Background: Impulse control disorders (ICDs) have an increased frequency in patients with Parkinson's disease (PD), mainly because of treatment with dopamine agonists (DA). Factors related with the country of origin (culture, economy, healthcare politics) may impact phenomenology.Objectives: To explore phenomenology of ICDs depending on the country.Methods: A systematic review following PRISMA guidelines was performed using Pubmed database. Articles published up to 2018 in which the prevalence of ICDs was analyzed were selected.Results: Thirty‐two studies from 22 countries worldwide were included. The highest prevalence of ICDs in each continent was found in UK (59%), USA (39.1%) and India (31.6%). Frequency of ICDs was higher in those studies with lower mean age, higher proportion of males, whenever a screening instrument was used and whenever prescription of DAs was more common. Prevalence of ICDs was higher in Western countries compared to Asian countries (20.8% vs. 12.8%, P < 0.001) as it was the proportion of patients treated with DAs (66% vs. 48.2%, P < 0.001). Hypersexuality was the most common ICD overall (up to 23.8%). The highest frequencies of compulsive buying and eating were found in Western countries. Gambling was less commonly diagnosed, but prevalence was relevant Japan (14%).Conclusion: We observed a tendency towards a different ICD profile in different geographical areas, which may be attributable to socio‐economical, cultural or political influences in the phenomenology of these disorders. Acknowledging these differences could help their early detection, which is critical for prognosis.

Multiple studies have shown the importance of blood-based biomarkers indicating axonal damage (serum neurofilament light chains [sNfL]) or astroglia activation (serum glial fibrillary acidic protein [sGFAP]) for monitoring different neurological diseases. However, normal values of these variables remain to be clearly defined, partly due to the influence of different demographic factors. We investigated demographic differences in a cohort of healthy volunteers. A cross-sectional study was conducted including 116 healthy controls with ages between 18 and 69 years (67.5% females; n = 79). sNfL and sGFAP concentrations were measured using single-molecule arrays. Age and body mass index affected sNfL values, and age was found to be the most important factor. The normal values changed with age, and we established normal values for individuals younger than 45 years as <10 pg/mL and for controls older than 45 years as <15 pg/mL. We established normal values at <10 pg/mL for individuals younger than 45 years and <15 pg/mL for older individuals. Alternatively, a Z-score of 1.5 was relevant for all controls. sGFAP was only affected by age. Differences in normal values were evident by 55 years. The highest normality limit for sGFAP was 140 pg/mL for controls under 55 years and 280 for older controls. We defined normal levels for sNfL and sGFAP and their corresponding age-associated changes. These data may contribute to the application of such variables in clinical practice.

Natalizumab is a highly effective therapy for multiple sclerosis (MS). The aim of this study was to evaluate serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) in patients with relapsing–remitting MS treated with Natalizumab. sNfL and sGFAP were analyzed at baseline, 6 and 12 months post treatment using the single-molecule array (SiMoA) technique. We recruited matched healthy controls for comparison. The study included 54 patients, with a median age of 33 years (Interquartile range (IQR), 29–41), with 32 women (60%) and 76 healthy controls. A decrease in sNfL was observed at 6 (67%, p = 0.005) and 12 (72%, p < 0.0001) months compared to baseline. After two years, six patients experienced evidence of disease activity (EDA-3). The remaining ones had no evidence of disease activity (NEDA-3). NEDA-3 presented a remarkable reduction in sNfL (p < 0.0001) and sGFAP (p = 0.01) after 6 months of treatment that continued to be observed after 12 months compared to baseline. EDA-3 only reached a significant decrease in sNfL after 12 months; there were no significant changes in sGFAP values. Natalizumab leads to a decrease in sNfL, which is higher and occurs earlier in NEDA-3 patients. Patients also showed a significant reduction in sGFAP levels, which was not observed in the EDA-3 group.

The combined use of serum and CSF biomarkers for prognostic stratification in multiple sclerosis (MS) remains underexplored. This multicenter observational study investigated associations between serum neurofilament light chain (sNfL), glial fibrillary acidic protein (sGFAP), and CSF lipid-specific IgM oligoclonal bands (LS-OCMB) with different forms of disability worsening, such as relapse-associated worsening (RAW), active progression independent of relapse activity (aPIRA), and non-active PIRA (naPIRA). A total of 535 patients with MS were included, all sampled within one year of disease onset. Biomarkers were quantified using single-molecule array and immunoblotting techniques, and CSF cell subsets were analyzed by flow cytometry. High sNfL z-scores and LS-OCMB positivity were independently associated with increased risk of RAW and aPIRA, collectively termed inflammatory-associated worsening (IAW), while elevated sGFAP levels predicted naPIRA. Patients with both high sNfL and LS-OCMB positivity had the highest risk of IAW. Among LS-OCMB–positive patients, higher regulatory T cell percentages were associated with lower sNfL levels, suggesting a protective role. Conversely, in LS-OCMB–negative patients, sNfL levels correlated with CSF C3 concentrations. These findings support the complementary role of sNfL, sGFAP, and LS-OCMB in identifying distinct mechanisms of disease worsening and may inform early personalized management strategies in MS.

Background: Plasma exchange (PE) is a therapeutic option for CNS demyelinating attacks unresponsive to corticosteroids, but the potential benefit of adding rituximab and IV immunoglobulins to PE (RTX-IVIG-PE), which has shown its efficacy and safety in other immune-mediated conditions, has not been investigated. Objective: We aimed to determine the overall effectiveness of PE in severe demyelinating attacks, identify predictors of response, and compare outcomes between standard PE and RTX-IVIG-PE protocols. Design: Retrospective analysis of patients with CNS demyelinating attacks treated with PE between 2012 and 2023 across five Spanish hospitals. Methods: Clinical response was measured as an improvement or a complete recovery at discharge and 6 months. Uni- and multivariable logistic regression models were developed to identify predictors of PE response. A logistic regression analysis, after inverse probability of treatment weighting (IPTW), was performed to compare the outcome of attacks treated with standard PE and RTX-IVIG-PE. Results: A total of 162 attacks in 150 patients (81 multiple sclerosis; 37 others acquired demyelinating syndromes; 27 neuromyelitis optica spectrum disorder; 5 myelin oligodendrocyte glycoprotein antibody-associated disease; 73% female; median age 43 years (interquartile range 33–52.75)) were analyzed. At discharge, 60% of all attacks showed clinical improvement, and 15% achieved complete recovery. At 6 months, these rates increased to 77% and 31%, respectively. Younger age, lower preattack Expanded Disability Status Scale, and treatment with RTX-IVIG-PE were independent predictors of complete recovery at 6 months. IPTW analysis showed that attacks treated with RTX-IVIG-PE (33) were associated with improvement (adjusted odds ratio (aOR) 5.4, p < 0.001) and complete recovery (aOR 4.55, p < 0.001) at 6 months compared to those treated with standard PE (129), without significant differences in the adverse event profile between the two protocols. Conclusion: PE improves the outcome of steroid-refractory CNS demyelinating attacks, and the addition of rituximab and IVIG to PE may increase the likelihood of long-term recovery.

BACKGROUND Progression independent of relapse activity (PIRA) is a major driver of long-term disability in multiple sclerosis (MS), yet its diagnosis remains retrospective, delaying treatment adaptation. Objective, real-time tools to anticipate PIRA are lacking. OBJECTIVE This study aimed to identify the most effective machine-learning model for PIRA prediction using passively acquired smartphone data and to evaluate its diagnostic performance. METHODS We conducted a prospective observational cohort study at a single MS center (November 2021–January 2024). Adults (≥18 years) with confirmed MS, compatible smartphone use, and ≥12 months follow-up were included. A custom mobile application continuously captured typing dynamics, extracting tapping speed as a digital biomarker. Time-series were analyzed using mixed linear regression and ARIMA models. Longitudinal data were transformed into a supervised learning dataset by segmenting the time series into overlapping temporal windows, each treated as an independent sample. The dataset was split into training, validation, and test sets (70/15/15) to develop and evaluate machine-learning models (XGBoost, TimesNet, and Transformers). The primary outcome was prediction of a clinically confirmed PIRA event. RESULTS Ninety-nine patients (median age 47 years, 63% women, median Expanded Disability Status Scale 2.5) completed follow-up; 14 developed PIRA. Baseline characteristics were comparable between groups. Time-series decomposition revealed distinct dynamics (Akaike Information Criterion [AIC]: 69.7 vs 108.0) between patients who developed PIRA and those who did not. A total of 25,440 temporal windows were generated. Among all models, TimesNet achieved the best performance, with accuracy 99.7%, precision 97% (95% CI 94–99), sensitivity 100% (95% CI 95–100), specificity 100% (95% CI 96–100), and F1-score 98%. CONCLUSIONS Passive smartphone monitoring enables continuous assessment of motor function in MS. TimesNet outperformed other models for early PIRA detection, supporting its potential as a digital biomarker of progression risk. These findings warrant prospective multicenter validation for individualized disease monitoring.