Gene-environment interactions in cardiovascular disease

Advantageous early-life environments cushion the genetic risk for ischemic heart disease

In one of the first papers on the impact of early-life conditions on individuals' health in older age, Barker and Osmond [Lancet, 327, 1077-1081 (1986)] show a strong positive relationship between infant mortality rates in the 1920s and ischemic heart disease in the 1970s. We merge historical data on infant mortality rates to 370,000 individual records in the UK Biobank using information on local area and year of birth. We replicate the association between the early-life infant mortality rate and later-life ischemic heart disease in our sample. We then go "beyond Barker," by showing considerable genetic heterogeneity in this association that is robust to within-area as well as within-family analyses. We find no association between the polygenic index and heart disease in areas with the lowest infant mortality rates, but a strong positive relationship in areas characterized by high infant mortality. These findings suggest that advantageous environments can cushion one's genetic disease risk.

A Gene-Acculturation Study of Obesity Among US Hispanic/Latinos: The Hispanic Community Health Study/Study of Latinos

OBJECTIVE

In the United States, Hispanic/Latino adults face a high burden of obesity; yet, not all individuals are equally affected, partly due in part to this ethnic group's marked sociocultural diversity. We sought to analyze the modification of body mass index (BMI) genetic effects in Hispanic/Latino adults by their level of acculturation, a complex biosocial phenomenon that remains understudied.

METHODS

Among 11,747 Hispanic/Latinos adults in the Hispanic Community Health Study/Study of Latinos aged 18 to 76 years from four urban communities (2008-2011), we a) tested our hypothesis that the effect of a genetic risk score (GRS) for increased BMI may be exacerbated by higher levels of acculturation and b) examined if GRS acculturation interactions varied by gender or Hispanic/Latino background group. All genetic modeling controlled for relatedness, age, gender, principal components of ancestry, center, and complex study design within a generalized estimated equation framework.

RESULTS

We observed a GRS increase of 0.34 kg/m 2 per risk allele in weighted mean BMI. The estimated main effect of GRS on BMI varied both across acculturation level and across gender. The difference between high and low acculturation ranged from 0.03 to 0.23 kg/m 2 per risk allele, but varied across acculturation measure and gender.

CONCLUSIONS

These results suggest the presence of effect modification by acculturation, with stronger effects on BMI among highly acculturated individuals and female immigrants. Future studies of obesity in the Hispanic/Latino community should account for sociocultural environments and consider their intersection with gender to better target obesity interventions.

Twelve Variants Polygenic Score for Low-Density Lipoprotein Cholesterol Distribution in a Large Cohort of Patients With Clinically Diagnosed Familial Hypercholesterolemia With or Without Causative Mutations

Background

A significant proportion of individuals clinically diagnosed with familial hypercholesterolemia (FH), but without any disease‐causing mutation, are likely to have polygenic hypercholesterolemia. We evaluated the distribution of a polygenic risk score, consisting of 12 low‐density lipoprotein cholesterol (LDL‐C)‐raising variants (polygenic LDL‐C risk score), in subjects with a clinical diagnosis of FH.

Methods and Results

Within the Lipid Transport Disorders Italian Genetic Network (LIPIGEN) study, 875 patients who were FH‐mutation positive (women, 54.75%; mean age, 42.47±15.00 years) and 644 patients who were FH‐mutation negative (women, 54.21%; mean age, 49.73±13.54 years) were evaluated. Patients who were FH‐mutation negative had lower mean levels of pretreatment LDL‐C than patients who were FH‐mutation positive (217.14±55.49 versus 270.52±68.59 mg/dL, P<0.0001). The mean value (±SD) of the polygenic LDL‐C risk score was 1.00 (±0.18) in patients who were FH‐mutation negative and 0.94 (±0.20) in patients who were FH‐mutation positive (P<0.0001). In the receiver operating characteristic analysis, the area under the curve for recognizing subjects characterized by polygenic hypercholesterolemia was 0.59 (95% CI, 0.56–0.62), with sensitivity and specificity being 78% and 36%, respectively, at 0.905 as a cutoff value. Higher mean polygenic LDL‐C risk score levels were observed among patients who were FH‐mutation negative having pretreatment LDL‐C levels in the range of 150 to 350 mg/dL (150–249 mg/dL: 1.01 versus 0.91, P<0.0001; 250–349 mg/dL: 1.02 versus 0.95, P=0.0001). A positive correlation between polygenic LDL‐C risk score and pretreatment LDL‐C levels was observed among patients with FH independently of the presence of causative mutations.

Conclusions

This analysis confirms the role of polymorphisms in modulating LDL‐C levels, even in patients with genetically confirmed FH. More data are needed to support the use of the polygenic score in routine clinical practice.

Social Determinants of Cardiovascular Disease

Social determinants of health (SDoH), which encompass the economic, social, environmental, and psychosocial factors that influence health, play a significant role in the development of cardiovascular disease (CVD) risk factors as well as CVD morbidity and mortality. The COVID-19 pandemic and the current social justice movement sparked by the death of George Floyd have laid bare long-existing health inequities in our society driven by SDoH. Despite a recent focus on these structural drivers of health disparities, the impact of SDoH on cardiovascular health and CVD outcomes remains understudied and incompletely understood. To further investigate the mechanisms connecting SDoH and CVD, and ultimately design targeted and effective interventions, it is important to foster interdisciplinary efforts that incorporate translational, epidemiological, and clinical research in examining SDoH-CVD relationships. This review aims to facilitate research coordination and intervention development by providing an evidence-based framework for SDoH rooted in the lived experiences of marginalized populations. Our framework highlights critical structural/socioeconomic, environmental, and psychosocial factors most strongly associated with CVD and explores several of the underlying biologic mechanisms connecting SDoH to CVD pathogenesis, including excess stress hormones, inflammation, immune cell function, and cellular aging. We present landmark studies and recent findings about SDoH in our framework, with careful consideration of the constructs and measures utilized. Finally, we provide a roadmap for future SDoH research focused on individual, clinical, and policy approaches directed towards developing multilevel community-engaged interventions to promote cardiovascular health.

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.

Global DNA Methylation in Dental Implant Failure Due to Peri-Implantitis: An Exploratory Clinical Pilot Study

Background: Peri-implantitis (PIT) is highly prevalent in patients with dental implants and is a challenging condition to treat due to the limited outcomes reported for non-surgical and surgical therapies. Therefore, epigenetic therapeutics might be of key importance to treat PIT. However, developing epigenetic therapeutics is based on understanding the relationship between epigenetics and disease. To date, there is still scarce knowledge about the relationship between epigenetic modifications and PIT, which warrants further investigations. Aim: The purpose of this study was to evaluate the level of global DNA methylation associated with implant failure (IF) due to PIT compared to periodontally healthy (PH) patients. Material and Methods: A total of 20 participants were initially enrolled in this pilot, exploratory, single-blinded, cross-sectional clinical human study in two groups: 10 in the PH group and 10 in the IF group. In the participants who have completed the study, gingival tissue and bone samples were harvested from each participant and were used to perform global DNA methylation analysis. The percentage of global DNA methylation (5-mC%) was compared (1) between groups (PH and IF); (2) between the subgroups of gingival tissue and bone separately; (3) in the whole sample, comparing gingival tissue and bone; (4) within groups, comparing gingival tissue and bone. Demographic, periodontal, and peri-implant measurements as well as periodontal staging, were also recorded. All statistical comparisons were made at the 0.05 significance level. Results: Out of the initially enrolled 20 patients, only 19 completed the study and, thus, were included in the final analysis; 10 patients in the PH group and 9 patients in the IF group, contributing to a total of 38 samples. One patient from the IF group was excluded from the study due to systemic disease. The mean implant survival time was 10.8 years (2.17–15.25 years). Intergroup comparison, stratified by group, indicated a similar 5-mC% between the PH and IF groups in both gingival tissue and bone (p = 0.599), only in bone (p = 0.414), and only in gingival tissue (p = 0.744). Intragroup comparison, stratified by the type of sample, indicated a significantly higher 5-mC% in gingival tissue samples compared to bone in both the PH and IF groups (p = 0.001), in the PH group (p = 0.019), and in the IF group (p = 0.009). Conclusions: Within the limitations of this study, higher global DNA methylation levels were found in gingival tissue samples compared to bone, regardless of the study groups. However, similar global DNA methylation levels were observed overall between the IF and PH groups. Yet, differences in the global DNA methylation levels between gingival tissues and bone, regardless of the study group, could reflect a different epigenetic response between various tissues within the same microenvironment. Further studies are necessary to elucidate the present findings and to evaluate the role of epigenetic modifications in IF due to PIT.

Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions

Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene-environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008-2011) and visit 2 (2014-2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified. Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m² increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m², p < 0.001). Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.

Exposure to volatile organic compounds - acrolein, 1,3-butadiene, and crotonaldehyde - is associated with vascular dysfunction

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Exposure to air pollution, specifically particulate matter of diameter ≤2.5 μm (PM2.5), is a well-established risk factor for CVD. However, the contribution of gaseous pollutant exposure to CVD risk is less clear.

OBJECTIVE

To examine the vascular effects of exposure to individual volatile organic compounds (VOCs) and mixtures of VOCs.

METHODS

We measured urinary metabolites of acrolein (CEMA and 3HPMA), 1,3-butadiene (DHBMA and MHBMA3), and crotonaldehyde (HPMMA) in 346 nonsmokers with varying levels of CVD risk. On the day of enrollment, we measured blood pressure (BP), reactive hyperemia index (RHI - a measure of endothelial function), and urinary levels of catecholamines and their metabolites. We used generalized linear models for evaluating the association between individual VOC metabolites and BP, RHI, and catecholamines, and we used Bayesian Kernel Machine Regression (BKMR) to assess exposure to VOC metabolite mixtures and BP.

RESULTS

We found that the levels of 3HPMA were positively associated with systolic BP (0.98 mmHg per interquartile range (IQR) of 3HPMA; CI: 0.06, 1.91; P = 0.04). Stratified analysis revealed an increased association with systolic BP in Black participants despite lower levels of urinary 3HPMA. This association was independent of PM2.5 exposure and BP medications. BKMR analysis confirmed that 3HPMA was the major metabolite associated with higher BP in the presence of other metabolites. We also found that 3HPMA and DHBMA were associated with decreased endothelial function. For each IQR of 3HPMA or DHBMA, there was a -4.4% (CI: -7.2, -0.0; P = 0.03) and a -3.9% (CI: -9.4, -0.0; P = 0.04) difference in RHI, respectively. Although in the entire cohort the levels of several urinary VOC metabolites were weakly associated with urinary catecholamines and their metabolites, in Black participants, DHBMA levels showed strong associations with urinary norepinephrine and normetanephrine levels.

DISCUSSION

Exposure to acrolein and 1,3-butadiene is associated with endothelial dysfunction and may contribute to elevated risk of hypertension in participants with increased sympathetic tone, particularly in Black individuals.

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

Gene-Environment Interaction: A Variable Selection Perspective

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Prediction of new potential associations between LncRNAs and environmental factors based on KATZ measure

The associations between genetic and environmental factors (EFs) are significant to understand the development and progression of many complex human diseases. There have been many research studies concerning genetic factors (protein-coding genes, microRNAs) and EFs but limited research addressing the associations between long noncoding RNAs (lncRNAs) and EFs. LncRNAs of more than 200 nucleotides are an important class of non-coding transcripts and are effective in the organization of gene expressions and, therefore, on the formation of diseases. Environmental factors can alter the expression patterns of some lncRNAs, so a thorough understanding of the associations between lncRNAs and environmental factors will contribute to the understanding of the mechanisms of many complex diseases at the molecular level. In this study, we have developed a model based on the KATZ measure to find potential new associations between lncRNAs and EFs by using the DLREFD database, which contains proven associations between lncRNAs and EFs. The KATZ measure and Gaussian interaction profile kernel similarity were used to predict new potential associations between lncRNAs and EFs. The AUC results obtained by global leave-one-out cross-validation and 2-fold and 5-fold cross-validations were 0.855, 0.827, 0.838, respectively. These results show that our model can predict new potential associations between lncRNAs and EFs with high reliability. Also, the results obtained in case studies demonstrate the effectiveness of our model.

Differential lipid metabolism outcomes associated with ADRB2 gene polymorphisms in response to two dietary interventions in overweight/obese subjects

BACKGROUND AND AIMS

A precise nutrigenetic management of hypercholesterolemia involves the understanding of the interactions between the individual's genotype and dietary intake. The aim of this study was to analyze the response to two dietary energy-restricted interventions on cholesterol changes in carriers of two ADRB2 polymorphisms.

METHODS AND RESULTS

A 4-month nutritional intervention was conducted involving two different hypo-energetic diets based on low-fat (LF) and moderately high-protein (MHP) dietary patterns. A total of 107 unrelated overweight/obese individuals were genotyped for two ADRB2 non-synonymous polymorphisms: Arg16Gly (rs1042713) and Gln27Glu (rs1042714). Genotyping was performed by next-generation sequencing and haplotypes were phenotypically screened. Anthropometric measurements and the biochemical profile were assessed by conventional methods. Both diets induced cholesterol decreases at the end of both nutritional interventions. Interestingly, phenotypical differences were observed according to the Arg16Gly polymorphism. Within the MHP group, Gly16Gly homozygotes had lower reductions in total cholesterol (-6.5 mg/dL vs. -24.2 mg/dL, p = 0.009), LDL-c levels (-1.4 mg/dL vs. -16.5 mg/dL, p = 0.005), and non-HDL-c (-4.5 mg/dL vs. -21.5 mg/dL, p = 0.008) than Arg16 allele carriers. Conversely, within the LF group, Gly16Gly homozygotes underwent similar falls in total cholesterol (-18.5 mg/dL vs. -18.7 mg/dL, ns), LDL-c levels (-9.7 mg/dL vs. -13.1 mg/dL, ns), and non-HDL-c (-15.3 mg/dL vs. -15.7 mg/dL, ns) than Arg16 allele carriers. The Gln27Glu polymorphism and the Gly16/Glu27 haplotype showed similar, but not greater effects.

CONCLUSIONS

An energy-restricted LF diet could be more beneficial than a MHP diet to reduce serum cholesterol, LDL-c, and non-HDL-c among Gly16Gly genotype carriers. CLINICALTRIALS.GOV: Identifier: NCT02737267.

Current Applications of Genetic Risk Scores to Cardiovascular Outcomes and Subclinical Phenotypes

Genetic risk scores are a useful tool for examining the cumulative predictive ability of genetic variation on cardiovascular disease. Important considerations for creating genetic risk scores include the choice of genetic variants, weighting, and comparability across ethnicities. Genetic risk scores that use information from genome-wide meta-analyses can successfully predict cardiovascular outcomes and subclinical phenotypes, yet there is limited clinical utility of these scores beyond traditional cardiovascular risk factors in many populations. Novel uses of genetic risk scores include evaluating the genetic contribution of specific intermediate traits or risk factors to cardiovascular disease, risk prediction in high-risk populations, gene-by-environment interaction studies, and Mendelian randomization studies. Though questions remain about the ultimate clinical utility of the genetic risk score, further investigation in high-risk populations and new ways to combine genetic risk scores with traditional risk factors may prove to be fruitful.

Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only

The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.

MicroRNAs associated with exercise and diet: a systematic review

MicroRNAs are posttranscriptional regulators of gene expression. MicroRNAs reflect individual biologic adaptation to exposures in the environment. As such, measurement of circulating microRNAs presents an opportunity to evaluate biologic changes associated with behavioral interventions (i.e., exercise, diet) for weight loss. The aim of this study was to perform a systematic review of the literature to summarize what is known about circulating microRNAs associated with exercise, diet, and weight loss. We performed a systematic review of three scientific databases. We included studies reporting on circulating microRNAs associated with exercise, diet, and weight loss in humans. Of 1,219 studies identified in our comprehensive database search, 14 were selected for inclusion. Twelve reported on microRNAs associated with exercise, and two reported on microRNAs associated with diet and weight loss. The majority of studies used a quasiexperimental, cross-sectional design. There were numerous differences in the type and intensity of exercise and dietary interventions, the biologic source of microRNAs, and the methodological approaches used quantitate microRNAs. Data from several studies support an association between circulating microRNAs and exercise. The evidence for an association between circulating microRNAs and diet is weaker because of a small number of studies. Additional research is needed to validate previous observations using methodologically rigorous approaches to microRNA quantitation to determine the specific circulating microRNA signatures associated with behavioral approaches to weight loss. Future directions include longitudinal studies to determine if circulating microRNAs are predictive of response to behavioral interventions.

A review on the genetic, environmental, and lifestyle aspects of the early-life origins of cardiovascular disease

This article is a comprehensive review on developmental origins of health and disease regarding various factors related to the origins of cardiovascular diseases from early life. It presents a summary of the impacts of various factors such as epigenetics; gene-environment interaction; ethnic predisposition to cardiovascular diseases and their underlying risk factors; prenatal factors; fetal programming; maternal weight status and weight gain during pregnancy; type of feeding during infancy; growth pattern during childhood; obesity; stunting; socioeconomic status; dietary and physical activity habits; active, secondhand, and thirdhand smoking, as well as environmental factors including air pollution and global climate change on the development and progress of cardiovascular diseases and their risk factors. The importance of early identification of predisposing factors for cardiovascular diseases for primordial and primary prevention of cardiovascular diseases from early life is highlighted.

MicroRNA associated with dyslipidemia and coronary disease in humans

MicroRNAs are structural components of an epigenetic mechanism of posttranscriptional regulation of messenger RNA translation. Recently, there has been significant interest in the application of microRNA as a blood-based biomarker of underlying physiological conditions. Dyslipidemia is a complex, heterogeneous condition conferring substantially increased risk for cardiovascular disease. The purpose of this review is to describe the current body of knowledge on the role of microRNA regulation of lipoprotein metabolism in humans and to discuss relevant methodological and study design considerations. We highlight the potential roles for microRNA in gene-environment interactions.