Dopamine replacement therapy normalizes reactive step length to postural perturbations in Parkinson's disease

Druggable targets for Parkinson's disease: transcriptomics based Mendelian randomization study

Parkinson's disease (PD) is a prevalent neurodegenerative disorder. Currently available drugs for PD, can only relieve the symptoms of PD, but cannot prevent the progression of the disease and have serious side effects. Other new druggable therapeutic targets for PD are needed. First, six GEO datasets with transcriptomic data from the substantia nigra (SN) region of the brain were downloaded to find dysregulated druggable genes in PD. Then, Mendelian randomization (MR) and summary statistics-based MR (SMR) analysis were conducted using eQTL data from both brain tissue and blood to investigate the relationship between gene expression and PD. Next, the association between the expression of candidate druggable targets and disease stage was validated in an additional dataset GSE49036. Finally, a phenome-wide MR analysis was carried out to investigate the potential impact of candidate druggable genes on several other complex diseases or traits. Our study revealed 313 differentially expressed genes that may be directly targetable and have an impact on PD (FDR-p < 0.1). Through MR and SMR analysis, P2RX7 and RNASET2 were identified as feasible PD therapeutic targets, which were highly expressed in PD tissues and increased as the Braak stages increased. Phenome-wide MR analysis revealed other effects of targeting RNASET2. This study presents genetic support for the potential therapeutic properties of targeting P2RX7 and RNASET2, which will be useful for developing druggable therapeutic targets for PD.

Extraction of the pull force from inertial sensors during the pull test for Parkinson's disease: A reliability study

OBJECTIVE

To examine the inter- and intra-rater reliability of the pull test in patients with Parkinson's disease (PD) using the extracted pull force.

METHODS

In this inter- and intra-rater reliability study, two raters performed a pull test on 30 patients with PD. The pull force was quantified using inertial sensors attached to the rater's right hand and the patient's lower trunk. In this study, the pull force was calculated as an extracted three-dimensional vector quantity, the resultant acceleration, and was expressed in m/s2. Inter- and intra-rater reliabilities were analyzed using the interclass correlation coefficient (ICC) for the pull force and Cohen's weighted kappa (κw) for the pull test score. Furthermore, Bland-Altman analysis was used to investigate systematic errors.

RESULTS

The inter- and intra-rater reliability of the pull force was very poor (ICC = 0.033-0.214). Bland-Altman analysis revealed no systematic errors in the pull forces between the two test points. Conversely, κw for the pull test scores ranged from 0.763 to 0.920, indicating substantial to almost perfect agreement.

CONCLUSION

The pull test score was reliable despite variations in the quantified pull force for inter- and intra-rater reliability. Our findings suggest that the pull test is a robust tool for evaluating postural instability in patients with PD and that the pull force probably does not affect scoring performance.

Distinguishing features of Parkinson's disease fallers based on wireless insole plantar pressure monitoring

Postural instability is one of the most disabling motor signs of Parkinson's disease (PD) and often underlies an increased likelihood of falling and loss of independence. Current clinical assessments of PD-related postural instability are based on a retropulsion test, which introduces human error and only evaluates reactive balance. There is an unmet need for objective, multi-dimensional assessments of postural instability that directly reflect activities of daily living in which individuals may experience postural instability. In this study, we trained machine-learning models on insole plantar pressure data from 111 participants (44 with PD and 67 controls) as they performed simulated static and active postural tasks of activities that often occur during daily living. Models accurately classified PD from young controls (area under the curve (AUC) 0.99+/- 0.00), PD from age-matched controls (AUC 0.99+/- 0.01), and PD fallers from PD non-fallers (AUC 0.91+/- 0.08). Utilizing features from both static and active postural tasks significantly improved classification performances, and all tasks were useful for separating PD from controls; however, tasks with higher postural threats were preferred for separating PD fallers from PD non-fallers.