Identifying Sex-Specific Sub-phenotypes of Alzheimer’s Disease Progression Using Longitudinal Electronic Health Records

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder strongly influenced by sex differences, with women comprising nearly two-thirds of cases. However, sex-specific progression patterns remain underexplored due to unclear clinical and molecular mechanisms. To address this gap, we developed a temporal autoencoder framework to identify sex-specific AD sub-phenotypes using longitudinal electronic health record (EHR) data from the OneFlorida+ Clinical Research Consortium. Sequential EHRs were encoded into latent representations and clustered to derive disease states, which were assembled into progression pathways. This approach uncovered five primary sex-stratified sub-phenotypes with distinct trajectories and phenotypic characteristics. Survival and cumulative prevalence analyses further revealed heterogeneous temporal dynamics of AD onset and comorbidity accumulation between female- and male-dominant groups. By integrating deep learning with large-scale real-world data, our framework advances understanding of sex-based heterogeneity in AD progression and provides a scalable tool for early risk stratification, personalized intervention, and improved clinical trial design.