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Axonal degeneration in multiple sclerosis: can we predict and prevent permanent disability?

ABSTRACT: Axonal degeneration is a major determinant of permanent neurological impairment during multiple sclerosis (MS). Due to the variable course of clinical disease and the heterogeneity of MS lesions, the mechanisms governing axonal degeneration may differ between disease stages. While the etiology of MS remains elusive, there now exist potential prognostic biomarkers that can predict the conversion to clinically definite MS. Specialized imaging techniques identifying axonal injury and drop-out are becoming established in clinical practice as a predictive measure of MS progression, such as optical coherence tomography (OCT) or diffusion tensor imaging (DTI). However, these imaging techniques are still being debated as predictive biomarkers since controversy surrounds their lesion-specific association with expanded disability status scale (EDSS). A more promising diagnostic measure of axonal degeneration has been argued for the detection of reduced N-acetyl aspartate (NAA) and Creatine ratios via magnetic resonance spectroscopic (MRS) imaging, but again fail with its specificity for predicting actual axonal degeneration. Greater accuracy of predictive biomarkers is therefore warranted and may include CSF neurofilament light chain (NF-L) and neurofilament heavy chain (NF-H) levels, for progressive MS. Furthermore, defining the molecular mechanisms that occur during the neurodegenerative changes in the various subgroups of MS may in fact prove vital for the future development of efficacious neuroprotective therapies. The clinical translation of a combined Na+ and Ca2+ channel blocker may lead to the establishment of a bona fide neuroprotective agent for the treatment of progressive MS. However, more specific therapeutic targets to limit axonal damage in MS need investigation and may include such integral axonal proteins such as the collapsin response mediator protein-2 (CRMP-2), a molecule which upon post-translational modification may propagate axonal degeneration in MS. In this review, we discuss the current clinical determinants of axonal damage in MS and consider the cellular and molecular mechanisms that may initiate these neurodegenerative changes. In particular we highlight the therapeutic candidates that may formulate novel therapeutic strategies to limit axonal degeneration and EDSS during progressive MS.

SUBMITTER: Lee JY

PROVIDER: S-EPMC4243718 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients. METHODS:We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked. RESULTS:Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (ΔAUC = 0.02 for RF and ΔAUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75±0.07 (mean and standard deviation)), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier). CONCLUSIONS:Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

Project description:Preventing chronic disease deterioration is an unmet need in people with multiple sclerosis, where axonal loss is considered a key substrate of disability. Clinically, chronic multiple sclerosis often presents as progressive myelopathy. Spinal cord cross-sectional area (CSA) assessed using MRI predicts increasing disability and has, by inference, been proposed as an indirect index of axonal degeneration. However, the association between CSA and axonal loss, and their correlation with demyelination, have never been systematically investigated using human post mortem tissue. We extensively sampled spinal cords of seven women and six men with multiple sclerosis (mean disease duration= 29 years) and five healthy controls to quantify axonal density and its association with demyelination and CSA. 396 tissue blocks were embedded in paraffin and immuno-stained for myelin basic protein and phosphorylated neurofilaments. Measurements included total CSA, areas of (i) lateral cortico-spinal tracts, (ii) gray matter, (iii) white matter, (iv) demyelination, and the number of axons within the lateral cortico-spinal tracts. Linear mixed models were used to analyze relationships. In multiple sclerosis CSA reduction at cervical, thoracic and lumbar levels ranged between 19 and 24% with white (19-24%) and gray (17-21%) matter atrophy contributing equally across levels. Axonal density in multiple sclerosis was lower by 57-62% across all levels and affected all fibers regardless of diameter. Demyelination affected 24-48% of the gray matter, most extensively at the thoracic level, and 11-13% of the white matter, with no significant differences across levels. Disease duration was associated with reduced axonal density, however not with any area index. Significant association was detected between focal demyelination and decreased axonal density. In conclusion, over nearly 30 years multiple sclerosis reduces axonal density by 60% throughout the spinal cord. Spinal cord cross sectional area, reduced by about 20%, appears to be a poor predictor of axonal density.

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Axonal degeneration in multiple sclerosis: can we predict and prevent permanent disability?

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