Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance.
Abstract
Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.
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Research summary
The paper addresses the concept of epigenetic age and its acceleration, which are relevant to understanding biological aging processes. However, it primarily focuses on methodological aspects and simulations rather than providing novel insights into the root causes of aging or potential interventions for lifespan extension. Its contributions are solid but limited in their broader implications for the field of longevity research.
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