Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21–1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation.

Aims/hypothesis Recent reports have suggested that genotypes at the FTO locus interact with physical activity to modify levels of obesity-related traits. We tested this hypothesis in two non-diabetic population-based cohorts, the first from southern Sweden and the second from the Botnia region of western Finland. Methods In total 2,511 Finnish and 15,925 Swedish nondiabetic middle-aged adults were genotyped for the FTO rs9939609 variant. Physical activity was assessed by questionnaires and standard clinical procedures were conducted, including measures of height and weight and glucose regulation. Tests of gene×physical activity interaction were performed using linear interaction effects to determine whether the effect of this variant on BMI is modified by physical activity. Results The minor A allele at rs9939609 was associated with higher BMI in both cohorts, with the per allele difference in BMI being about 0.13 and 0.43 kg/m 2 in the Swedish and Finnish cohorts, respectively (p<0.0001). The test of interaction between physical activity and the rs9939609 variant on BMI was not statistically significant after controlling for age and sex in either cohort (Sweden: p=0.71, Finland: p=0.18). Conclusions/interpretation The present report does not support the notion that physical activity modifies the effects of the FTO rs9939609 variant on obesity risk in the nondiabetic Swedish or Finnish adults studied here.

OBJECTIVE-Recent advances in type 2 diabetes genetics have culminated in the discovery and confirmation of multiple risk variants. Two important and largely unanswered questions are whether this information can be used to identify individuals most susceptible to the adverse consequences of sedentary behavior and to predict their response to lifestyle intervention; such evidence would be mechanistically informative and provide a rationale for targeting genetically susceptible subgroups of the population.RESEARCH DESIGN AND METHODS-Gene ϫ physical activity interactions were assessed for 17 polymorphisms in a prospective population-based cohort of initially nondiabetic middle-aged adults. Outcomes were 1) impaired glucose regulation (IGR) versus normal glucose regulation determined with either fasting or 2-h plasma glucose concentrations (n ϭ 16,003), 2) glucose intolerance (in mmol/l, n ϭ 8,860), or 3) incident type 2 diabetes (n ϭ 2,063 events). RESULTS-Testsof gene ϫ physical activity interactions on IGR risk for 3 of the 17 polymorphisms were nominally statistically significant: CDKN2A/B rs10811661 (P interaction ϭ 0.015), HNF1B rs4430796 (P interaction ϭ 0.026), and PPARG rs1801282 (P interaction ϭ 0.04). Consistent interactions were observed for the CDKN2A/B (P interaction ϭ 0.013) and HNF1B (P interaction ϭ 0.0009) variants on 2-h glucose concentrations. Where type 2 diabetes was the outcome, only one statistically significant interaction effect was observed, and this was for the HNF1B rs4430796 variant (P interaction ϭ 0.0004). The interaction effects for HNF1B on IGR risk and incident diabetes remained significant after correction for multiple testing (P interaction ϭ 0.015 and 0.0068, respectively).CONCLUSIONS-Our observations suggest that the genetic predisposition to hyperglycemia is partially dependent on a person's lifestyle.

The protein encoded by the PPARGC1A gene is expressed at high levels in metabolically active tissues and is involved in the control of oxidative stress via reactive oxygen species detoxification. Several recent reports suggest that the PPARGC1A Gly482Ser (rs8192678) missense polymorphism may relate inversely with blood pressure. We used conventional meta-analysis methods to assess the association between Gly482Ser and systolic (SBP) or diastolic blood pressures (DBP) or hypertension in 13,949 individuals from 17 studies, of which 6,042 were previously unpublished observations. The studies comprised cohorts of white European, Asian, and American Indian adults, and adolescents from South America. Stratified analyses were conducted to control for population stratification. Pooled genotype frequencies were 0.47 (Gly482Gly), 0.42 (Gly482Ser), and 0.11 (Ser482Ser). We found no evidence of association between Gly482Ser and SBP [Gly482Gly: mean = 131.0 mmHg, 95% confidence interval (CI) = 130.5–131.5 mmHg; Gly482Ser mean = 133.1 mmHg, 95% CI = 132.6–133.6 mmHg; Ser482Ser: mean = 133.5 mmHg, 95% CI = 132.5–134.5 mmHg; P = 0.409] or DBP (Gly482Gly: mean = 80.3 mmHg, 95% CI = 80.0–80.6 mmHg; Gly482Ser mean = 81.5 mmHg, 95% CI = 81.2–81.8 mmHg; Ser482Ser: mean = 82.1 mmHg, 95% CI = 81.5–82.7 mmHg; P = 0.651). Contrary to previous reports, we did not observe significant effect modification by sex (SBP, P = 0.966; DBP, P = 0.715). We were also unable to confirm the previously reported association between the Ser482 allele and hypertension [odds ratio: 0.97, 95% CI = 0.87–1.08, P = 0.585]. These results were materially unchanged when analyses were focused on whites only. However, statistical evidence of gene-age interaction was apparent for DBP [Gly482Gly: 73.5 (72.8, 74.2), Gly482Ser: 77.0 (76.2, 77.8), Ser482Ser: 79.1 (77.4, 80.9), P = 4.20 × 10−12] and SBP [Gly482Gly: 121.4 (120.4, 122.5), Gly482Ser: 125.9 (124.6, 127.1), Ser482Ser: 129.2 (126.5, 131.9), P = 7.20 × 10−12] in individuals <50 yr (n = 2,511); these genetic effects were absent in those older than 50 yr (n = 5,088) (SBP, P = 0.41; DBP, P = 0.51). Our findings suggest that the PPARGC1A Ser482 allele may be associated with higher blood pressure, but this is only apparent in younger adults.

Aims/hypothesis The PPARGC1A gene coactivates multiple nuclear transcription factors involved in cellular energy metabolism and vascular stasis. In the present study, we genotyped 35 tagging polymorphisms to capture all common PPARGC1A nucleotide sequence variations and tested for association with metabolic and cardiovascular traits in 2,101 Danish and Estonian boys and girls from the European Youth Heart Study, a multicentre school-based cross-sectional cohort study. Methods Fasting plasma glucose concentrations, anthropometric variables and blood pressure were measured.Habitual physical activity and aerobic fitness were objectively assessed using uniaxial accelerometry and a maximal aerobic exercise stress test on a bicycle ergometer, respectively. Results In adjusted models, nominally significant associations were observed for BMI (rs10018239, p=0.039), waist circumference (rs7656250, p = 0.012; rs8192678 [Gly482Ser], p=0.015; rs3755863, p=0.02; rs10018239, beta=−0.01 cm per minor allele copy, p=0.043), systolic blood pressure (rs2970869, p=0.018) and fasting glucose concentrations (rs11724368, p=0.045). Stronger associations were observed for aerobic fitness (rs7656250, p= 0.005; rs13117172, p=0.008) and fasting glucose concentrations (rs7657071, p=0.002). None remained significant after correcting for the number of statistical comparisons. We proceeded by testing for gene × physical activity interactions for the polymorphisms that showed nominal evidence of association in the main effect models. None of these tests was statistically significant. Conclusions/interpretation Variants at PPARGC1A may influence several metabolic traits in this European paediatric cohort. However, variation at PPARGC1A is unlikely to have a major impact on cardiovascular or metabolic health in these children.