Gene × physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry

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.

Sweet Taste Receptors' Genetic Variability in Advanced Potential Targets of Obesity

Background: Obesity, mainly visceral obesity, causes a low-grade of chronic inflammation (meta-inflammation), associated with comorbidities such as type 2 diabetes, cardiovascular diseases, and certain cancers. Precision Nutrition aims to understand the bidirectional crosstalk between the genome and diet to improve human health. Additionally, by leveraging individual data, Precision Nutrition seeks to predict how people will respond to specific foods or dietary patterns, with the ultimate goal of providing personalized nutritional recommendations tailored to their unique needs and lifestyle factors, including poor dietary habits (e.g., high intake of sugar or saturated fatty acids, alcohol consumption, etc.) and sedentary habits, exacerbate obesity in genetically predisposed individuals. Genetic, metabolic, and environmental factors can play a crucial role during obesity. Objective: To investigate the effects of genetic variability in sweet taste receptors and their downstream signaling pathways in the gut-brain axis on anthropometry, biochemistry, and lifestyle variables. Methods: A sample of 676 volunteers (mean age of 42.22 ± 12 years, ranging from 18 to 73 years) from the database of the GENYAL platform for nutritional trials at the IMDEA Food Institute were included in this study. We present a first-in-class genetic chip, Glucosensing, designed to interrogate 25 single-nucleotide polymorphisms (SNPs) located in genes encoding sweet taste receptors and components of downstream signaling pathways. These include elements of the gut-brain axis and its associated metabolic networks, enabling a comprehensive analysis of individual variability in sweet taste perception and metabolic responses. Results: Several significant associations were found after correction for multiple comparisons, representing potential targets for personalized interventions.

Genetic-by-age interaction analyses on complex traits in UK Biobank and their potential to identify effects on longitudinal trait change

BACKGROUND

Genome-wide association studies (GWAS) have identified thousands of loci for disease-related human traits in cross-sectional data. However, the impact of age on genetic effects is underacknowledged. Also, identifying genetic effects on longitudinal trait change has been hampered by small sample sizes for longitudinal data. Such effects on deteriorating trait levels over time or disease progression can be clinically relevant.

RESULTS

Under certain assumptions, we demonstrate analytically that genetic-by-age interaction observed in cross-sectional data can be indicative of genetic association on longitudinal trait change. We propose a 2-stage approach with genome-wide pre-screening for genetic-by-age interaction in cross-sectional data and testing identified variants for longitudinal change in independent longitudinal data. Within UK Biobank cross-sectional data, we analyze 8 complex traits (up to 370,000 individuals). We identify 44 genetic-by-age interactions (7 loci for obesity traits, 26 for pulse pressure, few to none for lipids). Our cross-trait view reveals trait-specificity regarding the proportion of loci with age-modulated effects, which is particularly high for pulse pressure. Testing the 44 variants in longitudinal data (up to 50,000 individuals), we observe significant effects on change for obesity traits (near APOE, TMEM18, TFAP2B) and pulse pressure (near FBN1, IGFBP3; known for implication in arterial stiffness processes).

CONCLUSIONS

We provide analytical and empirical evidence that cross-sectional genetic-by-age interaction can help pinpoint longitudinal-change effects, when cross-sectional data surpasses longitudinal sample size. Our findings shed light on the distinction between traits that are impacted by age-dependent genetic effects and those that are not.

Polygenic Interactions With Environmental Exposures in Blood Pressure Regulation: The HUNT Study

BACKGROUND

The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized.

METHODS AND RESULTS

We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene-environment interactions between genome-wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome-wide PRSs for systolic BP and 4 for genome-wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P < 2 × 10-4). We found evidence for gene-dependent influence of lifestyle factors such as cardiorespiratory fitness, dietary patterns, and tobacco exposure, as well as biomarkers such as serum cholesterol, creatinine, and alkaline phosphatase on BP.

CONCLUSIONS

Individuals that are genetically susceptible to high BP may be more vulnerable to common acquired risk factors for hypertension, but these effects appear to be modifiable. The gene-dependent influence of several common acquired risk factors indicates the potential of genetic data combined with lifestyle assessments in risk stratification, and gene-environment-informed risk modeling in the prevention and management of hypertension.

Gene-Environment Interactions Significantly Alter the Obesity Risk of SH2B1 rs7498665 Carriers

Background

Src homology 2 B adaptor protein 1 (SH2B1) gene and variants have been found to be associated with common obesity. We aimed to investigate the association between the common missense variant SH2B1 rs7498665 and common obesity risk as well as interactions with lifestyle variables in an Israeli population.

Methods

An adult cohort (n=3,070; ≥18 years) with the SH2B1 rs7498665 variant and lifestyle, behavior (online questionnaire), and blood glucose data was analyzed. Associations between this variant, obesity risk (body mass index [BMI] ≥25 and ≥30 kg/m2), and interactions with behavioral and lifestyle factors (stress levels, eating habits score [EHS], physical activity [PA], and wine consumption) were investigated. Association and gene-environment interactions were analyzed using binary logistic regressions with interaction.

Results

SH2B1 rs7498665 carriers were significantly (P<0.05) more likely to be overweight (BMI ≥25 kg/m2) or obese (BMI ≥30 kg/m2) in recessive (odds ratio [OR], 1.90 and 1.36, respectively), additive (OR, 1.24 and 1.14, respectively), and codominant (OR, 2.00 and 1.41, respectively) genetic models. SH2B1 rs7498665 interacted with lifestyle and behavioral factors as well as glucose levels. PA and moderate wine consumption (1 to 3 drinks/week) reduced obesity risk (OR, 0.35 and 0.71, respectively). Conversely, carriers of two risk alleles who reported high stress levels, had ≥median EHS, and who had a fasting glucose level ≥90 mg/dL had a significantly increased obesity risk (OR, 3.63 and 5.82, respectively).

Conclusion

Carrying SH2B1 rs7498665 significantly elevates the risk of obesity. Actionable lifestyle and behavioral factors significantly modulate the rs7498665 genetic predisposition to obesity; PA and moderate wine consumption attenuate the risk, while high stress, EHS, and fasting glucose level increase the obesity risk.

Comprehensive Genetic Analysis of Associations between Obesity-Related Parameters and Physical Activity: A Scoping Review

Genetic epidemiological studies have shown that numerous genetic variants cumulatively increase obesity risk. Although genetically predisposed individuals are more prone to developing obesity, it has been shown that physical activity can modify the genetic predisposition to obesity. Therefore, genetic data obtained from earlier studies, including 30 polymorphisms located in 18 genes, were analyzed using novel methods such as the total genetic score and Biofilter 2.4 software to combine genotypic and phenotypic information for nine obesity-related traits measured before and after the realization of the 12-week training program. The results revealed six genes whose genotypes were most important for post-training changes-LEP, LEPR, ADIPOQ, ADRA2A, ADRB3, and DRD2. Five noteworthy pairwise interactions, LEP × LEPR, ADRB2 × ADRB3, ADRA2A × ADRB3, ADRA2A × ADRB2, ADRA2A × DRD2, and three specific interactions demonstrating significant associations with key parameters crucial for health, total cholesterol (TC), high-density lipoprotein (HDL), and fat-free mass (FFM), were also identified. The molecular basis of training adaptation described in this study would have an enormous impact on the individualization of training programs, which, designed according to a given person's genetic profile, will be effective and safe intervention strategies for preventing obesity and improving health.

The interaction of genetics and physical activity in the pathogenesis of metabolic dysfunction associated liver disease

Genetic variants associated with increased liver fat and volume have been reported, but whether physical activity (PA) can attenuate the impact of genetic susceptibility to these traits is poorly understood. We aimed to investigate whether higher PA modify genetic impact on liver-related traits in the UK Biobank cohort. PA was self-reported, while magnetic resonance images were used to estimate liver fat (n = 27,243) and liver volume (n = 24,752). Metabolic dysfunction-associated liver disease (MASLD) and chronic liver disease (CLD) were diagnosed using ICD-9 and ICD-10 codes. Ten liver fat and eleven liver volume-associated genetic variants were selected and unweighted genetic-risk scores for liver fat (GRSLF) and liver volume (GRSLV) were computed. Linear regression analyses were performed to explore interactions between GRSLF/ GRSLV and PA in relation to liver-related traits. Association between GRSLF and liver fat was not different among lower (β = 0.063, 95% CI 0.041-0.084) versus higher PA individuals (β = 0.065, 95% CI 0.054-0.077, pinteraction = 0.62). The association between the GRSLV and liver volume was not different across different PA groups (pinteraction = 0.71). Similarly, PA did not modify the effect of GRSLF and GRSLV on MASLD or CLD. Our findings show that physical activity and genetic susceptibility to liver-related phenotypes seem to act independently, benefiting all individuals regardless of genetic risk.

A Novel Approach to Shaping the Lateral Abdomen: Simultaneous Application of High-Intensity Focused Electromagnetic (HIFEM) Therapy and Synchronized Radiofrequency at the Flanks: A Multicenter MRI Study

BACKGROUND

An accumulation of adipose tissue on the lateral abdomen (flanks) coupled with muscle deconditioning negatively affects core stability, muscular balance, and the intrinsic strength essential for maintaining optimal body mechanics and posture. This lateral fat accumulation and diminution of muscle result in an unfavorable abdominal profile and present challenges in finding appropriately fitting attire.

OBJECTIVES

The aim of this study was to explore the effectiveness and safety of the simultaneous application of high-intensity focused electromagnetic (HIFEM) therapy and synchronized radiofrequency for sculpting the lateral abdomen.

METHODS

All patients were scheduled to undergo four 30-minute treatments at approximately weekly intervals and then subsequent follow-up visits at 1 month and 3 months after the last treatment. The primary evaluation assessed changes in the oblique muscles, adipose tissue thickness, and cross-sectional area (CSA) by MRI performed at baseline and follow-ups. The secondary outcomes included digital photographs of the treated areas, a Subject Satisfaction Questionnaire, and a Therapy Comfort Questionnaire. Adverse events and side effects were monitored throughout the study duration.

RESULTS

The muscle tissue showed a substantial increase in thickness (+27.2%) and CSA (+29.0%). The adipose tissue measurements showed a decrease of -30.5% in CSA and -28.8% in thickness. As secondary outcomes, 81.8% of patients reported feeling more toned, and 84.9% of patients found the treatment comfortable and reported less than mild pain.

CONCLUSIONS

Based on the evaluation, the study suggests that the simultaneous application of HIFEM and synchronized radiofrequency is safe and effective for reducing adipose tissue and strengthening muscle in the area of the lateral abdomen.

Deciphering the Interplay between Genetic Risk Scores and Lifestyle Factors on Individual Obesity Predisposition

Obesity's variability is significantly influenced by the interplay between genetic and environmental factors. We aimed to integrate the combined impact of genetic risk score (GRSBMI) with physical activity (PA), sugar-sweetened beverages (SSB), wine intake, and eating habits score (EHS) on obesity predisposition risk. Adults' (n = 5824) data were analyzed for common obesity-related single nucleotide polymorphisms and lifestyle habits. The weighted GRSBMI was constructed and categorized into quartiles (Qs), and the adjusted multivariate logistic regression models examined the association of GRSBMI with obesity (BMI ≥ 30) and lifestyle factors. GRSBMI was significantly associated with obesity risk. Each GRSBMI unit was associated with an increase of 3.06 BMI units (p ≤ 0.0001). PA markedly reduced obesity risk across GRSBMI Qs. Inactive participants' (≥90 min/week) mean BMI was higher in GRSBMI Q3-Q4 compared to Q1 (p = 0.003 and p < 0.001, respectively). Scoring EHS ≥ median, SSBs (≥1 cup/day), and non-wine drinking were associated with higher BMI within all GRSBMI Qs compared to EHS < median, non-SSBs, and non-wine drinkers. Mean BMI was higher in GRSBMI Q4 compared to other quartiles (p < 0.0001) in non-wine drinkers and compared to Q1 for SSB's consumers (p = 0.07). A higher GRSBMI augmented the impact of lifestyle factors on obesity. The interplay between GRSBMI and modifiable lifestyle factors provides a tailored personalized prevention and treatment for obesity management.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity

Importance

Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity.

Objective

To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity.

Design, Setting, and Participants

In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis.

Exposure

Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared.

Main Outcome and Measures

Incident obesity (BMI ≥30).

Results

A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d.

Conclusions and Relevance

In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries

Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.

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.

Quantifying physical activity needed to mitigate genetic risk for obesity

Despite consistent public health recommendations, obesity rates continue to increase. Physical activity (e.g. daily steps) is a well-established modifier of body weight. Genetic background is an important, but typically uncaptured, contributor to obesity risk. Leveraging physical activity, clinical, and genetic data from the All of Us Research Program, we measured the impact of genetic risk of obesity on the level of physical activity needed to reduce incident obesity. For example, we show that an additional 3,310 steps per day (11,910 steps total) would be needed to mitigate a 25% higher than average genetic risk of obesity. We quantify the number of daily steps needed to mitigate obesity risk across the spectrum of genetic risk. This work quantifies the relationship between physical activity and genetic risk showing significant independent effects and provides a first step towards personalized activity recommendations that incorporate genetic information to reduce incident obesity risk.

Drinking Habits and Physical Activity Interact and Attenuate Obesity Predisposition of TMEM18 Polymorphisms Carriers

The transmembrane protein 18 (TMEM18) gene plays a central and peripheral role in weight regulation. TMEM18 genetic polymorphisms have been identified as an important risk factor for obesity, depending on ethnic population and age. This research aimed to study the association of common TMEM18 polymorphisms with obesity and their interactions with modifiable factors, namely drinking habits (sugar-sweetened beverages (SSBs), flavored water and wine) and physical activity (PA) in the Israeli population. Adults (n = 3089) were analyzed for common TMEM18 polymorphisms and lifestyle and nutrition habits were obtained from questionnaires using adjusted (age, sex) binary logistic regression models. TMEM18 rs939583 and rs1879523 were significantly associated with increased obesity risk (OR = 1.35, 95% CI (1.17−1.57) and OR = 1.66, 95% CI (1.29−2.15), respectively). TMEM18 rs939583 interacted with consumption of 1−3 weekly glasses of wine and PA to attenuate obesity risk (OR = 0.82 95% CI (0.74−0.9; p < 0.001) and OR = 0.74 95% CI (0.68−0.8), respectively), while physical inactivity, SSBs and flavored water consumption significantly enhanced obesity risk (OR = 1.54 95% CI (1.41−1.67), OR = 1.31 95% CI (1.14−1.51) and OR = 1.35 95% CI (1.13−1.62), respectively). PA duration was significantly associated with a lower BMI for rs939583 risk carriers, with a PA cutoff of >30 min/week (p = 0.005) and >90 min/week (p = 0.01). Common TMEM18 SNPs were significantly linked with adult obesity risk and interacted with modifiable lifestyle factors.

Comparison of the Concordance of Cardiometabolic Diseases and Physical and Laboratory Examination Findings between Monozygotic and Dizygotic Korean Adult Twins: A Cross-Sectional Study Using KoGES HTS Data

This study investigated the contribution of genetic and environmental factors to cardiometabolic diseases (CMDs) by comparing disease concordance in monozygotic and dizygotic twins. This cross-sectional study analyzed 1294 (1040 monozygotic and 254 dizygotic) twin pairs (>20 years) based on the Korean Genome and Epidemiology Study data (2005−2014). The odds ratios of disease concordance were calculated using binomial and multinomial logistic regression models. The occurrence of CMDs (hypertension, hyperlipidemia, type 2 diabetes, cerebral stroke, transient ischemic attack, and ischemic heart disease) and related physical and laboratory levels did not differ between the monozygotic and dizygotic twin groups. The odds for concordance of the presence/absence of CMDs and the likelihood of incident CMD within monozygotic twins were comparable to that of dizygotic twins. The absolute differences in hemoglobin A1c, insulin, low- and high-density lipoprotein cholesterol, total cholesterol, triglycerides, and systolic blood pressure were lower in monozygotic twins than in dizygotic twins. Absolute differences in fasting glucose and diastolic blood pressure did not differ between groups. Although baseline levels of several laboratory parameters related to CMD showed a strong likelihood of heritability in monozygotic twins, CMD phenotype appears to be largely affected by environmental factors.

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.

A quantile integral linear model to quantify genetic effects on phenotypic variability

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

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.

Physical Activity and Subsequent Change in Body Weight, Composition and Shape: Effect Modification by Familial Overweight

BACKGROUND

Physical activity (PA) has been shown to attenuate the genetic risk of obesity as measured using polygenic risk scores. However, familial obesity history might be an easier predictor. We examined associations between PA and subsequent changes in BMI, body fat percentage (BF%) and waist circumference (WC) among participants with and without adiposity and a familial overweight.

METHODS

In total, 1971 participants from the Danish MONICA cohort were included. Mean differences for 6-year changes in BMI, BF% and WC across PA levels were estimated. Association between walking and biking and subsequent change in adiposity were analysed. Effect modification by familial obesity was assessed by adding product terms to the models.

RESULTS

We observed weak associations between leisure PA level and changes in WC [participants with low PA: 3.4 cm (95%CI: 2.8;4.0), participants with high PA: 2.4 cm (95%CI: 1.8;3.0)], with no evidence of effect modification by familial obesity. We found effect modification in analyses on walking and biking in relation to changes in BMI (P-interaction<0.01) and BF% (P-interaction=0.04), suggesting lower gain with more hours of activity among participants with adiposity and familial overweight.

CONCLUSIONS

The results were modest but suggested that PA, especially walking and biking, may prevent future adiposity.

A polygenic-score-based approach for identification of gene-drug interactions stratifying breast cancer risk

An individual's genetics can dramatically influence breast cancer (BC) risk. Although clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly used medications are a promising set of modifiable factors, but no previous study has explored whether a range of widely taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGSs) that summarize BC genetic risk and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with an HR = 3.41 per-allele within corticosteroid users). In comparison, there are no differences in BC risk within the reference allele homozygotes. Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.

MD, Eckel-Passow J, Acosta A. Precision Medicine for Obesity

Obesity is a multifactorial disease with a variable and underwhelming weight loss response to current treatment approaches. Precision medicine proposes a new paradigm to improve disease classification based on the premise of human heterogeneity, with the ultimate goal of maximizing treatment effectiveness, tolerability, and safety. Recent advances in high-throughput biochemical assays have contributed to the partial characterization of obesity's pathophysiology, as well as to the understanding of the role that intrinsic and environmental factors, and their interaction, play in its development and progression. These data have led to the development of biological markers that either are being or will be incorporated into strategies to develop personalized lines of treatment for obesity. There are currently many ongoing initiatives aimed at this; however, much needs to be resolved before precision obesity medicine becomes common practice. This review aims to provide a perspective on the currently available data of high-throughput technologies to treat obesity.

Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative

Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.

The composite alliance of FTO locus with obesity-related genetic variants

Obesity has become a genuine global pandemic due to lifestyle and environmental modifications, and is associated with chronic lethal comorbidities. Various environmental factors such as lack of physical activity due to modernization and higher intake of energy-rich diets are primary obesogenic factors in pathogenesis of obesity. Genome-wide association study has identified the crucial role of FTO (fat mass and obesity) in human obesity. A bunch of SNPs in the first intron of FTO has been identified and subsequently correlated to body mass index and body composition. Findings of in silico, in vitro, and in vivo studies have manifested the robust role of FTO in regulation of energy expenditure and food consumption. Numerous studies have highlighted the mechanistic pathways behind the concomitant functions of FTO in adipogenesis and body size. Current investigation has also revealed the link of FTO neighbouring genes i.e., RPGRIP1L, IRX3 and IRX5 and epigenetic factors with obesity phenotypes. The motive behind this review is to cite the consequences of FTO on obesity vulnerability.

Joint association between education and polygenic risk score for incident coronary heart disease events: a longitudinal population-based study of 26 203 men and women

BACKGROUND

Genetic vulnerability to coronary heart disease (CHD) is well established, but little is known whether these effects are mediated or modified by equally well-established social determinants of CHD. We estimate the joint associations of the polygenetic risk score (PRS) for CHD and education on CHD events.

METHODS

The data are from the 1992, 1997, 2002, 2007 and 2012 surveys of the population-based FINRISK Study including measures of social, behavioural and metabolic factors and genome-wide genotypes (N=26 203). Follow-up of fatal and non-fatal incident CHD events (N=2063) was based on nationwide registers.

RESULTS

Allowing for age, sex, study year, region of residence, study batch and principal components, those in the highest quartile of PRS for CHD had strongly increased risk of CHD events compared with the lowest quartile (HR=2.26; 95% CI: 1.97 to 2.59); associations were also observed for low education (HR=1.58; 95% CI: 1.32 to 1.89). These effects were largely independent of each other. Adjustment for baseline smoking, alcohol use, body mass index, igh-density lipoprotein (HDL) and total cholesterol, blood pressure and diabetes attenuated the PRS associations by 10% and the education associations by 50%. We do not find strong evidence of interactions between PRS and education.

CONCLUSIONS

PRS and education predict CHD events, and these associations are independent of each other. Both can improve CHD prediction beyond behavioural risks. The results imply that observational studies that do not have information on genetic risk factors for CHD do not provide confounded estimates for the association between education and CHD.

Effect of interaction between obesity-promoting genetic variants and behavioral factors on the risk of obese phenotypes

The studies investigating gene-gene and gene-environment (or gene-behavior) interactions provide valuable insight into the pathomechanisms underlying obese phenotypes. The Pakistani population due to its unique characteristics offers numerous advantages for conducting such studies. In this view, the current study was undertaken to examine the effects of gene-gene and gene-environment/behavior interactions on the risk of obesity in a sample of Pakistani population. A total of 578 adult participants including 290 overweight/obese cases and 288 normal-weight controls were involved. The five key obesity-associated genetic variants namely MC4R rs17782313, BDNF rs6265, FTO rs1421085, TMEM18 rs7561317, and NEGR1 rs2815752 were genotyped using the TaqMan allelic discrimination assays. The data related to behavioral factors, such as eating pattern, diet consciousness, the tendency toward fat-dense food (TFDF), sleep duration, sleep-wake cycle (SWC), shift work (SW), and physical activity levels were collected via a questionnaire. Gene-gene and gene-behavior interactions were analyzed by multifactor dimensionality reduction and linear regression, respectively. In our study, only TMEM18 rs7561317 was found to be significantly associated with anthropometric traits with no significant effect of gene-gene interactions were observed on obesity-related phenotypes. However, the genetic variants were found to interact with the behavioral factors to significantly influence various obesity-related anthropometric traits including BMI, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and percentage of body fat. In conclusion, the interaction between genetic architecture and behavior/environment determines the outcome of obesity-related anthropometric phenotypes. Thus, gene-environment/behavior interaction studies should be promoted to explore the risk of complex and multifactorial disorders, such as obesity.

Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin

Rationale

Genome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene–environment interactions, but studies are few.

Methods

We analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case–control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV₁, FVC and FEV₁/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10⁻⁹) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r²=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions.

Results

Each trait was highly significantly associated with its GRS (all three p values<8.9×10⁻⁸). The inverse association of the GRS with FEV₁/FVC was stronger for current smokers (p_interaction=0.017) or former smokers (p_interaction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (p_interaction=0.053). No significant interactions were observed between any GRS and house dust endotoxin.

Conclusions

Evaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.

Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity

Obesity is routinely considered as a single disease state, which drives a "one-size-fits-all" approach to treatment. We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from pre-clinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively, we propose that it is critical to think of obesity as a heterogeneous disease with complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual.

Evidence for shared genetics between physical activity, sedentary behaviour and adiposity-related traits

Observational, cross-sectional and longitudinal studies showed that physical activity and sedentary behaviour are associated with adiposity-related traits, apparently in a bidirectional manner. Physical activity is also suggested to suppress the genetic risk of adiposity. Since phenotypic associations with genetic variants are not subject to reverse causation or confounding, they may be used as tools to shed light on cause and effect in this complex interdependency. We review the evidence for shared genetics of physical activity and adiposity-related traits and for gene-by-physical activity interactions on adiposity-related traits in human studies. We outline limitations, challenges and opportunities in studying and understanding of these relationships. In summary, physical activity and sedentary behaviour are genetically correlated with body mass index and fat percentage but may not be correlated with lean body mass. Mendelian randomisation analyses show that physical activity and sedentary behaviour have bidirectional relationships with adiposity. Several studies suggest that physical activity suppresses genetic risk of adiposity. No studies have yet tested whether adiposity enhances genetic predisposition to sedentariness. The complexity of the comprehensive causal model makes the assessment of the single or combined components challenging. Substantial progress in this field may need long-term intervention studies.

Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10^-6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10^-6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10^-9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10^-10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene-environment interaction affecting disease.

Genetic test for the prescription of diets in support of physical activity

Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.

Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity

BACKGROUND/OBJECTIVES

Quantile-dependent expressivity occurs when a gene's phenotypic expression depends upon whether the trait (e.g., BMI) is high or low relative to its distribution. We have previously shown that the obesity effects of a genetic risk score (GRSBMI) increased significantly with increasing quantiles of BMI. However, BMI is an inexact adiposity measure and GRSBMI explains <3% of the BMI variance. The purpose of this paper is to test BMI for quantile-dependent expressivity using a more inclusive genetic measure (h2, heritability in the narrow sense), extend the result to other adiposity measures, and demonstrate its consistency with purported gene-environment interactions.

SUBJECTS/METHODS

Quantile-specific offspring-parent regression slopes (βOP) were obtained from quantile regression for height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry (DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures. Heritability was estimated by 2βOP/(1 + rspouse) in 6227 offspring-parent pairs from the Framingham Heart Study, where rspouse is the spouse correlation.

RESULTS

Compared to h2 at the 10th percentile, genetic heritability was significantly greater at the 90th population percentile for BMI (3.14-fold greater, P < 10-15), waist girth/ht (3.27-fold, P < 10-15), hip girth/ht (3.12-fold, P = 6.3 × 10-14), waist-to-hip ratio (1.75-fold, P = 0.01), sagittal diameter/ht (3.89-fold, P = 3.7 × 10-7), DXA total fat/ht2 (3.62-fold, P = 0.0002), DXA leg fat/ht2 (3.29-fold, P = 2.0 × 10-11), DXA arm fat/ht2 (4.02-fold, P = 0.001), CT-visceral fat/ht2 (3.03-fold, P = 0.002), and CT-subcutaneous fat/ht2 (3.54-fold, P = 0.0004). External validity was suggested by the phenomenon's consistency with numerous published reports. Quantile-dependent expressivity potentially explains precision medicine markers for weight gain from overfeeding or antipsychotic medications, and the modifying effects of physical activity, sleep, diet, polycystic ovary syndrome, socioeconomic status, and depression on gene-BMI relationships.

CONCLUSIONS

Genetic heritabilities of anthropometric, CT, and DXA adiposity measures increase with increasing adiposity. Some gene-environment interactions may arise from analyzing subjects by characteristics that distinguish high vs. low adiposity rather than the effects of environmental stimuli on transcriptional and epigenetic processes.

Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model

The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (-log10p>7.3).

Polygenic approaches to detect gene-environment interactions when external information is unavailable

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Environment and Gene Association With Obesity and Their Impact on Neurodegenerative and Neurodevelopmental Diseases

Obesity is a multifactorial disease in which environmental conditions and several genes play an important role in the development of this disease. Obesity is associated with neurodegenerative diseases (Alzheimer, Parkinson, and Huntington diseases) and with neurodevelopmental diseases (autism disorder, schizophrenia, and fragile X syndrome). Some of the environmental conditions that lead to obesity are physical activity, alcohol consumption, socioeconomic status, parent feeding behavior, and diet. Interestingly, some of these environmental conditions are shared with neurodegenerative and neurodevelopmental diseases. Obesity impairs neurodevelopment abilities as memory and fine-motor skills. Moreover, maternal obesity affects the cognitive function and mental health of the offspring. The common biological mechanisms involved in obesity and neurodegenerative/neurodevelopmental diseases are insulin resistance, pro-inflammatory cytokines, and oxidative damage, among others, leading to impaired brain development or cell death. Obesogenic environmental conditions are not the only factors that influence neurodegenerative and neurodevelopmental diseases. In fact, several genes implicated in the leptin-melanocortin pathway (LEP, LEPR, POMC, BDNF, MC4R, PCSK1, SIM1, BDNF, TrkB, etc.) are associated with obesity and neurodegenerative and neurodevelopmental diseases. Moreover, in the last decades, the discovery of new genes associated with obesity (FTO, NRXN3, NPC1, NEGR1, MTCH2, GNPDA2, among others) and with neurodegenerative or neurodevelopmental diseases (APOE, CD38, SIRT1, TNFα, PAI-1, TREM2, SYT4, FMR1, TET3, among others) had opened new pathways to comprehend the common mechanisms involved in these diseases. In conclusion, the obesogenic environmental conditions, the genes, and the interaction gene-environment would lead to a better understanding of the etiology of these diseases.

Gene-Environment Interplay Between Physical Exercise and Fitness and Depression Symptomatology

Studies often report beneficial effects of physical exercise on depression symptomatology, both in clinical and community samples. In clinical samples, effects are observed using physical exercise as primary treatment and supplement to antidepressant medications and/or psychotherapies. Magnitudes vary with sample characteristics, exercise measure, and study rigor. Both propensity to exercise and vulnerability to depression show genetic influences, suggesting gene–environment interplay. We investigated this in a Danish Twin Registry-based community sample who completed a cycle fitness test and detailed assessments of depression symptomatology and regular exercise engagement that enabled estimates of typical total, intentional exercise-specific, and other metabolic equivalent (MET) expenditures. All exercise-related measures correlated negatively with depression symptomatology (− .07 to − .19). Genetic variance was lower at higher levels of cycle fitness, with genetic and shared environmental correlations of −  .50 and 1.0, respectively. Nonshared environmental variance in depression was lower at higher levels of total MET, with no indications of genetic or environmental covariance. Being physically active and/or fit tended to prevent depression, apparently because fewer participants with higher levels of activity and fitness reported high depression symptomatology. This was driven by nonshared environmental influences on activity but genetic influences on physical fitness. Genetic correlation suggested people less genetically inclined toward physical fitness may also be genetically vulnerable to depression, possibly because inertia impedes activity but also possibly due to social pressures to be fit. Exercise programs for general well-being should emphasize participation, not performance level or fitness. We discuss possible interrelations between fitness aptitude and metabolism.

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Some candidate genes have been robustly reported to be associated with complex traits, such as the fat mass and obesity-associated (FTO) gene on body mass index (BMI), and the fibroblast growth factor 5 (FGF5) gene on blood pressure levels. It is of interest to know whether an environmental factor (E) can attenuate or exacerbate the adverse influence of a candidate gene. To this end, we here evaluate the performance of “genetic risk score” (GRS) approaches to detect “gene-environment interactions” (G × E). In the first stage, a GRS is calculated according to the genotypes of variants in a candidate gene. In the second stage, we test whether E can significantly modify this GRS effect. This two-stage procedure can not only provide a p-value for a G × E test but also guide inferences on how E modifies the adverse effect of a gene. With systematic simulations, we compared several ways to construct a GRS. If E exacerbates the adverse influence of a gene, GRS formed by the elastic net (ENET) or the least absolute shrinkage and selection operator (LASSO) is recommended. However, the performance of ENET or LASSO will be compromised if E attenuates the adverse influence of a gene, and using the ridge regression (RIDGE) can be more powerful in this situation. Applying RIDGE to 18,424 subjects in the Taiwan Biobank, we showed that performing regular exercise can attenuate the adverse influence of the FTO gene on four obesity measures: BMI (p = 0.0009), body fat percentage (p = 0.0031), waist circumference (p = 0.0052), and hip circumference (p = 0.0001). As another example, we used RIDGE and found the FGF5 gene has a stronger effect on blood pressure in Han Chinese with a higher waist-to-hip ratio [p = 0.0013 for diastolic blood pressure (DBP) and p = 0.0027 for systolic blood pressure (SBP)]. This study provides an evaluation on the GRS approaches, which is important to infer whether E attenuates or exacerbates the adverse influence of a candidate gene.

The obesity epidemic - Nature via nurture: A narrative review of high-income countries

Over the last three decades, the prevalence of obesity has increased rapidly in populations around the world. Despite a wealth of research, the relative contributions of the different mechanisms underlying this global epidemic are not fully understood. While there is growing consensus that the rapid rise in obesity prevalence has been driven by changes to the environment, it is evident that biology plays a central role in determining who develops obesity and who remains lean in the current obesogenic environment. This review summarises evidence on the extent to which genes and the environment influence energy intake and energy expenditure, and as a result, contribute to the ongoing global obesity epidemic. The concept of genetic susceptibility to the environment driving human variation in body weight is discussed.

LMX1B rs10733682 Polymorphism Interacts with Macronutrients, Dietary Patterns on the Risk of Obesity in Han Chinese Girls

Previous studies have found that LMX1B rs10733682 polymorphism is associated with Body Mass Index (BMI) in European and American Indian adults. In this study, the association of rs10733682 polymorphism with obesity-related indicators, and its interaction with macronutrients and dietary patterns (DPs) were explored in Chinese children (n = 798). The rs10733682 polymorphism was genotyped by improved Multiple Ligase Detection Reaction (iMLDR). Four DPs were identified by factor analysis. The AA genotype had a higher incidence of overweight/obesity than GG+GA genotypes (P = 0.010) in girls (n = 398), but no difference in boys. The AA genotype in girls could interact with intake of energy, fat and carbohydrate, causing an increased triglyceride (TG), (P = 0.021, 0.003, 0.002, respectively), and also could interact with energy from protein, causing an elevated BMI (P = 0.023) and waist (P = 0.019). Girls inclining to the HED (high-energy density)-DP were associated with increased TG (P = 0.033), and girls inclining to the VEF (vegetables, eggs, and fishes based)-DP were associated with decreased total cholesterol (TC, P = 0.045) and decreased low density lipoprotein cholesterin (LDL, P = 0.016). The findings indicated that the AA genotype of rs10733682 and the HED-DP are potential risk factors of obesity in Chinese girls.

Emotional eating and obesity in adults: the role of depression, sleep and genes

Stress and other negative emotions, such as depression and anxiety, can lead to both decreased and increased food intake. The term 'emotional eating' has been widely used to refer to the latter response: a tendency to eat in response to negative emotions with the chosen foods being primarily energy-dense and palatable ones. Emotional eating can be caused by various mechanisms, such as using eating to cope with negative emotions or confusing internal states of hunger and satiety with physiological changes related to emotions. An increasing number of prospective studies have shown that emotional eating predicts subsequent weight gain in adults. This review discusses particularly three lines of research on emotional eating and obesity in adults. First, studies implying that emotional eating may be one behavioural mechanism linking depression and development of obesity. Secondly, studies highlighting the relevance of night sleep duration by showing that adults with a combination of shorter sleep and higher emotional eating may be especially vulnerable to weight gain. Thirdly, an emerging literature suggesting that genes may influence body weight partly through emotional eating and other eating behaviour dimensions. The review concludes by discussing what kind of implications these three avenues of research offer for obesity prevention and treatment interventions.

Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population

INTRODUCTION

The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly.

OBJECTIVE

Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates.

METHODS

2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1.

RESULTS

We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes.

DISCUSSION/CONCLUSION

The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.

Methodological challenges in constructing DNA methylation risk scores

Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.

Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents

Common genetic variants predispose to obesity with varying contribution by age. We incorporated known genetic variants into genetic risk scores (GRSs) and investigated their associations with overweight/obesity and central obesity in preadolescents. Furthermore, we compared GRSs with lifestyle factors, and tested if they predict the change in body size and shape in a 4-year follow-up. We utilized 1142 subjects from the Finnish Health in Teens (Fin-HIT) cohort. Overweight and obesity were defined with age- and gender-specific body mass index (BMI) z-score (BMIz), while central obesity by the waist-to-height ratio (WHtR). Background data on parental language, eating habits, leisure-time physical activity (LTPA) and sleep duration were included. Genotyping was performed with the Metabochip platform. Weighted, standardized GRSs were derived. Of the11-year-old children, 25.5% were at least overweight and 90.8% had Finnish speaking background. BMI-GRS was associated with higher risk for overweight with odds ratio (95% confidence interval) of 1.39 (1.20; 1.60) and obesity 1.41 (1.08; 1.83), but not with central obesity. BMI-GRS was weakly and inversely associated with the changes in BMIz and WHtR in the 4-year follow-up. Waist-to-hip ratio-GRS was not related to any obesity measures at baseline nor in the follow-up. The effect of BMI-GRS is similar to that of low LTPA on overweight. An interaction between parental language and BMI-GRS was noted (P = .019): BMI-GRS associated more strongly with overweight in Swedish than in Finnish speakers. We further identified two suggestive genetic variants near LOC101926977 and LOC105369677 associated with BMIz in preadolescents which were replicated in the adult population. In preadolescents, known genetic predisposing factors induce a risk for overweight comparable to low LTPA. However, the GRS was poor in predicting short-term changes in BMI or WHtR.

Precision behavioral medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment

This article reviews the concept of precision behavioral medicine and the progress toward applying genetics and genomics as tools to optimize weight management intervention. We discuss genetic, epigenetic, and genomic markers, as well as interactions between genetics and the environment as they relate to obesity and behavioral weight loss to date. Recommendations for the conditions under which genetics and genomics could be incorporated to support clinical decision-making in behavioral weight loss are outlined and illustrative scenarios of how this approach could improve clinical outcomes are provided. It is concluded that there is not yet sufficient evidence to leverage genetics or genomics to aid the treatment of obesity but the foundations are being laid. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A genetic sum score of risk alleles associated with body mass index interacts with socioeconomic position in the Heinz Nixdorf Recall Study

Body mass index (BMI) is influenced by genetic, behavioral and environmental factors, while interactions between genetic and socioeconomic factors have been suggested. Aim of the study was to investigate whether socioeconomic position (SEP) interacts with a BMI-related genetic sum score (GRS_BMI) to affect BMI in a population-based cohort. SEP-related health behaviors and a GRS associated with educational attainment (GRS_Edu) were included in the analysis to explore potential interactions underlying the GRS_BMIxSEP effect. Baseline information on SEP indicators (education, income), BMI, smoking, physical activity, alcohol consumption and genetic risk factors were available for 4,493 participants of the Heinz Nixdorf Recall Study. Interaction analysis was based on linear regression as well as on stratified analyses. In SEP-stratified analyses, the highest genetic effects were observed in the lowest educational group with a 0.24 kg/m² higher BMI (95%CI: 0.16; 0.31) and in the lowest income quartile with a 0.14 kg/m² higher BMI (95%CI: 0.09; 0.18) per additional risk allele. Indication for a GRS_BMIxSEP interaction was observed for education (ß_{GRSbmixeducation} = -0.02 [95%CI:-0.03; -0.01]) and income (ß_{GRSbmixincome} = -0.05 [95%CI: -0.08; -0.02]). When adjusting for interactions with the GRS_Edu and SEP-related health behaviors, effect size estimates of the GRS_BMIxSEP interaction remained virtually unchanged. Results gave indication for an interaction of BMI-related genetic risk factors with SEP indicators, showing substantially stronger genetic effects in low SEP groups. This supports the hypothesis that expression of genetic risks is higher in socioeconomically disadvantaged environments. No indication was observed that the GRS_BMIxSEP interaction was affected by other SEP-related factors included in the analysis.

RNAs and RNA-Binding Proteins in Immuno-Metabolic Homeostasis and Diseases

The increasing prevalence of worldwide obesity has emerged as a major risk factor for type 2 diabetes (T2D), hepatosteatosis, and cardiovascular disease. Accumulating evidence indicates that obesity has strong inflammatory underpinnings tightly linked to the development of metabolic diseases. However, the molecular mechanisms by which obesity induces aberrant inflammation associated with metabolic diseases are not yet clearly defined. Recently, RNAs have emerged as important regulators of stress responses and metabolism. RNAs are subject to changes in modification status, higher-order structure, and cellular localization; all of which could affect the affinity for RNA-binding proteins (RBPs) and thereby modify the RNA-RBP networks. Proper regulation and management of RNA characteristics are fundamental to cellular and organismal homeostasis, as well as paramount to health. Identification of multiple single nucleotide polymorphisms (SNPs) within loci of fat mass- and obesity-associated protein (FTO) gene, an RNA demethylase, through genome-wide association studies (GWAS) of T2D, and functional assessments of FTO in mice, support the concept that disruption in RNA modifications leads to the development of human diseases including obesity and metabolic disorder. In obesity, dynamic alterations in modification and localization of RNAs appear to modulate the RNA-RBP networks and activate proinflammatory RBPs, such as double-stranded RNA (dsRNA)-dependent protein kinase (PKR), Toll-like receptor (TLR) 3 and TLR7, and RNA silencing machinery. These changes induce aberrant inflammation and the development of metabolic diseases. This review will describe the current understanding of the underlying causes of these common and altered characteristics of RNA-RBP networks which will pave the way for developing novel approaches to tackle the pandemic issue of obesity.

Gene-by-environment interactions in Alzheimer's disease and Parkinson's disease

Diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) arise from complex interactions of genetic and environmental factors, with genetic variants regulating individual responses to environmental exposures (i.e. gene-by-environment interactions). Identifying gene-by-environment interactions will be critical to fully understanding disease mechanisms and developing personalized therapeutics, though these interactions are still poorly understood and largely under-studied. Candidate gene approaches have shown that known disease risk variants often regulate response to environmental factors. However, recent improvements in exposome- and genome-wide association and interaction studies in humans and mice are enabling discovery of novel genetic variants and pathways that predict response to a variety of environmental factors. Here, we highlight recent approaches and ongoing developments in human and rodent studies to identify genetic modulators of environmental factors using AD and PD as exemplars. Identifying gene-by-environment interactions in disease will be critical to developing personalized intervention strategies and will pave the way for precision medicine.

Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants

Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30–70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10^-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity.

The complex interplay of genetics and lifestyle makes obesity a challenging issue. Previous studies have found performing regular physical exercise could blunt the genetic effects on body mass index (BMI). However, BMI does not take into account lean body mass or identify central obesity. Moreover, it remains unclear what kinds of exercise could more effectively attenuate the genetic effects on obesity measures. With a sample of 18,424 unrelated Han Chinese adults, we comprehensively investigated gene-exercise interactions on 5 obesity measures: BMI, body fat percentage, waist circumference, hip circumference, and waist-to-hip ratio. Moreover, we tested whether the genetic effects on obesity measures could be modified by any of 18 kinds of self-reported regular exercise. Because no large genome-wide association studies on obesity have been done for Han Chinese, we constructed genetic risk scores with internal weights for analyses. Among these exercises, regular jogging consistently presented the strongest evidence to mitigate the genetic effects on all 5 obesity measures. Moreover, mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga attenuated the genetic effects on BMI. The benefits of regularly performing these 6 kinds of exercise are more impactful in subjects who are more predisposed to obesity.

Gene-Environment Interactions on Body Fat Distribution

The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.