Aging Clocks Show Promise for Longevity Interventions but Face Reliability Challenges
Aging clocks, developed using machine learning techniques on biological data, are designed to predict age-related conditions and mortality risk. These clocks utilize various data types, including omics data, clinical chemistry results from blood tests, and imaging data. When applied to individuals, a higher predicted age compared to chronological age often correlates with increased mortality risk and the presence of age-related conditions. The primary goal for these aging clocks is to provide a rapid assessment tool for novel longevity interventions, potentially eliminating the need for lengthy, decade-spanning clinical trials. However, a significant challenge remains: ensuring the reliability of these clocks in assessing the efficacy of new interventions. There is currently no direct, well-mapped connection between the data used to derive these clocks and the underlying mechanisms of aging, making it difficult to predict whether a given clock will accurately estimate the effects of a treatment without ext... 
