α-Synuclein Strain Dynamics Predict Cognitive Transitions in Parkinson's Disease

Abstract

-Synuclein (-syn) strains can serve as discriminators between Parkinsons disease (PD) and related -synucleinopathies. The relationship between -syn strain dynamics and clinical performance as patients transition from normal cognition (NC) to cognitive impairment (CI) is not known. Here, we show that the biophysical properties and neurotoxicity of -syn strains change as PD cognitive status transitions from NC to mild cognitive impairment (PD-MCI) and dementia (PD-D). Both cross-sectional and longitudinal analyses reveal distinct -syn strains in PD patients correlating to their level of cognitive impairment. Machine learning (ML) was employed to achieve high classification accuracy. The combination of thioflavin T (ThT) maximal fluorescence intensity (mfi), max slope of rise curve (forming rate), lag time (tlag), 20% time (t20), and half-time (t50), dynamic light scattering (DLS) (peak number, [1/2] peak size, [1/2] peak intensity) and neurotoxicity together with demographic variables for model training yielded superior performance (89[~]99% accuracy in the 4- and 2- classification schema) compared to individual features alone in classifying cognitive status. For the longitudinal study, DLS peak number emerged as the strongest predictor of cognitive transition (HR = 0.12, P = 0.002), with the optimal predictive model combining DLS peak number, sex, education, DLS peak 1 size, and DLS peak 2 polydispersity achieving high accuracy (C-index of [~]93%). This study presents evidence that individuals with PD have different -syn strains correlating to their cognitive status and highlights the potential of -syn strain dynamics to guide future diagnosis, management, and stratification of PD patients. One Sentence SummaryDistinct features of -syn strains change with cognitive decline in Parkinsons disease and AI-based analysis incorporating these combined characteristics serves as a powerful tool for PD clinical stratification.