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

Authors: WESTERMAN, KENNETH - Jean Mayer Human Nutrition Research Center On Aging At Tufts University; FERNANDEZ-SANLES, ALBA - Hospital Del Mar Medical Research Institute; PATIL, PRASAD - Boston University; SEBASTIANI, PAOLA - Boston University; JACQUES, PAUL - Jean Mayer Human Nutrition Research Center On Aging At Tufts University; STARR, JOHN - University Of Edinburgh; DEARY, IAN - University Of Edinburgh; LIU, QING - Brown University; LIU, SIMIN - Brown University; ELOSUA, ROBERTO - Hospital Del Mar Medical Research Institute; DEMEO, DAWN - Brigham & Women'S Hospital; ORDOVAS, JOSE - Jean Mayer Human Nutrition Research Center On Aging At Tufts University

Submitted to: Journal of the American Heart Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/10/2020
Publication Date: 4/20/2020
Citation: Westerman, K., Fernandez-Sanles, A., Patil, P., Sebastiani, P., Jacques, P.F., Starr, J.M., Deary, I.J., Liu, Q., Liu, S., Elosua, R., DeMeo, D.F., Ordovas, J.M. 2020. Epigenomic assessment of cardiovascular disease risk and interactions with traditional risk metrics. Journal of the American Heart Association. 9(8):e015299. https://doi.org/10.1161/JAHA.119.015299.
DOI: https://doi.org/10.1161/JAHA.119.015299

Interpretive Summary: DNA methylation is a biological process that adds methyl groups to the DNA molecule. Methylation can change the activity of a DNA segment without changing the sequence. This is part of a series of processes called epigenetics or epigenomics. DNA methylation has been implicated in cardiovascular disease, including atherosclerosis. DNA methylation may be used as an early biomarker of atherosclerosis since it may happen before lesions are observed, which may provide an early tool for detection and risk prevention. Previous studies have examined the relation between methylation and cardiovascular risk factors (e.g., cholesterol, glucose, blood pressure). However, few studies have sought to directly develop a predictor of CVD risk. Scientists at the HNRCA in Boston, in collaboration with researchers in the USA, UK, and Spain, analyzed existing DNA methylation data to create such a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. Using sophisticated statistical approaches, this investigation provides proof-of-concept for a CVD-specific epigenomic risk score. It suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk prediction approaches.

Technical 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.