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The genetic influence on body fat distribution
Drug Discovery Today: Disease Mechanisms
DRUG DISCOVERY
TODAY
Vol. 10, No. 1–2 2013
Editors-in-Chief Toren Finkel – National Heart, Lung and Blood Institute, National Institutes of Health, USA Charles Lowenstein – University of Rochester Medical Center, Rochester, NY.
DISEASE Mechanisms of Obesity MECHANISMS
The genetic influence on body fat distribution Robert Wagner1,2,3, Fausto Machicao1,2,3, Andreas Fritsche1,2,3, Norbert Stefan1,2,3, Hans-Ulrich Ha¨ring1,2,3, Harald Staiger1,2,3,* 1
Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tu¨bingen, Germany 2 Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tu¨bingen (IDM), Tu¨bingen, Germany 3 German Center for Diabetes Research (DZD), Neuherberg, Germany
Measures of general adiposity have limitations in the
with hypothesis-free approaches is the low effect size of
prediction of metabolic complications of obesity. Body
discovered variants and, in most cases, the lack of
fat compartments, such as abdominal visceral fat,
pathomechanistic explanations. Further studies using
interscapular fat, perivascular fat around the brachial
more sophisticated methods for the assessment of
artery, perivascular fat around the thoracic artery and
body fat distribution are needed to advance our knowl-
liver fat content, correlate better with insulin resis-
edge in this field.
tance, glucose intolerance, diabetes and hypertension than body mass index. Finding the origin of specific fat compartments could help in the development of new therapeutic strategies.
Section editor: Haiming Cao – NHLBI, National Institutes of Health, Bethesda, MD, USA.
The profound genetic determination of body fat distribution has been demonstrated in twin studies and
Introduction
complex segregation analyses. Genome-wide associa-
The increasing prevalence of obesity has become one of the most serious public health issues in industrialized countries. Obesity is a well-acknowledged risk factor for a series of diseases such as diabetes, hypertension, stroke, coronary heart disease, and cancer [1,2]. Beside the important aspect of a distinct lifestyle, genetic factors are also thought to largely impact on the development of obesity. In search of genetic determinants of obesity during the recent years, several genes were found. However, excess fat deposition has strikingly different patterns. This is especially true for moderate degrees of obesity. Probably, the first distinction of apple shaped (male type, ‘android’) and pear shaped (female type, ‘gynoid’) obesity has been made by Vague in 1947 [3]. Since the seminal work of Larsson and associates in 1984, we also know that different body fat distributions translate into
tion studies delivered clear evidence for an association of specific genes or genetic regions with waist-to-hip ratio, waist circumference, visceral fat area, and pericardial fat determined by computed tomography. Many of these SNPs and genes also associate with metabolic end-points, such as insulin resistance and diabetes. Candidate gene studies also discovered polymorphisms that are suggested to be associated with markers of body fat distribution. Although most of the results of small studies are not replicated, the problem *Corresponding author.: H. Staiger ([email protected]) 1740-6765/$ ß 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.ddmec.2013.05.003
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different disease risk profiles [4]. The recognition of a strong association between certain body fat distribution patterns and cardiovascular risk lead to a hugely increased interest in the study of this area. It was postulated that understanding the pathology of excess fat storage patterns would unravel new pathways in the disease mechanism of diabetes and atherosclerosis. Identifying potential drug targets to treat obesity and its metabolic consequences is crucial to extend our yet insufficient treatment armamentarium.
The assessment of body fat distribution Rationale Although the commonly used body mass index (BMI) is an easily measurable proxy of general fat mass, it has several limitations [5,6]. Among them the impact of ageing, race, muscle mass and weight loss with and without exercise largely influences the power of BMI to predict adiposity. Furthermore, general adiposity has considerable limitations in the prediction of adverse metabolic consequences. It was shown that approximately 30% of obese individuals have neither insulin resistance, nor early signs of atherosclerosis [7–9]. Conversely, a non-obese but metabolically abnormal phenotype exists as well [10], which is missed when using simple parameters such as BMI, but can be identified with more sophisticated methods [11]. In this aspect, the accumulation of deep abdominal fat, that is, visceral fat, emerged from early on as a better determinant of glucose intolerance than BMI [12]. Since then, several specific fat compartments have been identified as determinants of an adverse metabolic environment. In this regard, interscapular fat accumulation has been shown to be independently associated with insulin resistance [13]. Also, accumulation of fat in the pancreas impacts on glucose metabolism, probably by impairing insulin secretion [14]. Furthermore, a cluster of specific perivascular fat depots are independently correlated with metabolic complications (reviewed in [15]). Certain types, like perivascular fat around the brachial artery [16], seem to influence systemic manifestations of the metabolic syndrome such as insulin resistance. Other perivascular fat depots have impact on functions of the involved organ, such as renal sinus fat that potentially facilitates albuminuria [17] and causes hypertension [18]. Thoracic periaortic fat has been shown to be associated with peripheral arterial disease [19] and cardiovascular disease [11]. Adipocytes are cells dedicated to the accretion of triglycerides, and they constitute the frame for example, for perivascular fat accumulation patterns. However, deposition of large amounts of triglycerides in the skeletal and cardiac myocytes and in the liver is a pathologically different entity. This kind of triglyceride deposition constitutes ectopic fat. From specific types of ectopic fat, intramyocellular, but more importantly intrahepatic, fat are strong predictors of insulin e6
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resistance that explain large parts of the effects formerly attributed to visceral adipose tissue [20]. Present knowledge on the genetic determination of hepatic fat accumulation has been just reviewed extensively [21].
Methods Traditional techniques of body composition analysis include measurement of skin fold thickness, underwater weighing and radioisotope dilution methods (reviewed in [22]). Because T1-weighted magnetic resonance imaging (MRI) is a safe and non-invasive procedure which is particularly suitable for differentiation of lean and adipose tissue mass, this method evolved to be the gold standard for the volumetric determination of specific adipose tissue compartments [23]. Computed tomographic (CT) imaging of visceral fat area (VFA) and subcutaneous fat area (SFA) delivers similar results, but its use is limited by ethical concerns related to ionizing radiation [24]. Dual-energy X-ray absorptiometry has been shown to be a good alternative to CT in predicting total abdominal fat mass, however, it cannot differentiate subcutaneous and visceral fat better than anthropometric methods [25,26]. Abdominal body impedance analysis (‘VFA scanner’) can also be used to measure visceral fat with a good correlation to CT results [27]. Finally, ultrasound techniques can be applied to assess abdominal subcutaneous and intraabdominal fat thickness, however, their predictive power is rather small [28]. Anthropometric methods are less accurate, but easier to perform and cheaper. Thus, they are better suitable for large studies. Among the anthropometric parameters, waist circumference (waist girth) correlates stronger with CT-determined visceral abdominal fat mass than the commonly used waist-to-hip ratio (WHR) [29]. Waist circumference also outperformed WHR in regard to cardiovascular disease prediction [30] and was a stronger predictor of diabetes than BMI or the body adiposity index [31]. It is not known why the incorporation of hip circumference in a simple prediction model is of less value than the usage of waist circumference alone. Possibly a sampling error is more often introduced in large studies when hip circumference is measured.
Genetic factors determining body fat distribution Early evidence of genetic determination Since the mid 1980-ies, a solid body of evidence accumulated on the important role of the genetic background on body fat distribution, and these studies have been reviewed in the past [32,33]. A seminal work of Bouchard and associates involved 12 pairs of monozygotic twins who had been fed with a highcalorie diet for 84 days. The study demonstrated an intra-class correlation coefficient of 0.72 for the increase of CT-determined VFA after controlling for covariates, showing that a considerable part of the propensity for visceral fat gain is genetically determined [34]. Complex segregation analyses
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suggested that a major gene would account for about half of the variance in CT-determined abdominal visceral fat mass, but a Mendelian inheritance could not be demonstrated after adjustment for total fat mass [35,36].
Genome-wide associations studies The technical development of rapid array-based genotyping and computing enabled the hypothesis-free investigation of genomic common variations in a large number of individuals. Hundreds of thousands of such single nucleotide polymorphisms (SNPs) have been scanned for associations with body fat distribution phenotypes in genome-wide association studies (GWAS) since 2008. Altogether, 22 genes emerged with genome-wide significant association signals for measures of body fat composition (Table 1). Fifteen of these have been found in a GWAS looking for determinants of WHR [37,38] and 4 SNPs have been identified in studies investigating waist circumference [39–41]. An additional SNP in the LYPLAL1 gene (rs11118316) which is weakly (r2 = 0.28) linked to the WHR signal (rs4846567) has been found to be associated with the CT-measured ratio of visceral and subcutaneous abdominal fat mass and a SNP (rs1659258) in another genomic region between THNSL2 and FABP1 was associated with CT-measured visceral fat mass in women [42]. An example for dissociation of general adiposity and the metabolically more dangerous visceral adiposity is rs2943650 in IRS1. The major allele of this SNP was associated with lower total body fat mass at a genome-wide significance, but was also convincingly demonstrated (p = 6.1 106) as a determinant of higher visceral/subcutaneous fat ratio and an adverse metabolic profile in men [43]. Furthermore, a study involving altogether 9000 individuals in the combined discovery and validation cohorts located a SNP strongly associated with pericardial fat [44]. These analyses provided clear evidence for the existence of a sexual dimorphism with effects generally larger in women. The most striking difference in effect strength was observed at LYPLAL1, and opposite effect directions were observed at GRB14 [37]. For IRS1, the genetic effect was stronger in men [43]. What are the mechanisms of action of these SNPs? All the discovered signals are located in intergenic or intronic regions. But, even in intronic regions, the respective gene does not necessarily cause the observed phenotypic variance. Primary biological processes still remain a matter of speculation. Two of the SNPs lie in the vicinity of genes encoding homeobox-proteins (TBX15, HOXC13), known orchestrators of morphogenesis, suggesting that they may play a direct role in the ontogeny of fat depots. The SNP near ADAMTS9 belongs to a protein family which has also been implicated in the control of organ shape during development [45]. Other potentially involved genes encode transcription factors (TFAP2B, NFE2L3, CPEB4). Two SNPs may be implicated in
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wnt-signaling (RSPO3, ZNRF3-KREMEN1), and two SNPs might have a connection to body fat distribution through insulin signaling (IRS1, GRB14). In addition to the latter, a direct link to metabolism can also be suspected for the SNP near FABP1 which encodes a fatty acid binding protein. Altogether and unfortunately, GWAS did not largely improve our biological understanding about mechanisms regulating body fat distribution. The effect sizes of the discovered variants are relatively small (the largest effect is estimated at 1.48 cm for waist circumference and 0.04 for WHR SNPs, [46]). Furthermore, the 14 WHR SNPs discovered in 2010 explained only 1.34% of the variance of WHR in women and 0.46% in men [37]. Potential causes of the missing explained heritability in genome-wide obesity association studies are objects of broad discussion [46]. Both in the case of general obesity and body fat distribution, we postulate that the use of easily measurable but arbitrary anthropometric markers in part underlies the merely moderate success of most GWAS approaches. Anthropometric proxies of body fat distribution are resultants of a myriad of biological processes, and are, therefore, prone to effect modification and interaction with environmental factors which are too complex to be modeled. Anatomically and physiologically more precise proxies, such as fat volume computed by MRI, should better reflect the results of biological processes and could, therefore, provide more detection power. This is seen in the visceral fat and pericardial fat studies performed by Fox et al. who demonstrated genomewide significant effects in, for GWAS dimensions, smaller samples of 10,000 individuals [42,44].
Candidate gene studies Numerous genes have been postulated to control body fat distribution and were examined in hypothesis-driven linkage studies. Since none of these smaller studies delivers genomewide significant results generally accepted at p < 5 108, the validity heavily relies on the study quality [47]. The first variant which was demonstrated to be associated with central obesity was a polymorphism in the 50 -flanking region of the insulin (INS) gene [48]. A later study found that a deletion/ insertion variation in INS leads to increased CT-determined visceral fat mass in South African women [49]. The BclI restriction fragment length polymorphism (also known as rs41423247) of the glucocorticoid receptor gene (NR3C1), has been found to be associated with WHR and CT-determined abdominal fat mass in several small studies [50]. Another steroid hormone receptor gene, the estrogen receptor a gene (ESR1), has been implicated as a determinant of WHR in an investigation conducted with 1763 participants of the Framingham Heart Study. Rs1004467 in the CYP17A1 gene, encoding an enzyme of steroid hormone synthesis, has been suggested, alongside rs11191548 of NT5C2, to have an impact on subcutaneous and visceral fat area [51]. The initial www.drugdiscoverytoday.com
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Table 1. Variants influencing phenotypes of body fat distribution which were discovered in GWAS Alleles (major/minor)
Remarks on functione of the nearest gene(s) and further metabolic associations
6
C/G
Encodes a member of the thrombospondin type 1 repeat supergene family.
VEGFA
6
A/G
Encodes the vascular endothelial growth factor. This SNP may also be associated with insulin sensitivity in women [74]. Another SNP in this gene is associated with T2DM (rs9369425) [75,76] and possibly insulin secretion [80].
WHR [37]
TBX15-WARS2
1
G/C
TBX15 (T-box family) encodes a transcription factor that regulates a variety of developmental processes; WARS encodes mitochondrial tryptophanyl-tRNA synthetase 2
rs1055144
WHR [37]
NFE2L3
7
A/G
Encodes a member of the cap ‘n’ collar basic-region leucine zipper family of transcription factors.
rs10195252
WHR [37]
GRB14
2
C/T
Encodes a growth factor receptor-binding protein that interacts with insulin receptors and insulin-like growthfactor receptors. Also associated with T2DM in South East Asians [77].
rs2605100
WHR [41]
LYPLAL1
1
G/A
Encodes phopholipase-like 1. Marked sexual dimorphism, the effect on body fat distribution phenotypes is present in females only. Low to moderate LD between SNPs. Another SNP (rs3001032), which is in high LD with rs4846567, was associated with fasting insulin in a GWAS after adjustment for BMI [73].
rs4846567
WHR [37]
G/T
a
G/A
SNP
Phenotype and reference
Nearest gene
rs9491696
WHR [37]
RSPO3
rs6905288
WHR [37]
rs984222
Chr
rs11118316
VAT/SAT [42]
rs1011731
WHR [37]
DNM3-PIGC
1
A/G
DNM3 encodes dynamin 3 – the protein possesses mechanochemical properties involved in actin-membrane processes (membrane budding). PIGC encodes an endoplasmic reticulum associated protein that is involved in glycosyl-phosphatidylinositol lipid anchor biosynthesis.
rs718314
WHR [37]
ITPR2-SSPN
12
A/G
ITPR2 encodes inositol 1,4,5-trisphosphate receptor, type 2. SSPN encodes a member of the dystrophin-glycoprotein complex, which constitutes a structural trans-sarcolemmal link in muscle cells.
rs1294421
WHR [37]
LY86
6
G/T
CNV-tagging SNP. The LY86 gene encodes lymphocyte antigen 86.
rs1443512
WHR [37]
HOXC13
12
C/A
Encodes Homeobox C13 (transcription factor playing a role in morphogenesis).
rs6795735
WHR [37]
ADAMTS9
3
C/T
This gene encodes a member of the ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs) protein family. Another SNP in this region (rs4607103) is associated with T2DM [76]
rs4823006
WHR [37]
ZNRF3-KREMEN1
22
A/G
ZNRF3 encodes zinc and ring finger 3, a negative feedback regulator of wnt signaling. KREMEN1 encodes a high-affinity dickkopf homolog 1 (DKK1) transmembrane receptor that plays a role in the inhibition of wnt signaling.
rs6784615
WHR [37]
NISCH-STAB1
3
T/C
NISCH encodes nischarin, a nonadrenergic imidazoline-1 receptor protein. STAB1 encodes a large, transmembrane receptor protein, may play a role as a scavanger receptor. The SNP is correlated with 2 non-synonymous variants in the nearby DNAH1 and GLYCTK genes.
rs6861681
WHR [37]
CPEB4
5
G/A
Encodes cytoplasmic polyadenylation element binding protein 4
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Table 1 (Continued ) SNP
Phenotype and reference
Nearest gene
Chr
Alleles (major/minor)
Remarks on functione of the nearest gene(s) and further metabolic associations
rs2074356
WHR (in Asians) [38]
HECTD4 (C12orf51)
12
C/T
Probably encodes E3 ubiquitin protein ligase 4 (HECT domain). Rare variant in Europeans. Also associated with HDL and Gamma-glutamyl transferase (liver enzyme) in Asians [81].
rs10198628
Pericardial fatb [44]
TRIB2
2
G/A
Encodes tribbles homolog 2, one of three members of the Tribbles family.
rs1659258
VATc [42]
THNSL2-FABP1
2
A/G
THNSL2 encodes threonine synthase-like 2 (S. cerevisiae). FABP1 encodes fatty acid binding protein 1, the fatty acid binding protein found in liver. Another variant in the FABP1 gene also seemes to be associated with diabetes and insulin resistance [82]
rs10146997
Waist circumference [40]
NRXN3
14
A/G
Encodes neurexin 3, a member of a family of proteins that function in the nervous system as receptors and cell adhesion molecules
rs2943650
VAT/SATa and total body fatd [43]
IRS1
2
T/C
Encodes insulin receptor substrate 1 which is phosphorylated by insulin receptor tyrosine kinase. A linked variant (rs2943634, r2 = 0.78) associates with fasting insulin and probably diabetes [73]
rs987237
Waist circumference [41]
TFAP2B
6
A/G
Encodes transcription factor AP-2 beta (activating enhancer binding protein 2 beta) a member of the AP-2 family of transcription factors. This protein functions as both a transcriptional activator and repressor
rs545854 (merged from rs7826222)
Waist circumference [41]
MSRA
8
C/G
Encodes methionine sulfoxide reductase A. It carries out the enzymatic reduction of methionine sulfoxide to methionine
rs12970134
Waist circumference [39]
MC4R
18
G/A
Encodes melanocortin 4 receptor. The protein encoded by this gene is a membrane-bound receptor and member of the melanocortin receptor family. Associated with insulin sensitivity [39] and diabetes [83], independent of BMI
a
Visceral fat/subcutaneous fat ratio determined by CT. Determined by cardiac CT. c Determined by CT. d Determined by body impedance analysis and dual-energy X-ray absorptiometry. e Excerpt from the NCBI gene database available under http://www.ncbi.nlm.nih.gov/gene. b
hypothesis for testing 12 hypertension SNPs established in GWAS was a postulated common underlying pathomechanism for hypertension and metabolic syndrome, but the findings for body fat depots and hypertension were directionally not consistent [51]. The beta-3-adrenergic receptor has also been postulated as a relevant modulator of metabolism. The Trp64Arg substitution polymorphism (rs4994) in its gene (ADRB3) was associated with WHR in Finnish [52] and with CT-determined visceral fat area in Japanese [53] subjects. This SNP also significantly associated with C-reactive protein concentrations in a US cohort of women [54], suggesting a link to cardiovascular disease risk. Similar pathomechanistic concepts underlie the hypotheses on the involvement of uncoupling protein genes in the regulation of body fat distribution. Studies point to a possible impact of UCP1 [55] and UCP2 [56] on CT-determined abdominal fat area and WHR, respectively.
Data on genetic variation in the TNF-alpha gene (TNF) are somewhat controversial. The minor allele of rs1800629 (308A) increased the waist and hip circumferences, without changing their ratio in one study [57], but reduced WHR in another study [58]. Because thiazolidendiones, which target the peroxisome proliferator-activated receptor gamma (PPARG), improve insulin action and redistribute visceral fat to subcutaneous depots, corresponding genetic variation constitutes an evident hypothesis for associations with body fat distribution. In the overweight subgroup of a study involving 1051 Korean women, the minor allele (G) of the Pro12Ala polymorphism (rs1801282) was associated with higher WHR, subcutaneous adipose tissue and visceral adipose tissue [59]. Metabolic effects of this polymorphism have been extensively reviewed earlier [60]. Genetic variation in this SNP explained 6.2% of the variation in CT-determined subcutaneous fat area vs. 1.8% of that in visceral fat area, confirming the expectations www.drugdiscoverytoday.com
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Metabolic Traits
General adiposity trait (BMI)
NPC1 PFKP INSIG2
Body fat distribution traits
CTNNBL1
GRB14 VEGFA
PTER
TMEM18
PRL
LYPLAL1 RSPO3
ADAMTS9
TBX15-WARS
HECTD4
DNM3-PIGC
25 additional loci of minor effects
SH2B1
FABP1
NFE2L3
GPRC5B
FTO
MC4R
KCTD15 SEC16B
BDNF*
IRS1
GNPDA2 BCDIN3D/FAIM2
ITPR2-SSPN
TFAP2B
LY86
MTCH2
THNSL2-FABP1
MAF
NEGR1 CPEB4
FDFT1 TRIB2
NISCH-STAB1 ETV5-DGKG
ZNRF3-KREMEN1 MSRA
HOXC13 NRXN3
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Figure 1. Genes suggested to be associated with body fat distribution and body mass index in genome-wide association studies with their relationship to metabolic traits. Notes: SH2B1: associated with T2DM independently of BMI [72], probably also associates with visceral fat [62,71], and insulin sensitivity [84]. FTO: no association with T2DM in Europeans after adjustment for BMI; rs9939609 also associated with T2DM after adjustment for BMI in Japanese [85]. BDNF*: opposite effect direction between BMI and fasting glucose [72]. BCDIN3D/FAIM2 rs7138803: associates with diabetes after adjustment for BMI in the discovery study [86]; with waist circumference and WHR [72], and with coronary artery disease [87]. TMEM18: rs6548238 associated with T2DM after adjustment for BMI in Danes [88], and rs4854344 in Japanese [85]. TFAP2B: associated with T2DM in Japanese subjects [78], and insulin sensitivity in European children [79]. NEGR1: also associated with visceral adipose tissue [42]; no association after BMI-adjustment in the discovery study [86]; trend for an association with T2DM in Europeans [72].
that the gene has a larger influence on subcutaneous fat deposition [59]. However, the G allele confers a reduced risk for type 2 diabetes, especially in Asians [61], and a recent study could not replicate its effect on body fat distribution [62]. A polymorphism in the exon of the peroxisome proliferator-activated receptor gamma coactivator-1a (rs8192678) also influenced WHR in the obese subgroup of non-diabetic Chinese subjects [63]. In addition, the endocannabinoid system has been implicated in the regulation of body weight in humans. Rs12720071 in the cannabinoid type 1 receptor gene (CBR1) was shown to be associated with subscapular skinfold thickness and WHR [64], and another SNP (rs1049353) was found to be associated with MRI-determined visceral fat mass in 783 healthy young men [65]. Furthermore, variation in the proopiomelanocortin (POMC) gene, which plays a role in the regulation of appetite in the central nervous system, was nominally linked to WHR and visceral abdominal fat mass in one study [66]. e10
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Also genes regulating adipokine secretion are thought to affect body fat mass and distribution. The RARRES2 gene, encoding the adipokine chemerin, was shown to be associated with MRI-determined visceral adipose tissue mass [67]. The 420C > G promotor variant in RETN, which encodes the resistin gene, was associated with less visceral fat mass in men, independent of age and adiposity [68]. The ACE I/D polymorphism has been also suggested to be associated with abdominal adiposity [69,70]. Several GWAS-discovered obesity-associated SNPs have been examined for associations with body fat distribution. Some of these data suggest an association of SH2B1 with abdominal fat [62,71], and the variation between BCDIN3D-FAIM2 and WHR, waist circumference [72].
Conclusions Both, candidate gene approaches and GWAS have now been extensively used to identify major genes regulating body fat mass and -distribution. Interestingly, none of the associations
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established in candidate gene studies emerged in GWAS as important determinants. The candidate gene studies that investigated phenotypes of body fat distribution with CT- or MR-imaging, were generally small. Although the results were in most cases not corroborated by independent studies, they are nevertheless mechanistically plausible. On the other side, completely new genetic associations were discovered with GWAS. Some of these genes and genomic regions have not functionally been characterized well, and would have probably remained in the dark without these discoveries. The close mechanistic link between visceral fat mass and an adverse metabolic setting is reflected by the fact that several genes provide both signals for visceral obesity and metabolic traits in GWAS (see Fig. 1). The GWAS of Manning et al. [73] looked for determinants of fasting insulin and glucose after accounting for variation and interaction with BMI. They identified 3 SNPs which are in or near previously identified WHR genes (LYPLAL1, GRB14, IRS1). Furthermore, a hypothesis-driven study identified rs6905288 in VEGFA as a determinant of insulin sensitivity [74], and rs9369425 in this gene was suggested to be associated with type 2 diabetes [75,76]. ADAMTS9 [76] and GRB14 [77] were associated with type 2 diabetes in GWAS. Data on TFAP2B suggest an association with type 2 diabetes [78] and insulin sensitivity [79]. To narrow the hiatus between data delivered by hypothesis-free statistical methods and our current mechanistic understanding, further work is needed on both sides. Finemapping studies looking for the causal variants and mechanistic investigations trying to establish the pathophysiology of the GWAS-discovered genes are certainly needed from the part of the biologic research. Using more elaborate phenotypes such as MR-determined visceral fat mass or liver fat content measured by 1H-MRI in new studies and meta-analyses could identify other genetic variants that may have stronger impact on body fat mass and -distribution and the related metabolic abnormalities.
Conflicts of interest The authors declare that they have no conflicts of interest regarding this manuscript.
References 1 Mokdad, A.H.F.E. (2003) Prevalence of obesity, diabetes, and obesityrelated health risk factors, 2001. JAMA 289, 76–79 2 Carroll, K.K. (1998) Obesity as a risk factor for certain types of cancer. Lipids 33, 1055–1059 3 Vague, J. (1947) La diffe´rentiation sexuelle, facteur de´terminant des formes de l’obe´site´. Presse Me´d. 30, 339–340 4 Larsson, B. et al. (1984) Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br. Med. J. (Clinical Research ed) 288, 1401–1404 5 Garn, S.M. et al. (1986) Three limitations of the body mass index. Am. J. Clin. Nutr. 44, 996–997 6 Prentice, A.M. and Jebb, S.A. (2001) Beyond body mass index. Obesity Rev. 2, 141–147
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7 Stefan, N. et al. (2008) Identification and characterization of metabolically benign obesity in humans. Arch. Intern. Med. 168, 1609–1616 8 Wildman, R.P. et al. (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch. Intern. Med. 168, 1617–1624 9 McLaughlin, T. et al. (2007) Heterogeneity in the prevalence of risk factors for cardiovascular disease and type 2 diabetes mellitus in obese individuals: effect of differences in insulin sensitivity. Arch. Intern. Med. 167, 642–648 10 Ruderman, N.B. et al. (1981) The ‘metabolically-obese,’ normal-weight individual. Am. J. Clin. Nutr. 34, 1617–1621 11 Britton, K.A. et al. (2012) Prevalence, distribution, and risk factor correlates of high thoracic periaortic fat in the framingham heart study. J. Am. Heart Assoc. http://dx.doi.org/10.1161/JAHA. 112.004200 12 Despre´s, J.P. et al. (1989) Role of deep abdominal fat in the association between regional adipose tissue distribution and glucose tolerance in obese women. Diabetes 38, 304–309 13 Thamer, C. et al. (2010) Interscapular fat is strongly associated with insulin resistance. J. Clin. Endocrinol. Metab. 95, 4736–4742 14 Heni, M. et al. (2010) Pancreatic fat is negatively associated with insulin secretion in individuals with impaired fasting glucose and/or impaired glucose tolerance: a nuclear magnetic resonance study. Diabetes Metab. Res. Rev. 26, 200–205 15 Britton, K.A. and Fox, C.S. (2011) Ectopic fat depots and cardiovascular disease. Circulation 124, e837–e841 16 Rittig, K. et al. (2008) Perivascular fatty tissue at the brachial artery is linked to insulin resistance but not to local endothelial dysfunction. Diabetologia 51, 2093–2099 17 Wagner, R. et al. (2012) Exercise-induced albuminuria is associated with perivascular renal sinus fat in individuals at increased risk of type 2 diabetes. Diabetologia 55, 2054–2058 18 Foster, M.C. et al. (2011) Fatty kidney, hypertension, and chronic kidney disease: the Framingham Heart Study. Hypertension 58, 784–790 19 Fox, C.S. et al. (2010) Periaortic fat deposition is associated with peripheral arterial disease: the Framingham heart study. Circ. Cardiovasc. Imaging 3, 515–519 20 Stefan, N. et al. (2008) Causes and metabolic consequences of fatty liver. Endocr. Rev. 29, 939–960 21 Dongiovanni, P. et al. (2013) Genetic predisposition in NAFLD and NASH: impact on severity of liver disease and response to treatment. Curr. Pharm. Des. http://www.ncbi.nlm.nih.gov/pubmed/23394097 22 Lukaski, H.C. (1987) Methods for the assessment of human body composition: traditional and new. Am. J. Clin. Nutr. 46, 537–556 23 Machann, J. et al. (2010) Follow-up whole-body assessment of adipose tissue compartments during a lifestyle intervention in a large cohort at increased risk for type 2 diabetes. Radiology 257, 353–363 24 Ohsuzu, F. et al. (1998) Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat. Med. 16, 99–107 25 Snijder, M. et al. (2002) The prediction of visceral fat by dual-energy X-ray absorptiometry in the elderly: a comparison with computed tomography and anthropometry. Int. J. Obes. Relat. Metab. Disord. 26, 984 26 Clasey, J.L. et al. (1999) The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obesity Res. 7, 256–264 27 Ryo, M. et al. (2005) A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Dia. Care 28, 451–453 28 Armellini, F. et al. (1993) Total and intra-abdominal fat measurements by ultrasound and computerized tomography. Int. J. Obes. Relat. Metab. Disord. 17, 209–214 29 Ferland, M. et al. (1989) Assessment of adipose tissue distribution by computed axial tomography in obese women: association with body density and anthropometric measurements. Br. J. Nutr. 61, 139–148 30 Dobbelsteyn, C.J. et al. (2001) A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of
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38
39
40
41
42
43
44 45
46 47 48
49
50
51
52
53
54 55
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cardiovascular risk factors. Can. Heart Health Surveys. Int. J. Obesity 25, 652–661 Schulze, M.B. et al. (2012) Body adiposity index, body fat content and incidence of type 2 diabetes. Diabetologia 55, 1660–1667 Lemieux, S. (1997) Genetic susceptibility to visceral obesity and related clinical implications. Int. J. Obes. Relat. Metab. Disord. 21, 831–838 Bouchard, C. (1997) Genetic determinants of regional fat distribution. Hum. Reprod. 12 (Suppl 1), 1–5 Bouchard, C. et al. (1990) The response to long-term overfeeding in identical twins. N. Engl. J. Med. 322, 1477–1482 Bouchard, C. et al. (1996) Major gene for abdominal visceral fat area in the Que´bec Family Study. Int. J. Obes. Relat. Metab. Disord. 20, 420–427 Rice, T. et al. (1997) Segregation analysis of abdominal visceral fat: the HERITAGE Family Study. Obes. Res. 5, 417–424 Heid, I.M. et al. (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 Cho, Y.S. et al. (2012) Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat. Genet. 44, 67–72 Chambers, J.C. et al. (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40, 716–718 Heard-Costa, N.L. et al. (2009) NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet. 5, e1000539 Lindgren, C.M. et al. (2009) Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet. 5, e1000508 Fox, C.S. et al. (2012) Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 8, e1002695 Kilpela¨inen, T.O. et al. (2011) Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat. Genet. 43, 753–760 Fox, C.S. et al. (2012) Genome-wide association of pericardial fat identifies a unique locus for ectopic fat. PLoS Genet. 8, e1002705 Blelloch, R. and Kimble, J. (1999) Control of organ shape by a secreted metalloprotease in the nematode Caenorhabditis elegans. Nature 399, 586–590 Sandholt, C.H. et al. (2012) Beyond the fourth wave of genome-wide obesity association studies. Nutr. Diabetes 2, e37 Attia, J.I.J. (2009) How to use an article about genetic association: B: are the results of the study valid? JAMA 301, 191–197 Weaver, J.U. et al. (1992) Central obesity and hyperinsulinaemia in women are associated with polymorphism in the 50 flanking region of the human insulin gene. Eur. J. Clin. Invest. 22, 265–270 Berman, P. et al. (2009) Association between the 4 bp proinsulin gene insertion polymorphism (IVS-69) and body composition in black South African women. Obesity (Silver Spring) 17, 1298–1300 Van Rossum, E.F.C. and Lamberts, S.W.J. (2004) Polymorphisms in the glucocorticoid receptor gene and their associations with metabolic parameters and body composition. Recent Prog. Horm. Res. 59, 333–357 Hotta, K. et al. (2012) Genetic variations in the CYP17A1 and NT5C2 genes are associated with a reduction in visceral and subcutaneous fat areas in Japanese women. J. Hum. Genet. 57, 46–51 Wide´n, E. et al. (1995) Association of a polymorphism in the beta 3adrenergic-receptor gene with features of the insulin resistance syndrome in Finns. N. Engl. J. Med. 333, 348–351 Sakane, N. et al. (1997) Beta 3-adrenergic-receptor polymorphism: a genetic marker for visceral fat obesity and the insulin resistance syndrome. Diabetologia 40, 200–204 Fan, A.Z. et al. (2010) Gene polymorphisms in association with emerging cardiovascular risk markers in adult women. BMC Med. Genet. 11, 6 Kim, K.S. et al. (2005) The finding of new genetic polymorphism of UCP-1 A-1766G and its effects on body fat accumulation. Biochim. Biophys. Acta 1741, 149–155
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Martinez-Hervas, S. et al. (2012) Polymorphisms of the UCP2 gene are associated with body fat distribution and risk of abdominal obesity in Spanish population. Eur. J. Clin. Invest. 42, 171–178 57 Corbala´n, M.S. et al. (2004) Influence of two polymorphisms of the tumoral necrosis factor-alpha gene on the obesity phenotype. Diabetes Nutr. Metab. 17, 17–22 58 Um, J-Y. et al. (2004) Polymorphism of the tumor necrosis factor alpha gene and waist-hip ratio in obese Korean women. Mol. Cells 18, 340–345 59 Kim, K.S. et al. (2004) Effects of peroxisome proliferator-activated receptorgamma 2 Pro12Ala polymorphism on body fat distribution in female Korean subjects. Metab. Clin. Exp. 53, 1538–1543 60 Stumvoll, M. and Ha¨ring, H. (2002) The peroxisome proliferatoractivated receptor-gamma2 Pro12Ala polymorphism. Diabetes 51, 2341–2347 61 Gouda, H.N. et al. (2010) The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am. J. Epidemiol. 171, 645–655 62 Hotta, K. et al. (2012) Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography. J. Hum. Genet. 57, 305–310 63 Weng, S-W. et al. (2010) Gly482Ser polymorphism in the peroxisome proliferator-activated receptor gamma coactivator-1alpha gene is associated with oxidative stress and abdominal obesity. Metab. Clin. Exp. 59, 581–586 64 Russo, P. et al. (2007) Genetic variations at the endocannabinoid type 1 receptor gene (CNR1) are associated with obesity phenotypes in men. J. Clin. Endocrinol. Metab. 92, 2382–2386 65 Frost, M. et al. (2010) Polymorphisms in the endocannabinoid receptor 1 in relation to fat mass distribution. Eur. J. Endocrinol. 163, 407–412 66 Ternouth, A. et al. (2011) Association study of POMC variants with body composition measures and nutrient choice. Eur. J. Pharmacol. 660, 220–225 ¨ ssig, K. et al. (2009) RARRES2, encoding the novel adipokine chemerin, 67 Mu is a genetic determinant of disproportionate regional body fat distribution: a comparative magnetic resonance imaging study. Metab. Clin. Exp. 58, 519–524 68 Bouchard, L. et al. (2004) Human resistin gene polymorphism is associated with visceral obesity and fasting and oral glucose stimulated C-peptide in the Que´bec Family Study. J. Endocrinol. Invest. 27, 1003–1009 69 Riera-Fortuny, C. et al. (2005) The relation between obesity, abdominal fat deposit and the angiotensin-converting enzyme gene I/D polymorphism and its association with coronary heart disease. Int. J. Obes. (Lond.) 29, 78–84 70 Strazzullo, P. et al. (2003) Genetic variation in the renin-angiotensin system and abdominal adiposity in men: the Olivetti Prospective Heart Study. Ann. Intern. Med. 138, 17–23 71 Haupt, A. et al. (2010) Novel obesity risk loci do not determine distribution of body fat depots: a whole-body MRI/MRS study. Obesity (Silver Spring) 18, 1212–1217 72 Sandholt, C.H. et al. (2011) Studies of Metabolic Phenotypic Correlates of 15 Obesity Associated Gene Variants. PLoS One 6, e23531 73 Manning, A.K. et al. (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 74 Burgdorf, K.S. et al. (2012) Association studies of novel obesity-related gene variants with quantitative metabolic phenotypes in a populationbased sample of 6,039 Danish individuals. Diabetologia 55, 105–113 75 Zeggini, E. et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for Type 2 diabetes. Science 316, 1336–1341 76 Zeggini, E. et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638–645 77 Kooner, J.S. et al. (2011) Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989
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80 81
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Maeda, S. et al. (2005) Genetic variations in the gene encoding TFAP2B are associated with type 2 diabetes mellitus. J. Hum. Genet. 50, 283–292 Nordquist, N. et al. (2009) The transcription factor TFAP2B is associated with insulin resistance and adiposity in healthy adolescents. Obesity (Silver Spring) 17, 1762–1767 Staiger, H. et al. (2008) Novel meta-analysis-derived type 2 diabetes risk loci do not determine prediabetic phenotypes. PLoS One 3 Kim, Y.J. et al. (2011) Large-scale genome-wide association studies in east Asians identify new genetic loci influencing metabolic traits. Nat. Genet. 43, 990–995 Mansego, M.L. et al. (2012) Common variants of the liver fatty acid binding protein gene influence the risk of type 2 diabetes and insulin resistance in Spanish population. PLoS One 7, e31853 Xi, B. et al. (2012) Common polymorphism near the MC4R gene is associated with type 2 diabetes: data from a meta-analysis of 123,373 individuals. Diabetologia 55, 2660–2666
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86
87
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Fall, T. et al. (2012) The role of obesity-related genetic loci in insulin sensitivity. Diabet. Med. 29, e62–e66 Takeuchi, F. et al. (2011) Association of genetic variants for susceptibility to obesity with type 2 diabetes in Japanese individuals. Diabetologia http:// dx.doi.org/10.1007/s00125-011-2086-8 Thorleifsson, G. et al. (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 Huang, H. et al. (2012) Implication of genetic variants near TMEM18, BCDIN3D/FAIM2, and MC4R with coronary artery disease and obesity in Chinese: a angiography-based study. Mol. Biol. Rep. 39, 1739–1744 Thomsen, M. et al. (2012) b2-adrenergic receptor Thr164Ile polymorphism, obesity, and diabetes: comparison with FTO, MC4R, and TMEM18 polymorphisms in more than 64,000 individuals. J. Clin. Endocrinol. Metab. 97, E1074–E1079
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DRUG DISCOVERY
TODAY
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Editors-in-Chief Toren Finkel – National Heart, Lung and Blood Institute, National Institutes of Health, USA Charles Lowenstein – University of Rochester Medical Center, Rochester, NY.
DISEASE Mechanisms of Obesity MECHANISMS
The genetic influence on body fat distribution Robert Wagner1,2,3, Fausto Machicao1,2,3, Andreas Fritsche1,2,3, Norbert Stefan1,2,3, Hans-Ulrich Ha¨ring1,2,3, Harald Staiger1,2,3,* 1
Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tu¨bingen, Germany 2 Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tu¨bingen (IDM), Tu¨bingen, Germany 3 German Center for Diabetes Research (DZD), Neuherberg, Germany
Measures of general adiposity have limitations in the
with hypothesis-free approaches is the low effect size of
prediction of metabolic complications of obesity. Body
discovered variants and, in most cases, the lack of
fat compartments, such as abdominal visceral fat,
pathomechanistic explanations. Further studies using
interscapular fat, perivascular fat around the brachial
more sophisticated methods for the assessment of
artery, perivascular fat around the thoracic artery and
body fat distribution are needed to advance our knowl-
liver fat content, correlate better with insulin resis-
edge in this field.
tance, glucose intolerance, diabetes and hypertension than body mass index. Finding the origin of specific fat compartments could help in the development of new therapeutic strategies.
Section editor: Haiming Cao – NHLBI, National Institutes of Health, Bethesda, MD, USA.
The profound genetic determination of body fat distribution has been demonstrated in twin studies and
Introduction
complex segregation analyses. Genome-wide associa-
The increasing prevalence of obesity has become one of the most serious public health issues in industrialized countries. Obesity is a well-acknowledged risk factor for a series of diseases such as diabetes, hypertension, stroke, coronary heart disease, and cancer [1,2]. Beside the important aspect of a distinct lifestyle, genetic factors are also thought to largely impact on the development of obesity. In search of genetic determinants of obesity during the recent years, several genes were found. However, excess fat deposition has strikingly different patterns. This is especially true for moderate degrees of obesity. Probably, the first distinction of apple shaped (male type, ‘android’) and pear shaped (female type, ‘gynoid’) obesity has been made by Vague in 1947 [3]. Since the seminal work of Larsson and associates in 1984, we also know that different body fat distributions translate into
tion studies delivered clear evidence for an association of specific genes or genetic regions with waist-to-hip ratio, waist circumference, visceral fat area, and pericardial fat determined by computed tomography. Many of these SNPs and genes also associate with metabolic end-points, such as insulin resistance and diabetes. Candidate gene studies also discovered polymorphisms that are suggested to be associated with markers of body fat distribution. Although most of the results of small studies are not replicated, the problem *Corresponding author.: H. Staiger ([email protected]) 1740-6765/$ ß 2013 Elsevier Ltd. All rights reserved.
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different disease risk profiles [4]. The recognition of a strong association between certain body fat distribution patterns and cardiovascular risk lead to a hugely increased interest in the study of this area. It was postulated that understanding the pathology of excess fat storage patterns would unravel new pathways in the disease mechanism of diabetes and atherosclerosis. Identifying potential drug targets to treat obesity and its metabolic consequences is crucial to extend our yet insufficient treatment armamentarium.
The assessment of body fat distribution Rationale Although the commonly used body mass index (BMI) is an easily measurable proxy of general fat mass, it has several limitations [5,6]. Among them the impact of ageing, race, muscle mass and weight loss with and without exercise largely influences the power of BMI to predict adiposity. Furthermore, general adiposity has considerable limitations in the prediction of adverse metabolic consequences. It was shown that approximately 30% of obese individuals have neither insulin resistance, nor early signs of atherosclerosis [7–9]. Conversely, a non-obese but metabolically abnormal phenotype exists as well [10], which is missed when using simple parameters such as BMI, but can be identified with more sophisticated methods [11]. In this aspect, the accumulation of deep abdominal fat, that is, visceral fat, emerged from early on as a better determinant of glucose intolerance than BMI [12]. Since then, several specific fat compartments have been identified as determinants of an adverse metabolic environment. In this regard, interscapular fat accumulation has been shown to be independently associated with insulin resistance [13]. Also, accumulation of fat in the pancreas impacts on glucose metabolism, probably by impairing insulin secretion [14]. Furthermore, a cluster of specific perivascular fat depots are independently correlated with metabolic complications (reviewed in [15]). Certain types, like perivascular fat around the brachial artery [16], seem to influence systemic manifestations of the metabolic syndrome such as insulin resistance. Other perivascular fat depots have impact on functions of the involved organ, such as renal sinus fat that potentially facilitates albuminuria [17] and causes hypertension [18]. Thoracic periaortic fat has been shown to be associated with peripheral arterial disease [19] and cardiovascular disease [11]. Adipocytes are cells dedicated to the accretion of triglycerides, and they constitute the frame for example, for perivascular fat accumulation patterns. However, deposition of large amounts of triglycerides in the skeletal and cardiac myocytes and in the liver is a pathologically different entity. This kind of triglyceride deposition constitutes ectopic fat. From specific types of ectopic fat, intramyocellular, but more importantly intrahepatic, fat are strong predictors of insulin e6
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resistance that explain large parts of the effects formerly attributed to visceral adipose tissue [20]. Present knowledge on the genetic determination of hepatic fat accumulation has been just reviewed extensively [21].
Methods Traditional techniques of body composition analysis include measurement of skin fold thickness, underwater weighing and radioisotope dilution methods (reviewed in [22]). Because T1-weighted magnetic resonance imaging (MRI) is a safe and non-invasive procedure which is particularly suitable for differentiation of lean and adipose tissue mass, this method evolved to be the gold standard for the volumetric determination of specific adipose tissue compartments [23]. Computed tomographic (CT) imaging of visceral fat area (VFA) and subcutaneous fat area (SFA) delivers similar results, but its use is limited by ethical concerns related to ionizing radiation [24]. Dual-energy X-ray absorptiometry has been shown to be a good alternative to CT in predicting total abdominal fat mass, however, it cannot differentiate subcutaneous and visceral fat better than anthropometric methods [25,26]. Abdominal body impedance analysis (‘VFA scanner’) can also be used to measure visceral fat with a good correlation to CT results [27]. Finally, ultrasound techniques can be applied to assess abdominal subcutaneous and intraabdominal fat thickness, however, their predictive power is rather small [28]. Anthropometric methods are less accurate, but easier to perform and cheaper. Thus, they are better suitable for large studies. Among the anthropometric parameters, waist circumference (waist girth) correlates stronger with CT-determined visceral abdominal fat mass than the commonly used waist-to-hip ratio (WHR) [29]. Waist circumference also outperformed WHR in regard to cardiovascular disease prediction [30] and was a stronger predictor of diabetes than BMI or the body adiposity index [31]. It is not known why the incorporation of hip circumference in a simple prediction model is of less value than the usage of waist circumference alone. Possibly a sampling error is more often introduced in large studies when hip circumference is measured.
Genetic factors determining body fat distribution Early evidence of genetic determination Since the mid 1980-ies, a solid body of evidence accumulated on the important role of the genetic background on body fat distribution, and these studies have been reviewed in the past [32,33]. A seminal work of Bouchard and associates involved 12 pairs of monozygotic twins who had been fed with a highcalorie diet for 84 days. The study demonstrated an intra-class correlation coefficient of 0.72 for the increase of CT-determined VFA after controlling for covariates, showing that a considerable part of the propensity for visceral fat gain is genetically determined [34]. Complex segregation analyses
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suggested that a major gene would account for about half of the variance in CT-determined abdominal visceral fat mass, but a Mendelian inheritance could not be demonstrated after adjustment for total fat mass [35,36].
Genome-wide associations studies The technical development of rapid array-based genotyping and computing enabled the hypothesis-free investigation of genomic common variations in a large number of individuals. Hundreds of thousands of such single nucleotide polymorphisms (SNPs) have been scanned for associations with body fat distribution phenotypes in genome-wide association studies (GWAS) since 2008. Altogether, 22 genes emerged with genome-wide significant association signals for measures of body fat composition (Table 1). Fifteen of these have been found in a GWAS looking for determinants of WHR [37,38] and 4 SNPs have been identified in studies investigating waist circumference [39–41]. An additional SNP in the LYPLAL1 gene (rs11118316) which is weakly (r2 = 0.28) linked to the WHR signal (rs4846567) has been found to be associated with the CT-measured ratio of visceral and subcutaneous abdominal fat mass and a SNP (rs1659258) in another genomic region between THNSL2 and FABP1 was associated with CT-measured visceral fat mass in women [42]. An example for dissociation of general adiposity and the metabolically more dangerous visceral adiposity is rs2943650 in IRS1. The major allele of this SNP was associated with lower total body fat mass at a genome-wide significance, but was also convincingly demonstrated (p = 6.1 106) as a determinant of higher visceral/subcutaneous fat ratio and an adverse metabolic profile in men [43]. Furthermore, a study involving altogether 9000 individuals in the combined discovery and validation cohorts located a SNP strongly associated with pericardial fat [44]. These analyses provided clear evidence for the existence of a sexual dimorphism with effects generally larger in women. The most striking difference in effect strength was observed at LYPLAL1, and opposite effect directions were observed at GRB14 [37]. For IRS1, the genetic effect was stronger in men [43]. What are the mechanisms of action of these SNPs? All the discovered signals are located in intergenic or intronic regions. But, even in intronic regions, the respective gene does not necessarily cause the observed phenotypic variance. Primary biological processes still remain a matter of speculation. Two of the SNPs lie in the vicinity of genes encoding homeobox-proteins (TBX15, HOXC13), known orchestrators of morphogenesis, suggesting that they may play a direct role in the ontogeny of fat depots. The SNP near ADAMTS9 belongs to a protein family which has also been implicated in the control of organ shape during development [45]. Other potentially involved genes encode transcription factors (TFAP2B, NFE2L3, CPEB4). Two SNPs may be implicated in
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wnt-signaling (RSPO3, ZNRF3-KREMEN1), and two SNPs might have a connection to body fat distribution through insulin signaling (IRS1, GRB14). In addition to the latter, a direct link to metabolism can also be suspected for the SNP near FABP1 which encodes a fatty acid binding protein. Altogether and unfortunately, GWAS did not largely improve our biological understanding about mechanisms regulating body fat distribution. The effect sizes of the discovered variants are relatively small (the largest effect is estimated at 1.48 cm for waist circumference and 0.04 for WHR SNPs, [46]). Furthermore, the 14 WHR SNPs discovered in 2010 explained only 1.34% of the variance of WHR in women and 0.46% in men [37]. Potential causes of the missing explained heritability in genome-wide obesity association studies are objects of broad discussion [46]. Both in the case of general obesity and body fat distribution, we postulate that the use of easily measurable but arbitrary anthropometric markers in part underlies the merely moderate success of most GWAS approaches. Anthropometric proxies of body fat distribution are resultants of a myriad of biological processes, and are, therefore, prone to effect modification and interaction with environmental factors which are too complex to be modeled. Anatomically and physiologically more precise proxies, such as fat volume computed by MRI, should better reflect the results of biological processes and could, therefore, provide more detection power. This is seen in the visceral fat and pericardial fat studies performed by Fox et al. who demonstrated genomewide significant effects in, for GWAS dimensions, smaller samples of 10,000 individuals [42,44].
Candidate gene studies Numerous genes have been postulated to control body fat distribution and were examined in hypothesis-driven linkage studies. Since none of these smaller studies delivers genomewide significant results generally accepted at p < 5 108, the validity heavily relies on the study quality [47]. The first variant which was demonstrated to be associated with central obesity was a polymorphism in the 50 -flanking region of the insulin (INS) gene [48]. A later study found that a deletion/ insertion variation in INS leads to increased CT-determined visceral fat mass in South African women [49]. The BclI restriction fragment length polymorphism (also known as rs41423247) of the glucocorticoid receptor gene (NR3C1), has been found to be associated with WHR and CT-determined abdominal fat mass in several small studies [50]. Another steroid hormone receptor gene, the estrogen receptor a gene (ESR1), has been implicated as a determinant of WHR in an investigation conducted with 1763 participants of the Framingham Heart Study. Rs1004467 in the CYP17A1 gene, encoding an enzyme of steroid hormone synthesis, has been suggested, alongside rs11191548 of NT5C2, to have an impact on subcutaneous and visceral fat area [51]. The initial www.drugdiscoverytoday.com
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Table 1. Variants influencing phenotypes of body fat distribution which were discovered in GWAS Alleles (major/minor)
Remarks on functione of the nearest gene(s) and further metabolic associations
6
C/G
Encodes a member of the thrombospondin type 1 repeat supergene family.
VEGFA
6
A/G
Encodes the vascular endothelial growth factor. This SNP may also be associated with insulin sensitivity in women [74]. Another SNP in this gene is associated with T2DM (rs9369425) [75,76] and possibly insulin secretion [80].
WHR [37]
TBX15-WARS2
1
G/C
TBX15 (T-box family) encodes a transcription factor that regulates a variety of developmental processes; WARS encodes mitochondrial tryptophanyl-tRNA synthetase 2
rs1055144
WHR [37]
NFE2L3
7
A/G
Encodes a member of the cap ‘n’ collar basic-region leucine zipper family of transcription factors.
rs10195252
WHR [37]
GRB14
2
C/T
Encodes a growth factor receptor-binding protein that interacts with insulin receptors and insulin-like growthfactor receptors. Also associated with T2DM in South East Asians [77].
rs2605100
WHR [41]
LYPLAL1
1
G/A
Encodes phopholipase-like 1. Marked sexual dimorphism, the effect on body fat distribution phenotypes is present in females only. Low to moderate LD between SNPs. Another SNP (rs3001032), which is in high LD with rs4846567, was associated with fasting insulin in a GWAS after adjustment for BMI [73].
rs4846567
WHR [37]
G/T
a
G/A
SNP
Phenotype and reference
Nearest gene
rs9491696
WHR [37]
RSPO3
rs6905288
WHR [37]
rs984222
Chr
rs11118316
VAT/SAT [42]
rs1011731
WHR [37]
DNM3-PIGC
1
A/G
DNM3 encodes dynamin 3 – the protein possesses mechanochemical properties involved in actin-membrane processes (membrane budding). PIGC encodes an endoplasmic reticulum associated protein that is involved in glycosyl-phosphatidylinositol lipid anchor biosynthesis.
rs718314
WHR [37]
ITPR2-SSPN
12
A/G
ITPR2 encodes inositol 1,4,5-trisphosphate receptor, type 2. SSPN encodes a member of the dystrophin-glycoprotein complex, which constitutes a structural trans-sarcolemmal link in muscle cells.
rs1294421
WHR [37]
LY86
6
G/T
CNV-tagging SNP. The LY86 gene encodes lymphocyte antigen 86.
rs1443512
WHR [37]
HOXC13
12
C/A
Encodes Homeobox C13 (transcription factor playing a role in morphogenesis).
rs6795735
WHR [37]
ADAMTS9
3
C/T
This gene encodes a member of the ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs) protein family. Another SNP in this region (rs4607103) is associated with T2DM [76]
rs4823006
WHR [37]
ZNRF3-KREMEN1
22
A/G
ZNRF3 encodes zinc and ring finger 3, a negative feedback regulator of wnt signaling. KREMEN1 encodes a high-affinity dickkopf homolog 1 (DKK1) transmembrane receptor that plays a role in the inhibition of wnt signaling.
rs6784615
WHR [37]
NISCH-STAB1
3
T/C
NISCH encodes nischarin, a nonadrenergic imidazoline-1 receptor protein. STAB1 encodes a large, transmembrane receptor protein, may play a role as a scavanger receptor. The SNP is correlated with 2 non-synonymous variants in the nearby DNAH1 and GLYCTK genes.
rs6861681
WHR [37]
CPEB4
5
G/A
Encodes cytoplasmic polyadenylation element binding protein 4
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Drug Discovery Today: Disease Mechanisms | Mechanisms of Obesity
Table 1 (Continued ) SNP
Phenotype and reference
Nearest gene
Chr
Alleles (major/minor)
Remarks on functione of the nearest gene(s) and further metabolic associations
rs2074356
WHR (in Asians) [38]
HECTD4 (C12orf51)
12
C/T
Probably encodes E3 ubiquitin protein ligase 4 (HECT domain). Rare variant in Europeans. Also associated with HDL and Gamma-glutamyl transferase (liver enzyme) in Asians [81].
rs10198628
Pericardial fatb [44]
TRIB2
2
G/A
Encodes tribbles homolog 2, one of three members of the Tribbles family.
rs1659258
VATc [42]
THNSL2-FABP1
2
A/G
THNSL2 encodes threonine synthase-like 2 (S. cerevisiae). FABP1 encodes fatty acid binding protein 1, the fatty acid binding protein found in liver. Another variant in the FABP1 gene also seemes to be associated with diabetes and insulin resistance [82]
rs10146997
Waist circumference [40]
NRXN3
14
A/G
Encodes neurexin 3, a member of a family of proteins that function in the nervous system as receptors and cell adhesion molecules
rs2943650
VAT/SATa and total body fatd [43]
IRS1
2
T/C
Encodes insulin receptor substrate 1 which is phosphorylated by insulin receptor tyrosine kinase. A linked variant (rs2943634, r2 = 0.78) associates with fasting insulin and probably diabetes [73]
rs987237
Waist circumference [41]
TFAP2B
6
A/G
Encodes transcription factor AP-2 beta (activating enhancer binding protein 2 beta) a member of the AP-2 family of transcription factors. This protein functions as both a transcriptional activator and repressor
rs545854 (merged from rs7826222)
Waist circumference [41]
MSRA
8
C/G
Encodes methionine sulfoxide reductase A. It carries out the enzymatic reduction of methionine sulfoxide to methionine
rs12970134
Waist circumference [39]
MC4R
18
G/A
Encodes melanocortin 4 receptor. The protein encoded by this gene is a membrane-bound receptor and member of the melanocortin receptor family. Associated with insulin sensitivity [39] and diabetes [83], independent of BMI
a
Visceral fat/subcutaneous fat ratio determined by CT. Determined by cardiac CT. c Determined by CT. d Determined by body impedance analysis and dual-energy X-ray absorptiometry. e Excerpt from the NCBI gene database available under http://www.ncbi.nlm.nih.gov/gene. b
hypothesis for testing 12 hypertension SNPs established in GWAS was a postulated common underlying pathomechanism for hypertension and metabolic syndrome, but the findings for body fat depots and hypertension were directionally not consistent [51]. The beta-3-adrenergic receptor has also been postulated as a relevant modulator of metabolism. The Trp64Arg substitution polymorphism (rs4994) in its gene (ADRB3) was associated with WHR in Finnish [52] and with CT-determined visceral fat area in Japanese [53] subjects. This SNP also significantly associated with C-reactive protein concentrations in a US cohort of women [54], suggesting a link to cardiovascular disease risk. Similar pathomechanistic concepts underlie the hypotheses on the involvement of uncoupling protein genes in the regulation of body fat distribution. Studies point to a possible impact of UCP1 [55] and UCP2 [56] on CT-determined abdominal fat area and WHR, respectively.
Data on genetic variation in the TNF-alpha gene (TNF) are somewhat controversial. The minor allele of rs1800629 (308A) increased the waist and hip circumferences, without changing their ratio in one study [57], but reduced WHR in another study [58]. Because thiazolidendiones, which target the peroxisome proliferator-activated receptor gamma (PPARG), improve insulin action and redistribute visceral fat to subcutaneous depots, corresponding genetic variation constitutes an evident hypothesis for associations with body fat distribution. In the overweight subgroup of a study involving 1051 Korean women, the minor allele (G) of the Pro12Ala polymorphism (rs1801282) was associated with higher WHR, subcutaneous adipose tissue and visceral adipose tissue [59]. Metabolic effects of this polymorphism have been extensively reviewed earlier [60]. Genetic variation in this SNP explained 6.2% of the variation in CT-determined subcutaneous fat area vs. 1.8% of that in visceral fat area, confirming the expectations www.drugdiscoverytoday.com
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Metabolic Traits
General adiposity trait (BMI)
NPC1 PFKP INSIG2
Body fat distribution traits
CTNNBL1
GRB14 VEGFA
PTER
TMEM18
PRL
LYPLAL1 RSPO3
ADAMTS9
TBX15-WARS
HECTD4
DNM3-PIGC
25 additional loci of minor effects
SH2B1
FABP1
NFE2L3
GPRC5B
FTO
MC4R
KCTD15 SEC16B
BDNF*
IRS1
GNPDA2 BCDIN3D/FAIM2
ITPR2-SSPN
TFAP2B
LY86
MTCH2
THNSL2-FABP1
MAF
NEGR1 CPEB4
FDFT1 TRIB2
NISCH-STAB1 ETV5-DGKG
ZNRF3-KREMEN1 MSRA
HOXC13 NRXN3
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Figure 1. Genes suggested to be associated with body fat distribution and body mass index in genome-wide association studies with their relationship to metabolic traits. Notes: SH2B1: associated with T2DM independently of BMI [72], probably also associates with visceral fat [62,71], and insulin sensitivity [84]. FTO: no association with T2DM in Europeans after adjustment for BMI; rs9939609 also associated with T2DM after adjustment for BMI in Japanese [85]. BDNF*: opposite effect direction between BMI and fasting glucose [72]. BCDIN3D/FAIM2 rs7138803: associates with diabetes after adjustment for BMI in the discovery study [86]; with waist circumference and WHR [72], and with coronary artery disease [87]. TMEM18: rs6548238 associated with T2DM after adjustment for BMI in Danes [88], and rs4854344 in Japanese [85]. TFAP2B: associated with T2DM in Japanese subjects [78], and insulin sensitivity in European children [79]. NEGR1: also associated with visceral adipose tissue [42]; no association after BMI-adjustment in the discovery study [86]; trend for an association with T2DM in Europeans [72].
that the gene has a larger influence on subcutaneous fat deposition [59]. However, the G allele confers a reduced risk for type 2 diabetes, especially in Asians [61], and a recent study could not replicate its effect on body fat distribution [62]. A polymorphism in the exon of the peroxisome proliferator-activated receptor gamma coactivator-1a (rs8192678) also influenced WHR in the obese subgroup of non-diabetic Chinese subjects [63]. In addition, the endocannabinoid system has been implicated in the regulation of body weight in humans. Rs12720071 in the cannabinoid type 1 receptor gene (CBR1) was shown to be associated with subscapular skinfold thickness and WHR [64], and another SNP (rs1049353) was found to be associated with MRI-determined visceral fat mass in 783 healthy young men [65]. Furthermore, variation in the proopiomelanocortin (POMC) gene, which plays a role in the regulation of appetite in the central nervous system, was nominally linked to WHR and visceral abdominal fat mass in one study [66]. e10
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Also genes regulating adipokine secretion are thought to affect body fat mass and distribution. The RARRES2 gene, encoding the adipokine chemerin, was shown to be associated with MRI-determined visceral adipose tissue mass [67]. The 420C > G promotor variant in RETN, which encodes the resistin gene, was associated with less visceral fat mass in men, independent of age and adiposity [68]. The ACE I/D polymorphism has been also suggested to be associated with abdominal adiposity [69,70]. Several GWAS-discovered obesity-associated SNPs have been examined for associations with body fat distribution. Some of these data suggest an association of SH2B1 with abdominal fat [62,71], and the variation between BCDIN3D-FAIM2 and WHR, waist circumference [72].
Conclusions Both, candidate gene approaches and GWAS have now been extensively used to identify major genes regulating body fat mass and -distribution. Interestingly, none of the associations
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established in candidate gene studies emerged in GWAS as important determinants. The candidate gene studies that investigated phenotypes of body fat distribution with CT- or MR-imaging, were generally small. Although the results were in most cases not corroborated by independent studies, they are nevertheless mechanistically plausible. On the other side, completely new genetic associations were discovered with GWAS. Some of these genes and genomic regions have not functionally been characterized well, and would have probably remained in the dark without these discoveries. The close mechanistic link between visceral fat mass and an adverse metabolic setting is reflected by the fact that several genes provide both signals for visceral obesity and metabolic traits in GWAS (see Fig. 1). The GWAS of Manning et al. [73] looked for determinants of fasting insulin and glucose after accounting for variation and interaction with BMI. They identified 3 SNPs which are in or near previously identified WHR genes (LYPLAL1, GRB14, IRS1). Furthermore, a hypothesis-driven study identified rs6905288 in VEGFA as a determinant of insulin sensitivity [74], and rs9369425 in this gene was suggested to be associated with type 2 diabetes [75,76]. ADAMTS9 [76] and GRB14 [77] were associated with type 2 diabetes in GWAS. Data on TFAP2B suggest an association with type 2 diabetes [78] and insulin sensitivity [79]. To narrow the hiatus between data delivered by hypothesis-free statistical methods and our current mechanistic understanding, further work is needed on both sides. Finemapping studies looking for the causal variants and mechanistic investigations trying to establish the pathophysiology of the GWAS-discovered genes are certainly needed from the part of the biologic research. Using more elaborate phenotypes such as MR-determined visceral fat mass or liver fat content measured by 1H-MRI in new studies and meta-analyses could identify other genetic variants that may have stronger impact on body fat mass and -distribution and the related metabolic abnormalities.
Conflicts of interest The authors declare that they have no conflicts of interest regarding this manuscript.
References 1 Mokdad, A.H.F.E. (2003) Prevalence of obesity, diabetes, and obesityrelated health risk factors, 2001. JAMA 289, 76–79 2 Carroll, K.K. (1998) Obesity as a risk factor for certain types of cancer. Lipids 33, 1055–1059 3 Vague, J. (1947) La diffe´rentiation sexuelle, facteur de´terminant des formes de l’obe´site´. Presse Me´d. 30, 339–340 4 Larsson, B. et al. (1984) Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br. Med. J. (Clinical Research ed) 288, 1401–1404 5 Garn, S.M. et al. (1986) Three limitations of the body mass index. Am. J. Clin. Nutr. 44, 996–997 6 Prentice, A.M. and Jebb, S.A. (2001) Beyond body mass index. Obesity Rev. 2, 141–147
Drug Discovery Today: Disease Mechanisms | Mechanisms of Obesity
7 Stefan, N. et al. (2008) Identification and characterization of metabolically benign obesity in humans. Arch. Intern. Med. 168, 1609–1616 8 Wildman, R.P. et al. (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch. Intern. Med. 168, 1617–1624 9 McLaughlin, T. et al. (2007) Heterogeneity in the prevalence of risk factors for cardiovascular disease and type 2 diabetes mellitus in obese individuals: effect of differences in insulin sensitivity. Arch. Intern. Med. 167, 642–648 10 Ruderman, N.B. et al. (1981) The ‘metabolically-obese,’ normal-weight individual. Am. J. Clin. Nutr. 34, 1617–1621 11 Britton, K.A. et al. (2012) Prevalence, distribution, and risk factor correlates of high thoracic periaortic fat in the framingham heart study. J. Am. Heart Assoc. http://dx.doi.org/10.1161/JAHA. 112.004200 12 Despre´s, J.P. et al. (1989) Role of deep abdominal fat in the association between regional adipose tissue distribution and glucose tolerance in obese women. Diabetes 38, 304–309 13 Thamer, C. et al. (2010) Interscapular fat is strongly associated with insulin resistance. J. Clin. Endocrinol. Metab. 95, 4736–4742 14 Heni, M. et al. (2010) Pancreatic fat is negatively associated with insulin secretion in individuals with impaired fasting glucose and/or impaired glucose tolerance: a nuclear magnetic resonance study. Diabetes Metab. Res. Rev. 26, 200–205 15 Britton, K.A. and Fox, C.S. (2011) Ectopic fat depots and cardiovascular disease. Circulation 124, e837–e841 16 Rittig, K. et al. (2008) Perivascular fatty tissue at the brachial artery is linked to insulin resistance but not to local endothelial dysfunction. Diabetologia 51, 2093–2099 17 Wagner, R. et al. (2012) Exercise-induced albuminuria is associated with perivascular renal sinus fat in individuals at increased risk of type 2 diabetes. Diabetologia 55, 2054–2058 18 Foster, M.C. et al. (2011) Fatty kidney, hypertension, and chronic kidney disease: the Framingham Heart Study. Hypertension 58, 784–790 19 Fox, C.S. et al. (2010) Periaortic fat deposition is associated with peripheral arterial disease: the Framingham heart study. Circ. Cardiovasc. Imaging 3, 515–519 20 Stefan, N. et al. (2008) Causes and metabolic consequences of fatty liver. Endocr. Rev. 29, 939–960 21 Dongiovanni, P. et al. (2013) Genetic predisposition in NAFLD and NASH: impact on severity of liver disease and response to treatment. Curr. Pharm. Des. http://www.ncbi.nlm.nih.gov/pubmed/23394097 22 Lukaski, H.C. (1987) Methods for the assessment of human body composition: traditional and new. Am. J. Clin. Nutr. 46, 537–556 23 Machann, J. et al. (2010) Follow-up whole-body assessment of adipose tissue compartments during a lifestyle intervention in a large cohort at increased risk for type 2 diabetes. Radiology 257, 353–363 24 Ohsuzu, F. et al. (1998) Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat. Med. 16, 99–107 25 Snijder, M. et al. (2002) The prediction of visceral fat by dual-energy X-ray absorptiometry in the elderly: a comparison with computed tomography and anthropometry. Int. J. Obes. Relat. Metab. Disord. 26, 984 26 Clasey, J.L. et al. (1999) The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obesity Res. 7, 256–264 27 Ryo, M. et al. (2005) A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Dia. Care 28, 451–453 28 Armellini, F. et al. (1993) Total and intra-abdominal fat measurements by ultrasound and computerized tomography. Int. J. Obes. Relat. Metab. Disord. 17, 209–214 29 Ferland, M. et al. (1989) Assessment of adipose tissue distribution by computed axial tomography in obese women: association with body density and anthropometric measurements. Br. J. Nutr. 61, 139–148 30 Dobbelsteyn, C.J. et al. (2001) A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of
www.drugdiscoverytoday.com
e11
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31 32 33 34 35 36 37
38
39
40
41
42
43
44 45
46 47 48
49
50
51
52
53
54 55
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cardiovascular risk factors. Can. Heart Health Surveys. Int. J. Obesity 25, 652–661 Schulze, M.B. et al. (2012) Body adiposity index, body fat content and incidence of type 2 diabetes. Diabetologia 55, 1660–1667 Lemieux, S. (1997) Genetic susceptibility to visceral obesity and related clinical implications. Int. J. Obes. Relat. Metab. Disord. 21, 831–838 Bouchard, C. (1997) Genetic determinants of regional fat distribution. Hum. Reprod. 12 (Suppl 1), 1–5 Bouchard, C. et al. (1990) The response to long-term overfeeding in identical twins. N. Engl. J. Med. 322, 1477–1482 Bouchard, C. et al. (1996) Major gene for abdominal visceral fat area in the Que´bec Family Study. Int. J. Obes. Relat. Metab. Disord. 20, 420–427 Rice, T. et al. (1997) Segregation analysis of abdominal visceral fat: the HERITAGE Family Study. Obes. Res. 5, 417–424 Heid, I.M. et al. (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 Cho, Y.S. et al. (2012) Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat. Genet. 44, 67–72 Chambers, J.C. et al. (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40, 716–718 Heard-Costa, N.L. et al. (2009) NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet. 5, e1000539 Lindgren, C.M. et al. (2009) Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet. 5, e1000508 Fox, C.S. et al. (2012) Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 8, e1002695 Kilpela¨inen, T.O. et al. (2011) Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat. Genet. 43, 753–760 Fox, C.S. et al. (2012) Genome-wide association of pericardial fat identifies a unique locus for ectopic fat. PLoS Genet. 8, e1002705 Blelloch, R. and Kimble, J. (1999) Control of organ shape by a secreted metalloprotease in the nematode Caenorhabditis elegans. Nature 399, 586–590 Sandholt, C.H. et al. (2012) Beyond the fourth wave of genome-wide obesity association studies. Nutr. Diabetes 2, e37 Attia, J.I.J. (2009) How to use an article about genetic association: B: are the results of the study valid? JAMA 301, 191–197 Weaver, J.U. et al. (1992) Central obesity and hyperinsulinaemia in women are associated with polymorphism in the 50 flanking region of the human insulin gene. Eur. J. Clin. Invest. 22, 265–270 Berman, P. et al. (2009) Association between the 4 bp proinsulin gene insertion polymorphism (IVS-69) and body composition in black South African women. Obesity (Silver Spring) 17, 1298–1300 Van Rossum, E.F.C. and Lamberts, S.W.J. (2004) Polymorphisms in the glucocorticoid receptor gene and their associations with metabolic parameters and body composition. Recent Prog. Horm. Res. 59, 333–357 Hotta, K. et al. (2012) Genetic variations in the CYP17A1 and NT5C2 genes are associated with a reduction in visceral and subcutaneous fat areas in Japanese women. J. Hum. Genet. 57, 46–51 Wide´n, E. et al. (1995) Association of a polymorphism in the beta 3adrenergic-receptor gene with features of the insulin resistance syndrome in Finns. N. Engl. J. Med. 333, 348–351 Sakane, N. et al. (1997) Beta 3-adrenergic-receptor polymorphism: a genetic marker for visceral fat obesity and the insulin resistance syndrome. Diabetologia 40, 200–204 Fan, A.Z. et al. (2010) Gene polymorphisms in association with emerging cardiovascular risk markers in adult women. BMC Med. Genet. 11, 6 Kim, K.S. et al. (2005) The finding of new genetic polymorphism of UCP-1 A-1766G and its effects on body fat accumulation. Biochim. Biophys. Acta 1741, 149–155
www.drugdiscoverytoday.com
Vol. 10, No. 1–2 2013
56
Martinez-Hervas, S. et al. (2012) Polymorphisms of the UCP2 gene are associated with body fat distribution and risk of abdominal obesity in Spanish population. Eur. J. Clin. Invest. 42, 171–178 57 Corbala´n, M.S. et al. (2004) Influence of two polymorphisms of the tumoral necrosis factor-alpha gene on the obesity phenotype. Diabetes Nutr. Metab. 17, 17–22 58 Um, J-Y. et al. (2004) Polymorphism of the tumor necrosis factor alpha gene and waist-hip ratio in obese Korean women. Mol. Cells 18, 340–345 59 Kim, K.S. et al. (2004) Effects of peroxisome proliferator-activated receptorgamma 2 Pro12Ala polymorphism on body fat distribution in female Korean subjects. Metab. Clin. Exp. 53, 1538–1543 60 Stumvoll, M. and Ha¨ring, H. (2002) The peroxisome proliferatoractivated receptor-gamma2 Pro12Ala polymorphism. Diabetes 51, 2341–2347 61 Gouda, H.N. et al. (2010) The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am. J. Epidemiol. 171, 645–655 62 Hotta, K. et al. (2012) Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography. J. Hum. Genet. 57, 305–310 63 Weng, S-W. et al. (2010) Gly482Ser polymorphism in the peroxisome proliferator-activated receptor gamma coactivator-1alpha gene is associated with oxidative stress and abdominal obesity. Metab. Clin. Exp. 59, 581–586 64 Russo, P. et al. (2007) Genetic variations at the endocannabinoid type 1 receptor gene (CNR1) are associated with obesity phenotypes in men. J. Clin. Endocrinol. Metab. 92, 2382–2386 65 Frost, M. et al. (2010) Polymorphisms in the endocannabinoid receptor 1 in relation to fat mass distribution. Eur. J. Endocrinol. 163, 407–412 66 Ternouth, A. et al. (2011) Association study of POMC variants with body composition measures and nutrient choice. Eur. J. Pharmacol. 660, 220–225 ¨ ssig, K. et al. (2009) RARRES2, encoding the novel adipokine chemerin, 67 Mu is a genetic determinant of disproportionate regional body fat distribution: a comparative magnetic resonance imaging study. Metab. Clin. Exp. 58, 519–524 68 Bouchard, L. et al. (2004) Human resistin gene polymorphism is associated with visceral obesity and fasting and oral glucose stimulated C-peptide in the Que´bec Family Study. J. Endocrinol. Invest. 27, 1003–1009 69 Riera-Fortuny, C. et al. (2005) The relation between obesity, abdominal fat deposit and the angiotensin-converting enzyme gene I/D polymorphism and its association with coronary heart disease. Int. J. Obes. (Lond.) 29, 78–84 70 Strazzullo, P. et al. (2003) Genetic variation in the renin-angiotensin system and abdominal adiposity in men: the Olivetti Prospective Heart Study. Ann. Intern. Med. 138, 17–23 71 Haupt, A. et al. (2010) Novel obesity risk loci do not determine distribution of body fat depots: a whole-body MRI/MRS study. Obesity (Silver Spring) 18, 1212–1217 72 Sandholt, C.H. et al. (2011) Studies of Metabolic Phenotypic Correlates of 15 Obesity Associated Gene Variants. PLoS One 6, e23531 73 Manning, A.K. et al. (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 74 Burgdorf, K.S. et al. (2012) Association studies of novel obesity-related gene variants with quantitative metabolic phenotypes in a populationbased sample of 6,039 Danish individuals. Diabetologia 55, 105–113 75 Zeggini, E. et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for Type 2 diabetes. Science 316, 1336–1341 76 Zeggini, E. et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638–645 77 Kooner, J.S. et al. (2011) Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989
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78 79
80 81
82
83
Maeda, S. et al. (2005) Genetic variations in the gene encoding TFAP2B are associated with type 2 diabetes mellitus. J. Hum. Genet. 50, 283–292 Nordquist, N. et al. (2009) The transcription factor TFAP2B is associated with insulin resistance and adiposity in healthy adolescents. Obesity (Silver Spring) 17, 1762–1767 Staiger, H. et al. (2008) Novel meta-analysis-derived type 2 diabetes risk loci do not determine prediabetic phenotypes. PLoS One 3 Kim, Y.J. et al. (2011) Large-scale genome-wide association studies in east Asians identify new genetic loci influencing metabolic traits. Nat. Genet. 43, 990–995 Mansego, M.L. et al. (2012) Common variants of the liver fatty acid binding protein gene influence the risk of type 2 diabetes and insulin resistance in Spanish population. PLoS One 7, e31853 Xi, B. et al. (2012) Common polymorphism near the MC4R gene is associated with type 2 diabetes: data from a meta-analysis of 123,373 individuals. Diabetologia 55, 2660–2666
Drug Discovery Today: Disease Mechanisms | Mechanisms of Obesity
84 85
86
87
88
Fall, T. et al. (2012) The role of obesity-related genetic loci in insulin sensitivity. Diabet. Med. 29, e62–e66 Takeuchi, F. et al. (2011) Association of genetic variants for susceptibility to obesity with type 2 diabetes in Japanese individuals. Diabetologia http:// dx.doi.org/10.1007/s00125-011-2086-8 Thorleifsson, G. et al. (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 Huang, H. et al. (2012) Implication of genetic variants near TMEM18, BCDIN3D/FAIM2, and MC4R with coronary artery disease and obesity in Chinese: a angiography-based study. Mol. Biol. Rep. 39, 1739–1744 Thomsen, M. et al. (2012) b2-adrenergic receptor Thr164Ile polymorphism, obesity, and diabetes: comparison with FTO, MC4R, and TMEM18 polymorphisms in more than 64,000 individuals. J. Clin. Endocrinol. Metab. 97, E1074–E1079
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