Evidence map›Paper›PMID 40313288›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Decoding subphenotypes in electronic medical records within late-onset Alzheimer's disease reveals heterogeneity and sex-specific differences.

Yukari Katsuhara, Umair Khan, Zachary A Miller, Isabel E Allen, Tomiko T Oskotsky, Marina Sirota, Alice S Tang

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yukari KatsuharaBakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.ORCID 0000-0003-2335-5413
Umair KhanBakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.ORCID 0000-0002-6361-4996
Zachary A MillerMemory and Aging Center, Department of Neurology, UCSF Weill Institute for Neurosciences, UCSF, San Francisco, CA, USA.ORCID 0000-0002-5991-3053
Isabel E AllenDepartment of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.ORCID 0000-0001-7393-5120
Marina SirotaBakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.ORCID 0000-0002-7246-6083
Alice S TangBakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.ORCID 0000-0003-4745-0714

Funding

ApoE Genotype-Directed Drug Repositioning and Combination Therapy for Alzheimer's DiseaseR01AG057683 · NIA · J. DAVID GLADSTONE INSTITUTES · PI HUANG, YADONG, SIROTA, MARINA · 2017 to 2021
$4.3M
An Integrative Multi-Omics Approach to Elucidate Sex-Specific Differences in Alzheimers DiseaseR01AG060393 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2018 to 2022
$4.1M
Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s DiseaseF30AG079504 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TANG, ALICE SUMMER · 2022 to 2025
$187k
NIA NIH HHS F30 AG079504NIA NIH HHS R01 AG057683NIA NIH HHS R01 AG060393
6 · The paper itself

Abstract

We applied unsupervised learning techniques to electronic medical records from UCSF to identify distinct Alzheimer's disease subgroups based on comorbidity profiles. Given the well-known female sex predominance in Alzheimer's disease prevalence, we performed sex-stratified analyses to evaluate differences in disease manifestations based on sex. Findings were validated using an independent UC-Wide dataset. Among 8,363 patients, we identified five Alzheimer's disease subphenotypes, characterized by comorbidities related to cardiovascular conditions, gastrointestinal disorders, and frailty-related conditions such as pneumonia and pressure ulcers. We further refined significant comorbidity variations across clusters through sex-stratified analyses, observing a higher prevalence of circulatory diseases in males in Cluster 2 and bladder stones in females in Cluster 3. Key results were consistent across the UCSF and UC-Wide datasets. Our study identifies clinically meaningful Alzheimer's disease subgroups, along with sex-specific variations, suggesting underlying biological factors, and indicates the potential utility of these findings in informing individualized therapeutic regimens.

Indexed as

Alzheimer’scluster analysiselectronic medical recordsindividualized therapyphenotypesex differencesunsupervised machine learning

Identifiers

PMID40313288
PMCPMC12045436

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.