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.

The SNCA gene, encoding alpha-synuclein, is implicated in the pathogenesis of Parkinson’s disease (PD), with several single-nucleotide polymorphisms (SNPs) linked to increased risk. This study systematically evaluated the association between common SNCA polymorphisms and PD through a meta-analysis of cohort and case–control studies published before 20 November 2023. Eligible studies were identified via comprehensive searches of PubMed, Scopus, and Web of Science, and pooled odds ratios with 95% confidence intervals were calculated under allelic, dominant, and recessive models. Heterogeneity and publication bias were assessed, and subgroup and sensitivity analyses were performed. Twenty-seven studies were included. SNP rs11931074 showed consistent associations with PD across all models, with low heterogeneity and no evidence of publication bias. rs356219 and rs356165 were also significantly associated with PD, although regional differences contributed to heterogeneity. In contrast, rs2583988 showed marginal significance in the allelic model, which was lost after sensitivity analyses. No associations were found under dominant or recessive models for this SNP. These findings confirm rs11931074 as a robust PD risk variant and support the roles of rs356219 and rs356165 while suggesting weaker evidence for rs2583988. Large, multi-ethnic studies are warranted to elucidate underlying mechanisms and support precision medicine in PD.

Physical activity (PA) remains critical in the slowing of disease progression in early Parkinson's disease (PD), although the longitudinal follow-up of such studies remain scarce. Using data from an early PD Cohort, we longitudinally examined the impact of unprescribed PA on symptoms of early PD, controlling for demographics and medications. Over five years, the reported PA in early PD declined significantly annually. When maintained, the overall PA had significant association with improved motor symptoms, cognition, and quality of life . Higher PA maintained longitudinally is associated with a slower progression of motor and non-motor symptoms, and predict better quality of life in patients with early PD.

Background: Cognitive decline (CD) is a significant concern in Parkinson's disease (PD), highlighting the need for reliable risk prediction models for early intervention. 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 (MoCA) 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. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. 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. Conclusion: ML-based models offer potential for early identification of patients at high risk of CD, facilitating targeted interventions and improving patient outcomes in PD management.