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@ARTICLE{Huan:179931,
      author       = {T. Huan and S. Nguyen and E. Colicino and C. Ochoa-Rosales
                      and W. D. Hill and J. A. Brody and M. Soerensen and Y. Zhang
                      and A. Baldassari and M. A. Elhadad and T. Toshiko and Y.
                      Zheng and A. Domingo-Relloso and D. H. Lee and J. Ma and C.
                      Yao and C. Liu and S.-J. Hwang and R. Joehanes and M.
                      Fornage and J. Bressler and J. B. J. van Meurs and B.
                      Debrabant and J. Mengel-From and J. Hjelmborg and K.
                      Christensen and P. Vokonas and J. Schwartz and S. A. Gahrib
                      and N. Sotoodehnia and C. M. Sitlani and S. Kunze and C.
                      Gieger and A. Peters and M. Waldenberger and I. J. Deary and
                      L. Ferrucci and Y. Qu and P. Greenland and D. M. Lloyd-Jones
                      and L. Hou and S. Bandinelli and T. Voortman and B. Hermann
                      and A. Baccarelli and E. Whitsel and J. S. Pankow and D.
                      Levy},
      title        = {{I}ntegrative analysis of clinical and epigenetic
                      biomarkers of mortality.},
      journal      = {Aging cell},
      volume       = {21},
      number       = {6},
      issn         = {1474-9718},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {DKFZ-2022-00980},
      pages        = {e13608},
      year         = {2022},
      note         = {2022 Jun;21(6):e13608},
      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.},
      keywords     = {DNA methylation (Other) / cancer (Other) / cardiovascular
                      disease (Other) / machine learning (Other) / mortality
                      (Other)},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35546478},
      doi          = {10.1111/acel.13608},
      url          = {https://inrepo02.dkfz.de/record/179931},
}