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The Latest Articles on the Effect of Multiple Sclerosis on Physical Function

Published on: 20 Dec 2023 Viewed: 369

Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.

This week, we would like to share several latest articles on the effect of multiple sclerosis on physical function.

Title: The effect of lower back and lower-extremity kinesiology taping on static balance and physical function performance in people with multiple sclerosis: A pilot study
Authors: Emerson Sebastião, Shuqi Zhang, Nicholas E. Grahovec, Christopher M. Hill, Vitor A.A.A. Siqueira, Jocelyn Cruz, MahgolZahra Kamari
Type: Research Article
Abstract:

Introduction
Multiple sclerosis (MS) can lead to numerous deficits in body functions, including balance and mobility impairment. This study examined the effect of lower back and lower extremity kinesiology tape (KT) application on static balance and physical functioning performance in people with MS (pwMS) and compared that to a non-elastic tape.

Methods
This pilot randomized study recruited and enrolled 10 participants with MS that were allocated into two groups: kinesio (n = 6) and non-elastic (n = 4) tape. Participants were assessed with and without the respective tape on static balance with eyes open and closed and various physical function tests.

Results
Effect sizes for the Kinesio tape intervention were found to be small, while effect sizes for the sham tape/place condition varied from small to high. For both groups, the tendency was to reduce or maintain performance on the tests comparing tape and no tape. A subsequent, mixed-factor ANOVA revealed no significant difference between KT or sham tape/placebo.

Conclusion
Our findings suggest that KT applied on lower back and lower extremity muscles does not seem to improve static balance and physical function performance in pwMS.
Access this article: https://doi.org/10.1016/j.jbmt.2023.11.051

Title:Blood–brain barrier dysfunction in multiple sclerosis: causes, consequences, and potential effects of therapies
Authors: Bettina Zierfuss PhD, Catherine Larochelle MD PhD, Prof Alexandre Prat MD PhD
Type: Review Article

Summary:
Established by brain endothelial cells, the blood–brain barrier (BBB) regulates the trafficking of molecules, restricts immune cell entry into the CNS, and has an active role in neurovascular coupling (the regulation of cerebral blood flow to support neuronal activity). In the early stages of multiple sclerosis, around the time of symptom onset, inflammatory BBB damage is accompanied by pathogenic immune cell infiltration into the CNS. In the later stages of multiple sclerosis, dysregulation of neurovascular coupling is associated with grey matter atrophy. Genetic and environmental factors associated with multiple sclerosis, including dietary habits, the gut microbiome, and vitamin D concentrations, might contribute directly and indirectly to brain endothelial cell dysfunction. Damage to brain endothelial cells leads to an influx of deleterious molecules into the CNS, accelerating leakage across the BBB. Potential future therapeutic approaches might help to prevent BBB damage (eg, monoclonal antibodies targeting cell adhesion molecules and fibrinogen) and help to repair BBB dysfunction (eg, mesenchymal stromal cells) in people with multiple sclerosis.

Access this article: https://doi.org/10.1016/S1474-4422(23)00377-0

Title:Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment
Authors: Zichun Yan, Shiqi Yuan, Qiyuan Zhu, Xiaohua Wang, Zhuowei Shi, Yu Zhang, Jie Liu, Jinzhou Feng, Yiqiu Wei, Feiyue Yin, Shanxiong Chen, Yongmei Li
Type: Original article
Abstract:

Background
Cognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages.

Methods
One hundred and eighteen patients with MS were divided into CI and normal cognitive (NC) cohorts (62/56) as defined by the Montreal Cognitive Assessment (MoCA). All participants were randomly divided into train and test sets with a ratio of 7:3. The radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO) method. The discrimination models were built with the support vector machines (SVM) by the clinical data, radiomic features, and merge data, respectively. And the patients were further divided according to each cognitive domain including memory, visuospatial, language, attention and executive, and each domain model was applied by the most suitable classifier.

Results
A total of 2298 features were extracted, of which 36 were finally selected. The merge model showed the greatest performance with the area under the curve (AUC) of 0.86 (95 % confidence interval: 0.81–0.91), accuracy (ACC) of 0.78, sensitivity of 0.79 and specificity of 0.77 in test cohort. However, although the visuospatial domain model showed the highest AUC of 0.71 (95 % confidence interval: 0.61–0.81) among five domain models, other domain models did not meet satisfactory results with a relatively low AUC, ACC, sensitivity and specificity.

Conclusions
The radiomics model based on clinical data and cortical damages had a great potential to identify the MS patients with CI for clinical cognitive assessment.
Access this article: https://doi.org/10.1016/j.msard.2023.105348

Title: Risk of dementia in older veterans with multiple sclerosis
Authors: Nathaniel H Fleming, Amber Bahorik, Feng Xia, Kristine Yaffe
Type: Research article
Abstract:

Background
While it is widely accepted that multiple sclerosis (MS) often causes cognitive dysfunction, it is thought that these cognitive symptoms rarely progress to dementia. However, this has not been thoroughly investigated. The objectives of this cohort study are to determine whether people with MS have an increased risk of dementia compared to the general population and to identify factors, such as geographic latitude, which may modify this association.

Methods
We studied data from a random sample of US veterans aged ≥ 55 years followed at Veterans Affairs Health Care Systems nationwide from 1999 to 2019. We identified all patients diagnosed with MS using ICD codes over a two-year baseline period. We then identified a comparison cohort of patients without MS matched 1:1 on sex, age, race, and first encounter date. We constructed Cox proportional hazards regression models to determine the association between MS and dementia while controlling for demographic factors and comorbidities, with additional models to examine subgroup effects. We used Fine-Gray subdistribution hazard models accounting for competing risk of death to evaluate the sensitivity of the findings.

Results
The study included 4084 MS patients and a matched group of 4084 non-MS patients. Overall, patients had mean age 66, were 93.6% male, and 88.1% non-Hispanic White, with mean follow-up time 9.5 years (MS) and 10.8 years (non-MS). In unadjusted models, veterans with MS had greater risk of dementia compared to matched controls (cumulative incidence 16.7% vs 12.4%; Cox HR 1.58, 95% CI 1.41–1.78). The increased risk remained after adjustment for potential confounders (adjusted HR 1.56, 95% CI 1.39–1.76) and when considering death as a competing risk (Fine-Gray HR 1.36, 95% CI 1.21–1.53). The magnitude of the MS-dementia association increased with rising geographic latitude (North HR 1.86, 1.51–2.30; Central HR 1.61, 1.42–1.82; South HR 1.39, 1.18–1.64; interaction p = 0.04) and younger baseline age (interaction p<0.001).    

Conclusions
Among older veterans with MS, risk of dementia diagnosis was higher compared to matched controls even after controlling for comorbidities. The risk difference was highest in northern regions and in younger patients. Clinicians caring for older MS patients should be aware of this risk and offer screening and treatment accordingly.
Access this article: https://doi.org/10.1016/j.msard.2023.105372

Title: Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study
Authors: Oun Al-iedani, Stasson Lea, A. Alshehri, Vicki E. Maltby, Bente Saugbjerg, Saadallah Ramadan, Rodney Lea, Jeannette Lechner-Scott
Type: Research article
Abstract:

Background
Cognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years.

Methods
A cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome.

Results
The multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48–0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48–0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22–0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94).

Conclusions
Multi-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.
Access this article: https://doi.org/10.1016/j.msard.2023.105379

Ageing and Neurodegenerative Diseases
ISSN 2769-5301 (Online)

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