Edinburgh Research Explorer

DNA methylation-based measures of biological age: meta-analysis predicting time to death

Research output: Contribution to journalArticle

  • Brian H Chen
  • Elena Colicino
  • Marjolein J Peters
  • Cavin K Ward-Caviness
  • Pei-Chien Tsai
  • Nicholas S Roetker
  • Allan C Just
  • Ellen W Demerath
  • Weihua Guan
  • Jan Bressler
  • Myriam Fornage
  • Stephanie Studenski
  • Amy R Vandiver
  • Ann Zenobia Moore
  • Toshiko Tanaka
  • Douglas P Kiel
  • Liming Liang
  • Pantel Vokonas
  • Joel Schwartz
  • Kathryn L Lunetta
  • Joanne M Murabito
  • Stefania Bandinelli
  • Dena G Hernandez
  • David Melzer
  • Michael Nalls
  • Luke C Pilling
  • Timothy R Price
  • Andrew B Singleton
  • Christian Gieger
  • Rolf Holle
  • Anja Kretschmer
  • Florian Kronenberg
  • Sonja Kunze
  • Jakob Linseisen
  • Christine Meisinger
  • Wolfgang Rathmann
  • Melanie Waldenberger
  • Sonia Shah
  • Naomi R Wray
  • Allan F McRae
  • Oscar H Franco
  • Albert Hofman
  • André G Uitterlinden
  • Devin Absher
  • Themistocles Assimes
  • Morgan E Levine
  • Ake T Lu
  • Philip S Tsao
  • Lifang Hou
  • JoAnn E Manson
  • Cara L Carty
  • Andrea Z LaCroix
  • Alexander P Reiner
  • Tim D Spector
  • Andrew P Feinberg
  • Daniel Levy
  • Andrea Baccarelli
  • Joyce van Meurs
  • Jordana T Bell
  • Annette Peters
  • James S Pankow
  • Luigi Ferrucci
  • Steve Horvath

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Rights statement: Authors retain ownership of the copyright for their article, but they allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in Aging journal, so long as the original authors and source are cited.

    Final published version, 3 MB, PDF-document

    Licence: Creative Commons: Attribution (CC-BY)

Original languageEnglish
JournalAging
DOIs
Publication statusPublished - 28 Sep 2016

Abstract

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10(-9)), independent of chronological age, even after adjusting for additional risk factors (p<5.4x10(-4)), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10(-43)). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

Download statistics

No data available

ID: 28140681