PUMA: A Phenotypic Unsupervised Model of Aging Reveals Distinct Aging Dimensions

Research overview

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Aging is a multidimensional process, yet most aging models reduce it to a single score to estimate biological aging and predict health outcomes, disease risk, or mortality. Here, we introduce PUMA (Phenotypic Unsupervised Model of Aging), a framework that characterizes aging through multiple phenotypic dimensions. Applying PUMA to more than 1,000 traits spanning behavioral, psychological, social, physical, environmental, and biomedical domains in over 150,000 individuals from the Lifelines cohort, we identified seven distinct phenotypic aging dimensions. These dimensions were significantly associated with the future incidence of major age-related diseases—including cancer, diabetes, COPD, heart failure, stroke, and Parkinson’s disease— demonstrating the potential of PUMA to stratify individuals by disease risk and identify phenotypic domains for targeted intervention. Notably, dimensions reflecting psychosocial factors, particularly cumulative life stress, predicted disease risk as strongly as, or more strongly than, traditional biomedical risk factors, highlighting the importance of psychological and social influences in aging and disease risk. The observation that phenotypic aging dimensions are differentially associated with the future risk of age-related diseases supports a multidimensional model of aging, indicating that aging is not a single uniform process. Because PUMA relies on accessible phenotypic data, it provides an interpretable framework for disease risk stratification and targeted preventive strategies.

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