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@ARTICLE{Li:180173,
      author       = {X. Li$^*$ and B. Schöttker$^*$ and B. Holleczek$^*$ and H.
                      Brenner$^*$},
      title        = {{A}ssociations of {DNA} methylation algorithms of aging and
                      cancer risk: {R}esults from a prospective cohort study.},
      journal      = {EBioMedicine},
      volume       = {81},
      issn         = {2352-3964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2022-01145},
      pages        = {104083},
      year         = {2022},
      note         = {#EA:C070#LA:C070#LA:C120#},
      abstract     = {Previous studies have shown that three DNA methylation
                      (DNAm) based algorithms of aging, DNAm PhenoAge acceleration
                      (AgeAccelPheno), DNAm GrimAge acceleration (AgeAccelGrim),
                      and mortality risk score (MRscore), to be strong predictors
                      of mortality and aging related outcomes. We aimed to
                      investigate if and to what extent these algorithms predict
                      cancer.In four subsets (n = 727, 1003, 910, and 412) of a
                      population-based cohort from Germany, DNA methylation in
                      whole blood was measured using the Infinium Methylation EPIC
                      BeadChip kit or the Infinium HumanMethylation450K BeadChip
                      Assay (Illumina.Inc, San Diego, CA, USA). AgeAccelPheno,
                      AgeAccelGrim, and a revised MRscore based on 8 CpGs only
                      (MRscore-8CpGs), were calculated. Hazard ratios (HRs) were
                      calculated to assess associations of the three DNAm
                      algorithms with total cancer risk and risk of invasive
                      breast, lung, prostate, and colorectal cancer
                      incidence.During 17 years of follow-up, a total of 697
                      malignant tumors (thereof breast cancer = 75, lung cancer =
                      146, prostate cancer = 114, colorectal cancer = 155) were
                      observed. All three algorithms showed strong positive
                      associations with lung cancer risk in a dose response
                      manner, with adjusted HRs per SD increase in AgeAccelPheno,
                      AgeAccelGrim, and MRscore-8CpGs, of 1·37 (1·03-1·82),
                      1·74 (1·11-2·73), and 2·06 (1·39-3·06), respectively.
                      By contrast, strong inverse associations were seen with
                      breast cancer risk [adjusted HRs 0·65 (0·49-0·86), 0·45
                      (0·25-0·80), and 0·42 (0·25-0·70), respectively]. Weak
                      positive associations of MRscore-8CpGs were seen with total
                      cancer risk.The DNAm algorithms, particularly the
                      MRscore-8CpGs, have potential to contribute to site-specific
                      cancer risk prediction.The ESTHER study was funded by grants
                      from the Baden-Württemberg state Ministry of Science,
                      Research and Arts (Stuttgart, Germany), the Federal Ministry
                      of Education and Research (Berlin, Germany), the Federal
                      Ministry of Family Affairs, Senior Citizens, Women and Youth
                      (Berlin, Germany), and the Saarland State Ministry of
                      Health, Social Affairs, Women and the Family (Saarbrücken,
                      Germany). The work of Xiangwei Li was supported by a grant
                      from Fondazione Cariplo (Bando Ricerca Malattie
                      invecchiamento, #2017-0653).},
      keywords     = {Age acceleration (Other) / Cancer risk (Other) / DNA
                      methylation (Other) / Epigenetic clock (Other)},
      cin          = {C070 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35636319},
      doi          = {10.1016/j.ebiom.2022.104083},
      url          = {https://inrepo02.dkfz.de/record/180173},
}