{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T07:02:23Z","timestamp":1779778943100,"version":"3.53.1"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) frequently coexists with risk factors that modify its clinical course. The combined presence of cancer and metabolic encephalopathy (ME) in AD represents a particularly vulnerable and understudied phenotype. We investigated whether cancer-associated risk profiles differ between AD patients with and without metabolic encephalopathy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used multivariate logistic regression to identify clinical, vascular, pulmonary, neurocognitive, psychiatric, and treatment-related factors distinguishing (i) AD patients with metabolic encephalopathy with and without cancer (AD\u202f+\u202fME\u202f\u00b1\u202fC) and (ii) AD patients without metabolic encephalopathy with and without cancer (AD \u2013 ME\u202f\u00b1\u202fC). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were used to identify risk factors and phenotype-specific associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the total cohort, 10,516 patients had metabolic encephalopathy, and 118,253 did not. Cancer coexistence was present in 146\u202fAD\u202f+\u202fME patients and 1,167\u202fAD \u2013 ME patients. Among AD\u202f+\u202fME patients, cancer was strongly associated with cerebrovascular accident (OR\u202f=\u202f3.47, 95% CI 2.16\u20135.59), secondary dementia (OR\u202f=\u202f9.89, 95% CI 3.26\u201329.98), mild cognitive impairment (OR\u202f=\u202f5.20, 95% CI 1.98\u201313.27), chronic obstructive pulmonary disease (OR\u202f=\u202f7.66, 95% CI 5.20\u201311.29), and SSRI use (OR\u202f=\u202f3.27, 95% CI 2.21\u20134.87). In contrast, memantine, buspirone, and valproate were associated with AD\u202f+\u202fME without cancer. Among AD\u2013ME patients, cancer was associated with dyslipidemia, peripheral vascular disease, congestive heart failure, arteriosclerosis, COPD, and cutaneous ulcers, reflecting chronic systemic illness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Metabolic encephalopathy was associated with a different clinical profile in cancer-associated AD. Patients with ME exhibited increased systemic and neurologic vulnerability (e.g., vascular comorbidity and frailty indicators) rather than differences in baseline cognitive severity alone.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1810937","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T06:04:03Z","timestamp":1779775443000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Risk factors associated with cancer and metabolic encephalopathy in Alzheimer\u2019s disease patients"],"prefix":"10.3389","volume":"18","author":[{"given":"Danny","family":"Pham","sequence":"first","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Connor","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jillian","family":"Florez-Bhandari","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Faulstich","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timi","family":"Ojo","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Aloi","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Goodwin","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Molinaroli College of Engineering and Computing, North Greenville University","place":["Columbia, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurie","family":"Roley","sequence":"additional","affiliation":[{"name":"PRISMA Health Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel I.","family":"Nathaniel","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas I.","family":"Nathaniel","sequence":"additional","affiliation":[{"name":"University of South Carolina School of Medicine Greenville","place":["Greenville, SC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1668420","DOI":"10.3389\/fneur.2025.1668420","article-title":"Evaluating machine learning models for stroke prediction based on clinical variables","volume":"16","author":"Akinwumi","year":"2025","journal-title":"Front. 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