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.

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.

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.

New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability.

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.