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

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.