effect of dopamine receptor drd2 ankk1 …...ii effect of dopamine receptor drd2 and ankk1...
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Effect of Dopamine Receptor DRD2 and ANKK1 Polymorphisms on Dietary Compliance, Blood Pressure, and BMI in Type 2 Diabetic Patients
by
Shahad Abdulnour
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science, Faculty of Medicine University of Toronto
© Copyright by Shahad Abdulnour (2010)
ii
Effect of Dopamine Receptor DRD2 and ANKK1 Polymorphisms on Dietary Compliance, Blood Pressure,
and BMI in Type 2 Diabetic Patients
Shahad Abdulnour
Master of Science
Institute of Medical Science, Faculty of Medicine University of Toronto
2010
Abstract Reduction in dopamine receptor D2, has been associated with insufficient brain reward,
food addiction, obesity, and type 2 diabetes (T2D). Our aim was to assess whether the
genetic variability responsible for this reduction is associated with poor dietary
compliance and life style habits in T2D patients. Genetic-analysis was done for 109 T2D
individuals who completed a 24-week randomized clinical trial and were assigned to
follow either a low-GI or a high-fibre diet. Polymorphisms of TaqIA and C957T were
compared with physical and biochemical measures. Regardless of dietary treatments,
individuals with the C957T-T allele and the TaqIA-A2 allele were significantly
associated with blood pressure reduction. Carriers of the T allele significantly lowered
their body mass index (BMI) over the 24-week trial. Our findings suggest that the
presence of the TaqIA-A2 allele is associated with a decrease in blood pressure. The
C957T-T allele was associated with decrease in pressure and body weight.
iii
Dedication
I dedicate this body of work to the memory of my beloved
grandfather, Dr. Zaki Abdulnour, who inspired me in many ways
and ignited in me the sparks of curiosity by introducing me to the
infinite realm of science.
iv
Acknowledgments First and foremost, I praise and thank God for the wisdom, capability, perseverance, and
all the opportunities that He has bestowed upon me throughout my life: “I can do
everything through Him who gives me strength” (Philippians 4: 13). This thesis appears
in its current form due to the guidance, assistance, and encouragement of several people.
I would therefore like to extend my heartfelt gratitude to all of them:
I would like to express my heartfelt gratitude and deepest appreciation to my parents:
Thuraya De Buyzer and Nashwan Abdulnour. I thank you both for nurturing my faith,
compassion, and wonder. I would certainly not have been where I am today without you!
You have been an inspiration, source of guidance, and accompanied me through thick
and thin. I sincerely thank you for everything; from ensuring my well-being to your
unconditional love, support, and encouragement to pursue my interests. No words can
describe the love and appreciation I feel for you and all the things you have done.
I warmly thank my grandparents: Mary Denno, Zaki Abdulnour, George De Buyzer (may
they rest in peace), and Norma Saloom for being role models and providing me with
some of life’s wisest and valuable lessons which I will always treasure. To my brother,
Fahad Abdulnour, thank you for your everlasting presence and support. To my sister,
Rand Abdulnour, thank you for being my best friend and confidante. You have always
been there for me and always believed in me. I am lucky to have you as a sister! I would
also like to thank my aunts, uncles, cousins, and friends for all the love and
encouragements.
I am greatly indebted to my thesis supervisor, Dr. David J.A. Jenkins, whose patience
and kindness, as well as his academic experience have been invaluable to me. I thank Dr.
Jenkins for believing in me and making this research possible. I am grateful to him for
his supervision, mentorship and guidance. He has had an immense impact on my
personal and academic evolution, and for that I offer my everlasting gratitude.
v
Many thanks go to my graduate committee members: Dr. Ahmed El-Sohemy, Dr.
Anthony Hanley, and Dr. Joel Levine for their support during the whole period of the
study, especially for your patience and guidance through our committee meetings and
writing process. I would also like to thank my examiners Dr. James W. Anderson, Dr.
Roy Baker, and Dr. Pauline Darling for their encouragements as well as their helpful
suggestions and comments during my defense.
I am grateful to Dr. Cyril Kendall for his guidance and for mentoring me through my
fourth year undergraduate research project. The experience was most rewarding and
inspired me to further pursue graduate studies.
I have been fortunate to come across many great friends during this process. Special
thanks to: Sara Piran, and Zeynep Yilmaz for being supportive, encouraging, and being
there when I needed them. Our great memories together will forever be engraved in
mind.
Special thanks and sincere gratitude go to former and current Risk Factor Modification
Centre team/family: Ruba Abdulhadi, Monica Banach, Sonia Blanco, Amanda Carleton,
Laura Chiavaroli, Duncan Cushnie, Dorothea Faulkner, Kathy Galbraith, Chris Ireland,
Maria Jouikova, Claudia Meneses, Arash Mirrahimi, Sandra Mitchell, Stephanie Nishi,
Tri Nguyen, Tina Parker, Darshna Patel, Sandhya Sahye-Pudaruth, John Sievenpiper,
Kristie Srichaiku, Matthew Yu. I would also like to thank Clement Zai for all his
guidance and verification of the statistical analyses in this body of work. I am especially
thankful for the help and support of Nalini Irani, Balachandran Bashyam and Hyeon-Joo
Lee in technical assistance in the Lab. I have been blessed for knowing each and every
one of you!
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Table of Contents
Chapter Title Page
Abstract .............…………………....……………………………...……………… ii
Dedication..........…………………....……………………………...……………....iii
Acknowledgments…………………………………….…….…….…..……...… iv-v
Table of Contents……………………………………………..….…..………..vi-viii
List of Abbreviations………………………………….…….…….…..…..……..ix-x
List of Figures…………………………....……………….….…………………….xi
List of Tables………………………………………………..….…………………xii
1 Introduction…………....…………………………………….………………….1-3
2 Literature Review………………………………….………...….………..………4
2.1 General Background on Type 2 Diabetes….…….…………………….5
2.1.1 Type 2 Diabetes ..…………..……….…….……….………5-7
2.1.2 Diabetes and Obesity…....…..………….……………………7
2.1.2.1 Compulsive Eating and Compliance…….……….7-8
2.2 General Background on Dopamine Receptors………………………....8
2.2.1 Dopamine Receptors.………………………………………...8
2.2.1.1 Subtypes of Dopamine Receptor………………......9
2.2.1.2 D2 Receptor Family……….…………………...9-12
2.2.2 Associations of Dopamine Receptor on Behaviours…….….13
2.2.2.1 Dopamine and Food Addiction..……………....13-14
2.2.2.2 Association of Dopamine with Reward, Motivation,
and Stress…………….....……………………...15-16
2.2.2.3 Dopamine and Glucose Interaction....…....…....16-17
2.3 Genetic Aspects of Dopamine Receptors..……………………...….…17
2.3.1 Dopamine Receptor Gene………………....………….....17-18
2.3.2 DRD2-C957T Polymorphism……………...………….....18-20
2.3.3 ANKK1-TaqIA Polymorphism……......….……….……..20-22
vii
2.3.4 Linkage Disequilibrium between C957T and TaqIA....……23
2.4 Genetic Variability of Dopamine Receptor and Type 2 Diabetes...23-24
3 Hypotheses and Aims ……………………………..…………………….......…..25
3.1 Hypotheses………………………………………………………..…….....26-27
3.2 Aims………………………………………………………..…………..……..28
4 Background Methodology for the Genetic Study and Relevant Results of the
Original Low GI Study……………………………………….……………….....29
4.1 Subjects………………………………………………………………..…...30-31
4.2 Protocol………………………………………………………………….....32-33
4.3 Dietary Intervention………………………………………………...……...34-35
4.4 Biochemical Analyses ………………………………………………………...36
4.5 Results of the Original Low Glycemic Index Study...………………………....37
5 The Effect of DRD2 – C957T Polymorphism on Dietary Compliance, Blood
Pressure, BMI…………………………………………………………...………..38
5.1 Abstract…………………………………………………………………….......39
5.2 Introduction…………………………………………………………….......40-41
5.3 Methods………………………………………………………………….…….42
5.3.1 Participants………………………………….…………………….....42
5.3.2 Dietary Interventions ………………….………………………....42-43
5.3.3 Study Protocol – Genetic Analyses……………...………….……43-44
5.3.4 Statistical Analyses……………………………………...……….......44
5.4 Results………………………………………………………............……...45-52
5.5 Discussion……………………………….……………………………….....53-55
Link to Chapter 6…………………..……………………...……………...…...56-57
6 The Effect of ANKK1 – TaqIA Polymorphism on Dietary Compliance, Blood
Pressure, BMI…………………………………………………………...……......58
6.1 Abstract………………………………………………………………………...59
6.2 Introduction……………………………………………………….………........60
viii
6.3 Methods…………………………………………………………….……….….61
6.3.1 Participants………………………………….………….…………….61
6.3.2 Dietary Interventions ………………….…………………………61-62
6.3.3 Study Protocol – Genetic Analyses…………..…...……..…….…62-63
6.3.4 Statistical Analyses………………………….………...…..……........63
6.4 Results………………………………………………………................……64-73
6.5 Discussion…………………………………………………..………..……..74-75
7 The Effect of ANKK1 – TaqIA Polymorphism on Dietary Compliance, Blood
Pressure, BMI……………………………………………………………..……...76
7.1 Abstract……………………………………………………………....………...77
7.2 Introduction………………………………………………………………...78-79
7.3 Methods…………………………………………………………………….….80
7.3.1 Participants………………………………….……………………….80
7.3.2 Dietary Interventions ………………….…………………………80-81
7.3.3 Study Protocol – Genetic Analyses……………...…………….…81-82
7.3.4 Statistical Analyses…………………………………...………….......82
7.4 Results………………………………………………………................……83-89
7.5 Discussion…………………………………………………………….……..90-91
8 Overall Discussion, Limitations and Future Research…..….………..…………92
8.1 Overall Discussion………………………………………….………………93-97
8.2 Limitations………………………………………………………..……............98
8.3 Future Research………………………………………………….………..99-100
9 Implications: Clinical Application ..……………………………………….101-103
10 Summary…………………………………………………………………….104-105
11 References……………………………………………..…...........…………..106-121
Copyright Acknowledgments……………………………...……………………122
ix
List of Abbreviations
AC – Adenylyl Cyclase
ADA – American Diabetes Association
ANKK1 – Ankyrin Repeat and Kinase Domain Containing 1
ATP – Adenosine Triphosphate
BMI – Body Mass Index
cAMP – Cyclic Adenosine Monophosphate
CHD – Coronary Heart Disease
DBP – Diastolic Blood Pressure
DHEA – Dehydroepiandrosterone
DM – Diabetes Mellitus
DNA – Deoxyribonucleic Acid
DRD2 – Dopamine Receptor [Subtype 2]
FDA – Food and Drug Administration
GABA – Gamma-Aminobutyric Acid
GI – Glycemic Index
HbA1c – Glycosilated Hemoglobin
HDL – High-Density Lipoprotein
HDL-C – High-Density Lipoprotein Cholesterol
LD – Linkage Disequilibrium
LDL – Low- Density Lipoprotein
LDL-C – Low- Density Lipoprotein Cholesterol
LH – Lateral Hypothalamus
LOD – Logarithm of the Odds
mRNA – Messenger Ribonucleic Acid
MUFA – Monounsaturated Fatty Acid
NCEP ATP – National Cholesterol Education Program Adult Treatment Panel
PCR – Polymerase Chain Reaction
PET – Positron Emission Tomography
PFH – Perifornical Hypothalamus
x
PUFA – Polyunsaturated Fatty Acid
RFLP – Restriction Fragment Length Polymorphism
SAS – Statistical Analysis System
SBP – Systolic Blood Pressure
SEM – Standard Error Mean
SFA – Saturated Fatty Acid
SNP – Single Nucleotide Polymorphisms
SPSS – Statistical Package for the Social Sciences
T2D – Type 2 Diabetes
Total-C – Total Cholesterol
TG – Triglycerides
xi
List of Figures
Chapter 2
Figure 2-1: General mechanism of insulin resistance.
Figure 2-2: Distribution of Dopamine receptors in human brain.
Figure 2-3: A proposed mechanism for dopamine receptor D2
Figure 2-4: Human DRD2 and ANKK1 genes residing on Chromosome 11q23.
Chapter 4
Figure 4-1: Flowchart of participants’ distribution.
Chapter 5
Figure 5-1: Patient C957T Distribution.
Figure 5-2: Association of C957T with change in BMI over the 24-weeks of dietary treatment.
Figure 5-3: Association of C957T with change in blood pressure (SBP) over the 24-weeks of dietary
treatment.
Chapter 6
Figure 6-1: Patient TaqIA Distribution.
Figure 6-2: Association of TaqIA with change in blood pressure (SBP) over the 24-weeks of dietary
treatment.
Figure 6-3: Association of TaqIA with change in blood pressure (DBP) over the 24-weeks of dietary
treatment.
Chapter 7
Figure 7-1: Association of SNP-SNP combination (C957T and TaqIA) with change in BMI over the
24-weeks of dietary treatment.
Figure 7-2: Association of SNP-SNP combination (C957T and TaqIA) with change in SBP over the
24-weeks of dietary treatment.
xii
List of Tables
Chapter 5
Table 5-1: DRD2 – C957T SNP, Participant Distribution.
Table 5-2: DRD2 – C957T SNP, Baseline Measurements
Table 5-3: DRD2 – C957T SNP, Percentage Change of Measurements over the 24-weeks of Study
Table 5-4: DRD2 – C957T SNP, Baseline Measurements in Caucasians
Table 5-5: DRD2 – C957T SNP, Percentage Change of Measurements over the 24-weeks of Study
in Caucasians
Chapter 6
Table 6-1: ANKK1 – TaqIA SNP, Participant Distribution.
Table 6-2: ANKK1 – TaqIA SNP, Baseline Measurements
Table 6-3: ANKK1 – TaqIA SNP, Percentage Change of Measurements over the 24-weeks of Study
Table 6-4: ANKK1 – TaqIA SNP, Baseline Measurements in Caucasians
Table 6-5: ANKK1 – TaqIA SNP, Percentage Change of Measurements over the 24-weeks of Study
in Caucasians
Chapter 7
Table 7-1: SNP-SNP (C957T and TaqIA) Baseline Measurements
Table 7-2: SNP-SNP (C957T and TaqIA) Percentage Change of Measurements over the 24-weeks
of Study
1
Chapter I
Introduction
2
Introduction
Diabetes has become a major problem along with the growth in obesity in western
populations. Part of the growth in obesity lies in the fact that western populations
exercise less and have a greater variety of food, including more highly processed energy
dense foods. Our aim therefore was to see whether those who might have less control
over their consumption patterns were more vulnerable to poor diabetic control and
possibly responded less well to dietary interventions which required restraint.
DRD2 polymorphisms, that were shown to be responsible of the variability in
dopamine receptors’ availability in the brain, and which have been considered important
in addictive behaviours, possibly as a result of the interaction with other genes (such as
serotonin transporter, 5-HTT, gene) and effecting downstream mechanisms (M. Hirvonen
et al., 2004; Jocham et al., 2009; Montag, Hartmann, Merz, Burk, & Reuter, 2008;
Penttila et al., 2004; Wang et al., 2001). These possible interactions and effects may be
cues for satisfaction (Thomas, Tomlinson, & Critchley, 2000; Wang, et al., 2001). Hence,
those with less effective receptors require a greater stimulus (such as increased food
intake, alcohol, tobacco, etc.) to drive satisfaction through adequate brain stimulation. We
suggest that those who seek food with a higher glycemic index, GI, are likely to have
fewer receptors and may be at greater risk for hyperglycemia and poor diabetes control.
They may also be at greater risk of obesity through excess food intake, thus, leading to
worsening their diabetes control in the longer term.
Genetic variability in compulsive behaviours such as food compulsive behaviour
might thus explain the susceptibility to body weight increase and food consumption in
individuals, which in turn could lead to diabetes and other health risks. Weight loss can
3
help to improve blood glucose control, blood pressure, cholesterol and many other risk
factors (DeFronzo & Ferrannini, 1991; Patel et al., 2008).
The dopamine receptor polymorphisms may be partially related to the glycemic
index concept since the low GI food will provide less glycemic stimulus to satisfaction
(Eny, Corey, & El-Sohemy, 2009; Haltia et al., 2008; Hamdi, Onaivi, & Prasad, 1992).
There remains insufficient investigation into the precise genes or genetic factors that are
involved in food intake. We therefore, decided to assess the following polymorphism of
C957T SNP of DRD2 gene and the TaqIA SNP of the ANKK1 gene (that has been
suggested to regulate DRD2) in type 2 diabetic patients who have been provided with a
low GI or a high fibre diets. Thus, the purpose of this thesis is to investigate the potential
role genetic differences in DRD2 and ANKK1, and the role they may play in dietary
compliance through the control of HbA1c (Glycosilated Hemoglobin), body weight, and
blood pressure in type 2 diabetic patients. Other outcomes such as HDL-C (High-Density
Lipoprotein Cholesterol) have been included in this research. Ultimately, this will give
better understanding to dietitians and clinicians of whether further counseling programs
may be needed for those who carry the addictive genes.
4
Chapter II
Literature Review
5
2.1 General Background on Type 2 Diabetes
2.1.1 Type 2 Diabetes
Type 2 diabetes (T2D) is the most common form of diabetes; it accounts for more
than 90% of all diabetes worldwide (Horikawa et al., 2000; Tuomilehto et al., 2001;
Zimmet, Alberti, & Shaw, 2001) and it is known to be the sixth leading cause of death in
the US (Mokdad et al., 2003). T2D generally starts in the later part of life and is often
treated initially with diet modification alone or by oral medications. It usually occurs
when the body cannot use insulin (the hormone that normally allows glucose to enter the
cell to convert it into energy (Ferrannini & Natali, 1991) or to be stored (Perseghin,
Petersen, & Shulman, 2003; Reaven, 1993)). The exact mechanisms by which people get
insulin resistance in T2D are still being actively explored.
Most patients with type 2 diabetes produce normal to high amounts of insulin
initially. The first stage in T2D is a condition called insulin resistance, where the cells of
the body do not respond to insulin easily (Ferrannini & Natali, 1991; Ruderman,
Chisholm, Pi-Sunyer, & Schneider, 1998) (Figure 2-1). In patients with insulin resistance,
although insulin can attach normally to receptors on liver and muscle cells, defects in
signal mechanisms prevent the hormone from moving glucose from blood into these cells
where it is used (Perseghin, et al., 2003; Ruderman, et al., 1998). Glucose remains in the
blood leading to higher than normal blood glucose and as a result the body starts making
more and more insulin. In the beginning, this amount is usually sufficient to overcome
such resistance, but during the later phases of the disease the severity of the insulin
resistance increases and decreased Beta-cell function. The amount of insulin is
poroediroeu
6
Figure 2-1: A general mechanism of Insulin Resistance - In obese states, adipose tissue is under a constant state of metabolic stress, resulting in the activation of the stress and inflammatory responses, which leads to the accumulation of macrophages at this stage and affect the adipokines and pro-inflammatory cytokines via adipose tissue thereby potentially leading to insulin resistance and decreased Beta-cell function. (de Luca & Olefsky, 2006) with modification UCSF Medical Center – Diabetes Teaching Center Images (http://www.deo.ucsf.edu/types-of-diabetes/type-2.html).
7
insufficient to maintain glucose homeostasis. This leads to raised postprandial and then
high fasting blood glucose levels will increase free fatty acid released from adipose tissue
(de Luca & Olefsky, 2008; Ferrannini et al., 1987).
Over the long term, high blood glucose and insulin are associated with medical
complications such as coronary heart diseases (CHD) (Pyorala, 1979; Steppan et al.,
2001), kidney failure (Ritz, Rychlik, Locatelli, & Halimi, 1999; Steppan, et al., 2001),
nerve damage (Gabbay, 1975), infections (Rayfield et al., 1982; Ritz, et al., 1999;
Steppan, et al., 2001), and blindness (Steppan, et al., 2001). Not everyone with type 2
diabetes needs drug therapy or insulin replacement therapy and may be treated with
healthy routines, such as diet modifications, exercise, and weight loss if followed
effectively.
2.1.2 Diabetes and Obesity
2.1.2.1 Compulsive Eating and Compliance
Type 2 diabetes is nearly always associated with obesity. Obesity (BMI > 30) is
characterized by excess energy intake, a lack of exercise or energy expenditure, and the
influence of one’s own genes.
Overeating can be highly addictive activity (Salamone & Correa, 2002; Wise,
2006). There are individual differences in the food consumption patterns that relate to the
dissimilarities in eating and energy intake (Epstein et al., 2007; Wise, 2006). For
example, increases in carbohydrate and fat intake may contribute to increased overall
energy intake. It has been suggested that the food consumption behaviour could be
triggered through the same mechanisms as drug consumption behaviour (Bickel, Marsch,
8
& Carroll, 2000). Subjects who find food highly addictive may consume more energy in
an ad libitum eating situation than those who are low in food reinforcement (which is
characterized by the motivation to over eat and consume excess energy intake) (Epstein
et al., 2004). Thus, obesity may relate to food addiction and poor dietary compliance.
This is a key factor that could lead to diabetes and other serious health problems. Obese
individuals find food more compelling and are more motivated to eat than normal weight
individuals (Epstein, et al., 2004; Saelens & Epstein, 1996). This would be one of the
foremost aspects in patients with diabetes due to a high positive correlation between
obesity and T2D (Bickel, et al., 2000).
2.2 General Background on Dopamine Receptors
2.2.1 Dopamine Receptors
Dopamine is a catecholamine neurotransmitter found in neurons of both the
central and peripheral nervous systems (Ahlenius, 1979). It is responsible for a variety of
functions, including: cognition (Nieoullon, 2002; Nieoullon & Coquerel, 2003; Remy &
Samson, 2003), emotion (Nieoullon & Coquerel, 2003), locomotion, neuroendocrine
secretion (Balldin, Berggren, & Lindstedt, 1992), motivation and rewards (Nieoullon &
Coquerel, 2003; Noble, 2000). This neurotransmitter is stored in vesicles in axon
terminals and released when the neuron is depolarized (B. T. Chen & Rice, 2001).
Dopamine interacts with specific membrane receptors (dopamine receptors) to produce
its effects. However, dopamine’s effects cannot all be explained by interaction with a
single receptor. This led to the classification of dopamine receptors into subtypes, based
on physiological or biochemical responses.
9
2.2.1.1 Subtypes of Dopamine Receptor
There are five subtypes of dopamine receptors in the mammalian brain (subtypes
D1–D5) which are widely distributed throughout both the cerebral cortex and the limbic
system (Civelli, Bunzow, & Grandy, 1993; Lachowicz & Sibley, 1997). These subtypes
may be divided into D1-like (D1 and D5) and D2-like (D2, D3, and D4) receptors based on
their predicted transmembrane characteristics and functional properties (Binda, Kabbani,
Lin, & Levenson, 2002; Civelli, et al., 1993). Certain subtypes are also found in other
specific areas of the brain, for example the D1 and D2 receptors are expressed in the
corpus striatum (Figure 2-2) (Andersen et al., 1990; Thompson et al., 1997).
2.2.1.2 D2 Receptor Subfamily
This study was mainly focused on dopamine receptor, subtype 2 (DRD2). D2
receptors are present in dopaminergic projection areas (Andersen, et al., 1990; Levey et
al., 1993) (such as the striatum, nucleus accumbens, olfactory tubercle, hypothalamus and
pituitary) where they are involved in the modulation of locomotion (Benturquia et al.,
2007), reward (Noble, 2000; Wise, 2006), food addiction (Epstein, et al., 2007), memory
and learning (Wise, 2004).
The brain DRD2 is abundant in basal ganglia and is has major affects on
behaviour. It has been suggested that dopamine receptors mediate enzyme activities,
metabolic rates, and ion channels. D2 receptors are members of the G-protein coupled
receptor family (Castro & Strange, 1993; Hibert, Trumpp-Kallmeyer, Bruinvels, &
Hoflack, 1991).
10
Figure 2-2: Distribution of different dopamine receptors subtypes in specific areas of the brain (Other transmitters and modulators. In: Pharmacology, 4th edition)
The receptors are membrane receptors that have 7 transmembrane spanning a-
helices (Castro & Strange, 1993). Dopamine binding to the ‘binding groove’ on the
extracellular portion of the receptor activates the G-proteins, which initiate secondary
messenger signaling pathways (Lachowicz & Sibley, 1997). The D2 receptor is coupled
to inhibitory G-proteins (Gi unit). The G-inhibitor (Gi) proteins inhibit secondary messenger
signaling mechanisms that have been associated with brain reward and gratification
through unknown downstream signaling mechanisms (Katzung, Masters, Trevor, &
MyiLibrary., 2009). The dopamine receptors, D2, have been universally studied and are
11
of special interest as dopamine and other commonly prescribed antipsychotic drugs bind
with high affinity to the receptors (Grandy et al., 1989).
It has been suggested that D2 receptors reduce, or do not change, adenylyl cyclase
activity (adenylyl cyclase, [AC], the enzyme which converts adenosine triphosphate
[ATP] to cyclic adenosine monophosphate [cAMP] that mediates the postsynaptic
response to dopamine) (Enjalbert & Bockaert, 1983; Ferre et al., 1998; Glass & Felder,
1997; Stoof & Kebabian, 1982). This is important since the decrease in dopamine
receptors would lead to increase of cAMP. Studies have shown that site-specific injection
of cAMP-analogs in perifornical and lateral hypothalamus (PFH and LH) potently
increases food intake, although the mechanisms of such actions in the PFH and LH are
not yet clear (A. Z. Zhao, 2005).
There is also evidence that D2 increases intercellular K+ conductance (Stoof &
Kebabian, 1982). This could mean the low availability in dopamine receptors leads to
decrease in intercellular K+, hence, one way to keep the body in a homeostasis (the ability
of an organism or cell to maintain internal equilibrium) state is by sodium consumption.
High sodium intake may lead to hypertension.
12
Figure 2-3: Dopamine receptor D2 inhibits adenylyl cyclase (AC) activity to convert adenosine triphosphate [ATP] to cyclic adenosine monophosphate [cAMP] that mediates the postsynaptic response to dopamine activating protein kinases. Through unknown mechanisms, we propose, this inhibition may lead to self reward, and hence, decrease in food intake.
13
2.2.2 Associations of Dopamine Receptor on Behaviours 2.2.2.1 Dopamine and Food Addiction
Studies have shown the genetic variant of dopamine receptor gene that is
responsible for the decrease in the availability of dopamine receptors also responsible in
increasing BMI in obese individuals (Comings, Gade, MacMurray, Muhleman, & Peters,
1996); (Comings, Gade, et al., 1996; Wang, et al., 2001). An increase in appetite was
confirmed through drugs that block dopamine receptors, specifically D2, and result in
significant weight gain (Baptista, 1999) and vice-versa with drugs that increase brain
dopamine (Baptista, 1999; Towell, Muscat, & Willner, 1988) were shown to decrease
appetite.
It has been suggested that there is lower dopamine receptor availability in the
striatum of obese individuals than in normal individuals (Volkow et al., 2008; Will, Pratt,
& Kelley, 2006). Wang and colleagues showed a similar result: DRD2 measures were
negatively correlated with BMI (Wang, et al., 2001). The results lead to an association
between low D2 receptor amounts in obese individuals with severe eating disorder
(Wang, et al., 2001). This reduction in dopamine receptors D2 has also been associated
with addictive behaviours (Johnson & Kenny, 2010; Noble, 2000; Pontieri, Tanda, Orzi,
& Di Chiara, 1996).
Eating can be a highly compulsive behaviour for some; it can induce feelings of
gratification and pleasure (Wang, et al., 2001). Feeding increases extracellular dopamine
concentrations in the nucleus acumbens, (Bassareo & Di Chiara, 1999) which is believed
to add to the compulsivity effect (Pontieri, et al., 1996) on substance and food intake.
14
In the brain, eating is motivated by pleasure and reward. A very recent study in
rats showed that drug addiction and compulsive eating have the same effects on the brain
by desensitizing brain reward circuits (Johnson & Kenny, 2010). The researchers studied
three groups of rats and all three groups were allowed access to their standard (healthy)
chow at all times. In addition, rats had either no access, restricted access (1 hour per day),
or extended access (18-23 hours per day) to palatable energy dense food for 40 days.
Extended access rats gained weight rapidly, and were significantly heavier than
chow only or restricted access rats (Johnson & Kenny, 2010). Their calorie intake was
almost double that of the chow only rats. Even the restricted access rats developed binge
like eating behaviours, getting about 66% of their daily calories during their 1 hour of
access to the unhealthy food (Johnson & Kenny, 2010). These findings were traced to a
decrease in levels of specific dopamine receptors in the striatum region of the brain.
These exact neurobiological changes have been shown to occur in rats that are given
extended access to heroin or cocaine (Johnson & Kenny, 2010). This gave evidence to
aberrant cravings and overeating in obese individuals. It has been shown that this
abnormal behaviour is similar to that of the compulsive use of drugs in addicted subjects
(Johnson & Kenny, 2010; Wang, et al., 2001). Even though, it is still unknown whether
overeating/poor dietary habits leads to reduced dopamine receptor levels or whether the
reduction of the receptors are themselves responsible for making people compulsive
eaters, this compulsive behaviour in the face of negative consequences and is a
characteristic of addiction and poor dietary compliance.
15
2.2.2.2 Association of Dopamine with Reward, Motivation, and Stress
There is evidence of an involvement of dopamine in motivational behaviours that
may drive food intake. Wand and his team have shown that smelling, seeing, and talking
about food increases brain dopamine in non-obese, food deprived subjects (Wand et al.,
2007). It is suggested that eating, like other activities regulated by dopamine reward
circuits, is a highly addictive behaviour (Johnson & Kenny, 2010; Noble, 2000; Wand, et
al., 2007). Deficits in D2 receptor binding (for example, low availability) are found in
individuals who manifest addictive compulsive disorders, including cocaine and
methamphetamine abuse (Volkow et al., 2001; Volkow et al., 1990), heroin addiction
(Wang, et al., 2001), alcoholism (Volkow et al., 1996), and obesity (Wang, et al., 2001).
Human dopaminergic neurons are involved in the control of hormone secretion,
voluntary movement, and emotional behavior. Mediating these effects are the dopamine
D1 and D2 receptors. Dopamine activity plays an important role in behavioural activation
and goal directed behavior (Noble, 2003). Dopamine receptors are involved in
neurological signaling and may play roles in cognitive and emotional functions and
neurological disorders (Rognan et al., 1990; Sokoloff, Giros, Martres, Bouthenet, &
Schwartz, 1990). Moreover, chronic stress has been shown to cause reduction in
dopamine secretion/receptivity and the number of active dopamine cells (Gambarana,
Scheggi, Tagliamonte, Tolu, & De Montis, 2001; Masi et al., 2001), thereby increasing
the craving or need for reward compensation (Gambarana, et al., 2001; Miyasaka et al.,
2005; Moore, Rose, & Grace, 2001).
There is a clear connection between dopamine D2 Receptor gene polymorphisms
and excessive craving-induced aberrant behaviors. The combination of emotional
16
behaviour, learning, stress, and motivation may all attribute to food addiction habits. As
individuals with low availability of dopamine rectors would have difficulty in following
dietary guidance and may be less motivated to lose weight.
2.2.2.3 Dopamine and Glucose Interaction
Carbohydrates cause the release of the pleasure inducing brain chemical
dopamine (Wang, et al., 2001). Low availability of dopamine receptors, D2, has been
associated to induce and increase hyperglycemia in both animals models and humans
(Eny, et al., 2009; Frank et al., 2008; Haltia et al., 2007; Hamdi, et al., 1992), though,
men and women showed different mode of inheritance (the pattern in which a particular
genetic trait is passed from one generation to another). One study has shown that males
followed an additive mode of inheritance (where the combined effects of genetic alleles
at two or more gene loci are equal to the sum of their individual effects) and females
followed partial heterosis mode of inheritance (where the individuals with heterozygote
genotype have a more controlled or lower carbohydrate consumption than the
homozygotes) (Eny, et al., 2009).
A study by Shiroyama, in 1998, showed the direct effect of dopamine on glucose
release from primary cultured rat hepatocytes. The study assessed whether dopamine has
a direct effect on glucose release from hepatocytes through the glycogenolytic and/or
gluconeogenic pathways, and at the same time determined the main type of adrenergic
receptor involved in glucose release (Shiroyama, Moriwaki, & Yuge, 1998). Dopamine
caused release of glucose which was inhibited by the beta blocker propranolol
(Shiroyama, et al., 1998). Their study demonstrated that dopamine has a direct effect on
17
hepatocytes, increasing glucose release via both glycogenolytic and gluconeogenic
pathways and mediated by beta adrenergic receptors (Shiroyama, et al., 1998). Also, it
has been shown that intake of palatable foods, such as chocolate and shortcake, results in
an increase of extracellular dopamine in the nucleus accumbens (Martel & Fantino, 1996;
Wilson, Nomikos, Collu, & Fibiger, 1995). Thus, individuals with fewer dopamine
receptors would be prone to overconsumption of such palatable foods to compensate of
the insufficient amount of dopamine in the nucleus accumbens.
There is a general agreement that other pleasure inducing substances such as
alcohol and nicotine, like glucose, exert an effect on the dopaminergic pathways of the
brain. This also suggests the finding of common genetic thread of multiple addictions
(Blum, Sheridan, et al., 1996). Thus, this general agreement due to the association of low
dopamine receptor availability with the patterns of increase in glucose consumption, led
us to the hypothesis that diabetic individuals with genetic variances responsible for low
dopamine receptor density, who went through a low GI dietary intervention, would have
difficulties in complying with a given dietary guidance that was provided for them.
2.3 Genetic Aspects of Dopamine Receptor
2.3.1 Dopamine Receptor Gene
Type 2 diabetes and compulsive eating disorders that are shown to be associated
with the disease are very complex and it is likely that more than one defective gene is
involved. Indeed, there are several neurotransmitters (dopamine, GABA, norepinephrine,
serotonin) as well as peptides and amino acids that are involved in the regulation of food
intake (Wang, et al., 2001). Of particular interest is dopamine since this neurotransmitter,
18
as discussed above, seems to modulate motivation and reward circuits through the meso-
limbic circuitry (Nieoullon, 2002) and nucleus accumbens (Bassareo & Di Chiara, 1999).
Hence, dopamine deficiency in obese individuals may be responsible for pathological
eating as a means to compensate for decreased activation of these circuits.
It has been suggested that compulsive disorders such as smoking (Noble et al.,
1994), drug addiction (Volhttp://ca.wiley.com/WileyCDA/Section/id-302301.htmlkow,
Fowler, Wang, Swanson, & Telang, 2007), alcohol addiction (Blum et al., 1990),
gambling (Comings et al., 1996), learning (Wise, 2004), motivation (Wang, et al., 2001;
Wise, 2004), and obesity (Wang, et al., 2001) reflect “reward deficiency syndrome”
(Bowirrat & Oscar-Berman, 2005), that is thought to have an association with and be due,
relatively, to the change in dopamine receptors. Thus, we wanted to investigate the
potential role in genetic variability that is responsible for the amount of dopamine
receptor availability. It has been well documented that the two SNPs: the C957T of the
DRD2 gene and the TaqIA of the ANKK1 gene are associated with the dopamine receptor
D2 availability.
2.3.2 DRD2 – C975T Polymorphism
The dopamine receptor, DRD2, gene has been localized on chromosome 11q23.2
(Grandy, et al., 1989). Six DRD2 synonymous polymorphisms have been identified; these
include: C132T, T765C, C939T, C957T, G423A, and G1101A (Gejman et al., 1994;
Grandy, et al., 1989; Sarkar et al., 1991; Seeman et al., 1993). It has been shown that only
two of these six synonymous single nucleotide polymorphisms (SNPs) have functional
19
consequences on DRD2 mRNA stability and the regulation of DRD2 expression (Duan et
al., 2003). As a result this may lead to the variation in dopamine receptor density.
The C957T SNP, (with genotypes: CC, CT, and TT) rs6277, rather than being
‘silent’ (for example, not resulting in a change to the amino acid sequence of a protein),
has been shown to noticeably account for the variability in DRD2 binding characteristics
and mRNA folding (Hietala, Syvalahti, et al., 1994; Hietala, West, et al., 1994;
Pohjalainen et al., 1998; Pohjalainen, Rinne, Nagren, Syvalahti, & Hietala, 1998). It has
been shown that C957T changes dopamine-induced regulation of DRD2 expression
(Duan, et al., 2003). Functional in vitro studies have found that the T allele of the C957T
polymorphism of the DRD2 gene is associated with decrease in mRNA translation and
decrease stability of DRD2, leading to a 50% reduction in the level of the DRD2 protein
synthesis and dopamine-induced expression compared with the C allele (Duan, et al.,
2003).
However, other studies, for instance an in vivo setting using healthy human
subjects, showed that the C957T polymorphism might have been responsible for the 18%
of the striatal dopamine receptor D2 binding potential (J. Hirvonen et al., 2005; M.
Hirvonen, et al., 2004). Furthermore, an in vivo study of 45 healthy humans using
[11C]raclopride and positron emission tomography (PET) indicated that the C957T SNP
alters dopamine receptor D2 binding potential (BP; Bmax/KD) in the human striatum in a
different pattern with TT homozygous having the highest striatal DRD2 binding
potential, CT heterozygous had intermediate binding, and CC homozygotes had the
lowest (M. Hirvonen, et al., 2004) to that observed previously by Duan et al. in 2003.
20
Thus, the results that are shown in the in vivo studies are opposite than those done in
vitro.
The difference between the observed findings is likely to reflect differences
between in vivo versus in vitro genetic studies and regulation. It has so far remained
unclear how the C957T, related to changes in mRNA folding in humans, can lead to
variation in DRD2 availability. It is also unclear how the C957T SNP can influence
receptor-G protein coupling and receptor-receptor interactions. Different mechanisms
have been proposed for C957T SNP but nothing has been verified.
2.3.3 ANKK1 – TaqIA Polymorphism
Neville et al. (2004) have localized a restriction fragment length polymorphism
(RFLP) named Taq1A (rs1800497) to a region approximately 9.5 Kb downstream from
the DRD2 gene in the ankyrin repeat and kinase domain containing 1 (ANKK1) gene
(Dubertret et al., 2010; Dubertret, Hanoun, Ades, Hamon, & Gorwood, 2004; Neville,
Johnstone, & Walton, 2004) (Figure 2-4). ANKK1 gene is a member of an extensive
family of proteins involved in signal transduction pathways (T. J. Chen et al., 2007).
The TaqIA has been previously indicated to be in linkage disequilibrium (LD)
with the DRD2 gene (Dubertret, et al., 2004; Neville, et al., 2004). LD is defined by a
non-random association of alleles at two or more loci (Reich et al., 2001). However, it
has been shown that this linkage is found in some but not all ethnic populations. TaqIA
SNP, (with genotypes: A2A2, A1A2, and A1A1), (Duan, et al., 2003; Fossella, Green, &
Fan, 2006) have previously reported to act on DRD2 availability (Jonsson et al., 1999;
21
Noble, Blum, Ritchie, Montgomery, & Sheridan, 1991; Pohjalainen, Rinne, Nagren,
Lehikoinen, et al., 1998; Thompson, et al., 1997).
The presence of the TaqIA A1 allele has been associated with a 30%-40%
reduction in the density of DRD2 and weaker dopamine signalling (Jonsson, et al., 1999;
Pohjalainen, Rinne, Nagren, Lehikoinen, et al., 1998; Ritchie & Noble, 2003). Reduction
in dopamine, when looking at TaqIA SNP, A1 allele, has been reported to be associated
with addiction to alcoholism (Arinami, Gao, Hamaguchi, & Toru, 1997; Blum, et al.,
1990; Ishiguro et al., 1998; Noble, et al., 1991) and it has also been linked to increase in
obesity and blood pressure (Thomas, et al., 2000), induce emotional effect (Nieoullon &
Coquerel, 2003), higher prevalence in type 2 diabetic population (Barnard et al., 2009),
insufficient brain reward (Comings & Blum, 2000), inability to learn from mistakes
(Klein et al., 2007), and impulsive and addictive behaviours (Comings, Rosenthal, et al.,
1996; Huang et al., 2009; Rowe et al., 1999; Shahmoradgoli Najafabadi et al., 2005).
This has led us to predict that type 2 diabetic patients, who went on a dietary intervention
treatment, with A1 allele of the TaqIA SNP will have poor dietary compliance due to the
effect of A1 allele on reduction of dopamine receptor availability. Hence, those
individuals will crave for external stimulus such as food to compensate for the
insufficient brain reward and due to difficulties following a guideline.
22
Figure 2-4: Region of Chromosome 11q23 showing the human DRD2 and ANKK1 gene structure that includes C957T and Taq1A SNPs sites, respectively (Hill et al., 2008).
23
2.3.4 Linkage Disequilibrium between C957T and TaqIA
Since the TaqIA A1 allele has been associated with reduction in the density of
DRD2 and weaker dopamine signalling, this may contribute to the effects of the C957T
SNP in humans. There has been evidence for linkage disequilibrium between the C957T
and the TaqIA polymorphisms (Duan, et al., 2003; K. Xu et al., 2004). Functional studies
in human suggest that the C allele of the C957T SNP (DRD2 gene) and the A1 allele of
the TaqIA SNP (ANKK1 gene) are associated with reduced receptor density in the human
brain (Jonsson, et al., 1999; Pohjalainen, Rinne, Nagren, Lehikoinen, et al., 1998). Thus,
we have combined the effect of both the C957T and the TaqIA SNPs while looking at the
polymorphisms of type 2 diabetic patients with regards to eating habits and physiological
effects in this study.
2.4 Genetic Variability of Dopamine Receptor and Type 2 Diabetes
Type 2 diabetes is nearly always associated with obesity. Obesity may be due to
excess of energy intake, a lack of exercise, and the influence of genetic factors. The
concept that obesity may relate to food addiction and poor dietary compliance, may be a
key factor that could lead to diabetes and other serious health problems. Genetic
variability in compulsive behaviours such as compulsive eating might thus explain the
susceptibility to body weight increase and food consumption in individuals, which in turn
could lead to diabetes and other health risks. Weight loss can help to improve blood
glucose control, blood pressure, cholesterol and many other health problems.
The association of dopamine receptors and addictive behaviours (such as smoking
(Noble et al., 1994), drug addiction (Volhttp://ca.wiley.com/WileyCDA/Section/id-
24
302301.htmlkow, Fowler, Wang, Swanson, & Telang, 2007), alcohol addiction (Blum et
al., 1990), gambling (Comings et al., 1996), learning (Wise, 2004), and obesity (Wang, et
al., 2001) reflect “reward deficiency syndrome” (Bowirrat & Oscar-Berman, 2005)) have
been established in healthy human studies. The present study, therefore, aim to determine
whether dopamine receptor polymorphisms may influence the outcome of dietary advice
that was giving to type 2 diabetics.
25
Chapter III
Hypotheses and Aims
26
3.1 Hypotheses
Goal: The polymorphisms of DRD2 and ANKK1 may influence the physiological effects
resulting from dietary advice given to type 2 diabetic patients.
Hypothesis 1 (Chapter 5)
Hypothesis 2 (Chapter 6)
The T allele of the C957T polymorphism (DRD2 gene),
that has been shown to be responsible for maintaining
sufficient dopamine receptors in human studies, may
confer benefits in low glycemic index diets through
increasing dietary compliance leading to better outcomes
in HbA1c, and reductions in BMI and blood pressure in
type 2 diabetic patients.
The A2 (A1-) allele of the TaqIA polymorphism
(ANKK1 gene), that has been shown to be responsible
for maintaining sufficient dopamine receptors in human
studies, may confer benefits in low glycemic index diets
through increasing dietary compliance leading to better
outcomes in HbA1c, and reductions in BMI and blood
pressure in type 2 diabetic patients.
27
Hypothesis 3 (Chapter 7)
The TTxA1- combined genotypes of the C957T and
TaqIA polymorphisms (both alleles have been shown to
be responsible for maintaining sufficient dopamine
receptors in human studies) may confer benefits in low
glycemic index diets through increasing dietary
compliance leading to better outcomes in HbA1c, and
reductions in BMI and blood pressure in type 2 diabetic
patients.
28
3.2 Aims
AIM 1 (Chapter 5)
AIM 2 (Chapter 6)
AIM 3 (Chapter 7)
To assess whether the T allele of the C957T SNP, that is
responsible for sufficient dopamine receptor availability
in human studies, are associated with better outcomes in
HbA1c, BMI, and blood pressure following high fibre
or low GI dietary advice in type 2 diabetic patients.
.
To assess whether the A2 (A1-) allele of the TaqIA
SNP, that is responsible for sufficient dopamine
receptor availability in human studies, is associated with
better outcomes in HbA1c, BMI, and blood pressure
following high fibre or low GI dietary advice in type 2
diabetic patients.
To assess whether the combination of the T allele of
C957T polymorphism and the A2 (A1-) allele of the
TaqIA polymorphism, that are responsible for sufficient
dopamine receptor availability in human studies, are
associated with better outcomes in HbA1c, BMI, and
blood pressure following high fibre or low GI dietary
advice in type 2 diabetic patients.
.
29
Chapter IV
Background Methodology for the Genetic Study and Relevant Results of the Original Low GI Study
30
4.1 Subjects
Study participants were recruited from a previous study for Glycemic Index at the
Risk Factor Modification Center, St. Michael’s Hospital, Toronto, where all study
clinical activity took place. Of the 210 participants that were randomized, 155 completed
the study and 109 gave a genetic consent. All participants were men or postmenopausal
women with type 2 diabetes who had HbA1c values at screening between 6.5% and 8.0%
(Jenkins et al., 2008). None had clinically significant cardiovascular, renal, or liver
disease and none were undergoing treatment for cancer.
31
981 Attended information session
658 Screened
375 Not interested
82 Booked for information session twice but did not attend
575 Excluded200 Unable to reach for clarification
203 Not interested63 Unable to reach
137 with HbA1c too high 186 with HbA1c too low
66 Other health issues 5 Screened, found acceptable, but want to start later, post- closure
48 Screened, found acceptable, but lost interest after screening
6 Screened, but could not be contacted after screening
57 Excluded
7 Post-study closure calls
2220 Contacted by Telephone
210 Randomized
106 Low GI
80 Completed2 medication increases
13 medication reductions8 other reasons*
104 High Fiber28 Dropped out
5 Pre-study23 During Study
(1 medication reduction)
1 Withdrawn
75 Completed3 medication increases3 medication reductions
2 other reasons*
25 Dropped out6 Pre-study
19 During Study(1 medication increase)
1 Withdrawn(Raised HbA1c)
57 Per protocol67 Per protocol
Figure 4-1: Subjects distribution in the glycemic index study (Jenkins, et al., 2008) with modification.
56 With genetic consent
53 With genetic consent
32
4.2 Protocol
The study was a randomized parallel study with two treatments, a low glycemic
index test diet and a high wheat fibre control diet, each of 6 months duration (Jenkins, et
al., 2008). The technical staff, involved in the analyses, were blinded to treatment. In
addition, during the study, equal emphasis was placed by dietitians on the potential
importance of high wheat fibre foods and low glycemic index foods and appropriate
weekly checklists were developed for both treatments (Jenkins, et al., 2008).
Participants were seen at baseline, weeks 2 and 4, and thereafter at monthly
intervals until the end of the 6 month period (Jenkins, et al., 2008). During the first
month, subjects received instructions on the diet to which they were allocated.
Throughout the study this advice was reinforced by the dietitians. At each study visit,
participants were weighed in indoor clothing without shoes and a fasting blood sample
was taken (Jenkins, et al., 2008). Blood pressure was measured seated on 3 occasions at
1 minute intervals using an Omron automatic sphygmomanometer (OMRON Healthcare
Inc., Burlington, Ontario, Canada) and the average of the three measurements was taken
(Jenkins, et al., 2008). In addition, participants brought with them their 7-day food
record covering the week prior to the visit and this was discussed with the dietitian
together with their checklist of low glycemic index or wheat fibre food items recorded on
a daily basis throughout the study. The dietitian gave one of three ratings for individual
adherence: good, unsatisfactory and may need additional encouragement with a between
visit phone call, or non-adherent with definite need for phone calls between scheduled
visits.
33
The study was approved by the research ethics board of St. Michael’s Hospital
and the University of Toronto, and written consent was obtained from all participants.
Clinical Trial Registration numbers: NCT00438698.
34
4.3 Dietary Interventions
General dietary advice conformed to the National Cholesterol Education Program
Adult Treatment Panel III (NCEP ATP III) (Chandalia et al., 2000; “Executive Summary
of The Third Report of The National Cholesterol Education Program (NCEP) Expert
Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults
(Adult Treatment Panel III),” 2001) and the American Diabetes Association (ADA)
(Franz et al., 2004) guidelines to reduce saturated fat and cholesterol intakes. The
majority of the participants were overweight (85.2%, 179/210, BMI ≥ 25 kg/m2) or obese
(53.8%, 113/210, BMI ≥ 30 kg/m2) and wished to lose weight. They were informed that
this was not a weight loss study but appropriate advice was given on portion size and fat
intake to help them meet their body weight objectives.
Participants were also provided with a checklist with either low glycemic index or
high wheat fibre food options from different categories (breakfast cereals, breads,
vegetables, and fruit) as approximately 15 g carbohydrate servings. The number of
carbohydrate servings prescribed covered 42-43% of total dietary calories. On the low
glycemic index diet the following foods were emphasized: low GI breads and breakfast
cereals, pasta, parboiled rice, beans, peas, lentils and nuts.
On the high wheat fibre diet, subjects were advised to take the “brown” option:
whole grain breads; whole grain breakfast cereals; brown rice; potatoes with skins; and
high wheat fibre bread, crackers and breakfast cereals. Six servings were prescribed for a
1500 kcal diet, 8 servings for a 2000 kcal diet and 10 servings for a 2500 kcal diet. The
checklists were completed by participants on a daily basis throughout the study and 7 day
diet records were completed prior to each visit. Adherence was assessed from the 7-day
35
diet records. The daily checklists were of value in alerting the dietitian to problems with
adherence to the diet plan over the month prior to center attendance. Fruit and vegetables
were encouraged on both treatments.
On the low GI treatment, temperate fruit was the focus including apples, pears,
oranges, peaches, cherries and berries, while on the wheat fibre treatment, tropical fruit
such as bananas, mangos, guavas, grapes, raisins, watermelon, and cantaloupe were
emphasized (Jenkins, et al., 2008).
36
4.4 Biochemical Analyses
Blood glucose was measured in the hospital routine analytical laboratory by a
glucose oxidase method using a Random Access Analyzer and reagents (SYNCHRON
LX Systems, Beckman Coulter, Brea, CA) (Coefficient of variation between assays, CV
= 1.9%). HbA1c was analyzed within 2 days of collection on whole blood collected in
EDTA Vacutainer tubes and measured by a designated HPLC method (Tosoh G7
Automated HPLC Analyzer, Grove City, OH, USA) (CV=1.7%).
Serum was analyzed for total cholesterol (total-C), triglycerides (TG), and high-
density lipoprotein cholesterol (HDL-C), also using a Random Access Analyzer and
reagents (SYNCHRON LX Systems, Beckman Coulter, Brea, CA) (CV=1.5-2.4%)
(Jenkins, et al., 2008). Low-density lipoprotein-cholesterol (LDL-C) was calculated by
the method of Friedewald et al. in mmol/L (LDL-C = total-C – (TG/2.2 + HDL-C))
(Friedewald, Levy, & Fredrickson, 1972). C-reactive protein (CRP) was measured by
end-point nephelometry (Behring BN-100, N high-sensitivity CRP reagent, Dade-
Behring, Marburg, GmbH) (CV=2.3%). Diets were assessed for macronutrients, fatty
acids, cholesterol, fibre and glycemic index using a computer program based on USDA
data (Jenkins, et al., 2008) and international GI tables (Foster-Powell, Holt, & Brand-
Miller, 2002) with additional measurements made on local foods, especially specialty
breads used as part of the low GI diet (Jenkins, et al., 2008).
37
4.5 Results of the Original Low Glycemic Index Study
The glycemic index study showed that HbA1c was reduced by -6.6±0.8% (7.1%
to 6.7% absolute HbA1c units) on the low GI diet compared to -2.9±0.8% (7.1% to 6.9%)
on the control diet. The difference in percentage reduction in fasting blood glucose was
not significant (-3.8±1.7%, P=0.137). These improvements in glycemic control on the
low GI diet relative to the control diet were achieved with a non-significant greater
reduction in body weight (-0.8±0.5%, P=0.127).
Significant treatment effects were observed for HDL-C and triglyceride increased
on the low GI diet relative to the control diet by 4.7±1.7% (P=0.018). Systolic and
diastolic blood pressure dropped slightly on both diets but the difference in diastolic
blood pressure reduction was significantly greater on the low glycemic index diet by
1.5±1.1% (74 mmHg to 72 mmHg) (P=0.029).
38
Chapter V
The Effect of DRD2 – C957T Polymorphism on Dietary Compliance, Blood Pressure, and BMI
39
5.1 Abstract Hypothesis and Purpose: Variation in dopamine receptor D2, DRD2, has been associated with addiction, reward, pleasure, stress, learning, motivation, food intake, obesity and type 2 diabetes. There have been some conflicting results with respect to DRD2 gene findings, specifically, with the C957T SNP genotypes (CC, CT, and TT) and number of dopamine receptors that has been associated with addictive behaviours. That is, the more dopamine receptors available the less the addictive tendency. The aim of this study was to assess the association between the DRD2 - C957T polymorphisms with dietary compliance, BMI, and blood pressure in type 2 diabetic patients. Methods: Blood samples were collected from 109 type 2 diabetic participants who completed a 24-week randomized clinical trial and gave consent for genetic studies. They were assigned to follow either a low glycemic index (GI) or a high fibre (high GI) diet over the 24 weeks. C957T genotypes were determined for each patient and compared with blood pressure, BMI and their food intake. Results: Regardless of dietary treatment, the CT and TT genotypes of the C957T SNP were significantly associated with reduction in blood pressure and greater reduction in BMI over the 24-week trial (p=0.016 and P=0.009, respectively). Conclusion: In this study, the presence of the T allele in DRD2 was associated with decrease of both body weight and blood pressure in diabetic patients. These results were consistent with other studies in healthy non-diabetic participants.
40
5.2 Introduction
The brain dopamine D2 receptor (DRD2) is found in the striatum, nucleus
accumbens, olfactory tubercle, hypothalamus and pituitary of the brain and helps regulate
feelings of pleasure and modulates the rewarding properties of food (Bassareo & Di
Chiara, 1999). The C957T SNP, with C and T alleles, is one of the few genetic variants
shown to be responsible for the variability in DRD2 binding characteristics (Duan, et al.,
2003; Haltia, et al., 2007; Hietala, West, et al., 1994; Pohjalainen, Rinne, Nagren,
Lehikoinen, et al., 1998). Dopamine receptor is a major constituent of reward pathways
and is strongly believed to be involved in many forms of substance dependence and
addictive behaviours (Noble, 2000; Volkow, et al., 2001).
C957T related changes in DRD2 mRNA folding may translate in humans to
changes in dopamine receptor D2 availability. Human imaging studies using healthy
populations have found that the C allele is responsible for the decrease in striatal DRD2
density (M. Hirvonen, et al., 2004; M. M. Hirvonen et al., 2009). This decrease in the
striatal D2 receptor density has been in turn shown to be responsible for a range of
important functions and regulation of a number of physiological processes in the human
brain. As such, a population carrying the C allele would be prone to chronic stress (or
stressors) (Lawford et al., 2003), substance addiction (Fowler, Volkow, Kassed, &
Chang, 2007; Volkow, et al., 1990), and increase in food intake (Eny, et al., 2009;
Volkow, et al., 2008; Wang, et al., 2001) due to reduced dopamine secretion/receptivity
increasing the craving or need for reward compensation. Furthermore, other studies have
shown that BMI correlated negatively with the measures of dopamine receptor activity
41
(Volkow, et al., 2008). That is individuals with the C allele (lower dopamine receptors
density) had a very high BMI compared to the other allele.
Although, many of the mechanisms and genetic factors for type 2 diabetes are not
fully understood, genetic-environmental interactions are the likely to be involved
(Barnard, et al., 2009). Based on previous studies, we predict that individuals deficient in
dopamine receptors may need to eat more than people with sufficient dopamine receptor
levels to induce feelings of satisfaction and gratification. That is, individuals with C allele
might be more vulnerable to compulsive behaviours such as poor dietary compliance, and
aberrant sugar craving (Eny, et al., 2009) and food addiction (Epstein et al., 2004). This
vulnerability might explain the susceptibility to body weight increase and excess food
consumption in individuals who develop type 2 diabetes. As a result, we have looked at
the dopamine receptor, in particular the DRD2 gene, to see if there is an association
between different C957T polymorphisms of the DRD2 gene and food intake, BMI, and
blood pressure.
42
5.3 Methods
5.3.1 Participants
Subjects were participants in a previously published low glycemic diet study
(Jenkins, et al. 2008). From the original 210 participants, 109 completed the study and
consented to the genetic study (Figure 4-1). Eligible participants were men or
postmenopausal women with type 2 diabetes who were taking oral agents other than
acarbose to control their diabetes, with medications stable for the previous three months
and who had HbA1c values at screening between 6.5% and 8.0% (Jenkins, et al., 2008).
None had clinically significant cardiovascular, renal, or liver disease (ALT > 3 times the
upper limit of normal) and none were undergoing treatment for cancer. The final sample
size (n=109) consisted of 69 men and 40 women. The ethnicity of the group were African
(n=6), Asian (n=7), Caucasian (n=75), Hispanic (n=1), Native (n=1), and South Asian
(n=19). Further classification was done according to genotype taking account of sex and
dietary treatments.
The study and genetic consent were approved by the research ethics board of St.
Michael’s Hospital and the University of Toronto. Clinical Trial Registration number:
NCT00438698.
5.3.2 Dietary Interventions
General dietary advice conformed to the National Cholesterol Education Program
Adult Treatment Panel III (NCEP ATP III) (“Executive Summary of The Third Report of
The National Cholesterol Education Program (NCEP) Expert Panel on Detection,
Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel
43
III),” 2001) and the American Diabetes Association (ADA) (Franz, 2004) guidelines to
reduce saturated fat and cholesterol intakes. Participants were randomized onto the low
glycemic index diet or the high wheat fibre diet. Six servings were prescribed for a 1500
kcal diet, 8 servings for a 2000 kcal diet and 10 servings for a 2500 kcal diet (Jenkins, et
al., 2008). The checklists were completed by participants on a daily basis throughout the
study and 7 day diet records were completed prior to each visit. Adherence was assessed
from the 7-day diet records. The daily checklists were of value in alerting the dietitian to
problems with adherence to the diet plan over the month prior to center attendance.
5.3.3 Study Protocol - Genetic Analyses
Overnight fasting (12 hours) blood samples were collected at St. Michael's
Hospital, Toronto, Canada. The patients’ blood samples were centrifuged and buffy-coat
stored in the freezer (-70ºC) at the University of Toronto for DNA extraction. DNA was
extracted from leucocytes using standard MasterPureTM DNA Purification Kit for Blood
Version II – MB711400 (InterScience - EPICENTRE® Biotechnologies, Madison,
Wisconsin, USA) and subsequently used as a template for determination of genotypes.
The C957T polymorphism (rs6277) of the DRD2 was detected by real-time
polymerase chain reaction (PCR) using a TaqMan® SNP Genoypting Assay
(C__11339240_10) from Applied Biosystems (Foster City, California, USA). PCR
conditions were as follows: 50ºC for 2 min, 95ºC for 10 min followed by 40 cycles at
95ºC for 15 second and 60ºC for 1 min. Allelic discrimination was performed using the
ABI 7000 Sequence Detection System (Applied Biosystems, Foster City, California,
USA). A total of 5–10 ng of genomic DNA was amplified in 1 9 SYBR green PCR
44
master mix (Applied Biosystems) containing 0.4 lM of allele specific forward primer and
0.4 lM of common reverse primer in a 25 nm volume. Genotyping was verified using
positive controls in each plate as well as re-running at least 50% of the samples twice.
5.3.4 Statistical Analyses
The primary outcome of the main study was HbA1c. Blood pressure, BMI, and
HDL were secondary measures. All results are expressed as means ± SE. All analyses
were carried out using IBM® SPSS® software, version 17 (SPSS Inc., Chicago, Illinois,
USA). Analyses were undertaken of the completed and consented subject group.
For variables with time trends, the significance of treatment differences was
assessed using an ANOVA model with the percent change from baseline to end of study
as the response variable. Dietary treatment, sex, and diet by sex interactions were also
examined in all the consented population and in Caucasians separately.
Additional analyses were performed using change in fibre and carbohydrate
intake. Levene’s test was used to assess the equality of variances in different samples. For
skewed data, the results were confirmed with non-parametric analysis (Kruskal-Wallis
test), thus, both parametric and none parametric analyses were reported. The Mantel-
Haenszel, chi-squared and Fisher’s Exact tests were used to assess differences in
medication changes in the primary study (Jenkins, et al., 2008).
45
5.4 Results
C957T genotyping identified 32 (29.3%) participants as CC genotype, 49 (45.0%)
as CT genotype and 28 (25.7%) as TT genotype (Figure 5-1). These frequencies were in
Hardy-Weinberg equilibrium, Χ2(1,N=109) = 1.08, P = 0.30. The distribution of the
participants with respect to their genotypes is shown in Figure 5-1.
Figure 5-1: The distribution of the participants according to their C957T SNP genotypes. The frequency of the distribution were in Hardy-Winberg equilibrium, P=0.30
Table 5-1 was constructed to look at the dietary treatments, sex, and ethnicity
between genotypes of C957T SNP. From the 46 participant that were on the high fibre
diet treatment group: 18 were CC homozygous, 26 were CT heterozygous, and 12 were
TT homozygous. Almost similar distributions were observed in the low GI diet treatment
group: 14 were CC homozygous, 23 were CT heterozygous, and 16 were TT
46
homozygous. No significance differences were observed between dietary treatments
when evaluating different C957T genotypes and the biological markers that were taken
during week 0.
In the study, there were more males than females who completed and consented to
the genetic studies: 69 males versus 40 females. However, no significance differences
were observed between the association of C957T genotypes and the two sexes.
Table 5-1: DRD2 – C957T SNP, genotype distribution by dietary treatment, sex, and ethnicity. The values have been reported by sample size (percentage), n (%).
Subgroup CC CT TT
Dietary Treatment
High Fibre (Control) 18 (16.5%) 26 (23.9%) 12 (11.0%)
Low GI (Test) 14 (12.8%) 23 (21.1%) 16 (14.7%)
Sex
Male 20 (18.3%) 32 (29.4%) 17 (15.6%)
Female 12 (11.0%) 17 (15.6%) 11 (10.1%)
Ethnicity
African 6 (5.5%) 0 (0%) 0 (0%)
Asian 5 (4.6%) 1 (0.9%) 1 (0.9%)
Caucasian 13 (11.9%) 38 (34.9%) 24 (22.0%)
Hispanic 1 (0.9%) 0 (0%) 0 (0%)
Native 1 (0.9%) 0 (0%) 0 (0%)
South Asian 6 (5.5%) 10 (9.2%) 3 (2.8%)
Total 32 (29.3%) 49 (45.0%) 28 (25.7%)
47
Most of our patients were Caucasian (69% of the total consented population). The
Caucasians were mostly heterozygous for C957T and had greater frequency for the T
allele. Whereas other ethnic groups: Africans, Asians, Natives, and South Asians showed
a higher frequency for the C allele (Table 5-1).
Table 5-2: DRD2 – C957T SNP, Baseline of physical and biochemical markers and food consumption by genotype distribution. The values have been reported by Mean (SE)
Baseline CC
(n=32) (±SE)CT
(n=49) (±SE) TT
(n=28) (±SE) P-valueAge (years) 59.0 (1) 63.0 (1) 62.0 (2) 0.087 BMI (Kg/m2) 30.7 (1.3) 31.3 (0.8) 29.6 (0.8) 0.480HbA1c (%) 7.1 (0.1) 7.1 (0.1) 7.1 (0.1) 0.996 Lipids (mmol/L) Total Cholesterol 4.4 (0.2) 4.2 (0.1) 4.2 (0.1) 0.560Triglycerides 1.4 (0.2) 1.4 (0.1) 1.5 (0.1) 0.780HDL 1.1 (0.0) 1.1 (0.04) 1.1 (0.1) 0.540LDL 2.6 (0.1) 2.5 (0.1) 2.4 (0.1) 0.450 Blood Pressure (mmHg) Systolic (SBP) 123.2 (2.4) 126.7 (2.2) 133.1 (3.7) 0.060Diastolic (DBP) 73.0 (1.7) 73.8 (1.4) 75.6 (2.0) 0.590 Dietary intake (per day) Energy (Kcal) 1885 (96) 1807 (75) 1903 (87) 0.680 Carbohydrate (g) 198 (9) 200 (10) 210 (11) 0.710 Protein (g) 98 (5) 90 (3) 92 (4) 0.380 Fibre (g) 24 (2) 25 (2) 26 (1) 0.670 Fat (g) 76 (5) 70 (4) 72 (4) 0.630 MFA (mg) 31 (2) 28 (2) 28 (2) 0.640 PUFA (mg) 16 (1) 14 (1) 14 (1) 0.580 SFA (mg) 23 (2) 21 (2) 23 (2) 0.680 Alcohol (g) 3 (1) 3 (1) 7 (2) 0.060
GI 83 (1) 81 (1) 81 (1) 0.250
48
The baseline for all subjects was assessed and compared to see if any dietary
consumption and phenotypically significant differences existed between different
genotypes (prior to any intervention). Table 5-2 shows that there were no significance
differences in BMI, HbA1c, cholesterol, blood pressure, and dietary consumption
associated with C957T SNP at baseline.
Over the 24-week study period, no treatment differences were seen in the change
in measurements over either diets when related to the C and T alleles (P-values>0.05).
Moreover, the effect of genotypes did not differ between men and women at baseline and
over the 24 weeks. Thus, the participants were pooled regardless of their sex and dietary
treatment to have a larger sample size.
Figure 5-2: Percentage change in BMI over the 24 weeks of dietary intervention for C957T SNP in type 2 diabetic patients (n=109) with: 32 homozygous CC, 49 heterozygous CT, and 27 homozygous TT. The BMI significantly decreased by percentage change of -2.1 ± 0.5%, -3.6 ± 0.5%, and -5.0 ± 0.9% for CC homozygous, CT heterozygous and TT homozygous, respectively (P=0.009).
-7
-6
-5
-4
-3
-2
-1
0C/C C/T T/T
Per
cent
age
Chan
ge in
BM
I (%
)
49
Through the pooled population for this study, the T allele of C957T was
significantly associated with a greater reduction in BMI than the C allele over time
(P=0.009) (Figure 5-2).
Over the 24 weeks of the study, the T allele (heterozygous CT and homozygous
TT) of C957T was also significantly associated with improved outcomes in response to
blood pressure (Figure 5-3), specifically systolic blood pressure (SBP). A pattern was
observed: individuals with homozygous CC genotype increased their SBP by 2.7 ± 1.6%,
while the individuals with T allele decreased their SBP by 2.72 ± 1.3% and 3.34 ± 1.8%
for heterozygous CT and homozygous TT, respectively (P=0.016).
Figure 5-3: Percentage change in systolic blood pressure (SBP) over the 24 weeks of dietary intervention for C957T SNP in type 2 diabetic patients (n=109). There were 32 homozygous CC, 49 heterozygous CT, and 27 homozygous TT. The blood pressure (SBP) significantly decreased by percentage change of -2.72 ± 1.3% for CT heterozygous and -3.34 ± 1.8% TT homozygous; while patients with CC homozygous genotype increased their blood pressure by a percentage change of 2.69 ± 1.6%. (P=0.016).
-6
-4
-2
0
2
4
6C/C C/T T/T
Per
cent
age
Cha
nge
in S
BP
(%)
50
This significant SBP difference was observed between the CC and the CT
genotypes (Table 5-3). It was also observed in the CC and the TT genotypes. However,
there were no significant differences between the CT and the TT in response to SBP and
in response to diastolic blood pressure (DBP). Again, this difference was seen
irrespective of dietary treatment or sex (Table 5-3).
Table 5-3: DRD2 – C957T SNP, Percentage change in physical and biochemical markers and food consumption by genotype distribution over the 24-week study. The values have been reported by Mean Percentage Change (SE).
Percentage Change (%) CC
(n=32) (±SE)CT
(n=49) (±SE)TT
(n=28) (±SE) P-value BMI -2 (1) -4 (0) -5 (1) 0.009HbA1c -5 (1) -6 (1) -9 (2) 0.186 Lipids Total Cholesterol 1 (3) 1 (2) -1 (2) 0.905Triglycerides 8 (5) 7 (7) -15 (7) 0.066HDL 3 (2) 3 (2) 4 (3) 0.882LDL 0 (5) 1 (5) 5 (3) 0.805 Blood Pressure Systolic (SBP) 3 (2) -3 (1) -3 (2) 0.016Diastolic (DBP) 1 (2) -2 (2) -4 (2) 0.081 Dietary Intake (per day) Energy -11 (4) -10 (3) -10 (4) 0.983 Carbohydrate -5 (4) -4 (4) -4 (5) 0.989 Protein -8 (4) -4 (3) -2 (4) 0.553 Fibre 25 (9) 31 (10) 25 (8) 0.885 Fat -16 (7) -17 (6) -19 (5) 0.952 MFA -17 (8) -17 (7) -19 (5) 0.977 PUFA -15 (10) -12 (8) -12 (9) 0.969 SFA -16 (7) -23 (5) -24 (5) 0.577 Alcohol 0 (1) 0 (0) -3 (2) 0.160
GI -5 (2) -7 (2) -10 (3) 0.236
51
Due to the allelic frequency differences in genetic variances between different
ethnic groups, a separate analysis was done to show the baseline and the percentage
changes in Caucasians. There were 75 Caucasians out of 109 participants in the study
with: 13 homozygous CC, 38 heterozygous CT, and 24 homozygous TT. These
frequencies were in Hardy-Weinberg equilibrium, Χ2(1,N=75) = 0.095 (P = 0.76).
There were no significance differences at the baseline in the biological markers
and food consumption in Caucasians (Table 5-4).
Table 5-4: DRD2 – C957T SNP, Baseline of physical and biochemical markers and food consumption by genotype distribution in Caucasians. The values have been reported by Mean (SE).
Baseline CC
(n=13) (±SE) CT
(n=38) (±SE) TT
(n=24) (±SE) P-value BMI (Kg/m2) 32.4 (2.2) 32.0 (0.8) 29.4 (0.9) 0.136HbA1c (%) 6.9 (0.1) 7.1 (0.1) 7.1 (0.1) 0.503 Lipids (mmol/L) Total Cholesterol 4.5 (0.3) 4.1 (0.1) 4.1 (0.2) 0.302Triglycerides 1.7 (0.3) 1.5 (0.1) 1.5 (0.2) 0.754HDL 1.1 (0.08) 1.0 (0.04) 1.1 (0.1) 0.477LDL 2.7 (0.3) 2.4 (0.1) 2.2 (0.1) 0.313 Blood Pressure (mmHg) Systolic (SBP) 123.1 (3.2) 129.2 (2.4) 133.1 (4.2) 0.207Diastolic (DBP) 70.2 (2.2) 74.4 (1.6) 74.2 (2.1) 0.383 Dietary intake (per day) Energy (Kcal) 2063 (139) 1838 (88) 1867 (75) 0.409 Carbohydrate (g) 207 (16) 200 (11) 203 (10) 0.929 Protein (g) 104 (10) 92 (4) 92 (3) 0.279 Fibre (g) 28 (3) 26 (2) 26 (1) 0.820 Fat (g) 88 (11) 72 (5) 70 (4) 0.140 MFA (mg) 35 (4) 29 (2) 27 (2) 0.119 PUFA (mg) 18 (2) 15 (1) 14 (1) 0.152 SFA (mg) 28 (4) 23 (2) 22 (2) 0.289 Alcohol (g) 4 (2) 3 (1) 8 (3) 0.149 GI -7 (3) -6 (2) -11 (3) 0.907
52
There were also no significance differences, at the end of the study, in the
percentage changes of all the measurements between week 0 to week 24, in Caucasians
(Table 5-5).
Table 5-5: DRD2 – C957T SNP, Percentage change in physical and biochemical markers and food consumption by genotype distribution over the 24-week study in Caucasians. The values have been reported by Mean Percentage Change (SE). Percentage Change
(%) CC
(n=13) (±SE)CT
(n=38) (±SE)TT
(n=24) (±SE) P-value
BMI -3 (1) -4 (1) -5 (1) 0.274
HbA1c -4 (2) -6 (2) -8 (1) 0.583
Lipids
Total Cholesterol -7 (3) 2 (3) 0 (2) 0.133
Triglycerides -4 (8) 7 (9) -15 (7) 0.204
HDL 8 (4) 3 (2) 4 (3) 0.584
LDL -12 (5) 3 (6) 6 (4) 0.192
Blood Pressure
Systolic (SBP) 1 (3) -3 (2) -4 (2) 0.322
Diastolic (DBP) -2 (2) -3 (2) -5 (2) 0.589
Dietary intake (per day)
Energy -8 (6) -10 (3) -9 (4) 0.922
Carbohydrate -1 (7) -6 (4) -3 (5) 0.843
Protein -2 (6) -2 (3) -3 (4) 0.988
Fibre 34 (17) 22 (9) 25 (10) 0.788
Fat -16 (12) -15 (7) -18 (5) 0.951
MFA -18 (15) -16 (8) -17 (6) 0.986
PUFA -15 (16) -9 (9) -17 (6) 0.822
SFA -22 (9) -21 (5) -24 (6) 0.899
Alcohol 0 (1) 0 (1) -3 (2) 0.063
GI 80 (2) 81 (1) 80 (1) 0.305
53
5.5 Discussion
In this study our findings were consistent with findings from other human studies
(Epstein, et al., 2007; M. M. Hirvonen, et al., 2009). That is, the C957T C allele that was
shown to be associated with the decrease in dopamine receptor might be associated with
increased food intake and poor dietary compliance. The T allele of the C957T SNP
(DRD2 gene) had greater significant positive changes over the 24 weeks of dietary
intervention in diabetic patients with respect to blood pressure and BMI.
These significances were observed when the participants were pooled together.
There were no significant differences when looking at the different dietary treatments or
the sexes in association with the C957T SNP genotypes. Moreover, no significant
differences were observed when separating the Caucasians to avoid genetic variability in
different ethnic groups. One possible reason for the non-significant values may be lower
sample sizes and lower power affect. However, the pattern of response was similar to
when the data from the whole group was used.
Rodriguez-Jimenez had shown in his recent study that the CC genotype was
selected as the “risky” genotype based on a previous research association with cognitive
aspects of impulsivity in healthy adults (Rodriguez-Jimenez et al., 2006; H. Xu et al.,
2007; K. Xu, et al., 2004) and supported by data showing reduced striatal DRD2 binding
in this group (M. M. Hirvonen, et al., 2009). Moreover, studies on cognition in healthy
subjects have associated the C allele to poorer cognitive performance in decision-making
(Rodriguez-Jimenez, et al., 2006) and working memory (H. Xu, et al., 2007).
The CC genotype has also been associated with psychopathic traits in alcohol
dependent patients (Ponce et al., 2008). In contrast, studies focusing on addiction have
54
found inconsistent associations for the presence of the C allele or the T allele with
alcohol dependence (Hill, et al., 2008; Ponce, et al., 2008) and nicotine response
(Jacobsen, Pugh, Mencl, & Gelernter, 2006; Lerman et al., 2006).
The conflict between findings on the T allele of C957T is dependent on the fact
whether the research was done in vivo or in vitro. In vitro study, the T allele was
associated with reduced mRNA stability and translation, and dramatically decrease of
dopamine receptor availability induced by DRD2 expression (Duan, et al., 2003). This
therefore are inconsistency in Duan et al’s (2003) in vitro study which showed a negative
effect of the T allele and Hirvonen et al.’s (2004, 2009) in vivo study which showed both
a benefit effect of the presence of the T allele and a negative association for the C allele
in terms of food addiction and dietary compliance.
Environmental factors may explain the conflicting findings reported for
associations between alternate models of C957T inheritance in human studies (Conner,
Hellemann, Ritchie, & Noble, 2010). It is likely there are multiple environmental and
genetic risk factors contributing to poor dietary compliance and food addictive behaviour.
It has been shown that a significant increase in blood pressure, associated with acute
environmental stress, was correlated to impulsive behaviours (Bogdan & Pizzagalli,
2006; Roberti, 2003) such as food intake. However, we did not find any correlation
between blood pressure and BMI.
These findings provide the opportunity to provided insight on dopamine receptors
and their influence on reward and addictive behaviours that can be applied to clinical
settings. They also provide support for the importance of studying food addiction as a
contributor to obesity and type 2 diabetes. These findings are consistent with those
55
studies done in humans demonstrating that stress may alter dopamine metabolism
(Lawford, et al., 2003), may contribute to overeating (Epstein, et al., 2007), and
increases in other abnormal craving behaviours that lead to weight gain (Baptista, 1999).
Food intake can be very compulsive and can be used as a reward (Noble, 2000). It
can also be as reinforcing as drugs and substance abuse (Blum, et al., 1990; Conner, et
al., 2010). Conceptualizing overeating as increased motivation to eat because of increased
food addiction is similar to drug abuse (Madden, Petry, Badger, & Bickel, 1997). This
hypothesis suggests that polymorphisms that are related to the density of DRD2 receptors
can affect food reinforcement, obesity, and energy intake, and also provide a mechanistic
explanation for how dopaminergic activity may influence obesity and diabetes risk. A
limitation of the current study is its modest sample size, requiring a cautious
interpretation of the results.
We suggest that the T allele which was shown to provides sufficient dopamine
receptors to convey the necessary physiological response to prevent anxiety.
Accordingly, individuals with adequate dopamine receptors are less likely to have
addictive behaviours since they have the self gratification to avoid unhealthy food
consumption and so avoid weight gain. On the other hand, individuals with less
availability of dopamine receptors are prone to addictive behaviours such as food
addiction to stimulate the dopaminergic system and compensate for the lack of
stimulation of reward/pleasure circuits that the chemical generates. As a result deficiency
of dopamine receptor in the brain may explain why some individuals engage in
pathological overeating, resulting in weight gain that in turn may increase blood pressure
and the risk of developing diseases such as diabetes.
56
Link to Chapter VI
57
A restriction fragment length polymorphism (RFLP) named Taq1A (rs1800497),
that has been historically described as residing in the DRD2 gene, has been localized to a
region approximately 9.5 kb downstream from the DRD2 gene in the ankyrin repeat and
kinase domain containing 1 (ANKK1) gene (Grandy, et al., 1989; Hill, et al., 2008;
Neville, et al., 2004). This may contribute to the effects of the C957T SNP. In human
beings, studies have shown a higher prevalence of the TaqIA A1 allele is linked with
lower amounts of dopamine D2 receptors (Thompson, et al., 1997) in obese individuals
(Blum, Braverman, et al., 1996) and may therefore have contributed to their excess
calorie intake.
58
Chapter VI
The Effect of ANKK1 – TaqIA Polymorphism on Dietary Compliance, Blood Pressure, and BMI
59
6.1 Abstract Hypothesis and Purpose: The presence of the TaqIA A1 allele has been associated with a 30%-40% reduction in the density of the dopamine receptor D2. It has also been associated with addiction, lack of brain reward, stress, slow learning, lack of motivation, increase in food consumption and obesity, and type 2 diabetes. The aim of this study was to assess the association between TaqIA (genotypes: A1- for homozygous A2A2, A1+ for heterozygous A1A2 and homozygous A1A1) polymorphisms with dietary compliance, blood pressure, and BMI in type 2 diabetic patients. Methods: Blood samples were collected from 109 type 2 diabetic participants who completed a 24-week randomized clinical trial and gave consent for genetic testing. They were assigned to follow either a low glycemic index (GI) or a high fibre (high GI) diet over the 24 weeks. TaqIA genotypes were determined for each patient and compared with blood pressure, BMI, and their food intake. Results: No diet treatments specific differences were observed in the influence of the TaqIA genotypes. When both treatments were combined, the A1 allele of the TaqIA SNP was significantly associated with greater change in blood pressure (both systolic and diastolic blood pressure) over the 24-week trial (p=0.014 and P=0.021, respectively). Conclusion: In this study, the presence of the A1 allele in the ANKK1 was associated with increase of blood pressure in diabetic patients. This finding was consistent with other studies that were done in diabetic and non-diabetic participants.
60
6.2 Introduction
TaqIA comes in two allelic forms: A1 and A2. The presence of the TaqIA A1
allele has been associated with a 30%-40% reduction in the density of the dopamine
receptors, DRD2, and weaker dopamine signalling (Jonsson, et al., 1999; Pohjalainen,
Rinne, Nagren, Lehikoinen, et al., 1998; Ritchie & Noble, 2003). A low number of
dopamine D2 receptors in individuals carrying the Taq1A A1 variant suggests a
hypodopaminergic function (Gardner, Hall, & Strange, 1997).
When there is a scarcity of dopamine receptors the individual will be more likely
to seek any substance or behaviours (Gardner, et al., 1997) (such as food addiction,
alcohol, cocaine, heroin, nicotine, gambling, and as well as a number of behaviours that
preferentially release dopamine at the nucleus accumbens) that stimulate the
dopaminergic system to compensate for the lack of stimulation of reward/pleasure
circuits that the chemical generates. Blum (1990) reported a similar strong association
with addiction (specifically, substance and alcoholism abuse) and the Taq1A A1 allele
(Blum, et al., 1990). Other more recent studies further support the association of this
allele with substance abuse and other addictive behaviours (Blum et al., 1997; Comings,
Rosenthal, et al., 1996; Parsian, Cloninger, & Zhang, 2000; K. Xu, et al., 2004; Young et
al., 2002).
We have therefore studied the effect of the TaqIA polymorphism on blood
pressure, BMI, and dietary compliance in type 2 diabetic participants who had taken part
in a study of low glycemic index (GI) and high fibre diets (Jenkins, et al., 2008).
61
6.3 Methods 6.3.1 Participants
Subjects were participants in a previously published low glycemic diet study
(Jenkins, et al., 2008). From the original 210 participants, 109 completed the study and
consented to the genetic testing (Figure 4-1). Eligible participants were men or
postmenopausal women with type 2 diabetes who were taking oral agents other than
acarbose to control their diabetes, with medications stable for the previous three months
and who had HbA1c values at screening between 6.5% and 8.0%. None had clinically
significant cardiovascular, renal, or liver disease (ALT > 3 times the upper limit of
normal) and none were undergoing treatment for cancer. The final sample size (n=109)
consisted of 69 men and 40 women. The ethnicity of the group were African (n=6), Asian
(n=7), Caucasian (n=75), Hispanic (n=1), Native (n=1), and South Asian (n=19). Further
classification was done according to genotype and subgroup.
The study and genetic consent were approved by the research ethics board of St.
Michael’s Hospital and the University of Toronto. Clinical Trial Registration number:
NCT00438698.
6.3.2 Dietary Interventions
General dietary advice conformed to the National Cholesterol Education Program
Adult Treatment Panel III (NCEP ATP III) (Executive Summary of The Third Report of
The National Cholesterol Education Program (NCEP) Expert Panel on Detection,
Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel
62
III), 2001) and the American Diabetes Association (ADA) (Franz, 2004) guidelines to
reduce saturated fat and cholesterol intakes. Participants were randomized onto the low
glycemic index diet or the high wheat fibre diet. Six meal servings were prescribed for a
1500 kcal diet, 8 servings for a 2000 kcal diet and 10 meal servings for a 2500 kcal diet.
The checklists were completed by participants on a daily basis throughout the study and 7
day diet records were completed prior to each visit. Adherence was assessed from the 7-
day diet records. The daily checklists were of value in alerting the dietitian to problems
with adherence to the diet plan over the month prior to center attendance.
6.3.3 Study Protocol - Genetic Analyses
Overnight fasting (12 hours) blood samples were collected at St. Michael's
Hospital, Toronto, Canada. The patients’ blood samples were centrifuged and buffy-coat
stored in the freezer (-70ºC) at the University of Toronto for DNA extraction. DNA was
extracted from leucocytes using standard MasterPureTM DNA Purification Kit for Blood
Version II – MB711400 (InterScience - EPICENTRE® Biotechnologies, Madison,
Wisconsin, USA) and subsequently used as a template for determination of genotypes.
The TaqIA polymorphism (rs1800497) of the ANKK1 was detected by real-time
polymerase chain reaction (PCR) using a TaqMan® SNP Genoypting Assay
(C___7486676_10) from Applied Biosystems (Foster City, California, USA). PCR
conditions were as follows: 50ºC for 2 min, 95ºC for 10 min followed by 40 cycles at
95ºC for 15 second and 60ºC for 1 min. Allelic discrimination was performed using the
ABI 7000 Sequence Detection System (Applied Biosystems, Foster City, California,
USA). A total of 5–10 ng of genomic DNA was amplified in 1 9 SYBR green PCR
63
master mix (Applied Biosystems) containing 0.4 l M of allele specific forward primer and
0.4 lM of common reverse primer in a 25 nm volume. Genotyping was verified using
positive controls in each plate as well as re-running at least 50% of the samples twice.
6.3.4 Statistical Analyses
The primary outcome was HbA1c of the main study. Blood pressure, BMI, and
HDL were secondary measures. All results are expressed as means ± SE (standard error).
Also, all analyses were carried out using IBM® SPSS® software, version 17 (SPSS Inc.,
Chicago, Illinois, USA). Analyses were undertaken of the completed and consented
subject group.
For variables with time trends, the significance of treatment differences was
assessed using an ANOVA model with the percent change from baseline to end of study
as the response variable. Dietary treatment, sex, and diet by sex interaction were also
examined in the total consented population and in Caucasians separately.
Additional analyses were performed using change in fibre intake, carbohydrate
intake, and GI value. Levene’s test was used to assess the equality of variances in
different samples. For skewed data, the results were confirmed with non-parametric
analysis (Kruskal-Wallis test), thus, both parametric and none parametric analyses are
reported.
64
6.4 Results
TaqIA genotyping identified 73 (70.0%) participants as A2A2 (A1-) genotype, 32
(29.3%) as A1A2 genotype, and 4 (3.7%) as A1A1 genotype. These frequencies are in
Hardy-Weinberg equilibrium, Χ2(1,N=109) = 0.04, P = 0.83. Since the sample size of
participants with A1A1 genotype was very small, analyses were performed combining
the A1 (A1A1 and A1A2) genotypes and referred to as A1+ due to the presence of A1
allele, these were compared to A2A2 (referred to as A1- due to the absence of A1 allele)
genotype. The distribution of the participants with respect to their genotypes is shown in
Figure 6-1.
Figure 6-1: The distribution of the participants according to their TaqIA SNP genotypes. The frequency of the distribution were in Hardy-Weinberg equilibrium, P=0.83
65
A table was constructed to look at the dietary treatments, sex, and ethnicity
between genotypes of TaqIA polymorphism. From the 46 participant that were on High
fibre diet treatment group: 37 were A1- and 18 were A1+ genotype. Almost similar
distributions were observed in the low GI diet treatment group: 36 were A1- and 18 were
A1+ genotype.
No significance differences were observed at baseline between dietary treatments
when evaluating different TaqIA genotypes and their physical and biochemical
measurements that were taken during week 0.
Table 6-1: ANKK1 – TaqIA SNP, genotype distribution by dietary treatment, sex, and ethnicity. The values have been reported by sample size (percentage), n (%).
Subgroup A1- A1+
A2A2 A1A2 A1A1
Dietary Treatment
High Fibre (Control) 37 (33.9%) 16 (14.7%) 3 (2.8%)
Low GI (Test) 36 (33%) 16 (14.7%) 1 (0.9%)
Sex
Male 42 (38.5%) 25 (22.9%) 2 (1.8%)
Female 31 (28.4%) 7 (6.4%) 2 (1.8%)
Ethnicity
African 4 (3.7%) 2 (1.8%) 0 (0%)
Asian 3 (2.7%) 3 (2.8%) 1 (0.9%)
Caucasian 55 (50.4%) 19 (17.4%) 1 (0.9%)
Hispanic 1 (0.9%) 0 (0%) 0 (0%)
Native 0 (0%) 0 (0%) 1 (0.9%)
South Asian 10 (9.2%) 8 (7.3%) 1 (0.9%)
Total 73 (67.0%) 32 (29.3%) 4 (3.7%)
66
In the study, there were more males than females who completed and consented to
the genetic testing: 69 males versus 40 females. However, no significance differences
were observed between the association of TaqIA genotypes and the two sexes.
Most of our patients were Caucasian (69% of the total consented population). The
Caucasians and other ethnic groups (Africans, Asians, and South Asians) were mostly
homozygous A1-, thus, had greater allelic frequency for the A2 allele (Table 6-1).
The baseline for all subjects was assessed and compared to see if any dietary,
physical, or biochemical differences existed between different genotypes (prior to any
intervention). Table 6-2 shows that there were significant mean differences in BMI and
HDL at baseline. Diabetic individuals in the study with A1- were overweight while
diabetic individuals with A1+ genotypes were obese at baseline (P=0.022). Also, the
HDL for individuals with A1- genotype was significantly higher than people with A1+,
1.2 ± 0.04 and 1.0 ± 0.04, respectively (P=0.010) at baseline. GI was significantly higher
in patients with A1+ genotypes (P=0.008) at baseline. Other measurements such as
HbA1c, cholesterol, blood pressure, and dietary consumption patterns were not related to
differences in TaqIA polymorphism at baseline.
Over the 24-week of study period, no differences were seen in the change in
measurements over either diets when related to the to the A1- and A1+ genotypes (P-
values>0.05). Moreover, the effect of genotype does not differ between men and women
at baseline and over the 24 weeks. Thus, the participants were pooled regardless of their
sex and dietary treatment to have a larger sample size.
67
Table 6-2: ANKK1 – TaqIA SNP, Baseline of physical and biochemical markers and food consumption by genotype distribution. The values have been reported by Mean (SE).
Baseline A1-
(n=73) (±SE) A1+
(n=36) (±SE) P-valueAge (years) 61 (1) 62 (1) 0.283 BMI (Kg/m2) 29.8 (0.6) 32.4 (1.2) 0.022HbA1c (%) 7.1 (0.1) 7.0 (0.1) 0.394 Lipids (mmol/L) Total Cholesterol 4.3 (0.1) 4.3 (0.2) 0.847Triglycerides 1.3 (0.1) 1.6 (0.1) 0.041HDL 1.2 (0.04) 1.0 (0.04) 0.010LDL 2.5 (0.1) 2.5 (0.1) 0.771 Blood Pressure (mmHg) Systolic (SBP) 128.7 (1.9) 124.4 (2.5) 0.185Diastolic (DBP) 74.0 (1.2) 74.1 (1.6) 0.929 Dietary intake (per day) Energy (Kcal) 1823 (53) 1917 (102) 0.365 Carbohydrate (g) 197 (7) 211 (11) 0.271 Protein (g) 91 (3) 96 (5) 0.296 Fibre (g) 26 (1) 23 (2) 0.212 Fat (g) 71 (3) 74 (5) 0.580 MFA (mg) 29 (1) 29 (2) 0.977 PUFA (mg) 15 (1) 15 (1) 0.976 SFA (mg) 21 (1) 24 (2) 0.170 Alcohol (g) 4 (1) 3 (1) 0.451
GI 81 (1) 84 (1) 0.008
Over the 24 weeks of the study, the A1- genotype of TaqIA was significantly
associated with improved outcomes in response to blood pressure, that is, both systolic
(SBP) and diastolic blood pressure (DBP). A pattern was observed: individuals with A1-
genotype decreased their SBP and DBP by percentage change of 2.8 ± 1.1% and 3.4 ±
68
1.2%, respectively. However, individuals with A1+ genotype increased their SBP and
DBP by percentage change of 1.9 ± 1.5% and 1.5 ± 1.8%, respectively (P=0.014 for SBP
and P= 0.021for DBP).
Figure 6-2: Percentage change in systolic blood pressure (SBP) over the 24 weeks of dietary intervention for TaqIA SNP in type 2 diabetic patients (n=109). There were 73 individuals with A1- genotype and 36 individuals with A1+ genotype. The SBP significantly decreased by percentage change of 2.8 ± 1.1% for A1- genotype and increased by percentage change of 1.9 ± 1.5% for A1+ genotype (P=0.014).
-5
-4
-3
-2
-1
0
1
2
3
4
5A1- A1+
Perc
enta
ge C
hang
e in
SBP
(%)
69
Figure 6-3: Percentage change in diastolic blood pressure (DBP) over the 24 weeks of dietary intervention for TaqIA SNP in type 2 diabetic patients (n=109). There were 73 individuals with A1- genotype and 36 individuals with A1+ genotype. The SBP significantly decreased by percentage change of 2.8 ± 1.1% for A1- genotype and increased by percentage change of 1.2 ± 1.5% for A1+ genotype (P=0.021).
-5
-4
-3
-2
-1
0
1
2
3
4A1- A1+
Perc
enta
ge C
hang
e in
DB
P (%
) >
70
Table 6-3: ANKK1 – TaqIA SNP, Percentage change in physical and biochemical markers and food consumption by genotype distribution over the 24-week study. The values have been reported by Mean Percentage Change (SE).
Percentage Change (%) A1-
(n=73) (±SE) A1+
(n=36) (±SE) P-valueBMI -4 (0) -3 (1) 0.139
HbA1c -7 (1) -5 (2) 0.253
Lipids
Total Cholesterol 0 (2) 1 (2) 0.857
Triglycerides 2 (4) 1 (10) 0.847
HDL 2 (2) 6 (2) 0.104
LDL 4 (3) -3 (4) 0.232
Blood Pressure
Systolic (SBP) -3 (1) 2 (2) 0.014Diastolic (DBP) -3 (1) 1 (2) 0.021
Dietary intake (per day)
Energy -9 (3) -12 (4) 0.498
Carbohydrate -2 (3) -8 (4) 0.264
Protein -4 (3) -5 (4) 0.832
Fibre 32 (7) 18 (9) 0.233
Fat -16 (5) -18 (6) 0.753
MFA -16 (5) -20 (6) 0.675
PUFA -13 (6) -11 (9) 0.844
SFA -21 (4) -22 (6) 0.926
Alcohol -1 (1) 0 (0) 0.584
GI -7 (2) -7 (2) 0.872
Due to the allelic frequency differences in genetic variances between different
ethnic groups, a separate analysis was done to show the baseline and percentage change
in Caucasians. There were 75 Caucasians out of 109 participants in the study with: 55
homozygous A2A2 (A1-), 19 heterozygous A1A2, and 1 homozygous A1A1 (the A1A2
71
heterozygous and A1A1 homozygous were combined in one group, A1+). These
frequencies are in Hardy-Weinberg equilibrium, Χ2(1,N=75) = 0.20, P = 0.65.
BMI, HDL, and the consumption of saturated fatty acids (SFA) were significant at
baseline. Caucasian diabetic individuals in the study with A1- genotype had significantly
lower BMI with a baseline value of 30.1 ± 4.7 Kg/m2 versus 34.5 ± 6.2 Kg/m2 (P=0.002).
Individuals with A1- genotype had higher HDL, of 1.1 ± 0.05 mmol/L versus 1.0 ± 0.04
mmol/L (P=0.041). Individuals with A1- genotype were also significantly associated with
lower consumption of SFA, a value of 22 ± 1% of energy versus 29 ± 3% of energy
(P=0.014) at baseline (Table 6-4). No other measurements were significant at the baseline
amongst the biological markers and food consumption patterns in Caucasians (Table 6-4).
In the assessment of changes across the intervention, Caucasian diabetics with
A1- genotype significantly increased their LDL by 5.2 ± 3.8%, whereas, individuals with
A1+ decreased their LDL by 10.2 ± 6.5% over the 24 weeks with a p-value of 0.043
(Table 6-5). No other differences between A1- and A1+ were significant (Table 6-5).
72
Table 6-4: ANKK1 – TaqIA SNP, Baseline of physical and biochemical markers and food consumption by genotype distribution in Caucasians. The values have been reported by Mean (SE).
Baseline A1-
(n=55) (±SE) A1+
(n=20) (±SE) P-valueBMI (Kg/m2) 30.1 (4.7) 34.5 (6.2) 0.002HbA1c (%) 7.1 (0.5) 7.0 (0.4) 0.294 Lipids (mmol/L) Total Cholesterol 4.2 (0.1) 4.2 (0.2) 0.863Triglycerides 1.4 (0.1) 1.8 (0.2) 0.134HDL 1.1 (0.05) 1.0 (0.04) 0.041LDL 2.4 (0.1) 2.4 (0.2) 0.845 Blood Pressure (mmHg) Systolic (SBP) 130.6 (2.3) 126.1 (3.2) 0.294Diastolic (DBP) 73.5 (1.4) 74.2 (1.8) 0.786 Dietary intake (per day) Energy (Kcal) 1828 (58) 2045 (161) 0.114 Carbohydrate (g) 198 (7) 215 (17) 0.294 Protein (g) 92 (3) 100 (7) 0.217 Fibre (g) 27 (1) 24 (2) 0.232 Fat (g) 70 (3) 84 (8) 0.053 MFA (mg) 28 (1) 33 (3) 0.131 PUFA (mg) 14 (1) 16 (2) 0.351 SFA (mg) 22 (1) 29 (3) 0.014 Alcohol (g) 5 (1) 4 (2) 0.588
GI 80 (1) 83 (1) 0.078
73
Table 6-5: ANKK1 – TaqIA SNP: Percentage change in physical and biochemical markers and food consumption by genotype distribution over the 24-week study in Caucasians. The values have been reported by Mean Percentage Change (SE).
Percentage Change (%) A1-
(n=55) (±SE) A1+
(n=20) (±SE) P-valueBMI -4 (1) -4 (1) 0.673
HbA1c -6 (9) -6 (11) 0.932
Lipids
Total Cholesterol 0 (14) -2 (15) 0.606
Triglycerides -2 (5) 0 (16) 0.874
HDL 3 (2) 9 (3) 0.115
LDL 5 (4) -10 (7) 0.043 Blood Pressure Systolic (SBP) -4 (1) 1 (2) 0.075
Diastolic (DBP) -4 (1) -1 (2) 0.160
Dietary intake (per day)
Energy -8 (3) -13 (4) 0.302
Carbohydrate -2 (3) -10 (5) 0.171
Protein -3 (3) -1 (5) 0.773
Fibre 26 (7) 22 (13) 0.754
Fat -15 (5) -21 (6) 0.505
MFA -14 (6) -23 (7) 0.440
PUFA -13 (7) -11 (13) 0.865
SFA -21 (4) -26 (7) 0.556
Alcohol -1 (1) 0 (1) 0.494
GI -7 (2) -8 (3) 0.777
74
6.5 Discussion
Our findings suggest that the presence of the TaqIA A1 allele (that has been
associated with lower density of DRD2 receptors) of the ANKK1 may serve as a risk
factor. It has been shown previously that individuals with the A1 allele of TaqIA have 30
to 40% reduction in the D2 receptor density (Wang, et al., 2001) and it has been also
associated with human obesity (Jonsson, et al., 1999; Noble, 2000; Pohjalainen, Rinne,
Nagren, Lehikoinen, et al., 1998; Stice, Yokum, Bohon, Marti, & Smolen, 2010; Wang,
et al., 2001). A study of healthy humans, A1 form of the dopamine ANKK1 gene has
shown to be associated with an increase in fat storage and a high BMI. In this study,
diabetic patients with A1+ genotype, when pooled together, were significantly different
in BMI at baseline. Individuals with A1- genotype were overweight (25 kg/m2 < BMI <
30 kg/m2) while individuals with A1+ genotype were obese (BMI > 30 Kg/m2) at
baseline (P = 0.022). Also, individuals with A1+ genotypes had significantly higher
triglyceride, lower HDL, and greater SFA consumption level than individuals with A1-
genotype at baseline.
Over the 24 weeks of study, patients with A1- allele of the TaqIA SNP had a
significant reduction with respect to blood pressure (both SBP and DBP) with p-values of
0.014 and 0.021, respectively, but no significant differences were seen in body weight or
BMI. There were no significant differences when looking at the different dietary
treatments or the sexes in association with the TaqIA SNP genotypes. Other studies have
not reported differences with respect TaqIA polymorphisms and sex.
When separating Caucasians to avoid genetic frequency variability between
different ethnic groups, there were significant differences in BMI, HDL, and SFA at
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baseline. Thus, individuals with A1- had significantly lower BMI, higher HDL, and
consumed less SFA than individuals with A1+ genotypes with p-values of 0.002, 0.041,
and 0.014, respectively. These findings were consistent with other human studies
(Barnard, et al., 2009; Blum, Braverman, et al., 1996; Thomas, et al., 2000; Wang, et al.,
2001). However, over the 24 weeks the individuals with A1- genotype failed improve
their LDL level, instead, an increase in LDL was observed, while individuals with A1+
genotypes decreased their LDL level (P= 0.043) with no change in HDL-C with either
alleles.
For most of these findings one could suggest the following explanation: due to the
decrease in dopamine receptor density through down-regulation of the TaqIA SNP in
obese diabetics, individuals with A1+ genotype are in need of overestimation from
feeding to compensate for level of dopamine. Thus, patients with A1+ genotypes of the
TaqIA SNP may be more vulnerable to addictive behaviours including compulsive food
intake and weight gain leading to increase in risk factors.
Having the A1 allele of TaqIA polymorphism suggests that an individual might be
more at risk for poor dietary compliance and overeating due to reduced dopamine
production secondary to impaired D2 receptor function. The results of this study have
important implications for the development and treatment for diabetic patients through
further enhancing personalized dietary counseling based on genetic differences. In
addition, the ability to characterize people genetically as at risk for diabetes on the basis
of behavioral and neurobiological factors would provide the opportunity to develop
treatment programs that are tailored to individuals with specific patterns of risk factors.
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Chapter VII
C957T SNP x TaqIA SNP Interaction and Additive SNP Effect on Dietary Compliance, Blood
Pressure, and BMI
77
7.1 Abstract Hypothesis and Purpose: The C allele of C957T SNP and the A1 allele of the TaqIA have been shown to have an association with reduction in the density of the dopamine receptor subtype 2, DRD2. This reduction has been associated with addiction, lower level of reward and pleasure, stress, food addiction, and obesity. Previous studies had indicated that the two SNPs are in linkage disequilibrium. The aim of this study was to assess the interaction and the additive effect of the two SNPs (C957T and TaqIA) polymorphisms with dietary compliance, BMI and blood pressure in type 2 diabetic patients. Methods: Blood samples were collected from 109 type 2 diabetic participants who completed a 24-week randomized clinical trial and gave consent. They were assigned to follow either a low glycemic index (GI) or a high fibre (high GI) diet over the 24 weeks. C957T and TaqIA genotypes were determined for each patient and compared with blood pressure, BMI, and their food intake. Results: Regardless of dietary treatments, the CCxA1+ combination of C957T and TaqIA SNPs, respectively, was significantly associated with increase in blood pressure and the lowest decrease in their BMI. Whereas, the TTxA1- combination were significantly associated with highest decrease in blood pressure and BMI over the 24-week trial (p=0.017 and P=0.038, respectively). There was no significant SNP-SNP interaction. Conclusion: The findings indicate that there is an additive effect when combining the two SNPs together. Having the TTxA1- and CCxA1+ combinations were associated with the best and worst outcomes, respectively, in blood pressure and BMI changes.
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7.2 Introduction
Dopamine can modulate the activity of neuronal reward pathways (Comings &
Blum, 2000) and lower dopamine receptor number increases impulsive (Comings,
Rosenthal, et al., 1996), addictive and compulsive behaviours (Bowirrat & Oscar-
Berman, 2005), including overeating and carbohydrate bingeing (Eny, et al., 2009; Wang,
et al., 2001; Wise, 2006). The C allele of the C957T SNP and the A1 allele of the TaqIA
SNP have been associated with reduced striatal DRD2 density in human studies (J.
Hirvonen, et al., 2005; M. Hirvonen, et al., 2004; M. M. Hirvonen, et al., 2009; Jonsson,
et al., 1999; Pohjalainen, Rinne, Nagren, Syvalahti, et al., 1998).
ANKK1 gene has been shown to be adjacent to the DRD2 gene (Hill, et al., 2008;
Neville, et al., 2004). TaqIA polymorphism of the ANKK1 is located 9.5 kb downstream
from the coding region of the DRD2 gene (Grandy, et al., 1989; Neville, et al., 2004).
TaqIA has been shown to be in linkage disequilibrium with C957T of the DRD2 gene
(Hill, et al., 2008). A recent study in humans has shown this linkage disequilibrium to be
true in Caucasians but not other ethnic groups (Barnard, et al., 2009; Young, Lawford,
Nutting, & Noble, 2004). Thus, we assessed whether the polymorphisms of the two genes
(DRD2 and ANKK1) are more likely to be inherited together or not in the individuals in
our study through LOD score.
From this study, we predict that individuals with the combination of the C and A1
alleles as their genotype might have greater additive effect on brain circuits involved in
reward and, thus, more vulnerable to addictive behaviours leading to poor dietary
compliance and overeating. Thus, individuals having the combination of C and A1 alleles
will be placed at a higher health risks in an attempt to compensate for the relative lack of
79
a reward stimulus. Thus, in this study we investigated the potential of SNP-SNP (C957T
SNP of the DRD2 gene and TaqIA SNP of the ANKK1 gene) interactions and additive
genetic effect on dietary compliance and other measurements such as HbA1c, BMI, and
blood pressure.
80
7.3 Methods 7.3.1 Participants
Subjects were participants in a previously published low glycemic diet study
(Jenkins, et al., 2008). From the original 210 participants, 109 completed the study and
consented to the genetic study (Figure 4-1). Eligible participants were men or
postmenopausal women with type 2 diabetes who were taking oral agents other than
acarbose to control their diabetes, with medications stable for the previous three months
and who had HbA1c values at screening between 6.5% and 8.0%. None had clinically
significant cardiovascular, renal, or liver disease (ALT > 3 times the upper limit of
normal) and none were undergoing treatment for cancer. The final sample size (n=109)
consisted of 69 men and 40 women. The ethnicity of the group were African (n=6), Asian
(n=7), Caucasian (n=75), Hispanic (n=1), Native (n=1), and South Asian (n=19).
The study and genetic consent were approved by the research ethics board of St.
Michael’s Hospital and the University of Toronto. Clinical Trial Registration number:
NCT00438698.
7.3.2 Dietary Interventions
General dietary advice conformed to the National Cholesterol Education Program
Adult Treatment Panel III (NCEP ATP III) (“Summary of the second report of the
National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation,
and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II),” 1993)
and the American Diabetes Association (ADA) (Franz, et al., 2004) guidelines to reduce
saturated fat and cholesterol intakes.
81
Participants were randomized onto the low glycemic index diet or the high wheat
fibre diet. Six meal servings were prescribed for a 1500 kcal diet, 8 meal servings for a
2000 kcal diet and 10 meal servings for a 2500 kcal diet. The checklists were completed
by participants on a daily basis throughout the study and 7 day diet records were
completed prior to each visit. Adherence was assessed from the 7-day diet records. The
daily checklists were of value in alerting the dietitian to problems with adherence to the
diet plan over the month prior to center attendance.
7.3.3 Study Protocol - Genetic Analyses
Overnight fasting (12 hours) blood samples were collected at St. Michael's
Hospital, Toronto, Canada. The patients’ blood samples were centrifuged and buffy-coat
stored in the freezer (-70ºC) at the University of Toronto for DNA extraction. DNA was
extracted from leucocytes using standard MasterPureTM DNA Purification Kit for Blood
Version II – MB711400 (InterScience - EPICENTRE® Biotechnologies, Madison,
Wisconsin, USA) and subsequently used as a template for determination of genotypes.
The C957T of the DRD2 gene (rs6277) and the TaqIA of the ANKK1 gene
(rs1800497) polymorphisms were detected by real-time polymerase chain reaction (PCR)
using a TaqMan® SNP Genoypting Assays: (C__11339240_10) for C957T and
(C___7486676_10) for TaqIA from Applied Biosystems (Foster City, California, USA).
PCR conditions were as follows: 50ºC for 2 min, 95ºC for 10 min followed by 40 cycles
at 95ºC for 15 second and 60ºC for 1 min. Allelic discrimination was performed using the
ABI 7000 Sequence Detection System (Applied Biosystems, Foster City, California,
USA). A total of 5–10 ng of genomic DNA was amplified in 1 9 SYBR green PCR
82
master mix (Applied Biosystems) containing 0.4 lM of allele specific forward primer and
0.4 lM of common reverse primer in a 25 nm volume. Genotyping was verified using
positive controls in each plate as well as re-running at least 50% of the samples twice.
7.3.4 Statistical Analyses
The primary outcome was HbA1c of the original Low GI study. Blood pressure,
BMI, and HDL were secondary measures. All results are expressed as means ± SE. All
analyses were carried out using IBM® SPSS® software, Version 17 (SPSS Inc., Chicago,
Illinois, USA). Analyses were undertaken of the completed and consented subject group.
For variables with time trends, the significance of treatment differences was
assessed using an ANOVA model with the percent change from baseline to end of study
as the response variable. SNP-SNP (C957TxTaqIA) interaction and additive effects were
examined in all consented population through two-way ANOVA. Moreover, HaploView
Version 4.1 (Barrett, Fry, Maller, & Daly, 2005) was used to detect any linkage
disequilibrium (LD) between C957T and TaqIA.
Additional analyses were performed using percentage change in fibre and
carbohydrate intake. Levene’s test and test of homogeneity of variances were used to
assess the equality of variances in different samples. For skewed data, the results were
confirmed with non-parametric analysis, Kruskal-Wallis test, via ranking scale.
83
7.4 Results
Due to sample size of participants with A1A1 genotype (n=4), analyses were
performed combining the A1A1 and A1A2 genotypes and they were identified as A1+
due to the presence of A1 allele. By combining the two SNPs C957T and TaqIA, we
identified: 26 (24%) participants having TTxA1- genotype, 31 (28%) having CTxA1-
genotype, 18 (16%) having CTxA1+ genotype, 16 (15%) having CCxA1- genotype, 1
(1%) having TTxA1+ genotype, and 17 (16%) having CCxA1+ genotype. It must be
noted that by reason of the small sample size of the combined SNPs effect, we were not
able to see any significant differences between dietary treatments or between sexes. Thus,
the participants were pooled regardless of their sex and dietary treatment to achieve a
larger sample size.
The baseline for all subjects was assessed and compared to see if any dietary,
physical or biochemical measures were significantly different between the combined
SNPs genotypes. Table 7-1 shows that there were significant differences at baseline in
triglyceride and GI with p-values of 0.042 and 0.047, respectively.
84
Table 7-1: C957T and TaqIA SNP-SNP combination - Baseline of physical and biochemical markers and food consumption by combined genotype distribution. The values have been reported by Mean (SE).
Baseline TT x A1-
(n=26) (±SE) CT x A1-
(n=31) (±SE) CT x A1+
(n=18) (±SE) CC x A1-
(n=16) (±SE) TT x A1+
(n=1) (±SE) CC x A1+
(n=17) (±SE) P-
value
BMI (Kg/m2) 29.5 (0.8) 30.8 (1.0) 32.1 (1.2) 28.3 (0.8) 30.8 (NA) 33.1 (2.3) 0.170
HbA1c (%) 7.1 (0.1) 7.1 (0.1) 7.0 (0.1) 7.2 (0.2) 7.1 (NA) 7.0 (0.2) 0.950
Lipids (mmol/L)
Total Cholesterol 4.1 (0.1) 4.2 (0.2) 4.2 (0.2) 4.6 (0.2) 5.1 (NA) 4.2 (0.2) 0.498
Triglycerides 1.5 (0.2) 1.3 (0.1) 1.5 (0.1) 0.9 (0.1) 1.4 (NA) 1.8 (0.3) 0.042
HDL 1.1 (0.08) 1.1 (0.06) 1.0 (0.05) 1.3 (0.06) 1.2 (NA) 1.0 (0.06) 0.058
LDL 2.3 (0.1) 2.5 (0.2) 2.6 (0.2) 2.9 (0.2) 3.3 (NA) 2.4 (0.1) 0.209
Blood Pressure (mmHg)
Systolic (SBP) 133.9 (3.9) 125.8 (2.7) 128.2 (3.6) 126.2 (2.7) 123.0 (NA) 120.2 (3.9) 0.159
Diastolic (DBP) 75.5 (2.1) 72.7 (1.7) 75.8 (2.4) 74.1 (2.5) 77.0 (NA) 71.9 (2.2) 0.765
Dietary Intake (per day)
Energy (Kcal) 1923 (91) 1796 (77) 1826 (157) 1718 (121) 1658 (NA) 2052 141 0.453
Carbohydrate (g) 211 (12) 200 (11) 201 (18) 172 (8) 202 (NA) 224 (14) 0.243
Protein (g) 93 (4) 88 (3) 93 (6) 93 (8) 79 (NA) 102 (7) 0.529
Fibre (g) 26 (1) 27 (2) 22 (2) 22 (2) 21 (NA) 25 (3) 0.835
Fat (g) 72 (4) 69 (5) 70 (8) 71 (7) 60 (NA) 80 (8) 0.835
MFA (mg) 29 (2) 29 (2) 27 (3) 29 (3) 23 (NA) 32 (3) 0.832
PUFA (mg) 14 (1) 15 (2) 14 (1) 15 (1) 11 (NA) 16 (2) 0.787
SFA (mg) 23 (2) 20 (1) 24 (4) 21 (3) 22 (NA) 25 (3) 0.663
Alcohol (g) 8 (3) 3 (1) 3 (1) 3 (1) 0 (NA) 4 (2) 0.187
GI 81 (1) 80 (1) 82 (1) 80 (1) 83 (NA) 86 (1) 0.047
85
Diabetic individuals in the study with CCxA1+ had a significantly higher level of
triglyceride at baseline compared to individuals with combination of CCxA1- had the lowest
level with a p-value of 0.042 (Table 7-1). Moreover, the GI values for individuals with CCxA1+
genotypes at baseline were shown to be significantly higher than individuals with other
combinations with a value of 86, P=0.047 (Table 7-1). The significant differences in triglyceride
and GI values were also observed in previous chapter for the TaqIA SNP. Other measurements
such as HbA1c, cholesterol, blood pressure, and dietary consumption were not significant in this
respect.
The percentage of change, over the 24-week period of study, for all subjects was assessed
and compared to see if any dietary consumption, physical, and biochemical significant
differences existed between the combined SNPs genotypes. Table 7-2 shows that there were
significant differences in percentage change of BMI and systolic blood pressure (SBP) with p-
values of 0.038 and 0.017, respectively.
86
Table 7-2: C957T and TaqIA SNP-SNP combination – Percentage change in physical and biochemical markers and food consumption by combined genotype distribution over the 24-week study. The values have been reported by Mean Percentage Change (SE) Percentage Change
(%) TT x A1-
(n=26) (±SE) CT x A1-
(n=31) (±SE) CT x A1+
(n=18) (±SE) CC x A1-
(n=16) (±SE) TT x A1+
(n=1) (±SE) CC x A1+
(n=17) (±SE) P-
value
BMI -4.8 (0.9) -3.6 (0.6) -3.6 (0.8) -2.7 (0.6) -9.0 (NA) -1.4 (0.8) 0.038
HbA1c -9.0 (1.7) -5.9 (1.7) -6.1 (2.5) -6.4 (1.7) -10.3 (NA) -3.5 (2.2) 0.519
Lipids
Total Cholesterol 0.01 (2.1) 1.5 (3.0) 0.03 (3.7) -1.7 (5.3) -18.0 (NA) 3.0 (3.1) 0.801
Triglycerides -13.3 (6.7) 7.0 (6.3) 7.1 (17.3) 18.5 (6.9) -55.6 (NA) -3.3 (7.1) 0.132
HDL 4.9 (2.74) -0.1 (2.24) 7.9 (3.66) 0.4 (2.96) -9.0 (NA) 5.4 (3.17) 0.268
LDL 5.5 (3.3) 6.8 (6.2) -9.1 (7.2) -4.2 (8.2) -13.8 (NA) 4.9 (4.6) 0.379
Blood Pressure
Systolic (SBP) -3.5 (1.9) -3.2 (1.7) -2.0 (1.9) -1.0 (2.0) 0.0 (NA) 6.4 (2.2) 0.017
Diastolic (DBP) -4.5 (2.0) -3.2 (1.9) -1.3 (2.5) -2.2 (2.2) -4.4 (NA) 4.9 (2.5) 0.099
Dietary Intake (per day)
Energy -9 (4) -9 (4) -13 (5) -10 (6) -13 (NA) -12 (6) 0.992
Carbohydrate -3 (5) -2 (5) -7 (5) -1 (6) -15 (NA) -9 (7) 0.921
Protein -1 (4) -4 (4) -5 (6) -10 (6) -7 (NA) -6 (6) 0.902
Fibre 28 (9) 34 (14) 25 (14) 35 (14) -16 (NA) 14 (12) 0.966
Fat -19 (5) -13 (8) -23 (8) -17 (12) -13 (NA) -14 (8) 0.966
MFA -20 (6) -14 (9) -22 (10) -16 (14) -13 (NA) -19 (8) 0.992
PUFA -12 (9) -10 (10) -16 (15) -23 (15) -10 (NA) -6 (12) 0.962
SFA -25 (6) -21 (6) -28 (9) -16 (12) -14 (NA) -16 (8) 0.881
Alcohol -27 (16) -2 (6) 9 (17) 27 (25) 0 (NA) -22 (10) 0.146
GI -12 (3) -6 (3) -9 (3) -3 (3) 5 (NA) -6 (3) 0.224
87
Over the 24 weeks of the study, the additive effect of TTxA1- genotype was significantly
associated with best improved outcomes in response to BMI (Figure 7-1) compared to other
combinations. A pattern was observed: individuals with TTxA1- genotype decreased their BMI
by percentage change of 4.8 ± 0.9% while CCxA1+ showed the smallest percentage change at -
1.4 ± 0.8% (P=0.038). The combination of TTxA1+ was not included in the Figure 7-1. The
percentage change of TTxA1+ was misleading due to the sample size (n=1).
Figure 7-1: Percentage change in BMI over the 24 weeks of dietary intervention for additive effect of SNP-SNP (C957T of DRD2 gene and TaqIA of ANKK1 gene) combination in type 2 diabetic patients (n=109). A significant pattern of genotype combination is shown in response to BMI (P=0.038). The TTxA1+ was not included since the sample size was unrepresentative (n=1).
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
TT x A1-(n=26)
CT x A1-(n=31)
CT x A1+(n=18)
CC x A1-(n=16)
CC x A1+(n=17)
Perc
enta
ge C
hang
e in
BM
I (%
)
88
The additive effect pattern was also observed in response to SBP. Over the 24 weeks of
the study, TTxA1- genotype was significantly associated with improved outcomes in response to
SBP (Figure 7-2) compared to other combinations. Individuals with TTxA1- genotype decreased
their SBP by a percentage change of -3.5 ± 1.9% while CCxA1+ increased their SBP by a
percentage change of 6.4 ± 2.2% (P=0.017). The combination of TTxA1+ was not included in
the Figure 7-2. The percentage change of TTxA1+ was misleading due to the sample size (n=1).
Figure 7-2: Percentage change in SBP over the 24 weeks of dietary intervention for additive effect of SNP-SNP (C957T of DRD2 gene and TaqIA of ANKK1 gene) combination in type 2 diabetic patients (n=109). A significant pattern of genotype combination is shown in response to SBP with a significant value of 0.017. The TTxA1+ was not included since the sample size was unrepresentative (n=1).
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
TT x A1-(n=26)
CT x A1-(n=31)
CT x A1+(n=18)
CC x A1-(n=16)
CC x A1+(n=17)
Perc
enta
ge C
hang
e in
SB
P (%
)
89
Other measurements such as HbA1c, cholesterol, and dietary consumption patterns such
as fibre, fat, and protein were not significantly related to the combinations of polymorphisms
over the 24-weeks.
Patterns of additive effect were shown but no significant interactions were seen when
combining the two SNPs. Furthermore, a significant linkage disequilibrium, LD, (the non-
random association of alleles) was detected between C957T and TaqIA (D’0.63, R2=0.11, LOD
score 4.07) for all combined ethnic groups. Pairwise (LD) was also assessed for Caucasians. The
LD between the two SNP was not as strong in the Caucasian group (D’0.618, R2=0.102, LOD
score 2.23). D’ of 1 or -1 means no evidence for recombination between the markers. R2 of 1
implies the markers provide exactly identical information (Frisse, et al., 2001).
90
7.5 Discussion
Our findings are consistent with other studies’ results that the C allele of C957T (DRD2
gene) and the A1 allele of TaqIA (ANKK1 gene), that have been linked with a lower density of
DRD2 receptors, are associated with severe risk factors (Bassareo & Di Chiara, 1999; Castro &
Strange, 1993; Volkow, et al., 2008; Wang, et al., 2001) and increase in food intake and poor
dietary compliance (Barnard, et al., 2009; Comings & Blum, 2000; Eny, et al., 2009; Epstein, et
al., 2007; Epstein, et al., 2004; Haltia, et al., 2007; Stice, et al., 2010; Volkow, et al., 2008;
Wang, et al., 2001). In this study, at baseline diabetic patients with CCxA1+ genotype, when
pooled together, had significantly higher triglyceride and GI values compared to other combined
genotypes. Individuals with the A1- were at the lowest half of triglyceride and GI values. These
patterns were seen in the previous chapter for TaqIA polymorphism of the ANKK1 gene and that
might be the reason behind the observed dominant role of A1 allele in the combinations.
However, this pattern might also indicate that the TaqIA A1 allele might be associated with an
increase of fat storage, but further studies are needed to confirm this observation.
Over the 24 weeks of study, patients with TTxA1- combined genotype had a greater
reduction in BMI and blood pressure (SBP) compared to other combinations with p-values of
0.038 and 0.017, respectively. This shows that there are additive effects when combining the two
SNPs (C957TxTaqIA) since the value of the percentage changes becomes greater when
compared to percentage changes of each SNP solely. These findings suggest that due to the
decrease in dopamine receptor density through the C allele of C957T polymorphism and the A1
allele of the TaqIA polymorphism in diabetes, individuals with combined CCxA1+ (that is CC
with A1A1 or CC with A1A2) genotypes may need to overeat to compensate for a deficiency in
dopamine receptor production compared to other genotype combinations. In other words,
91
patients with CCxA1+ genotypes maybe more susceptible to addictive behaviours including poor
dietary compliance, compulsive food intake, and weight gain. When looking at these findings,
other explanations such as change in exercise and misreporting of diet must be considered.
No significant interactions were observed between the two SNPs: C957T of the DRD2
and the TaqIA of the ANKK1 (P >0.05). Also, there were no significant differences when looking
at the different dietary treatments or the sexes individually as the sample size was too small to
show any effects, if there were any.
We conclude that having the combined CCxA1+, of the C957T and TaqIA
polymorphisms, genotype may make the carrier susceptible to a greater stimulation before
satisfaction is obtained. Such individuals may be more vulnerable to addictive behaviours such
as food addiction and this in turn may lead to health problems such as obesity and resulting
hypertension. The results of this study can have important implications for future enhanced
personalized dietary counseling based on genetic differences.
92
Chapter VIII
Overall Discussion, Limitations, and Future Research
93
8.1 Overall Discussion
Our results in this study showed that there were no significant differences in dietary
treatments and sexes with C957T and TaqIA polymorphisms, and therefore, we had grouped our
diabetic patients regardless of treatments.
In this study, diabetic patients with the C allele of the C957T and the A1 allele of the
TaqIA had less reduction in BMI, when compared to other alleles of the same SNPs, and an
increase in blood pressure. Those alleles may be influencing food addiction and poor dietary
consumption habits that is most likely contributing to their diabetes.
It is likely there are multiple environmental and genetic risk factors contributing to
diabetes (specifically, type 2 diabetes) and poor eating habits. Certain dopaminergic alleles such
as the C allele of C957T SNP (DRD2 gene) and the A1 allele of TaqIA SNP (ANKK1 gene) have
shown to reduce dopamine receptors availability in the brain (M. Hirvonen, et al., 2004; Jonsson,
et al., 1999; Thompson, et al., 1997), and therefore, employing different phenotypical behaviours
and physiological characteristics.
From previous studies, it has been suggested individuals with low numbers of D2
receptors may be more vulnerable to addictive behaviours including compulsive food intake
(Epstein, et al., 2007; Haltia, et al., 2007). This was seen to be true for both the C957T SNP of
the DRD2 gene and TaqIA SNP of the ANKK1. The presence of the C allele of C957T SNP and
the A1 allele of TaqIA SNP, that are associated with lower density of dopamine receptors D2,
(Jonsson, et al., 1999; Pohjalainen, Rinne, Nagren, Lehikoinen, et al., 1998; Ritchie & Noble,
2003) have been also shown to increase BMI and blood pressure (Thomas, et al., 2000), increase
in carbohydrate consumption (Eny, et al., 2009; Haltia, et al., 2008), induce emotional effects
94
(Nieoullon & Coquerel, 2003) such as depression, prevail highly in type 2 diabetic population
(Barnard, et al., 2009), induce insufficient brain reward (Comings & Blum, 2000), having the
inability to learn from mistakes (Klein, et al., 2007), and act on impulsive and addictive
behaviours (Comings, Rosenthal, et al., 1996; Huang, et al., 2009; Rowe, et al., 1999;
Shahmoradgoli Najafabadi, et al., 2005).
This has led us to predict that type 2 diabetic patients, on a dietary intervention treatment,
with the C allele of the C957T SNP and the A1 allele of the TaqIA SNP will have poor dietary
compliance due to the effect of C and A1 alleles on reduction of dopamine receptor availability.
Hence, those individuals most likely have less sufficient brain rewards and by other means look
for other stimulus such as food to compensate for it. We also predicted that individuals with C
and A1 alleles would also be more vulnerable to poor compliance in following low glycemic
index diet due to the low glycemia available in diet signifying fewer stimuli.
Our findings are consistent with other human studies. Individuals with the C allele for
C957T, which is suggested to be associated with reduction in DRD2 receptors density, reduction
in D2 stability, and mRNA translation (Duan, et al., 2003; Hill, et al., 2008; Pohjalainen, Rinne,
Nagren, Lehikoinen, et al., 1998), were susceptible to addictive behaviours had a less significant
reduction in BMI and significantly increased their blood pressure (SBP) over the 24 week of the
study regardless of treatment and sex. Moreover, there were no significant differences observed
when assessing Caucasians, nonetheless, the pattern of blood pressure and BMI that were
observed when grouping all subjects together were also observed in the Caucasian group.
In this study, diabetic patients who completed and consented with A1 allele of TaqIA
polymorphism, A1+ genotype (due to the combination of A1A1 and A1A2 genotypes), were
95
significantly obese versus overweight at baseline. This finding consists with other studies that
had shown the association of A1 allele with decrease in dopamine receptors D2 availability, and
therefore, playing a major role in compulsive eating habits and food addiction (Barnard, et al.,
2009; Blum, Braverman, et al., 1996). Over the 24-weeks of study regardless of treatment and
sex, individuals with A1 allele had significantly increased their blood pressure (both SBP and
DBP) whereas individuals with A1- genotype reduced their blood pressure.
We have also assessed the combination of the two SNPs with patients’ baseline and
changes over 24 weeks of physiological characteristics and dietary consumption for interaction
or additive effect pattern. Over the 24 weeks of the study, patients with TTxA1- combination
genotypes had a greater reduction in BMI and blood pressure (SBP) compared to other
combinations. Thus, additive genetic effects were observed when combining the two SNPs.
These findings suggest that individuals with combined alleles CCxA1+ genotype may need to
overeat in order to compensate for the brain reward deficiency. In other words, patients with
CCxA1+ genotypes may be more susceptible to addictive behaviours including poor dietary
compliance, compulsive food intake, and weight gain to balance for the inadequate stimuli that
has been associated with the reduction in dopamine receptors density. There were no significant
interactions observed between the two SNPs: C957T of the DRD2 and the TaqIA of the ANKK1.
The significant differences in both BMI and blood pressure are most likely due to the
association of BMI with blood pressure, that is, individuals who increase their BMI are
susceptible increases in blood pressure (W. H. Zhao et al., 2000). Another explanation is that
decrease in dopamine receptors, D2, availability has also been linked to stress and depression
(Lawford, et al., 2003). Stress has been correlated with high blood pressure in obese individuals
96
(Steptoe, Cropley, Griffith, & Joekes, 1999). However, the latter explanation needs to be further
tested possibly through oxidative stress values that were obtained in the original Low GI study.
TaqIA A1 allele lies 9.5 kb downstream of the DRD2 gene and it resides at the adjacent
gene, the ANKK1 gene (Huang, et al., 2009; Neville, et al., 2004). Polymorphisms in the TaqIA
may directly influence DRD2 expression. Studies have shown that the TaqIA is in linkage
disequilibrium with functional DRD2 variants including C957T (Neville et al., 2004). Thus, it is
useful to show whether the polymorphisms of the two genes (DRD2 and ANKK1) are more likely
to be inherited together or not. Since C957T and TaqIA are on the same chromosome
(chromosome 11), and their loci are close together, is it has been suggested to be in linkage
disequilibrium and inherited together (Dick et al., 2007; McAllister et al., 2008). A recent study
in humans has shown this to be true in Caucasians but not other ethnic groups (Barnard, et al.,
2009; Young, et al., 2004).
This linkage can be shown through the LOD (logarithm of the odds) score. LOD is a
statistical estimate of whether two loci are likely to lie near each other on a chromosome and are
therefore likely to be inherited together. LOD score of three and above indicates a high
probability of genetic linkage. We have used HaploView Version 4.1 (Barrett, Fry, Maller, &
Daly, 2005) to investigate whether the two SNPs: C957T and TaqIA are in linkage
disequilibrium (LD). When combining all the participants together, the LOD score was 4.07.
Thus, this suggests the two SNPs are in linkage disequilibrium for all subjects in the study, but,
this did not valid for Caucasians, as the LOD score was less than the value of three, LOD = 2.23.
It is worthy to note that the patients were diabetic and not healthy which adds another
aspect to our findings. Also, diabetes is a result of obesity and a collection of metabolic
97
dysfunctions influenced by variation of and/or amplified genetic expressions (polymorphisms),
as well as poor lifestyle decisions which could be due to the reduction in the dopamine receptor
density.
Although in this study, the focus is on dopamine polymorphisms, it is important to
recognize that the regulation of body weight and food compliance are complex and involve other
physiological mechanisms and other neurotransmitters. In particular, the brain serotonergic and
noradrenergic systems as well as the leptin receptor are also important targets in the treatment
diabetic patients and other obesity related disorders.
98
8.2 Limitations
The primary limitation of the study is the lack of the treatment difference which was
hypothesized to exist between the low glycemic index of high fibre diets. In view of the lack of
difference it is possible that both high fibre and low glycemic index diets are equally hard to
follow and thus the presence of the T allele or A1- allele made no significant difference. This
lack of the treatment difference might also be a null result. Thus, more investigation is needed to
validate this.
Other important limitations of study are the small sample size and ethnicity admixture.
Research designed to identify genes that are associated with or responsible for specific behaviors
usually requires much larger sample sizes and control of population stratification that can arise
when multiple racial and ethnic groups are studied, and usually involves tests of a large number
of genes to provide data on the association between specific dopaminergic genes and dietary
compliance.
99
8.3 Future Research
In the past it was thought that DNA was a static non-changeable component of an
organism. Today it is well established that environmental and pharmaceutical elements can have
influential effects on gene expression or alter polymorphic gene expressions through the
methylation process. Another example of these phenomena is the influence of folic acid upon the
methylation of DNA that may influence the predisposition to abnormal sugar craving behaviour,
together with excessive carbohydrates binging in there with the D2 dopamine receptor A1 allele,
which increases pleasure of eating in the reward circuitry of the brain
The folic acid and other amino acids result in the DNA methylation responsible for
neurotransmitters synthesis which may regulate feeding behaviours including over eating
(Cooney, Dave, & Wolff, 2002; Popendikyte et al., 1999; Vucetic, Kimmel, Totoki, Hollenbeck,
& Reyes). It has been suggested in the aspect that the actual gene expression depends on an
individual’s life style and nutritional status rather than the DNA by itself (Blum et al., 2000).
Thus, further studies are necessary to provide more insight into the gene-expression regulation
and epigenetic dysregulation of the dopaminergic neurotransmission in the pathophysiology of
dietary compliance and food addiction.
Other future research that could help in understanding food addiction and poor dietary
compliance in diabetic patients includes examining the interaction of DRD2 with other factors
that control dopaminergic activity along with how dopaminergic activity interacts with other
neurotransmitters, such as opioids or serotonin. Dopamine is influenced by and in turn influences
other neurotransmitter pathways. For example, serotonin has been shown to relate to sugar
cravings and the so called sweet tooth phenomenon. The interplay of serotonergic and
100
dopaminergic systems are well known and established throughout the neuropharmacological
literature mostly in healthy individuals. While this strongly suggests that dopamine metabolism
should be a primary target for therapeutic intervention, and potentially the ANKK1 protein, it is
also evident that there is a therapeutic need to include pathways like serotonin, leptin,
Neuropeptide Y, and potentially other systems that influence on and/or are influenced by
dopamine directly or indirectly.
101
Chapter IX
Implications: Clinical Application
102
9.1 Implications: Clinical Application
Polymorphisms of the dopamine receptor (DRD2) and other associated/regulatory
dopamine genes are associated with a number of impulsive addictive compulsive disorders such
food addiction and obesity (Blum et al, 1996, 1990, 2000; Gardner, 1997; Noble, 2003; Xu et al,
2004). Dopamine activity is related to both the density of dopamine receptors and the amount of
the dopamine transporters. Thus, we decided to look at dopaminergic effect on food addiction
and poor dietary compliance in diabetic patients where obesity is a common factor for the
disease.
In this study, the results came from type 2 diabetic participants, who completed a 24-
week dietary intervention study and consented to genetic research, with the T allele of the C957T
SNP (DRD2 gene) and the A2 allele of the TaqIA SNP (ANKK1 gene), although no treatment
differences were observed and treatments were combined, blood pressure and BMI were reduced
significantly. Many factors could contribute to diabetes (specifically type 2 diabetes), but the
genetic influence can be a major key. Having the homozygous CC of the C957T and the A1+ of
the TaqIA as genotypes suggests that there might be a powerful brain circuit involved in reward
that can make an individual more at risk for poor dietary compliance and overeating. The results
of this study have important implications for the development and treatment of diabetic patients
through further enhancing personalized dietary counseling based on genetic difference. The
ability to characterize people as at risk for diabetes on the basis of behavioral and
neurobiological factors will provide the opportunity to develop treatment programs that are
tailored to individuals with specific patterns of risk factors, especially those carrying the C and
the A1 alleles of C957T and TaqIA polymorphisms, respectively.
103
Strategies to enhance dopaminergic function could involve educational and behavioural
interventions such as exercise. In humans and animal models, exercise has been found to
increase dopamine release (Hattori, Naoi, & Nishino, 1994) and to raise D2 receptors (Foley &
Fleshner, 2008; Liste, Guerra, Caruncho, & Labandeira-Garcia, 1997; MacRae, Spirduso,
Walters, Farrar, & Wilcox, 1987). Obesity treatment programs generally provide a one-size-fits-
all approach, with the same treatments for all participants. Weight loss and maintenance might be
dramatically improved if treatment approaches would differentially focus on the individual
characteristics of the participants. It is possible that a prevention or treatment strategy for
someone who is vulnerable to food addiction would be very different from the strategy for
someone with no vulnerability to food craving but higher in activity reinforcement. Thus, one
could also recommend exercise and specific diet programs that is specific to those individuals
with C and A1- alleles.
The identification of individual differences in behavioral and neurobiological factors that
are related to obesity may make the potential for individualizing prevention or treatment
approaches to diabetes a reality. High quality lifestyle factors such as following a healthy diet
(that could be reinforced with further dietary counseling) can exert a positive influence on gene
expression.
104
Chapter X
Summary
105
10.1 Summary
Summary 1 (Chapter 5)
Summary 2 (Chapter 6)
Summary 3 (Chapter 7)
The T allele of C957T polymorphism (DRD2 gene), that
has been shown to be responsible for maintaining
sufficient dopamine receptors in human studies, was
significantly associated with greater reductions in BMI
and blood pressure in type 2 diabetic patients who
followed high fibre or low GI dietary advice.
The A2 (A1-) allele of TaqIA polymorphism (ANKK1
gene), that has been shown to be responsible for
maintaining sufficient dopamine receptors in human
studies, was significantly associated with greater
reduction in blood pressure in type 2 diabetic patients
who followed high fibre or low GI dietary advice.
The TTxA1- combined alleles of C957T and TaqIA
polymorphisms, that have been shown to be responsible
for maintaining adequate amount of dopamine receptors
in human studies, were significantly associated with
greater reductions in blood pressure and BMI in type 2
diabetic patients who followed high fibre or low GI
dietary advice.
106
Chapter XI
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107
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