This project develops behavioral aging clocks to estimate the gap between behavioral and chronological age. It uses global datasets to analyze how individual behaviors and macrosocial exposures—such as inequality, pollution, and migration—contribute to accelerated or delayed aging. Advanced machine learning techniques are applied to uncover key patterns across diverse regions. The findings aim to support public health strategies and promote more equitable aging outcomes. This work contributes to a deeper, more comprehensive understanding of aging trajectories worldwide.
Fellow: Hernán Hernández
Advisor: Agustín Ibáñez
Support: Davos Alzheimer’s Collaborative
