The acute effects of contraction and insulin on the glucose transport and GLUT4 glucose transporter translocation were investigated in rat soleus muscles by using a 3-O-methylglucose transport assay and the sensitive exofacial labeling technique with the impermeant photoaffinity reagent 2-N-4-(1-azi-2,2,2-trifluoroethyl)benzoyl-1,3-bis(Dmannose-4-yloxy)-2-propylamine (ATB-BMPA), respectively. Addition of wortmannin, which inhibits phosphatidylinositol 3-kinase, reduced insulin-stimulated glucose transport (8.8 ± 0.5 ,lmol per ml per h vs. 1.4 ± 0.1 ,umol per ml per h) and GLUT4 translocation [2.79 ± 0.20 pmol/g (wet muscle weight) vs. 0.49 ± 0.05 pmol/g (wet muscle weight)]. In contrast, even at a high concentration (1 ,uM), wortmannin had no effect on contraction-mediated glucose uptake (4.4 ± 0.1 ,imol per ml per h vs. 4.1 ± 0.2 ,umol per ml per h) and GLUT4 cell surface content [1.75 ± 0.16 pmol/g (wet muscle weight) vs. 1.52 ± 0.16 pmol/g (wet muscle weight)]. Contraction-mediated translocation of the GLUT4 transporters to the cell surface was closely correlated with the glucose transport activity and could account fully for the increment in glucose uptake after contraction. The combined effects of contraction and maximal insulin stimulation were greater than either stimulation alone on glucose transport activity (11.5 + 0.4 ,umol per ml per h vs. 5.6 + 0.2 ,umol per ml per h and 9.0 ± 0.2 ,umol per ml per h) and on GLUT4 translocation [4.10 + 0.20 pmol/g (wet muscle weight) vs. 1.75 ± 0.25 pmol/g (wet muscle weight) and 3.15 + 0.18 pmol/g (wet muscle weight)]. The results provide evidence that contraction stimulates translocation of GLUT4 in skeletal muscle through a mechanism distinct from that of insulin.

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.

Glycemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated hemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P <5x10 -8 ), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

Aims/hypothesis Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for gene-lifestyle studies are lacking. This casecohort study aims to investigate how genetic and potentially modifiable lifestyle and behavioural factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes. Methods Incident cases of type 2 diabetes occurring in European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts between 1991 and 2007 from eight of the ten EPIC countries were ascertained and verified. Prenticeweighted Cox regression and random-effects meta-analyses were used to investigate differences in diabetes incidence by age and sex. Results A total of 12,403 verified incident cases of type 2 diabetes occurred during 3.99 million person-years of follow-up of 340,234 EPIC participants eligible for InterAct. We defined a centre-stratified subcohort of 16,154 individuals for comparative analyses. Individuals with incident diabetes who were randomly selected into the subcohort (n=778) were included as cases in the analyses. All prevalent diabetes cases were excluded from the study. InterAct cases were followed-up for an average of 6.9 years; 49.7% were men. Mean baseline age and age at diagnosis were 55.6 and 62.5 years, mean BMI and waist circumference values were 29.4 kg/m 2 and 102.7 cm in men, and 30.1 kg/m 2 and 92.8 cm in women, respectively. Risk of type 2 diabetes increased linearly with age, with an overall HR of 1.56 (95% CI 1.48-1.64) for a 10 year age difference, adjusted for sex. A male excess in the risk of incident diabetes was consistently observed across all countries, with a pooled HR of 1.51 (95% CI 1.39-1.64), adjusted for age. Conclusions/interpretation InterAct is a large, well-powered, prospective study that will inform our understanding of the interplay between genes and lifestyle factors on the risk of type 2 diabetes development.