2026-06-14T03:43:12Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/151652025-03-26T11:29:08Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608
Repisalud
author
Huan, Tianxiao
author
Nguyen, Steve
author
Colicino, Elena
author
Ochoa-Rosales, Carolina
author
Hill, W David
author
Brody, Jennifer A
author
Soerensen, Mette
author
Zhang, Yan
author
Baldassari, Antoine
author
Elhadad, Mohamed Ahmed
author
Toshiko, Tanaka
author
Zheng, Yinan
author
Domingo-Relloso, Arce
author
Lee, Dong Heon
author
Ma, Jiantao
author
Yao, Chen
author
Liu, Chunyu
author
Hwang, Shih-Jen
author
Joehanes, Roby
author
Fornage, Myriam
author
Bressler, Jan
author
van Meurs, Joyce B J
author
Debrabant, Birgit
author
Mengel-From, Jonas
author
Hjelmborg, Jacob
author
Christensen, Kaare
author
Vokonas, Pantel
author
Schwartz, Joel
author
Gahrib, Sina A
author
Sotoodehnia, Nona
author
Sitlani, Colleen M
author
Kunze, Sonja
author
Gieger, Christian
author
Peters, Annette
author
Waldenberger, Melanie
author
Deary, Ian J
author
Ferrucci, Luigi
author
Qu, Yishu
author
Greenland, Philip
author
Lloyd-Jones, Donald M
author
Hou, Lifang
author
Bandinelli, Stefania
author
Voortman, Trudy
author
Hermann, Brenner
author
Baccarelli, Andrea
author
Whitsel, Eric
author
Pankow, James S
author
Levy, Daniel
funder
National Institutes of Health (Estados Unidos)
funder
NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos)
funder
NIH - Center for Information Technology (Estados Unidos)
funder
NIH - National Institute of Neurological Disorders and Stroke (NINDS) (Estados Unidos)
funder
NIH - National Institute on Aging (NIA) (Estados Unidos)
funder
Fundación Merck Salud
funder
Laughlin Family
funder
Alpha Phi Foundation
funder
Locke Charitable Foundation
funder
National Center for Advancing Translational Sciences (Estados Unidos)
funder
NIH - National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (Estados Unidos)
funder
Danish Council for Independent Research
funder
Unión Europea. Comisión Europea. 7 Programa Marco
funder
United States Department of Health and Human Services
funder
NIH - National Institute of Environmental Health Sciences (NIEHS) (Estados Unidos)
funder
Biotechnology and Biological Sciences Research Council (Reino Unido)
funder
Royal Society (Reino Unido)
funder
Scottish Government (Reino Unido)
funder
Age UK (Reino Unido)
funder
Centre for Cognitive Ageing and Cognitive Epidemiology (Reino Unido)
funder
Wellcome Trust
funder
University of Edinburgh (Reino Unido)
funder
University of Queensland (Australia)
funder
Medical Research Council (Reino Unido)
funder
United States Department of Veterans Affairs
funder
Massachusetts Veterans Education Research and Information Center (MAVERIC)
2022-11-16T11:20:06Z
2022-11-16T11:20:06Z
2022-06
Aging Cell. 2022 Jun;21(6):e13608.
10.1111/acel.13608
1474-9726
1474-9718
Aging cell
35546478
http://hdl.handle.net/20.500.12105/15165
DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10-7 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, PMR = 4.1 × 10-4 ) and negatively associated with longevity (Beta = -1.9, PMR = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.
eng
Cancer
Cardiovascular disease
DNA methylation
Machine learning
Mortality
Integrative analysis of clinical and epigenetic biomarkers of mortality
research article
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IntegrativeAnalysisClinicalEpigenetic_2022.pdf
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IntegrativeAnalysisClinicalEpigenetic_2022.pdf.txt