2026-08-29T15:33:50Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/103162024-09-27T08:04:40Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19605

00925njm 22002777a 4500 dc Westerman, Kenneth author Fernández-Sanlés, Alba author Patil, Prasad author Sebastiani, Paola author Jacques, Paul author Starr, John M author J Deary, Ian author Liu, Qing author Liu, Simin author Elosua, Roberto author DeMeo, Dawn L author Ordovas, Jose M author 2020-04-21 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. J Am Heart Assoc. 2020; 9(8):e015299 10.1161/JAHA.119.015299 2047-9980 Journal of the American Heart Association 32308120 http://hdl.handle.net/20.500.12105/10316 Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.