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. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful.

Enhancing the clinical value of serum neurofilament light chain measurement.

BACKGROUNDSerum neurofilament light chain (sNFL) is becoming an important biomarker of neuro-axonal injury. Though sNFL correlates with CSF NFL (cNFL), 40% to 60% of variance remains unexplained. We aimed to mathematically adjust sNFL to strengthen its clinical value.METHODSWe measured NFL in a blinded fashion in 1138 matched CSF and serum samples from 571 patients. Multiple linear regression (MLR) models constructed in the training cohort were validated in an independent cohort.RESULTSAn MLR model that included age, blood urea nitrogen, alkaline phosphatase, creatinine, and weight improved correlations of cNFL with sNFL (from R2 = 0.57 to 0.67). Covariate adjustment significantly improved the correlation of sNFL with the number of contrast-enhancing lesions (from R2 = 0.18 to 0.28; 36% improvement) in the validation cohort of patients with multiple sclerosis (MS). Unexpectedly, only sNFL, but not cNFL, weakly but significantly correlated with cross-sectional MS severity outcomes. Investigating 2 nonoverlapping hypotheses, we showed that patients with proportionally higher sNFL to cNFL had higher clinical and radiological evidence of spinal cord (SC) injury and probably released NFL from peripheral axons into blood, bypassing the CSF.CONCLUSIONsNFL captures 2 sources of axonal injury, central and peripheral, the latter reflecting SC damage, which primarily drives disability progression in MS.TRIAL REGISTRATIONClinicalTrials.gov NCT00794352.FUNDINGDivision of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH (AI001242 and AI001243).

Prognostic value of serum/plasma neurofilament light chain for COVID-19-associated mortality.

OBJECTIVE: Given the continued spread of coronavirus 2, the early predictors of coronavirus disease 19 (COVID-19) associated mortality might improve patients' outcomes. Increased levels of circulating neurofilament light chain (NfL), a biomarker of neuronal injury, have been observed in severe COVID-19 patients. We investigated whether NfL provides non-redundant clinical value to previously identified predictors of COVID-19 mortality. METHODS: We measured serum or plasma NfL concentrations in a blinded fashion in 3 cohorts totaling 338 COVID-19 patients. RESULTS: In cohort 1, we found significantly elevated NfL levels only in critically ill COVID-19 patients. Longitudinal cohort 2 data showed that NfL is elevated late in the course of the disease, following the two other prognostic markers of COVID-19: decrease in absolute lymphocyte count (ALC) and increase in lactate dehydrogenase (LDH). Significant correlations between ALC and LDH abnormalities and subsequent rise of NfL implicate that the multi-organ failure is the most likely cause of neuronal injury in severe COVID-19 patients. The addition of NfL to age and gender in cohort 1 significantly improved the accuracy of mortality prediction and these improvements were validated in cohorts 2 and 3. INTERPRETATION: A substantial increase in serum/plasma NfL reproducibly enhanced COVID-19 mortality prediction. Combined with other prognostic markers, such as ALC and LDH that are routinely measured in ICU patients, NfL measurements might be useful to identify the patients at a high risk of COVID-19-associated mortality, who might still benefit from escalated care.

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. Proteins secreted by toxic astrocytes are elevated in the cerebrospinal fluid (CSF) of MS patients and reproducibly correlate with the rates of accumulation of neurological disability and brain atrophy. This suggests a pathogenic role for neurotoxic astrocytes in MS. METHODS: Here, we applied a commercially available library of small molecules that are either Food and Drug Administration-approved or in clinical development to an in vitro model of toxic astrogliosis to identify drugs and signaling pathways that inhibit inflammatory transformation of astrocytes to a neurotoxic phenotype. RESULTS: Inhibitors of three pathways related to the endoplasmic reticulum stress: (1) proteasome, (2) heat shock protein 90 and (3) mammalian target of rapamycin reproducibly decreased inflammation-induced conversion of astrocytes to toxic phenotype. Dantrolene, an anti-spasticity drug that inhibits calcium release through ryanodine receptors expressed in the endoplasmic reticulum of central nervous system cells, also exerted inhibitory effect at in vivo achievable concentrations. Finally, we established CSF SERPINA3 as a relevant pharmacodynamic marker for inhibiting toxic astrocytes in clinical trials. CONCLUSION: Drug library screening provides mechanistic insight into the generation of toxic astrocytes and identifies candidates for immediate proof-of-principle clinical trial(s).

Prognostic Value of Serum/Plasma Neurofilament Light Chain for COVID-19 Associated Mortality.

Given the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), early predictors of coronavirus disease 19 (COVID-19) mortality might improve patients’ outcomes. Increased levels of circulating neurofilament light chain (NfL), a biomarker of neuro-axonal injury, have been observed in patients with severe COVID-19. We investigated whether NfL provides non-redundant clinical value to previously identified predictors of COVID-19 mortality. We measured serum or plasma NfL concentrations in a blinded fashion in 3 cohorts totaling 338 COVID-19 patients. In cohort 1, we found significantly elevated NfL levels only in critically ill COVID-19 patients compared to healthy controls. Longitudinal cohort 2 data showed that NfL is elevated late in the course of the disease, following two other prognostic markers of COVID-19: decrease in absolute lymphocyte count (ALC) and increase in lactate dehydrogenase (LDH). Significant correlations between LDH and ALC abnormalities and subsequent rise of NfL implicate multi-organ failure as a likely cause of neuronal injury at the later stages of COVID-19. Addition of NfL to age and gender in cohort 1 significantly improved the accuracy of mortality prediction and these improvements were validated in cohorts 2 and 3. In conclusion, although substantial increase in serum/plasma NfL reproducibly enhances COVID-19 mortality prediction, NfL has clinically meaningful prognostic value only close to death, which may be too late to alter medical management. When combined with other prognostic biomarkers, rising longitudinal NfL measurements triggered by LDH and ALC abnormalities would identify patients at risk of COVID-19 associated mortality who might still benefit from escalated care.

Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients.

Composite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm. The prospectively acquired MS patients, divided into training (n = 172) and validation (n = 83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx™ App that automatically computes disability scales. qMRI features were computed by lesion-TOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort. COMRISv2 models validated moderate correlation with cognitive disability [Spearman Rho = 0.674; Lin's concordance coefficient (CCC) = 0.458; p < 0.001] and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p < 0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy. COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data.

Confounder-adjusted MRI-based predictors of multiple sclerosis disability.

Introduction: Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as "accelerated aging." Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Methods: Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Results: Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. Conclusion: GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.

Smartphone-based symbol-digit modalities test reliably captures brain damage in multiple sclerosis.

As the burden of neurodegenerative diseases increases, time-limited clinic encounters do not allow quantification of complex neurological functions. Patient-collected digital biomarkers may remedy this, if they provide reliable information. However, psychometric properties of digital tools remain largely un-assessed. We developed a smartphone adaptation of the cognitive test, the Symbol-Digit Modalities Test (SDMT) by randomizing the test's symbol-number codes and testing sequences. The smartphone SDMT showed comparable psychometric properties in 154 multiple sclerosis (MS) patients and 39 healthy volunteers (HV). E.g., smartphone SDMT achieved slightly higher correlations with cognitive subscores of neurological examinations and with brain injury measured by MRI (R2 = 0.75, Rho = 0.83, p < 0.0001) than traditional SDMT. Mathematical adjustment for motoric disability of the dominant hand, measured by another smartphone test, compensates for the disadvantage of touch-based test. Averaging granular home measurements of the digital biomarker also increases accuracy of identifying true neurological decline.

Outcomes in Previously Healthy Cryptococcal Meningoencephalitis Patients Treated With Pulse Taper Corticosteroids for Post-infectious Inflammatory Syndrome.

BACKGROUND: Cryptococcal meningoencephalitis (CM) is a major cause of mortality in immunosuppressed patients and previously healthy individuals. In the latter, a post-infectious inflammatory response syndrome (PIIRS) is associated with poor clinical response despite antifungal therapy and negative cerebrospinal fluid (CSF) cultures. Data on effective treatment are limited. METHODS: Between March 2015 and March 2020, 15 consecutive previously healthy patients with CM and PIIRS were treated with adjunctive pulse corticosteroid taper therapy (PCT) consisting of intravenous methylprednisolone 1 gm daily for 1 week followed by oral prednisone 1 mg/kg/day, tapered based on clinical and radiological response plus oral fluconazole. Montreal cognitive assessments (MOCA), Karnofsky performance scores, magnetic resonance imaging (MRI) brain scanning, ophthalmic and audiologic exams, and CSF parameters including cellular and soluble immune responses were compared at PIIRS diagnosis and after methylprednisolone completion. RESULTS: The median time from antifungal treatment to steroid initiation was 6 weeks. The most common symptoms at PIIRS diagnosis were altered mental status and vision changes. All patients demonstrated significant improvements in MOCA and Karnofsky scores at 1 month (P < .0003), which was accompanied by improvements in CSF glucose, white blood cell (WBC) count, protein, cellular and soluble inflammatory markers 1 week after receiving corticosteroids (CS) (P < .003). All patients with papilledema and visual field deficits also exhibited improvement (P < .0005). Five out of 7 patients who underwent audiological testing demonstrated hearing improvement. Brain MRI showed significant improvement of radiological findings (P = .001). CSF cultures remained negative. CONCLUSIONS: PCT in this small cohort of PIIRS was associated with improvements in CM-related complications with minimal toxicity in the acute setting.

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test.

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the "Spiral tracing." We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.

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.e., interferon-beta [IFN-beta] and glatiramer acetate [GA]) and high-efficacy drugs (i.e., natalizumab, daclizumab, and ocrelizumab) on MS-related cellular abnormalities using propensity score matching. Physiological gender differences are less pronounced in the CSF compared to blood, and age-related changes suggest decreased immunosurveillance of CNS by activated HLA-DR+T cells associated with natural aging. Results from patient samples support the concept of MS being immunologically single disease evolving in time. Initially, peripherally activated innate and adaptive immune cells migrate into CSF to form MS lesions. With progression, T cells (CD8+ > CD4+), NK cells, and myeloid dendritic cells are depleted from blood as they continue to accumulate, together with B cells, in the CSF and migrate to CNS tissue, forming compartmentalized inflammation. All MS drugs inhibit non-physiological accumulation of immune cells in the CSF. Although low-efficacy drugs tend to normalize it, high-efficacy drugs overshoot some aspects of CSF physiology, suggesting impairment of CNS immunosurveillance. Comparable inhibition of MS-related CSF abnormalities advocates changes within CNS parenchyma responsible for differences in drug efficacy on MS disability progression. Video summarizing all results may become useful educational tool.

Idebenone does not inhibit disability progression in primary progressive MS.

BACKGROUND: Multiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disorder of the central nervous system (CNS). While current MS therapies target the inflammatory processes, no treatment explicitly targets mitochondrial dysfunction and resulting axonal loss. Therefore, the aim of this study was to determine whether idebenone inhibits mitochondrial dysfunction and accumulation of disability in primary progressive MS (PPMS) and to enhance understanding of pathogenic mechanisms of PPMS progression using cerebrospinal fluid (CSF) biomarkers. METHODS: The double-blind, placebo-controlled Phase I/II clinical trial of Idebenone in patients with Primary Progressive MS (IPPoMS; NCT00950248) was an adaptively designed, baseline-versus-treatment, placebo-controlled, CSF-biomarker-supported trial. Based on interim analysis of the 1-year pre-treatment data, change in the area under the curve of Combinatorial Weight-Adjusted Disability Score (CombiWISE) became the primary outcome, with >80% power to detect ≥40% efficacy with 28 patients/arm treated for 2 years in baseline versus treatment paradigm. Changes in traditional disability scales and in brain ventricular volume were secondary outcomes. Exploratory outcomes included CSF biomarkers of mitochondrial dysfunction (Growth/differentiation factor 15 [GDF15] and lactate), axonal damage (neurofilament light chain [NFL]), innate immunity (sCD14), blood brain barrier leakage (albumin quotient) and retinal nerve fiber layer thinning. RESULTS: Idebenone was well tolerated but did not inhibit disability progression or CNS tissue destruction. Concentrations of GDF15, secreted predominantly by astrocytes and choroid plexus epithelium in vitro, increased after exposure to mitochondrial toxin rotenone, validating the ability of this biomarker to measure intrathecal mitochondrial damage. CSF GDF15 levels correlated strongly with age and MS patients had CSF levels of GDF15 significantly above age-adjusted healthy volunteers, with highest levels measured in PPMS. Idebenone did not change CSF GDF15 levels. CONCLUSION: Mitochondrial dysfunction exceeding normal aging reflected by age-adjusted CSF GDF15 is present in the majority of PPMS patients, but it is not inhibited by idebenone.

Aging and efficacy of disease-modifying therapies in multiple sclerosis: a meta-analysis of clinical trials.

Background: Disease-modifying therapies (DMTs) for multiple sclerosis (MS) are approved for the treatment of disease activity and are effective in reducing relapses and new magnetic resonance imaging (MRI) lesions. However, disease activity generally subsides with time, and age-dependent changes in DMT efficacy are not well-established. We aimed to investigate whether age impacts the efficacy of DMTs in treating disease activity in patients with relapsing-remitting MS (RRMS). Methods: DMT efficacy related to age was assessed through a meta-analysis of clinical trials that evaluated the efficacy of DMTs in RRMS patients as measured by reductions in the annualized relapse rate (ARR), new T2 lesions, and gadolinium-enhanced lesions on MRI. Using the mean baseline patient age from each trial, a weighted linear regression was fitted to determine whether age was associated with treatment efficacy on a group level. Results: Group-level data from a total of 28,082 patients from 26 trials of 14 different DMTs were included in the meta-analysis. There were no statistically significant associations between age and reductions in ARR, new T2 lesions, and gadolinium-enhanced lesions of the treatment group compared with placebo. Conclusion: DMTs for RRMS show efficacy in treating disease activity independent of age as demonstrated by group-level data from DMT clinical trials. Nevertheless, clinical trials select for patients with baseline disease activity regardless of age, thereby not representing real-world patients with RRMS, where disease activity declines with age.

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. 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.

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

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.

Smartphone Level Test Measures Disability in Several Neurological Domains for Patients With Multiple Sclerosis.

Our long-term goal is to employ smartphone-embedded sensors to measure various neurological functions in a patient-autonomous manner. The interim goal is to develop simple smartphone tests (apps) and evaluate the clinical utility of these tests by selecting optimal outcomes that correlate well with clinician-measured disability in different neurological domains. In this paper, we used prospectively-acquired data from 112 multiple sclerosis (MS) patients and 15 healthy volunteers (HV) to assess the performance and optimize outcomes of a Level Test. The goal of the test is to tilt the smartphone so that a free-rolling ball travels to and remains in the center of the screen. An accelerometer detects tilting and records the coordinates of the ball at set time intervals. From this data, we derived five features: path length traveled, time spent in the center of the screen, average distance from the center, average speed while in the center, and number of direction changes underwent by the ball. Time in center proved to be the most sensitive feature to differentiate MS patients from HV and had the strongest correlations with clinician-derived scales. Its superiority was validated in an independent validation cohort of 29 MS patients. A linear combination of different Level features failed to outperform time in center in an independent validation cohort. Limited longitudinal data demonstrated that the Level features were relatively stable intra-individually within 4 months, without definitive evidence of learning. In contrast to previously developed smartphone tests that predominantly measure motoric functions, Level features correlated strongly with reaction time and moderately with cerebellar functions and proprioception, validating its complementary clinical value in the MS app suite. The Level Test measures neurological disability in several domains in two independent cross-sectional cohorts (original and validation). An ongoing longitudinal cohort further investigates whether patient-autonomous collection of granular functional data allows measurement of patient-specific trajectories of disability progression to better guide treatment decisions.

Body Mass Index in Multiple Sclerosis modulates ceramide-induced DNA methylation and disease course.

BACKGROUND: Multiple Sclerosis (MS) results from genetic predisposition and environmental variables, including elevated Body Mass Index (BMI) in early life. This study addresses the effect of BMI on the epigenome of monocytes and disease course in MS. METHODS: Fifty-four therapy-naive Relapsing Remitting (RR) MS patients with high and normal BMI received clinical and MRI evaluation. Blood samples were immunophenotyped, and processed for unbiased plasma lipidomic profiling and genome-wide DNA methylation analysis of circulating monocytes. The main findings at baseline were validated in an independent cohort of 91 therapy-naïve RRMS patients. Disease course was evaluated by a two-year longitudinal follow up and mechanistic hypotheses tested in human cell cultures and in animal models of MS. FINDINGS: Higher monocytic counts and plasma ceramides, and hypermethylation of genes involved in negative regulation of cell proliferation were detected in the high BMI group of MS patients compared to normal BMI. Ceramide treatment of monocytic cell cultures increased proliferation in a dose-dependent manner and was prevented by DNA methylation inhibitors. The high BMI group of MS patients showed a negative correlation between monocytic counts and brain volume. Those subjects at a two-year follow-up showed increased T1 lesion load, increased disease activity, and worsened clinical disability. Lastly, the relationship between body weight, monocytic infiltration, DNA methylation and disease course was validated in mouse models of MS. INTERPRETATION: High BMI negatively impacts disease course in Multiple Sclerosis by modulating monocyte cell number through ceramide-induced DNA methylation of anti-proliferative genes. FUND: This work was supported by funds from the Friedman Brain Institute, NIH, and Multiple Sclerosis Society.

Cerebrospinal fluid biomarkers link toxic astrogliosis and microglial activation to multiple sclerosis severity.

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 DNA-aptamers (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. CONCLUSION: Microglial activation and toxic astrogliosis are associated with MS disease process and may partake in CNS tissue destruction. This hypothesis should be tested in new clinical trials.

NeurEx: digitalized neurological examination offers a novel high-resolution disability scale.

OBJECTIVE: To develop a sensitive neurological disability scale for broad utilization in clinical practice. METHODS: We 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. RESULTS: In 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. INTERPRETATION: NeurEx 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.

Identifying and Quantifying Neurological Disability via Smartphone.

Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.