Cognitive rehabilitation represents a cornerstone of disease management in multiple sclerosis (MS), targeting the progressive cognitive decline that affects a significant proportion of patients. Despite growing evidence supporting its clinical utility, rehabilitation outcomes remain variable, and identifying modifiable factors that limit its efficacy has become a research priority. Sleep disorders are common in MS and have been increasingly linked to cognitive impairment, yet evidence based on objective polysomnographic assessment remains limited, and the specific parameters that influence cognitive function are poorly understood. This review synthesizes evidence from polysomnographic studies examining how sleep disturbances influence cognitive performance in MS patients. Following a systematic search of PubMed, EMBASE, and the Cochrane Library, 488 patients were included. Sleep fragmentation, reduced sleep efficiency, and oxygen desaturation indices were associated with impairments in attention, information processing speed, and verbal memory, with nocturnal hypoxia emerging as a potentially important mechanism of cognitive impairment. These findings suggest that identifying and treating sleep disorders may be essential for optimizing cognitive rehabilitation outcomes in MS. Further longitudinal studies are needed to determine whether addressing sleep pathology can enhance rehabilitation efficacy and preserve cognitive function over time.

Background/Objectives: Uric acid (UA), a major circulating antioxidant, has been consistently reported at lower levels in multiple sclerosis (MS) patients, yet long-term UA trajectories stratified by disease-modifying therapy (DMT) efficacy remain unexplored. This study aimed to evaluate the 5-year longitudinal evolution of serum UA levels in relapsing-remitting MS (RRMS), comparing high-efficacy (HE-DMT) and moderate-efficacy (ME-DMT) treatment groups. Methods: This retrospective longitudinal cohort study included adult RRMS patients who initiated or switched DMTs between January 2020 and June 2025. Serum UA was measured at treatment initiation (T0) and annually for up to 5 years (T1–T5). Linear mixed-effects models adjusted for sex, age, and EDSS were used to examine longitudinal UA trajectories. Results: A total of 222 patients were included and followed for up to 5 years across HE-DMT and ME-DMT treatment groups. Groups were comparable at baseline regarding serum UA levels despite significant differences in age, EDSS, and therapy status. Linear mixed-effects modeling demonstrated that serum UA levels changed significantly over time (F(5, 307.04) = 11.03, p < 0.01), with a significant main effect of treatment type (F(1, 378.31) = 5.25, p = 0.02) and a significant time × treatment interaction (F(5, 307.05) = 2.42, p = 0.04), indicating that UA trajectories differed between groups across the follow-up period. In the HE-DMT group, UA levels increased progressively from 4.27 mg/dL at baseline to 5.58 mg/dL at year 4, whereas the ME-DMT group showed an initial decline at year 1 followed by a more gradual increase from 4.19 mg/dL to 5.04 mg/dL at year 4. Sex was a significant independent predictor of UA levels (p < 0.01), whereas age and EDSS were not. Conclusions: HE-DMTs were associated with an earlier and more pronounced increase in serum UA levels over 5 years compared with ME-DMTs, with distinct trajectories depending on treatment efficacy. These findings suggest that longitudinal UA assessment may serve as a complementary exploratory indicator of the metabolic context associated with DMT efficacy.

Background/Objectives: Sleep disturbances are common but frequently underrecognized in multiple sclerosis (MS), independently predicting reduced quality of life and worsening fatigue, cognitive impairment, and depression. While pain, nocturia, fatigue, and mood symptoms are established contributors, cardiometabolic, and inflammatory factors remain largely unexplored despite their known links to poor sleep in other populations. This study used interpretable machine learning to determine the relative contribution of disease-related, cardiometabolic, and inflammatory parameters to sleep quality, assessed by the PSQI, in patients with MS. Methods: This cross-sectional, observational, single-center cohort study enrolled adult patients with MS. Sleep quality (PSQI), daytime sleepiness (ESS), and restless legs syndrome severity (IRLS) were assessed alongside clinical, anthropometric, hemodynamic, and laboratory parameters, including inflammatory and metabolic indices. Three regression models, Support Vector Regression (SVR), Random Forest, and XGBoost, were trained on 28 predictors to predict PSQI global scores and evaluated on an internal test set, with feature contributions examined using SHAP analysis. Results: A total of 173 patients with MS were included (mean age 39.66 ± 11.86 years, 69.9% female, 90.2% RRMS), with 48.0% classified as poor sleepers (PSQI > 5) and a mean PSQI score of 6.06 ± 3.47. XGBoost achieved the best predictive performance (test R² = 0.451). SHAP analysis identified IRLS severity as the strongest predictor of PSQI across all three models, followed by EDSS score and depression in the Random Forest and XGBoost models, while daytime sleepiness (ESS) ranked consistently among the top predictors. Cardiometabolic and inflammatory parameters contributed inconsistently, with several showing effects opposite to physiological expectation. Conclusions: Sleep impairment in MS was most strongly associated with restless legs syndrome severity and neurological disability, with depression also contributing in the two best-performing models. Disease-related and symptomatic factors outweighed cardiometabolic and inflammatory contributions. These findings support the value of interpretable machine learning for identifying clinically relevant correlates of sleep quality in MS, though the exploratory design and modest sample size warrant confirmation in larger, independent cohorts.

Combined central and peripheral demyelination (CCPD) is a rare neuroimmunological condition involving inflammatory demyelination of both the central nervous system (CNS) and peripheral nervous system (PNS). We report a chronic progressive CCPD case initially diagnosed as chronic inflammatory demyelinating polyneuropathy (CIDP) and treated with conventional CIDP-directed immunotherapies, with subsequent development of multiple sclerosis (MS)-like CNS demyelination. An extensive diagnostic evaluation excluded alternative infectious, metabolic, paraneoplastic, and antibody-mediated etiologies affecting either compartment. In the absence of a unifying pathogenic autoantibody, the combined clinical, radiological, cerebrospinal fluid, and electrophysiological findings support a shared immune-mediated process. Within this framework, B cells are implicated through antibody-independent mechanisms, including antigen presentation, pro-inflammatory cytokine production (e.g., IL-6), and amplification of Th1/Th17-driven inflammation. Interactions between B cells and the complement system via CR1 (CD35) and CR2 (CD21), together with dysfunction of the blood–brain barrier (BBB) and blood–nerve barrier (BNB), may facilitate parallel immune activation across both compartments. In this case, the observed radiological and electrophysiological stabilization under anti-CD20 therapy is consistent with a B-cell-driven pathogenic model in CCPD.

Background/Objectives: Sleep disturbances are common but frequently underrecognized in multiple sclerosis (MS), independently predicting reduced quality of life and worsening fatigue, cognitive impairment, and depression. While pain, nocturia, fatigue, and mood symptoms are established contributors, cardiometabolic and inflammatory factors remain largely unexplored despite their known links to poor sleep in other populations. This study used interpretable machine learning to determine the relative contribution of disease-related, cardiometabolic, and inflammatory parameters to sleep quality, assessed by the PSQI, in patients with MS. Methods: This cross-sectional, observational, single-center cohort study enrolled adult patients with MS. Sleep quality (PSQI), daytime sleepiness (ESS), and restless legs syndrome severity (IRLS) were assessed alongside clinical, anthropometric, hemodynamic, and laboratory parameters, including inflammatory and metabolic indices. Three regression models, Support Vector Regression, Random Forest, and XGBoost, were trained on 28 predictors to predict PSQI global scores and evaluated on a independent test set, with feature contributions examined using SHAP analysis. Results: A total of 173 patients with MS were included (mean age 39.66 ± 11.86 years, 69.9% female, 90.2% RRMS), with 48.0% classified as poor sleepers (PSQI > 5) and a mean PSQI score of 6.06 ± 3.47. XGBoost achieved the best predictive performance (test R² = 0.451). SHAP analysis identified IRLS severity as the strongest predictor of PSQI across all three models, followed by EDSS score and depression in the Random Forest and XGBoost models, while daytime sleepiness (ESS) ranked consistently among the top predictors. Cardiometabolic and inflammatory parameters contributed inconsistently, with several showing effects opposite to physiological expectation. Conclusions: Sleep impairment in MS was driven mainly by restless legs syndrome severity and neurological disability, with depression also contributing in the two best-performing models. Disease-related and symptomatic factors outweighed cardiometabolic and inflammatory contributions. These findings support the value of interpretable machine learning for identifying the multifactorial drivers of sleep quality in MS.