Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status

A Systematic Review of the Effect of Gene-Lifestyle Interactions on Metabolic-Disease-Related Traits in South Asian Populations

CONTEXT

Recent data from the South Asian subregion have raised concern about the dramatic increase in the prevalence of metabolic diseases, which are influenced by genetic and lifestyle factors.

OBJECTIVE

The aim of this systematic review was to summarize the contemporary evidence for the effect of gene-lifestyle interactions on metabolic outcomes in this population.

DATA SOURCES

PubMed, Web of Science, and SCOPUS databases were searched up until March 2023 for observational and intervention studies investigating the interaction between genetic variants and lifestyle factors such as diet and physical activity on obesity and type 2 diabetes traits.

DATA EXTRACTION

Of the 14 783 publications extracted, 15 were deemed eligible for inclusion in this study. Data extraction was carried out independently by 3 investigators. The quality of the included studies was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS), the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I), and the methodological quality score for nutrigenetics studies.

DATA ANALYSIS

Using a narrative synthesis approach, the findings were presented in textual and tabular format. Together, studies from India (n = 8), Pakistan (n = 3), Sri Lanka (n = 1), and the South Asian diaspora in Singapore and Canada (n = 3) reported 543 gene-lifestyle interactions, of which 132 (∼24%) were statistically significant. These results were related to the effects of the interaction of genetic factors with physical inactivity, poor sleep habits, smoking, and dietary intake of carbohydrates, protein, and fat on the risk of metabolic disease in this population.

CONCLUSIONS

The findings of this systematic review provide evidence of gene-lifestyle interactions impacting metabolic traits within the South Asian population. However, the lack of replication and correction for multiple testing and the small sample size of the included studies may limit the conclusiveness of the evidence. Note, this paper is part of the Nutrition Reviews Special Collection on Precision Nutrition.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration No. CRD42023402408.

Methylation Risk Score Modelling in Endometriosis: Evidence for Non-Genetic DNA Methylation Effects in a Case-Control Study

Endometriosis is a chronic gynaecological disease characterised by endometrial-like tissue found external to the uterus. While several studies have reported strong evidence of a genetic contribution to the disease, studies on the environmental impact on endometriosis are limited. DNA methylation (DNAm) can be influenced by genetic and environmental factors and serves as a useful biological marker of the effects of genetic and environmental exposures on complex diseases. This study aims to develop a methylation risk score (MRS) for endometriosis to increase the power to detect DNAm signals associated with the disease and enhance our understanding of the pathogenesis of the disease. Endometrial methylation and genotype data from 318 controls and 590 cases were analysed. MRSs were developed using several different models. MRS performances were evaluated by splitting samples into training and test sets based on independent cohort institutions, and the area under the receiver-operator curve (AUC) was calculated. The maximum AUC obtained from the best-performing MRS is 0.6748, derived from 746 DNAm sites. The classification performance of MRS and polygenic risk score (PRS) combined was consistently higher than PRS alone. This study demonstrates that there are DNAm signals independent of common genetic variants associated with endometriosis.

Internet usage elevates elderly obesity: evidence from a difference-in-differences analysis of the broadband China policy

BACKGROUND

The global aging population is rapidly increasing, which has led to a growing prevalence of obesity among the elderly. Body mass index (BMI) is a crucial measure of obesity and is linked to an increased risk of various chronic diseases. At the same time, the widespread use of the internet and digital technologies has significantly influenced the health behaviors and outcomes of the elderly.

OBJECTIVE

This study aims to examine the causal relationship between internet usage and BMI among the elderly, addressing a gap in existing research and providing evidence for the development of health policies targeted at the elderly population.

METHODS

Utilizing China's "Broadband China" strategy as a quasi-natural experiment, we employed a difference-in-differences (DID) approach to analyze panel data from the CHARLS covering the years 2011-2015. By comparing the treatment and control groups before and after the policy's implementation, we identify causal effects.

RESULTS

Our findings indicate that the "Broadband China" strategy significantly increased BMI among the elderly. The mechanisms underlying this effect include reduced sleep duration, decreased physical activity levels, and worsened mental health. Furthermore, the impact of internet usage on obesity is particularly pronounced among urban residents, those without chronic diseases, and individuals with fewer surviving children.

CONCLUSIONS

Policy recommendations include promoting healthy internet usage practices, enhancing community-based activity facilities, and providing comprehensive mental health support to mitigate obesity rates and improve health outcomes among the elderly.

Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and environmental (E) variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (rg) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRSs) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP heritability across E bins. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average n = 325,000) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with rg significantly <1 (false discovery rate < 5%); 28 trait-E pairs with significant PRSxE and significant SNP heritability differences across E bins; and 15 trait-E pairs with significant PRSxE but no SNP heritability differences across E bins. Across the three scenarios, eight of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of these scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait variance.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Accelerometry-assessed sleep clusters and obesity in adolescents and young adults: a longitudinal analysis in GINIplus/LISA birth cohorts

BACKGROUND

Some studies have revealed various sleep patterns in adolescents and adults using multidimensional objective sleep parameters. However, it remains unknown whether these patterns are consistent from adolescence to young adulthood and how they relate to long-term obesity.

METHODS

Seven-day accelerometry was conducted in German Infant Study on the influence of Nutrition Intervention PLUS environmental and genetic influences on allergy development (GINIplus) and Influence of Lifestyle factors on the development of the Immune System and Allergies in East and West Germany (LISA) birth cohorts during the 15-year and 20-year follow-ups, respectively. Five sleep clusters were identified by k-means cluster analysis using 12 sleep characteristics at each follow-up. Adjusted linear and logistic regression models using generalized estimating equations were examined. Further, the interaction effects with time of follow-ups and polygenic risk scores (PRS) for body mass index (BMI) were tested.

RESULTS

Five sleep clusters were classified consistently in both adolescence (n = 1347, aged 14.3-16.4 years) and young adulthood (n = 1262, aged 19.5-22.4 years). Adolescents in the "good sleep", "delayed sleep phase", and "fragmented sleep" clusters displayed greater stability transitioning into young adulthood, while those in the "sleep irregularity and variability", and "prolonged sleep latency" clusters showed lower stability (n = 636). Compared to the "good sleep" cluster, the "prolonged sleep latency" cluster exhibited associations with higher BMI [β = 0.56, 95% confidence interval (CI) = (0.06, 1.05)] and increased odds of overweight/obesity [Odds ratio = 1.55, 95% CI = (1.02, 2.34)]. No significant PRS-sleep cluster interaction was found for BMI or overweight/obesity. Among males only, the "delayed sleep phase", "sleep irregularity and variability" and "fragmented sleep" clusters showed stronger associations with overweight/obesity as age increased.

CONCLUSION

Adolescents and young adults shared five consistent sleep patterns, with the "prolonged sleep latency" pattern linked to higher BMI and overweight/obesity.

Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies

BACKGROUND

Diet is an important determinant of health and may moderate genetic susceptibility to obesity, but meta-analyses of available evidence are lacking.

OBJECTIVES

This study aimed to systematically review and meta-analyze evidence on the moderating effect of diet on genetic susceptibility to obesity, assessed with polygenic risk scores (PRS).

METHODS

A systematic search was conducted using MEDLINE, EMBASE, Web of Science, and the Cochrane Library to retrieve observational studies that examined PRS-diet interactions on obesity-related outcomes. Dietary exposures of interest included diet quality/dietary patterns and consumption of specific food and beverage groups. Random-effects meta-analyses were performed for pooled PRS- healthy eating index (HEI) interaction coefficients on body mass index (BMI) (on the basis of data from 4 cohort studies) and waist circumference (WC) (on the basis of data from 3 cohort studies).

RESULTS

Out of 36 retrieved studies, 78% were conducted among European samples. Twelve out of 21 articles examining dietary indices/patterns, and 16 out of 21 articles examining food/beverage groups observed some significant PRS-diet interactions. However, within many articles, findings are inconsistent when testing different combinations of obesity PRS-dietary factors and outcomes. Nevertheless, higher HEI scores and adherence to plant-based dietary patterns emerged as the more prominent diet quality/patterns that moderated genetic susceptibility to obesity, whereas higher consumption of fruits and vegetables, and lower consumption of fried foods and sugar-sweetened beverages emerged as individual food/beverage moderators. Results from the meta-analysis suggest that a higher HEI attenuates genetic susceptibility on BMI (pooled PRS∗HEI coefficient: -0.08; 95% confidence interval (CI): -0.15, 0.00; P = 0.0392) and WC (-0.37; 95% CI: -0.60, -0.15; P = 0.0013).

CONCLUSIONS

Current observational evidence suggests a moderating role of overall diet quality in polygenic risk of obesity. Future research should aim to identify genetic loci that interact with dietary exposures on anthropometric outcomes and conduct analyses among diverse ethnic groups.

TRIAL REGISTRATION NUMBER

This study was registered at the International Prospective Register of Systematic Reviews as CRD42022312289.

Effects of Gene-Lifestyle Interaction on Obesity Among Students

BACKGROUND

Obesity is a global health issue influenced primarily by genetic variants and environmental factors. This study aimed to examine the relationship between genetic and lifestyle factors and their interaction with obesity risk among university students.

METHODS

A total of 658 students from the same university participated in this study, including 531 females (mean age (SD): 21.6 (3.9) years) and 127 males (21.9 (4.6) years). Among them, 550 were classified as normal weight or underweight (456 females and 94 males), while 108 were identified as overweight or obese (75 females and 33 males). All the participants underwent anthropometric and genetic screening and completed lifestyle and sleep quality questionnaires.

RESULTS

The polygenic risk score, based on seven genetic variants (ADCY3 rs11676272, CLOCK rs1801260, GPR61 rs41279738, FTO rs1421085, RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597), explained 8.3% (p < 0.0001) of the variance in body mass index (BMI). On the other hand, lifestyle factors-such as meal frequency, frequency of overeating, nut consumption as a snack, eating without hunger, frequency of antibiotic use in the past year, symptoms of dysbiosis, years of physical activity, sleep duration, bedtime, ground coffee consumption frequency, and evening coffee consumption time-accounted for 7.8% (p < 0.0001) of the variance in BMI. The model based on gene-environment interactions contributed 15% (p < 0.0001) to BMI variance.

CONCLUSIONS

This study revealed that individuals with a higher genetic predisposition, as defined by the seven polymorphic loci, are more susceptible to becoming overweight or obese under certain lifestyle conditions.

Predictors of emotional eating behaviors in adolescents with overweight and obesity

PURPOSE

Mood symptoms and disordered eating behaviors are common in adolescents with overweight and obesity. This study aimed to investigate the relationships between mood symptoms, difficulties in emotion regulation, and emotional eating behaviors in adolescents with overweight and obesity.

METHODS

In this cross-sectional study, adolescents with normal weight (N = 45), overweight (N = 45), or obesity (N = 55) were assessed using semi-structured clinical interviews and self-report scales. Path analysis was used to examine factors contributing to emotional eating behaviors, considering psychiatric comorbidities, mood symptoms, emotional/binge/restrictive eating behaviors, difficulties in emotion regulation, and self-esteem.

RESULTS

Adolescents with overweight and obesity exhibited more depressive, anxiety, and anger symptoms; restrictive, emotional, and external eating behaviors; and psychopathologies such as mood disorders, anxiety disorders, and attention-deficit/hyperactivity disorder (ADHD) compared with normal-weight adolescents. ADHD diagnosis, difficulties in emotion regulation, hypomanic/manic symptoms, and anger symptoms directly predicted emotional eating behavior, while self-esteem was an indirect predictor.

CONCLUSION

Adolescents with overweight and obesity tend to exhibit similar psychological characteristics, including increased mood symptoms and maladaptive eating behaviors associated with higher rates of anxiety and mood disorders, compared with their normal-weight peers. The most significant predictors of emotional eating behaviors were ADHD and difficulties in emotion regulation. Given the frequency of psychological comorbidities in obesity, their detection and management should be encouraged.

How do lifestyle factors modify the association between genetic predisposition and obesity-related phenotypes? A 4-way decomposition analysis using UK Biobank

BACKGROUND

Obesity and central obesity are multifactorial conditions with genetic and non-genetic (lifestyle and environmental) contributions. There is incomplete understanding of whether lifestyle modifies the translation from respective genetic risks into phenotypic obesity and central obesity, and to what extent genetic predisposition to obesity and central obesity is mediated via lifestyle factors.

METHODS

This is a cross-sectional study of 201,466 (out of approximately 502,000) European participants from UK Biobank and tested for interactions and mediation role of lifestyle factors (diet quality; physical activity levels; total energy intake; sleep duration, and smoking and alcohol intake) between genetic risk for obesity and central obesity. BMI-PRS and WHR-PRS are exposures and obesity and central obesity are outcomes.

RESULTS

Overall, 42.8% of the association between genetic predisposition to obesity and phenotypic obesity was explained by lifestyle: 0.9% by mediation and 41.9% by effect modification. A significant difference between men and women was found in central obesity; the figures were 42.1% (association explained by lifestyle), 1.4% (by mediation), and 40.7% (by modification) in women and 69.6% (association explained by lifestyle), 3.0% (by mediation), and 66.6% (by modification) in men.

CONCLUSIONS

A substantial proportion of the association between genetic predisposition to obesity/central obesity and phenotypic obesity/central obesity was explained by lifestyles. Future studies with repeated measures of obesity and lifestyle would be needed to clarify causation.

Assessment of Adolescents' Overweight and Obesity Risk Factors Among Alabnaa Schools in Tabuk City, Saudi Arabia

BACKGROUND

The prevalence of overweight and obesity among adolescents in Saudi Arabia has been progressively increasing. Obesity is associated with an increased risk of various morbidities and mortality. Identifying the factors that contribute to obesity in this age group is crucial for implementing targeted prevention measures.

AIM

The aim of this study was to identify risk factors for overweight and obesity among adolescents aged nine to 17 years residing in Tabuk City, Saudi Arabia.

METHODS

A case-control study was conducted during the 2021-2022 academic year at Alabnaa Schools in Tabuk City, Saudi Arabia. The study included overweight/obese individuals (cases, n = 125) and normal-weight individuals (controls, n = 201) who were selected based on their body mass index and classified according to the World Health Organization's reference for defining overweight and obesity in individuals aged five to 19 years. Data were collected from both groups using a self-administered questionnaire.

RESULTS

The study analyzed 125 overweight/obese students and 201 normal-weight students who were matched for sex and age (p > 0.05). Logistic regression analysis identified several risk factors for overweight or obesity among adolescents. A family history of obesity was found to be associated with a 5.735 times increased likelihood of obesity (95% CI: 3.318-9.912, p < 0.001). Another significant contributing risk factor for obesity was frequent consumption of four or more meals per day (adjusted odds ratio: 3.091, 95% CI: 1.094-8.736, p = 0.033). Students who used electronic devices for more than five hours were 2.422 times more likely to exhibit obesity (p = 0.006).

CONCLUSIONS

Certain factors may increase the risk of overweight or obesity in adolescents aged nine to 17 years. These factors include frequent eating, prolonged use of electronic devices, family history of obesity, and the misconception that obesity is not an illness. Tailored school health programs are needed to improve students' healthy lifestyles and eating behaviors, minimize sedentary entertainment and use of electronic devices, and engage children in physical activity.

Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

From the reward network to whole-brain metrics: structural connectivity in adolescents and young adults according to body mass index and genetic risk of obesity

BACKGROUND

Obesity is a multifactorial condition. Genetic variants, such as the fat mass and obesity related gene (FTO) polymorphism, may increase the vulnerability of developing obesity by disrupting dopamine signaling within the reward network. Yet, the association of obesity, genetic risk of obesity, and structural connectivity of the reward network in adolescents and young adults remains unexplored. We investigate, in adolescents and young adults, the structural connectivity differences in the reward network and at the whole-brain level according to body mass index (BMI) and the FTO rs9939609 polymorphism.

METHODS

One hundred thirty-two adolescents and young adults (age range: [10, 21] years, BMI z-score range: [-1.76, 2.69]) were included. Genetic risk of obesity was determined by the presence of the FTO A allele. Whole-brain and reward network structural connectivity were analyzed using graph metrics. Hierarchical linear regression was applied to test the association between BMI-z, genetic risk of obesity, and structural connectivity.

RESULTS

Higher BMI-z was associated with higher (B = 0.76, 95% CI = [0.30, 1.21], P = 0.0015) and lower (B = -0.003, 95% CI = [-0.006, -0.00005], P = 0.048) connectivity strength for fractional anisotropy at the whole-brain level and of the reward network, respectively. The FTO polymorphism was not associated with structural connectivity nor with BMI-z.

CONCLUSIONS

We provide evidence that, in healthy adolescents and young adults, higher BMI-z is associated with higher connectivity at the whole-brain level and lower connectivity of the reward network. We did not find the FTO polymorphism to correlate with structural connectivity. Future longitudinal studies with larger sample sizes are needed to assess how genetic determinants of obesity change brain structural connectivity and behavior.

Lifestyle factors, genetic susceptibility to obesity and their interactions on coronary artery disease risk: A cohort study in the UK Biobank

OBJECTIVE

We aimed to evaluate potential modifying effects of genetic susceptibility to obesity on the association of lifestyle factors with coronary artery disease (CAD) risk.

METHODS

A total of 328,606 participants (54% women) were included using data from the UK Biobank. We evaluated the risk of developing CAD associated with obesity-related polygenic scores (PGSs) and healthy lifestyle scores (HLSs). HLSs were constructed using six lifestyle factors. Obesity PGSs were created using genetic variants identified by genome-wide association studies, including 941 variants for body mass index (BMI) and 457 for waist-to-hip ratio (WHR). Both HLSs and PGSs were categorized into three groups.

RESULTS

During a 9-year median follow-up, 14,541 participants developed CAD. An unhealthy lifestyle was significantly associated with an increased CAD risk (hazard ratio [HR] = 2.24, 95% confidence interval [CI] = 2.09-2.40). High BMI and WHR PGSs were each significantly associated with an increased CAD risk (HRBMI = 1.23, 1.17-1.29; HRWHR = 1.15, 1.09-1.21). Lifestyle factors explained 41% (95% CI = 38%-45%) of CAD, while genetic variants for BMI explained only 10% (7%-14%). Risks of CAD were increased with poorer HLS independent of obesity-related PGSs. Individuals with the most unhealthy lifestyle and highest BMI PGS had the highest risk of CAD risk (HR = 2.59, 95% CI = 2.26-2.97), compared with participants with the healthiest lifestyle and lowest BMI PGS.

CONCLUSIONS

While the observational nature of the study precludes the establishment of causality, our study provides supports for a causal association between obesity and CAD risk and the importance of lifestyle modification in the prevention of CAD.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

A multidimensional social risk atlas of depression and anxiety: An observational and genome-wide environmental interaction study

Background

Mental disorders are largely socially determined, yet the combined impact of multidimensional social factors on the two most common mental disorders, depression and anxiety, remains unclear.

Methods

We constructed a polysocial risk score (PsRS), a multidimensional social risk indicator including components from three domains: socioeconomic status, neighborhood and living environment and psychosocial factors. Supported by the UK Biobank cohort, we randomly divided 110 332 participants into the discovery cohort (60%; n = 66 200) and the replication cohort (40%; n = 44 134). We tested the associations between 13 single social factors with Patient Health Questionnaire (PHQ) score, Generalized Anxiety Disorder Scale (GAD) score and self-reported depression and anxiety. The significant social factors were used to calculate PsRS for each mental disorder by considering weights from the multivariable linear model. Generalized linear models were applied to explore the association between PsRS and depression and anxiety. Genome-wide environmental interaction study (GWEIS) was further performed to test the effect of interactions between PsRS and SNPs on the risk of mental phenotypes.

Results

In the discovery cohort, PsRS was positively associated with PHQ score (β = 0.37; 95% CI = 0.35-0.38), GAD score (β = 0.27; 95% CI = 0.25-0.28), risk of self-reported depression (OR = 1.29; 95% CI = 1.28-1.31) and anxiety (OR = 1.19; 95% CI = 1.19-1.23). Similar results were observed in the replication cohort. Emotional stress, lack of social support and low household income were significantly associated with the development of depression and anxiety. GWEIS identified multiple candidate loci for PHQ score, such as rs149137169 (ST18) (Pdiscovery = 1.08 × 10-8, Preplication = 3.25 × 10-6) and rs3759812 (MYO9A) (Pdiscovery = 3.87 × 10-9, Preplication = 6.21 × 10-5). Additionally, seven loci were detected for GAD score, such as rs114006170 (TMPRSS11D) (Pdiscovery = 1.14 × 10-9, Preplication = 7.36 × 10-5) and rs77927903 (PIP4K2A) (Pdiscovery = 2.40 × 10-9, Preplication = 0.002).

Conclusions

Our findings reveal the positive effects of multidimensional social factors on the risk of depression and anxiety. It is important to address key social disadvantage in mental health promotion and treatment.

New Insights into Polygenic Score-Lifestyle Interactions for Cardiometabolic Risk Factors from Genome-Wide Interaction Analyses

The relationship between lifestyles and cardiometabolic outcomes varies between individuals. In 382,275 UK Biobank Europeans, we investigate how lifestyles interact with polygenic scores (PGS) of cardiometabolic risk factors. We identify six interactions (PGS for body mass index with meat diet, physical activity, sedentary behaviour and insomnia; PGS for high-density lipoprotein cholesterol with sedentary behaviour; PGS for triglycerides with meat diet) in multivariable linear regression models including an interaction term and show stronger associations between lifestyles and cardiometabolic risk factors among individuals with high PGSs than those with low PGSs. Genome-wide interaction analyses pinpoint three genetic variants (FTO rs72805613 for BMI; CETP rs56228609 for high-density lipoprotein cholesterol; TRIB2 rs4336630 for triglycerides; PInteraction < 5 × 10-8). The associations between lifestyles and cardiometabolic risk factors differ between individuals grouped by the genotype of these variants, with the degree of differences being similar to that between individuals with high and low values for the corresponding PGSs. This study demonstrates that associations between lifestyles and cardiometabolic risk factors can differ between individuals based upon their genetic profiles. It further suggests that genetic variants with interaction effects contribute more to such differences compared to those without interaction effects, which has potential implications for developing PGSs for personalised intervention.

The Increase in Childhood Obesity and Its Association with Hypertension during Pandemics

There has been a major ongoing health impact of the COVID-19 pandemic on children's lives, including lifestyle and overall health. Enforcement of prevention measures, such as school closures and social distancing, has significantly affected children's daily routines and activities. This perspective manuscript aims to explore the rise in childhood obesity and its association with hypertension during pandemics. The COVID-19 pandemic has led to significant disruptions in children's routines, including reduced physical activity, increased sedentary behavior, and changes in dietary patterns. These factors, coupled with the psychological impact of the pandemic, have contributed to an alarming increase in childhood obesity rates. This paper has highlighted the concerning increase in childhood obesity and hypertension during pandemics. The disruptions caused by the COVID-19 pandemic, including reduced physical activity, increased sedentary behaviors, and changes in dietary patterns, have contributed to the rise in these health conditions. It is crucial to recognize the long-term consequences of childhood obesity and hypertension and the urgent need for a comprehensive approach to address them.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Gene-Environment Interplay in the Development of Overweight

PURPOSE

Overweight in youth is influenced by genes and environment. Gene-environment interaction (G×E) has been demonstrated in twin studies and recent developments in genetics allow for studying G×E using individual genetic predispositions for overweight. We examine genetic influence on trajectories of overweight during adolescence and early adulthood and determine whether genetic predisposition is attenuated by higher socioeconomic status and having physically active parents.

METHODS

Latent class growth models of overweight were fitted using data from the TRacking Adolescents' Individual Lives Survey (n = 2720). A polygenic score for body mass index (BMI) was derived using summary statistics from a genome-wide association study of adult BMI (N = ∼700,000) and tested as predictor of developmental pathways of overweight. Multinomial logistic regression models were used to examine effects of interactions of genetic predisposition with socioeconomic status and parental physical activity (n = 1675).

RESULTS

A three-class model of developmental pathways of overweight fitted the data best ("non-overweight", "adolescent-onset overweight", and "persistent overweight"). The polygenic score for BMI and socioeconomic status distinguished the persistent overweight and adolescent-onset overweight trajectories from the non-overweight trajectory. Only genetic predisposition differentiated the adolescent-onset from the persistent overweight trajectory. There was no evidence for G×E.

DISCUSSION

Higher genetic predisposition increased the risk of developing overweight during adolescence and young adulthood and was associated with an earlier age at onset. We did not find that genetic predisposition was offset by higher socioeconomic status or having physically active parents. Instead, lower socioeconomic status and higher genetic predisposition acted as additive risk factors for developing overweight.

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation

Recently, a non-parametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate genome-wide association study (GWAS) summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here, we propose an extension of the method to impute both genetic and environmental components of a trait using both single nucleotide polymorphism (SNP)-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations and were more highly correlated with the original observed BMI values.

Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review

Background

Obesity represents a major and preventable global health challenge as a complex disease and a modifiable risk factor for developing other non-communicable diseases. In recent years, obesity prevalence has risen more rapidly in low- and middle-income countries (LMICs) compared to high-income countries (HICs). Obesity traits are shown to be modulated by an interplay of genetic and environmental factors such as unhealthy diet and physical inactivity in studies from HICs focused on populations of European descent; however, genetic heterogeneity and environmental differences prevent the generalisation of study results to LMICs. Primary research investigating gene-environment interactions (GxE) on obesity in LMICs is limited but expanding. Synthesis of current research would provide an overview of the interactions between genetic variants and environmental factors that underlie the obesity epidemic and identify knowledge gaps for future studies.

Methods

Three databases were searched systematically using a combination of keywords such as "genes", "obesity", "LMIC", "diet", and "physical activity" to find all relevant observational studies published before November 2022.

Results

Eighteen of the 1,373 articles met the inclusion criteria, of which one was a genome-wide association study (GWAS), thirteen used a candidate gene approach, and five were assigned as genetic risk score studies. Statistically significant findings were reported for 12 individual SNPs; however, most studies were small-scale and without replication.

Conclusion

Although the results suggest significant GxE interactions on obesity in LMICs, updated robust statistical techniques with more precise and standardised exposure and outcome measurements are necessary for translatable results. Future research should focus on improved quality replication efforts, emphasising large-scale and long-term longitudinal study designs using multi-ethnic GWAS.

Effect of synbiotics on weight loss and metabolic health in adults with overweight and obesity: A randomized controlled trial

OBJECTIVE

The study aimed to investigate the effect of synbiotics on body composition and metabolic health in individuals with excessive body weight.

METHODS

The 12-week randomized, double-blind, placebo-controlled clinical trial had individuals aged 30 to 60 years with BMI of 25 to 34.9 kg/m2 . In total, 172 participants were randomly allocated to either synbiotic V5 or V7 groups or the placebo group. The primary outcome was change in BMI and body fat percentage. Secondary outcomes were changes in weight, other metabolic health and inflammatory markers, gastrointestinal quality of life, and eating behaviors.

RESULTS

The V5 and V7 groups had a significant reduction in BMI (p < 0.0001) from baseline to the end of the study, as opposed to the nonsignificant change in the placebo group (p = 0.0711). This reduction in the V5 and V7 groups was statistically significant when compared individually with the change in the placebo group (p < 0.0001). This corresponded well with the decrease in body weight with V5 and V7 (p < 0.0001). In addition, compared with placebo, the increase in high-density lipoprotein was statistically significant in the V5 (p < 0.0001) and V7 groups (p = 0.0205). A similar trend was observed in the high-sensitivity C-reactive protein levels, with a statistically significant decrease in the V5 (p < 0.0001) and V7 (0.0005) groups.

CONCLUSIONS

The study demonstrates that synbiotic V5 and V7 were effective in reducing body weight in individuals with lifestyle modification.

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

OBJECTIVE

Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity.

RESEARCH DESIGN AND METHODS

Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459).

RESULTS

The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations.

CONCLUSIONS

Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

Gene-environment interaction explains a part of missing heritability in human body mass index

Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.

Advances in DNA, histone, and RNA methylation mechanisms in the pathophysiology of alcohol use disorder

Alcohol use disorder (AUD) has a complex, multifactorial etiology involving dysregulation across several brain regions and peripheral organs. Acute and chronic alcohol consumption cause epigenetic modifications in these systems, which underlie changes in gene expression and subsequently, the emergence of pathophysiological phenotypes associated with AUD. One such epigenetic mechanism is methylation, which can occur on DNA, histones, and RNA. Methylation relies on one carbon metabolism to generate methyl groups, which can then be transferred to acceptor substrates. While DNA methylation of particular genes generally represses transcription, methylation of histones and RNA can have bidirectional effects on gene expression. This review summarizes one carbon metabolism and the mechanisms behind methylation of DNA, histones, and RNA. We discuss the field's findings regarding alcohol's global and gene-specific effects on methylation in the brain and liver and the resulting phenotypes characteristic of AUD.

Evolutionary mismatch and the role of GxE interactions in human disease

Globally, we are witnessing the rise of complex, non-communicable diseases (NCDs) related to changes in our daily environments. Obesity, asthma, cardiovascular disease, and type 2 diabetes are part of a long list of "lifestyle" diseases that were rare throughout human history but are now common. A key idea from anthropology and evolutionary biology-the evolutionary mismatch hypothesis-seeks to explain this phenomenon. It posits that humans evolved in environments that radically differ from the ones experienced by most people today, and thus traits that were advantageous in past environments may now be "mismatched" and disease-causing. This hypothesis is, at its core, a genetic one: it predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions and have differential health effects in ancestral versus modern environments. Here, we discuss how this concept could be leveraged to uncover the genetic architecture of NCDs in a principled way. Specifically, we advocate for partnering with small-scale, subsistence-level groups that are currently transitioning from environments that are arguably more "matched" with their recent evolutionary history to those that are more "mismatched". These populations provide diverse genetic backgrounds as well as the needed levels and types of environmental variation necessary for mapping GxE interactions in an explicit mismatch framework. Such work would make important contributions to our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and sociocultural contexts.

Genetics of circadian rhythms and sleep in human health and disease

Circadian rhythms and sleep are fundamental biological processes integral to human health. Their disruption is associated with detrimental physiological consequences, including cognitive, metabolic, cardiovascular and immunological dysfunctions. Yet many of the molecular underpinnings of sleep regulation in health and disease have remained elusive. Given the moderate heritability of circadian and sleep traits, genetics offers an opportunity that complements insights from model organism studies to advance our fundamental molecular understanding of human circadian and sleep physiology and linked chronic disease biology. Here, we review recent discoveries of the genetics of circadian and sleep physiology and disorders with a focus on those that reveal causal contributions to complex diseases.

Precision Nutrition: Recent Advances in Obesity

"Precision nutrition" is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps develop customized dietary plans and interventions to maintain optimal individual health. It encompasses nutritional genomic (gene-nutrient interactions), epigenetic, microbiome, and environmental factors. Obesity is a complex disease that is affected by genetic and environmental factors and thus a relevant target of precision nutrition-based approaches. Recent studies have shown significant associations between obesity phenotypes (body weight, body mass index, waist circumference, and central and regional adiposity) and genetic variants, epigenetic factors (DNA methylation and noncoding RNA), microbial species, and environment (sociodemographics and physical activity). Additionally, studies have also shown that the interactions between genetic variants, microbial metabolites, and epigenetic factors affect energy balance and adiposity. These include variants in FTO, MC4R, PPAR, APOA, and FADS genes, DNA methylation in CpG island regions, and specific miRNAs and microbial species such as Firmicutes, Bacteriodes, Clostridiales, etc. Similarly, studies have shown that microbial metabolites, folate, B-vitamins, and short-chain fatty acids interact with miRNAs to influence obesity phenotypes. With the advent of next-generation sequencing and analytical approaches, the advances in precision nutrition have the potential to lead to new paradigms, which can further lead to interventions or customized treatments specific to individuals or susceptible groups of individuals. This review highlights the recent advances in precision nutrition as applied to obesity and projects the importance of precision nutrition in obesity and weight management.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Meta-analytic evidence for a sex-diverging association between alcohol use and body mass index

Alcohol use is an important health issue and has been suggested to contribute to the burden produced by obesity. Both alcohol use and obesity are subject to sex differences. The available studies on the relationship between alcohol use and body mass index (BMI) report inconsistent results with positive, negative, and null findings which requests a meta-analytic approach. Therefore, we conducted a meta-analysis of case-control, cohort, and cross-sectional studies. The systematic literature search and data extraction was performed by 3 independent raters. We conducted sex-separated meta-analyses and -regressions to investigate how alcohol consumption associates with BMI. Our systematic literature search resulted in 36 studies with 48 data sets (Nmen = 172,254; kmen = 30; Nwomen = 24,164; kwomen = 18; Nunknown sex = 672,344; kunknown sex = 24). Alcohol use was associated with higher BMI in men (g = 0.08 [0.07; 0.09]) and lower BMI in women (g = - 0.26 [- 0.29; - 0.22]). Moreover, we found the amount of daily alcohol intake in men (β = 0.001 [0.0008; 0.0014]) and ethnicity in women (g[Caucasians] = - 0.45 versus g[Asians] = - 0.05; z = 11.5, p < 0.0001) to moderate these effects. We here identified sex-diverging relationships between alcohol use and BMI, found daily alcohol intake and ethnicity to sex-specifically moderate these effects, and argue that sex-specific choice of beverage type and higher amount of daily alcohol use in men than in women account for these observations. Future research is needed to provide empirical evidence for the underlying mechanisms.

The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index

Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.

Do gene-environment interactions have implications for the precision prevention of type 2 diabetes?

The past decades have seen a rapid global rise in the incidence of type 2 diabetes. This surge has been driven by diabetogenic environmental changes that may act together with a genetic predisposition to type 2 diabetes. It is possible that there is a synergistic gene-environment interaction, where the effects of the diabetogenic environment depend on the genetic predisposition to type 2 diabetes. Randomised trials have shown that it is possible to delay, or even prevent the development of type 2 diabetes in individuals at elevated risk through behavioural modification, focusing on weight loss, physical activity and diet. There is wide heterogeneity between individuals regarding the effectiveness of these interventions, which could, in part, be due to genetic differences. However, the studies of gene-environment interactions performed thus far suggest that behavioural modifications appear equally effective in reducing the incidence of type 2 diabetes from the stage of impaired glucose tolerance, regardless of the known underlying genetic predisposition. Recent studies suggest that there may be several subtypes of type 2 diabetes, which give new opportunities for gaining insight into gene-environment interactions. At present, the role of gene-environment interactions in the development of type 2 diabetes remains unclear. With many puzzle pieces missing in the general picture of type 2 diabetes development, the available evidence of gene-environment interactions is not ready for translation to individualised type 2 diabetes prevention based on genetic profiling.

The most effective exercise to prevent obesity: A longitudinal study of 33,731 Taiwan biobank participants

Regular physical exercise is recommended to reduce the risk of obesity. However, it remains unclear which activities are more effective in preventing obesity. In this study, five obesity indices and lifestyle factors of 33,731 Taiwan Biobank adults were measured/collected twice with a mean time interval of 4.06 years. A linear mixed effects model was fitted to assess the associations of exercises with obesity indices, in which a random intercept term was used to account for individual differences. The five obesity indices included body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). Among 23 exercises, jogging and yoga were consistently the most effective choices across all five obesity indices. One more weekly hour to jog was associated with a 0.093 kg/m2 decrease in BMI (p = 4.2E-20), a 0.297% decrease in BFP (p = 3.8E-36), a 0.398 cm decrease in WC (p = 1.6E-21), and a 2.9E-3 decrease in WHR (p = 1.3E-17). One more weekly hour to perform yoga was associated with a 0.225 cm decrease in HC (p = 6.4E-14). Jogging is an exercise for the entire body. Arms swing, waist turn, legs and feet run, and shoulders and abdomen are also involved in this act. By contrast, many yoga poses use muscles around the hips and pelvis, and therefore yoga is the most effective exercise to reduce HC.

Cultural evolution may influence heritability by shaping assortative mating

Uchiyama et al. productively discuss how culture can influence genetic heritability and, by modifying environmental conditions, limit the generalizability of genome-wide association studies (GWASs). Here, we supplement their account by highlighting how recent changes in culture and institutions in industrialized, westernized societies - such as increased female workforce participation - may have increased assortative mating. This alters the distribution of genotypes themselves, increasing heritability and phenotypic variance, and may be detectable using the latest methods.

Chronic Stress, Genetic Risk, and Obesity in US Hispanic/Latinos: Results From the Hispanic Community Health Study/Study of Latinos

OBJECTIVE

This study aimed to investigate whether the association of chronic stress with obesity is independent of genetic risk and test whether it varies by the underlying genetic risk.

METHODS

The analysis included data from the Hispanic Community Health Study/Study of Latinos, a community-based study of Hispanic/Latinos living in four US communities (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA). The sample consisted of 5336 women and 3231 men who attended the Hispanic Community Health Study/Study of Latinos second in-person examination, had measures of obesity, and chronic stress, and were genotyped. Chronic stress burden was assessed by an eight-item scale. An overall polygenic risk score was calculated based on the summary statistics from GIANT and UK BioBank meta-analysis of body mass index (BMI) genome-wide association studies. Mixed-effect models were used to account for genetic relatedness and sampling design, as well as to adjust for potential confounders.

RESULTS

A higher number of chronic stressors were associated with both BMI ( β [log odds] = 0.31 [95% confidence interval = 0.23-0.38]) and obesity ( β [log odds] = 0.10 [95% confidence interval = 0.07-0.13]), after adjustment for covariates and genetic risk. No interactions were found between chronic stress and the genetic risk score for BMI or obesity.

CONCLUSIONS

We did not find evidence for an interaction between chronic stress and polygenic risk score, which was not consistent with other publications that showed greater BMI or obesity in the groups with high stressors and elevated genetic risk.

Association between fat-soluble vitamins and self-reported health status: a cross-sectional analysis of the MARK-AGE cohort

Self-rated health (SRH) is associated with higher risk of death. Since low plasma levels of fat-soluble vitamins are related to mortality, we aimed to assess whether plasma concentrations of vitamins A, D and E were associated with SRH in the MARK-AGE study. We included 3158 participants (52 % female) aged between 35 and 75 years. Cross-sectional data were collected via questionnaires. An enzyme immunoassay quantified 25-hydroxyvitamin D and HPLC determined α-tocopherol and retinol plasma concentrations. The median 25-hydroxyvitamin D and retinol concentrations differed significantly (P < 0·001) between SRH categories and were lower in the combined fair/poor category v. the excellent, very good and good categories (25-hydroxvitamin D: 40·8 v. 51·9, 49·3, 46·7 nmol/l, respectively; retinol: 1·67 v. 1·75, 1·74, 1·70 µmol/l, respectively). Both vitamin D and retinol status were independently associated with fair/poor SRH in multiple regression analyses: adjusted OR (95 % CI) for the vitamin D insufficiency, deficiency and severe deficiency categories were 1·33 (1·06-1·68), 1·50 (1·17-1·93) and 1·83 (1·34-2·50), respectively; P = 0·015, P = 0·001 and P < 0·001, and for the second/third/fourth retinol quartiles: 1·44 (1·18-1·75), 1·57 (1·28-1·93) and 1·49 (1·20-1·84); all P < 0·001. No significant associations were reported for α-tocopherol quartiles. Lower vitamin A and D status emerged as independent markers for fair/poor SRH. Further insights into the long-term implications of these modifiable nutrients on health status are warranted.

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

Contribution of brain network connectivity in predicting effects of polygenic risk and childhood trauma on state-trait anxiety

BACKGROUND

Anxiety is usually attributed to adverse environmental factors, but it is known as a polygenic inheritance disease. Gene-environment interactions on the occurrence and severity of anxiety are still unclear. The role of brain network connectivity in the gene-environment effects on anxiety has not been explored and may be key to understanding neuropathogenesis and guiding treatment.

METHODS

This study recruited 177 young adults from the community that completed functional magnetic resonance imaging, Childhood Trauma Questionnaire (CTQ), state-trait anxiety scores, and whole exome sequencing. We calculated polygenic risk score (PRS) for anxiety and the sum score of CTQ, which are genetic and environmental factors that may affect anxiety, respectively. Abnormal brain network connectivity determined by the gene-environment effects and its associations with anxiety scores were then explored.

RESULTS

Except for the main effect of PRS or CTQ on intra-network connectivity, significant interactions were found in intra-network connectivity of visual network, default mode network, self-reference network, and sensorimotor network. Moreover, altered network connectivity was related to anxious tendency. In particular, the effect of CTQ on trait anxiety was mediated by the disrupted sensorimotor network, accompanied by a significant direct effect. However, the PRS influence on anxiety was mainly mediated through sensorimotor network paths, which exceeded the direct influence and was moderated by childhood trauma levels.

CONCLUSIONS

These network-specific functional changes related to individual gene-environment risks advance our understanding of psychiatric pathogenesis of anxiety and provide new insights for clinical intervention.

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.

Educational attainment as a modifier for the effect of polygenic scores for cardiovascular risk factors: cross-sectional and prospective analysis of UK Biobank

BACKGROUND

Understanding the interplay between educational attainment and genetic predictors of cardiovascular risk may improve our understanding of the aetiology of educational inequalities in cardiovascular disease.

METHODS

In up to 320 120 UK Biobank participants of White British ancestry (mean age = 57 years, female 54%), we created polygenic scores for nine cardiovascular risk factors or diseases: alcohol consumption, body mass index, low-density lipoprotein cholesterol, lifetime smoking behaviour, systolic blood pressure, atrial fibrillation, coronary heart disease, type 2 diabetes and stroke. We estimated whether educational attainment modified genetic susceptibility to these risk factors and diseases.

RESULTS

On the additive scale, higher educational attainment reduced genetic susceptibility to higher body mass index, smoking, atrial fibrillation and type 2 diabetes, but increased genetic susceptibility to higher LDL-C and higher systolic blood pressure. On the multiplicative scale, there was evidence that higher educational attainment increased genetic susceptibility to atrial fibrillation and coronary heart disease, but little evidence of effect modification was found for all other traits considered.

CONCLUSIONS

Educational attainment modifies the genetic susceptibility to some cardiovascular risk factors and diseases. The direction of this effect was mixed across traits considered and differences in associations between the effect of the polygenic score across strata of educational attainment was uniformly small. Therefore, any effect modification by education of genetic susceptibility to cardiovascular risk factors or diseases is unlikely to substantially explain the development of inequalities in cardiovascular risk.

The Niche of n-of-1 Trials in Precision Medicine for Weight Loss and Obesity Treatment: Back to the Future

PURPOSE OF REVIEW

The n-of-1 clinical trials are considered the epitome of individualized health care. They are employed to address differences in treatment response and adverse events between patients, in a comparative effectiveness manner, extending beyond the delivery of horizontal recommendations for all.

RECENT FINDINGS

The n-of-1 design has been applied to deliver precision exercise interventions, through eHealth and mHealth technologies. Regarding personalized and precision medical nutrition therapy, few trials have implemented dietary manipulations and one series of n-of-1 trials has applied comprehensive genetic data to improve body weight. With regard to anti-obesity medication, pharmacogenetic data could be applied using the n-of-1 trial design, although none have been implemented yet. The n-of-1 clinical trials consist of the only tool for the delivery of evidence-based, personalized obesity treatment (lifestyle and pharmacotherapy), reducing non-responders, while tailoring the best intervention to each patient, through "trial and error". Their application is expected to improve obesity treatment and mitigate the epidemic.

The dynamic effect of genetic variation on the in vivo ER stress transcriptional response in different tissues

The genetic regulation of gene expression varies greatly across tissue-type and individuals and can be strongly influenced by the environment. Many variants, under healthy control conditions, may be silent or even have the opposite effect under diseased stress conditions. This study uses an in vivo mouse model to investigate how the effect of genetic variation changes with cellular stress across different tissues. Endoplasmic reticulum stress occurs when misfolded proteins accumulate in the endoplasmic reticulum. This triggers the unfolded protein response, a large transcriptional response which attempts to restore homeostasis. This transcriptional response, despite being a conserved, basic cellular process, is highly variable across different genetic backgrounds, making it an ideal system to study the dynamic effects of genetic variation. In this study, we sought to better understand how genetic variation alters expression across tissues, in the presence and absence of endoplasmic reticulum stress. The use of different mouse strains and their F1s allow us to also identify context-specific cis- and trans- regulatory variation underlying variable transcriptional responses. We found hundreds of genes that respond to endoplasmic reticulum stress in a tissue- and/or genotype-dependent manner. The majority of the regulatory effects we identified were acting in cis-, which in turn, contribute to the variable endoplasmic reticulum stress- and tissue-specific transcriptional response. This study demonstrates the need for incorporating environmental stressors across multiple different tissues in future studies to better elucidate the effect of any particular genetic factor in basic biological pathways, like the endoplasmic reticulum stress response.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

The Impact of ACEs on BMI: An Investigation of the Genotype-Environment Effects of BMI

Adverse Childhood Experiences are stressful and traumatic events occurring before the age of eighteen shown to cause mental and physical health problems, including increased risk of obesity. Obesity remains an ongoing national challenge with no predicted solution. We examine a subset of the Healthy Nevada Project, focusing on a multi-ethnic cohort of 15,886 sequenced participants with recalled adverse childhood events, to study how ACEs and their genotype-environment interactions affect BMI. Specifically, the Healthy Nevada Project participants sequenced by the Helix Exome+ platform were cross-referenced to their electronic medical records and social health determinants questionnaire to identify: 1) the effect of ACEs on BMI in the absence of genetics; 2) the effect of genotype-environment interactions on BMI; 3) how these gene-environment interactions differ from standard genetic associations of BMI. The study found very strong significant associations between the number of adverse childhood experiences and adult obesity. Additionally, we identified fifty-five common and rare variants that exhibited gene-interaction effects including three variants in the CAMK1D gene and four variants in LHPP; both genes are linked to schizophrenia. Surprisingly, none of the variants identified with interactive effects were in canonical obesity-related genes. Here we show the delicate balance between genes and environment, and how the two strongly influence each other.

Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA)

PURPOSE

The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA.

PARTICIPANTS

A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit).

FINDINGS TO DATE

Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease.

FUTURE PLANS

The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

Understanding gene-lifestyle interaction in obesity: the role of mediation versus moderation

BACKGROUND

Obesity results from complex interactions between genetic susceptibility to weight gain and poor eating and lifestyle behaviors. The approach that has been traditionally used in genetics to investigate gene-environment/lifestyle interaction in obesity is based on the concept of moderation, or effect modification. Another approach called mediation analysis can be used to investigate gene-environment interaction in obesity. The objective of this review article is to explain the differences between the concepts of moderation and mediation and summarize the studies that have used mediation analysis to support the role of eating or lifestyle behaviors as putative mediators of genetic susceptibility to obesity.

SUMMARY

Moderation is used to determine whether the effect of an exposure (genes associated with obesity) on an outcome (obesity phenotype) differs in magnitude and/or direction across the spectrum of environmental exposure. Mediation analysis is used to assess the extent to which the effect of the exposure on the outcome is explained by a given set of hypothesized mediators with the aim of understanding how the exposure could lead to the outcome. In comparison with moderation, relatively few studies used mediation analyses to investigate gene-environment in obesity. Most studies found evidence that traits related to appetite or eating behaviors partly mediated genetic susceptibility to obesity in either children or adults. Key messages: Moderation and mediation represent two complementary approaches to investigate gene-environment interaction in obesity and address different research questions pertaining to the cause-effect relationship between genetic susceptibility to obesity and various obesity outcomes. More studies relying on mediation are needed to better understand the role eating and lifestyle habits in mediating genetic susceptibility to obesity.