Interplay between gait and neuropsychiatric symptoms in Parkinson’s Disease

The impact of anxiety on gait impairments in Parkinson's disease: insights from sensor-based gait analysis

BACKGROUND

Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson's disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD.

METHODS

We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments.

RESULTS

During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)).

CONCLUSION

Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.

Inhibition of ferroptosis underlies EGCG mediated protection against Parkinson's disease in a Drosophila model

Ferroptosis, a new type of cell death accompanied by iron accumulation and lipid peroxidation, is implicated in the pathology of Parkinson's disease (PD), which is a prevalent neurodegenerative disorder that primarily occurred in the elderly population. Epigallocatechin-3-gallate (EGCG) is the major polyphenol in green tea with known neuroprotective effects in PD patients. But whether EGCG-mediated neuroprotection against PD involves regulation of ferroptosis has not been elucidated. In this study, we established a PD model using PINK1 mutant Drosophila. Iron accumulation, lipid peroxidation and decreased activity of GPX, were detected in the brains of PD flies. Additionally, phenotypes of PD, including behavioral defects and dopaminergic neurons loss, were ameliorated by ferroptosis inhibitor ferrostatin-1 (Fer-1). Notably, the increased iron level, lipid peroxidation and decreased GPX activity in the brains of PD flies were relieved by EGCG. We found that EGCG exerted neuroprotection mainly by restoring iron homeostasis in the PD flies. EGCG inhibited iron influx by suppressing Malvolio (Mvl) expression and simultaneously promoted the upregulation of ferritin, the intracellular iron storage protein, leading to a reduction in free iron ions. Additionally, EGCG downregulated the expression of Duox and Nox, two NADPH oxidases that produce reactive oxygen species (ROS) and increased SOD enzyme activity. Finally, modulation of intracellular iron levels or regulation of oxidative stress by genetic means exerted great influence on PD phenotypes. As such, the results demonstrated that ferroptosis has a role in the established PD model. Altogether, EGCG has therapeutic potentials for treating PD by targeting the ferroptosis pathway, providing new strategies for the prevention and treatment of PD and other neurodegenerative diseases.

Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease

The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.

Probing cellular health at the muscle level-Multi-frequency bioimpedance in Parkinson's disease

Bioimpedance (mfBIA) non-invasively assesses cellular muscle health. Our aim was to explore whether mfBIA captures abnormal cellular muscle health in patients with Parkinson's Disease (PD) and how such changes are modulated with the use of Parkinson's medication. In patients with PD (n = 20) mfBIA measurements were made of biceps brachii, triceps, and extensor carpi radialis longus muscles of the more affected arm whilst at rest, using a mobile mfBIA device (IMPEDIMED, Australia). mfBIA and assessment of motor symptoms were performed in a pragmatic off-medication state, as well as one and 3 h after oral intake of 200 mg levodopa. Age and sex-matched healthy subjects (HC; n = 20) served as controls. PD and HC mfBIA parameters were compared by applying an unpaired two-tailed adjusted t-test and ANOVA with Tukey's correction for multiple comparisons (p ≤ 0.05). The PD group consisted of 13 men (71 ± 17 years) and 7 women (65 ± 7 years). Independent of medication, internal (Ri ) and external resistance (Re ) were found to be significantly higher, and membrane capacitance (Mc) significantly lower, in m.biceps brachii in PD subjects compared to HC. Center frequency (fc) was significantly higher in m.biceps brachii of PD subjects in the "medication-off" state. There was no difference between PD and HC in mfBIA parameters in the measured extensor muscles. The upper limb flexor muscle shows a difference in mfBIA parameters in PD compared to HC. mfBIA may be useful in the diagnosis and assessment of PD patients and is objective, non-invasive, reliable, and easy to use.