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oai:repisalud.isciii.es:20.500.12105/151652025-03-26T11:29:08Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608

00925njm 22002777a 4500 dc 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 author 2022-06 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. 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 Cancer Cardiovascular disease DNA methylation Machine learning Mortality Integrative analysis of clinical and epigenetic biomarkers of mortality