Optimizing timing and cost-effective use of plasma biomarkers in Alzheimer's disease.

Abstract

Background and objectives: Early and cost-effective identification of amyloid positivity is crucial for Alzheimer's disease (AD) diagnosis. While amyloid PET is the gold standard, plasma biomarkers such as phosphorylated tau 217 (pTau217) provide a potential alternative. This study evaluates the diagnostic accuracy of a combined-panel approach using machine learning models and evaluated the biomarker significance.

Methods: We enrolled 371 participants, including AD (n = 143), non-AD (n = 159), and cognitively unimpaired (CU, n = 69) controls. Combined panels of pTau217, pTau181, glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), Aβ42/40, and total tau were measured prior to the amyloid PET scan. The multiclass logistic (LR) regression, support vector machines, decision trees, and random forests (RF)-were applied to classify amyloid positivity (A+) at all stages or at early clinical stages (1-3). In AD, we tested whether the biomarker may define the clinical stagings.

Results: When benchmarked against amyloid PET, plasma biomarker-based stratification achieves an optimal balance between diagnostic accuracy and cost-effectiveness. The multi-class LR performed equivalently with RF model in identifying A+. The combined plasma panel reached an > 92% accuracy in identifying A+, with performance increasing to 93.4% at early clinical stages. We ranked the importance of individual biomarkers and pTau217 alone achieved comparable accuracy (> 90%) and was the top-ranked biomarker in the LR or RF model. NFL and GFAP correlated significantly with Mini-Mental State Examination; however, these plasma biomarkers did not enhance clinical staging stratification.

Discussion: The use of multiclass LR model enhances amyloid classification, particularly at earlier clinical stages. While the combined-panel approach is most accurate, pTau217 alone provides a cost-effective alternative for screening. These findings support the integration of plasma biomarkers and ML into clinical workflows for early detection and patient stratification.