconeogenesis, glucose metabolism (McCance et al., 2006).
Circulating antagonists of insulin, such as cortisol, glucagon, catecholamines, and growth
hormones, increase during this time and serve as additional facilitators of an insulin
resistant state (Reynolds & Walker, 2003). In and of itself, this process is expressed as an
effort to adapt and protect internal systems. When the feedback systems of the body
either are ineffectively turned off once the stressor is removed or not turned on at all,
damaging effects occur. Continued upregulation potentially influences future stress
19
responses contingent upon the frequency and impact leading to a decreased adaptive
reserve that ultimately accelerates tissue and cellular deterioration evidenced by disease
development. For example, chronic elevation of cortisol levels induces Cushings disease,
diabetes, abdominal adiposity, and gastric ulcer formation (Selye, 1950; 1952; 1974).
In addition, animal models have provided similar evidence related to the
consequence of stress exposure in relation to hormone reactivity, abdominal adiposity,
insulin resistance, and cardiovascular disease. In the recent work of Kaufman et al.
(2007), nursing bonnet macaque monkeys that experienced maternal food insecurity were
found to predispose offspring to obesity and insulin resistance due to early-life stress
experienced in utero during neurological development. By peripubertal development, the
offspring had significant differences from normally reared monkeys. These differences
included increased abdominal girth, weight, BMI, HOMAIR and lower glucose disposal
rates. Similar findings by Shively and colleagues were noted in earlier works. Shively
and Clarkson (1988) discovered that Sprague-Dawley rats experiencing chronic stress
had increased lipoprotein lipase activity and heavier fat pads associated with larger
adipocytes. Later, cynomolgus monkeys encountering social stressors unable to
successfully overcome or adapt developed depression, glucose intolerance and abdominal
obesity (Shively, Laber-Laird, & Anton, 1997). Lastly, when observing rhesus monkeys
for the development of obesity-related diabetes onset, Bodkin and Metzger (1989) found
high associations with decreased peripheral insulin sensitivity measured by euglycemic
clamping and increased fasting insulin, which was not always followed by overt glucose
intolerance development.
20
The human response to stress has had corresponding findings in epidemiologic
studies. The influence of childhood social disadvantages and adult life experiences play a
vital role in how an individual appraises a stressful event. Further, these experiences
affect how the individual handles new situations and relationships. Several experimental
human studies attest to the prenatal effects of stress on the fetus. For example, Enringer et
al. (2008) established increased prenatal exposure to psychosocial stressors resulted in
higher insulin resistance among maternal offspring, independent of birthweight, familial
history of diabetes, gestational diabetes, BMI, and smoking. Kim-Dorner, McKenzie,
Poth, and Deuster (2009) examined psychological associations with a physiological
correlate of insulin resistance, measured by HOMAIR, among African Americans (AAs)
and Caucasians and found increased HOMAIR to be associated with negative appraisal
coping styles with the highest scores among AAs; although there were no differences in
the appraisal of stress among each group.
Prolonged exposure to perceived psychosocial, environmental, or socio-economic
injuries has been implicated as a precursor to many chronic illnesses. Individual factors,
such as social support, education, and income also increase the risk of experiencing a
stressful encounter. The role of stress in disease development should not be
underestimated because increased exposure and excess taxation on physiological systems
facilitate unhealthy states and premature development of disease.
For AA, the perception of being discriminated against during daily life
experiences or when pursuing housing, education, employment, or service in public
settings can be one of these chronic stressors. The end of the Civil Rights movement
coupled with more recent discussions on cultural competency/sensitivity would lead one
21
to think there are minimal to no discriminatory acts committed in the 21st century.
However, AA and other minorities still experience both overt and subtle acts they
perceive as racially motivated unfairness (Williams & Mohammed, 2001; Carlson &
Chamberlain, 2004). Moreover, given the segregative history of the U.S., previous
researchers have investigated the effects of discrimination on health (Ahmed,
Mohammed, & Williams, 2007; Myers, 2009; Williams, Neighbors, & Jackson, 2003).
Woods-Giscombe and Lobel (2008) took these results a step further by evaluating racegender relationships between stress and health specific to AAW. Because AAW are
beginning to shoulder much of the socioeconomic responsibility in AA families, AAW
could be a vulnerable population to stress-related illness (Blitstein, 2009; Findlow, 2006;
Geronimus, Hicken, Keene, & Bound, 2006). The Sojourner Syndrome (Lekan, 2009)
and Superwoman Schema (Woods-Giscombé, 2010) are depictions of how race, gender,
class, age, and perceptions of stress converge influencing the health outcomes
experienced by AAW. These concepts collectively describe how AAW attempt to prove
themselves as an asset, countering what they perceive as negative societal perceptions by
maintaining an appearance of strength in the face of adversity, suppressing negative
emotions as a means of survival, which often results in strained interpersonal
relationships and increased stress (Lekan, 2009; Woods-Giscombé, 2010).
If previous studies hold true, socioeconomic status and education will not be
protective in this instance, because AA who have obtained some level of success report
feelings of pessimism (Geronimus & Keene, 2006); citing “tokenism” within
organizations that need to meet quotas, or being seen as the “affirmative action hire” as
common themes ( Kelly, 2007; Reskin, McBriar, & Kmec, 1999; Yoder, 1996). When
22
considering work environments, those with high demand, low decision latitude (Agardh
et al., 2007; Karasek, Barker, Marxer, Ahlborn, & Theorell, 1981) and effort-reward
deficits (Din-Dzietham, Nembhard, & Collins, 2004; Wadsworth et al., 2007) are at risk
for higher stress levels, which some researchers have found to be associated with
coronary heart disease and type 2 diabetes (Björntorp, 2001). AA also perceive a sense of
strategic “blackballing” which disallows upward mobility equal to their Caucasian
counterparts for senior positions due to intentional “homosocial” reproduction by way of
biased selection, mentorship and promotion of individuals like oneself. Such selection
stifles professional growth and hinders minorities from reaping the assumed benefit of
acquiring higher education (Allison, 2000; James 2000; Kanter, 1977; Outley & Dean,
2007). Therefore, even if an AAW achieves higher socioeconomic status and is college
educated, there is the stress of achieving the dream without self-fulfillment; instead, the
revelation of yet another glass ceiling (Cotter, Hermsen, Ovadia & Vanneman, 2001).
For this reason, discrimination is considered a relevant measure of stress because
it accounts for the frustration of being seen as AA or “black” first, and a “person” second
(Brondolo, Gallo, & Myers, 2009; Carlson & Chamberlain, 2004). In addition, AA’s
feelings of stress over social inequalities underscored by racism are then validated. This
notion has given race-specific meaning to “social” or “environmental” determinants of
health, that sets the tone for “health disparities” (Smedley, Stith, & Nelson, 2003). The
risk of ambiguity between feelings of stress relevant to their source and contribution from
the cause, hence inappropriate appraisal, suggests that discussion of generic acute and
chronic stress should be included to elucidate the extent to which each type of stress
measure specifically impacts the outcome variable. The JHS concurrently measured
23
scales on depression, coping, hostility, anxiety and hassles; however these concepts were
not included in the present study and will not be discussed further.
Allostatic Load
Given the likelihood of experiencing dsyregulation of one or more systems during
disease development, it is wise not to discount the effects of multiple compensatory
mechanisms firing simultaneously on an individual. McEwen’s definitions of allostasis
and allostastic load account for the “cumulative biological burden exacted on the body
through multiple antecedent challenges and attempts to adapt to life’s demands”
(Schulkin, 2004, p.113). When the body completes this task satisfactorily, allostasis has
been achieved, evidencing the body’s ability to meet and overcome the immediate
environmental demands exerted upon it. Persistent lingering of abnormal operating
ranges and dysregulation in one or more feedback systems results in damage to
regulatory systems, assisting in the transition to allostatic load. Allostatic load is a
concept that can be appraised by both longitudinal and cross-sectional assessments,
although limited inferences may be made from cross-sections.
Allostatic load has been measured in several studies in recent years. When
identifying elevated-risk zones, high scores on allostatic load based on physiologic
markers indicate cause for concern regarding health status. The use of scores low enough
not to be clinically significant but high enough to capture those at increased risk, whether
guided by clinical practice guidelines or quartiles, attempts to seek out the evidence of
wear and tear on the body before it manifests itself in disease (Singer et al., 2004). A
more relevant point to make would be to reset the cut-point identifying those at high-risk
24
prior to approaching levels that are clinically significant resulting in disease diagnosis.
Essentially, risk zones are set such that forewarning is given to the clinician and patient
regarding their health. The MacArthur Successful Aging Study (Seeman, Singer, Rowe,
Horwitz, & McEwen, 1997; Seeman, McEwen, Rowe, & Singer, 2001) was a
longitudinal study of 70-79 year old Caucasian adults with a high level of functioning.
The health outcome of interest was physical and cognitive functioning, as well as 7-year
mortality. The cut points in this study were the upper and lower quartiles based on 10
biomarkers. The primary mediators were urinary cortisol, urinary epinephrine and
norepinephrine, and dehydroepiandrosterone sulphate (DHEA-S). The secondary
mediators were systolic and diastolic blood pressure (SBP/DBP), waist-hip ratio, highdensity lipoprotein (HDL), total HDL cholesterol ratio, and glycosylated hemoglobin
(A1C). Summing of all biomarkers in the high risk quartile, defined as the 75th
percentile, was given a value of ‘1’. All others were assigned ‘0’ and totaled to yield an
allostatic load score (Seeman et al., 1997).
Allostatic load measurement is intended to
reflect the presence of dysregulation in biological pathways that attempt to identify
antecedents of disease and signal impending onset (Singer, Ryff, & Seeman, 2004). The
concurrent presence of these biomarkers may be associated with several chronic disease
processes, such as impaired cognitive functioning, type 2 diabetes, CVD, and metabolic
syndrome (McEwen, 1998a, 1998b, 1999, 2004, 2005). Illness among AAs related to the
effects of stress from a social or environmental perspective, including discrimination
(Clark, Anderson, Clark, & Williams, 1999; Krieger, 2001; Sims, Wyatt, Gutierrez,
Taylor, & Williams, 2009), have been associated with insulin resistance (McEwen, 2004;
Räkkönen et al., 1996), depression, mental illness (Thompson, 2002), infant mortality
25
(Gamble & Stone, 2006), type 2 diabetes, hypertension, coronary heart disease, stroke,
and CVD (Clark & Adams, 2004; Mays, Cochran, & Barnes, 2007; Warren-Findlow,
2006; Wyatt et al., 2003).
Geronimus et al. (2006) posits that “weathering”, in addition to allostatic load, is
the physiological repercussion of continually engaging with acute and chronic stressors in
a race conscious society. Consequently, AAs experience morbidity and mortality by
middle-age equivalent to Caucasians that are much older. When evaluating the NHANES
IV (1999-2002) for race-gender variances based on age-related allostatic load scores,
AAW maintained the highest scores among AA men and Caucasians. Even AAW with
high socioeconomic status had no measurable advantage in this comparison (Geronimus
et al., 2006).
In this sub-study, allostatic load was measured by summarizing levels of
physiological markers specific to body systems that evidence a level of insulin resistance.
In addition, the summarized scores will account for use of antihypertensive, antidiabetic,
or antilipidemic medication in the past two weeks as stated in the Medication Survey
Form of the JHS. The allostatic load factors included (see Figure 4): serum cortisol, A1C,
waist circumference, systolic blood pressure, diastolic blood pressure, high density
lipoproteins, triglycerides, low density lipoproteins, and/or the use of antihypertensive or
antilipidemic medications. The highest quartiles for each allostatic load factor were used,
except for high density lipoproteins, for which lower quartiles were used, to identify
scores low enough not to be clinically significant but high enough to capture those at
increased risk in this cohort (Singer et al., 2004). Higher allostatic load scores depict the
cumulative effect of these factors on insulin resistance, while lower scores potentially
26
indicate those individuals had decreased risk of insulin resistance at the time of
examination. Table 1 highlights ten studies with varying populations, designs, and
operationalization of allostatic load.
Figure 4: Allostatic Load Factors
Insulin Resistance
The debate continues regarding the risk factors that best predict when an insulin
resistant state is approaching. Four scientific organizations, the World Health
Organization, Adult Treatment Panel III, International Diabetes Federation, and European
Association for the Study of Diabetes, have established criteria they assert are of greatest
relevance to insulin resistance disease, with modest amounts of variability noted (see
Table 2). The variations include feasible use in clinical research versus practice, ethnicspecific cut-points, waist circumference measurement, BMI classification cut points, and
the inclusion of a specific metabolic measure. However insulin resistance is defined, the
27
scientific community agrees the overarching goal is to identify critical intercessory points
at which the clinical course toward disease can be somehow changed or halted.
Insulin resistance by definition is a progressive β-cell dysfunction caused by an
admixture of genetic and environmental factors that influence metabolic aberrations,
causing an increase in hepatic glucose production and impaired insulin-mediated
peripheral glucose uptake prefacing disease processes such as overt type 2 diabetes,
hypertension, coronary heart disease and cardiovascular disease (Garvey & Hermayer,
1998; Reynolds & Walker, 2003). At this time, individuals experience varying levels of
hyperinsulinemia, glucose intolerance and dyslipidemia (Garvey & Hermayer, 1998).
In addition, key modulators of insulin-signaling are ineffective, which directly impacts
lipoprotein metabolism and inflammation.
Circulating antagonists of insulin, such as cortisol, glucagon, catecholamines, and
growth hormones, are also increased during this time, serving as additional facilitators of
an insulin resistant state (Reynolds & Walker, 2003). Adipose tissue or excessive fat
mass was long thought to be a receptacle for excess calories; however, it has an ability to
function like an endocrine organ, secrete bioactive peptides or adipokines, [such as leptin,
adiponectin, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), plasminogen
activation inhibitor-1 (PAI-1), angiotensinogen, CRP, and resistin] that influence
metabolic activity and insulin resistance, thus, adipose tissue facilitates the progression
toward type 2 diabetes and CVD (Aronne & Isoldi, 2007; Appel, Oster, Floyd, & Ovalle,
2009; LeRoith, 2007).
Past studies in the literature have found declining insulin sensitivity as one of the
first occurrences preceding type 2 diabetes, noting abnormal insulin secretion 3-5 years
28
prior to obvious hyperglycemia, with actual diagnosis coming 4-7 years later (Gerich,
2007; Goldstein, 2003). During this masked or subclinical period, 20% of undiagnosed
patients with type 2 diabetes begin to exhibit microvascular complications and are 2-5
times as likely to experience cardiovascular events (Gerich, 2007; Mitrakou, 2006). The
current recommendations by the National Cholesterol Education Program Adult
Treatment Panel III (NCEP-ATP III) guidelines, indirectly measures insulin resistance,
and theoretical underpinnings of allostatic load support the measurement of the allostatic
load score in the proposed study as evidence of an insulin resistant state in nondiabetic
AAW.
The direct measurement of insulin resistance, defined by Homeostatic Model
Assessment of insulin resistance (HOMAIR), as the health outcome of the proposed study
requires a single measurement of fasting glucose and insulin to evaluate hepatic glucose
production and β-cell insulin secretion. Euglycemic clamping is the gold standard for
evaluating insulin sensitivity, but is also time consuming, expensive, lacks utility in
clinical practice, and is absent of the ability to reflect insulin-glucose dynamics under
physiologically stressed conditions, such as OGTT or a meal (Muniyappa, Lee, Chen, &
Quon, 2008). The HOMAIR is the best surrogate marker with an acceptable linear
correlation to euglycemic clamping. HOMAIR shows glucose-insulin homeostasis by
revealing the effects of fasting state glucose and insulin on pancreatic β-cell function
(Muniyappa et al., 2008). Considering NCEP-ATP III has characterized the development
of type 2 diabetes as a “CVD risk equivalent” (Bianchi et al., 2008; Buse et al., 2007;
National Cholesterol Education Program [NCEP-ATP III], 2004; Singh & Deedwania,
2006; Sultan, Thuan, & Avignon, 2006) and the risks associated with CVD potentially
29
have linear relationships to glycemic states below diagnostic thresholds, alternative
methods of identifying risks should be explored. Insulin resistance measured by
HOMAIR, has been found to have a level of significance for AAW, but is not currently an
NCEP-ATP III criterion (Appel et al., 2005, 2009; Resnick, et al., 2002; Song et al.,
2007).
The literature search performed attempted to keep the overarching purpose of the
JHS in context, as well as the proposed dissertation study. The ultimate purpose of the
JHS is to identify CVD and associated cardiovascular risk factors in AA populations.
Insulin resistance is considered a predecessor to type 2 diabetes and CVD. AAW
succumb to disparate rates of morbidity and mortality annually due to cardiovascularrelated illness or CHD than either AA men or Caucasians (Taylor et al., 2002; Office of
Minority Health [OMH-Heart Disease and African Americans], 2009). The consequence
of such high incidence and prevalence rate for AAW renders them more susceptible to
the microvascular and macrovascular complications attributed to poor glycemic control
and dyslipidemia, experiencing death as an endpoint 2-3 times more often than Caucasian
women (Cannon, 2007; Keyserling et al., 2002; Samuel-Hodge et al., 2000).
Table 3 depicts the relationship between HOMAIR and insulin sensitivity to
establish relevance of this measure as a predictor of insulin resistance. Table 4 outlines
pivotal studies that used HOMAIR as a predictor of CVD or associated cardiovascular risk
factors to evidence the importance of identifying nondiabetic AAW in an insulin resistant
state. While type 2 diabetes and CVD are not the endpoint for this study, there is still no
agreement on how an investigator establishes an individual’s overall risk for type 2
diabetes, nor is there agreement on mean number of years to disease development.
30
Discussion of the relationship between HOMAIR and CVD supports the relative
importance of early detection and prevention in nondiabetic AAW as an effort to curtail
negative health outcomes associated with insulin resistance, heightening the awareness of
the consequential effects both, type 2 diabetes and CVD.
31
CHAPTER 3
METHODOLOGY
This chapter presents a comprehensive discussion of the research design and
methods used in this study, including protection of human subjects, setting, population
and sample, instruments, data collection procedure, data management, and data analysis.
In addition, the strategies used to answer the research question.
Research Design
A cross-sectional, secondary data analysis of the JHS will be used to answer the
research questions. In this study, the investigator examined the independent contribution
of perceived stress as a construct (measured by Global Stress, Weekly Stress Inventory,
and Discrimination Survey) and allostatic load factors [fasting insulin, serum cortisol,
Hemoglobin A1C (A1C), waist circumference (WC), systolic blood pressure (SBP),
diastolic blood pressure (DBP), high-density lipoprotein (HDL), triglycerides (TG), lowdensity lipoprotein (LDL), including also the possible use of antihypertensive,
antidiabetic, or antilipidemic medication], as explanatory variables of insulin resistance.
Insulin resistance measured by HOMAIR was the dependent variable. The covariates
included age, income, marital status, education, physical activity, and smoking.
32
Protection of Human Subjects
The study was conducted after the manuscript proposal had been approved by the
Jackson Heart Study Steering Committee. This process also included approval from the
Institutional Review Board (IRB) of the University of Alabama at Birmingham (UAB).
The enrollment period for the first exam began in 2000 and ended in 2004.
Setting
The subjects for this study were all participants in the Jackson Heart Study. The
JHS is funded by the National Heart, Lung, and Blood Institute (NHLBI) and the National
Center on Minority Health and Health Disparities at the National Institutes of Health
(NCMHD), National Institute of Biomedical Imaging and Bioengineering (NIBIB), and
sponsored via a partnership and collaboration with Jackson State University, Tougaloo
College, and the University of Mississippi Medical Center. The JHS consists of four
enrollment pools: a family cohort (31%), random sampling from the Accudata America
commercial listing (17%), volunteer (30 %), and the Jackson Atherosclerosis Risk in
Communities (ARIC) population (22%). Jackson, Mississippi was one of four clinic sites
of the ARIC study established in 1987 (Taylor et al., 2005). The sampled population for
the JHS cohort encompasses the tri-county area of Hinds, Madison and Rankin counties in
the Jackson metropolitan area. The JHS is a the largest longitudinal population-based
cohort study to focus on traditional and non-traditional risk factors essential to CVD
development and progression in AAs (Taylor et al., 2003; 2005).
33
Population and Sample
The target population in this study was nondiabetic AAW of the JHS. The total
cohort included 5,301 participants, of which 5,031 were 35-84 years of age. The ratio of
females to males was almost 2:1 at 64.1% versus 35.9%, respectively. Although the JHS
cohort was 5,301, only nondiabetic AAW in the JHS, aged 21-84 years from 2000-2004
were included in the sample for this study. In the Demographics & Risk Factors charts
available on the JHS website, 2,542 (76%) of the women are without diabetes, in
comparison to 647 (16%) with type 2 diabetes. Again, the largest population of women
was 35-84 years of age with a mean average of 55 years. The population with a negative
history of type 2 diabetes is important to note in estimation of the possible sample based
on the following exclusion criteria: a) pregnant, b) fasting plasma glucose > 126 mg/dL,
or c) diagnosed by a physician as an individual with type 2 diabetes.
Instruments
Perceived Stress. The perceived stress measures were defined as acute, chronic,
global, and discrimination measures. Perceived stress as an acute stress measure was
measured by the Weekly Stress Inventory (WSI), an 87-item scale administered at from
September 2000 to March 2004 in the JHS. The WSI is designed to measure the
occurrence and impact of minor stressors over the course of a week (Brantley, Jones,
Boudreaux, & Catz, 1997). The purpose of the study was to evaluate the associations
between individual stressors of daily living and insulin resistance. Although one would
think major life events would be more predictive of stress, in actuality, repetitive daily
engagement with minor stressors, such as arguments, disturbed sleep or financial
34
difficulties, on a daily basis has far greater repercussions to one’s state of health
(Brantley et al., 1988).
The WSI was originally derived from three scales: the Daily Stress Inventory
(DSI) (Brantley & Jones, 1989), the Hassles Scale (Kanner et al., 1981), and the
Inventory of Small Life Events (ISLE) (Zautra et al., 1986). The normative sample was
522 community-dwelling males and females, 17 years of age or older. The readability of
the instrument is at the 7th grade level. The WSI is a self-report scale to measure the
number and stressfulness of the events occurring in one week on a 7-point Likert scale
ranging from 1 (occurred but was not stressful) to 7 (extremely stressful) that are coded
as numeric values. The WSI is constructed of eight subscales containing items that share
similar themes. The eight subscales are: work/school; money; transportation;
marital/family; household; social; personal; and leisure.
Two scores evolve from the WSI scale, a WSI-Event and WSI-Impact score. The
Event score sums the number of items the participant reported occurred on during that
week. The Impact score may be influenced by the number of stressful events, but also
factors in the participant’s appraisal of the event. High scores indicate poor coping skills,
while low scores could be biased by a participant being in denial about the stressors in
their life or inability to appropriately perceive the event as stressful because they are
constantly engaged in a high demand state. WSI is not intended to diagnose stress
disorder; therefore cutoff scores have not been devised.
Internal consistency reliability coefficients were established in Brantley (1989) in
a sample of headache patients and Mosley et al. (1991,1997) in a sample of coronary
heart disease patients with WSI-Event and -Impact Cronbach α = .92-.96 and .93-.97,
35
respectively. Mosley et al. (1997) validated the WSI in a sample of CHD participants in
the ARIC study, one of the formative sample groups of the JHS. Test-retest reliability is
also important to note when administering the WSI. The overall goal is to measure minor
stressors occurring throughout the day, when considering weeks, assessing these effects
in the same week as opposed to over several weeks at different time points should result
in test-retest coefficients that progressively get smaller. Same-week stability in 170
college students resulted in WSI-Event r =.83 and WSI-Impact r =.80 (Brantley et al.,
1989). One-week stability assessed over two consecutive weeks in 130 patients with
complaints of headache resulted in WSI-Event r = .76 and WSI-Impact r =.78. In Mosley
et al. (1991), the WSI was monitored over several weeks and found test-retest
coefficients assessments at week 1 and week 3 had progressively lessened. The WSIEvent coefficient ranged from r = .84 at week 1, to .64 at week 3. Likewise, the WSIImpact coefficient at week 1 was .70 and .56 at week 3 (Brantley & Jones, 1989).
Concurrent validity was evaluated by associating the WSI-Event and -Impact
scores with scores from the Hassles Scale (Kanner et al., 1981). The premise for such
associations is that the Hassles Scale correlates with physical and psychological
symptomology, except the assessment considers occurrences within the past month. Even
though the WSI assesses one week and Hassles Scale considers one month there were
acceptable coefficients, ranging from .61 to .69, respectively.
Global Stress (STS) was a chronic stress measure, which was an eight-item selfreport survey developed for the JHS. Each item is rated on a four-point response scale
ranging from “not stressful” (1) to “very stressful” (4). This instrument was modified
from Kohn and MacDonald's Survey of Recent Life Experiences (1992), Cohen et al.'s
36
Perceived Stress Scale (1983), and Sarason et al.'s Life Events Scale (1978). The STS
measured the perception of the severity of stress experienced over a prior period of
twelve months in several domains including employment, relationships, related to one's
neighborhood, caring for others, legal problems, medical problems, racism and
discrimination, and meeting basic needs. Participants responded to the items rating the
stress severity for each domain on a 4-point scale ranging from "not stressful" to "very
stressful," and the summary score was used as a continuous variable. The JHS is
currently working on the validation of this instrument in another substudy. Boykin et al.
(under review) reported a Cronbach's alpha= 0.72 for reliability of the STS in the JHS.
Unlike daily or weekly measures, this instrument aims to measure the participant’s
perception of chronic stress over the previous 12 months regarding job pressures,
relationships, neighborhood influences, caregiver responsibilities, legal and medical
problems, racism and discrimination, and challenges acquiring basic needs, such as food
and housing. Forthcoming data regarding the psychometric properties of the STS
instrument are pending.
The JHS Discrimination (DIS) survey was developed for the JHS. The goal was to
assess not only daily experiences with discrimination, but also major life events related to
discrimination. In addition, some items measure frequency, attribution, coping
mechanisms, lifetime burden, and effect of skin color (Sims, Wyatt, Gutierrez, Taylor, &
Williams, 2009). The JHS DIS was derived from a literature review, evolving themes of
focus groups, the Everyday Discrimination instrument, including two measures specific
to skin color (Williams, Yu, Jackson, & Anderson, 1997), two items from the MacArthur
37
Foundation Midlife Development survey (Kessler, Mickelson, & Shanyang, 1997), and
two newly developed items (Sims et al., 2009).
Measuring everyday experiences of discrimination with the JHS DIS focused on
attributes such as, courtesy, respect, service in public places, underestimation of
competence, challenging one’s integrity or honesty, name calling, and feeling threatened
or harassed. Major life events were measured by the following nine domains derived
from Krieger (1990) and Krieger & Sidney (1996): school, getting a job, work, housing,
resources or money, medical care, street/public place, getting services, and other ways.
The scoring ranged from A (several times a day) to G (never). The Discrimination Scale
(Krieger, 1996), Everyday Racial Discrimination Questionnaire (Williams et al., 1997),
and the Perceived Racism Scale (McNeilly, 1996) were fundamental in the development
of the Discrimination survey administered in the JHS.
Chronic perceived discrimination was measured by nine items assessing “day-today basis” experiences with discrimination. For example, “How often on a day-to-day
basis do you have the following experiences?” Responses for these items ranged from 1
(several times a day) to 7 (never). Reverse coding allowed a higher score to be
interpreted as the participant perceiving he or she was more unfairly treated. The scores
were summed and averaged.
Acute perceived discrimination was measured by nine items assessing lifetime
experiences of unfair treatment over their lifetime. The items focus on unfair treatment
over their lifetime specific to environments, such as school or training, getting a job,
work, obtain resources or money, housing/purchasing a home, in public or on the street,
and receiving public services. Discriminatory ranking was created when a participant
38
responds “yes.” Each “yes” received a score of “1,” with the rank computed by summing
the scores. The values for this discrimination rank ranged from 0 (minimum) to 9
(maximum). Higher scores indicate discrimination was perceived to be more severe over
the lifetime of a participant.
Allostatic load (AL). Given the likelihood of an individual being experiencing
more than one disease process, it is wise not to underestimate the effects co-existing or
co-morbid disease can have on an individual. McEwen’s definition of allostasis and
allostatic load account for the “cumulative biological burden exacted on the body through
multiple antecedent challenges and attempts to adapt to life’s demands” (Schulkin, 2004,
p.113).
There are three means by which allostatic load can be measured: 1.) summation of
physiological markers at or above an identified level, 2.) weighted summation of
standardized physiological markers with canonical weights, and 3.) recursive partitioning
of participants into high, intermediate, or low categories of allostatic load. Summed
scores at or above the identified threshold is how allostatic load was measured in this
study. When identifying elevated-risk zones, usually high scores of allostatic load based
on physiologic markers indicate cause for concern regarding health status. A more
relevant point to make would be to reset the cut-point identifying those at high-risk prior
to approaching levels that are clinically significant resulting in disease diagnosis.
Essentially, risk zones are set such that forewarning is given to the clinician and patient
regarding their health. The use of scores low enough not to be clinically significant but
high enough to capture those at increased risk, whether guided by clinical practice
39
guidelines or quartiles, attempts to seek out the evidence of wear and tear on the body
before it manifests itself in disease (Singer et al., 2004).
The MacArthur Successful Aging Study (Seeman et al., 1997; Seeman et al.,
2001), a pivotal study in operationalizing allostatic load, was a longitudinal study of 7079 year old Caucasian adults with a high level of functioning. The health outcome of
interest was physical and cognitive functioning, as well as 7-year mortality. The cut
points in this study were the upper and lower quartiles based on following 10 biomarkers:
SBP, DBP, WHR, ratio TC/HDL, A1C, urine cortisol, urine epinephrine, urine
norepinephrine, HDL, and DHEA-S. Summing of all biomarkers based on the 75th and
25th percentile that fell into high risk quartiles were totaled to yield an allostatic load
score (Seeman et al., 1997). A similar approach will be used to analyze the biophysical
markers relevant to the proposed study in review of the associations between perceived
stress, allostatic load, and insulin resistance among nondiabetic AAW of the JHS.
More specifically for this analysis, allostatic load was defined by summarizing
biophysiological markers specific to body systems that evidence a level of insulin
resistance. In addition, the summarized scores accounted for use of antihypertensive,
antidiabetic, or fibrate medication in the past two weeks as stated in the Medication
Survey Form of the JHS. The allostatic load factors included: fasting insulin, serum
cortisol, A1C, waist circumference, systolic blood pressure, diastolic blood pressure, high
density lipoproteins, triglycerides, low density lipoproteins, and/or the use of
antihypertensive or antilipidemic medications. Of these 13 factors, participants were
selected by quartiles based on the score distribution. Each allostatic load measure was
summed based on those exceeding the parameters identified, except high density
40
lipoprotein, where the parameter was set according to the lowest quartile. The use of
lowest/highest quartiles, as well as concurrent use of medication, was meant to express
those with the greatest risk of experiencing disease or already expressing a degree of
dysregulation. The highest and lowest quintiles for each allostatic load measure in this
cohort aimed to identify scores low enough not to be clinically significant but high
enough to capture those at increased risk (Singer et al., 2004). The investigative team
asserts this approach would be more appropriate in establishing the cutoff values for the
following reasons: a) due to the scarcity of specific representation in the literature for the
proposed study sample and some of the variables of interest; and b) of the represented
studies, the cutoffs may under or overestimate the presence of disease in nondiabetic
AAW. Higher allostatic load scores represent the cumulative effect of these factors on
insulin resistance, while lower scores potentially indicate those individuals with
decreased risk of insulin resistance at the time of examination.
Covariates. Each analysis was adjusted for individual factors defined as age,
marital status, education, and income. Age was a continuous variable based on selfreported birthdate and age at clinic visit. Marital status was a nominal or categorical
variable defined as the following groups: married (M); divorced (D); single (S); or
widowed (W). Education was an ordinal or ranked variable defined as less than high
school, high school or GED, more than high school, but less than college, or college or
greater. Lastly, income was also an ordinal or ranked variable defined as, <$24,999
(lower), $25,000-49,999 (lower-middle), $50,000-99,999 (upper-middle), or > $100,000
(affluent).
41
Health outcome: Insulin Resistance. The Homeostatic Model Assessment of
insulin resistance (HOMAIR) is the best surrogate marker of insulin resistance with an
acceptable linear correlation to euglycemic clamping. HOMAIR depicts glucose-insulin
homeostasis by revealing the effects of fasting state glucose and insulin on pancreatic βcell function (Muniyappa, Lee, Chen, & Quon, 2008). Insulin resistance, calculated by
HOMAIR, has been found to have a high level of significance for AAW, but is not
currently an NCEP-ATP III criterion (Appel et al., 2005, 2009). The following formula
was used to calculate HOMAIR (mmol/L x μU/mL) = fasting glucose (mmol/L) x fasting
insulin (μU/mL)/22.5. Upon enrollment, JHS participants were not categorized as insulin
resistant by HOMAIR during data collection. However, baseline data revealed some
AAW were already experiencing insulin resistance to some degree with prediabetes and
type 2 diabetes rates at 13.1% and 19.8%, respectively (Taylor et al., 2005). HOMAIR
was measured as a continuous dependent variable.
Data Collection Procedure
According to the design and methods publication for the JHS by Taylor et al.
(2005), baseline examinations were completed during the voluntary home induction
interview, initial clinic visit, or 24-hour follow-up. The initial exam took 4.5 hours for
discussion and procedures. Informed consent was included in this process with
audiovisuals to give participants a clear understanding. The following procedures were
implemented during data collection relevant to the proposed study: (1) a fasting state for
at least 12 hours, (2) avoiding alcohol or nicotine consumption one hour prior to
providing venous specimens or blood pressure, (3) sitting blood pressures were measured
42
prior to venous specimen collection, (4) interview/examination before the medical
review, (5) venous specimens were obtained between the hours of 0900-1030 after being
in the supine position at least 20 minutes, (6) the anthropometric measures were collected
as follows: weight was rounded to the nearest kilogram (kg); height was measured to the
nearest centimeter (cm) with heels and back of head in Frankfort’s horizontal plane;
abdominal girth and neck circumference were taken in the horizontal plane at the
umbilicus and cricothyroid membrane; (7) the following instruments were taken home
and collected at the 24-hour follow-up visit: (a) Global Stress; (b) the Weekly Stress
Inventory (WSI); and (c) Discrimination survey.
Data Management
The data management and quality control of the JHS Coordinating Center
involves two laboratories and five reading centers. The University of Mississippi Medical
Center and University of Minnesota are the laboratory sites. The five reading centers
located at the JHS Coordinating Center are the electrocardiograms, echocardiograms,
carotid ultrasonograms, pulmonary function tests, and ambulatory blood pressure
monitoring. The JHS Coordinating Center also involves protocol development, data
management, statistical analyses, and operational support (Carpenter et al., 2004). Since
the investigator conducted a secondary data analysis of the JHS, issues regarding missing
data or completeness were addressed as instructed by the biostatistician investigator
affiliated with the JHS. This was done to ensure consistency when analyzing and
reporting findings from the JHS data as not to misrepresent or misinterpret previously
reported studies, or subsequent studies to the proposed study. The investigator used the
descriptive statistics to assess sample sizes and sub analyses when analyzing the data.
43
Once these tasks were performed, the investigator coded the data and entered them into
PAWS 18.0.
When performing multiple regression analyses, studentized residuals, DFFITS,
and DFBETAS diagnostic statistics were used to identify outlying and influential
observations. Outliers were identified; however, it was determined that this did not occur
due to an error during data entry, therefore all data was retained.
Data Analysis
The data were analyzed by using descriptive and inferential statistics. The
Predictive Analytics Software (PASW) 18.0 (formerly SPSS), was used for data analysis
(Norušis, 2010). Descriptive statistics (frequencies, percentages, means, and standard
deviations) were used to describe the distribution of the independent variables associated
with the constructs, perceived stress and allostatic load, as well as the individual factors.
Hypotheses testing for statistical significance maintained an a prior alpha level of 0.05.
The internal consistency reliability of the WSI, STS, and JHS DIS questionnaires were
assessed by evaluating the Cronbach’s alpha coefficients.
The investigator hypothesized that perceived stress and the allostatic load factors
would have a significant effect on insulin resistance, measured by HOMAIR, among
nondiabetic AAW in the JHS. In addition, the investigator hypothesized higher allostatic
load scores would be associated with insulin resistance (defined as HOMAIR) above and
beyond individual and perceived stress group levels among nondiabetic AAW of the JHS.
Each research question was stratified among glycemic profiles assessing for group
differences in the following procedure: Ho= µ1= µ2; There are no significant differences
44
between euglycemic and prediabetic groups. The covariates in the proposed study were:
age, income, education, smoking, physical activity, and marital status.
Research Question One
The first research question examined the associations between perceived stress
and insulin resistance among nondiabetic AAW, stratified as euglycemic and prediabetic,
in the JHS. The investigator will be testing Ho=ρ = 0, hypothesizing in bivariate
correlations analysis, increased perceived stress is associated with insulin resistance,
measured by HOMAIR, among nondiabetic AAW. More specifically, increased
associations are hypothesized among the chronic stress measurements, STS and DIS,
evaluating global stress and perceived discrimination. The investigator analyzed the
hypothesized associations between perceived stress and insulin resistance, testing for
strength and direction of the variables. Bivariate correlations are appropriate in
determining the associations between perceived stress and insulin resistance (HOMAIR).
Perceived stress associations were individually analyzed by evaluating the correlations of
the acute and chronic stress measures (i.e., STS, WSI, and DIS) with insulin resistance.
Bivariate correlations analysis is also necessary prior to implementing regression analysis
to assess the independent variables for issues of multicollinearity. Differences in stress
measures between euglycemic and prediabetic groups were also evaluated. T tests were
used for normally distributed continuous measures. Chi-square was used for categorical
measures.
Research Question Two
The second research question examined the associations between allostatic load
factors and insulin resistance among nondiabetic AAW, stratified as euglycemic and
45
prediabetic, in the JHS. The investigator tested Ho=ρ = 0, hypothesizing in bivariate
correlations analysis, increased allostatic load summed factor scores are associated with
insulin resistance (HOMAIR). The investigator analyzed the hypothesized associations
between each allostatic load factor and insulin resistance for strength and direction of the
variables using bivariate correlations analysis (r). Because they were continuous
variables, bivariate correlations were used to determine the associations among the 9
allostatic load measures (fasting insulin, serum cortisol, A1C, waist circumference,
systolic blood pressure, diastolic blood pressure, high density lipoprotein, triglycerides,
low density lipoprotein) and insulin resistance (HOMAIR). Bivariate correlations analysis
also allowed for the hypothesized relationships of allostatic load to be evaluated for
strength and direction among each independent/covariate variable and HOMAIR. T test
for normally distributed continuous measures were used when evaluating differences
among the stratified groups; Chi square was used for categorical data, such as concurrent
use of antihypertensive and antilipidemic medications.
Research Question Three
The third research question assesses the extent to which individual factors,
perceived stress and allostatic load measures are predictors of insulin resistance (defined
as HOMAIR) among nondiabetic AAW, stratified as euglycemic and prediabetes, in the
JHS. The investigator hypothesized allostatic load scores would be associated with
insulin resistance (defined as HOMAIR) above and beyond each set of variables for the
individual and perceived stress factors among nondiabetic AAW of the JHS. Figure 5
depicts a forward hierarchical multiple linear regression approach was used to evaluate
the independent contribution of allostatic load factor levels in predicting HOMAIR in
46
models already containing perceived stress levels while controlling for the following
covariates: age, education, marital status, physical activity, and smoking. Separate
models were developed for euglycemic and prediabetes groups. An F-test was performed
to test
Education +
Ho= R2= 0; when HOMA IR = Yi = βo + [β1 x Age + β2 x Physical Activity + β3 x
β4 x Marital Status + β4 x Smoking] + [β1 x STS + β2 x DIS-ED + β3 x DIS-LT] + [β1 x LDL +
β2 x HDL + β3 x TG …+ β12 x Oral
med]
+ εI.
Figure 5: Forward Hierarchical Linear Regression of Study Variables
47
48
CHAPTER 4
FINDINGS
This chapter presents a description of the sample and statistical analyses used in
the current study and a discussion of the study findings. The discussion includes a
comparison of the sample cohort and excluded participants for the study. Also discussed
are the results and summarization of the analyses for the sample cohort based on the three
research questions for the study.
The sample size for this study before consideration of the exclusion criteria was
3,189 nondiabetic AAW from the JHS. A power analysis was conducted, using Cohen’s
L (as cited in Munro, 2005), to determine the necessary sample size to achieve power of
80% (β=.20) at an alpha of 0.05 and using multiple regression as the analytic method.
The sample size (N) was calculated as follows: N= [L*(1-R2)/ R2] + u + 1, where L =
21.5 (based on L for 20 predictors = 15.5 plus .6 for each additional predictor), u = 20,
and R2 = .13 for a moderate effect size. Based on these criteria, the calculated N was 165.
According to Soper (2010), the post hoc power analysis was sufficient. In the prediabetic
group, an observed power of .995 was achieved with a sample size of 335 participants,
with an alpha of 0.05, set of predictors A = 6, set of predictors B = 17, and f2= 0.13.
Among euglycemic participants (n=1,910), an observed power of 1.000 was found with
the same alpha level, set and number of predictors, and f2 (Soper).
49
Description of the Cohort
The Jackson Heart Study enrolled 5,301 participants from September 2000 to
March 2004. Of that number, 3,348 are women, ranging from 21 to greater than 85 years
of age with an average age of 55 years old. The exclusion criteria for this sub-study were
fasting plasma glucose greater than or equal to 126 mg/dL, a history or use of antidiabetic medication, or history of physician diagnosed type 2 diabetes mellitus (T2DM)
by self-report. Participants who met the classification of having T2DM at baseline
examination were excluded (n=647). In addition, 78 were found to have missing data
regarding a history of T2DM; therefore the remaining sample was 2,623 nondiabetic
AAW.
To determine if there were any differences in participants with a diagnosis of
T2DM versus non-T2DM, t-tests for the comparison of means were performed on the
following variables: age, physical activity, and BMI. Chi square analyses were
performed to evaluate differences in proportions on education, income, marital status, and
smoking. Table 5 presents the results from the t-tests; table 6 summarizes the Chi square
values and p-values for the categorical variables between the two groups. On each
variable analyzed, individuals with a classification of T2DM were found to be
significantly different from nondiabetic participants. Chi square analyses indicated that
individuals classified as having T2DM were significantly different from those without
T2DM based on education (χ2 = 69.60, p <.001), income (χ2 = 33.96, p <.001), marital
status (χ2 = 27.02, p <.001), and smoking (χ2 = 16.13, p <.001). The T2DM group had
lower educational levels and income levels, and had a higher frequency of widowed
marital status and former smoking status. The results of the t-tests on age, BMI, and
50
physical activity indicated that there was a significant difference between the two groups
only on mean age (60.48 versus 54.08), with the T2DM group being older. Mean
physical activity scores were slightly higher in the nondiabetic group, but the difference
was not statistically different. BMI indices also were not statistically different (p=0.863).
Table 5
Demographics of Cohort: Age, BMI, and Physical Activity of African American Women
of the JHS with and without T2DM
Non-T2DMb
p-value
Variables
T2DMa
t
df
Mean (SD)
Mean (SD)
Age (yrs)
60.4 (10.1)
(N=647)
54.0 (13.0)
(N=2537)
-11.65
3268
.001*
Body Mass Index
35.1 (7.5)
(N=645)
32.3 (7.3)
(N=2621)
-8.59
3264
.863
Physical Activity
7.29 (2.6)
(N=611)
8.37 (2.5)
(N=2477)
3086
.155
9.442
Note: *t-test p < .05. Superscripts a,b denote the exclusion (defined as fasting blood glucose > 126 mg/dL
or physician diagnosed T2DM) and inclusion (defined as fasting blood glucose 70-125 mg/dL) groups.
51
Table 6
Demographics of Cohort: Education, Income, Marital Status, Smoking of African
American Women of the JHS with and without T2DM
Non-T2DMb
Variables
T2DMa
χ2
p-value
(N)
(N)
Education
647
2537
69.60
.001*
< High School
High School
>High School
but < Bachelor’s
> Bachelor’s
28.8%
15.1%
20%
20.8%
24.7%
29.7%
26.5%
34.4%
Income
543
2218
Lower
24.5%
17%
Lower Middle
31.5%
25.8%
Upper Middle
25.4%
30.3%
Affluent
18.6%
26.9%
Marital Status
Divorced
647
2623
14.2%
18.2%
Married
42%
46.2%
Never Married
13%
14%
Separated
5.6%
Widowed
24.6%
Smoking
646
33.96
<.001*
27.02
<.001*
16.13
<.001*
4.3%
17%
2602
Never
70.7%
75.4%
Former
20.4%
14.1%
Current
8.8%
10.49%
Note: *Chi-square statistic p < .05.Superscripts a,b denote the exclusion (defined as fasting blood glucose
> 126 mg/dL or physician diagnosed T2DM) and inclusion (defined as fasting blood glucose 70-125
mg/dL) groups.
52
Characteristics of the Study Sample
Of the 2,623 participants included in the study, 84% were euglycemic defined as a
fasting glucose of 70-99 mg/dL and 16% were prediabetic defined as fasting plasma
glucose 100-125 mg/dL. A total of 378 participants were excluded from the analyses due
to missing allostatic load factors (n = 65) or covariate data (n = 313), leaving 2,245
participants for analysis in the full regression model that met the inclusion criteria.
When evaluating differences, stratified by glycemic profile, based on age, BMI,
and physical activity, there were significant differences in age, but not in physical activity
and BMI (see Table 7). The average age of those participants classified as euglycemic
was significantly lower (p < .001).
Table 7
Demographics of Study Sample: Age, BMI, and Physical Activity of Euglycemic and
Prediabetic African American Women of the JHS
p-value
Variables
Euglycemica Prediabeticb
F
t
df
Mean (SD)
Mean (SD) statistic
38.17
-11.46
2535
<.001*
34.4 (6.9)
(N=399)
.745
-6.10
2533
.388
7.7 (2.5)
(N=364)
.684
5.42
2393
.408
Age (yrs)
52.8 (13.0)
(N=2137)
60.7 (10.8)
(N=399)
Body Mass Index
31.9 (7.5)
(N=2136)
Physical Activity
8.51 (2.51)
(N=2031)
Note: *t-test p < .05. Superscripts a,b denote euglycemic (defined as fasting blood glucose 70-99 mg/dL)
and prediabetic (defined as fasting blood glucose 100-125 mg/dL) groups.
53
Table 8
Demographics of Study Sample: Education, Income, Marital Status, and Smoking of
Euglycemic and Prediabetic African American Women of the JHS
χ2
Variables
Euglycemica
Prediabetesb
p-value
(N)
(N)
Education
2131
397
48.40
<.001*
<High School
13.3 %
23.9%
High School
20.3%
23.2%
>High School
but < Bachelor’s
> Bachelor’s
30.8%
24.7%
35.6%
28.2%
Income
1790
Lower
352
16.1%
20.2%
Lower-Middle
25%
29%
Upper Middle
31%
28.4%
27.9%
22.4%
Affluent
Marital Status
2137
397
Divorced
18.3%
16.8%
Married
47.4%
42%
Never Married
14.9%
8.0%
Separated
4.2%
5.5%
Widowed
15.1%
26.8%
Smoking
2123
394
Never
76.2%
72.8%
Former
13.5%
17.5%
Current
10.3%
9.6%
21.342
.011*
55.92
<.001*
10.22
.115
Note: *Chi-square statistic p < .05. Superscripts a,b denote the euglycemic (defined as fasting blood
glucose 70-99 mg/dL) and prediabetic (defined as fasting blood glucose 100-125 mg/dL) groups.
54
Chi square analyses indicated that the classification by glycemic profile was
found to be associated with education, income, and marital status (see Table 8). The
prediabetic group had lower educational levels and income levels, and had a higher
frequency of widowed marital status. However, there was not a significant difference in
smoking status between the euglycemic and prediabetic groups.
Table 9 depicts the means and standard deviations for the each of the continuous
variable that define allostatic load, except the concurrent use of medications classified as
antidiabetic, antihypertensive, or antihyperlipidemic. The percentages for each glycemic
profile were examined to identify differences in this instance. The glycemic profiles
differed significantly on hemoglobin A1C (p <.001), triglycerides (p <.001), and fasting
insulin (p <.001). The prediabetic group had higher A1C levels than the euglycemic
group. The mean fasting insulin (22.6 versus 15.5) and A1C levels (6.0 versus 5.4) were
higher in the prediabetic group than euglycemic. Of the fasting lipid panel, mean
triglycerides were the only significant difference, with higher triglyceride levels noted in
the prediabetic group (116.8 versus 91.4).
Table 10 displays the differences among glycemic profiles when evaluating
concurrent medication use. Neither of the groups was found to be taking antidiabetic
medications; however 74.19% of the prediabetic group was on antihypertensives, in
comparison to 45% of the euglycemic group. Less than 9% of the euglycemic group was
on antihyperlipidemic medication, while 14.1% of the prediabetics were found to be on
this class of medications.
55
Table 9
Comparison Mean and Standard Deviations for Allostatic Load Factors
Variable
Prediabetica Euglycemicb
t
Mean (SD)
Mean (SD)
df
p-value
5.4 (.45)
(N=2076)
-20.533
2460
.000*
206.4 (40)
(N=399)
199.7 (38.0)
(N=2115)
-3.125
2512
.272
HDL (mg/dL)
52.6 (14.7)
(N=399)
56.1 (14.8)
(N=2115)
4.3545 2512
.931
LDL(mg/dL)
131.0 (38.0)
(N=397)
125.5 (35.8)
(N=2111)
-2.789
2512
.063
Triglyceride Level (mg/dL)
116.8(65.7)
(N=399)
91.4 (49.0)
(N=2115)
-8.966
2512
.000*
Insulin (uU/mL)
22.6 (12.0)
(N=399)
15.5 (8.0)
(N=2117)
-14.749
2514
<.001*
Waist circumference
105.6 (15.7)
(N=399)
97.1 (16.0)
(N=2136)
-9.791
2533
.624
Systolic Blood Pressure
131.2 (18.2)
(N=399)
124.4(18.3)
(N=2134)
-6.907
2531
.971
Diastolic Blood Pressure
78.2 (9.9)
(N=399)
77.8 (10.1)
(N=2134)
-.860
2531
.929
Cortisol (ug/dL)
9.9 (3.9)
(N=399)
2.71 (1.95)
(N=399)
8.6 (3.8)
(N=2112)
-6.209
2509
.587
4.53 (1.89)
(N=2112)
-19.251
2509
.000
Hemoglobin A1C
6.0 (.58)
(N=386)
Total Cholesterol (mg/dL)
Allostatic Load Score
Notes: *p < .0001; M = Mean; SD; = Standard deviations. Superscripts a,b denote prediabetic (defined as
fasting blood glucose 100-125 mg/dL) and euglycemic (defined as fasting blood glucose 70-99 mg/dL).
56
Table 10
Concurrent Medication Use by Glycemic Profile
Class of Medications
Euglycemica Prediabetesb
(N)
(N)
Antihypertensive Medications
2043
379
Yes
45.3%
74.1%
No
54.7%
25.9%
Antihyperlipidemic Medications
1998
362
Yes
8.2%
14.1%
No
91.8%
85.9%
χ2
p-value
108.16
<.001*
13.545
.004*
Note: *Chi-square statistic p < .05. Superscripts a,b denote the euglycemic (defined as fasting blood
glucose 70-99 mg/dL) and prediabetic (defined as fasting blood glucose 100-125 mg/dL).
Research Questions
Research Question One
What are the associations between perceived stress scores at baseline and insulin
resistance (HOMAIR) among non-diabetics in the JHS, stratified by glycemic profiles?
The first research question examined the associations between perceived stress
and insulin resistance among nondiabetic AAW, stratified as euglycemic and prediabetic,
in the JHS. The investigator hypothesized that, in bivariate correlation analysis, increased
perceived stress would be positively associated with insulin resistance, as measured by
HOMAIR.
Among the stress measures, the Everyday Discrimination subscale of the DIS
instrument was the only one that approached significance when examining insulin
resistance with bivariate analysis. In the euglycemic group, Everyday Discrimination
57
yielded a p=.057 when correlated with HOMAIR; however, this did not meet the a priori
level of significance of .05. In both groups, the Everyday Discrimination subscale was
negatively associated with insulin resistance. Major Life Events, another subscale of the
DIS instrument, was also negatively associated but not found to be significant with
insulin resistance among either group. All other stress measures were negatively
associated as well, but again, were not statistically significant.
Table 11
Bivariate Analysis: Perceived Stress Measures and Insulin Resistance of Euglycemic and
Prediabetic African American Women of the JHS
Glycemic Profile: Euglycemic
WSI
WIS
DIS-ED
DIS-ML
STS
HOMAIR
1
.243**
.250**
.427**
-.014
1
.541**
.341**
-.042
1
.350**
-.008
1
.008
DIS-ED
DIS-ML
STS
Glycemic Profile: Prediabetic
WSI
DIS-ED
DIS-ML
STS
WIS
DIS-ED
DIS-ML
STS
HOMAIR
1
.191**
.269**
.312**
-.091
1
.541**
.328**
-.076
1
.282**
-.064
1
.030
Notes: WSI-Weekly Stress Inventory; DIS-ED-Everyday Discrimination; DIS-ML- Major Life
Events Discrimination; STS-Global Stress Scale; HOMAIR- Homeostatic Insulin Resistance
*p < .05; ** p < .01; ***p < .0001
58
Research Question Two
What are the associations between individual allostatic load factors, as well as the
summed allostatic load score, and insulin resistance (HOMAIR) among non-diabetics in
the JHS, stratified by glycemic profiles?
The second research question examined the associations between allostatic load
factors and insulin resistance among nondiabetic AAW, stratified as euglycemic and
prediabetic, in the JHS. The investigator hypothesized that allostatic load summed factor
scores were positively associated with insulin resistance (HOMAIR). The investigator
analyzed the hypothesized associations between allostatic load factors and insulin
resistance for strength and direction of the variables using bivariate correlation analysis
(r).
In the euglycemic group, 8 of 10 allostatic load factors were associated with
insulin resistance. HDL, cortisol, and diastolic blood pressure were all negatively
associated with insulin resistance, with HDL being significant. Fasting insulin and
HOMAIR were very highly correlated, with r=.992. This finding was expected given that
fasting insulin is considered in the calculation of HOMAIR. Fasting LDL and systolic
blood pressure were modestly associated in this group with the remaining allostatic load
factors. A1C, triglycerides, and waist circumference were more strongly associated with
insulin resistance.
The prediabetic group had similar findings. The allostatic load factors that
maintained negative associations with insulin resistance were fasting cortisol and HDL.
In addition, systolic blood pressure and fasting LDL were also negatively associated. Of
these variables, fasting HDL and cortisol were found to be significant. Other correlations
among the variables with insulin resistance have strong associations with A1C,
59
triglycerides, and waist circumference. Fasting insulin was again highly correlated with
HOMAIR, with r=.992. The allostatic load factors that did not have significant findings
among the prediabetic group were systolic and diastolic blood pressure, and fasting LDL.
Irrespective of the glycemic profile, the summed allostatic load indices were positively
and strongly correlated with insulin resistance (see Table 12).
Table 12
Bivariate Analysis: Allostatic Load Factors and HOMAIR of Euglycemic and Prediabetic
African American Women of the JHS
Euglycemic
Prediabetic
Glycemic Profile ⇒
Allostatic Load Factors
r
r
Glycosylated A1C
.252***
.151***
Fasting Insulin
.994***
.992***
Fasting Cortisol
-.023
-.118**
Fasting HDL
-.189***
-.286***
Fasting LDL
.052*
-.022
Fasting Triglycerides
.200***
.156***
Fasting Cholesterol
.020
-.077
Waist Circumference
.281***
.277***
Systolic Blood Pressure
.053*
-.053
Diastolic Blood Pressure
-.004
.061
Summed Allostatic Load Score
.314***
.345***
*p < .05. **p < .01. **p < .0001.
60
Research Question Three
To what extent, after adjustment for age, education, marital status, smoking, and
physical activity, do perceived stress and allostatic load factors predict insulin resistance
(defined as HOMAIR) among nondiabetic AAW in the JHS, when stratified by glycemic
profile?
Forward hierarchical linear regression methods were used to investigate the third
research question. Prior to testing this research question, the bivariate correlations were
examined to identify any potential for multicollinearity. Regression diagnostic statistics
such as Cook’s distance, leverage statistics, studentized residuals, DFFITS, and
DFBETAS were used to identify outliers and influential observations. Because there was
no supporting evidence to justify case deletion, outliers in this study were retained. The
Durbin Watson’s statistic, tolerance, and VIF were also evaluated. The variables, such as
marital status, educational, and smoking status were coded using effect-coding vectors
prior to analysis.
61
Table 13
Forward Hierarchical Regression Analysis of the Prediabetic Group (n = 335)
Variable
Standardized β weights
Step 1
Step 2
-.047
.292***
.057
-.018
-.085
-.018
-.046
.298***
.098
-.013
-.080
-.082
----
.086
-.072
-.078
--
--
1.863
.116***
---
.132
.015
.222***
.090
.085
-.077
-.055
Model 3
Allostatic Load Scores
R∆
DurbinWatson
-.107
.215***
.083
-.002
-.078
-.065
Model 2
Perceived Stress
Global Stress
DIS Everyday
DIS Lifetime
R
2
Step 3
Model 1
Covariates
Age
BMI
Education
Marital status
Smoking
Physical Activity
2
.314***
*p < .05. **p < .01. ***p < .0001.
The null hypothesis tested was that the summed allostatic load scores do not
contribute explained variance above and beyond the covariates and stress measures,
stratified by glycemic profile. As described in Table 13, the results demonstrated that
there was a significant relationship between insulin resistance and the set of predictor
variables, including the selected covariates in the prediabetic group. Thus, the ordered
62
entry of hierarchical regression guided by theory allowed the evaluation of the change in
explained variation after each block was entered to identify the unique variance in insulin
resistance accounted for by the independent variables. First, the covariates of age,
education, marital status, physical activity, smoking and BMI were entered in block one.
Next, the perceived stress measures, excluding the WSI instrument, were entered into
block two. (The WSI scores were not included due to the low response rate for this
instrument.) Last, the summed allostatic load scores were entered into the third block.
An a priori level of significance was determined as a p < 0.05.
In the prediabetic group, hierarchical linear regression analysis found that of the
covariates, only BMI was statistically significant. While the covariates explained 11.6%
of the variance, covariates other than BMI were not contributing to the model. The
perceived stress measures (i.e., STS, DIS), were not significantly associated with insulin
resistance and only explained 1.5% of the variance in insulin resistance. Upon entering
the summed allostatic load scores into block three, a 9.0% increase in the explained
variance occurred, with 22.2% of variance explained by the full model. Allostatic load
scores were found to be positively associated with insulin resistance in the prediabetic
group. The Durbin Watson statistic yielded a value of 1.854 indicating the assumption of
independent error was upheld (Polit, 2010). The tolerance value in the model ranged
from .611 to .950 and the variance inflation factor (VIF) ranged from 1.053 to 1.593,
indicating there were no highly intercorrelated independent variables in the model (Polit).
In the euglycemic group, hierarchical linear regression analysis found that the
covariates explained 14.3% of the variance (see Table 14). Both BMI and physical
activity maintained statistical significance in Model 1 and 2; however physical activity
63
was no longer statistically significant when allostatic load entered the model. BMI
continued to be a significant contributor to the explained variance in all three models.
Age was not found to be significant until the perceived stress measures were entered and
strengthened its contribution upon entering allostatic load, as evidenced by a change in
p-value. The perceived stress measures (i.e., STS, DIS) explained less than 1% of the
variance in the model. However, the Everyday Discrimination subscale of the DIS
instrument was significantly associated with insulin resistance when entered into block
two and maintained its contribution in the final model that included allostatic load. Upon
entering the summed allostatic load scores into block three, a 10.7% increase in the
explained variance was noted, with 25.3% of variance explained by the full model.
Likewise, allostatic load scores were found to be positively associated with insulin
resistance in the euglycemic group. In this analysis, the Durbin Watson statistic was
1.914 indicating independence among the errors. The tolerance values ranged from .641
to .971, with accompanying VIF values ranging from 1.023 to 1.560. The tolerance
values and VIF evidence there is no appearance of problematic intercorrelation among
the independent variables.
64
Table 14
Forward Hierarchical Regression Analysis of Euglycemic Group (n = 1910)
Variable
Standardized β weights
Step 1
Step 2
-.037
.363***
-.023
.004
-.004
-.071***
----
--
---
-.051* -.172***
.366*** .264***
-.016
-.066
.003
-.001
-.002
-.030
-.067*** -.031
-.005
-.074**
.032
--
.146
.004
.253***
.107
-.016
-.060**
.024
Model 3
Allostatic Load Scores
R∆
DurbinWatson
1.914
.143***
Model 2
Perceived Stress
Global Stress
DIS Everyday
DIS Lifetime
R
2
Step 3
Model 1
Covariates
Age
BMI
Education
Marital status
Smoking
Physical Activity
2
.370***
*p < .05 **p < .01 ***p<.001
Summary of the Results
The results of the bivariate correlations analyses, for both glycemic profiles,
indicated that there were positive intercorrelations among each of the perceived stress
measures. With the exception associations noted between DIS-ED and DIS-ML
(r=.541), all other intercorrelations observed were weak, with none exceeding an r=
.328. However, there was no association among any of the perceived stress variables and
65
HOMAIR as hypothesized. When assessing for associations between each allostatic load
factor and HOMAIR stratified by glycemic profile, the predicted relationships were
maintained. Among the euglycemic group, bivariate correlations indicated that as some
allostatic load factors increased, so does HOMAIR. More specifically, factors such as
A1C, fasting triglycerides, fasting insulin, waist circumference, and summed allostatic
load scores increase, HOMAIR increases. There were positive associations, albeit weak,
observed for LDL and systolic blood pressure as well. HDL had a strong, negative
association with HOMAIR, noting that as HDL increased, HOMAIR decreased. The
remaining allostatic load factors (fasting cholesterol, diastolic blood pressure, and fasting
cortisol) were not found to be associated with HOMAIR. In the prediabetic group, HDL
maintained a strong negative association with HOMAIR.. Fasting cortisol, unlike in the
euglycemic group, also had a negative association with HOMAIR. The positive
associations among the allostatic load factors in the euglycemic group were sustained in
the prediabetic group (A1C, fasting insulin, fasting triglycerides, summed allostatic load
scores, and waist circumference), except for systolic blood pressure. In addition to
fasting cholesterol and diastolic blood pressure, fasting LDL and systolic blood pressure
were not associated with HOMAIR among participants in the prediabetic group.
Using the forward hierarchical linear regression method, among participants
classified as euglycemic, results indicated that the summed allostatic load score was a
significant predictor of insulin resistance; while only one of the perceived stress measures,
Everyday Discrimination, contributed to the model after adjusting for age, BMI, education,
marital status, smoking, and physical activity. Of the covariates, age was not a significant
contributor until the perceived stress measures was entered in Step 2. In the final model,
66
Step 3, the summed allostatic load scores attenuated the contribution of the physical
activity variable in the model. Although the only significant effect among the covariates
was BMI, individuals classified as prediabetic experienced increased insulin resistance
for every one unit increase in BMI and summed allostatic load scores, with minimal
contribution from the perceived stress measures. In closing, the noted differences in
explained variance, stratified by glycemic profile, indicates that of the study variables,
summed allostatic load scores is more predictive of an insulin resistant state as
hypothesized when controlling for the identified covariates. With the exception of the
Everyday Discrimination measure, all other perceived stress measures had minimal to no
contribution to the model, irrespective of glycemic profile. The assumption that increased
perceived stress is predictive of an insulin resistant state was only upheld in the
euglycemic group in one of three stress measures evaluated.
67
CHAPTER 5
DISCUSSION
This chapter introduces discussion of the research study findings relevant to the
conceptual framework and literature review. In addition, a discussion of strengths and
limitations of the study are presented. Implications for nursing research, practice, and
education are also presented.
Discussion Related to Conceptual Framework
McEwen’s allostatic load was the framework guiding this cross-sectional study.
The framework asserts that the cumulative net effect of “wear and tear” on multiple
systems in efforts to counter repeated stress exposure over long periods manifest into
disease (2004). It was hypothesized that individuals, experiencing increased levels of
perceived stress and allostatic load scores, would also evidence an increased insulin
resistant state (operationalized by the HOMAIR score). According to Räkkönen et al.
(1996) and McEwen (2004), increased stress has been found to be associated with
insulin resistance. In this study, when evaluating findings from bivariate correlations, this
assumption was not upheld. There were no associations between perceived stress and
insulin resistance irrespective of the glycemic profile as indicated in Table 11. On the
other hand, findings from the forward hierarchical linear regression analyses only
indicated such findings among the euglycemic participants, but did not predict
relationships between perceived stress, allostatic load, and insulin resistance in the
68
prediabetic group, as indicated in Table 13 and 14. Moreover, of the perceived stress
measures specific to discrimination, Everyday Discrimination and Major Lifetime event
subscales, the Everyday Discrimination subscale was the only stress measure noted as a
contributor to the explained variance in any of the regression analyses. These findings are
consistent with the precept that it is the chronic, day-to-day exposure to unfair treatment
(such as being treated with less respect, insulted, treated with suspicion, or having
dehumanizing interactions) that manifests into disease over time, not acute exposure to
stress related to discrimination (such as getting fired or lack of promotion) (Clark et al.,
1999; Krieger, 2001; McEwen, 2004; Sims et al., 2009). It is of particular interest that this
finding was among the younger euglycemic participants, even though the mean average
age of this group is 52 years of age. This suggests that in the presence of euglycemia, or
adequate glucose disposal and metabolism, chronic stress is a potential predictor of
diseases, such as insulin resistance.
The lack of association between other general perceived stress measures and insulin
resistance measure warrants further discussion. It is important to acknowledge that there
may have been relationships between the perceived stress measures and insulin resistance
that were attenuated by coping mechanisms or confounded by co-existing feelings of
depression, hostility, or anxiety (Golden, 2007; Surwit et al., 2002); however, these
measures were not included in this study. Furthermore, gender-specific differences
relevant to perceptions of stress were not considered in the design of this study as this is a
homogenous group of AAW. Equally important is the use of McEwen’s allostatic load
framework to guide the study. While allostatic load has been measured in both
longitudinal and cross-sectional designs, studies with limited data collection on
69
psychosocial measures restrict the ability to assess interrelationships and predictiveness
of allostatic load (Singer et al., 2004). Another explanation for the findings among the
perceived stress measures is that all these measures relied on self-report; hence, were
subject to recall bias (LoBiondo-Wood & Haber, 2006).
Finally, the omission of the WSI instrument in the final analysis may have
impacted the findings relevant to perceived stress and insulin resistance. The WSI 87item instrument was administered by the JHS as a take-home survey to be mailed back
upon completion. Missing data is a shared issue of both primary and secondary
databases (Williams, 2006). Most statisticians agree that no more than 5-10% of the data
can be missing without consequence. Data exceeding this percentage should then be
considered for imputation, listwise deletion, or omission from the final model (Donders,
van der Heijden, Stijnen, & Moons, 2006). The WSI instrument exceeded 10% missing
data. There was less than a 60% response rate for the overall cohort, and less than 50%
for the analyzed sample. Response burden and failure to submit completed surveys are
tenable reasons for such high attrition for this particular stress measure, potentially
explaining the observed lack of association between the perceived stress measures and
insulin resistance. There was concern that the inclusion of the WSI instrument would not
yield adequate power to stratify by glycemic profile. In addition, there were no data
available in the literature regarding the imputation or listwise deletion of the WSI
instrument from previous studies; therefore, the variable was omitted.
Future recommendations for evaluating the associations between perceived
stress, allostatic load, and insulin resistance would be to include AA men of the JHS
study, retain the WSI instrument for final analysis if attrition is not of concern, and also
70
investigate the mediating/moderating effects of coping, which the literature indicates
has occurred in AA populations and other minorities (Broman, 2000; Schulz, et al.,
2006; Wei, Ku, & Russell, 2008; Gee, 2008). Also, further analysis including other
psychosocial measures collected by the JHS, such as the Beck Depression Inventory
(Beck & Steer, 1987; Golden, 2007), Cook-Medley Hostility Scale (Cook & Medley,
1954; Surwit et al., 2002)) and Spielberg’s State Trait Anxiety Inventory (Spielberg,
Gorsuch, & Lushene, 1970) would be critical to the refinement of this study.
Discussion Related to Literature Review
Instruments
The recent validation and psychometric testing of the multidimensional JHS DIS
instrument began the foundation for further examinations between perceived
discrimination as a stressor among AA populations in the JHS (Sims et al., 2009). With
almost all of the participants of the JHS completing the survey (n=5200), significant
differences in the demographics of the analyzed sample are noteworthy. In Sims et al.
(2009), the Everyday Discrimination measure was more prevalent among participants 2145 years of age. In addition, men were more likely to report experiences of
discrimination than women. The Major Lifetime Discrimination measure was more
commonly noted among participants over 45 years old or males. In this study, men were
excluded and the mean average age, based on glycemic profile, was 52 and 61 years of
age for euglycemic and prediabetic groups, respectively. Therefore, the exclusion criteria
and mean average age of the analyzed sample group could be influencing the associations
between the study variables. The observed differences of perceived discrimination in this
71
study could be potentially impacted by gender, age, or failure to report discrimination as
a stressor if adequate coping mechanisms are intact, in comparison to the overall cohort.
The JHS investigators developed the Global Stress (STS) measure from three well
validated instruments, Kohn and MacDonald's Survey of Recent Life Experiences (1992),
Cohen et al.'s Perceived Stress Scale (1983), and Sarason et al.'s Life Events Scale
(1978). Psychometric testing and validation of this measure is ongoing within the overall
sample of the JHS. These results were not available as of early 2011. Once available,
they can be used to guide use of the instrument in future analyses.
Predictors of Insulin Resistance
The associations observed between some of the allostatic load factors and insulin
resistance in this study revealed important findings as well. For AAW, according to the
Triglyceride and Cardiovascular Risk in African Americans (TARA) study, AAW did not
evidence glucose intolerance until a BMI > 30 kg/m2 was observed. In addition, waist
circumference greater than 98 cm was the best predictor insulin resistance. These
findings exceed NCEP-ATP III by 10cm, and the International Diabetes Federation by 18
cm (Sumner et al., 2008). In this study, participants classified as euglycemic were found
to have a waist circumference with a mean average of 97.07 cm. Individuals in this
group also had strong, positive associations with insulin resistance. As expected, those
classified in the prediabetes group had similar correlative associations, but the mean
average waist circumference was 105.61 cm. According to Sumner et al. (2008; 2010),
increasing the waist circumference threshold and adjusting for race and gender, given the
strong correlation to insulin resistance, would allow for proper diagnosis and appropriate
72
treatment. Further studies in search of the best predictor for AAW related to BMI
estimations and waist circumference should be pursued (Fontaine et al., 2003; Redden,
Wang, Westfall, & Allison, 2003; Aronne, Brown, & Isoldi, 2007; Sumner et al, 2007).
Triglyceride and HDL levels are also two allostatic load factors that require
further discussion in this study. Hypertriglyceridemia is an independent risk factor for
coronary heart disease. The triglyceride levels mean average (range) was 91.39 (16-587
mg/dL) in the euglycemic group and 116.82 (32-657 mg/dL) in the prediabetic group. In
addition, the HDL mean average (range) in the euglycemic group was 56.10 (18-127
mg/dL) and 52.61 (20-143 mg/dL) in the prediabetic group. Some investigators suggest
that AAW have been found to have higher HDL and lower triglyceride levels than
Caucasian women (Sumner & Cowie, 2008; Hall et al., 2003; Dubbert et al., 2002).
Taylor et al. (2008) and Sumner et al. (2005), both predicate this finding is a result of the
absence of impaired postheparin-lipoprotein lipase (LPL) activity in insulin resistant
AAs.
LPL is an enzyme that facilitates triglyceride removal from immediate circulation.
This activity is more pronounced among AAs in comparison to Caucasians during a
healthy state. In the face of an insulin resistant state, Caucasians experience decreased
LPL activity, while AAs preserve adequate LPL functioning, and possibly display a
cardioprotective mechanism (Sumner et al., 2005). In this study, irrespective of glycemic
classification, there were similar findings. The euglycemic group did not (and was not
expected to) exceed the fasting triglyceride or HDL high-risk threshold; however, the
prediabetic group was also well below the a priori cut-off value. Further race-gender
73
studies elucidating LPL turnover rate and triglyceride levels in both, healthy and insulin
resistant AAW would be needed to explain this phenomenon (Sumner & Cowie, 2008).
With regard to the outcome variable, insulin resistance, controversy and debate
continue to exist over the best predictor and measure of insulin resistance (direct or
indirect); as well as, whether it is an insulin resistant “state” or metabolic “syndrome” the
individual is experiencing (Alberti & Zimmet, 1998). The World Health Organization
(1998), and many other members of the scientific community (Kylin, 1923; Reaven,
1998; Zimmet, 2005) are not in agreement about whether the appropriate way to term the
sequela is “metabolic syndrome” or “insulin resistance syndrome”. This explains some
of the variability noted in the literature related to how we define and clinically measure
insulin resistance. Insulin resistance, measured by HOMAIR, has been found to have a
level of significance for AAW, but is not currently an NCEP-ATP III criterion (Appel et
al., 2005, 2009).
In this study, the research team aimed to specifically measure HOMAIR as an
outcome and evaluate a direct measure of insulin resistance through fasting insulin and
glucose (constituents of HOMAIR assessment that are strongly associated with
development of both type 2 diabetes and CVD), instead of using indirect measures of
insulin resistance provided by traditional clinical practice guidelines defining metabolic
syndrome (Nakai et al., 2002; Song et al., 2007; Bonora et al., 2002; Rutter et al., 2008;
Ausk et al., 2010). Including allostatic load scores as a predictor of insulin resistance can
be criticized because many of the parameters define metabolic syndrome (Seeman et al.,
2001). However, the use of allostatic load scores, as opposed to metabolic syndrome
criteria, has been proven to have predictive ability that exceeds the current criteria
74
defined by National Cholesterol Education Program-Adult Treatment Panel III (NCEPATP III) (NCEP, 2004; Seeman et al., 2004; Crews, 2007; Karlamangla et al., 2002).
Further, the current NCEP-ATP III guidelines do not account for concurrent use
of medications classified as antihypertensives or antilipidemic. In more recent studies,
individuals on any class of these medications are assigned an additional point, even if
they do not meet or exceed the high-risk threshold (Crimmins et al., 2003; Geronimus et
al., 2006; Mattei et al., 2010). In this study, the prevalence of antihypertensive and
antilipidemic medication use was 45% and 8.2%, respectively, for individuals classified
as euglycemic. The concurrent use of antihypertensive medication among prediabetics
was much higher, estimating over 74% and 85.9% using antilipidemics which may have
weakened the observed associations between allostatic load factors, such as LDL, fasting
cholesterol, SBP and DBP. In this study, had the operational definition of allostatic load
not accounted for medication use, as NCEP ATP III guidelines neglects to do, an
inaccurate measure of associated dysregulation evidencing allostatic load may have
impacted the current findings in the analyzed cohort.
There have been several studies previously published in the literature, such as
Haffner et al. (1996), Bonora et al. (1998), Matsumoto et al. (1997), Song et al. (2007),
and Esteghamati et al. (2010), supporting the use of HOMAIR as a measure of insulin
resistance (see Table 3 and 4). In a recent analysis of the JHS 2000-2004 cohort, the
prevalence of metabolic syndrome (the current standard for indirectly identifying insulin
resistant individuals in clinical practice) and its relationship to CVD was evaluated.
When referencing other population-based cohorts similar in design to the JHS, metabolic
syndrome prevalence rates among nondiabetic individuals were 24% in the Framingham
75
Offspring Study and 30% in the San Antonio Heart Study (Meigs et al., 2003). The JHS
2000-2004 overall prevalence rates of metabolic syndrome for ages 21-94 was 39.8%,
while age-adjusted rates for ages 35-84 years was 44% for women and 33.4 % among
men. The JHS 2000-2004 analysis found that members of the Jackson metropolitan area
have one of the highest prevalence rates of metabolic syndrome coupled with the largest
burden of CVD in the nation (Taylor et al., 2008). In future studies, the researcher will
consider evaluating associations between the study variables after redefining insulin
resistance conceptually, such that it is congruent with how Taylor et al. (2008) defined
insulin resistance in the JHS study. This could clarify whether or not the operational
definition of insulin resistance in this study impacted the ability to observe the prevalence
of this disease among the study variables in a cross sectional design.
76
Study Strengths and Limitations
Study Strengths
The JHS is the largest epidemiologic, population-based study to focus on
traditional and non-traditional risk factors essential to CVD development and progression
in the African Americans. This study presents a unique opportunity to investigate health
disparities specific to race and gender among AAW. The highly sampled population
decreases the likelihood of overlooking a significant difference, or committing a Type II
error (Polit & Beck, 2004). The large sample size of AAW stratified by glycemic profile
ensured the study was adequately powered to support that the findings among these groups
were not due by chance alone. After performing a post hoc power analysis, this condition
was confirmed (Soper, 2010).
In addition, because 64% of the overall JHS cohort are AAW and men were
excluded, the analyzed sample had adequate representation of minority women to meet the
National Institutes of Health Revitalization Act of 1993 (PL 103-43), entitled Women and
Minorities as Subjects in Clinical Research, which was revised and mandated in 1994 that
women and minorities be included in all of its clinical research studies. A recent report by
the Institute of Medicine (2010), Women’s Health Research: Progress, Pitfalls, & Promise,
confirmed that over the past twenty years there has been tremendous progress in women’s
research reducing the burden of disease and decreasing mortality; however, gaps persist and
gender-specific studies such as this are still of critical importance.
Another strength was the inclusion nondiabetic AAW stratified by glycemic profile.
Even though the results of the hierarchical regression analysis did not yield high percentages
of explained variance, stratifying by glycemic profile in this study may provide additional
77
knowledge to the scientific community about the associations that exist between perceived
stress, allostatic load, and insulin resistance among this population. For instance, because the
analyses were stratified by glycemic profile, the investigator was able to observe that
euglycemic participants were less likely to be on an antilipidemic medication and more
likely to be on antihypertensive medications. On the other hand, prediabetic participants
were found to self-report high usage of both antihypertensive and antilipidemic
medications. These findings may indicate that a critical point to intervene in euglycemic
individuals is once hypertension has been diagnosed, given its association to insulin
resistance. For prediabetic individuals, aggressive management of blood pressure and
dyslipidemia may be important measures healthcare providers can take to prevent the
transition from an insulin resistant state to overt type 2 diabetes.
Considering only one of the perceived stress variables, Everyday Discrimination,
was predictive of insulin resistance among younger euglycemic women of the JHS, this
finding supports the precept that experiences with day-to-day discrimination can have
deleterious effects on health outcomes (Krieger, 2001; Clark, Anderson, Clark, &
Williams, 1999; Sims, Wyatt, Gutierrez, Taylor & Williams, 2009). There is hope that
continued research evaluating the associations between discrimination and health
outcomes will provide insight for health policy that can result in measurable change at
the societal, economic, cultural, and environmental level.
As noted in Table 1, the measurement of allostatic load in most studies regarding
its operationalization defines it by the highest quartiles. This is appropriate and in
keeping with the seminal studies by Seeman et al. (1997); however, there appears to be a
paradigm shift (supported by Seeman) moving toward defining allostatic load in way that
78
translates into clinical practice (Crimmins et al., 2003; Mattei et al., 2010). Mattei et al.
(2010) used the clinical guidelines provided by the NCEP ATP III, as well as accounting
for concurrent medication use. Crimmins et al. (2003) defined allostatic load as a
combination of clinical guidelines and quartiles to identify their cutoffs, as was done in
this study. In support of the paradigm shift, defining allostatic load in this fashion is a
more practical, assertive approach for healthcare providers in clinical practice who are
attempting to identify critical intercessory points among high-risk individuals, such as
AAW, prior to transitioning to a diseased state.
The wide education and income gradient present in this study is also important to
note. The ability to observe the associations between perceived stress, allostatic load, and
insulin resistance, particularly in AAW with high levels of education and income, proved
not to be protective in this instance which is consistent with the findings of Geronimus et
al. (2006). Finally, and more importantly, is the insight gained from identifying the
absence of associations or predictive ability among some of the study variables. This may
contribute to optimal use of time and financial resources in the design of future research
that will be informative as we search for answers to reduce disease burden and
consequential mortality in a population that experiences disease at disparate rates.
Limitations
This study has limitations which warrant further discussion and offer
opportunities for refinement in future studies. First, the homogenous nature of the
sample only includes the tri-county area of Hinds, Madison, and Rankin counties in
Mississippi. In addition, the exclusion of AA males and limited representation of
79
participants, either less than 21 years or greater than 85 years may limit generalizability
in some instances. A comparison among another highly sampled AA cohort with similar
aims in a different geographical region would be helpful in assessing consistency in
findings when adjusting for regions. Also, given the size of the JHS cohort, it would be
insightful to evaluate the presence of insulin resistance between AAW, AA males,
Hispanics and their Caucasian counterparts. While Hispanics and Caucasians are not
included in this cohort, collaborations with other expert researchers at other institutions
may yield such opportunities.
An additional limitation is that two variables initially identified for inclusion in
the analyses were omitted. As discussed previously, low response rates to the WIS
subscale led to its omission in the regression analyses. In addition, self-reported income
was initially proposed to be among the covariates controlled for but poor response rates
threatened the researcher’s ability to maintain adequate power when stratifying on
glycemic profile, and subsequently resulted in the deletion of this variable from the final
model.
With regard to allostatic load, the use of scores low enough not to be clinically
significant but high enough to capture those at increased risk, whether guided by clinical
practice guidelines or quartiles, attempts to seek out the evidence of wear and tear on the
body before disease manifests. One limitation of identifying the high risk parameters is
that scoring does not allow full analysis of all available data, only those at the cut-off
quartiles. The allostatic load factors that were included, while clinically relevant, may
not be exhaustive of all significant surrogate measures of insulin resistance. Moreover,
there is the risk of underestimation or overestimation of insulin resistance when using
80
clinical practice guidelines to identify the cut-off point because the seminal studies
establishing NCEP ATP III guidelines were not normed in a sample with adequate AA
representation. The summing of the selected allostatic load factors may also potentially
be masking the individual contribution of an allostatic load factor (for example, waist
circumference) or disallowing the evaluation of interaction effects among the factors
(for example, waist circumference, age, and BMI). Future studies could be redesigned
to include more than a one-time measure to gain better insight on multi-system changes
measured by the allostatic load scores overtime; as well as, consider canonical
correlations, recursive partitioning, or a factor analysis (other documented statistical
methods of analyzing allostatic load) in search of more rigorous ways of
operationalizing allostatic load that allow full analysis of the data without the need to
establish a cut-off value.
Allostatic load is a concept that can be appraised by both longitudinal and crosssectional assessments, but there are limited inferences to be made by a cross-sectional,
correlational design. On the other hand, the use of a cross-sectional design did allow for
the exploration of associations among the study variables to help refine the current study
for future studies; as well as, avoided internal threats to validity, such as maturation,
mortality, and loss to follow-up (Polit & Beck, 2004).
Unlike cross-sectional or longitudinal designs, a secondary data analysis is not a
study design. Secondary data analyses may be challenged by the primary study’s
scientific integrity, data quality, and construct validity (Lacey & Hughes, 2007). The
purpose or conceptual framework guiding the original study may also impart limitations
because interrelationships among the new variables of interest are lacking. The
81
investigator may find the data set is not comprehensive and variables they would wish to
control for or add for analysis may be missing (Coyer & Gallo, 2004). Some of the
aforementioned limitations to a secondary data analysis were present in this study. For
example, the JHS was not purposed to directly assess insulin resistance so there are
inherent risks to consider regarding the secondary data analysis of this study. As noted
in Table 3 and 4, the researcher attempted to design the study such that the overarching
purpose of the JHS was maintained and identified adequate literature that evidenced
insulin resistance as a predictor of CVD and type 2 diabetes, a CVD risk equivalent. In
addition, all of the psychosocial instruments and serum cortisol levels were only
collected once at baseline with no opportunity for further evaluation in future studies to
observe the influence of perceived stress at other time periods of the JHS.
In addition, there are key modulators such as leptin, adiponectin, interleukin-6
(IL-6), tumor necrosis factor-α (TNF-α), plasminogen activation inhibitor-1 (PAI-1),
angiotensinogen, CRP, and resistin that would have been of interest to this study, but
will not be collected until the next wave of data collection in 2012. All of these
modulators are tenable factors in the development of an insulin resistant state facilitating
the progression toward type 2 diabetes and CVD (Aronne & Isoldi, 2007; LeRoith,
2007; Appel, Oster, Floyd, & Ovalle, 2009). Also, the measurement of visceral adipose
tissue (VAT), the best method of measuring the metabolic activity of adipose tissue in
comparison to waist circumference, is also being collected by the JHS but was not
available in the 2000-2004 data collection. The exploration of VAT volume at another
time period may be more informative or suggestive when evaluating the pathogenic role
of insulin resistance development (Appel et al., 2006; Perry et al., 2000).
82
Implications for Nursing Research
Research that considers race-gender differences provides nurses and nurse
researchers with a bridge to close the gaps between scientific discovery and clinical
application. The findings from this study, with refinement, can contribute to the
current body of knowledge relevant to insulin resistance among nondiabetic AAW.
The evidence provided by this study implies that it is important to consider the role
day-to-day stressors, such as discrimination, play in individuals of various age ranges
as they progress from a euglycemic state to insulin resistance. Data from this study
also indicated that age, BMI, and to some degree, physical activity among euglycemic
AAW of the JHS are significantly associated with insulin resistance. BMI was the
only variable predictive of insulin resistance among the prediabetic group. In future
studies, it would be additive to evaluate the level of predictiveness based on obesity
classification, measured by BMI.
There is evidence that posits, when identifying associations, it is more insightful
to evaluate BMI and its relevance to disease states based on obesity classification: (a)
Underweight – BMI < 18.5 kg/m2; (b) Normal – BMI 18.5 – 24.9 kg/m2; (c)
Overweight – BMI 25 – 29.9 kg/m2; (d) Obesity (Class I) – BMI 30.0–34.9 kg/m2; (e)
Obesity (Class II) - BMI 35.0 – 39.9 kg/m2; (f) Obesity (Class III) - BMI > 40 kg/m2
(National Institute of Heart, Lung, and Blood Institute [NHLBI], n.d).
83
Implications for Nursing Education
On August 31, 2010, A Summary of the February 2010 Forum on the Future of
Nursing Education Workshop Summary was published in print by the Institute of
Medicine (IOM, 2010). In this publication, one of the recommendations for basic and
advanced nursing education was to prepare future nurses to embrace issues of diversity
by being more innovative and culturally competent. Further, the advance practice nurse
should engage in cutting edge approaches to research by investigating diverse
populations through multidisciplinary collaborations, along with other nurse scientists.
The findings from this study can generate information that further defines race-gender
specific expressions of insulin resistance among AAW in the absence of type 2 diabetes.
The data from this study can also provide insight on race-gender specific differences
related to perceived stress and insulin resistance. For nursing students, this study
provides evidence that nurse educators should encourage students to perform a
comprehensive psychosocial assessment that is patient-centered and relevant to the
individual’s perception of the issue. This study can increase cultural sensitivity and
competence among nurses by heightening their awareness to the presence of
discrimination as a plausible stressor for AAs; as well as, consider the effects this stressor
has on the health outcomes of AAs.
Implications for Nursing Practice
The findings of this study have important implications for nursing practice. The
results provide an opportunity to address the paucity of literature and evidence needed to
address insulin resistance among nondiabetic AAW. Board certified advance practice
nurses credentialed to diagnose and treat individuals should be aware that insulin
84
resistance may manifest differently based on race and gender. More importantly, when
attempting to detect those individuals at risk, there are measurable psychological and
physiological contributors to this process. By assessing AAs perception of not only
general stress, but discrimination as well, the advanced practice nurse is empowered to
devise a competent, comprehensive plan of care. This plan of care may include
nonpharmacological and pharmacological agents, stress-reduction exercises, counseling,
or psychotherapy. Moreover, this study can serve as a springboard for the continued
research and development of culturally appropriate behavioral modification interventions,
patient-centered stress management approaches, and disease prevention educational
materials specific to AAW.
Advanced practice nurses and nurse researchers should also consider the presence
of multiple allostatic load factors could potentially evidence multisystem dysregulation
that can quickly manifest as overt disease if left undetected. Currently in clinical practice,
the only guidelines purposed to identify insulin resistance, and similar to the measurement
of allostatic load, is the NCEP ATP III guidelines. For AAW in this study, the NCEPATP III diagnostic criteria overestimate the appropriate threshold for waist circumference
and HDLs; while triglycerides are underestimated.
Given the antecedent relationship of insulin resistance with type 2 diabetes and
CVD, this reinforces the need for continued research related to the study variables that
can be translated into clinical practice distinguishing race-gender difference. In addition,
the formulation of new approaches to research using novel markers, such as LPL activity,
and the development of revised race-gender specific guidelines for the current criteria must
85
be pursued if health equity is going to be achieved for a population of women that
experience some of the highest rates of consequential morbidity and mortality.
86
Table-1 Operationalization of Allostatic Load in Research Studies
Author (s)
Study
Population/
Design
*
Operationalization
of Allostatic Load
(AL)
Measureme
nt of
Allostatic
Load
Determinant
of cut point
Seeman,
McEwen,
Rowe, &
Singer (2001)
Seeman et al.
(2002)
MacArthur
Studies-AL as
Biological Risk
Caucasian/
10 Biomarkers
Summing
scores
Highest/Lowest
Quartile
10 Biomarkers
Summing
Scores
Highest/Lowest
Quartile
Karlamangla
et al. (2002)
AL as a predictor
of functional
decline
MacArthur
studies
Allostatic Load
& work
conditions
Caucasian/
10 Biomarkers
Canonical
correlation
analysis
Highest/Lowest
Quartile
Summing
scores
Highest/Lowest
Quartile
Age differences
in AL: NHANES
III (1998-1994)
Caucasian/
African
American
men and
women
10 Biomarkers +
TNFα, CRP, BMI
Workers in German
airline
SBP, DBP, TC/HDL,
A1C, HDL, Albumin,
CRP, Fibrinogen,
Peak flow, creatinine
clearance, and
Homocysteine
Summing
scores
Clinical Practice
Guidelines/
Highest/Lowest
Quartile
Schnorpfeil
et al. (2002)
Crimmins,
Johnson,
Hayward &
Seeman
(2003)
Social
relationships,
gender, and AL
Longitudinal
study
Caucasian/
African
American
Cross-section
of longitudinal
Study
Longitudinal
study
German men
& women/
Cross-section
90th & 10th
10 Biomarkers + IGF- Summing
Taiwanese
Biomarkers
scores
percentile
I, IL-6, FPG, BMI
men
related to
physical and
Cross-section
mental wellbeing
Summing
Highest/Lowest
10 Biomarkers +
African
“Weathering”
Geronimus,
scores
Quartile
BMI, Albumin,
American/
and age patterns
Hicken,
Creatinine clearance,
Caucasian
of Blacks and
Keene, &
TG, CRP, and
Bound (2006) Whites-Homocysteine
Cross-section
NHANES IV
(1999-2002)
*The original 10 biomarkers were the following: systolic blood pressure (SBP), diastolic blood pressure (DBP),
waist hip ratio (WHR), Ratio TC/HDL (total cholesterol/high density lipoproteins), A1C (glycosylated hemoglobin),
urine cortisol, urine epinephrine, urine norepinephrine, HDL (high density lipoproteins), and DHEA-S (Seeman,
Singer, Rowe, Horwitz, & McEwen, 1997).
Seplaki,
Goldman,
Weinstein, &
Lin (2004)
87
Table-1 Operationalization of Allostatic Load in Research Studies (continued)
Author (s)
Study
Population/
Design
*
Operationalization
of Allostatic Load
(AL)
Measurement
of Allostatic
Load
Determinant
of cut point
Sun, Wang,
Zhang, & Li
(2007)
Job stress & AL
in Chinese
employees
Chinese
10 Biomarkers + IGR,
BMI, TG, Fibrinogen,
CRP
Summing scores
Highest/Lowest
Quartile
Crews
(2007)
Associations of
physiological
and
psychological
stress and
diabetes,
American
Samoan men
and women
**SBP, DBP, WHR,
A1C, TC, HDL, LDL,
TG, FPG, FSI, BMI,
triceps skinfolds,
subscapular skinfolds,
and relative fat pattern
index.
10 Biomarkers (with
CRP added to the
model in a separate
analysis)
Summing scores
Highest/Lowest
Quartile
Cross-section
Cross-section
Clinical
Practice
Guidelines
(except for
DHEAS, urine
cortisol, urine
epinephrine
and
norepinephrine)
*The original 10 biomarkers were the following: systolic blood pressure (SBP), diastolic blood pressure (DBP),
waist hip ratio (WHR), Ratio TC/HDL (total cholesterol/high density lipoproteins), A1C (glycosylated hemoglobin),
urine cortisol, urine epinephrine, urine norepinephrine, HDL (high density lipoproteins), and DHEA-S (Seeman,
Singer, Rowe, Horwitz, & McEwen, 1997).
** Additional abbreviations cited in Crews (2007): low density lipoprotein (LDL), triglycerides (TG), fasting plasma
glucose (FPG), fasting serum insulin (FSI), body mass index (BMI)
Mattei et al.
(2010)
Association of
AL to CVD,
Obesity,
Hypertension,
Diabetes,
Arthritis, and
Cancer
Boston Puerto
Rican Heart
Study
Cross-section
2004-2008
Summing scores
88
Table 2 History and Description of the Emerging Definition of Insulin Resistance
1923
Kylin, E.: Described the clustering of hypertension, hyperglycemia and gout as a syndrome
(Kylin, 1923).
1947
Vague,J.: Seminal observation that android obesity or male-patterned upper body obesity
associated with abnormalities seen in type 2 diabetes and CVD (Vague, 1947).
1988
Reaven, G.: Described the existence of a clustering of metabolic abnormalities linked to insulin
resistance (Reaven, 1988).
1998
World Health Organization (WHO): Made a proposal to call the condition metabolic syndrome
versus insulin resistance syndrome, since insulin resistance alone at that time could not offer a
sole explanation for all components of the syndrome (Alberti & Zimmet, 1998).
1999
World Health Organization (WHO): Publishes their global definition of metabolic syndrome
(WHO, 1999).
1999
European Group for the Study of Insulin Resistance (EGIR): Supported WHO definition, but felt
it needed to be simplified for clinicians to use in practice. Therefore, published a modified
version of WHO's definition of metabolic syndrome (Balkau & Charles, 1999).
2001
National Cholesterol Education Program Adult Treatment Panel III (NCEP/ ATP III): Identified
metabolic syndrome as a risk factor for cardiovascular disease (CVD). Defined Metabolic
syndrome as any 3 of the following: abdominal central obesity, hypertriglyceridemia, low HDLC, hypertension, and/or impaired fasting glucose (NCEP/ATP III, 2001).
2003
American Association of Clinical Endocrinologist (AACE), Task Force on Insulin Resistance
Syndrome: Modified the ATPIII definition to include a marker of insulin resistance as key
factors.
2005
International Diabetes Foundation (IDF): Introduced a new definition for metabolic syndrome in
hopes of clarifying controversy among consensus groups. They are the first to provide ethnic
specific waist criteria for some groups (Asians, Europeans and Euro-Americans). IDF suggested
if an individual's body mass index (BMI) exceeds 30 kg/m2 that they be identified as having
central obesity (Zimmet, Alberti & Shaw, 2005).
Joint Statement from the American Diabetes Association and European Association for Study of
Diabetes: Questioned the condition as a syndrome, and stated it can not be a syndrome since, the
cause is unknown (Kahn, Buse, & Stern, 2005).
2005
2005
2005
American Heart Association (AHA)/National Heart, Lung, and Blood Institute (NHLBI)
Scientific Statement: A statement on the diagnosis and management of the metabolic syndrome
by AHA and NHLBI reaffirmed their commitment to the ATPIII definition of metabolic
syndrome. Revision: NCEP-ATPIII-R
American Association of Clinical Endocrinologist (AACE), Task Force on Rapid Response Ad
hoc Committee: Respond regarding their Insulin Resistance Syndrome Consensus Statement of
2003. The ACE/AACE reaffirm their 2003 consensus conference position statement on IRS,
while endorsing the usefulness of viewing IRS/metabolic syndrome as a "syndrome."
Appel, S., Moore, T., & Giger, J.N. (2006). An overview and update on the Metabolic Syndrome:
implications for identifying cardiometabolic risk among African American women. Journal of
National Black Nurses Association, 12 (8), 48-63.
89
Table 3: HOMAIR and Insulin Sensitivity
Authors
Purpose
Population
Design/
Years-tofollowup
Glycemic
Profile
Cutpoint
determinant
Haffner,
Gonzalez,
Miettinen,
Kennedy, &
Stern (1996)
To evaluate
HOMAIR as a
measure of insulin
resistance from
fasting
glucose/insulin
levels in the
prediction of
NIDDM
1,449
Mexican
Americans
Prospective
Nondiabetic
Log transformed
Odds Ratio at
10th percentile:
HOMAIR =1.0
Haffner,
Miettnen, &
Stern (1997)
To evaluate
HOMAIR as a
measure of insulin
resistance from
fasting
glucose/insulin
levels in the
prediction of
NIDDM
2,465
MexicanAmericans
and
Caucasians
To prospectively
evaluate whether
HOMAIR and
HOMAB were
associated with
T2DM risk among
postmenopausal
women
82, 069
Caucasian,
African
American ,
Hispanic, and
Asian/Pacific
Islanders
Song et al.,
(2007)
Appel et al.,
(2009)
To evaluate the
relationship
between HOMAIR,
acanthosis nigrans,
hsCRP, and PAI-1
when comparing
the ATP III/IDF
guidelines
Esteghamati
et al.,
(2009)
Assess the
threshold of top
quintile HOMAIR
value to determine
associations of IR
with MetS based
on ATP III and
IDF guidelines.
3.5 years
The Mexico
City Diabetes
Study
90th percentile:
HOMAIR =6.8
Crosssectional
analysis
Nondiabetic
Quartiles (75th)
HOMAIR =2.7
(Mexicans)
HOMAIR =2.1
(Caucasians)
Effects on
Outcome
Variable:
HOMAIR
HOMAIR was found
to be a useful
measure of insulin
resistance and
insulin secretion
HOMAIR was found
to be a useful
measure of insulin
resistance and β-cell
function
The San
Antonio
Heart Study
Women’s
Health
Initiative
Observation
Study
33 African
American
women
1,276 Iranian
men and
women
Nondiabetic
Median
split/
cutpoints:
HOMAIR
=1.439
Elevated HOMAIR
and decreased
HOMAB were
strongly associated
with increased risk
for T2DM
Descriptive
pilot study
Nondiabetic
Quartiles
HOMAIR =2.7
HOMAIR was
strongly associated
with an insulin
resistant state among
AAW that did not
meet ATP III/IDF
guidelines
Crosssectional
study
2005-2008
Nondiabetic
Lower limit of
top Quintile
HOMAIR =1.78
HOMAIR cut point
for Iranian
populations was
lower than other
populations in
previous studies
Prospective
Observational
5.9 years
90
Table 3: HOMAIR and Insulin Sensitivity (continued)
Authors
Purpose
Population
Design/
Years-tofollowup
Glycemic
Profile
Cutpoint
determinant
Esteghamati
et al., (2010)
To determine the
optimal cutpoint
for HOMAIR as a
measure of insulin
resistance and MetS
in nondiabetic
residents of Tehran
1,162 Iranian
men and women
Prospective
Populationbased
2005-2007
Nondiabetic/
Diabetic
50th percentile/
95th percentile:
HOMAIR
=1.775
To evaluate
HOMAIR as a
measure of insulin
resistance from
fasting
glucose/insulin
levels in the
prediction of
NIDDM
2,465 MexicanAmericans and
Caucasians
Crosssectional
analysis
Nondiabetic
Lower limit of
top quintile:
HOMAIR
=2.77
HOMAIR was found
to be a useful
measure of insulin
resistance and β-cell
function
Matsumoto
et al., (1997)
To evaluate insulin
secrection and
insulin resistance in
Japanese.
756 Japanese
participants
Crosssectional
study
Nondiabetic/
Obese/
nonobese
90th Percentile
HOMAIR
=1.97
Elevated HOMAIR
and decreased
HOMAB were
strongly associated
with increased risk
for T2DM
Geloneze
et al., (2006)
Assess HOMAIR to
determine
conclusive
thresholds that are
clinically
significant among
insulin resistant
Brazilian men and
women
312 Brazilian
men and women
Crosssectional
study
Nondiabetic
90th percentile
HOMAIR
=2.71
Assess HOMAIR to
determine
conclusive
thresholds that are
clinically
significant among
insulin resistant &
sensitive nonobese Japanese
participants
161 Japanese
men and women
(sensitive)
Crosssectional
study
Diabetic
(Insulin
Resistant/
Sensitive)
90th percentile
Insulin
Sensitive
HOMAIR =1.7
HOMAIR cut-off
was strongly
associated with
Bonora et al. (1998)
noting laboratory
assessment of
insulin resistance in
heterogeneous
Brazilian
participants may be
sufficient
Ethnic differences
exist for HOMAIR
scores when
evidencing insulin
sensitivity and
resistance among
Type 2 diabetic,nonobesee Japanese
men and women
Bonora et
al., (1998)
Nakai et al.,
(2002)
Third National
Surveillance of
Risk Factors of
NonCommunicable
Diseases
The San
Antonio Heart
Study
The Brazilian
Metabolic
Syndrome Study
(BRAMS)
346 Japanese
men and women
(resistant)
90th percentile
Insulin
Sensitive
HOMAIR =1.8
Effects on
Outcome
Variable:
HOMAIR
HOMAIR= 1.775
was found to be
optimal cutpoint for
diagnosis of MetS in
nondiabetic and
diabetic Iranian men
and women.
91
Table 4: HOMAIR and CVD Risk Prediction
Authors
Purpose
Population
Design/
Years-tofollowup
Glycemic
Profile
Bonora et
al. (2002)
To evaluate whether
HOMAIR is an
independent
predictor of CVD in
T2DM
1,326 T2DM not
being treated with
insulin at baseline
Prospective
T2DM
included
Hanley et
al., (2002)
Resnick et
al. (2003)
Resnick et
al. (2002)
Rutter et
al. (2005)
Jeppesen
et al.
(2007)
Bonora et
al. (2007)
Rutter et
al., (2008)
To evaluate the
association between
HOMAIR , as well as
insulin levels with
fatal and nonfatal
CVD
To evaluate the
whether insulin
resistance and MetS
independently
predict CVD
1. To evaluate two
surrogate insulin
resistance measures,
HOMAIR and FSIGT
2. Compare insulin
sensitivity of
nondiabetic and
T2DM in SAH with
IRAS
To evaluate baseline
MetS and two
surrogate insulin
resistance measures,
HOMAIR and ISI0.120
for their independent
relationship to 7-yr
CVD risk
To evaluate whether
insulin resistance
would predict CVD
independent of MetS
To evaluate if
insulin resistance
predicts CVD and
explore traditional
and nontraditional
CVD risk factors
To evaluate HOMAIR
as a surrogate marker
of insulin resistance
and its’ ability to
predict T2Dm and
CVD in a
community cohort
Verona Diabetes
Complication
Study
2,569 MexicanAmericans and
Caucasians
The San Antonio
Heart Study
2,283 American
Indians
The Strong Heart
Study
61 American
Indians
Strong Heart Study
4.5 years
Prospective
populationbased
HOMAIR was found to be an
independent predictor of CVD in
patients with T2DM
Nondiabetic
HOMAIR was found to be
associated with risk of CVD
Nondiabetic
HOMAIR and ATP III-MetS
were predictive of T2DM, but no
independent prediction of CVD
Nondiabetic
HOMAIR was not found to be
useful when identifying
nondiabetic AI at risk for
cardiovascular disease. AI of the
SHS had lower S1 than all
participants of the IRAS
Nondiabetic
HOMAIR was not found to be an
independent predictor of CVD,
but strongly associated fasting
insulin, waist circumference, and
HDL
T2DM
included
HOMAIR and NCEP ATP III
were an independent predictor of
CVD
Nondiabetic
IFG, IGT, and
T2DM
included
HOMAIR was found to be an
independent predictor of CVD
and several nontraditional CVD
risk factors
Nondiabetic
HOMAIR predictive value for
T2DM yielded an aROC of 80%
and 86% with a multivariate
model (age, sex, fasting glucose,
and insulin) at the 76th percentile.
CVD prediction aROC was 63%
8 years
Prospective
populationbased
7.6 + 1.8
years
Communitybased
observational
study
Cross-section
1994-1997
2,898 (1,596
women)
Caucasians
Associative/Predictive
Ability of Outcome
Variable:
HOMAIR
Communitybased
observational
study
Framingham
Offspring Study
cohort
6.7 years
2,493 Caucasian
men and women
Populationbased study
Dannish cohort
9.4 years
919 Caucasian men
and women
Prospective
populationbased
The Bruneck Study
15 years
2,720 Caucasian
men and women
Framingham
Offspring Study
Prospective
observational
study
7- and 11years
92
Table 4: HOMAIR and CVD risk prediction (continued)
Authors
Purpose
Population
Design/
Years-tofollowup
Glycemic
Profile
Ausk et al.
(2010)
To evaluate if
HOMAIR is
associated with allcause mortality and
disease-specific
mortality; Subanalyses of CVD and
cancer mortality
5,51, adult
participants
Cross-section
study (19881994)
Nondiabetic
HOMAIR was associated with
increased all-cause mortality in
nondiabetic, nonobese
population;
HOMAIR was found to be
associated with CVD in the
lowest quartile.
No associations with cancer
mortality
Cross-section
study
Nondiabetic/
Diabetic
Increased HOMAIR , and
triglyceride/HDLc ratio are
positively correlated with
coronary artery disease
Bertoluci
et al.,
(2010)
To evaluate the
associations between
HOMAIR,
triglyceride/HDLc
ratio, and coronary
artery disease
Third US National
Health and
Nutrition
Examination
Survey (19881994)
131 Caucasian men
and women
Associative/Predictive
Ability of Outcome
Variable:
HOMAIR
93
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APPENDIX A
EXPEDITED INSTITUTIONAL REVIEW BOARD APPROVAL
123
124
APPENDIX B
JACKSON HEART STUDY APPROVAL
125
JACKSON HEART STUDY
Jackson Medical Mall, Suite 701 ♦ 350 West Woodrow Wilson Drive ♦ Jackson, MS 39213 ♦ Phone: 601­979­8700 ♦ Fax: 601­979­8701 July 22, 2009
Alethea N. Hill
University of Alabama at Birmingham
1465 Wilkins Road,
Mobile, Alabama
Dear Ms. Hill,
Thank you for submitting the JHS manuscript proposal entitled, “Risk Assessment of the
Associations between Perceived Stress, Insulin Resistance and Allostatic Load among
nondiabetic African American women of the Jackson Heart Study” to the Publications
and Presentations Subcommittee. The sequential number P#0216 has been assigned to
your proposal, so please refer to this number when making reference to this proposal. The
committee voted to “Accept pending recommended revisions”. A copy of the evaluation
comments is included (see attachments) for your review.
Lead authors of proposals that are to “accept pending recommended revisions” must
revise the proposal addressing each required revision and resubmit it along with a cover
letter addressed to the PPS Chair indicating that the proposal is being resubmitted and
describing the revisions. This should be done within 30 days of the date on the
notification letter.
We thank you for this proposal and we look forward to the final product.
Sincerely,
Nimr Fahmy, PhD
Chair, Publications and Presentations Subcommittee
cc: Frances Henderson, Errol Crook, Susan Appel, Daniel Sarpong, Mario Sims
126
JACKSON HEART STUDY
Jackson Medical Mall, Suite 701 ♦ 350 West Woodrow Wilson Drive ♦ Jackson, MS 39213 ♦ Phone: 601­979­8700 ♦ Fax: 601­979­8701 October 21, 2009
Alethea N. Hill
University of Alabama at Birmingham
1465 Wilkins Road,
Mobile, Alabama
Dear Ms. Hill,
Thank you for re-submitting the JHS manuscript proposal entitled, “Risk Assessment of
the Associations between Perceived Stress, Insulin Resistance and Allostatic Load among
nondiabetic African American women of the Jackson Heart Study” to the Publications
and Presentations Subcommittee. The sequential number P#0216 has been assigned to
your proposal, so please refer to this number when making reference to this proposal. The
committee voted to “accept your revisions.”
A period of 2 years is allowed for publication of the manuscript from a given proposal from
the date of the availability of data. “Languishing manuscripts” (those inactive after 2 years)
are eligible for recommendation by the PPS for reassignment to other interested investigators.
We thank you and wish you much success in completing your dissertation.
Sincerely,
Nimr Fahmy, PhD
Chair, Publications and Presentations Subcommittee
cc: Frances Henderson, Errol Crook, Susan Appel, Daniel Sarpong, Mario Sims
127
APPENDIX C
LETTERS OF PERMISSION
128
From:
To:
Date:
Subject:
Jonas Burén <[email protected]>
"Alethea Hill" <[email protected]>
12/3/07 7:21AM
Re: Stress & Diabetes
Hi Alethea.
Thanks for your request. I hereby approve your request. You could state
something like "Figure (adopted) from Jonas Burén and Jan W Eriksson.
Reproduced with permission." Or something similar...
I wish you good luck with your literature review. If possible, you could
maybe send me a PDF copy of your literature review/dissertation once it is
finished. It is an interesting research area you are working with.
Best,
Jonas
_____________________
Jonas Burén, PhD
Department of Medicine
Umeå University Hospital
6M, 4tr
S-901 85 Umeå, Sweden
Phone: +46-(0)90-785 31 72
E-mail: [email protected]
129
130
131
132
From:
To:
Date:
Subject:
roland rosmond <[email protected]>
Alethea Hill <[email protected]>
12/1/07 4:02PM
RE: Stress and HPA axis
Dear Alethea
Please feel free to use, in our material, the figure of the HPA axis published in the paper
"Stress induced disturbances of the HPA axis: A pathway to diabetes" in Med Sci
Monitor, 9 (2), RA35-39.
Best regards
Roland Rosmond
> Date: Sat, 1 Dec 2007 15:36:52 -0600> From: [email protected]> To:
[email protected]> Subject: Stress and HPA axis> > Hello,> My name is
Alethea Hill. I am currently a Clinical Assistant Professor at the University of South
Alabama and a doctoral student at the University of Alabama at Birmingham completing
coursework. The culminating project for my research practicum course working toward
dissertation, is to write a literature review. The concepts I am focusing on are stress and
Type 2 diabetes, specifically the influence of perceived stress has on glycemic control in
police officers, measuring BMI, HbA1c, and waist circumference. During my literature
search, I found your (2003) publication "Stress induced disturbances of the HPA axis: A
pathway to diabetes" in Med Sci Monitor, 9 (2), RA35-39, in which has a figure
depicting the HPA axis. I would like to adopt this picture and integrate into my paper. I
am seeking your written permission for use restricted to the literature review for my
dissertation studies.> > I look forward to hearing from you soon. Please feel free to
contact me via email or at 251-434-3599.> > Regards,> AH>
_________________________________________________________________
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