Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance

Area of Science:

• Epigenetics
• Genomics
• Biostatistics

Background:

• Epigenetic Age (EA) estimates biological age using DNA methylation (DNAm).
• Epigenetic Age Acceleration (EAA) represents deviations from chronological aging and is linked to health outcomes.
• Longitudinal studies on EA are increasing, but methodological variations exist.

Purpose of the Study:

• To evaluate the robustness of different statistical models for analyzing longitudinal Epigenetic Age (EA) data.
• To identify optimal methods for estimating associations between predictors and longitudinal EA or Epigenetic Age Acceleration (EAA).

Main Methods:

• Simulations were used to compare various statistical models (linear mixed models, GEE) and outcome variables (EA vs. EAA).
• Chronological age and age-independent time variables were assessed as temporal dynamics.
• Real-world data with biological sex and birthweight as predictors were analyzed using the optimal model.

Main Results:

• Linear mixed models or generalized estimating equations using chronological age provided the most accurate effect size estimates.
• The choice between EA and EAA as an outcome had minimal impact on estimates.
• Males exhibited accelerated EA, and higher birthweight was associated with decelerated EA over time in children.

Conclusions:

• The findings offer guidance for selecting appropriate methodologies in future longitudinal Epigenetic Age (EA) studies.
• Accurate estimation of EA dynamics is crucial to avoid over- or underestimation of associations.
• Methodological choices significantly influence the interpretation of EA and EAA trends.