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Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10 -8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10 -8 ) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10 -3 ) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Background Differences in risk factors for atrial fibrillation (AF) and heart failure (HF) are incompletely understood. Aim of this study was to understand whether risk factors and biomarkers show different associations with incident AF and HF and to investigate predictors of subsequent onset and mortality. Methods and Results In N=58 693 individuals free of AF/HF from 5 population-based European cohorts, Cox regressions were used to find predictors for AF, HF, subsequent onset, and mortality. Differences between associations were estimated using bootstrapping. Median follow-up time was 13.8 years, with a mortality of 15.7%. AF and HF occurred in 5.0% and 5.4% of the participants, respectively, with 1.8% showing subsequent onset. Age, male sex, myocardial infarction, body mass index, and NT-proBNP (N-terminal pro-B-type natriuretic peptide) showed similar associations with both diseases. Antihypertensive medication and smoking were stronger predictors of HF than AF. Cholesterol, diabetes mellitus, and hsCRP (high-sensitivity C-reactive protein) were associated with HF, but not with AF. No variable was exclusively associated with AF. Population-attributable risks were higher for HF (75.6%) than for AF (30.9%). Age, male sex, body mass index, diabetes mellitus, and NT-proBNP were associated with subsequent onset, which was associated with the highest all-cause mortality risk. Conclusions Common risk factors and biomarkers showed different associations with AF and HF, and explained a higher proportion of HF than AF risk. As the subsequent onset of both diseases was strongly associated with mortality, prevention needs to be rigorously addressed and remains challenging, as conventional risk factors explained only 31% of AF risk.

Objectives: The aims of this study were to characterize the association of high-sensitivity cardiac troponin I (hs-cTnI) with heart failure (HF), to determine its predictive value beyond classical cardiovascular risk factors (CVRFs) and N-terminal pro-B-type natriuretic peptide, and to derive a relevant cutoff for potential clinical application., Background: HF is an important contributor to the overall burden of cardiovascular disease. Early identification of individuals at risk could be beneficial for preventive therapies., Methods: Based on the Biomarker for Cardiovascular Risk Assessment in Europe consortium, we analyzed individual-level data from 4 prospective population-based cohort studies including 48,455 individuals. Participants with myocardial infarction, HF, and stroke at baseline were excluded. We investigated the value of adding hs-cTnI to CVRFs and N-terminal pro-B-type natriuretic peptide using Cox proportional hazards survival models and for prediction by calculating C-statistics and Brier score., Results: The median age of the study population was 51 years, and the median follow-up time for occurrence of HF was 6.61 years. Cox regression models adjusted for age, sex, and CVRFs revealed a significant association of hs-cTnI with incident HF (hazard ratio: 1.42 per log [ng/l] unit change [95% confidence interval: 1.31 to 1.53]). The best predictive value was achieved in the model with CVRFs (base model) and both biomarkers (C-index = 0.862; 95% confidence interval: 0.841 to 0.882). Optimal hs-cTnI cutoff values of 2.6 ng/l for women and 4.2 ng/l for men were derived for selecting individuals at risk., Conclusions: In this large dataset from the general population, hs-cTnI could show its independence for the prognosis of HF., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: Limited evidence is available on the temporal relationship between atrial fibrillation (AF) and ischaemic stroke and their impact on mortality in the community. We sought to understand the temporal relationship of AF and ischaemic stroke and to determine the sequence of disease onset in relation to mortality., Methods and Results: Across five prospective community cohorts of the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project we assessed baseline cardiovascular risk factors in 100 132 individuals, median age 46.1 (25th-75th percentile 35.8-57.5) years, 48.4% men. We followed them for incident ischaemic stroke and AF and determined the relation of subsequent disease diagnosis with overall mortality. Over a median follow-up of 16.1 years, N = 4555 individuals were diagnosed solely with AF, N = 2269 had an ischaemic stroke but no AF diagnosed, and N = 898 developed both, ischaemic stroke and AF. Temporal relationships showed a clustering of diagnosis of both diseases within the years around the diagnosis of the other disease. In multivariable-adjusted Cox regression analyses with time-dependent covariates subsequent diagnosis of AF after ischaemic stroke was associated with increased mortality [hazard ratio (HR) 4.05, 95% confidence interval (CI) 2.17-7.54; P < 0.001] which was also apparent when ischaemic stroke followed after the diagnosis of AF (HR 3.08, 95% CI 1.90-5.00; P < 0.001)., Conclusion: The temporal relations of ischaemic stroke and AF appear to be bidirectional. Ischaemic stroke may precede detection of AF by years. The subsequent diagnosis of both diseases significantly increases mortality risk. Future research needs to investigate the common underlying systemic disease processes., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Importance: Risk stratification for coronary heart disease (CHD) remains challenging because of the complex causative mechanism of the disease. Metabolomic profiling offers the potential to detect new biomarkers and improve CHD risk assessment., Objective: To evaluate the association between circulating metabolites and incident CHD in a large European cohort., Design, Setting, and Participants: This population-based study used the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) case-cohort to measure circulating metabolites using a targeted approach in serum samples from 10 741 individuals without prevalent CHD. The cohort consisted of a weighted, random subcohort of the original cohort of more than 70 000 individuals. The case-cohort design was applied to 6 European cohorts: FINRISK97 (Finland), Monitoring of Trends and Determinants in Cardiovascular Diseases/Cooperative Health Research in the Region of Augsburg (MONICA/KORA; Germany), MONICA-Brianza and Moli-sani (Italy), DanMONICA (Denmark), and the Scottish Heart Health Extended Cohort (United Kingdom)., Main Outcomes and Measures: Associations with time to CHD onset were assessed individually by applying weighted and adjusted Cox proportional hazard models. The association of metabolites with CHD onset was examined by C indices., Results: In 10 741 individuals (4157 women [38.7%]; median [interquartile range] age, 56.5 [49.2-62.2] years), 2166 incident CHD events (20.2%) occurred over a median (interquartile range) follow-up time of 9.2 (4.5-15.0) years. Among the 141 metabolites analyzed, 24 were significantly associated with incident CHD at a nominal P value of .05, including phosphatidylcholines (PCs), lysoPCs, amino acids, and sphingolipids. Five PCs remained significant after correction for multiple testing: acyl-alkyl-PC C40:6 (hazard ratio [HR], 1.13 [95% CI, 1.07-1.18]), diacyl-PC C40:6 (HR, 1.10 [95% CI, 1.04-1.15]), acyl-alkyl-PC C38:6 (HR, 1.11 [95% CI, 1.05-1.16]), diacyl-PC C38:6 (HR, 1.09 [95% CI, 1.04-1.14]) and diacyl-PC C38:5 (HR, 1.10 [95% CI, 1.05-1.16]). Lower levels of these metabolites were associated with increased risk of incident CHD. The strength of the associations competes with those of classic risk factors (C statistics: acyl-alkyl-PC C40:6, 0.756 [95% CI, 0.738-0.774], diacyl-PC C40:6, 0.754 [95% CI, 0.736-0.772], acyl-alkyl-PC C38:6, 0.755 [95% CI, 0.736-0.773], diacyl-PC C38:6, 0.754 [95% CI, 0.736-0.772]), diacyl-PC C38:5, 0.754 [95% CI, 0.736-0.772]). Adding metabolites to a base risk model including classic risk factors high-sensitivity C-reactive protein and high-sensitivity troponin I did not improve discrimination by C statistics., Conclusions and Relevance: Five PCs were significantly associated with increased risk of incident CHD and showed comparable discrimination with individual classic risk factors. Although these metabolites do not improve CHD risk assessment beyond that of classic risk factors, these findings hold promise for an improved understanding of the pathophysiology of CHD.