No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels

Abstract

Gene–lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13–0.31], P = 1.63 × 10−6). All SNPs were associated with 2-h glucose (β = 0.06–0.12 mmol/allele, P ≤ 1.53 × 10−7), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene–lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women

“…Although heterogeneity of interaction effects between obesity subgroups was observed for G6PC2 and FADS1, our findings overall are consistent with those of previous metaanalysis and epidemiologic studies, and their gene functions have been established. A TCF7L2 variant that has been related to T2DM (Nettleton, McKeown, & Kanoni, 2010;Palmer, McDonough, & Hicks, 2012;Saxena, Elbers, & Guo, 2012;Scott, Chu, & Grarup, 2012;Zeggini et al, 2008) indicates involvement in beta-cell malfunction; thus, in this and other studies Kirchhoff et al, 2008;Nettleton et al, 2013;Walford et al, 2012), the common variant in this gene is inversely related to insulin levels with increased glucose levels. Moreover, we found a more profound effect of this variant among obese or inactive women, implying a gene-obesity interaction that may contribute to the heterogeneity of T2DM etiology.…”

Genetic Drivers of Pancreatic Islet Function

“…We used LocusZoom (PRUIM et al 2010) to generate regional association plots at each of the candidate gene loci for diabetes-associated phenotypes measured in the MAGIC studies, e.g., fasting glucose, insulin, or HbA1c (DUPUIS et al 2010;SORANZO et al 2010;MANNING et al 2012;SCOTT et al 2012a;SCOTT et al 2012b). While many of the SNPs associated with these phenotypes are sub-threshold for a genome-wide query, the relatively small number of SNPs interrogated in our analysis (<400) greatly reduces the multiple-testing penalty for these single-gene searches.…”

How accurate can genetic predictions be?

“…When there are no gene-environment interactions, this difference is the proportion of phenotypic variation that these features explain independently of genes. For example, weekly physical activity can explain 4% of phenotypic variance of T2D (see Methods), is moderately heritable [20], and was found to not interact with well-known gene variants in T2D [21]. Accordingly, the proportion of variance explained by the integrated predictor comprised of genomic profile and physical activity does not increment by the full 4% beyond the heritability of T2D.…”