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Review
. 2019 Jan 1;74(1):57-61.
doi: 10.1093/gerona/gly060.

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

Riccardo E Marioni  1   2 , Matthew Suderman  3 , Brian H Chen  4 , Steve Horvath  5   6 , Stefania Bandinelli  7 , Tiffany Morris  8 , Stephan Beck  8 , Luigi Ferrucci  4 , Nancy L Pedersen  9 , Caroline L Relton  3 , Ian J Deary  1   10 , Sara Hägg  9
Affiliations
Review

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

Riccardo E Marioni et al. J Gerontol A Biol Sci Med Sci. .

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: 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 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.

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Figures

Figure 1.
Figure 1.
Mean linear longitudinal trajectories of epigenetic Δage. For each data set, mixed models were applied and predicted values, derived from the model intercept and fixed effect estimates for age, were plotted to illustrate the Δage trajectories across the life span. The x-axis represents the age where the cohort specific trajectory is plotted corresponding to the age span covered in that cohort. The y-axis shows the Δage.
Table 2.
Table 2.
Pearson Correlations of Epigenetic Δage Between Waves in Each Cohort
Figure 2.
Figure 2.
Within-cohort correlations of epigenetic Δage across study waves. For each data set, correlations between all possible combinations of waves were calculated and plotted. On the x-axis is the time between the two measurements in years and on the y-axis is the correlation coefficient.
See this image and copyright information in PMC

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