{"doi":"10.1101/2024.10.22.619694","title":"α-Synuclein strain dynamics predict cognitive transitions in Parkinson’s disease","abstract":"Abstract α-Synuclein (α-syn) strains can serve as discriminators between Parkinson’s disease (PD) and related α-synucleinopathies. The relationship between α-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 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 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 (t lag ), 20% time (t 20 ), and half-time (t 50 ), dynamic light scattering (DLS) (peak number, ½ peak size, ½ 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 α-syn strains correlating to their cognitive status and highlights the potential of α-syn strain dynamics to guide future diagnosis, management, and stratification of PD patients. One Sentence Summary Distinct features of α-syn strains change with cognitive decline in Parkinson’s disease and AI-based analysis incorporating these combined characteristics serves as a powerful tool for PD clinical stratification.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":487443,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9567,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1208966,"name":"Ning Wang","orcid":"0000-0002-3410-8509","position":1,"is_corresponding":false},{"id":1331742,"name":"Kyungdo Kim","orcid":"0000-0001-5797-0612","position":2,"is_corresponding":false},{"id":663394,"name":"Enquan Xu","orcid":"0000-0003-3463-0337","position":3,"is_corresponding":false},{"id":935097,"name":"Xiaodi Zhang","orcid":"0000-0001-5452-4027","position":4,"is_corresponding":false},{"id":590403,"name":"Hanyu Li","orcid":"0000-0002-7848-731X","position":5,"is_corresponding":false},{"id":1025535,"name":"Jacob Deyell","orcid":"0000-0003-4080-7506","position":6,"is_corresponding":false},{"id":1331743,"name":"Jun Yang","orcid":"0000-0002-2581-5244","position":7,"is_corresponding":false},{"id":371069,"name":"Anthony Wang","orcid":"0000-0002-9509-6257","position":8,"is_corresponding":false},{"id":1331744,"name":"Youngjae Cha","orcid":"0000-0002-2194-1788","position":9,"is_corresponding":false},{"id":685052,"name":"Ramhari Kumbhar","orcid":"0000-0002-8975-2502","position":10,"is_corresponding":false},{"id":1208971,"name":"Haiqing Liu","orcid":"0000-0002-2085-1991","position":11,"is_corresponding":false},{"id":1331745,"name":"Lili Niu","orcid":"0000-0002-7489-2633","position":12,"is_corresponding":false},{"id":970166,"name":"Rong Chen","orcid":"0000-0001-9291-5539","position":13,"is_corresponding":false},{"id":1010539,"name":"Shu Zhang","orcid":"0000-0003-2543-5943","position":14,"is_corresponding":false},{"id":710852,"name":"Catherine C. 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