Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics

Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease

Alba Fernández‐Sanlés¹  Roberto Elosua¹  Dawn L. DeMeo²  Paola Sebastiani³  Prasad Patil³  Qing Liu⁴  Simin Liu⁴  John M. Starr⁵  Ian J. Deary⁵  Kenneth Westerman⁶  José M. Ordovás⁶  Paul Jacques⁶ 

[1] Cardiovascular Epidemiology and Genetics Research Group REGICOR Study Group IMIM (Hospital del Mar Medical Research Institute) Barcelona Catalonia Spain;Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital Boston MA;Department of Biostatistics Boston University School of Public Health Boston MA;Department of Epidemiology Brown University School of Public Health Providence RI;Department of Psychology University of Edinburgh United Kingdom;JM‐USDA Human Nutrition Research Center on Aging at Tufts University Boston MA;

关键词: cardiovascular disease;    DNA methylation;    epigenomics;    risk prediction;   

DOI  :  10.1161/JAHA.119.015299

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown