{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:17:22Z","timestamp":1777396642944,"version":"3.51.4"},"reference-count":94,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2023,12,6]]},"abstract":"<jats:p>Background: Blood biomarkers have the potential to transform Alzheimer\u2019s disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored. Objective: This study aims to enhance blood biomarkers\u2019 sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration\u2019s efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features. Methods: We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity. Results: The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance. Conclusions: In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.<\/jats:p>","DOI":"10.3233\/jad-230755","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T11:34:59Z","timestamp":1700566499000},"page":"1529-1546","source":"Crossref","is-referenced-by-count":4,"title":["Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer\u2019s Disease in the HABS-HD Study"],"prefix":"10.1177","volume":"96","author":[{"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Health and Aging Brain Study (HABS-HD) Study Team","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melissa","family":"Petersen","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leigh","family":"Johnson","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, USA"},{"name":"Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Hall","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, University of North Texas Health Science Center, 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