Circulating Fatty Acids and Prostate Cancer Risk: Individual Participant Meta-Analysis of Prospective Studies

Background: Individual studies have suggested that some circulating fatty acids are associated with prostate cancer risk, but have not been large enough to provide precise estimates of associations, particularly by stage and grade of disease. Methods: Principal investigators of prospective studies on circulating fatty acids and prostate cancer were invited to collaborate. Investigators provided individual participant data on circulating fatty acids (weight percent) and other characteristics of prostate cancer cases and controls. Prostate cancer risk by study-specific fifths of 14 fatty acids was estimated using multivariable-adjusted conditional logistic regression. All statistical tests were two-sided. Results: Five thousand and ninety-eight case patients and 6649 control patients from seven studies with an average follow-up of 5.1 (SD = 3.3) years were included. Stearic acid (18:0) was inversely associated with total prostate cancer (odds ratio [OR] Q5 vs Q1 = 0.88, 95% confidence interval [CI] = 0.78 to 1.00, P trend = .043). Prostate cancer risk was, respectively, 14% and 16% greater in the highest fifth of eicosapentaenoic acid (20:5n-3) (OR = 1.14, 95% CI = 1.01 to 1.29, P trend = .001) and docosapentaenoic acid (22:5n-3) (OR = 1.16, 95% CI = 1.02 to 1.33, P trend = .003), but in each case there was heterogeneity between studies (P = .022 and P < .001, respectively). There was heterogeneity in the association between docosapentaenoic acid and prostate cancer by grade of disease (P = .006); the association was statistically significant for low-grade disease but not high-grade disease. The remaining 11 fatty acids were not statistically associated with total prostate cancer risk. Conclusion: There was no strong evidence that circulating fatty acids are important predictors of prostate cancer risk. It is not clear whether the modest associations of stearic, eicosapentaenoic, and docosapentaenoic acid are causal

Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study

Abstract Background Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored. Methods As part of the ‘Population Architecture using Genomics and Epidemiology (PAGE)’ Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA) and 39,716 European Americans (EA)). We assessed effect modification by testing an interaction term for each SNP and smoking (current vs. former/never) in the linear regression and by stratified analyses. Results We did not observe strong evidence for interactions and only observed two interactions with p-values <0.1: for rs6548238/TMEM18, the risk allele (C) was associated with BMI only among AA females who were former/never smokers (β = 0.018, p = 0.002), vs. current smokers (β = 0.001, p = 0.95, pinteraction = 0.10). For rs9939609/FTO, the A allele was more strongly associated with BMI among current smoker EA females (β = 0.017, p = 3.5x10-5), vs. former/never smokers (β = 0.006, p = 0.05, pinteraction = 0.08). Conclusions These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results. Clinical Trial Registration NCT0000061

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes

Lymphoma risk is elevated for relatives with common non-Hodgkin lymphoma (NHL) subtypes, suggesting shared genetic susceptibility across subtypes. To evaluate the extent of mutual heritability among NHL subtypes and discover novel loci shared among subtypes, we analyzed data from eight genome-wide association studies within the InterLymph Consortium, including 10,629 cases and 9505 controls. We utilized Association analysis based on SubSETs (ASSET) to discover loci for subsets of NHL subtypes and evaluated shared heritability across the genome using Genome-wide Complex Trait Analysis (GCTA) and polygenic risk scores. We discovered 17 genome-wide significant loci (P &lt; 5 × 10−8) for subsets of NHL subtypes, including a novel locus at 10q23.33 (HHEX) (P = 3.27 × 10−9). Most subset associations were driven primarily by only one subtype. Genome-wide genetic correlations between pairs of subtypes varied broadly from 0.20 to 0.86, suggesting substantial heterogeneity in the extent of shared heritability among subtypes. Polygenic risk score analyses of established loci for different lymphoid malignancies identified strong associations with some NHL subtypes (P &lt; 5 × 10−8), but weak or null associations with others. Although our analyses suggest partially shared heritability and biological pathways, they reveal substantial heterogeneity among NHL subtypes with each having its own distinct germline genetic architecture

Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study

<p>Abstract</p> <p>Background</p> <p>Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored.</p> <p>Methods</p> <p>As part of the ‘Population Architecture using Genomics and Epidemiology (PAGE)’ Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA) and 39,716 European Americans (EA)). We assessed effect modification by testing an interaction term for each SNP and smoking (current vs. former/never) in the linear regression and by stratified analyses.</p> <p>Results</p> <p>We did not observe strong evidence for interactions and only observed two interactions with p-values <0.1: for rs6548238/<it>TMEM18</it>, the risk allele (C) was associated with BMI only among AA females who were former/never smokers (β = 0.018, p = 0.002), vs. current smokers (β = 0.001, p = 0.95, p_interaction = 0.10). For rs9939609/<it>FTO</it>, the A allele was more strongly associated with BMI among current smoker EA females (β = 0.017, p = 3.5x10^-5), vs. former/never smokers (β = 0.006, p = 0.05, p_interaction = 0.08).</p> <p>Conclusions</p> <p>These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results.</p> <p>Clinical Trial Registration</p> <p>NCT00000611</p