Tracking the Epigenetic Clock Across the Human Life Course: A Meta-analysis of Longitudinal Cohort Data

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 Δ(age) (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 Δ(age) in the following cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (n = 986, total age-range 7–19 years, 2 waves), ALSPAC mothers (n = 982, 16–60 years, 2 waves), InCHIANTI (n = 460, 21–100 years, 2 waves), SATSA (n = 373, 48–99 years, 5 waves), Lothian Birth Cohort 1936 (n = 1,054, 70–76 years, 3 waves), and Lothian Birth Cohort 1921 (n = 476, 79–90 years, 3 waves). Linear mixed models were used to track longitudinal change in Δ(age) within each cohort. RESULTS: For both epigenetic age measures, Δ(age) showed a declining trend in almost all of the cohorts. The correlation between Δ(age) 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.