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

Author

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  • Alice S. Tang

    (Bakar Computational Health Sciences Institute, UCSF
    Graduate Program in Bioengineering, UCSF
    School of Medicine, UCSF)

  • Tomiko Oskotsky

    (Bakar Computational Health Sciences Institute, UCSF
    Department of Pediatrics, 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
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai)

  • Caroline Warly Solsberg

    (Pharmaceutical Sciences and Pharmacogenomics, UCSF
    University of California, San Francisco
    Memory and Aging Center, 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
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai)

  • Marina Sirota

    (Bakar Computational Health Sciences Institute, UCSF
    Department of Pediatrics, UCSF)

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.

Suggested Citation

  • 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. , 2022. "Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28273-0
    DOI: 10.1038/s41467-022-28273-0
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    References listed on IDEAS

    as
    1. Cathryn M. Delude, 2015. "Deep phenotyping: The details of disease," Nature, Nature, vol. 527(7576), pages 14-15, November.
    2. Matthias Arnold & Kwangsik Nho & Alexandra Kueider-Paisley & Tyler Massaro & Kevin Huynh & Barbara Brauner & Siamak MahmoudianDehkordi & Gregory Louie & M. Arthur Moseley & J. Will Thompson & Lisa St , 2020. "Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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