Investigating the Role of Serum IL-6 in Predicting Outcomes of B- Cell Depleting Therapy in Multiple Sclerosis: A Retrospective Cohort Study

Investigating the Role of Serum IL-6 in Predicting Outcomes of B- Cell Depleting Therapy in Multiple Sclerosis: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Role of Serum IL-6 in Predicting Outcomes of B- Cell Depleting Therapy in Multiple Sclerosis: A Retrospective Cohort Study Tristan Kölsche, Ramona Hagler, Niklas Huntemann, Lars Masanneck, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8885644/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Interleukin-6 (IL-6) plays a pivotal role in autoimmune inflammation through its effects on B-cell differentiation, Th17 expansion, and regulatory T-cell suppression. Given ocrelizumab’s (OCR) mechanism of selective CD20 + B-cell depletion, baseline IL-6 levels have been hypothesized to predict disease activity and long-term outcomes in multiple sclerosis (MS). However, the prognostic value of serum IL-6 in OCR-treated patients remains unclear. Methods: This retrospective study included 73 patients with relapsing–remitting MS (RRMS, n = 30) or primary progressive MS (PPMS, n = 43) who initiated OCR at University Hospital Düsseldorf between 2018 and 2023. Baseline serum IL-6 was compared with 87 healthy controls (HC) and correlated with clinical, radiological, and biomarker outcomes over 24 months. Clinical endpoints included confirmed progression independent of relapse activity (PIRA), and relapse-associated worsening (RAW), alongside MRI activity and Serum Neurofilament Light Chain (sNfL) and Serum Glial Fibrillary Acidic Protein (sGFAP) levels. Between-group comparisons used Welch’s t-test or Wilcoxon rank-sum test, paired longitudinal comparisons used paired t-tests or Wilcoxon signed-rank tests, and associations between baseline biomarkers and outcomes were evaluated using Cox regression, multivariable linear or logistic regression, and rank-based linear models for IL-6. Results: Baseline serum IL-6 levels showed no significant differences between the overall MS cohort and HC, nor between RRMS and PPMS subgroups. No baseline biomarker, including IL-6, sNfL, and sGFAP predicted disease activity. Longitudinal analysis under OCR revealed largely stable IL-6 concentrations but patients maintaining No Evidence of Disease Activity-3 (NEDA-3) showed 50% reduction in IL-6 at 12 months, whereas those with loss of NEDA-3 remained stable. Conclusion: Baseline serum IL-6 alone is insufficient to predict clinical or radiological outcomes in OCR-treated MS patients. However, the significant longitudinal decline specifically in stable patients suggests that IL-6 dynamics, rather than static baseline measures, may better reflect sustained therapeutic response. These findings underscore the limited utility of serum IL-6 alone as a biomarker and support further exploration of longitudinal, multiparametric approaches. Multiple Sclerosis Interleukin-6 (IL-6) Ocrelizumab B-cell Depletion Primary Progressive Multiple Sclerosis (PPMS) Relapsing-Remitting Multiple Sclerosis (RRMS) Progression Independent of Relapse Activity (PIRA) Biomarker Serum Neurofilament Light Chain (sNfL) Serum Glial Fibrillary Acidic Protein (sGFAP) Figures Figure 1 Figure 2 Figure 3 Introduction Multiple sclerosis (MS) is a chronic demyelinating and inflammatory disease of the central nervous system in which both T-cells and B-cells play critical roles in driving tissue damage (1). Among pro-inflammatory mediators, interleukin-6 (IL-6) has emerged as a particularly interesting cytokine due to its multifaceted effects on immune cell differentiation and function. IL-6 promotes B-cell differentiation and immunoglobulin production, potentially contributing to MS pathology. It also facilitates Th17 cell expansion while inhibiting regulatory T-cell development, fostering a pro-inflammatory environment that may exacerbate neuroinflammation and demyelination (2). Consequently, IL-6 is considered a key driver of autoimmune pathology and a potential therapeutic target in MS (3). Beyond MS, IL-6 has been implicated in other autoimmune and inflammatory conditions, including systemic lupus erythematosus (SLE) and neuromyelitis optica spectrum disorders (NMOSD), where IL-6 receptor blockade reduces relapse rates in the latter disease (4,5). Emerging data also associates peripheral blood IL-6 alterations with various neuropsychiatric conditions, underscoring its broader potential as a biomarker across central nervous system disorders (4). Previous investigations into serum IL-6 in MS cohorts have yielded diverse results, highlighting the complexity of peripheral cytokine monitoring. While IL-6 is frequently elevated in the cerebrospinal fluid (CSF) during clinical relapses (6,7), its presence in the serum is often lower or undetectable compared to central compartments (6). Furthermore, recent studies in diverse populations have shown that while serum IL-6 can be elevated during relapses, it does not consistently correlate with all clinical subtypes (8). Ocrelizumab (OCR) is a humanized monoclonal antibody that selectively depletes CD20 + B-cells and is effective in both relapsing-remitting (RRMS) and primary progressive MS (PPMS) (9). Given IL-6’s role in B-cell function, it has been hypothesized that baseline IL-6 levels may predict disease activity or long-term outcomes in OCR-treated patients. However, existing data remains limited and inconclusive (10). Building on this hypothesis, we investigated whether baseline serum IL-6 levels correlate with established markers of disease activity in MS patients initiating OCR therapy, and we compared these levels to healthy controls (HC). Methods Study Population This retrospective study used data from the MS database of the Department of Neurology, Heinrich-Heine-University Düsseldorf, Germany (2018–2023). Clinical data was recorded during routine visits every six months and made available for research after written informed consent. Only patients diagnosed with RRMS or PPMS according to the 2017 McDonald criteria were included. Additional inclusion criteria comprised: (1) initiation of ocrelizumab (OCR) therapy and (2) availability of baseline serum samples collected prior to the first OCR infusion. Patients who experienced a relapse within three months prior to OCR initiation or were unable to provide consent were excluded. For comparison, baseline IL-6 levels from 87 healthy individuals without any history of autoimmune or chronic inflammatory disease were included as HC. This study was conducted in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cohort studies. To minimize confounding from acute inflammation, baseline C-reactive protein (CRP) levels and leukocyte counts were obtained. Patients with evidence of active infection- defined as CRP above the reference range, leukocytosis, or clinical signs of infection at the time of sample collection were excluded from the analysis. Disability status and disease severity of MS patients were assessed using the Expanded Disability Status Scale (EDSS), a clinician-rated scale ranging from 0 (normal neurological examination) to 10 (death due to MS)(11) EDSS was recorded at baseline (within one month prior to treatment initiation) and every six months thereafter, with changes from baseline analyzed to evaluate disability progression. Outcome Definitions Disability progression and disease activity were defined using established clinical and radiological criteria. Confirmed Disability Accumulation (CDA) was defined as an increase in EDSS of ≥1.0 points for patients with baseline EDSS ≤5.5, or ≥0.5 points for baseline EDSS >5.5, confirmed at a subsequent visit ≥12 weeks later. Progression Independent of Relapse Activity (PIRA) was defined as CDA occurring without a preceding relapse between the two relevant EDSS assessments or within the 12 weeks prior to baseline(12) Relapse-Associated Worsening (RAW) was defined as CDA occurring during a relapse, with sustained disability progression persisting for ≥12 weeks; transient EDSS changes related to relapses without confirmation were excluded from both PIRA and RAW(13) Relapses were defined as neurological deterioration lasting more than 24 hours, unrelated to infection, and verified within 7 days. Absence of EDSS progression was defined as no confirmed disability increase over six months, with thresholds depending on baseline EDSS (0: ≥1.5 points; 1–5: ≥1 point; >5: ≥0.5 points). MRI activity was defined by new T1 gadolinium-enhancing lesions or new/enlarging T2 lesions in clinical routine measurements. No Evidence of Disease Activity-3 (NEDA-3) was defined as the absence of: (1) new or enlarging T2 lesions or T1 gadolinium-enhancing lesions on MRI, (2) clinical relapses, and (3) confirmed disability accumulation (CDA) over the observation period. Conversely, loss of NEDA-3 (loss of NEDA-3) was defined as the occurrence of any of these events. This composite outcome was assessed according to the standardized criteria previously described(14). Laboratory Measures Blood samples were obtained from patients with MS at baseline and every six months for up to two years, scheduled within two weeks prior to each subsequent OCR infusion; HCs were sampled at baseline only. Blood was drawn via a short catheter from an antecubital vein into Monovettes (Sarstedt, Nümbrecht, Germany). Serum tubes were allowed to clot for 30–60 minutes, centrifuged at 3000 rpm for 10 minutes, aliquoted, and initially frozen at −20 °C. Within four weeks, samples were transferred to a −80 °C freezer for long-term storage until final analysis. EDTA tubes were used for leukocyte counts, which were analyzed directly without centrifugation. Serum was used for C‑reactive protein (CRP), immunoglobulin G (IgG), immunoglobulin A (IgA), immunoglobulin M (IgM), serum neurofilament light chain (sNfL), serum glial fibrillary acidic protein (sGFAP), and IL‑6. All biomarkers were assessed at baseline, whereas IL‑6 was additionally measured at six-month intervals throughout the study period. Quantification of sNfL and sGFAP was performed at the Central Institute for Clinical Chemistry and Laboratory Diagnostics, Medical Faculty, University Hospital Düsseldorf, Germany. Both biomarkers were analyzed using research-use-only electrochemiluminescence immunoassays (ECLIA) in a two-step sandwich format on the Roche Cobas 8000 analyzer (Elecsys® module), according to the manufacturer’s instructions. Laboratory personnel were blinded to clinical data. Z-scores for sNfL and sGFAP were calculated based on previously published methods (15,16) Statistical Analysis All analyses were conducted in RStudio. Demographic, clinical, and biomarker data were summarized using descriptive statistics. Normal distribution was tested using the Shapiro–Wilk test. Non-normally distributed data were presented as median and interquartile range (IQR), whereas normally distributed data were reported as mean ± standard deviation (SD). Between-group comparisons of continuous variables were performed using Welch’s t-test for normally distributed data or the Wilcoxon rank-sum test for non-normal data. Paired longitudinal comparisons within groups were conducted using paired t-tests or Wilcoxon signed-rank tests, as appropriate. Associations between baseline biomarkers and clinical outcomes, including CDA, PIRA, RAW, NEDA-3, loss of NEDA-3, and MRI activity, were assessed using Cox proportional hazards models, multivariable linear regression, or logistic regression, adjusting for age and sex where relevant. Rank-based linear models were applied for comparisons of IL-6 levels across MS subtypes and over time. A p-value <0.05 was considered statistically significant, with correction for multiple comparisons applied where relevant. Results A total of 73 MS patients were included, comprising 43 with PPMS (58.9%) and 30 with RRMS (41.1%). Baseline characteristics are presented in Table 1.1 and 1.2. The mean (SD) age of the entire MS cohort was 52.2 ± 12.7 years, and 54.8% were female, with PPMS patients being older than those with RRMS (58.3 ± 9.3 vs. 43.5 ± 12 years, p < 0.001) and showing a slight male predominance (58.1% vs. 50%, p = 0.654). HC (n = 86) had a mean age of 46.1 ± 11.8 years and were in 72.1% female. Compared with MS patients HC were significantly younger (p < 0.001). Median disease duration was significantly longer in PPMS (115 months, IQR 78) compared with RRMS (73 months, IQR 48; p < 0.001). Baseline EDSS was also higher in PPMS (median 4.0, IQR 3.375) than in RRMS (median 2.0, IQR 2.5; p < 0.001) (Table 1.2 ). Baseline IL-6 levels were similar between the overall MS cohort and HC (MS: 1.90 [IQR 1.8] pg/mL vs. HC: 1.96 [IQR 1.1] pg/mL; rank-based linear model adjusted for age and sex; p = 0.178) (Fig. 1 A). Subgroup analysis by MS type revealed that baseline mean IL-6 level was higher in PPMS (2.30 [IQR 2.7] pg/mL), compared to RRMS patients (1.75 [IQR 0.6] pg/mL; p = 0.024). A significant overall group effect was observed (ANOVA; p = 0.011); however, post-hoc pairwise comparisons adjusted for multiple testing did not reveal statistically significant differences between individual groups, although a trend toward lower IL-6 levels in RRMS compared with HC was evident (Fig. 1 B). While baseline sGFAP z-scores were similar between MS subtypes, median baseline sNfL z-scores were elevated in RRMS (2.12 [IQR 1.9]) compared with PPMS (− 0.41 [IQR 1.6]; p < 0.001) (Suppl. Figure 1). CRP levels were slightly higher in PPMS compared to healthy controls, though all retained samples were below the clinical threshold for acute infection (Suppl. Figure 2). During the 24-month follow-up, 22 MS patients (30.1%) experienced CDA, 20 experienced PIRA (27.4%), and 2 experienced RAW (2.7%), with loss of NEDA-3 observed in 31 of 73 patients (42.5%). Cox proportional hazard models revealed no significant associations between baseline IL-6 and occurrence of CDA (Suppl. Table 1.3). Also, after dichotomization of IL-6 at the median, there was no statistically significant association with the risk of CDA (HR 1.83; 95% CI 0.73–4.61; p adj = 0.989) (Fig. 2 ). In RRMS, baseline IL-6 was not associated with relapse occurrence over two years (median 1.75 vs. 1.8 pg/mL; rank-based linear model adjusted for age and sex; p = 0.913). Multivariable Cox regression for RAW was not performed due to the limited number of events (n = 3). Multivariable linear regression identified baseline EDSS as the sole predictor of EDSS at 24 months (β = 0.74; 95% CI 0.41–1.08; p = 0.005), while no other biomarkers, including sNfL and sGFAP z-scores and baseline IL-6, were associated with final disability accrual or PIRA. Similarly, logistic regression demonstrated no relationship between baseline IL-6 and new T2 lesions on follow-up MRI (OR 0.86; 95% CI 0.53–1.39; p = 0.53), with subgroup analyses in RRMS (OR 0.27; 95% CI 0.01–5.21; p = 0.38) and PPMS (OR 1.04; 95% CI 0.61–1.77; p = 0.88) showing consistent findings. Longitudinal analysis of serum IL-6 levels of 25 patients over 12 months during B-cell depleting therapy revealed largely stable concentrations, with no significant differences observed at any follow-up compared to baseline. This stability persisted across MS subtypes (RRMS and PPMS). Upon stratification by clinical response, early IL-6 dynamics between baseline and 6 months did not differ significantly between patients maintaining NEDA-3 and those with loss of NEDA-3 (p = 0.126). However, by 12 months post-ocrelizumab initiation, patients maintaining NEDA-3 exhibited a mean decrease in IL-6 from baseline to 12 months (-1.30 pg/mL), whereas patients with disease activity showed no significant change (0.09 pg/mL). The between-group difference in IL-6 change over 12 months was statistically significant (p = 0.0318) (Fig. 3 ). The between-group difference in IL-6 changes from baseline to 12 months indicated a trend toward significance after adjustment for multiple comparisons (p = 0.064). No immediate temporal correlation was found between the exact timing of a relapse and a spike in IL-6, though the limited number of RAW events (n = 3) restricted this analysis. Discussion In this retrospective cohort of 73 MS patients, we found that baseline serum IL-6 levels do not predict clinical or radiological outcomes over 24 months of ocrelizumab therapy. However, we observed that patients who pertained NEDA-3 status experienced a significant 50% reduction in longitudinal IL-6 levels, a change not seen in those with disease activity. Ocrelizumab therapy was intentionally incorporated in our cohort to investigate biomarkers predictive of disease activity in both PPMS and RRMS patients. The rationale for this investigation lies in the complex interplay between B cells and IL-6. IL-6-producing B-cells drive proinflammatory Th17 responses integral to MS pathogenesis, and ocrelizumab effectively targets these cells, thereby reducing inflammation and relapses (17). Hypothetically, the depletion of these CD20 + B-cells should result in a concurrent reduction of serum IL-6, serving as a marker for therapeutic efficacy. However, despite effective B-cell depletion, a subset of patients continued to experience disease activity, indicating that compensatory or IL-6-independent pathways underpin progression in these cases(18). Within this context, our findings indicate that serum IL-6 lacks sufficient sensitivity to predict clinical disease activity or associated neurodegenerative markers. Although serum IL-6 did not differentiate MS patients from controls, mean baseline levels were highest in PPMS, suggesting a possible association with progressive disease. This pattern aligns with the concept that PPMS is characterized by chronic, low-grade inflammation and neurodegeneration, processes known to drive increased systemic IL-6 production, particularly with advancing age. Notably, previous transcriptomic studies have reported reduced IL-6 receptor expression in PPMS compared with RRMS, a finding that may reflect receptor downregulation or shedding in the setting of chronically elevated IL-6 levels (19). The higher baseline IL-6 in PPMS patients might also reflect age-dependent immune signatures, as our PPMS cohort trended towards an older age. This chronic low-grade systemic inflammation may contribute to the relatively high rate of lo-NEDA-3 (42.5%) and CDA observed in this real-world cohort(20). Taken together, higher IL-6 concentrations in PPMS combined with lower IL-6R expression may represent a compensatory or exhaustion-related immune signature rather than an acute inflammatory response. Furthermore, this may indicate that elevated IL-6 is more characteristic of PPMS and could help distinguish disease courses, though the small sample size limits firm conclusions. Notably, sNfL and GFAP z-scores in this cohort did not predict outcomes, in contrast to other studies (15,21), which may be explained by the higher age of our patients and the relatively low relapse activity. In our subgroup with available longitudinal serum samples, IL-6 levels remained largely stable over 24 months of B-cell depleting therapy. When patients were stratified by NEDA-3 status at 12 months, those who maintained NEDA-3 showed significant reductions in IL-6, whereas patients experiencing loss of NEDA-3 exhibited minimal change. The between-group difference at 12 months suggested a trend toward significance after correction for multiple testing, although sample sizes were limited. These observations raise the possibility that longitudinal IL-6 dynamics may reflect clinical stability under anti-CD20 therapy, warranting further investigation in larger cohorts to determine its potential utility as a biomarker. Notably, prior research consistently links cerebrospinal fluid (CSF) IL-6 - not serum IL-6 - with MS disease activity and disability. Elevated CSF IL-6 correlates with severity and is higher in PPMS compared with controls. This divergence likely reflects cytokine compartmentalization and blood-brain barrier selectivity limiting IL-6 spill-over into serum, except possibly in progressive MS with chronic blood-brain barrier disruption (22). Our lack of CSF IL-6 data notwithstanding, this mechanistic insight contextualizes our negative serum IL-6 findings, underscoring CSF IL-6 as a more sensitive CNS inflammation biomarker. Thus, serum IL-6 alone cannot capture the full spectrum of inflammatory and neurodegenerative processes in MS progression under B-cell therapy. Research should emphasize the multifaceted B-cell - IL-6 axis and develop composite biomarkers covering diverse pathophysiological routes, improving patient stratification and refining therapeutic targeting beyond IL-6. Comparatively, IL-6 is a robust biomarker in neuromyelitis optica spectrum disorder (NMOSD), where it closely correlates with disease activity, relapse severity, and disability both in serum and CSF. IL-6 drives NMOSD pathogenesis by promoting plasmablast survival, aquaporin-4 antibody production, blood-brain barrier disruption, and enhanced proinflammatory T-cell activation. IL-6 receptor blockade significantly reduces NMOSD relapses, underscoring IL-6’s role as both biomarker and therapeutic target(23). In contrast, MS exhibits heterogeneous immunopathology with multiple cytokines and mechanisms fluctuating by stage and subtype, limiting IL-6’s predictive value. This disease-specific distinction emphasizes the importance of appropriate biomarker validation tailored to distinct CNS autoimmune disorders. Several methodological and biological factors may also explain our lack of serum IL-6 predictive value in MS: moderate sample size and incomplete longitudinal sampling limit statistical power; IL-6 variability from external factors obscures specific signals; single baseline measures miss post-treatment cytokine dynamics; and retrospective real-world design entails sampling heterogeneity reducing biomarker sensitivity. In conclusion, our data reinforce IL-6’s limited utility as a serum biomarker for disease monitoring in B-cell–treated MS. Future studies should integrate longitudinal, multiparametric approaches combining cytokine profiles, neuroaxonal injury markers, glial activation, and advanced imaging across larger, balanced cohorts to clarify IL-6’s combined biomarker potential within the broader inflammatory milieu governing MS progression and therapy response. This will facilitate individualized disease monitoring and precision therapeutic strategies. Abbreviations CDA: Confirmed Disability Accumulation CRP: C-reactive Protein CSF: Cerebrospinal Fluid ECLIA: Electrochemiluminescence Immunoassay EDSS: Expanded Disability Status Scale HC: Healthy Controls IgA: Immunoglobulin A IgG: Immunoglobulin G IgM: Immunoglobulin M IL-6: Interleukin-6 IQR: Interquartile Range MRI: Magnetic Resonance Imaging MS: Multiple Sclerosis NEDA-3: No Evidence of Disease Activity-3 NMOSD: Neuromyelitis Optica Spectrum Disorders OCR: Ocrelizumab PIRA: Progression Independent of Relapse Activity PPMS: Primary Progressive Multiple Sclerosis RAW: Relapse-Associated Worsening RRMS: Relapsing–Remitting Multiple Sclerosis SD: Standard Deviation sGFAP: Serum Glial Fibrillary Acidic Protein SLE: Systemic Lupus Erythematosus sNfL: Serum Neurofilament Light Chain NA: Not Applicable Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the local ethics committee of Heinrich-Heine-University Düsseldorf (registry number 5951R, approval date 28.06.2018, and registry number 2021–1775, approval date 12.07.2022). Written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials : The dataset used is available from the corresponding author on reasonable request. Competing interests TK: reports no conflicts of interest in connection to this study. RH: reports no conflicts of interest in connection to this study. NH: reports no conflicts of interest in connection to this study. LM: reports no conflicts of interest related to this study. He reports honoraria for lecturing, consulting and travel expenses for attending meetings from Biogen, Merck, argenX, Bial, Roche, Hexal, Neuraxpharm, Sanofi, Alexion and Novartis, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B.Braun Foundation and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 493659010. MM: reports no conflicts of interest in connection to this study. BJK: reports no conflicts of interest in connection to this study. JMB: reports no conflicts of interest in connection to this study. PK: received honoraria for lectures, consultancy or travel support for attending meetings from Deutsche Gesellschaft für Neurologie (DGN), Hexal, Roche, Sanofi, Merck, Neuraxpharm and Viatris. KSB: reports no conflicts of interest in connection to this study. OA: received speaking honoraria and travel grants from Alexion, Almirall, Biogen, Celgene, Merck, Novartis, Roche, and VielaBio. SGM: receives honoraria for lecturing, travel expenses and for attending meetings from Academy 2, Argenx, Alexion, Almirall, Amicus Therapeutics Germany, AstraZeneca, Bayer Health Care, Biogen, BioNTech, BMS, Celgene, Datamed, Demecan, Desitin, Diamed, Diaplan, DIU Dresden, DPmed, Gen Medicine and Healthcare products, Genzyme, Hexal AG, IGES, Impulze GmbH, Janssen Cilag, KW Medipoint, MedDay Pharmaceuticals, Medmile, Merck Serono, MICE, Mylan, Neuraxpharm, Neuropoint, Novartis, Novo Nordisk, ONO Pharma, Oxford PharmaGenesis, QuintilesIMS, Roche, Sanofi, Springer Medizin Verlag, STADA, Chugai Pharma, Teva, UCB, Viatris, Wings for Life international and Xcenda. His research is funded by the German Ministry for Education and Research (BMBF), German Federal Institute for Risk Assessment (BfR), German Research Foundation (DFG), Else Kröner Fresenius Foundation, Gemeinsamer Bundesausschuss (G-BA), German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies (IZKF) Muenster, German Foundation Neurology, Ministry of Culture and Science of the State of North Rhine-Westphalia, The Daimler and Benz Foundation, Multiple Sclerosis Society North Rhine-Westphalia Regional Association (dmsg), Peek & Cloppenburg Düsseldorf Foundation, Hempel Foundation for Science, Art and Welfare, German Alzheimer Society e.V. Dementia self-help and Alexion, Almirall, Amicus Therapeutics Germany, Argenx, Bayer Vital GmbH, BGP Products Operations (Viatris Company), Biogen, BMS, Demecan, Diamed, DGM e.v., Fresenius Medical Care, Genzyme, Gesellschaft von Freunden und Förderern der Heinrich-Heine-Universität Düsseldorf e.V., HERZ Burgdorf, Hexal, Janssen, Merck Serono, Novartis, Novo Nordisk Pharma, ONO Pharma, Roche and Teva. TR: received honoraria and/or research support from Alexion, argenx, Biogen, Merck, Novartis, Sanofi, UCB, J&J, and Roche. MP: received honoraria for lecturing and travel expenses for attending meetings from Alexion, ArgenX, Bayer Health Care, Biogen, Hexal, Merck Serono, Novartis, Roche, Sanofi-Aventis, Takeda and Teva. His research is funded by ArgenX, Biogen, Demecan, Hexal, Horizon Merck Serono, Novartis, Roche, Viatris, Takeda and Teva, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B. Braun Foundation and the German Alzheimer Society. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions TK: Conceptualization (equal); Investigation (lead); Formal analysis (equal); Writing – original draft preparation (lead); Visualization (equal); Validation (equal); Writing – review & editing (equal). RH: Conceptualization (equal); Investigation (lead); Formal analysis (equal); Writing – original draft preparation (lead); Visualization (equal); Validation (equal); Writing – review & editing (equal). NH: Data curation (supporting); Investigation (supporting); Writing – review & editing (equal). LM: Data curation (supporting); Methodology (supporting); Writing – review & editing (equal). BJK: Data curation (supporting); Investigation (supporting); Writing – review & editing (equal). MM: Data curation (equal); Investigation (equal); Writing – review & editing (equal). JB: Writing – review & editing (equal). PK: Investigation (supporting); Writing – review & editing (equal). KSB: Resources (lead); Investigation (supporting); Methodology (supporting); Writing – review & editing (equal). SGM: Supervision (supporting); Resources (lead); Writing – review & editing (equal). TR: Supervision (supporting); Writing – review & editing (equal). MP: Conceptualization (lead); Methodology (lead); Project administration (lead); Supervision (lead); Writing – review & editing (lead); Validation (supporting). All authors read and approved the final manuscript. References Stampanoni Bassi M, Iezzi E, Drulovic J, Pekmezovic T, Gilio L, Furlan R, et al. IL-6 in the Cerebrospinal Fluid Signals Disease Activity in Multiple Sclerosis. Front Cell Neurosci [Internet]. 2020 Jun 23 [cited 2025 Mar 15];14:535794. Available from: www.frontiersin.org Holloman JP, Axtell RC, Monson NL, Wu GF. The Role of B Cells in Primary Progressive Multiple Sclerosis. 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Standardized Definition of Progression Independent of Relapse Activity (PIRA) in Relapsing-Remitting Multiple Sclerosis. JAMA Neurol [Internet]. 2025 Jun 1 [cited 2025 Dec 14];82(6):614–25. Available from: https://jamanetwork.com/journals/jamaneurology/fullarticle/2832250 Kappos L, Wolinsky JS, Giovannoni G, Arnold DL, Wang Q, Bernasconi C, et al. Contribution of Relapse-Independent Progression vs Relapse-Associated Worsening to Overall Confirmed Disability Accumulation in Typical Relapsing Multiple Sclerosis in a Pooled Analysis of 2 Randomized Clinical Trials. JAMA Neurol [Internet]. 2020 Sep 1 [cited 2025 Mar 16];77(9):1132–40. Available from: https://pubmed.ncbi.nlm.nih.gov/32511687/ Masanneck L, Rolfes L, Regner-Nelke L, Willison A, Räuber S, Steffen F, et al. Detecting ongoing disease activity in mildly affected multiple sclerosis patients under first-line therapies. Mult Scler Relat Disord [Internet]. 2022 Jul 1 [cited 2025 Mar 15];63:103927. 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Available from: https://www.neurology.org/doi/pdf/10.1212/NXI.0000000000000841 Tables Table 1.1: Baseline characteristics of the MS cohort and healthy controls Variable Total MS cohort (n=73) HC (n=86) p-value Age (years, mean ± SD) 52.2 ± 12.7 46.1 ± 11.8 <0.001 Female sex (n, %) 40 (54.8%) 62 (72.1%) 0.036 Months since disease manifestation (median; range; IQR) 91 (16–427; IQR 71) NA NA EDSS (median; range; IQR) 3 (0–7; IQR 3.3) NA NA Relapse within 24M (n, %) 6 (7.8%) NA NA CDA within 24M (n, %) 22 (30.1%) NA NA PIRA within 24M (n, %) 20 (27.4%) NA NA RAW within 24M (n, %) 2 (2.7%) NA NA Lo-NEDA-3 within 24M (n, %) 31 (42.5%) NA NA IL-6 [pg/ml] (median; IQR) 1.90 (IQR 1.8) 1.96 (IQR 1.1) 0.334 sNfL z-score (median; IQR) -0.10 (IQR 2.6) NA NA sGFAP z-score (median; IQR) 0.28 (IQR 1.5) NA NA CRP [mg/L] (median; IQR) 0.09 (IQR 0.2) 0.07 (IQR 0.1) <0.001 IgG [mg/dl] (median; IQR) 952 (IQR 348.5) 885 (IQR 250) 0.761 IgM [mg/dl] (median; IQR) 86 (IQR 58.5) NA NA Vitamin D [ng/ml] (median; IQR) 23.0 (IQR 19) NA NA Continuous variables are presented as mean ± SD or median (range; IQR). Categorical variables are presented as n (%). P-values represent comparisons between the total MS cohort and HC using Welch’s t-test or Wilcoxon rank-sum test. Abbreviations: CDA: Confirmed Disability Accumulation; CRP: C-reactive Protein; EDSS: Expanded Disability Status Scale; HC: Healthy Controls; IQR: Interquartile Range; loss of NEDA-3: Loss of No Evidence of Disease Activity-3; MS: Multiple Sclerosis; NA: Not Applicable; PIRA: Progression Independent of Relapse Activity; RAW: Relapse-Associated Worsening; SD: Standard Deviation; sGFAP: Serum Glial Fibrillary Acidic Protein; sNfL: Serum Neurofilament Light Chain. Table 1.2: Baseline characteristics of PPMS and RRMS patients Variable PPMS (n=43) RRMS (n=30) p-value Age (years, mean ± SD) 58.3 ± 9.3 43.5 ± 12 <0.001 Female sex (n, %) 25 (58.1%) 15 (50%) 0.654 Months since disease manifestation (median; range; IQR) 115 (16–427; IQR 78) 73 (37–415; IQR 48) <0.001 EDSS (median; range; IQR) 4 (1.5–7; IQR 3.4) 2 (0.0–6.5; IQR 2.5) <0.001 Relapse within 24M (n, %) NA 6 (20%) NA CDA within 24M (n, %) 18 (41.9%) 4 (13.3%) 0.019 PIRA within 24M (n, %) 18 (41.9%) 2 (6.7%) 0.002 RAW within 24M (n, %) NA 2 (8%) NA Lo-NEDA-3 within 24M (n, %) 20 (46.5%) 11 (36.7%) 0.551 IL-6 [pg/ml] (median; IQR) 2.30 (IQR 2.7) 1.75 (IQR 0.6) 0.024 sNfL z-score (median; IQR) -0.41 (IQR 1.6) 2.12 (IQR 1.9) 0.005 sGFAP z-score (median; IQR) 0.28 (IQR 1.1) 0.62 (IQR 2.6) 0.806 CRP [mg/L] (median; IQR) 0.10 (IQR 0.3) 0.09 (IQR 0.11) 0.341 IgG [mg/dl] (median; IQR) 969 (IQR 225) 829 (IQR 459) 0.018 IgM [mg/dl] (median; IQR) 87 (IQR 62) 82 (IQR 68) 0.153 Vitamin D [ng/ml] (median; IQR) 22.0 (IQR 20) 23.5 (IQR 16.5) 0.742 Characteristics are stratified by Multiple Sclerosis subtype. P-values represent comparisons between PPMS and RRMS groups using Welch’s t-test or Wilcoxon rank-sum test.. Abbreviations: CDA: Confirmed Disability Accumulation; CRP: C-reactive Protein; EDSS: Expanded Disability Status Scale; IQR: Interquartile Range; loss of NEDA-3: Loss of No Evidence of Disease Activity-3; NA: Not Applicable; PIRA: Progression Independent of Relapse Activity; PPMS: Primary Progressive Multiple Sclerosis; RAW: Relapse-Associated Worsening; RRMS: Relapsing-Remitting Multiple Sclerosis; SD: Standard Deviation; sGFAP: Serum Glial Fibrillary Acidic Protein; sNfL: Serum Neurofilament Light Chain. Additional Declarations Competing interest reported. TK: reports no conflicts of interest in connection to this study. RH: reports no conflicts of interest in connection to this study. NH: reports no conflicts of interest in connection to this study. LM: reports no conflicts of interest related to this study. He reports honoraria for lecturing, consulting and travel expenses for attending meetings from Biogen, Merck, argenX, Bial, Roche, Hexal, Neuraxpharm, Sanofi, Alexion and Novartis, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B.Braun Foundation and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 493659010. MM: reports no conflicts of interest in connection to this study. BJK: reports no conflicts of interest in connection to this study. JMB: reports no conflicts of interest in connection to this study. PK: received honoraria for lectures, consultancy or travel support for attending meetings from Deutsche Gesellschaft für Neurologie (DGN), Hexal, Roche, Sanofi, Merck, Neuraxpharm and Viatris. KSB: reports no conflicts of interest in connection to this study. OA: received speaking honoraria and travel grants from Alexion, Almirall, Biogen, Celgene, Merck, Novartis, Roche, and VielaBio. SGM: receives honoraria for lecturing, travel expenses and for attending meetings from Academy 2, Argenx, Alexion, Almirall, Amicus Therapeutics Germany, AstraZeneca, Bayer Health Care, Biogen, BioNTech, BMS, Celgene, Datamed, Demecan, Desitin, Diamed, Diaplan, DIU Dresden, DPmed, Gen Medicine and Healthcare products, Genzyme, Hexal AG, IGES, Impulze GmbH, Janssen Cilag, KW Medipoint, MedDay Pharmaceuticals, Medmile, Merck Serono, MICE, Mylan, Neuraxpharm, Neuropoint, Novartis, Novo Nordisk, ONO Pharma, Oxford PharmaGenesis, QuintilesIMS, Roche, Sanofi, Springer Medizin Verlag, STADA, Chugai Pharma, Teva, UCB, Viatris, Wings for Life international and Xcenda. His research is funded by the German Ministry for Education and Research (BMBF), German Federal Institute for Risk Assessment (BfR), German Research Foundation (DFG), Else Kröner Fresenius Foundation, Gemeinsamer Bundesausschuss (G-BA), German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies (IZKF) Muenster, German Foundation Neurology, Ministry of Culture and Science of the State of North Rhine-Westphalia, The Daimler and Benz Foundation, Multiple Sclerosis Society North Rhine-Westphalia Regional Association (dmsg), Peek & Cloppenburg Düsseldorf Foundation, Hempel Foundation for Science, Art and Welfare, German Alzheimer Society e.V. Dementia self-help and Alexion, Almirall, Amicus Therapeutics Germany, Argenx, Bayer Vital GmbH, BGP Products Operations (Viatris Company), Biogen, BMS, Demecan, Diamed, DGM e.v., Fresenius Medical Care, Genzyme, Gesellschaft von Freunden und Förderern der Heinrich-Heine-Universität Düsseldorf e.V., HERZ Burgdorf, Hexal, Janssen, Merck Serono, Novartis, Novo Nordisk Pharma, ONO Pharma, Roche and Teva. TR: received honoraria and/or research support from Alexion, argenx, Biogen, Merck, Novartis, Sanofi, UCB, J&J, and Roche. MP: received honoraria for lecturing and travel expenses for attending meetings from Alexion, ArgenX, Bayer Health Care, Biogen, Hexal, Merck Serono, Novartis, Roche, Sanofi-Aventis, Takeda and Teva. His research is funded by ArgenX, Biogen, Demecan, Hexal, Horizon Merck Serono, Novartis, Roche, Viatris, Takeda and Teva, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B. Braun Foundation and the German Alzheimer Society. Supplementary Files SupplementaryData.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 18 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 15 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Boeckers","email":"","orcid":"","institution":"Ruhr University Bochum, BG University Hospital Bergmannsheil","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"M.","lastName":"Boeckers","suffix":""},{"id":595059655,"identity":"94139ec4-87b6-4737-a9e4-98c91ed21e05","order_by":7,"name":"Patricia Kirschner","email":"","orcid":"","institution":"Medical Faculty University Hospital Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Kirschner","suffix":""},{"id":595059658,"identity":"336f1848-8953-44e1-ba99-e2ac4878856a","order_by":8,"name":"Karin Schulze-Bosse","email":"","orcid":"","institution":"University Hospital Düsseldorf, Heinrich-Heine-University","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Schulze-Bosse","suffix":""},{"id":595059660,"identity":"5a222b16-5f6d-40ea-bca7-84f7b125fbb2","order_by":9,"name":"Orhan Aktas","email":"","orcid":"","institution":"Medical Faculty University Hospital Düsseldorf","correspondingAuthor":false,"prefix":"","firstName":"Orhan","middleName":"","lastName":"Aktas","suffix":""},{"id":595059661,"identity":"4dd36f03-13c4-45a5-960b-7223db9e4af3","order_by":10,"name":"Sven G. 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No significant difference was observed (p = 0.178, rank-based linear model adjusted for age and sex). \u003cem\u003e(B)\u003c/em\u003eSubgroup analysis of IL-6 levels across RRMS, PPMS, and HC. While an overall group effect was noted (p = 0.0106, ANOVA), post-hoc pairwise comparisons did not reveal significant differences between individual groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: HC: Healthy Controls; IL-6: Interleukin-6; MS: Multiple Sclerosis; PPMS: Primary Progressive Multiple Sclerosis; RRMS: Relapsing-Remitting Multiple Sclerosis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8885644/v1/b79c5469edaa4d2c55062177.png"},{"id":103345577,"identity":"685bd87b-1957-4a8e-b59a-1e7494d3de97","added_by":"auto","created_at":"2026-02-24 16:13:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100232,"visible":true,"origin":"","legend":"\u003cp\u003eTime to confirmed disability accumulation (CDA) over 24 months stratified by baseline IL-6. Kaplan-Meier survival analysis showing the probability of remaining free from CDA. Patients were dichotomized into \"High\" and \"Low\" groups based on the median baseline IL-6 level. No significant difference in CDA risk was observed between the two groups (HR = 1.83; 95% CI: 0.73–4.61; p-value = 0.989).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CDA: Confirmed Disability Accumulation; CI: Confidence Interval; HR: Hazard Ratio; IL-6: Interleukin-6.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8885644/v1/001faacce447c5430b635109.png"},{"id":103506717,"identity":"f1efb79c-896d-47e6-bc58-5bea4ceb4d1e","added_by":"auto","created_at":"2026-02-26 13:39:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56382,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal change in serum IL-6 levels at 12 months by loss of NEDA-3 status. Boxplots represent the mean change in IL-6 concentrations from baseline to 12 months post-ocrelizumab initiation. Patients maintaining NEDA-3 status exhibited a significant reduction in IL-6 (mean -1.30 pg/mL), whereas those with loss of NEDA-3 (loss of NEDA-3) remained stable (mean 0.09 pg/mL). The between-group difference was statistically significant (p = 0.0318), suggesting a potential association between IL-6 reduction and sustained clinical stability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e IL-6: Interleukin-6; loss of NEDA-3: Loss of No Evidence of Disease Activity-3; NEDA-3: No Evidence of Disease Activity-3.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8885644/v1/cfc6c57a231583541aebbcc0.png"},{"id":103510988,"identity":"6637e9ba-76e7-46d6-b767-9989deb2854f","added_by":"auto","created_at":"2026-02-26 14:08:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8885644/v1/2e7b020e-5948-475d-aeb0-fd8f30336f8c.pdf"},{"id":103345578,"identity":"61ae68be-4cc8-4713-8c82-5b79989e8867","added_by":"auto","created_at":"2026-02-24 16:13:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":73343,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-8885644/v1/e65570c2346d3259f7dc6f73.docx"}],"financialInterests":"Competing interest reported. TK: reports no conflicts of interest in connection to this study.\nRH: reports no conflicts of interest in connection to this study.\nNH: reports no conflicts of interest in connection to this study.\nLM: reports no conflicts of interest related to this study. He reports honoraria for lecturing, consulting and travel expenses for attending meetings from Biogen, Merck, argenX, Bial, Roche, Hexal, Neuraxpharm, Sanofi, Alexion and Novartis, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B.Braun Foundation and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 493659010.\nMM: reports no conflicts of interest in connection to this study.\nBJK: reports no conflicts of interest in connection to this study.\nJMB: reports no conflicts of interest in connection to this study.\nPK: received honoraria for lectures, consultancy or travel support for attending meetings from Deutsche Gesellschaft für Neurologie (DGN), Hexal, Roche, Sanofi, Merck, Neuraxpharm and Viatris.\nKSB: reports no conflicts of interest in connection to this study.\nOA: received speaking honoraria and travel grants from Alexion, Almirall, Biogen, Celgene, Merck, Novartis, Roche, and VielaBio.\nSGM: receives honoraria for lecturing, travel expenses and for attending meetings from Academy 2, Argenx, Alexion, Almirall, Amicus Therapeutics Germany, AstraZeneca, Bayer Health Care, Biogen, BioNTech, BMS, Celgene, Datamed, Demecan, Desitin, Diamed, Diaplan, DIU Dresden, DPmed, Gen Medicine and Healthcare products, Genzyme, Hexal AG, IGES, Impulze GmbH, Janssen Cilag, KW Medipoint, MedDay Pharmaceuticals, Medmile, Merck Serono, MICE, Mylan, Neuraxpharm, Neuropoint, Novartis, Novo Nordisk, ONO Pharma, Oxford PharmaGenesis, QuintilesIMS, Roche, Sanofi, Springer Medizin Verlag, STADA, Chugai Pharma, Teva, UCB, Viatris, Wings for Life international and Xcenda.\nHis research is funded by the German Ministry for Education and Research (BMBF), German Federal Institute for Risk Assessment (BfR), German Research Foundation (DFG), Else Kröner Fresenius Foundation, Gemeinsamer Bundesausschuss (G-BA), German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies (IZKF) Muenster, German Foundation Neurology, Ministry of Culture and Science of the State of North Rhine-Westphalia, The Daimler and Benz Foundation, Multiple Sclerosis Society North Rhine-Westphalia Regional Association (dmsg), Peek \u0026 Cloppenburg Düsseldorf Foundation, Hempel Foundation for Science, Art and Welfare, German Alzheimer Society e.V. Dementia self-help and Alexion, Almirall, Amicus Therapeutics Germany, Argenx, Bayer Vital GmbH, BGP Products Operations (Viatris Company), Biogen, BMS, Demecan, Diamed, DGM e.v., Fresenius Medical Care, Genzyme, Gesellschaft von Freunden und Förderern der Heinrich-Heine-Universität Düsseldorf e.V., HERZ Burgdorf, Hexal, Janssen, Merck Serono, Novartis, Novo Nordisk Pharma, ONO Pharma, Roche and Teva.\nTR: received honoraria and/or research support from Alexion, argenx, Biogen, Merck, Novartis, Sanofi, UCB, J\u0026J, and Roche.\nMP: received honoraria for lecturing and travel expenses for attending meetings from Alexion, ArgenX, Bayer Health Care, Biogen, Hexal, Merck Serono, Novartis, Roche, Sanofi-Aventis, Takeda and Teva. His research is funded by ArgenX, Biogen, Demecan, Hexal, Horizon Merck Serono, Novartis, Roche, Viatris, Takeda and Teva, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B. Braun Foundation and the German Alzheimer Society.","formattedTitle":"Investigating the Role of Serum IL-6 in Predicting Outcomes of B- Cell Depleting Therapy in Multiple Sclerosis: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic demyelinating and inflammatory disease of the central nervous system in which both T-cells and B-cells play critical roles in driving tissue damage (1). Among pro-inflammatory mediators, interleukin-6 (IL-6) has emerged as a particularly interesting cytokine due to its multifaceted effects on immune cell differentiation and function. IL-6 promotes B-cell differentiation and immunoglobulin production, potentially contributing to MS pathology. It also facilitates Th17 cell expansion while inhibiting regulatory T-cell development, fostering a pro-inflammatory environment that may exacerbate neuroinflammation and demyelination (2). Consequently, IL-6 is considered a key driver of autoimmune pathology and a potential therapeutic target in MS (3).\u003c/p\u003e \u003cp\u003eBeyond MS, IL-6 has been implicated in other autoimmune and inflammatory conditions, including systemic lupus erythematosus (SLE) and neuromyelitis optica spectrum disorders (NMOSD), where IL-6 receptor blockade reduces relapse rates in the latter disease (4,5). Emerging data also associates peripheral blood IL-6 alterations with various neuropsychiatric conditions, underscoring its broader potential as a biomarker across central nervous system disorders (4). Previous investigations into serum IL-6 in MS cohorts have yielded diverse results, highlighting the complexity of peripheral cytokine monitoring. While IL-6 is frequently elevated in the cerebrospinal fluid (CSF) during clinical relapses (6,7), its presence in the serum is often lower or undetectable compared to central compartments (6). Furthermore, recent studies in diverse populations have shown that while serum IL-6 can be elevated during relapses, it does not consistently correlate with all clinical subtypes (8).\u003c/p\u003e \u003cp\u003eOcrelizumab (OCR) is a humanized monoclonal antibody that selectively depletes CD20\u003csup\u003e+\u003c/sup\u003e B-cells and is effective in both relapsing-remitting (RRMS) and primary progressive MS (PPMS) (9). Given IL-6\u0026rsquo;s role in B-cell function, it has been hypothesized that baseline IL-6 levels may predict disease activity or long-term outcomes in OCR-treated patients. However, existing data remains limited and inconclusive (10).\u003c/p\u003e \u003cp\u003eBuilding on this hypothesis, we investigated whether baseline serum IL-6 levels correlate with established markers of disease activity in MS patients initiating OCR therapy, and we compared these levels to healthy controls (HC).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study used data from the MS database of the Department of Neurology, Heinrich-Heine-University Düsseldorf, Germany (2018–2023). Clinical data was recorded during routine visits every six months and made available for research after written informed consent. Only patients diagnosed with RRMS or PPMS according to the 2017 McDonald criteria were included. Additional inclusion criteria comprised: (1) initiation of ocrelizumab (OCR) therapy and (2) availability of baseline serum samples collected prior to the first OCR infusion. Patients who experienced a relapse within three months prior to OCR initiation or were unable to provide consent were excluded. For comparison, baseline IL-6 levels from 87 healthy individuals without any history of autoimmune or chronic inflammatory disease were included as HC. This study was conducted in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cohort studies.\u003c/p\u003e\n\u003cp\u003eTo minimize confounding from acute inflammation, baseline C-reactive protein (CRP) levels and leukocyte counts were obtained. Patients with evidence of active infection- defined as CRP above the reference range, leukocytosis, or clinical signs of infection at the time of sample collection were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003eDisability status and disease severity of MS patients were assessed using the Expanded Disability Status Scale (EDSS), a clinician-rated scale ranging from 0 (normal neurological examination) to 10 (death due to MS)(11) EDSS was recorded at baseline (within one month prior to treatment initiation) and every six months thereafter, with changes from baseline analyzed to evaluate disability progression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome Definitions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDisability progression and disease activity were defined using established clinical and radiological criteria. Confirmed Disability Accumulation (CDA) was defined as an increase in EDSS of ≥1.0 points for patients with baseline EDSS ≤5.5, or ≥0.5 points for baseline EDSS \u0026gt;5.5, confirmed at a subsequent visit ≥12 weeks later. Progression Independent of Relapse Activity (PIRA) was defined as CDA occurring without a preceding relapse between the two relevant EDSS assessments or within the 12 weeks prior to baseline(12) Relapse-Associated Worsening (RAW) was defined as CDA occurring during a relapse, with sustained disability progression persisting for ≥12 weeks; transient EDSS changes related to relapses without confirmation were excluded from both PIRA and RAW(13) Relapses were defined as neurological deterioration lasting more than 24 hours, unrelated to infection, and verified within 7 days. Absence of EDSS progression was defined as no confirmed disability increase over six months, with thresholds depending on baseline EDSS (0: ≥1.5 points; 1–5: ≥1 point; \u0026gt;5: ≥0.5 points). MRI activity was defined by new T1 gadolinium-enhancing lesions or new/enlarging T2 lesions in clinical routine measurements. No Evidence of Disease Activity-3 (NEDA-3) was defined as the absence of: (1) new or enlarging T2 lesions or T1 gadolinium-enhancing lesions on MRI, (2) clinical relapses, and (3) confirmed disability accumulation (CDA) over the observation period. Conversely, loss of NEDA-3 (loss of NEDA-3)\u0026nbsp;was defined as the occurrence of any of these events. This composite outcome was assessed according to the standardized criteria previously described(14).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory Measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were obtained from patients with MS at baseline and every six months for up to two years, scheduled within two weeks prior to each subsequent OCR infusion; HCs were sampled at baseline only. Blood was drawn via a short catheter from an antecubital vein into Monovettes (Sarstedt, Nümbrecht, Germany). Serum tubes were allowed to clot for 30–60 minutes, centrifuged at 3000 rpm for 10 minutes, aliquoted, and initially frozen at −20 °C. Within four weeks, samples were transferred to a −80 °C freezer for long-term storage until final analysis. EDTA tubes were used for leukocyte counts, which were analyzed directly without centrifugation.\u003c/p\u003e\n\u003cp\u003eSerum was used for C‑reactive protein (CRP), immunoglobulin G (IgG), immunoglobulin A (IgA), immunoglobulin M (IgM), serum neurofilament light chain (sNfL), serum glial fibrillary acidic protein (sGFAP), and IL‑6.\u0026nbsp;All biomarkers were assessed at baseline, whereas IL‑6 was additionally measured at six-month intervals throughout the study period.\u003c/p\u003e\n\u003cp\u003eQuantification of sNfL and sGFAP was performed at the Central Institute for Clinical Chemistry and Laboratory Diagnostics, Medical Faculty, University Hospital Düsseldorf, Germany. Both biomarkers were analyzed using research-use-only electrochemiluminescence immunoassays (ECLIA) in a two-step sandwich format on the Roche Cobas 8000 analyzer (Elecsys® module), according to the manufacturer’s instructions. Laboratory personnel were blinded to clinical data. Z-scores for sNfL and sGFAP were calculated based on previously published methods (15,16)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in RStudio. Demographic, clinical, and biomarker data were summarized using descriptive statistics. Normal distribution was tested using the Shapiro–Wilk test. Non-normally distributed data were presented as median and interquartile range (IQR), whereas normally distributed data were reported as mean ± standard deviation (SD). Between-group comparisons of continuous variables were performed using Welch’s t-test for normally distributed data or the Wilcoxon rank-sum test for non-normal data. Paired longitudinal comparisons within groups were conducted using paired t-tests or Wilcoxon signed-rank tests, as appropriate. Associations between baseline biomarkers and clinical outcomes, including CDA, PIRA, RAW, NEDA-3, loss of NEDA-3, and MRI activity, were assessed using Cox proportional hazards models, multivariable linear regression, or logistic regression, adjusting for age and sex where relevant. Rank-based linear models were applied for comparisons of IL-6 levels across MS subtypes and over time. A p-value \u0026lt;0.05 was considered statistically significant, with correction for multiple comparisons applied where relevant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 73 MS patients were included, comprising 43 with PPMS (58.9%) and 30 with RRMS (41.1%). Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1.1\u003c/span\u003e and 1.2. The mean (SD) age of the entire MS cohort was 52.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 years, and 54.8% were female, with PPMS patients being older than those with RRMS (58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 vs. 43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showing a slight male predominance (58.1% vs. 50%, p\u0026thinsp;=\u0026thinsp;0.654). HC (n\u0026thinsp;=\u0026thinsp;86) had a mean age of 46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 years and were in 72.1% female. Compared with MS patients HC were significantly younger (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Median disease duration was significantly longer in PPMS (115 months, IQR 78) compared with RRMS (73 months, IQR 48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Baseline EDSS was also higher in PPMS (median 4.0, IQR 3.375) than in RRMS (median 2.0, IQR 2.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1.2\u003c/span\u003e). Baseline IL-6 levels were similar between the overall MS cohort and HC (MS: 1.90 [IQR 1.8] pg/mL vs. HC: 1.96 [IQR 1.1] pg/mL; rank-based linear model adjusted for age and sex; p\u0026thinsp;=\u0026thinsp;0.178) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subgroup analysis by MS type revealed that baseline mean IL-6 level was higher in PPMS (2.30 [IQR 2.7] pg/mL), compared to RRMS patients (1.75 [IQR 0.6] pg/mL; p\u0026thinsp;=\u0026thinsp;0.024). A significant overall group effect was observed (ANOVA; p\u0026thinsp;=\u0026thinsp;0.011); however, post-hoc pairwise comparisons adjusted for multiple testing did not reveal statistically significant differences between individual groups, although a trend toward lower IL-6 levels in RRMS compared with HC was evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile baseline sGFAP z-scores were similar between MS subtypes, median baseline sNfL z-scores were elevated in RRMS (2.12 [IQR 1.9]) compared with PPMS (\u0026minus;\u0026thinsp;0.41 [IQR 1.6]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Suppl. Figure\u0026nbsp;1). CRP levels were slightly higher in PPMS compared to healthy controls, though all retained samples were below the clinical threshold for acute infection (Suppl. Figure\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eDuring the 24-month follow-up, 22 MS patients (30.1%) experienced CDA, 20 experienced PIRA (27.4%), and 2 experienced RAW (2.7%), with loss of NEDA-3 observed in 31 of 73 patients (42.5%).\u003c/p\u003e \u003cp\u003eCox proportional hazard models revealed no significant associations between baseline IL-6 and occurrence of CDA (Suppl. Table\u0026nbsp;1.3). Also, after dichotomization of IL-6 at the median, there was no statistically significant association with the risk of CDA (HR 1.83; 95% CI 0.73\u0026ndash;4.61; p\u003csub\u003eadj\u003c/sub\u003e = 0.989) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn RRMS, baseline IL-6 was not associated with relapse occurrence over two years (median 1.75 vs. 1.8 pg/mL; rank-based linear model adjusted for age and sex; p\u0026thinsp;=\u0026thinsp;0.913). Multivariable Cox regression for RAW was not performed due to the limited number of events (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003eMultivariable linear regression identified baseline EDSS as the sole predictor of EDSS at 24 months (β\u0026thinsp;=\u0026thinsp;0.74; 95% CI 0.41\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.005), while no other biomarkers, including sNfL and sGFAP z-scores and baseline IL-6, were associated with final disability accrual or PIRA. Similarly, logistic regression demonstrated no relationship between baseline IL-6 and new T2 lesions on follow-up MRI (OR 0.86; 95% CI 0.53\u0026ndash;1.39; p\u0026thinsp;=\u0026thinsp;0.53), with subgroup analyses in RRMS (OR 0.27; 95% CI 0.01\u0026ndash;5.21; p\u0026thinsp;=\u0026thinsp;0.38) and PPMS (OR 1.04; 95% CI 0.61\u0026ndash;1.77; p\u0026thinsp;=\u0026thinsp;0.88) showing consistent findings.\u003c/p\u003e \u003cp\u003eLongitudinal analysis of serum IL-6 levels of 25 patients over 12 months during B-cell depleting therapy revealed largely stable concentrations, with no significant differences observed at any follow-up compared to baseline. This stability persisted across MS subtypes (RRMS and PPMS). Upon stratification by clinical response, early IL-6 dynamics between baseline and 6 months did not differ significantly between patients maintaining NEDA-3 and those with loss of NEDA-3 (p\u0026thinsp;=\u0026thinsp;0.126). However, by 12 months post-ocrelizumab initiation, patients maintaining NEDA-3 exhibited a mean decrease in IL-6 from baseline to 12 months (-1.30 pg/mL), whereas patients with disease activity showed no significant change (0.09 pg/mL). The between-group difference in IL-6 change over 12 months was statistically significant (p\u0026thinsp;=\u0026thinsp;0.0318) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The between-group difference in IL-6 changes from baseline to 12 months indicated a trend toward significance after adjustment for multiple comparisons (p\u0026thinsp;=\u0026thinsp;0.064). No immediate temporal correlation was found between the exact timing of a relapse and a spike in IL-6, though the limited number of RAW events (n\u0026thinsp;=\u0026thinsp;3) restricted this analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort of 73 MS patients, we found that baseline serum IL-6 levels do not predict clinical or radiological outcomes over 24 months of ocrelizumab therapy. However, we observed that patients who pertained NEDA-3 status experienced a significant 50% reduction in longitudinal IL-6 levels, a change not seen in those with disease activity.\u003c/p\u003e \u003cp\u003eOcrelizumab therapy was intentionally incorporated in our cohort to investigate biomarkers predictive of disease activity in both PPMS and RRMS patients. The rationale for this investigation lies in the complex interplay between B cells and IL-6. IL-6-producing B-cells drive proinflammatory Th17 responses integral to MS pathogenesis, and ocrelizumab effectively targets these cells, thereby reducing inflammation and relapses (17).\u003c/p\u003e \u003cp\u003eHypothetically, the depletion of these CD20\u003csup\u003e+\u003c/sup\u003e B-cells should result in a concurrent reduction of serum IL-6, serving as a marker for therapeutic efficacy. However, despite effective B-cell depletion, a subset of patients continued to experience disease activity, indicating that compensatory or IL-6-independent pathways underpin progression in these cases(18). Within this context, our findings indicate that serum IL-6 lacks sufficient sensitivity to predict clinical disease activity or associated neurodegenerative markers.\u003c/p\u003e \u003cp\u003eAlthough serum IL-6 did not differentiate MS patients from controls, mean baseline levels were highest in PPMS, suggesting a possible association with progressive disease. This pattern aligns with the concept that PPMS is characterized by chronic, low-grade inflammation and neurodegeneration, processes known to drive increased systemic IL-6 production, particularly with advancing age. Notably, previous transcriptomic studies have reported reduced IL-6 receptor expression in PPMS compared with RRMS, a finding that may reflect receptor downregulation or shedding in the setting of chronically elevated IL-6 levels (19).\u003c/p\u003e \u003cp\u003eThe higher baseline IL-6 in PPMS patients might also reflect age-dependent immune signatures, as our PPMS cohort trended towards an older age. This chronic low-grade systemic inflammation may contribute to the relatively high rate of lo-NEDA-3 (42.5%) and CDA observed in this real-world cohort(20).\u003c/p\u003e \u003cp\u003eTaken together, higher IL-6 concentrations in PPMS combined with lower IL-6R expression may represent a compensatory or exhaustion-related immune signature rather than an acute inflammatory response. Furthermore, this may indicate that elevated IL-6 is more characteristic of PPMS and could help distinguish disease courses, though the small sample size limits firm conclusions.\u003c/p\u003e \u003cp\u003eNotably, sNfL and GFAP z-scores in this cohort did not predict outcomes, in contrast to other studies (15,21), which may be explained by the higher age of our patients and the relatively low relapse activity.\u003c/p\u003e \u003cp\u003eIn our subgroup with available longitudinal serum samples, IL-6 levels remained largely stable over 24 months of B-cell depleting therapy. When patients were stratified by NEDA-3 status at 12 months, those who maintained NEDA-3 showed significant reductions in IL-6, whereas patients experiencing loss of NEDA-3 exhibited minimal change. The between-group difference at 12 months suggested a trend toward significance after correction for multiple testing, although sample sizes were limited. These observations raise the possibility that longitudinal IL-6 dynamics may reflect clinical stability under anti-CD20 therapy, warranting further investigation in larger cohorts to determine its potential utility as a biomarker.\u003c/p\u003e \u003cp\u003eNotably, prior research consistently links cerebrospinal fluid (CSF) IL-6 - not serum IL-6 - with MS disease activity and disability. Elevated CSF IL-6 correlates with severity and is higher in PPMS compared with controls. This divergence likely reflects cytokine compartmentalization and blood-brain barrier selectivity limiting IL-6 spill-over into serum, except possibly in progressive MS with chronic blood-brain barrier disruption (22). Our lack of CSF IL-6 data notwithstanding, this mechanistic insight contextualizes our negative serum IL-6 findings, underscoring CSF IL-6 as a more sensitive CNS inflammation biomarker.\u003c/p\u003e \u003cp\u003eThus, serum IL-6 alone cannot capture the full spectrum of inflammatory and neurodegenerative processes in MS progression under B-cell therapy. Research should emphasize the multifaceted B-cell - IL-6 axis and develop composite biomarkers covering diverse pathophysiological routes, improving patient stratification and refining therapeutic targeting beyond IL-6.\u003c/p\u003e \u003cp\u003eComparatively, IL-6 is a robust biomarker in neuromyelitis optica spectrum disorder (NMOSD), where it closely correlates with disease activity, relapse severity, and disability both in serum and CSF. IL-6 drives NMOSD pathogenesis by promoting plasmablast survival, aquaporin-4 antibody production, blood-brain barrier disruption, and enhanced proinflammatory T-cell activation. IL-6 receptor blockade significantly reduces NMOSD relapses, underscoring IL-6\u0026rsquo;s role as both biomarker and therapeutic target(23). In contrast, MS exhibits heterogeneous immunopathology with multiple cytokines and mechanisms fluctuating by stage and subtype, limiting IL-6\u0026rsquo;s predictive value. This disease-specific distinction emphasizes the importance of appropriate biomarker validation tailored to distinct CNS autoimmune disorders.\u003c/p\u003e \u003cp\u003eSeveral methodological and biological factors may also explain our lack of serum IL-6 predictive value in MS: moderate sample size and incomplete longitudinal sampling limit statistical power; IL-6 variability from external factors obscures specific signals; single baseline measures miss post-treatment cytokine dynamics; and retrospective real-world design entails sampling heterogeneity reducing biomarker sensitivity.\u003c/p\u003e \u003cp\u003eIn conclusion, our data reinforce IL-6\u0026rsquo;s limited utility as a serum biomarker for disease monitoring in B-cell\u0026ndash;treated MS. Future studies should integrate longitudinal, multiparametric approaches combining cytokine profiles, neuroaxonal injury markers, glial activation, and advanced imaging across larger, balanced cohorts to clarify IL-6\u0026rsquo;s combined biomarker potential within the broader inflammatory milieu governing MS progression and therapy response. This will facilitate individualized disease monitoring and precision therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCDA:\u003c/strong\u003e Confirmed Disability Accumulation\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCRP:\u003c/strong\u003e C-reactive Protein\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCSF:\u003c/strong\u003e Cerebrospinal Fluid\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eECLIA:\u003c/strong\u003e Electrochemiluminescence Immunoassay\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEDSS:\u003c/strong\u003e Expanded Disability Status Scale\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHC:\u003c/strong\u003e Healthy Controls\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIgA:\u003c/strong\u003e Immunoglobulin A\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIgG:\u003c/strong\u003e Immunoglobulin G\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIgM:\u003c/strong\u003e Immunoglobulin M\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIL-6:\u003c/strong\u003e Interleukin-6\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIQR:\u003c/strong\u003e Interquartile Range\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMRI:\u003c/strong\u003e Magnetic Resonance Imaging\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMS:\u003c/strong\u003e Multiple Sclerosis\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNEDA-3:\u003c/strong\u003e No Evidence of Disease Activity-3\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNMOSD:\u003c/strong\u003e Neuromyelitis Optica Spectrum Disorders\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOCR:\u003c/strong\u003e Ocrelizumab\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePIRA:\u003c/strong\u003e Progression Independent of Relapse Activity\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePPMS:\u003c/strong\u003e Primary Progressive Multiple Sclerosis\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRAW:\u003c/strong\u003e Relapse-Associated Worsening\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRRMS:\u003c/strong\u003e Relapsing\u0026ndash;Remitting Multiple Sclerosis\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSD:\u003c/strong\u003e Standard Deviation\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003esGFAP:\u003c/strong\u003e Serum Glial Fibrillary Acidic Protein\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSLE:\u003c/strong\u003e Systemic Lupus Erythematosus\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003esNfL:\u003c/strong\u003e Serum Neurofilament Light Chain\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNA:\u003c/strong\u003e Not Applicable\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the local ethics committee of Heinrich-Heine-University D\u0026uuml;sseldorf (registry number 5951R, approval date 28.06.2018, and registry number 2021\u0026ndash;1775, approval date 12.07.2022). Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTK: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eRH: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eNH: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eLM:\u0026nbsp;reports no conflicts of interest related to this study. He reports honoraria for lecturing, consulting and travel expenses for attending meetings from Biogen, Merck, argenX, Bial, Roche, Hexal, Neuraxpharm, Sanofi, Alexion and Novartis, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B.Braun Foundation and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u0026ndash; 493659010.\u003c/p\u003e\n\u003cp\u003eMM: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eBJK: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eJMB: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003ePK: received honoraria for lectures, consultancy or travel support for attending meetings from Deutsche Gesellschaft f\u0026uuml;r Neurologie (DGN), Hexal, Roche, Sanofi, Merck, Neuraxpharm and Viatris.\u003c/p\u003e\n\u003cp\u003eKSB: reports no conflicts of interest in connection to this study.\u003c/p\u003e\n\u003cp\u003eOA: received speaking honoraria and travel grants from Alexion, Almirall, Biogen, Celgene, Merck, Novartis, Roche, and VielaBio.\u003c/p\u003e\n\u003cp\u003eSGM: receives honoraria for lecturing, travel expenses and for attending meetings from Academy 2, Argenx, Alexion, Almirall, Amicus Therapeutics Germany, AstraZeneca, Bayer Health Care, Biogen, BioNTech, BMS, Celgene, Datamed, Demecan, Desitin, Diamed, Diaplan, DIU Dresden, DPmed, Gen Medicine and Healthcare products, Genzyme, Hexal AG, IGES, Impulze GmbH, Janssen Cilag, KW Medipoint, MedDay Pharmaceuticals, Medmile, Merck Serono, MICE, Mylan, Neuraxpharm, Neuropoint, Novartis, Novo Nordisk, ONO Pharma, Oxford PharmaGenesis, QuintilesIMS, Roche, Sanofi, Springer Medizin Verlag, STADA, Chugai Pharma, Teva, UCB, Viatris, Wings for Life international and Xcenda.\u003c/p\u003e\n\u003cp\u003eHis research is funded by the German Ministry for Education and Research (BMBF), German Federal Institute for Risk Assessment (BfR), German Research Foundation (DFG), Else Kr\u0026ouml;ner Fresenius Foundation, Gemeinsamer Bundesausschuss (G-BA), German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies (IZKF) Muenster, German Foundation Neurology, Ministry of Culture and Science of the State of North Rhine-Westphalia, The Daimler and Benz Foundation, Multiple Sclerosis Society North Rhine-Westphalia Regional Association (dmsg), Peek \u0026amp; Cloppenburg D\u0026uuml;sseldorf Foundation, Hempel Foundation for Science, Art and Welfare, German Alzheimer Society e.V. Dementia self-help and Alexion, Almirall, Amicus Therapeutics Germany, Argenx, Bayer Vital GmbH, BGP Products Operations (Viatris Company), Biogen, BMS, Demecan, Diamed, DGM e.v., Fresenius Medical Care, Genzyme, Gesellschaft von Freunden und F\u0026ouml;rderern der Heinrich-Heine-Universit\u0026auml;t D\u0026uuml;sseldorf e.V., HERZ Burgdorf, Hexal, Janssen, Merck Serono, Novartis, Novo Nordisk Pharma, ONO Pharma, Roche and Teva.\u003c/p\u003e\n\u003cp\u003eTR: received honoraria and/or research support from Alexion, argenx, Biogen, Merck, Novartis, Sanofi, UCB, J\u0026amp;J, and Roche.\u003c/p\u003e\n\u003cp\u003eMP: received honoraria for lecturing and travel expenses for attending meetings from Alexion, ArgenX, Bayer Health Care, Biogen, Hexal, Merck Serono, Novartis, Roche, Sanofi-Aventis, Takeda and Teva. His research is funded by ArgenX, Biogen, Demecan, Hexal, Horizon Merck Serono, Novartis, Roche, Viatris, Takeda and Teva, all outside the scope of this work. His research is funded by the by the German Multiple Sclerosis Foundation (DMSG), the B. Braun Foundation and the German Alzheimer Society.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTK:\u003c/strong\u003e Conceptualization (equal); Investigation (lead); Formal analysis (equal); Writing \u0026ndash; original draft preparation (lead); Visualization (equal); Validation (equal); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRH:\u003c/strong\u003e Conceptualization (equal); Investigation (lead); Formal analysis (equal); Writing \u0026ndash; original draft preparation (lead); Visualization (equal); Validation (equal); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNH:\u003c/strong\u003e Data curation (supporting); Investigation (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLM:\u003c/strong\u003e Data curation (supporting); Methodology (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBJK:\u003c/strong\u003e Data curation (supporting); Investigation (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMM:\u003c/strong\u003e Data curation (equal); Investigation (equal); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJB:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePK:\u003c/strong\u003e Investigation (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKSB:\u003c/strong\u003e Resources (lead); Investigation (supporting); Methodology (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSGM:\u003c/strong\u003e Supervision (supporting); Resources (lead); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTR:\u003c/strong\u003e Supervision (supporting); Writing \u0026ndash; review \u0026amp; editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMP:\u003c/strong\u003e Conceptualization (lead); Methodology (lead); Project administration (lead); Supervision (lead); Writing \u0026ndash; review \u0026amp; editing (lead); Validation (supporting).\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStampanoni Bassi M, Iezzi E, Drulovic J, Pekmezovic T, Gilio L, Furlan R, et al. IL-6 in the Cerebrospinal Fluid Signals Disease Activity in Multiple Sclerosis. Front Cell Neurosci [Internet]. 2020 Jun 23 [cited 2025 Mar 15];14:535794. Available from: www.frontiersin.org\u003c/li\u003e\n\u003cli\u003eHolloman JP, Axtell RC, Monson NL, Wu GF. The Role of B Cells in Primary Progressive Multiple Sclerosis. Front Neurol [Internet]. 2021 Jun 7 [cited 2025 Mar 15];12:680581. Available from: www.frontiersin.org\u003c/li\u003e\n\u003cli\u003eJones BE, Maerz MD, Buckner JH. IL-6: a cytokine at the crossroads of autoimmunity. Curr Opin Immunol [Internet]. 2018 Dec 1 [cited 2025 Dec 14];55:9\u0026ndash;14. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0952791518300530?via%3Dihub\u003c/li\u003e\n\u003cli\u003eDing J, Su S, You T, Xia T, Lin X, Chen Z, et al. Serum interleukin-6 level is correlated with the disease activity of systemic lupus erythematosus: a meta-analysis. Clinics [Internet]. 2020 Oct 19 [cited 2025 Mar 24];75:e1801. 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Journal of Experimental Medicine [Internet]. 2012 May 7 [cited 2025 Dec 14];209(5):1001\u0026ndash;10. Available from: www.jem.org/cgi/doi/10.1084/jem.20111675\u003c/li\u003e\n\u003cli\u003eYong HYF, Camara-Lemarroy C. Progression independent of relapsing biology in multiple sclerosis: a real-word study. Front Neurol [Internet]. 2025 May 29 [cited 2025 Dec 14];16:1595929. Available from: https://BioRender.com/xsuz0zr.\u003c/li\u003e\n\u003cli\u003eKoch MW, Ilnytskyy Y, Golubov A, Metz LM, Yong VW, Kovalchuk O. Global transcriptome profiling of mild relapsing-remitting versus primary progressive multiple sclerosis. Eur J Neurol [Internet]. 2018 Apr 1 [cited 2025 Dec 14];25(4):651\u0026ndash;8. Available from: /doi/pdf/10.1111/ene.13565\u003c/li\u003e\n\u003cli\u003eEschborn M, Pawlitzki M, Wirth T, Nelke C, Pfeuffer S, Schulte-Mecklenbeck A, et al. Evaluation of Age-Dependent Immune Signatures in Patients With Multiple Sclerosis. Neurology(R) neuroimmunology \u0026amp; neuroinflammation [Internet]. 2021 Nov 1 [cited 2026 Feb 1];8(6). Available from: https://pubmed.ncbi.nlm.nih.gov/34667129/\u003c/li\u003e\n\u003cli\u003eBenkert P, Maleska Maceski A, Schaedelin S, Oechtering J, Zadic A, Vilchez Gomez JF, et al. Serum Glial Fibrillary Acidic Protein and Neurofilament Light Chain Levels Reflect Different Mechanisms of Disease Progression under B-Cell Depleting Treatment in Multiple Sclerosis. Ann Neurol [Internet]. 2025 Jan 1 [cited 2025 Dec 18];97(1):104\u0026ndash;15. Available from: /doi/pdf/10.1002/ana.27096\u003c/li\u003e\n\u003cli\u003eItorralba J, Brand-Arzamendi K, Saab G, Muccilli A, Schneider R. Intrathecal interleukin-6 levels are associated with progressive disease and clinical severity in multiple sclerosis. BMC Neurol [Internet]. 2025 Dec 1 [cited 2025 Dec 18];25(1). Available from: https://pubmed.ncbi.nlm.nih.gov/40175894/\u003c/li\u003e\n\u003cli\u003eFujihara K, Bennett JL, de Seze J, Haramura M, Kleiter I, Weinshenker BG, et al. Interleukin-6 in neuromyelitis optica spectrum disorder pathophysiology. Neurology(R) neuroimmunology \u0026amp; neuroinflammation [Internet]. 2020 Sep 3 [cited 2025 Dec 14];7(5):e841. Available from: https://www.neurology.org/doi/pdf/10.1212/NXI.0000000000000841\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.1: Baseline characteristics of the MS cohort and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal MS cohort (n=73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (n=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years, mean ± SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.2 ± 12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.1 ± 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale sex (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonths since disease manifestation (median; range; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91 (16–427; IQR 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS (median; range; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (0–7; IQR 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDA within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIRA within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAW within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLo-NEDA-3 within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6 [pg/ml] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.90 (IQR 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.96 (IQR 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esNfL z-score (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.10 (IQR 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esGFAP z-score (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28 (IQR 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP [mg/L] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 (IQR 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07 (IQR 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIgG [mg/dl] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e952 (IQR 348.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e885 (IQR 250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIgM [mg/dl] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (IQR 58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin D [ng/ml] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.0 (IQR 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables are presented as mean ± SD or median (range; IQR). Categorical variables are presented as n (%). P-values represent comparisons between the total MS cohort and HC using Welch’s t-test or Wilcoxon rank-sum test. \u003cem\u003eAbbreviations:\u003c/em\u003e CDA: Confirmed Disability Accumulation; CRP: C-reactive Protein; EDSS: Expanded Disability Status Scale; HC: Healthy Controls; IQR: Interquartile Range; loss of NEDA-3: Loss of No Evidence of Disease Activity-3; MS: Multiple Sclerosis; NA: Not Applicable; PIRA: Progression Independent of Relapse Activity; RAW: Relapse-Associated Worsening; SD: Standard Deviation; sGFAP: Serum Glial Fibrillary Acidic Protein; sNfL: Serum Neurofilament Light Chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.2:\u0026nbsp;\u003c/strong\u003eBaseline characteristics of PPMS and RRMS patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPMS (n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRRMS (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years, mean ± SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.3 ± 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.5 ± 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale sex (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonths since disease manifestation (median; range; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (16–427; IQR 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73 (37–415; IQR 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS (median; range; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (1.5–7; IQR 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.0–6.5; IQR 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDA within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIRA within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAW within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLo-NEDA-3 within 24M (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (46.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6 [pg/ml] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.30 (IQR 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75 (IQR 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esNfL z-score (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.41 (IQR 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.12 (IQR 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esGFAP z-score (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28 (IQR 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62 (IQR 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP [mg/L] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10 (IQR 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 (IQR 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIgG [mg/dl] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e969 (IQR 225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e829 (IQR 459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIgM [mg/dl] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87 (IQR 62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (IQR 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin D [ng/ml] (median; IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.0 (IQR 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.5 (IQR 16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics are stratified by Multiple Sclerosis subtype. P-values represent comparisons between PPMS and RRMS groups using Welch’s t-test or Wilcoxon rank-sum test.. \u003cem\u003eAbbreviations:\u003c/em\u003e CDA: Confirmed Disability Accumulation; CRP: C-reactive Protein; EDSS: Expanded Disability Status Scale; IQR: Interquartile Range; loss of NEDA-3: Loss of No Evidence of Disease Activity-3; NA: Not Applicable; PIRA: Progression Independent of Relapse Activity; PPMS: Primary Progressive Multiple Sclerosis; RAW: Relapse-Associated Worsening; RRMS: Relapsing-Remitting Multiple Sclerosis; SD: Standard Deviation; sGFAP: Serum Glial Fibrillary Acidic Protein; sNfL: Serum Neurofilament Light Chain.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple Sclerosis, Interleukin-6 (IL-6), Ocrelizumab, B-cell Depletion, Primary Progressive Multiple Sclerosis (PPMS), Relapsing-Remitting Multiple Sclerosis (RRMS), Progression Independent of Relapse Activity (PIRA), Biomarker, Serum Neurofilament Light Chain (sNfL), Serum Glial Fibrillary Acidic Protein (sGFAP)","lastPublishedDoi":"10.21203/rs.3.rs-8885644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8885644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Interleukin-6 (IL-6) plays a pivotal role in autoimmune inflammation through its effects on B-cell differentiation, Th17 expansion, and regulatory T-cell suppression. Given ocrelizumab’s (OCR) mechanism of selective CD20\u003csup\u003e+\u003c/sup\u003e B-cell depletion, baseline IL-6 levels have been hypothesized to predict disease activity and long-term outcomes in multiple sclerosis (MS). However, the prognostic value of serum IL-6 in OCR-treated patients remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective study included 73 patients with relapsing–remitting MS (RRMS, n = 30) or primary progressive MS (PPMS, n = 43) who initiated OCR at University Hospital Düsseldorf between 2018 and 2023. Baseline serum IL-6 was compared with 87 healthy controls (HC) and correlated with clinical, radiological, and biomarker outcomes over 24 months. Clinical endpoints included confirmed progression independent of relapse activity (PIRA), and relapse-associated worsening (RAW), alongside MRI activity and Serum Neurofilament Light Chain (sNfL) and Serum Glial Fibrillary Acidic Protein (sGFAP) levels. Between-group comparisons used Welch’s t-test or Wilcoxon rank-sum test, paired longitudinal comparisons used paired t-tests or Wilcoxon signed-rank tests, and associations between baseline biomarkers and outcomes were evaluated using Cox regression, multivariable linear or logistic regression, and rank-based linear models for IL-6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Baseline serum IL-6 levels showed no significant differences between the overall MS cohort and HC, nor between RRMS and PPMS subgroups. No baseline biomarker, including IL-6, sNfL, and sGFAP predicted disease activity. Longitudinal analysis under OCR revealed largely stable IL-6 concentrations but patients maintaining No Evidence of Disease Activity-3 (NEDA-3) showed 50% reduction in IL-6 at 12 months, whereas those with loss of NEDA-3 remained stable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Baseline serum IL-6 alone is insufficient to predict clinical or radiological outcomes in OCR-treated MS patients. However, the significant longitudinal decline specifically in stable patients suggests that IL-6 dynamics, rather than static baseline measures, may better reflect sustained therapeutic response. These findings underscore the limited utility of serum IL-6 alone as a biomarker and support further exploration of longitudinal, multiparametric approaches.\u003c/p\u003e","manuscriptTitle":"Investigating the Role of Serum IL-6 in Predicting Outcomes of B- Cell Depleting Therapy in Multiple Sclerosis: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:12:54","doi":"10.21203/rs.3.rs-8885644/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-11T15:19:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T04:38:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226904172901520711448839207303571459954","date":"2026-03-09T04:07:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T15:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123386857567222860956238181841611481589","date":"2026-03-03T12:42:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T09:44:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178047699422483530399327249008191592830","date":"2026-02-19T06:08:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T03:22:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T03:05:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T03:05:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-02-15T11:21:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe93c838-40ab-4c13-8f9f-6bf5214c9cde","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-28T14:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:12:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8885644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8885644","identity":"rs-8885644","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}