Integrative analysis of clinical and epigenetic biomarkers of mortality
Huan, Tianxiao ; Nguyen, Steve ; Colicino, Elena ; Ochoa-Rosales, Carolina ; Hill, W David ; Brody, Jennifer A ; Soerensen, Mette ; Zhang, Yan ; Baldassari, Antoine ; Elhadad, Mohamed Ahmed ... show 10 more
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Nguyen, Steve
Colicino, Elena
Ochoa-Rosales, Carolina
Hill, W David
Brody, Jennifer A
Soerensen, Mette
Zhang, Yan
Baldassari, Antoine
Elhadad, Mohamed Ahmed
Toshiko, Tanaka
Zheng, Yinan
Domingo-Relloso, Arce
Lee, Dong Heon
Ma, Jiantao
Yao, Chen
Liu, Chunyu
Hwang, Shih-Jen
Joehanes, Roby
Fornage, Myriam
Bressler, Jan
van Meurs, Joyce B J
Debrabant, Birgit
Mengel-From, Jonas
Hjelmborg, Jacob
Christensen, Kaare
Vokonas, Pantel
Schwartz, Joel
Gahrib, Sina A
Sotoodehnia, Nona
Sitlani, Colleen M
Kunze, Sonja
Gieger, Christian
Peters, Annette
Waldenberger, Melanie
Deary, Ian J
Ferrucci, Luigi
Qu, Yishu
Greenland, Philip
Lloyd-Jones, Donald M
Hou, Lifang
Bandinelli, Stefania
Voortman, Trudy
Hermann, Brenner
Baccarelli, Andrea
Whitsel, Eric
Pankow, James S
Levy, Daniel
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UMass Chan Affiliations
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Abstract
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.
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Huan T, Nguyen S, Colicino E, Ochoa-Rosales C, Hill WD, Brody JA, Soerensen M, Zhang Y, Baldassari A, Elhadad MA, Toshiko T, Zheng Y, Domingo-Relloso A, Lee DH, Ma J, Yao C, Liu C, Hwang SJ, Joehanes R, Fornage M, Bressler J, van Meurs JBJ, Debrabant B, Mengel-From J, Hjelmborg J, Christensen K, Vokonas P, Schwartz J, Gahrib SA, Sotoodehnia N, Sitlani CM, Kunze S, Gieger C, Peters A, Waldenberger M, Deary IJ, Ferrucci L, Qu Y, Greenland P, Lloyd-Jones DM, Hou L, Bandinelli S, Voortman T, Hermann B, Baccarelli A, Whitsel E, Pankow JS, Levy D. Integrative analysis of clinical and epigenetic biomarkers of mortality. Aging Cell. 2022 Jun;21(6):e13608. doi: 10.1111/acel.13608. Epub 2022 May 12. PMID: 35546478; PMCID: PMC9197414.