Show simple item record

dc.contributor.authorWesterman, Kenneth
dc.contributor.authorFernández-Sanlés, Alba
dc.contributor.authorPatil, Prasad
dc.contributor.authorSebastiani, Paola
dc.contributor.authorJacques, Paul
dc.contributor.authorStarr, John M
dc.contributor.authorJ Deary, Ian
dc.contributor.authorLiu, Qing
dc.contributor.authorLiu, Simin
dc.contributor.authorElosua, Roberto
dc.contributor.authorDeMeo, Dawn L
dc.contributor.authorOrdovas, Jose M 
dc.date.accessioned2020-06-10T07:11:49Z
dc.date.available2020-06-10T07:11:49Z
dc.date.issued2020-04-21
dc.identifier.citationJ Am Heart Assoc. 2020; 9(8):e015299es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/10316
dc.description.abstractBackground 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.es_ES
dc.description.sponsorshipThis work was supported by the US Department of Agriculture, Agriculture Research Service (8050–51000-098-00D). Dr. Westerman was additionally supported by National Institutes of Health predoctoral training grant 5T32HL069772-14. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. This article was prepared in collaboration with investigators of the WHI but has not been reviewed by the WHI and does not necessarily reflect the opinions of the WHI investigators or the National Heart, Lung, and Blood Institute. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with Boston University (Contract No. N01-HC-25195 and HHSN268201500001I). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or National Heart, Lung, and Blood Institute. The LBC 1936 is supported by Age UK (Disconnected Mind program) and the Medical Research Council (MR/M01311/1). Methylation typing in LBC 1936 was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. LBC 1936 work was conducted in the Centre for Cognitive Ageing and Cognitive Epidemiology, which supported Dr. Deary and is supported by the medical Research Council and Biotechnology and Biological Sciences Research Council (MR/K026992/1).es_ES
dc.language.isoenges_ES
dc.publisherAmerican Heart Associationes_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleEpigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.es_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución-NoComercial 4.0 Internacional*
dc.identifier.pubmedID32308120es_ES
dc.format.volume9es_ES
dc.format.number8es_ES
dc.format.pagee015299es_ES
dc.identifier.doi10.1161/JAHA.119.015299es_ES
dc.contributor.funderDepartment of Agriculture (United States)es_ES
dc.contributor.funderNational Institutes of Health (United States)es_ES
dc.contributor.funderNational Heart, Lung, and Blood Institute (United States)es_ES
dc.contributor.funderMedical Research Council (United Kingdom)es_ES
dc.contributor.funderWellcome Trustes_ES
dc.description.peerreviewedes_ES
dc.identifier.e-issn2047-9980es_ES
dc.relation.publisherversionhttps://doi.org/10.1161/JAHA.119.015299es_ES
dc.identifier.journalJournal of the American Heart Associationes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Imagen Cardiovascular y Estudios Poblacionaleses_ES
dc.repisalud.institucionCNICes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


Files in this item

Acceso Abierto
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Atribución-NoComercial 4.0 Internacional
This item is licensed under a: Atribución-NoComercial 4.0 Internacional