ArticleAlzheimer's research & therapy2025
Optimizing timing and cost-effective use of plasma biomarkers in Alzheimer's disease.
Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Plasma Biomarkers for Alzheimer's Disease Across the Continuum: A Systematic Review of SIMOA-Based Studies.Cellular and molecular neurobiology · 2026Pooled it
- Evaluation of plasma p-tau217 biomarkers in detecting amyloid pathology and predicting cognitive outcomes: Observations from Japanese Alzheimer's disease neuroimaging initiative cohort.The journal of prevention of Alzheimer's disease · 2026Article
- Glial biomarkers improve classification of cognitive impairment: an explainable artificial intelligence study using CSF biomarkers.Frontiers in neurologyArticle
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8 authors.
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Abstract
BACKGROUND AND
objectivesEarly 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.
methodsWe 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.
resultsWhen 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.
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