CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits

Rate-Limiting Enzymes in Cardiometabolic Health and Aging in Humans

INTRODUCTION

Rate-limiting enzymes (RLEs) are innate slow points in metabolic pathways, and many function in bio-processes related to nutrient sensing. Many RLEs carry causal mutations relevant to inherited metabolic disorders. Because the activity of RLEs in cardiovascular health is poorly characterized, our objective was to assess their involvement in cardiometabolic health and disease and where altered biophysical and biochemical functions can promote disease.

METHODS

A dataset of 380 human RLEs was compared to protein and gene datasets for factors likely to contribute to cardiometabolic disease, including proteins showing significant age-related altered expression in blood and genetic loci with variants that associate with common cardiometabolic phenotypes. The biochemical reactions catalyzed by RLEs were evaluated for metabolites enriched in RLE subsets associating with various cardiometabolic phenotypes. Most significance tests were based on Z-score enrichment converted to p values with a normal distribution function.

RESULTS

Of 380 RLEs analyzed, 112 function in mitochondria, and 53 are assigned to inherited metabolic disorders. There was a depletion of RLE proteins known as aging biomarkers. At the gene level, RLEs were assessed for common genetic variants that associated with important cardiometabolic traits of LDL-cholesterol or any of the five outcomes pertinent to metabolic syndrome. This revealed several RLEs with links to cardiometabolic traits, from a minimum of 26 for HDL-cholesterol to a maximum of 45 for plasma glucose. Analysis of these GWAS-linked RLEs for enrichment of the molecular constituents of the catalyzed reactions disclosed a number of significant phenotype-metabolite links. These included blood pressure with acetate (p = 2.2 × 10-4) and NADP+ (p = 0.0091), plasma HDL-cholesterol and triglyceride with diacylglycerol (p = 2.6 × 10-5, 6.4 × 10-5, respectively) and diolein (p = 2.2 × 10-6, 5.9 × 10-6), and waist circumference with d-glucosamine-6-phosphate (p = 1.8 × 10-4).

CONCLUSION

In the context of cardiometabolic health, aging, and disease, these results highlight key diet-derived metabolites that are central to specific rate-limited processes that are linked to cardiometabolic health. These metabolites include acetate and diacylglycerol, pertinent to blood pressure and triglycerides, respectively, as well as diacylglycerol and HDL-cholesterol.

G × E interactions as a basis for toxicological uncertainty

To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of "genetic" influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present "gene expression" influences is summarized here as Ge. Also, the concept of "environment" needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge. This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et. We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM).

Prevalence and clinical correlates of abnormal lipid metabolism in first-episode and drug-naïve patients with major depressive disorder: A large-scale cross-sectional study

OBJECTIVE

Studies have shown an association between abnormal lipid profiles and MDD, but there are few studies on the clinical correlates of lipid abnormalities in patients with major depressive disorder (MDD). The purpose of this study was to investigate the prevalence of abnormal lipid metabolism and its correlates in Chinese first-episode and drug-naïve MDD patients, which has not yet been reported.

METHODS

A total of 1718 outpatients with first-episode and drug-naïve MDD were included. Demographic data were collected by a standardized questionnaire and blood lipid levels were measured, including total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL-C), high density lipoprotein (HDL-C). The Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Positive and Negative Syndrome Scale (PANSS) positive subscale, and Clinical Global Impression of Severity Scale (CGI-S) were assessed for each patient.

RESULTS

The prevalence of abnormal lipid metabolism was 72.73% (1301/1718), and the rates of high TC, high TG, high LDL-C and low HDL-C were 51.05% (877/1718), 61.18% (1051/1718), 30.09% (517/1718), 23.40% (402/1718), respectively. Logistic regression showed the risk factors for abnormal lipid metabolism were severe anxiety, HAMD score, CGI-S score, BMI and systolic blood pressure (SBP). Multiple linear regression analysis showed that age at onset, SBP, HAMD score, HAMA score, PANSS positive subscale score, and CGI-S were independently associated with TC levels. BMI, HAMD score, PANSS positive subscale score and CGI-S score were independently associated with TG levels. SBP, HAMD score, PANSS positive subscale score and CGI-S score were independently associated with LDL-C levels. Age of onset, SBP and CGI-S score were independently associated with HDL-C levels.

CONCLUSIONS

The prevalence of abnormal lipid metabolism in first-episode and drug-naïve MDD patients is quite high. The severity of psychiatric symptoms may be closely associated with the presence of abnormal lipid metabolism in patients with MDD.

Dietary Responses of Dementia-Related Genes Encoding Metabolic Enzymes

The age-related loss of the cognitive function is a growing concern for global populations. Many factors that determine cognitive resilience or dementia also have metabolic functions. However, this duality is not universally appreciated when the action of that factor occurs in tissues external to the brain. Thus, we examined a set of genes involved in dementia, i.e., those related to vascular dementia, Alzheimer's disease, Parkinson's disease, and the human metabolism for activity in 12 metabolically active tissues. Mining the Genotype-Tissue Expression (GTEx) data showed that most of these metabolism-dementia (MD) genes (62 of 93, 67%) exhibit a higher median expression in any of the metabolically active tissues than in the brain. After identifying that several MD genes served as blood-based biomarkers of longevity in other studies, we examined the impact of the intake of food, nutrients, and other dietary factors on the expression of MD genes in whole blood in the Framingham Offspring Study (n = 2134). We observed positive correlations between flavonoids and HMOX1, taurine and UQCRC1, broccoli and SLC10A2, and myricetin and SLC9A8 (p < 2.09 × 10^-4). In contrast, dairy protein, palmitic acid, and pie were negatively correlated, respectively, with the expression of IGF1R, CSF1R, and SLC9A8, among others (p < 2.92 × 10^-4). The results of this investigation underscore the potential contributions of metabolic enzyme activity in non-brain tissues to the risk of dementia. Specific epidemiological or intervention studies could be designed using specific foods and nutrients or even dietary patterns focused on these foods and nutrients that influence the expression of some MD genes to verify the findings presented here.

Research gaps and opportunities in precision nutrition: an NIH workshop report

Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On 11-12 January 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition. The workshop proceeded in 3 parts: part I covered many aspects of genetics and physiology that mediate the links between nutrient intake and health conditions such as cardiovascular disease, Alzheimer disease, and cancer; part II reviewed potential contributors to interindividual variability in dietary exposures and responses such as baseline nutritional status, circadian rhythm/sleep, environmental exposures, sensory properties of food, stress, inflammation, and the social determinants of health; part III presented the need for systems approaches, with new methods and technologies that can facilitate the study and implementation of precision nutrition, and workforce development needed to create a new generation of researchers. The workshop concluded that much research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, the field must proceed with appropriate levels of caution and make sure the factors listed above are all considered, and systems approaches and methods are incorporated. It will be important to develop and train an expanded workforce with the goal of reducing health disparities and improving precision nutritional advice for all Americans.

Personalized nutrition: A review of genotype-based nutritional supplementation

Nutritional disorders have become a major public health issue, requiring increased targeted approaches. Personalized nutrition adapted to individual needs has garnered dramatic attention as an effective way to improve nutritional balance and maintain health. With the rapidly evolving fields of genomics and nutrigenetics, accumulation of genetic variants has been indicated to alter the effects of nutritional supplementation, suggesting its indispensable role in the genotype-based personalized nutrition. Additionally, the metabolism of nutrients, such as lipids, especially omega-3 polyunsaturated fatty acids, glucose, vitamin A, folic acid, vitamin D, iron, and calcium could be effectively improved with related genetic variants. This review focuses on existing literatures linking critical genetic variants to the nutrient and the ways in which these variants influence the outcomes of certain nutritional supplementations. Although further studies are required in this direction, such evidence provides valuable insights for the guidance of appropriate interventions using genetic information, thus paving the way for the smooth transition of conventional generic approach to genotype-based personalized nutrition.

Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation

PURPOSE OF REVIEW

This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits.

RECENT FINDINGS

Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.

Effects of SNVs in ABCA1, ABCG1, ABCG5, ABCG8, and SCARB1 Genes on Plasma Lipids, Lipoproteins, and Adiposity Markers in a Brazilian Population

Several proteins are involved in cholesterol homeostasis, as scavenger receptor class B type I and ATP-binding cassette (ABC) transporters including ABCA1, ABCG1, ABCG5, and ABCG8. This study aimed to determine the effects of single nucleotide variants (SNVs) rs2275543 (ABCA1), rs1893590 (ABCG1), rs6720173 (ABCG5), rs6544718 (ABCG8), and rs5888 (SCARB1) on plasma lipids, lipoproteins, and adiposity markers in an asymptomatic population and its sex-specific effects. Volunteers (n = 590) were selected and plasma lipids, lipoproteins, and adiposity markers (waist-to-hip and waist-to-height ratios, lipid accumulation product and body adiposity index) were measured. Genomic DNA was isolated from peripheral blood cells according to the method adapted from Gross-Bellard. SNVs were detected in the TaqMan® OpenArray® Real-Time polymerase chain reaction platform and data analyses were performed using the TaqMan® Genotyper Software. The rs2275543*C point to an increase of high-density lipoprotein size in females while in males very-low-density lipoprotein, cholesterol, and triglycerides were statistically lower (P value < 0.05). The rs1893590*C was statistically associated with lower apolipoprotein A-I levels and higher activities of paraoxonase-1 and cholesteryl ester transfer protein (P value < 0.05). The rs6720173 was statistically associated with an increase in cholesterol and low-density lipoprotein cholesterol in males; moreover, rs6544718*T reduced adiposity markers in females (P value < 0.05). Regarding the rs5888, a decreased adiposity marker in the total population and in females occurred (P value < 0.05). Multivariate analysis of variance showed that SNVs could influence components of high-density lipoprotein metabolism, mainly through ABCG1 (P value < 0.05). The ABCA1 and ABCG5 variants showed sex-specific effects on lipids and lipoproteins, while SCARB1 and ABCG8 variants might influence adiposity markers in females. Our data indicate a possible role of ABCG1 on HDL metabolism.

Dietary recommendations for prevention of atherosclerosis

This review aims at summarizing updated evidence on cardiovascular disease (CVD) risk associated with consumption of specific food items to substantiate dietary strategies for atherosclerosis prevention. A systematic search on PubMed was performed to identify meta-analyses of cohort studies and RCTs with CVD outcomes. The evidence is highly concordant in showing that, for the healthy adult population, low consumption of salt and foods of animal origin, and increased intake of plant-based foods-whole grains, fruits, vegetables, legumes, and nuts-are linked with reduced atherosclerosis risk. The same applies for the replacement of butter and other animal/tropical fats with olive oil and other unsaturated-fat-rich oil. Although the literature reviewed overall endorses scientific society dietary recommendations, some relevant novelties emerge. With regard to meat, new evidence differentiates processed and red meat-both associated with increased CVD risk-from poultry, showing a neutral relationship with CVD for moderate intakes. Moreover, the preferential use of low-fat dairies in the healthy population is not supported by recent data, since both full-fat and low-fat dairies, in moderate amounts and in the context of a balanced diet, are not associated with increased CVD risk; furthermore, small quantities of cheese and regular yogurt consumption are even linked with a protective effect. Among other animal protein sources, moderate fish consumption is also supported by the latest evidence, although there might be sustainability concerns. New data endorse the replacement of most high glycemic index (GI) foods with both whole grain and low GI cereal foods. As for beverages, low consumption not only of alcohol, but also of coffee and tea is associated with a reduced atherosclerosis risk while soft drinks show a direct relationship with CVD risk. This review provides evidence-based support for promoting appropriate food choices for atherosclerosis prevention in the general population.

Biobank Scale Pharmacogenomics Informs the Genetic Underpinnings of Simvastatin Use

Studying drug-metabolizing enzymes, encoded by pharmacogenes, may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, pharmacogenes could not be studied at biobank scale. In 7,649 unrelated African-ancestry (AFR) and 326,214 unrelated European-ancestry (EUR) participants from the UK Biobank, we associated pharmacogene haplotypes from 50 genes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models. In EUR, N-acetyltransferase 2 (NAT2) metabolizer phenotype and activity score were associated with simvastatin use. The dose of NAT2*1 was associated with simvastatin use when compared with NAT2*5 (the most common haplotype). This association was robust to effects of low-density lipoprotein cholesterol (LDL-C) concentration (NAT2*1 odds ratio (OR) = 1.07, 95% CI: 1.05-1.09, P = 1.14 × 10^-8 ) and polygenic risk for LDL-C concentration (NAT2*1 OR = 1.09, 95% CI: 1.04-1.14, P = 2.26 × 10^-4 ). Interactive effects between NAT2*1 and simvastatin use on LDL-C concentration (OR = 0.957, 95% CI: 0.916-0.998, P = 0.045) were replicated in the electronic Medical Records and Genomics Pharmacogenetic Sequencing Pilot (eMERGE-PGx) cohort (OR = 0.987, 95% CI: 0.976-0.998, P = 0.029). We used biobank-scale data to uncover and replicate an association between NAT2 locus variation and better response to statin therapy. Testing NAT2 alleles may be useful for making clinical decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL-C concentration prior to statin treatment.

Ketogenic Diets and Cardio-Metabolic Diseases

While the prevalence of cardio-metabolic diseases (CMDs) has become a worldwide epidemic, much attention is paid to managing CMDs effectively. A ketogenic diet (KD) constitutes a high-fat and low-carbohydrate diet with appropriate protein content and calories. KD has drawn the interests of clinicians and scientists regarding its application in the management of metabolic diseases and related disorders; thus, the current review aimed to examine the evidences surrounding KD and the CMDs to draw the clinical implications. Overall, KD appears to play a significant role in the therapy of various CMDs, which is manifested by the effects of KDs on cardio-metabolic outcomes. KD therapy is generally promising in obesity, heart failure, and hypertension, though different voices still exist. In diabetes and dyslipidemia, the performance of KD remains controversial. As for cardiovascular complications of metabolic diseases, current evidence suggests that KD is generally protective to obese related cardiovascular disease (CVD), while remaining contradictory to diabetes and other metabolic disorder related CVDs. Various factors might account for the controversies, including genetic background, duration of therapy, food composition, quality, and sources of KDs. Therefore, it's crucial to perform more rigorous researches to focus on clinical safety and appropriate treatment duration and plan of KDs.

Quantile-Dependent Expressivity and Gene-Lifestyle Interactions Involving High-Density Lipoprotein Cholesterol

BACKGROUND

The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent.

METHOD

The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity.

SUMMARY

Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

Interplay between the Mediterranean diet and C-reactive protein genetic polymorphisms towards inflammation in adolescents

AIM

From a nutrigenetics perspective, we aim to investigate the moderating role of the Mediterranean diet and each of its subgroups in the association between C-reactive protein (CRP) gene polymorphisms and CRP blood concentration in adolescents.

METHODS

In 562 adolescents (13-17 y) of the European HELENA study, data was available on circulating CRP levels as inflammatory biomarker, three CRP gene SNPs (rs3093068, rs1204, rs1130864), food intake determined by a self-administered computerized 24 h-dietary recall for 2 days, and body composition. A 9-point Mediterranean diet score and each food subgroup were tested as moderator via SNP*diet interaction. Analyzes were adjusted for age, sex, puberty, adiposity and socioeconomic status.

RESULTS

The minor allele frequencies of rs3093068 and rs1130864 SNPs (GG and TT, respectively) were associated with higher CRP concentrations, while rs1205 (CT/TT) was associated with lower CRP concentrations. There were significant interactions between rs3093068 and Mediterranean diet (B = -0.1139, p = 0.011), or the fish food subgroup (B = -0.0090, p = 0.022), so that those with the highest genetic CRP risk underwent the highest CRP attenuation by a healthier diet. Although the effect of diet and SNP was substantial, the explained variance by interaction was only 1%.

CONCLUSION

Greater adherence to the Mediterranean diet and particularly its fish component was associated with a lower CRP blood concentrations especially in those at highest genetic risk due to the rs3093068 SNP.

Quantile-dependent expressivity of postprandial lipemia

Purpose

“Quantile-dependent expressivity” describes an effect of the genotype that depends upon the level of the phenotype (e.g., whether a subject’s triglycerides are high or low relative to its population distribution). Prior analyses suggest that the effect of a genetic risk score (GRS) on fasting plasma triglyceride levels increases with the percentile of the triglyceride distribution. Postprandial lipemia is well suited for testing quantile-dependent expressivity because it exposes each individual’s genotype to substantial increases in their plasma triglyceride concentrations. Ninety-seven published papers were identified that plotted mean triglyceride response vs. time and genotype, which were converted into quantitative data. Separately, for each published graph, standard least-squares regression analysis was used to compare the genotype differences at time t (dependent variable) to average triglyceride concentrations at time t (independent variable) to assess whether the genetic effect size increased in association with higher triglyceride concentrations and whether the phenomenon could explain purported genetic interactions with sex, diet, disease, BMI, and drugs.

Results

Consistent with the phenomenon, genetic effect sizes increased (P≤0.05) with increasing triglyceride concentrations for polymorphisms associated with ABCA1, ANGPTL4, APOA1, APOA2, APOA4, APOA5, APOB, APOC3, APOE, CETP, FABP2, FATP6, GALNT2, GCKR, HL, IL1b, LEPR, LOX-1, LPL, MC4R, MTTP, NPY, SORT1, SULF2, TNFA, TCF7L2, and TM6SF2. The effect size for these polymorphisms showed a progressively increasing dose-response, with intermediate effect sizes at intermediate triglyceride concentrations. Quantile-dependent expressivity provided an alternative interpretation to their interactions with sex, drugs, disease, diet, and age, which have been traditionally ascribed to gene-environment interactions and genetic predictors of drug efficacy (i.e., personalized medicine).

Conclusion

Quantile-dependent expressivity applies to the majority of genetic variants affecting postprandial triglycerides, which may arise because the impaired functionalities of these variants increase at higher triglyceride concentrations. Purported gene-drug interactions may be the manifestations of quantile-dependent expressivity, rather than genetic predictors of drug efficacy.

FTO Variant rs1421085 Associates With Increased Body Weight, Soft Lean Mass, and Total Body Water Through Interaction With Ghrelin and Apolipoproteins in Arab Population

Association studies have implicated single nucleotide polymorphisms (SNPs), particularly rs1421085, from the fat mass and obesity-associated (FTO) gene with body composition phenotypes, obesity, dietary intake, and physical activity in European, East Asian, and African populations. However, the impact of the rs1421085 variant has not been sufficiently tested in ethnic populations (such as Arabs) with high levels of obesity. Further, there is a lack of studies identifying biomarkers that interact with FTO. Therefore, we investigated the association of rs1421085 with obesity and body composition traits and metabolic biomarkers in Arab population. We genotyped rs1421085 SNP in 278 Arab individuals, where multiple biomarkers relating to obesity, inflammation, and other metabolic pathways were quantified. We performed genetic association tests under additive mode of inheritance using linear regression models and found association of rs1421085_C allele with higher levels of body weight, soft lean mass (SLM), and total body water. Examination (using linear regression models under dominant mode of inheritance) of correlation among biomarkers and interaction with genotypes at the variant revealed that measures of these three body composition traits were found mediated by interaction between carrier genotypes (TC+CC) and measures of ghrelin, ApoA1, and ApoB48. Lean body mass (LBM), to which SLM contributes, is an important determinant of physical strength and is a focal point in studies on sarcopenia. Low LBM is known to be associated with higher risk of cardiometabolic disorders. Thus, the finding on the FTO variant as a genetic determinant of SLM via interaction with ghrelin, ApoA1, and ApoB48 is important.

Metabolomic Links between Sugar-Sweetened Beverage Intake and Obesity

BACKGROUND

Sugar-sweetened beverage (SSB) consumption is highly associated with obesity, but the metabolic mechanism underlying this correlation is not understood.

OBJECTIVE

Our objective was to examine metabolomic links between SSB intake and obesity to understand metabolic mechanisms.

DESIGN

We examined the association of plasma metabolomic profiles with SSB intake and obesity risk in 781 participants, aged 45-75 y, in the Boston Puerto Rican Health Study (BPRHS) using generalized linear models, controlling for potential confounding factors. Based on identified metabolites, we conducted pathway enrichment analysis to identify potential metabolic pathways that link SSB intake and obesity risk. Variants in genes encoding enzymes known to function in identified metabolic pathways were examined for their interactions with SSB intake on obesity.

RESULTS

SSB intake was correlated with BMI (β = 0.607, P=0.045). Among 526 measured metabolites, 86 showed a significant correlation with SSB intake and 148 with BMI (P ≤ 0.05); 28 were correlated with both SSB intake and BMI (P ≤ 0.05). Pathway enrichment analysis identified the phosphatidylcholine and lysophospholipid pathways as linking SSB intake to obesity, after correction for multiple testing. Furthermore, 8 of 10 genes functioning in these two pathways showed strong interaction with SSB intake on BMI. Our results further identified participants who may exhibit an increased risk of obesity when consuming SSB.

CONCLUSIONS

We identified two key metabolic pathways that link SSB intake to obesity, revealing the potential of phosphatidylcholine and lysophospholipid to modulate how SSB intake can increase obesity risk. The interaction between genetic variants related to these pathways and SSB intake on obesity further supports the mechanism.

Systemic low-grade inflammation-associated lifestyle, diet, and genetic factors: A population-based cross-sectional study

OBJECTIVES

Systemic low-grade inflammation (SLGI) is an intermediary common condition to the physiopathology of chronic noncommunicable diseases and targeting its determinants could lead to more efficient public health strategies. We aimed to investigate SLGI-independent associations with lifestyle, diet, and genetic factors in a population-based sample of adults using a systemic low-grade inflammation score (SIS).

METHODS

The study sample is composed of 269 participants from the cross-sectional population-based Health Survey of Sao Paulo (2008-2010), ages 20 to 59 y, whose data on socioeconomic variables, lifestyle, health parameters, and blood samples were available. Diet was assessed by two 24-h recalls, and the Brazilian Health Eating Index-Revised (BHEI-R) was scored. From blood samples, 30 single nucleotide polymorphisms on inflammatory genes were genotyped, and plasma eleven inflammatory biomarkers levels that composed the SIS were determined. A multiple, stepwise, linear regression was used to investigate SIS-independent associated factors.

RESULTS

Factors independently associated with SIS were BHEI-R score (partial R² = 5.1; β = -0.13; P = 0.003), body mass index (partial R² = 3.4; β = 0.19; P = 0.001), TLR4 rs5030728 GA + AA genotype (partial R² = 3.1; β = -1.37; P = 0.008), age 50 to 59 y (partial R² = 2.5; β = 1.93; P = 0.029) in comparison with the reference category (20 to 29 y), and commuting physical activity >150 min/wk (partial R² = 2.2; β = -1.29; P = 0.043) after adjustment for current smoking status, medication use, and dietary misreporting.

CONCLUSIONS

Eating a lower quality diet, having a higher body mass index score and age, being GG homozygous for TLR4 rs5030728, and spending <150 min/wk in transportation physical activity are independent determinants of SLGI.

Using Genetic Burden Scores for Gene-by-Methylation Interaction Analysis on Metabolic Syndrome in African Americans

With the rapid advancement of omics-based research, particularly big data such as genome- and epigenome-wide association studies that include extensive environmental and clinical variables, data analytics have become increasingly complex. Researchers face significant challenges regarding how to analyze multifactorial data and make use of the findings for clinical translation. The purpose of this article is to provide a scientific exemplar for use of genetic burden scores as a data analysis method for studies with both genotype and DNA methylation data in which the goal is to evaluate associations with chronic conditions such as metabolic syndrome (MetS). This study included 739 African American men and women from the Genetic Epidemiology Network of Arteriopathy Study who met diagnostic criteria for MetS and had available genetic and epigenetic data. Genetic burden scores for evaluated genes were not significant after multiple testing corrections, but DNA methylation at 2 CpG sites (dihydroorotate dehydrogenase cg22381196 pFDR = .014; CTNNA3 cg00132141 pFDR = .043) was significantly associated with MetS after controlling for multiple comparisons. Interactions between the marginally significant CpG sites and burden scores, however, were not significant. More work is required in this area to identify intermediate biological pathways influenced by environmental, genetic, and epigenetic variation that may explain the high prevalence of MetS among African Americans. This study does serve, however, as an example of the use of the genetic burden score as an alternative data analysis approach for complex studies involving the analysis of genetic and epigenetic data simultaneously.

Genome-wide association study identifies novel recessive genetic variants for high TGs in an Arab population

Abnormal blood lipid levels are influenced by genetic and lifestyle/dietary factors. Although many genetic variants associated with blood lipid traits have been identified in Europeans, similar data in Middle Eastern populations are limited. We performed a genome-wide association study with Arab individuals (discovery cohort: 1,353; replication cohort: 1,176) from Kuwait to identify possible associations of genetic variants with high lipid levels. We used Illumina HumanOmniExpress BeadChip and candidate SNP genotyping in the discovery and replication phases, respectively. For association tests, we used genetic models that were based on additive and recessive modes of inheritance. High triglycerides (TGs) were recessively associated with six risk variants (rs1002487/RPS6KA1, rs11805972/LAD1) rs7761746/Or5v1, rs39745/CTTNBP2-LSM8, rs2934952/PGAP3, and rs9626773/RP11-191L9.4-CERK) at genome-wide significance (P  6.12E-09), and another six variants (rs10873925/ST6GALNAC5, rs4663379/SPP2-ARL4C, rs10033119/NPY1R, rs17709449/LINC00911-FLRT2, rs11654954/CDK12-NEUROD2, and rs9972882/STARD3) were associated at borderline significance (P  5.0E-08). High TG was also additively associated with rs11654954. All of the 12 identified markers are novel and are harbored in runs of homozygosity. Literature evidence supports the involvement of these gene loci in lipid-related processes. This study in an Arab population augments international efforts to identify genetic regulation of lipid traits.

Precision nutrition for prevention and management of type 2 diabetes

Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some-LPL and APOB-that require further validation to clarify their involvement in T2DM.

Diet and Cardiovascular Disease: Advances and Challenges in Population-Based Studies

In this Minireview, we provide an epidemiologist's perspective on the debate and recent advances in determining the relationship between diet and cardiovascular health. We conclude that, in order to reduce the global burden of cardiovascular disease, there should be a greater emphasis on improving overall diet quality and food sources of macronutrients, such as dietary fats and carbohydrates. In addition, building a strong evidence base through high-quality intervention and observational studies is crucial for effective policy changes, which can greatly improve the food environment and population health.

Environmental and epigenetic regulation of postprandial lipemia

PURPOSE OF REVIEW

Postprandial lipemia (PPL), the prolonged increase in plasma triglyceride-rich lipoproteins following food consumption, is an independent risk factor for cardiovascular disease. Genetic variation, environment and the interplay between these direct an individual's postprandial lipid response. From such interplay, inducible and reversible epigenetic changes arise. Increasing evidence suggests epigenetic variation contributes to postprandial response in lipids and risk.

RECENT FINDINGS

Diet and exercise are central agents affecting postprandial lipemia - triglyceride, but heterogeneity of the findings warrant more and larger studies. Several epigenetic loci identified from a human intervention study account for a substantial proportion of PPL phenotype variation, but the burden to conduct an intervention study of postprandial responses likely limits translation to personalized nutrition.

SUMMARY

The impact of both DNA methylation patterns and environmental factors such as diet, exercise, sleep and medication on PPL is multifaceted. Discovery of interactions that modify the association between CpG (oligodeoxydinucleotide) methylation and postprandial phenotypes is unfolding.

Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale

BACKGROUND

Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results.

METHODS AND RESULTS

The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene-lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene-lifestyle or more generally gene-environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects.

CONCLUSIONS

The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene-lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies

Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region (p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region (p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice

Nutrigenetic research examines the effects of inter-individual differences in genotype on responses to nutrients and other food components, in the context of health and of nutrient requirements. A practical application of nutrigenetics is the use of personal genetic information to guide recommendations for dietary choices that are more efficacious at the individual or genetic subgroup level relative to generic dietary advice. Nutrigenetics is unregulated, with no defined standards, beyond some commercially adopted codes of practice. Only a few official nutrition-related professional bodies have embraced the subject, and, consequently, there is a lack of educational resources or guidance for implementation of the outcomes of nutrigenetic research. To avoid misuse and to protect the public, personalised nutrigenetic advice and information should be based on clear evidence of validity grounded in a careful and defensible interpretation of outcomes from nutrigenetic research studies. Evidence requirements are clearly stated and assessed within the context of state-of-the-art 'evidence-based nutrition'. We have developed and present here a draft framework that can be used to assess the strength of the evidence for scientific validity of nutrigenetic knowledge and whether 'actionable'. In addition, we propose that this framework be used as the basis for developing transparent and scientifically sound advice to the public based on nutrigenetic tests. We feel that although this area is still in its infancy, minimal guidelines are required. Though these guidelines are based on semi-quantitative data, they should stimulate debate on their utility. This framework will be revised biennially, as knowledge on the subject increases.

A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.

Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study

Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

Detection of gene-environment interactions in a family-based population using SCAD

Gene-environment interaction (GxE) is emphasized as one potential source of missing genetic variation on disease traits, and the ultimate goal of GxE research is prediction of individual risk and prevention of complex diseases. However, there are various challenges in statistical analysis of GxE. In this paper, we focus on the three methodological challenges: (i) the high dimensions of genes; (ii) the hierarchical structure between interaction effects and their corresponding main effects; and (iii) the correlation among subjects from family-based population studies. In this paper, we propose an algorithm that approaches all three challenges simultaneously. This is the first penalized method focusing on an interaction search based on a linear mixed effect model. For verification, we compare the empirical performance of our new method with other existing methods in simulation study. The results demonstrate the superiority of our method under overall simulation setup. In particular, the outperformance obviously becomes greater as the correlation among subjects increases. In addition, the new method provides a robust estimate for the correlation among subjects. We also apply the new method on Genetics of Lipid Lowering Drugs and Diet Network study data. Copyright © 2017 John Wiley & Sons, Ltd.

Systems biology of personalized nutrition

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person's health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology-based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of "systems flexibility" is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed.

Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

BACKGROUND

Obesity is a multifactorial condition influenced by both genetics and lifestyle. The aim of this study was to investigate whether the association between a validated genetic profile risk score for obesity (GPRS-obesity) and body mass index (BMI) or waist circumference (WC) was modified by macronutrient intake in a large general population study.

METHODS

This study included cross-sectional data from 48 170 white European adults, aged 37-73 years, participating in the UK Biobank. Interactions between GPRS-obesity and macronutrient intake (including total energy, protein, fat, carbohydrate and dietary fibre intake) and its effects on BMI and WC were investigated.

RESULTS

The 93-single-nucleotide polymorphism (SNP) GPRS was associated with a higher BMI (β: 0.57 kg m^-2 per s.d. increase in GPRS (95% confidence interval: 0.53-0.60); P=1.9 × 10^-183) independent of major confounding factors. There was a significant interaction between GPRS and total fat intake (P_{(interaction)}=0.007). Among high-fat-intake individuals, BMI was higher by 0.60 (0.52, 0.67) kg m^-2 per s.d. increase in GPRS-obesity; the change in BMI with GPRS was lower among low-fat-intake individuals (β: 0.50 (0.44, 0.57) kg m^-2). Significant interactions with similar patterns were observed for saturated fat intake (high β: 0.66 (0.59, 0.73) versus low β: 0.49 (0.42, 0.55) kg m^-2, P_{(interaction)}=2 × 10^-4) and for total energy intake (high β: 0.58 (0.51, 0.64) versus low β: 0.49 (0.42, 0.56) kg m^-2, P_{(interaction)}=0.019), but not for protein intake, carbohydrate intake and fibre intake (P_{(interaction)} >0.05). The findings were broadly similar using WC as the outcome.

CONCLUSIONS

These data suggest that the benefits of reducing the intake of fats and total energy intake may be more important in individuals with high genetic risk for obesity.

Genetic variation in the developmental regulation of cortical avpr1a among prairie voles

Early experiences can have enduring impacts on brain and behavior, but the strength of these effects can be influenced by genetic variation. In principle, polymorphic CpGs (polyCpGs) may contribute to gene-by-environment interactions (G × E) by altering DNA methylation. In this study, we investigate the influence of polyCpGs on the development of vasopressin receptor 1a abundance in the retrosplenial cortex (RSC-V1aR) of prairie voles (Microtus ochrogaster). Two alternative alleles ('HI'/'LO') predict RSC avpr1a expression, V1aR abundance and sexual fidelity in adulthood; these alleles differ in the frequency of CpG sites and in methylation at a putative intron enhancer. We hypothesized that the elevated CpG abundance in the LO allele would make homozygous LO/LO voles more sensitive to developmental perturbations. We found that genotype differences in RSC-V1aR abundance emerged early in ontogeny and were accompanied by differences in methylation of the putative enhancer. As predicted, postnatal treatment with an oxytocin receptor antagonist (OTA) reduced RSC-V1aR abundance in LO/LO adults but not their HI/HI siblings. Similarly, methylation inhibition by zebularine increased RSC-V1aR in LO/LO adults, but not in HI/HI siblings. These data show a gene-by-environment interaction in RSC-V1aR. Surprisingly, however, neither OTA nor zebularine altered adult methylation of the intronic enhancer, suggesting that differences in sensitivity could not be explained by CpG density at the enhancer alone. Methylated DNA immunoprecipiation-sequencing showed additional differentially methylated regions between HI/HI and LO/LO voles. Future research should examine the role of these regions and other regulatory elements in the ontogeny of RSC-V1aR and its developmentally induced changes.

Influence of toll-like receptor 4 gene variants and plasma fatty acid profile on systemic inflammation: A population-based cross-sectional study

OBJECTIVE

The aim of this study was to investigate the interaction of toll-like receptor 4 (TLR4) gene single nucleotide polymorphism (SNP) and plasma fatty acid (FA) profile in modulating risk for systemic inflammation.

METHODS

In all, 262 adult (19-59 y) participants of the Health Survey of São Paulo met the inclusion criteria. Anthropometric parameters, blood pressure, plasma inflammatory biomarker concentration, and fatty acid profile were measured and four SNPs of the TLR4 gene (rs4986790, rs4986791, rs11536889, and rs5030728) were genotyped. Multivariate cluster analysis was performed to stratify individuals based on levels of 11 plasma inflammatory biomarkers into two groups: inflammatory (INF) and noninflammatory (NINF).

RESULTS

No association was found between any of the SNPs studied and systemic inflammation. The INF cluster had higher palmitic acid levels (P = 0.039) and estimated stearoyl coenzyme A desaturase activity (P = 0.045) and lower polyunsaturated fatty acid (P = 0.011), ω-6 fatty acid (P = 0.018), arachidonic acid (P = 0.002) levels, and estimated δ-5 desaturase activity (P = 0.025) compared with the NINF cluster. Statistically significant interaction between rs11536889 and arachidonic acid/eicosapentaenoic acid (AA/EPA) ratio (P = 0.034) was found to increase the odds of belonging to the INF cluster when individuals had the variant allele C and were at the higher percentile of AA/EPA plasma ratio.

CONCLUSION

Plasma fatty acid profile modulated the odds of belonging to the INF cluster depending on genotypes of TRL4 gene polymorphisms.

The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.

Epigenome-wide association study of triglyceride postprandial responses to a high-fat dietary challenge

Postprandial lipemia (PPL), the increased plasma TG concentration after consuming a high-fat meal, is an independent risk factor for CVD. Individual responses to a meal high in fat vary greatly, depending on genetic and lifestyle factors. However, only a few loci have been associated with TG-PPL response. Heritable epigenomic changes may be significant contributors to the unexplained inter-individual PPL variability. We conducted an epigenome-wide association study on 979 subjects with DNA methylation measured from CD4⁺ T cells, who were challenged with a high-fat meal as a part of the Genetics of Lipid Lowering Drugs and Diet Network study. Eight methylation sites encompassing five genes, LPP, CPT1A, APOA5, SREBF1, and ABCG1, were significantly associated with PPL response at an epigenome-wide level (P < 1.1 × 10⁻⁷), but no methylation site reached epigenome-wide significance after adjusting for baseline TG levels. Higher methylation at LPP, APOA5, SREBF1, and ABCG1, and lower methylation at CPT1A methylation were correlated with an increased TG-PPL response. These PPL-associated methylation sites, also correlated with fasting TG, account for a substantially greater amount of phenotypic variance (14.9%) in PPL and fasting TG (16.3%) when compared with the genetic contribution of loci identified by our previous genome-wide association study (4.5%). In summary, the epigenome is a large contributor to the variation in PPL, and this has the potential to be used to modulate PPL and reduce CVD.

Interaction of an S100A9 gene variant with saturated fat and carbohydrates to modulate insulin resistance in 3 populations of different ancestries

BACKGROUND

S100 calcium-binding protein A9 (S100A9) has previously been identified as a type 2 diabetes (T2D) gene. However, this finding requires independent validation and more in-depth analyses in other populations and ancestries.

OBJECTIVES

We aimed to replicate the associations between an S100A9 variant and insulin resistance and T2D and to initiate an investigation of potential interactions with the habitual diet in several independent populations.

DESIGN

We investigated the association of the S100A9 variant rs3014866 with insulin resistance and T2D risk and its interactions with diet in 3 diverse populations as follows: the CORDIOPREV (Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention; n = 711), which consisted of Spanish white adults; the GOLDN (Genetics of Lipids Lowering Drugs and Diet Network; n = 818), which involved North American non-Hispanic white adults; and Hispanic adults who participated in the BPRHS (Boston Puerto Rican Health Study; n = 1155).

RESULTS

Meta-analysis indicated that T carriers presented a lower risk of T2D than CC carriers (pooled OR: 0.714; 95% CI: 0.584, 0.845; P = 0.002). In all 3 populations (CORDIOPREV, GOLDN, and BPRHS), we showed a significant interaction between the rs3014866 single nucleotide polymorphism and dietary SFA:carbohydrate ratio intake for the homeostasis model assessment of insulin resistance (HOMA-IR) (P = 0.028, P = 0.017, and P = 0.026, respectively). CC carriers had a significantly higher HOMA-IR only when SFA:carbohydrate intake was high (P = 0.045 for the CORDIOPREV, P = 0.033 for the GOLDN, and P = 0.046 for the BPRHS) but not when SFA:carbohydrate ratio intake was low.

CONCLUSIONS

The minor allele (T) of the S100A9 variant rs3014866 is associated with lower T2D risk in 3 populations of different ancestries. Note that individuals with the high-risk CC genotype may be more likely to benefit from a low SFA:carbohydrate ratio intake to improve insulin resistance as evaluated with the use of the HOMA-IR. These trials were registered at clinicaltrials.gov as NCT00924937 (CORDIOPREV), NCT00083369 (GOLDN), and NCT01231958 (BPRHS).

Genetic susceptibility to dyslipidemia and incidence of cardiovascular disease depending on a diet quality index in the Malmö Diet and Cancer cohort

BACKGROUND

By taking diet quality into account, we may clarify the relationship between genetically elevated triglycerides (TG) and low-density lipoprotein-cholesterol (LDL-C), and better understand the inconsistent results regarding genetically elevated high-density lipoprotein-cholesterol (HDL-C), and cardiovascular disease (CVD) risk.

METHODS

We included 24,799 participants (62 % women, age 44-74 years) from the Malmö Diet and Cancer cohort. During a mean follow-up time of 15 years, 3068 incident CVD cases (1814 coronary and 1254 ischemic stroke) were identified. Genetic risk scores (GRSs) were constructed by combining 80 validated genetic variants associated with higher TG and LDL-C or lower HDL-C. The participants' dietary intake, assessed by a modified diet history method, was ranked according to a diet quality index that included six dietary components: saturated fat, polyunsaturated fat, fish, fiber, fruit and vegetables, and sucrose.

RESULTS

The GRSLDL-C (P = 5 × 10(-6)) and GRSHDL-C (P = 0.02) but not GRSTG (P = 0.08) were significantly associated with CVD risk. No significant interaction between the GRSs and diet quality was observed on CVD risk (P > 0.39). A high compared to a low diet quality attenuated the association between GRSLDL-C and the risk of incident ischemic stroke (P interaction = 0.01).

CONCLUSION

We found some evidence of an interaction between diet quality and GRSLDL-C on ischemic stroke.

Nutrigenomics, the Microbiome, and Gene-Environment Interactions: New Directions in Cardiovascular Disease Research, Prevention, and Treatment: A Scientific Statement From the American Heart Association

Cardiometabolic diseases are the leading cause of death worldwide and are strongly linked to both genetic and nutritional factors. The field of nutrigenomics encompasses multiple approaches aimed at understanding the effects of diet on health or disease development, including nutrigenetic studies investigating the relationship between genetic variants and diet in modulating cardiometabolic risk, as well as the effects of dietary components on multiple "omic" measures, including transcriptomics, metabolomics, proteomics, lipidomics, epigenetic modifications, and the microbiome. Here, we describe the current state of the field of nutrigenomics with respect to cardiometabolic disease research and outline a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at understanding mechanisms and developing new therapeutic options for cardiometabolic disease treatment and prevention.

Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

BACKGROUND

Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature.

METHODS

To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed.

RESULTS

A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest.

CONCLUSION

Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings.

Physical activity attenuates the effect of the FTO genotype on obesity traits in European adults: The Food4Me study

OBJECTIVE

To examine whether the effect of FTO loci on obesity-related traits could be modified by physical activity (PA) levels in European adults.

METHODS

Of 1,607 Food4Me participants randomized, 1,280 were genotyped for FTO (rs9939609) and had available PA data. PA was measured objectively using accelerometers (TracmorD, Philips), whereas anthropometric measures [BMI and waist circumference (WC)] were self-reported via the Internet.

RESULTS

FTO genotype was associated with a higher body weight [β: 1.09 kg per risk allele, (95% CI: 0.14-2.04), P = 0.024], BMI [β: 0.54 kg m(-2) , (0.23-0.83), P < 0.0001], and WC [β: 1.07 cm, (0.24-1.90), P = 0.011]. Moderate-equivalent PA attenuated the effect of FTO on BMI (P[interaction]  = 0.020). Among inactive individuals, FTO increased BMI by 1.06 kg m(-2) per allele (P = 0.024), whereas the increase in BMI was substantially attenuated among active individuals (0.16 kg m(-2) , P = 0.388). We observed similar effects for WC (P[interaction]  = 0.005): the FTO risk allele increased WC by 2.72 cm per allele among inactive individuals but by only 0.49 cm in active individuals.

CONCLUSIONS

PA attenuates the effect of FTO genotype on BMI and WC. This may have important public health implications because genetic susceptibility to obesity in the presence of FTO variants may be reduced by adopting a physically active lifestyle.

Genome-wide association study of triglyceride response to a high-fat meal among participants of the NHLBI Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)

OBJECTIVE

The triglyceride (TG) response to a high-fat meal (postprandial lipemia, PPL) affects cardiovascular disease risk and is influenced by genes and environment. Genes involved in lipid metabolism have dominated genetic studies of PPL TG response. We sought to elucidate common genetic variants through a genome-wide association (GWA) study in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).

METHODS

The GOLDN GWAS discovery sample consisted of 872 participants within families of European ancestry. Genotypes for 2,543,887 variants were measured or imputed from HapMap. Replication of our top results was performed in the Heredity and Phenotype Intervention (HAPI) Heart Study (n = 843). PPL TG response phenotypes were constructed from plasma TG measured at baseline (fasting, 0 hour), 3.5 and 6 hours after a high-fat meal, using a random coefficient regression model. Association analyses were adjusted for covariates and principal components, as necessary, in a linear mixed model using the kinship matrix; additional models further adjusted for fasting TG were also performed. Meta-analysis of the discovery and replication studies (n = 1715) was performed on the top SNPs from GOLDN.

RESULTS

GOLDN revealed 111 suggestive (p < 1E-05) associations, with two SNPs meeting GWA significance level (p < 5E-08). Of the two significant SNPs, rs964184 demonstrated evidence of replication (p = 1.20E-03) in the HAPI Heart Study and in a joint analysis, was GWA significant (p = 1.26E-09). Rs964184 has been associated with fasting lipids (TG and HDL) and is near ZPR1 (formerly ZNF259), close to the APOA1/C3/A4/A5 cluster. This association was attenuated upon additional adjustment for fasting TG.

CONCLUSION

This is the first report of a genome-wide significant association with replication for a novel phenotype, namely PPL TG response. Future investigation into response phenotypes is warranted using pathway analyses, or newer genetic technologies such as metabolomics.

Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits

OBJECTIVE

Common circadian-related gene variants associate with increased risk for metabolic alterations including type 2 diabetes. However, little is known about whether diet and sleep could modify associations between circadian-related variants (CLOCK-rs1801260, CRY2-rs11605924, MTNR1B-rs1387153, MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations.

RESEARCH DESIGN AND METHODS

We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

RESULTS

We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (<7 h) and higher FG and replicated known MTNR1B associations with glycemic traits. No interactions were evident after accounting for multiple comparisons. However, we observed nominally significant interactions (all P < 0.01) between carbohydrate intake and MTNR1B-rs1387153 for FG with a 0.003 mmol/L higher FG with each additional 1% carbohydrate intake in the presence of the T allele, between sleep duration and CRY2-rs11605924 for HDL-cholesterol with a 0.010 mmol/L higher HDL-cholesterol with each additional hour of sleep in the presence of the A allele, and between long sleep duration (≥9 h) and MTNR1B-rs1387153 for BMI with a 0.60 kg/m(2) higher BMI with long sleep duration in the presence of the T allele relative to normal sleep duration (≥7 to <9 h).

CONCLUSIONS

Our results suggest that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian-related genetic variants. Until further mechanistic examination of the nominally significant interactions is conducted, recommendations applicable to the general population regarding diet—specifically higher carbohydrate and lower fat composition—and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants.