Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms.

While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes.

Drug library screen identifies inhibitors of toxic astrogliosis.

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disorder, in which activated immune cells directly or indirectly induce demyelination and axonal degradation. Inflammatory stimuli also change the phenotype of astrocytes, making them neurotoxic. The resulting 'toxic astrocyte' phenotype has been observed in animal models of neuroinflammation and in MS lesions.

Extensive Healthy Donor Age/Gender Adjustments and Propensity Score Matching Reveal Physiology of Multiple Sclerosis Through Immunophenotyping.

Quantifying cell subpopulations in biological fluids aids in diagnosis and understanding of the mechanisms of injury. Although much has been learned from cerebrospinal fluid (CSF) flow cytometry in neuroimmunological disorders, such as multiple sclerosis (MS), previous studies did not contain enough healthy donors (HD) to derive age- and gender-related normative data and sufficient heterogeneity of other inflammatory neurological disease (OIND) controls to identify MS specific changes. The goals of this blinded training and validation study of MS patients and embedded controls, representing 1,240 prospectively acquired paired CSF/blood samples from 588 subjects was (1) to define physiological age-/gender-related changes in CSF cells, (2) to define/validate cellular abnormalities in blood and CSF of untreated MS through disease duration (DD) and determine which are MS-specific, and (3) to compare effect(s) of low-efficacy (i.

Intrathecal, Not Systemic Inflammation Is Correlated With Multiple Sclerosis Severity, Especially in Progressive Multiple Sclerosis.

To test the hypothesis that Multiple Sclerosis (MS) patients have increased peripheral inflammation compared to healthy donors and that this systemic activation of the immune system, reflected by acute phase reactants (APRs) measured in the blood, contributes to intrathecal inflammation, which in turn contributes to the development of disability in MS. Eight serum APRs measured in a prospectively-collected cross-sectional cohort with a total of 51 healthy donors and 291 untreated MS patients were standardized and assembled into related biomarker clusters to derive global measures of systemic inflammation. The resulting APR clusters were compared between diagnostic categories and correlated to equivalently-derived cerebrospinal fluid (CSF) biomarkers of innate and adaptive immunity.

Genetic model of MS severity predicts future accumulation of disability.

No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis.