Deep phenotyping of Alzheimer’s disease leveraging electronic medical records identifies sex-specific clinical associations
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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.
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The fifth real-world data study used one EHR database to identify medications enriched among AD cases and controls, with validation in an independent EHR.
“The fifth study used one EHR database to identify medications enriched among AD cases and controls, with validation in an independent EHR (Tang et al., 2022).”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Tang et al., 2022
One study leveraged data from two large-scale independent EHR databases.
“and one study leveraged data from two large-scale independent EHR databases (Tang et al., 2022).”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Tang et al., 2022
The AD repurposing candidates suggested and validated by the five studies using real-world data for validation included fluticasone, mometasone, dexamethasone, pioglitazone, febuxostat, atenolol, and sildenafil.
“The AD repurposing candidates suggested and validated by the five studies using real-world data for validation included fluticasone and mometasone (inhaled corticosteroids) (Xu et al., 2021), dexamethasone (a systemic corticosteroid) (Tang et al., 2022), pioglitazone (an antidiabetic drug) (Fang et al., 2022), febuxostat (a uric acid-lowering drug used in treatment of gout) (Fang et al., 2022),”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Tang et al., 2022
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“Going forward, leveraging this big data from diverse datasets (e.g., the National Institutes of Health All of Us Research Program, UK Biobank, and local EHRs) will be critical to identify clinically meaningful drug repurposing candidates for AD.”
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Self-citation
- Authors
- Tang, A. S., Oskotsky, T., Havaldar, S., Mantyh, W. G., Bicak, M., Solsberg, C. W., Woldemariam, S., Zeng, B., Hu, Z., Oskotsky, B., Dubal, D., Allen, I. E., Glicksberg, B. S., & Sirota, M.
- Affiliations
- University of California, San Francisco (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of Minnesota (US)
- Corresponding author
- Tang, Alice S.
- ORCID
- Venue
- Nature Communications
- Published
- 2022-02-03
- Volume
- 13
- Issue
- 1
- Pages
- 675-675
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8814236
- OpenAlex
- W4210339973
- Cited by
- 45
- References
- 129
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 11 · 2023: 5 · 2022: 4 · 2021: 1
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (RF1AG059319, AG059319, RF1AG068325, R01AG060393, R01AG057683)
- National Institute of General Medical Sciences (T32GM007618, T32GM007175)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, sex differences, clinical phenotyping, electronic health records, comorbidity networks, sex-stratified analysis, clinical associations, drug repurposing, graph embedding
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Mental Disorders, California, Female, Humans, Male, Musculoskeletal Diseases, Nervous System Diseases, New York, Phenotype, Sex Factors, Vascular Diseases, Cohort Studies, Comorbidity, Chi-Square Distribution, Databases, Factual, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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