Comorbidity patterns and sex differences in late-onset Alzheimer’s disease using electronic records
Abstract
In this retrospective study, we applied unsupervised learning techniques to electronic medical records from the University of California, San Francisco (UCSF) to characterize Alzheimer's disease (AD) comorbidity patterns. Given the well-known female predominance in AD, we performed sex-stratified analyses to explore differences in comorbidity patterns based on sex. Findings were evaluated using an independent UC-wide dataset. Among 8,363 patients in the UCSF dataset, we observed five data-driven comorbidity clusters related to cardiovascular conditions, gastrointestinal disorders, and frailty-related conditions such as pneumonia and pressure ulcers, as well as groups with higher and lower overall comorbidity burden. Sex-stratified analyses within clusters identified variation in comorbidity patterns, including circulatory diseases in males in cluster 2 and bladder stones in females in cluster 3. Key results demonstrate partial consistency in the UC-wide dataset. Our study provides a structured characterization of comorbidity heterogeneity in AD and highlights sex-related differences, though findings should be interpreted as descriptive and hypothesis-generating.
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BibTeX
@article{katsuhara2026comorbidity,
title = {{Comorbidity patterns and sex differences in late-onset Alzheimer’s disease using electronic records}},
author = {Yukari Katsuhara and Umair Khan and Zachary A. Miller and Isabel E. Allen and Tomiko T. Oskotsky and Marina Sirota and Alice S. Tang},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.117753},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03134-2},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Comorbidity patterns and sex differences in late-onset Alzheimer’s disease using electronic records
AU - Yukari Katsuhara
AU - Umair Khan
AU - Zachary A. Miller
AU - Isabel E. Allen
AU - Tomiko T. Oskotsky
AU - Marina Sirota
AU - Alice S. Tang
PY - 2026
DA - 2026/10/08
JO - iScience
DO - 10.1016/j.isci.2026.117753
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03134-2
AB - In this retrospective study, we applied unsupervised learning techniques to electronic medical records from the University of California, San Francisco (UCSF) to characterize Alzheimer's disease (AD) comorbidity patterns. Given the well-known female predominance in AD, we performed sex-stratified analyses to explore differences in comorbidity patterns based on sex. Findings were evaluated using an independent UC-wide dataset. Among 8,363 patients in the UCSF dataset, we observed five data-driven comorbidity clusters related to cardiovascular conditions, gastrointestinal disorders, and frailty-related conditions such as pneumonia and pressure ulcers, as well as groups with higher and lower overall comorbidity burden. Sex-stratified analyses within clusters identified variation in comorbidity patterns, including circulatory diseases in males in cluster 2 and bladder stones in females in cluster 3. Key results demonstrate partial consistency in the UC-wide dataset. Our study provides a structured characterization of comorbidity heterogeneity in AD and highlights sex-related differences, though findings should be interpreted as descriptive and hypothesis-generating.
SN - 2589-0042
ER -