A Comprehensive Analysis of the Association Between SNCA Polymorphisms and the Risk of Parkinson's Disease

Background: Various studies have reported associations between synuclein alpha (SNCA) polymorphisms and Parkinson's disease (PD) risk. However, the results are inconsistent. We conducted a comprehensive meta-analysis of the associations between SNCA single-nucleotide polymorphisms (SNPs) and PD risk in overall populations and subpopulations by ethnicity.Methods: Standard meta-analysis was conducted according to our protocol with a cutoff point of p < 0.05. To find the most relevant SNCA SNPs, we used a cutoff point of p < 1 × 10−5 in an analysis based on the allele model. In the subgroup analysis by ethnicity, we divided the overall populations into five ethnic groups. We conducted further analysis on the most relevant SNPs using dominant and recessive models to identify the contributions of heterozygotes and homozygotes regarding each SNP.Results: In our comprehensive meta-analysis, 24,075 cases and 22,877 controls from 36 articles were included. We included 16 variants in the meta-analysis and found 12 statistically significant variants with p < 0.05. After narrowing down the variants using the p < 1 × 10−5 cutoff, in overall populations, seven SNPs increased the risk of PD (rs2736990, rs356220, rs356165, rs181489, rs356219, rs11931074, and rs2737029, with odds ratios [ORs] of 1.22–1.38) and one SNP decreased the risk (rs356186, with an OR of 0.77). In the East Asian group, rs2736990 and rs11931074 increased the risk (with ORs of 1.22–1.34). In the European group, five SNPs increased the risk (rs356219, rs181489, rs2737029, rs356165, and rs11931074, with ORs of 1.26–1.37) while one SNP decreased the risk (rs356186, with an OR of 0.77). The heterozygotes and homozygotes contributed differently depending on the variant.Conclusions: In summary, we found eight SNCA SNPs associated with PD risk, which had obvious differences between ethnicities. Seven SNPs increased the risk of PD and one SNP decreased the risk in the overall populations. In the East Asian group, rs2736990 and rs11931074 increased the risk. In the European group, rs356219, rs181489, rs2737029, rs356165, and rs11931074 increased the risk while rs356186 decreased the risk. Variants with the highest ORs and allele frequencies in our analysis should be given priority when carrying out genetic screening

Recent Advances in Biomarkers for Parkinson’s Disease

Parkinson’s disease (PD) is one of the common progressive neurodegenerative disorders with several motor and non-motor symptoms. Most of the motor symptoms may appear at a late stage where most of the dopaminergic neurons have been already damaged. In order to provide better clinical intervention and treatment at the onset of disease, it is imperative to find accurate biomarkers for early diagnosis, including prodromal diagnosis and preclinical diagnosis. At the same time, these reliable biomarkers can also be utilized to monitor the progress of the disease. In this review article, we will discuss recent advances in the development of PD biomarkers from different aspects, including clinical, biochemical, neuroimaging and genetic aspects. Although various biomarkers for PD have been developed so far, their specificity and sensitivity are not ideal when applied individually. So, the combination of multimodal biomarkers will greatly improve the diagnostic accuracy and facilitate the implementation of personalized medicine

Factors Associated With Dyskinesia in Parkinson's Disease in Mainland China

Background and Objectives: Studies examining the risk factors for dyskinesia in Parkinson's disease (PD) have been inconsistent, and racial differences exist. Since there have been no systematic studies of the characteristics of dyskinesia in the Mainland Chinese population, we sought to elucidate the risk factors for dyskinesia.Methods: A total of 1974 PD patients from Mainland China were systematically investigated by univariable and multivariable analyses. PD patients with and without dyskinesia were stratified into 4 groups according to levodopa equivalent daily dose (LEDD) and analyzed by a Cox proportional hazards model. A longitudinal study of 87 patients with dyskinesia was classified into 3 groups according to the duration from onset of PD to the initiation of levodopa, and comparisons among groups were analyzed by the Mann-Whitney test.Results: Early age of onset, long disease duration, being female, high LEDD, low UPDRS III scores (ON-state) and high Hoehn-Yahr stage (ON-state) were predictors of dyskinesia. Dyskinesia was levodopa dosage-dependent, and the incidence increased remarkably when LEDD exceeded 300 mg/d (p < 0.05). The emergence of dyskinesia had no association with the initiation time of levodopa, and if the latter was more than 4 years, the duration of time on chronic levodopa free of motor complications was significantly shortened.Conclusions: We found risk factors for the prediction of dyskinesia. Our data shows that physicians should be cautious if the LEDD exceeds 300 mg/d. The development of dyskinesia was not correlated with the time of levodopa initiation

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

ObjectiveAlthough risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).Materials and methodsA total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.ResultsAt the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.ConclusionIn this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy