DOI: 10.1101/19006965Sep 23, 2019Paper

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

MedRxiv : the Preprint Server for Health Sciences
Kenneth WestermanJosé M. Ordovás

Abstract

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. Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across three 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 predictions into an ensemble predictor. The methylation-based risk score (MRS) predicted CVD time-to-event in a held-out fraction of the Framingham dataset (HR per SD = 1.28, p = 2e-3) and predicted myocardial infarction status in the independent REGICOR dataset (OR per SD = 2.14, p = 9e-7). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net mo...Continue Reading

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