Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci.

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits.

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits  to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. 'Physical activity' and 'General Health' were the exposure categories containing the highest number of 'tentative signals' in analyses assessing the average association of lifestyle variables, while 'Tobacco use' was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.

Blood pressure and bladder cancer risk in men by use of survival analysis and in interaction with NAT2 genotype, and by Mendelian randomization analysis.

The association between blood pressure (BP) and bladder cancer (BC) risk remains unclear with confounding by smoking being of particular concern. We investigated the association between BP and BC risk among men using conventional survival-analysis, and by Mendelian Randomization (MR) analysis in an attempt to disconnect the association from smoking. We additionally investigated the interaction between BP and N-acetyltransferase-2 (NAT2) rs1495741, an established BC genetic risk variant, in the association. Populations consisting of 188,167 men with 502 incident BC's in the UK-biobank and 27,107 men with 928 incident BC's in two Swedish cohorts were used for the analysis. We found a positive association between systolic BP and BC risk in Cox-regression survival analysis in the Swedish cohorts, (hazard ratio [HR] per standard deviation [SD]: 1.14 [95% confidence interval 1.05-1.22]) and MR analysis (odds ratio per SD: 2-stage least-square regression, 7.70 [1.92-30.9]; inverse-variance weighted estimate, 3.43 [1.12-10.5]), and no associations in the UK-biobank (HR systolic BP: 0.93 [0.85-1.02]; MR OR: 1.24 [0.35-4.40] and 1.37 [0.43-4.37], respectively). BP levels were positively associated with muscle-invasive BC (MIBC) (HRs: systolic BP, 1.32 [1.09-1.59]; diastolic BP, 1.27 [1.04-1.55]), but not with non-muscle invasive BC, which could be analyzed in the Swedish cohorts only. There was no interaction between BP and NAT2 in relation to BC on the additive or multiplicative scale. These results suggest that BP might be related to BC, more particularly MIBC. There was no evidence to support interaction between BP and NAT2 in relation to BC in our study.

Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries.

AIMS/HYPOTHESIS: Gene-macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes. METHODS: We investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution. RESULTS: No interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0-51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined. CONCLUSIONS/INTERPRETATION: Genetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions.

The heritable basis of gene-environment interactions in cardiometabolic traits.

AIMS/HYPOTHESIS: Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. METHODS: Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software. RESULTS: All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. CONCLUSIONS/INTERPRETATION: Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

Do Genetic Factors Modify the Relationship Between Obesity and Hypertriglyceridemia? Findings From the GLACIER and the MDC Studies.

BACKGROUND: Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability. METHODS AND RESULTS: We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmö Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRSTG) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRSTG were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m(2)) associated with 2.8% (P=8.4×10(-84)) higher triglyceride concentration and each additional WGRSTG unit with 2% (P=7.6×10(-48)) higher triglyceride concentration. Each unit of the WGRSTG was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants (Pinteraction=0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRSTG×BMI interaction effect (Pinteraction=6.0×10(-4)), which was strengthened by including data from the Danish cohorts (Pinteraction=6.5×10(-7)). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRSTG×BMI×sex) was observed (Pinteraction=0.03), where the WGRSTG×BMI interaction was only statistically significant in females. Using protein-protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci. CONCLUSIONS: Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.

Innate biology versus lifestyle behaviour in the aetiology of obesity and type 2 diabetes: the GLACIER Study.

AIMS/HYPOTHESIS: We compared the ability of genetic (established type 2 diabetes, fasting glucose, 2 h glucose and obesity variants) and modifiable lifestyle (diet, physical activity, smoking, alcohol and education) risk factors to predict incident type 2 diabetes and obesity in a population-based prospective cohort of 3,444 Swedish adults studied sequentially at baseline and 10 years later. METHODS: Multivariable logistic regression analyses were used to assess the predictive ability of genetic and lifestyle risk factors on incident obesity and type 2 diabetes by calculating the AUC. RESULTS: The predictive accuracy of lifestyle risk factors was similar to that yielded by genetic information for incident type 2 diabetes (AUC 75% and 74%, respectively) and obesity (AUC 68% and 73%, respectively) in models adjusted for age, age(2) and sex. The addition of genetic information to the lifestyle model significantly improved the prediction of type 2 diabetes (AUC 80%; p = 0.0003) and obesity (AUC 79%; p < 0.0001) and resulted in a net reclassification improvement of 58% for type 2 diabetes and 64% for obesity. CONCLUSIONS/INTERPRETATION: These findings illustrate that lifestyle and genetic information separately provide a similarly high degree of long-range predictive accuracy for obesity and type 2 diabetes.

Gene-lifestyle and gene-pharmacotherapy interactions in obesity and its cardiovascular consequences.

Obesity is a highly prevalent complex trait that raises the risk of other chronic diseases such as type 2 diabetes, certain cancers, sleep apnea, and cardiovascular disease, and shortens lifespan. Clinical intervention studies focused on weight loss and epidemiological studies of obesity indicate that genetic variation may modify the relationship between lifestyle behaviors and weight loss or weight gain. Similar observations have also emerged from pharmacogenetic studies. The literature includes several reports from these studies, but few examples of interactions have been adequately replicated. In this review we introduce the topics of population genetics and gene x environment interaction research. We also provide a systematic review of the published literature on gene x lifestyle (physical activity and dietary factors) and gene x drug interactions in relation to obesity. Finally, we overview the scope and findings from these studies and discuss some of their strengths and limitations.