Objective: Biomarker development for Parkinson’s disease and related dementia.

Background: α-Synuclein (α-syn) strains can serve as discriminators of Parkinson’s disease (PD) from other α-synucleinopathies. However, the relationship between α-syn strain dynamics and clinical performance as patients transition from normal cognition (NC) to cognitive impairment (CI) has not been well understood.

Method: In this study, researchers demonstrate that the biophysical properties and neurotoxicity of α-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 α-syn strains in PD patients based on their cognitive status. Tree-based models in Machine learning (ML) were employed to achieve high accuracy rates in different classification tasks.

Results: The use of a combination of thioflavin T (ThT) (maximal fluorescence intensity (mfi), lag time (tlag) and half-time (t50), dynamic light scattering (DLS) (peak number, ½ peak height, ½ peak intensity) and neurotoxicity features with ML methods yielded superior performance (95~99% accuracy in

classification tasks) compared to using individual features alone in predicting cognitive status.

Conclusion: This study highlights that PD individuals have different α-syn strains based on their cognitive status, and the potential of α-syn strain dynamics to guide future diagnosis, management, and stratification of PD patients.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/new-%ce%b1-synuclein-strain-biomarker-in-parkinsons-disease-and-related-dementia/