Aging-Related Diseases in the U.S. Population: A Data-Driven Approach Using All of Us Electronic Health Record Data.

Background

Aging is the strongest risk factor for most chronic diseases, yet aging-related diseases (ARDs) lack consistent, data-driven definitions. Prior work has proposed frameworks to classify ARDs based on age-specific onset patterns, but applications have largely relied on demographically homogeneous populations.

Methods

We extended a data-driven framework integrating unsupervised clustering and actuarial modeling to electronic health record data from 633,547 participants in the U.S. All of Us Research Program. Age-specific onset rates for 274 high-burden diseases between ages 21 and 74 were analyzed. Standardized onset curves were grouped using hierarchical agglomerative clustering. Aging-related patterns were assessed with Gompertz and Gompertz-Makeham (GM) models to quantify age-dependent increases in disease onset.

Results

Eleven clusters of disease onset trajectories were identified, with four main clusters encompassing 96.0% of diseases. Two clusters exhibited pronounced late-life increases in onset rate, while one showed earlier but moderate age-related increases, and another displayed largely age-independent patterns. Cardiovascular diseases and cancers were enriched in clusters with strong aging-related onset trajectories. Overall, 165 diseases demonstrated strong evidence of being aging-related, characterized by positive Gompertz age coefficients and good GM model fit. Median onset ages varied across clusters and disease categories and showed statistically significant discrepancies compared with U.K. populations.

Conclusions

In a large, racially diverse U.S. cohort, we identified distinct ARD clusters with shared age-related onset patterns. These findings extend prior work to a more diverse population and support integrated clustering and actuarial approaches to systematically define ARDs and inform aging research and public health strategies.