Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics

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• dc.contributor.author Westerman, Kenneth
• dc.contributor.author Fernández Sanlés, Alba, 1988-
• dc.contributor.author Patil, Prasad
• dc.contributor.author Sebastiani, Paola
• dc.contributor.author Jacques, Paul
• dc.contributor.author Starr, John M.
• dc.contributor.author Deary ,Ian J.
• dc.contributor.author Liu, Qing
• dc.contributor.author Liu, Simi
• dc.contributor.author Elosua Llanos, Roberto
• dc.contributor.author DeMeo, Dawn L
• dc.contributor.author Ordovás, José M.
• dc.date.accessioned 2020-10-09T07:09:04Z
• dc.date.available 2020-10-09T07:09:04Z
• dc.date.issued 2020
• dc.description.abstract Background Epigenome-wide association studies for cardiometabolic risk factors have discovered multiple loci associated with incident cardiovascular disease (CVD). However, few studies have sought to directly optimize a predictor of CVD risk. Furthermore, it is challenging to train multivariate models across multiple studies in the presence of study- or batch effects. Methods and Results Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. We trained Cox proportional hazards-based elastic net regressions for incident CVD separately in each cohort and used a recently introduced cross-study learning approach to integrate these individual scores into an ensemble predictor. The methylation-based risk score was associated with CVD time-to-event in a held-out fraction of the Framingham data set (hazard ratio per SD=1.28, 95% CI, 1.10-1.50) and predicted myocardial infarction status in the independent REGICOR (Girona Heart Registry) data set (odds ratio per SD=2.14, 95% CI, 1.58-2.89). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net models trained on a directly merged data set. Additionally, we investigated interactions between the methylation-based risk score and both genetic and biochemical CVD risk, showing preliminary evidence of an enhanced performance in those with less traditional risk factor elevation. Conclusions This investigation provides proof-of-concept for a genome-wide, CVD-specific epigenomic risk score and suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk metrics.
• dc.format.mimetype application/pdf
• dc.identifier.citation Westerman K, Fernández-Sanlés A, Patil P, Sebastiani P, Jacques P, Starr JM. Et al. Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics. J Am Heart Assoc. 2020 Apr 21; 9(8):e015299. DOI: 10.1161/JAHA.119.015299
• dc.identifier.doi http://dx.doi.org/10.1161/JAHA.119.015299
• dc.identifier.issn 2047-9980
• dc.identifier.uri http://hdl.handle.net/10230/45448
• dc.language.iso eng
• dc.publisher Wiley-Blackwell
• dc.relation.ispartof Journal of the American Heart Association. 2020 Apr 21;9(8):e015299
• dc.rights Copyright © 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
• dc.subject.keyword DNA methylation
• dc.subject.keyword Cardiovascular disease
• dc.subject.keyword Epigenomics
• dc.subject.keyword Risk prediction
• dc.title Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion