OBJECTIVE: Existing biological age (BA) models often oversimplify aging's complexity, offering single-dimensional metrics. However, these fail to capture the critical heterogeneity of aging across organs.. This study aims to develop a machine learning-based unified framework to assess and interpret multi-organ biological aging comprehensively.
METHOD: Using data from UK Biobank participants, we trained and integrated organ-specific BA estimates to assess multidimensional BA within an ensemble learning framework, and uncover distinct aging patterns.
RESULT: Our Fusion BA (an overall estimation of BA) was significantly correlated with chronological age (CA) (mean absolute error (MAE): 4.473 years; Pearson correlation: 0.718, P < 0.01). Accelerated Fusion BA derived from the ensemble model (contrast between Fusion BA and CA) predicted 10-year mortality (HR=1.504, 95 % CI: 1.438-1.574). Organ-specific BA correlated with organ disease risk and effectively captured distinct aging patterns.
CONCLUSION: This framework enables systemic and organ-specific aging assessment, provides actionable tools and insights for clinical risk.
