ASSOCIATIONS OF DIETARY FATTY ACIDS AND CARBOHYDRATES INTAKE WITH
COGNITION IN SCHOOL-AGED CHILDREN
Sehrish Naveed
Master's thesis
Public Health
School of Medicine
Faculty of Health Sciences
University of Eastern Finland
April 2017
1
UNIVERSITY OF EASTERN FINLAND, Faculty of Health Sciences
Institute of Public Health and Clinical Nutrition, Public Health
NAVEED, S.: Associations of dietary fatty acids and carbohydrates intake with cognition in
school-aged children
Master's thesis, 74 pages.
Instructors: Arja Erkkilä and Eero A. Haapala
April 2017
Key words: Diet, fatty acid, carbohydrate, children, brain, cognition
ASSOCIATIONS OF DIETARY FATTY ACIDS AND CARBOHYDRATES INTAKE WITH
COGNITION IN SCHOOL-AGED CHILDREN
Diet quality during childhood plays a vital role in achieving the optimal growth of neural tissues
and cognitive development. Evidence on the association of diet quality and cognition in children
is limited. Therefore, the aim of this study was to investigate the associations of the quality of
dietary fats and carbohydrates intake with cognitive function in school-aged children.
The data for this study were obtained from the PANIC study. The participants were 487 children
of age 6-8 years (250 boys and 237 girls). The dietary intakes were calculated from food records
of consecutive four days taken at home, school, in afternoon care, and elsewhere outside home.
Non-verbal reasoning skills as a measure of cognitive function were assessed by the Raven’s
Colored Progressive Matrices (RCPM) score, a higher score indicating better reasoning skills.
The associations of dietary intakes of carbohydrates and fats with non-verbal reasoning were
investigated with correlational analyses and multivariate linear regression models.
The analyses of dietary factors demonstrated significantly higher daily energy intake in boys than
in girls with greater consumption of sucrose, starch, insoluble fibers and fats among boys. While
the intake of glucose and fructose were equal in both genders. The analyses of association of
dietary intake of fatty acids or carbohydrates with RCPM score did not show correlation among
girls or when boys and girls were analyzed together. However, in boys, higher dietary intakes of
fructose (β = 0.23, P = <0.01), total fiber ((β = 0.15, P = 0.02) and soluble fiber (β = 0.15, P =
0.03) were associated with better RCPM score.
It was concluded that dietary intake of fructose, total fiber and soluble fiber demonstrated
positive correlation with cognition among school-aged boys, however none of the other dietary
carbohydrates and fats showed association with cognition among school-aged girls.
2
PREFACE
I am thankful to Assistant Professor Arja Erkkilä and Doctor Eero Haapala for throughout
guidance and feedback in the accomplishment of this study. I am also thankful to Professor Timo
Lakka for his comments and supervision. My special thanks to my parents and family for their
endless support in my life.
Kuopio, April 2017
Sehrish Naveed
3
ABBREVIATIONS
AA
ADHD
ALA
AX-CPTs
BDNF
CREB
DHA
DPA
DXA
EFs
EPA
FAs
GAP-43
GI
GLUT5
HA
IQ
LA
LDL-C
MetS
MUFAs
NePSY
The PANIC study
PUFAs
RCPM
SCFAs
SFAs
VA
WISC
Arachidonic acid
Attention deficit hyperactivity disorder
α-linolenic acid
AX Continuous Performance Tasks
Brain derived neurotrophic factor
Cyclic-AMP response element-binding protein
Docosahexaenoic acid
Docosapentaenoic acid
dual energy X-ray absorptiometry
Executive functions
Eicosapentaenoic acid
Fatty acids
Growth associated protein 43
Glycemic index
Glucose transporter 5
Heptadecenoic acid
Intelligence quotient
Linoleic acid
Low density lipoprotein-cholesterol
Metabolic syndrome
Monounsaturated fatty acids
Neuropsychological mental health association
The Physical activity and nutrition in children study
Polyunsaturated fatty acids
Raven’s colored progressive matrix
Short-chain fatty acids
Saturated fatty acids
Vaccenic acid
Wechsler Intelligence Scale for Children
4
CONTENTS
1.
INTRODUCTION................................................................................................................... 6
2.
LITERATURE REVIEW ...................................................................................................... 8
2.1.
Cognition ........................................................................................................................... 8
2.1.1.
Cognitive development .............................................................................................. 9
2.1.2.
Role of cognition in academic achievement ............................................................ 10
2.1.3.
Measures of cognitive function ................................................................................ 12
2.2.
Association of dietary fats and cognition ........................................................................ 17
2.2.1.
Fatty acids and their quality ..................................................................................... 17
2.2.2.
Structural importance of fats in brain ....................................................................... 18
2.2.3.
Functional importance of fats in neurodevelopment ................................................ 19
2.2.4.
Association of dietary saturated fatty acids with cognition in childhood ................ 20
2.2.5.
Association of dietary unsaturated fatty acids with cognition in childhood ............ 21
2.3.
Association of dietary carbohydrates and cognition........................................................ 23
2.3.1.
Dietary carbohydrates and their quality ................................................................... 23
2.3.2.
Structural and functional importance of carbohydrates in brain .............................. 25
2.3.3.
Association of dietary complex carbohydrates with cognition in childhood ........... 26
2.3.4.
Association of dietary simple carbohydrates with cognition in childhood .............. 27
2.4.
Summary of literature ...................................................................................................... 29
3.
RESEARCH QUESTIONS .................................................................................................. 30
4.
METHODOLOGY ............................................................................................................... 31
5.
6.
4.1.
Study population and design............................................................................................ 31
4.2.
Measurements .................................................................................................................. 34
4.2.1.
Food records ............................................................................................................. 34
4.2.2.
Measurements of non-verbal reasoning skills .......................................................... 34
4.2.3.
Other measurements ................................................................................................. 34
4.2.4.
Statistical methods.................................................................................................... 35
RESULTS .............................................................................................................................. 36
5.1.
Baseline characteristics.................................................................................................... 36
5.2.
Associations of dietary fatty acids and carbohydrates intake with cognition .................. 40
DISCUSSION ........................................................................................................................ 45
5
6.1.
Overview of the study...................................................................................................... 45
6.2.
Association of dietary fats with cognition ....................................................................... 45
6.3.
Association of dietary carbohydrates and cognition........................................................ 46
6.4.
Strengths and limitations of study ................................................................................... 47
7.
CONCLUSION ..................................................................................................................... 49
8.
REFERENCES ...................................................................................................................... 50
6
1.
INTRODUCTION
The development of human nervous system starts from infancy and continues throughout the
adolescence. Isaacs & Oates (2008) argued that sufficient quantity and quality of different
nutrients are needed to attain the optimal neural growth. Growth spurts in the human brain occur
between 2-4, 6-8, 10-12 and 14-16 years (Bourre 2006, Lenroot & Giedd 2006, Luna 2009). The
myelination of brain areas such as frontal lobe, which is associated with higher executive
function like planning, sequencing, regulating and controlling an action, is continued throughout
childhood (Toga et al. 2006) especially during initial 2 years of life, between 7-9 years and 14-16
years of age (Thatcher 1991, Bryan et al. 2004). This maturation of the brain areas has been
linked with the acquisition of cognitive skills like reading comprehension, language development
and memory function (Nagy et al. 2004, Giedd et al. 2010). Therefore, childhood is an important
phase in cognitive development and acquiring academic knowledge which contributes in making
future work force and human capital for economic growth of a country (Ross & Mirowsky 1999).
The development of central nervous system is dependent on sufficient intake of various nutrients
which provide fundamental components for optimum structural and functional development of
the young brain (Georgieff, 2007, Giedd et al. 2010, Nyaradi et al. 2013). Diet is an important
source of energy for cellular metabolism (Hoyland et al. 2008). The key building blocks required
for manufacturing DNA, cellular growth and development, signal transmission, enzymes for
metabolism and hormonal regulation, are dependent on the quality as well as the adequacy of
nutrients in diet (Bhatnagar & Taneja 2001, Lozoff & Georgieff 2006, Zeisel 2009, De Souza et
al. 2011, Zimmermann 2011). In addition to this, better diet quality also protects nervous system
by ameliorating the inflammatory responses of systemic infections (Jang et al. 2010, Sherry et al.
2010). According to Zipp & Aktas (2006) inflammation is associated with brain atrophy.
Optimal cognitive development is needed for performing complex and difficult tasks involving
interaction of different brain areas such as frontal lobe, prefrontal lobe and hippocampus. This
leads to better performance not only in academic career but in professional competence as well.
Studies have depicted the positive relationship between diet quality and cognition in school-aged
children (Florence et al. 2008, Khan et al. 2015). Carbohydrates and fats are one of the main
components of our daily diet and have important role in structural and functional maintenance of
7
the brain (Wang et al. 2003, Nyaradi et al. 2013). Therefore, the purpose of this thesis was to
investigate the relationship of dietary intake of fatty acids and carbohydrates to cognition in
children aged 6-8 years.
8
2.
LITERATURE REVIEW
2.1. Cognition
2.1.1. Definition and classification
Cognition is defined as an ability to flexibly use the acquired information to modify behavior in
order to accomplish a goal directed activity (Atallah et al. 2004, Wainwright and Colombo 2006).
Cognitive control or executive functions (EFs) have prime role in decision-making and
controlling emotions (Diamond 2013). EFs are defined as a family of the mental process needed
to think rationally and focus attentively as well as to inhibit relying on instinct perceptions if they
are unsatisfactory or misguiding (Miller & Cohen 2001, Espy 2004, Diamond 2013). There are
three basic types of EFs (Miyake et al. 2000, Lehto et al. 2003): Inhibition and selective attention,
working memory, and mental flexibility. Figure 1 is demonstrating the cognitive functions and
their interactions to accomplish different tasks.
Inhibition comprises of supervising emotions, thoughts, and behavior in order to prevail internal
or external provocations and biases. Selective attention or executive attention also works in
similar fashion to focus on the preferred thought while suppressing extra stimuli. Working
memory involves organizing the information in mind to produce a meaningful action plan.
Working memory assists in creating innovative thoughts while using conceptual knowledge and
past experiences. Unlike working memory, short-term memory involves only memorizing the
information without manipulating it. While, working memory involves manipulation of given
information and making subject to recollect and organize the previous information to carry out an
action. Mental flexibility or cognitive flexibility is the capability to change one’s perception and
to adjust the old information into new situation effectively. (Diamond 2013)
All three EFs interact with each other to perform higher order executive functions as described in
figure 1. Higher-ordered cognitive functions include planning and fluid intelligence. Fluid
intelligence involves problem solving skills and reasoning skills. Reasoning skills refer to the
ability to recognize the patterns and connections among objects (Ferrer et al. 2009). Reasoning
skills do not depend on acquired knowledge (Cattell 1987), these instead help children to acquire
other cognitive abilities (Cattell 1971, Cattell 1987, Blair 2006). According to Gottfredson
9
(1997), reasoning skills are the accurate predictor of school performance in childhood. Therefore,
reasoning skills are essential for cognitive development and play a crucial role in mental and
physical health for successful school life and professional competence (Diamond 2013).
Figure 1. Cognitive functions and their interactions (Diamond 2013).
2.1.2. Cognitive development
EFs development continuously progress throughout childhood and reaches to maximum in early
adulthood, but subsequently decline with increasing age (Diamond 2013). Brain undergoes
marked structural and functional changes during childhood (Lenroot & Giedd 2006, Luna 2009)
to develop the brain for future competition. The imaging studies of the brain have shown
maximum developmental changes as the child ages, especially in the prefrontal cortex (Frith &
Dolan 1996, Miller 2000, O'Hare & Sowell 2008). These changes in neural connections are
driven in by the experiences learned from surrounding environment resulting in efficiently
10
working complex neural connections, linked with the improved cognitive performance as child
ages (O'Hare & Sowell 2008, Best & Miller 2010).
Inhibitory control gradually develops in young children. At the age of 4, children can
successfully perform simple and complex inhibition tasks (Best & Miller 2010). Continuous
improvement has been seen predominantly at age of 5-8 years (Romine & Reynolds 2005), when
a child can perform tasks combining inhibition with working memory (Gerstadt et al. 1994,
Carlson 2005). In contrast to inhibition, working memory starts developing from infancy and
young children demonstrate fairly long retention time for one or two things (Diamond 1995,
Nelson et al. 2012). Working memory matures slowly with age and enables one to hold many
items in memory and manipulate them simultaneously (Crone et al. 2006, Davidson et al. 2006,
Cowan et al. 2011). Cognitive flexibility builds on working memory and inhibitory control. It
develops much slowly in U-shape manner, reaches maximum at 20 years and declines after the
age of 60 (Davidson et al. 2006, Cepeda et al. 2011).
Cattell (1987) explains that reasoning skills emerge after the development of general, perceptual,
attentional and motor capabilities in the first 2 to 3 years of life. According to McArdle et al.
(2002), reasoning skills advance rapidly in early to middle childhood, continue to develop until
early adolescence and become steady at mid to late-adolescence stage. Afterwards, reasoning
skills start to decline.
2.1.3. Role of cognition in academic achievement
Academic achievement denotes a child´s performance and grades in school and can be measured
and graded upon by the child’s ability to perform mathematical and reading tasks. The acquisition
of these academic skills depends on the development of cognitive functions. Accordingly, EFs
has been found to better predict academic achievement in school-aged children (Agostino et al.
2010, Borella et al. 2010) than intelligence quotient (IQ) (Best et al. 2011, Röthlisberger et al.
2013). Inhibition and cognitive flexibility exhibited close correlation with pre-math and prereading tasks in school-aged children (Bull et al. 2008, Pritchard et al. 2010, Roebers et al. 2011,
Willoughby et al. 2012). However, working memory demonstrated close association with
mathematical tasks (De Smedt et al. 2009, Toll et al. 2011, Van der Ven et al. 2012), reading
tasks (Sesma et al. 2009, Christopher et al. 2012) and spelling tasks (Hooper et al., 2011).
11
Although all three EFs are playing important role in academic achievement during early
childhood (Espy et al. 2004, Bull et al. 2008, Clark et al. 2010, Roebers et al. 2011), working
memory is more important determinant of academic achievement in school-aged children
(Altemeier et al. 2008, Toll et al. 2011, Lee et al. 2012).
According to Sesma et al. (2009), there was an association of working memory with reading
capability. Verbal working memory demonstrated important role in understanding the written
language or commands, also known as reading comprehension (Daneman & Carpenter 1980,
Carpenter & Just 1988, Just & Carpenter 1992, Swanson & Alexander 1997, Swanson 1999,
Swanson & Jerman 2007). Working memory works by engaging the cognitive resources to
decode the written word by recollecting the former existing word vocabulary, processing the
previous familiar knowledge to understand and memorize the new words. Good comprehension
skill depends on highly developed working memory as it provides sufficient cognitive capital to
run multiple processes concurrently to understand the written commands and also to interpret the
unaccustomed words by using the previous knowledge of comparable words, ultimately making a
person carry out the difficult task successfully (Sesma et al. 2009).
Reading comprehension is also linked to another component of higher level EFs known as
planning skills which enable us to reason deductively and analyze critically (Vellutino et al.
2000, Sesma et al. 2009). In addition to cognitive skills, good reading comprehension also
depends on the use of metacognitive skills (Palincsar & Brown 1984, Tierney & Cunningham
1984, Pearson & Fielding 1991, Pressley 2000). Metacognition is defined as ability and
awareness of individual to effectively plan, control, adjust and organize higher EFs in learning
tasks (Baker & Brown 1984, Paris et al. 1984, Palincsar 1986), and specifically in text
comprehension (Wong 1991, Trahan and Swanson 1996).
Dyslexia is a developmental deficit in reading the written language (Jeffries & Everatt 2004). A
comparison of cognitive functioning of non-dyslexic children with dyslexic children by Reiter et
al. (2005) demonstrated deficiencies in working memory, inhibition and fluency tasks in dyslexic
children. Swanson (2003) compared the development of verbal working memory of mainstream
children aged 7-20 years with those having reading disabilities. He observed that working
memory skills improved with increasing age in normally developing individuals as compared to
12
slightly upgrading dyslexic children. This difference gradually progresses with time,
transforming a normally developing child into a skilled reader while little improvement in a
dyslexic child.
2.1.4. Measures of cognitive function
Various approaches are used to evaluate the language skills, cognitive flexibility, working
memory, focus, visuospatial performance, as well as determining the IQ score for example, the
Wechsler Intelligence Scale for Children (WISC) (Wechsler 2010) and the Developmental
Neuropsychological Assessment (NEPSY) (Korkman 2008). Working memory can also be
assessed by using self-ordered pointing task and n-back task (Diamond 2013). In the self-ordered
pointing task, 3-12 items are displayed on the screen for a subject to touch in any order without
repetition. For instance, items can be boxes, designs or lines. When all the items have been
touched, the items appear on the screen with new alignment and this time the subject has to
remember the same order used previously to touch each item. The n-back task or AX Continuous
Performance Tasks (AX-CPTs) are designed in a same way to evaluate the working memory. In
this task, the subject is required to repeat the name of item previously read to him by the assessor.
Digit span and Corsi block test are modified to evaluate the short-term memory (Diamond 2013).
The inhibition and selective attention can be assessed by performing Stroop task and the flanker
task (Diamond 2013). These tasks involve congruent trials with compatible stimuli and
incongruent trials with incompatible stimuli to increase the amount of inhibition involved in the
cognitive assessment. The congruent trial, for example Stroop task involves test subjects to read a
word “red” written in red color. However, incongruent trials require reading a word “red” printed
in green color, thus inhibiting the visual clue (Diamond 2013). The flanker task assesses selective
attention by making test subject attentive to central focus surrounded with same or different
adjoining stimuli (Eriksen & Eriksen 1974). The complexity of tasks can be further enhanced by
using incompatible response conditions i.e. to answer in opposite direction from the given
direction (Diamond 2013).
Cognitive flexibility develops much later in life and relies on the development of previous two
EFs (Davidson et al. 2006, Garon et al. 2008). According to Diamond (2013), cognitive
flexibility can be assessed by using various tasks such as verbal fluency, design fluency and
13
category (semantic) fluency. These tasks were further explained by quoting examples like asking
subject to think about the uses of the table, words beginning with letter F, and alternating animal
and foods names (Baldo & Shimamura 1997, Baldo et al. 2001, Van der Elst et al. 2011, Chi et
al. 2012). Moreover, cognitive flexibility can be examined through task-switching and setshifting tasks. The classic example of these is the Wisconsin Card Sorting Task (Milner 1964,
Stuss et al. 2000) in which sorting of cards is carried out according to the criteria set by the
assessor. Participants are supposed to follow the card sorting rules and flexibly switch to the
changes in the set criteria.
Higher-order EFs embodying reasoning skills, planning and problem solving skills and are
assessed by many tests (Diamond 2013, Mungkhetklang et al. 2016). These include Raven’s
colored progressive matrix (RCPM) (Raven et al. 1995), Non-verbal Intelligence test-fourth
edition (TONI-4) (Brown et al. 2010), Comprehensive Test of Non-verbal Intelligence-Second
Edition (CTONI-2) (Hammill et al. 2009), and Wechsler Non-verbal Scale of Ability (WNV)
(Wechsler & Naglieri 2006). Table 1 below is demonstrating detailed comparison of these tests
which are designed to assess non-verbal reasoning skills in children, adults and mentally
impaired persons. The current study is focusing on non-verbal reasoning skills to assess higher
order EFs in primary school-children. These require minimal interference from examiner, does
not depend on linguistic skills and thus provide better idea about higher order EFs in children.
The current study used RCPM to assess non-verbal reasoning skills and is formulated for children
aged 5.5 to 11 years and subjects over 65 years (Basso et al. 1987, Raven et al. 1995). RCPM is
most widely used test and considered ideal for assessing non-verbal reasoning, independent of
acquired knowledge, reading, writing and language skills (McCallum & SpringerLink 2003).
The test design involves progressive matrices, comparing tasks on the basis of similarities,
differences or finding out the discrete patterns. The test has 36 incomplete non-figurative colored
designs arranged on the basis of increasing complexity (Basso et al. 1987). Each task in this test
contains a set of small objects, a large object or a pattern of objects. The test subject is provided
with one large incomplete object or pattern which has to be completed by selecting from given
items (Basso et al. 1987). The correct responses altogether make the RCPM score, ranging 0 – 36
(Basso et al. 1987).
14
The TONI-4 also assess problem-solving ability and non- verbal reasoning abilities. Test design
involves two multiple-choice format forms (A&B). Form A contain 45 items in the form of
abstract figures to be matched with one of the five or six given responses. Test is not dependent
on linguistic skills, but is tested only in English speaking subjects. Participant of the test needed
to understand the communication and instruction given by examiner and answers are recorded in
the form of pointing, head nodding or blinking. Test is considered reliable but the validity is low
as compared to RCPM (shown in table 1) (Brown et al. 2010).
The WNV is designed to be used clinically to assess cognitive abilities and IQ of individuals
aged 4-21 years. The test design has individual based four-subtests containing matrices and
pictures (for perceptual reasoning), coding (for graphomotor speed) and spatial span (for working
memory). The test is dependent on the acquired knowledge but is free of linguistic limitations but
is tested only in English speaking subjects. The validity of test is low as compared to RCPM
(Wechsler & Naglieri 2006).
The CTONI-2 consists of six subtests including pictures and geometric analogies, categories and
sequences. It is useful in assessing the intelligence of individuals with developmental delay in
language or motor abilities (Hammill et al. 2009). The NNAT-2 is a simple and fast method to
nonverbal reasoning and general problem solving abilities and is also not limited by educational
level of child (Naglieri 2008). Test design is adopted from RCPM and comprises of progressive
matrices on two parallel forms with increasing difficulty levels. Both CTONI-2 and NNAT-2
tests require minimal language skills or fine motor skills but the validity is controversial because
of sample bias (McCallum 2013).
15
Table 1. Comparison of tests for assessment of non-verbal reasoning skills.
Tests to measure
non-verbal
cognitive skills
Age group
Time
required for
completion
Test protocol
Limitations
Reliability
of test
score
Raven's Colored
Progressive Matrices
(RCPM)
5-11 years,
elderly >65
age, mentally
and physically
impaired
persons.
20-45
minutes
No language or communicative
limitations
0.65 to 0.94
Test of Non-verbal
Intelligence-fourth
edition (TONI-4)
6–89 years
15-20 min
English language and/or
communicative limitations.
0.94 to 0.97
0.73 - 0.79
Comprehensive Test
of Non-verbal
Intelligence-Second
Edition (CTONI-2)
6-89 years
60 minutes
Communicative limitations.
0.90
0.70
Wechsler Nonverbal Scale of
Ability; (WNV)
4-21 years
Full battery:
45 minutes;
Brief
version: 1520 minutes
• Most widely used test.
• can be used in a variety of settings,
such as testing culturally diverse
populations,
• no language bias,
• predicting potential educational
success
• a variety of research applications with
children and adults.
• Standardized in the US and UK
• contains 2 forms (A&B)
• screening test
• yields overall IQ score, abstract
reasoning and problem solving
• Standardized in US
• contains 6 subtests
• yields three IQ score, analogical
reasoning, categorical classification,
and sequential reasoning
• Standardized in the US and UK
• contains 6 subtests
• yield a full-scale IQ and scores for
verbal comprehension, perceptual
reasoning, working memory and
processing speed
Validity
compared
to other
non-verbal
intelligence
tests
0.50–0.85
English language and/or
communicative limitations.
0.91
0.71 - 0.82
Table 1 continued
16
Test name
Age group
Completion
time
Naglieri Nonverbal
Ability Test)
(NNAT)
5-17 years
25-30
minutes
Test protocol
• Contains two parallel forms with wide
range of difficulty levels in two-colour
shapes and designs.
• independent of prior learning.
• yields nonverbal-reasoning and
problem-solving skills.
Limitations
Reliability
of score
Simple instructions can be
communicated nonverbally, if
necessary
0.24 - 0.80
Validity
compared
to other
non-verbal
intelligence
tests
Restricted
17
2.2. Association of dietary fats and cognition
2.2.1. Fatty acids and their quality
Fatty acids (FAs) are divided in two main classes on the basis of presence of double or triple
bond in their structure i.e. saturated fatty acids (SFAs) and unsaturated fatty acids (Lobb & Chow
2000) (shown in figure 2 below). SFA have no double bond with general formula of R-COOH,
where R is the straight chain of hydrocarbons. SFA are further divided in to short and long chain
SFAs. Short-chain SFAs contain 4 to 18 carbons straight chain such as butyric acid (4C), palmitic
acid (16C), stearic acid (18C). Long-chain SFAs contain ≥19 carbon straight chain such as
arachidic acid (20C) and cerotic acid (26C). SFAs are solid at room temperature (DeMan 2007).
They are abundant in animal products like meat, milk and dairy products (Palmquist & Jensen
2007, Wood et al. 2007). Unsaturated FAs contain at least one double which add bends or kicks
in structure. They are liquid at room temperature (DeMan 2007). Unsaturated FAs are mainly
obtained from plants (Bruckner & Peng 2007) and fish (Ackman 2007) and further classified on
the basis number of double bonds in carbon chain into monounsaturated FAs (MUFAs) and
polyunsaturated FAs (PUFAs). They are further categorized on the basis of location of double
bond in hydrocarbon chain such as omega-9 contains double bond at ninth carbon counting from
methyl(-CH3) end of chain. MUFAs contain omega-9 FA oleic acid and PUFAs contain omega-3
alpha-linolenic acid (ALA), omega-6 linoleic acid (LA) and omega-6 arachidonic acid (AA)
(Lobb & Chow 2000). This classification is illustrated in figure 2 below.
Essential FAs are the unsaturated FAs that cannot be prepared in human body therefore, it is
indispensable to take them from external environment. There are two essential FAs i.e. LA and
ALA (Lobb & Chow 2000) which can be obtained from plant oils (Bruckner & Peng 2007).
Human body has limited tendency to convert ALA in to long chain omega-3 FAs such as
eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). These become essential if they
are not synthesized sufficiently by the body therefore, called “conditionally essential FAs” (Lobb
& Chow 2000) and can be obtained from fish sources (Ackman 2007).
18
Fatty Acids
Unsaturated
Saturated (SFA)
(contain double
bond)
(no double bond)
Short-chain SFA
(4-18C)
Butyric acid
Palmiticacid
Stearic acid
Long-chain SFA
(≥19C)
arachidic acid
cerotic acid
Monounsaturted
(MUFA)
Polyunsaturated
(PUFA)
(one double bond)
(≥1 double bonds)
Omega-9
oliec acid
Omega-6
Omega-3
Arachidonic acid
EPA andDHA
Linoleic acid
α-linolenic acid
Essential Fatty acids
Figure 2. Classification of Fatty acids
Essential Fatty acids
2.2.2. Structural importance of fats in brain
FAs are the major structural components of the human brain particularly unsaturated FAs, mainly
including omega-9, omega-6, and omega-3 FAs (McNamara & Carlson 2006). Omega-9 makes
20% of brain FAs, while omega-6 and omega-3 form 17% and 14% of FAs in brain respectively
(McNamara & Carlson 2006). DHA alone makes 10-20% of total FAs in the brain (McNamara &
Carlson 2006). Figure 3 demonstrates various proportions of FAs present in the prefrontal frontal
cortex of the human brain.
19
Figure 3. Fatty acid composition of the human post-mortem prefrontal frontal cortex (Brodmann
Area 10) (Adapted from McNamara & Carlson 2006).
Abbreviations: Docosahexaenoic acid (DHA), α-linolenic acid (ALA), eicosapentaenoic acid
(EPA), docosapentaenoic acid (DPA, 22:5n-3), arachidonic acid (AA), omega-6 fatty acids
linoleic acid (LA), docosapentaenoic acid (DPA, 22:5n-6), heptadecenoic acid (HA, 17:1n-7) and
vaccenic acid (VA, 18:1n-7) and intermediate fatty acid metabolites (Remainder).
2.2.3. Functional importance of fats in neurodevelopment
Lipids are vital for the functional development of the nervous system. They are structural
components of neural cell membranes, maintain membrane fluidity and play role in modulation
of enzyme activities and receptor functions. In addition to these, lipids are integral components of
steroid hormones which regulate expression of genes. In this way, fats stimulate the growth of
neuron and their branches, thus help in making new connections between growing nerve cells.
Therefore, lipids can impact the efficacy of processing and transmission of signals (Eilander et al.
2007, Cetina 2008, Wurtman 2008, Ramakrishnan et al. 2009, Ryan et al. 2010, Schuchardt et al.
2010, De Souza et al. 2011, Nyaradi et al. 2013).
20
As discussed above, lipids change the nerve cell membrane and myelin sheath composition
resultantly influencing the neuronal signal transmission (Isaacs & Oates 2008). Especially, the
essential FAs, LA and ALA, despite of their low content in brain, play a vital role in functional
development and maturation of intellect and other cognitive functions (Giovannini et al. 1992,
Lifshitz & Tarim 1996, Broadhurst et al. 1998, Simopoulos 1999). According to Georgieff
(2007), mouse brain exhibited the role of EPA and DHA, in stimulating the myelin-related
proteins expression. He further explained that myelin is the outer covering of the axons of the
neurons which increases the speed of electrical signal transmission and helps in swift execution
of tasks, thus associated with better cognitive ability. These observations demonstrated that lipids
are the essential part of the structural and functional maintenance of the brain. However, most
studies in this regard are done in elderly subjects (Heude et al. 2003, Swanson 2012, Luchtman
2013).
2.2.4. Association of dietary saturated fatty acids with cognition in childhood
SFAs are the major structural components of the human brain (McNamara & Carlson 2006).
However, limited work has been done to find the importance of SFAs intake in cognitive
development among school-aged children. The majority of the studies done in children are
inconclusive in establishing the definite impact of dietary restriction of SFAs on cognitive
development. For example, Rask-Nissilä et al. (2000, 2002) presented a clinical trial of continued
restriction of dietary SFAs (11.7% of daily energy) from infancy to 5 years of age. The results of
the trial demonstrated no impact on the physical or mental growth of children. Obarzanek et al.
(2001) also reported similar results of no impact on cognitive development by performing
another trial of restricting saturated fat intake (<8 % of daily energy) in children who had a high
level of age-specific serum cholesterol (LDL-C).
Conversely, sufficient evidence is found on the detrimental effects of high consumption of SFAs
on cognitive development. Both animal and human studies have linked increased dietary intake
of SFAs with cognitive decline. Animal studies focused on high SFAs intake in rodents
demonstrated a decline in the performance of memory tasks (Kesslak et al. 1998, Mizuno et al.
2000). This has been related to decreased levels of brain-derived neurotrophic factor (BDNF).
BDNF has been suggested to cause neurogenesis and neural survival in the hippocampus - an
21
important brain structure for learning and memory (Kesslak et al. 1998, Mizuno et al. 2000).
Similarly, a diet rich in SFAs negatively influence other neural growth factors like synapsin I,
growth associated protein 43 (GAP-43), and cyclic AMP response element-binding protein
(CREB). These factors are necessary for neural development and function by mediating
transcription of neurotransmitters, axonal growth and synaptic connections (Molteni et al. 2002,
Kanoski et al. 2010).
Among human studies, the evidence on the association of SFAs intake and cognitive function in
children was found limited but adequate evidence was available in adults. For instance, Zhang et
al. (2005), found no relationship of SFAs intake with neurocognitive performance in school aged
children of 6-16 years. Baym et al. (2014) linked the higher consumption of SFAs with poor
working memory in school-aged children of 7-9 years. According to Morris et al. (2004), high
intake of SFAs associated with decline in executive functions in old adults. Many prospective
and cross-sectional studies revealed the association of high intake of SFAs during midlife decline
in cognitive functions in adults. High dietary intake of SFAs has been associated with risk of
acquiring Alzheimer’s disease in old age (Kalmijn et al. 1997, Luchsinger et al. 2002, Heude et
al. 2003, Morris et al. 2003, Solfrizzi 2006, Eskelinen et al. 2008, Devore et al. 2009, Vercambre
2009). From these studies, it can be assumed that high intake of SFAs can cause detrimental
effects on cognitive development, therefore it is very important to investigate their impact on
childhood cognitive development to avoid these unfavorable effects on growing brains.
2.2.5. Association of dietary unsaturated fatty acids with cognition in childhood
According to Broadhurst et al. (1998), unsaturated FAs play vital role in the cognitive
development. Dietary deficiency or low intake of FAs, particularly ALA, in children of 6 years of
age can lead to psychomotor, visual and behavioral disturbances (Holman et al. 1982). However,
these neurological abnormalities reverse to normal when FAs intake return to an adequate level
(Holman 1988, Holman et al. 1982). Therefore, it is highly important to reverse the impact of
diet-related deficiencies which can impact cognitive functioning in childhood. Stevens et al.
(1996) also associated the decreased consumption of DHA with learning and behavioral problems
in 6–12 years old boys. Another study done in young rats by Lim et al. (2005), associated DHAdeficient diet with decreased olfactory discrimination and olfactory-based learning tasks. Thus,
22
there is some evidence that deficiency of unsaturated FAs has negative effects on cognitive
abilities. However, the knowledge about beneficial effects of PUFAs intake is controversial.
According to Ryan et al. (2010), meta-analysis of epidemiological studies and supplemental trials
of adding DHA in the diet of children showed inconsistent results on improvement of cognitive
functions. Another meta-analysis of randomized control trials by Jiao et al. (2014) to study the
impact of adding omega-3 dietary supplement on cognitive function during the entire course of
life, demonstrated no improvement in cognitive functions at any phase of life except infancy,
either in terms of composite memory, execution, processing speed, recognition or recall memory.
They also found no improvement in cognitive ability in Alzheimer’s disease.
Some studies showed the association of cognitive improvement with PUFAs intake. For example,
cross-sectional studies have related consumption of PUFAs with improved memory in children
(Hu et al. 2001, Zhang et al. 2005, Baym et al. 2014). Johnson et al. (2016) conducted in a
randomized controlled trial of three months’ supplementation with EPA and DHA along with
gamma-linolenic acid in main stream school aged children. Results demonstrated improved
reading ability in all children, especially in those with attention deficits. Supplementation trials
further elaborated the positive impact of higher EPA+DHA intake and cognition especially
among children with reading disabilities, psychological disorders such as ADHD (Bloch &
Qawasmi 2011), malnourishment (Portillo-Reyes et al. 2014) or low socio-economic status
(Dalton et al. 2009).
23
2.3. Association of dietary carbohydrates and cognition
2.3.1. Dietary carbohydrates and their quality
Dietary carbohydrates are classified on the basis of their chemical structure and physiology into
many categories. But this study is considering only two classifications of carbohydrates. The
figure 4 below is briefly demonstrating both structural and physiological classification of
carbohydrates included in this study. Structurally they are classified in to three main groups on
the basis of number of monomeric (single sugar) units. These include sugars (containing 1-2
monomers), oligosaccharides (containing 3-9 monomers) and polysaccharides (containing ≥ 10
monomers) (Cummings et al. 1997, FAO/WHO 1998). Sugars are further divided into
monosaccharides, disaccharides and polyols- sugar alcohols. Oligosaccharides are classified into
α-glucans (malto-oligosaccharides) and non-α-glucan. Polysaccharides are classed as starch and
non-starch polysaccharides.
Carbohydrates are physiologically divided into glycaemic and non-glycaemic carbohydrates
based on the ability to provide glucose for energy upon metabolism inside body. Glycaemic
carbohydrates metabolize to glucose following digestion or absorption in small gut for example
mono- & disaccharides, maltodextrins and starches. Non-glycemic carbohydrates are hard to
digest by small-gut enzymes and pass to the large intestine without being metabolized for
example polyols, oligosaccharides (non-alpha-glucan), resistant and modified starches, nonstarch polysaccharides. Glycaemic carbohydrates are further divided on the basis of rapidity of
their metabolism known as glycaemic index (GI) (Venn & Green 2007). GI indicates the increase
in the level of blood glucose after consumption of a carbohydrate compared to standard
carbohydrate (sugar, white bread or glucose) (Frost et al. 2000, Jenkins et al. 2002).
24
Dietary
Carbohydrates
(Glucose, Fructose,
Glactose)
Polysaccharides
(≥ 10 monomer units)
α-Glucan
Starch
(Maltodextrins)
(Amylose,
Amylopectin)
Disaccharides
(Sucrose, Lactose,
Maltose)
Starch
(some modified and
resistant starches)
Polyols
Non-α-Glucan
(Sorbitol, Mannitol,
Lactitol)
(Raffinose, Inulin,
Polydextrose)
Non-Starch
(Cellulose, Hemicellulose,
Pectin, Gums, Mucilages)
Glycaemic
carbohydrates
Monosaccharides
Oligosaccharides
(3-9 monomer units)
Non-glycaemic
carbohydrates
Sugars
(1-2 monomer units)
Figure 4. Brief structural and physiological classification of carbohydrates (Cummings et al. 1997,
Cummings & Stephen 2007, FAO/WHO 1998)
Most mono- and disaccharides, some oligosaccharides (maltodextrins), rapidly digested starches
have high GI, which means a rapid rise in blood glucose level after ingestion. These rapidly
digestible carbohydrates are also called as simple sugars. Slowly digestible starches such as
modified and resistant starches (high amylose and amylopectin content), have low GI because
they generate glucose less rapidly and are thus termed as complex carbohydrates (Asp 1994,
Cummings & Stephen 2007). This classification is elaborated in Table 2 below. The GI of a
carbohydrate is dependent on many factors including biology of host body, overall meal
composition and the intrinsic properties of the food such as non-starch polysaccharides/ fiber
contents (Cummings & Stephen 2007). Thus, the contents of diet play role in regulating
25
carbohydrate metabolism. High fiber contents of diet reflect a slow rise in blood glucose level
after ingestion therefore contributing to low GI (British nutrition foundation 1990).
As described above, dietary fibers are associated with good quality of carbohydrates. These are
obtained from plants and are categorized into water-soluble and insoluble fibers (Trowell et al.
1976, Asp 1994). Insoluble fibers include lignins and cellulose, which help in regulating bowel
movements and slow down the stomach emptying. Soluble fibers (pectins, gums, mucilages,
algal-polysaccharides, some storage polysaccharides, and hemicelluloses) turned into a viscous
gel (gelatine) on the addition of water, which is fermented by gut bacteria into short-chain fatty
acids (SCFAs) such as butyrate (Theuwissen 2008, Khan 2015).
Table 2. Classification of carbohydrates on the basis of glycaemic index.
Carbohydrate class
Simple carbohydrates
Property
High glycaemic index
(rapidly metabolized)
Complex carbohydrates
Low glycaemic index
(slowly metabolized)
Example
• Most mono- and disaccharides
• some
oligosaccharides
(maltodextrins)
• rapidly digestible starches
(low amylose, amylopectin
contents)
• Modified
and
resistant
starches (high amylose and
amylopectin contents)
2.3.2. Structural and functional importance of carbohydrates in brain
Glucose, metabolite from glycaemic carbohydrates, is the principal energy (ATP) source for
neuronal cells and is subtle for their survival (Volpe 1995). Brain cells are very sensitive to the
deprivation of glucose which effects brain development profoundly. For example, studies on
young mice brain demonstrated neuronal degenerations and decreased synaptic plasticity after
repeated or acute hypoglycemia of 4 hours respectively, particularly in the hippocampus and
cortical areas linked with cognition (Yamada et al. 2004, Kim et al. 2005).
26
Carbohydrates also play a vital role in the structural maintenance of nerve cells especially
glucose and galactose, present in sialic acid. Sialic acids are naturally occurring nine-carbon
monosaccharides attached to the terminals of carbohydrate chains in the cell-membranes and
water-soluble proteins (Schauer et al 1995, Schauer & Kamerling 1997). These are vital for
myelin formation, making nerve connections and therefore signal transduction (Kunz et al. 2000,
Wang & Brand-Miller 2003, Wang et al. 2003). Animal studies have also demonstrated the
importance of sialic acid supplementation in increasing the quantity of vital glycolipids and
glycoproteins in rats’ brain which is necessary for the development and maturation of executive
functions (Carlson & House 1986). However, this is still unproved for human brains.
Dietary soluble fibers have a neuroprotective role. Study of mouse brain exhibited an association
between higher intake of pectins and decline in the level of inflammatory markers (Sherry et al.
2010). Butyrate has been associated to amplify the production of BDNF, which is vital for
maintaining the structural integrity of neurons (Schroeder et al. 2007). However, the exact
mechanism of the relationship of insoluble fibers with cognition is still unknown. Insoluble fibers
have been associated with slow rise in blood glucose and insulin levels (Pereira et al. 2002). This
property of insoluble fiber is linked with decreased risk of metabolic syndrome (Khan et al.
2015). Metabolic dysfunction/syndrome (MetS) is defined as “weight gain, dyslipidaemia,
impaired glucose tolerance, decreased insulin levels and high blood pressure” (Grundy et al 2004,
Alberti 2006).
2.3.3. Association of dietary complex carbohydrates with cognition in childhood
Complex carbohydrates intake has been associated with better memory performance among
children and adults within 1-2 hrs. of intake (Benton et al. 2007). Similar association has been
found with the consumption of breakfast having complex carbohydrates with less variations in
blood glucose level than those containing simple carbohydrates resulting in better cognitive
function in children (Taki et al. 2010). Another study performed on 11-14 years old children
demonstrated that complex carbohydrates have association with the better impact on learning and
cognitive outcome when compared to simple carbohydrates breakfast (Micha et al. 2011). A
clinical trial observed that decreasing GI in children’s diet by replacing the simple carbohydrates
with protein did not show impact on cognitive performance tasks (Brindal et al. 2012).
27
Due to their protective effects on neurons, dietary fibers have shown correlation with cognition in
children and older adults. For example, according to Ortega et al. (1997), higher intake of dietary
fibers demonstrated improved cognitive abilities among elderly subjects. A cross-sectional study
found a positive association of dietary fiber intake with cognitive skills, especially in tasks
involving selective attention and inhibition in children 7-9 years of age (Khan et al. 2015).
2.3.4. Association of dietary simple carbohydrates with cognition in childhood
The impact of simple carbohydrates consumption on cognitive development has been studied
more in terms of their role in causing metabolic dysfunction which indirectly impact the
cognitive functioning. For example, sucrose is the major sweetener in soft drinks which carry the
increased risk of obesity and diabetes (Kanoski & Davidson 2011). Some studies related
cognitive decline such as Alzheimer’s disease in non-diabetic adults with the intake of high GI
carbohydrates (Kanoski & Davidson 2011; Moreira 2013). It is also elaborated in terms of
glucose tolerance because better glucose metabolism and control on blood glucose level showed
better cognitive performances in adult females (Benton & Nabb 2006).
Limited evidence is seen in human studies however, animal studies show a link of
neurodegeneration and cognitive decline with high fructose consumption present in refined sugar
products like sucrose and fructose corn syrup. A cross-sectional study done by Ye et al. (2011) in
middle-aged adults evaluated the association of fructose intake with cognitive impairment. The
results of the study demonstrated no association of fructose consumption from fruits and
vegetables with cognition. But the fructose, sucrose, and glucose from refined sugars
demonstrated lower cognitive scores.
Fructose intake has been linked with higher susceptibility to develop neurocognitive impairment
(Yau et al. 2012, Singh 2006). The exact mechanism is not known however it has been
established in studies on animals that fructose can cause direct impact on neuronal functions
(Funari 2007). Fructose intake resulted in decreased blood flow in most areas of cerebral cortex
(Page et al. 2013) leading to a drop in synaptic connections and their density (Calvo-Ochoa et al.
2014). Direct administration of fructose in rats’ brain induced glucose transporter 5 (GLUT5),
which is known as a specific transport channel for fructose (Kayano et al. 1990, Burant et al.
1992) and increases the uptake of fructose in brain cells unopposed by glucose (Shu et al. 2006,
28
Messier et al. 2007, Payne et al. 1997). Fructose is considered as a cause of the accumulation of
beta amyloid protein in neuronal cells, which has been associated with Alzheimer’s disease
(Mielke et al. 2005, Cao et al. 2007).
In addition to these derangements, animal studies also exhibited the relationship of higher
fructose consumption with increased risk of MetS (Johnson et al. 2007, Agrawal et al. 2012, Rios
et al. 2014). According to Cristernas et al. (2015), inducing MetS in rats by feeding them with
fructose for seven weeks demonstrated decline in cognitive function especially memory and
learning tasks due to deterioration in synaptic structure and function. They also found a reversal
of these impairments by discontinuing the fructose diet.
Sucrose is also linked to impaired cognitive functioning in animal studies. Rats fed with high
sucrose containing diet showed poor spatial memory (D’Hooge et al. 2001), object recognition
memory (Jurdak & Kanarek 2009) and also long-term memory (Jurdak et al. 2008). In addition
to this, it was observed that fasting glucose level was higher in rats fed with sucrose, indicating
metabolic derangements (Jurdak & Kanarek 2009) with the same mechanism as explained in
fructose section above. However, these negative associations of cognitive functioning with higher
intake of fructose or sucrose are not yet shown in humans. A review study done by White and
Wolraich in 1995, refuses the concept of cognitive or behavior alteration and hyperactivity
syndrome in children with higher sugar intake, more specifically with sucrose.
Glucose has been associated with enhancing the memory storage in adults immediately after its
consumption (White & Wolraich 1995, Kennedy & Scholey 2000, Scholey & Kennedy 2004).
The positive impact of breakfast maintains the blood glucose level and thus has been associated
with increased attention, good performance in mathematical and problem-solving, memory and
deductive reasoning tasks among children of 9–11 years age (Pollitt et al. 1981, Conners &
Blouin 1982, Márquez et al. 2001). Studies showed inconsistent results with skipping breakfast in
well-nourished children but malnourished children were found to be more vulnerable to show
decreased cognitive ability on these tasks (Adolphus 2016, Lovino et al. 2016).
29
2.4. Summary of literature
Limited work has been done to find the relation of dietary factors with cognitive functions among
primary-school children. Studies have depicted the positive relationship between diet quality and
cognition in school-aged children. Nutritionally deficient diet in children has been associated
with declined cognitive functioning. SFAs consumption in higher amounts has been related to
poorer working memory. In-adequate or low dietary PUFAs especially omega-3 has been
associated with poor performance in hippocampal-dependent memory tasks. However, the
knowledge about beneficial effects of PUFAs intake is controversial. Meta-analysis of
epidemiological studies, randomized control and supplemental trials of adding omega-3 in the
diet of children demonstrated inconsistent results on improvement of cognitive functions.
A higher intake of simple carbohydrates like fructose and sucrose from refined sugars have been
linked with neurocognitive impairment in animals but this is not yet proved in human studies.
Complex carbohydrates intake has been associated with better memory performance among
children. A positive association of dietary fiber intake with cognitive skills, especially in tasks
involving selective attention and inhibition, was seen in children 7-9 years of age while worse
cognitive outcomes has been observed with decreased intake of fiber among children.
In fact, cognitive functions among children of developed world are prone to be effected by the
inadequate nutritional contents of their diet. Given the importance of diet quality in childhood as
a vital determinant of optimal cognitive development and thus, acquiring academic knowledge
and contributing in future work force for the economic growth of a country, it is very important
to elaborate the role of various nutrients in cognitive development. Therefore, the purpose of this
study was to investigate the relationship of dietary intake of fatty acids and carbohydrates with
cognition in school children aged 6-8 years.
30
3.
RESEARCH QUESTIONS
The aim of current study was to evaluate the associations of dietary intake of fats and
carbohydrates with non-verbal reasoning in 6-8 years old school-aged children. Following
questions were evaluated by using the baseline data obtained from the Physical Activity and
Nutrition in Children (PANIC) study.
1. Are dietary fatty acids (saturated and unsaturated) associated with non-verbal reasoning skills
in school-aged children?
2. Are dietary carbohydrates (glucose, fructose, sucrose, starch, soluble and insoluble fiber)
associated with non-verbal reasoning skills in school-aged children?
31
4.
METHODOLOGY
4.1. Study population and design
The data for this study was derived from Physical Activity and Nutrition in Children (PANIC)
study. It is an ongoing interventional study on controlled diet and physical activity conducted on
the children residing in city of Kuopio, Finland (Clinical trial number NCT01803776). The
Institute of Biomedicine, University of Eastern Finland, Kuopio campus, Finland is primarily
involved in carrying out this study. The baseline sample was recruited in 2007-2009 by sending
invitation letters by mail to the parents of children registered for the first grade in 16 public
primary schools of Kuopio. The non-responding parents were further approached by telephone to
ask for their willingness to participate in the study (PANIC study 2016).
Immigrants, children with severe physical disability and private schools were excluded from the
study. The children were entitled to take part in study if they were registered for the first class,
had no severe disability that could influence the participation in the assessments or in the
intervention, had a custodian who was able to understand and communicate in Finnish to fill out
questionnaires and to participate in the intervention.
Of the 736 invited children, 512 (70%) responded to participate in the baseline study. These
included 248 (48.4%) girls and 264 (51.6%) boys. The participation rate varied between 55 to
87% (data not shown). The recruited study population was compared with register data from the
standard school health examinations done by school nurses on all Finnish children before the 1st
grade started. Baseline data demonstrated that the recruited sample population of children did not
differ in age, sex distribution, or BMI-standard deviation score (BMI-SDS) from all children who
were registered for the first grade in Kuopio during the years 2007–2009 (data not shown).
Six children from the study population were further excluded based dropout during baseline
examination. The remainder 506 children were divided in to control (200 children from 7
schools, 40%) and intervention (306 children from 9 schools, 60%) groups on the basis of the
location (urban or rural) and size (large or small) of schools. These division criteria were
designed to minimize variances in baseline characteristics between intervention and control
groups and also to avoid un-intentional intervention to the controlled group.
32
The ratio of intervention group participants was kept higher than control group to keep the
statistical power sufficient for comparison as large number of drop-outs were expected from
intervention group. As compared to control group, both children and parents in intervention
group were provided with intensive (seven times in two years) life-style and behavioral
counselling based on Finnish nutrition and exercise recommendations. As a part of dietary
intervention, the parents and children were encouraged to participate in cooking clubs supervised
by nutritionist to know about cooking healthy meals and snacks. The children were also offered
to participate in once a week after-school exercise clubs, supervised by trained exercise
instructor. Follow up assessments (similar to baseline) were carried out after two years of
counselling in 2009-2012. The study was continued with less intensive intervention till next
follow up in adolescence (2015-2017) and early adulthood (2020-2022). (PANIC study 2017)
The parents of participating children showed their informed consent in writing. Personal
information of all the participants is kept highly confidential and cannot be disclosed beyond the
study team. The Research Ethics Committee of the Hospital District of Northern Savo, Kuopio
approved the protocol of PANIC study (PANIC study 2017).
The data for current study was taken from the baseline measurements of the PANIC study. The
study follows a cross-sectional study design with no intervention to the study population.
Therefore, the sample population from baseline data was treated as a cohort. The target group of
this study was 6-8 years old primary school going children in the city of Kuopio, Finland. This
study included 487 children on the basis of their complete data on dietary factors and cognition.
The excluded children were having incomplete cognitive data, a lower parental education and
income than the included children (data not shown). We used data on non-verbal skills, dietary
and confounding factors for current evaluation of the association of dietary intake of fats and
carbohydrates with cognition. Figure 5 below is demonstrating briefly the recruited study
population from PANIC study.
33
Total number of children registered
for the 1st grade in schools of Kuopio
in 2007-2009,
n=2663
Children excluded
from study (include
private schools,
immigrants, severe
physical disability)
n=1927
Total number of children invited to
participate in study from 16 public
schools
n=736
Total number of children participated
in study
n=512
(girls=248
boys=264)
Drop out during
baseline examination
n=6
Final study population after baseline
examination
n=506
Current study
population
n= 487
Control group
n=206
Intervention group
n=300
Figure 5. Flow chart of the Physical Activity and Nutrition in Children (PANIC) Study
population and the recruited sample for current study.
34
4.2. Measurements
4.2.1. Food records
To assess the energy, food and nutrient intakes, parents were requested to collect the food records
of their children on four consecutive days including weekdays and weekend (Eloranta 2012).
Parents were individually explained by clinical nutritionist to measure the food and drinks intake
at home by using measuring units like tablespoons, grams and deciliters as well as record the
food eaten outside home by asking their children. A clinical nutritionist contacted the catering
company to know the food menus and recipes served at schools and afternoon day-care as well as
considered the picture booklet of portion sizes to complete the food records (National Public
Health Institute 2006). Total energy and nutrients intake were calculated from the collected food
data by using the Micro Nutrica® dietary analysis software, Version 2.5 (The Social Insurance
Institution of Finland).
4.2.2. Measurements of non-verbal reasoning skills
A trained researcher assessed the non-verbal reasoning skills by using Raven´s Colored
Progressive Matrices (RCPM) score (Raven et al. 1998). RCPM comprises of 36 incomplete
picture puzzles. The children were asked to complete the puzzles by choosing correct part from
six alternatives. RCPM is independent of linguistic skills or learnt knowledge and assesses the
capability to figure out discrete patterns on the basis of similarities and differences (Raven et al.
1998). The RCPM score (0 – 36) was calculated on the basis of correct answers. According to
Diamond (2013), RCPM score reflects all three basic components executive functions shown in
Figure 1.
4.2.3. Other measurements
In the start of PANIC study, a clinical examination of all participants was conducted. Qualified
researchers measured the bare-footed height of children in Frankfurt plane by using a stadiometer
fixed on the wall. In order to calculate accurate body weight, children were asked to fast
overnight. The weight of children was calculated in underwear by using InBody® 720
bioelectrical impedance device to an accuracy of 100 g with empty bladder (Biospace, Seoul,
35
Korea). Similar protocol of light clothing, empty bladder and removing all metallic objects were
incorporated to measure body fat percentage and lean body mass in supine position by using
Lunar dual x-ray absorptiometry (DXA) device (Lunar Prodigy Advance; GE Medical Systems,
Madison, WI, USA) (Eloranta 2012, Tompuri et al. 2015). Parents were asked to fill the
questionnaire about their education and family income.
4.2.4. Statistical methods
The statistical tool SPSS, Version 21.0 (IBM Corp., Armonk, NY, USA) was used to analyze the
data. The Student´s t-test, the Mann-Whitney´s U-test, and the Chi-Square test were applied to
compare the basic characteristics among boys and girls. Then, the associations of dietary intakes
of carbohydrates and fats with non-verbal reasoning were investigated with correlational analyses
and multivariate linear regression models. The data were adjusted for age, sex, body fat,
household income, parental education and daily energy intake in multivariate linear regression
models.
36
5.
RESULTS
5.1. Baseline characteristics
The baseline characteristics of all participants included in the analyses are described in Table 3.
These analyses were conducted on the basis of data collected from 487 children (250 boys, 237
girls) and data were explored separately for both genders. Both groups had a mean age of 7.6
years. Boys were taller, had lower body fat percentage, and came from families with higher
annual income and parental education level as compared to girls. No differences in the RCPM
score were found between boys and girls.
The exploration of dietary variables (Table 4) demonstrated that boys had significantly higher
daily energy intake than girls, and this was contributed by significantly greater consumption of
carbohydrates and fat in boys. The intake of sucrose and starch in boys was significantly higher,
while glucose and fructose ingestion was almost equal in both genders. Similarly, the intake of
insoluble fiber was significantly higher in boys. The intake of insoluble fiber was almost two
times higher than the intake of soluble fiber in both genders. In the same way, fat consumption
exhibited a pattern of higher intake in boys with main difference in the intake of SFA and MUFA
among boys and girls.
37
Table 3. Baseline characteristics of all participants*.
P value†
Variables
All
Boys
Girls
Study Sample(n)
487
250
237
Age (years)
7.6±0.4
7.6±0.4
7.6±0.4
0.23
Height (cm)
128.7±5.6
129.6±5.5
127.8±5.6
<0.01
26.9±5.1
27.2±4.9
26.5±5.1
0.10
18.7±8.2
15.0±8.0
20.5±7.7
<0.01
Weight(kg)
‡
Body fat percentage
Household income (%)
0.02
<30,000€
19.6
17.1
22.3
30,000-60,000€
41.9
39.0
45.0
>60,000€
35.8
40.2
31.1
Parental education (%)
0.01
Vocational school or less
19.2
19.9
18.5
Polytechnic
44.0
37.1
51.3
University degree
35.6
41.4
29.4
23.9±5.1
23.8±5.2
24.1±4.9
RCPM score
0.57
* Data were presented in the form of means ± SD, medians and interquartile range (%) of baseline characteristics of all boys and girls
included in the study using Student’s t test or the Mann–Whitney U test for continuous variables and x2 test for categorical variables.
†P values refer to statistical significance for differences between boys and girls.
‡ Median.
Abbreviation: RCPM score- Raven’s colored progressive matrix score.
38
Table 4. Dietary intake in boys and girls*.
Boys
(n = 250)
1709±310.6
Girls
(n = 237)
1537±284.4
P value†
Daily energy intake (kcal/day)
All
(n = 487)
1625± 310.0
Carbohydrate Intake (g/day)
Total Carbohydrate
Sucrose
Glucose
Fructose
Starch
210.2±45.7
51.2±19.8
11.3±5.0
9.6±4.8
96.2±23.8
219.6±47.6
53.5±21.3
11.7±5.3
9.7±5.2
101.0±25.2
200.3±41.4
48.7±17.7
11.0±4.6
9.5±4.4
91.1±21.0
<0.01
<0.01
0.12
0.75
<0.01
14.2±4.1
3.3±1.0
6.3±2.0
14.6±4.3
3.4±1.1
6.5±2.1
13.8±3.9
3.2±1.0
6.0±1.9
0.04
0.21
<0.01
Variables
Fiber Intake (g/day)
Total fiber
Soluble fiber
Insoluble fiber
<0.01
Fat Intake (g/day)
Total Fat
54.6±14.9
57.8±14.5
51.1±14.5
<0.01
SFA
22.1±7.1
23.4±6.9
20.7±7.1
<0.01
MUFA
18.1±5.1
19.2±5.0
16.9±5.0
<0.01
PUFA
8.9±2.8
9.5±3.0
8.3±2.6
<0.01
Palmitic acid (C16)
9.0±3.1
9.6±3.1
8.4±3.1
<0.01
Stearic acid (C18)
4.1±1.6
4.4±1.6
3.7±1.5
<0.01
LA (C18:2)
6.6±2.4
7.0±2.5
6.1±2.3
<0.01
ALA (C18:3)
1.4±0.5
1.5±0.5
1.3±0.4
<0.01
Arachidonic acid (C20:4)
0.09±0.1
0.1±0.1
0.08±0.1
<0.01
EPA (C20:5 n-3)
0.04±0.1
0.04±0.1
0.03±0.1
0.14
DHA (C22:6 n-6)
0.1±0.1
0.1±0.1
0.1±0.1
0.11
* Data were presented in the form of means ± SD of dietary factors of all boys and girls by using Student’s t test or the Mann–Whitney
U test for continuous variables and x2 test for categorical variables
† P values refer to statistical significance for differences between boys and girls.
39
Abbreviations: SFA-saturated fatty acid, MUFA-monounsaturated fatty acid, PUFA-polyunsaturated fatty acids, EPA,
eicosapentaenoic acid; DHA, docosahexaenoic acid; ALA, alpha-linoleic acid; LA, linoleic acid.
40
5.2. Associations of dietary fatty acids and carbohydrates intake with cognition
There were significant positive correlations of RCPM score with age, parental education and
household income in girls (Table 5). Analyses of dietary factors (Table 5) demonstrated the
significant positive association of cognitive function with dietary intake of fructose, total fiber
and soluble fiber in boys, however girls did not show association of cognition with these dietary
factors. All other variables for carbohydrate (total carbohydrate, glucose, sucrose, starch),
insoluble fiber and fat intake (total fat, SFA, MUFA, PUFA, palmitic acid, stearic acid, LA,
ALA, AA, EPA, DHA) did not show significant correlation with RCPM score in boys or girls.
After adjusting for age, regression analyses (Model 1, Table 6) also demonstrated results in
accordance with correlation analyses. Fructose intake depicted significant positive association
with RCPM score for boys, whereas results for girls showed a non-significant association. Total
fiber and soluble fiber intake in boys were also found to show significant positive correlation. All
other measures of dietary intakes were not associated with RCPM score following adjustment for
covariates. Further adjustment for body fat percentage, household income, parental education and
daily energy intake (Model 2, Table 6) had no effect on these associations.
41
Table 5. The associations of baseline characteristics and dietary intakes with Raven’s colored progressive matrix score. *
Variables
Age (years)
Height (cm)
Weight(kg)
Body fat percentage
Household income (%)
Parental education
Daily Energy Intake (kcal/day)
All Children
(n = 487)
r
p value
0.11
0.01
0.07
0.10
<-0.01
0.97
-0.01
0.75
0.13
<0.01
0.10
0.01
-0.01
0.81
Boys
(n = 250)
R
p value
0.05
0.43
0.04
0.47
-0.03
0.60
-0.04
0.45
0.08
0.21
0.09
0.12
-0.01
0.83
Girls
(n = 237)
r
p value
0.19
<0.01
0.11
0.07
0.03
0.56
<0.01
0.90
0.19
<0.01
0.12
0.04
0.01
0.90
Daily Carbohydrate Intake (g/day)
Total Carbohydrate
Glucose
Sucrose
Fructose
Starch
0.01
0.02
0.02
0.13
0.01
0.74
0.55
0.60
<0.01
0.93
0.02
0.06
-0.01
0.20
<0.01
0.71
0.33
0.88
0.01
0.97
0.01
-0.01
0.76
0.35
0.01
0.77
0.84
0.25
0.59
0.76
Daily Fiber Intake (g/day)
Total fiber
Soluble fiber
Insoluble fiber
0.07
0.06
0.05
0.10
0.18
0.25
0.14
0.13
0.09
0.02
0.03
0.13
-0.01
-0.02
0.01
0.90
0.70
0.92
Daily Fat Intake (g/day)
Total Fat
SFA
MUFA
PUFA
Palmitic acid (C16) (g/day)
Stearic acid (C18)
LA (C18:2)
-0.03
-0.04
-0.02
0.002
-0.03
-0.03
-0.003
0.46
0.33
0.55
0.96
0.50
0.42
0.95
-0.06
-0.07
-0.05
-0.001
0.06
-0.06
-0.01
0.33
0.21
0.43
0.98
0.27
0.30
0.83
0.01
0.01
0.01
0.01
0.02
0.009
0.02
0.89
0.96
0.89
0.77
0.68
0.88
0.73
Table 3 continued
42
All children
Boys
Girls
(n = 487)
(n = 250)
(n = 237)
r
p value
R
p value
r
p value
ALA (C18:3)
0.02
0.60
0.04
0.44
<0.01
0.99
Arachidonic acid (C20:4)
-0.05
0.21
-0.02
0.66
-0.09
0.16
EPA (C20:5 n-3)
0.01
0.72
0.06
0.32
-0.04
0.54
DHA (C22:6 n-6)
0.008
0.86
0.05
0.40
-0.06
0.31
* Bivariate correlation analyses of Raven’s colored progressive matrix score with other variables in study.
Abbreviations: SFA-saturated fatty acid, MUFA-monounsaturated fatty acid, PUFA-polyunsaturated fatty acids, EPA,
eicosapentaenoic acid; DHA, docosahexaenoic acid; ALA, alpha-linoleic acid; LA, linoleic acid.
Variables
43
Table 4. Association of dietary intakes with Raven’s colored progressive matrix (RCPM) score following adjustment of
confounding factors.
All children
Variables
Model 1
Boys
Model 2
Model 1
Girls
Model 2
Model 1
Model 2
Β
P
β
P
β
P
β
P
Β
P
β
P
Daily Carbohydrate
Intake (g/day)
Total Carbohydrate
Glucose
Sucrose
Fructose
Starch
0.19
0.04
0.02
0.13
0.009
0.67
0.37
0.65
<0.01
0.84
0.13
0.05
0.05
0.14
0.01
0.15
0.31
0.42
<0.01
0.81
0.02
0.06
-0.01
0.20
0.002
0.73
0.30
0.85
<0.01
0.97
1.2
0.10
0.00
0.23
0.01
0.21
0.15
0.98
<0.01
0.87
0.01
0.007
0.05
0.53
0.20
0.77
0.91
0.35
0.41
0.75
0.2
-0.02
0.10
0.4
0.1
0.82
0.78
0.23
0.66
0.86
Daily Fiber Intake
(g/day)
Total fiber
Soluble fiber
Insoluble fiber
0.08
0.07
0.06
0.06
0.12
0.18
0.08
0.07
0.03
0.11
0.16
0.48
0.14
0.13
0.09
0.02
0.04
0.13
0.15
0.15
0.08
0.02
0.03
0.21
0.01
0.01
0.01
0.83
0.88
0.79
-0.01
-0.3
-0.2
0.88
0.72
0.68
Daily Fat Intake
(g/day)
Total Fat
SFA
MUFA
PUFA
Palmitic acid (C16)
Stearic acid (C18)
-0.04
-0.05
-0.03
0.001
-0.04
-0.04
0.34
0.22
0.44
0.99
0.35
0.30
-0.09
-0.09
-0.06
0.01
-0.06
-0.06
0.18
0.14
0.32
0.83
0.37
0.32
-0.06
-0.08
-0.05
-0.00
-0.08
-0.06
0.29
0.18
0.39
0.96
0.21
0.27
-1.4
-1.7
-0.9
0.1
-1.3
-1.1
0.14
0.08
0.34
0.86
0.18
0.24
-0.21
-0.02
-0.01
-0.00
-0.00
-0.02
0.75
0.70
0.79
0.98
0.92
0.66
0.1
0.0
0.2
0.3
0.4
-0.0
0.88
0.96
0.83
0.75
0.64
0.96
Table 4 continued
44
All children
Model 1
β
-0.00
0.01
-0.05
P
0.94
0.70
0.23
Boys
Model 2
β
-0.01
0.02
-0.07
P
0.87
0.70
0.15
Model 1
β
-0.01
0.04
-0.02
P
0.83
0.48
0.65
Girls
Model 2
β
-0.0
0.9
-0.5
P
0.94
0.35
0.56
Model 1
Β
0.001
-0.01
-0.08
P
0.98
0.77
0.18
Model 2
β
0.3
-0.3
-1.4
P
0.70
0.71
0.13
LA (C18:2)
ALA (C18:3)
Arachidonic acid
(C20:4)
EPA (C20:5 n-3)
0.02
0.60
0.02
0.64
0.06
0.30
1.3
0.19
-0.02
0.66
-0.5
0.56
DHA (C22:6 n-6)
0.01
0.74
0.01
0.74
0.07
0.26
1.4
0.15
-0.05
0.40
-1.0
0.31
Model 1: Regression analysis of fat and carbohydrate intakes with cognition following adjustment for age.
Model 2: Regression analysis of fat and carbohydrate intakes with cognition following adjustment for age, body fat, household income,
parental education and daily energy intake.
Abbreviations: SFA-saturated fatty acid, MUFA-monounsaturated fatty acid, PUFA-polyunsaturated fatty acids, EPA, eicosapentaenoic
acid; DHA, docosahexaenoic acid; ALA, alpha-linoleic acid; LA, linoleic acid.
45
6.
DISCUSSION
6.1. Overview of the study
Diet during childhood is very important for developing optimum cognitive abilities for future
competition. Therefore, this study was designed to evaluate the impact of different types of
FAs and carbohydrates on the cognitive performance of primary-school children of 6-8 years
old. The dietary data was collected by training the parents the methods of measuring the food
intake, thus improved the assessment of dietary factors.
The ethnic bias was omitted by the fact that all the children were from Finnish families.
Further, the effect of confounders like sex, body fat, household income, parental education
and daily energy intake, was also minimized by adjusting the data in final analyses. The
higher order cognitive function i.e. non-verbal reasoning skills were assessed by using the
RCPM to nullify the impact of acquired knowledge or language skills in the study children.
6.2. Association of dietary fats with cognition
The first objective of this study was to identify the association of dietary FAs with cognition
in school age children of 6-8 years. The results demonstrated that dietary intake of saturated
(SFAs, palmitic acid, stearic acid) or unsaturated fats (MUFAs, PUFAs, linoleic acid, alinolenic acid, arachidonic acid, EPA, DHA) were not associated with cognitive functions in
both genders. One explanation of non-significant results can be attributed to the small
variation in the dietary FAs intake by the study population. This fact is supported by other
studies that the intake of FAs has no correlation with cognitive performance in mainstream
healthy children with no psychological or reading disabilities (Ryan et al. 2010, Jiao et al.
2014, Johnson et al. 2016). On the evaluation of baseline dietary data of PANIC study, it was
observed that the average intake of nutrients among children are close to the Finnish national
recommendations (VRN 2016). For example, as demonstrated in table 4 mean intakes of
SFAs among all children was 22.7±7.1g/day (approx. 10-12% E) while recommended intake
is <20g/day (i.e. <10% E). Similarly, consumption of PUFAs and MUFAs was 8.9±2.8g/day
(5% E) and 18.1±5.1g/day (10% E) respectively, which is in the recommended range of 5-
46
10%E for PUFAs and 10–20 E% for MUFAs given by Finnish national nutrition council
(VRN 2016).
The other reason for null association of FAs intake and cognition can be the more pronounced
impact of dietary FAs on certain cognitive abilities. For example, animal studies on rats
related high FAs diet to more errors in tasks involving spatial working memory (Murray et al.
2009). Spatial working memory is involved in recording information from surrounding
environment and represent both short and long term memory (Kanoskia & Davidson 2011). In
humans, SFAs impaired the tasks dependent on prospective memory (Kanoskia & Davidson
2011) which is involved in remembering a planned action to perform at some point in future
(Ellis 2014). The pronounced impact of dietary FAs on reasoning skills did not show
association in this cross-sectional study. Therefore, to explore the association follow up
studies are needed.
6.3. Association of dietary carbohydrates and cognition
The second objective of this study was to find out association of dietary carbohydrates with
cognition in school aged children of 6-8 years. The results demonstrated the significant
positive correlation of fructose, total fiber and soluble fiber intake with cognitive function in
boys only. These findings were significant even after adjusting the total daily energy intake
along with other confounding factors including age, height, parental education and household
income. However, other carbohydrates such as glucose, sucrose, starch and insoluble fibers
did not show significant association with cognition among both genders.
The significance of the results could be due to the natural source of the fructose, total and
soluble fibers i.e. from fruits and vegetables. Although, the source of nutrients has not been
discussed in this study, yet the data on food record from PANIC study showed that main
sources of fructose and fibers were fruits and vegetables. Similar findings were also reflected
in previous studies, for example intake of fructose obtained from refined sugars was
associated with declined cognitive performance, while that obtained from natural sources like
fruits and vegetables demonstrated positive correlation with cognition (Tucker et al. 2005,
Akbaraly et al. 2007, Ye et al. 2011).
47
Moreover, there is evidence that dietary factors have a stronger association with boys’
cognitive development than that of girls (Isaacs et al.
2008). This is attributed to the
association of diet with structural development of brain i.e. caudate lobe volume, which
demonstrated direct correlation with IQ level in boys only (Isaacs et al. 2008). However, the
literature is limited on the differential susceptibility of male and female cognitive
development to the dietary factors.
The reason for non-significant association of cognitive function with other dietary
carbohydrates such as glucose, sucrose, starch and insoluble fibers is the difference in the
degree of impact of dietary carbohydrates on cognitive abilities. This association was
demonstrated by Papanikolaou et al. (2006) that verbal memory tasks had profound negative
association with high GI carbohydrate meal in adults while little impact was observed on digit
span working memory task. Another study performed on children exhibited lowered working
memory and executive function following intake of high GI meal in contrast to low GI meal
while sustained attention did not show association with the type of carbohydrate consumed
(Benton et al. 2007).
6.4. Strengths and limitations of study
The dietary data was carefully collected, for which parents were trained on the methods of
measuring the food intake by skilled nutritionists. This approach increased the reliability of
dietary data. Another strength of this study was the omission of ethnic bias from data by
selecting all the participant children from Finnish families. The current study is focusing on
non-verbal reasoning skills to assess higher order EFs in primary school-children. These
require minimal interference from examiner, does not depend on linguistic skills and thus
provide better idea about higher order EFs in children.
The higher order cognitive function i.e. non-verbal reasoning skills were assessed by using
the RCPM to nullify the impact of acquired knowledge or language skills in the study
children. RCPM is one of the oldest and most widely used method to assess non-verbal
reasoning skills. It has good reliability (0.65 to 0.94) and moderate validity (0.50-0.85) as
compared to other methods used for assessing non-verbal reasoning skills (compared in table
48
1). Further, the effect of confounders like sex, body fat, household income, parental education
and daily energy intake, was also minimized by adjusting the data in final analyses.
The study has limitation because of its cross-sectional design which cannot assess the
causality of the relationship between dietary factors and cognition. Secondly, dietary data also
had some limitations. Although full record of food eaten at home and outside was obtained,
but the dietary intake was recorded only once on four days, that cannot reflect the habitual
diet of the study group. In addition to this, the diet earlier in life was not recorded, which is
also an important factor in determining cognitive functioning.
Another limitation is the assessment of cognitive function by RCPM test only. However, it
omits the need of acquired knowledge or language skills and is considered reliable for this
age. However, the validity of the test is being complicated by the complexity of cognitive
interactions involved in higher executive functioning. This complexity makes it difficult to
arrange the items in tasks to assess the different problem-solving approaches (Guthke 1972,
Carlson & Wiedl 1979).
49
7.
CONCLUSION
Childhood has elaborated importance in cognitive development therefore, diet during this
phase of life play vital role in achieving optimum growth of neural tissues. The objective of
this study was to evaluate the association of cognitive functions with dietary fats and
carbohydrates in school children of 6-8 years old. Therefore, the association of average intake
of the daily diet of children with reasoning skills was analyzed.
The results demonstrated the significant direct association of higher intake of fructose, total
fiber and soluble fiber with better cognitive function in boys only. However, girls did not
show association with any of these dietary factors and cognition. On the other hand, no
correlation was seen among cognitive function and the intake of other carbohydrates (glucose,
sucrose, starch), insoluble fibers or any type of fats in both genders.
Concluding all, dietary fats are not associated with cognitive function in school-aged children.
However, the intake of fructose, total fiber, and soluble fiber is associated with higher
cognitive functioning in school-aged boys. The current study is contributing to the existing
literature, whereas longitudinal study needed to evaluate the role of dietary carbohydrates and
fats in cognitive function of school-aged children. as well as to find the impact of gender on
the association of cognition with dietary factors.
50
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