Neurology Neuroimmunology & Neuroinflammation

Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system with a complex etiology. Recent genomic studies highlight the contribution of expression quantitative trait loci (eQTLs) in modulating gene expression and disease susceptibility. Given the emerging role of circular RNAs (circRNAs) in MS, we hypothesized that genetic variants may regulate circRNA expression through circRNA-specific eQTLs (circ-eQTLs). We performed a cis-circ-eQTL analysis integrating circRNA expression and whole-genome genotyping data from 30 MS patients and 18 healthy controls using a linear regression model adjusted for disease status and sex. Candidate circ-eQTLs were prioritized based on MS-associated regions and known splicing QTLs (sQTLs) from GTEx and validated in an independent cohort (67 MS, 64 controls). Association analysis in a larger cohort (2831 MS, 3191 controls) evaluated two candidate variants for MS risk. We identified 42,077 significant cis-circ-eQTLs and validated three. Two SNPs, rs7214410 and rs11079784, modulated hsa_circ_0106983 expression, and rs7214410 also acted as an sQTL affecting EFCAB13 splicing. rs7214410 showed stronger association with MS than rs11079784. Our findings reveal extensive genetic regulation of circRNA expression and highlight rs7214410 as a dual-function variant refining the MS susceptibility locus on chromosome 17.

Background and ObjectivesMultiple sclerosis (MS) is a chronic autoimmune disease with limited treatment options. Thus, drug discovery and repurposing are essential to enhance treatment efficacy and safety. MethodsWe obtained summary statistics for protein quantitative trait loci (pQTL) of 2,004 plasma proteins and 1,443 brain proteins, a genome-wide association study (GWAS) of MS susceptibility with 14,802 cases and 26,703 controls, and expression quantitative trait loci (eQTL) for 8,000 genes in peripheral blood and 16,704 genes in brain tissue. Our integrative analysis included a proteome-wide association study to identify MS-associated proteins, followed by summary-data-based Mendelian randomization (SMR) to determine causal associations. We used the HEIDI test and Bayesian colocalization analysis to distinguish pleiotropy from linkage. Proteins passing SMR, HEIDI, and colocalization analyses were considered potential drug targets. We further conducted pathway annotations, protein-protein interaction (PPI) network analysis, and examined mRNA levels of these targets. ResultsWe identified hundreds of MS-associated proteins in plasma and brain, confirming the causal roles of 18 proteins (nine in plasma and nine in brain). Among these, we found 78 annotated pathways and 16 existing non-MS drugs targeting six proteins. We also discovered intricate PPIs among seven potential drug targets and 19 existing MS drug targets, as well as PPIs of four targets across plasma and brain. Combining expression data, we identified two targets adhering to the central dogma of molecular biology. DiscussionWe prioritized 18 potential drug targets in plasma and brain, elucidating the underlying pathology and providing evidence for drug discovery and repurposing in MS.

ImportanceThe identification of a prodrome in multiple sclerosis (MS) is the key to early prevention and the targeting of new interventions. ObjectiveTo assess the associations between health conditions diagnosed in primary care and the risk of incident MS relative to other autoimmune inflammatory diseases. DesignA case-control study in the UK and France was conducted from Jan 1, 1996 to March 28, 2022 in the UK and from Jan 4, 1998 to March 28, 2022 in France. SettingData were obtained from electronic health records from the Health Improvement Network database. ParticipantsWe included all individuals with at least two years of history in the database and a recorded diagnosis of either MS, lupus or Crohns disease. Three controls matched for sex, age at index date and year at index date were randomly assigned to each individual with a diagnosis of MS. Main outcome measuresWe agnostically tested the associations between 113 different diagnoses and multiple sclerosis diagnosis during the five years before or the five years after the diagnosis of MS. Unadjusted odds ratios (ORs) and 95% CIs were estimated, and p values were corrected for multiple comparisons. We also stratified for sex, age at diagnosis, and year of diagnosis. A logistic regression analysis (adjusted for sex and age at diagnosis) was performed to compare MS patients with lupus and Crohns disease patients. ResultsThe study population consisted of patients with MS (UK: 15,808; and France: 4,366), Crohns disease (UK: 20,872; and France: 9,605) or lupus (UK: 5,296; and France: 2,041). We identified twelve health conditions as significantly positively associated with the risk of MS. After considering health conditions suggestive of demyelinating events as the first diagnosis of MS, five health conditions remained significantly associated with MS: depression (UK OR 1.22 [95%CI 1.11-1.34]), sexual dysfunction (1.47 [1.11-1.95]), constipation (1.5 [1.27-1.78]), cystitis (1.21 [1.05-1.39]), and urinary tract infections (1.38 [1.18-1.61]). However, none of these conditions was selectively associated with MS in comparisons with both lupus and Crohns disease. During the five years after MS diagnosis, all five health conditions identified here were still associated with MS. Conclusion and relevanceThe identified symptoms may be considered to be not only prodromal, but also early-stage symptoms, albeit not specific to MS. Key pointsO_ST_ABSQuestionC_ST_ABSWhat prediagnostic manifestations of multiples sclerosis (MS) occur in primary care settings and how do they differ from those of other autoimmune diseases? ResultsIn this agnostic study of 15,808 MS patients from the UK and 4,366 MS patients from France, we identified five health conditions as positively associated with the risk of MS when recorded in the five years preceding MS diagnosis. We show that most of these health conditions were also present in the early presentations of lupus and Crohns disease. MeaningOur findings suggest that the prodromal phase of MS is largely similar to the prediagnostic manifestations of other autoimmune diseases.

While current treatments of multiple sclerosis (MS) effectively inhibit formation of focal lesions and relapses, most patients experience progression independent of relapse activity (PIRA). To understand PIRA, we analyzed nine prospectively acquired clinical and imaging outcomes in 176 relapsing-remitting and 215 progressive MS patients and 45 healthy volunteers, along with matched cellular and >5000 protein data in 1,042 cerebrospinal fluid (CSF) samples. Regressing out physiological aging and sex effects identified MS-related processes. Among these, compartmentalized inflammation and its effector mechanisms such as pyroptosis showed the strongest association with MS severity, irrespective of clinical categorization of patients. However, molecular processes affected localization of CNS injury: patients with predominant brain damage had proportionally higher neuroinflammation, while fibrosis and tissue hypoxia were linked to principal involvement of spinal cord. We did not identify inflammation-unrelated neurodegeneration; instead, CNS-related processes were beneficial, such as synaptogenesis. Machine learning-based CSF biomarker models predicted nine clinical and volumetric imaging outcomes in the independent cohort with accuracy exceeding published MS models. These data show intra-individual diversity of putative disease mechanisms in MS and implicate processes related to compartmentalized neuroinflammation as leading candidate mechanisms of PIRA. Future drug development should include CNS-penetrant anti-inflammatory agents.

BackgroundDimethyl fumarate (DMF) and ocrelizumab are two effective immunomodulators for multiple sclerosis (MS) with distinct mechanisms of action. Identifying overlapping therapeutic effects between both agents may elucidate common pathways in preventing disease activity. ObjectivesIn this study we analyzed cytokine and immune-profiling data to evaluate the similarities and differences between the two disease-modifying therapies for MS. Methods: Plasma and PBMCs from MS patients were collected at baseline, 3 months and 6 months after treatment with DMF (n=16) and ocrelizumab (n=13). Immunophenotyping was performed with mass cytometry (CyTOF) and analyzed with gating based on cell surface markers. Cytokine analysis from plasma was performed with Olink assays and analyzed with linear mixed effects models. ResultsDMF reduced both effector T and memory B cell populations while increasing CD56bright natural killer (NK) cells. Ocrelizumab exerted its main immunomodulatory effect by reducing the frequency of all B cells and increasing frequency of NK cells. At 6 months, naive B-cells began to reconstitute; however, memory B cells remain depleted. DMF treatment was associated with a significant reduction of plasma cytokines involved in inflammatory pathways, such as IL-6, IL-12, and Dectin-1 signaling. In addition, DMF lowered plasma cytokines that are dysregulated in psoriasis and involved in allograft rejection pathways. Ocrelizumab treatment led to the upregulation of neurotropic proteins in the plasma of MS patients, including proteins involved in NAD+ biosynthesis and tryptophan catabolism. ConclusionOur high-dimensional immunophenotyping results suggest that to exert their effects on MS patients, DMF and ocrelizumab both increase NK cells in addition to affecting different immune cell populations and cytokine pathways. Detecting similarities between the mechanisms of the two drugs may contribute to identifying more specific therapeutic targets.

BackgroundMultiple sclerosis (MS) is an inflammatory disease characterized by demyelination and neuro-axonal degeneration in the central nervous system. Except for neurofilament light protein, identification of biomarkers has been difficult to assess in the blood, presumably due partly to sensitivity. To detect traces of disease activities in the periphery and identify low-abundance protein biomarkers, this study conducts an exploratory examination of the plasma proteome of MS using proximity extension technology, a high-sensitivity multiplex PCR-based immunoassay. MethodsA case-control cohort consisting of 52 MS cases (relapsing-remitting=30, progressive=22) and 17 healthy controls were enrolled at the Karolinska University Hospital. EDTA plasma was analyzed for 1157 unique protein targets across thirteen proximity extension assays. Protein associations to disease outcomes and related clinical measures were assessed using a multivariable linear regression model corrected for sex and age at sampling. ResultsAHCY and CHR levels were higher among MS cases than controls, while FABP2 was lower among those with relapsing-remitting disease than controls (Pdiscovery<0.05, Preplication<0.05), although not significant after multiple test corrections. Furthermore, PTN and CYR61 levels were higher in progressive MS than in relapsing-remitting disease (P<0.0002, PFDR<0.05), and CRNN and CXCL13 were associated with more severe disability at sampling (P<0.0001, PFDR<0.05), independent of disease course. CTSF was positively correlated with disease duration (P=4.1x10-5, PFDR=0.044), while RRM2B level correlated with intrathecal immunoglobulin production (IgG Index) in relapsing-remitting MS (P=1.7x10-5, PFDR=0.018). ConclusionWe provide several candidates for characterizing MS, particularly progressive disease, which may help monitor disease progression and treatment response in a clinical setting.

Background and ObjectivesOral cladribine tablet (CladT) therapy is efficacious for relapsing multiple sclerosis (MS). However, the mechanisms by which cladribine exerts benefit in MS remain unclear, particularly regarding its effects on compartmentalized inflammation within the cerebrospinal fluid (CSF). MethodsTranscriptional profiles along with T and B lymphocyte receptor repertoires from CSF and blood were obtained by single cell sequencing methods from a single site participating in a phase IV clinical trial investigating the impact of cladribine treatment for MS. All subjects provided blood and CSF samples immediately before starting CladT therapy and were randomized to also provide samples at either five weeks, ten weeks, one year, or two years post-CladT therapy. Thirty-four samples from 13 individuals with relapsing MS before and after treatment were available to test the hypothesis that CladT alters the composition and phenotype of lymphocytes in the CSF. ResultsWe found that treatment with CladT profoundly altered cellular composition, but not the transcriptional phenotype, of immune cells in the CSF. In particular, we identified a reduction of switched memory B cells but recovery of naive B cells in the CSF, similar to our findings in blood. Additionally, populations of CD4 Treg cells emerged early after CladT therapy and remained elevated one year later in the CSF, but not in the blood. Antigen receptor sequencing revealed a moderate decrease in numbers of large clonally expanded CD8 T cell clones (>10 cells/clone) primarily in the CSF, but also in the blood post-CladT treatment. DiscussionOur results identified unique cellular dynamics and changes in T and B cell clonality in both tissues which can potentially explain long-term beneficial effects of CladT therapy in MS, including preservation of immune function and relatively low number of side effects. Altogether, this study demonstrates that CladT treatment had a substantial impact not only on blood, but also on the CSF compartment highlighting the importance of cross-tissue analysis for better understanding of effect and the mechanism of action of DMTs.

Multiple sclerosis (MS), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and neuromyelitis optica spectrum disorder (NMOSD) are immune-mediated inflammatory disorders of the central nervous system (CNS). The temporal relationship between disease-specific autoantibodies and biomarkers of CNS injury before diagnosis remains unclear and is relevant for understanding early pathobiology. Here, we conducted a multicentre retrospective longitudinal case-control study using prediagnostic plasma from 362 individuals who later developed MS, 145 who developed MOGAD, and 60 who developed NMOSD. Plasma IgG levels against CNS antigens, MOG, and AQP4, as well as neurofilament light chain (pNfL), were quantified, and temporal relationships between immune activation, neuroaxonal injury, and clinical disease onset were modelled using linear mixed-effects models and survival analyses. In MS, EBNA-1-specific and CNS-cross-reactive IgG were elevated up to 77.8 months before diagnosis, preceding pNfL increases by 44.9 months. In NMOSD, AQP4-IgG seroconversion occurred 32.5 months before diagnosis and preceded pNfL elevations by 40.4 months. In MOGAD, pNfL elevations preceded MOG-IgG seroconversion by 11.2 months. Thus, in MS and NMOSD, humoral autoimmunity precedes detectable CNS injury, whereas in MOGAD, neuroaxonal injury occurs before circulating MOG-IgG. These distinct temporal patterns suggest differing early immunopathological trajectories and may provide a framework for future studies of early disease biology and biomarker-guided risk stratification.

BackgroundMultiple Sclerosis (MS) is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study (GWAS) findings in MS into therapeutics and effective preventive strategies has been limited to date. MethodsWe used Summary Data-Based Mendelian Randomisation (SMR) to synthesise findings from public expression quantitative trait locus (eQTL; eQTLgen and CAGE), methylation quantitative trait locus (mQTL; Lothian Birth Cohort and Brisbane Systems Genetics Study), and MS GWAS datasets (International Multiple Sclerosis Genetics Consortium). By correlating the effects of methylation on MS (M-2-MS), methylation on expression (M-2-E), and expression on MS susceptibility (E-2-MS), we prioritise genetic loci with strong evidence of causally influencing MS susceptibility. We overlay these findings onto a list of druggable genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and STRING to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr Virus-infected B cells, Lymphoblastoid Cell Lines (LCLs). We conducted a systematic review of prioritised genes using the Open Targets platform to identify completed and planned trials targeted prioritised genes in MS and related disease areas. ResultsExpression of 45 genes in peripheral was strongly associated with MS susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leukocyte signalling. We refined this prioritised gene list by restricting to loci where CpG site methylation was associated with MS susceptibility (M-2-MS), with gene expression (M-2-E), and where expression was associated with MS susceptibility (E-2-MS). This approach yielded a list of 15 prioritised druggable target genes for which there was evidence of a causal pathway linking methylation, expression, and MS. Five of these 15 genes are targeted by existing drugs (CD40, ERBB2, VEGFB, MERTK, and PARP1), and three were replicated in a smaller eQTL dataset (CD40, MERTK, and PARP1). In LCLs, SMR prioritised 7 druggable gene targets, of which only one was priortised by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritised genes in disorders related to MS. ConclusionsWe use public datasets and SMR to identify a list of prioritised druggable genetic targets in Multiple Sclerosis. We hope our findings could be translated into effective repurposing of existing drugs to provide novel therapies for MS and, potentially, provide a platform for developing preventive therapies.

Multiple sclerosis (MS) is a tissue-specific autoimmune disease of the central nervous system in which the antigen(s) remains elusive. Antibodies targeting the flotillin-1/2 (FLOT-1/2) complex have been described in 1-2% of the patients in a recent study. Other candidate antigens as anoctamin-2 (ANO2) or neurofascin-155 (NF155) have been previously described in MS patients, although their clinical relevance remains uncertain. Our study aims to analyse the frequency and clinical relevance of antibodies against NF155, ANO2 and the FLOT-1/2 complex in MS. Serum (n=252) and CSF (n=50) samples from 282 MS patients were included in the study. The control group was composed of 260 serum samples (71 healthy donors and 189 with other neuroinflammatory disorders). Anti-FLOT1/2, anti-ANO2 and anti-NF155 antibodies were tested by cell-based assays using transfected-HEK293 cells. We identified 6 MS patients with antibodies against the FLOT-1/2 complex (2.1%) and 1 MS patient with antibodies against ANO2 (0.35%). All MS patients were negative for anti-NF155 antibodies. Three of the anti-FLOT1/2 positive patients showed anti-FLOT-1/2 positivity in other serum samples extracted at different moments of their disease. IgG subclasses of anti-FLOT-1/2 antibodies were predominantly IgG1 and IgG3. We confirm that antibodies targeting the Flotillin-1/2 complex are present in a subgroup of patients with MS. Further studies are needed to understand the clinical and pathological relevance of anti-FLOT-1/2 autoantibodies in MS.

Spinal cord pathology is a major determinant of irreversible disability in progressive multiple sclerosis. The demyelinated lesion is a cardinal feature. The well-characterised anatomy of the spinal cord and new analytic approaches allows the systematic study of lesion topography and its extent of inflammatory activity unveiling new insights into disease pathogenesis. We studied cervical, thoracic, and lumbar spinal cord tissue from 119 pathologically confirmed multiple sclerosis cases. Immunohistochemistry was used to detect demyelination (PLP) and classify lesional inflammatory activity (CD68). Prevalence and distribution of demyelination, staged by lesion activity, was determined and topographical maps were created to identify patterns of lesion prevalence and distrtibution using mixed models and permutation-based voxelwise analysis. 460 lesions were observed throughout the spinal cord with 76.5% of cases demonstrating at least 1 lesion. The cervical level was preferentially affected by lesions. 58.3% of lesions were inflammatory with 87.9% of cases harbouring at least 1 inflammatory lesion. Topographically, lesions consistently affected the dorsal and lateral columns with relative sparing of subpial areas in a distribution mirroring the vascular network. The presence of spinal cord lesions and the proportion of active lesions related strongly with clinical disease milestones, including time from onset to wheelchair and onset to death. We demonstrate that spinal cord demyelination is common, highly inflammatory, has a predilection for the cervical level, and relates to clinical disability. The topography of lesions in the dorsal and lateral columns and relative sparing of subpial areas points to a role of the vasculature in lesion pathogenesis, suggesting short-range cell infiltration from the blood and signaling molecules circulating in the perivascular space incite lesion development. These findings challenge the notion that end-stage progressive multiple sclerosis is burnt out and an outside-in lesional gradient predominates in the spinal cord. Taken together, this study provides support for long-term targeting of inflammatory demyelination in the spinal cord and nominates vascular dysfunction as a potential target for new therapeutic approaches to limit irreversible disability.

Multiple sclerosis (MS) is associated with vascular abnormalities including blood-brain-barrier disruption, neurovascular uncoupling and cerebral hypoperfusion. As pericytes regulate vascular integrity and capillary diameter, pericyte dysfunction may contribute to MS pathophysiology. To investigate this, we generated induced pluripotent stem cell-derived pericytes (iPericytes) from individuals with or without relapsing-onset MS (RoMS). RoMS iPericytes exhibited intrinsic, cell-autonomous alterations in gene expression, were larger, and responded poorly to the vasoconstrictor, endothelin-1. Despite intrinsic differences, Control and RoMS iPericytes phagocytosed myelin debris and responded robustly to lesion-relevant stimuli. Hypoxia induced iPericyte proliferation, elongation and angiogenic and immune-related gene expression, while exposure to MS-relevant cytokines (TNF and IFN{gamma}) exacerbated the endothelin-1-evoked contraction. Both RoMS and cytokine exposure altered the expression of calcium and myosin signalling genes downstream of endothelin-1, including the key contractile regulator MYLK. These data support intrinsic pericyte dysfunction and signalling within MS-lesions as drivers of hypoperfusion and neurodegeneration in MS.

BackgroundPredicting response to treatment and risk of long-term disability in multiple sclerosis (MS) is challenging. In other disease areas, combining genetic risk variants enabled detection of relevant clinical endophenotypes associated with important outcomes, but this has never been applied to MS. MethodsWe applied unsupervised hierarchical clustering to genomic risk scores of two cohorts (the prospective cohort study of 1455 Welsh MS patients was used as the discovery cohort and replication was performed in a multi-centre post-mortem Netherlands Brain Bank cohort of 272 MS patients) to predict relevant disease outcomes using survival analysis for time to disability milestones (expanded disability status scale, EDSS), and ANOVA to compare linear clinical outcomes. ResultsThree genomic clusters were identified, in each cluster patients had similar genetic profiles. Baseline demographic characteristics were similar between clusters. Welsh patients in cluster 1 attained key disability milestones later, reaching EDSS6, 6 years later (p=0.003) and EDSS8, 13 years later (p=0.02) than those in clusters 2 and 3. Time to EDSS6 was also significantly longer for patients in cluster 1 versus cluster 2 in the NBB-MS cohort (6 years, p=0.04). Genomic clustering is an independent predictor for disease progression compared with well-validated risk factors (Hazard ratio for time to EDSS6 1.3-2.0, all p<0.05). Welsh patients in cluster 2 and 3 also had a significantly greater annual increase in T2 lesion load on serial MR imaging (p=0.04). In cluster 2, patients who had received MS disease modifying treatments (DMT) had a longer time to EDSS6 (p=0.003) compared to those that had received no DMTs, whereas no differences were observed in either cluster 1 or cluster 3. In the NBB-MS cohort, we also observed differences in symptomatology, including earlier development of swallowing problems (p=0.02) or muscle spasticity (p= 0.0008) in cluster 2 patients. ConclusionThis study demonstrates that unsupervised genetic clustering has utility to detect clinically relevant endophenotypes of MS, with genetic cluster 2 patients having a more severe phenotype and higher risk of disability. Moreover, genetic stratification is able to predict response to DMTs and could potentially be used for precision medicine in MS management.

Peripheral B cell depletion via anti-CD20 treatment is a highly effective disease-modifying treatment in multiple sclerosis (MS) patients. A drawback of anti-CD20 treatment is poor immune responses to vaccination. While this can be mitigated by treatment interruption of at least six months prior to vaccination, the timing to resume treatment while maintaining subsequent vaccine responses remains undetermined. We characterized SARS-CoV-2 S-directed antibody and B cell responses throughout three BNT162b2 doses in MS patients, where the first two doses were given during treatment interruption. The last anti-CD20 rituximab infusion was given 1.3 years (median) prior to the first vaccine dose and re-administered four weeks after the second vaccine dose. After two vaccine doses, antibody-mediated responses in SARS-CoV-2-naive MS patients were comparable to vaccinated healthy controls, albeit with greater variation. We could demonstrate that the response to the second dose of vaccination was predictive of a boost effect after a third dose, even after re-initiation of rituximab. MS patients also exhibited lower frequencies of Decay Accelerating Factor-negative memory B cells, a suggested proxy for germinal centre activity, than healthy individuals. Our findings also offer a first indication on the potential importance of antigenic stimulation of CD27-IgD- double negative B cells and the possible long-term impairment of germinal centre activity in rituximab-treated MS patients.

ImportanceThe risk of multiple sclerosis (MS) is significantly influenced by polygenic inheritance. Polygenic risk scores (PRS) for MS can help identify high-risk individuals and stratify populations for clinical trials. However, most genome-wide association studies (GWAS) have been conducted in populations of European ancestry, raising questions about the accuracy of these PRS in other ancestries. ObjectiveTo determine whether a PRS for MS can effectively stratify individuals of non-European ancestries. Design, setting and participantsThis cross-sectional study utilized prospectively collected data from the All of Us Research Program (2018-2023). It included participants who had both whole genome sequencing and electronic health record (EHR) data. Exposure(s)A PRS comprising 282 independent single nucleotide variants for MS, divided into quintiles. Main Outcome(s) and Measure(s)Prevalence of multiple sclerosis ascertained through ICD-10 or SNOMED codes. ResultsIn this study population, MS cases comprised 1.0% (327 cases) of the European population, 0.56% (183 cases) of the African population, and 0.46% (150 cases) of the Latino/admixed American population. In analyses adjusting for age, sex, and genetic principal components, the PRS associates with MS risk in the European population, with a 141% increase in the risk of MS for individuals in the highest compared to the lowest PRS quintile (OR: 2.41 [1.69-3.50], test-for-trend p<0.001). Similarly, the PRS appropriately partitions the Latino/admixed population into increasing MS risk groups (OR: 2.56 [1.45-4.78], test-for-trend p-value <0.001). However, it did not significantly stratify the African population into distinct MS risk categories (OR: 1.45 [0.95-2.25], test-for-trend p=0.10). Conclusions and RelevanceA PRS for MS effectively stratified individuals of European and Latino/admixed ancestries but not African ancestry. This highlights the need for ancestry-specific PRS development to ensure accurate risk prediction across diverse populations, emphasizing the importance of including non-European groups in MS genetic research.

ObjectiveThe field of multiple sclerosis (MS) has seen a tremendous expansion of treatments in the past decade. However, treatment response in individual patients can currently be determined only by waiting for breakthrough disease activity to occur. This highlights a critical need for biomarkers that can predict treatment response and stratify the risk of impeding disease activity before damage is inflicted to the CNS. Here we show that CCR6+CD3+ T-cell surveillance in peripheral blood can be used to discriminate responders and non-responders to dimethyl-fumarate. MethodsA cohort of 101 treatment-naive, dimethyl-fumarate (DMF) treated MS patients and healthy controls was immunophenotyped and then responders and non-responders were determined retrospectively after clinical and radiographic follow up. Receiver operating characteristic (ROC) curve, linear and logistic regression, mixed effects models, and cox proportional hazards were used for the analysis. ResultsAmong various clinical and immunophenotypic metrics, the percentage of CCR6+CD3+ T-cells was the most significant predictor of impending disease activity. This immunophenotypic metric was able to discriminate responders and non-responders to DMF with an area under the ROC of 0.85 (95% CI: 0.71-0.99), which was higher than that achieved using surrogate metrics for T-helper-1-like T-helper-17 or T-cytotoxic-17 cells. DMF-treated patients with the highest percentage of CCR6+CD3+ T-cells had a significantly higher risk of impending disease activity compared to patients with a low percentage. InterpretationChanges in CCR6+CD3+ T-cells in the periphery could precede disease activity by many months and potentially serve as an early biomarker of treatment response, at least for DMF. These results have implications for novel personalized treatment strategies in MS.

ObjectiveSerum neurofilament light chain (sNFL) is becoming an important biomarker of neuroaxonal injury. While sNFL correlates with cerebrospinal fluid NFL (cNFL), 40-60% of variance remains unexplained. Assuming that for diseases of the central nervous system (CNS), such as multiple sclerosis (MS), the cNFL better reflects CNS injury, our goal was to develop and validate adjustment of sNFL for relevant confounders, to strengthen cNFL-sNFL correlations. MethodsWe used 1,378 matched cNFL-sNFL pairs divided into training and validation cohort with matching data on 11 confounders, neuroexam, and magnetic resonance imaging (MRI). The effect of confounders on cNFL-sNFL relationship was tested using multiple linear regression (MLR) model. Propensity score matching was used to identify effect of spinal cord damage on sNFL levels. ResultsIn the training cohort (n=898) we correlated 11 confounders with the residuals from cNFL-sNFL linear regression. Four non-overlapping confounders explaining highest proportion of variance (12%: age, 8.7%: blood urea nitrogen, 3%: alkaline phosphatase, and 3.9%: weight) were used in MLR model. The model strengthened the cNFL-sNFL correlation from R2 = 0.52 to 0.64 in the independent validation cohort and strengthened correlation of adjusted sNFL with number of contrast-enhancing lesions (from R2 0.11 to 0.18). However, only sNFL, but not cNFL correlated with MS severity outcomes. Using propensity score matching, we demonstrated that subjects with proportionally higher sNFL to cNFL levels have significantly higher clinical and radiological evidence of spinal cord injury. InterpretationSuperiority of sNFL likely resides in the release of NFL from axons of lower motor or dorsal ganglia neurons directly to blood.

Multiple sclerosis (MS) is a heterogeneous disorder with regards to clinical presentation and pathophysiology. Stratification into biologically distinct subgroups could enhance prognostication and efficacious allocation to disease-modifying therapies. In this study, we identified MS subgroups by performing a clustering analysis on neuropathology data collected for MS donors in the Netherlands Brain Bank (NBB) autopsy cohort. The input dataset contained detailed information on white matter lesion load, the proportion of active, mixed active/inactive, inactive and remyelinating lesions, microglia morphology in these lesions, and the presence of microglial nodules, perivascular cuffs and cortical lesions for 228 donors. A factor analysis was performed to reduce noise and redundancy prior to hierarchical clustering with K-means consolidation. Four subgroups with distinct patterns of white matter lesions were identified. These were subsequently validated with additional clinical, neuropathological and genetic data. The subgroups differed with regards to disease progression and duration, the timing of motor, sensory and other relevant signs and symptoms, patterns of cortical lesions and the presence of B cells. Age at MS onset and sex, previously associated with milder forms of MS, did not differ between the subgroups; the subgroups could also not be distinguished based on the manifestation of clinical signs and symptoms. The available genetic data was used to calculate MS polygenic risk scores (PRSs) for donors included in the NBB cohort. The MS PRS did not differ between the subgroups, but was significantly correlated with the first and second dimension of the factor analysis, the latter lending genetic support to our subdivision. Taken together, these findings suggest a complex relationship between neuropathological subgroups and clinical characteristics, indicating that post-mortem cohort studies are critical to better stratify patients and understand underlying neuropathophysiological mechanisms, in order to ultimately achieve personalised medicine in MS.

ObjectiveBiomarkers could inform disease worsening and severity in people with MS (pwMS). Few studies have examined blood biomarkers informative of patient-reported outcome (PRO) of disability in pwMS. In this study we examine the associations between serum protein biomarker profiles and patient-reported disability in pwMS. MethodsThis cross-sectional study included adults with a neurologist-confirmed diagnosis of MS from the University of Pittsburgh Medical Center (Pittsburgh, PA) and the Rocky Mountain MS Clinic (Salt Lake City, Utah) between 2017 and 2020. For exposure, we included 19 serum protein biomarkers potentially associated with MS inflammatory disease activity and 7 key clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying treatment, and time interval between sample collection and closest PRO assessment). Using 6 machine learning approaches (Least Absolute Shrinkage and Selection Operator [LASSO] regression, Random Forest [RF], XGBoost, Support-Vector Machines [SVM], stacking ensemble learning, and stacking classification algorithm), we examined model performance in predicting Patient Determined Disease Steps (PDDS) as the primary outcome. We assessed model prediction of Patient-Reported Outcomes Measurement Information System (PROMIS) physical function in a subgroup. We reported model performance using the held-out testing set. ResultsWe included 431 unique participants (mean age 49 years, 81% women, 94% non-Hispanic White). Using binary outcomes, models comprising both routine clinical factors and the 19 proteins as features consistently outperformed base models (containing clinical features alone or clinical features plus single protein) in predicting severe (PDDS[&ge;]4, PROMIS<35) versus mild/moderate (PDDS<4, PROMIS[&ge;]35) disability for all machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS=0.91, AUCPROMIS=0.90). Using ordinal/continuous outcomes, LASSO models with combined clinical factors and 19 proteins as features (R2PDDS=0.31, R2PROMIS =0.35) again outperformed base models. The four LASSO models (PDDS, PROMIS; both binary and ordinal/continuous) with combined clinical and protein features shared 2 clinical features (disease subtype, disease duration) and 4 protein biomarkers (CDCP1, IL-12B, NEFL, PRTG). ConclusionsSerum protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus individual protein biomarker, reaching clinically actionable performance.

Despite important advances in understanding the etiopathology of multiple sclerosis, factors determining disease progression remain partially understood and often difficult to predict. Specific diagnostic and prognostic biomarkers are needed to optimize the risk-benefit ratio of treatment for each patient. The aim of our study was to identify a cerebrospinal fluid proteomic signature associated with diagnosis and short- to mid-term prognosis across the multiple sclerosis continuum. Our multicentric cohort study analyzed CSF samples from 120 patients using a proteomics data-independent acquisition strategy. Differentially expressed proteins were identified across diagnostic groups: 62 patients with multiple sclerosis, 15 patients with clinically isolated syndrome, and 43 healthy controls. We also compared the CSF of patients with no evidence of disease activity with those with disease activity at 2 and 5 years of follow-up. A diagnostic and prognostic classification model was built using iterative cross-validated logistic regression models on shared differentially expressed proteins across these two comparisons. A total of 1,257 proteins were quantified, and 162 differentially expressed proteins were identified across comparisons. We identified a set of ten proteins associated with the diagnosis and prognosis of multiple sclerosis, including previously identified potential biomarkers (CH3L2, IGHG1, IGKC, LAMP2, ADA2), proteins known to be involved in the pathophysiology of multiple sclerosis (A0A8J8YUT9, AT2A2, CO3A1) and two yet unreported proteins (DSC2 and MMRN2). Multivariate models based on these proteins achieved good accuracy for the diagnosis of MS compared with CIS (area under the receiver operating characteristics curve [AUROC] up to 80% using 3 proteins) and prognosis (NEDA vs. EDA; AUROC up to 96% at 2 and 5 years; using 5 proteins). These results, which will require further investigation to validate the new biomarkers, open new perspectives on multiple sclerosis pathophysiology and therapeutic targets.