Interaction between peroxisome proliferator-activated receptor gamma polymorphism and obesity on type 2 diabetes in a Chinese Han population

Diabetes mellitus (DM) is a group of common metabolic disorders that share the phenotype of hyperglycemia. Globally, about 5.4% of adult population has been estimated to have type 2 diabetes mellitus (T2DM) [1]. Several types of diabetes mellitus exist and are caused by a complex interaction of genetics and environmental factors [2]. Type 2 diabetes has a strong genetic component. There is evidence that the Pro12Ala polymorphism is linked to obesity and T2DM [3].

The peroxisome proliferator-activated receptor gamma (PPARG) is now recognized to play a main PPARG entered the spotlight as a major player in metabolic regulation in the early 1990s [4] through the discovery of thiazolidinediones (TZDs) as potent synthetic insulin-sensitizing drugs. TZDs quickly passed clinical trials and became front-line agents in the treatment of type II diabetes for their robust insulin-sensitizing and glucose-lowering actions, although their popularity has recently decreased in response to safety concerns. Activation of PPARG results in systemic insulin sensitization through complex mechanisms involving multiple. PPARG was the first gene reproducibly associated with T2DM. The association between the substitution of alanine by proline at codon 12 of PPARG2 (Ala12 allele) and the risk for T2DM has been widely studied since Yen et al. [5], first reported this polymorphism. Some studies indicated that rs1805192 SNP plays a key role in regulating the expression of numerous genes involved in lipid metabolism, metabolic syndrome, inflammation, and atherosclerosis [6, 7]. Some environmental risk factors for T2DM were suggested, such as obesity, which was reported in different populations [8‐10]. However, till now, no study focused on the impact of gene- environment interaction between PPARG and obesity on T2DM risk in Chinese population. So the aim of this study was to investigate the association of PPARG, and additional PPARG gene- obesity interaction with T2DM risk based on a Chinese population.

A total of 1297 subjects including 647 T2DM patients and 650 normal controls were included in this study, including 606 males and 691 females. The mean age of all subjects was 54.3 ± 15.8 years old. Participants characteristics stratified by T2DM cases and controls are shown in Table 2. The distributions of males, smoking, alcohol consumption, high fat diet, low fiber diet and mean of age were not different between cases and controls. The means of BMI, WC, TG, TC and HDL were significantly different between cases and controls.

All genotypes were distributed according to Hardy–Weinberg equilibrium (all p values were more than 0.05). There were significant differences in rs1805192 and rs3856806 alleles and genotypes distributions between cases and controls (Table 3). The frequency for G allele of rs1805192 was higher in cases (49.0% in T2DM patients and 37.5% in controls, p < 0.001). Logistic analysis showed that T2DM risk was significantly higher in individuals with rs1805192-G allele (p < 0.05). The carriers of G allele of the rs1805192 polymorphism revealed increased T2DM risk than those with CC variants (CG + GG versus CC, adjusted OR (95% CI) 1.76 (1.45–2.06), p < 0.001). The frequency for T allele of rs3856806 was higher in cases (50.2% in T2DM patients and 38.9% in controls, p < 0.001). Logistic analysis showed that T2DM risk was significantly higher in individuals with rs3856806-T allele (p < 0.05). The carriers of T allele of the rs3856806 polymorphism revealed increased T2DM risk than those with CC variants (CT + TT versus CC, adjusted OR (95% CI) 1.25 (1.17–1.76), p < 0.001). However, we did not find any significant association between other two SNPs and T2DM risk before or after covariates adjustment.

We employed the GMDR analysis to assess the impact of the PPARG gene- obesity interaction on T2DM risk, after adjustment for gender, age, smoke and alcohol consumption status, high fat diet, low fiber diet, TC and HDL. Table 4 summarizes the results obtained from GMDR analysis for two- to five-locus models. There was a significant two-locus model (p = 0.0107) involving rs1805192 and obesity, indicating a potential gene–environment interaction among rs1805192 and obesity. Overall, the two- locus models had a cross-validation consistency of 10 of 10, and had the testing accuracy of 62.17%.

In order to obtain the odds ratios and 95% CI for the joint effects of rs1805192 genotype and obesity on T2DM, we conducted interaction analysis between rs1805192 and obesity. We found that obese subjects with CG or GG genotype have the highest T2DM risk, compared to subjects with CC genotype and normal BMI (OR 2.40, 95% CI 1.68–3.63), after adjustment for gender, age, smoke and alcohol status, high fat diet, low fiber diet, TC and HDL (Table 5).

The result of this study indicated that rs1805192- G allele and rs3856806-T allele of PPARG is significantly associated with higher T2DM risk, however, we did not find any association between the others SNPs and T2DM. Although many studies have taken a candidate gene approach to investigate the genetic etiology of T2DM, implicating the potential candidate genes in T2DM risk factors, which include genes for rs1805192 and rs3856806 polymorphism of PPARG [16], however the results of association between rs1805192 and T2DM are inconsistent. Wang et al. [17] demonstrated that the presence of the Ala allele may contribute to improved insulin secretory capacity and may confer protection from T2DM and obesity in the Chinese population. Moreover, a meta-analysis confirmed the association between the PPARG2 Pro12 allele and T2DM, and suggested that patients who carry the Pro12 allele have a 1.27-fold higher risk for developing T2DM than Ala12 carriers. This seemingly modest effect translates into a staggering 25% population-attributable risk because of the higher frequency of the Pro12 allele, especially in Japanese and European populations [18]. However, Bener et al. [19] suggested that no significant association between the Pro12Ala polymorphism of the PPARG2 gene and T2DM in Qatari’s population. Al-Safar et al. [20] also suggested that confirmed that Pro12Ala mutation in PPARG2 is not associated with T2DM risk in this population. In a study for South Africa population, Vergotine et al. [21] reported that the Pro12Ala of PPARG2 is significantly associated with insulin resistance and this polymorphism interacts with IRS1 Gly972Arg, to increase the risk of T2DM. Hahn et al. [22] indicated that our data confirm a beneficial effect of the PPARG1 12Ala allele on insulin resistance and glucose metabolism in PCOS women. Furthermore, the Ala allele appears to be associated with less severe hirsutism, independent of hyperandrogenemia. Tellechea et al. [23] conducted a study for males of Argentinian blood donors of self-reported European ancestry and indicated that healthy men, in particular nonsmokers, carrying the Ala12 allele of PPARG rs1801282 polymorphism, have a high risk for metabolic syndrome (MetS) and insulin resistance (IR). Fan et al. [24] indicated that the genotypes with minor allele variants at the rs1805192, rs709158 and rs3856806 loci are associated with increased LDL-C levels, which was a risk factor of T2DM. Gu et al. [25] also suggested that in the codominant and log-additive models, rs1805192 and rs3856806 were all associated with increased dyslipidemia risk. A study conducted by Phani et al. [26] showed that PPARG2 variants are associated with increased T2DM susceptibility when associated with adiposity in Indian population. These data reflect an association of analyzed PPARG gene polymorphisms with values of insulin, HDL, LDL and total cholesterol which indicates an important role of these genes in lipid metabolism and pathogenesis of T2DM and MetS [16].

T2DM is associated with both environmental and genetic factors. Several metabolic abnormalities are implicated in its pathogenesis, such as obesity and other environmental factors. Several studies have confirmed the association between obesity and T2DM. However, till now, no study focused on impact of gene-environment interaction between PPARG and obesity on T2DM risk in Chinese population. This study investigated the impact of additional PPARG gene-obesity interaction on T2DM risk based on a Chinese population by using GMDR model. We found that a significant two-locus model involving rs1805192 and obesity, indicating a potential gene-environment interaction among rs1805192 and obesity. Obese subjects with CG or GG genotype have the highest T2DM risk, compared to subjects with CC genotype and normal BMI.

Several limitations of this study should be considered. Firstly, More SNPs of PPARG gene should been chosen. The limited SNPs were not sufficient to capture most genetic information of PPARG. Secondly, there was a relatively small sample size in the study, other larger sample studies should be conducted in the future. Thirdly, we did not obtain any information on IR level.

Our results support an important association between rs1805192 and rs3856806 minor allele (G allele) of PPARG and increased T2DM risk, the interaction analysis shown a combined effect of G- obesity interaction between rs1805192 and obesity on increased T2DM risk.