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

Area of Science:

• Epigenetics
• Aging Research
• Biomarkers of Aging

Background:

• Epigenetic clocks correlate strongly with chronological age in cross-sectional studies.
• Longitudinal data on epigenetic age changes over the human lifespan is scarce.
• Understanding longitudinal Δage (epigenetic age - chronological age) is crucial for aging research.

Purpose of the Study:

• To investigate the longitudinal changes in Δage across the human life course.
• To determine if Δage remains constant or varies from childhood to old age.
• To utilize multiple longitudinal DNA methylation datasets for comprehensive analysis.

Main Methods:

• Analysis of DNA methylation data from five diverse longitudinal cohorts.
• Calculation of Δage using Hannum and Horvath epigenetic clock measures.
• Application of linear mixed models to track longitudinal Δage trends within cohorts.

Main Results:

• A declining trend in Δage was observed in most cohorts for both epigenetic age measures.
• Correlation between Δage across waves varied (0.22-0.82 for Horvath, 0.25-0.71 for Hannum).
• Stronger Δage associations were found when sample collection times were closer.

Conclusions:

• Epigenetic age tends to increase at a slower rate than chronological age throughout life.
• This slower rate is particularly evident in older populations.
• Potential drivers include survival bias and the age distribution of training data for epigenetic clocks.