Exploring the performance of outcome measures in MS for predicting cognitive and clinical progression in the following years

Weak grip strength among persons with multiple sclerosis having minimal disability is not related to agility or integrity of the corticospinal tract

INTRODUCTION

Mobility impairment is common in multiple sclerosis (MS); however, agility has received less attention. Agility requires strength and neuromuscular coordination to elicit controlled propulsive rapid whole-body movement. Grip strength is a common method to assess whole body force production, but also reflects neuromuscular integrity and global brain health. Impaired agility may be linked to loss of neuromuscular integrity (reflected by grip strength or corticospinal excitability).

OBJECTIVES

We aimed to determine whether grip strength would be associated with agility and transcranial magnetic stimulation (TMS)-based indices of corticospinal excitability and inhibition in persons with MS having low disability. We hypothesized that low grip strength would predict impaired agility and reflect low corticospinal excitability.

METHODS

We recruited 34 persons with relapsing MS (27 females; median [range] age 45.5 [21.0-65.0] years) and mild disability (median [range] Expanded Disability Status Scale 2.0 [0-3.0]), as well as a convenience sample of age- and sex-matched apparently healthy controls. Agility was tested by measuring hop length during bipedal hopping on an instrumented walkway. Grip strength was measured using a calibrated dynamometer. Corticospinal excitability and inhibition were examined using TMS-based motor evoked potential (MEP) and corticospinal silent period (CSP) recruitment curves, respectively.

RESULTS

MS participants had significantly lower grip strength than controls independent of sex. Females with and without MS had weaker grip strength than males. There were no statistically significant sex or group differences in agility. After controlling for sex, weaker grip strength was associated with shorter hop length in controls only (r = 0.645, p < .05). Grip strength did not significantly predict agility in persons with MS, nor was grip strength predicted by corticospinal excitability or inhibition.

CONCLUSIONS

In persons with MS having low disability, grip strength (normalized to body mass) was reduced despite having intact agility and walking performance. Grip strength was not associated with corticospinal excitability or inhibition, suggesting peripheral neuromuscular function, low physical activity or fitness, or other psychosocial factors may be related to weakness. Low grip strength is a putative indicator of early neuromuscular aging in persons with MS having mild disability and normal mobility.

Predictive value of brain atrophy, serum biomarkers and information processing speed for early disease progression in multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is a chronic autoimmune-mediated demyelinating disease of the central nervous system (CNS). A clinical presentation of the disease is highly differentiated even from the earliest stages of the disease. The application of stratifying tests in clinical practice would allow for improving clinical decision-making including a proper assessment of treatment benefit/risk balance.

METHODS

This prospective study included patients with MS diagnosed up to 1 year before recruitment. We analyzed serum biomarkers such as CXCL13, CHI3L1, OPN, IL-6, and GFAP and neurofilament light chains (NfLs); brain MRI parameters of linear atrophy such as bicaudate ratio (BCR), third ventricle width (TVW); and information processing speed were measured using the Symbol Digit Modalities Test (SDMT) during the 2 years follow-up.

RESULTS

The study included a total of 50 patients recruited shortly after the diagnosis of MS diagnosis (median 0 months; range 0-11 months), and the mean time of observation was 28 months (SD = 4.75). We observed a statistically significant increase in the EDSS score (Wilcoxon test: Z = 3.06, p = 0.002), BCR (Wilcoxon test: Z = 4.66, p < 0.001), and TVW (Wilcoxon test: Z = 2.84, p = 0.005) after 2 years of disease. Patients who had a significantly higher baseline level of NfL suffered from a more severe disease course as per the EDSS score (Mann-Whitney U-test: U = 107, Z = -2,74, p = 0.006) and presence of relapse (Mann-Whitney U-test: U = 188, Z = -2.01, p = 0.044). In the logistic regression model, none of the parameters was a significant predictor for the achieving of no evidence of disease activity status (NEDA). In the model considering all assessed parameters, only the level of NfL had a significant impact on disease progression, measured as the increase in EDSS (logistic regression: β = 0.002, p = 0.017).

CONCLUSION

We confirmed that NfL levels in serum are associated with more active disease. Moreover, we found that TVW at the time of diagnosis was associated with an impairment in cognitive function measured by information processing speed at the end of the 2-year observation. The inclusion of serum NfL and TVW assessment early in the disease may be a good predictor of disease progression independent of NEDA.

Are we ready to define cognitive worsening in MS? How different cutoffs detect future cognitive worsening after six years of follow-up

BACKGROUND

Although cognitive evaluation has been incorporated in recent MS clinical trials, the definition of cognitive progression is not clear and recent data are questioning the 4-point cutoff using the SDMT at the individual level. We aimed to evaluate the behavior of cognitive performance over time using different cutoffs.

METHODS

Cognitive performance over six years was analyzed in a cohort of 42 relapsing-remitting MS patients and 30 controls using verbal/visual memory and information processing speed tests. Fixed cutoffs were: 10% and 20% change (all tests) and a 4- and 8-point change (SDMT). The relative cutoff established by regression-based models was a 1SD change.

RESULTS

The distributions of "worsening", "stability", and "improvement" showed low concordance rates across the cutoffs (p < 0.001 for most comparisons). Most patients classified with worsening initially using fixed cutoffs had subsequent improvement in all cognitive tests, yielding a low sensitivity to predict later cognitive worsening. Using the relative cutoff, the proportion of patients with subsequent improvement was noticeably smaller.

CONCLUSIONS

Fixed cutoffs classify a high proportion of patients with cognitive improvement. Most patients categorized with worsening initially presented subsequent improvement. Instead, the relative cutoff generally had a better performance. These data raise concerns about how we are defining cognitive worsening so far, especially at the individual level.

Machine learning techniques for prediction of multiple sclerosis progression

Patients afflicted by multiple sclerosis experience a relapsing-remitting course in about 85% of the cases. Furthermore, after a 10/15-year period their situation tends to worse, resulting in what is considered the second phase of multiple sclerosis. While treatments are now available to reduce the symptoms and slow down the progression of the disease, the administration of drugs must be adapted to the course of the disease, and predicting relapsing periods and the worsening of the symptoms can greatly improve the outcome of the treatment. For this reason, indicators such as the patient-reported outcome measures (PROMs) have been largely used to support early diagnosis and prediction of future relapsing periods in patients affected by multiple sclerosis. However, such indicators are insufficient, as the prediction they provide is often not accurate enough. In this paper, machine learning techniques have been applied to data obtained from clinical trial, in order to improve the prediction capabilities and provide doctors with an additional instrument to evaluate the clinical situation of patients. After the application of correlation indicators and the use of principal component analysis for the reduction of the dimensionality of the feature space, classification algorithms have been applied and compared, in order to identify the best suiting one for our purposes. After the application of re-balance algorithms, the accuracy of the machine learning-based prediction system reaches 79%, demonstrating the capability of the framework to correctly predict future progression of disability.

Cognitive assessment of Brazilian patients with multiple sclerosis: weighing the impact of disability and depressive symptoms

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system. Cognition is not routinely assessed in patients with MS though they frequently have cognitive complaints or dysfunction.

Objective

The aim of this study was to compare the cognitive status of patients with MS with age, sex, and schooling matched controls and to evaluate the potential influence of clinical parameters on cognition.

Methods

A total of 35 patients with MS (mean±SD age 37.9 years±11.44, M/F: 12/23) and 33 healthy controls (mean±SD age 38.8 years±12.6, M/F: 12/21) were enrolled in this study. All subjects underwent a structured clinical assessment and the cognitive tools are as follows: Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Rey Auditory Verbal Learning Test (RAVLT), Digit Span, and Verbal Fluency Tests (letters F, A, and S and animal category). Psychopathology was assessed with the Mini International Neuropsychiatric Interview and the Beck Depression Inventory (BDI). The Expanded Disability Status Scale (EDSS) was used for patients.

Results

Patients performed worse than controls in almost all tests, with approximately 70% of patients presenting cognitive impairment. The most affected cognitive domain was episodic memory (45.7%), followed by verbal fluency (42.8%) and information processing speed (22.8%). SDMT was inversely correlated with disease severity, as assessed by the EDSS. Depression did not influence cognitive performance in this cohort.

Conclusions

Cognitive dysfunction is common among patients with MS. While motor impairment was associated with information processing speed, depression did not influence cognitive performance.