There is great heterogeneity in both the clinical presentation and rate of disease progression among patients with Parkinson’s disease (PD). This can pose prognostic difficulties in a clinical setting, and a greater understanding of the risk factors that contribute to modify disease course is of clear importance for optimizing patient care and clinical trial design. Genetic variants in SNCA are an established risk factor for PD and are candidates to modify disease presentation and progression. This systematic review aimed to summarize all available primary research reporting the association of SNCA polymorphisms with features of PD. We systematically searched PubMed and Web of Science, from inception to 1 June 2020, for studies evaluating the association of common SNCA variants with age at onset (AAO) or any clinical feature attributed to PD in patients with idiopathic PD. Fifty-eight studies were included in the review that investigated the association between SNCA polymorphisms and a broad range of outcomes, including motor and cognitive impairment, sleep disorders, mental health, hyposmia, or AAO. The most reproducible findings were with the REP1 polymorphism or rs356219 and an earlier AAO, but no clear associations were identified with an SNCA polymorphism and any individual clinical outcome. The results of this comprehensive summary suggest that, while there is evidence that genetic variance in the SNCA region may have a small impact on clinical outcomes in PD, the mechanisms underlying the association of SNCA polymorphisms with PD risk may not be a major factor driving clinical heterogeneity in PD.

Objectives: To evaluate the impact of SNCA polymorphisms originally identified as risk factors for Parkinson's disease (PD) on the clinical presentation and progression of the disease in a large cohort of population-based patients with incident PD.Methods: Four hundred thirty-three patients and 417 controls from three longitudinal cohorts were included in the study. Disease progression was recorded annually for up to 9 years using the Unified Parkinson's Disease Rating Scale (UPDRS) or Mini-Mental State Examination. Genotypes for five variants within the SNCA locus (rs2870004, rs356182, rs5019538, rs356219, and rs763443) were determined. We studied the association between each variant and disease progression using linear mixed-effects regression models.Results: The clinical profile of the patients with PD at the point of diagnosis was highly uniform between genotype groups. The rs356219-GG genotype was associated with a higher UPDRS II score than A-allele carriers (β = 1.52; 95% confidence interval 0.10–2.95; p = 0.036), but no differences were observed in the rate of progression of the UPDRS II scores. rs356219-GG was also associated with a faster annual change in Mini-Mental State Examination score compared with A-carriers (β = 0.03; 95% confidence interval 0.00–0.06; p = 0.043).Conclusions: We show that the known PD-risk variant rs356219 has a minor effect on modifying disease progression, whereas no differences were associated with rs2870004, rs356182, rs5019538, and rs763443. These findings suggest that SNCA variants associated with PD risk may not be major driving factors to the clinical heterogeneity observed for PD.

Background and ObjectivesNeuroinflammation contributes to Parkinson disease (PD) pathology, and inflammatory biomarkers may aid in PD diagnosis. Proximity extension assay (PEA) technology is a promising method for multiplex analysis of inflammatory markers. Neuroinflammation also plays a role in related neurodegenerative diseases, such as dementia with Lewy bodies (DLB) and Alzheimer disease (AD). The aim of this work was to assess the value of inflammatory biomarkers in newly diagnosed patients with PD and in patients with DLB and AD.MethodsPatients from the Norwegian ParkWest and Dementia Study of Western Norway longitudinal cohorts (PD, n = 120; DLB, n = 15; AD, n = 27) and 44 normal controls were included in this study. A PEA inflammation panel of 92 biomarkers was measured in the CSF. Disease-associated biomarkers were identified using elastic net (EN) analysis. We assessed the discriminatory power of disease-associated biomarkers using receiver operating characteristic (ROC) curve analysis and estimated the optimism-adjusted area under the curve (AUC) using the bootstrapping method.ResultsEN analysis identified 9 PEA inflammatory biomarkers (ADA, CCL23, CD5, CD8A, CDCP1, FGF-19, IL-18R1, IL-6, and MCP-2) associated with PD. Seven of the 9 biomarkers were included in a diagnostic panel, which was able to discriminate between those with PD and controls (optimism-adjusted AUC 0.82). Our 7-biomarker PD panel was also able to distinguish PD from DLB and from AD. In addition, 4 inflammatory biomarkers were associated with AD and included in a panel, which could distinguish those with AD from controls (optimism-adjusted AUC 0.87). Our 4-biomarker AD panel was also able to distinguish AD from DLB and from PD.DiscussionIn our exploratory study, we identified a 7-biomarker panel for PD and a 4-biomarker panel for AD. Our findings indicate potential inflammation-related biomarker candidates that could contribute toward PD-specific and AD-specific diagnostic panels, which should be further explored in other larger cohorts.