Decoding subphenotypes in electronic medical records within late-onset Alzheimer's disease reveals heterogeneity and sex-specific differences.

We applied unsupervised learning techniques to electronic medical records from UCSF to identify distinct Alzheimer's disease subgroups based on comorbidity profiles. Given the well-known female sex predominance in Alzheimer's disease prevalence, we performed sex-stratified analyses to evaluate differences in disease manifestations based on sex. Findings were validated using an independent UC-Wide dataset. Among 8,363 patients, we identified five Alzheimer's disease subphenotypes, characterized by comorbidities related to cardiovascular conditions, gastrointestinal disorders, and frailty-related conditions such as pneumonia and pressure ulcers. We further refined significant comorbidity variations across clusters through sex-stratified analyses, observing a higher prevalence of circulatory diseases in males in Cluster 2 and bladder stones in females in Cluster 3. Key results were consistent across the UCSF and UC-Wide datasets. Our study identifies clinically meaningful Alzheimer's disease subgroups, along with sex-specific variations, suggesting underlying biological factors, and indicates the potential utility of these findings in informing individualized therapeutic regimens.