TY  - JOUR
AU  - Huan, Tianxiao
AU  - Nguyen, Steve
AU  - Colicino, Elena
AU  - Ochoa-Rosales, Carolina
AU  - Hill, W David
AU  - Brody, Jennifer A
AU  - Soerensen, Mette
AU  - Zhang, Yan
AU  - Baldassari, Antoine
AU  - Elhadad, Mohamed Ahmed
AU  - Toshiko, Tanaka
AU  - Zheng, Yinan
AU  - Domingo-Relloso, Arce
AU  - Lee, Dong Heon
AU  - Ma, Jiantao
AU  - Yao, Chen
AU  - Liu, Chunyu
AU  - Hwang, Shih-Jen
AU  - Joehanes, Roby
AU  - Fornage, Myriam
AU  - Bressler, Jan
AU  - van Meurs, Joyce B J
AU  - Debrabant, Birgit
AU  - Mengel-From, Jonas
AU  - Hjelmborg, Jacob
AU  - Christensen, Kaare
AU  - Vokonas, Pantel
AU  - Schwartz, Joel
AU  - Gahrib, Sina A
AU  - Sotoodehnia, Nona
AU  - Sitlani, Colleen M
AU  - Kunze, Sonja
AU  - Gieger, Christian
AU  - Peters, Annette
AU  - Waldenberger, Melanie
AU  - Deary, Ian J
AU  - Ferrucci, Luigi
AU  - Qu, Yishu
AU  - Greenland, Philip
AU  - Lloyd-Jones, Donald M
AU  - Hou, Lifang
AU  - Bandinelli, Stefania
AU  - Voortman, Trudy
AU  - Hermann, Brenner
AU  - Baccarelli, Andrea
AU  - Whitsel, Eric
AU  - Pankow, James S
AU  - Levy, Daniel
TI  - Integrative analysis of clinical and epigenetic biomarkers of mortality.
JO  - Aging cell
VL  - 21
IS  - 6
SN  - 1474-9718
CY  - Oxford [u.a.]
PB  - Wiley-Blackwell
M1  - DKFZ-2022-00980
SP  - e13608
PY  - 2022
N1  -  2022 Jun;21(6):e13608
AB  - 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
KW  - DNA methylation (Other)
KW  - cancer (Other)
KW  - cardiovascular disease (Other)
KW  - machine learning (Other)
KW  - mortality (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:35546478
DO  - DOI:10.1111/acel.13608
UR  - https://inrepo02.dkfz.de/record/179931
ER  -