Decoding the clinical masking effect in SAA-positive Parkinson’s disease: from central mechanistic dichotomy to a peripheral diagnostic panel

Abstract

Despite global efforts to develop disease-modifying therapies for Parkinson’s disease (PD), recurrent trial failures persist, likely driven by profound and unstratified molecular heterogeneity. The advent of α-syn seed amplification assays (SAA) has enabled biologically pure cohorts, yet underlying disease trajectories remain highly variable. In this study, we leveraged deep baseline dual-compartment proteomics (4,785 CSF and 5,400 plasma proteins) from a deeply phenotyped, strictly SAA-positive PD cohort (N = 114) with a 5-year longitudinal clinical follow-up. Unsupervised machine learning revealed two robust biological subtypes driven by diametrically opposed micro-pathological crises: a synaptic/neuronal failure versus a lysosomal/glial collapse. Strikingly, despite this massive molecular divergence, these subtypes exhibited indistinguishable macroscopic clinical phenotypes, uniform striatal dopaminergic denervation (DaTscan) profiles, and strictly parallel disease progression over 5 years. Furthermore, this dichotomy occurred entirely independently of canonical genetic mutations. This pervasive “clinical masking effect” explains how targeting unstratified cohorts inadvertently dilutes subtype-specific therapeutic signals into statistical noise. To overcome this macroscopic disguise, we translated our central findings into a non-invasive, 5-protein machine-learning plasma panel (FAM3B, ACTA2, ATL3, PEBP4, and BTNL10P), achieving a cross-validated Area Under the Curve (AUC) of 0.867. Using a large-scale multi-center replication dataset, we definitively demonstrated that critical peripheral biomarker signals are completely submerged in unstratified clinical cohorts, yet they are robustly restored when stratified by our mechanism-driven framework. Ultimately, this signature provides an actionable non-invasive roadmap for precision patient stratification in future clinical trials.