Substantial MRI activity, particularly if in combination with clinical relapses, during the first year of treatment with interferon-β indicates significant risk of treatment failure and EDSS worsening in the short term.

This study identifies and validates the role of rs9828519, an intronic variant in SLC9A9, in IFNβ-treated subjects, demonstrating a successful pharmacogenetic screen in MS. Functional characterization suggests that SLC9A9, an Na(+) -H(+) exchanger found in endosomes, appears to influence the differentiation of T cells to a proinflammatory fate and may have a broader role in MS disease activity, outside of IFNβ treatment.

for the Italian MS registry IMPORTANCE Availability of new disease-modifying therapies (DMTs) and changes of therapeutic paradigms have led to a general improvement of multiple sclerosis (MS) prognosis in adults. It is still unclear whether this improvement also involves patients with pediatric-onset MS (POMS), whose early management is more challenging. OBJECTIVE To evaluate changes in the prognosis of POMS over time in association with changes in therapeutic and managing standards. DESIGN, SETTING, AND PARTICIPANTS Retrospective, multicenter, observational study. Data were extracted and collected in May 2019 from the Italian MS Registry, a digital database including more than 59 000 patients. Inclusion criteria were MS onset before age 18 years, diagnosis before January 2014, and disease duration of at least 3 years. Exclusion criteria were primary progressive MS, Expanded Disability Status Scale (EDSS) score of at least 8 one year after onset, unavailability of diagnosis date, and less than 2 EDSS score evaluations. Eligible patients were 4704 patients with POMS. According to these criteria, we enrolled 3198 patients, excluding 1506. EXPOSURES We compared time to reach disability milestones by epoch of MS diagnosis (<1993 , 1993-1999, 2000-2006, and 2007-2013), adjusting for possible confounders linked to EDSS evaluations and clinical disease activity. We then analyzed the difference among the 4 diagnosis epochs regarding demographic characteristics, clinical disease activity at onset, and DMTs management.MAIN OUTCOMES AND MEASURES Disability milestones were EDSS score 4.0 and 6.0, confirmed in the following clinical evaluation and in the last available visit. RESULTSWe enrolled 3198 patients with POMS (mean age at onset, 15.2 years; 69% female; median time to diagnosis, 3.2 years; annualized relapse rate in first 1 and 3 years, 1.3 and 0.6, respectively), with a mean (SD) follow-up of 21.8 (11.7) years. Median survival times to reach EDSS score of 4.0 and 6.0 were 31.7 and 40.5 years. The cumulative risk of reaching disability milestones gradually decreased over time, both for EDSS score of 4.0 (hazard ratio [HR],

Objective:To perform systematic transcriptomic analysis of multiple sclerosis (MS) risk genes in peripheral blood mononuclear cells (PBMCs) of subjects with distinct MS stages and describe the pathways characterized by dysregulated gene expressions.Methods:We monitored gene expression levels in PBMCs from 3 independent cohorts for a total of 297 cases (including clinically isolated syndromes (CIS), relapsing-remitting MS, primary and secondary progressive MS) and 96 healthy controls by distinct microarray platforms and quantitative PCR. Differential expression and pathway analyses for distinct MS stages were defined and validated by literature mining.Results:Genes located in the vicinity of MS risk variants displayed altered expression in peripheral blood at distinct stages of MS compared with the healthy population. The frequency of dysregulation was significantly higher than expected in CIS and progressive forms of MS. Pathway analysis for each MS stage–specific gene list showed that dysregulated genes contributed to pathogenic processes with scientific evidence in MS.Conclusions:Systematic gene expression analysis in PBMCs highlighted selective dysregulation of MS susceptibility genes playing a role in novel and well-known pathogenic pathways.

A lower baseline MRI and clinical activity have been identified as predictors of DMT efficacy in patients with RRMS in routine clinical practice. Evaluation of clinical and MRI activity at 1 year is recommended to monitor patients over time.

Our study confirmed, in a daily clinical setting, that MRS had a better specificity and accuracy than RS in identifying the patients who will have a poor response to long-term IFN-β treatment.

Objective:To track the effects of fingolimod, an approved drug for multiple sclerosis (MS), on the activation of myeloid cells from the periphery to the CNS.Methods:In vitro and ex vivo immunologic studies coupled with flow cytometry were performed to evaluate the action of fingolimod on lipopolysaccharide (LPS)–induced expression of activation markers in human monocytes from healthy participants, participants with untreated MS, and participants with fingolimod-treated MS. In vivo administration of fingolimod during experimental autoimmune encephalomyelitis (EAE) was established to verify the activation state of splenic, CNS infiltrating, and CNS resident myeloid cells ex vivo at flow cytometer.Results:We found that in vitro exposure of human monocytes to fingolimod inhibited LPS-induced CD25 and CD150 expression and tumor necrosis factor–α (TNF-α) secretion without altering immune cell survival. Further, EAE treatment with fingolimod led to reduced amounts of TNF-α produced by myeloid cells in vivo in the spleen and CNS. Finally, while displaying normal induction of CD25 and CD150 levels at high LPS concentration, monocytes from patients with fingolimod-treated MS showed significantly higher activation threshold at suboptimal LPS stimulation than controls.Conclusions:The inhibition of myeloid cell activation may be part of the immunosuppressive action of fingolimod and take place in the periphery and in the CNS.

Summary Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS.