During the past decade, mutations affecting liability to central obesity have been discovered at a phenomenal rate, and despite few consistently replicated findings, a number of intriguing results have emerged in the literature. Association studies have been proposed to identify the genetic determinants of complex traits such as central obesity. The advantages of the association method include its relative robustness to genetic heterogeneity and the ability to detect much smaller effect sizes than is detectable using feasible sample sizes in linkage studies. However, the current literature linking central obesity to genetic variants is teeming with reports of associations that either cannot be replicated or for which corroboration by linkage has been impossible to find. Explanations for this lack of reproducibility are well rehearsed, and typically include poor study design, incorrect assumptions about the underlying genetic architecture, and simple overinterpretation of data. These limitations create concern about the validity of association studies and cause problems in establishing robust criteria for undertaking association studies. In this article, the current status of the literature of association studies for genetic dissection of central obesity is critically reviewed.
Our knowledge of human obesity has progressed beyond the simple generalization that obesity is fully explained by inappropriate eating. In addition, the location of body fat has emerged as an important predictor of the health hazards of obesity. Sites of body fat predominance are easily measured by the ratio of waist-to-hip circumferences. High ratios are associated with higher risks for diabetes, hypertension, heart disease, or their associated risk factors.1 The genetic heritability of the central obesity phenotype accounts for about 30–50% of the age- and gender-adjusted phenotypic variances.2
The investigation of heritable susceptibility to disease is ultimately an effort to associate disease phenotype with underlying genotype. The usual strategy has been to use linkage mapping in affected families to identify chromosomal loci from which candidate genes and genotypes can be tested for association with disease. Significant technological advances for identification of single-nucleotide polymorphisms (SNPs) and use of microarrays have further strengthened research methodologies for genetic analysis. The candidate gene approach is potentially very powerful, but a major problem is that the efficiency in the choice of candidates is inevitably a function of the level of previous understanding of disease pathophysiology. Association studies represent an important paradigm for investigation of complex traits, such as central obesity, both to follow-up regions of interest from linkage studies and for pure functional studies. The genotype is usually obtained from a polymorphic marker. This may be a short tandem repeat or microsatellite in which the number of copies of a 2-, 3-, or 4-base pair DNA pattern varies or it may be an SNP in which a particular DNA base pair varies. A marker is thought to be polymorphic if the frequency of the most frequent allele is less than 99%. Microsatellites generally have 4–12 alleles, whereas SNPs generally have 2. For a discrete trait, the simplest sort of association study counts the frequency of each allele at a polymorphic marker in two groups of unrelated individuals. Association exists when the allele frequencies differ between subject with disease (cases) and subject without disease (controls). Association between a marker and a phenotype may occur when the relationship is causal and the genotyped marker is itself functional, or when the genotyped marker is not itself functional, but is in linkage disequilibrium with other polymorphisms that are functional, or finally, it is possible that an association is due to population stratification. Linkage disequilibrium, discussed in greater detail below, is a function of both physical proximity between the marker and the functional polymorphism on the same chromosome, and their shared history in the population. Population stratification refers to the case in which a correlation between a marker and a phenotype is due to each being correlated with a third, nongenetic factor.
This article reviews the literature published during the past 10 y within the context of the methodological issues and limitations inherent in the use of association studies linking central obesity to genetic polymorphisms. The research findings to date will be discussed with focus on several candidate genes that have aroused particular interest.
Central obesity phenotype
Excess body fat, obesity, is one of the most common disorders in clinical practice. The location of the body fat is a major determinant of the degree of excess morbidity and mortality due to obesity.1 At least two components of body fat are associated with obesity-related adverse health outcomes. These are the amount of subcutaneous truncal or abdominal fat, and the amount of visceral fat located in the abdominal cavity. Each of these components of body fat is associated with varying degrees of metabolic abnormalities and independently predicts adverse health outcomes.
The current definition of central obesity is based on the waist-to-hip ratio (WHR) where waist is defined as the smallest circumference between the rib cage and the illiac crests, and hip measurement is the largest circumference between the waist and the thighs. Women should have a ratio of less than 0.85 (or a waist no larger than 88 cm or 35 in); men, less than 1.0 (or a waist no larger than 102 cm or 40 in).3 Ratios above these values reflect abdominal and/or visceral obesity and a greater risk of obesity-related disorders.
Principle of the association paradigm
Many complex traits are thought to be inherited since they often run in families. However, these complex traits do not show typical mendelian pedigree patterns. These nonmendelian diseases may depend on several susceptibility loci, with a variable contribution from environmental factors. Discovering the major susceptibility locus may be the key to advances in understanding the pathophysiology of a disease. The identification of susceptibility loci can be pursued by different strategies.4 In the general population, association studies are the most used for discovering susceptibility loci. Furthermore, the simplicity of identifying genetic markers by polymerase chain reaction has contributed to a rapid increase in association studies between candidate genes and disease.5
Genetic association studies assess correlations between marker alleles and trait differences on a population scale. Significant associations between marker alleles and disease status may emerge artifactually by incorrect matching between proband and control groups (population stratification).6 Assuming that the proband and control groups are correctly matched, genetic variants and trait scores can become associated by two mechanisms: (i) direct involvement of the genetic variant in the disease process, or (ii) linkage disequilibrium, also known as gametic phase disequilibrium or allelic association.7 Linkage disequilibrium occurs when an allele at one genetic locus is situated on the same chromosome with a specific allele at another locus more often than would be expected by chance. Linkage disequilibrium between a mutation and surrounding polymorphisms is an artifact of the history of the mutation. Markers that are closer to a new mutation are likely to be in stronger disequilibrium with it. As generations pass, more recombinations occur, and disequilibrium between the mutation and surrounding markers continually decreases. Eventually, the mutation reaches equilibrium with the surrounding markers. At equilibrium, the probability of finding a particular combination of alleles occurring together is simply the product of their individual allele frequencies. In addition to recombination, a particular mutation may have arisen multiple times in different individuals on different chromosomal backgrounds. In this case, disequilibrium with surrounding markers may only exist in population subgroups that may not be easily identified. Moreover, as a result of, for instance, regional variability in recombination patterns, genetic drift, local chromosomal composition, and the pattern of mating within a population, patterns of linkage disequilibrium can vary significantly within and between different populations.8
Despite the increasingly important role in detecting disease genes, the inconsistency of association data is still a strong feature of this approach. The most common explanation for association without linkage is population stratification. Despite this commonly being used as an explanation for nonreplicable associations, there are few actual examples to support this assumption,9 suggesting that this problem has been overemphasized. In contrast, the importance of overinterpreting marginal findings and of publication bias has been underemphasized. Consequently, methodological development focusing on issues such as multilocus association, background linkage disequilibrium levels and large-scale study design, independent replication of data and careful attention to the effects of multiple testing might decrease the inconsistency of association data in the future. Much can be learned from the experience of epidemiological research, which attempts to understand the relationship between environment and disease, particularly where the effects are small and the disease is heterogeneous.10 Epidemiological methods are applicable to genetic association studies and are encompassed in the unified field of genetic epidemiology.11,12
Association studies in the literature
There have been several studies using association approaches in order to undertake systematic searches for candidate genes in obesity defined as elevated body mass index (BMI, kg/m2).13 By contrast, only a few of these studies give sufficient attention to phenotypes reflecting central obesity (eg abdominal visceral fat, WHR, waist circumference, sagittal abdominal diameter). Table 1 presents a summary of the genetic polymorphisms associated with central obesity phenotypes. Positive associations have been found with markers of agouti-related protein homolog (AGRP),14 adiponectin (APM1),15 β2-adrenergic receptor (ADRB2),16,17,18,19 β3-adrenergic receptor (ADRB3),20,21,22,23,24,25,26 apolipoprotein A-II (APOA2),27 apolipoprotein A-IV (APOA4),28 apolipoprotein B (APOB),29 apolipoprotein E (APOE),30 core-binding factor, α subunit 2 (CBFA2T1),31 cytochrome P450, subfamily XIX (CYP19),32 estrogen receptor 1 (ERS1),33 fatty acid-binding protein 2 (FABP2),34 resistin (FIZZ3/ADSF),35 γ-aminobutyric acid A receptor, subunit α6 (GABRA6),36 glucagon receptor (GCGR),37 11β-hydroxysteroid dehydrogenase, type I (HSD11B1),38 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A),39 insulin (INS),40 leptin receptor (LEPR),41,42,43 hormone-sensitive lipase (LIPE),44,45 lamin A/C (LMNA),46,47 melanocortin 3 receptor (MC3R),48 melanocortin 4 receptor (MC4R),49 neuropeptide Y (NPY),50 glucocorticoid receptor (NR3C1),19,51,52,53 peroxisome proliferative activated receptor γ (PPARG),54,55 peroxisome proliferative activated receptor γ, coactivator 1 (PPARGC1),56 tumor necrosis factor (TNF),57,58 tumor necrosis factor receptor superfamily, member 1B (TNFRSF1B),59 uncoupling protein 2 (UCP2),60 and uncoupling protein 3 (UCP3).61
In addition to the positive association studies summarized in Table 1, the number of ‘negative’ reports, which are only partly reviewed below, is also increasing very rapidly. Given the large number of negative studies, it is important to point out that in most studies, lack of power is a major problem. Negative findings that result from lack of power do not refute the hypothesis and should thus be approached with considerable skepticism, especially considering that there is a strong inverse relationship between the frequency of a given allele in a population and the number of probands required to test its contribution to the phenotype, particularly for a complex trait such as central obesity.62 Besides lack of power, most studies are also confounded by lack of adequate controls. Indeed, the real challenge in genetic association studies often lies in the recruitment of healthy volunteers. It is often convenient to use hospital staff or patients suffering from another disease as controls. Depending on the source of controls (blood banks, spouses, healthy hospital workers, etc), control groups are generally confounded by a selection bias that may influence the genetic make-up of that population.
To date, no finding comes close to the stringent level of nominal statistical significance demanded for conventional levels of genome-wide significance. However, several polymorphisms have attracted interest and these include interesting positive reports that await robust validation. These will be considered in more detail in the following sections.
β2-Adrenergic receptor gene (ADRB2)
Genes that are involved in the regulation of catecholamine function may be of particular importance for human obesity because of the central role catecholamines play in energy expenditure, both as hormones and as neurotransmitters.63,64 This regulation is in part affected by stimulating lipid mobilization through lipolysis in fat cells. The β2-adrenergic receptor is a major lipolytic receptor in human fat cells,63,64 Recent data indicate that the muscle sympathetic nerve activity is increased in men with elevated WHR and visceral fat area.65
Four variants in the ADRB2 have been identified that cause changes in the encoded amino acids at residues 16, 27, 34, and 164.66 The most frequent polymorphisms were arginine 16 to glycine (Arg16 → Gly) and glutamine 27 to glutamic acid (Gln27 → Glu).66 In a group of 140 Swedish women with a BMI ranging from 17.8 to 60.0 kg/m2, homozygotes for Glu27 had significantly higher WHR (Glu27 allele frequency 0.40).67 However, no significant association with changes in β2-adrenergic receptor function was observed. In contrast, the Arg16Gly polymorphism was associated with a five-fold increased agonist sensitivity of the β2-adrenergic receptor.67 This variant was not significantly linked with obesity.67 However, in a cohort of 267 Swedish men, the Arg16Gly genotype showed significant relationships to WHR (Glu27 allele frequency 0.41; Gly16 allele frequency 0.55).18 In a nonrandomly selected subgroup of the 140 previously investigated Swedish women, homozygosity for the Glu27 allele was no longer significantly associated with elevated WHR (Glu27 allele frequency 0.38).68 In a relatively large sample of the northern French population, Gln27Gln men had higher waist circumference and WHR compared with Glu27 carriers (Glu27 allele frequency 0.41).16,17 These analyses were stratified on gender and physical activity, and were adjusted for age, blood pressure, and consumption of tobacco and alcohol.16 In a smaller group of unrelated Japanese men, the Gln27Glu heterozygotes had significantly higher age-adjusted BMI but not visceral fat area.69 The potential effect of Glu27 homozygosity was not investigated in this study since only one Glu27 homozygote was detected (Glu27 allele frequency 0.05).69 In another sample of Japanese men, the Gln27Glu and Arg16Gly polymorphisms of the ADRB2 were not associated with either BMI or WHR (Glu27 allele frequency 0.05; Gly16 allele frequency 0.48).70 Similar results were obtained in a sample of Korean men and women (Glu27 allele frequency 0.10; Gly16 allele frequency 0.38).71
These studies present a confused picture. Explanations proposed for the discrepancy in results would typically include differences in subject characteristics (age, sex, etc), differences in the phenotype studied, and the evidently lower Glu27 allele frequency in the East Asian studies. However, with one exception,16 a striking feature of these studies is the failure to control for environmental exposure (fat intake, physical activity, etc). Considering that the pathogenesis of central obesity is most likely a multistage process in which several genetic and environmental events underlie each stage, controlling for environmental confounders provides a rational strategy to achieve disease susceptibility gene identification.
β3-Adrenergic receptor gene (ADRB3)
The β3-adrenergic receptor, located mainly in adipose tissue, is involved in the regulation of lipolysis and thermogenesis. In 1995, a series of papers published in the New England Journal of Medicine indicated a potential relevance of a missense mutation in the ADRB3 to human obesity.20,72,73 A cytosine-to-thymidine substitution was identified resulting in the replacement of tryptophan by arginine (Trp64Arg) in the first intracellular loop of the receptor.20,72,73
In a group of 108 female Finns, the Arg64 allele was significantly associated with an elevated WHR.20 When linkage analysis was conducted in subjects with predisposition to type II diabetes, no evidence for linkage to either BMI or WHR was found.74 Moreover, Arg64 carriers had significantly lower WHR than Trp64 homozygotes.74 Based upon both association and linkage analyses, no significant difference in abdominal visceral fat between carriers and noncarriers for the Trp64Arg mutation was identified in two large cohorts: the Quebec Family Study (QFS) and the Swedish Obese Subjects (SOS).75 In two Japanese cohorts of 131 women and 221 men, the visceral fat area measured by computerized tomography (CT) scan was greater in Arg64Arg and Trp64Arg subjects than in subjects homozygous for the Trp64 allele.22,24 It should be noted that the Arg64 allele frequency among these populations is variable as it ranges from 0.04 and 0.08 in the QFS and SOS cohorts to 0.23 in the Japanese studies. Similar Arg64 allele frequency as in the SOS cohort has been reported in two other negative studies of Caucasian subjects.76,77 In sib-pairs identified among Mexican-American families participating in the San Antonio Family Heart Study, the presence of the Arg64 variant was associated with borderline significantly higher values in waist circumference (mean inter-sib difference: 75 mm; P=0.050).
Over the last few years, a number of negative findings have been reported,55,78,79,80,81 which raises questions as to the validity of the previously positive findings. Furthermore, attempts to assess the functional properties of the Trp64Arg variant have yielded conflicting results.82,83,84,85,86,87. The apparent discrepancy between the studies may reflect differences in methodology. Another possibility might be that the Trp64Arg variant per se is a neutral SNP in linkage disequilibrium with other neighboring regions of the ADRB3 that may harbor mutations that influence central obesity.
Leptin receptor gene (LEPR)
The ob gene product leptin is an adipocyte-derived hormone that acts on the brain through specific receptors to stimulate a signaling cascade resulting in the inhibition of several orexigenic neuropeptides, while stimulating several anorexigenic peptides.88 Leptin has also extrahypothalamic functions, and potential sites of action correspond to the distribution of leptin receptors.88 The leptin receptor is a single transmembrane protein belonging to the superfamily of cytokine receptors and has several alternatively spliced isoforms.89
Several SNPs have been described in the human leptin receptor gene (eg Gln223Arg, Ser343Ser, Ser492Thr, Lys656Asn).90,91 One of these, Lys109Arg, is reported to be associated with low BMI and abdominal sagittal diameter, as well as low systolic and diastolic blood pressure.41 These findings suggest the existence of protective alleles and might explain why not all obese men are hypertensive. Similar protective alleles against coronary heart disease92 and type II diabetes93 are established. It has been suggested that an important question for obesity is how to find such alleles.94
In postmenopausal women, carriers of the Asn656 allele had increased total abdominal fat measured by CT scan.42 Total abdominal fat was also higher in Gln223Gln homozygotes.42 In a population sample of Brazilian men of European descent, the Arg223Arg genotype was more frequent in subjects with a waist circumference equal or above 102 cm.43 No differences in genotype frequencies among women with and without central obesity were observed.43
Recently, Heo et al95,96 analyzed data pooled from nine studies on the LEPR for the association of three alleles (Lys109Arg, Gln223Arg, Lys656Asn) with BMI and waist circumference. A total of 3263 related and unrelated subjects from diverse ethnic backgrounds including African American, Danish, Finnish, French Canadian, and Nigerian were studied. In the process, the authors encountered the several methodological challenges such as missing data, multivariate analysis, multiallele analysis at multiple loci, heterogeneity, and epistasis (ie nonallelic interaction between different genes).95,96 The results showed that none of the effects were significant at the 0.05 level of significance.95,96
Glucocorticoid receptor gene (NR3C1)
Cortisol regulates adipose-tissue differentiation, function, and, distribution, and in excess, causes visceral obesity.97 In addition, cortisol plays an important role in both glucose homeostasis and blood pressure regulation, as demonstrated most strikingly in Cushing's syndrome. Glucocorticoids exert their cellular action by complexing with a specific cytoplasmic receptor, the glucocorticoid receptor, which in turn translocates to the nucleus and binds to specific sites on chromatin.97 Mutations in the NR3C1 gene were first reported in 1991.97 To date, a number of mutations within the human NR3C1 gene have been described.97
In the QFS cohort, two studies have reported that a biallelic (4.5- and 2.3-kb alleles) polymorphism is associated with a higher abdominal visceral fat (AVF) area in both men and women, independently of total body fat mass.19,51 However, the association with AVF was seen only in subjects of the lower tertile of the percent body fat level. In these subjects, the polymorphism was found to account for 41 and 35%, in men and women, respectively, of the total variance in AVF area.51 In a general population of non-Cushingoid middle-aged Swedish men, the 4.5-kb fragment was associated with elevated BMI, WHR, and abdominal sagittal diameter.52 In a randomly selected subgroup of the Newcastle Heart Project, a polymorphism of the NR3C1 resulting in an asparagine-to-serine substitution at codon 363, previously described, tested, and proven to lack any functional effects in vitro,97 was found to be associated with WHR in Caucasian men.53 To observe an association with WHR in this report, the authors needed to correct for a number of other factors that associate with central obesity, hence the multivariate approach (Redfern, personal communication). In a cohort study of 268 Swedish men, all aged 51 y, the Asp363Ser variant was neither associated with BMI nor with measurements of central obesity.98 When using a similar multivariate approach as in the previously mentioned report, the lack of association remained (unpublished). The frequency of the Ser363 allele was 0.05 in this cohort, which is comparable to the previous report.53 In a Danish case–cohort study, comprising 741 obese subjects and 854 random control subjects, no differences in BMI, WHR, or weight gain were seen within any of the groups when defined according to the Asp363Ser genotype.99 Recently, in a subgroup of 202 healthy elderly subjects of the Rotterdam Study, an arginine-to-lysine amino acid change at codon 23, which does not seem to alter the activity of the receptor in vitro,100 was found to be related to lower insulin and fasting glucose levels.101 No differences between the genotypes in BMI or WHR were observed.101
Several independent clinical investigations have suggested that the 4.5/2.3-kb polymorphism, whose detection requires laborious Southern blotting due to lack of sequence information, is associated with hyperinsulinemia, visceral obesity, blood pressure, and hypersensitivity of the hippocampus to glucocorticoids.97 The exact mechanism through which this variant introduces these pathological phenotypes is still uncertain.
Tumor necrosis factor gene (TNF)
Tumor necrosis factor (TNF) is a multifunctional proinflammatory cytokine, and the biological actions of this cytokine are concentration dependent.102 At low concentrations, TNFs act locally as a paracrine and autocrine regulator of immunoinflammation.102 At high concentrations, TNF (namely TNFα) enters the blood stream where it can act as an endocrine factor.102 The first biallelic TNF polymorphism in humans was detected in 1992,103 and involved a single base change from guanine to adenine at position −308 in the promoter region of the gene. In vitro experiments have demonstrated that the −308 variant increases transcriptional activation of the TNF gene.104,105
Some, but not all studies, have indicated a key role for the −308 variant of the TNF gene in the pathogenesis of obesity and obesity-associated insulin resistance.57,58,59,106,107,108,109,110,111 However, only four of these studies include anthropometric indices of central obesity.57,58,110,111 Among these studies, two reported associations between the G−308A variant of the TNF and measurements of central obesity.57,58 In one of the negative studies, men homozygous for the A allele had a trend toward greater central adiposity and higher insulin and glucose levels.111 Since the statistical power is less than 50% in this study, it should be recognized that the sample size has but a modest chance to provide any robust statistical protection against type II error, which in turn delays the prospect of detecting plausible effect sizes for susceptibility genes.9,112,113
Besides methodological issues, the conflicting results raise questions about the penetrance of the G−308A variant, the presence of genetic modifiers, and how TNF interacts with low- or moderate-penetrance genes, which likely cumulate to contribute to an overall central obesity risk and to disease characteristics. In this regard, when several SNPs occur in the same gene such as the TNF,114 it will be crucial to establish in each individual tested using haplotyping, if possible, an exhaustive genomic profiling for all selected sequence variants.
Identifying central obesity susceptibility alleles
The case–control study has been the most widely applied strategy of association studies for characterizing the genetic contribution to central obesity. This approach, however, is most prone to identify gene variants that prove to be spuriously associated with disease.115 The nature of spurious association is the failure to replicate the data and the inability to provide evidence for genetic linkage.62 An important difficulty with this study design is the choice of controls, which are often identified and ascertained after collection of the disease group.115 Ideally, cases and controls should be matched by ethnic origin, age, and gender, and this can normally only be carried out by seeking community-based controls. Thus, a population-based control population, representing probands with a wide range of demographic and physical characteristics, would provide a crucial resource for association studies.
As we move into the postgenome era it can be anticipated that the ever-improving laboratory and statistical methodologies will provide increasing power to the association paradigm.116 However, despite the level of statistical evidence in favor of an allele–central obesity association, it will be necessary to demonstrate the biological mechanism underlying the putative pathogenic association. For instance, the demonstration that an SNP of the gene that encodes the peroxisome proliferative activated receptor γ (PPARγ) associated with central obesity 54,55 is also associated with a moderate reduced transcriptional activity of PPARγ adds to the credibility of this polymorphism as a potential genetic marker for central obesity.
Many common diseases ranging from cancer to Alzheimer's disease are polygenic in nature, with more than one gene contributing to disease susceptibility in the population. Association studies considering only one or a small fraction of genetic variants may well fail to demonstrate differences in allele frequency, which in itself have a role in the development of central obesity. The selection of several polymorphisms within one candidate gene and the analysis of their respective frequencies as well as their combination (haplotypes) in cases and controls may reveal mutations in linkage disequilibrium with a given haplotype, or help identify a series of interacting SNPs acting synergistically at a given locus. However, it is difficult to understand the haplotype and linkage disequilibrium structure with only two or three markers per candidate gene.8 Indeed, as many as 100 000 SNP markers will only yield about one etiologic signal per gene.8 To work within such broad range of multiple genotyping will clearly pose a laboratory and statistical challenge.
In general, progress in complex disease genetics has been hampered by the low detectance power of mapping approaches.8 This problem reflects heterogeneity in both genetic and environmental influences. For many of the candidate genes in central obesity that have been studied, positive and negative results have been published, possibly because the studies were conducted in populations differing in their demographic parameters (age, sex, ethnicity) or the phenotype studied. It is obvious that environmental factors may produce dramatic phenotypic changes, and even pre- and postnatal environmental conditions correlate strongly with phenotypes expressed in later life such as central obesity.117 Since the genetic heritability of the central obesity phenotype is less than 50%,2 most of the variation in phenotype is not genetic in any simple sense. For this reason it would seem imperative to put more effort in controlling for potential environmental confounders in the study design,12,118,119
No gene has yet been confirmed as a susceptibility or modifying locus for central obesity. Interesting findings have been reported with several candidate genes/polymorphisms suggesting that they may play a modest role in susceptibility to illness or modification of the course of illness, at least in a subset of individuals with central obesity.
The pathogenesis of central obesity is complex. Identification of an effect of a polymorphism will be the first, simple step on a more challenging path toward elucidation of the biological pathways involved, and crucially, the gene–gene and gene–environment interactions. If a genotype–phenotype relationship is to be found, then as much of the background noise from nongenetic factors as can be eliminated should be eliminated, and future studies must fulfill more stringent criteria than those adopted in the majority of reports published.
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Rosmond, R. Association studies of genetic polymorphisms in central obesity: a critical review. Int J Obes 27, 1141–1151 (2003). https://doi.org/10.1038/sj.ijo.0802397
- association study
- central obesity
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