Reliable biomarkers for Parkinson's disease (PD) diagnosis are urgently needed. Alpha-synuclein (α-syn) and its proteoforms play a key role in PD pathology but in vivo measurements have raised conflicting results, and whether α-syn in blood could distinguish PD patients from healthy controls is still controversial. A systematic literature search yielded 35 eligible studies for meta-analysis reporting the concentration of total, oligomeric or phosphorylated α-syn in plasma and/or serum of PD patients and healthy controls. Standardized mean differences (SMD) were pooled using multivariate/multilevel linear mixed-effects models. Meta-regression analyses were conducted to investigate possible modifiers. A meta-analysis of 32 articles involving 2683 PD patients and 1838 controls showed a significant overall effect of PD on total α-syn levels (SMD = 0.85, p = 0.004). Meta-regression showed that increased SMD of total α-syn in PD was significantly associated with lower age, shorter disease duration, mild motor impairment, and Immunomagnetic Reduction assay for protein quantification. In contrast, no significant differences were observed for oligomeric or phosphorylated α-syn between PD and controls but increased oligomeric α-syn was significantly associated with shorter disease duration. The heterogeneity among studies was high (>98%). These findings suggest that increased total plasma/serum α-syn levels in PD primarily occur in early phases of the disease. The evidence obtained from a small number of studies measuring plasma/serum concentrations of oligomeric and phosphorylated species of α-syn shows no difference. The clinical applicability of measuring plasma or serum α-syn species for differentiating PD from healthy control warrants further studies with better clinical profiling of PD patients.

Objective: This study was undertaken to analyze longitudinal changes of retinal thickness and their predictive value as biomarkers of disease progression in idiopathic Parkinson's disease (iPD).Methods: Patients with Lewy body diseases were enrolled and prospectively evaluated at 3 years, including patients with iPD (n = 42), dementia with Lewy bodies (n = 4), E46K‐SNCA mutation carriers (n = 4), and controls (n = 17). All participants underwent Spectralis retinal optical coherence tomography and Montreal Cognitive Assessment, and Unified Parkinson's Disease Rating Scale score was obtained in patients. Macular ganglion cell–inner plexiform layer complex (GCIPL) and peripapillary retinal nerve fiber layer (pRNFL) thickness reduction rates were estimated with linear mixed models. Risk ratios were calculated to evaluate the association between baseline GCIPL and pRNFL thicknesses and the risk of subsequent cognitive and motor worsening, using clinically meaningful cutoffs.Results: GCIPL thickness in the parafoveal region (1‐ to 3‐mm ring) presented the largest reduction rate. The annualized atrophy rate was 0.63μm in iPD patients and 0.23μm in controls (p < 0.0001). iPD patients with lower parafoveal GCIPL and pRNFL thickness at baseline presented an increased risk of cognitive decline at 3 years (relative risk [RR] = 3.49, 95% confidence interval [CI] = 1.10–11.1, p = 0.03 and RR = 3.28, 95% CI = 1.03–10.45, p = 0.045, respectively). We did not identify significant associations between retinal thickness and motor deterioration.Interpretation: Our results provide evidence of the potential use of optical coherence tomography–measured parafoveal GCIPL thickness to monitor neurodegeneration and to predict the risk of cognitive worsening over time in iPD. ANN NEUROL 2021;89:165–176

Non-motor symptoms are common in Parkinson’s disease (PD) and they represent a major source of disease burden. Several non-motor manifestations, such as rapid eye movement sleep behavior disorder, olfactory loss, gastrointestinal abnormalities, visual alterations, cognitive and mood disorders, are known to precede the onset of motor signs. Nonetheless, the mechanisms mediating these alterations are poorly understood and probably involve several neurotransmitter systems. The dysregulation of GABAergic system has received little attention in PD, although the spectrum of non-motor symptoms might be linked to this pathway. This Mini Review aims to provide up-to-date information about the involvement of the GABAergic system for explaining non-motor manifestations in early stages of PD. Therefore, special attention is paid to the clinical data derived from patients with isolated REM sleep behavior disorder or drug-naïve patients with PD, as they represent prodromal and early stages of the disease, respectively. This, in combination with animal studies, might help us to understand how the disturbance of the GABAergic system is related to non-motor manifestations of PD.

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.

Background: Retinal optical coherence tomography findings in Lewy body diseases and their implications for visual outcomes remain controversial. We investigated whether region‐specific thickness analysis of retinal layers could improve the detection of macular atrophy and unravel its association with visual disability in Parkinson's disease.Methods: Patients with idiopathic Parkinson's disease (n = 63), dementia with Lewy bodies (n = 8), and E46K mutation carriers in the α‐synuclein gene (E46K‐SNCA) (n = 4) and 34 controls underwent Spectralis optical coherence tomography macular scans and a comprehensive battery of visual function and cognition tests. We computed mean retinal layer thicknesses of both eyes within 1‐, 2‐, 3‐, and 6‐mm diameter macular discs and in concentric parafoveal (1‐ to 2‐mm, 2‐ to 3‐mm, 1‐ to 3‐mm) and perifoveal (3‐ to 6‐mm) rings. Group differences in imaging parameters and their relationship with visual outcomes were analyzed. A multivariate logistic model was developed to predict visual impairment from optical coherence tomography measurements in Parkinson's disease, and cutoff values were determined with receiver operating characteristic analysis.Results: When compared with controls, patients with dementia with Lewy bodies had significant thinning of the ganglion cell–inner plexiform layer complex within the central 3‐mm disc mainly because of differences in 1‐ to 3‐mm parafoveal thickness. This parameter was strongly correlated in patients, but not in controls, with low contrast visual acuity and visual cognition outcomes (P < .05, False Discovery Rate), achieving 88% of accuracy in predicting visual impairment in Parkinson's disease.Conclusion: Our findings support that parafoveal thinning of ganglion cell–inner plexiform complex is a sensitive and clinically relevant imaging biomarker for Lewy body diseases, specifically for Parkinson's disease. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.