The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e−06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.

Objective Biomarkers aid diagnosis, allow inexpensive screening of therapies and guide selection of patient-specific therapeutic regimens in most internal medicine disciplines. In contrast, neurology lacks validated measurements of the physiological status, or dysfunction(s) of cells of the central nervous system (CNS). Accordingly, patients with chronic neurological diseases are often treated with a single disease-modifying therapy without understanding patient-specific drivers of disability. Therefore, using multiple sclerosis (MS) as an example of a complex polygenic neurological disease, we sought to determine if cerebrospinal fluid (CSF) biomarkers are intra-individually stable, cell type-, disease- and/or process-specific and responsive to therapeutic intervention. Methods We used statistical learning in a modeling cohort (n=225) to develop diagnostic classifiers from DNA-aptamer-based measurements of 1128 CSF proteins. An independent validation cohort (n=85) assessed the reliability of derived classifiers. The biological interpretation resulted from in-vitro modeling of primary or stem cell-derived human CNS cells and cell lines. Results The classifier that differentiates MS from CNS diseases that mimic MS clinically, pathophysiologically and on imaging, achieved a validated area under receiver-operator characteristic curve (AUROC) of 0.98, while the classifier that differentiates relapsing-remitting from progressive MS achieved a validated AUROC of 0.91. No classifiers could differentiate primary-from secondary-progressive MS better than random guessing. Treatment-induced changes in biomarkers greatly exceeded intra-individual- and technical variabilities of the assay. Interpretation CNS biological processes reflected by CSF biomarkers are robust, stable, and disease- or even disease-stage specific. This opens opportunities for broad utilization of CSF biomarkers in drug development and precision medicine for CNS disorders.

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. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression.

ObjectiveTo develop a sensitive neurological disability scale for broad utilization in clinical practice.MethodsWe employed advances of mobile computing to develop an iPad‐based App for convenient documentation of the neurological examination into a secure, cloud‐linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician‐ and App‐computed traditional clinical scales.ResultsIn the cross‐sectional comparison of 906 neurological examinations, the variance between App‐computed and clinician‐scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)‐trained clinicians. By eliminating rating ambiguity, App‐computed scales achieved greater accuracy in measuring disability progression over time (n = 191 patients studied over 880.6 patient‐years). The NeurEx score had no apparent ceiling effect and more than 200‐fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5.InterpretationNeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.

Background: Once multiple sclerosis (MS) reaches the progressive stage, immunomodulatory treatments have limited efficacy. This suggests that processes other than activation of innate immunity may at least partially underlie disability progression during late stages of MS. Pathology identified these alternative processes as aberrant activation of astrocytes and microglia, and subsequent degeneration of oligodendrocytes and neurons. However, we mostly lack biomarkers that could measure central nervous system (CNS) cell-specific intrathecal processes in living subjects. This prevents differentiating pathogenic processes from an epiphenomenon. Therefore, we sought to develop biomarkers of CNS cell-specific processes and link them to disability progression in MS.Methods: In a blinded manner, we measured over 1000 proteins in the cerebrospinal fluid (CSF) of 431 patients with neuroimmunological diseases and healthy volunteers using modified DNAaptamers (SOMAscan®). We defined CNS cell type-enriched clusters using variable cluster analysis, combined with in vitro modeling. Differences between diagnostic categories were identified in the training cohort (n = 217) and their correlation to disability measures were assessed; results were validated in an independent validation cohort (n = 214).Results: Astrocyte cluster 8 (MMP7, SERPINA3, GZMA and CLIC1) and microglial cluster 2 (DSG2 and TNFRSF25) were reproducibly elevated in MS and had a significant and reproducible correlation with MS severity suggesting their pathogenic role. In vitro studies demonstrated that proteins of astrocyte cluster 8 are noticeably released upon stimulation with proinflammatory stimuli and overlap with the phenotype of recently described neuro-toxic (A1) astrocytes.

Objective: 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.Methods: 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. Finally, correlations were calculated between biomarkers of systemic and intrathecal inflammation and MS severity measures, which predict future rates of disability progression.Results: While two blood APR clusters were elevated in MS patients, only one exhibited a weak correlation with MS severity. All CSF inflammation clusters, except CSF albumin, correlated with at least one measure of MS severity, with biomarkers of humoral adaptive immunity exhibiting the strongest correlations, especially in Progressive MS.Conclusion: Systemic inflammation does not appear to be strongly associated with intrathecal inflammation in MS. Positive correlations between markers of intrathecal inflammation, especially of humoral immunity, with MS severity measures support a pathogenic role of intrathecal (compartmentalized) inflammation in central nervous system tissue destruction, including in Progressive MS.

The inability to measure putative pathogenic processes in the central nervous system (CNS) of living subjects precludes the determination of their temporal distribution, intra-individual heterogeneity, and their ability to predict disease course. Using multiple sclerosis (MS) as an example of a complex neurological disorder, we sought to determine if cerebrospinal fluid (CSF) biomarkers can be aggregated to predict future rates of MS progression and provide molecular insight into mechanisms of CNS destruction. 1,305 CSF biomarkers were analyzed blindly in the longitudinal training dataset (N=129) of untreated MS patients, using DNA-aptamer assay. Random forest models, validated in an independent longitudinal cohort (N=64), uncovered signatures of MS severity, measured by clinical scales and volumetric brain imaging. Cluster analysis revealed intra-individual molecular heterogeneity of disease mechanisms that include both CNS- and immune-related pathways and may represent novel targets for inhibiting MS progression.

An increased risk for Systemic Autoimmune Diseases (SAID) was reported in the population of Libby, Montana, where extensive exposure to asbestiform amphiboles occurred through mining and use of asbestiform fiber-laden vermiculite. High frequencies of antinuclear autoantibodies (ANA) were detected in individuals and mice exposed to Libby Asbestiform Amphiboles (LAA). Among the 6603 individuals who have undergone health screening at the Center for Asbestos Related Diseases (CARD, Libby MT), the frequencies of rheumatoid arthritis, systemic lupus erythematosus, sarcoidosis, and systemic sclerosis are significantly higher than expected prevalence in the United States. While these data support the hypothesis that LAA can trigger autoimmune responses, evidence suggests that chrysotile asbestos does not. Serological testing was therefore performed in subjects exposed to LAA or predominantly chrysotile (New York steamfitters) using multiplexed array technologies. Analyses were performed in order to determine a) autoantibody profiles in each cohort, and b) whether the two populations could be distinguished through predictive modeling. Analysis using perMANOVA testing confirmed a significant difference between autoantibody profiles suggesting differential pathways leading to autoantibody formation. ANA were more frequent in the LAA cohort. Specific autoantibodies more highly expressed with LAA-exposure were to histone, ribosomal P protein, Sm/Ribonucleoproteins, and Jo-1 (histidyl tRNA synthetase). Myositis autoantibodies more highly expressed in the LAA cohort were Jo-1, PM100, NXP2, and Mi2a. Predictive modeling demonstrated that anti-histone antibodies were most predictive for LAA exposure, and anti-Sm was predictive for the steamfitters' exposure. This emphasizes the need to consider fiber types when evaluating risk of SAID with asbestos exposure.