SELECTED CANDIDATE GENES AND OBESITY AMONG GHANAIAN ADULTS: A CASE CONTROL STUDY AT THE KORLE-BU TEACHING HOSPITAL (DIETHERAPY UNIT) ACCRA AMOS AGYEI GYAMFI (10231377) DEPARTMENT OF DIETETICS SCHOOL OF ALLIED HEALTH SCIENCES COLLEGE OF HEALTH SCIENCES UNIVERSITY OF GHANA THIS DISSERTATION IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON, IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MSC DIETETICS DEGREE JULY, 2014 University of Ghana http://ugspace.ug.edu.gh ii DECLARATION I, hereby declare that this is the result of my own research work under the supervision of Dr. Charles A. Brown and Dr. Samuel Antwi-Baffour. There has not been any previous submission for a Master of Science degree here or elsewhere. Also, references to works of other authors cited have been duly acknowledged. Signature …………………………….. …………….. AMOS AGYEI GYAMFI DATE (STUDENT) Signature ………………………………… …………….. DR. CHARLES BROWN DATE (SUPERVISOR) Signature ………………………………… …………….. DR. SAMUEL ANTWI-BAFFOUR DATE (SUPERVISOR) University of Ghana http://ugspace.ug.edu.gh iii ABSTRACT Background: Obesity is a complex, heterogeneous group of disorders that is determined by genes, environmental factors and interaction between genes and environment. Several studies have all established associations of certain candidate genes to obesity and their variations with respect to race, geographical location/country of origin. A study of this nature to investigate the relationship between some of these selected genes and obesity has not yet been conducted in Ghana. Aim: The aim of this study was to investigate the relationship between single nucleotide polymorphisms (SNPS) in selected candidate genes (β3-adrenergic receptors (ADRB3), leptin receptor (LEPR), and peroxisome proliferator activator γ receptor (PPARγ)) and obesity among Ghanaian adults. Methods: This was a case-control study. The cases were newly diagnosed obese (BMI ≥ 30 kg/m2) male and female Ghanaian adults (18 and 60 years) who visited the Dietherapy Unit (KBTH) for dietary counselling. Controls were students of the School of Allied Health Sciences and various staff members of Korle bu Teaching Hospital. A validated questionnaire was used to collect data on demographic characteristics, feeding habits, dietary history and anthropometric measures. Dietary intake was assessed using a food frequency questionnaire. DNA was extracted from mouth rinse water samples and SNPS in the ADRB3, LEPR and PPARγ genes were determined by polymerase chain reaction restriction fragment length polymorphism (PCR –RFLP). Results: A total of 56 subjects (71.4% females), consisting of 24 cases (41.63 ± 11.50 years) and 32 controls (26.42 ± 5.48 years), were recruited for the study. All the subjects were non-smokers, with 80.3% not consuming alcohol. There were statistically significant differences in BMI, waist to hip ration (WHR) and mid-upper arm circumference (MUAC) measurements between cases and controls (all ps˂ 0.0001). University of Ghana http://ugspace.ug.edu.gh iv There was no statistically significant difference (p = 0.3032) between the mean energy intakes of the cases and controls. DNA fragments for the PPARγ gene was amplified in 55 out of the 56 respondents. Only the presence of CC (Pro12Pro) genotype was detected after the RFLP. No relationship was observed between PPARγ gene polymorphism and BMI, MUAC and WHR among the participants. For LEPR, amplification was successful in 15 cases and 14 controls. However, only amplicons of six cases, all in the OBClass 1 to OBClass 3 range, after RFLP indicated the presence of the Gln223Arg genotype. A significant difference (all ps <0.05) emerged for the LEPR gene Gln223Arg genotype polymorphism frequencies between the cases and the controls for BMI, MUAC and WHR. For ADRB3, amplification was successful in 19 cases and 24 controls. RFLP indicated the presence of the Trp64Trp and the Trp64Arg genotypes. No significant difference (all ps >0.05) emerged for the ADRB3 gene Try64Arg genotype polymorphism frequencies between the cases and the controls for BMI, MUAC and WHR. Conclusions: No relationships were observed between ADRB3 and PPARγ genotype polymorphisms and BMI, MUAC and WHR. However, LEPR gene Gln223Arg genotype polymorphism was statistically associated with BMI, MUAC and WHR. A larger sample size would have to be considered in order to establish any potential associations between LEPR and obesity in the Ghanaian population. University of Ghana http://ugspace.ug.edu.gh v DEDICATION This dissertation is dedicated to THE LORD GOD ALMIGHTY for bringing me this far. It is also to Yauniuck my “friend” and Patrick my “brother” for their encouragement and assistance in diverse ways in the completion of this work. University of Ghana http://ugspace.ug.edu.gh vi ACKNOWLEDGEMENTS To start with, I would like to thank Dr. Charles Brown, my Principal Supervisor who was not only the catalyst for this research but also a vast source of inspiration, knowledge and encouragement towards the completion of my MSc dissertation. I would also like to thank my co-supervisor, Dr. Samuel Antwi-Baffour who assiduously assessed the literature review and the final work. Thanks should also be bestowed upon Dr. Anita Ghansah (Parasitology Department) and Mr. Richard Fordjour, Opong, “Vincent”, “Nana Efua”, Miss. Javada Appenteng, all staff of Clinical Pathology, Noguchi Memorial Institute for Medical Research (NMIMR) for the wonderful experience and assistance offered throughout this project. Appreciation also goes to Mr. Richard Asmah, Miss. Gloria Amegatcher, Mr. Christian Adabor, Miss Marjorie Quarchie and staff of Molecular Biology Laboratory, SAHS for their tireless efforts concerning the success of this work. I am also grateful to the Administration, Head and staff, Department of Dietetics (KBTH) where data was collected for this research, and especially to the participants, for their willingness and cooperation. I would also like to thank my family; Dad, Mum, Stone and Sammy for their constant support and love throughout my demanding moments. University of Ghana http://ugspace.ug.edu.gh vii TABLE OF CONTENTS DECLARATION ................................................................................................................ ii ABSTRACT ....................................................................................................................... iii DEDICATION .................................................................................................................... v ACKNOWLEDGEMENTS ............................................................................................... vi TABLE OF CONTENTS .................................................................................................. vii LIST OF FIGURES ............................................................................................................ x LIST OF TABLES ............................................................................................................. xi ABBREVIATIONS .......................................................................................................... xii CHAPTER ONE ................................................................................................................. 1 1.0 INTRODUCTION ........................................................................................................ 1 1.1 BACKGROUND ....................................................................................................... 1 1.3 SIGNIFICANCE OF THE STUDY .......................................................................... 4 1.4 HYPOTHESIS .......................................................................................................... 5 1.5 AIM ........................................................................................................................... 5 1.6 SPECIFIC OBJECTIVES ......................................................................................... 5 CHAPTER TWO ................................................................................................................ 6 2.0 LITERATURE REVIEW ............................................................................................. 6 2.1 OBESITY .................................................................................................................. 6 2.1.1 Definition ............................................................................................................ 6 2.1.2 Description ......................................................................................................... 6 2.1.3 Prevalence ........................................................................................................... 8 2.1.3.1 Global .......................................................................................................... 8 2.3.1.2 Africa ........................................................................................................ 11 2.1.3.3 Ghana ........................................................................................................ 13 2.1.4 Risk Factors ...................................................................................................... 13 2.1.4.1 Modifiable risk factors .............................................................................. 13 2.1.4.1.1 Environmental factors ........................................................................ 13 2.1.4.1.2 Behavioural factors ............................................................................ 14 2.1.4.2 Non modifiable risk factors ...................................................................... 16 2.1.4.2.1 Genetic factors ................................................................................... 16 2.2 GENETIC AETIOLOGY OF OBESITY ................................................................ 19 2.2.1 Monogenic Obesity .......................................................................................... 19 2.2.2 Polygenic Obesity ............................................................................................. 20 2.3 CANDIDATE GENES STUDIES .......................................................................... 21 University of Ghana http://ugspace.ug.edu.gh viii 2.4 GENE IDENTIFICATION BEFORE THE GENOME-WIDE ASSOCIATION ERA ............................................................................................................................... 24 2.5 GENOME-WIDE LINKAGE STUDIES ................................................................ 24 2.6 GENOME WIDE ASSOCIATION STUDIES ....................................................... 25 2.7 PEROXISOME PROLIFERATOR ACTIVATED- RECEPTOR GENE AND OBESITY ...................................................................................................................... 28 2.8 BETA-2/3 ADRENERGIC RECEPTOR GENE AND OBESITY ........................ 30 2.9 LEPTIN/LEPTIN RECEPTOR GENE AND OBESITY ....................................... 31 CHAPTER THREE .......................................................................................................... 35 3.0 METHODOLOGY ..................................................................................................... 35 3.1 STUDY DESIGN .................................................................................................... 35 3.2 STUDY SITE .......................................................................................................... 35 3.3 PARTICIPANTS ..................................................................................................... 36 3.4 SAMPLE SIZE CALCULATION .......................................................................... 36 3.5 ETHICAL CONSIDERATION .............................................................................. 37 3.6 DATA COLLECTION ............................................................................................ 37 3.6.1 Questionnaire Administration .......................................................................... 37 3.6.2 Anthropometric Measurements ........................................................................ 38 3.6.3 Waist to Hip Ratio (WHR) Determination ....................................................... 38 3.7 MOLECULAR LABORATORY METHODS ....................................................... 39 3.7.1 DNA Extraction ................................................................................................ 39 3.7.2 PCR Amplification ........................................................................................... 40 3.7.3 Restriction Fragment Length Polymorphism (RFLP) Analysis ....................... 45 3.7.4 Agarose Gel Electrophoresis ............................................................................ 45 3.8 STATISTICAL ANALYSIS ................................................................................... 46 CHAPTER FOUR ............................................................................................................. 47 4.0 RESULTS ................................................................................................................... 47 4.1 SOCIO-DEMOGRAPHIC CHARACTERISTICS OF RESPONDENTS ............. 47 4.2 ANTHROPOMETRIC INDICES ........................................................................... 48 4.2.1 BMI ................................................................................................................... 48 4.2.2 MUAC .............................................................................................................. 49 4.2.3 WHR ................................................................................................................. 50 4.3 ENERGY INTAKE ................................................................................................. 50 4.4 CANDIDATE GENES ............................................................................................ 50 University of Ghana http://ugspace.ug.edu.gh ix 4.4.1 PPARγ gene polymorphism among respondents ............................................. 50 4.4.1.1 Relationship between PPARγ gene polymorphism and BMI ................... 52 4.4.1.2 Relationship between PPARγ gene polymorphism and MUAC .............. 52 4.4.1.3 Relationship between PPARγ gene polymorphism and WHR ................. 52 4.4.2 Leptin receptor gene (LEPR) polymorphism among respondents ................... 52 4.4.2.1 Relationship between LEPR polymorphism and BMI ............................. 53 4.4.2.2 Relationship between LEPR polymorphism and MUAC ......................... 54 4.4.2.3 Relationship between LEPR polymorphism and WHR ............................ 54 4.4.3 ADRB3 genotype polymorphism among respondents ..................................... 56 4.4.3.1 Relationship between ADRB3 genotype polymorphism and BMI .......... 56 4.4.3.2 Relationship between ADRB3 genotype polymorphism and MUAC ...... 56 4.4.3.3 Relationship between ADRB3 genotype polymorphism and WHR ......... 56 CHAPTER FIVE .............................................................................................................. 59 5.0 DISCUSSION AND CONCLUSION ........................................................................ 59 5.1 DISCUSSION ......................................................................................................... 59 5.2 CONCLUSION ....................................................................................................... 65 REFERENCES ................................................................................................................. 67 APPENDIXES .................................................................................................................. 94 University of Ghana http://ugspace.ug.edu.gh x LIST OF FIGURES Fig. 1. WHO estimated global overweight and obesity prevalence for males, Aged 15+, 2010 Fig. 2 WHO estimated global overweight and obesity prevalence for females, Aged 15+, 2010 Fig. 3 WHO estimated African overweight and obesity prevalence for males, Aged 15+, 2010 Fig. 4 WHO estimated African overweight and obesity prevalence for females, Aged 15+, 2010 Fig. 5 Genetic map for monogenic obesity genes and their distribution range throughout chromosome Fig. 6 Mean body mass index (BMI) of respondents Fig. 7 Mean Mid-upper arm circumference (MUAC) of respondents Fig 8 Mean energy intakes of the respondents Fig. 9 A representative agarose gel electrophoregram of PCR products (band size 270bp) of PPAR-γ gene Fig. 10 A representative agarose gel electrophoregram of PCR products (band size 80 bp) of LEPR gene Fig. 11 A representative agarose gel electrophoregram of PCR products (band size 210 bp) of the ADRB3 gene. University of Ghana http://ugspace.ug.edu.gh xi LIST OF TABLES Table 1 Some genes showing polymorphisms involved in obesity development according to candidate genes studies along with their location and their main function within metabolism Table 2 Nearest genes to loci discovered throughout Genome Wide Association Studies (GWAS), and BMI increase (kg/m2) per risk allele in the genotype Table 3 Sequence details of primers used for the genotyping of the selected candidate genes Table 4a PPAR- PCR reaction mixture Table 4b PPAR- PCR cycling conditions Table 5a ADRB3 PCR reaction mixture Table 5b ADRB3 PCR cycling conditions Table 6a Leptin PCR reaction mixture Table 6b Leptin PCR cycling conditions Table 7 Socio-demographic characteristics of respondents Table 8 Weight classification of respondents Table 9 Respondent‟s MUAC measurements Table 10 Waist-to-hip measurements of respondents Table 11 LEPR polymorphism among respondents Table 12 Relationship between LEPR polymorphism and BMI of respondents Table 13 Relationship between LEPR polymorphism and MUAC of respondents Table 14 Relationship between LEPR polymorphism and WHR of respondents Table 15 ADRB3 polymorphism among respondents Table 16 Relationship between ADRB3 polymorphism and BMI of respondents Table 17 Relationship between ADRB3 polymorphism and MUAC of respondents Table 18 Relationship between ADRB3 polymorphism and WHR of respondents University of Ghana http://ugspace.ug.edu.gh xii ABBREVIATIONS ADRB3 Adrenergic Beta 3 Receptor BMI Body Mass Index BP Blood Pressure CDC Centre for Disease Control CPHA Canadian Public Health Association DHS Demographic Health Survey DNA Deoxyribonucleic acid dNTPs Deoxynucleotide triphosphates FAO Food and Agriculture Organisation GHS Ghana Health Service – GSS Ghana Statistical Service GWAS Genome Wide Association Studies KBTH Korle Bu Teaching Hospital LEPR Leptin Receptor MESA Multi‐Ethnic protocol for the Study of Atherosclerosis mRNA messenger RNA MUAC Mid Upper Arm Circumference NHLBI National Heart Lung and Blood Institute NIH National Institute of Health PCR Polymerase Chain Reaction PPARG Peroxisome Proliferator Activated Receptor Gamma RFLP Restriction fragment length polymorphism RNA Ribonucleic acid SNPs Single Nucleotide Polymorphisms TNES A buffer comprising; Tris, NaOH, EDTA, and SDS UNICEF United Nations International Children‟s Emergency Fund WHO World Health Organization WHR Waist to Hip Ratio University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0 INTRODUCTION 1.1 BACKGROUND Obesity is an accumulation of excess body fat, to such an extent that health might be impaired (WHO, 2000; 2013a). Obesity is a complex condition involving environmental, behavioural and genetic factors as well as interactions among them. Despite its advancement in pandemic proportions, associated morbidity and mortality, and the financial burden for governments and society, it remains a major unsolved medical problem (Kopelman. 2005; Arora & Anubhuti 2006). Obesity increases the risk for developing chronic diseases, including type 2 diabetes (T2M), coronary heart diseases (CHDs), hypertension, osteoarthritis, and certain cancers (endometrial, breast, and colon), besides the social and psychological consequences (Neary et al., 2003; WHO, 2013a). Overweight and obesity are the fifth leading risk for global deaths. At least 2.8 million adults die each year as a result of being overweight or obese. In addition, 44% of the diabetes burden, 23% of the ischaemic heart disease burden and between 7% and 41% of certain cancer burdens are attributable to overweight and obesity (WHO 2013a). Obesity is the most common nutritional disorder in the world. Globally, there are more than 1.4 billion overweight adults, of these over 500 million are obese (WHO, 2013a). About 65% of the world‟s population live in countries with high mortality rates from overweight and obesity compared to underweight (WHO, 2013a). The United States has the highest prevalence of obesity among the developed nations (CDC, 2010; WHO, University of Ghana http://ugspace.ug.edu.gh 2 2013a). However, increases in the prevalence of overweight and obesity have been observed throughout the world. An estimated 68.8% of U.S. adults are overweight, and 35.7% are obese (CDC, 2012). In Canada 36% of adults are overweight and another 23% are obese (Statistics Canada, 2010). In Europe, between 30% and 80% of the adult population are overweight. About 31.9% to 79.3% of the men, and 27.8% to 77.8% of the women are overweight while 5.4% to 22.8% adult males and 7.1% to 35.6% adult females are obese (WHO, 2007; WHO, 2013b). In the United States, prevalence of obesity is higher in black and Hispanic populations, especially among Mexican American women (CDC, 2012). The prevalence of obesity in West Africa rapidly increased during the last two decades of the 20th century and continues to increase in the 21st century. A recent review of obesity prevalence in West Africa indicates that between 2000 and 2004, 10% of West African adults were obese. According to the same study, half of the urban population in general and 60% of urban West African women were either overweight or obese (Abubakari et al., 2008). In Ghana, the Demographic and Health Survey (DHS) in 2008 indicated that the percentage of women aged 15-49 overweight or obese grew from 25% to 30% between 2003 and 2008 with the highest values among urban women (GSS, GHS, and ICF Macro, 2009). Wave 1 of the Women‟s Health Study of Accra (WHSA-1) found 62.2% of 1,237 non-pregnant women living in Accra to be overweight or obese (Duda et al., 2007). Genetic factors may influence the development of obesity in a number of ways (Thomas & Bishop 2007). The number of fat cells, regional distribution of body fat, and resting metabolic rate (RMR) are also influenced by genes. Studies of twins confirm that genes University of Ghana http://ugspace.ug.edu.gh 3 determine 50% to 70% of the predisposition to obesity (Prentice et al., 2005). However in 2007, genome-wide association studies to identify obesity-related genes discovered the first obesity-related gene variants; the fat mass and obesity-associated (FTO) gene on chromosome 16 (Dina et al., 2007; Frayling et al., 2007). These gene variants are fairly common, and those carrying them do have a 20 – 30% higher risk of obesity than people without it (Loos et al., 2008; Qi, 2008). There are numerous genes involved in genetic predisposition to obesity. Some have received much attention, the ob gene (produces leptin), the adiponectin (ADIPOQ) gene, the B3-adrenoreceptor gene located primarily in the adipose tissue, which is thought to regulate RMR and fat oxidation in humans, and the FTO gene (predisposes to diabetes by its effect on body mass) (Frayling et al., 2007). Mutations in the ob gene, leptin receptor (LEPR), or ADIPOQ genes can result in obesity or metabolic syndrome (MetS), especially if diet provides too much saturated fat (Ferguson et al., 2010). A much greater proportion of the population is also likely to exhibit polymorphisms in other genes which have a significant but less dramatic effect on body weight (e.g. mutations in uncoupling proteins, the B3-adrenergic receptor (ADRB3) or peroxisome proliferator activator receptor γ (PPARγ). These genes may influence a wide range of metabolic and behavioural characteristics that together determine an individual‟s susceptibility to obesity (Thomas & Bishop 2007). A candidate gene is defined as that part of the DNA molecule that directs the synthesis of a specific polypeptide chain closely associated with a particular disease. Candidate genes for obesity can be chosen for their possible effects on body fat composition, anatomical distribution of fat, food intake and energy expenditure (Kopelman, 2000). Despite the evidence that genetic factors play a role in determining changes in adiposity, very few University of Ghana http://ugspace.ug.edu.gh 4 studies have investigated the role of obesity candidate genes. Results from other studies have shown that the B-2/B-3 adrenergic receptor (ADRB2/ADRB3), LEP/LEPR and PPARγ genes are associated with adiposity changes over time (van Rossum et al., 2002). 1.2 PROBLEM STATEMENT In Ghana, obesity was formally regarded as a disease of the „western world‟ and of the affluent. Current statistical data, however, indicates this trend has changed (Sobal and Stunkard, 1989; Duda et al., 2007) and obesity now runs across the entire socio- economic divide of the population. Several associations have been linked with obesity such as environmental and behavioural. Lately, genetic linkage is showing dominance in most recent scientific researches on obesity. The observation in this trend is not so in Ghana. Several studies (Bouchard et al., 1988; Malczewska-Malec et al., 2004; Podolsky et al., 2007) have all established associations of certain candidate genes to obesity and their variations with respect to race, geographical location/country of origin. While little or no scientifically researched information in this area of obesity research exist in Ghana, the evidences from other published works call for more studies by Ghanaian medical researchers to investigate variations if any that exist within the Ghanaian population. 1.3 SIGNIFICANCE OF THE STUDY Investigation into these genes will help clarify the aetiology of obesity, its metabolic consequences and complications. In addition, it will also identify at-risk individuals or groups in terms of their genetic profile with the goal of developing personalized prevention and treatment strategies to enhance the quality of life. It will provide insight during dietetic counselling for obese individuals. University of Ghana http://ugspace.ug.edu.gh 5 This project will also provide baseline information for future research in this field, since there is lack of scientific information on the genetic bases of obesity in Ghana. 1.4 HYPOTHESIS There is no relationship between the selected candidate genes and obesity. 1.5 AIM The aim of this study was to investigate the relationship between single nucleotide polymorphisms (SNPS) in selected candidate genes (β3-adrenergic receptors (ADRB3), leptin receptor (LEPR), and peroxisome proliferator activator γ receptor (PPARγ) and obesity among Ghanaian adults. 1.6 SPECIFIC OBJECTIVES a) To detect single nucleotide polymorphisms (SNPS) in the receptors of the ADRB3, LEPR and PPARγ in the Ghanaian population. b) To determine whether there is a relation between the SNPS in the receptors of the selected candidate genes and obesity in the Ghanaian population. University of Ghana http://ugspace.ug.edu.gh 6 CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 OBESITY 2.1.1 Definition WHO (2000; 2013a) defined obesity as an accumulation of excess body fat, to such an extent that health might be impaired. Kumar and Clark (2009), also explain that obesity is an excess storage of fat, which can easily be detected by looking at the undressed patient. They further explained that, overweight, a precursor to obesity, occurs as a result of an imbalance between food consumed and physical activity. If energy expenditure (mechanical work and heat) is smaller than the intake, energy will be stored. As emphasized by O‟Rahilly (2009), obesity results from a chronic surplus of energy intake compared to energy expenditure, which leads to storage of excessive amounts of triglycerides in adipose tissue. The World Health Organization uses body mass index (BMI) as a surrogate measure of overall obesity, and thus classifies a person with a BMI≥30 kg/m2 as obese and a BMI≥40 kg/m2 as extremely obese (WHO, 1998; 2010). 2.1.2 Description The case of obesity is a complex one, involving environmental, behavioural and genetic factors as well as interactions among them (Kumar and Clark, 2009). The multifactorial facets of obesity: genetics, behavioural (diet and physical activity patterns), environmental, psychosocial and sociocultural factors with emphasis on genetics influencing the development of obesity in an individual have been established by several authors (Stunkard et al., 1990; Maes et al., 1997; Farooqi et al., 1999; Farooqi and O‟Rahilly, 2005; Ruiz et al., 2010). University of Ghana http://ugspace.ug.edu.gh 7 The causes of this imbalance can be traced to a number of factors including specific genetic traits which may convey a metabolic predisposition, behavioural factors and environmental circumstances (Jebb, 1997). These factors may act alone or in combination to modulate intake and/or expenditure and hence determine the likelihood of an individual becoming obese (Thomas & Bishop 2007). Over the years, many hypotheses have evolved to explain why some people become fat and others remain lean, and why it is so difficult for reduced-obese persons to maintain weight loss (Mahan et al., 2012). No single theory can completely explain all manifestations of obesity or apply consistently to all persons. Moreover, it is a reputable fact that rapid globalization of the westernized lifestyle is enhancing this emerging obesity epidemic. Nonetheless, not everyone in this contemporary obesogenic environment develops obesity, highlighting the multifactorial nature of the condition. Undeniably, obesity arises through the combined actions of multiple genetic and environmental factors, i.e. the obesogenic environment increases the risk of obesity, particular in those who are already genetically susceptible (Loos, 2009). Obesity causes or exacerbates many health problems, both independently and in association with other diseases. In particular, it is associated with the development of type 2 diabetes mellitus, coronary heart disease (CHD), hypertension, an increased incidence of certain forms of cancer, respiratory complications (obstructive sleep apnoea), osteoarthritis of large and small joints (Kopelman, 2000), besides the social and psychological consequences (Neary et al., 2003). Despite its advancement in pandemic proportions, associated morbidity and mortality, and the financial burden for government and society, it also remains a major unsolved medical problem (Kopelman, 2005; Arora & Anubhuti, 2006; Shamseddeen et al., 2011). University of Ghana http://ugspace.ug.edu.gh 8 Most patients suffer from simple obesity but in certain conditions such as Prader-Willi syndrome, Laurence-moon-biedl syndrome, hypothyroidism/Cushing‟s syndrome, Stein- leventhal syndrome and hypothalamic damage due to trauma/tumours, obesity is an associated feature (Kumar and Clark, 2009). In these and other cases of obesity, excess weight arises as a consequence of a long term excess of energy consumed relative to an individual‟s energy requirement. The Build and Blood Pressure Study has shown that the adverse effects of excess weight tend to be delayed, sometimes for ten years or longer (Society of Actuaries, 1979). Life-insurance data and epidemiological studies confirm that increasing degrees of overweight and obesity are important predictors of decreased longevity (Lew, 1985). In the Framingham Heart Study, the risk of death within 26 years increased by 1% for each extra pound (0.45 kg) increase in weight between the ages of 30 years and 42 years, and by 2% between the ages of 50 years and 62 years (Hubert, 1986). Despite this evidence, many clinicians consider obesity to be a self-inflicted condition of little medical significance (Kopelman, 2000). 2.1.3 Prevalence 2.1.3.1 Global Obesity is the most common nutritional disorder in the world, globally; there are more than 1.4 billion overweight adults, of whom more than 500 million are obese (WHO, 2013a). The prevalence is generally higher in developed than developing countries, but the rate of increase in some areas, especially in countries undergoing rapid economic transition, is marked. University of Ghana http://ugspace.ug.edu.gh 9 There are marked trends in obesity with age. Among 16-24 year olds, only 10% are obese, rising to over 30% in 55-64 year olds, after which there is a modest decline (Thomas & Bishop 2007). The United States has the highest prevalence of obesity among the developed nations (Figs. 1 and 2) (CDC, 2010; WHO, 2013a). However, increases in the prevalence of overweight and obesity have been observed throughout the world. The international trend is often called "globesity." The estimates of overweight and obesity among children and adults are based on measured weights and heights from the National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (CDC, 2007). An estimated 66% of U.S. adults are overweight, and 32% are obese. In Canada 36% of adults are overweight and another 23% are obese (Statistics Canada, 2010). In Europe between 15% and 25% of adults are obese. In the United States, prevalence of obesity is higher in Non-Hispanic black women and Hispanic populations, with about 41.9% and 30.7% respectively (CDC, 2010). Unfortunately, children are not immune to the epidemic. Obesity is now the most common paediatric nutritional problem in the United States. Nearly a third of children and adolescents between ages 2 and 19 years are overweight (Mahan et al., 2012). Obesity is almost invariable in developed countries and almost all people accumulate some fat as they get older. The WHO acknowledges that obesity (body mass index > 30 kg/m2) is a worldwide problem which also affects many developing countries (Kumar and Clark, 2009). Obesity is a worldwide communal health problem. The problem does not only affect developed countries, as there is now a significant increase in overweight and obesity throughout the developing world (CPHA. 2006). University of Ghana http://ugspace.ug.edu.gh 10 Fig. 1: WHO estimated global overweight and obesity prevalence for males, Aged 15+, 2010 Fig. 2: WHO estimated global overweight and obesity prevalence for females, Aged 15+, 2010. University of Ghana http://ugspace.ug.edu.gh 11 2.3.1.2 Africa It is projected by the WHO that about three quarters of the obese population worldwide will be in non-industrialised countries by 2025 (WHO, 2005). There is a rapid ascendency in the prevalence of obesity in sub-Saharan Africa, especially among women (Figs. 3 and 4). The prevalence of obesity in West Africa rapidly increased during the last two decades of the 20th century and continues to increase in the 21st century. A review of obesity prevalence in West Africa indicated that between 2000 and 2004, 10% of West African adults were obese. According to the same study, half of the urban population in general and 60% of urban, West African women were either overweight or obese (Abubakari et al., 2008). A Demographic and Health Survey data review from seven African countries by Ziraba, et al. (2009) showed that the prevalence of urban overweight and obesity increased by nearly 35% during the period, with an estimated annual increase of about 5%. They also found that overweight/obesity did not differ significantly between the poor and the rich, but was significantly higher among non-educated women than educated women. The prevalence of overweight and obesity in urban sub-Saharan Africa was found to range between 23% in Malawi to 35% in Niger and Ghana, and 38% in Kenya. Another study conducted in two North African countries, Morrocco and Tunisia, also gave similar findings. Half of the women were overweight or obese (BMI > 25) with 51.3% in Morrocco and 50.9% in Tunisia (Mokhtar et al., 2001). The study also showed significantly higher rates of obesity among women than men in both countries (18% versus 5.7% in Morrocco and 22.7% versus 6.7% in Tunisia). University of Ghana http://ugspace.ug.edu.gh 12 Fig. 3: WHO estimated African overweight and obesity prevalence for males, Aged 15+, 2010 Fig. 4: WHO estimated African overweight and obesity prevalence for females, Aged 15+, 2010 University of Ghana http://ugspace.ug.edu.gh 13 Overweight and obesity was found to be prevalent among women in urban areas and lower education levels (Puoane et al., 2002; Ziraba et al., 2009). Puoane et al., (2002) conducted studies in South Africa which divulged the prevalence of overweight and obesity to be higher among women (56.5%) than in men (29.2%). They also found that the risk of obesity increased with age. 2.1.3.3 Ghana In Ghana, the Demographic and Health Surveys (DHS) indicate that the percentage of women aged 15-49 overweight or obese grew from 25% to 30% between 2003 and 2008 with the highest values among urban women (GSS, GHS, and ICF Macro; 2009). Wave 1 of the Women‟s Health Study of Accra (WHSA-1) found 62.2% of 1,237 non-pregnant women living in Accra were overweight or obese; the same study found no relationship between obesity prevalence and socio- economic status (Duda et al., 2007). Amoah, (2003) conducted a similar study in Ghana, which buttressed the fact that overweight and obesity actually were higher among women (47.3%) than men (21.1%), with a ratio of more than 2:1 respectively in the capital, Accra. The Kumasi Metropolitan Health Profile Report (2008) provided a startling revelation in the prevalence of obesity among adult residents in Kumasi. In that report, about three times more women (26%) were obese compared to the (8%) men. The national prevalence of obesity was 5.5% as at 2005 (Biritwum et al., 2005) 2.1.4 Risk Factors 2.1.4.1 Modifiable risk factors 2.1.4.1.1 Environmental factors On the global level, the marked increase in obesity which accompanies urbanization and economic development provides strong evidence that environmental factors per se play a University of Ghana http://ugspace.ug.edu.gh 14 strong part in the aetiology of obesity or unleash a latent genetic predisposition to obesity (Prentice et al., 2005). The environment has changed dramatically during the last century and the increased availability of highly palatable energy dense foods as well as a lower physical activity levels are thought to play a major role in the development of obesity (Prentice & Jebb, 1995; 2003). It is clear that the modern urban environment promotes consumption through the accessibility, affordability and advertising of food, while simultaneously decreasing energy needs through increasing reliance on vehicles, labour-saving devices in the home and garden and a decline in manual occupation (Thomas & Bishop 2007). Several factors such as finance and the availability of sweets and snacks, tend to affect food intake. Some people consume more during situations of intense exercise or pregnancy and are unable to return to previous eating patterns. It should be noted that the increase in obesity is usually related to the type of food consumed such as food containing sugar and fats. According to Kumar and Clark (2009), obese patients eat more than they admit, and over the years a very small daily excess of intake over expenditure can lead to a large accumulation of fat. 2.1.4.1.2 Behavioural factors Our diet and physical activity patterns are the source of day to day variations in energy balance. The state of positive energy balance that started the obesity epidemic must have resulted from changes in behaviour. Experts debate about the extent to which changes in diet versus changes in physical activity led to the obesity epidemic (Hill, 2006). Overweight and obesity are well understood to be the result of an energy imbalance; consuming more calories than are equivalently expended in physical activity. University of Ghana http://ugspace.ug.edu.gh 15 Two popularly cited theories for the West African change in energy balance include increases in urbanization and westernization. In theory, urbanization and westernization lead to decreased physical activity and increased food supply, including access to high caloric fast foods and sugar sweetened beverages (Duda et al., 2007). Although there is some indication that energy intake has increased and physical activity has declined over the past few decades, it is surprisingly difficult to accurately quantify these changes (Heitmann et al., 2000). Increased time spent in sedentary behaviours and decreased time spent in moderate-to-vigorous–intensity physical activity have been reported to be independently associated with the risk of metabolic syndrome and its components (Bertrais et al., 2005; Ford et al., 2005; Dunstan et al., 2005, 2007; Healy et al., 2006; Li et al., 2007). Furthermore, it is difficult to separate cause from effect between energy intake and obesity. For example, energy intake in the NHANES surveys aligns well with body weight. The jump in body weight and obesity seen from NHANES II to NHANES III was mirrored by a jump in average energy intake (Briefel et al., 1995). Most experts speculate that technological changes have reduced lifestyle physical activity (Hill & Peters, 1998). One of the most controversial areas of obesity research is the role of diet composition on body weight. Flatt, (1988) provided a theoretical basis for how diet composition could impact body weight regulation. He argued that achieving energy balance is largely a matter of achieving substrate balance and particularly fat balance. There is little functional capacity for storage of additional protein or carbohydrate in the body, but capacity for fat storage is essentially unlimited. It is important that protein and carbohydrate balance be regulated acutely, and the body developing effective means for oxidizing excesses of these nutrients. Fat balance can be regulated over the long term University of Ghana http://ugspace.ug.edu.gh 16 because fat can be brought in and out of storage as needed (Hill, 2006). Thus, the way the body re-achieves energy balance after a perturbation (e.g. overfeeding) will involve restoration of protein and carbohydrate balance before fat balance. Nordmann et al., (2006) conducted a meta-analysis of non-energy-restricted, low-carbohydrate diets and concluded that they were at least as effective as low-fat diets over a period of one year. Diet composition can impact body weight in individuals who are in energy balance. Astrup et al., (2000) reviewed a large number of studies where diet composition was altered in individuals who were in energy balance and found that body weight is reduced slightly as dietary fat content of the diet is lowered. This is because carbohydrate produces more thermic effect than fat, reducing dietary fat and increasing dietary carbohydrate would also be expected to produce a slight increase in the thermic effect of food (Hill et al., 1993). Portion size is another factor that can influence total energy intake. Kral et al. (2004) demonstrated that energy intake increases as the portion size of the food offered increases. Other factors, such as increased variety of food, low cost, and accessibility also may increase energy intake (Woods et al., 2000). 2.1.4.2 Non modifiable risk factors 2.1.4.2.1 Genetic factors Body weight is established by a combination of genetic and environmental factors related to lifestyle (diet, physical activity or sedentary lifestyle), as well as by interactions between those factors. Therefore, obesity appears as a consequence of a positive energy balance involving any alteration in one or several of these factors (Razquin et al., 2011; Ordovaz et al., 2011). University of Ghana http://ugspace.ug.edu.gh 17 There are a number of ways in which genetic factors may influence the development of obesity (Thomas et al., 2007) and an individual‟s response to „obesogenic‟ environments is largely determined by genetic susceptibility (Podolsky et al., 2007). Halaas et al. (1995) accentuated that hereditary component of bodyweight regulation was studied for the first time in the first decades of the twentieth century, but there were no objective and consistent data about specific genes involved in obesity development and onset. In 1994, the discovery of ob gene and leptin, contributed to promoting a momentous progress in the knowledge and understanding on the genetic component of body weight regulation. Family and twin studies showed that genetic factors contribute 40–70% to the inter individual variation in common obesity (Maes et al., 1997). Furthermore, Hinney et al., (2010) suggested that about 50- 80 % of a population BMI variation could be due to genetic factors as revealed from their twin, families and adoption studies. Many hormonal and neural factors involved in weight regulation are determined genetically. These include the short and long term signals that determine satiety and feeding activity. Small defects in their expression or interaction could contribute significantly to weight gain (Lenard et al., 2009). The number of fat cells, regional distribution of body fat, and resting metabolic rate (RMR) are also influenced by genes. Studies of twins confirm that genes determine 50% to 70% of the predisposition to obesity (Prentice et al., 2005). Although numerous genes are involved, several have received much attention such as the ob gene which produces leptin, the adiponectin (ADIPOQ) gene, the FTO gene, and the B3-adrenoreceptor gene. University of Ghana http://ugspace.ug.edu.gh 18 Mutations in the ob gene, leptin receptor (LEPR), or ADIPOQ genes can result in obesity or metabolic syndrome (MetS), especially if the diet provides too much saturated fat (Ferguson et al., 2010). The B3-adrenoreceptor gene, located primarily in the adipose tissue, is thought to regulate resting metabolic rate (RMR) and fat oxidation in humans. The FTO gene predisposes to diabetes by its effect on body mass (Frayling et al., 2007). In laboratory animals, a number of monogenic disorders are associated with severe obesity. A number of human obesity syndromes characterized by similar gene defects, including defects in the ob gene, resulting in congenital leptin deficiency, mutations in the leptin ( LEP/LEPR) and melanocortin (MC4) receptor genes, and defective pro- opiomelaninocortin (POMC) processing has been reported (Farooqi and O‟Rahilly 2005). In humans, each of these syndromes is associated with marked hyperphagia and severe obesity, although in animal models there may be additional effects on energy expenditure. A much greater proportion of the population is likely to exhibit polymorphisms in other genes which have a significant but less dramatic effect on body weight (e.g. mutations in uncoupling proteins, the B3-adrenergic receptor (ADBR) or peroxisome proliferator activated receptor gamma (PPARγ). These genes may influence a wide range of metabolic and behavioural characteristics that together determine an individual‟s susceptibility to obesity (Thomas & Bishop 2007). University of Ghana http://ugspace.ug.edu.gh 19 2.2 GENETIC AETIOLOGY OF OBESITY Obesity as a result of genetic causes has been grouped into two major categories namely monogenic and polygenic obesity due to their varied causal association pathways (Farooqi and O‟Rahilly, 2005; Hinney & Hebebrand, 2008). Recently, Herrera, & Lindgren, (2010) also indicate that there are three commonly classified subgroups depending on suspected aetiology: monogenic obesity (extremely severe obesity in the absence of developmental delays), syndromic obesity (clinically obese subjects additionally distinguished by mental retardation, dysmorphic features, and organ-specific developmental abnormalities), and polygenic or common obesity, which affects the general population (but may have associated health risks, such as increased risk of CVD). 2.2.1 Monogenic Obesity Present data indicates that, monogenic obesity due to a single chromosome‟ polymorphism is responsible for almost 5% of the entire obesity cases. It has been proven that some genetic variations in several genes that codify for proteins implicated in appetite regulation and satiety are responsible for pathologic changes leading to the phenotypic manifestations of obesity (Farooqi and O'Rahilly, 2005). Up to now, about 11 genes have been associated to this type of obesity (Fig. 5). Among these genetic variants, the LEP, LEPR, POMC, MC4R and PCSK1 are especially important, since they account for the 5% of severe obesity cases of early development in children (Farooqi and O'Rahilly, 2005; Moleres et al., 2013). University of Ghana http://ugspace.ug.edu.gh 20 Fig. 5: Genetic map for monogenic obesity genes and their distribution range throughout chromosome (Moleres et al., 2012). 2.2.2 Polygenic Obesity Polygenic, or common, obesity arises when an individual‟s genetic makeup is susceptible to an environment that promotes energy consumption over energy expenditure. It is determined by the presence of genes of enough prevalence but with a relatively low effect (Perusse et al., 2005). Most Westernized societies have an environment that favours weight gain rather than loss because of food abundance and lack of physical activity, thus positioning common obesity as a major epidemic currently challenging these societies (Mutch and Cle´ment, 2006). The last 2005 update of the Human Obesity Gene Map (Rankinen et al., 2006) describes more than a hundred obesity candidate genes associated with polygenic obesity (Moleres et al., 2013). University of Ghana http://ugspace.ug.edu.gh 21 In relation to polygenic obesity studies, two main approaches have been applied namely, candidate gene studies and genome-wide association studies (GWAS). Genome- wide association study is now the more frequent tool used for the discovery of new genetic variants associated to obesity. In addition to these two approaches, a new tool, copy number variations (CNVs), a structural alteration of DNA that results in an abnormal number of copies of one or more sections of the DNA as well as epigenetic processes and nutrigenetics have been developed as new strategies to search for obesity causes (Moleres et al., 2013). 2.3 CANDIDATE GENES STUDIES Candidate obesity gene approach analyses genes involved in key metabolic pathways or those that have been shown to be important for obesity development in animal studies (Hinney et al., 2010). A candidate gene is defined as that part of the deoxyribonucleic acid (DNA) molecule that directs the synthesis of a specific polypeptide chain closely associated with a particular disease (Kopelman, 2000). The search for obesity genes requires a multifaceted approach that involves studies of potential candidate genes derived from animal models, human obesity syndromes and a genome-wide search using microsatellites covering the human genome. Candidate genes for obesity can be chosen for their possible effects on body fat composition, anatomical distribution of fat, food intake and energy expenditure (Kopelman, 2000). Loos (2009) also outlined a detailed description of candidate genes studies as a hypothesis-driven study which relied on the current understanding of the biology and pathophysiology that underlied the susceptibility to obesity. Genes, for which there is University of Ghana http://ugspace.ug.edu.gh 22 evidence for a role in regulation of the energy balance in animal models or in extreme/monogenic forms of obesity, are tested for association with obesity-related traits at the population level. In the mid-1990s, when genotyping was still an expensive and tedious task, candidate gene studies typically examined only one or a few variants per gene, with a focus on non-synonymous variants because of their potential functional implications. Recent advancements and publicly available datasets, such as dbSNP and the International HapMap, have provided deeper insight into genetic variation in genes. This has also led to more comprehensive studies that systematically examine the association of all common variation in a gene of interest by means of carefully selected tagSNPs and their haplotypes under the supposition that a causal variant would be in high linkage disequilibrium with one of the tagSNPs or at least captured by the haplotypes. Nonetheless, a significant limitation of this study approach is the reduced sample size (n<1000) that prevents it from having the necessary power to identify modest genetic effects on obesity. To overcome this limitation, in the last years, studies with a high number of participants and meta-analyses that gather the published information have been carried out and from them, strong associations between obesity and several genetic variants have been found (Vimaleswaran & Loos, 2010). Since the first candidate gene studies for obesity related traits, more than fifteen years ago, the number of proposed obesity susceptibility genes has grown steadily (Table 1). The latest update of the Human Obesity Gene Map reported one-hundred and twenty seven candidate genes for which at least one study reported a positive association with obesity-related traits (Rankinen et al., 2006). University of Ghana http://ugspace.ug.edu.gh 23 Table 1: Some genes showing polymorphisms involved in obesity development according to candidate genes studies along with their location and their main function within metabolism FUNCTION GENE LOCATION Involved in dietary intakea INS 11p15.5 LEP 7q31.3 MC4R 18q22 NPY 7p15.3 POMC 2p23.3 AGRP 16q22 CARTPT 5q13.2 FTO 16q12.2 LEPR 1p31 Involved in energy expenditureb ADBR2 5q31-q32 ADRB3 8p12 UCP1 4q28-q31 UCP2 11q13 UCP3 11q13.4 CLOCK 4q12 Adipose tissue growth and developmentc PPARG2 3p25 CEBPA 19q13.11 IL6 7p21-p15 FAB94 8q21.13 PNPLA3 22q13.31 PLPIN5 19p13.3 aLarsen et al., 2012; Moleres et al., 2012 bOchoa et al., 2006; Galbete et al., 2012 cRazquin et al., 2009; Corpeleijn et al., 2010). University of Ghana http://ugspace.ug.edu.gh 24 The rapid expansion reflects the availability of efficient genotyping technologies to a wider scientific community. Nonetheless, replication in subsequent studies for many of the proposed candidate genes has been incoherent, making the overall conclusion on association indistinct. Meta-analyses and the use of large sample sizes (n> 5000) has proven robust associations for non-synonymous variants in the melanocortin 4 receptor (MC4R), pro-hormone convertase 1/3 (PCSK1), brain-derived neurotrophic factor (BDNF), and -adrenergic receptor 3 (ADRB3) genes (Loos, 2009). 2.4 GENE IDENTIFICATION BEFORE THE GENOME-WIDE ASSOCIATION ERA Although recent success of GWAS has drawn a lot of attention, gene identification in recent years has been based on two broad genetic epidemiological approaches, i.e. candidate gene and genome-wide linkage studies. For over a decade, the scientific community interested in obesity genetics was privileged to have a Human Obesity Gene Map that catalogued all genetic variants and chromosomal loci ever associated with or linked to obesity-related traits (Rankinen et al., 2006). Its first edition was published in 1996, followed by ten yearly updates, the latest in 2006 covering the literature available as of the end of 2005. The map continues to be a useful resource, in print and web-based versions (http://obesitygene.pbrc.edu), providing a comprehensive and easily accessible overview of the literature on candidate gene and genome-wide linkage studies published before 2006 (Loos, 2009). 2.5 GENOME-WIDE LINKAGE STUDIES Genome wide linkage scans exploit familial relationship and rely on use of highly polymorphic markers that spread across the whole genome to pinpoint the location of University of Ghana http://ugspace.ug.edu.gh 25 genes, followed by calculating the degree of linkage of the marker to a disease trait (Bell et al., 2005). Genome-wide linkage studies rely on the relatedness of study participants and test whether certain chromosomal regions co-segregate with a disease or trait across generations. A whole-genome linkage survey requires 400–600 highly polymorphic markers, genotyped at 10-cM intervals. Genome-wide linkage studies have a rather coarse resolution and typically identify broad intervals that require follow-up genotyping to pinpoint the genes that underlie the linkage signal. Since the first genome-wide linkage study was published in 1997, the number of chromosomal loci linked to obesity-related traits has grown exponentially. The latest Human Obesity Gene Map update reported 253 loci from 61genome-wide linkage scans, of which 15 loci have been replicated in at least three studies. Yet none of these replicated loci could be narrowed down sufficiently to pinpoint the genes or variants that underlie the linkage signal (Rankinen et al 2006). Despite substantial power, a meta-analysis of 37 genome-wide linkage studies with data on more than 31,000 individuals from 10,000 families of European origin could not locate a single obesity or BMI locus with convincing evidence (Saunders et al., 2007). This meta-analysis indicates that genome-wide linkage might not be an effective approach for identifying genetic variants for common obesity. 2.6 GENOME WIDE ASSOCIATION STUDIES Genome Wide Association Studies (GWAS) is one of the most recent, common and useful tool in discovering new genetic variants associated with obesity and its related co- morbidities. In this study method, the genome of a group of people with the trait under study (cases) is compared with another group of people without it (controls) in order to University of Ghana http://ugspace.ug.edu.gh 26 detect genetic variants associated with this trait. In relation to candidate gene studies, there are major advantages when GWAS is used. These include having a much higher resolution, being more profitable, and allowing for the analyses of larger sample sizes hence providing solid findings (Frayling, 2007). The fat mass and obesity associated gene (FTO) was the first gene from GWAS to be indisputably related to obesity after being replicated in different populations (Dina et al., 2007; Frayling et al., 2007; Scuteri et al., 2007). Afterwards, more waves of GWAS for body mass index (BMI), with a sample size of about 123,865 individuals, have been performed identifying 32 loci extensively linked with this trait (Willer et al., 2009; Speliotes et al., 2010; Loos , 2012). The discovery of these new loci is of great importance since it has allowed for new biological pathways involved in the development and onset of obesity to be studied in depth. In the midst of the waves of GWAS for BMI, six loci have been confirmed to have a strong association with this trait. These loci are located in FTO gene, near MC4R, near KCTD15, near NEGR1, near TMEM18 and in SH2B1 gene (Loos, 2012). The identified loci associated with BMI after GWAS are shown in the Table 2. Despite the evidence that genetic factors play a role in determining changes in adiposity, very few studies have investigated the role of obesity candidate genes. Results from other studies have shown that the ADRB2/ADRB3, LEP/LEPR, NOS3 and PPARγ genes are associated with adiposity changes over time (van Rossum et al., 2002). University of Ghana http://ugspace.ug.edu.gh 27 Table 2: Nearest genes to loci discovered throughout Genome Wide Association Studies (GWAS), and BMI increase (kg/m2) per risk allele in the genotype (Thorleifsson et al., 2009; Willer et al., 2009; Loos, 2012) Loci Genome Wide Association Studies (GWAS) BMI increase (kg/m2) per risk allele FTO [19–21] 0.39 Near TMEM18 [19–21] 0.31 NearMC4R [19–21] 0.23 SH2B1 [19–21] 0.15 Near NEGR1 [19–21] 0.13 Near KCTD15 [19–21] 0.06 Near SEC16B [19–21] 0.22 BDNF [19–21] 0.19 Near GNPDA2 [19–21] 0.18 NearETV5 [19–21] 0.14 Near FAIM2 [19–21] 0.12 MTCH2 [19–21] 0.06 SLC39A8 19 0.19 Near GPRC5B 19 0.17 Near PRKD1 19 0.17 QCPTL 19 0.15 Near RBJ 19 0.14 TFAP2B 19 0.13 MAP2K5 19 0.13 NRXN3 19 0.13 Near FLJ35779 19 0.10 Near FANCL 19 0.10 CADM2 19 0.10 Near TMEM160 19 0.09 Near LRP1B 19 0.09 MTIF3 19 0.09 TNN13K 19 0.07 Near ZNF608 19 0.07 Near PTBP2 19 0.06 Near RPL27A 19 0.06 NUDT3 19 0.06 University of Ghana http://ugspace.ug.edu.gh 28 Although much effort has been and continues to be invested in finding genetic polymorphisms associated with obesity itself, much less attention has been paid to finding genetic polymorphisms associated with the development of obesity (Rankinen et al., 2006). 2.7 PEROXISOME PROLIFERATOR ACTIVATED- RECEPTOR GENE AND OBESITY Obesity is a complex metabolic disorder with strong genetic components (Bouchard et al., 1988). Candidate genes are numerous and may involve both structural and regulatory proteins from various tissues. The development of obesity requires the continuous differentiation of new adipocytes throughout life (Flier, 1995). This process of adipocyte differentiation from pre-adipocytes has been shown to be controlled by members of the peroxisome proliferator activated receptor gamma (PPARγ), adipocyte determination and differentiation-dependent factor-1 (ADD1)/SREBP1, and CCAAT/enhancer-binding protein (C/EBP) nuclear receptor families (Lin & Lane, 1994; Tontonoz et al., 1994a; Tontonoz et al., 1994b; MacDougald and Lane, 1995; Kim and Spiegelman, 1996). The PPARγ gene is located on chromosome 3 (Beamer et al., 1997). There are three types of PPAR due to the alternative splicing of its mRNA into these isoforms. Alternative promoter regions within the PPARγ gene allow for the formation of three PPARγ isoforms: PPARγ1, γ2 and γ3 (Vidal-Puig et al., 1997). Among these isoforms, PPARγ has been shown to regulate adipocyte differentiation and lipid metabolism through the activation of adipocyte- specific genes (Spiegelman & Flier, 1996). It is also secreted in adipose tissue and controls adipocyte differentiation (Tontonoz et al., 1994; Forman et al., 1995; Kliewer et al., 1995) and regulates lipid and glucose homeostasis (Auwerx et al., 1996; Spiegelman & Flier, 1996). University of Ghana http://ugspace.ug.edu.gh 29 PPARγ is an important regulator of adipocyte differentiation and a modulator of intracellular insulin-signalling events (Spiegelman, 1998). It also acts through the transactivation of adipose specific genes, including those that encode for proteins involved in lipid storage and metabolism to result in fat cell differentiation (Tontonoz et al., 1994; Tontonoz et al., 1995; Sears et al., 1996). Forced expression of PPAR γ2 and C/EBP-A in fibroblasts (Tontonoz et al., 1994a) and myoblasts (Hu et al., 1995) is capable of differentiating these cells into adipocytes. PPARγ has been shown to have the highest adipogenic activity among the members of the PPAR family (Brun et al., 1996). PPARγ also controls the transcription of target genes by the formation of a heterodimeric complex with retinoid x receptors, and this complex binds to specific PPARγ response elements in the promoter regions of these genes (Tontonoz et al., 1994a). While PPARγ1 mRNA is found in a wide range of tissues (including heart, liver, skeletal muscle and adipose tissue), PPARγ2 is most abundant in adipose tissue). PPARγ3 is expressed in macrophages, colon epithelium and adipose tissue (Fajas et al., 1998). PPARγ2 contains 30 additional amino acids at its N-terminus, which may confer a greater ligand-independent activation function (augmented by insulin) upon this isoform (Werman et al., 1997). PPARγ2 expression is increased in adipose tissue taken from obese subjects compared with lean subjects, and is reduced during weight loss (Vidal-Puig et al., 1997). Results from animal models experimentation and in vitro cell culture systems indicate that PPARγ enhances expression of genes such as lipoprotein lipase (Schoonjans et al., 1996), aP2 (Tontonoz et al., 1994), phosphoenolpyruvate carboxykinase (Tontonoz et al., 1995), and acylCoA synthase ( Schoonjans et al., 1995), while it down-regulates expression of the leptin gene (Kallen & Lazar, 1996; De Vos et al., 1996; Zhang et al., 1996; Hollenberg et al., 1997). University of Ghana http://ugspace.ug.edu.gh 30 Dominant negative PPARγ mutations are associated with severe insulin resistance, hypertension and alterations in lipid profile (low high-density lipoprotein (LDL), and high triglycerides (Barosso et al., 1999). The Pro12Ala polymorphism of the PPARγ2 isoform has been shown to decrease PPARγ activity (Hara et al., 2000; Masugi et al., 2000) and to be associated with higher BMI (Deeb et al., 1998). In addition, prospective and intervention studies have shown that the Pro12Ala polymorphism could be associated with weight regain after a diet-induced weight loss (Ek et al. 1999; Nicklas et al., 2001). Pro115Gln PPARγ mutation is associated with excessive adipose tissue accumulation through an intrinsic increase in adipogenic activity (Ristow et al., 1998). 2.8 BETA-2/3 ADRENERGIC RECEPTOR GENE AND OBESITY Adipose tissue forms an integral part of the regulation, storage and mobilization of energy, and has been the focus of the efforts to identify candidate genes for obesity. One such gene is the B3-adrenergic receptor (Emorine et al.,1989; Nahmias et al., 1991) which is the main receptor involved in the regulation of thermogenesis and lipolysis in brown and white adipose tissue in rodents (Arch & Kaumann, 1993). In humans, the B3- adrenergic receptor (Emorine et al., 1989) is expressed predominantly in fat and adipocytes lining the gastrointestinal tract (Krief et al., 1993). The receptor‟s primary role is thought to be the regulation of the resting metabolic rate and lipolysis. (Emorine et al., 1994). The suggested role of the B3-adrenergic receptor in fat metabolism, its functional deficiency in genetically obese mice (Collins et al., 1994; Arbeeny et al., 1994) and the results of studies in which the gene for the receptor has been disrupted in mice (Susulic et al., 1994) has impelled numerous investigations into the function of this gene and its mutated variants such as (Trp64Arg) in patients with morbid obesity. University of Ghana http://ugspace.ug.edu.gh 31 The B3-adrenergic receptor is expressed in visceral fat in humans (Krief et al., 1993) and is considered responsible for increases in lipolysis and the delivery of free fatty acid into the portal vein (Lonnqvist et al., 1995). An increase in visceral fat mass, in turn, correlates with resistance to insulin in skeletal muscle (Colberg et al., 1995). An abnormality in the B3-adrenergic receptor could therefore explain the link between abdominal obesity and insulin resistance. 2.9 LEPTIN/LEPTIN RECEPTOR GENE AND OBESITY The ground-breaking discovery of leptin by the year 1994 ignited the field of obesity research in providing the first direct evidence for a hormonal system primarily involved in body weight regulation (Oswal & Yeo, 2010). According to Zhang et al. (1994), leptin, is the circulating product of the ob gene, which is secreted by adipose tissue, and the expression of this hormone rises with increasing fat stores in animals and humans (Maffei et al., 1995; Considine et al., 1996). Considine et al. (1996) asserted that the hormone acts as a sensor of fat mass through a negative feedback loop to maintain body fat stores at required levels. Klok et al. (2007) did confirm the fact that, the concentration of leptin in circulation is parallel to body fat stores, to such an extent that, a rise in adiposity increases leptin production, hence restraining food intake and vice versa. Accordingly, humans and mice with inherited loss of function mutations of the genes encoding either leptin or its receptor display severe early onset obesity (Pelleymounter et al., 1995; White & Tartaglia, 1996; Montague et al., 1997; Clément et al., 1998; Farooqi et al., 2007). Studies have shown that leptin deficiency is usually common among people with monogenic obesity syndromes who do respond well to treatment with recombinant leptin (Farooqi et al., 1999; Licinio et al., 2004; Farooqi and O‟Rahilly, 2005). University of Ghana http://ugspace.ug.edu.gh 32 There are several systemic effects that are attributed to leptin secretion such as body mass control, lipid and glucose metabolism, thermogenesis, angiogenesis, immunity, reproductive, endocrine and cardiovascular functions, among others (Korner et al., 2009). Mutations in the ob gene lead to defective/elevated leptin production and obesity in the ob/ob mouse (Zhang et al., 1994), but no such mutations had yet been identified in humans (Considine et al., 1996b; Niki et al., 1996; Maffei et al., 1996). However, Maffei et al. (1995) and Considine et al. (1996) both asserted that most obese humans also have elevated blood leptin levels, possibly implying that defects in the leptin receptor (OB-R) gene contribute to obesity in humans. The leptin receptor is a single membrane spanning protein with structural similarity to the class 1 cytokine receptor family (Tartaglia et al., 1995; Lee et al., 1996). Though the array of clinical phenotypes of congenital leptin and leptin receptor deficiency is related, the leptin receptor deficiency in humans leads to a less severe phenotype (Farooqi et al., 2007). It is also considered to be less prevailing than leptin deficiency, accounting for up to 3% of all cases of extreme early onset obesity. The hypothalamic arcuate nucleus (ARC) is the main locus for leptin signalling and leptin resistance (Chen et al., 1996; Mercer et al., 1996; Thornton et al., 1997). Within it are two distinct classes of neurons; one class expressing the POMC (proopiomelanocortin) and cocaine and amphetamine–related transcript peptides which reduce food intake, while the other expresses the NPY (neuropeptide Y) and AgRP (agouti-related protein) peptides which stimulate feeding behaviour (Coll et al., 2007; Coll et al., 2008). Leptin receptors which are highly expressed on the membranes of University of Ghana http://ugspace.ug.edu.gh 33 these neurons allow leptin to reciprocally regulate their populations (Oswal and Yeo, 2010). Several varied alternatively spliced isoforms of the receptor (ObR) exist (Lee et al., 1996), with each having a characteristic intracellular domain. The isoforms are either classified as short or long depending upon the length of the intracellular domain. The signaling capacity of the short isoforms (ObRa, ObRb, ObRc, ObRd, ObRe, and ObRf) are limited whereas the long isoform ObRb is believed to be the primary signalling form of the receptor (Lee et al., 1996; Bjorbaek et al., 1997; Fei et al., 1997). The cerebral micro vessels constituting the blood–brain barrier expresses the ObRa and ObRc isoforms at high levels and play key roles in the transport of leptin into the central nervous system (Hileman et al., 2002). Both diet-induced obese mice and mice lacking the ObR have distinctly impaired leptin transport (Hileman et al., 2002). It is generally assumed that this impairment results from a saturation of the leptin transporter due to the high endogenous leptin levels of these mice. To buttress this argument, the ObR deficient Koletsky rat has shown normalized leptin transport when a perfusion method that negates the effect of serum leptin levels was used. It appears however, that the activity of a separate transporter for leptin is actually modulated by the short isoforms of the receptor, whose identity remains unknown (Banks et al., 2002). The long form of the receptor, ObRb is markedly expressed in the hypothalamus where it regulates the effects of leptin on energy homeostasis (Fei et al., 1997; Elmquist et al., 1998). The OB-R gene–related protein which controls endocytic internalization, in part regulates the membrane expression of the ObRb (Couturier et al., 2007). University of Ghana http://ugspace.ug.edu.gh 34 A lack of functional ObRb is responsible for the obesity and metabolic syndrome observed in the db/db mouse model (White & Tartaglia, 1996). In mice, only the Ob-Rb isoform with a long cytoplasmic tail appears fully functional and is thought to mediate the hypothalamic actions of leptin (Lee et al., 1996). A mutation in the OB-R gene of db/db mice leads to abnormal mRNA splicing that replaces the Ob-Rb with the short Ob-Ra isoform (Lee et al., 1996, Chen et al., 1996), giving rise to abnormal signal transduction (Ghilardi et al., 1996), complete leptin resistance and obesity (Halass et al., 1995; Stephen et al., 1995). University of Ghana http://ugspace.ug.edu.gh 35 CHAPTER THREE 3.0 METHODOLOGY 3.1 STUDY DESIGN The research design was a case-control study using a convenient sampling method to select Ghanaian adults at the Dietherapy Unit of the Korle-Bu Teaching Hospital, Accra. 3.2 STUDY SITE The study was conducted at the Dietherapy Unit, Korle-Bu Teaching Hospital (KBTH), Accra. Korle Bu Teaching Hospital was established on October 9, 1923, as a general hospital to address the health needs of the indigenous people of Accra. Today, it is the third largest hospital in Africa and the leading national referral centre in Ghana and has grown from an initial 200 bed capacity to 2,000, with 17 clinical and diagnostic Departments/Units and a daily average attendance of 1,500 patients (KBTH, 2014). The Dietherapy Department of KBTH provides essential services to individuals and groups as part of the hospital‟s multidisciplinary approach to health care delivery. Dietetic consultations start from 8 a.m. to 4 p.m., Mondays to Fridays. A patient can walk into the department for dietetic counselling on weight management, or requires a referral from a doctor for dietetic consultation on all other medical issues. Generally, expert consultations are provided by the department in several areas including nutritional screening, assessment and reassessment, nutritional education and counselling, community nutrition outreach programs and activities etc. Evidence-based, patient- centred dietetic counselling services are provided to individuals and groups visiting the department either on in-patient or out-patient basis. University of Ghana http://ugspace.ug.edu.gh 36 3.3 PARTICIPANTS Male and female Ghanaian adults who visited the Dietherapy Unit (KBTH) for dietary counselling were recruited as cases. The age range for inclusion was between 18 and 60 years. They were also newly diagnosed subjects, not on any drugs influencing lipid/glucose metabolism and on a normal/usual diet. Selected subjects had refrained from alcohol, and caffeine consumption for a month or more, prior to commencement of the study, and were non-smokers with BMI ≥ 30 kg/m2, according to WHO classification for overweight and obesity (WHO, 2010). Controls were mainly recruited from students of the School of Allied Health Sciences, staff of the Dietherapy Unit and hospital management staff of KBTH. 3.4 SAMPLE SIZE CALCULATION A sample size of 80 was determined based on an absolute precision of 5% and a confidence interval of 95% (Daniel, 1999). : n = [z] ² P[1-P] E² n = (1.96) 2 × 0.055 (1-0.055) (0.05)2 = 79.9, approximately 80 participants Where: n is estimated minimum sample size E is the allowable margin of error. University of Ghana http://ugspace.ug.edu.gh 37 z is the critical z score based on the desired level of significance. P is estimated prevalence of 5.5% for obesity (Biritwum et al., 2005) However, due to time constraints, finances and logistics, a fraction 2/3 (50-56) of the sample size was used for the research. 3.5 ETHICAL CONSIDERATION Permission was sought from the Ethics and Protocol Review Committee of the School of Allied Health Sciences (SAHS), College of Health Sciences, University of Ghana before commencement of this study. Authorization to conduct the study was also obtained from the management of Korle-Bu Teaching Hospital (Acting Chief Administrator, Director of Medical Affairs, Head‟ Public/Occupational Health Unit, and Head‟ Department of Dietetics) to use their facility as a study site, before data collection was done. Voluntary written informed consent (Appendix II) was also obtained after a detailed explanation of the study to the subjects before recruitment. Patient confidentiality was ensured at all times and participation in this study was absolutely voluntary. 3.6 DATA COLLECTION 3.6.1 Questionnaire Administration A structured questionnaire (Appendix I) to identify newly diagnosed obese (BMI ≥ 30kg/m2) subjects was administered. The first part of the questionnaire had a detailed consent form which was signed by both the participant and researcher. The second part comprised a checklist for baseline assessment, with four sub-divisions: demography, anthropometry, lifestyle information and diet history. University of Ghana http://ugspace.ug.edu.gh 38 The lifestyle information dealt with frequency of alcohol consumption, smoking and caffeine use. A diet history of subject‟s meal pattern, encompassing meal timings, types and quantities was also recorded. 3.6.2 Anthropometric Measurements Height was measured with a stadiometer (SECA 213, Humberg, Germany) to the nearest milimeter (mm). Weight was measured to the nearest kilogram (kg) at one decimal place with a standardised weighing scale (TANITA BC-533, Tokyo, Japan), blood pressure (mmHg), was taken with Omron MX2 Basic (HEM-742-E2, Kyoto, Japan) sphygmomanometer for three consecutive times while patient was seated and the mean calculated. Body mass index (weight/height2) in kg/m2 was then calculated. Mid upper arm circumference (MUAC) was measured, preferably on the right arm at the midpoint between the tips of the shoulder (acromion process) and elbow (olecranon process), in centimetres using S0145630 MUAC tape (Ramsey, 1991; Taylor et al., 1998; CDC, 1998; Bild et al., 2002; CDC, 2007; UNICEF, 2009). 3.6.3 Waist to Hip Ratio (WHR) Determination According to the WHO STEPS protocol for measuring waist circumference, the measurement should be made at the approximate midpoint between the lower margin of the last palpable rib and the top of the iliac crest. Taylor et al., (1998) and quite a few protocols from the United States also indicated that waist girth should be measured at the minimum circumference top of or between the iliac crest and the rib cage. However, the recommendation by the United States National Institute of Health (NIH) Multi‐Ethnic protocol for the Study of Atherosclerosis (MESA) which stated that, the measurement of waist circumference should be made at the level of the umbilicus, or navel was used. Waist and hip girths in (cm) were measured with an anthropometric tape over light University of Ghana http://ugspace.ug.edu.gh 39 clothing. The measurement was taken at the end of a normal expiration, when the lungs were at their functional residual capacity. The subjects were made to relax, and took a few deep, natural breaths before the actual measurement was made. This was to minimize the inward pull of the abdominal contents during the waist measurement. The hip circumference measurement was taken around the widest portion of the buttocks, at the maximum width over the greater trochanters with the use of a stretch‐resistant tape that was snugged around the body, but not pulled so tightly, preventing constriction. The tape provided a constant tension throughout its use, reducing differences in tightness and ensuring parallelism to the floor at the level at which the measurement was made. The subject stood with body erect, arms at the sides, feet positioned close together, and weight evenly distributed across the feet. The waist-to-hip ratio (WHR) was then calculated (CDC, 1998; Taylor et al., 1998; NHLBI, 2000; Bild et al., 2002; CDC, 2007; WHO, 2008). 3.7 MOLECULAR LABORATORY METHODS 3.7.1 DNA Extraction Participants were provided with 500 ml sachet water and asked to rinse mouth three times; discarding after each rinse to get rid of any food particles. They were then required to fill mouth with water upon the fourth rinse, rubbing the tongue against inner cheeks, palate, and inner lips against teeth, while swigging for about 30 seconds to one minute. Mouth content was spat into a 50ml conical tube, ensuring the 50 ml mark was reached. Samples were placed in a pre-packed ice container and transported to the Department of Clinical Pathology, Noguchi Memorial Institute for Medical Research (NMIMR). University of Ghana http://ugspace.ug.edu.gh 40 Samples were spun down at 2000 rpm for 20 minutes using table top centrifuge (Tomy LC-200, Digital Biology Co., Tokyo, Japan). The supernatants were carefully poured off and the pellets were then re-suspended in 600 l TNES digestion buffer (10 mM Tris- HCl (pH 7.5), 400 mM NaCl, 100 mM EDTA, 0.60% SDS) by pipetting up and down. The re-suspended pellets were separately transferred into a 1.7 ml Eppendorf tubes, the 35 l of proteinase K were added to each tube and incubated overnight in a water bath (Memmert WNB 10, Memmert GmbH+ Co. KG, Schwabach, Germany) at 55oC. Samples were retrieved, 200 l 5M NaCl was added to each tube and vortexed briefly. The contents were then spun down at 14000rpm for 30 minutes in a microcentrifuge (Hettich Mikro 200, Andreas Hettich GmbH & Co. KG Hettich, Tuttlingen, Germany). The supernatants were transferred into 2ml Eppendorf tubes, and 800ul 100% ethanol added to each tube and rocked gently back and forth. Deoxyribonucleic acid (DNA) precipitation was visible at this point. Samples were then stored in a -80oC freezer for two hours. They were retrieved, allowed to thaw, and spun down at 14000rpm for 30 minutes. The absolute ethanol was carefully poured off pellet, 500ul 70% ethanol added and spun down again at 14000rpm for five minutes. The 70% ethanol was also poured off after spinning; the tubes were blotted, and air-dried. The formed pellets were finally re-suspended in 500ul TE and stored at -20oC 3.7.2 PCR Amplification Primers (Table 3) and PCR methods described by Liao et al. (2006) for PPAR (Tables 4a and 4b), Widén et al. (1995) for ADRB3 (Tables 5a and 5b), Gotoda et al. (1997) for LEPR (Tables 6a and 6b) were used. The PCR assays and reaction conditions were slightly modified. University of Ghana http://ugspace.ug.edu.gh 41 Table 3: Sequence details of primers used for the genotyping of the selected candidate genes Gene and Primer Primer sequence (5’- 3’) SNP PPAR- PPARG2f GCCAATTCAAGCCCAGTC Pro12Ala PPARG2r GATATGTTTGCAGACAGTGTATCAGTGAAGGAATCGCTTTCCG Leptin LEPRf AAACTCAACGACACTCTCCTT Gln223Arg LEPRr TGAACTGACATTAGAGGTGAC ADRB3 BSTNUP CGCCCAATACCGCCAACAC Trp64Arg BSTNDOWN CCACCAGGAGTCCCATCACC University of Ghana http://ugspace.ug.edu.gh 42 Table 4a: PPAR- PCR reaction mixture Reagents Volume per sample (µl) Final concentration Sterile distilled water 9.075 10X PCR buffer 3.0 1X 10Mm DNTP mix 2 0.2mM each 10 M PPARG2f 0.4 0.2 M 10 M PPARG2r 0.4 0.2 M Taq polymerase (5U/ l) 0.125 0.025U DNA template 5 Total volume 20 Table 4b: PPAR- PCR PCR cycling condition Cycle Temperature Time No. of cycles Initial Denaturation 94ºC 5 minutes 1 Denaturation 94ºC 30 seconds 35 Annealing 51ºC 40 seconds Extension 72ºC 40 seconds Final extension 72ºC 7 minutes 1 PCR product size = 270bp University of Ghana http://ugspace.ug.edu.gh 43 Table 5a: ADRB3 PCR reaction mixture Reagents Volume per sample (µl) Final concentration Sterile distilled water 9.075 10X PCR buffer 3.0 1X 10Mm DNTP mix 2 0.2mM each 10 M BSTN UP 0.4 0.2 M 10 M BSTN DOWN 0.4 0.2 M Taq polymerase (5U/ l) 0.125 0.025U DNA template 5 Total volume 20 Table 5b: ADRB3 PCR cycling conditions Cycle Temperature Time No. of cycles Initial Denaturation 94ºC 5 minutes 1 Denaturation 94ºC 30 seconds 35 Annealing 61ºC 30 seconds Extension 72ºC 30 seconds Final extension 70ºC 7 minutes 1 PCR product size = 210bp University of Ghana http://ugspace.ug.edu.gh 44 Table 6a: Leptin PCR reaction mixture Reagents Volume per sample (µl) Final concentration Sterile distilled water 9.075 10X PCR buffer 3.0 1X 10Mm DNTP mix 2 0.2mM each 10 M LEPRf 0.4 0.2 M 10 M LEPRr 0.4 0.2 M Taq polymerase (5U/ l) 0.125 0.025U DNA template 5 Total volume 20 Table 6b: Leptin PCR cycling conditions Cycle Temperature Time No. of cycles Initial Denaturation 94ºC 5 minutes 1 Denaturation 94ºC 30 seconds 35 Annealing 50ºC 45 seconds Extension 72ºC 60 seconds Final extension 70ºC 7 minutes 1 PCR product size = 80bp University of Ghana http://ugspace.ug.edu.gh 45 3.7.3 Restriction Fragment Length Polymorphism (RFLP) Analysis After PCR amplification, the amplified products were subjected to restriction enzyme digestion. Appropriate restriction enzymes were added to the amplified PCR products and the digestions were carried out as recommended by the manufacturers (New England Biolabs, USA). The final reaction volume of 20 l contained 5-10 l of the amplified products. For ADRB3, the amplified fragments of 210 bp were digested with Mva I (BstNI). The fragment size was judged as the Arg64Arg type if 161 base pairs; the Trp64Trp type if 99 and 62 base pairs; the Trp64Arg type if 161, 99, and 62 base pairs. For PPAR , the amplified fragments of 270 bp were digested with BstUI. A single 270 bp fragment indicates the presence of CC (Pro12Pro) genotype, two fragments of 227 and 43 bp indicate the presence of GG (Ala12Ala) genotype and three fragments of 270, 227 and 43 bp indicate the presence of GC (Pro12Ala) genotype. For LEPR, the amplified fragments of 80 bp were digested with MspI. A single 80 bp fragment indicates the presence of Gln223Gln genotype, two fragments of 80 and 58 bp indicate the presence of Gln223Arg genotype and a single fragment of 58 bp indicate the presence of Arg223Arg genotype 3.7.4 Agarose Gel Electrophoresis Following the PCR, 10 µl of each PCR product was added to 2µl of 6 x bromophenol blue loading dye and electrophoresed in 2% agarose gel stained with 0.5 g/ml ethidium bromide. The gels were prepared and run in 1x TAE buffer at 100V for 30 -45 mins and University of Ghana http://ugspace.ug.edu.gh 46 were observed and photographed over a UV transilluminator at short wavelength using a Kodak EDAS 290 gel documentation system. The sizes of the PCR products were estimated by comparing with the mobility of a standard 100bp DNA ladder. Restriction digests were run in 3% agarose gels, following the same procedure. 3.8 STATISTICAL ANALYSIS Results are presented as mean ± standard deviation (SD), and categorical variables are expressed as numbers counted. The exact test was used to verify Hardy Weinberg equilibrium of genotype frequencies. Differences in demographic characteristics between cases and controls were tested by chi-square test for categorical data and Student‟s t-test for numerical data. Odds ratio (OR) and 95% confidence interval (CI) for the association between genotype and BMI, MUAC and WHR was computed. The level of statistical significance was set at P<.05. All analyses were performed using IBM SPSS 21. University of Ghana http://ugspace.ug.edu.gh 47 CHAPTER FOUR 4.0 RESULTS 4.1 SOCIO-DEMOGRAPHIC CHARACTERISTICS OF RESPONDENTS A total of 56 subjects, 24 cases and 32 controls, were recruited for the study. The average age for the cases was 41.63 ± 11.50 years and that of the controls was 26.42 ± 5.48 years (Table 7). There were more females (71.4%) compared to males (28.5%). There were also more married people among the cases (30.4%) compared to singles (51.8%) within the controls. All the respondents were non-smokers, with the majority (80.3%) not consuming alcohol. Table 7: Socio-demographic characteristics of respondents Variable Cases (%) Controls (%) Total N 24 (42.9) 32 (57.1) 56 Age (years) 41.63±11.50 26.42±5.48 Sex Male 4 (7.1) 12 (21.4) 16 Female 20 (35.7) 20 (35.7) 40 Marital status Yes 17 (30.4) 3 (5.4) 20 No 7 (12.5) 29 (51.8) 36 Alcohol consumption Yes 5 (8.9) 6 (10.7) 11 No 19 (33.9) 26 (46.4) 45 Smoking Yes 0 0 0 No 24 (42.9) 32 (57.1) 56 University of Ghana http://ugspace.ug.edu.gh 48 4.2 ANTHROPOMETRIC INDICES 4.2.1 BMI There was a statistically significant difference in BMI between cases and the controls (p˂ 0.0001) [Figure 6]. All the cases were in the WHO classification of overweight to obese class 3: eight were within the obese class III range (Table 8). The majority of the controls fell within the normal range: there were five overweight respondents. Mean ± SD Figure 6. Mean body mass index (BMI) of respondents. Table 8. Weight classification of respondents Weight classification* BMI (kg/m2) Subjects Cases (%) Control (%) s Underweight <18.5 0 2 (3.6) Normal 18.5 - 24.9 0 25 (44.6) Overweight 25 - 29.9 2 (3.6) 5 (8.9) Obese Class 1 30 - 34.9 7 (12.5) 0 Obese Class 2 35 - 39.9 7 (12.5) 0 Obese Class 3 ≥ 40 8 (14.3) 0 Total 24 32 *WHO 2010. B M I ( k g / m 2 ) 15 20 25 30 35 40 45 50 Cases Controls University of Ghana http://ugspace.ug.edu.gh 49 4.2.2 MUAC Mid-upper arm circumference (MUAC) measurements (Figure 7) between cases and controls were statistically significant (p ˂ 0.00001). All the cases had MUAC measurements and corresponding BMI values which indicated that they were likely to be obese (Table 9). Two controls had MUAC measures < 23.5cm and corresponding BMI < 20 kg/m2. Figure 7: Mean mid-upper arm circumference (MUAC) of respondents. Table 9. Respondent‟s MUAC measurements MUAC Subjects Cases (%) Controls (%) Yes* 23 (95.8) 0 (0.0) No 1 (4.2) 32 (100.0) Total 24 32 *MUAC Cut-off: Yes = MUAC is >32.0 cm and BMI is >30 kg/m2 i.e. subject is likely to be obese (The Malnutrition Action Group, British Association of Parenteral and Enteral Nutrition (BAPEN). 1 (2003); UNICEF/WHO (2009). M U A C ( c m ) 20 25 30 35 40 45 50 Cases Controls University of Ghana http://ugspace.ug.edu.gh 50 4.2.3 WHR Majority (75.0%) of the cases as compared to the controls (3.1%) were obese based on WHR classifications (Table 10). WHR measurements between cases and controls were also statistically significant (p ˂ 0.00001). Table 10. Waist-to-hip measurements of respondents Waist to hip ratio Subjects Total Cases (%) Control (%) s Yes* 18 (75.0) 1 (3.1) 19 No 6 (25.0) 31 (96.9) 37 Total 24 32 56 *Yes: Men (M) = more than 0.90, Women (F) = more 0.85, WHO (1999). 4.3 ENERGY INTAKE Though, the minimum daily energy for the cases (1956.80 kcal) and controls (1938.40 kcal) was about the same, the maximum intakes (7214.90 kcal for controls and 11207 kcal for cases), differed. However, there was no statistically significant difference (p = 0.3032) between the mean energy intakes of the controls and the cases (Figure 8). 4.4 CANDIDATE GENES 4.4.1 PPARγ gene polymorphism among respondents The expected 270 bp DNA fragment for the PPARγ gene (Fig. 9) was amplified in 55 out of the 56 respondents. None of the 55 amplicons was digested with BstUI which indicated the presence of CC (Pro12Pro) genotype University of Ghana http://ugspace.ug.edu.gh 51 Figure 8: Mean energy intakes of the respondents Figure 9. A representative agarose gel electrophoregram of PCR products (band size 270 bp) of PPAR-γ gene. Lane M = 100bp marker; Lanes 1-9 = PCR positives. T o t a l c a l o r i e s ( D a i l y ) 0 2000 4000 6000 8000 10000 12000 Cases Controls M 1 2 3 4 5 6 7 8 9 500 bp 200 bp University of Ghana http://ugspace.ug.edu.gh 52 4.4.1.1 Relationship between PPARγ gene polymorphism and BMI No relationship was observed between PPARγ gene polymorphism and BMI of the respondents. Only the Pro12Pro genotype, was observed among both the cases and controls. 4.4.1.2 Relationship between PPARγ gene polymorphism and MUAC No relationship was observed between PPARγ gene polymorphism and MUAC of the respondents. Only the Pro12Pro genotype, was observed among both the cases and controls. 4.4.1.3 Relationship between PPARγ gene polymorphism and WHR No relationship was observed between PPARγ gene polymorphism and WHR of the respondents. Only the Pro12Pro genotype, was observed among both the cases and controls. 4.4.2 Leptin receptor gene (LEPR) polymorphism among respondents Amplification was successful in 29 respondents (15 cases and 14 controls). For each of these, the expected 80 bp fragment of the LEPR gene was seen (Fig. 10). However, only the amplicons of six of the cases out of the 29 respondents were digested with MspI (Table 11) which indicated the presence of the Gln223Arg genotype. The genotype frequencies for LEPR Gln223Arg SNP were distributed in compliance with Hardy Weinberg equilibrium (p>0.05). University of Ghana http://ugspace.ug.edu.gh 53 Figure 10. A representative agarose gel electrophoregram of PCR products (band size 80 bp) of LEPR gene. Lane M = 100bp marker; Lanes 1 and 2 = PCR positives; Lanes 3- 7 = PCR negatives. Table 11: LEPR polymorphism among respondents LEPR RFLP Subjects Cases Controls Yes (Gln223Arg) 6 0 No (Gln223Gln) 9 14 Total 15 14 4.4.2.1 Relationship between LEPR polymorphism and BMI The six cases with LEPR SNPs were in the OBClass 1 to OBClass 3 range (Table 12). A significant difference emerged for the LEPR gene Gln223Arg genotype polymorphism frequencies between the cases and the controls (p = 0.0169, OR = 19.842, 95%CI = 0.9966 to 395.07). 500bp 100bp M 1 2 3 4 5 6 7 University of Ghana http://ugspace.ug.edu.gh 54 Table 12: Relationship between LEPR polymorphism and BMI of respondents LEPR RFLP Subjects Total Cases Controls Yes Body Mass Index (kg/m2) OBClass1 2 0 2 OBClass2 1 0 1 OBClass3 3 0 3 No Body Mass Index (kg/m2) Underweight 0 2 2 Normal 0 10 10 Overweight 1 2 3 OBClass1 3 0 3 OBClass2 3 0 3 OBClass3 2 0 2 Total 15 14 29 4.4.2.2 Relationship between LEPR polymorphism and MUAC Six cases had LEPR SNPs and all six had MUAC measurements >32 cm (Table 13). A significant difference emerged for the LEPR gene Gln223Arg genotype polymorphism frequencies between the cases and the controls (p = 0.0169, OR = 19.842, 95%CI = 0.9966 to 395.07). 4.4.2.3 Relationship between LEPR polymorphism and WHR Six cases had LEPR SNPs and all six had WHR measurements >0.85 cm (Table 14). A significant difference emerged for the LEPR gene Gln223Arg genotype polymorphism frequencies between the cases and the controls (p = 0.0169, OR = 19.842, 95%CI = 0.9966 to 395.07). University of Ghana http://ugspace.ug.edu.gh 55 Table 13: Relationship between LEPR polymorphism and MUAC of respondents LEPR RFLP Subjects Total Cases Controls Yes Mid-upper arm circumference (cm) Yes* 6 0 6 No 0 0 0 No Mid-upper arm circumference (cm) Yes* 9 0 9 No 0 14 14 Total 15 14 29 *MUAC Cut-offs: Yes = MUAC is >32.0 cm and BMI is >30 kg/m2 Table 14: Relationship between LEPR polymorphism and WHR of respondents LEPR RFLP Subjects Total Cases Controls Yes WHR Yes* 6 0 10 No 0 0 5 No WHR Yes* 9 0 6 No 0 14 8 Total 15 14 29 *Yes: Men (M) = more than 0.90, Women (F) = more 0.85, WHO (1999). University of Ghana http://ugspace.ug.edu.gh 56 4.4.3 ADRB3 genotype polymorphism among respondents Amplification was successful in 43 respondents (19 cases and 24 controls). In each of these, the expected 210 bp fragment of the ADRB3 gene was seen (Fig. 11). Restriction enzyme digestion of the amplicons with MvaI indicated the presence of the Trp64Trp and the Trp64Arg genotypes (Table 15). The genotype frequencies were distributed in compliance with Hardy Weinberg equilibrium (p>0.05). 4.4.3.1 Relationship between ADRB3 genotype polymorphism and BMI Details of the ADRB3 genotype polymorphism and BMI is shown in table 16. No significant difference emerged for the ADRB3 gene Try64Arg genotype polymorphism frequencies between the cases and the controls (p = 0.2088, OR = 0.3846, 95% CI = 0.1068 to 1.385). 4.4.3.2 Relationship between ADRB3 genotype polymorphism and MUAC Details of the ADRB3 genotype polymorphism and MUAC is shown in table 17. No significant difference emerged for the ADRB3 gene Try64Arg genotype polymorphism frequencies between the cases and the controls (p = 0.2088, OR = 0.3846, 95% CI = 0.1068 to 1.385). 4.4.3.3 Relationship between ADRB3 genotype polymorphism and WHR Details of the ADRB3 genotype polymorphism and WHR is shown in table 16. No significant difference emerged for the ADRB3 gene Try64Arg genotype polymorphism frequencies between the cases and the controls (p = 0.2088, OR = 0.3846, 95% CI = 0.1068 to 1.385). University of Ghana http://ugspace.ug.edu.gh 57 Figure 11. A representative agarose gel electrophoregram of PCR products (band size 210 bp) of the ADRB3 gene. Lane M = 100bp marker; Lanes 1-7, 10-13 = PCR positives; Lanes 8, 9 and 14 = PCR negatives Table 15: ADRB3 polymorphism among respondents ADRB3 RFLP Subjects Total Cases Controls Yes Trp64Arg 6 12 18 No Trp64Trp 13 10 23 Total 19 22 41 Table 16: Relationship between ADRB3 polymorphism and BMI of respondents ADRB3 RFLP Subjects Total Cases Controls Yes Body Mass Index (kg/m2) Underweight 0 1 1 Normal 0 9 9 Overweight 0 2 2 OBClass1 2 0 2 OBClass2 3 0 3 OBClass3 1 0 1 No Body Mass Index (kg/m2) Underweight 0 1 1 Normal 0 7 7 Overweight 1 2 3 OBClass1 6 0 6 OBClass2 1 0 1 OBClass3 5 0 5 Total 19 22 41 M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 500 bp 200 bp University of Ghana http://ugspace.ug.edu.gh 58 Table 17. Relationship between ADRB3 polymorphism and MUAC of respondents ADRB3 RFLP Subjects Total Cases Controls Yes Mid-upper arm circumference (cm) Yes* 6 0 6 No 0 12 0 No Mid-upper arm circumference (cm) Yes* 13 0 13 No 0 10 14 Total 19 22 29 *MUAC Cut-offs: Yes = MUAC is >32.0 cm and BMI is >30 kg/m2 Table 18: Relationship between ADRB3 polymorphism and WHR of respondents ADRB3 RFLP Subjects Total Cases Controls Yes WHR Yes* 6 0 6 No 0 12 12 No WHR Yes* 13 0 13 No 0 10 8 Total 19 22 41 *Yes: Men (M) = more than 0.90, Women (F) = more 0.85, WHO (1999). University of Ghana http://ugspace.ug.edu.gh 59 CHAPTER FIVE 5.0 DISCUSSION AND CONCLUSION 5.1 DISCUSSION This study looked at selected candidate genes (PPARγ, ADRB3 and LEPR) of obesity, in 24 newly diagnosed obese patients at the Dietherapy Unit (KBTH) and 32 controls. All the cases studied had BMIs equal to or greater than 30. However, only five out of the controls were overweight but not obese. In comparison with the WHO obesity classification, 25 controls were within the normal limits, while two were underweight. There was a statistically significant difference (p˂ 0.0001) between the BMIs of the cases studied and the controls. This implies a positive association between higher BMIs and obesity. According to Hiza et al., (2000), BMI is one of the most frequently used methods of evaluating body fat percentage. It is also used in an individual‟s nutritional and health status appraisal. Thus far, BMI is the best available anthropometric estimate of body fatness for public health purposes (Hall & Cole, 2008). Higher BMIs generally mean higher body fat and as BMI increases, particularly from values equal to or greater than 30, health risks increase (Hiza et al., 2000). BMI measures excess body weight for a particular height and has been shown to correlate with body fat although it is not a direct measure of body fat (Nihiser, 2007). BMI cut points recommended in 1998 by the WHO were the first such cut-offs accepted at the international level (Weisell, 2002). As argued by Ashwell (2011) and Kesavachandran (2012) who provided results from a cross-sectional and prospective epidemiological survey, it was evident that the cut-offs of BMI provided by the World Health Organisation (WHO, 1995), do not effectively reflect the overweight or obesity University of Ghana http://ugspace.ug.edu.gh 60 status of all populations. A case in point is a higher body fat percentage correlating with lower BMIs among Asians while among Pacific Islanders, higher BMIs tend to be associated with more muscle mass and less body fat (Weisell, 2002). Excluding ethnicity, the universal applicability of BMI has also been raised concerning age, physical activity and most importantly, as a less reliable predictor of cardiovascular risk (Muralidhara, 2008). Debates regarding the usefulness of BMI in predicting body fat may significantly affect several specialised areas such as risks of coronary heart diseases, diabetes, weight loss programs, population health surveys, epidemiological studies of diet and health, sports and fitness training among others. Despite its drawbacks, BMI remains one of the most widely used tools to screen obesity risk in several target populations as it is simple, inexpensive and non-invasive (Bhurosy & Jeewon, 2013). Twenty-two (41%) of the cases had MUAC measures greater than 32cm and BMI also greater than 30kgm-2. This was in congruence with the MUAC and BMI cut-off set by The Malnutrition Action Group, from the British Association of Parenteral and Enteral Nutrition, for assessing malnutrition in individuals (The Malnutrition Action Group, 2003). The use of MUAC is beneficial over BMI and skin-fold measures because it is non- invasive compared to other anthropometric measures. It also requires inexpensive equipment, which does not need to be calibrated or maintained. Moreover, in low-income settings, MUAC proves to be very useful in the absence of inappropriate weighing scales and stadiometer (Haboubi et al., 2010). As proposed by Mazicioglu et al. (2010) MUAC closely reflects body fat tissue, it can therefore be used along with BMI to assess obesity University of Ghana http://ugspace.ug.edu.gh 61 risk. Despite being a relatively easy method, small measurement errors such as incorrect choice of arm, inappropriate positioning of the tape measure at the midpoint between the acromion and the olecranon processes, incorrect horizontal positioning and insufficient compressive force can lead to invalid interpretations (Almeida et al., 2003). Majority of the cases (75%) had WHRs that were higher than the normal ranges for both men and women. This implies that, they were obese when WHR was used as the index for assessing obesity. According to Dekkers et al. (2008), waist circumference (WC) and WHR are measures of abdominal or central obesity. Assessment of central obesity is crucial since it is associated with high risks of cardiovascular and metabolic diseases (Mushtaq et al., 2011). No SNPs of the Pro12Ala allele for the PPARγ gene was detected among all the participants involved in this research. According to Deeb et al. (1998), the Pro12Ala polymorphism of the PPARγ2 isoform has an association with higher BMI. In agreement with this, association studies performed by Beamer et al. (1998) in two independent Caucasian populations also linked the Pro12Ala PPARγ2 isoform with higher BMI which is suggestive of a susceptibility to obesity in humans. However, all 24 cases in this study did not show this SNP even though they were obese. This may confirm the fact that variations do exist with certain candidate genes of obesity with respect to race, geographical location/country of origin (Bouchard et al., 1988; Malczewska-Malec et al. 2004; Podolsky et al., 2007). Perhaps, a larger sample size would help to establish this fact among Ghanaian adults. Six cases had the Gln223Arg polymorphism for LEPR. Although these cases were obese a strong association of this polymorphism with obesity could not be established as also University of Ghana http://ugspace.ug.edu.gh 62 indicated by Gotoda et al. (1997). Leptin receptor gene, located on chromosome 1p31 (Thompson et al., 1998) and leptin gene have several systemic effects attributed to them such as, body mass control, lipid and glucose metabolism, thermogenesis, angiogenesis, immunity, reproductive, endocrine and cardiovascular functions, among other (Korner et al., 2009). Humans and mice with inherited loss of function mutations of LEP/LEPR display severe early onset obesity (Pelleymounter et al., 1995; White & Tartaglia, 1996; Montague et al., 1997; Clément et al., 1998; Farooqi et al., 2007). Studies by Farooqi et al. (1999), Farooqi and O‟Rahilly (2005), and Licinio et al. (2004) have shown that leptin deficiency is usually common among people with monogenic obesity syndromes and treatment with recombinant leptin yields positive outcome. Adipose tissue forms an integral part of the regulation, storage and mobilization of energy, and has been the focus of the efforts to identify candidate genes for obesity. One such gene is the B3-adrenergic receptor (ADRB3) (Emorine et al., 1989; Nahmias et al., 1991) which is the main receptor involved in the regulation of thermogenesis and lipolysis in brown and white adipose tissue in rodents (Arch & Kaumann, 1993). In humans, ADRB3 receptor (Emorine et al., 1989) is expressed predominantly in fat and adipocytes lining the gastrointestinal tract (Krief et al., 1993). The receptor‟s primary role is thought to be the regulation of the resting metabolic rate and lipolysis (Emorine et al., 1994). SNPs were identified for the Trp64Arg allele for the ADRB3 gene. Six cases and 12 controls had the Trp64Arg polymorphism. No significant difference (all ps >0.05) emerged for the ADRB3 gene Try64Arg genotype polymorphism frequencies between the cases and the controls for BMI, MUAC and WHR and thus an association of this polymorphism with obesity could not be established. University of Ghana http://ugspace.ug.edu.gh 63 The suggested role of the B3-adrenergic receptor in fat metabolism, its functional deficiency in genetically obese mice (Collins et al., 1994; Arbeeny et al., 1994) and the results of studies in which the gene for the receptor has been disrupted in mice (Susulic et al., 1994) has impelled numerous investigations into the function of this gene and its mutated variants such as (Trp64Arg) in patients with morbid obesity. The B3-adrenergic receptor is expressed in visceral fat in humans (Krief et al., 1993) and is considered responsible for increases in lipolysis and the delivery of free fatty acid into the portal vein (Lonnqvist et al., 1995). An increase in visceral fat mass, in turn, correlates with resistance to insulin in skeletal muscle (Colberg et al., 1995). The Trp64Arg allele of the B-3 Adrenergic receptor is associated with abdominal obesity and resistance to insulin and may contribute to the early onset of non-insulin dependent diabetes mellitus (Widén et al., 1995). Energy intakes for all the subjects were above the WHO/FAO (2002) recommendations. This alone may not be responsible for the weight gain in especially the cases. Long-term increases in energy intake without a corresponding increase in energy expenditure results in weight gain due to accumulation of excess body fat (Bray, 1987). It is possible that all the 56 subjects consumed significantly more carbohydrates than normal. There was no statistically significant difference in energy intake of both cases and control, though they all consumed a minimum of 2000 kcals, the cases had a higher upper limit above 8000 kcal, compared to 7000 kcal for the controls. Some studies also found no relationship between total dietary energy, dietary fat and overweight/obesity (Prentice & Jebb, 1995; Willet, 1998; Goran et al., 1998; Treuth et al., 1998). In contrast, other studies reported an association between dietary energy, dietary fat and weight gain (Bray & Popkin, 1998; Lanza et al., 2001; Mokhtar et al., 2001; Bes-Rastrollo et al., 2008). The conflicting University of Ghana http://ugspace.ug.edu.gh 64 reports could be linked to differences in research design and bias associated with self- reported food intake. High carbohydrate intake seems to affect the high prevalence of overweight/obesity among the subjects in this study. In a prospective longitudinal study, Bes-Rastrollo et al. (2008) found that dietary energy density was positively correlated with dietary carbohydrate. The authors also found a significantly greater weight gain among women who increased their dietary energy density during an 8-year time period than those who decreased their dietary energy density. Food choices and preferences which also affect energy intakes in the long term are also influenced by an individual‟s sociocultural background and environmental factors. Preference for the traditional diets which are high in carbohydrates could explain the higher intake of carbohydrates, hence increased energy intake. The “westernized” dietary pattern which has become a common practice in urban Ghanaian cities could also account for the high energy intakes. A higher carbohydrate intake and more sedentary behaviour could explain the high rate of overweight/obesity among the cases. The dietary pattern of the subjects in this study could reflect the impact of urbanisation, westernisation, increasing affluence and food market globalisation as has been observed in urban societies of some developing countries (Bourne et al., 1993; Monteiro et al., 2000; Popkin, 2001; Popkin & Gordon-Larsen, 2004; Prentice, 2006). Increasing affluence is usually followed by increased food accessibility and intake of larger food portions. Urbanisation, westernisation and food market globalisation has also led to the prevalence of fast food chains and energy dense exotic soft drinks on the Ghanaian market, especially in urban communities like Accra, Takoradi and Kumasi. The nutritional outcome is a shift from intake of traditional diets high in complex carbohydrates, pulses and low in fats toward an increased consumption of foods high in University of Ghana http://ugspace.ug.edu.gh 65 fats, simple sugars and total calories. This situation is similar to that reported elsewhere by Bourne et al., (1993) that urbanization led to imprudent dietary behaviour in South African populations. Thus, unhealthy dietary behaviour could impact on the high prevalence of overweight and obesity found among the cases in this study. In the present study there were some limitations. The small sample size might limit the applications of the potential associations detected. The use of self-reported dietary intakes might have been subjected to under-reporting or over-reporting bias in subject‟s actual intakes. There were also no locally accepted handy measure estimates for the local soups, stews, and other uniquely Ghanaian dishes consumed. Estimates elsewhere were employed. This might have introduced bias in the caloric estimations, hence over or under –estimating energy intakes. A conveniently randomized selection of newly diagnosed obese subjects within the stipulated age range was carried out, although a direct age matching for cases and controls, was not considered. This might have potentially influenced the weight distribution data for the various ages within the cases and controls. 5.2 CONCLUSION There were statistically significant differences in BMI, WHR and MUAC measurements between cases and controls (all ps˂ 0.0001). SNPS in the receptors of the ADRB3 and LEPR, but not PPARγ genes, were detected in the subjects for the study. No relationships were observed between ADRB3 (Try64Arg) and PPARγ (Pro12pro) genotype polymorphisms and BMI, MUAC and WHR of the subjects. However, significant differences emerged for the LEPR gene Gln223Arg genotype polymorphism frequencies between the cases and the controls (p = 0.0169, OR = 19.842, 95%CI = 0.9966 to 395.07) University of Ghana http://ugspace.ug.edu.gh 66 and BMI, MUAC and WHR. Energy intakes for all the subjects were above the WHO/FAO recommendations. However, there was no statistically significant difference (p = 0.3032) between the mean energy intakes of the controls and the cases. It is recommended that: 1. A larger sample size should be considered during further studies in this area in order to establish any potential associations between the selected candidate genes and obesity in the Ghanaian population. 2. Direct age matching for cases and controls, in addition to the other inclusion criteria used, should be considered for improved correlations during further studies. 3. Studies to establish accepted protocols for estimating dietary intakes other than self-reporting, and handy measures for local soups, stews, and other uniquely Ghanaian dishes consumed should be considered by the department of dietetics. This will help improve future localized researches requiring these information to avoid over or under –estimating subject‟s energy intakes. University of Ghana http://ugspace.ug.edu.gh 67 REFERENCES Abubakari, A.R., Lauder, W., Agyemang, C., Jones, M., Kirk, A. & Bhopal, R.S. (2008) Prevalence and time trends in obesity among adult West African populations: a meta-analysis. Obesity Reviews 9: 297-311. Almeida, C.A.N., Ciampo, L.A.D., Ricco, R.G., Silva, S.M., Naves, R.B. & Pina, J.F. (2003). Assessment of mid-upper arm circumference as a method for obesity screening in pre-school children. Journal of Pediatrics 79(5): 455-460. Amoah, A.G.B. (2003). Socio-demographic variations in obesity among Ghanaian adults. Public Health Nutrition 6(8): 751-757. Appiah, C.A. (2011). Dietary intake, physical activity and body size preference: implication on the prevalence of overweight and obesity among women in Kumasi, Ghana. M. Phil Thesis. Department of Dietetics, School of Allied Health Sciences, University of Ghana. Arbeeny, C.M., Meyers, D.S, Hillyer, D.E. & Bergquist, K.E. (1994). Brown adipose tissue recruitment and improvement in glucose sensitivity in ob/ob mice following chronic stimulation of the B-adrenergic receptor. International Journal of Obesity 18 S2:4. Abstract. Arch, J.R.S. & Kaumann, A.J. (1993) B3 and atypical B-adrenoreceptors. Medicinal Research Reviews 13: 663-729. Arora, S. & Anubhuti. (2006). Role of neuropeptides in appetite regulation and obesity- a review. Neuropeptides 40(6): 375-401. Ashwell M. (2011). Charts based on body mass index and waist-to-height ratio to assess the health risks of obesity: A review. The Open Obesity Journal 3: 78 Astrup, A., Ryan, L., Grunwald, G.K., Storgaard, M., Saris, W., Melanson, E. & Hill, J.O. (2000). The role of dietary fat in body fatness: evidence from a preliminary meta-analysis of ad libitum low-fat dietary intervention studies. British Journal of Nutrition 83: S25–S32. Auwerx, J., Martin, G., Guerre-Millo, G. & Staels, B. (1996) Transcription, adipocyte differentiation, and obesity. Journal of Molecular Medicine 74: 347–352. University of Ghana http://ugspace.ug.edu.gh 68 Banks, W.A., Niehoff, M.L., Martin, D. & Farrell, C.L. (2002). Leptin transport across the blood-brain barrier of the Koletsky rat is not mediated by a product of the leptin receptor gene. Brain Researches 950: 130-136. Barosso, I., Gurnell, M, Crowley, V.E.F., Agostini, M., Schwabe, J.W., Soos, M.A. Maslen, G.L., Williams, T.D., Lewis, H., Schafer, A.J., Chatterjee, V.K., & O'Rahilly, S. (1999) Dominant negative mutations in human PPARG associated with severe insulin resistance, diabetes mellitus and hypertension. Nature 402: 880-883. Beamer, B.A., Negri, C., Yen, C.J., Gavrilova, O., Rumberger, J.M., Durcan, M.J., Yarnall, D.P., Hawkins, A.L., Griffin, C.A., Burns, D.K., Roth, J., Reitman, M. & Shuldiner, A.R. (1997) Chromosomal localization and partial genomic structure of the human peroxisome proliferator activated receptor-gamma (hPPAR gamma) gene. Biochemical and Biophysical Research 233: 756–759. Beamer, B.A., Yen C.J., Andersen R.E., Muller D., Elahi D., Cheskin L.J, Andres R, Roth J & Shuldiner A.R (1998) Association of the Pro12Ala variant in the peroxisome proliferator-activated receptor- 2 gene with obesity in two caucasian populations. Diabetes 47:1806-1808. Bell, C.G., Walley, A.J. & Froguel, P. (2005). The genetics of human obesity. Nature Review Genetics 6: 221-234. Bertrais, S., Beyeme-Ondoua, J.P., Czernichow, S., Galan, P., Hercberg, S. & Oppert, J.M. (2005). Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obesity Research 13: 936–944. Bes-Rastrollo, M., Van Dam, R.M., Martinez-Gonzalez, M.A., Li, T.Y., Sampson, L.L. & Hu, F.B., (2008). Prospective study of dietary energy density and weight gain in women. American Journal of Clinical Nutrition 88: 769-777. Bhurosy, T. & Jeewon, R. (2013) Pitfalls of using body mass index (BMI) in assessment of obesity risk. Current Research in Nutrition and Food Science Journal 1(1):71-76. University of Ghana http://ugspace.ug.edu.gh 69 Bild, D.E., Bluemke, D.A., Burke, G.L., Detrano, R., Diez Roux, A.V., Folsom, A.R., Greenland, P., Jacob, D.R. Jr., Kronmal, R., Liu, K., Nelson, J.C., O'Leary, D., Saad, M.F., Shea, S., Szklo, M. & Tracy, R.P. (2002). Multi-ethnic study of atherosclerosis: objectives and design. American Journal of Epidemiology 156(9): 871-81. Biritwum, R.B., Gyapong, J. & Mensah, G. (2005). The epidemiology of obesity in Ghana. Ghana Medical Journal 39(3) 82-85 Bjorbaek, C., Uotani, S., da Silva, B. & Flier, J.S. (1997) Divergent signalling capacities of the long and short isoforms of the leptin receptor. Journal of Biological Chemistry 272: 32686-32695. Bouchard, C., Pérusse, L., Leblanc, C., Tremblay, A. & Theriault, G. (1988). Inheritance of the amount and distribution of human body fat. International Journal of Obesity 12: 205–215. Bourne, L.T., Langenhoven, M.L., Steyn, K., Jooste, P.L., Laubscher, J.A., Van der Vyver, E. (1993). Nutrient intake in the urban African population of the Cape Peninsula, South Africa. The Brisk Study. Central African Journal of Medicine 39(12): 238-247. Bray, G.A. & Popkin, B.M. (1998) Dietary fat intake does affect obesity! American Journal of Clinical Nutrition 68: 1157–1173. Bray, G.A. (1987) Obesity: a disease of nutrient or energy balance? Nutrition Reviews 45: 33–43. Briefel, R.R., McDowell, M.A., Alaimo, K., Caughman, C.R., Bischof, A.L., Carroll, M.D. & Johnson, C.L. (1995). Total energy intake of the US population: the third National Health and Nutrition Examination Survey, 1988–1991. American Journal of Clinical Nutrition 62: 1072S–1080S. Brun, R.P., Tontonoz, P., Forman, B.M., Ellis, R., Chen, J., Evans, R.M &. Spiegelman, B.M. (1996). Differential activation of adipogenesis by multiple PPAR isoforms. Genes & Development 10: 974–984. Canadian Public Health Association, CPHA (2006). The continuing challenge of obesity. Canadian Journal of Public Health 97: 428. University of Ghana http://ugspace.ug.edu.gh 70 CDC (1998) National Health and Nutrition Examination Survey (NHANES) III. Centers for Disease Control and Prevention. National Center for Health Statistics Hyattsville, MD: US Accessed, 6th February, 2013 from http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/NCHS/MANUALS/ANT HRO.PDF. CDC (2007) Anthropometry Procedures Manual. Centers for Disease Control and Prevention. National Center for Health Statistics. National Health and Nutrition Examination Survey. Hyattsville, MD: U.S. Accessed 9th February, 2013 from http://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf. CDC (2010) Overweight and obesity Centers for Disease Control and Prevention. National Center for Health Statistics (NCHS). U.S. Department of Health and Human Services Accessed 30th April, 2014 from http://www.cdc.gov/vitalsigns, www http://www.cdc.gov/obesity. . CDC (2012) Healthy weight, overweight, and obesity among adults aged 20 and over, by selected characteristics: selected years 1960–1962 through 2009–2012. Centers for Disease Control and Prevention. National Center for Health Statistics (NCHS). Hyattsville, MD: United States. Accessed 30th April, 2014 from http://www.cdc.gov/nchs/hus/contents2012.htm#068. Chen, H., Charlat, O., Tartaglia, L.A., Woolf, E.A., Weng, X., Ellis, S.J., Lakey, N.D., Culpepper, J., Moore, K.J., Breitbart, R.E., Duyk, G.M., Tepper, R.I. & Morgenstern, J.P. (1996) Evidence that the diabetes gene encodes the leptin receptor: identification of a mutation in the leptin receptor gene in db/db mice. Cell 84: 491–495. Clément, K., Vaisse, C., Lahlou, N. Cabrol S, Pelloux V, Cassuto D, Gourmelen M, Dina C, Chambaz J, Lacorte JM, Basdevant A, Bougnères P, Lebouc Y, Froguel P, & Guy-Grand B. (1998). A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392: 398–401. Colberg, S.R., Simoneau, JA., Thaete, F.L. & Kelley, D.E. (1995) Skeletal muscle utilization of free fatty acids in women with visceral obesity. Journal of Clinical Investigation 95: 1846-1853. Coll, A.P., Farooqi, I.S. & O‟Rahilly, S. (2007). The Hormonal control of food intake. Cell 129:251-262. University of Ghana http://ugspace.ug.edu.gh 71 Coll, A.P., Yeo, G.S., Farooqi, I.S. & O‟Rahilly, S. (2008) SnapShot: the hormonal control of food intake. Cell 135: 572.e1-572.e2. Collins, S., Daniel, K.W., Rohlfs, E.M., Pamkumar, V., Taylor, I.L. & Gettys, T.W. (1994) Impaired expression and functional activity of the B3- and B1- adrenergic receptors in adipose tissue of congenitally obese (C57BL/6J ob/ob) mice. Molecular Endocrinology 8: 518-27. Considine, R.V., Considine, E.L., Williams, C.J., Nyce, M.R., Zhang, P., Opentanova, I., Ohannesian, J.P., Kolaczynski, J.W., Bauer, T.L., Moore, J.H. & Caro, J.F. (1996b) Mutation screening and identification of a sequence variation in the human ob gene coding region. Biochemical and Biophysical Research Communications 220: 735–739. Considine, R.V., Sinha, M.K., Heiman, M.L., Kriauciunas, A., Stephens, T.W., Nyce, M.R., Ohannesian, J.P., Marco, C.C., McKee, L.J., Bauer, T.L. & Caro, J.F. (1996) Serum immunoreactive-leptin concentrations in normal-weight and obese humans. New England Journal of Medicine 334: 292–295. Corpeleijn, E., Petersen, L., Holst, C., Saris, W.H., Astrup, A., Langin, D., MacDonald, I., Martinez, J.A., Oppert, J.M., Polak, J., Pedersen, O., Froguel, P., Arner, P., Sørensen, T.I., & Blaak, E.E. (2010) Obesity-related polymorphisms and their associations with the ability to regulate fat oxidation in obese Europeans: the NUGENOB study. Obesity (Silver Spring) 18 (7): 1369–1377. Couturier, C., Sarkis, C., Seron, K. Belouzard S, Chen P, Lenain A, Corset L, Dam J, Vauthier V, Dubart A, Mallet J, Froguel P, Rouillé Y & Jockers R (2007) Silencing of OB-RGRP in mouse hypothalamic arcuate nucleus increases leptin receptor signaling and prevents diet-induced obesity. Proceedings of the National Academy of Sciences (PNAS) USA 104: 19476-19481. Daniel, W.W. (1999) Biostatistic: A Foundation for Analysis in the Health Sciences. 7th edition. New York: John Wiley & Sons. De Vos, P., Lefebvre, A.M., Miller, S.G., Guerre-Millo, M., Wong, K., Saladin, R., Hamann, L.G., Staels, B., Briggs, M.R., & Auwerx, J. (1996). Thiazolidinediones repress ob gene expression in rodents via activation of peroxisome proliferator activated receptor gamma. Journal of Clinical Investigation 98(4): 1004-1009. University of Ghana http://ugspace.ug.edu.gh 72 Deeb, S.S., Fajas, L., Nemoto, M., Pihlajamaki, J., Mykkanen, L., Kuusisto, J., Laakso, M., Fujimoto, W. & Auwerx, J. (1998) A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nature Genetics 20: 284–287. Dekkers, J.C., Wier M.F.V., Hendriksen I.J.M., Twisk J.W.R. & Mechelen W.V. (2008). Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Medical Research Methodology 8: 69 Dina, C., Meyre, D., Gallina, S., Durand, E., Körner, A., Jacobson, P., Carlsson, L.M., Kiess, W., Vatin, V., Lecoeur, C., Delplanque, J., Vaillant, E., Pattou, F., Ruiz, J., Weill, J., Levy-Marchal, C., Horber, F., Potoczna, N., Hercberg, S., Le Stunff, C., Bougnères, P., Kovacs, P., Marre, M., Balkau, B., Cauchi, S., Chèvre, J.C., & Froguel, P. (2007). Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genetics 39: 724–6. Duda, R.B., Dark, R., Seffah, J., Adanu, R.M.K., Anarfi, J.K. & Hill, A.G. (2007) Prevalence of obesity in women of Accra, Ghana. African Journal of Health Sciences 14: 154-159. Dunstan, D.W., Salmon, J., Healy, G.N., Shaw, J.E., Jolley, D., Zimmet, P.Z. & Owen, N. (2007) Association of television viewing with fasting and 2-h post challenge plasma glucose levels in adults without diagnosed diabetes. Diabetes Care 30: 516–522. Dunstan, D.W., Salmon, J., Owen, N., Armstrong, T., Zimmet, P.Z., Welborn, T.A., Cameron, A.J., Dwyer, T., Jolley, D. & Shaw, J.E. (2005) Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia 48: 2254–2261. Ek, J., Urhammer, S.A., Sorensen, T.I., Andersen, T., Auwerx, J. & Pedersen, O. (1999) Homozygosity of the Pro12Ala variant of the peroxisome proliferation-activated receptor-gamma2 (PPAR-gamma2): divergent modulating effects on body mass index in obese and lean Caucasian men. Diabetologia 42: 892–895. University of Ghana http://ugspace.ug.edu.gh 73 Elia, M., & Russell, C.A., (Eds.) 2003. The Malnutrition Action Group, British Association of Parenteral and Enteral Nutrition (BAPEN) and collaborators. The „MUST‟ Report. Nutritional screening for adults: a multidisciplinary responsibility. MAG Standing Committee. London, UK. Elmquist, J.K., Bjorbaek, C., Ahima, R.S., Flier, J.S. & Saper, C.B. (1998) Distributions of leptin receptor mRNA isoforms in the rat brain. Journal of Comparative Neurology 395: 535-547. Emorine, L., Blin, N. & Strosberg, A.D. (1994) The human B3-adrenoreceptor: the search for a physiological function. Trends in Pharmacological Sciences 15: 3- 7. Emorine, L.J., Marullo, S., Briend-Sutren, M.M., Patey, G., Tate, K., Delavier-Klutchko, C., & Strosberg, A.D. (1989) Molecular characterization of the B3-adrenergic receptor. Science 245: 1118-1121. Erlich, H.A. (Ed.) 1989. PCR technology: principles and applications for DNA amplifications. Pp. 246. MacMillan Publishers Ltd, Stockton Press, New York. Fajas, L., Fruchart, J.C. & Auwerx, J. (1998) PPARG3 mRNA: a distinct subtype transcribed from an independent promoter. FEBS Letters 438 (1-2): 55-60. FAO (1990) Food and Agriculture Organization. Recommended energy intakes. http://www.fao.org/docrep/w0073e/w0073e08.htm#P9793_1161767. Accessed 12th June, 2014. FAO/WHO (1973) Energy and protein requirements. Food and Agriculture Organization/ World Health Organization.: Report of a joint FAO/WHO adhoc expert committee. http://www.fao.org/3/a-y5686e.pdf. Accessed 11th March, 2015. FAO/WHO (1973). Energy and protein requirements: FAO Nutrition Meetings Report Series No. 52. WHO Technical Report Series No. 522. Report of a joint FAO/WHO ad hoc expert committee. Rome and Geneva. University of Ghana http://ugspace.ug.edu.gh 74 FAO/WHO (2001) Human energy requirements. Nutrition Meetings Report Series No. 52. WHO Technical Report Series No. 522. Food and nutrition technical report series.. Report of a joint FAO/WHO/UNU Expert consultation Rome, ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf. Accessed 12th June, 2014. Farooqi, I.S., Jebb, S.A., Langmack, G., Lawrence E, Cheetham CH, Prentice AM, Hughes IA, McCamish MA & O'Rahilly S. (1999a) Effects of recombinant leptin therapy in a child with congenital deficiency of the leptin receptor. New England Journal of Medicine 341(12): 879-884. Farooqi, S., Jebb, S.A., Cook, G., Lawrence, E., Cheetham, K.H., Prentice, A.M., Hughes, I.A., McCamish, M. & O‟Rahilly S. (1999b) Recombinant leptin induces weight loss in human congenital leptin deficiency. New England Journal of Medicine 341: 879-884. Farooqi, I.S., Wangensteen, T., Collins, S., Kimber, W., Matarese, G., Keogh, J.M., Lank, E., Bottomley, B., Lopez-Fernandez, J., Ferraz-Amaro, I., Dattani, M.T., Ercan, O., Myhre, A.G., Retterstol, L., Stanhope, R., Edge, J.A., McKenzie, S., Lessan, N., Ghodsi, M., De Rosa, V., Perna, F., Fontana, S., Barroso, I., Undlien, D.E., & O'Rahilly, S. (2007) Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. New England Journal of Medicine 356: 237–247. Farooqi, S. & O‟Rahilly, S. (2005) Monogenic obesity in humans: Annual Review of Medicine 56: 443-458. Fei, H., Okano, H.J., Li, C., Lee, G.H., Zhao, C., Darnell, R., & Friedman, J.M. (1997) Anatomic localization of alternatively spliced leptin receptors (Ob-R) in mouse brain and other tissues. Proceedings of the National Academy of Sciences (PNAS) USA 94: 7001-7005. University of Ghana http://ugspace.ug.edu.gh 75 Ferguson, J.F., Phillips, C.M., Tierney, A.C., Pérez-Martínez, P., Defoort, C., Helal, O., Lairon, D., Planells, R., Shaw, D.I., Lovegrove, J.A., Gjelstad, I.M., Drevon, C.A., Blaak, E.E., Saris, W.H., Leszczynska-Golabek, I., Kiec-Wilk, B., Risérus, U., Karlström, B., Miranda, J.L., & Roche, H.M. (2010) Gene-nutrient interactions in the metabolic syndrome: single nucleotide polymorphisms in ADIPOQ and ADIPORI interact with plasma saturated fatty acids to modulate insulin resistance. American Journal of Clinical Nutrition 91(3): 794-801 Flatt, J.P. (1988) Importance of nutrient balance in body weight regulation. Diabetes/Metabolism Review 4: 571–581. Flier, J.S. (1995) The adipocyte: storage depot or node on the energy information Super highway. Cell 80: 15–18. Ford, E.S., Kohl, H.W., 3rd, Mokdad, A.H. & Ajani, U.A. (2005) Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obesity Research 13: 608–614. Forman, B.M., Tontonoz, P., Chen, J., Brun, R.P., Spiegelman, B.M., Evans, R.M. (1995). 15Deoxy-delta 12, 14-prostaglandin J2 is a ligand for the adipocyte determination factor PPAR gamma. Cell 83: 803–12. Frayling, T.M. (2007) Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nature Reviews Genetics 8: 657–62. Frayling, T.M., Timpson, N.J., Weedon, M.N., Zeggini, E., Freathy, R.M., Lindgren, C.M., Perry, J.R., Elliott, K.S., Lango, H., Rayner, N.W., Shields, B., Harries, L.W., Barrett, J.C., Ellard, S., Groves, C.J., Knight, B., Patch, A.M., Ness, A.R., Ebrahim, S., Lawlor, D.A., Ring, S.M., Ben-Shlomo, Y., Jarvelin, M.R., Sovio, U., Bennett, A.J., Melzer, D., Ferrucci, L., Loos, R.J., Barroso, I., Wareham, N.J., Karpe, F., Owen, K.R., Cardon, L.R., Walker, M., Hitman, G,A., Palmer, C.N., Doney, A.S., Morris, A.D., Smith, G.D., Hattersley, A.T., & McCarthy, M.I. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316 (5826): 889- 894. University of Ghana http://ugspace.ug.edu.gh 76 Galbete, C., Contreras, R., Martinez, J.A., Martínez-González, M.Á., Guillén-Grima, F., & Marti, A. (2012). Physical activity and sex modulate obesity risk linked to 3111T/C Gene Variant of the CLOCK Gene in an Elderly Population: The SUN Project. Chronobiology International 29: 1397–404. Ghana Statistical Service (GSS), Ghana Demographic and Health Survey 2008. Ghana Health Service (GHS) and ICF Macro. (2009). Accra, Ghana. Ghilardi, N., Ziegler, S., Wiestner, A., Stoffel, R., Heim, M.H. & Skoda, R.C. (1996) Defective STAT signaling by the leptin receptor in diabetic mice. Proceedings of the National Academy of Sciences (PNAS) USA 93: 6231–6235. Goran, M.I., Shewchuk, R., Gower, B.A., Nagy, T.R., Carpenter, W.H. & Johnson, R. (1998). Longitudinal changes in fatness in white children: no effect of childhood energyexpenditure. American Journal of Clinical Nutrition 67: 309 – 316. Gotoda, T., Manning, B.S., Goldstone, A.P., Imrie, H., Evans, A.L., Strosberg, A.D., McKeigue, P.M., Scott, J., Aitman, T.J. (1997) Leptin receptor gene variation and obesity: lack of association in a white British male population. Human Molecular Genetics 6(6): 869-76. Haboubi, H.N., Kennedy, A.J., Sheriff, S.P. & Haboubi, N.Y. (2010). Mid-upper arm circumference to estimate BMI in obese subjects. Gastroenterology Today, 20(2): 41-45. Halaas, J.L., Gajiwala, K.S., Maffei, M., Cohen, S.L., Chait, B.T., Rabinowitz, B., Lallone, R.L., Burley, S.K. & Friedman, J.M. (1995) Weight-reducing effects of the plasma protein encoded by the obese gene. Science 269: 543–546. Hall D.M.B. and Cole T.J. (2006) What use is the BMI? Archives of Disabled Children 91(4): 283-286 Hara, K., Kubota, N., Tobe, K., Terauchi, Y., Miki, H., Komeda, K., Tamemoto, H., Yamauchi, T., Hagura, R., Ito, C., Akanuma, Y., & Kadowaki, T. (2000) The role of PPARg as a thrifty gene both in mice and humans. British Journal of Nutrition 84 S2: S235–S239. Healy, G.N., Dunstan, D.W., Shaw, J.E., Zimmet, P.Z. & Owen, N. (2006) Beneficial associations of physical activity with 2-h but not fasting blood glucose in Australian adults: the The Australian Diabetes, Obesity and Lifestyle (AusDiab) study. Diabetes Care 29: 2598–2604. University of Ghana http://ugspace.ug.edu.gh 77 Heitmann, B.L., Lissner, L. & Osler, M. (2000) Do we eat less fat, or just report so? International Journal of Obesity and Related Metabolic Disorders 24: 435–442. Herrera, B. M. & Lindgren, C. M. (2010) The genetics of obesity. Current Diabetes Reports 10(6): 498–505. Hileman, S.M., Pierroz, D.D., Masuzaki, H., Bjørbaek, C., El-Haschimi, K., Banks, W.A., & Flier, J.S. (2002) Characterization of short isoforms of the leptin receptor in rat cerebral microvessels and of brain uptake of leptin in mouse models of obesity. Endocrinology 143(3): 775-783. Hill, J.O. & Peters, J.C. (1998) Environmental contributions to the obesity epidemic. Science 280: 1371–1374. Hill, J.O. (2006) Understanding and addressing the epidemic of obesity: an energy balance perspective. Endocrine Reviews 27(7): 750-761. Hill, J.O., Drougas, H. & Peters, J.O. (1993) Obesity treatment: can diet composition play a role? Annals of Internal Medicine 119: 694–697. Hinney, A. & Hebebrand, J. (2008) Polygenic obesity in humans. Obesity Facts 1: 35– 42. Hinney, A., Vogel, C.I., Hebebrand, J. (2010) From monogenic to polygenic obesity: recent advances. European Child & Adolescent Psychiatry 19: 297–310. Hiza, H.A., Pratt, C., Mardis, M.D. & Anand, R. (2000). Body Mass Index and Health Nutritional Insights 16: 1 Hu, E., Tontonoz, P., & Spiegelman, B.M. (1995). Transdifferentiation of myoblasts by the adipogenic transcription factor PPAR-gamma and C/EBP-α. Proceedings of National Academy of Science, USA. 92: 9856-9860. Hubert, H. B. (1986). The importance of obesity in the development of coronary risk factors and disease: the epidemiological evidence. Annual Review of Public Health 7: 493–502. IPGRI (2003). International Plant Genetic Resources Institute (and Cornell University. Using molecular marker technology in studies on plant genetic diversity (DNA- based technologies). PCR-based technologies. Ithaca, NY 14850, United States. Jebb, S.A. (1997). Aetiology of obesity. British Medical Bulletin 53: 264-285. University of Ghana http://ugspace.ug.edu.gh 78 Kallen, C.B. & Lazar, M.A. (1996). Antidiabetic thiazolidinediones inhibit leptin (ob) gene expression in 3T3-L1 adipocytes. Proceedings of the National Academy of Sciences (PNAS) USA 93: 5793–5796. KBTH (2014). Korle-Bu Teaching Hospital. Brief history of the Korle-Bu Teaching Hospital. http://www.kbth.gov.gh/index.php?id=126. Accessed 30th April 30, 2014. KBTH (2014). Korle-Bu Teaching Hospital. Department of dietherapy. http://www.kbth.gov.gh/index.php?id=18. Accessed 1st May, 2014. Kesavachandran C.N., Bihari, V., & Mathur, N. (2012) The normal range of body mass index with high body fat percentage among male residents of Lucknow city in north India. Indian Journal of Medical Research 135: 72-77 Kim, J. & Spiegelman, B. (1996). ADD1/SREBP1 promotes adipocyte differentiation and gene expression linked to fatty acid metabolism. Genes & Development 10: 1096–1107. Kliewer, S.A., Lenhard, J.M., Willson, T.M., Patel, I., Morris, D.C. & Lehmann, J.M. (1995). A prostaglandin J2 metabolite binds peroxisome proliferator-activated receptor γ and promotes adipocyte differentiation. Cell 83: 813–819. Klok, M.D., Jakobsdottir, S. & Drent, M.L. (2007). The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review. Obesity Reviews 8: 21-34. KMA (2008). Kumasi Metropolitan Health Profile Report (2008). Kumasi Metropolitan District Health Directorate, Kumasi Metropolitan Assembly, Kumasi, Ghana. Kopelman, P.G. (2000). Obesity as a medical problem. Nature 404: 635- 643. Kopelman, P.G., (2005). Clinical treatment of obesity: are drugs and surgery the answer? Proceedings of the Nutrition Society 64: 65-71. Korner, J., Woods, S.C., & Woodworth, K.A., (2009). Regulation of energy homeostasis and health consequences in obesity. American Journal of Medicine 122 (S1):S12-8. Kral, T.V.E., Roe, L.S. & Rolls, B.J. (2004). Combined effects of energy density and portion size on energy intake in women. American Journal of Clinical Nutrition 79: 962–968. University of Ghana http://ugspace.ug.edu.gh 79 Krief, S., Lönnqvist, F., Raimbault, S., Baude, B., Van Spronsen, A., Arner, P., Strosberg, A.D., Ricquier, D., & Emorine, L.J. (1993). Tissue distribution of B3-adrenergic receptor mRNA in man. Journal of Clinical Investigation 91(1): 344-349. Kumar, P. & Clark, M., (2009). Kumar & Clark‟s Clinical medicine. 7th Ed. pp 228-229. Saunders Elsevier Ltd.UK. Lanza, E., Schatzkin, A., Daston, C., Corle, D., Freedman, L., Ballard-Barbash, R., Caan, B., Lance, P., Marshall, J., Ibert, F., Shike, M., Weissfeld, J., Slattery, M., Paskett, E., Mateski, D. & Albert, P. (2001). Implementation of a 4-y, high fiber, high-fruit-and vegetable, low-fat dietary intervention: results of dietary changes in the Polyp Prevention trial. American Journal of Clinical Nutrition 74(3): 387- 401. Larsen, L.H., Angquist, L., Vimaleswaran, K.S., Hager, J., Viguerie, N., Loos, R.J.F., Handjieva-Darlenska, T., Jebb, S.A., Kunesova, M., Larsen, T.M., Martinez, J.A., Papadaki, A., Pfeiffer, A.F.H., van Baak, M.A., Sørensen, T.I., Holst, C., Langin, D., Astrup, A. & Saris, W.H.M. (2012). Analyses of single nucleotide polymorphisms in selected nutrient-sensitive genes in weight-regain prevention: the DIOGENES study. American Journal of Clinical Nutrition 95 (5). 1254- 1260. Lee, G.H., Proenca, R., Montez, J.M., Carroll, K.M., Darvishzadeh, J.G., Lee, J.I. & Friedman, J.M. (1996). Abnormal splicing of the leptin receptor in diabetic mice. Nature 379: 632–635. Lenard, N.R, Shin, A.C., Berthoud, H.R. & Zheng, H. (2009). Appetite control and energy balance regulation in the modern world: reward-driven brain overrides repletion signals. International journal of obesity 33(2): S8-13. Lew, E.A. (1985). Mortality and weight: Insured lives of the American cancer study. Annals of Internal Medicine 103:1024-1029. Li, C.L., Lin, J.D., Lee, S.J. & Tseng, R.F. (2007). Associations between the metabolic syndrome and its components, watching television and physical activity. Public Health 121: 83–91. University of Ghana http://ugspace.ug.edu.gh 80 Liao, S.Y., Zeng, Z.R., Leung, W,K., Zhou, S.Z., Chen, B., Sung, J.J. & Hu, P.J. (2006). Peroxisome proliferator-activated receptor-gamma Pro12Ala polymorphism, Helicobacter pylori infection and non-cardia gastric carcinoma in Chinese. Alimentary Pharmacology & Therapeutics 23(2): 289-94. Licinio, J., Caglayan, S., Ozata, M., Yildiz, B.O., de Miranda, P.B., O'Kirwan, F., Whitby, R., Liang, L., Cohen, P., Bhasin, S., Krauss, R.M., Veldhuis, J.D., Wagner, A.J., DePaoli, A. M., McCann, S.M., & Wong, Ma-Li (2004) Phenotypic effects of leptin replacement on morbid obesity, diabetes mellitus, hypogonadism, and behaviour in leptin deficient adults. Proceedings of National Academy of Science USA 101(13): 4531-4536 Lin, F.T. &. Lane, M.D. (1994). CCAAT/enhancer binding protein alpha is sufficient to initiate the 3T3-L1 adipocyte differentiation program. Proceedings of the National Academy of Sciences (PNAS) USA 91: 8757–8761. Lonnqvist, F., Thorne, A., Nilsell, K., Hoffstedt, J. & Arner, P. (1995). A pathogenetic role of visceral fat B3-adrenoreceptor in obesity. Journal of Clinical Investigation 95: 1109-1116. Loos, R.J. (2012). Genetic determinants of common obesity and their value in prediction. Best Practice and Research Clinical Endocrinology and Metabolism 26: 211– 26. Loos, R.J., Lindgren, C.M., Li, S., Wheeler, E., Zhao, J.H., Prokopenko, I., Inouye, M., Freathy, R.M., Attwood, A.P., Beckmann, J.S., Berndt, S.I., Bergmann, S., Bennett, A.J., Bingham, S.A., Bochud, M., Brown, M., Cauchi, S., Connell, J.M., Cooper, C., Smith, G.D., Day, I., Dina, C., De, S., Dermitzakis, E.T., Doney, A.S.F., Elliott, K.S., Elliott, P., Evans, D.M., Farooqi, I.S., Froguel, P., Ghori, J., Groves, C.J., Gwilliam, R., Hadley, D., Hall, A.S., Hattersley, A.T., Hebebrand, J. et al.,(2008). Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genetics 40: 768-775. Loos, R.J.F. (2009). Recent progress in the genetics of common obesity. British Journal of Clinical Pharmacology 68(6): 811–829. MacDougald, O.A. & Lane, M.D. (1995). Transcriptional regulation of gene expression during adipocyte differentiation. Annual Review of Biochemistry 64: 345–373. University of Ghana http://ugspace.ug.edu.gh 81 Maes, H.H., Naele, M.C. & Eaves, L.J. (1997). Genetic and environmental factors in relative body weight and human adiposity. Behavioural Genetics 27(4): 325- 351. Maffei, M., Halaas, J., Ravussin, E., Pratley, R.E., Lee, G.H., Zhang, Y., Fei, H., Kim, S., Lallone, R., Ranganathan, S., Kern, P.A. & Friedman, J.M. (1995). Leptin levels in human and rodent: measurement of plasma leptin and ob RNA in obese and weight-reduced subjects. Nature Medicine 1: 1155–1161. Maffei, M., Stoffel, M., Barone, M., Moon, B., Dammerman, M., Ravussin, E., Bogardus, C., Ludwig, D.S., Flier, J.S., Talley, M., Auerbach, S. & Friedman, J.M. (1996b). Absence of mutations in the human OB gene in obese/diabetic subjects. Diabetes 45: 679–682. Mahan, K.L., Escott-Stump, S. & Raymond, J.L. (2012). Krause‟s Food and the nutrition care process. 13th Ed. Pp 465-467. Elsevier Saunders Ltd. USA. Malczewska-Malec, M., Wybranska, I., Leszczynska-Golabek, I., Partyka, L., Hartwich, J., Jabrocka, A., Kiec-Wilk, B., Kwasniak, M., Motyka, M. & Dembinska-Kiec, A. (2005). Analysis of candidate genes in Polish families with obesity. Clinical Chemistry and Laboratory Medicine 42(5): 487–493. Masugi, J, Tamori, Y., Mori, H., Koike, T. & Kasuga, M. (2000) Inhibitory effect of a proline-to-alanine substitution at codon 12 of peroxisome proliferator-activated receptor-gamma 2 on thiazolidinedione-induced adipogenesis. Biochemical and Biophysical Research Communications 268: 178–182. Mazicioglu, M.M., Hatipoglu, N., Ozturk, A., Cicek, B., Ustunbas, H.B., & Kurtoglu, S. (2010). Waist circumference and mid-upper arm circumference in evaluation of obesity in children aged between 6 and 17 years. Journal of Clinical Research into Paediatric Endocrinology 2(4): 144-150. Meister, B. (2007) Neurotransmitters in key neurons of the hypothalamus that regulate feeding behavior and body weight. Physiology & Behavior 92(1-2): 263-271. Mercer, J.G., Hoggard, N., Williams, L.M., Lawrence, C.B., Hannah, L.T., & Trayhurn, P.(1996) Localization of leptin receptor mRNA and the long form splice variant (Ob-Rb) in mouse hypothalamus and adjacent brain regions by in situ hybridization. FEBS letters 387(2-3): 113-116. University of Ghana http://ugspace.ug.edu.gh 82 Mokhtar, N., Elati, J., Chabir, R., Bour, A., Elkari, K., Schlossman, N.P., Caballero, B. & Aguenaou, H. (2001) Diet culture and Obesity in Northern Africa. Journal of Nutrition 131: 887S-892S. Moleres, A., Martinez, J.A. & Marti, A. (2013) Genetics of Obesity. Current Obesity Reports 2(1): 23-31. Moleres, A., Ochoa, M.C., Rendo-Urteaga, T., Martínez-González, M.A., Azcona San Julián, M.C., Martínez, J.A., Marti, A.(2012) Dietary fatty acid distribution modifies obesity risk linked to the rs9939609 polymorphism of the fat mass and obesity-associated gene in a Spanish case control study of children. British Journal of Nutrition 107 (4): 533–538. Montague, C.T., Farooqi, I.S., Whitehead, J.P., Soos, M.A., Rau, H., Wareham, N.J., Sewter, C.P., Digby, J.E., Mohammed, S.N., Hurst, J.A., Cheetham, C.H., Earley, A.R., Barnett, A.H., Prins, J.B., & O'Rahilly, S. (1997) Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 307: 903-908. Monteiro, C.A., Benicio, M.H.D.A., Mondini, L. & Popkin, B.M. (2000). Shifting obesity trends in Brazil. European Journal of Clinical Nutrition 54: 342–346. Muralidhara, D.V. (2008) Body Mass Index and its adequacy in capturing body fat. Thai Journal of Physiological Sciences 20(2): 97 Mushtaq M.U., Gull S., Abdullah H.M., Shahid U., Shad M.A. & Akram J. (2011) Waist circumference, waist-hip ratio and waist-height ratio percentiles and central obesity among Pakistani children aged five to twelve years. Biomed Central Pediatrics 11: 105 Mutch, D.M & Cle´ment, K. (2006) Unravelling the genetics of human obesity. Public Library of Science (PLOS) Genetics 2(12), e188. Nahmias, C., Blin, N., Elalouf, J.M., Mattei, M.G., Strosberg, A.D. & Emorine, L.J. (1991) Molecular characterization of the mmouse B3-adrenergic receptor: relationship with the atypical receptor of adipocytes. European Molecular Biology Organization (EMBO) Journal 10: 3721-3727. Neary, N.M., Small, C.J. & Brown, S.R. (2003) Gut and mind. Gut 52(7): 918-921. University of Ghana http://ugspace.ug.edu.gh 83 NHLBI (2000). The practical guide: Identification, evaluation and treatment of overweight and obesity in adults. National Heart, Lung, and Blood Institute. National Institutes of Health (NIH Publication Number 00‐4084) Nicklas, B.J., van Rossum, E.F., Berman, D.M., Ryan, A.S., Dennis, K.E. & Shuldiner, A.R. (2001) Genetic variation in the peroxisome proliferator-activated receptor- gamma2 gene (Pro12Ala) affects metabolic responses to weight loss and subsequent weight regain. Diabetes 50(9): 2172–2176. Nihiser A.J., Lee S.M., Wechsler H., McKenna M., Odom E., Reinold C., Thompson D. & Grummer-Strawn L. (2007) Body mass index measurement in schools. Journal of School Health 77(10): 651-671. Niki, T., Mori, H., Tamori, Y., Kishimoto-Hashiramoto, M., Ueno, H., Araki, S., Masugi, J., Sawant, N., Majithia, H.R., Rais, N., Hashiramoto, M., Taniguchi, H. & Kasuga, M. (1996) Human obese gene: molecular screening in Japanese and Asian Indian NIDDM patients associated with obesity. Diabetes 45: 675– 678. Nordmann, A.J., Nordmann, A., Briel, M., Keller, U., Yancy, W.S. Jr., Brehm, B.J. & Bucher, H.C. ( 2006) Effects of low-carbohydrate vs low-fat diets on weight loss and cardiovascular risk factors: a meta-analysis of randomized controlled trials. Archives of Internal Medicine 166: 285–293. O’Rahilly, S. (2009) Human genetics illuminates the paths to metabolic disease. Nature 462: 307–314. Ochoa, M.C., Moreno-Aliaga, M.J., Martinez-Gonzalez, M.A., Martínez, J.A., & Marti, A. (2006). TV watching modifies obesity risk linked to the 27Glu polymorphism of the ADRB2 gene in girls. International Journal of Pediatric Obesity 1: 83–88. Ordovas, J.M., Robertson, R. & Cleirigh, E.N. (2011) Gene-gene and gene environment interactions defining lipid-related traits. Current Opinion in Lipidology 22: 129– 36. Oswal, A. & Yeo, G. (2010) Leptin and the Control of Body Weight: A review of its diverse central targets, signaling mechanisms, and role in the pathogenesis of Obesity. Nature Reviews 18: 221–229. University of Ghana http://ugspace.ug.edu.gh 84 Pelleymounter, M.A., Cullen, M.J., Baker, M.B., Hecht, R., Winters, D., Boone, T., & Collins, F. (1995) Effects of the obese gene product on body weight regulation in ob/ob mice. Science 269: 540–543. Perusse, L., Rankinen, T., Zuberi, A., Chagnon, Y.C., Weisnagel, S.J., Argyropoulos, G., Walts, B., Snyder, E.E., & Bouchard, C. (2005) The human obesity gene map: the 2004 update. Obesity Research 13: 381–490. Pi-Sunyer, X. (2009). The medical risks of obesity. Journal of Postgraduate Medicine 121: 21–33. Podolsky, R.H., Barbeau, P., Kang, H-S., Zhu, H., Treiber, F.A. & Snieder, H. (2007). Candidate genes and growth curves for adiposity in African- and European- American youth. International Journal of Obesity 31: 1491-1499. Popkin, B.M. & Gordon-Larsen, P. (2004). The nutrition transition: worldwide obesity dynamics and their determinants. International Journal of Obesity 28: S2-S9. Popkin, B.M. (2001). The nutrition transition and obesity in the developing world. Journal of Nutrition 131: 871S-873S. Prentice, A.M. & Jebb, S.A. (1995) Obesity in Britain: gluttony or sloth? British Medical Journal 311: 437- 439. Prentice, A.M. & Jebb, S.A. (2003) Fast foods, energy density and obesity: a possible mechanistic link. Obesity Review 4: 187-194. Prentice, A.M. (2006). The emerging epidemic of obesity in developing countries. International Journal of Epidemiology 35: 93-99. Prentice, A.M., & Jebb, S.A. (1995) Obesity in Britain: gluttony or sloth? British Medical Journal 311: 437–439. Prentice, A.M., Rayco-Solon, P. & Moore, S. (2005) Insights from the developing world: thrifty genotypes and thrifty phenotypes. Proceedings of the Nutrition Society 64: 153-161. Puoane, T., Steyn, K., Bradshaw, D., Laubscher, R., Fourie, J., Lambert, V. & Mbanga N. (2002) Obesity in South Africa: The South African Demographic and Health Survey. Obesity Research 10: 1038-1048. University of Ghana http://ugspace.ug.edu.gh 85 Qi, L., Kraft, P., Hunter, D.J., Hu, F.B., (2008). The common obesity variant near MC4R gene is associated with higher intakes of total energy and dietary fat, weight change and diabetes risk in women. Human Molecular Genetics 17: 3502-3508. Ramsey, M. 3rd (1991) Blood pressure monitoring: automated oscillometric devices. Journal of Clinical Monitoring 7(1): 56–67. Rankinen, T., Zuberi, A., Chagnon, Y.C., Weisnagel, S.J., Argyropoulos, G., Walts, B., Perusse, L. & Bouchard, C. (2006) The human obesity gene map: the 2005 update. Obesity (Silver Spring) 14: 529-644. Raymond, S. & Raymond, R.H. (1964) Process for gel electrophoresis. United States Patent Office 3129158. 538 Walnut Lane, Swarthmore, Pa. Filed Ian. 24, 1961, Ser. No. 84,729. Razquin, C., Marti, A. & Martinez, J.A. (2011) Evidences on three relevant obesogenes: MC4R FTO and PPAR gamma. Approaches for personalized nutrition. Molecular Nutrition & Food Research 55: 136–49. Razquin, C., Martinez, J.A., Martinez-Gonzalez, M.A., Corella, D., Santos, J.M., & Marti, A. (2009) The Mediterranean diet protects against waist circumference enlargement in 12Ala carriers for the PPARgamma gene: 2 years' follow-up of 774 subjects at high cardiovascular risk. British Journal of Nutrition 102: 672–9. Ristow, M., Muller-Wieland, D., Pfieffer, A., Krone, W. & Kahn, C.R. (1998) Obesity associated with a mutation in a genetic regulator of adipocyte differentiation. New England Journal of Medicine 339: 953-959. Ruiz, J.R., Layen, I., Ortega, F.B., Lagry, V., Moreno, L.A., Dallongeville, J., Martinez- Gomez, D., Borkor, S., Manois, Y., Ciarapica, D., Gottrand, F., De Henauw, S., Molnar, D., Sjorstrom, M. & Meirhaeghe, A. (2010) Attenuation of the effect of the FTO rs9939609 polymorphism on total and central body fat by physical activity in adolescents: The HELENA study. Archives of Pediatrics and Adolescent Medicine 164(4): 328-333. Saunders, C.L., Chiodini, B.D., Sham, P., Lewis, C.M., Abkevich, V., Adeyemo, A.A., de Andrade, M., Arya R, Berenson, G.S., Blangero, J., Boehnke, M., Borecki, I.B., Chagnon, Y.C., Chen, W., Comuzzie, A.G., Deng. et al., (2007) Meta- analysis of genome-wide linkage studies in BMI and obesity. Obesity (Silver Spring) 15: 2263–2275. University of Ghana http://ugspace.ug.edu.gh 86 Schoonjans, K., Peinado-Onsurbe, J., Lefebvre, A.M., Heyman, R.A., Briggs, M., Deeb, S., Staels, B. & Auwerx, J. (1996) PPARα and PPARγ activators direct a tissue- specific transcriptional response via PPRE in the lipoprotein lipase gene. European Molecular Biology Organization (EMBO) Journal 15: 5336–5348. Schoonjans, K., Watanabe, M., Suzuki, H., Mahfoudi, A., Krey, G., Wahli, W., Grimaldi, P., Staels, B., Yamamoto, T. & Auwerx, J. (1995) Induction of the acyl-coenzyme A synthetase gene by fibrates and fatty acids is mediated by a peroxisome proliferator response element in the C promoter. Journal of Biological Chemistry 270: 19269–19276. Scuteri, A., Sanna, S., Chen, WM., Uda, M., Albai, G., Strait, J., Najjar, S., Nagaraja, R., Orrú, M., Usala, G., Dei, M., Lai, S., Maschio, A., Busonero, F., Mulas, A., Ehret, G.B., Fink, A.A., Weder, A.B., Cooper, R.S., Galan, P., Chakravarti, A., Schlessinger, D., Cao, A., Lakatta, E., & Abecasis, G.R. (2007) Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. Public Library of Science (PLOS) Genetics 3, e115. Sears, I., MacGinnitie, M., Kovacs, L. & Graves, R. (1996) Differentiation-dependent expression of brown adipocyte uncoupling protein gene: regulation by peroxisome proliferator-activated receptor gamma. Molecular Cell Biology 16: 3410–3419. Shamseddeen, H., Getty, J.Z., Hamdallah, IN. & Ali, M.R. (2011) Epidemiology and economic impact of obesity and type 2 diabetes. Surgical Clinics of North America 91: 1163–1172 Sobal, J. & Stunkard, A.J. (1989) Socioeconomic status and obesity: A review of literature. Psychological Bulletin 105(2): 260-275. Society of Actuaries and Association of Life Insurance Medical Directors (1979). The Build Study 1979, 8(4). Recording and Statistical Corporation, Chicago. Southern, E.M. (1976) Detection of specific sequences among DNA fragments separated by gel electrophoresis. Journal of Molecular Biology 98: 503–517. University of Ghana http://ugspace.ug.edu.gh 87 Speliotes, E.K., Willer, C.J., Berndt, S.I., Monda, K.L., Thorleifsson, G., Jackson, A.U., Lango Allen, H., Lindgren, C.M., Luan, J., Mägi, R., Randall, J.C., Vedantam, S., Winkler, T.W., Qi, L., Workalemahu, T., Heid, I.M., Steinthorsdottir, V., Stringham, H.M., Weedon, M.N., Wheeler, E., Wood, A.R., Ferreira, T., Weyant, R.J., Segrè, A.V., Estrada, K., Liang, L. et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genetics 42: 937–948. Spiegelman, B.M. & Flier, J.S. (1996) Adipogenesis and obesity: rounding out the big picture. Cell 87: 377–389. Spiegelman, B.M. (1998) PPARG: adipogenic regulator and thiazolidinedione receptor. Diabetes 47: 507–514, Statistics Canada (2010) 2010 National Census: obesity prevalence rate in Canada. Statistics catalogue volume 32, no.2 Ottawa. www.phac-aspc.gc.ca Accessed 4th December, 2013 Stellwagen, N.C. (1998) DNA gel electrophoresis. Nucleic acid electrophoresis laboratory manual. (Tietz, D Ed.). Springer Verlag. Berlin-Heidelberg-New York.Stockton Press, NY. Stephens, T.W., Basinski, M., Bristow, P.K., Bue-Valleskey, J.M., Burgett, S.G., Craft, L., Hale, J., Hoffmann, J., Hsiung, H.M., Kriauciunas, A., MacKeller, W., Rosteck, P.R.,Jr, Schoner, B., Smith, D., Tinsley, F.C., Zhang, X.-Y. & Heiman, M. (1995) The role of neuropeptide Y in the antiobesity action of the obese gene product. Nature 377: 530–532. Stunkard, A.J., Harris, J.R., Pedersen, N.L. & McClearn, G.E. (1990). The body mass index of twins who have been reared apart. New England Journal of Medicine 322(21): 1483-1487. Susulic, V.S., Lawitts, J.A., Flier, J.S. & Lowell, B.B., (1994) Targeted disruption of the B3-adrenergic receptor gene. International Journal of Obesity 18: Suppl 2:5 abstract. University of Ghana http://ugspace.ug.edu.gh 88 Tartaglia, L.A., Dembski, M., Weng, X., Deng, N., Culpepper, J., Devos, R., Richards, G.J., Campfield, L.A., Clark, F.T., Deeds, J., Muir, C., Sanker, S., Moriarty, A., Moore, K.J., Smutko, J.S., Mays, G.G., Woolf, E.A., Monroe, C.A. & Tepper, R.I. (1995) Identification and expression cloning of a leptin receptor, OB-R. Cell 83: 1263–1271. Taylor, R.W., Keil, D., Gold, E.J., Williams, S.M. & Goulding, A., (1998) Body mass index, waist girth, and waist-to-hip ratio as indexes of total and regional adiposity in women: evaluation using receiver operating characteristic curves. American Journal of Clinical Nutrition 67: 44–49. www.ajcn.nutrition.org/content/67/1/44.full. . Accessed 2nd December, 2013. Thomas, B. & Bishop, J. (2007) Manual of dietetic practice. 4th Ed. Pp 567-571. Blackwell Scientific Publishing Ltd. UK Thompson, D.B., Ravussin, E., Bennett, P.H. & Bogardus, C. (1998) Structure and sequence variation at the human leptin receptor gene in lean and obese Pima Indians. Human Molecular Genetics 6(5): 675-679. Thorleifsson, G., Walters, G.B., Gudbjartsson, D.F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., Styrkarsdottir, U., Gretarsdottir, S., Thorlacius, S., Jonsdottir, I., Jonsdottir, T., Olafsdottir, E.J., Olafsdottir, G.H., Jonsson, T., Jonsson, F., Borch-Johnsen, K., Hansen, T., Andersen, G., Jorgensen, T., Lauritzen, T., Aben, K.K., Verbeek, A.L., Roeleveld, N., Kampman, E., Yanek, L.R., Becker, L.C., Tryggvadottir, L., Rafnar, T., Becker, D.M., Gulcher, J., Kiemeney, L.A., Pedersen, O., Kong, A., Thorsteinsdottir, U., & Stefansson, K. (2009) Genome- wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature Genetics 41: 18–24. Thornton, J.E., Cheung, C.C., Clifton, D.K. & Steiner, R.A., (1997) Regulation of hypothalamic proopiomelanocortin mRNA by leptin in ob/ob mice. Endocrinology 138: 5063-5066. Tontonoz, P., Graves, R., Budavari, A., Erdjument-Bromage, H., Lui, M., Hu, E., Temps, P. & Spiegelman, B. (1994a) Adipocyte-specific transcription factor ARF 6 is a heterodimeric complex of two nuclear hormone receptors, PPARγ and RXRα . Nucleic Acids Research 22: 5628–5634. University of Ghana http://ugspace.ug.edu.gh 89 Tontonoz, P., Hu, E. & Spiegelman, B.M. (1994b) Stimulation of adipogenesis in fibroblasts by PPARγ2, a lipid-activated transcription factor. Cell 79: 1147– 1156. Tontonoz, P., Hu, E. Devine, J., Beale, E. & Spiegelman, B. (1995) PPARγ2 regulates adipose expression of the phospoenolpyruvate carboxykinase gene. Molecular Cell Biology 15: 351–357. Tontonoz, P., Hu, E., Graves, R., Budavari, A.I. & Spiegelman,B.M. (1994) mPPARγ2: tissue-specific regulator of an adipocyte enhancer. Genetics and Development 8: 1224–1234. Treuth, M.S., Figueroa-Colon, R., Hunter, G.R., Weinsier, R.L., Butte, N.F. & Goran, M.I. (1998). Energy expenditure and physical fitness in overweight vs non- overweight prepubertal girls. International Journal of Obesity and Related Metabolic Disorders 22: 440– 447. UNICEF/WHO (2009) United Nations International Children's Emergency Fund. Technical Bulletin No.13. MUAC Tapes. UNICEF Supply Division. HIV/AIDS and Health Centre, Essential Medicines and Nutrition Unit www.unicef.org/.../Mid_Upper_Arm_Circumference_Measuring_Tapes. Accessed 2nd May, 2014. UNSCN (2008) United Nations System Standing Committee On Nutrition, Task Force On Assessment, Monitoring, And Evaluation, Fact Sheets On Food And Nutrition Security Indicators: Mid-Upper Arm Circumference SCN News, www.unsystem.org/scn and www.unscn.org/files/Task_Forces/Assessment...and.../MUAC.pdf. Accesses 2nd May, 2014. UNU (2006) United Nations University. United Nations University Press/ United Nations System Standing Committee on Nutrition. SCN Nutrition Policy Paper No 21. WHO, UNICEF, and SCN informal consultation on community-based management of severe malnutrition in children. Food and Nutrition Bulletin (sup). 27;3. September 2006. http://www.unu.edu/unupress/food/FNB_v27n3_suppl.pdf, www.who.int/nutrition/.../severemalnutrition - . Accessed 2nd May, 2014. University of Ghana http://ugspace.ug.edu.gh 90 van Rossum, C.T., Hoebee, B., Seidell, J.C., Bouchard, C., van Baak, M.A., de Groot, C.P., Chagnon, M., de Graaf, C., & Saris, W.H. (2002) Genetic factors as predictors of weight gain in young adult Dutch men and women. International Journal of Obesity and Related Metabolic Disorders 26: 517- 528. Vidal-Puig, A.J., Considine, R.V., Jimenez-Linan, M., Werman, A., Pories, W.J., Caro, J.F. & Flier, J.S. (1997) Peroxisome proliferator-activated receptor gene expression in human tissues: effects of obesity, weight loss, and regulation by insulin and glucocorticoids. Journal of Clinical Investigation 99: 2416–2422. Vimaleswaran, K.S. & Loos, R.J. (2010) Progress in the genetics of common obesity and type 2 diabetes. Expert Reviews in Molecular Medicine 12, e7. Webster-Gandy, J., Madden, A. & Holdsworth, M. (eds.) 2012. Oxford handbook of nutrition, and dietetics. Oxford University Press, Oxford. 2nd edition : 412. Weisell R.C. (2002) Body mass index as an indicator of obesity. Asia Pacific Journal of Clinical Nutrition 11: Suppl 8:S681-684. Werman, A., Wollenberg, A., Solanes, G., Bjorkbaek, C., Vidal-Puig, A.J. & Flier, J.S. (1997) Ligand-independent activation domain in the N-terminus of peroxisome proliferator-activated receptor G (PPARG). Journal of Biological Chemistry 272: 20230-20235. White, D.W. & Tartaglia, L.A. (1996) Leptin and OB-R: body weight regulation by a cytokine receptor, Cytokine Growth Factor. Reviews 7: 303–309. WHO (1985) World Health Organization. Energy and protein requirements: Report of a joint FAO/WHO/UNU expert consultation. WHO Technical Report Series No. 724. Geneva. http://www.fao.org/docrep/w0073e/w0073e08.htm#P9793_1161767. Accessed, 12th June, 2014. WHO (1995) World Health Organization Expert Committee on Physical Status.: the use and interpretation of anthropometry, report of a WHO expert committee. Geneva, 1995 (WHO Technical Report Series, No. 854; http://whqlibdoc.who.int/trs/WHO_TRS_854.(chp3).pdf. Accessed 2nd May, 2014. University of Ghana http://ugspace.ug.edu.gh 91 WHO (1998) World Health Organization. Obesity: preventing and managing the global epidemic: Report of a WHO Consultation on Obesity, Geneva, 3–5 June, 1997. Geneva: World Health Organization. whqlibdoc.who.int/hq/1998/WHO_NUT_NCD_98.1_(p1-158).pdf. Accessed 10th October, 2013. WHO (1999) World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: Report of a WHO Consultation on Diagnosis and Classification of Diabetes Mellitus. Geneva: World Health Organization. whqlibdoc.who.int/hq/1999/who_ncd_ncs_99.2.pdf. Accessed 9th October, 2013. WHO (2000) Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. WHO Technical Report Series No. 894 i-xii, 1-253. Geneva. www.ncbi.nlm.nih.gov/pubmed/11234459. Accessed, 11th June, 2014. WHO (2002) World Health Day 2002 “Move for Health”. Questions and Answers. www.who.int/world-healthday/q_and_a.en.shtml., whqlibdoc.who.int/hq/2002/WHO_NMH_NPH_WHD_02.13.pdf. Accessed 12th June, 2014. WHO (2005) World Health Organisation. Preventing chronic disease, a vital investment. WHO Global Report. World Health Organisation. Geneva. whqlibdoc.who.int/publications/2005/9241563001_eng.pdf. Accessed 4th December, 2013. WHO (2007) World Health Organization. The challenge of obesity in the WHO European Region and strategies for response. Copenhagen: WHO Regional Office for Europe; 2007. www.euro.wh . Accessed 2nd May, 2014. WHO (2008) World Health Organization. WHO STEPwise approach to surveillance (STEPS). Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation, Geneva. www.whqlibdoc.who.int/publications/2011/9789241501491_eng.pdf. Accessed 3rd December, 2013. University of Ghana http://ugspace.ug.edu.gh 92 WHO (2010) World Health Organization. Obesity. Preventing and managing the global epidemic. Report of a WHO Consultation on Obesity. World Health Organization Geneva, WHO (1998). (Revised 2010) http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed 30th April, 2014. WHO (2013a). World Health Organization. Media center, Obesity and overweight review http://www.who.int/mediacentre/factsheets/fs311/en/. Accessed 29th April, 2014. WHO (2013b) World Health Organization. Prevention and control of noncommunicable diseases in the European Region: A progress report, www.euro.who.int/__data/assets/pdf_file/0004/235975/Prevention-and-control- of-noncommunicable-diseases-in-the-European-Region-A-progress-report- Eng.pdf?ua=1. Accessed, 2nd May, 2014. WHO/FAO (2002) Joint WHO/FAO Expert Consultation on Diet. WHO technical report series 916. Nutrition and the Prevention of Chronic Diseases. Draft 28 March 2002. Geneva. whqlibdoc.who.int/trs/who_trs_916.pdf. Accessed 12th June, 2014. Widén, E., Lehto, M., Kanninen ,T., Walston, J., Shuldiner, A.R. & Groop, L.C. (1995) Association of a polymorphism in the beta 3-adrenergic-receptor gene with features of the insulin resistance syndrome in Finns. New England Journal of Medicine 333(6): 348-351. Willer, C.J., Speliotes, E.K., Loos, R.J., Li, S., Lindgren, C.M., Heid, I.M., Berndt, S.I., Elliott, A.L., Jackson, A.U., Lamina, C., Lettre, G., Lim, N., Lyon, H.N., McCarroll, S.A., Papadakis, K., Qi, L., Randall, J.C., Roccasecca, R.M., Sanna, S., Scheet, P., Weedon, M.N., Wheeler, E., Zhao, J.H., Jacobs, L.C., Prokopenko, I., Soranzo, N., Tanaka, T., Timpson, N.J., Almgren, P., et al. (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genetics 41: 25–34. Willett, W.C. (1998) Is dietary fat a major determinant of body fat? American Journal of Clinical Nutrition 67(suppl): 556S–62S. Woods, S.C., Schwartz, M.W., Baskin, D.G. & Seeley, R.J. (2000) Food intake and the regulation of body weight. Annual Review of Psychology 51: 255–277. University of Ghana http://ugspace.ug.edu.gh 93 Zhang, B., Graziano,M.P., Doebber, T.W., Leibowitz, M.D., White-Carrington, S., Szalkowski, D.M., Hey, P.T., Wu,M., Cullinan, C.A., Bailey, P., Collmann, B., Frederich, R., Flier, J.S., Strader, C.D. & Smith, R.G. (1996) Down-regulation of the expression of the obese gene by antidiabetic thiazolidinediones in Zucker diabetic fatty rats and db/db mice. Journal of Biological Chemistry 271: 9455– 9459. Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L. & Friedman, J.M. (1994) Positional cloning of the mouse obese gene and its human homologue. Nature 372: 425–432. Ziraba, A.K., Fotso, J.C. & Ochako, R. (2009) Overweight and obesity in urban Africa: a problem of the rich or the poor? Biomedcentral (BMC) Public Health 9: 465. University of Ghana http://ugspace.ug.edu.gh 94 APPENDIXES APPENDIX I: INFORMED CONSENT FORM Name of Researcher: AMOS AGYEI GYAMFI Name of Institution: School of Allied Health Sciences College of Health Sciences University of Ghana. Name of Supervisors: DR. CHARLES A. BROWN (School of Allied Health Sciences, University of Ghana) DR. SAMUEL ANTWI-BAFFOUR (School of Allied Health Sciences, University of Ghana) Project Title: SELECTED CANDIDATE GENES AND OBESITY AMONG GHANAIAN ADULTS: A CASE STUDY AT THE KORLE- BU TEACHING HOSPITAL (DIETHERAPY UNIT) ACCRA University of Ghana http://ugspace.ug.edu.gh 95 Consent You have been invited to take part in the above titled research. The purpose of which is to investigate the relationship between the selected candidate genes and their role in obesity among Ghanaian adults. About 5 ml of your venous blood will be drawn for DNA analyses after your blood pressure, weight, height and waist-to-hip ratio have been measured. Your confidentiality will be assured at all times. All information will be safeguarded. Your data will only be known to the researcher, it will also be kept under an encryption/password protection. Your privacy and anonymity will be ensured in the collection, storage and publication of the research material. Should the information be published in any scientific journal, you will not be identified by name. You have the right to refuse to participate at anytime you wish to. I understand my participation is totally voluntary and free, and that I am not going to be subjected to any risk, danger or discomfort. I have been informed that the confidentiality of the information will be safeguarded and that my privacy and anonymity will be ensured in the collection, storage and publication of the research material. I have the right to refuse to participate at anytime I wish to. I have read the information provided. All questions have been answered to my satisfaction. I consent voluntarily to participate in this study. Signature of participant:.……………….……………………………… Date:………………………………/……../ 2013 Signature of researcher:.…………………………………………… Date:…………..…….../..……./ 2013 University of Ghana http://ugspace.ug.edu.gh 96 QUESTIONNAIRE FOR DATA COLLECTION SELECTED CANDIDATE GENES AND OBESITY AMONG GHANAIAN ADULTS: A CASE CONTROL STUDY AT THE KORLE-BU TEACHING HOSPITAL (DIETHERAPY UNIT) ACCRA Demography 1. Age............ 2. Sex: M F 3. Religion: Christian Moslem Traditional Other 4. Marital Status: S M 5.Occupation……………………….. 6. Location…………………………………….. Anthropometry 7. Height…………. 8. Weight…………….. 9. BMI………….. 10. BP…………. 11. MUAC………….. 12. Waist-to-hip ratio…………. Lifestyle Information 13. Smoking: Y N 14. Alcohol consumption Y N If yes to 14, how often 15. Daily Weekly Monthly Other 16. Caffeine consumption Y N If yes to 16, from which source 17. Tea bag Coffee Milo Energy drinks Other How often to question 17, 18. Daily Weekly Monthly Other University of Ghana http://ugspace.ug.edu.gh 97 19. DIET HISTORY TIME TYPE OF FOOD PORTION SIZE (ESTIMATING FROM HANDY MEASURES) 20. SAMPLE COLLECTION (MOUTH RINSE (SALIVA)/BUCCAL SWAB/ 3ml VENOUS BLOOD) Has venous blood/ buccal swab /mouth rinse been taken: Y N University of Ghana http://ugspace.ug.edu.gh 98 APPENDIX II ETHICAL IDENTIFICATION NUMBER University of Ghana http://ugspace.ug.edu.gh 99 APPENDIX III SAMPLE COLLECTION University of Ghana http://ugspace.ug.edu.gh 100 APPENDIX IV SAMPLE PREPARATION (DNA EXTRACTION) (Courtesy Prof. D. T. Burke. Department of Human Genetics, University of Michigan Medical School). University of Ghana http://ugspace.ug.edu.gh