Background: People with Multiple Sclerosis (MS) frequently experience cognitive impairment and neurobehavioral issues including depression and fatigue. We aim to define neuropsychological phenotypes in MS based on cognitive function, depression and fatigue.Methods: In this cross‐sectional study, we collected clinical and neuropsychological data for 600 MS patients. Neuropsychological assessments included Brief International Cognitive Assessment for MS (BICAMS), Beck Depression Inventory (BDI‐II) and Modified Fatigue Impact Scale (MFIS). We employed a latent profile analysis (LPA) to unveil latent neuropsychological profiles in MS patients, clustering individuals in unobserved groups, and we compared clinical and MRI features between groups.Results: LPA identified five neuropsychological phenotypes: Normal neuropsychological phenotype, with normal cognitive and neurobehavioral scores; Isolated cognitive impairment, showing mild reduction in BICAMS scores; Isolated neurobehavioral impairment, showing increased BDI‐II and MFIS scores; Mild neuropsychological impairment, showing mildly reduced BICAMS scores, and mildly increased BDI‐II and MFIS scores; Severe neuropsychological impairment, showing severely reduced BICAMS scores, and great increase in BDI‐II and MFIS scores vs patients with normal neuropsychological phenotype.Relapsing‐onset and paediatric‐onset patients are more likely in the normal neuropsychological phenotype and isolated cognitive impairment class. Patients with severe neuropsychological impairment showed lower cortical grey matter, thalamic, amygdala, pallidum and putamen volume compared with those with isolated neurobehavioral impairment.Conclusions: We identified five neuropsychological phenotypes in patients with MS that showed distinct clinical and radiologic features, as from different stages of the disease pathology accrual. Clinical and MRI features may help distinguishing patients requiring closer monitoring of cognitive or neurobehavioral symptoms.Disclosures: Antonio Luca Spiezia, Fabrizia Falco, Federica Lamagna, Martina Eliano, Valerio Nicolella, Antonio Esposito and Cristina Di Monaco have nothing to disclose. Marcello Moccia has received financial support by the MUR PNRR Extended Partnership (MNESYS no. PE00000006, and DHEAL‐COM no. PNC‐E3‐2022‐23,683,267); research grants from the ECTRIMS‐MAGNIMS, the UK MS Society, and Merck; salary as Assistant Editor of Neurology; and honoraria from Abbvie, Biogen, BMS Celgene, Ipsen, Jansenn, Merck, Novartis, Roche, and Sanofi‐Genzyme. Sirio Cocozza has served on scientific advisory board for Amicus Therapeutics, has received speaker honoraria from Sanofi and research grants from Fondazione Italiana Sclerosi Multipla and Telethon. Giuseppe Pontillo was supported by the MAGNIMS/ECTRIMS (2020), ESNR (2021), and ECTRIMS (2022) research fellowship programs. Roberta Lanzillo has received honoraria from Biogen, Merck, Novartis, Roche and Teva. VBM has received research grants from the Italian MS Society, and Roche, and honoraria from Bayer, Biogen, Merck, Mylan, Novartis, Roche, Sanofi‐ Genzyme and Teva. Vincenzo Brescia Morra has received research grants from Italian MS Federation and Roche; and honoraria from Almirall, Biogen, BMS Celgene, Janssen, Merck, Novartis, Roche, Sanofi‐Genzyme, and Viatris. Maria Petracca discloses travel/meeting expenses from Novartis, Janssen, Roche and Merck; speaking honoraria from HEALTH&LIFE S.r.l., AIM Education S.r.l., Biogen, Novartis and FARECOMUNICAZIONE E20; honoraria for consulting services and advisory board participation from Biogen; research grants from Baroni Foundation and the Italian Ministry of University and Research. Antonio Carotenuto disclosed research grants from ECTRIMS‐MAGNIMS and Almirall, travel/meeting expenses from Novartis, Janssen, Roche and Merck and speaking honoraria from Merk, BMS, Biogen, Novartis, Roche and Almirall.

Plasma neurofilament light chain (pNfL) is an established biomarker of neuroaxonal injury in multiple sclerosis (MS), but analytical variability across platforms may limit clinical comparability. We compared pNfL values obtained using Lumipulse® and Atellica® assays, and evaluated the clinical correlates (associations with disability status, disease activity, MRI outcomes, and treatments) of Atellica® pNfL in a real-world MS cohort. Baseline plasma samples from people with MS were analysed using both immunoassays. Agreement was assessed using Passing-Bablok regression, Lin's concordance correlation coefficient, and Bland-Altman analysis. Associations between Atellica® pNfL and demographic and clinical variables were evaluated using multivariable regression. We included 394 people with MS (mean age 41.0 ± 13.0 years; 61.5% female; median EDSS 2.5). Lumipulse® and Atellica® pNfL values showed strong association, with Passing-Bablok regression indicating proportional bias (slope 0.78; 95% CI 0.70-0.86; intercept 3.24; 95% CI 2.46-3.90) and moderate concordance (ρc = 0.49). Higher Atellica® pNfL levels were independently associated with older age and greater disability, but not with short-term clinical or radiological activity. Atellica® and Lumipulse® showed strong association but only moderate concordance in pNfL measurements. These findings indicate that the two platforms cannot be considered interchangeable. Atellica® pNfL reflects disability status and represents a clinically meaningful biomarker when interpreted within assay-specific frameworks in real-world MS care.

Objective: We aimed to explore associations between plasma neurofilament light chain (pNfL) and cognition through patient-reported outcomes (PROs) in multiple sclerosis (MS). Methods: In this cross-sectional study, we included 211 people with MS (PwMS) and collected data from pNfL (fully automated chemiluminescent enzyme immunoassay), EDSS, education, cognition (the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test-II (CVLT II), and Brief Visuospatial Memory Test–Revised (BVMT-R)), the Modified Fatigue Impact Scale (MFIS), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Results: On multivariate linear regression models, higher educational attainment was significantly associated with lower pNfL (high school: Coeff = −0.22, 95% CI = −0.41 to −0.04, p = 0.019; university: Coeff = −0.22, 95% CI = −0.42 to −0.02, p = 0.030). In logistic regression models, the likelihood of having pNfL levels above normal thresholds increased by 56% for each one-point increment in the EDSS score (OR = 1.56, 95% CI = 1.23 to 1.98, p < 0.001) and was 2.5 times greater in individuals with impaired SDMT (OR = 2.50, 95% CI = 2.20 to 5.21, p = 0.014). No statistically significant associations were observed between pNfL and CVLT-II, BVMT-R, BDI-II, MFIS, BAI, or PSQI. Conclusions: Neuro-axonal damage in people with MS manifests clinically as increased disability and reduced attention and processing speed. However, these effects may be mitigated by greater brain resilience, as suggested by the protective role of higher educational attainment. The PROs assessed in this study showed no significant associations with pNfL levels, possibly due to measurement errors and heterogeneity, with limited sensitivity to neuro-axonal damage.

Background: Speech patterns in multiple sclerosis (MS) are poorly characterized. Software such as Dysarthria Analyzer (DA) performs a detailed speech analysis. Objective: The objective of the study is (1) to explore speech features in patients with MS and (2) to investigate correlations between speech features and clinic-demographic characteristics. Methods: Patients underwent clinical assessments (Expanded Disability Status Scale (EDSS) and Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS)) and speech evaluation using the DA. Each patient performed 6 vocal tasks: phonation A, phonation I, reading, monologue, fast syllables repetitions. The DA extracted 23 speech features as age-, gender-, and education-adjusted z-scores against healthy controls. A principal component analysis was conducted. Correlations between the components and clinic-demographic features were assessed through stepwise analysis. Results: We enrolled 72 patients (50 females; mean age 48.1 ± 10.9 years; median EDSS of 3 (1.5–6.5)). Principal component analysis (PCA) identified a 7-component model: Voice Intensity, Voice Intensity Variation, Motor Coordination, Speech Diadochokinesis, Speech Speed, Voice Strength, Speech Latency. Higher EDSS is associated with worse Voice Intensity Variation ( r = –0.23; p = 0.05), Speech Speed ( r = –0.40; p = 0.001) and Speech Latency ( r = –0.23; p = 0.05). Progressive MS patients showed worse Voice Intensity Variation (coeff. = −1.20; p = 0.002). Worse cognitive functioning correlated with worse Speech Speed (coeff. = −0.38; p = 0.02). Conclusion: Detailed speech analysis can reveal useful clinical information about physical and cognitive disability in MS possibly representing an additional tool to assess disability.