Progression in multiple sclerosis - a long-term problem.

Multiple sclerosis (MS) is an important cause of progressive neurological disability, typically commencing in early adulthood. There is a need for safe and effective therapy to prevent the progressive central nervous system (CNS) damage and resultant disability that characterize the disease course. Increasing evidence supports a chronic autoimmune basis for CNS damage in MS. In the present study, we review current concepts of autoimmune pathogenesis in MS, assess current therapies aimed at countering autoimmune attack and discuss potential therapeutic strategies. Among currently available therapies, beta-interferon and glatiramer acetate have a modest effect on reducing relapses and slowing the accumulation of disability in relapsing-remitting MS. Beta-interferon is of doubtful efficacy in secondary progressive MS and appears to aggravate primary progressive MS, possibly by increasing antibody-mediated CNS damage through inhibition of B-cell apoptosis. Mitoxantrone may reduce relapses and slow disability progression in relapsing-remitting and secondary progressive MS, but its use is limited by the risk of cardiomyopathy. There are currently no effective treatments for primary progressive MS. Many therapies that are effective in the animal model, experimental autoimmune encephalomyelitis (EAE), are either ineffective in MS or--in the case of gamma-interferon, lenercept and altered peptide ligands--actually make MS worse. This discrepancy may be explained by the occurrence in MS of defects in immunoregulatory mechanisms, the integrity of which is essential for the efficacy of these treatments in EAE. It is likely that the development of safe, effective therapy for MS will depend on a better understanding of immunoregulatory defects in MS.

To investigate disease progression and risk factors in a large geographically based population with multiple sclerosis (MS), using two different inception points--clinical onset and date of birth. The authors reviewed a database of subjects with definite MS and symptom onset prior to July 1988. The main outcome was sustained progression to Expanded Disability Status Scale (EDSS) 6 (requires a cane), using the date of birth and date of MS onset as inception points in separate analyses. Risk factors examined were sex, relapsing vs primary progressive course, onset age, and onset symptoms. The study included 2,837 patients, followed prospectively for 22,723 patient years. The median time to EDSS 6 was 27.9 years, 15 years after onset; only 21% reached EDSS 6, and by age 50, 28% required a cane. Men progressed 38% more quickly than women from onset (p < 0.0005), yet both required canes at similar ages: 58.8 years for men and 60.1 for women (p = 0.082). A younger onset age predicted a slower progression, but those older at onset were consistently older when reaching EDSS 6. A primary progressive course predicted a more rapid progression from both onset (p < 0.0005) and birth (hazard ratio = 2.7 [95% CI: 2.2 to 3.3]). No onset symptom consistently predicted progression. Disability progression in multiple sclerosis (MS) accrued more slowly than found in earlier longitudinal studies. The authors also challenged two fundamental concepts in MS, demonstrating that neither male sex nor older onset age was associated with worse disease outcome.

Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 ± 0.0019 and 0.8384 ± 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.

To investigate the association between serum levels of neurofilament light chain (sNfL) and atrophied T2 lesion volume (LV) and other MRI measures indicative of inflammation and neurodegeneration, and disability progression (DP) in multiple sclerosis (MS).

PurposeClinical validation of “BrainLossNet”, a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the discrimination between MS patients with versus without disability progression. Materials and methodsA longitudinal normative database of healthy controls (n = 563), two mono-scanner MS cohorts (n = 414, 156) and a mixed-scanner cohort acquired for various indications (n = 216) were included retrospectively. Mean observation period from the baseline (BL) to the last follow-up (FU) MRI scan was 2–3 years. Expanded Disability Status Scale (EDSS) at BL and FU was available in 149 MS patients. Annual BVL was computed using BrainLossNet and Siena and then adjusted for age. Repeated-measures ANOVA and Cohen's effect size were used to compare BrainLossNet and Siena regarding the detection of accelerated BVL and the differentiation between MS patients with versus without EDSS progression. ResultsCohen's effect size for the differentiation of patients from healthy controls based on the age-adjusted annual BVL was larger with BrainLossNet than with Siena (MS cohort 1: 0.927 versus 0.495, MS cohort 2: 0.671 versus 0.456, mixed-scanner cohort: 0.918 versus 0.730, all p < 0.001). Cohen's effect size for the discrimination between MS patients with (n = 51) versus without (n = 98) EDSS progression was larger with BrainLossNet (0.503 versus 0.400, p = 0.048). ConclusionBrainLossNet can provide added value in clinical routine and MS therapy trials regarding the detection of accelerated BVL in MS and the differentiation between MS patients with versus without disability progression.

BackgroundThere is a lack of understanding of the mechanisms by which the CNS is injured in multiple sclerosis (MS). Since Theiler’s murine encephalomyelitis virus (TMEV) infection in SJL/J mice is an established model of progressive disability in MS, and CNS atrophy correlates with progressive disability in MS, we used in vivo MRI to quantify total ventricular volume in TMEV infection. We then sought to identify immunological and virological biomarkers that correlated with increased ventricular size.MethodsMice, both infected and control, were followed for 6 months. Cerebral ventricular volumes were determined by MRI, and disability was assessed by Rotarod. A range of immunological and virological measures was obtained using standard techniques.ResultsDisability was present in infected mice with enlarged ventricles, while infected mice without enlarged ventricles had Rotarod performance similar to sham mice. Ventricular enlargement was detected as soon as 1 month after infection. None of the immunological and virological measures correlated with the development of ventricular enlargement.ConclusionsThese results support TMEV infection with brain MRI monitoring as a useful model for exploring the biology of disability progression in MS, but they did not identify an immunological or virological correlate with ventricular enlargement.

The presence of disability progression in multiple sclerosis (MS) is an important hallmark for MS patients in the course of their disease. The transition from relapsing remitting (RRMS) to secondary progressive forms of the disease (SPMS) represents a significant change in their quality of life and perception of the disease. It could also be a therapeutic key for opportunities, where approaches different from those in the initial phases of the disease can be adopted. The characterization of structural biomarkers (e.g., magnetic resonance imaging or neurofilament light chain) has been proposed to differentiate between both phenotypes. However, there is no definite threshold between them. Whether the risk of clinical progression can be predicted by structural markers at early disease phases is still a focus of clinical research. However, several theories and pathological evidence suggest that both disease phenotypes are part of a continuum with common pathophysiological mechanisms. In this case, the clinical evaluation of the patients would play a preponderant role above destruction biomarkers for the early identification of disability progression and SPMS. For this purpose, the use of clinical tools beyond the Expanded Disability Status Scale (EDSS) should be considered. Besides established functional tests such as the Multiple Sclerosis Functional Composite (MSFC), patient's neurological history or digital resources may help neurologists in the decision-taking. In this article, we discuss arguments for the use of clinical markers in the detection of secondary progressive MS and the characterization of progressive disease activity.

Short-term disability progression was predicted from a baseline evaluation in patients with multiple sclerosis (MS) using their three-dimensional T1-weighted (3DT1) magnetic resonance images (MRI). One-hundred-and-eighty-one subjects diagnosed with MS underwent 3T-MRI and were followed up for two to six years at two sites, with disability progression defined according to the expanded-disability-status-scale (EDSS) increment at the follow-up. The patients’ 3DT1 images were bias-corrected, brain-extracted, registered onto MNI space, and divided into slices along coronal, sagittal, and axial projections. Deep learning image classification models were applied on slices and devised as ResNet50 fine-tuned adaptations at first on a large independent dataset and secondly on the study sample. The final classifiers’ performance was evaluated via the area under the curve (AUC) of the false versus true positive diagram. Each model was also tested against its null model, obtained by reshuffling patients’ labels in the training set. Informative areas were found by intersecting slices corresponding to models fulfilling the disability progression prediction criteria. At follow-up, 34% of patients had disability progression. Five coronal and five sagittal slices had one classifier surviving the AUC evaluation and null test and predicted disability progression (AUC > 0.72 and AUC > 0.81, respectively). Likewise, fifteen combinations of classifiers and axial slices predicted disability progression in patients (AUC > 0.69). Informative areas were the frontal areas, mainly within the grey matter. Briefly, 3DT1 images may give hints on disability progression in MS patients, exploiting the information hidden in the MRI of specific areas of the brain.

ObjectivesOver the past few decades, there has been an improvement in the rate of disability progression in multiple sclerosis (MS) patients, and most studies relate this evolvement to the introduction of disease-modifying therapies. However, several other factors have changed over this period, including access to MRI and newer diagnostic criteria. The aim of this study is to investigate changes in the natural course of MS over time in a near-complete and geographically well-defined population from the south-east of Norway.MethodsWe examined disease progression and demographics over two decades and assessed the effect of disease-modifying therapies using linear mixed-effect models.ResultsIn a cohort of 2097 patients, we found a significant improvement in disability as measured by the Expanded Disability Status Scale (EDSS) stratified by age, and the improvement remained significant after adjusting for time on disease-modifying medications, gender and progressive MS at onset. The time from disease onset to EDSS 6 in the total cohort was 29.8 years (95% CI 28.5–31.1) and was significantly longer in patients diagnosed after 2006 compared to patients diagnosed before. There are significant differences between patient demographics, as well as time to EDSS 6, in the near-complete, geographically well-defined population compared to an additional cohort from the capital Oslo and its suburbs.ConclusionThe natural course of MS is improving, but the improvement seen in disease progression has multifaceted explanations. Our study underlines the importance of completeness of data, relevant timeframes and demographics when comparing different MS populations. Studies on incomplete populations should be interpreted with caution.

Disability in progressive MS is associated with T2 lesion changes

Background and objectivesThe influence of body weight across the life course on multiple sclerosis (MS) progression remains incompletely understood. While excess body mass at diagnosis is associated with disability progression, it is unclear how early-life and adult BMI jointly affect long-term outcomes. We aimed to investigate the separate and combined effects of BMI at age 20 and at diagnosis on MS progression.MethodsWe studied 2940 individuals with relapsing-onset MS from a population-based case–control study with prospective follow-up through the Swedish MS registry. BMI was calculated from self-reported weight at age 20 and at diagnosis. Outcomes included confirmed disability worsening (CDW), and time to reach EDSS 3 and EDSS 4. Cox regression and general linear models were used to examine associations between BMI and MS progression, including interaction terms.ResultsHigh BMI (> 28 kg/m2) at age 20 was associated with higher disability at diagnosis (β = 0.15, p = 0.0015), while BMI at diagnosis predicted increased risk of progression. Compared to individuals with BMI ≤ 28 kg/m2 at both time points, those with persistent elevated BMI had higher risks of CDW (HR 1.28, 95% CI 1.01–1.63), EDSS 3 (HR 1.64, 95% CI 1.21–2.24), and EDSS 4 (HR 1.51, 95% CI 1.00–2.39). Risks were increased, though less pronounced, among those with high BMI only at diagnosis. Early-life excess weight alone was not associated with progression. Interaction models suggested a stronger effect of adult BMI in the presence of early excess weight.ConclusionsHigh BMI at diagnosis was associated with faster disability progression, particularly when present since early adulthood. These findings underscore the potential benefits of early weight management in MS.

To perform a meta-analysis of randomized, blinded, multiple sclerosis (MS) clinical trials, to test the hypothesis that efficacy of immunomodulatory disease-modifying therapies (DMTs) on MS disability progression is strongly dependent on age. We performed a literature search with pre-defined criteria and extracted relevant features from 38 clinical trials that assessed efficacy of DMTs on disability progression. We fit a linear regression, weighted for trial sample size, and duration, to examine the hypothesis that age has a defining effect on the therapeutic efficacy of immunomodulatory DMTs. More than 28,000 MS subjects participating in trials of 13 categories of immunomodulatory drugs are included in the meta-analysis. The efficacy of immunomodulatory DMTs on MS disability strongly decreased with advancing age (R2 = 0.6757, p = 6.39e-09). Inclusion of baseline EDSS did not significantly improve the model. The regression predicts zero efficacy beyond approximately age 53 years. The comparative efficacy rank derived from the regression residuals differentiates high- and low-efficacy drugs. High-efficacy drugs outperform low-efficacy drugs in inhibiting MS disability only for patients younger than 40.5 years. The meta-analysis supports the notion that progressive MS is simply a later stage of the MS disease process and that age is an essential modifier of a drug efficacy. Higher efficacy treatments exert their benefit over lower efficacy treatments only during early stages of MS, and, after age 53, the model suggests that there is no predicted benefit to receiving immunomodulatory DMTs for the average MS patient.

ABSTRACTIntroduction: Multiple sclerosis (MS) is a chronic inflammatory, demyelinating disease of the central nervous system affecting both white matter and grey matter in the earliest phases of its course. The crucial role of neurodegeneration in disability progression in MS, regardless of white matter damage, has been confirmed by several imaging and neuropathological studies. Fingolimod is an effective immunomodulator of the sphingosine 1-phosphate receptor, approved in relapsing remitting MS and able to cross the blood–brain barrier and to slow disability progression and brain volume loss. However, it remains unclear whether this neuroprotective action is due to a peripheral anti-inflammatory effect and/or to a direct effect on neuronal cells.Areas covered: In this review, the authors summarize the published preclinical and clinical studies on the effect of Fingolimod in limiting the focal and diffuse grey matter damage in MS.Expert opinion: Fingolimod might have a significant neuroprotective effect on relapsing remitting MS based on its modulatory effect on oligodendroglial cells and astrocytes, and on its direct effect on cortical neurons. Future clinical studies including measures of grey matter damage are required to confirm in vivo such neuroprotective effect.

Disability in multiple sclerosis is associated with vascular factors: An ultrasound study

To assess whether biological aging as measured by leukocyte telomere length (LTL) is associated with clinical disability and brain volume loss in multiple sclerosis (MS). Adults with MS/clinically isolated syndrome in the University of California, San Francisco EPIC cohort study were included. LTL was measured on DNA samples by quantitative polymerase chain reaction and expressed as telomere to somatic DNA (T/S) ratio. Expanded Disability Status Scale (EDSS) and 3-dimensional T1-weighted brain magnetic resonance imaging were performed at baseline and follow-up. Associations of baseline LTL with cross-sectional and longitudinal outcomes were assessed using simple and mixed effects linear regression models. A subset (n = 46) had LTL measured over time, and we assessed the association of LTL change with EDSS change with mixed effects models. Included were 356 women and 160 men (mean age = 43 years, median disease duration = 6 years, median EDSS = 1.5 [range = 0-7], mean T/S ratio = 0.97 [standard deviation = 0.18]). In baseline analyses adjusted for age, disease duration, and sex, for every 0.2 lower LTL, EDSS was 0.27 higher (95% confidence interval [CI] = 0.13-0.42, p < 0.001) and brain volume was 7.4mm3 lower (95% CI = 0.10-14.7, p = 0.047). In longitudinal adjusted analyses, those with lower baseline LTL had higher EDSS and lower brain volumes over time. In adjusted analysis of the subset, LTL change was associated with EDSS change over 10 years; for every 0.2 LTL decrease, EDSS was 0.34 higher (95% CI = 0.08-0.61, p = 0.012). Shorter telomere length was associated with disability independent of chronological age, suggesting that biological aging may contribute to neurological injury in MS. Targeting aging-related mechanisms is a potential therapeutic strategy against MS progression. ANN NEUROL 2019;86:671-682.