Non‐Invasive Classification Approach for Spinocerebellar Ataxia Type 3 via Metabolic Fingerprints Enhanced by Bimetallic Alloys

Spinocerebellar ataxia type 3 (SCA3) is a progressive inherited neurodegenerative disorder for which accessible blood-based classification approaches are needed. In this retrospective, single-center study, plasma metabolic fingerprints were acquired from 202 participants using mesoporous PdPt nanoparticle-assisted laser desorption/ionization mass spectrometry (LDI-MS) and analyzed using a Tabular Prior-data Fitted Network (TabPFN) classifier. The model achieved AUCs of 0.952 in the discovery cohort and 0.963 in the internal temporal validation cohort. A reduced 21-metabolite panel retained discriminatory performance, with AUCs of 0.901 and 0.932, respectively. Exploratory clinical and pathway analyses provided additional biological context. These findings highlight the potential of PdPt-assisted plasma metabolic fingerprinting for SCA3 classification.