Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations

Alice S. Tang, Tomiko Oskotsky, Shreyas Havaldar, William G. Mantyh, Mesude Bicak, Caroline Warly Solsberg, Sarah Woldemariam, Billy Zeng, Zicheng Hu, Boris Oskotsky, Dena Dubal, Isabel E. Allen, Benjamin S. Glicksberg, Marina Sirota

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Alzheimer’s Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.

Original languageEnglish
Article number675
JournalNature Communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022

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