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Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4 – 23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Visceral adipose tissue (VAT)—fat stored around the internal organs—has been suggested as an independent risk factor for cardiovascular and metabolic disease1–3, as well as all-cause, cardiovascular-specific and cancer-specific mortality4,5. Yet, the contribution of genetics to VAT, as well as its disease-related effects, are largely unexplored due to the requirement for advanced imaging technologies to accurately measure VAT. Here, we develop sex-stratified, nonlinear prediction models (coefficient of determination = 0.76; typical 95% confidence interval (CI) = 0.74–0.78) for VAT mass using the UK Biobank cohort. We performed a genome-wide association study for predicted VAT mass and identified 102 novel visceral adiposity loci. Predicted VAT mass was associated with increased risk of hypertension, heart attack/angina, type 2 diabetes and hyperlipidemia, and Mendelian randomization analysis showed visceral fat to be a causal risk factor for all four diseases. In particular, a large difference in causal effect between the sexes was found for type 2 diabetes, with an odds ratio of 7.34 (95% CI = 4.48–12.0) in females and an odds ratio of 2.50 (95% CI = 1.98–3.14) in males. Our findings bolster the role of visceral adiposity as a potentially independent risk factor, in particular for type 2 diabetes in Caucasian females. Independent validation in other cohorts is necessary to determine whether the findings can translate to other ethnicities, or outside the UK.

Alcohol consumption and smoking, 2 major risk factors for cardiovascular disease (CVD), often occur together. The objective of this study is to use a wide range of CVD risk factors and outcomes to evaluate potential total and direct causal roles of alcohol and tobacco use on CVD risk factors and events. Using large publicly available genome-wide association studies (GWASs) (results from more than 1.2 million combined study participants) of predominantly European ancestry, we conducted 2-sample single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR) to simultaneously assess the independent impact of alcohol consumption and smoking on a wide range of CVD risk factors and outcomes. Multiple sensitivity analyses, including complementary Mendelian randomization (MR) methods, and secondary alcohol consumption and smoking datasets were used. SVMR showed genetic predisposition for alcohol consumption to be associated with CVD risk factors, including high-density lipoprotein cholesterol (HDL-C) (beta 0.40, 95% confidence interval (CI), 0.04-0.47, P value = 1.72 × 10-28), triglycerides (TRG) (beta -0.23, 95% CI, -0.30, -0.15, P value = 4.69 × 10-10), automated systolic blood pressure (BP) measurement (beta 0.11, 95% CI, 0.03-0.18, P value = 4.72 × 10-3), and automated diastolic BP measurement (beta 0.09, 95% CI, 0.03-0.16, P value = 5.24 × 10-3). Conversely, genetically predicted smoking was associated with increased TRG (beta 0.097, 95% CI, 0.014-0.027, P value = 6.59 × 10-12). Alcohol consumption was also associated with increased myocardial infarction (MI) and coronary heart disease (CHD) risks (MI odds ratio (OR) = 1.24, 95% CI, 1.03-1.50, P value = 0.02; CHD OR = 1.21, 95% CI, 1.01-1.45, P value = 0.04); however, its impact was attenuated in MVMR adjusting for smoking. Conversely, alcohol maintained an association with coronary atherosclerosis (OR 1.02, 95% CI, 1.01-1.03, P value = 5.56 × 10-4). In comparison, after adjusting for alcohol consumption, smoking retained its association with several CVD outcomes including MI (OR = 1.84, 95% CI, 1.43, 2.37, P value = 2.0 × 10-6), CHD (OR = 1.64, 95% CI, 1.28-2.09, P value = 8.07 × 10-5), heart failure (HF) (OR = 1.61, 95% CI, 1.32-1.95, P value = 1.9 × 10-6), and large artery atherosclerosis (OR = 2.4, 95% CI, 1.41-4.07, P value = 0.003). Notably, using the FinnGen cohort data, we were able to replicate the association between smoking and several CVD outcomes including MI (OR = 1.77, 95% CI, 1.10-2.84, P value = 0.02), HF (OR = 1.67, 95% CI, 1.14-2.46, P value = 0.008), and peripheral artery disease (PAD) (OR = 2.35, 95% CI, 1.38-4.01, P value = 0.002). The main limitations of this study include possible bias from unmeasured confounders, inability of summary-level MR to investigate a potentially nonlinear relationship between alcohol consumption and CVD risk, and the generalizability of the UK Biobank (UKB) to other populations. Evaluating the widest range of CVD risk factors and outcomes of any alcohol consumption or smoking MR study to date, we failed to find a cardioprotective impact of genetically predicted alcohol consumption on CVD outcomes. However, alcohol was associated with and increased HDL-C, decreased TRG, and increased BP, which may indicate pathways through impact CVD risk, warranting further study. We found smoking to be a risk factor for many CVDs even after adjusting for alcohol. While future studies incorporating alcohol consumption patterns are necessary, our data suggest causal inference between alcohol, smoking, and CVD risk, further supporting that lifestyle modifications might be able to reduce overall CVD risk.

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.

Drug development in cardiovascular disease is stagnating, with lack of efficacy and adverse effects being barriers to innovation. Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization, with genetic support for causation increasing the probability of a clinical trial succeeding. Mendelian randomization applied to quantitative traits can identify risk factors for disease that are both causal and amenable to therapeutic modification. However, important differences exist between genetic investigations of a biomarker (such as HDL cholesterol) and a drug target aimed at modifying the same biomarker of interest (such as cholesteryl ester transfer protein), with implications for the methodology, interpretation and application of Mendelian randomization to drug development. Differences include the comparative nature of the genetic architecture — that is, biomarkers are typically polygenic, whereas protein drug targets are influenced by either cis-acting or trans-acting genetic variants — and the potential for drug targets to show disease associations that might differ from those of the biomarker that they are intended to modify (target-mediated pleiotropy). In this Review, we compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits. We explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.In this Review, Holmes and colleagues compare and contrast the use of Mendelian randomization to evaluate potential drug targets versus quantitative traits and explain how genetic epidemiological studies can be used to assess the aetiological roles of biomarkers in disease and to prioritize drug targets, including designing their evaluation in clinical trials.

The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

BackgroundHigher body mass index (BMI) and waist-to-hip ratio (WHR) increase the risk of cardiovascular disease, but the extent to which this is mediated by blood pressure, diabetes, lipid traits, and smoking is not fully understood.MethodsUsing consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits, and smoking mediated an effect of BMI and WHR on the risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke.ResultsThe odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% −23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI −20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome.ConclusionsMeasures to reduce obesity will lower the risk of cardiovascular disease primarily by impacting downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.

MicroRNAs (miRNAs) are a key factor in epigenetic regulation of gene expression, but miRNA responses to fine particulate matter (PM ) air pollution and their potential contribution to cardiovascular effects of PM are unknown. We explored the potential influence of PM on the expression of selected cytokines relevant to systemic inflammation, coagulation, and vasoconstriction, and on miRNAs that may regulate their expression. We designed a double-blind, randomized crossover study in which true and sham air purifiers were used to expose 55 healthy young adult students in Shanghai, China, to reduced or ambient levels of indoor PM during two-week periods, and we measured the expression (mRNA and protein) of 10 serum cytokines, and miRNAs that target them, after each intervention period. We used linear mixed-effect models to estimate associations of the intervention, and time-weighted personal PM exposures, with the cytokines, mRNA, and miRNAs; we also explored potential mediation by miRNAs. The findings were generally consistent for associations with the intervention and for associations with an interquartile range increase in time-weighted PM . Specifically, higher PM exposure was positively associated with the expression (mRNA, protein, or both) of interleukin-1 (encoded by ), IL6, tumor necrosis factor (encoded by ), toll-like receptor 2 (encoded by ), coagulation factor 3 (encoded by ), and endothelin 1 (encoded by ), and was negatively associated with miRNAs (miR-21-5p, miR-187-3p, miR-146a-5p, miR-1-3p, and miR-199a-5p) predicted to target mRNAs of , , , and . Our findings require confirmation but suggest that effects of PM on cardiovascular diseases may be related to acute effects on cytokine expression, which may be partly mediated through effects of PM on miRNAs that regulate cytokine expression. https://doi.org/10.1289/EHP1447.

Background Whether a modestly elevated homocysteine level is causally associated with an increased risk of cardiovascular disease remains unestablished. We conducted a Mendelian randomization study to assess the associations of circulating total homocysteine (tHcy) and B vitamin levels with cardiovascular diseases in the general population. Methods Independent single nucleotide polymorphisms associated with tHcy ( n  = 14), folate ( n  = 2), vitamin B6 ( n  = 1), and vitamin B12 ( n  = 14) at the genome-wide significance level were selected as instrumental variables. Summary-level data for 12 cardiovascular endpoints were obtained from genetic consortia, the UK Biobank study, and the FinnGen consortium. Results Higher genetically predicted circulating tHcy levels were associated with an increased risk of stroke. For each one standard deviation (SD) increase in genetically predicted tHcy levels, the odds ratio (OR) was 1.11 (95% confidence interval (CI), 1.03, 1.21; p  = 0.008) for any stroke, 1.26 (95% CI, 1.05, 1.51; p  = 0.013) for subarachnoid hemorrhage, and 1.11 (95% CI, 1.03, 1.21; p  = 0.011) for ischemic stroke. Higher genetically predicted folate levels were associated with decreased risk of coronary artery disease (OR SD , 0.88; 95% CI, 0.78, 1.00, p  = 0.049) and any stroke (OR SD , 0.86; 95% CI, 0.76, 0.97, p  = 0.012). Genetically predicted increased vitamin B6 levels were associated with a reduced risk of ischemic stroke (OR SD , 0.88; 95% CI, 0.81, 0.97, p  = 0.009). None of these associations persisted after multiple testing correction. There was no association between genetically predicted vitamin B12 and cardiovascular disease. Conclusions This study reveals suggestive evidence that B vitamin therapy and lowering of tHcy may reduce the risk of stroke, particularly subarachnoid hemorrhage and ischemic stroke.

Background Circadian rhythms regulate key biological processes influencing metabolic pathways. Disregulation is associated with type 2 diabetes (T2D) and cardiovascular diseases (CVD). Circadian rhythms are generated by a transcriptional autoregulatory feedback loop involving core clock genes. CLOCK (circadian locomotor output cycles protein kaput), one of those core genes, is known to regulate glucose metabolism in rodent models. Cross-sectional studies in humans have reported associations between this locus and obesity, plasma glucose, hypertension and T2D prevalence, supporting its role in cardiovascular risk. However, no longitudinal study has investigated the association between CLOCK gene variation and T2D or CVD incidence. Moreover, although in a previous work we detected a gene-diet interaction between the CLOCK -rs4580704 (C > G) single nucleotide polymorphism (SNP) and monounsaturated (MUFA) intake on insulin resistance, no interventional study has analyzed gene-diet interactions on T2D or CVD outcomes. Methods We analyzed the association between the CLOCK -rs4580704 SNP and incidence of T2D and CVD longitudinally in 7098 PREDIMED trial (ISRCTN35739639) participants after a median 4.8-year follow-up. We also examined modulation by Mediterranean diet (MedDiet) intervention (high in MUFA) on these associations. Results We observed a significant association between the CLOCK -rs4580704 SNP and T2D incidence in n = 3671 non-T2D PREDIMED participants, with variant allele (G) carriers showing decreased incidence (dominant model) compared with CC homozygotes (HR: 0.69; 95 % CI 0.54–0.87; P = 0.002). This protection was more significant in the MedDiet intervention group (HR: 0.58; 95 % CI 0.43–0.78; P < 0.001) than in the control group (HR: 0.95; 95 % CI 0.63–1.44; P = 0.818). Moreover, we detected a statistically significant interaction (P = 0.018) between CLOCK -rs4580704 SNP and T2D status on stroke. Thus, only in T2D subjects was CLOCK -rs4580704 SNP associated with stroke risk, G-carriers having decreased risk (HR: 0.61; 95 % CI 0.40–0.94; P = 0.024 versus CC) in the multivariable-adjusted model. Conclusions In agreement with our previous results showing a protective effect of the G-allele against hyperglycemia, we extended our findings by reporting a novel association with lower T2D incidence and also suggesting a dietary modulation. Moreover, we report for the first time an association between a CLOCK polymorphism and stroke in T2D subjects, suggesting that core clock genes may significantly contribute to increased CVD risk in T2D.