Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques

Article type: Research Article

Authors: Chen, Yanxi^a | Su, Yi^b | Wu, Jianfeng^a | Chen, Kewei^c | Atri, Alireza^{b; d; e} | Caselli, Richard J.^f | Reiman, Eric M.^b | Wang, Yalin^{a; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA | [b] Banner Alzheimer’s Institute, Phoenix, AZ, USA | [c] College of Health Solutions, Arizona State University, Tempe, AZ, USA | [d] Banner Sun Health Research Institute, Sun City, AZ, USA | [e] Department of Neurology, Center for Brain/Mind Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA | [f] Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA

Correspondence: [*] Correspondence to: Dr. Yalin Wang, School of Computing and Augmented Intelligence, Arizona State University, P.O. Box 878809, Tempe, AZ 85287, USA. Tel.: +1 480 965 6871; Fax: +1 480 965 2751; E-mail: ylwang@asu.edu.

Note: [1] Data used in preparing this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database(http://adni.loni.usc.edu). As such, many investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract: Background: Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer’s disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer’s Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model’s superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS:Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer’s disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Keywords: Aβ positivity, amyloid PET, blood-based biomarkers, image features, MRI

DOI: 10.3233/JAD-231162

Journal: Journal of Alzheimer's Disease, vol. 98, no. 4, pp. 1415-1426, 2024