The associations of cerebrospinal fluid biomarkers with cognition, and rapid eye movement sleep behavior disorder in early Parkinson's disease

The α-Synuclein Seeding Amplification Assay for Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Currently, PD is incurable, and the diagnosis of PD mainly relies on clinical manifestations. The central pathological event in PD is the abnormal aggregation and deposition of misfolded α-synuclein (α-Syn) protein aggregates in the Lewy body (LB) in affected brain areas. Behaving as a prion-like seeding, the misfolded α-syn protein can induce and facilitate the aggregation of native unfolded α-Syn protein to aggravate α-Syn protein aggregation, leading to PD progression. Recently, in a blood-based α-Syn seeding amplification assay (SAA), Kluge et al. identified pathological α-Syn seeding activity in PD patients with Parkin (PRKN) gene variants. Additionally, pathological α-syn seeding activity was also identified in sporadic PD and PD patients with Leucine-rich repeat kinase 2 (LRRK2) or glucocerebrosidase (GBA) gene variants. Principally, the α-Syn SAA can be used to detect pathological α-Syn seeding activity, which will significantly enhance PD diagnosis, progression monitoring, prognosis prediction, and anti-PD therapy. The significance and future strategies of α-Syn SAA protocol are highlighted and proposed, whereas challenges and limitations of the assay are discussed.

Machine Learning for Early Detection of Cognitive Decline in Parkinson's Disease Using Multimodal Biomarker and Clinical Data

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson's Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods-AutoScore, Random Forest, K-Nearest Neighbors and Neural Network-were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

An interactive web application to identify early Parkinsonian non-tremor-dominant subtypes

BACKGROUND

Parkinson's disease (PD) patients with tremor-dominant (TD) and non-tremor-dominant (NTD) subtypes exhibit heterogeneity. Rapid identification of different motor subtypes may help to develop personalized treatment plans.

METHODS

The data were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI). Following the identification of predictors utilizing recursive feature elimination (RFE), seven classical machine learning (ML) models, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting, etc., were trained to predict patients' motor subtypes, evaluating the performance of models through the area under the receiver operating characteristic curve (AUC) and validating by the follow-up data.

RESULTS

The feature subset engendered by RFE encompassed 20 features, comprising some clinical assessments and cerebrospinal fluid α-synuclein (CSF α-syn). ML models fitted in the RFE subset performed better in the test and validation sets. The best performing model was support vector machines with the polynomial kernel (P-SVM), achieving an AUC of 0.898. Five-fold repeated cross-validation showed the P-SVM model with CSF α-syn performed better than the model without CSF α-syn (P = 0.034). The Shapley additive explanation plot (SHAP) illustrated that how the levels of each feature affect the predicted probability as NTD subtypes.

CONCLUSION

An interactive web application was developed based on the P-SVM model constructed from feature subset by RFE. It can identify the current motor subtypes of PD patients, making it easier to understand the status of patients and develop personalized treatment plans.