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

Westerman, Kenneth; Fernández Sanlés, Alba, 1988-; Patil, Prasad; Sebastiani, Paola; Jacques, Paul; Starr, John M.; Deary ,Ian J.; Liu, Qing; Liu, Simi; Elosua Llanos, Roberto; DeMeo, Dawn L; Ordovás, José M.

doi:http://dx.doi.org/10.1161/JAHA.119.015299

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

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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

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.