The occurrence of dyslipidaemia, which is an established risk factor for cardiovascular diseases, has been attributed to multiple factors including genetic and environmental factors. We used a genetic risk score (GRS) to assess the interactions between genetic variants and dietary factors on lipid-related traits in a cross-sectional study of 190 Brazilians (mean age: 21 ± 2 years). Dietary intake was assessed by a trained nutritionist using three 24-h dietary recalls. The high GRS was significantly associated with increased concentration of TAG (beta = 0·10 mg/dl, 95 % CI 0·05–0·16; P < 0·001), LDL-cholesterol (beta = 0·07 mg/dl, 95 % CI 0·04, 0·11; P < 0·0001), total cholesterol (beta = 0·05 mg/dl, 95 % CI: 0·03, 0·07; P < 0·0001) and the ratio of TAG to HDL-cholesterol (beta = 0·09 mg/dl, 95 % CI: 0·03, 0·15; P = 0·002). Significant interactions were found between the high GRS and total fat intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03) and between the high GRS and SFA intake on TAG:HDL-cholesterol ratio (Pinteraction = 0·03). A high intake of total fat (>31·5 % of energy) and SFA (>8·6 % of energy) was associated with higher TAG:HDL-cholesterol ratio in individuals with the high GRS (beta = 0·14, 95 % CI: 0·06, 0·23; P < 0·001 for total fat intake; beta = 0·13, 95 % CI: 0·05, 0·22; P = 0·003 for SFA intake). Our study provides evidence that the genetic risk of high TAG:HDL-cholesterol ratio might be modulated by dietary fat intake in Brazilians, and these individuals might benefit from limiting their intake of total fat and SFA.

Mitochondria play a key role in cell homeostasis as a major source of intracellular energy (adenosine triphosphate), and as metabolic hubs regulating many canonical cell processes. Mitochondrial dysfunction has been widely documented in many common diseases, and genetic studies point towards a causal role in the pathogenesis of specific late-onset disorder. Together this makes targeting mitochondrial genes an attractive strategy for precision medicine. However, the genetics of mitochondrial biogenesis is complex, with over 1,100 candidate genes found in two different genomes: the nuclear DNA and mitochondrial DNA (mtDNA). Here, we review the current evidence associating mitochondrial genetic variants with distinct clinical phenotypes, with some having clear therapeutic implications. The strongest evidence has emerged through the investigation of rare inherited mitochondrial disorders, but genome-wide association studies also implicate mtDNA variants in the risk of developing common diseases, opening to door for the incorporation of mitochondrial genetic variant analysis in population disease risk stratification.

Gene–nutrient interactions (GeNuIne) collaboration, a large-scale collaborative project, has been initiated to investigate the impact of gene–nutrient interactions on cardiometabolic diseases using population-based studies from ethnically diverse populations. In this project, the relationship between deficiencies of vitamins B12 and D, and metabolic diseases was explored using a nutrigenetic approach. A genetic risk score (GRS) analysis was used to examine the combined effect of several genetic variations that have been shown to be associated with metabolic diseases and vitamin B12 and D deficiencies, respectively. In Sri Lankan, Indonesian and Brazilian populations, those carrying a high B12-GRS had an increased risk of metabolic diseases under the influence of dietary protein, fibre and carbohydrate intakes, respectively; however, in Asian Indians, genetically instrumented metabolic disease risk showed a significant association with low vitamin B12 status. With regards to nutrigenetic studies on vitamin D status, although high metabolic-GRS showed an interaction with dietary carbohydrate intake on vitamin D status, the study in Indonesian women demonstrated a vitamin D GRS–carbohydrate interaction on body fat percentage. In summary, these nutrigenetic studies from multiple ethnic groups have provided evidence for the influence of the dietary factors on the relationship between vitamin B12/D deficiency and metabolic outcomes. Furthermore, these studies highlight the existence of genetic heterogeneity in gene–diet interactions across ethnically diverse populations, which further implicates the significance of personalised dietary approaches for the prevention of these micronutrient deficiencies and metabolic diseases.

Several studies on gene–diet interactions (nutrigenetics) have been performed in western populations; however, there are only a few studies to date in lower middle-income countries (LMIC). A large-scale collaborative project called gene–nutrient interactions (GeNuIne) Collaboration, the main objective of which is to investigate the effect of GeNuIne on cardiometabolic traits using population-based studies from various ethnic groups, has been initiated at the University of Reading, UK. While South Asians with higher genetic risk score (GRS) showed a higher risk of obesity in response to a high-carbohydrate diet, South East and Western Asian populations with higher GRS showed an increased risk of central obesity in response to a high-protein diet. The paper also provides a summary of other gene–diet interaction analyses that were performed in LMIC as part of this collaborative project and gives an overview of how these nutrigenetic findings can be translated to personalised and public health approaches for the prevention of cardiometabolic diseases such as obesity, type 2 diabetes and CVD.

Metabolic syndrome (MetS) risk is influenced by genetic and environmental factors. The present study explored genetic risk scores (GRS) of genetic variants that influence the MetS and the effect of interactions between GRS and nutrient intake on MetS risk. The genetic variants that influence MetS risk were selected by genome-wide association study after adjusting for age, sex, area of residence and BMI in 8840 middle-aged adults. GRS were calculated by summing the risk alleles of the selected SNP and divided into low (0–1), medium (2–3) and high (4–7) risk groups, and the relationships between the MetS and GRS were determined by logistic regression after adjusting covariates involved in MetS risk. We also analysed the interaction between GRS and lifestyles. Four genetic variants (APOA5_rs651821, EFCAB4B_rs4766165, ZNF259_rs2160669 and APOBEC1_rs10845640) were selected because they increased MetS risk after adjusting for covariates. Individuals with medium-GRS and high-GRS alleles had a higher MetS risk by 1·48- and 2·23-fold, respectively, compared with those with low-GRS after adjusting for covariates. The increase in MetS risk was mainly related to serum TAG and HDL-cholesterol concentrations. The GRS had an interaction with carbohydrate (CHO) and Na intakes and daily physical activities for MetS risk. In conclusion, Asian middle-aged adults with high-GRS alleles were at increased MetS risk mainly due to dyslipidaemia. High daily physical activity (≥1 h moderate activity per d) reduced the MetS risk but a low-CHO diet (<65 % of total energy intake) increased the risk in carriers with high-GRS alleles. Low Na intake (<1·6 g Na intake/4 MJ) did not decrease its risk.

No risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene–environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene–environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

The present study investigated whether dietary patterns could interact with fat mass and obesity-associated (FTO) polymorphisms in relation to changes in BMI and waist circumference (WC) over 3⋅6 years of follow-up. Subjects were selected from participants of the Tehran Lipid and Glucose Study (n 4292, 43⋅2 % male). Dietary data were collected using a valid and reliable FFQ. Dietary patterns were determined using factor analysis. The genotypes of polymorphisms (rs1421085, rs1121980, rs17817449, rs8050136, rs9939973 and rs3751812) were determined. Genetic risk score (GRS) was calculated using the weighted method. Mean ages of men and women were 42·6 (sd 14) and 40⋅4 (sd 13) years, respectively. The healthy (e.g. vegetables and fruits) and the Western dietary patterns (WDP; e.g. soft drinks and fast foods) were extracted. In carriers of the risk alleles rs1121980, rs1421085, rs8050136, rs1781799 and rs3751812, BMI was approximately 2-fold higher in individuals in the higher quartile of WDP score, compared with the first quartile (P < 0⋅05). WC increased with increasing WDP score in carriers of the risk alleles rs1121980 and rs3751812, but not in individuals who did not carry any risk alleles. BMI and WC increased to a greater extent in the high GRS group while increasing quartiles of the WDP score, compared with the low GRS group (BMI change; Q1: 1⋅04 (se 0⋅34) v. Q4: 2⋅26 (se 0⋅36)) (WC change; Q1: 0⋅47 (se 0⋅32) v. Q4: 0⋅95 (se 0⋅34)) (Pinteraction < 0⋅05). These results suggest that adults with higher genetic predisposition to obesity are more susceptible to the harmful effects of adherence to the WDP, which emphasised the need to reduce the consumption of unhealthy foods for the prevention of obesity.

The prevalence of childhood obesity has increased worldwide. Although it is considered a polygenic inheritance disease, little is known about its susceptibility when the additive effect is considered. The aim of this study is to investigate whether the genetic risk score (GRS) based on previously associated obesity polymorphisms (SNP) rs9939609 (fat mass and obesity-associated (FTO)), rs6548238 (transmembrane protein 18 (TMEM18)) and rs16835198 (fibronectin type III domain containing 5 (FNDC5)) could serve as a predictor for anthropometric characteristics in a sample of Brazilian children and adolescents. This is a cross-sectional study with 1471 children and adolescents aged 6–17 years. BMI, waist circumference (WC) and percentage of body fat and metabolic parameters were verified. In all, three SNP were genotyped by TaqMan™ allelic discrimination. The metabolic and anthropometric parameters were compared between the genotypes, and the unweighted and weighted GRS (GRS and wGRS, respectively) were created to test the additive effect of these genetic polymorphisms on anthropometric parameters. The prevalence of overweight plus obesity was 41 %. Significant associations were identified for FTO rs9939609, TMEM18 rs6548238 and FNDC5 rs16835198 and for GRS and wGRS with anthropometric phenotypes. The higher score of wGRS was associated with obesity (OR: 2·65, 95 % CI 1·40, 5·04, P=0·003) and with greater WC (OR: 2·91, 95 % CI 1·57, 5·40, P=0·001). Our results suggest that these genetic variants contribute to obesity susceptibility in children and adolescents and reinforce the idea that the additive effect may be useful to elucidate the genetic component of obesity.

Blood eosinophil count is associated with a variety of common complex outcomes in epidemiological observation. The aim of this study was to explore the causal association between determined blood eosinophil count and 20 common complex outcomes (10 metabolic, 6 cardiac, and 4 pulmonary). Through Mendelian randomization, we investigated genetic evidence for the genetically determined eosinophil in association with each outcomes using individual-level LifeLines cohort data (n = 13,301), where a weighted eosinophil genetic risk score comprising five eosinophil associated variants was created. We further examined the associations of the genetically determined eosinophil with those outcomes using summary statistics obtained from genome-wide association study consortia (6 consortia and 14 outcomes). Blood eosinophil count, by a 1-SD genetically increased, was not statistically associated with common complex outcomes in the LifeLines. Using the summary statistics, we showed that a higher genetically determined eosinophil count had a significant association with lower odds of obesity (odds ratio (OR) 0.81, 95% confidence interval (CI) [0.74, 0.89]) but not with the other traits and diseases. To conclude, an elevated eosinophil count is unlikely to be causally associated to higher risk of metabolic, cardiac, and pulmonary outcomes. Further studies with a stronger genetic risk score for eosinophil count may support these results.

Serum uric acid (SUA), a causative agent for gout among others, is affected by both genetic and dietary factors, perhaps differentially by sex. We evaluated cross-sectional (SUAbase) and longitudinal (SUArate) associations of SUA with a genetic risk score (GRS), diet and sex. We then tested the interactive effect of GRS, diet and sex on SUA. Longitudinal data on 766 African-American urban adults participating in the Healthy Aging in Neighborhood of Diversity across the Lifespan study were used. In all, three GRS for SUA were created from known SUA-associated SNP (GRSbase (n 12 SNP), GRSrate (n 3 SNP) and GRStotal (n 15 SNP)). Dietary factors included added sugar, total alcohol, red meat, total fish, legumes, dairy products, caffeine and vitamin C. Mixed-effects linear regression models were conducted. SUAbase was higher among men compared with that among women, and increased with GRStotal tertiles. SUArate was positively associated with legume intake in women (γ=+0·14; 95 % CI +0·06, +0·22, P=0·001) and inversely related to dairy product intake in both sexes combined (γ=−0·042; 95 % CI −0·075, −0·009), P=0·010). SUAbase was directly linked to alcohol consumption among women (γ=+0·154; 95 % CI +0·046, +0·262, P=0·005). GRSrate was linearly related to SUArate only among men. Legume consumption was also positively associated with SUArate within the GRStotal’s lowest tertile. Among women, a synergistic interaction was observed between GRSrate and red meat intake in association with SUArate. Among men, a synergistic interaction between low vitamin C and genetic risk was found. In sum, sex–diet, sex–gene and gene–diet interactions were detected in determining SUA. Further similar studies are needed to replicate our findings.