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

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-β