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Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.

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.

journal_name

J Am Heart Assoc

journal_title

Journal of the American Heart Association

authors

Westerman K,Fernández-Sanlés A,Patil P,Sebastiani P,Jacques P,Starr JM,J Deary I,Liu Q,Liu S,Elosua R,DeMeo DL,Ordovás JM

doi

10.1161/JAHA.119.015299

subject

Has Abstract

pub_date

2020-04-21 00:00:00

pages

e015299

issue

issn

2047-9980

journal_volume

pub_type

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Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.