A Bayesian classification model for differential diagnosis of Alzheimer's disease and frontotemporal dementia using plasma biomarkers

J Alzheimers Dis. 2026 Apr 6:13872877261439950. doi: 10.1177/13872877261439950. Online ahead of print.

ABSTRACT

BackgroundAlzheimer's disease (AD) and frontotemporal dementia (FTD) have distinct pathologies but frequently overlapping clinical presentations, making early and atypical differential diagnosis challenging. Blood-based biomarkers offer a minimally invasive alternative to cerebrospinal fluid and neuroimaging measures, yet their diagnostic performance-alone and in combination-remains to be fully established.ObjectiveTo quantify the discriminative ability of plasma biomarkers for differentiating AD, FTD, and healthy controls (HC).MethodsWe used a fully Bayesian classification framework, estimating Bayesian logistic regression models for all single, pairwise, and triplet combinations of six plasma biomarkers-phosphorylated tau at threonine 217 (pTau217), brain-derived tau (BD-Tau), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), amyloid-β₄₀ (Aβ₄₀), and amyloid-β₄₂ (Aβ₄₂)-in AD (n = 97), FTD (n = 255), and HC (n = 70). Models were fitted across three contrasts (AD versus HC, FTD versus HC, AD versus FTD) and evaluated via posterior distributions of cross-validated AUC, precision, recall, F1 score, and Brier score.ResultsAcross 41 candidate models, NfL was the top single biomarker (mean AUC = 0.85), achieving strong discrimination for FTD versus HC (AUC = 0.94). The best two-marker panel (pTau217 + NfL) improved AD versus HC (AUC = 0.96) and AD versus FTD (AUC = 0.90). Adding Aβ₄₂ produced the highest-ranked triplet (AUC = 0.95) with modest, consistent gains. Posterior coefficients were biologically coherent (AD-specific pTau217 effects; severity-linked NfL), and calibration was satisfactory, with minor overconfidence only at extreme probabilities.ConclusionsA parsimonious pTau217 + NfL panel captures most diagnostic information in the full plasma profile, providing an accurate probabilistic classifier with interpretable uncertainty to support differential diagnosis and clinical triage in precision neurology.

PMID:41940846 | DOI:10.1177/13872877261439950