Postural sway correlates with cognition and quality of life in Parkinson's disease

Cognitive performance in healthy clinical trial participants and patients with the NeuroCart, a neurodegenerative disease measured with an automated neuropsychological and neurophysiological test battery

BACKGROUND

The prevalence of neurodegenerative diseases increases significantly with increasing age. Neurodegeneration is the progressive loss of function of neurons that eventually leads to cell death, which in turn leads to cognitive disfunction. Cognitive performance can therefore also be considered age dependent. The current study investigated if the NeuroCart can detect age related decline on drug-sensitive CNS-tests in healthy volunteers (HV), and whether there are interactions between the rates of decline and sex. This study also investigated if the NeuroCart was able to differentiate disease profiles of neurodegenerative diseases, compared to age-matched HV and if there is age related decline in patient groups.

METHODS

This retrospective study encompassed 93 studies, performed at CHDR between 2005 and 2020 that included NeuroCart measurements, which resulted in data from 2729 subjects. Five NeuroCart tests were included in this analysis: smooth and saccadic eye movements, body sway, adaptive tracking, VVLT and N-back. Data from 84 healthy male and female volunteer studies, aged 16-90, were included. Nine studies were performed in patients with Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) or vascular dementia (VaD). The data were analyzed with regression analyses on age by group, sex, sex by age, group by sex and group by sex by age. Least square means (LSMs) and 95% confidence intervals (CIs) were calculated for each group at the average age of the group, and at the average age of each of the other groups, and per sex.

RESULTS

Mean age and standard deviation (SD) for all groups was: HV 36.2 years (19.3), 68.3 CE years (8), PD 62.7 years (8.5), HD 51.4 years (9.8) and VaD 66.9 years (8.1). Performance on all NeuroCart tests decreased significantly each year in HV. Saccadic peak velocity (SPV) was increased in AD compared to age-matched HV (+26.28 degrees/s, p = 0.007), while SPV was decreased for PD and HD compared to age-matched HV (PD: -15.87 degrees/s, p = 0.038, HD: -22.52 degrees/s, p = 0.018). In HD patients SPV decreased faster with age compared to HV. On saccadic peak velocity the slopes between HD vs HV were significantly different, indicating a faster decline in performance on this task for HD patients compared to HV per age year. Smooth pursuit showed an overall significant difference between subject groups (p = 0.037. Significantly worse performance was found for AD (-12.87%, p ≤0.001), PD (-4.45%, p ≤0.001) and VaD (-5.69%, p = 0.005) compared to age-matched HV. Body sway significantly increased with age (p = 0.021). Postural stability was decreased for both PD and HD compared to age-matched HV (PD: +38.8%, p ≤0.001, HD: 154.9%, p ≤0.001). The adaptive tracking was significantly decreased with age (p ≤0.001). Adaptive tracking performance by AD (-7.54%, p ≤0.001), PD (-8.09%, p ≤0.001), HD (-5.19%, p ≤0.001) and VaD (-5.80%, p ≤0.001) was decreased compared to age-matched HV. Adaptive tracking in PD patients vs HV and in PD vs HD patients was significantly different, indicating a faster decline on this task per age year for PD patients compared to HV and HD. The VVLT delayed word recall showed an overall significant effect of subject group (p = 0.006. Correct delayed word recall was decreased for AD (-5.83 words, p ≤0.001), HD (-3.40 words, p ≤0.001) and VaD (-5.51 words, p ≤0.001) compared to age-matched HV.

CONCLUSION

This study showed that the NeuroCart can detect age-related decreases in performance in HV, which were not affected by sex. The NeuroCart was able to detect significant differences in performance between AD, PD, HD, VaD and age-matched HV. Disease durations were unknown, therefore this cross-sectional study was not able to show age-related decline after disease onset. This article shows the importance of investigating age-related decline on digitalized neurocognitive test batteries. Performance declines with age, which emphasizes the need to correct for age when including HV in clinical trials. Patients with different neurogenerative diseases have distinct performance patterns on the NeuroCart, which this should be considered when performing NeuroCart tasks in patients with AD, PD, HD and VaD.

Postural Sway in Parkinson's Disease and Multiple Sclerosis Patients During Tasks With Different Complexity

Neurological diseases are associated with static postural instability. Differences in postural sway between neurological diseases could include "conceptual" information about how certain symptoms affect static postural stability. This information might have the potential to become a helpful aid during the process of finding the most appropriate treatment and training program. Therefore, this study investigated static postural sway performance of Parkinson's disease (PD) and multiple sclerosis (MS) patients, as well as of a cohort of healthy adults. Three increasingly difficult static postural tasks were performed, in order to determine whether the postural strategies of the two disease groups differ in response to the increased complexity of the balance task. Participants had to perform three stance tasks (side-by-side, semi-tandem and tandem stance) and maintain these positions for 10 s. Seven static sway parameters were extracted from an inertial measurement unit that participants wore on the lower back. Data of 47 healthy adults, 14 PD patients and 8 MS patients were analyzed. Both healthy adults and MS patients showed a substantial increase in several static sway parameters with increasingly complex stance tasks, whereas PD patients did not. In the MS patients, the observed substantial change was driven by large increases from semi-tandem and tandem stance. This study revealed differences in static sway adaptations between PD and MS patients to increasingly complex stance tasks. Therefore, PD and MS patients might require different training programs to improve their static postural stability. Moreover, this study indicates, at least indirectly, that rigidity/bradykinesia and spasticity lead to different adaptive processes in static sway.

Balance factors affecting the quality of life in patients with knee osteoarthritis

Background

Knee osteoarthritis (OA) affects the quality of life (QOL) and balance control of elderly people; our study explored the balance factors that affected the QOL in patients with knee OA.

Objectives

To determine the balance factors that affected the QOL of patients with knee OA who attended general clinics.

Method

A total of 30 healthy controls and 60 patients with mild-to-moderate bilateral knee OA, all aged 55–75 years, were enrolled in our cross-sectional study. All participants were interviewed; the Medical Outcomes Study 36-Item Short-Form Health Survey was used to assess their QOL in eight dimensions, and the Balance Master System was used to evaluate their balance control according to six parameters. Descriptive statistics were used to reduce the data; an independent t-test determined differences between the two groups, and a multiple regression analysis was undertaken to establish associations between variables from the balance control test and SH36 physical and mental health components. The level of statistical significance was set at 5%.

Results

In the OA group, significant negative correlations were observed between sway velocity and the physical health component (p = 0.003) and between sway velocity and the mental health component (p = 0.006). Thus, sway velocity had a major impact on the QOL of patients with knee OA.

Conclusions

The sway velocity at the centre of gravity in balance control was a crucial factor for determining the QOL of patients with bilateral knee OA.

Clinical implications

Sway velocity is a key factor affecting the QOL and may provide a basis to formulate preventive actions and design treatment goals for patients with knee OA.

Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset

Multi‐site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical‐based studies. A multi‐site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE‐p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site‐effects quantitatively and allow the harmonization of data. This method is user‐friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites.