High-dimensional Ageome Representations of Biological Aging across Functional Modules

Area of Science:

• Gerontology
• Computational Biology
• Epigenetics

Background:

• Aging is characterized by molecular changes leading to functional decline and increased disease risk.
• Current epigenetic aging clocks provide single age estimates but lack mechanistic detail.
• Biological aging may be better represented by the collective aging of multiple functional modules.

Purpose of the Study:

• To challenge the paradigm of single biological age estimates.
• To introduce Ageome, a computational framework for multi-pathway epigenetic age measurement.
• To provide a high-dimensional representation of aging dynamics.

Main Methods:

• Developed the Ageome computational framework.
• Applied Ageome to measure epigenetic ages of thousands of molecular pathways in mice and humans.
• Analyzed longevity intervention models and human cohorts.

Main Results:

• Ageome provides a high-dimensional view of biological aging across cellular functions.
• Distinct patterns of pathway-specific age deceleration were observed in longevity interventions.
• Cell reprogramming showed mixed effects, rejuvenating some modules while accelerating others.
• Ageome revealed heterogeneity in mortality prediction and improved prediction for age-related diseases like cancer in human cohorts.

Conclusions:

• The Ageome framework offers a comprehensive and interpretable approach to aging assessment.
• It provides insights into aging mechanisms and potential intervention targets.
• Ageome moves beyond single-point biological age estimates for a more nuanced understanding of aging.