Causality-enriched epigenetic age uncouples damage and adaptation.

Area of Science:

• Genetics
• Epigenetics
• Computational Biology

Background:

• Machine learning models predict biological age using DNA methylation but lack causal insights.
• Existing epigenetic clocks do not fully capture the causal mechanisms of aging.

Purpose of the Study:

• To identify CpG sites causally linked to aging-related traits using epigenome-wide Mendelian randomization.
• To develop novel causality-enriched epigenetic clocks for aging research.

Main Methods:

• Epigenome-wide Mendelian randomization applied to large-scale genetic and DNA methylation data.
• Development of DamAge and AdaptAge clocks incorporating causal information.
• Correlation analysis with adverse outcomes (mortality) and beneficial adaptations.

Main Results:

• Identified CpG sites with potential causal roles in aging, distinct from existing clocks.
• Developed DamAge clock correlating with mortality and AdaptAge clock with beneficial adaptations.
• Demonstrated sensitivity of causality-enriched clocks to short-term interventions.

Conclusions:

• Established a framework for causal insights in epigenetic clocks, advancing aging biomarker development.
• Provided a landscape of CpG sites causally linked to lifespan and healthspan.
• Highlighted potential for assessing and reversing age-associated changes.