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Background Alzheimer's Disease (AD) is a complex neurodegenerative disorder, with women comprising nearly two-thirds of individuals with AD. However, sex-specific heterogeneity in AD progression remains insufficiently understood. A data-driven approach is needed to characterise such heterogeneity from longitudinal electronic health records (EHRs). Methods We developed a deep learning-based framework to uncover sex-specific AD sub-phenotypes using longitudinal EHRs from OneFlorida+ Clinical Research Consortium. We constructed temporal representations of these EHRs and employed an autoencoder architecture to generate latent embeddings, followed by clustering to derive sex-specific sub-phenotypes with associated progression patterns. We also performed statistical and survival analyses to unravel the characteristics of our identified sub-phenotypes. Findings From 1665 individuals with AD (961 females, 704 males), we identified five major sex-specific sub-phenotypes of AD with distinct progression pathways and comorbidity patterns. Female-dominant sub-phenotypes presented later AD onset, longer disease duration, and enrichment of respiratory and neurological disorders. Male-dominant sub-phenotypes exhibited earlier onset, shorter duration, and higher prevalence of endocrine and metabolic conditions. Survival analysis showed significant differences in time to AD onset across sub-phenotypes. Interpretation Our findings revealed distinct disease trajectories and comorbidity patterns between male- and female-dominant subgroups with AD. This study provides insight into sex-specific AD progression and demonstrates a data-driven framework for characterising disease heterogeneity using longitudinal EHRs. Funding This study was supported by grants from the Florida Department of Health, the Centers for Disease Control and Prevention, the National Institute of Environmental Health Sciences, and the NIH National Center for Advancing Translational Sciences.
Meng et al. (Thu,) studied this question.