Seven dimensions of aging linked to disease risk in people
An observational analysis of over 150,000 people identified seven aging dimensions from more than 1,000 traits, spanning behavior, social circumstances and biomedical measures.
150,000 participants
Limit: The analysis did not measure whether targeting these dimensions prevents disease
Edited by Boris Djordjevic · AgingResearch.News
In an observational analysis of over 150,000 people from the Lifelines cohort, researchers used more than 1,000 traits to build a model of aging. The traits covered behavior, psychological and social factors, physical characteristics, environment and biomedical measures. Rather than reducing aging to one score, the model identified seven distinct dimensions, or patterns across traits.
These dimensions were associated with the future development of diseases including cancer, diabetes, heart failure, stroke and Parkinson’s disease. Dimensions reflecting psychological and social factors, particularly accumulated life stress, predicted disease risk as strongly as, or more strongly than, traditional biomedical risk factors. The authors said the different associations with disease risk supported a model in which aging is not a single, uniform process.
Why it matters
Models of biological aging aim to anticipate age-related disease. The authors propose that measuring several aspects of aging could help identify areas for targeted prevention.
Caveats
The observational analysis cannot establish that these trait patterns caused disease or that targeting them would prevent it. The findings come from a preprint that has not been peer-reviewed.
The paper
PUMA: A Phenotypic Unsupervised Model of Aging Reveals Distinct Aging Dimensions
University Medical Center Groningen
bioRxiv, Jul 27, 2026, Open access, CC BY, Preprint, not peer-reviewed
