THE GENETICS OF OBESITY AND EATING DISORDERS
GENETICS OF NUTIRENT CONSUMPTION AND AN EVOLUTIONARY
PERSPECTIVE OF EATING DISORDERS
By ALEXANDRA JEAN MAYHEW, B.A.SC
A Thesis submitted to the School of Graduate studies in partial fulfilment of the
requirements for the degree Master of Science
McMaster University © Copyright by Alexandra Jean Mayhew, July 2014
McMaster University MASTER OF SCIENCE (2014) Hamilton, Ontario (Health
Research Methodology)
TITLE: Genetics of Nutrient Consumption and an Evolutionary Perspective of Eating
Disorders
AUTHOR: Alexandra Jean Mayhew, B.A.Sc (University of Guelph)
SUPERVISOR: Dr. David Meyre
NUMBER OF PAGES: 146
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ABSTRACT
GENETICS OF NUTRIENT CONSUMPTION AND AN EVOLUTIONARY
PERSPECTIVE OF EATING DISORDERS
Alexandra Jean Mayhew
McMaster University, 2014
Advisor: Dr. David Meyre
Committee Member: Dr. Sonia Anand
Committee Member: Dr. Andrew Mente
Obesity prevalence continues to increase worldwide, yet few safe and effective
treatment options are available suggesting there needs to be a greater emphasis on
preventing rather than treating obesity. This research investigated the association of
obesity predisposing SNPs and a gene score with nutrient consumption patterns including
total energy intake and macronutrient distribution in a European ancestry population as
well as discussing an evolutionary perspective on eating disorders using current
epidemiological evidence to identify genes which may be involved. The association of
two of the 14 obesity predisposing SNPs and the gene score with BMI was confirmed in
the EpiDREAM population. Novel associations between two SNPs located in or near
BDNF (rs6265 and rs1401635) were found with total fat, MUFA, and PUFA intake.
Rs1401635 was also associated with total energy and trans fat intake. Novel associations
of rs6235 (PCSK1) and the gene score were found with total energy intake. The novel
associations found indicate that food related behaviours are one of the mechanisms of
action through which obesity predisposing SNPs cause obesity and therefore warrant
further investigation. The lack of association among all genes and the modest association
of the gene score show that mechanisms other than food consumption are important. The
investigation of the evolutionary history of eating disorders revealed that the adapted to
flee famine hypothesis is a plausible theory explaining anorexia nervosa while the thrifty
genotype hypothesis provides a possible explanation for bulimia nervosa and binge eating
disorder. These evolutionary theories can be applied to identify new candidate genes as
well as phenotypic traits to investigate to better understand the genetic architecture of
eating disorders. Understanding genes associated with disordered eating patterns may
highlight future areas for obesity prevention.
Key words: obesity, polygenic obesity, BMI, SNP, gene score, energy intake,
macronutrient, eating disorders, anorexia nervosa, bulimia nervosa, binge eating disorder,
thrifty genotype hypothesis, adapted to flee famine hypothesis.
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ACKNOWLEDGEMENTS
I would like to begin by thanking my advisor, Dr. David Meyre for his continued
support and guidance throughout the duration of my Masters. His passion for
understanding the genetics of obesity, dedication to his students, and willingness to teach
made this thesis a reality. My research also benefited greatly from my committee
members Drs. Sonia Anand and Andrew Mente who provided exceptional suggestions for
improving this thesis. A special thank you goes out to my peers and additional mentors,
Russell de Souza, Aihua Li, and Sébastien Robiou-du-Pont, who played important roles
in developing my skills and understanding.
I thank my parents, John and Rose Mayhew for encouraging me from day one to
pursue a career in academia and providing support in countless ways over the many years
of school. I thank my friends Lindsay Morris, Claudia Chan, Jill Patrick, Victor and Tori
Da Silva Sa, Allison Davis, Chris Bonanno, Florence Wilson, Ray Flores, Matt and Julia
Stoop, and Nicole Seymour for boosting my spirits when overwhelmed and for their
continued friendship. Lastly, I thank my partner Alexander Jensen for providing the
support necessary to push forward during the most challenging times through his endless
patience and unwavering belief in my abilities.
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TABLE OF CONTENTS
Title Page…………………………………………………………………………………..i
Descriptive note…………………………………………………………………………...ii
Abstract…………………………………………………………………………………...iii
Acknowledgements……………………………………………………………………….iv
Table of contents…………………………………………………………………………..v
List of figures and tables………………………………………………………………..viii
List of abbreviations……………………………………………………………………...xi
Declaration of academic achievement…………………………………………………...xii
1.0
Introduction .............................................................................................................. 1
2.0
Literature Review..................................................................................................... 4
2.1 Heritability of obesity ............................................................................................... 4
2.2 Heritability of eating characteristics ......................................................................... 7
2.2.1 Energy consumption and macronutrient distribution ......................................... 7
2.2.2 Food consumption patterns ................................................................................ 9
2.2.3 Food consumption behaviours ......................................................................... 10
2.2.4 Conclusions ...................................................................................................... 12
2.3 Current state of knowledge of the genetics of obesity ............................................ 13
2.3.1 Monogenic obesity ........................................................................................... 13
2.3.2 Polygenic forms of obesity .............................................................................. 16
2.4 Current state of knowledge of the genetics of eating behaviour ............................. 20
2.5 Background of eating disorders .............................................................................. 23
2.5.1 Diagnostic criteria ............................................................................................ 23
2.5.2 Symptoms, comorbidities, and mortality of eating disorders .......................... 24
2.5.3 Treatment ......................................................................................................... 25
2.5.4 Risk factors for eating disorders ...................................................................... 27
2.6 Heritability of eating disorders ............................................................................... 39
2.7 Genetics of eating disorders .................................................................................... 40
v
2.7.1 Serotonin .......................................................................................................... 41
2.7.2 Catecholamine pathway ................................................................................... 44
2.7.3 Norephinephrine .............................................................................................. 47
2.7.4 Neuropeptides and Feed Regulations............................................................... 48
2.7.5 Summary .......................................................................................................... 55
3.0 Justification and Objectives ........................................................................................ 56
4.0 The Association of Obesity SNPs with Food Consumption Patterns ......................... 59
4.1 Introduction ............................................................................................................. 59
4.2 Methods................................................................................................................... 62
4.2.1 Participants ....................................................................................................... 62
4.2.2 Phenotyping ..................................................................................................... 64
4.2.3 Genotyping ....................................................................................................... 64
4.2.4 Statistical methods ........................................................................................... 65
4.3 Results ..................................................................................................................... 68
4.3.1 Association of obesity predisposing SNPs and genotype score with BMI ...... 70
4.3.2 Association of obesity predisposing SNPs and genotype score with dietary
intake parameters ...................................................................................................... 73
4.4 Discussion ........................................................................................................... 73
4.5 Conclusions ............................................................................................................. 78
5.0 An Evolutionary Perspective on Eating Disorders ..................................................... 79
5.1 Introduction ............................................................................................................. 79
5.2 Suppression of reproduction and sexual competition ............................................. 79
5.3 Adapted to flee famine hypothesis and rogue hibernation...................................... 81
5.4 Thrifty gene hypothesis........................................................................................... 84
5.5 Summary of evolutionary theories .......................................................................... 89
5.6 Coexistence of Anorexia Nervosa and Bulimia Nervosa in binge/purge subtype
Anorexia Nervosa ......................................................................................................... 92
5.6.1 Binge eating as protection against Anorexia Nervosa ..................................... 92
5.6.2 Accumulation of independent genes leading to binge eating and Anorexia
Nervosa ..................................................................................................................... 93
5.6.3 Mutations/structural gene variants with opposite effects in the same gene lead
to AN or BN .............................................................................................................. 94
vi
5.6.4 Mutations in nearby genes result in partial linkage disequilibrium of Anorexia
Nervosa and binge eating traits ................................................................................. 95
5.6.5 Summary of the role of genetics in the coexistence of Anorexia Nervosa and
Bulimia Nervosa in binge/purge subtype Anorexia Nervosa ................................... 96
5.7 Conclusions ............................................................................................................. 97
6.0 Ethical Considerations .............................................................................................. 100
7.0 Conclusions and Future Work .................................................................................. 103
8.0 References ................................................................................................................. 109
vii
LIST OF FIGURES AND TABLES
FIGURES
Figure 1. The role of monogenic obesity genes in the melanocortin pathway………….15
Figure 2. EpiDREAM participant flow chart……………………………………………63
Figure 3. Statistical power for detecting associations between individual SNPs and BMI
according to allele frequency and beta-coefficients with a sample size of 1,850
participants……………………………………………………………………………….67
Figure 4. Framework for the risk of developing an eating disorder based on genotypic
category and presence or absence of pressure to be thin………………………………...99
TABLES
Table 1. Gene symbols and proteins coded……………………………………………...14
Table 2. Genotypic information for obesity predisposing SNPs………………………...65
Table 3. EpiDREAM Participant Characteristics………………………………………..68
Table 4. Pearson correlations for unadjusted nutrient intakes....………………………...69
Table 5. Association of energy and energy adjusted nutrients with BMI……………….69
Table 6. The association of obesity predisposing SNPs with BMI and energy adjusted
nutrients…………………………………………………………………………..71
viii
LIST OF ABBREVIATIONS AND SYMBOLS
5-HIAA – 5-hydroxyindoleacetic acid
5-HT – Serotonin
5-HT2A – 5-hydroxytryptamine receptor 2A
5-HTTLPR – Serotonin-transporter-linked polymorphic region
AFFH – Adapted to flee famine hypothesis
AGRP – Agouti related peptide
AN – Anorexia Nervosa
AN(BP) – Anorexia Nervosa binge/purge subtype
AN(R) – Anorexia Nervosa restrictive subtype
BDNF – Brain-derived neurotrophic factor
BED – Binge eating disorder
BMI – Body mass index (kg/m2)
BN – Bulimia nervosa
CBT – Cognitive behaviour therapy
COMT – catecholamine-O-methyltransferase
CSF – Cerebrospinal fluid
CVD – Cardiovascular disease
D2 – Dopamine receptor 2
D3 – Dopamine receptor 3
D4 – Dopamine receptor 4
DAT1 – Dopamine transporter 1
DNA – Deoxyribonucleic acid
DRD2 – Dopamine receptor 2D
DRD3 – Dopamine receptor 3D
DRD4 – Dopamine receptor 4D
DSM – Diagnostic and Statistical Manual of Mental Disorders
ix
DZ – Dizygotic twins
FFQ – Food frequency questionnaire
FTO – Fat mass and obesity associated gene
GWAS – Genome wide association study
HEW – Hardy-Weinberg equilibrium
HOXB5 – homebox protein
HVA – Homovanillic acid
IBC – ITMAT Broad Care
KCTD14 – Potassium channel tetramerization domain containing 14
LEP – Leptin
LEPR – Leptin receptor
LOD – Logarithm of odds
MAOA – Monoamine oxidase A
MC3R – Melanocortin 3 receptor
MC4R – Melanocortin 4 receptor
MISTRA – Minnesota Study of Twins Reared Apart
MRAP2 – Melanocortin 2 receptor accessory protein 2
MUFA – Monounsaturated fatty acid
MZ – Monozygotic twins
NEGR1 – Neuronal growth regulator 1
NET – Norepinephrine transporter
NHLBI – National Heart, Lung, and Blood Institute
NIMH – National Institute of Mental Health
NMDAr – Glutamate receptor
NPL – Nonparametric linkage
NPY – Neuropeptide Y
NSAL – National Survey of American Life
NTRK2 – Neurotrophic tyrosine kinase receptor type 2 gene
x
OGTT – Oral glucose tolerance test
OLFM4 – Olfactomedin
OPRD1 – Opioid delta receptor
PCSK1 – Prohormone convertase 1
PET – Positron emission tomography
POMC - Proopiomelanocortin
PUFA – Polyunsaturated fatty acid
PVN – Paraventricular nucleus
PYY – Peptide YY
RCT – Randomized controlled trial
RNA – Ribonucleic acid
SH2B1 – SH2B adaptor protein 1
SHARE – Study of Health Assessment and Risk Evaluation
SIM1 – Single-minded homolog 1
SNPs – Single nucleotide polymorphisms
SPSS – Statistical Package for the Social Sciences
TPH – Tryptophan hydroxylase
xi
DECLARATION OF ACADEMIC ACHIEVEMENT
My supervisor Dr. David Meyre and myself developed the plans of analysis for the
association of obesity predisposing SNPs with dietary consumption patterns with
feedback from my committee members, Dr. Sonia Anand and Dr. Andrew Mente. The
data used had previously been collected for the EpiDREAM study and the principle
investigator, Dr. Hertzel Gerstein provided comments on the proposal. I performed the
data cleaning and all statistical analysis for the project. Dr. Meyre and I conceptualized
the paper on the evolutionary history of eating disorders together with all data collection
and writing being performed by myself.
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
1.0 Introduction
Obesity is caused by a positive energy balance in which more calories are consumed
than expended per day, leading to excessive fat accumulation in adipose tissue. Body mass
index (BMI) is typically used to determine obesity at a population level. Using
predetermined BMI categories, individuals under 18.5kg/m2 are underweight, 18.5 to 24.9
kg/m2 are normal weight, 25.0 to 29.9 kg/m2 are overweight and anyone over 30 kg/m2 is
obese (World Health Organization). The studies used to determine the current cut off points
for weight categories were conducted in European populations. Recent evidence from a
multiethnic study showed that the cut-off point for obesity should be approximately 6kg/m2
lower in non-European populations to more accurately reflect the metabolic risk associated
with weight such as blood pressure, glucose metabolism, and lipid metabolism (Razak et
al. 2007), supporting the need for the development and use of ethnic specific BMI
categories. Additionally, the prevalence of obesity has been increasing globally. In 2008,
it was estimated that 1.46 billion adults worldwide were overweight or obese with average
BMI increasing by 0.4 to 0.5kg/m2 per decade from the 1980s (Finucane et al. 2011). The
greatest burden of disease is in Western countries. The United States has the highest
percentage of obese adults (Finucane et al. 2011). However, developing countries are
experiencing the greatest increases in incidence of obesity and in the future will be the
greatest contributors to the obesity epidemic (Finucane et al. 2011).
Obesity is associated with many health complications including type 2 diabetes,
cardiovascular disease (CVD), fatty liver disease, cancer, obstructive sleep apnea,
breathing difficulties, and musculoskeletal disability and pain (Schelbert 2009). These
complications contribute to severe forms of obesity decreasing life expectancy by 5 to 20
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
years (Fontaine et al. 2003). In addition to affecting health, obesity is also an economic
burden. In the United States, the annual healthcare cost of obesity is estimated to be $56
billion with a positive association between BMI and per capita expenditure. Though
difficult to measure, this number is much higher when considering the indirect costs of
obesity, such as absenteeism, disability, and premature death (Lehnert et al. 2013). The
treatments available for obese patients vary based on the degree of obesity and associated
co-morbidities as well as the potential side effects of the therapeutic options. Interventions
range from lifestyle modifications involving food restriction and increased energy
expenditure through physical activity for those with mild obesity, to pharmacotherapy
options for moderate obesity, and surgical interventions for those with the most extreme
forms of obesity (Gray et al. 2012; Wyatt 2013). Despite the availability of diverse
treatment options the effect of lifestyle interventions is modest and the more effective
pharmacotherapy and surgical options are associated with serious complications. The lack
of adequate treatments indicates that prevention may be the best strategy for curbing the
obesity epidemic (Gortmaker et al. 2011).
One of the barriers to obesity prevention is the abundance of contributing causes of the
disease. The availability of low cost, calorically dense foods and less physical activity
demands are often cited as the two biggest contributors to the rising prevalence of obesity
(McAllister et al. 2009), particularly in developing countries which are increasingly
influenced by Western culture through globalization (Malik et al. 2013). While energy
balance is the most cited cause of obesity, other risk factors such as infections, epigenetic
effects, maternal age, assortative mating, reproductive fitness, sleep debt, endocrine
disruptors, pharmaceutical side effects, ambient temperature, and intrauterine and
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
intergenerational effects have at minimum epidemiological evidence supporting their
association with obesity (McAllister et al. 2009). Obesity risk is also partially attributable
to genetic factors. Family and twin studies have established that 50 to 80% of the interindividual variation in predisposition to obesity is because of genetic factors (Choquet and
Meyre 2011b). In an effort to determine how to better prevent obesity, genome wide
association studies (GWAS) and candidate gene studies have identified close to 70 loci
associated with obesity-related traits. Despite the identification of obesity predisposing
single nucleotide polymorphisms (SNPs), the underlying mechanisms are poorly
understood. Food behaviors such as energy consumption, macronutrient consumption
distribution, food preferences, and satiety responsiveness have been investigated as
possible mechanisms with some evidence of association with obesity promoting genes
(Breen et al. 2006; Hasselbalch et al. 2008; Llewellyn et al. 2012; Cornelis et al. 2014). In
addition to exploring the association of already known obesity promoting genes with food
consumption parameters, examining the evolutionary history of obesity and other
disordered eating patterns may also shed light on possible mechanisms.
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
2.0 Literature Review
2.1 Heritability of obesity
To determine the heritability of traits or diseases, including obesity, four
contributing factors that lead to the development of the phenotype are quantified
(Boomsma et al. 2002). These factors and their symbols include (Rijsdijk and Sham 2002):

A: Additive genetic influences which are calculated as the sum of the effect of each
allele at all loci which influence the phenotype

C: Shared environmental factors which are influences that would be common to
members of a family such as socioeconomic status

D: Non-additive genetic influences which include the interaction between alleles at
the same locus (dominance) or at different loci (epistasis)

E: Unique environmental influences such as differences in parental treatment,
prenatal environmental, and life events
Twin studies are a frequently used method to determine heritability taking advantage
of the nearly 100% shared genetic data for monozygotic (MZ) twins. Theoretically, MZ
pairs do not differ from one another genetically (A and D) and also have the same shared
environmental factors (C), therefore the difference in phenotypic traits can be attributed to
unique environmental influences (E) (Rijsdijk & Sham, 2002). Like other sibling pairs
dizygotic twins (DZ) only share 50% of their genetic data, however are assumed to have
the same shared environment (C) and for the individual environment (E) to vary to the
same extent as is observed in MZ pairs (Rijsdijk & Sham, 2002). Using the assumption
that the unique environment (E) contributes to the development of the phentoype of interest
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
equally in MZ and DZ pairs, comparing the correlation of a phenotype in MZ pairs versus
DZ pairs allows for an estimate of heritability as the difference in correlation should be
attributable to genetics, both additive and non-additive effects (Boomsma et al. 2002). The
most straightforward method of calculating heritability is Falconer’s formula which
subtracts the phenotypic correlation in DZ pairs from the phenotypic correlation in MZ
pairs and multiplies the result by two [H2= 2(rMZ – 2DZ)] (Rijsdijk & Sham, 2002). Family
studies are also used to determine the heritability of phenotypes. Genotypic data can be
collected using either genetic marker data or estimates of the expected genetic relatedness
based upon the relationship between the two individuals. Creating a regression model of
the genetic relatedness and the phenotypic trait of interest allows for an estimate of
heritability (Vinkhuyzen et al. 2014). Modelling can also be used in twin studies, however
models are poorly equipped to determine the non-additive genetic component (D) because
non-additive genetic factors are confounded with shared environment and therefore cannot
be estimated in the same model (Elks et al. 2012). Classic twin studies also have some
limitations. The assumptions that gene-environment correlations and interactions are
minimal as well as assuming that the amount of variance explained by environmental
factors is identical in MZ and DZ pairs are questioned (Rijsdijk and Sham 2002). Making
these assumptions increases the risk that heritability estimates are artificially inflated by
incorrectly attributing the contribution of these factors to genetics (Manolio et al. 2009).
The study design can have a substantial impact on heritability estimates. A recent metaanalysis of studies investigating BMI heritability found that in 88 independent twin studies
(n=140,525), heritability estimates ranged from 0.47 to 0.90 while the estimates from 27
family studies (n=42,968) were lower, ranging from 0.24 to 0.81 (Elks et al. 2012). Another
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
meta-analysis estimated the heritability to be 0.20 to 0.80 using data from family studies
and 0.20 to 0.60 from adoption studies (Maes et al. 1997). Though the assumption that the
amount of variance explained by environmental factors is the same in MZ and DZ pairs
has been criticized for inflating heritability estimates (Rijsdijk and Sham 2002), a study
conducted in 93 MZ pairs raised separately found that the intra-pair correlation for BMI in
MZ pairs reared apart was 0.70 for men and 0.66 for females. For MZ pairs reared apart,
there is no shared environment (C) and the variance explained by unique environmental
influences can be correctly assumed to be appropriately equal in the separated twins. In
this unique situation, the intra-pair correlations are estimates of the amount of the variance
explained by only genetic factors. The same study used a classical twin design with MZ
(n=154) and DZ (n=208) pairs to provide a heritability estimate of 0.82 for males and 0.78
for females (Stunkard et al. 1990). The difference in heritability estimates indicates that
the classic twin design may have provided inflated heritability estimates due to
confounding from gene-environment correlations and interactions (Rijsdijk and Sham
2002). The study supplemented their results by using maximum-likelihood model fitting
analyses yielding heritability estimates of 0.74 for males and 0.69 for females. The
similarities between these estimates and those provided through the twins reared apart
support the use of modeling techniques for estimated heritability. The study was also the
first to show that shared environment has a very modest effect on the variation in BMI
(Stunkard et al. 1990).
The broad range of heritability estimates for BMI can in part be explained by different
study methodologies including family studies and twin studies, different statistical
procedures such as using Falconer’s formula versus various modeling techniques, errors in
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
phenotyping, or the incorrect classification of MZ and DZ pairs. From the most recent
meta-analysis, the median heritability estimate from twin studies was 0.75 and 0.46 for
family studies (Elks et al. 2012) indicating that that genes explain a moderate to high degree
of the variability in obesity. Heritability estimates for BMI are also age-dependent,
increasing with age and reaching a plateau at young adulthood (Silventoinen and Kaprio
2009; Silventoinen et al. 2010; Elks et al. 2012). Based on this information, exploration
into the specific genes that may be contributing to obesity risk is warranted.
2.2 Heritability of eating characteristics
Increased total energy intake is a well-established risk factor for obesity (McAllister
et al. 2009), however many other eating behaviours are similarly found to be associated
with body weight or increased caloric consumption. Strong epidemiological evidence
supports a positive association between dietary fat consumption and weight (Bray et al.
2004) and a negative association between dietary carbohydrate consumption and weight
(Gaesser 2007). In both adult and adolescent populations there is a growing body of
evidence that food responsiveness including liking of food and satiety responsiveness,
eating in the absence of hunger, disinhibition, and impulsivity/self-control are associated
with BMI and overall food/energy intake (French et al. 2012). Because of the association
of these eating characteristics with weight the same techniques used to determine the
heritability of obesity have also been used to determine the hereditably of food
consumption behaviour.
2.2.1 Energy consumption and macronutrient distribution
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
A strong positive association between energy intake and weight as well as the
difference in energy density of macronutrients have led researchers to investigate if
consumption levels are a heritable trait. A study conducted in twin pairs from the Quebec
Newborn Twin Study (n=379) (Dubois et al. 2013) found that the heritability of mean
energy intake measured in kilocalories (kcal) is 0.42 (95% confidence interval 0.31 to
0.53), while the heritability of intake in grams of protein, fat, and carbohydrates is 0.35
(0.24 to 0.47), 0.34 (0.22 to 0.46), and 0.42 (0.31 to 0.53) respectively. The study also
investigated the percentage of total daily calories from each macronutrient with only the
percentage of calories from fat having significant heritability with an estimate of 0.28 (0.16
to 0.40). Studies conducted in adult populations including twin pairs (n=600) from the
Danish Twin Registry, (Hasselbalch et al. 2008), twins and non-related individuals from
the Minnesota Study of Twins Reared Apart (MISTRA) (n=335) (Hur et al. 1998), and
families from the San Antonio Family Heart Study (n=1431) (Mitchell et al. 2003; Cai et
al. 2004) have come to similar conclusions. The Danish Twin Registry (Hasselbalch et al.
2008) produced heritability estimates for the additive genetic effect of 0.38 (0.24 to 0.51)
in males and 0.32 (0.12 to 0.48) in females for total energy consumption, 0.28 (0.12 to
0.43) in males and 0.01 (0.00 to 0.39) in females for percentage of calories from protein,
0.36 (0.22 to 0.49) in males and 0.49 (0.35 to 0.61) in females for percentage of calories
from carbohydrates, and 0.01 (0.00 to 0.45) in males and 0.01 (0.00 to 0.57) from
percentage of calories in fat. For the variables in which low heritability was found for
additive genetic effects, there was a moderate to strong contribution of non-additive genetic
effects (Hasselbalch et al. 2008). In MISTRA (Hur et al. 1998) the additive effects of
genetics accounted for an average of 0.22 of the variability of nutrients investigated
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
including 0.32 (0.10 to 0.51) for caloric consumption, 0.40 (0.20 to 0.57) for calories/kg
consumed, 0.16 (0.00 to 0.37) for protein in grams, 0.35 (0.14 to 0.52) for fat in grams, and
0.25 (0.03 to 0.45) of carbohydrates in grams. In the San Antonio Family Heart Study
(Mitchell et al. 2003; Cai et al. 2004), the heritability estimates were considerably lower,
ranging from 0.09 to 0.21 (Cai et al. 2004) for total energy intake and grams of nutrients
consumed per day, and between 0.13 to 0.26 for the percentage of total calories coming
from macronutrients (Mitchell et al. 2003). A potential cause of the lower heritability
estimates from the San Antonio Family Heart Study compared to the other studies is that
they used related individuals opposed to twins requiring different statistical methods to be
employed to estimate heritability. Dietary estimation methods used in these studies such as
food frequency questionnaires (FFQs) and 24 hour recalls also have a significant amount
of error associated with them which could also lead to different estimates.
2.2.2 Food consumption patterns
In addition to assessing the heritability of the consumption of energy and
macronutrients, studies have also investigated if certain dietary pattern types, for example
diets high in fruits and vegetables, are heritable. In children, there is evidence that the types
of food most commonly consumed are heritable. The MacArthur Longitudinal Study of
Twins (n=792) found that using a 24 hour dietary recall, seven food categories investigated
had significant heritability estimates, including an estimate of 0.79 for the consumption of
peanut butter and jelly in males and 0.56 for the consumption of fish and lemons in females
(Faith et al. 2008). Using factor analysis on 77 food items, Breen et al (2006) found four
categories, dessert foods, moderate for vegetables, moderate for fruits, and high liking for
protein foods with heritability estimates of 0.20 (0.04 to 0.38), 0.37 (0.20 to 0.58), 0.51
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
(0.37 to 0.68), and 0.78 (0.63 to 0.92) respectively. In adult female twins from the United
Kingdom (n=3262), five diet patterns, high fruit and vegetable, high alcohol, traditional
English, dieting, and low meat accounted for 22% of the variance in dietary patterns with
heritability estimates ranging from 0.41 to 0.48 (Teucher et al. 2007). In adult Finnish twin
pairs (n=2009) factor analysis revealed four dietary patterns, healthy foods, high-fat foods,
sweet foods, and meat with heritability estimates of 0.49 (0.40 to 0.56), 0.44 (0.34 to 0.53),
0.42 (0.32 to 0.51), and 0.39 (0.30 to 0.48) in males, and 0.54 (0.47 to 0.60), 0.47 (0.39 to
0.53), 0.43 (0.36 to 0.50), and 0.44 (0.36 to 0.51) in females respectively (Keskitalo et al.
2008a). Results from 4640 male and female twins in the United States aged 50 and over
found two dietary patterns, items high in fat, salt, and sugar, and the other which was
described as a healthy diet. The heritability estimate for the unhealthy diet pattern was 0.30
and 0.40 for the healthy diet pattern (van den Bree et al. 1999). The results from these
studies indicate that in both children and adults, a moderate to high amount of the variance
in food types consumed can be attributed to genetics.
2.2.3 Food consumption behaviours
The heritability of other food consumption behaviours thought to be associated with
obesity have also been investigated. Neophobia, a fear of trying new foods, has been
studied in children and adults. It is proposed that previously, neophobia motivated humans
to eat familiar foods to avoid consuming new items which may not be safe. Between 20
and 30% of children are estimated to be neophobic with the trait being associated with
decreased consumption of fruits, vegetables, and protein foods (Wardle and Cooke 2008).
In two independent twin studies conducted in pediatric populations using modeling
techniques, heritability estimates of 0.72 and 0.78 were produced (Cooke et al. 2007; Faith
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et al. 2013). Similar estimates were found in young adults, 0.61 in females, (Knaapila et al.
2011) and in adult Finnish and British twins, 0.66 (Knaapila et al. 2007). Shared
environment did not account for any of the variability in neophobia in any study except for
males from the study by Knaapila et al. (2011), in which shared environment was estimated
to account for 45% of the variability.
Studies have also found that in twin adolescents (n=254), the number of bites taken
per minute is associated with BMI as well as hereditable with an estimate of 0.62 (0.45 to
0.74) (Llewellyn et al. 2008). In the Gemini Twin Study (n=4804) at three months of age,
enjoyment of food, food responsiveness, slowness in eating, satiety responsiveness, and
appetite size were all associated with weight with slowness in eating, satiety
responsiveness, and appetite size having heritability estimates of 0.42 (0.26 to 0.54), 0.45
(0.32 to 0.56), and 0.41 (0.28 to 0.52) respectively (Llewellyn et al. 2012). The Gemini
Study had previously looked at the same population prior to three months of age and found
higher heritability estimates for slowness in eating, satiety responsiveness, and appetite
size (Llewellyn et al. 2010) indicating that the relative influence of genetics on these
phenotypes may change over time. In older children, satiety responsiveness has been
estimated to have a heritability of 0.63 (0.39 to 0.81) and 0.75 (0.52 to 0.85) for food cue
responsiveness (Carnell et al. 2008), and 0.51 (+/- 0.10) for eating in the absence of hunger
(Fisher et al. 2007).
In adult populations, the Three Factor Eating Questionnaire has been used to
determine the heritability of cognitive restraint, uncontrolled eating, and emotional eating.
In male and female Finnish and British twins, heritability estimates were 0.26 to 0.63 for
cognitive restraint, 0.45 to 0.69 for uncontrolled eating, and 0.09 to 0.45 for emotional
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eating when divided by sex and ethnicity (Keskitalo et al. 2008b). In another study of adult
twins (n=110), the same questionnaire provided heritability estimates of 0.44 for cognitive
restraint, 0.24 for perceived hunger, and 0.58 for restraint score scales (de Castro and
Lilenfeld 2005). A Swedish twin study (n=782 pairs) found heritability estimates of 0.59
(0.52 to 0.66) for cognitive restraint, 0.60 (0.52 to 0.66) for emotional eating and 0.45 (0.36
to 0.53) for uncontrolled eating (Tholin et al. 2005). Number of meals consumed per day
is also hereditable with an estimate of 0.44 in adult twin pairs (de Castro 1993). Despite
the variation in heritability estimates which could be due to sampling effects, different
ethnicities, differences in measuring phenotypes, the effect of age, and different statistical
methods, there is strong evidence that food consumption behaviour is at least moderately
hereditable.
2.2.4 Conclusions
In both children and adults there is a growing body of evidence that a higher intake
of calories, the total amount and relative intake of macronutrients including protein, fat,
and carbohydrates, food consumption patterns such as eating more high fat foods, more
fruits and vegetables, or high protein diets, as well as eating behaviours are moderately to
highly heritable traits. At this point in time, not enough studies have been conducted to
determine if heritability changes across the lifespan, what the effect of sex is, and how it
might change between cultures and ethnicities. Interestingly, most of the studies concluded
that the shared environment element (C), did not significantly contribute to the variation in
phenotype with the individual environment playing a much larger role.
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2.3 Current state of knowledge of the genetics of obesity
2.3.1 Monogenic obesity
To date, the investigation of monogenic forms of obesity have provided the
majority of the information regarding the genetics of obesity. Monogenic forms of obesity
include both syndromic obesity which is associated with specific clinical phenotypes in
addition to obesity, often including mild to severe cognitive deficits and unusual behaviour
(Chung 2012) as well as non-syndromic obesity which is characterized by early-onset,
severe obesity with extreme hyperphagia resulting from a single gene disorder (Choquet
and Meyre 2011b). There are over 30 types of syndromic obesity (Choquet and Meyre
2010), however the mechanisms through which the genetic mutations effect energy
homeostasis are not understood in all cases (Chung 2012). Prader Willi syndrome is the
most common form of syndromic obesity affecting 1 in 15 000 to 20 000 live births and
involves a loss of expression of paternal genes in the 15q11-13 region. Other well-known
forms of syndromic obesity include Bardet-Biedl syndrome, Alstrom syndrome, and
WAGR syndrome (Chung 2012). Though the precise mechanisms of action may not be
known, all forms of monogenic obesity involve the central nervous system (CNS) (Choquet
and Meyre 2010). Specifically, monogenic forms of obesity involve genes that impact the
neuronal differentiation of the periventricular nucleus (PVN) and the leptin/melanocortin
pathway. Eleven genes leading to monogenic obesity have been discovered including
SH2B1 (Doche et al. 2012), LEP, LEPR, POMC, PCSK1, MC4R, MC3R (Mencarelli et al.
2011), MRAP2 (Asai et al. 2013), SIM1, BDNF and its receptor TrkB coded by the NTRK2
(Choquet and Meyre 2011b). See Table 1 for information about proteins coded by the
obesity predisposing SNPs. The leptin/melanocortin pathway plays a critical role in food
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consumption regulation (Figure 1). Leptin is a cytokine like hormone secreted by
adipocytes that acts in the hypothalamus to regulate food intake, energy expenditure, and
nutrient portioning. Individuals unable to produce leptin or who have leptin resistance due
to faulty receptors experience hyperphagia and consequent obesity (Chung 2012). The
exact prevalence of monogenic obesity is unknown as a random cohort of obese subjects
has not yet been genotyped, however it is estimated to be between 5 and 10% of all obese
individuals for the eleven currently known genes and the 16p11.2 deletion (Choquet and
Meyre 2011a).
Gene Symbol
SH2B1
LEP
LEPR
POMC
PCSK1
MC4R
MC3R
MRAP2
SIM1
BDNF
NTRK2
Protein Coded
SH2B adaptor protein 1
Leptin
Leptin receptor
Proopiomelanocortin prohormone convertase 1
Proprotein convertase 1/3
Melanocortin 4 receptor
Melanocortin 3 receptor
Melanocortin 2 receptor accessory protein 2
Single-minded homolog 1
Brain derived neurotrophic factor
Nueurotrophic tyrosine kinase receptor type 2 – codes TRkB receptor
Table 1. Gene Symbols and Proteins Coded
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Figure 1. The role of monogenic obesity genes in the melanocortin pathway. White adipose tissue
releases leptin in response to increased adipose tissue (Doche et al. 2012). Leptin travels to the brain,
specifically the hypothalamus located in the periventricular nucleus. There it binds to leptin receptors on
POMC neurons and AgRP neurons (do Carmo et al. 2013). SH2B1 plays a role in the leptin response by
modulating the ability of leptin to bind to leptin receptors (Doche et al. 2012). On the POMC neurons,
leptin binding causes the synthesis of which is then cleaved by PC 1/3 to produce α-MSH (Farooqi et al.
2007; Jung and Kim 2013). On AgPR neurons, leptin binding decreases the neuronal activity of NPY and
AgRP. AgRP and α-MSH compete for binding on MC3R and MC4R (Jung and Kim 2013). If α-MSH
binds to MC3R and MC4R, the activity of the protein is increased causing a suppression of appetite. If
AgRP binds the activity of MC3R and MC4R are reduced and appetite is increased (Jung and Kim 2013).
MRAP2 is an accessory protein that causes a small but significant reduction in the surface expression of
MC4R and in combination with MRAP also reduces the function of MC3R (Chan et al. 2009). Similarly, a
deficiency in SIM1 causes a lower concentration of MC4R mRNA in the periventricular nucleus (Zegers et
al. 2014). Though the precise mechanisms are unknown BDNF and its receptor TRkB play a role in
moderating the effect of the melanocortin pathway downstream (Bariohay et al. 2009).
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2.3.2 Polygenic forms of obesity
Three different study types have been used to determine gene variants associated
with polygenetic obesity (Choquet and Meyre 2011b). Candidate gene studies involve
choosing genetic variants based on biological, physiological, or pharmacological evidence
of their role in weight regulation. Both MC4R and BDNF (Stutzmann et al. 2007) were
discovered using candidate gene studies but otherwise the approach has not been successful
for identifying obesity predisposing SNPs. Genome wide linkage studies are an extension
of family studies in which families who have the phenotypic trait of interest are recruited
and the association of various genetic markers with obesity are investigated. This strategy
has not yielded many significant results, likely because of the small effect size of individual
polygenic genes on obesity and confounding caused by the environment. Lastly, GWAS
involve several hundred thousand SNPs across the human genome being investigated in a
large population for their association with a phenotypic trait. As of June 2014, more than
2894 GWAS have been conducted (Yu et al. 2008). GWAS studies improve upon other
designs because the large sample size of individuals allows researchers to find statistically
significant results despite the small effect size of the gene (Manolio et al. 2009). Recent
studies have provided firm evidence that common variants in or near 70 loci modestly
increase BMI variation/obesity risk (Choquet and Meyre 2011b). Despite the highly
significant association of many of the SNPs with BMI, the identified genetic variants only
account for approximately 2% of the total heritability of obesity (Speliotes et al. 2010; Elks
et al. 2012).
Nine SNPs identified through GWAS studies (BDNF, NTRK2, LEPR, SH2B1,
PCSK1, POMC, MC4R, TUB, SDCCAG8) overlap with monogenic forms of syndromic
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and non-syndromic obesity (Thorleifsson et al. 2009; Choquet and Meyre 2011a).
However, the most frequently studied SNPs are within the fat mass and obesity associated
gene (FTO). Shortly after the incidental discovery of an association of rs9939609 (FTO)
and obesity in a GWAS study for type 2 diabetes conducted in a European population
(Frayling et al. 2007), a GWAS investigating obesity-related quantitative traits in Sardinia
found the same association which the authors then replicated in European Americans and
Hispanic Americans (Scuteri et al. 2007). A study looking at extremely obese young
Germans and their controls found a significant association of rs9939609 (FTO) as well as
five other SNPs located in FTO with extreme, early onset obesity (Hinney et al. 2007).
This study was followed by another research group concluding that rs1421085 and
rs17817449 in FTO are both significantly associated with obesity in a European population
using family data to exclude a potential undetected stratification effect (Dina et al. 2007).
The association of rs9939609 has also been confirmed in African and Asian populations
(Tung and Yeo 2011; Tan et al. 2014). Of all the obesity predisposing SNPs, rs9939609
(FTO) has the largest effect size, with those homozygous for the risk allele weighing on
average 3kg more than those homozygous for the protective allele (Frayling et al. 2007).
FTO belongs to the superfamily of Fe(II) 2-oxogluterate-dependent dioxygenases which
catalyze the Fe(II)- and 2-oxoluterate-dependent demethylation of 3-methylthymine, 3methyluracil, and N6-methyladensoine in single stranded DNA (deoxyribonucleic acid)
and RNA (ribonucleic acid). FTO is mostly expressed in the brain, specifically in the
arcurate, paraventricular, dorsomedial, and ventromedial hypothalamic nuclei which is
responsible for the control of energy homeostasis. Within the arcuate nuclei, FTO is
regulated as a function of nutritional status with fasting causing a decrease in FTO
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expression and high fat diets causing an increase. Specifically, FTO levels appear to be
regulated by the availability of essential amino acids. Decreased FTO expression increases
food intake while increased FTO expression decreases food intake. Though the biological
mechanism of action of FTO regulating total food intake is in line with studies showing an
association of FTO SNPs with food intake, it is still unknown how FTO obesity-related
SNPs influence FTO. However, based on the location of the SNPs, it appears that the SNPs
are likely either up or down regulating FTO expression opposed to being functional
mutations (Gulati and Yeo 2013).
One of the challenges in understanding the genetics of obesity today is the
discrepancy between heritability estimates and the amount of variance which is explained
by known genetic variants, identified through GWAS (Golan and Rosset 2011). Despite
being highly statistically associated with obesity, the SNPs identified by GWAS only
account for approximately 2% of the total heritability of obesity (Elks et al. 2012). Because
the effect sizes of obesity predisposing SNPs are relatively small (Manolio et al. 2009), it
is possible that many SNPs associated with obesity are not detected because of the
statistical significance requirements for a genome wide association, p < 5 x 10-8,
(Dudbridge and Gusnanto 2008) and low minor allele frequencies (Yang et al. 2010). New
strategies for handling GWAS data including linear model analysis and random effects
models to estimate heritability without needing to know all of the genetic variants
associated with a phenotype have been proposed (Yang et al. 2010; Golan and Rosset
2011). Yang et al. (2010) applied the linear model analysis method to human height which
is estimated to be approximately 80% hereditable. Previously, 50 variants accounted for
approximately 5% of the phenotypic variance. Using the linear model analysis method
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produced a heritability estimate of 84% indicating that statistical techniques may be
appropriate for verifying heritability estimates from twin and family studies. The inclusion
of SNPs that meet a minimum threshold for association with the disease but are not yet
confirmed in modeling techniques can improve the accuracy of disease risk predictions,
particularly when used in conjunction with novel technology such as machine learning
algorithms which allow for models to take advantage of interactions between genetic
markers. This technique was applied to GWAS data for Type 1 Diabetes which
significantly improved the predictive value of the model compared to only using confirmed
SNPs in regression models (Wei et al. 2009). These findings also indicate that there are
many more genetic variants associated with obesity to be discovered. Broadening the scope
of GWAS to non-European populations (Manolio et al. 2009) and working towards larger
samples sizes to increase power are amongst the steps that can be taken to improve our
understanding of the genetics of obesity. Including SNPs that are nominally significant
with obesity/BMI increases the variance attributable to genetics, however the heritability
estimates still remain far lower than those determined by family and twin studies (Yang et
al. 2010). Other explanations of the “missing heritability” include rare variants which may
have a large effect size but are not investigated because of their low minor allele frequency,
genetic interactions as total heritability estimates assume that the risk alleles do not interact
and have an additive effect, gene by environment interactions in which environmental
factors modify the effect of risk alleles on the disease (Marian 2012), and copy number
variations (Zhao et al. 2012).
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2.4 Current state of knowledge of the genetics of eating behaviour
After the identification of FTO and other obesity predisposing SNPs, researchers
began to look at the association of obesity-predisposing SNPs with eating behaviours in an
attempt to better understand the mechanism of action of genes in increasing obesity risk.
Behaviours associated with obesity that have been identified to be under genetic control in
heritability studies such as energy consumption, macronutrient distribution patterns, food
consumption patterns, and eating behaviours have also been found to be associated with
obesity predisposing SNPs.
Several SNPs have consistently been shown to be associated with increased
energy intake including rs9939609 (FTO) in children (Cecil et al. 2008; Timpson et al.
2008) and in adults (Speakman et al. 2008), and rs8050136 (FTO) (Haupt et al. 2009).
Some genes have also been found to be associated with macronutrient consumption such
as rs7498665 (SH2B1) with increased fat, saturated fat, and monounsaturated fat,
rs368794 (KCTD15) with increased carbohydrate intake, rs2568958 (NEGR1) with
decreased saturated fat and monounsaturated fat intake (Bauer et al. 2009), rs8050136
(FTO) with an increased percentage of calories from fat and decreased percentage of
calories from carbohydrates (Park et al. 2013), and rs1421085 (FTO) with higher protein
intake (Tanaka et al. 2013). To take into account that generally as BMI increases energy
requirements also need to increase, leading to higher energy consumption, models were
adjusted either for BMI/body weight (Cecil et al. 2008; Timpson et al. 2008; Haupt et al.
2009; Tanaka et al. 2013), total energy consumption (Bauer et al. 2009; Park et al. 2013),
or basal metabolic rate (Speakman et al. 2008). By adjusting for BMI in the case of
studies looking at the association of SNPs with total energy consumption, we can be
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more sure that the individual is consuming more food because of the SNP rather than
because their increased body size requires it. Similarly, by adjusting for total energy
intake when considering the association of SNPs with intakes of macronutrients, it
indicates that the individual has a greater proportion of their diet coming from a specific
macronutrient rather than simply consuming more energy and consequently a
proportionate amount more of each macronutrient. Another method of determining if
SNPs influence what is consumed is to look at food categories. There is evidence that the
risk variant of rs4788099 (SH2B1) is associated with more servings of dairy products per
day, and BDNF risk variants (rs10767664, rs6265, and/or rs1401625) are associated with
more servings of meat, eggs, nuts and beans (Mccaffery et al. 2012).
Some genes also seem to influence how and when people eat food. Having the risk
variant for rs142085 (FTO) increases the number of eating episodes per day (Mccaffery et
al. 2012), rs17782313 (near MC4R) is associated with an increase in disinhibition and
emotional eating scores in females from the Three Factor Eating Questionnaire (Horstmann
et al. 2013), nominally significant associations have been found between increased
snacking with rs295946 (BDNF) and rs7498665 (SH2B1) (Robiou-du-Pont et al. 2013),
statistically significant increases in snacking with rs17782313 (near MC4R) (Stutzmann et
al. 2009), an association of decreased fullness after food consumption for rs9939609 (FTO)
(Dougkas et al. 2013), increased food responsiveness in children is associated with
rs9939609 (FTO) (Velders et al. 2012) as well as reduced satiety (Wardle et al. 2008) and
increased food consumption (Wardle et al. 2009). A genotype score, which sums the
obesity predisposing alleles for obesity, may also be used to determine if genetics are
influencing food related behaviours. Using a gene score, a negative association was found
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with satiety responsiveness in children while the gene score was positively associated with
adiposity and BMI (Llewellyn et al. 2014), and an unexpectedly negative association with
total energy intake and higher fibre intake (Rukh et al. 2013).
In summary, numerous associations have been found between obesity predisposing
genes and food intake behaviour, however many results have not been replicated and the
studies have mostly been conducted in European populations. An exception is FTO which
has consistently been shown to be associated with food consumption parameters including
total energy intake (Cecil et al. 2008; Speakman et al. 2008; Timpson et al. 2008; Haupt et
al. 2009), total fat intake (Timpson et al. 2008; Tanofsky-Kraff et al. 2009; Lee et al. 2010;
Lear et al. 2011; Park et al. 2013), protein intake (Chu et al. 2013; Tanaka et al. 2013),
carbohydrate intake (Park et al. 2013), food category consumption (Brunkwall et al. 2013),
satiety (Wardle et al. 2008; Hoed et al. 2009; Rutters et al. 2010; Dougkas et al. 2013),
number of eating episodes per day (Mccaffery et al. 2012), and restraint/control of food
consumption (Tanofsky-Kraff et al. 2009; Velders et al. 2012; Cornelis et al. 2014). The
lack of replications is indicative of the unavailability of food consumption data in large
genotyped populations, differences in measurement of dietary parameters, as well as small
sample sizes exacerbated by the small effect size of obesity-predisposing SNPs. However,
the scatter results also show that though some SNPs may work through modifying food
consumption patterns, some genes may be operating through other mechanisms such as
altering metabolism.
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2.5 Background of eating disorders
2.5.1 Diagnostic criteria
The DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, version 5)
currently describes three eating disorders, anorexia nervosa (AN), bulimia nervosa (BN),
and binge eating disorder (BED). AN is characterized by patients being severely
underweight (<85% of expected weight), having an intense fear of gaining weight or
becoming fat, a disturbance in body image, a heavy emphasis on body shape for selfevaluation, and denial of the seriousness of their current weight loss (American Psychiatric
Association, 2013). There are two types of AN, binge-eating/purging type (ANBP)
involving overeating and purging, and the restricting type (ANR) involving dieting without
binging or compensatory behaviours. It is unclear if these are distinct subgroups or
different phases of the disease (Hartmann et al. 2013). Similar to AN, people with BN place
a heavier emphasis on body shape for self-evaluation. The other key characteristic of BN
is having recurrent episodes of binge eating during which larger than reasonable quantities
of food are consumed during a discrete period of time. During these episodes, people feel
a lack of control, either unable to stop eating or limit the amount consumed. After binges,
inappropriate compensatory behaviour to prevent weight gain is used. In the purging type
of BN, self-induced vomiting, laxative misuse, diuretics or enemas are used. In the nonpurging type of BN the compensatory behaviours include excessive exercise or abuse of
medication. This behaviour must occur on average a minimum of once a week for three
months to be diagnosed. BED has the same diagnostic criteria as BN, without the
inappropriate compensatory behaviour (American Psychiatric Association, 2013).
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2.5.2 Symptoms, comorbidities, and mortality of eating disorders
Of the three eating disorders, AN is considered the most dangerous. More than 10%
of patients with AN will die from it in their lifetime, the highest mortality of any mental
disorder (Hoek 2006). Short term symptoms of AN include dizziness, headaches, brain
fogginess, feelings of coldness, nausea, weakness, poor sleep and blurred vision while long
term issues include osteoporosis, cardiovascular disturbances, electrolyte imbalances,
diabetes mellitus, thyroid disorders, gastrointestinal disorders and fertility and pregnancy
problems (Fairburn & Harrison, 2003; Meczekalski, Podfigurna to Stopa, & Katulski,
2013). Mood disorders are also common in people with AN, with estimates from 31.0 to
88.9% of patients suffering from other mental health issues. Depression is the most
prevalent (40 to 45%), however bipolar affective disease, social phobia, obsessive
compulsive disorder, and substance abuse are also common (Meczekalski et al. 2013).
BN has a significantly lower mortality rate than AN, estimated to between 0% to
3.3% (Keel and Brown 2010; Franko et al. 2013). There is significant overlap of BN and
AN, with 5% of people diagnosed with BN eventually developing AN and 25 to 30% of
people receiving treatment for BN previously having been diagnosed with AN (Kaye et al.
2000). The medical complications of BN in comparison to AN are also generally less
severe. Permanent dental erosion caused by contact with gastric acid during self-induced
vomiting is one of the most common side effects, though it is also found in people with
AN who purge. Electrolyte imbalances such as hypokalemia, hypochloremia, and
hyponatremia, can occur as a result of repeated vomiting. These electrolyte imbalances can
cause arrhythmias. Severe but rare side effects include gastric rupture or esophageal tears
(Klein and Walsh 2003). Similarly to those with AN, people with BN suffer from
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psychiatric comorbidities with a lifetime prevalence of between 64.1% to 84.8% (Jaite et
al. 2013; Kessler et al. 2013). Specific disorders include depressive disorders 40.0% to
45.8% (Jaite et al. 2013; Keski-Rahkonen et al. 2013), anxiety disorders - 15.7% (Jaite et
al. 2013), and somatoform disorders – 21.1% (Jaite et al. 2013).
BED is highly associated with overweight/obesity (Reichborn-Kjennerud et al.
2004; Field et al. 2012) with 2 to 25% of those seeking treatment for obesity being
diagnosed with BED (Fairburn et al. 2000; Fairburn and Harrison 2003; Yanovski 2003).
The estimated mortality of BED is 0 to 3%, however the follow up period for most studies
has been relatively short which may not capture outcomes such as premature death that
may occur as a result of obesity (Keel and Brown 2010). An estimated 63.6% of those with
BED have some type of psychiatric comorbidity (Hudson et al. 2008), particularly anxiety
and mood disorders (Reichborn-Kjennerud et al. 2004; Preti et al. 2009). There is evidence
that some patients with BED may also develop BN, with up to 9% of patients developing
BN 12 years after the diagnosis of BED (Keel and Brown 2010; Field et al. 2012).
2.5.3 Treatment
The treatment of AN has four main components, helping patients recognize they
need help and to maintain their motivation to get better after receiving help, weight
restoration, addressing patients’ over evaluation of self based on shape and weight and
preoccupation with eating habits, and lastly using compulsory treatment when medically
necessary (Fairburn and Harrison 2003). Whether outpatient, day-patient, or inpatient care
is provided is dependent on the psychological and physiological severity of the disease.
Various forms of therapy are used including family-based treatment in younger patients,
and cognitive behaviour therapy (CBT) (Fairburn and Harrison 2003). An estimated 50%
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of people suffering from AN will recover almost fully, while 30% will continue to have
symptoms intermittently throughout their life. The remaining 20% will chronically suffer
from the disease, often with death from AN as the final outcome (Kaye et al. 2000).
The treatment of BN has been relatively well studied with over 50 randomized
controlled trails (RCT). The findings strongly indicate that CBT focusing on modifying the
specific behaviours and ways of thinking that maintain the eating disorder is crucial with
approximately 20 individual sessions over a 5 month period resulting in one third to one
half of patients making a full recovery (Kaye et al. 2000; Fairburn and Harrison 2003;
Keski-Rahkonen et al. 2009). The results of the RCTs have also shown that antidepressant
drugs have an anti-bulimic effect, decreasing the frequency of binge eating and purging.
However, anti-depressant drugs are not as effective as CBT and the results are often not
sustained. To date, there is no reliable evidence of predictors for successful treatment
(Fairburn and Harrison 2003). An estimated 15 to 30% of patients still meet the diagnostic
criteria for BN within 5 to 10 years after diagnosis and the remainder will experience
intermittent relapses over their lifetime (Fairburn et al. 2000; Kaye et al. 2000).
Treatments for BED focus on two aspects, weight loss and controlling binge eating
(Brownley et al. 2007). Treatment options include pharmacotherapy such as
antidepressants
(fluoxetine,
fluvoxamine,
sertraline,
citalopram,
imipramine,
desipramine), anticonvulsants (topiramate), appetite suppressants (subutramine), fat
absorption inhibitors (orlistat), behavioural interventions with CBT being the most
common, though dialectical behaviour therapy has been investigated, and more unique
methods such as virtual reality and self-help books (Carter and Fairburn 1998).
Combinations of drugs and behavioural interventions are also common (Brownley et al.
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2007). Overall, the evidence is limited as to which treatment options are best but the
evidence suggests that as with BN, CBT in addition to pharmacotherapy options may
provide the best results for controlling binge eating and reducing weight (Brownley et al.
2007). The remission rates reported by studies are highly variable, ranging from 25 to 82%
(Fairburn et al. 2000; Keel and Brown 2010), further emphasizing the need for more studies
involving BED.
2.5.4 Risk factors for eating disorders
Eating disorders, particularly AN and BN are considered a Western disease
affecting mostly females in adolescence and early adulthood. Other general risk factors
include having a family history of any type of eating disorder, depression, substance
misuse, as well as either a family history of obesity or being obese in the case of BN. Life
experiences including early menarche (BN), high parental expectations, sexual abuse,
family dieting, criticism about one’s shape and size, and participation in activities
promoting a low fat mass like distance running, swimming, dancing, and modelling
increase risk (Klein and Walsh 2003). Personality characteristics including low selfesteem, perfectionism (particularly in the case of AN), anxiety, and anxiety disorders are
also risk factors for eating disorders (Fairburn and Harrison 2003).
Personal characteristics: Common personality characteristics of individuals
diagnosed with AN and BN include perfectionism, obsessive-compulsiveness,
neuroticism, negative emotionality, harm avoidance, low self-directedness, low
cooperativeness, and traits associated with avoidant personality disorder (Cassin and von
Ranson 2005; Kaye 2008). People with the restrictive type of AN tend to have low novelty
seeking, low emotional responsiveness, decreased pleasure, decreased seeking of pleasure,
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and reduced social spontaneity compared to those with BN who tend to be impulsive, seek
out new experiences, and have characteristics of borderline personality disorder (Cassin
and von Ranson 2005; Kaye 2008). BED is associated with perfectionism, sensation
seeking, and obsessive compulsive personality disorder, overall having many more
personality characteristics in common with BN and the purging form of AN compared to
the restrictive form of AN (Cassin and von Ranson 2005).
Family environment: Historically, many theories of the development of eating
disorders focused on the family environment. Typically family dynamics that involve
overprotective, intrusive, controlling, emotionally unresponsive, and rigid parents as well
as enmeshment of parents and children, poor conflict resolution, and a lack of opportunity
for children to express themselves were thought to increase eating disorder risk (Klump et
al. 2002; Kardum et al. 2008; O’Shaughnessy and Dallos 2009). The development of an
eating disorder was theorized to be a means for communicating avoided messages by
adolescents to their parents (O’Shaughnessy and Dallos 2009). Due to the challenges in
classifying, diagnosing, and studying eating disorders with the additional complication of
family dynamics being difficult to study, there is limited evidence of if and how the family
environment affects eating disorder risk. Broadly, a meta-analysis including 17 studies
found that families which had at least one person suffering from an eating disorder reported
worse family function in comparison to control families. A wide spectrum of features were
used to define family functioning including cohesion, adaptability, conflict, affective
expression, affective involvement, communication, task accomplishment, problem solving,
achievement orientation, role performance, family hierarchy, behaviour control, adherence
to values and norms, and constraining implicit family rules. The results across studies for
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which specific domains of family dysfunction were most relevant to all eating disorders or
specific disorders were inconclusive. Some studies found significant differences between
family functioning domains for different eating disorder subgroups, however none of the
results were replicated across studies (Holtom-Viesel and Allan 2014). A preoccupation
with weight in the form of family dieting or receiving criticism about weight, eating or
body shape, similarly can increase risk (Fairburn and Harrison 2003). Weight
preoccupations enforced by non-family factors, such as participating in sports that
encourage thinness like dance or gymnastics or that heavily emphasize weight such as
wrestling can also be damaging (Mitchison and Hay 2014). Though not always related to
the family, childhood trauma has been found to increase the risk of developing an eating
disorder, particularly if the abuse was sexual or physical in nature (Smolak and Murnen
2002; Mitchison and Hay 2014).
Gender: AN is most prevalent in females, with a ratio of 10-20 females
diagnosed for each male and an overall lifetime prevalence of 0.3 to 3.0% for females
and 0.24 to 0.30% for males in Western countries (Favaro et al. 2003; Hoek and van
Hoeken 2003; Currin et al. 2005; Hoek 2006; Wade et al. 2006; Hudson et al. 2008; Preti
et al. 2009; Marques et al. 2011; Heaner and Walsh 2013; Meczekalski et al. 2013). BN is
similarly more prevalent in females with approximately 0.88 to 4.6% of females suffering
from the disease in Western countries (Favaro et al. 2003; Hoek and van Hoeken 2003;
Hoek 2006; Wade et al. 2006; Hudson et al. 2008; Keski-Rahkonen et al. 2009; Preti et
al. 2009; Smink et al. 2012) compared to 0.10 to 1.5% of males (Hudson et al. 2008; Preti
et al. 2009; Smink et al. 2012). Compared to AN and BN, BED is more equally
distributed between the genders. The lifetime prevalence is estimated to be between 2.5
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to 3.5% for females and 1.5 to 2.0% for males (Spitzer et al. 1992, 1993; Hay 1998;
Kinzl et al. 1999; Hudson et al. 2008). A general estimate is that up to 25% people with
BED are male (Fairburn and Harrison 2003).
Age: AN and BN both predominantly affect young people, though AN has a
slightly earlier age of onset compared to BN (Fairburn and Harrison 2003). The peak
incidence of AN is between the years of 10 and 19 (Fornari et al. 1994; Favaro et al.
2003; Hoek and van Hoeken 2003; Currin et al. 2005; Son et al. 2006; Wade et al. 2006;
Franko et al. 2013; Kessler et al. 2013) with an estimated 40% of all cases of AN
occurring in those 15 to 19 years of age (Hoek and van Hoeken 2003). For BN, there is
greater variability in the estimates of peak incidence, which in part could be because of
the use of different age cut off points as well as the relatively recent changes in diagnostic
criteria. The peak incidence is estimated to be as low as 10 to 20 years of age (Currin et
al. 2005; Keski-Rahkonen et al. 2009), or as high as 25 to 29 years (Son et al. 2006) with
other estimates falling in between those ranges (Kinzl et al. 1999; Hoek and van Hoeken
2003; Preti et al. 2009).The average age of onset provides more specific estimates, 20.0
years of age for BN purging type, 19.0 years for BN non-purging type (Wade et al. 2006)
and between 17.0 and 19.7 years for both types combined (Fornari et al. 1994; Favaro et
al. 2003; Hudson et al. 2008).
There are currently no reliable estimates of peak incidence for BED. However,
three studies have found that the incidence of the disease is relatively equally distributed
across the lifespan. (Kinzl et al. 1999; Alegria et al. 2007; Grucza et al. 2007). Point
estimates for the age of onset of BED range from 18.3 years (Favaro et al. 2003) to 25.4
years (Barry et al. 2002; Wade et al. 2006; Hudson et al. 2008). In a study consisting of
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participants from six European countries, it was found that while there were no new cases
of AN diagnosed after 20 years of age and only a limited number of cases of BN, BED
continued to be regularly diagnosed in people above the age of 40 (Preti et al. 2009). A
possible explanation for the differences in peak incidence estimates for BED is provided
by Spurrell et al. (1997) who found that there was a statistically significant difference in
the age that individuals first met the criteria for BED between those who starting binging
first versus those who started dieting first. In the binge first group, the average age of
meeting the criteria for BED was 18.81 (+/- 10.74) years and in the diet first group, the
average age of meeting the criteria for BED was 33.20 (+/-11.95) years. Similar results
were found in a study by Grilo and Masheb (2000).
Time Trends: A study conducted in six European countries found an inverse
association between cohort (age at interview) and lifetime risk of eating disorders, with
the younger cohort (18 to 29 years) having an odds ratio of 7.9 (3.89 to 16.20) of
developing any eating disorder in their lifetime compared to the 45 years and older age
category (Preti et al. 2009). This relationship was true for all subcategories of eating
disorders with the lowest odds ratio for BN (4.1), and the highest for AN (14.5). A metaanalysis of the diagnosis of AN and BN globally with a focus on Westernized countries
found a correlation of 0.35 for the association between time and the increase of AN and a
correlation of 0.89 for BN (Keel and Klump 2003). A small review by Hoek and van
Hoeken (2003) supports that there has been an overall increase in the incidence of BN in
the United States, Netherlands, and United Kingdom from the early 1980s to the early
1990s as well as an upwards trend since the 1950s of the incidence in AN, specifically in
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15 to 24 year old females, though the same increase in incidence was not found in males
and females over 25 years of age.
Studies published after the reviews have provided conflicting evidence of if the
incidence of eating disorders are increasing over time. A population based sample (n=3001
in 1995, and 3047 in 2005) from metropolitan South Australia, found that there was a 2.4
times greater risk for being diagnosed with BED in 2005 compared to 1995 (Hay et al.
2008). Similarly in the United Kingdom, there was 3 fold increase in the early 1990s for
BN, particularly amongst females aged 10 to 39 years. The same study found that since
1996 the incidence has been declining, likely in part because of decreased symptom
recognition and changes in service use (Currin et al. 2005). Contrary to other studies,
evidence from a cohort of American college aged males and females from 1982, 1992, and
2002, concluded that the prevalence of BN decreased over time, from 4.2%, to 1.3% to
1.7% in females, and 1.1%, 0.4%, and 0% in males (Keel et al. 2006). Possible explanations
of the discrepant findings are the altering of diagnostic criteria, shifting attitudes about
mental health disorders, the inherent challenge in identifying patients who often try to keep
their disease hidden, and various methodologies used (Keel and Klump 2003). Sampling
poses a specific issue as some studies recruited from very specific populations such as
university students or public school students decreasing the generalizability of the findings.
Small sample sizes of less than 500 people are also common. An example of the possibility
that revised diagnostic criteria may lead to changes in the frequency of diagnosis of a
disease is the dramatic increase in the incidence of BN from pre 1980 to post 1980. The
true incidence may have increased or the increase may be attributed to the publication of
the DSM-III where BN was first recognized as its own diagnostic category (Hoek and van
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Hoeken 2003). While the results should be interpreted with caution, the evidence seems to
indicate that in Western societies, all three types of eating disorders, AN, BN, and BED
may be becoming more common.
Ethnicity and Culture: Understanding the roles of ethnicity and culture in eating
disorder development is challenging. The concepts of ethnicity and culture are often
inappropriately interchanged and are intrinsically connected making it next to impossible
to study the effect of ethnicity or culture without confounding. Studying eating disorders
in a global context adds additional challenges because environmental factors like the
availability of doctors may be unrelated to culture but greatly impact the diagnosis and
measurement of the disease. Several studies of eating have been conducted in multiple
ethnicities from the same country in an attempt to understand ethnic and cultural effects on
the development of eating disorders. Though it is tempting to assume that all people living
in the same country have a shared culture and that the differences found between ethnicities
would related to their genetics, this is an appropriate conclusion. Within a country there
are innumerable subcultures, many of which are highly associated with ethnicity.
Therefore, the results of the studies included in this review are unable to conclude if
ethnicity is influencing the development of eating disorders because of underlying genetic
differences or because people from different ethnic backgrounds are part of different
subcultures.
Anorexia Nervosa - Eating disorders are often cited as a Western disease however
there is evidence that AN is found worldwide. In non-Western countries the prevalence of
AN is estimated to be between 0.002% to 0.9%, significantly lower than the estimated
prevalence of 0.3 to 3% for females and 0.24% for males in Western countries (Hoek 2006;
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Wade et al. 2006; Hudson et al. 2008; Preti et al. 2009; Marques et al. 2011; Heaner and
Walsh 2013; Meczekalski et al. 2013). A meta-analysis of all reports including grey
literature of AN in non-Western nations, taking country, diagnostic criteria, prevalence of
cases, presence of weight concern in patients, and level of Western influence into
consideration found that there are reported cases of AN worldwide, even in people who are
unlikely to have experienced any Western influence. The most significant difference
between the cases of AN in Western countries versus non-Western countries is the presence
of weight concerns as a motivating factor for food refusal. If weight concerns are removed
as a diagnostic criteria for AN, the prevalence of AN is very similar in Western and nonWestern countries (Simpson 2002; Keel and Klump 2003).
Though Keel and Klump (2003) concluded that AN occurs in all ethnicities and in
all cultures, a study in Curacao, a Caribbean island, found an overall incidence of 1.82
cases of AN per 100 000 persons per year without a single case in Black females (Harten
et al. 2005) despite Curacao being under a strong Western influence at the time of the study.
Similarly, in the United States, a study including 2046 females from the NHLBI (The
National Heart, Lung, and Blood Institute) Growth and Health Survey found that of the
1061 black females participating in the study, not one was diagnosed with AN, despite
1.5% of the white females participating having the disease (Striegel-Moore et al. 2003).
Another study conducted in the United States investigating 5191 adults and 1170
adolescents who are African American and Caribbean Black found that the lifetime
prevalence of AN in the adult population was 0.15% for African Americans and 0% for
Caribbean Blacks (Taylor et al. 2013), significantly lower than the estimated prevalence of
0.3 to 3% for females and 0.24% for males in Western countries. (Hoek and van Hoeken
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
2003; Currin et al. 2005; Hoek 2006; Wade et al. 2006; Hudson et al. 2008; Preti et al.
2009; Marques et al. 2011; Heaner and Walsh 2013; Meczekalski et al. 2013). The study
by Taylor et al. (2003) partially supports Keel and Klump's (2003) theory that
preoccupation with weight is a culturally driven attribute. When preoccupation with weight
was removed as a diagnostic criteria, there was an increase in prevalence of AN of 0.21%
in the African American population though there was no increase in the Caribbean Black
population. This result highlights the confusion regarding the role of culture and the role
in genetics in ethnicity. The increase in prevalence of AN in African Americans but not
Caribbean Blacks could be because of underlying genetic differences that alter the
predisposition to the disease, or it could be reflective of African Americans and Caribbean
Blacks being part of different subcultures that could alter the risk of AN. One study
interested in determining the genetic versus cultural impact of ethnicity investigated the
degree of acculturation of Asian-Americans and found that the prevalence of AN was lower
than in the White American population independent of acculturation (Nicdao et al. 2007).
While the results indicate underlying genetic differences, the results still may be
confounded as the degree of acculturation may not capture all relevant elements of culture.
Bulimia Nervosa – BN is significantly less studied than AN, particularly in the
international context limiting the conclusions that can be made about the role of ethnicity
and Westernization in the disease (Keel and Klump 2003). Despite the limited studies
available, it is generally accepted that the prevalence of BN is lower in non-Western
countries compared to Western countries. A possible explanation is the unique
environment necessary for BN which includes the ability to access large quantities of food
as well as having weight concerns driving the need to purge (Keel and Klump 2003). The
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
meta-analysis by Keel and Klump (2003) which found that AN exists worldwide no matter
the degree the Western influence found the opposite for BN. There were no studies in nonWestern countries reporting the presence of BN in an individual without exposure to
Western ideals. Extrapolating from the evidence for AN which found that as countries
became more Westernized, there was a transmission of Western beauty ideals emphasizing
thinness which coincided with increases in the prevalence of AN (Simpson 2002) it is
possible that BN is not as prevalent in non-Westernized countries because people who do
not idealize thinness would not fear gaining weight and therefore would not purge to
compensate after binging episodes. The importance of Western influence in causing weight
concerns motivating eating disorders is supported by a study conducted in the United States
Latino population which found that recent immigrants had the lowest rates of BN while
those who have resided in the United States for 70% or more of their lives had the highest
rates (Alegria et al. 2007). Similarly to the research conducted for AN, the prevalence of
BN has been looked at in multiple ethnicities from one country. A study including 2046
females from the NHLBI Growth and Health Survey in the United States found that 2.3%
of white females, but only 0.4% of black females had a lifetime diagnosis of BN (StriegelMoore et al. 2003). In a study of African American and Black Caribbean adults living in
the United States, the lifetime prevalence of BN was 1.40% for the African Americans and
1.98% for the Caribbean Blacks, lower than the estimated prevalence of 0.88 to 4.6% for
all females (Favaro et al. 2003; Hoek and van Hoeken 2003; Hoek 2006; Wade et al. 2006;
Hudson et al. 2008; Keski-Rahkonen et al. 2009; Preti et al. 2009; Smink et al. 2012) and
0.10 to 1.5% for males (Hudson et al. 2008; Preti et al. 2009; Smink et al. 2012) in Western
countries. Removing preoccupation with body weight resulted in a modest increase in the
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
prevalence of BN (Taylor et al. 2013). These ethnic differences may reflect the
consequences of subcultures, specifically what type of body shape is considered ideal, or
the potential for underlying genetic differences predisposing individuals to BN.
Binge Eating Disorder - BED is distinct from AN and BN because it does not
include weight preoccupation as a diagnostic criteria (American Psychiatric Association,
2013). Weight preoccupation is predominantly a Western concern (Simpson 2002) and
perhaps also ethnicity specific as evidenced by White American females experiencing a
larger discrepancy between their current weight and ideal weight and being more
concerned about dieting, regardless of current BMI compared to their African American
counterparts (Gluck and Geliebter 2002). Unlike AN and BN where generally there is a
higher prevalence in populations of European heritage, most studies have found that the
prevalence of BED is as high as or higher in Black Africans compared to Whites. Very
limited data is available regarding other ethnicities. The lifetime prevalence of BED is
estimated to be between 2.5 to 3.5% for females and 1.5 to 2.0% for males in Western
countries (Spitzer et al. 1992, 1993; Kinzl et al. 1999; Ghaderi and Scott 2001; Hudson et
al. 2008). A study conducted in African Americans and Caribbean Blacks in the United
States partaking in the National Survey of American Life (NSAL) found that the lifetime
prevalence of BED in African American adults is 5.02%, and 5.78% for Caribbean Black
adults (Taylor et al. 2013). While this study had an exceptionally high prevalence of BED,
a study including the pooled data from the NIMH (National Institute of Mental Health)
Collaborative Psychiatric Epidemiological Studies in the United States found the lifetime
prevalence of BED to be 1.91% in non-Latino Whites, 2.71% in Latinos, 1.66% in Asians,
and 2.22% in African Americans females and 0.75%, 1.67%, 0.95%, and 0.97%
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respectively in males. The only statistically significant differences between groups existed
for non-Latino White males versus Latino males, and non-Latino White versus African
American males (Marques et al. 2011). Similar lifetime prevalence estimated were found
in another study in the United States focusing on the Latino population, 1.55% for males,
and 2.31% for females (Alegria et al. 2007). A study involving a biracial population-based
cohort of males and females participating in a longitudinal study of cardiovascular risk
factor development had similar results indicating the prevalence of BED is approximately
the same in White and Black populations. The Revised Questionnaire on Eating and
Weight patterns was used to establish BED status among the 3948 (55% females, 48%
Black) participants, who were 28 to 49 years of age. Prevalence of BED was 1.5% overall,
with similar rates among Black females (2.2%), White females (2.0%), and White males
(1.2%), while Black males had a significant lower prevalence (0.4%). (Smith et al. 1998).
While less studied, it appears that the lifetime prevalence of BED in Asian Americans is
within the general range of the White population with 2.67% of females and 1.36% of
males being affected by the disease (Nicdao et al. 2007)
The higher prevalence of BED in Blacks compared to Whites may be explained by
this lack of weight preoccupation. BN, which involves the same binge eating behaviour
seen in BED, but also includes a purging element motivated by the fear of gaining is more
prevalent in White females compared to Black females. Even within the BED population,
White females are significantly more concerned about eating, dietary restraint, shape
concern, and weight concern compared to Black females, despite on average being less
overweight (Pike et al. 2001). Because a smaller proportion of African Americans have
weight concerns, there is the potential that the population is less likely on average to engage
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in purging behaviour compared to their White American counterparts. Therefore they
receive a diagnosis of BED rather than BN. This is supported by studies such as (Taylor et
al. 2013) which found that when weight preoccupation was removed as a criteria, the
prevalence of BN in African Americans and Caribbean Blacks increases.
Summary of the role of ethnicity and culture in the development of eating
disorders – It is generally accepted that the lifetime prevalence of AN according to the
current diagnostic criteria is higher in the White population compared to other ethnicities.
However, the degree of Westernization may play an important role, especially in the
development of AN and BN which both require an individual to have preoccupations with
their weight to receive a diagnosis. The idealization of a slim body type and weight
preoccupation is a Western phenomenon and there is evidence that the more a person is
influenced by Western culture, the more likely it is for them to develop either AN or BN.
However, studies have also shown that regardless of acculturation to Western ideals, there
are still some significant differences between the prevalence of eating disorders in people
of different ethnic backgrounds who have been raised in the same country. These
differences may be reflective of subcultures existing within countries that may be strongly
associated with ethnicity or that the genetic differences between ethnicities may alter the
baseline risking of an individual for developing an eating disorder and that exposure to
Western ideals, particularly that a low weight makes one attractive, may modulate the risk.
2.6 Heritability of eating disorders
There is very strong evidence that eating disorders are moderately to highly
heritable. In a 2005 review of family studies investigating the heritability of AN and BN,
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all but one study reported increased risk of eating disorders in those with family members
displaying eating disorders (Slof-Op ’t Landt et al. 2005). It is estimated that the lifetime
risk of AN and BN among female relatives of a person with an eating disorder is 7 to 20
times higher than that of the general population (Strober et al. 2000; Klump et al. 2001).
Estimates of the heritability of eating disorders range from 0.28 to 0.76 for twin studies of
AN and 0.30 to 0.83 for twin studies of BN. In many of these studies, broad definitions for
disease diagnosis were used to increase statistical power because of the low prevalence of
the diseases (Slof-Op ’t Landt et al. 2005). Interestingly, a large portion of the remaining
variance for the risk of developing either AN or BN is accounted for by non-shared
environmental factors, estimated to be between 0.17 to 0.50 compared to the relatively low
estimates of the effect of shared environmental factors (Klump et al. 2002; Slof-Op ’t Landt
et al. 2005). The lifetime risk of having BED is estimated to be between 1.9 and 2.2 times
higher for those with a relative with the disease and twin studies have estimated the
heritability is between 0.41 and 0.57 (Thornton et al. 2011).
2.7 Genetics of eating disorders
A variety of different types of studies have been conducted to determine the genetic
architecture of eating disorders including linkage studies, candidate gene approaches, and
GWAS. Linkage studies use related individuals to identify regions of the genome
containing genes that predispose individuals to a disease have provided limited (Teare and
Barrett 2005). Candidate gene studies investigate genes that have been selected based on
their physiological, biochemical, and functional aspects (Zhu and Zhao 2007). GWAS are
studies attempt to identify common genetic variants that contribute to disease risk using
markers from the entire genome (Bush and Moore 2012).
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Linkage studies have provided limited results in the context of eating disorders. In
a collaboration of 5 studies, there were no AN-affected relative pairs with nonparametric
linkage (NPL) scores above 1.80, however people with the restrictive subtype of AN had
a linkage peak at 1p33-36 (NPL score = 3.03) and a peak at 4q12-14 (NPL score = 2.44).
Investigating the traits of drive for thinness and obsessionality in the AN sample revealed
three possible linkages on chromosome 1q31 (LOD (logarithm of odds) score = 3.46) for
both traits, 2p11 (LOD score = 2.22) for obsessionality, and 13q13 (LOD score = 2.5) for
drive for thinness. For BN, chromosome 10p13 (LOD = 2.92), and 10p14 (LOD score =
2.7) achieved significant linkage results, while 14q22-23 were almost significant (LOD
score = 1.97). When regular vomiting in those with BN was used as a phenotype, the
linkage on chromosome 10p13 was strengthened (LOD=3.39) (Slof-Op ’t Landt et al.
2005).
Genome wide association studies (GWAS) are studies which identify common
genetic variants that contribute to disease risk (Bush and Moore 2012) as well as candidate
gene studies investigating genes selected based on physiological, biochemical, and
functional aspects (Zhu and Zhao 2007) have also been widely used. The results of these
studies in relation to eating disorders can be categorized into three main biological
pathways, the serotonin pathway, the catecholamine pathway, and the pathway involved
with neuropeptide and feeding regulation (Slof-Op ’t Landt et al. 2005)
2.7.1 Serotonin
Serotonin (5-HT) is involved with normal brain function including mood state,
hunger, sex, sleep, memory, emotion, anxiety, and endocrine function (Haleem 2012).
Symptoms of AN such as depression (Meczekalski et al. 2013), refusal to consume food,
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binging behaviour (American Psychiatric Association, 2013), and hyperactivity (Sternheim
et al. 2014) are all linked to circulating serotonin levels (Haleem 2012). Serotonin levels
are determined using 5-hydroxyindoleacetic acid (5-HIAA), the primary metabolite of
serotonin. 5-HIAA level are elevated in both those suffering from AN and BN as well as
those recovered from the diseases indicating the possible role of hyperserotonergic function
(Scherag et al. 2010). Furthermore, there is evidence that people suffering from and who
have recovered from AN have a reduced ability to experience reward, specifically in the
context of food. Though anhedonia, a decreased ability to experience pleasure (Ho and
Sommers, 2013) is often cited as the reason for decreased pleasure from food in people
with AN, there is also the possibility of reward contamination (Keating et al. 2012). The
theory of reward contamination stems from people suffering from eating disorders
originally gaining pleasure from losing weight and practicing weight loss activities which
then contaminates the usual reward responses, ultimately causing a neural overlap in
circuits processing reward and punishment making it difficult for people to correctly
regulate their behaviour (Keating 2010).
The association of SNPs related to the serotonin pathway with eating disorders is
conflicted. The most promising polymorphism is within the 5-HT2A (5-hydroxytryptamine
receptor 2A) receptor gene (1438G/A, A is risk allele). In a meta-analysis of 11 studies of
individuals with AN, the odds ratio for having the disease if the risk allele (-1438A) is
present is 1.22 (95% confidence interval: 1.09 to 1.35) (Martaskova et al. 2009). However
high between-study heterogeneity reduces the confidence in the statistically significant
increase in risk (Gorwood 2003; Slof-Op ’t Landt et al. 2005). For the association of
1438G/A with BN, five of eight studies were not significant, two were positive indicating
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the A allele as the risk allele and one study indicated that the G allele is the risk allele (Maj
and Monteleone 2008). To our knowledge, no studies have investigated the association of
5-HT2A with BED. 5-HT2A is regulated by estrogen which provides biological support for
the association of 5-HT2A and eating disorders, particularly AN, because eating disorders
are significantly more prevalent in females than males and in females usually begin at the
time of puberty (Frank et al 2002, Kaye et al 2001). Further evidence comes from a study
indicating that people currently suffering from or who have recovered from AN have
reduced binding of 5-HT2A (Kaye et al. 2013).
5-HTTLPR (serotonin-transporter-linked polymorphic region), the promoter region
of the serotonin transporter coded by the SLC64A gene has provided some promising
results in relation to eating disorders. Individuals either have the short allele (S) with only
14 repeats of a sequence versus those with the long allele (L) with 16 repeats. Carriers of
the short allele only make half as much of the serotonin transporter protein as those with
the long allele (Trace et al. 2013). A meta-analysis using a random effects model of eight
independent samples of individuals with AN provided a z-score of 2.54 (p=0.1) for having
the S-containing genotype versus the L/L genotype (Calati et al. 2011), similar results to
an earlier meta-analysis involving four studies which found an odds ratio of 1.38 (95%
confidence interval: 1.16 to 1.72) for people with the short allele variant compared to the
long allele variant (Gorwood 2004). In a meta-analysis of individuals from 6 studies with
BN, the results were non-significant (Polsinelli et al. 2012) with Calati et al. (2011)
concluding that there was no significant difference in the occurrence of the S-containing
genotype versus the L/L genotype in BN patients from 7 studies. Interestingly, there is a
relationship between 5-HTTLPR and the development of BN in individuals who already
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have AN indicating the possibility of differences within the eating disorder population
based on impulse control (Bailer et al. 2007; Castellini et al. 2012; Trace et al. 2013). To
our knowledge, no studies have investigated the association of 5-HTTLPR with BED,
however (Akkermann et al. 2012) determined that the 5-HTTLPR risk allele interacted with
adverse life events in 18 year olds to increase the risk of binge eating indicating there may
be some effect.
Other serotonin receptors including the 5HT1B, 5HT1DB, 5HT2C, 5HTR3B, 5HT3B, 5-HT1D, and 5HT7, and the enzymes tryptophan hydroxylase (TPH) responsible
for the synthesis of serotonin, and monoamine oxidase A (MAOA) which breaks down
serotonin have been studied in relation to eating disorders. However, the studies
investigating these receptors and enzymes in the context of their association with eating
disorders is limited and inconclusive at this point in time (Rask-Andersen et al. 2010;
Scherag et al. 2010; Trace et al. 2013).
2.7.2 Catecholamine pathway
The main neurotransmitters involved in the catecholamine pathway are dopamine
and norepinephrine (Rask-Andersen et al. 2010; Baik 2013). Dopamine is the primary
catecholamine neurotransmitter found in the brain and affects physiological functions such
as motor activity, hormone secretion, motivation, and emotional behaviours (Baik 2013;
Trace et al. 2013). In the context of eating disorders, it is theorized that increased dopamine
activity is associated with symptoms such as weight loss, altered satiety, impulse control,
mood, hyperactivity, amenorrhea, body image distortion, and obsessive-compulsive
behaviour (Kaye et al. 2013; Trace et al. 2013). Two main theories similar to those for
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serotonin exist on how dopamine may play a role in the development of AN. The first is
that dieting and exercise are initially rewarding behaviours and as time progresses the
behaviour is maintained through conditioning (Scheurink et al. 2010), and the second is
that people with AN have reduced food consumption because they do not receive pleasure
from food (Kontis and Theochari 2012). The effects of dopamine may be different in BN
and BED. Blocking dopamine receptors has been found to increase appetite and cause
weight gain, therefore overeating during binge periods may be a compensation method for
the blunting of the pleasurable response to eating (Barry et al. 2009). Severely obese
individuals have been found to have fewer striatal dopamine receptors compared to normal
weight people with a decrease in the number of dopamine receptors as weight increases
(Wang et al. 2001). Therefore, overweight/obese people may binge on highly palatable and
calorically dense foods which stimulate the greatest amount of dopamine activity (Abizaid
et al. 2006). It is unknown at this time if low dopamine activity is the cause of overeating
(Barry et al. 2009), or if overeating causes elevated dopamine levels leading to the down
regulation of dopamine receptors (Wang et al. 2004).
SNPs involved with three dopamine receptors (D2, D3, and D4), a dopamine
transporter (DAT1), as well as catecholamine-O-methyltransferase (COMT), an enzyme
involved in monoamine metabolism, have been investigated for their association with
eating disorders. Overall, the evidence is at best weak with more studies being conducted
in people with AN than BN or BED. For DRD2 (dopamine receptor 2), a statistically
significant relationship was found between DRD2 - +725 3’ G>T and TaqIA C>T with the
purging subtype of AN (Bergen et al. 2005) as well as an association between rs1800497,
rs2283265 and rs6277 (DRD2) with BED (Davis et al. 2012). However, Nisoli et al (2007)
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did not find between group differences of TaqA1 C>T in those with AN, BN, those who
are obese, and a control group, and Gervasini et al (2013) found no association between
rs1800497 (DRD2) and AN. DRD3 has been less studied, however both Gervasini et al.
(2013) and Bruins-Slot et al. (1998) found no association between AN and the DRD3
Ser/Gly variant and DRD3 with Val1 polymorphism in exon 1 respectively. Two studies
have found a positive association between DRD4 (7 repeat allele) and a greater risk of AN
(OR=1.74, 95% CI 1.01 to 2.97) and BED (OR=3.25, 95%CI 1.43 to 7.41) (Gervasini et
al. 2013). Other positive results include an association between AN and DRD4-616C/C (
odds ratio: 3.83, 95% confidence interval: 1.05 to 13.98) (Gervasini et al. 2013) and an
association between DRD4 - C-521T’C’ allele and AN (Bachner-Melman et al. 2007).
However two studies found no between group differences for DRD4 (13-bp and 48-bp
deletion) in patients with AN, underweight students, and obese children/adolescents
(Hinney et al. 1999) and DRD4 (22, 24, 33, 34, 44 repeats, 27, 37, 47 repeats, and 77
repeats) prevalence in female siblings, one with and one without AN (Karwautz et al.
2001). Only one study investigated DAT1 and AN and found no association (Gervasini et
al. 2013). For COMT, only AN has been reasonably well studied, with a meta-analysis of
COMT (rs4680) and AN including 11 datasets (2021 cases, 2848 controls, 89 informative
trios) concluding there is no association and very little between study heterogeneity
(Brandys et al. 2012). To conclude, the evidence supporting the link between dopamine
genes and eating disorders is extremely limited, with the strongest, yet still weak evidence,
supporting an association with DRD4. Potential reasons for the inconsistencies and lack of
findings are small sample sizes and changing diagnostic criteria for eating disorders.
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Despite the lack of success in genetic association studies, there is significant
biological evidence of the role of serotonin in eating disorders. PET (positron emission
tomography) studies have revealed that patients recovered from AN have increased binding
of DA D2/D3 receptors in the anterior ventral striatum relative to controls, indicating either
a reduction in the intrasynaptic dopamine concentrations or an increase in the density or
affinity of the D2 and D3 receptors. A decrease in homovanillic acid (HVA), a metabolite
of dopamine in the cerebrospinal fluid of recovered AN patients supports a decrease in
intrasynamptic dopamine concentrations (Kaye et al. 2013). There is also evidence that
patients recovered from AN have feelings of anxiety after the release of dopamine (contrary
to the usual euphoria in controls without the disease), indicating that patients with AN may
restrict eating to avoid the release of anxiety causing dopamine. (Kaye et al. 2013).
Specifically in patients with BN, peripheral and central HVA are lower compared to people
without the disease and there is evidence that HVA levels which are depressed while
suffering from the disease can be returned to normal levels after treatment (Bello and
Hajnal 2011)
2.7.3 Norephinephrine
Norepinephrine is thought to influence eating patterns through its actions on the
hypothalamic paraventricular nucleus (PVN). Norepinephrine can either increase or reduce
eating, depending on where it is and the balance of α1- and α2- adrenoreceptors present. α1adrenoreceptors supress appetite, while α2- adrenoreceptors increase appetite (Wellman
2000). Though there is a plausible role for norepinephrine in eating disorders, very little
research has been conducted in the area. One study found a novel 343-bp sequence with 5
additional AAGG repeat islands within the norepinephrine transporter (NET) gene
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promoter region. In the NET gene promoter polymorphic region (NETpPR), a 4-bp deletion
(S4) or insertion (L4) in the AAGG4 resulted in the net loss or gain, respectively, of a
putative Elk-1 transcription factor site. There was a significant preferential transmission of
L4 (odds ratio 2.1) from parent to child with restricting AN (Urwin et al. 2002). Hu et al.
(2007) attempted to replicate the results, however did not find any statistically significant
results in a sample of 142 family trios with AN from London, United Kingdom, and
Vienna, Austria.
2.7.4 Neuropeptides and Feed Regulations
Opioids: Opioid receptors are involved in food intake, reward sensitivity, and pain
and are thought to play a role in vulnerability to addictive disorders (Trace et al. 2013).
Endogenous opioids are linked to the enjoyment of food with sweet and fatty foods
increasing opioid receptor binding and binge eating leading to changes in the endogenous
opioid system, creating a cyclical pattern of binging (Mathes et al. 2010; Bello and Hajnal
2011). The current hypothesis is that people with AN have a dysregulation of the opioid
system and are predisposed towards being addicted and that restriction and exercise are a
means to compensate for the diminished response to reward (Trace et al. 2013). Betaendorphin, an agonist of opioid receptors has been measured in people with eating
disorders. In both AN and BN patients it is unclear if beta-endorphin levels, are increased,
decreased, or unchanged though overall evidence indicates decreased (Brewerton et al.
1992; Johnson 1995). A genome wide linkage analysis of 192 families was conducted
finding an association between marker D1S3721 on chromosome 1p with restrictive type
AN (Grice et al. 2002) leading to the investigation of opioid delta receptor (OPRD1),
located on chromosome 1 (chr1p36.3-34.3) as a candidate gene. Three of five OPRD1
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SNPs were significantly associated with AN (OPRD1(8214T>C), p=0.045 for alleles,
OPRD1 (23340A>G), p=0.046 for alleles, OPRD1(47821A>G), p=0.003 for genotypes
and 0.01 for alleles) (Bergen et al. 2003). The association of OPRD1 SNPs with AN was
further supported when three gene polymorphisms (rs569356, p=0.0011 for genotype for
all types of AN, rs521809, p=0.163 for restrictive subtype, rs4654327, p=0.0246) were
found to be associated with AN (Brown et al. 2007). To date, no genetic association studies
have been conducted with BN or BED patients.
Neuropeptide Y (NPY): Neuropeptide Y is released by both the sympathetic
nervous system and from isolated adipocytes and plays a role in adipocyte regulation.
Activation of Y2 receptors in abdominal fat prompts the body to store abdominal fat,
contributing to obesity as well as increasing appetite (Bulloch and Daly 2014). Activation
is thought to occur when body fat stores are diminished and the body is attempting to
achieve homeostasis, however in patients with AN, despite increased NPY levels in the
spinal fluid and brain, the disease persists (Södersten et al. 2008). In patients with BN,
cerebrospinal fluid (CSF) level concentrations are not significantly different than those
without the disease, however, plasma concentrations are significantly elevated (Smitka et
al. 2013). Only one study to date has tested the genetic association between NPY and eating
disorders, finding no association between NPY gene SNP (rs16139) and all eating disorders
combined, or AN, BN, and BED separately, p<0.479 for genotype (Kindler et al. 2011).
Peptide YY (PYY): PYY is secreted by the digestive tract after food consumption,
promoting satiety. Two forms of PYY are secreted, PYY1-36 and PYY3-36, with evidence
supporting that PYY3-36 may exert its anorectic effect by inhibiting dopamine and
norepinephrine release through its bonding on NPY Y2 receptors in the hypothalamus.
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Negative associations between PYY with leptin and grehlin have also been found. Limited
evidence exists about PYY concentrations in those with eating disorders. For those with
AN and BN there is conflicting evidence on basal PYY levels, however there may be some
effect on PYY after food is consumed (Smitka et al. 2013).
Melanocortin Pathway (MC4R): MC4R deficiency is the most common cause of
monogenic obesity in addition to contributing to polygenic obesity. MC4R exerts its effect
on obesity by regulating appetite through the leptin-melanocortin signalling system
(Valette et al. 2013a). Leptin, a hormone released by adipocytes that causes feelings of
satiety though the release of anorexigenic peptides, such as POMC, as well as supressing
AgRP (Rask-Andersen et al. 2010; Valette et al. 2013a). When POMC is cleaved, it
produces α-MSH which then binds to MC4R to decrease food intake (Valette et al. 2013a).
The agouti protein, closely related to AgRP, which has been extensively studied in mice
because of its link to obesity, hyperphagia, hyperinsulimia, hyperglycemia (in males), and
ability to antagonize MC4R preventing α-MSH from binding (Corander and Coll 2011).
When AGRB binds to MC4R it promotes food consumption (Valette et al. 2013a).
Leptin: Leptin levels are negatively associated with obesity and people suffering
from AN have significantly reduced leptin levels compared to controls, as well as increased
soluble leptin receptor levels (Brewerton et al. 2000; Dolezalova et al. 2007). The evidence
is less clear with BN patients who may experience a decrease, increase, or no change in
leptin levels. It appears that severity and duration of the disease may play a role in
decreasing leptin levels. In BED, no changes in leptin have been observed (Monteleone et
al. 2004). The leptin receptor was of interest because it is located in 1p31.2, close to the
marker at 1p34.2 previously identified for increasing susceptibility to AN through linkage
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studies (Grice et al. 2002). Despite mutations causing a complete lack of circulating leptin
irrevocably leading to severe obesity without exogenous leptin treatment (Bastard and Feve
2012), there is no evidence that genetic variants in LEP or LEPR are associated with any
eating disorders. Three LEPR SNPs (Lys109Arg, Gln223Arg, Lys656Asn) were compared
in females with and without AN, however there were no significant differences in allele or
genotype frequency, even when AN patients were categorized into restrictive and purging
types (Quinton et al. 2004). Similarly, two novel mutations in the coding region of the
leptin gene (ser-91-ser; glu-126-gln), and a novel polymorphism (-1387 G/A) in the leptin
gene linked upstream region (LEGLUR) were not found to be associated with AN, BN,
underweight, or early onset obesity (Hinney et al. 1998)
Proopiomelancortin Gene (POMC): There are not any studies to date
investigating genetic variants in POMC and eating disorders.
Agouti Related Peptide (AgRP): In patients with AN, AgRP is elevated, however
after returning to a normal weight, AgRP are similar to healthy controls (Moriya et al.
2006; Merle et al. 2011). In BN, there is a negative correlation between AgRP and selfreported BN symptoms (Lofrano-Prado et al. 2011). Only two studies have investigated
the genetic relationship between AgRP SNPs and eating disorders. A significant effect of
Ala67Thr-AGRP (p=0.046) and AN was found (Dardennes et al. 2007) as well as a
relationship between two mutations in perfect linkage disequilibrium, 760A and 526A in
AgRP and AN with carriers 2.63 times more likely to develop AN (Vink et al. 2001).
Melanocortin 4 Receptor (MC4R): Four studies have investigated the association
of functional MC4R mutations and BED in obese people. No studies have been conducted
for BN or AN. Three of the four studies found no association (Hebebrand et al. 2004;
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Lubrano-Berthelier et al. 2006; Valette et al. 2013b) while one study found that all obese
people with MC4R functional mutations display binge eating behaviour compared to 14.2%
of obese people without mutations and 0% of normal weight subjects without mutations
(p<0.001) (Branson et al. 2003). However, this study used a different questionnaire to
diagnose BED and was done in a sample eligible for bariatric surgery that may not
generalizable to other obese populations.
Ghrelin: Ghrelin is an appetite-stimulating hormone that increases during fasting
and decreases following a meal and that is inversely associated with BMI (Atalayer et al.
2013; Trace et al. 2013). Ghrelin is predominantly produced in the stomach and takes
action in the hypothalamus by activating NPY and AgRP containing neurons to increase
appetite. A review of the evidence of ghrelin in eating disorders found that in 11 of 16
studies, people with AN had higher ghrelin levels than controls, 6 out 9 studies found that
people with BN have no difference in ghrelin levels, and 2 out of 2 studies found that
people with BED have less ghrelin than controls (Atalayer et al. 2013). Further support of
the role of ghrelin in eating disorders comes from exogenous ghrelin administration
increasing food intake in females with AN (Hotta et al. 2009). Overall, genetic studies seem
to indicate that polymorphisms of the ghrelin gene are not associated with risk of eating
disorders. Of 5 studies investigating AN, four found no association between Arg51Gln
(rs34911341), Leu72Met (rs696217), Gln90Leu (rs4684677), 3056T>C (rs2075356)
(Ando et al. 2006; Cellini et al. 2006; Monteleone et al. 2006; Kindler et al. 2011) and one
study found a statistically significant relationship for the transmission of Leu72Met to
offspring with AN (p=0.004), including the binging subtype specifically (p=0.005)
(Dardennes et al. 2007). Three of four studies investigating BN found no association
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between Arg51Gln (rs34911341), Leu72Met (rs696217), and Gln90Leu (rs4684677), with
BN (Cellini et al. 2006; Monteleone et al. 2006; Kindler et al. 2011) and one study
conducted in Japanese females found that Leu72Met (rs696217, p=0.0410, OR=1.48,
95%CI: 1.01 to 2.15) and 3056T>C(rs2075356, p=0.0035, OR=1.63, 95%CI: 1.17 to 2.26)
are associated with BN as well as an association between all eating disorders and 3056T>C
(rs2075356, p=0.0110, OR 1.37, 95%CI: 1.07 to 1.74) (Ando et al. 2006). Only one study
investigated BED and found no association with the SNPs Arg51Gln (rs34911341),
Leu72Met (rs696217), Gln90Leu (rs4684677) (Kindler et al. 2011).
Brain Derived Neurotrophic Factor (BDNF): BDNF is part of the nerve growth
family which binds to a specific receptor, tropomyosin receptor kinase (Trk) B, that is
responsible for the growth and regulation of neuronal cells by moderating survival,
development, and enhanced synaptic activity in addition to BDNF’s role of modulating
neurotransmitters including dopamine, serotonin, and glutamate providing a biological
pathway for BDNF to influence food consumption (Nakazato et al. 2012). Serum BDNF
has strong evidence indicating it is positively associated with BMI, however there is
conflicting evidence of if BDNF levels increase or decrease in patients with eating
disorders (Nakazato et al. 2012). Further evidence of the role of BDNF in food
consumption behaviours comes from studies of humans with mutation deletions in the
BDNF region. Individuals with the deletion mutation suffer from extreme hyperphagia and
obesity (Gray et al. 2006; Han et al. 2008).
A 2007 meta-analysis of the effect of BDNF Val66Met (rs6265) including five
studies estimated that individuals with the Met/Met or Met/Val genotypes have a 36%
greater chance of developing an eating disorder than those with the Val/Val genotype.
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Though the pooled effect was positive, only two of the five studies had statistically
significant results and one of the studies had a negative association (Gratacòs et al. 2007).
Since the meta-analysis, further studies have yielded inconclusive results. For Val66Met
(rs6265) specifically, four out of five studies investigating the gene found no statistically
significant relationship with AN (Dardennes et al. 2007; Ando et al. 2012; Brandys et al.
2013; Gamero-Villarroel et al. 2013) and the study with the positive outcome for Val66Met
(rs6265) as well as rs2030324 was no longer significant after adjusting for multiple testing
(p-values between 0.002 to 0.21 for genotypes and 0.00 to 0.039 for alleles) (DmitrzakWeglarz et al. 2013). Two studies published prior to the meta-analysis but not included
found positive associations between the Met allele of the Val66Met BDNF polymorphism
and restricting type AN and minimum BMI in a Spanish population (Ribasés et al. 2003)
and a positive association between the Met allele of the Val66Met BDNF polymorphism
and restrictive type AN, and minimum BMI (Ribasés et al. 2005). One study found a
positive result for BDNF (rs7934165A/270T) and AN (p=0.008) (Mercader et al. 2007).
Addition BDNF SNPs that were found to not have an association include rs11030102,
rs10835210, rs16917237, rs56164415, and rs11030119 with both AN and BN (GameroVillarroel et al. 2013), and rs712442, rs11030107, rs7103873, rs11030123, rs17309930,
rs2049048, and rs1491851 with AN (Slof-Op ’t Landt et al. 2011)
Other Mechanisms: There are other proposed mechanisms that may have genetic
links to eating disorders though they are less well studied. Cholecystokinin, a hormone
which decreases appetite through receptors in the CNS as well as stimulating the digestion
of lipids and proteins in the small intestine has had suggestive evidence linking it to AN
(Rask-Andersen et al. 2010). Glutamate receptor (NMDAr) also plays a role in eating
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behaviour through its action on the reward system for food consumption and is associated
with the SK3 Channel which in the brain, regulates the ion flow through the NMDA
receptor. Genes coding for SK3 and NMDAr have both been shown to be associated with
AN (Rask-Andersen et al. 2010).
2.7.5 Summary
Though there is an undeniable genetic component to the development of eating
disorders, with heritability estimates from twin studies ranging from 0.28 to 0.76 for AN
and 0.30 to 0.83 for BN (Strober et al. 2000; Klump et al. 2001), and between 0.41 to
0.57 for BED (Thornton et al. 2011). Our review of genetic association studies found
very little evidence supporting the role of functional variants in genes that may influence
eating disorders. There was inconclusive evidence for genes related to serotonin (5HT2A, 5-HTTLPR, 5-HT1B, 5-HT1DB, 5-HT2C, 5-HTR3B, 5-HT1D, 5-HT7), dopamine
(DRD2, DRD3, DRD4, COMT), norepinephrine (NET), and peptide YY with studies
finding a range of negative, positive, and no effect results. Genes associated with
Neuropeptide Y, Leptin (LEPR), and ghrelin, had evidence indicating a lack of
association with eating disorders while AGRP was found to have a possible association
with AN, MC4R a possible association with BED, and BDNF with all eating disorders.
The strongest evidence was for OPRD1 (opioid receptor) with AN.
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3.0 Justification and Objectives
As the prevalence of obesity continues to increase worldwide (Finucane et al.
2011), there is a dire need to better understand the disease to curb the obesity epidemic and
help prevent health complications (Schelbert 2009), decreases in life expectancy (Fontaine
et al. 2003), and reduce the economic burden (Lehnert et al. 2013). Obesity treatments fall
on spectrum of effectiveness. Lifestyle interventions are very limited in their success,
pharmacotherapy options are successful in some cases, and bariatric surgeries can lead to
significant weight loss. However, the more effective the treatment, the higher the risk for
serious complications (Gray et al. 2012; Wyatt 2013). Investigating methods of preventing
obesity, rather than treating it may be the most viable solution (Gortmaker et al. 2011). The
moderate to high hereditability of obesity, estimated to be between 0.47 and 0.90 using
twin studies and between 0.24 and 0.81 using family studies (Elks et al. 2012), the
hereditability of eating characteristics such as energy consumption and dietary
macronutrient distribution (Hur et al. 1998; Faith et al. 2004; Fisher et al. 2007;
Hasselbalch et al. 2008; Dubois et al. 2013), and the identification of common variants
located in or near approximately 70 loci that are associated with obesity (Choquet and
Meyre 2011b) suggest that understanding the underlying genetic architecture of obesity
may be an important step in determining how to prevent obesity.
Researchers are investigating ways in which genetics may be causing weight
increase by testing the association of obesity predisposing SNPs with eating behaviours
among other endophenotypes (e.g. energy expenditure). The strongest evidence currently
involves total energy intake (Cecil et al. 2008; Speakman et al. 2008; Timpson et al. 2008;
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Haupt et al. 2009), though some SNPs have also been found to be associated with
macronutrient intakes (Bauer et al. 2009; Park et al. 2013; Tanaka et al. 2013) or amount
of food consumed from specific food groups (Mccaffery et al. 2012). Studies have also
shown associations between obesity predisposing SNPs and how people eat such as the
number of eating episodes, the amount of disinhibition when eating, snacking habits, and
satiety (Wardle et al. 2008; Stutzmann et al. 2009; Mccaffery et al. 2012; Robiou-du-Pont
et al. 2013). Though there is sufficient evidence to conclude that eating behaviours at least
partially mediate the relationship between the obesity predisposing SNPs and measures of
obesity, the limited access to genotypic and phenotypic data in large populations as well as
small effect sizes require a further investigation of these relationships to confirm current
findings as well as to look at other eating patterns.
Eating disorders, including AN, BN, and BED are extreme eating patterns that are
also worthy of investigation. They are heritable diseases with hereditability estimates of
0.28 to 0.78 for AN, 0.30 to 0.83 for BN (Slof-Op ’t Landt et al. 2005), and 0.41 to 0.57
for BED (Thornton et al. 2011). Despite the moderate to high heritability of the diseases,
there is little evidence supporting the role of functional mutations in genes that may be
increasing risk for the eating disorders. Because eating disorders are heritable there is
reason to believe that historically, characteristics of the diseases would have offered a
competitive advantage for survival. Theorizing about potential evolutionary advantages of
symptoms of eating disorders with the support of modern epidemiological evidence may
help future gene identification efforts. With adequate sample sizes GWAS should be able
to identify genes predisposing to eating disorders and the evolutionary theories may help
provide strong biological evidence strengthening the findings of GWAS. Understanding
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the genetic architecture of eating disorders, particularly BN and BED which involve
excessive food consumption, may help to identify new genes associated with obesity or
disordered eating behaviours as well as determining biological pathways through which
genes are regulating feeding behaviour.
The objectives of this thesis are:
1. To investigate the association of obesity predisposing SNPs and a gene score with
nutrient consumption parameters including the intake of total energy, total fat,
saturated fat, trans fat, monounsaturated fat, polyunsaturated fat, carbohydrates,
and protein intake in a population of European ancestry.
2. To look at eating disorders from an evolutionary perspective using current
epidemiological evidence to help in future gene identification efforts for eating
disorders.
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4.0 The Association of Obesity SNPs with Food
Consumption Patterns
4.1 Introduction
The rise of obesity in the past three decades has reached worldwide epidemic
proportions (Finucane et al. 2011). Obesity is associated with numerous psychosocial and
health-related complications including type 2 diabetes mellitus, gallbladder disease,
osteoarthritis, coronary heart disease, some types of cancer, and decreased life expectancy
(Must et al. 1999; Calle et al. 2003; Fontaine et al. 2003). The treatment options offered to
obese patients vary based on the degree of obesity and associated co-morbidities as well as
the potential side effects of the therapeutic options. For those with moderate forms of
obesity, lifestyle interventions including food restriction and increased energy expenditure
through physical activity are recommended. As the degree of obesity increases or in the
presence of co-morbidities, pharmacotherapy options which work to decrease appetite such
as sibutramine or decrease fat absorption such as orlistat may be offered and for the most
obese patients, surgical interventions may be considered (Gray et al. 2012; Wyatt 2013).
Though pharmacotherapy options have yielded successful results, they are associated with
complications such as gastrointestinal problems with orlistat or cardiovascular risk with
sibutramine (Gray et al. 2008). Bariatric surgery is associated with significant weight loss
(Kral and Näslund 2007). However the most effective techniques involve rerouting the
digestive tract. This can lead to malabsorption which can cause nutritional deficiencies if
an adequate diet and supplementation regime is not maintained as well as post-operative
complications including hemorrhage, staple-line dehiscence, thromboembolism, and death
(Runkel et al. 2011). Despite the availability of diverse treatment options, there is evidence
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of only a modest effect of lifestyle interventions and pharmacotherapy and surgical options
are associated with numerous complications, suggesting that preventing opposed to
treating obesity may be the best strategy for curbing the obesity epidemic (Gortmaker et
al. 2011).
An excessive consumption of calories is the main risk factor for obesity with
environmental and societal factors encouraging calorie consumption (Young and Nestle
2002). The current “obesogenic” environment is characterized by calorically dense food
being inexpensive, abundant, served in large portions (Young and Nestle 2002), and
heavily marketed while demands for physical activity decrease (Mitchell et al. 2011;
Swinburn et al. 2011). Though the environment is an important contributor to obesity, the
inter-individual differences in obesity (Elks et al. 2012) and food behaviors such as energy
consumption, macronutrient consumption distribution, food preferences, and satiety
responsiveness in the presence of the obesogenic environment can in part be explained by
genetic factors (Breen et al. 2006; Hasselbalch et al. 2008; Llewellyn et al. 2012; Cornelis
et al. 2014). In addition, heritability studies in twins evidenced a shared genetic component
between obesity and food behaviors (Keskitalo et al. 2008b; Llewellyn et al. 2012). This
suggests that the genetic architecture of food behaviors and obesity may be at least partially
overlapping.
Monogenic forms of obesity include both syndromic types in which obesity occurs
in conjunction with other symptoms and non-syndromic types in which obesity is the main
clinical feature. Both types of monogenic obesity are characterized by severe hyperphagia
(Choquet and Meyre 2011b). GWAS and candidate gene studies have identified close to
70 loci associated with obesity-related traits, nine of which (BDNF, NTRK2, LEPR, SH2B1,
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PCSK1, POMC, MC4R, TUB, SDCCAG8) overlap with monogenic forms of syndromic
and non-snydromic obesity (Thorleifsson et al. 2009; Choquet and Meyre 2011a). To date,
only a limited number of obesity-predisposing SNPs (FTO, MC4R, BDNF, SH2B1,
NEGR1, MTCH2) have been reliably associated with food-related traits (Cecil et al. 2008;
Bauer et al. 2009; Stutzmann et al. 2011; Mccaffery et al. 2012; Robiou-du-Pont et al.
2013; Rukh et al. 2013).
Though an obesity genotype score consisting of 13 risk alleles was associated with
higher body mass index (BMI), it was unexpectedly associated with lower total energy
intake and higher intake of fiber in 26,107 Northern European subjects, (Rukh et al. 2013)
. A genotype score consisting of 24 risk alleles was not associated with snacking behavior
in 7,502 European subjects (Robiou-du-Pont et al. 2013). A recent two-stage genome-wide
association meta-analysis for macronutrient intake in more than 70,000 European subjects
identified FTO and a new locus (FGF21) not previously associated with obesity as
significant contributors to protein and carbohydrate / lipid intake, respectively (Chu et al.
2013; Tanaka et al. 2013). Collectively, these data suggest that the genetic predisposition
to polygenic obesity is at best selectively related to food intake and food behavior meaning
that other possible mechanisms involved in genetic predisposition to obesity should be
explored.
The positive association between increased energy intake and BMI is well
established (Prentice and Jebb 2004), however some questions still remain such as if
increased energy intake is the cause of increased BMI or a consequence of it. Higher total
energy intake has been associated with subsequent weight gain in children in longitudinal
studies (Berkey et al. 2000) but the association between total energy intake and weight gain
61
M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
across time is controversial in adults (Jørgensen et al. 1995; Halkjaer et al. 2006). This
complex pattern of association may relate to the fact that increased BMI results in higher
basal metabolic rates and higher energy requirements (Byrne et al. 2012). This question
also applies to genetic variants associated both with food-related traits and obesity
(Robiou-du-Pont et al. 2013). To gain more insight into the role of obesity predisposing
SNPs on energy intake and macronutrient distribution, we investigated the association
between 14 obesity predisposing SNPs as well as a genotype score on overall energy intake,
macronutrients, different types of fat, using a validated FFQ in 1,850 Canadian subjects of
European ancestry.
4.2 Methods
4.2.1 Participants
The EpiDREAM study included a total of 24,872 individuals from 191 centres in
21 countries who were screened for eligibility to enter in the DREAM clinical trial (Anand
et al. 2012). All individuals who were deemed to be at risk for dysglycemia defined by
family history, ethnicity and abdominal obesity, between the ages of 18 to 85 years, were
screened using a 75 gram oral glucose tolerance test (OGTT) from July 2001 to August 2,
2003. Detailed methods and description of the study cohort have been described earlier
(Anand et al. 2012). We included in the present study 1,850 Canadian subjects of European
ancestry having both FFQ and genotypic information available at baseline (Figure 2). All
non-Canadians with FFQ data were removed because country specific FFQs were used,
limiting the comparability of participants across countries. We excluded participants with
>5% of FFQ data not filled out or those with implausible dietary intakes. Self-reported
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white European ethnicity has been validated using the Eigensoft software
(http://genepath.med.harvard.edu/~reich/Software.htm). Samples that failed to cluster with
individuals of European ancestry were removed. The EpiDREAM study has been approved
by local ethics committee and informed consent was obtained from each subject before
participating in the study, in accordance with the Declaration of Helsinki.
Figure 2. EpiDREAM Participant Flow Chart
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
4.2.2 Phenotyping
Height (m) and weight (kg) were measured in clinical centers by a trained medical
staff. Standing height was measured to the nearest 0.1 cm with the participant looking
straight ahead in bare feet and with his/her back against a wall. Weight was measured to
the nearest 0.1 kg in light clothing. BMI was calculated as weight in kilograms (kg) divided
by height in meters (m) squared.
Diet was measured using a validated semiquantitative FFQ previously developed
for the SHARE (Study of Health Assessment and Risk Evaluation) study. Pearson
correlation coefficients between the diet records and FFQ in the European population
ranged from 0.30 for polyunsaturated fat to 0.65 for saturated fat (Kelemen et al. 2003).
4.2.3 Genotyping
Buffy coats for DNA extraction have been collected from all willing participants of
the EpiDREAM study. DNA has been extracted by the Gentra System. Genotyping was
performed using the Illumina CVD bead chip microarray ITMAT Broad Care (IBC) array
(Keating et al. 2008). Genotyping was performed at the McGill University and Genome
Quebec Innovation Centre using the Illumina Bead Studio genotyping module, version 3.2.
Marker with a missing call rate of greater than 10% and individuals with a 3% or greater
call rate were removed, as well as marker with a minor allele frequency of less than
0.00001. We selected 14 SNPs that display genome-wide significant association (P<5×108
) for BMI and / or binary obesity status in the literature and were genotyped on the versions
1 and 2 of the IBC 50K SNP array. The 14 SNPs are: rs1514176 (TNN13K), rs6235
(PCSK1), rs6232 (PCSK1), rs2206734 (CDKAL1), rs2272903 (TFAP2B), rs1211166
64
M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
(NTRK2), rs6265 (BDNF), rs1401635 (BDNF), rs997295 (MAP2K5), rs7203521 (FTO),
rs9939609 (FTO), rs1805081 (NPC1), rs2075650 (TOMM40/APOE/APOC1), rs11671664
(GIPR). The 14 SNPs showed no deviation from Hardy-Weinberg equilibrium (HWE) (all
P > 0.06, Table 2. The call rate for each of the 14 SNPs was 100%.
Gene
SNP
Risk
Allele
Major
Allele
Minor
Allele
Genotype
Count
Genotyping
Call Rate (%)
HWE pvalue
TNN13K
rs1514176
G
A
G
650 893 307
100
0.9922
PCSK1
rs6235
C
G
C
1018 693 139
100
0.1639
PCSK1
rs6232
G
A
G
1660 187 3
100
0.3389
CDKAL1
rs2206734
C
C
T
1179 606 65
100
0.2314
TFAP2B
rs2272903
G
G
A
1494 341 15
100
0.3535
NTRK2
rs1211166
A
A
G
1212 565 73
100
0.4828
BDNF
rs6265
G
G
A
1213 577 60
100
0.3903
BDNF
rs1401635
C
G
C
932 748 170
100
0.2611
MAP2K5
rs997295
T
T
G
670 885 295
100
0.9230
FTO
rs7203521
A
A
G
746 828 276
100
0.0637
FTO
rs9939609
A
T
A
639 871 340
100
0.1543
NPC1
rs1805081
A
A
G
662 891 297
100
0.9227
TOMM40/AP
OE/APOC1
rs2075650
A
A
G
1400 413 37
100
0.3140
GIRP
rs11671664
G
A
A
1478 351 21
100
0.9749
Table 2: Genotypic Information for Obesity Predisposing SNPs
4.2.4 Statistical methods
Statistical analyses were performed using SPSS (IBM Corp 2011). The overlap of
information between nutrition measures was assessed using Pearson correlation. For each
of the 14SNPs, the previously identified obesity risk allele was used as the risk allele.
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Single SNP analyses were performed under the additive model. A genotype score was
calculated by summing the alleles predisposing to obesity for the 14 SNPs so that the score
ranged from 0 to 28. We used a unweighted score as Janssen et al (2007). previously
showed that weighting had no major impact on the score. We constructed multivariate
linear regression models to determine the effect of each of the 14 SNPS and the genotype
score on BMI in addition to food consumption parameters. The regression models were
adjusted for sex, age and BMI. To determine the effect of the relative intake of nutrients
independent of caloric intake, nutrient intakes were adjusted for caloric intake by
regression analysis (Willett and Stampfer 1986). Two-tailed P-values are presented in this
manuscript and P < 0.05 were considered as nominally significant. After applying a
Bonferroni’s correction for multiple testing, a p <1.754x10-4 (0.05/285 tests) was
considered as significant. QUANTO (Gauderman and Morrison 2001), was used to
measure the power of finding an association between individual SNPs and BMI for varying
allelic frequencies and beta-coefficients (Figure 3) .
66
M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
A
1
0.9
0.8
Power
0.7
Beta 0.1
0.6
Beta 0.2
0.5
Beta 0.3
0.4
Beta 0.4
0.3
Beta 0.5
0.2
Beta 0.6
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Allele Frequency
B
0.2
0.18
0.16
Power
0.14
Beta 0.1
0.12
Beta 0.2
0.1
Beta 0.3
0.08
0.06
Beta 0.4
0.04
Beta 0.5
0.02
Beta 0.6
0
0
0.2
0.4
0.6
0.8
1
Allele Frequency
Figure 3. Statistical power for detecting associations between individual SNPs and BMI
according to allele frequency and beta-coefficients with a sample size of 1,850 participants.
A – Power for testing associations according to risk allele frequency and beta-coefficient,
assuming a two-sided P-value of 0.05 unadjusted for multiple testing. B – Power for resting
associations according to risk allele frequency and beta-coefficient, assuming a two-sided Pvalue of 1.754x10-4 after adjustment for multiple testing.
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
4.3 Results
Table 3 describes the characteristics of the 1,850 study participants. The different
nutrient components (total energy, total fat, saturated fat, monounsaturated fat (MUFA),
polyunsaturated fat (PUFA), trans-fat, carbohydrates and protein) were all significantly
correlated with each other in the study (Pearson correlation coefficient range 0.311 to
0.986; Table 4). The correlation between carbohydrates and total fat and fat subcomponents
was more modest, as well as the correlation between trans-fat and all other nutrient
components. BMI was significantly associated with total energy intake as well as all
energy-adjusted nutrients (Table 5).
All (n=1850)
Males (n=643, 34.8%)
Females (n=1207, 65.2%)
Age
53.07 (+/- 10.579)
55.20 (+/- 10.850)
51.93 (+/- 10.256)
BMI (kg/m2)
30.83 (+/-6.441)
30.37 (+/- 5.172)
31.07 (+/- 7.014)
Total energy (kcal)
1891.75 (+/- 657.076)
2103.13 (+/- 691.026)
1779.15 (+/- 609.286)
Total fat (g/day)
64.90 (+/- 27.354)
72.19 (+/- 30.655)
61.02 (+/- 24.257)
Saturated fat (g/day)
22.24 (+/- 10.278)
24.92 (+/- 11.531)
20.82 (+/- 9.237)
Monounsaturated fat
(g/day)
24.82 (+/- 11.031)
27.90 (+/- 12.405)
23.19 (+/- 9.845)
Polyunsaturated
(g/day)
9.86 (+/- 4.477)
10.85 (+/-4.979)
9.33 (+/- 4.091)
Trans fat (g/day)
0.63 (+/- 0.739)
0.71 (+/- 0.805)
0.591 (+/- 0.699)
Carbohydrates
(g/day)
241.84 (+/- 91.252)
263.48 (+/- 95.074)
230.31 (+/- 87.016)
Protein (g/day)
82.636 (+/- 31.146)
89.11 (+/- 32.606)
79.19 (+/- 29.783)
fat
Table 3: EpiDREAM Participant Characteristics
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
0.844
0.819
0.416
0.91
0.882
0.951
0.986
0.918
0.523
0.652
0.844
0.912
0.769
0.463
0.593
0.787
0.936
0.53
0.605
0.816
0.573
0.631
0.774
0.312
0.311
Polyunsaturated Fat
Trans Fat
0.711
Carbohydrates
Protein
All correlations significant at the 0.01 level (two-tailed)
Table 4: Pearson Correlations for Unadjusted Nutrient Intakes
Association with BMI
Energy
Total Fat
Saturated Fat
Monounsaturated Fat
Polyunsaturated Fat
Trans Fat
Carbohydrates
Protein
Protein
Trans Fat
0.819
Monounsaturated Fat
Carbohydrates
Polyunsaturated Fat
0.878
Saturated Fat
Monounsaturated
Fat
Saturated Fat
Total Fat
Total Fat
Energy
Energy
Beta
SE
p-value
0.001
0.057
0.080
0.117
0.243
0.603
-0.011
0.037
2.340E-04
0.012
0.027
0.027
0.060
0.239
0.004
0.010
0.002
3.000E-06
0.003
1.700E-05
4.900E-05
0.012
0.006
3.640E-04
Table 5: Association of Energy and Energy Adjusted Nutrients with BMI
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
4.3.1 Association of obesity predisposing SNPs and genotype score with BMI
Two out of 14 SNPs showed a directionally consistent and nominally significant
association with BMI: rs9939609 SNP in FTO (beta = 0.72 ± 0.21, P = 0.001) and
rs1514176 in TNNI3K (beta = 0.57 ± 0.21, P = 0.008) (Table 6). The genotype score was
also positively associated with BMI (beta / per additional risk allele = 0.14 ± 0.06, P =
0.03). Further adjustment for total energy intake did not modify the association between
rs9939609 and rs1514176 SNPs in FTO and TNNI3K or the genotype score and BMI
(Table 5).
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M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
Gene/SNP
GIRP
rs11671664
Gene Score
-
TOMM40/A
POE/APOC1
- rs2075650
NPC1
rs1805081
-
FTO
rs9939609
-
FTO
rs7203521
-
MAP2K5
rs997295
-
BDNF
rs1401635
-
BDNF
rs6265
-
beta
0.571
-0.228
-0.065
0.246
0.003
0.033
-0.055
0.302
-0.071
0.089
0.717
0.053
-0.360
0.336
0.140
SE
0.215
0.237
0.481
0.269
0.356
0.266
0.273
0.229
0.216
0.214
0.210
0.216
0.309
0.343
0.064
p-value
0.008
0.336
0.893
0.360
0.993
0.900
0.842
0.187
0.744
0.678
0.001
0.806
0.244
0.328
0.030
beta
0.552
-0.256
-0.038
0.240
0.028
0.035
-0.089
0.264
-0.061
0.108
0.721
0.041
-0.370
0.308
0.132
SE
0.215
0.236
0.480
0.268
0.355
0.266
0.273
0.229
0.215
0.214
0.210
0.216
0.309
0.343
0.064
p-value
0.010
0.278
0.937
0.371
0.937
0.895
0.743
0.250
0.776
0.613
0.001
0.848
0.231
0.368
0.039
beta
0.018
0.028
-0.016
0.012
-0.015
0.001
0.022
0.026
-0.007
-0.011
-0.001
0.010
0.004
0.025
0.007
SE
0.012
0.013
0.026
0.015
0.019
0.014
0.015
0.012
0.012
0.012
0.011
0.012
0.017
0.019
0.003
p-value
0.115
0.028
0.550
0.411
0.431
0.932
0.138
0.034
0.570
0.331
0.922
0.407
0.827
0.180
0.039
beta
0.011
-0.010
0.000
0.002
-0.004
-0.010
0.018
0.017
-0.007
-0.011
-0.001
0.002
-0.001
0.015
0.001
SE
0.007
0.007
0.015
0.008
0.011
0.008
0.009
0.007
0.007
0.007
0.007
0.007
0.010
0.011
0.002
p-value
0.102
0.197
0.993
0.769
0.715
0.222
0.032
0.020
0.281
0.091
0.881
0.776
0.892
0.177
0.662
beta
0.013
-0.014
0.010
-0.002
-0.011
-0.014
0.016
0.016
0.001
-0.015
-0.007
-0.005
-0.004
0.008
-0.001
SE
0.009
0.010
0.020
0.011
0.015
0.011
0.011
0.009
0.009
0.009
0.009
0.009
0.013
0.014
0.003
p-value
0.149
0.155
0.613
0.828
0.445
0.212
0.164
0.083
0.923
0.096
0.397
0.604
0.757
0.549
0.689
beta
0.013
-0.012
0.001
0.002
-0.004
-0.008
0.021
0.017
-0.014
-0.013
0.004
0.005
0.002
0.020
0.001
SE
0.008
0.009
0.018
0.010
0.013
0.010
0.010
0.008
0.008
0.008
0.008
0.008
0.011
0.013
0.002
71
NTRK2
rs1211166
-
TFAP2B
rs2272903
-
CDKAL1
rs2206734
MUFA
-
Saturated Fat
PCSK1
rs6232
Total Fat
-
Total Energy Intake
PCSK1
rs6235
BMI adjusted for total energy intake
-
TNN13K
rs1514176
BMI
M.Sc. Thesis – A. Mayhew; McMaster University – Health Research Methodology
PUFA
Trans Fat
Carbohydrates
Protein
p-value
0.107
0.187
0.945
0.874
0.742
0.395
0.036
0.046
0.080
0.090
0.571
0.565
0.859
0.107
0.540
beta
0.017
-0.008
-0.012
0.008
0.008
-0.010
0.029
0.024
-0.020
-0.007
0.010
0.007
0.000
0.013
0.004
SE
0.008
0.009
0.019
0.011
0.014
0.010
0.011
0.009
0.008
0.008
0.008
0.008
0.012
0.013
0.003
p-value
0.050
0.403
0.523
0.423
0.565
0.323
0.007
0.008
0.017
0.386
0.223
0.392
0.970
0.346
0.126
beta
0.068
-0.015
0.093
0.003
0.034
-0.090
0.022
0.090
0.004
-0.003
-0.009
-0.013
-0.042
-0.052
0.005
SE
0.031
0.034
0.069
0.039
0.051
0.038
0.039
0.033
0.031
0.031
0.030
0.031
0.045
0.049
0.009
p-value
0.029
0.671
0.178
0.933
0.505
0.019
0.576
0.006
0.889
0.929
0.758
0.678
0.342
0.289
0.558
beta
-0.009
0.010
0.002
0.004
-0.002
0.002
-0.009
-0.004
0.005
0.002
-0.001
-0.005
-0.006
-0.001
-0.001
SE
0.005
0.006
0.012
0.007
0.009
0.007
0.007
0.006
0.005
0.005
0.005
0.005
0.008
0.009
0.002
p-value
0.113
0.079
0.860
0.577
0.797
0.758
0.192
0.471
0.350
0.663
0.921
0.337
0.458
0.866
0.679
beta
-0.005
0.002
-0.004
0.001
0.001
0.004
0.001
-0.004
-0.011
0.005
0.005
0.005
-0.001
0.002
0.000
SE
0.006
0.007
0.013
0.008
0.010
0.007
0.008
0.006
0.006
0.006
0.006
0.006
0.009
0.010
0.002
p-value
0.433
0.782
0.781
0.944
0.928
0.605
0.873
0.550
0.066
0.363
0.371
0.417
0.865
0.875
0.948
Table 6: The Association of Obesity Predisposing SNPs with BMI and Energy Adjusted Nutrients
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4.3.2 Association of obesity predisposing SNPs and genotype score with dietary intake
parameters
Six out of the 14 SNPs and the genotype score displayed nominally significant associations
with at least one energy adjusted dietary intake parameter: rs1514176 (TNN13K) with trans-fat
(beta = 0.068 ± 0.031, p = 0.029), rs6235 (PCSK1) with total energy (beta = 0.028 ± 0.013, p =
0.028), rs1211166 (NTRK2) with trans-fat (beta = -0.09 ± 0.038, p = 0.019), rs6265 (BDNF) with
total fat (beta = 0.018 ± 0.009, p = 0.032), MUFA (beta = 0.021 ± 0.01, p = 0.036), and PUFA
(beta = 0.029 ± 0.011, p = 0.007), rs1401635 (BDNF) with total energy (beta = 0.026 ± 0.012, p =
0.034), total fat (beta = 0.017 ± 0.007, p = 0.020), MUFA (beta = 0.017 ± 0.008, p = 0.046), PUFA
(beta = 0.024 ± 0.009, p = 0.008), and trans fat (beta = 0.090 ± 0.033, p = 0.006), rs997295
(MAP2K5) with PUFA (beta = -0.020 ± 0.008, p = 0.017), and the genotype score with total energy
(beta/ additional risk allele = 0.007 ± 0.003, p = 0.039). Further adjustment for BMI did not
significantly modify any of the relationships (Table 6).
4.4 Discussion
This study confirms the association of BMI with total energy intake, macronutrient intake,
and the subcomponents of fat as well as the association of rs9939609 (FTO) and rs1514176
(TNN13K), and genotype score with BMI in a North American population of European ancestry.
Six of the 14 SNPs as well as the genotype score were nominally associated with at least one of
the dietary intake parameters. Of particular interest was the association between two separate SNPs
located in or near BDNF (rs6265 and rs1401635), with total fat, MUFA, and PUFA intake with
rs1401635 also being associated with total energy and trans-fat intake. The study also found an
association between rs6235 (PCSK1) and the genotype score and total energy intake.
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After applying the Bonferroni correction to adjust for the 285 tests conducted for analysis
a p-value <1.754x10-4 was considered significant. After adjustment, only the association between
total fat, MUFA, and PUFA intake with BMI remained significant. Because our results are
otherwise nominally significant, at least some of them are likely false positive results. However,
the literature supports most of our results, though the more novel discoveries require further
replication. We replicated findings of the association of FTO (Dina et al. 2007; Frayling et al.
2007; Willer et al. 2009), and TNNI3K (Willer et al. 2009; Hong and Oh 2012; Mccaffery et al.
2012; Cornelis et al. 2014) with BMI. The lack of association of other SNPs with BMI reflects the
modest power of the study, supported by the nominal association of the 14 SNP genotype score
with BMI. Though we did not find a significant association between either BDNF SNPs with BMI
in the current study, the association between BDNF and obesity is well established with BDNF
being linked to both monogenic hyperphagic obesity (Gray Diabetes 2006) and polygenic human
obesity (Thorleifsson et al. 2009). In mouse models, heterozygous BDNF+/- are hyperphagic and
gain significant weight in young adulthood compared to wild type mice (Lyons et al. 1999). Mice
which have BDNF eliminated from the brain after birth similarly display hyperphagia, consuming
74% more food and experiencing a 80-150% increase in body weight compared to controls (Rios
et al. 2001). Additionally, BDNF (rs925946) has previously been found to be associated with food
related behaviours, such as increased snacking (Robiou-du-Pont et al. 2013) and increased energy
consumption (rs10767664 and rs6265) (Mccaffery et al. 2012). No previous studies have found an
association between BDNF risk variants and fat consumption, however an association between
BDNF and an increased number of servings of both dairy products and products from the meat,
eggs, nuts, and beans food group was found (Mccaffery et al. 2012). Dairy products as well as
meat, eggs, and nuts, can be higher sources of fat in the diet therefore potentially supporting a link
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between BDNF risk variants and higher fat consumption. Additional evidence of a link between
BDNF and fat consumption comes from mice models in which young BDNF mutants experienced
premature development of hyperphagia when fed a high fat diet but not when fed a balanced diet
(Fox and Byerly 2004). In this study, two BDNF variants were associated with total fat, MUFA,
and PUFA consumption reducing the likelihood that the relationship is a spurious result.
Similarly to BDNF, PCSK1, is linked to both monogenic hyperphagic obesity (Farooqi et
al. 2007) and polygenic forms of obesity (Benzinou et al. 2008). To the author’s knowledge, this
study is the first to find an association between PCSK1 and increased energy consumption.
Evidence from the monogenic form of obesity involving PCSK1 supports a hypothetical
relationship. In the monogenic form of obesity, a total PCSK1 deficiency causes a prohormone
convertase 1/3 deficiency leading to extreme hyperphagia because of an inability to cleave
prohormones related to metabolism such as pro-insulin or pro-POMC (Creemers et al. 2012; Frank
et al. 2013). It is conceivable that other functional variants in PCSK1 could similarly decrease
prohormone convertase 1/3 levels, leading to overeating and a milder form of obesity as seen in
polygenic forms of obesity. The reduced effect of the functional variants compared to total PCSK1
deficiency may be part of the reason no previous studies have detected a relationship as the effect
size is small. Mice models also support the role of prohormone convertase in obesity with a study
finding that mice with a novel allele of mouse prohormone convertase (PC1, N222D) experienced
increased energy intake and elevated body weight compared to wild type littermates. The mutation
is semi-dominant as mice heterozygous for the trait experienced a more mild increase in energy
consumption and obesity (Lloyd et al. 2006).
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The current evidence associating a genotype score with dietary intake parameters is
inconclusive. Both (Cornelis et al. 2014) and (Rukh et al. 2013) found that a higher genotype risk
score is associated with high BMI, however while (Cornelis et al. 2014) found that the genotype
risk score had a positive association with emotional and uncontrolled eating, (Rukh et al. 2013)
and (Robiou-du-Pont et al. 2013) found that an increased risk score was associated with a decrease
in energy consumption and had no association with snacking respectively. In our study we found
that the genotype score was positively associated with both BMI and total energy consumption.
One possible explanation for the discrepancy between studies is that while monogenic forms of
obesity are always associated with increased energy consumption (Choquet and Meyre 2011a), the
association of obesity predisposing SNPs may modulate BMI through other mechanisms than food
intake. For example, rs9939609 (FTO) has consistently been shown to interact with high energy
or high saturated fat diets to lead to obesity (Ahmad et al. 2011; Corella et al. 2011; Moleres et al.
2012; Phillips et al. 2012). PPARG which encodes peroxisome proliferator-activated receptor y
(PPARy), a nuclear receptor involved in fatty acid sensing and the regulation of adipocyte
differentiation, lipid metabolism, fat storage, and insulin sensitivity is associated with obesity and
BMI and suggests that lipid metabolism may be an important component of obesity (Cecil et al.
2012). Various SNPs located in or near MC4R, a gene involved in monogenic forms of obesity,
have been found to be associated with basal metabolic rate (rs11872992), respiratory quotient, fat
oxidation, total energy expenditure, insulin resistance, and leptin (SNP – 1385), and total activity
levels (SNP – 1704) (Cole et al. 2010).
Some obesity SNPs likely work through mechanisms other than increased energy
consumption. For example olfactomedin 4 (OLFM4) and homebox protein (HOXB5) are
associated with common early-onset obesity. OLFM4 plays a role in immunity function against
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Helicobacter pylori infection with increased weight being associated with more bacteria and
homebox protein (HOXB5), coded for by the HOXB5 gene, is proposed to play a role in gut
function with transcription factors increasing after fat loss (Bradfield et al. 2012). However, there
is also substantial evidence that some SNPs, in particular FTO are associated with energy
consumption independently of BMI (Cecil et al. 2008; Speakman et al. 2008; Timpson et al. 2008;
Haupt et al. 2009). Our study found an association between rs1401635 (BDFN), rs1401625
(PCSK1) and the genotype score and total energy consumption supporting the view that obesity
predisposing SNPs are causally involved in the regulation of energy intake. Currently, excessive
energy intake is considered the primary risk factor for obesity, far more so than decreased physical
activity (Speakman and O’Rahilly 2012). People who are genetically predisposed to obesity, either
from rare and severe monogenic forms or from common polygenic variants with an accumulation
of variants with individual modest effect on disease risk, are much more vulnerable to the effects
of the “obesogenic” environment in which food is inexpensive, abundant, served in large portions,
and heavily marketed (Mitchell et al. 2011). With the knowledge from this study and previous
research, we can conclude that because many people are genetically predisposed to obesity and
certain negative food consumption patterns, changes to the accessibility and quality of the food
available are required which can be accomplished through changes to policy.
Strengths of our study included using a FFQ validated for total energy intake,
macronutrients, and three of the four subcomponents of fat we investigated in the study. An
ethnically homogenous sample while decreasing the generalizability of our findings, decreases the
risk that the FFQ would less accurately estimate food consumption for certain ethnicities and helps
to control for culturally specific habits that may relate to obesity. Though a modest sample size
limited the statistical power for the study, our nominally significant results are well supported by
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either prior research or additional biological evidence, decreasing the likelihood that results are
spurious (Ioannidis et al. 2008). Additionally, the use of a genotype score is relatively novel.
Limitations of this study include using a cross-sectional sample which does not allow for the
temporal relationship between energy intake and obesity to be examined in the context of obesity
predisposing SNPs, the use of FFQ data opposed to ad libitum tests or dietary recall methods which
decreases the accuracy of the food parameter estimates, and the need for some of the novel
nominally significant results to be replicated.
4.5 Conclusions
In conclusion, our study showed nominally significant associations of rs9939609 (FTO)
and rs1514176 (TNN13K), and genotype score with BMI, an association of six of the 14 SNPs and
the genotype score with dietary intake parameters. The associations of BDNF (rs6265 and
rs1401635), with total fat, MUFA, and PUFA intake with rs1401635 also being associated with
total energy and trans-fat intake and rs6235 (PCSK1) and the genotype score and total energy
intake in particular warrant further investigation. Our data supports the view that the genetic
predisposition of obesity includes some obesity SNPs influencing energy consumption (O’Rahilly
and Farooqi 2008).
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5.0 An Evolutionary Perspective on Eating Disorders
5.1 Introduction
There is an ongoing debate about the causes of eating disorders. It is clear that there is a
complex interplay between life experiences, personal characteristics/environmental factors, and
genetics. The life experiences that are associated with eating disorders, such as early menarche
(BN), high parental expectations, sexual abuse, family dieting, criticism about one’s shape and
size, and participation in activities promoting a low fat mass like distance running, swimming,
dancing, and modelling (Klein and Walsh 2003) are well established.
In addition to life
experiences, personal characteristics and environmental factors such as gender, age, birth cohort,
ethnicity, and culture are likely important contributors to the development of eating disorders.
Despite the high hereditability of eating disorders, estimated to be between 0.28 and 0.76 for AN,
0.30 to 0.83 for BN (Slof-Op ’t Landt et al. 2005), and 0.41 to 0.57 for BED (Thornton et al. 2011),
there is very limited evidence of robust association between eating disorders and candidate genes
or associations detected through GWAS. The high heritability of eating disorders as well as their
relatively high prevalence, at least in Western societies, indicate that eating disorders, or
behaviours affiliated with eating disorders, may have previously provided an evolutionary
advantage. Using personal characteristics and environmental factors which have been identified as
risk factors for eating disorders, different evolutionary theories are investigated.
5.2 Suppression of reproduction and sexual competition
Some believe that AN is a method through which females can either supress or delay
reproduction to avoid procreating at a time when their offspring will have threatened survival. This
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would ultimately be an advantage as females who supressed reproduction until more favourable
environmental conditions would likely have higher lifetime reproductive success therefore would
be more likely to pass on the genes responsible for AN characteristics. The theory also states that
through social stress, females can try to supress the reproduction of other females in an attempt to
increase the chance of finding a high quality mate and create a better environment for their
offspring (Wasser and Barash 1983). While it may be advantageous for dominant females to
supress the reproductive capacity of others, Gatward (2007) proposes that it may also be in the
best interest of subordinate females to supress their reproduction through restricting food access.
Historically, being accepted as part of a group was critical for survival. While weight loss could
be used to become more attractive, severe weight loss such as with AN could be interpreted as a
sign of subordination and the potential lack of fertility would remove the subordinate female as a
threat to the dominant female. If the subordinate female poses no threat to the dominant female,
there is no reason for her to be excluded from the group. Another element of suppression of
reproduction is in young girls who have not yet reached sexual maturity. Maintaining a lower
weight may delay menarche allowing them to pursue other activities such as academics in modern
times before having children (Surbey 1987). The theories involving suppression of reproduction
are limited however, because they do not explain the hyperactivity seen in AN patients, the denial
of the seriousness of their disease and distorted body image, or the presence of AN in males
(Kardum et al. 2008).
A theory of inter-female competition excluding the concept of purposeful suppressed
reproduction has also been developed (Abed 1998). The hypothesis states that from an
evolutionary perspective, the ability to restrict food access may have been beneficial for females
in order to attract potential partners. A “nubile” shape consisting of a low waist to hip ratio is a
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sign of reproductive potential because it indicates youthfulness as well as no current pregnancy.
Being more attractive would increase a female’s likelihood of obtaining and maintaining a high
quality partner, therefore providing an advantage to her offspring explaining the high heritability
of eating disorders. This theory translates well into modern times where females have been
delaying having children until later in life, therefore females may decrease their weight in order to
stay competitive with younger females. However, despite the potential for the prevalence of eating
disorders to be increasing over time (Hoek and van Hoeken 2003; Preti et al. 2009), the disease
still predominantly affects young females with an peak in incidence between 10 to 19 years (Hoek
and van Hoeken 2003; Currin et al. 2005; Son et al. 2006) which is not the demographic
hypothesized to be trying to lose weight to compete with younger females. As with the suppression
of reproductive potential theories, there are limitations to the hypothesis including a lack of
applicability to males, the potentially incorrect assumption that a thin body type was/is universally
desirable, and that the theory was developed in the context of modern observations. The
suppression of reproduction theory and the sexual competition theory are also contradictory. The
suppression of reproduction theory indicates that anorectic tendencies decrease fertility while the
sexual competition theory indicates that anorectic tendencies will increase the quality of mate and
subsequent offspring and not have a negative consequence on fertility.
5.3 Adapted to flee famine hypothesis and rogue hibernation
The adapted to flee famine hypothesis (AFFH) provides an explanation for a possible
adaptive advantage to what are now known as symptoms of AN. It proposes that food restricting
behaviour, hyperactivity, and denial of seriousness of weight loss previously enabled migration
during periods of local food insecurity. These characteristics would allow individuals to ignore
their near starvation state and continue to be physically active until reaching a more food secure
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area. These adaptive mechanisms would be positively selected for as only those capable of
reaching food secure areas would be able to survive and reproduce, explaining the high heritability
of eating disorders (Guisinger 2003).
Elaborating upon this theory, Scolnick and Mostofsky (2014) propose that in today’s food
abundant environment, people with AN are trapped in a state of semi-starvation, hypothermia, and
hyperactivity because their metabolic pathways are falsely signaling that they are in a food scare
environment. This is based upon the metabolic similarities between AN and another physiological
adaptation to food scarcity, mammalian hibernation. Mammalian hibernation involves a decreased
metabolic rate, hypothermia, and a switch in the metabolic fuel supply from carbohydrates to lipids
and ketones. Hibernation allows the animal to survive cold, nutrient depleted winters. The cycle
of weight accumulation pre-hibernation, weight loss during hibernation, and subsequent weight
gain in spring after hibernation mimic periods of binging and purging in AN (Scolnick and
Mostofsky 2014). For both the AFFH and rogue hibernation theories of AN, it is proposed that in
modern food secure environments, the traits of restricting food consumption, hyperactivity, and
denial of seriousness of weight loss can be turned on by dieting or other contextual reasons for
decreasing food consumption (Guisinger 2003; Scolnick and Mostofsky 2014). The idea that
motivations other than the sole intention to lose weight can initiate AN may be important to
consider in an international context. Keel and Klump (2003) determined that if weight concerns
were removed as diagnostic criteria for AN, the prevalence would be similar in Western and nonWestern countries.
Both of these theories are supported by epidemiological information on AN. For example
the peak incidence of AN is between the years of 10 and 19 (Hoek and van Hoeken 2003; Currin
et al. 2005; Son et al. 2006). Fertility declines as age increases (Nelson et al. 2013) therefore it
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would be most advantageous for young females capable of bearing children in the future to be able
to relocate during times of food insecurity. Similarly, AN is mostly found in females with a ratio
of 10 to 20 females diagnosed for each male (Favaro et al. 2003; Hoek and van Hoeken 2003;
Currin et al. 2005; Hoek 2006; Wade et al. 2006; Hudson et al. 2008; Preti et al. 2009; Marques et
al. 2011; Heaner and Walsh 2013; Meczekalski et al. 2013). A far greater number of females would
be required to re-establish a society compared to males as female’s capacity to reproduce is
comparatively far lower.
In our review of the ethnic and cultural differences in the prevalence of eating disorders
we concluded that differences in prevalence of AN in different ethnicities likely have a strong
cultural component, but that there is the possibility that underlying genetic differences between
ethnicities may also be a contributing factor. The studies conducted have been limited by the
challenges of separating culture and genetics and there is no real evidence confirming or denying
the possible role of genetic differences between ethnicities influencing the risk of developing
eating disorders. However, if genetic differences are found to be an important risk factor for the
development of eating disorders, the evidence would fit in well with the AFFH. People of
European and African have some distinct genetic differences. It is possible that the genes
promoting AN characteristics were more advantageous in European ancestors compared to African
ancestors because of the vastly different climates and consequent patterns of food availability. The
migration patterns of Africans versus all other ethnic groups, including Europeans, may have
selected for different genes as well. Europeans migrated significantly further than Africans which
may have made genes allowing people to continue to migrate despite food scarcity a much more
significant advantage. This may explain why AN is much more prevalent in people of European
descent compared to other ethnicities.
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Culture has an important role in modern day AN. To receive the diagnosis of AN, an
individual must have a preoccupation with their weight which is likely a cultural construct
stemming from the beauty ideal of a thin shape. Keel and Klump (2003) found that if preoccupation
with body weight is removed as a diagnostic criterion, a similar proportion of the general and
psychiatric populations in Western and non-Western countries are found to have the disease. Using
the AFFH which suggests that food restriction causes the metabolic changes leading to the
symptoms of AN, regardless of if the goal is to lose weight or not, other food restriction behaviours
like fasting, or decreased food consumption because of stress could also bring on the disease.
However, in these cases, the disease would not be considered AN because the individuals would
not have a weight preoccupation. Further support for the AFFH and rogue hibernation theories
comes from the general lack of association of AN with genes related to serotonin, dopamine, and
opioids which all play roles in the pleasure and enjoyment of food. Though levels of serotonin,
dopamine, opioids, and related metabolites and receptors appear to be altered in people in AN, the
lack of evidence showing a link between relevant genes and the disease indicate that the abnormal
physiological state may be a result of the disease, rather than the cause of it.
5.4 Thrifty gene hypothesis
Fewer theories exist about how symptoms of BN and BED previously could have been an
evolutionary advantage compared to AN. The leading theory relates to the thrifty genotype
hypothesis which focuses on how historically, having extra fat stores were protective. Adipocytes
are used for fat storage in the form of triglycerides. Having these fat stores play a role in helping
to avoid malnutrition, regulate reproduction, and allow for survival during variations in the energy
supply. Both hunter/gathers as well as agricultural societies experienced fluctuations in the food
supply. However hunter/gathers were able to migrate more easily decreasing the fluctuation in
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energy available to them which relates back to the AFFH for AN (Wells 2006). Mass immigration
is also thought to have selected for thrifty genes because people are often escaping food scarce
conditions and may be exposed to more food scarcity through the immigration process and intense
competition for food (Wells 2006). As agricultural societies developed, people were less able to
relocate to avoid famine and as a result, “thrifty” genes were selected for (Neel 1962, 1999; Wells
2006). These genes which previously promoted survival in food insecure environments are no
longer protective in the modern “obesogenic” environment, characterized by food that is
inexpensive, abundant, served in large portions, and heavily marketed while demands for physical
activity decrease (Wells 2006; Mitchell et al. 2011; Swinburn et al. 2011).
While the thrifty gene hypothesis provides an explanation for the underlying predisposition
to obesity, it alone does not explain many of the disordered eating patterns observed. In particular,
it does not explain AN, which has an underlying advantage of allowing people to function well
despite having little energy available to them. However, the development of BN involving a cycle
of binging and purging, BED which is similar to BN but without purging after periods of binging,
as well as people with AN who also binge (Klein and Walsh 2003) may in part be explained by
the thrifty genotype hypothesis. Both BN and BED involve binging on food, estimated to be
between 3000 – 4500kcal in a binge episode (Wolfe et al. 2009). It is plausible that the loss of
control and drive to eat, particularly energy dense foods, is caused by the same genes that
previously promoted fat accumulation and food consumption according to the thrifty gene
hypothesis. The historical food context may also help explain the benefits of binge eating. In hunter
gather societies, a successful hunt would provide a large quantity of food that would be shared.
Being able to consume large amounts of food in a short time span would be beneficial to
individuals, allowing them to have a greater portion of the available food. Furthermore, before the
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invention of refrigeration and preservation techniques, food had a relatively short life span and
being able to consume large quantities before the food became unsafe to eat would have been
advantageous.
The purging behaviour seen in some people with AN and those with BN may be a modern
iteration of the historical benefits of purging. Prior to the development of the obesogenic
environment, it is unlikely that many people were able to find enough food to binge on to gain an
undesirable amount of weight and thinness may not have been idealized as it now is in Western
society. However, with the current overabundance of food available it is very easy for people to
gain weight and most people with BN are within normal weight ranges (Kaye et al. 2000) while
those with BED are overweight or obese (Reichborn-Kjennerud et al. 2004; Field et al. 2012). It
is possible that people with BN, who by definition of the disease are preoccupied and concerned
about their weight (American Psychiatric Association 2013) are driven by the genes that would be
associated with the thrifty gene hypothesis to eat, however because of societal pressures to be thin
have adopted purging behaviours to compensate. Though the inter-female competition theory is
specific to AN (Abed 1998), it is possible that the same motivation to attract a high quality partner
that is proposed to cause AN may also cause purging behaviour in BN.
Support of the role of the thrifty gene hypothesis in conjunction with inter female
competition in the development of BN and BED can be found by comparing and contrasting the
diseases. Applying the thrifty gene concept, we hypothesize that the root causes of binging are the
same in BN and BED. There is a biological drive to consume food when available which is a relic
from the period of time in human evolution when food was scarce. The significant difference
between BN and BED is that BN involves purging in addition to binging which may be a cultural
linked behaviour or a combination of AN and BED present in an individual. Evidence supporting
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this theory comes from the risk factors for the two diseases. The risk of developing BN increases
in family environments in which parents have high expectations and focus on weight as well as
when individuals are involved with activities which promote a lean shape (Klein and Walsh 2003).
This suggests that people with BN have been taught that being overweight is bad and use purging
to avoid it. The ratio of males to females with BN is lower than that of BED. While both males
and females are influenced by media exposure for beauty ideals, there is evidence that it affects
females more, particularly the ideal of being thin (Hausenblas et al. 2013). Therefore, it is logical
that if BN and BED both stem from the same genetic predisposition to over consume food, more
females would have BN rather than BED because of a greater impact of societal standards of
beauty. The peak incidence of developing the disease provide further evidence. For BN the peak
incidence is between 17.0 and 19.7 years of age (Fornari et al. 1994; Favaro et al. 2003; Hudson
et al. 2008) whereas BED appears to be more equally distributed across the lifespan (Kinzl et al.
1999; Alegria et al. 2007; Grucza et al. 2007). Biologically, the need to store energy to survive
future famines would continue to occur across the lifespan, however concerns about being at an
ideal weight to attract an appropriate partner would be more relevant at the younger ages seen in
BN patients. We found that compared to BN, BED is more equally distributed across ethnicities
(Smith et al. 1998; Striegel-Moore et al. 2003; Alegria et al. 2007; Nicdao et al. 2007; Marques et
al. 2011; Taylor et al. 2013). This could in part be because weight preoccupation is predominantly
a Western concern (Pike et al. 2001; Simpson 2002), therefore people from non-Western countries
who binge may not feel the same drive to purge compared to their Western counterparts.
The thrifty genotype has been focused upon in this thesis, however it is important to note
that the thrifty genotype is a controversial theory to explain obesity. Speakman has been one of
the most vocal opponents of the thrifty genotype hypothesis. His main arguments include that
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mortality during times of famine is not enough to lead to genetic selection nor are the people who
often die during famine, the elderly and the young the ones that would strongly influence the gene
pool and that historical patterns of famine are incompatible with a selective hypothesis (Speakman
2008). Others have agreed with Speakman that the mortality rates in famine are not sufficient to
explain the possible genetic selection, however argue the effect of famine on fertility selection can
explain the genetic selection. Speakman’s argument that the historical patterns of famine are
incompatible with the thrifty genotype hypothesis has been strongly refuted, including by Neel
himself (Prentice et al. 2008). The original thrifty genotype hypothesis states that the selective
pressure for thrifty genes would have existed during the Paleolithic era in hunter gather societies
(Neel 1962). It is generally accepted that famines and seasonal food shortages are much more
applicable to agricultural societies in comparison to hunter gathers societies (Prentice et al. 2008).
As an alternative to the thrifty genotype hypothesis, Speakman proposed what is now referred to
as the drifty gene hypothesis proposing that obesity was previously selected against by the risk of
predators. The upper limit of human weight was limited by predators as people who were too large
to run away or adequately protect themselves would not survive. Speakman proposes that with the
development of social behaviour, weapons, and fire the risk of predators was significantly
decreased and that the distribution of body fatness increased because there were no longer any
selective pressures to not have a higher BMI. Because the selective pressures to maintain a
minimum weight were still in place, such as immune function and decreased risk of starvation,
random mutations and drifting gradually increased the distribution of BMI in the population
(Speakman 2007). Another evolutionary theory of obesity that has been proposed is the thrifty
phenotype which proposes that maternal nutrition, low birth weight, and a prediction of low food
sources cause an adaptive insulin resistance (Hales and Barker 2013; Levitan and Wendland 2013).
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The thrifty phenotype theory applies to diabetes reasonably well, however is limited for explaining
obesity in developed countries. Very few mothers experience malnutrition during pregnancy
despite the high prevalence of obesity in developed countries (Levitan and Wendland 2013). A
final theory called the thrifty epigenotype combines the thrifty genotype and thrifty phenotype. It
postulates that through genetic canalization, genes coding for a thrifty genotype have been well
preserved in the genome across all ethnicities and in all individuals. The theory also suggests the
epigenetics, changes in gene activity that are not caused by alternations of DNA sequences but
DNA methylation, histone modifications, remodeling of chromatin, and non-coding RNAs, is a
critical element which is in line with the thrifty genotype theory indicating that the fetal
environment influences adult health (Stöger 2008).
5.5 Summary of evolutionary theories
Of the evolutionary theories proposed, the AFFH best explains AN. Historically, having
the motivation to be physically active, avoiding a preoccupation with food during caloric
scarcity, and denying the seriousness of weight loss would enable individuals to migrate from
food insecure to food secure areas and consequently increase their chances of survival and future
reproduction. Though the people most commonly afflicted by AN are from Western countries
where food scarcity is generally not an issue, Scolnick and Mostofsky (2014) proposed that
behaviour such as dieting could trigger the same physiological adaptations as living in a food
scarce environment. The diagnosis of AN today requires an individual to be preoccupied with
their weight. This may be best viewed as a contributing factor to the development of the disease,
rather than a symptom of it. The AFFH is supported by the general patient characteristics of AN.
AN mostly occurs in young females of child bearing age which would be the most desirable
individuals to be able to flee from famine as more females than males required to re-establish the
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population. While the contribution of genetics and culture to ethnic differences in the prevalence
of AN is still unresolved, if there are genetic differences the AFFH also can explain why AN is
predominantly found in White populations (Keel and Klump 2003; Striegel-Moore et al. 2003;
Taylor et al. 2013). The evolutionary pressure on Europeans in regards to food would be
different compared to other regions of the world, therefore the genes promoting AN
characteristics may have been more beneficial to Europeans compared to other ethnicities. The
lack of association of genes related to serotonin, dopamine, and opioids which have been heavily
explored because of their role in the pleasure and enjoyment of food indicate that altered levels
of these neurotransmitters could be a result of the disease, rather than the cause of it, supporting
the AFFH proposition that the biochemical changes are in response to famine.
The thrifty gene hypothesis was initially developed to explain obesity. It may also explain
the overeating behaviour seen in BN and BED. Historically, accumulating fat during times of
food abundance would be beneficial to survival during periods of food insecurity as the fat could
be used as energy stores. However, in the food abundant environments that people in the
Western world live in today, the genes allowing for the overconsumption of food when it was
available may be now encouraging chronic overconsumption. This can explain the binging seen
in BN and BED, however to explain the purging seen in BN, an elaboration of the theory in
necessary. The purging behaviour can be explained by the desire to stay thin despite the binging
episodes. BN is predominantly found in Western cultures where thinness is idealized while BED
is seen more equally across ethnicities. We propose that there could be common genes associated
with BN and BED, with the desire to be thin determining which disease category individuals fall
within based on purging behaviour.
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Examining the evolutionary theories of eating disorders can help future gene
identification efforts by focusing on novel candidate genes as well as guiding the development of
biological evidence to support the findings of GWAS. With the increasing use of GWAS
approaches, evolutionary theories of eating disorders can also help determine new phenotypes to
use as outcome variables. Genetic studies to date have provided limited success, which may be
because they are focusing on symptoms of the disease, especially in AN, rather than the causes
of it. For AN, abnormal neurotransmitter and hormone levels have been widely observed, but
genetic association studies have been widely negative. However, if the altered neurotransmitter
and hormone levels are a result of a physiological adaptation to low food consumption rather
than one of the causes of the low food consumption as we propose, then we would not expect
genes involved with the production and reception of those neurotransmitters and hormones to be
altered. We propose that genes related to metabolism, specifically genes relating to mammalian
hibernation in other species, would be more beneficial to investigate compared to genes involved
in the production of neuropeptides. The genes relating to neuropeptide production in BN and
BED may still be relevant, however because BED is equally prevalent worldwide, it indicates
that the genes promoting an increased consumption of food may be more broadly found with
almost undetectable effect sizes. Though looking at genes related to metabolism may lead to a
further understand the genetic architecture of eating disorders, there is also the possibility that
hormones relevant to eating disorders are yet to be discovered. Investigating metabolites,
transcription factors, and protein profiles in people with eating disorders and comparing these to
levels after recovery of the disease or between episodes as well as with controls may help to
identify novel biomarkers and pathways in the etiology of eating disorders.
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5.6 Coexistence of Anorexia Nervosa and Bulimia Nervosa in binge/purge subtype
Anorexia Nervosa
The evolutionary theories of eating disorders clearly distinguish between AN and the
other two eating disorders, BN and BED. However, there is a significant amount of overlap
between the diseases, particularly when looking at the subtypes of AN. AN(R) (Anorexia
Nervosa restrictive subtype) is the diagnosis given to people who weigh less than 85% of what
they should weight and consume a limited amount of calories per day while AN(BP) (Anorexia
Nervosa binge/purge) is the diagnosis given to people who weigh less than 85% of their ideal
weight and go through cycles of binging and purging (American Psychiatric Association 2013).
AN(BP) is very similar to BN, except that individuals with BN do not meet the weight cut offs
for AN. In addition to the similarities of some of the diagnostic criteria, there is a significant
amount of cross-over between those with AN(R), AN(BP), and BN. In seven years of follow-up,
49% of women initially diagnosed with AN crossed over between AN subtypes and 34% crossed
over from AN to BN. Of those initially diagnosed with AN(R) 55% changed diagnostic
categories to AN(BP) and 10% crossed over to a BN diagnosis. Women also crossed over from a
diagnosis of BN to AN, with 14% crossing over to AN(BP) and 4% then crossing over to AN(R)
(Eddy et al. 2013). Below, we propose theories as to why the traits seen in BN, AN(R), and
AN(BP) overlap from a genetic perspective.
5.6.1 Binge eating as protection against Anorexia Nervosa
AN is considered the most dangerous of all mental disorders with more than 10% of
patients dying from the disease (Hoek 2006). Theoretically, the denial of nutrients to the body
seen in the restrictive subtype of AN could switch on a pathway encouraging the consumption of
calories, explaining the binging behaviour seen in people with the binge/purge subtype of AN.
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The cross-over of patients from the subcategories of AN supports this theory when considering
that more people cross over from the restrictive subtype to the binge/purge subtype (Eddy et al.
2013). Similarly, approximately 25 to 30% of people receiving treatment for BN have been
found to previously have been diagnosed with AN while only 5% of people initially diagnosed
with BN being diagnosed with either subtype of AN (Kaye et al. 2000). This supports that the
restrictive behaviour seen in AN(R) may be difficult to maintain and consequently cross over to
AN(BP) when the drive to eat becomes overwhelming. The cross-over of individuals from
AN(BP) to BN shows that the binging behaviour may slowly lead to weight gain, as the only
significant diagnostic difference between AN(BP) and BN is weight with people with AN being
less than 85% of their ideal weight (American Psychiatric Association 2013). However, this
theory ignores that binge eating is seen outside of the context of AN in both BN and BED and
that some people with AN(R) do not cross-over to AN(BP) during the duration of their disease.
5.6.2 Accumulation of independent genes leading to binge eating and Anorexia Nervosa
Taking into account the limitations of the previous theory as to why binge/purging
behaviour coexists with AN, we have hypothesized that it is possible that independent genes lead
to the binge/purging behaviour seen in BN and the extreme weight loss seen in AN. Through
random segregation within populations, it is possible that some people cumulate both the genes
predisposing to AN and the genes predisposing to BN. This theory is unlikely though because
both AN and BN are relatively rare diseases. Using an estimated prevalence of 0.3 to 3.0% of
AN in females (Favaro et al. 2003; Hoek and van Hoeken 2003; Currin et al. 2005; Hoek 2006;
Wade et al. 2006; Hudson et al. 2008; Preti et al. 2009; Marques et al. 2011; Heaner and Walsh
2013; Meczekalski et al. 2013) and estimated prevalence of 0.88 to 4.6% for BN in females
(Favaro et al. 2003; Hoek and van Hoeken 2003; Hoek 2006; Wade et al. 2006; Hudson et al.
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2008; Keski-Rahkonen et al. 2009; Preti et al. 2009; Smink et al. 2012), the prevalence of both
AN and BN, therefore the AN(BP) subtype should be between 0.0026 and 0.138% while the
actual prevalence of AN(BP) is likely somewhere between 0.15 to 1.5% of women based on half
of the people with AN having the binge/purge subtype (Eddy et al. 2013). Therefore, we can
conclude that because the actual prevalence of AN(BP) is higher than would be expected from
accumulation of independent genes leading to BN and AN, the genes associated with the
individual diseases are either in linkage disequilibrium or the same genes are causing the two
different diseases.
5.6.3 Mutations/structural gene variants with opposite effects in the same gene lead to AN
or BN
The theory that the accumulation of independent genes causing binging behaviour and
extreme weight loss explain the cross-over of diagnostic criteria in eating disorders is likely
incorrect based on the much higher prevalence of AN(BP) in the population compared to
theoretical prevalence of the genes crossing over. As discussed in the evolutionary theories
section, the ability to consume large amounts of food when readily available may have been
advantageous leading to the selection of genes that now promote binge eating in the modern food
abundant environment (Neel 1962, 1999; Wells 2006; Wells et al. 2006; Mitchell et al. 2011;
Swinburn et al. 2011). The ability to also be highly functioning with minimal caloric intake may
also have been beneficial as it would enable migration from food insecure areas to more food
secure areas promoting genes that may now lead to what is known as AN (Guisinger 2003;
Scolnick and Mostofsky 2014). It is possible that mutations or structural variants such as
deletion/duplication, gain of function/loss of function, gain of expression/loss of expression in
the same genes may lead to AN and binging behaviour. This ties in with the evolutionary
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theories as these genes may have been positively selected for in the past under different
environmental conditions and people with both types of mutations may have been favoured. This
could lead to partial linkage disequilibrium between mutations in the population, leading to the
high prevalence of the mixed phenotypes. In the past, people possessing both the loss of
function and gain of function mutations may not have suffered from any negative side effects
allowing for the transmission of the genes on to future generations. It is feasible that the mixed
phenotypes are now only problematic in the obesogenic environment. Weight concerns are a
relatively modern concept and dieting to lose weight could trigger the metabolic changes
allowing for survival despite low caloric consumption. However, if the same genes are also
causing binging behaviour, individuals may purge in order to balance the conflicting biological
drives to survive with minimal caloric consumption and to take advantage of the food available
to them. The possibility of a single gene having both loss of function and gain of function
mutations is supported by recent findings on MC4R and the 16p locus where deletion/loss of
function mutations lead to hyperphagic obesity while duplications/gain of function mutations
lead to leanness and food-related phenotypes (Geller et al. 2004). To test this hypothesis, a genelevel association using a whole-exome sequencing approach could be conducted in patients with
AN(R), AN(BP), BN, BED, and healthy controls could be conducted.
5.6.4 Mutations in nearby genes result in partial linkage disequilibrium of Anorexia
Nervosa and binge eating traits
Similar to the theory that the same genes may contain loss of function/gain of function
mutations causing the cross-over of traits seen in eating disorders is the theory that the genes
coding for the different characteristics of eating disorders are in genes located near each other,
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5.6.5 Summary of the role of genetics in the coexistence of Anorexia Nervosa and Bulimia
Nervosa in binge/purge subtype Anorexia Nervosa
We have proposed four theories for why there is an overlap of characteristics seen in
AN(R), AN(BP), BN, and BED.
1. Binge eating is used as a protective mechanism against starvation in people with AN who
are not meeting their energy requirements
2. AN(BP) occurs in individuals who have both the independent genes for AN and the
independent genes for binge eating
3. Mutations/structural gene variants with opposite effects in the same gene lead to AN or
BN
4. Mutations in nearby genes result in partial disequilibrium of the genes for AN and the
genes for binge eating causing a higher than expected number of people to display the
mixed phenotype AN(BP) than would be expected if the genes were not in partial linkage
disequilibrium
We found that it is unlikely that that binge eating is purely a protective mechanism against AN
because it doesn’t account for the high proportion of people who binge eat without having prior
starvation as seen in BN and BED. We also discount the theory that AN(BP) occurs in
individuals who have the genes for both AN(R) and binge eating as the prevalence of AN(BP)
far surpasses what would be expected if the genes for AN(R) and binge eating are independent of
each other. It is feasible that both mutations/structural gene variants in the same gene causing
opposite effects and that mutations in nearby genes resulting in partial disequilibrium for the
genes causing AN(R) and binge eating can explain the overlap of phenotypes seen in AN(BP).
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5.7 Conclusions
We combined the evolutionary perspective of eating disorders with the possibility of the
genetic overlap causing mixed phenotypes, and the epidemiological evidence of differences in
prevalence of eating disorders in different ethnicities and cultures to create a framework
suggesting under which conditions each eating disorder will occur (Figure 4). In this framework,
we propose that there are separate genes predisposing individuals to AN(R) and genes that
predispose individuals to binge eating. These genes are likely to overlap because they are in
partial linkage disequilibrium, either because mutations/structural variants within the same gene
code for loss of function/gain of function mutations or because the genes are located close to
each other. Therefore, a greater number of people would have characteristics of both diseases
than would be expected if the genes were independent. Thus three genotypic categories were
created, people with only AN(R) genes, people with only binge eating genes, and people with
genes predisposing to both diseases. Another critical element of the framework is the differences
in prevalence of the eating disorders seen in different cultures/ethnicities. Because of the
challenges in untangling the effect of culture versus ethnicity it is impossible to determine how
much of the between-ethnicity variability in the prevalence of eating disorders is attributable to
genetics versus how much is attributable to culture. However, it is well established that White
women are more concerned about weight have lower weight ideals in comparison to Black
women (Pike et al. 2001; Gluck and Geliebter 2002; Simpson 2002) and it is very feasible that
at least some of the greater prevalence of AN and BN in White females is because they feel an
increased pressure to be thin. The pressure to be thin is not exclusively related to ethnicity and
consequently in our framework we created a binary variable of either the presence or absence of
pressure to be thin.
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The two independent variables consisting of the three genotypic groups and the presence
or absence of the pressure to be thin create six categories. Individuals with only the genes
predisposing to AN(R) will develop AN(R) if there is sufficient pressure to be thin using the
elaboration of AFFH and rogue hibernation theories which indicate that dieting can cause the
metabolic shift leading to the diseases. In the absence of weight concerns, an individual would
not develop AN(R) despite having predisposing genes unless starvation was achieved through
famine, fasting, or other non-weight related reasons why food consumption may be lowered.
People with only the genes predisposing to binge eating in the presence of the pressure to be thin
will be at risk for developing BN because genetically they are driven to eat however have a
strong desire to maintain a normal weight and therefore use inappropriate compensatory
behaviours. Without the pressure to maintain a certain weight, only BED will occur because the
individual is not driven to compensate for the overconsumption of calories. For those with the
mixed genotype, in the presence of having a weight preoccupation AN(BP) occurs because the
individual cannot avoid binging on food, however is highly motivated to maintain a minimum
weight and has the genetic advantage of being able to be functional despite their low energy
intake. People with the mixed phenotype who do not feel pressure to be thin will be at increased
risk for BED because they are driven to binge, yet do not feel the need to compensate for the
overconsumption of calories.
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Genes predisposing Genes predisposing
to Anorexia Nervosa to AN (restrictive
(restrictive subtype) subtype) and genes
predisposing to
binge eating
Genes predisposing
to binge eating
Presence of pressure
to be thin
Anorexia Nervosa
(restrictive subtype)
Anorexia Nervosa
(binge/purge subtype)
Bulimia Nervosa
Absence of pressure
to be thin
No disease – unless
Anorexia Nervosa is
triggered by factors
other than weight
loss, such as lack of
food availability or
fasting
Binge Eating
Disorder
Binge Eating
Disorder
Figure 4: Framework for the risk of developing an eating disorder based on genotypic category and
presence or absence of pressure to be thin
This framework provides a simplified view of the interaction of the potential genetics of
eating disorders and the environment, specifically the pressure to be thin. There are many other
factors that contribute to the risk of developing eating disorders not captured in the model such
as epigenetics, gene by gene interactions, gene by environment interactions in addition to the
pressure of being thin, any potential genetic factors which contribute to the predisposition of
being more sensitive to the pressure of being thin, and a plethora of other environmental factors.
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6.0 Ethical Considerations
The investigation of genetic susceptibility of diseases and the subsequent research to
determine the biological pathways through which the genes are functioning is working towards
personalized medicine. The concept of personalized medicine involves using an individual’s
unique genetics as well their life context to predict which diseases or medical conditions they will
suffer from and ideally prevent the disease from occurring, as well as providing targeted treatment
plans if a disease is not preventable (Joyner and Prendergast 2014). Examples of genotyping being
used to help health care practitioners decide on the treatment or management of disease exist today.
Screening for the BRCA1 and BRCA2 genes in females with familial breast cancer allows for
increased screening or preventative measures to be taken if necessary (Foulkes and Shuen 2013).
Phenylketonuria, an inability to convert phenylalanine to tyrosine caused by mutations in the gene
encoding for phenylalanine hydroxylase can be detected by blood tests and genetic testing can
confirm the diagnosis. If identified at birth, phenylketonuria can be effectively managed, however
if not it causes severe mental retardation amongst other issues (van Spronsen 2010).
Despite the current successes using genetic information to improve human health, the
ability to sequence the entire human genome has created an ethical debate about how to best protect
the privacy of individuals who participate in genomic research (Callier et al. 2014). A specific
ethical consideration of genetic research is how to handle disclosure to the individual (Li and
Meyre 2014)) and to the family (Rothstein 2013). Telling an individual that they have an increased
risk for a medical condition, particularly one for which preventative measures or treatments may
be unavailable could cause the participant stress. On the other hand, knowing about late onset,
non-preventable diseases may allow people to plan reproductive decisions and finances
accordingly (Li and Meyre 2014). However, not telling participants if they are at risk could also
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be ethically unsound because of an individual’s right to know that information and take whatever
actions they see fit. More complicated is if an individual involved in a study is found to have a
genetic disorder that could be passed on to their family. In some cases the participant may be
contacted to determine if they would like to tell family members but in cases where the participant
has passed away, an ethical dilemma occurs. There are no current guidelines indicating if
disclosing the information to the family is legal, ethical, or what the consequences could be
(Rothstein 2013). Researchers can in part avoid this situation by asking about the release of
information to family members in life and in death on the consent form, however in participants
who do not want the information shared, there can still be an ethical dilemma if the lives of family
members could be saved by knowing the information (Rothstein 2013).
Beyond the concerns of how genetic information can be used, there are also issues about
for whom genetic testing will be available. The cost to sequence the whole human genome has
rapidly dropped from approximately $2.7 billion for the first genome sequenced to $5000 in June
2013. As genetic testing becomes more affordable, physicians will need to start determining who
needs genetic testing versus which individuals it is medically unnecessary for to avoid using health
care dollars inappropriately. Another consequence of the increasing affordability of genetic testing
is that individuals can seek out private testing. This could create system in which people of higher
socioeconomic status could be in a position to receive better health care. However, the traditional
health care system has not yet fully transitioned to genomic medicine and only 10% of American
doctors feel comfortable using approved genetic tests. Though patients want doctors to interpret
their genetic testing results, many of them also do not feel that doctors are sufficiently trained to
do the task. (Li and Meyre 2014).
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Specific to the research included in this thesis, the only ethical consideration is keeping the
data collected from participants confidential which has been built into the study design of
EpiDREAM. The EpiDREAM study was approved by the local ethics committee and informed
consent was obtained from each subject before participating in the study, in accordance with the
Declaration of Helsinki. The individual and collective effect size of obesity predisposing genes on
obesity and food consumption patterns is extremely modest. Therefore, the conclusions of this
study while supporting the role of genetics in obesity risk also are not significant enough to warrant
telling participants or their families about their slightly enhanced risk for obesity. As the field of
personalized medicine advances forward and genetic testing becomes more frequent, it is
important that guidelines for both research and clinical applications are developed to take the
advancements into consideration. For genetics research, it is especially important to determine
what results will be passed on to the participants and/or their families and to have this information
included as part of the informed consent process. Continued guideline development will be
necessary protect individuals from genetic discrimination from insurance companies or potential
employers and what information from genetic testing should be provided to individuals and their
families. For clinical applications, there will need to be a sufficient number of genetic counsellors
available, or extra training provided to relevant physicians, to discuss the implications of the results
of the genetic testing.
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7.0 Conclusions and Future Work
The first half of this thesis focused on the association of 14 obesity predisposing SNPs and
a gene score with dietary consumption parameters including daily intakes of total energy, total fat,
saturated fat, trans fat, monounsaturated fat, polyunsaturated fat, carbohydrates and protein. Our
results confirmed the positive association of two of the obesity predisposing SNPs as well as the
gene score with BMI. Adjusting for energy did not significantly change the association, indicating
that these SNPs are associated with BMI independent of how much food is consumed, supporting
that an increase in energy consumption is not the only biological mechanism leading to obesity.
We also found statistically significant relationships between six of the obesity predisposing SNPs
and the gene score with at least one energy adjusted dietary parameter, specifically with total
energy, total fat, and subcategories of fat. Adjusting these relationships for BMI did not
significantly attenuate the relationships demonstrating that participants were not consuming more
energy or having higher fat diets because of their increased weight. The most significant finding
was the novel association of two separate SNPs located in or near BDNF (rs6265 and rs1401635)
with total fat, MUFA, and PUFA intake, with rs1401635 also being associated with total energy
and trans fat intake. Biological support of the relationship comes from the role of BDNF in
monogenic forms of obesity which are characterized by hyperphagia and mouse models in which
dietary fat moderates the relationship between mice with BDNF mutations and hyperphagia. The
association of rs6235 (PCSK1) and the genotype score with total energy intake were similarly
novel with biological evidence supporting PCSK1 because it is also a gene affiliated with
monogenic forms of obesity and that a total PCSK1 deficiency leads to extreme hyperphagia.
The second half of this thesis discussed the evolutionary history of eating disorders. Using
current epidemiological evidence, we propose that the AFFH best explains AN because it takes
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into account the weight loss element, increased energy expenditure, and ethnic/cultural differences
observed in people with AN. Theories involving suppression of reproduction and sexual
competition do not take the increased energy expenditure into account. The sexual competition
theories have a further flaw as they indicate that often it is older females who attempt to lose weight
to compete with younger females. However, eating disorders, particularly AN are much more
prevalent in young females compared to old females. Furthermore, the suppression of fertility that
can occur with severe eating disorders does not account for the high heritability of the diseases as
those with supressed reproduction would have a less opportunity to reproduce and pass on the
genes.
We also propose that BN and BED may not be distinct diseases but instead categories of
the same disease. Historically, a drive to consume food when available may have been protective
during times of food scarcity, however in today’s calorie abundant environment it is an undesirable
trait. Both BN and BED feature binge eating as a diagnostic criteria. People with BN use
inappropriate compensatory behaviours, often purging, after binges and people with BED do not
use inappropriate compensatory behaviours. Evidence that BED and BN have the same basic cause
comes from the ethnic differences. The prevalence of BED is relatively equal across ethnicities
whereas BN is mostly seen in Western cultures where thinness is idealized. Therefore it could be
the socially constructed strive for thinness causing the purging, rather than a unique genetic
determinant. The much higher prevalence of AN in Western cultures may also be explained by
environmental factors encouraging the drive for thinness. The AFFH does not involve the
preoccupation with food which is now part of the AN diagnostic criteria, but instead proposes that
the metabolic changes seen in AN came about because of food scarcity. Voluntary food restriction
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in the form of dieting because of a preoccupation with weight may trigger the same biological
response.
Together, the two components of this thesis provide evidence supporting that obesity and
disordered eating patterns have a strong genetic basis. However, both sections also indicate that
there is a need to widen the scope of biological mechanisms and genes being investigated in the
context of obesity and eating disorders. Most research conducted in obesity promoting SNPs is
focused on excessive energy intake, however the lack of association of most of the obesity
promoting SNPs and the modest association of the gene score with energy consumption point to
alternative biological mechanisms, such as alterations in metabolism. Despite the high likelihood
that there are genetically linked features other than food consumption that are associated with
weight, there is still the need of adequately powered studies to confirm the currently known
associations of obesity predisposing SNPs with food behaviour as well as detect unknown
associations. Using novel populations such as obese people who are successfully losing weight
through caloric restriction, or those who are gaining weight for GWAS may also help identify
relevant genes. Having a better understanding of the behaviours that SNPs are modifying obesity
through will help determine the specific biological pathways that are being impacted. This
information will be crucial in the development of preventative strategies for obesity. Great strides
have been made in conducting GWAS for obesity in ethnicities other than Europeans, however
studies focusing on nutrient consumption patterns and other factors that could be causing obesity
are still mostly limited to Europeans. The evidence indicates that some obesity predisposing genes
are ethnicity specific. If the ultimate goal of understanding the genetics of obesity is to come up
with strategies to avoid weight gain, researchers need to have the information required to create
ethnic specific programs. As the field of obesity genetics grows, more emphasis will also need to
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be placed on understanding gene by gene interactions, gene by environment interactions, and
epigenetics. Creating consortiums by pooling large amounts of genetic and phenotypic data from
multiple research groups with help to facilitate this research by providing the sample sizes required
to have adequately powered studies.
To better understand the genetics of eating disorders, many critical questions need to be
answered. Not specifically pertaining to genetics is the need for better phenotyping of eating
disorders. In our review, we propose that weight preoccupation which is a requirement for the
diagnosis of AN may not a relevant diagnostic criteria. Using the adapted to flee famine
hypothesis, any food restriction could bring on the metabolic changes seen in AN. In Western
cultures, dieting may be the most frequent and relevant instigator but evidence shows that AN
occurs world wide, regardless of the idealization of thinness. To determine if the theory that the
preoccupation with thinness is a Western ideal instead of an intrinsic part of eating disorders is
correct, biochemical data including hormones, metabolites, transcription factors, and protein
profiles should be compared between patients who present with all symptoms of AN except for a
preoccupation with weight and patients who have all symptoms of AN including a preoccupation
with weight. If the biochemical changes are similar in the two populations, this would indicate that
the diagnostic criteria for AN should be broadened. While the treatment for people with and
without weight occupations may be different, widening the diagnostic criteria may allow for more
people in need to receive treatment. Similarly, more research is required to determine if purging,
the only difference between BN and BED, is purely because of a desire to be thin, or if there are
other biological mechanisms that induce purging.
For genetic studies, the next step should be changing the focus from looking at genes
associated with neuropeptides and hormones which may only be a symptom of the disease to
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looking at genes that are linked to metabolism. Small sample sizes are a significant problem in
eating disorder research as many people attempt to hide the disease. Establishing clearer
phenotypes and subdividing into categories such as people with AN who have weight
preoccupations and those who do not have weight preoccupations could increase sample sizes as
would collaborations. Because eating disorders are extreme abnormalities in eating patterns, if
common gene variants are found to be associated with the diseases, then they are likely to be good
candidate genes to investigate for obesity as well, particularly genes associated with BN and BED.
Future candidate genes could also be discovered by studying hormones, metabolites, transcription
factors, and protein profiles in specific phenotypes, such as people who have successfully
recovered from eating disorders versus those who have not. If genes associated with AN can be
correctly identified and the biological mechanisms deduced, there is the possibility that the
findings could be used to not only help treatment people with AN, but also those who are obese.
For example, if the genes that allow people with AN to avoid fixation with food despite low caloric
intake can be identified and the biochemical pathway they act through be determined,
pharmacotherapy options capitalizing on the information could be developed. The continued
decrease in cost of next-generation methods of genome sequencing and the creation of consortiums
providing access to genetic information for extremely large sample sizes will also play a significant
role in determining the genetic architecture of eating disorders. The large sample sizes will provide
adequate power for detecting the small effect sizes seen in polygenic forms of the diseases.
However, for GWAS methods to be the most successful, eating disorder phenotypes need to be
clearly defined and the same phenotypes used in multiple studies to allow for potential replication
of findings.
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The moderate to high heritability of obesity and eating disorders in conjunction with the
findings that the shared environment has little effect highlight the need for environmental changes
to help people lead healthier lives. Obesity specifically is often viewed as the fault of the individual
but understanding that weight is under genetic control and the way people are raised has little effect
on their weight point to the need for stronger policies to help curb the obesogenic environment.
Many people who are obese, as well as those who have BN and BED, struggle to moderate what
and how much they are eating, showing a preference for unhealthy options. If these options were
less readily available, or if health information was more accessible in conjunction with higher
health literacy, people may be more equipped to make healthy choices. AN is a more complex
disease, but if the desire to be thin is causing the voluntary restriction in food intake prompting the
majority of cases of the disease in Western countries, there is a strong argument to invest more in
changing the social environment to accept bodies of all shapes and sizes. For future research of
both obesity and eating disorders, better defining phenotypes, taking advantage of large
consortium data, and investigating novel hormones, metabolites, transcription factors, and protein
profiles will be critical in better understanding the diseases. Novel phenotypes should also be
investigated as the current missing heritability can be partially attributed to only looking at a
limited range of phenotypes, such as just obese people rather than looking at people gaining and
losing weight.
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