Visceral fat accumulation has an important role in the development of several metabolic disorders, such as type 2 diabetes, dyslipidemia and hypertension. New genetic loci that contribute to the development of type 2 diabetes have been identified by genome-wide association studies. To examine the association of type 2 diabetes susceptibility loci and visceral fat accumulation, we genotyped 1279 Japanese subjects (556 men and 723 women), who underwent computed tomography for measurements of visceral fat area (VFA) and subcutaneous fat area (SFA) for the following single-nucleotide polymorphisms (SNPs): NOTCH2 rs10923931, THADA rs7578597, PPARG rs1801282, ADAMTS9 rs4607103, IGF2BP2 rs1470579, VEGFA rs9472138, JAZF1 rs864745, CDKN2A/CDKN2B rs564398 and rs10811661, HHEX rs1111875 and rs5015480, TCF7L2 rs7901695, KCNQ1 rs2237892, KCNJ11 rs5215 and rs5219, EXT2 rs1113132, rs11037909, and rs3740878, MTNR1B rs10830963, DCD rs1153188, TSPAN8/LGR5 rs7961581, and FTO rs8050136 and rs9939609. None of the above SNPs were significantly associated with VFA. The FTO rs8050136 and rs9939609 risk alleles exhibited significant associations with body mass index (BMI; P=0.00088 and P=0.0010, respectively) and SFA (P=0.00013 and P=0.00017, respectively). No other SNPs were significantly associated with BMI or SFA. Our results suggest that two SNPs in the FTO gene are associated with subcutaneous fat accumulation. The contributions of other SNPs are inconclusive because of a limitation of the sample power.
Metabolic syndrome is defined by four conditions: visceral fat obesity, impaired glucose tolerance, dyslipidemia and hypertension.1 Various adipocytokines secreted from adipocytes have been identified and shown to cause dyslipidemia, hypertension and insulin resistance.2, 3 Previous studies on adipocytokines indicate that visceral fat obesity has a central role in the development of metabolic syndrome. The determination of visceral fat mass is performed in terms of waist circumference, waist–hip ratio, or visceral fat area (VFA). Waist circumference and waist–hip ratio are commonly used, because they are simple and convenient. However, VFA measured using computed tomography (CT) is one of the most precise methods to assess fat distribution.1, 4, 5 There is an abundance of evidence showing that body fat distribution is influenced by genetic loci.6, 7, 8 Genome-wide association studies (GWAS) were conducted to identify the loci linked to waist circumference and waist–hip ratio in the Caucasian population.9, 10 Among the reported loci, we have reported that the rs1558902 and rs1421085 genotypes of the fat mass- and obesity-associated gene (FTO) were significantly associated with VFA, as well as with subcutaneous fat area (SFA) and body mass index (BMI).11
GWAS and meta-analysis of GWAS have also identified metabolic syndrome trait-associated genetic variations, including obesity, type 2 diabetes, dyslipidemia and hypertension.12 We have previously reported that among the single-nucleotide polymorphisms (SNPs) susceptible to obesity,13, 14, 15 rs7498665 in the SH2B adaptor protein 1 (SH2B1) gene was associated with VFA,16 and that among hypertension-susceptible SNPs,17, 18 rs1004467 in the cytochrome P450, family 17, subfamily A, polypeptide 1 (CYP17A1) gene and rs11191548 in the 5′-nucleotidase, cytosolic II (NT5C2) gene were significantly associated with both reduced VFA and SFA in women, indicating a genetic background to central obesity.19 Visceral fat obesity is also a risk factor for the development of type 2 diabetes. Several studies have investigated the association of type 2 diabetes loci from GWAS with body fat and BMI.20, 21, 22 However, there are few reports of the association between type 2 diabetes susceptibility SNPs and visceral fat accumulation.21, 23, 24 Associations between type 2 diabetes susceptibility SNPs, except those exited in the FTO gene and visceral fat mass determined by using more precise method, such as CT and magnetic resonance imaging, have not been elucidated yet. Therefore, we investigated the association between type 2 diabetes susceptibility SNPs and fat distribution (VFA and SFA) as determined by CT.
Materials and methods
In this study, we enrolled 1279 Japanese subjects from outpatient clinics as described previously.16, 19 These patients agreed to undergo CT testing (in the supine position) to determine the VFA and SFA values at the umbilical level (L4–L5). Both VFA and SFA values were calculated using the FatScan software program (N2system, Osaka, Japan).25 The clinical characteristics of the subjects are summarized in Table 1. Metabolic syndrome and metabolic abnormalities were diagnosed according to the criteria released by the Japanese Committee for the Diagnostic Criteria of Metabolic Syndrome in April 2005.4, 5 Written informed consent was obtained from each subject, and the protocol was approved by the ethics committee of each institution and of Kyoto University.
DNA extraction and SNP genotyping
Genomic DNA was extracted using Genomix (Talent Srl, Trieste, Italy) from blood samples collected from each subject. We selected 23 SNPs identified as susceptibility loci for type 2 diabetes by GWAS26, 27, 28, 29, 30, 31, 32, 33 and constructed Invader probes (Third Wave Technologies, Madison, WI, USA) for the following SNPs: notch 2 (NOTCH2) rs10923931, thyroid adenoma-associated (THADA) rs7578597, peroxisome proliferator-activated receptor γ (PPARG) rs1801282, ADAM metallopeptidase with thrombospondin type 1 motif, 9 (ADAMTS9) rs4607103, insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) rs1470579, vascular endothelial growth factor A (VEGFA) rs9472138, JAZF zinc finger 1 (JAZF1) rs864745, cyclin-dependent kinase inhibitor 2A and cyclin-dependent kinase inhibitor 2B (CDKN2A/CDKN2B) rs564398 and rs10811661, hematopoietically expressed homeobox (HHEX) rs1111875 and rs5015480, transcription factor 7-like 2 (TCF7L2) rs7901695, potassium voltage-gated channel, KQT-like subfamily, member 1 (KCNQ1) rs2237892, potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) rs5215 and rs5219, exostosin 2 (EXT2) rs1113132, rs11037909, and rs3740878, melatonin receptor 1B (MTNR1B) rs10830963, dermcidin (DCD) rs1153188, tetraspanin 8/leucine-rich repeat containing G protein-coupled receptor 5 (TSPAN8/LGR5) rs7961581, and FTO rs8050136 and rs9939609. The SNPs were genotyped using Invader assays as previously described.34 The success rate of these assays was >99.0%.
For the additive model, we coded the genotypes as 0, 1 or 2 depending on the number of copies of the risk alleles. Risk alleles refer to the type 2 diabetes-associated alleles, according to previous reports.26, 27, 28, 29, 30, 31, 32, 33 Multiple linear regression analyses were performed to test the independent effects of the risk alleles on BMI, VFA and SFA by taking into account the effects of other variables (i.e., age and gender) that were assumed to be independent of the effect of each SNP. The values of BMI, VFA and SFA were logarithmically transformed before multiple linear regression analysis. Odds ratios and P-values adjusted for age, gender and BMI were calculated using multiple logistic regression analysis with genotypes, age, gender and log-transformed BMI as the independent variables. The Hardy–Weinberg equilibrium was assessed using the χ2-test.35 All statistical analyses was performed using software R (http://www.r-project.org/). P-values were corrected by Bonferroni adjustment and P<0.0021 (0.05/23) was considered statistically significant.
The clinical characteristics and genotypes of the subjects are listed in Tables 1 and 2, respectively. All SNPs remained in the Hardy–Weinberg equilibrium, except rs5215 and rs5219 in KCNJ11 (P=0.03) and rs1153188 in DCD genes (P=0.04). The minor allele frequencies did not diverge from those reported in the HapMap database. The BMI, VFA and SFA values for each SNP genotype are presented in Table 3. Multiple linear regression analyses of the anthropometric parameters with respect to the 23 SNPs analyzed are listed in Table 4. Two SNPs (rs8050136 and rs9939609) in the FTO gene were significantly associated with BMI in an additive model (P=0.00088 and 0.0010, respectively). As the results in Table 3 showed that the dominant model was the best-fit model, we analyzed the above two SNPs in the dominant model. Significant associations of BMI were observed with rs8050136 (P=0.00052) and rs9939609 (P=0.00061) in a dominant model.
There is no SNP associated with VFA. Only SNPs (rs8050136 and rs9939609) in the FTO gene were marginally associated with VFA (P<0.05). Two SNPs in the FTO gene were significantly associated with SFA (P=0.00013 at rs8050136 and P=0.00017 at rs9939609), whereas all other SNPs did not associate with SFA. BMI, VFA and SFA are known to be affected by gender; therefore, we compared SFA in men and women separately. Associations between FTO rs8050136 and rs9939609 SNPs, and BMI were not significant in either men (P=0.045 at rs8050136 and P=0.048 at rs9939609) or women (P=0.0064 at rs8050136 and P=0.0075 at rs9939609) in an additive model. Associations of FTO rs8050136 and rs9939609 SNPs with SFA were not significant in either men (P=0.0066 at rs8050136 and P=0.0069 at rs9939609) or women (P=0.0058 at rs8050136 and P=0.0079 at rs9939609) in an additive model. This negative association is most likely due to the decrease in the number of each genotype.
We conducted the power analysis of linear regression (additive model) with a significance level of 0.05, using age and gender as explanatory parameters. The estimated effect sizes per allele (regression coefficients) for logarithmically transformed BMI, VFA and SFA were 0.010, 0.017 and 0.028, respectively, assuming from the values of rs8050136 and rs9939609 (Table 3). The power of our statistical test was calculated on the basis of these estimated effect sizes and by performing 10 000 simulations. When the allele frequency was assumed to be 0.2, the power was estimated to be 0.71 for BMI, 0.25 for VFA and 0.81 for SFA; however, when the allele frequency was assumed to be 0.1, the respective powers were estimated to be 0.47, 0.16 and 0.56.
Finally, we examined the association of these SNPs with impaired glucose tolerance. Although the number of subjects with impaired glucose tolerance was relatively small (n=353), we could replicate the association of rs10811661 in the CDKN2A/B gene (P=0.049), rs2237892 in the KCNQ1 gene (P=0.00013), and rs1113132 in the EXT2 gene (P=0.043) with impaired glucose tolerance (Table 5).
Obesity, especially visceral fat obesity, is an important risk factor for the development of metabolic syndromes, including type 2 diabetes. Recent GWAS revealed the genetic susceptibility factors for type 2 diabetes. The relationship between type 2 diabetes-associated SNPs and visceral fat obesity is of considerable interest. The CT-based analyses are more accurate for evaluating the association of SNPs with visceral fat mass than BMI, waist circumference, or dual energy X-ray absorptiometry-based abdominal fat mass analysis.4, 5 Therefore, we investigated the association of type 2 diabetes susceptibility SNPs with VFA and SFA. We demonstrated that only SNPs in the FTO gene are associated with SFA and BMI, but not with VFA. FTO rs8050136 has been shown to be marginally associated with visceral fat mass measured by magnetic resonance imaging in Germany (P=0.05)36 and rs9939609 with intra-abdominal adipose tissue estimated by dual energy X-ray absorptiometry in Danish (P=4.7 × 10−3).37 SNP rs8050136 has been reported to be marginally associated with VFA measured by CT in African Americans (P=0.05), but not in Hispanic Americans.38 Our study showed the weak association VFA and rs8050136 (P=0.043). Although the measurements of visceral fat were different, rs8050136 would be associated with visceral fat accumulation. As the simulation study showed the power of this study for VFA were relatively low (0.16 to 0.35), large sample size would be necessary to confirm the association. SNP rs8050136 has been reported to be marginally associated with SFA measured by CT in African Americans (P=0.038) and in Hispanic Americans (P=0.019),38 indicating that rs8050136 would be related with SFA in various populations.
Other type 2 diabetes susceptibility SNPs were not associated with BMI, VFA or SFA. Pecioska et al.21 reported that among the type 2 diabetes loci (SLC30A8, CDKN2A/B, CDKAL1, IGF2BP2, HHEX, PPARG, KCNJ11, TCFL2 and FTO), only FTO rs8050136 was associated with BMI, fat mass index (fat mass/height/height) and waist circumference. Our results were in agreement with those reported by Pecioska et al.21 The T allele of rs2237892 in the KCNQ1 gene was reported to be associated with increased waist circumference (P=0.043).24 However, the subjects with T allele had lower VFA in our study. A lack of association of type 2 diabetes loci, and VFA and SFA may be due to the low power of this study. The associations between SNPs and type 2 diabetes were adjusted for BMI, age and gender. Thus, SNPs associated with both obesity and type 2 diabetes would be excluded in the screening stage. Indeed, FTO was first discovered as a susceptibility gene for type 2 diabetes in an analysis that was not adjusted for BMI; after adjustment for BMI, the effect on type 2 diabetes was abolished.39
Most risk factors for type 2 diabetes are believed to act through perturbation of insulin secretion rather than through insulin action (insulin resistance).40 It is believed that CDKN2A and CDKN2B genes are involved in reducing β-cell mass, whereas MTNR1B, TCFL2 and KCNJ11 genes may be involved in β-cell dysfunction. Only the FTO gene can act through insulin resistance caused by obesity. Our results, demonstrating no significant association of type 2 diabetes susceptibility SNPs with VFA or SFA, except for SNPs in the FTO gene, confirm the above mechanism for the development of type 2 diabetes.
In summary, we showed that two SNPs (rs8050136 and rs9939609) in the FTO gene are significantly associated with SFA and BMI, and that other SNPs susceptible for type 2 diabetes are not associated with fat distribution. Our results suggest that only SNPs in the FTO gene are involved in the mechanism of insulin resistance.
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This study was supported by a Grant-in-Aid from the Ministry of Education, Science, Sports and Culture of Japan (21591186 to K Hotta, 23791027 to AK, and 23701082 to TK).
The authors declare no conflict of interest.
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