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Objective: Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders. Methods: This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis. Results: The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity. Conclusion: Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.

the clinical range of post-coronavirus disease 2019 symptoms in patients with Parkinson's disease (PD) has not yet been thoroughly characterized, with the exception of a few small case studies. the aim of the present study was to investigate the motor and non-motor progression of patients with PD (PWP) and post-CoVID-19 syndrome (PCS) at baseline and at 6 months after infection with CoVID-19. a cross-sectional prospective study of 38 PWP+/PCS+ and 20 PWP+/PCS-matched for age, sex and disease duration was conducted. all patients were assessed at baseline and at 6 months using a structured clinicodemographic questionnaire, the Unified Parkinson's Disease Rating Scale Part III (the UPDrS III), the Montreal Cognitive assessment, the Hoehn and Yahr scale, the Geriatric Depression Scale and the levodopa equivalent daily dose (lEDD). there was a statistically significant difference in the LEDD (P=0.039) and UPDRS III (P=0.001) at baseline and at 6 months after infection with CoVID-19 between the PWP with PCS groups. the most common non-motor PCS symptoms were anosmia/hyposmia, sore throat, dysgeusia and skin rashes. there was no statistically significant difference in demographics or specific scores between the two groups, indicating that no prognostic factor for PCS in PWP could be identified. The novelty of the present study is that it suggests the new onset of non-motor PCS symptoms of PWP with a mild to moderate stage.

Background: Apolipoprotein E (APOE) genotype may be associated with the development of cognitive decline in idiopathic Parkinson's disease i(PD), however its effect in genetic PD is understudied. Objectives: In the current work we aimed to assess the impact of APOE genotype on cognition in iPD as well as in genetic PD with mutations in the Alpha-synuclein (SNCA) and Glycocerebrosidase (GBA1) genes. Methods: Two independent PD cohorts were analyzed: The first cohort (Athens) included 50 iPD patients, 35 patients with the p.A53T SNCA mutation and 59 patients with GBA1 mutations (13 mild /46 severe). The second cohort (Tuebingen) included 292 patients with GBA1 mutations (170 risk/ 52 mild/ 70 severe). All patients underwent cognitive testing and were genotyped for APOE. Results: In the iPD subgroup, carriers of at least one APOE exhibited lower Montreal Cognitive Assessment test (MoCA) score as compared to non-carriers (p=0.044). Notably, in the p.A53T SNCA subgroup, APOE carriers also had lower MoCA scores compared to non-carriers (p=0.039). There were no APOE-related differences in the two GBA1 subgroups (Athens, p=0.729; Tuebingen p=0.585). Conclusions: We confirm the impact of APOE on cognitive decline in iPD and for the first time report a similar effect in p.A53T SNCA mutation carriers, who represent the prototypical genetic synucleinopathy. Contrary, the lack of such an effect in two independent cohorts of GBA1 mutation carriers, who are thought to also manifest a predominant alpha-synuclein-driven cognitive decline, suggests differences in factors associated with cognitive dysfunction between different genetic forms of synucleinopathies.

Background Nonmotor cognitive symptoms are widely being recognized in both Parkinson's Disease (PD) and Essential Tremor (ET), the two most common movement disorders. Clock-drawing (CD) test seems to be impaired early in the process of cognitive (executive) decline in PD. However, the optimal measures for detecting cognitive changes in ET patients have not been established. Examining whether the CD test is a quick test could identify frontal and visuospatial deficits in patients with Parkinson's disease (PD) and essential tremor (ET). Methods Visuospatial performance was assessed in 58 consecutive patients with ET and 75 with PD and 22 healthy controls (HC) who visited two neurological clinics of Athens in Greece. The CD and copy (CC) items of the PD-Cognitive Rating Scale were used as a test of visuospatial function. Results Both CD and CC scores were lower for ET compared to PD patients and HC (p=<0.001 for both comparisons). A binomial logistic regression showed that both CD and CC items predict if participants had ET or PD with high sensitivity 94.7% and specificity 87.9% and an area under the curve (AUC) 0.980 (95% confidence interval, 0.962-0.997). The model explained 86.1% (Nagelkerke R2) of the variance in the disease variable (ET/PD) and correctly classified 91.7% of the cases. Conclusion Patients with ET have more visuospatial deficits compared to PD and HC. CD task may be an easy, useful tool to track cognitive changes in nondemented patients with ET in clinical practice.

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