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.

Introduction Parkinson’s disease (PD) is more frequent in the elderly and increases the risk of respiratory infections. Previous data on PD and SARS-CoV-2 are scarce, suggesting a poor prognosis in advanced disease and second-line therapies. Methods A retrospective case–control study comparing patients with PD and COVID-19 and patients with PD without COVID-19 was conducted during the pandemic period in Spain (March 1st–July 31st 2020) in a tertiary university hospital. Results Thirty-nine (COVID-19 +) and 172 (COVID-19-) PD patients were included. Fifty-nine percent were males in both groups, with similar age (75.9 ± 9.0 COVID-19 + , 73.9 ± 10.0 COVID-19-), disease duration (8.9 ± 6.2 COVID-19 + , 8.5 ± 5.6 COVID-19-) and PD treatments. COVID-19 was mild in 10 (26%), required admission in 21 (54%) and caused death in 8 (21%) patients. Dementia was the only comorbidity more frequent in COVID-19 + patients (36% vs. 14%, p = 0.0013). However, in a multivariate analysis, institutionalization was the only variable associated with COVID-19 + (OR 17.0, 95% CI 5.0–60.0, p < 0.001). When considering severe COVID-19 (admission or death) vs. mild or absent COVID-19, institutionalization, neoplasm, dementia and a lower frequency of dopamine agonists were associated with severe COVID-19. In multivariate analysis, only institutionalization [OR 5.17, 95% CI 1.57–17, p = 0.004] and neoplasm [OR 8.0, 95%CI 1.27–49.8, p = 0.027] remained significantly associated. Conclusion In our experience, institutionalization and oncologic comorbidity, rather than PD-related variables, increased the risk of developing COVID-19, and impacted on its severity. These findings suggest that epidemiologic factors and frailty are key factors for COVID-19 morbidity/mortality in PD. Appropriate preventive strategies should be implemented in institutionalized patients to prevent infection and improve prognosis.

MicroRNAs (miRNAs) are promising biomarkers for many diseases, including multiple sclerosis (MS). The neurofilament light chain (NfL) is a biomarker that can detect axonal damage in different neurological diseases. The objective of this study was to evaluate the association of the expression profile of pre-selected miRNAs and NfL levels with clinical and radiological variables in MS patients. We conducted a 1-year longitudinal prospective study in MS patients with different clinical forms. We measured clinical disability using the expanded disability status scale (EDSS), the magnetic resonance imaging (MRI) volumetry baseline, and cognitive functioning using the processing speed test (PST) at baseline and 1 year later. Selected serum miRNAs and serum NfL (sNfL) levels were quantified. Seventy-three patients were recruited. MiR-126.3p correlated with EDSS and cognitive status at baseline and miR-126.3p and miR-9p correlated with cognitive deterioration at 1 year. Correlations with regional brain volumes were observed between miR-126.3p and the cortical gray matter, cerebellum, putamen, and pallidum; miR-146a.5p with the cerebellum and pallidum; miR-29b.3p with white matter and the pallidum; miR-138.5p with the pallidum; and miR-9.5p with the thalamus. sNfL was correlated with miR-9.5p. miR-146a.5p was also associated with the MS phenotype. These data justify future studies to further explore the utility of miRNAs (mirR-126.3p, miR-146.5p, and miR.9-5p) and sNfL levels as biomarkers of MS.

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.

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.