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Growing evidence links COVID-19 with acute and long-term neurological dysfunction. However, the pathophysiological mechanisms resulting in central nervous system involvement remain unclear, posing both diagnostic and therapeutic challenges. Here we show outcomes of a cross-sectional clinical study (NCT04472013) including clinical and imaging data and corresponding multidimensional characterization of immune mediators in the cerebrospinal fluid (CSF) and plasma of patients belonging to different Neuro-COVID severity classes. The most prominent signs of severe Neuro-COVID are blood-brain barrier (BBB) impairment, elevated microglia activation markers and a polyclonal B cell response targeting self-antigens and non-self-antigens. COVID-19 patients show decreased regional brain volumes associating with specific CSF parameters, however, COVID-19 patients characterized by plasma cytokine storm are presenting with a non-inflammatory CSF profile. Post-acute COVID-19 syndrome strongly associates with a distinctive set of CSF and plasma mediators. Collectively, we identify several potentially actionable targets to prevent or intervene with the neurological consequences of SARS-CoV-2 infection. Both acute and chronic COVID-19 disease (also known as long-COVID) may affect the central nervous system. Here authors characterize the immunological profile of peripheral blood and cerebrospinal fluid of COVID-19 patients in order to identify the main factors that contribute to neurological impairment and the severity of neurological symptoms in Sars-CoV-2 infection.

Background Neuromyelitis optica spectrum disorder (NMOSD) is a frequently disabling neuroinflammatory syndrome with a relapsing course. Blood-based disease severity and prognostic biomarkers for NMOSD are a yet unmet clinical need. Here, we evaluated serum glial fibrillary acidic protein (sGFAP) and neurofilament light (sNfL) as disease severity and prognostic biomarkers in patients with aquaporin-4 immunoglobulin (Ig)G positive (AQP4-IgG + ) NMOSD. Methods sGFAP and sNfL were determined by single-molecule array technology in a prospective cohort of 33 AQP4-IgG + patients with NMOSD, 32 of which were in clinical remission at study baseline. Sixteen myelin oligodendrocyte glycoprotein IgG-positive (MOG-IgG + ) patients and 38 healthy persons were included as controls. Attacks were recorded in all AQP4-IgG + patients over a median observation period of 4.25 years. Results In patients with AQP4-IgG + NMOSD, median sGFAP (109.2 pg/ml) was non-significantly higher than in MOG-IgG + patients (81.1 pg/ml; p = 0.83) and healthy controls (67.7 pg/ml; p = 0.07); sNfL did not substantially differ between groups. Yet, in AQP4-IgG + , but not MOG-IgG + patients, higher sGFAP was associated with worse clinical disability scores, including the Expanded Disability Status Scale (EDSS, standardized effect size = 1.30, p = 0.007) and Multiple Sclerosis Functional Composite (MSFC, standardized effect size = − 1.28, p = 0.01). While in AQP4-IgG + , but not MOG-IgG + patients, baseline sGFAP and sNfL were positively associated (standardized effect size = 2.24, p = 0.001), higher sNfL was only non-significantly associated with worse EDSS (standardized effect size = 1.09, p = 0.15) and MSFC (standardized effect size = − 1.75, p = 0.06) in patients with AQP4-IgG + NMOSD. Patients with AQP4-IgG + NMOSD with sGFAP > 90 pg/ml at baseline had a shorter time to a future attack than those with sGFAP ≤ 90 pg/ml (adjusted hazard ratio [95% confidence interval] = 11.6 [1.3–105.6], p = 0.03). In contrast, baseline sNfL levels above the 75 th age adjusted percentile were not associated with a shorter time to a future attack in patients with AQP4-IgG + NMOSD. Conclusion These findings suggest a potential role for sGFAP as biomarker for disease severity and future disease activity in patients with AQP4-IgG + NMOSD in phases of clinical remission.

BackgroundGranulocyte invasion into the brain is a pathoanatomical feature differentiating neuromyelitis optica spectrum disorder (NMOSD) from multiple sclerosis (MS). We aimed to determine whether granulocyte activation markers (GAM) in cerebrospinal fluid (CSF) can be used as a biomarker to distinguish NMOSD from MS, and whether levels associate with neurological impairment.MethodsWe quantified CSF levels of five GAM (neutrophil elastase, myeloperoxidase, neutrophil gelatinase-associated lipocalin, matrixmetalloproteinase-8, tissue inhibitor of metalloproteinase-1), as well as a set of inflammatory and tissue-destruction markers, known to be upregulated in NMOSD and MS (neurofilament light chain, glial fibrillary acidic protein, S100B, matrix metalloproteinase-9, intercellular adhesion molecule-1, vascular cellular adhesion molecule-1), in two cohorts of patients with mixed NMOSD and relapsing-remitting multiple sclerosis (RRMS).ResultsIn acute NMOSD, GAM and adhesion molecules, but not the other markers, were higher than in RRMS and correlated with actual clinical disability scores. Peak GAM levels occurred at the onset of NMOSD attacks, while they were stably low in MS, allowing to differentiate the two diseases for ≤21 days from onset of clinical exacerbation. Composites of GAM provided area under the curve values of 0.90–0.98 (specificity of 0.76–1.0, sensitivity of 0.87–1.0) to differentiate NMOSD from MS, including all anti-aquaporin-4 protein (aAQP4)-antibody-negative patients who were untreated.ConclusionsGAM composites represent a novel biomarker to reliably differentiate NMOSD from MS, including in aAQP4− NMOSD. The association of GAM with the degree of concurrent neurological impairment provides evidence for their pathogenic role, in turn suggesting them as potential drug targets in acute NMOSD.

Inclusion of cerebrospinal fluid (CSF) oligoclonal IgG bands (OCGB) in the revised McDonald criteria increases the sensitivity of diagnosis when dissemination in time (DIT) cannot be proven. While OCGB negative patients are unlikely to develop clinically definite (CD) MS, OCGB positivity may lead to an erroneous diagnosis in conditions that present similarly, such as neuromyelitis optica spectrum disorders (NMOSD) or neurosarcoidosis. To identify specific, OCGB-complementary, biomarkers to improve diagnostic accuracy in OCGB positive patients. We analysed the CSF metabolome and proteome of CDMS (n=41) and confirmed non-MS patients (n=64) comprising a range of CNS conditions routinely encountered in neurology clinics. OCGB discriminated between CDMS and non-MS with high sensitivity (85%), but low specificity (67%), as previously described. Machine learning methods revealed CCN5 levels provide greater accuracy, sensitivity, and specificity than OCGB (79%, +5%; 90%, +5%; and 72%, +5% respectively) while glial fibrillary acidic protein (GFAP) identified CDMS with 100% specificity (+33%). A multiomics approach improved accuracy further to 90% (+16%). The measurement of a few additional CSF biomarkers could be used to complement OCGB and improve the specificity of MS diagnosis when clinical and radiological evidence of DIT is absent.

Background Treatment decisions for persons with relapsing–remitting multiple sclerosis (RRMS) rely on clinical and radiological disease activity, the benefit-harm profile of drug therapy, and preferences of patients and physicians. However, there is limited evidence to support evidence-based personalized decision-making on how to adapt disease-modifying therapy treatments targeting no evidence of disease activity, while achieving better patient-relevant outcomes, fewer adverse events, and improved care. Serum neurofilament light chain (sNfL) is a sensitive measure of disease activity that captures and prognosticates disease worsening in RRMS. sNfL might therefore be instrumental for a patient-tailored treatment adaptation. We aim to assess whether 6-monthly sNfL monitoring in addition to usual care improves patient-relevant outcomes compared to usual care alone. Methods Pragmatic multicenter, 1:1 randomized, platform trial embedded in the Swiss Multiple Sclerosis Cohort (SMSC). All patients with RRMS in the SMSC for ≥ 1 year are eligible. We plan to include 915 patients with RRMS, randomly allocated to two groups with different care strategies, one of them new (group A) and one of them usual care (group B). In group A, 6-monthly monitoring of sNfL will together with information on relapses, disability, and magnetic resonance imaging (MRI) inform personalized treatment decisions (e.g., escalation or de-escalation) supported by pre-specified algorithms. In group B, patients will receive usual care with their usual 6- or 12-monthly visits. Two primary outcomes will be used: (1) evidence of disease activity (EDA3: occurrence of relapses, disability worsening, or MRI activity) and (2) quality of life (MQoL-54) using 24-month follow-up. The new treatment strategy with sNfL will be considered superior to usual care if either more patients have no EDA3, or their health-related quality of life increases. Data collection will be embedded within the SMSC using established trial-level quality procedures. Discussion MultiSCRIPT aims to be a platform where research and care are optimally combined to generate evidence to inform personalized decision-making in usual care. This approach aims to foster better personalized treatment and care strategies, at low cost and with rapid translation to clinical practice. Trial registration ClinicalTrials.gov NCT06095271. Registered on October 23, 2023

[This corrects the article DOI: 10.3389/fimmu.2021.811351.].

Background Reliable biomarkers for predicting disease progression in multiple sclerosis (MS) are crucial for advancing precision medicine and optimising treatment strategies. This study evaluates the predictive potential of serum nuclear magnetic resonance (NMR)-based metabolomics, individually and in combination with well-established biomarkers of neuroinflammation (serum glial fibrillary acidic protein, sGFAP) and axonal damage (neurofilament light chain, sNfL), in an extreme-phenotype subset of the Swiss Multiple Sclerosis Cohort (SMSC). Methods Serum samples were analysed using NMR-based metabolomics, along with quantification of sNfL and sGFAP. Supervised multivariate analysis was performed to differentiate MS phenotypes and identify future progressors. Multivariable receiver operating characteristic (ROC) analysis evaluated predictive performance, with key metabolite findings validated in an independent Oxford MS cohort. Results NMR-based metabolomics reliably distinguishes relapsing-remitting MS (RRMS) from secondary-progressive MS (SPMS) and predicts individual transitions. The identified predictive metabolites (lipoproteins, glutamine, alanine, valine, glucose) are also associated with progression independent of relapse activity (PIRA), a clinically relevant marker of sustained disability worsening. This demonstrates that the approach can both stage disease and forecast progression irrespective of stage. ROC analysis shows strong predictive performance (AUC = 0.81, p  = 0.001), with external validation confirming robustness. Integration of NMR-metabolomics with sGFAP and sNfL further improves accuracy, yielding AUCs of 0.91 ( p  < 0.0001) and 0.87 ( p  = 0.0002), respectively, supported by independent validation. Conclusions The integration of metabolic and protein biomarkers enables both accurate staging of RRMS versus SPMS and, critically, early prediction of progression irrespective of stage. This dual capability provides a clinically actionable, serum-based tool that can refine monitoring, improve therapeutic decision-making, and support a shift towards stage-agnostic, progression-focused care in MS. Plain Language Summary Predicting how multiple sclerosis (MS) will develop is challenging but essential for guiding treatment. Disability can worsen even in the absence of relapses, and current assessment tools provide limited ability to anticipate this. In this study, we analysed blood samples from people with MS using metabolomics, a method that profiles many small molecules reflecting energy and lipid metabolism. We combined this information with two established blood proteins that indicate nerve damage and inflammation. Together, these measures distinguished between different stages of disease and identified individuals at greater risk of progression. This approach points towards the development of a simple blood test that could support more personalised care through earlier and better-informed treatment decisions. Kacerova et al., predict multiple sclerosis progression using serum metabolomics integrated with protein biomarkers. The combined approach distinguishes disease stage and identifies patients at risk of worsening disability, enabling earlier and tailored care.

BackgroundPredicting disease progression in multiple sclerosis (MS) remains challenging. PET imaging with 18 kDa translocator protein (TSPO) radioligands can detect microglial and astrocyte activation beyond MRI-visible lesions, which has been shown to be highly predictive of disease progression. We previously demonstrated that nuclear magnetic resonance (NMR)-based metabolomics could accurately distinguish between relapsing-remitting (RRMS) and secondary progressive MS (SPMS). This study investigates whether combining TSPO imaging with metabolomics enhances predictive accuracy in a similar setting.MethodsBlood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand 11C-PK11195 in Finland. Patient disability was assessed using the expanded disability status scale (EDSS) at baseline and 1 year later. Serum metabolomics was performed to identify biomarkers associated with TSPO binding and disease progression.ResultsGreater TSPO availability in the normal-appearing white matter and perilesional regions correlated with higher EDSS. Serum metabolites glutamate (p=0.02), glutamine (p=0.006), and glucose (p=0.008), detected by NMR, effectively distinguished future progressors. These three metabolites alone predicted progression with the same accuracy as TSPO-PET imaging (AUC 0.78; p=0.0001), validated in an independent cohort. Combining serum metabolite data with PET imaging significantly improved predictive power, achieving an AUC of 0.98 (p<0.0001).ConclusionMeasuring three specific serum metabolites is as effective as TSPO imaging in predicting MS progression. However, integrating TSPO imaging with serum metabolite analysis substantially enhances predictive accuracy. Given the simplicity and affordability of NMR analysis, this approach could lead to more personalised, accessible treatment strategies and serve as a valuable tool for clinical trial stratification.

We determined activation profiles of the classical and alternative complement pathway in 39 treatment‐naïve patients with early relapse‐onset MS. Plasma concentrations of complement fragments were unchanged in MS compared to 32 patients with non‐inflammatory neurological diseases. Profiles in patients experiencing clinical exacerbations did not differ from patients with stable disease and did not correlate with baseline EDSS, numbers of T2 lesions and time to second relapse. Long‐term EDSS outcomes 4 years after diagnosis did not significantly correlate with baseline complement levels. These data do not support the use of complement activation products as biomarkers for disease activity in early MS.

Background Persistence to B cell depleting therapies (BCDT) like ocrelizumab and rituximab may be higher compared with other disease-modifying therapies (DMT) in multiple sclerosis (MS). Clinical trials directly comparing these treatments are lacking. Objective To compare the risk of treatment discontinuation, relapse, and confirmed disability worsening in patients starting BCDT vs other DMT within real-world settings. Methods In a longitudinal cohort study, patients with relapsing MS starting BCDT (ocrelizumab/rituximab, n = 269) after enrolment into the Swiss MS Cohort (SMSC) were evaluated for treatment discontinuation, occurrence of relapses, and disability worsening in comparison with platform (n = 57) and oral (n = 454) DMT, or natalizumab (n = 73) using Cox regression with double robust adjustment for baseline covariates. Results Patients starting BCDT were less likely to discontinue treatment than all other DMT combined (HR = 0.26, 95% CI = 0.18–0.36, p < .01), oral DMT (HR = 0.28, 95% CI = 0.20–0.39, p < .01) and natalizumab (HR = 0.35, 95% CI = 0.21–0.58, p < .01). BCDT were associated with lower risk of relapses as compared to oral DMT HR = 0.59, 95% CI = 0.39–0.88, p < .01), but not to natalizumab (HR = 0.90, 95% CI = 0.45–1.82, p = .778). Disability worsening was not significantly different between treatment groups. Conclusion We provide real-world evidence for patients being more persistent to BCDT than to other treatments, and better clinical outcomes may partly explain this.