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Tracking the epigenetic clock across the human life course: A meta-analysis of longitudinal cohort data

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  • Riccardo Marioni
  • Matthew J Suderman
  • Brian H Chen
  • Steve Horvath
  • Stefania Bandinelli
  • Tiffany J Morris
  • Stephan Becker
  • Luigi Ferrucci
  • Nancy L Pedersen
  • Caroline L. Relton
  • Ian Deary
  • Sara Hägg

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Original languageEnglish
Article numbergly060
Pages (from-to)1-5
JournalJournals of Gerontology, Series A
Early online date20 Mar 2018
DOIs
Publication statusE-pub ahead of print - 20 Mar 2018

Abstract

Background
Epigenetic clocks based on DNA methylation yield high correlations with chronological age in cross-sectional data. Due to a paucity of longitudinal data, it is not known how Δage (epigenetic age – chronological age) changes over time or if it remains constant from childhood to old age. Here, we investigate this using longitudinal DNA methylation data from five datasets, covering most of the human life course.

Methods
Two measures of the epigenetic clock (Hannum and Horvath) are used to calculate Δage in the following cohorts: ALSPAC offspring (n~950, total age-range 7-19 years, 2 waves), ALSPAC mothers (n~900, 16-60 years, 2 waves), InCHIANTI (n~460, 21-100 years, 2 waves), SATSA (n~370, 48-99 years, 5 waves), Lothian Birth Cohort 1936 (n~1,050, 70-76 years, 3 waves), and Lothian Birth Cohort 1921 (n~470, 79-90 years, 3 waves). Linear mixed models were used to track longitudinal change in Δage within each cohort.

Results
For both epigenetic age measures, Δage showed a declining trend in almost all of the cohorts. The correlation between Δage across waves ranged from 0.22 to 0.82 for Horvath and 0.25 to 0.71 for Hannum, with stronger associations in samples collected closer in time.

Conclusions
Epigenetic age increases at a slower rate than chronological age across the life course, especially in the oldest population. Some of the effect is likely driven by survival bias, where healthy individuals are those maintained within a longitudinal study, although other factors like the age distribution of the underlying training population may also have influenced this trend.
Key words: epigenetic clock, longitudinal, life-course perspective 

    Research areas

  • epigenetic clock, longitudinal, life-course perspective

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