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.

Baseline serum neurofilament light chain levels differentiate aggressive from benign forms of relapsing-remitting multiple sclerosis: a 20-year follow-up cohort

BACKGROUND AND OBJECTIVES

Serum biomarkers are emerging as useful prognostic tools for multiple sclerosis (MS); however, long-term studies are lacking. We aimed to evaluate the long-term prognostic value of the serum levels of neurofilament light chain (NfL), total tau, glial fibrillary acidic protein (GFAP), and chitinase 3-like-1 (CHI3L1) measured close to the time of MS onset.

METHODS

In this retrospective, exploratory, observational, case and controls study, patients with relapsing-remitting MS (RRMS) with available baseline serum samples and prospectively follow-up in our MS unit for a long time were selected based on their clinical evolution to form two groups: (1) a benign RRMS (bRRMS) group, defined as patients with an Expanded Disability Status Scale (EDSS) score of ≤ 3 at ≥ 10 years of follow-up; (2) an aggressive RRMS (aRRMS) group, defined as patients with an EDSS score of ≥ 6 at ≤ 15 years of follow-up. An age-matched healthy control (HC) group was selected. NfL, total tau, and GFAP serum levels were quantified using a single-molecule array (SIMOA), and CHI3L1 was quantified using ELISA.

RESULTS

Thirty-one patients with bRRMS, 19 with aRRMS, and 10 HC were included. The median follow-up time from sample collection was 17.74 years (interquartile range, 14.60-20.37). Bivariate and multivariate analyses revealed significantly higher NfL and GFAP levels in the aRRMS group than in the bRRMS group. A receiver operating characteristic curve analysis identified serum NfL level as the most efficient marker for distinguishing aRRMS from bRRMS.

DISCUSSION

This proof-of-concept study comparing benign and aggressive RRMS groups reinforces the potential role of baseline NfL serum levels as a promising long-term disability prognostic marker. In contrast, serum GFAP, total tau, and CHI3L1 levels demonstrated a lower or no ability to differentiate between the long-term outcomes of RRMS.

Sex differences in multiple sclerosis relapse presentation and outcome: a retrospective, monocentric study of 134 relapse events

Background

Reporting of sex-specific analyses in multiple sclerosis (MS) is sparse. Disability accrual results from relapses (relapse-associated worsening) and independent thereof (progression independent of relapses).

Objectives

A population of MS patients during relapse treated per standard of care was analyzed for sex differences and short-term relapse outcome (3-6 months) as measured by Expanded Disability Status Scale (EDSS) change.

Design

Single-center retrospective study.

Methods

We analyzed 134 MS relapses between March 2016 and August 2020. All events required relapse treatment (steroids and/or plasma exchange). Demographic, disease, and paraclinical characteristics [cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI)] were displayed separated by sex. Multivariable linear regression was run to identify factors associated with short-term EDSS change.

Results

Mean age at relapse was 38.4 years (95% confidence interval: 36.3-40.4) with a proportion of 71.6% women in our cohort. Smoking was more than twice as prevalent in men (65.8%) than women (32.3%). In- and after-relapse EDSSs were higher in men [men: 3.3 (2.8-3.9), women: 2.7 (2.4-3.0); men: 3.0 (1.3-3.6); women: 1.8 (1.5-2.1)] despite similar relapse intervention. Paraclinical parameters revealed no sex differences. Our primary model identified female sex, younger age, and higher EDSS at relapse to be associated with EDSS improvement. A higher immunoglobulin G (IgG) quotient (CSF/serum) was associated with poorer short-term outcome [mean days between first relapse treatment and last EDSS assessment 130.2 (79.3-181.0)].

Conclusion

Sex and gender differences are important in outcome analyses of MS relapses. Effective treatment regimens need to respect putative markers for a worse outcome to modify long-term prognosis such as clinical and demographic variables, complemented by intrathecal IgG synthesis. Prospective trials should be designed to address these differences and confirm our results.

Predictive factors and treatment challenges in malignant progression of relapsing-remitting multiple sclerosis

Objective

Our objective was to uncover the predictive factors that can help anticipate the malignant progression of individuals with Relapsing-Remitting Multiple Sclerosis (RRMS). Additionally, we sought to analyze and compare the response to treatment between patients with benign and malignant forms of RRMS.

Methods

This cohort study included RRMS patients categorized as benign (≥10 years since disease onset, Expanded Disability Status Scale (EDSS) ≤ 1) or malignant (≤5 years since disease onset, EDSS ≥6). Patients' data, including demographics, medical history, treatment, and MRI (Magnetic Resonance Imaging) scans, were collected and statistically analyzed.

Results

Among the 254 patients diagnosed with RRMS, 174 were found to have benign RRMS, while the remaining 80 were diagnosed with malignant RRMS. Notably, patients with malignant RRMS exhibited a significantly higher mean age of onset (32.00 ± 7.96 vs. 25.70 ± 17.19; P < 0.001) and a greater prevalence of males (40% vs. 18.4%; P = 0.014). Additionally, within the initial five years of diagnosis, patients with malignant RRMS experienced a higher number of relapses (median: 4 vs. 2; P < 0.001) and hospitalizations (median: 2 vs. 1; P = 0.006) compared to those with benign RRMS. Clinical presentations of malignant RRMS were predominantly characterized by multifocal attacks, whereas unifocal attacks were more prevalent in patients with benign RRMS. MRI scans revealed that malignant RRMS patients displayed a higher burden of plaques in the infratentorial and cord regions, as well as a greater number of black hole lesions. Conversely, benign RRMS patients exhibited a higher number of Gadolinium-enhanced lesions. Utilizing Disease-Modifying Therapies (DMTs) with an escalating approach has shown effectiveness in managing benign RRMS. However, it has proven insufficient in addressing malignant RRMS, resulting in frequent transitions to higher-line DMTs. As a result, it places a considerable burden on patients with malignant RRMS, consuming valuable time and resources, and ultimately yielding subpar outcomes.

Conclusion

Our study identifies prognostic factors for malignant progression in RRMS, including older age of onset, male gender, increased relapses and hospitalizations, multifocal attacks, higher plaque load, and black hole lesions. The current escalation strategy for DMTs is insufficient for managing malignant RRMS, requiring alternative approaches for improved outcomes. In other words, MS is a spectrum rather than a single disease, and some patients progress to a malignant phenotype of MS that is not effectively treated by the current approach.

Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

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.

Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes

BACKGROUND

Early risk-stratification in multiple sclerosis (MS) may impact treatment decisions. Current predictive models have identified that clinical and imaging characteristics of aggressive disease are associated with worse long-term outcomes. Serum biomarkers, neurofilament (sNfL) and glial fibrillary acidic protein (sGFAP), reflect subclinical disease activity through separate pathological processes and may contribute to predictive models of clinical and MRI outcomes.

METHODS

We conducted a retrospective analysis of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB study), where patients with multiple sclerosis are seen every 6 months and undergo Expanded Disability Status Scale (EDSS) assessment, have annual brain MRI scans where volumetric analysis is conducted to calculate T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and donate a yearly blood sample for subsequent analysis. We included patients with newly diagnosed relapsing-remitting MS and serum samples obtained at baseline visit and 1-year follow-up (both within 3 years of onset), and were assessed at 10-year follow-up. We measured sNfL and sGFAP by single molecule array at baseline visit and at 1-year follow-up. A predictive clinical model was developed using age, sex, Expanded Disability Status Scale (EDSS), pyramidal signs, relapse rate, and spinal cord lesions at first visit. The main outcome was odds of developing of secondary progressive (SP)MS at year 10. Secondary outcomes included 10-year EDSS, brain T2LV and BPF. We compared the goodness-of-fit of the predictive clinical model with and without sNfL and sGFAP at baseline and 1-year follow-up, for each outcome by area under the receiver operating characteristic curve (AUC) or R-squared.

RESULTS

A total 144 patients with median MS onset at age 37.4 years (interquartile range: 29.4-45.4), 64% female, were included. SPMS developed in 25 (17.4%) patients. The AUC for the predictive clinical model without biomarker data was 0.73, which improved to 0.77 when both sNfL and sGFAP were included in the model (P = 0.021). In this model, higher baseline sGFAP associated with developing SPMS (OR=3.3 [95%CI:1.1,10.6], P = 0.04). Adding 1-year follow-up biomarker levels further improved the model fit (AUC = 0.79) but this change was not statistically significant (P = 0.15). Adding baseline biomarker data also improved the R-squared of clinical models for 10-year EDSS from 0.24 to 0.28 (P = 0.032), while additional 1-year follow-up levels did not. Baseline sGFAP was associated with 10-year EDSS (ß=0.58 [95%CI:0.00,1.16], P = 0.05). For MRI outcomes, baseline biomarker levels improved R-squared for T2LV from 0.12 to 0.27 (P<0.001), and BPF from 0.15 to 0.20 (P = 0.042). Adding 1-year follow-up biomarker data further improved T2LV to 0.33 (P = 0.0065) and BPF to 0.23 (P = 0.048). Baseline sNfL was associated with T2LV (ß=0.34 [95%CI:0.21,0.48], P<0.001) and 1-year follow-up sNfL with BPF (ß=-2.53% [95%CI:-4.18,-0.89], P = 0.003).

CONCLUSIONS

Early biomarker levels modestly improve predictive models containing clinical and MRI variables. Worse clinical outcomes, SPMS and EDSS, are associated with higher sGFAP levels and worse MRI outcomes, T2LV and BPF, are associated with higher sNfL levels. Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice.

Frailty and relapse activity in multiple sclerosis: A longitudinal observation

Recent cross-sectional investigations suggest a relationship between frailty, as measured by Frailty Index (FI), and multiple sclerosis (MS). However, if and how frailty is associated with relapse activity in MS is still unknown. To explore this issue, a one-year follow-up study involving 471 patients was conducted. A univariate regression model showed an inverse association between baseline FI score and the presence of relapse, which was also confirmed in the multivariate model. These results suggest that frailty may reflect pathophysiological mechanisms involved in MS disease activity and that the FI may be used as an enrichment criterion in clinical trials.

Lessons from immunotherapies in multiple sclerosis

The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.

Characterization of a late-onset multiple sclerosis Portuguese cohort

BACKGROUND

Late-onset multiple sclerosis (LOMS) is defined as the onset of symptoms above 50 years, corresponding to an increasingly recognized subset of MS. This study aimed at comparing demographic and clinical data of patients with LOMS to those of early-onset MS (EOMS) from a Portuguese cohort.

METHODS

Retrospective chart review of an MS cohort from a Portuguese tertiary center.

RESULTS

From 746 patients with MS (61.7% female), we identified 39 cases with presentation after 50 years of age (22 males and 17 females), corresponding to 5.3%. The mean age at onset was 55.4 (±5.0) for LOMS and 29.5 (±8.9) for EOMS. There was no significant difference in disease duration. The most common type was relapsing-remitting MS, accounting for 51.5% and 83.9% of LOMS and EOMS patients, respectively. Primary-progressive MS (PPMS) was significantly more represented in the LOMS group (41.0%) (p < 0.01). The median EDSS was significantly higher in the LOMS group (4.75, 0.0-7.5) when compared to the EOMS group (2.0, 0.0-9,0). The most frequent presenting feature was myelitis in both LOMS (48.7%) and EOMS patients (47.4%), resulting in significantly higher EDSS (p = 0.003).

CONCLUSIONS

LOMS is associated with higher EDSS when considering the same disease duration, translating into increased disability.

External validation of a clinical prediction model in multiple sclerosis

BACKGROUND

Timely initiation of disease modifying therapy is crucial for managing multiple sclerosis (MS).

OBJECTIVE

We aimed to validate a previously published predictive model of individual treatment response using a non-overlapping cohort from the Middle East.

METHODS

We interrogated the MSBase registry for patients who were not included in the initial model development. These patients had relapsing MS or clinically isolated syndrome, a recorded date of disease onset, disability and dates of disease modifying therapy, with sufficient follow-up pre- and post-baseline. Baseline was the visit at which a new disease modifying therapy was initiated, and which served as the start of the predicted period. The original models were used to translate clinical information into three principal components and to predict probability of relapses, disability worsening or improvement, conversion to secondary progressive MS and treatment discontinuation as well as changes in the area under disability-time curve (ΔAUC). Prediction accuracy was assessed using the criteria published previously.

RESULTS

The models performed well for predicting the risk of disability worsening and improvement (accuracy: 81%-96%) and performed moderately well for predicting the risk of relapses (accuracy: 73%-91%). The predictions for ΔAUC and risk of treatment discontinuation were suboptimal (accuracy < 44%). Accuracy for predicting the risk of conversion to secondary progressive MS ranged from 50% to 98%.

CONCLUSION

The previously published models are generalisable to patients with a broad range of baseline characteristics in different geographic regions.

Diagnosis and treatment of progressive multiple sclerosis: A position paper

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is an unpredictable disease characterised by a highly variable disease onset and clinical course. Three main clinical phenotypes have been described. However, distinguishing between the two progressive forms of MS can be challenging for clinicians. This article examines how the diagnostic definitions of progressive MS impact clinical research, the design of clinical trials and, ultimately, treatment decisions.

METHODS

We carried out an extensive review of the literature highlighting differences in the definition of progressive forms of MS, and the importance of assessing the extent of the ongoing inflammatory component in MS when making treatment decisions.

RESULTS

Inconsistent results in phase III clinical studies of treatments for progressive MS, may be attributable to differences in patient characteristics (e.g., age, clinical and radiological activity at baseline) and endpoint definitions. In both primary and secondary progressive MS, patients who are younger and have more active disease will derive the greatest benefit from the available treatments.

CONCLUSIONS

We recommend making treatment decisions based on the individual patient's pattern of disease progression, as well as functional, clinical and imaging parameters, rather than on their clinical phenotype. Because the definition of progressive MS differs across clinical studies, careful selection of eligibility criteria and study endpoints is needed for future studies in patients with progressive MS.

CSF β-amyloid is not a prognostic marker in multiple sclerosis patients

BACKGROUND

Multiple sclerosis (MS) is the most common chronic inflammatory, demyelinating disorder. Given its variable prognosis, the identification of new prognostic biomarkers is needed.

OBJECTIVES

The aims of our study were to assess the prognostic values of CSF β-amyloid-42 (Aβ42) and β-amyloid-40 (Aβ40) levels in MS patients.

METHODS

Eighty-nine (55 RRMS, 34 PPMS) patients with a recent diagnosis and 27 controls were included in this single-centre retrospective study. Clinical, MRI and CSF data have been collected and were analysed to evaluate the potential value of CSF Aβ42 and Aβ40 levels as MS biomarkers.

RESULTS

CSF Aβ levels as well as Aβ42/Aβ40 ratio were identical in MS patients and controls. Although CSF Aβ42 and Aβ40 levels were higher in PPMS than in RRMS and in patients with higher EDSS, a multivariate analysis including age and EDSS demonstrated that only age of patients was associated with CSF amyloid levels. Additionally, 55 RRMS patients were followed for 3 years. We found no association between baseline amyloid levels and 3-year disability.

CONCLUSION

Our data do not support an association between CSF amyloid levels and MS status and disease severity. We suggest that CSF amyloid levels are not a prognostic biomarker in recently diagnosed RRMS.

Early onset multiple sclerosis and the effect of disease onset age on neurological disability in multiple sclerosis

OBJECTIVES

The incidence of early onset multiple sclerosis (EOMS) is increasing. We therefore aimed to compare the demographic, clinical, and magnetic resonance imaging features of early onset and adult onset multiple sclerosis patients. Furthermore, the effects of age of onset were evaluated for patients who reached an expanded disability status scale (EDSS) scores of six.

PATIENTS AND METHODS

This was a retrospective study of MS patient medical charts between 1977 and 2021, which were registered in the MS database. Only patients with relapsing remitting MS longer than 1 year were included in the study. The patients included in the study were divided into the EOMS and adult onset MS (AOMS) groups. General demographic datas, clinical datas such as the characteristics of the first clinical period, the time between the first two attacks, the attack rate in the first 2 and 3 years, the treatment status, the EDSS at the first evaluation, the EDSS score at 6 month intervals, the time to reach an EDSS score of six, and magnetic resonance imaging features such brain and spinal T2 lesions were recorded.

RESULTS

Total of 3477 including 353 (10.2 %) EOMS and 3124 (89.8 %) AOMS patients were analyzed. There was no statistically significant difference in symptom patterns between the EOMS and AOMS groups ( p = 0.649). Supratentorial clinical features at first attack were more common in AOMS patients, while optic neuropathy at first attack was more common in EOMS patients. Using univariable analysis, clinical supratentorial features at first attack, clinical optic neuropathy at first attack, clinical spinal cord at fist attack, spinal cord lesions, first EDSS score, relapse in the first 3 years, and onset patterns in terms of age were found to be statistically significant risk factors. In multivariable-adjusted analysis, clinical supratentorial features at first attack, clinical spinal cord lesions at first attack, first EDSS scores relapses in the first 3 years, and onset patterns in terms of age were found to be independent risk factors for EDSS in reaching a score of six. Early treatment start was associated with reduced hazard rate of reaching an EDSS score of 6.

CONCLUSION

Onset pattern in terms of age was an independent prognostic factor for neurological disabilities in MS patients.

Younger age at multiple sclerosis onset is associated with worse outcomes at age 50

OBJECTIVE

Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50.

METHODS

We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50.

RESULTS

We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037).

CONCLUSIONS

All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor.

Interrogating large multiple sclerosis registries and databases: what information can be gained?

PURPOSE OF REVIEW

Although substantial progress has been made in understanding the natural history of multiple sclerosis (MS) and the development of new therapies, many questions concerning disease behavior and therapeutics remain to be answered. Data generated from real-world observational studies, based on large MS registries and databases and analyzed with advanced statistical methods, are offering the scientific community answers to some of these questions that are otherwise difficult or impossible to address. This review focuses on observational studies published in the last 2 years designed to compare the effectiveness of escalation vs. induction treatment strategies, to assess the effectiveness of treatment in pediatric-onset and late-onset MS, and to identify the clinical phenotype of secondary progressive (SP)MS.

RECENT FINDINGS

The main findings originating from real-world studies suggest that MS patients who will qualify for high-efficacy disease-modifying therapies (DMTs) should be offered these as early as possible to prevent irreversible accumulation of neurological disability. Especially pediatric patients derive substantial benefits from early treatment. In patients with late-onset MS, sustained exposure to DMTs may result in more favorable outcomes. Data-driven definitions are more accurate in defining transition to SPMS than diagnosis based solely on neurologists' judgment.

SUMMARY

Patients, physicians, industry, and policy-makers have all benefited from real-world evidence based on registry data, in answering questions of diagnostics, choice of treatment, and timing of treatment decisions.

Immunological Subsets Characterization in Newly Diagnosed Relapsing-Remitting Multiple Sclerosis

Objectives

Using flow cytometry, we characterized myeloid, B, and T cells in patients recently diagnosed with relapsing–remitting multiple sclerosis (RRMS) naive to disease-modifying therapies (DMTs).

Methods

This prospective case–control study was conducted in the tertiary MS center of Catania, Italy. Demographic/clinical data and peripheral bloods were collected from 52 naive patients recently diagnosed with RRMS and sex/age-matched healthy controls (HCs) in a 2:1 ratio. We performed flow cytometry on isolated peripheral blood mononuclear cells to assess immune cell subsets differences between RMMS patients and HCs. We explored the biomarker potential of cell subsets using receiver operating characteristic (ROC) curves and relative area under the curve (AUC) analyses.

Results

Monocytic myeloid-derived suppressor cells (Mo-MDSCs CD14+/HLADR^−/low) and inflammatory monocytes (CD14+CD16+) displayed higher frequencies in RRMS patients when compared with HCs (p <.05). A lower percentage of B-unswitched memory cells was observed in RRMS patients when compared with HCs (p = .026). T cells had a higher frequency of T-helper CD4+ cells and their subset, CD4+CD161+, in RRMS patients when compared with HCs (p <.001). ROC analyses revealed an AUC >70% for Mo-MDSCs CD14+/HLADR^−/low and inflammatory CD14+CD16+, T-helper CD3+CD4+, and T-helper CD4+CD161+.

Conclusions

Patients with a recent RRMS diagnosis and naive to DMTs, showed peculiar myeloid, B-, and T-cell immunophenotypes.

Preventing disease progression in multiple sclerosis-insights from large real-world cohorts

Multiple sclerosis is a chronic neuroinflammatory disease with a highly heterogeneous disease course. Preventing lasting disability requires early identification of persons at risk and novel approaches towards patient stratification for personalized treatment decisions. In this comment, we discuss the importance of large datasets of real-world cohorts in order to address this unmet need.

Multiple Sclerosis Severity Score (MSSS) improves the accuracy of individualized prediction in MS

Background:

The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability.

Objective:

To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS.

Methods:

The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients’ demographic and clinical characteristics. The model fit with and without MSSS was assessed with penalized r2 and Harrell C.

Results:

A total of 5866 MS patients were started on disease-modifying therapy during prospective follow-up (age 38.4 ± 10.6 years; 72% female; disease duration 8.5 ± 7.7 years). Including MSSS into the model improved the accuracy of individual prediction of relapses by 31%, of CDA by 23%, and of CDI by 24% (Harrell C) and increased the amount of variance explained for relapses by 49%, for CDI by 11%, and for CDA by 10% as compared with the original model.

Conclusion:

Addition of a single, readily available metric, MSSS, to the comprehensive MSBase prediction model considerably improved the individual accuracy of prognostics in MS.

Mesenchymal Stem Cell-Derived Extracellular Vesicles and Their Therapeutic Use in Central Nervous System Demyelinating Disorders

Autoimmune demyelinating diseases-including multiple sclerosis, neuromyelitis optica spectrum disorder, anti-myelin oligodendrocyte glycoprotein-associated disease, acute disseminated encephalomyelitis, and glial fibrillary acidic protein (GFAP)-associated meningoencephalomyelitis-are a heterogeneous group of diseases even though their common pathology is characterized by neuroinflammation, loss of myelin, and reactive astrogliosis. The lack of safe pharmacological therapies has purported the notion that cell-based treatments could be introduced to cure these patients. Among stem cells, mesenchymal stem cells (MSCs), obtained from various sources, are considered to be the ones with more interesting features in the context of demyelinating disorders, given that their secretome is fully equipped with an array of anti-inflammatory and neuroprotective molecules, such as mRNAs, miRNAs, lipids, and proteins with multiple functions. In this review, we discuss the potential of cell-free therapeutics utilizing MSC secretome-derived extracellular vesicles-and in particular exosomes-in the treatment of autoimmune demyelinating diseases, and provide an outlook for studies of their future applications.

Prediction in treatment outcomes in multiple sclerosis: challenges and recent advances

INTRODUCTION

Multiple Sclerosis (MS) is a chronic autoimmune and neurodegenerative disease of the central nervous system with a course dependent on early treatment response. Increasing evidence also suggests that despite eliminating disease activity (relapses and lesions), many patients continue to accrue disability, highlighting the need for a more comprehensive definition of treatment success. Optimizing disability outcome measures, as well as continuously improving our understanding of neuroinflammatory and neurodegenerative biomarkers is required.

AREAS COVERED

This review describes the challenges inherent in classifying and monitoring disease phenotype in MS. The review also provides an assessment of clinical, radiological, and blood biomarker tools for current and future practice.

EXPERT OPINION

Emerging MRI techniques and standardized patient outcome assessments will increase the accuracy of initial diagnosis and understanding of disease progression.

Pregnancy in multiple sclerosis women with relapses in the year before conception increases the risk of long-term disability worsening

BACKGROUND

The influence of pregnancy on long-term disability in multiple sclerosis (MS) is still controversial.

OBJECTIVE

To assess the risk of long-term disability worsening after pregnancy in MS women as compared with a propensity-score (PS) matched group of MS women without pregnancy.

METHODS

In the setting of the Italian Pregnancy Dataset, MS patients with (pregnancy group (PG)) and without pregnancy (control group (CG)) were recruited. Time to disability worsening on the Expanded Disability Status Scale (EDSS) was assessed through a multivariable Cox regression model.

RESULTS

The PS-matching retained 230 PG and 102 CG patients. After a follow-up of 6.5 +/- 3.1 years, disability worsening occurred in 87 (26.2%) women. In the multivariable analysis, disability worsening was associated with pregnancy in women with relapses in the year before conception (adjusted hazard ratio (aHR) = 1.74; 95% confidence interval (CI) 1.06-2.84; p = 0.027), higher EDSS (aHR = 1.39; 95% CI 1.12-1.74; p = 0.003), younger age (aHR = 0.95; 95% CI 0.91-0.99; p = 0.022) and shorter DMD exposure over the follow-up (p < 0.008).

CONCLUSION

Pregnancy in MS women with relapses in the year before conception increases the risk of long-term disability worsening. Our findings underscore the importance of counselling in MS women facing a pregnancy that should be planned after a period of clinical stability, favouring treatment optimization in patients with recent disease activity.

Stopping disease-modifying therapy in relapsing and progressive multiple sclerosis

PURPOSE OF REVIEW

To assess the reasons for considering discontinuation of disease-modifying therapies (DMTs)in patients with multiple sclerosis (MS). Relevant aspects of the natural history, pathology, and immunology are analyzed.

RECENT FINDINGS

A number of retrospective observational studies in aggregate indicate that stopping DMTs may be attempted in older individuals with stable disease. Prognostic factors have been identified informing about the risk of recurrence of disease activity after DMT discontinuation.

SUMMARY

Several clinical scenarios provide a rationale to stop DMTs in people with MS. Cumulative evidence has been gathered recently allowing us to more precisely weigh the risks against the benefits. This information aids in the decision process.

Leveraging electronic health records data to predict multiple sclerosis disease activity

Objective

No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk.

Methods

Using data from a clinic‐based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1‐year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor‐intensive chart review is impractical. We pursued two‐stage algorithm development: (1) L₁‐regularized logistic regression (LASSO) to phenotype past 1‐year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1‐year relapse risk using imputed prior 1‐year relapse status and other algorithm‐selected features.

Results

The final model, comprising age, disease duration, and imputed prior 1‐year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1‐year relapse history. The predicted risk probability declined with disease duration and age.

Conclusion

Our novel machine‐learning algorithm predicts 1‐year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR‐based two‐stage approach of outcome prediction may have application to neurological disease beyond MS.

Recent advances and remaining questions of autologous hematopoietic stem cell transplantation in multiple sclerosis

The judicious use of autologous hematopoietic stem cell transplantation (AHSCT) for MS requires understanding the potential benefits, identifying the most appropriate patient, and acknowledging the risks and differences between different protocols. Recently, AHSCT for MS is occurring more frequently, with a better safety profile than earlier studies. This review assesses recently published studies to determine the advances that have been made and remaining questions that future studies are poised to answer. We included studies from January 2016 to November 2020 with 20 or more patients. The benefits of AHSCT, including "no evidence of disease activity", functional and patient-reported outcomes, novel biomarkers such as brain atrophy or neurofilament light chain, and cost-effectiveness were assessed. The patient selection, treatment protocols, and safety outcomes differ between reports. The overall efficacy of AHSCT is better than standard treatments. Younger patients with highly active disease have greater chance for improvement, while patients who have comorbidities, failed more treatments, and are transitioning to a more progressive phase may not respond as well to AHSCT. The safety profiles for all AHSCT protocols is improving, however the durability of treatment response may not be the same for all protocols. The goal of AHSCT is to stop disease activity, avoid worsening disability, and obviate the need for further disease-modifying treatment, while improving patient quality of life and minimizing treatment-related risk. Results from currently enrolling randomized controlled trials, as well as ongoing registries, will provide more evidence for the safe and appropriate use of AHSCT.

First line treatment failure: Predictive factors in a cohort of 863 Relapsing Remitting MS patients

BACKGROUND

The advent of new, potent, disease-modifying therapies has dramatically changed the management of multiple sclerosis (MS). Along with these possibilities, it is crucial to better recognize patients who are at risk of first line treatment (FLT) failure and switch to highly effective therapies (HET).

OBJECTIVES

To identify baseline prognostic factors associated with FLT failure in relapsing remitting MS (RR-MS) patients.

METHODS

We included recently diagnosed RR-MS patients starting an FLT identified from 3 French MS centers databases. Baseline characteristics were included in a multivariable Cox analysis to identify the main factors associated with FLT failure.

RESULTS

Eight hundred sixty-three patients were included. We observed an overall rate of treatment failure of 23.5%. The main baseline characteristics associated with treatment failure were age <26 years at treatment start (HR= 2.1, p<0.001), EDSS ≥2 (HR=2.1, p<0.001) and ≥2relapses in the previous year (HR=1.5, p=0.04). The association with the presence of gadolinium enhancement on MRI was not statistically significant. EDSS progression was only significantly associated with age at treatment start and treatment failure.

CONCLUSION

Our series demonstrates that some clinical and imaging factors are associated with treatment failure, and should be considered when planning treatment strategy in patients with recently diagnosed RR-MS.