Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer’s Disease in the HABS-HD Study

Article type: Research Article

Authors: Zhang, Fan^{a; b; *} | Petersen, Melissa^{a; b} | Johnson, Leigh^{a; c} | Hall, James^{a; b} | O’Bryant, Sid E.^{a; b} | for the Health and Aging Brain Study (HABS-HD) Study Team¹

Affiliations: [a] Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, USA | [b] Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA | [c] Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA

Correspondence: [*] Correspondence to: Fan Zhang, Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX 76107, USA. Tel.: +1 817 735 2947; E-mail: fan.zhang@unthsc.edu.

Note: [1] HABS-HD MPIs: Sid E O’Bryant, Kristine Yaffe, Arthur Toga, Robert Rissman, and Leigh Johnson; and the HABS-HD Investigators: Meredith Braskie, Kevin King, James R Hall, Melissa Petersen, Raymond Palmer, Robert Barber, Yonggang Shi, Fan Zhang, Rajesh Nandy, Roderick McColl, David Mason, Bradley Christian, Nicole Philips, Stephanie Large, Joe Lee, Badri Vardarajan, Monica Rivera Mindt, Amrita Cheema, Lisa Barnes, Mark Mapstone, Annie Cohen, Amy Kind, Ozioma Okonkwo, Raul Vintimilla, Zhengyang Zhou, Michael Donohue, Rema Raman, Matthew Borzage, Michelle Mielke, Beau Ances, Ganesh Babulal, Jorge Llibre-Guerra, Carl Hill and Rocky Vig.

Abstract: Background:Blood biomarkers have the potential to transform Alzheimer’s disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored. Objective:This study aims to enhance blood biomarkers’ sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration’s 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.

Keywords: Alzheimer’s disease, blood biomarkers, comorbidities, machine learning, recursive feature elimination, support vector machine

DOI: 10.3233/JAD-230755

Journal: Journal of Alzheimer's Disease, vol. 96, no. 4, pp. 1529-1546, 2023