期刊
JOURNAL OF THE AMERICAN HEART ASSOCIATION
卷 9, 期 8, 页码 -出版社
WILEY
DOI: 10.1161/JAHA.119.015299
关键词
cardiovascular disease; DNA methylation; epigenomics; risk prediction
资金
- US Department of Agriculture, Agriculture Research Service [8050-51000-098-00D]
- National Institutes of Health predoctoral training grant [5T32HL069772-14]
- National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services [HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, HHSN268201600004C]
- National Heart, Lung, and Blood Institute
- Boston University [N01-HC-25195, HHSN268201500001I]
- Age UK
- Medical Research Council [MR/M01311/1, MR/K026992/1]
- Centre for Cognitive Ageing and Cognitive Epidemiology
- Wellcome Trust Institutional Strategic Support Fund
- University of Edinburgh
- University of Queensland
- Biotechnology and Biological Sciences Research Council [MR/K026992/1]
- Medical Research Council [G0700704] Funding Source: researchfish
- MRC [G0700704] Funding Source: UKRI
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.
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