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 and Marina Sirota ()
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Alice S. Tang: Bakar Computational Health Sciences Institute, UCSF
Tomiko Oskotsky: Bakar Computational Health Sciences Institute, UCSF
Shreyas Havaldar: Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai
William G. Mantyh: University of Minnesota School of Medicine
Mesude Bicak: Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai
Caroline Warly Solsberg: Pharmaceutical Sciences and Pharmacogenomics, UCSF
Sarah Woldemariam: Bakar Computational Health Sciences Institute, UCSF
Billy Zeng: School of Medicine, UCSF
Zicheng Hu: Bakar Computational Health Sciences Institute, UCSF
Boris Oskotsky: Bakar Computational Health Sciences Institute, UCSF
Dena Dubal: University of California, San Francisco
Isabel E. Allen: Department of Epidemiology and Biostatistics, UCSF
Benjamin S. Glicksberg: Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai
Marina Sirota: Bakar Computational Health Sciences Institute, UCSF

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28273-0

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