The current meta-analysis, which includes data on 8,536 participants, indicates that the PPARGC1A Ser482 allele may increase the risk of type 2 diabetes. It is possible, however, that publication bias may explain the observed association between this genetic variant and risk of type 2 diabetes. We could not detect any statistically significant association of the Gly482Ser polymorphism with BMI, fasting insulin or fasting glucose levels in a meta-analysis comprised of 4,486 normoglycaemic study participants.
Over the last 6 years the role of PPARGC1A in several aspects of energy expenditure, metabolism and glucose homeostasis has been elucidated. This has led to several studies assessing the association among polymorphisms, risk of type 2 diabetes and related metabolic traits. The only common non-synonymous change, the Gly482Ser polymorphism, has generated conflicting results. In this study we aimed to address this issue by performing a meta-analysis of the effect of the Gly482Ser polymorphism on type 2 diabetes, BMI, fasting insulin and fasting glucose levels.
In this meta-analysis, which included data from eight studies comprising 8,536 participants, there was evidence for a small linear effect of the Gly482Ser in type 2 diabetes risk (OR=1.07, p=0.044), despite the presence of marginal between-study heterogeneity (p=0.069). The summary odds ratio increased to 1.1 (p=0.004) and there was no between-study heterogeneity after elimination of one of the studies. We also found significant between-study heterogeneity in the quantitative trait analyses. However, in this analysis we found no statistically significant association between the Gly482Ser polymorphism and the three traits analysed (BMI, fasting insulin and fasting glucose). In each instance the heterogeneity observed in the association with type 2 diabetes or with the quantitative traits could be explained by a different constituent population of the meta-analysis. This heterogeneity could result from differences in other genetic and environmental factors between study populations, particularly in relation to selection and inclusion of participants and their demographic, metabolic and clinical characteristics. Other possible explanations for the observed between-study heterogeneity include genotype errors, publication bias, lack of statistical power to detect true effects and simple random variation around the true estimate of risk.
The variable effect of the Gly482Ser polymorphism on type 2 diabetes risk and related quantitative traits between populations could also be explained by the presence of other functional variants in linkage disequilibrium (LD) with Gly482Ser. The amount of LD between these putative variants and Gly482Ser could vary between the different populations tested, hence accounting for the observed differences in the effect of Gly482Ser in diabetes association and in mean values for phenotypic traits between populations. Other possible explanations for the heterogeneity observed are gene–gene and gene-environment interactions such that the effect of the Gly482Ser polymorphism is different in different populations. Indeed, evidence for gene-environment interaction at this gene has been demonstrated [23–25]. Gene-environment interactions may lower the power to detect true associations when performing cross-sectional studies, including meta-analysis of cross-sectional studies, such as in this study.
In conclusion, our results, based on 8,536 study participants, suggest that the PPARGC1A Gly482Ser polymorphism is associated with risk of type 2 diabetes. However, larger scale studies are required to reliably confirm any association between this variant and the risk of type 2 diabetes.