{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T17:56:27Z","timestamp":1793642187660,"version":"4.5.1"},"posted":{"date-parts":[[2025,4,25]]},"group-title":"Neurology","reference-count":40,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"crossref","award":["R01AG060393"],"award-info":[{"award-number":["R01AG060393"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"crossref","award":["R01AG057683"],"award-info":[{"award-number":["R01AG057683"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,10]]},"abstract":"<jats:title>Summary<\/jats:title>\n                <jats:p>In this retrospective study, we applied unsupervised learning techniques to electronic medical records from UCSF to characterize Alzheimer\u2019s disease comorbidity patterns. Given the well-known female predominance in Alzheimer\u2019s disease, 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.<\/jats:p>\n                <jats:sec>\n                  <jats:title>Highlights<\/jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>Among an EHR cluster analysis, Alzheimer\u2019s disease patients stratify into five data-set derived comorbidity clusters.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Clinical patterns and comorbidity burdens vary across patient subgroups.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Sex-specific differences influence comorbidity profiles in selected patient clusters.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Highly granular cluster configurations lack robust cross-dataset reproducibility.<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.1101\/2025.04.24.25326306","type":"posted-content","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:55:13Z","timestamp":1745607313000},"source":"Crossref","is-referenced-by-count":1,"title":["Comorbidity patterns and sex differences in late-onset Alzheimer\u2019s disease using electronic records"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2335-5413","authenticated-orcid":false,"given":"Yukari","family":"Katsuhara","sequence":"first","affiliation":[{"name":"Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA"},{"name":"Health Data Science Graduate Program, UCSF, San Francisco, CA, 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