Alzheimer'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.
Objective
To quantify the discriminative ability of plasma biomarkers for differentiating AD, FTD, and healthy controls (HC).
Methods
We 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.
Results
Across 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.
Conclusions
A 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.
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