Prognosis of multiple sclerosis: clinical factors predicting the late evolution for an early treatment decision

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Sex-specific hypothalamic expression of kisspeptin, gonadotropin releasing hormone, and kisspeptin receptor in progressive demyelination model

Demyelinating diseases, such as multiple sclerosis, decrease the quality of life of patients and can affect reproduction. Assisted reproductive therapies are available, which although effective, aggravate motor symptoms. For this reason, it is important to determine how the control of the hypothalamus-pituitary-gonadal axis is affected in order to develop better strategies for these patients. One way to determine this is using animal models such as the taiep rat, which shows progressive demyelination of the central nervous system, and was used in the present study to characterize the expression of gonadotrophin-releasing hormone (GnRH), Kisspeptin, and kisspeptin receptor (Kiss1R) and luteinizing hormone (LH) secretion. The expression of kisspeptin, GnRH, and Kiss1R was determined at the hypothalamic level by immunofluorescence and serum LH levels were determined by ELISA. The expression of kisspeptin at the hypothalamic level showed sexual dimorphism, where there was an increase in males and a decrease in females during oestrus. There was no change in the expression of GnRH or kisspeptin receptor, regardless of sex. However, a decrease in serum LH concentration was observed in both sexes. The taiep rat showed changes in the expression of kisspeptin at the hypothalamic level. These changes are different from those reported in the literature with the use of animals with experimental allergic encephalomyelitis, this is because both animal models represent different degrees of progression of multiple sclerosis. Our results suggest that the effects on the hypothalamus-pituitary-gonadal axis depend on the differences between the demyelinating processes, their progression, and even individual factors, and it is thus important that fertility treatments are individualized to maximize therapeutic effects.

Cerebrospinal Tau levels as a predictor of early disability in multiple sclerosis

INTRODUCTION

Axonal loss is an important feature of Multiple Sclerosis (MS), being strongly related to irreversible disability accumulation. Nonetheless, the exact mechanisms underlying axonal loss remain unclear. Cerebrospinal fluid (CSF) levels of Tau and Beta-amyloid (Abeta) currently represent diagnostic biomarkers in other neurodegenerative diseases. In MS, studies on CSF Tau and Abeta provided preliminary informations on disease prognosis, but results have not yet been replicated.

METHODS

We investigated whether CSF Tau and Abeta levels could predict early disability accumulation in MS patients. 100 patients underwent CSF analysis during their diagnostic work-up. Demographic, clinical, radiological features and CSF were collected at baseline. MS severity score (MSSS) and age-related MSSS (ARMSS) were calculated at last follow-up. We performed Mann-Whitney test, Spearman's coefficient, and multiple regression analysis for significant predictors of disability based on CSF Abeta and Tau levels, gender, age at diagnosis and MRI characteristics at baseline.

RESULTS

Baseline CSF Tau levels moderately correlated with MSSS (r=0.372 p=0.0001) and weakly with ARMSS (r=0.237 p=0.0176) after a mean two years follow-up. Predictors of early disability evaluated with MSSS and ARMSS were CSF Tau (Beta:0.258 p=0.009 and Beta:0.252 p=0.01) and spinal cord involvement (Beta:0.196 p=0.029 and Beta:0.240 p=0.008); as well as age at MS diagnosis (Beta:0.286 p=0.001) for MSSS, and high brain lesion load (Beta:0.207 p=0.02) for ARMSS.

CONCLUSION

CSF Tau levels at diagnosis possibly has a predictive value along with MRI features and age at diagnosis. We hypothesize that Tau levels may express chronic axonal damage, possibly contributing to early MS disability.

A comparison of brain magnetic resonance imaging lesions in multiple sclerosis by race with reference to disability progression

BACKGROUND

We compared the magnetic resonance imaging (MRI) features between Japanese and Caucasian patients with multiple sclerosis (MS), and identified the relationships between MRI features and disability.

METHODS

From the baseline data of phase II fingolimod trials, 95 Japanese and 246 Caucasian relapsing-remitting MS patients were enrolled. The number, volume, and distribution of brain MRI lesions were evaluated using T2-weighted (T2W) images. Cross-sectional total normalized brain volume (NBV), normalized cortical gray matter volume, normalized deep gray matter volume (NDGMV), normalized white matter volume (NWMV), and normalized thalamic volume were measured.

RESULTS

Japanese patients had significantly lower Expanded Disability Status Scale (EDSS) scores than Caucasian patients (mean 2.0 vs. 2.3, p = 0.008), despite a similar disease duration. Japanese patients showed a trend towards fewer T2W-lesions (median 50 vs. 65, p = 0.08) and significantly lower frequencies of cerebellar and parietal lobe lesions (p = 0.02 for both) than Caucasian patients. There were no differences in T2W-lesion volume between races, whereas Japanese patients had a significantly larger T2W-lesion volume per lesion compared with Caucasian patients (median 140 mm³ vs. 85 mm³, p < 0.0001). T2W-lesion volumes were positively correlated with EDSS scores in Japanese patients (p < 0.0001). In both races, NBV, normalized cortical gray matter volume, NDGMV, and thalamic volume were negatively correlated with disease duration and EDSS scores (p < 0.01 for all). NWMV was negatively correlated with disease duration and EDSS scores only in Caucasian patients (p = 0.03 and p = 0.004, respectively). NBV, NDGMV, NWMV, and thalamic volume were consistently smaller in Japanese compared with Caucasian patients throughout the entire examined disease duration (p = 0.046, p = 0.01, p = 0.005, and p = 0.04, respectively). Japanese patients had a significantly faster reduction in NDGMV (p = 0.001), particularly for thalamic volume (p = 0.001), with disease duration compared with Caucasian patients.

CONCLUSIONS

Gray matter atrophy is a common denominator for disability in Japanese and Caucasian patients. Additional contributory factors for disability include T2W-lesion volume in Japanese patients and white matter atrophy in Caucasian patients. Less frequent parietal and cerebellar involvement with fewer T2W-lesions may underlie milder disability in Japanese patients.

Early predictors of non-response to interferon in multiple sclerosis

OBJECTIVE

To identify early clinical and MRI predictors of non-response to interferon (IFN) treatment in multiple sclerosis (MS).

METHODS

In 172 patients with relapsing-remitting MS treated with IFNβ, we evaluated prediction of future treatment non-response. Candidate predictors comprised disability and its sustained progression, relapse score (combining frequency and severity of relapses), brain volume change, brain parenchymal fraction, number of new T2 lesions, and T2 and T1 lesion volume within the initial year of treatment. Treatment non-response was evaluated as confirmed disability progression or overall average annual relapse score exceeding 1 over the following 5 years. Logistic regression model was adjusted for patient age, gender, disease duration and changes in treatment.

RESULTS

Ninety patients (52%) reached the status of IFN non-responders in years 2-6. Patients with ≥1 new T2 lesion and relapse score ≥2 (odds ratio ≥5.7) or those with ≥3 new T2 lesions regardless of the relapse score (odds ratio = 3) were in a significantly higher risk of future treatment non-response.

CONCLUSIONS

In patients with MS treated with IFNβ for 1 year, number of new T2 lesions and annualized relapse score predict individual risk of treatment non-response over the following 5 years.

Immunomodulatory therapies delay disease progression in multiple sclerosis

BACKGROUND

Few studies have analysed long-term effects of immunomodulatory disease modifying drugs (DMDs).

OBJECTIVE

Assessment of the efficacy of DMDs on long-term evolution of multiple sclerosis, using a Bayesian approach to overcome methodological problems related to open-label studies.

METHODS

MS patients from three different Italian multiple sclerosis centres were divided into subgroups according to the presence of treatment in their disease history before the endpoint, which was represented by secondary progression. Patients were stratified on the basis of the risk score BREMS (Bayesian risk estimate for multiple sclerosis), which is able to predict the unfavourable long-term evolution of MS at an early stage.

RESULTS

We analysed data from 1178 patients with a relapsing form of multiple sclerosis at onset and at least 10 years of disease duration, treated (59%) or untreated with DMDs. The risk of secondary progression was significantly lower in patients treated with DMDs, regardless of the initial prognosis predicted by BREMS.

CONCLUSIONS

DMDs significantly reduce the risk of multiple sclerosis progression both in patients with initial high-risk and patients with initial low-risk. These findings reinforce the role of DMDs in modifying the natural course of the disease, suggesting that they have a positive effect not only on the inflammatory but also on the neurodegenerative process. The study also confirms the capability of the BREMS score to predict MS evolution.

IL-7Rα confers susceptibility to experimental autoimmune encephalomyelitis

Multiple sclerosis (MS) is a neurological disorder that causes paralysis in young adults and affects women more frequently than men. The etiology of MS is not known, but it is generally viewed as an autoimmune disease of the central nervous system (CNS), influenced by genetic and environmental factors. Recent studies have identified interleukin-7 receptor α (IL-7Rα) as a risk factor for MS. But the role of IL-7Rα in experimental autoimmune encephalomyelitis (EAE) model of MS is not known. In this study we demonstrate that IL-7Rα-deficient (IL-7Rα(-/-)) mice remain resistant to MOGp35-55-induced EAE. When compared with C57BL/6 wild-type mice, IL-7Rα(-/-) mice showed less severe inflammation and demyelination in the CNS. The attenuation of EAE in IL-7Rα(-/-) mice was associated with a decrease in T-helper (Th) 1 and Th17 responses in the CNS and lymphoid organs. IL-7Rα(-/-) mice also showed an increase in Th2 response and CD4(+)Foxp3(+) regulatory T cells. These findings highlight that IL-7Rα confers susceptibility by influencing autoimmune Th1/Th17 responses in EAE model of MS.

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

Background

The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.

Methods

In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.

Results

We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).

Conclusion

We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.

Longitudinal predictors of attitudes toward aging among women with multiple sclerosis

The purpose of the study was to explore the impact of change in functional limitation (FL), controlling for social support (SS), on attitudes toward aging using longitudinal survey data collected over a 7-year period. The 503 women with multiple sclerosis (MS; age, M=57 years, SD=10.25) were mostly Anglo (93%) and married (69%). First, the authors identified a quadratic growth model as the best for describing change in FL. Next, they considered SS as a time-varying covariate of FL to assess both within- and between-individual effects of SS on FL over time. Within individuals, higher FL levels were associated with lower SS levels. Between individuals, level of but not change in FL was associated with average SS level. Finally, average SS and response level and change in FL were studied as predictors of attitudes toward aging, accounting for 38% of the variance. Women with higher FL and lower SS scores had more negative views of aging. Negative views of aging among women with MS may have long-term consequences for their health outcomes as well as for the quality of their later years.

Factors predicting incomplete recovery from relapses in multiple sclerosis: a prospective study

Objective To prospectively evaluate predictors of incomplete recovery after the first attacks in a cohort of patients with clinically isolated syndrome or relapsing—remitting multiple sclerosis.

Methods Seventy-two consecutive patients recruited from January 2001 to December 2003, evaluated every six months or at any relapse up to 31 July 2005. Relapse intervals were calculated from the date of onset, nadir, onset of improvement and maximum improvement. Predictive factors analysed were relapse-related (age at relapse onset, season and severity of the relapse, type of symptoms, speed of onset, plateau and total duration, number of affected Functional systems, preceding infections) and individual-related (gender, age at first attack, season of birth and first attack, characteristics of first brain MRI and cerebrospinal fluid oligoclonal bands, Link Index, IgG).

Results We counted 209 attacks: 44 (21%) left mild sequelae, and 27 (13%) severe. The highest probability of sequelae was associated with sphincteric symptoms (9/20; 45%), followed by sensitive (38/113; 34%), motor (20/84; 24%), visual (13/61; 21%), cerebellar (4/24; 17%), brainstem (5/44; 11%) and others (0/6) ( P 0.005). Four variables were still relevant to predict sequelae after multivariate analysis: mild, moderate or severe relapses versus very mild (Odds ratio = 17.2, 95% confidence limits = 2.2—136.4), intermediate or long relapses versus short (3.2, 1.5—6.9), age ≥ 30 at relapse onset (2.9, 1.5—5.7) and bi-polysymptomatic versus monosymptomatic (2.2, 1.1—4.3).

Conclusions Factors predicting incomplete recovery are more closely linked to the characteristics of the single relapse (extension and duration of tissue damage) than to the patient's genetic and environmental background. Multiple Sclerosis 2008; 14: 485—493. http://msj.sagepub.com

Emerging therapies for multiple sclerosis

This review examines the mode of action, safety profile and clinical efficacy of some of the most promising new therapeutic strategies for multiple sclerosis. Autologous hematopoietic stem cell transplantation can regenerate a new and tolerant immune system and is a potentially effective rescue therapy in a subset of patients with aggressive forms of MS refractory to approved immunomodulatory and immunosuppressive agents. High-dose cyclophosphamide without stem cell support is suggested to induce prolonged remissions through similar immunological mechanisms with less toxicity. Fingolimod (FTY720) is a novel oral immunomodulating agent that acts through preventing lymphocyte recirculation from lymphoid organs. Monoclonal antibody therapy has provided scientists and clinicians the opportunity to rationally direct the therapeutic intervention against specific molecules. Targeting molecules of the immune system such as CD52 (alemtuzumab), CD25 (daclizumab), VLA-4 (natalizumab) and CD20 (rituximab) have resulted in potent immunomodulatory effects through sometimes unpredicted mechanisms. The potential of immunoglobulins to induce remyelination in the CNS is being investigated in an attempt to develop therapies promoting tissue repair and functional recovery. The evidence supporting the potential of these emerging immunotherapies suggests that strong progress is being made in the development of effective cures for multiple sclerosis.

Prognosis of the individual course of disease--steps in developing a decision support tool for Multiple Sclerosis

Background

Multiple sclerosis is a chronic disease of uncertain aetiology. Variations in its disease course make it difficult to impossible to accurately determine the prognosis of individual patients. The Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) developed an "online analytical processing (OLAP)" tool that takes advantage of extant clinical trials data and allows one to model the near term future course of this chronic disease for an individual patient.

Results

For a given patient the most similar patients of the SLCMSR database are intelligently selected by a model-based matching algorithm integrated into an OLAP-tool to enable real time, web-based statistical analyses. The underlying database (last update April 2005) contains 1,059 patients derived from 30 placebo arms of controlled clinical trials. Demographic information on the entire database and the portion selected for comparison are displayed. The result of the statistical comparison is provided as a display of the course of Expanded Disability Status Scale (EDSS) for individuals in the database with regions of probable progression over time, along with their mean relapse rate. Kaplan-Meier curves for time to sustained progression in the EDSS and time to requirement of constant assistance to walk (EDSS 6) are also displayed. The software-application OLAP anticipates the input MS patient's course on the basis of baseline values and the known course of disease for similar patients who have been followed in clinical trials.

Conclusion

This simulation could be useful for physicians, researchers and other professionals who counsel patients on therapeutic options. The application can be modified for studying the natural history of other chronic diseases, if and when similar datasets on which the OLAP operates exist.