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| Name | Class |
|---|---|
| National Institute of Allergy and Infectious Diseases (NIAID) | NIH |
| University of Colorado, Denver | OTHER |
| University of Ottawa | OTHER |
| Montana State University |
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To determine if biomarker-based CSF testing is reliably detecting differences between patients with Multiple Sclerosis (MS), different MS-subtypes, and other central nervous system (CNS) diseases. This study will also look to identify biomarkers that could be used for the prediction, at the time of diagnosis, of the future disease clinical course and response to therapy. The SOMAscan assay will be used for CSF samples analysis.
Using machine learning, the investigators have developed from SOMAScan:
Because these results are derived from a single research center (NIAID/NDS), it is imperative to determine their performance in real clinical practice settings as a necessary step for their potential regulatory approval.
Consequently, his application has 2 specific aims:
AIM 1. To independently validate afore-mentioned CSF-biomarker-based tests for their clinical value within the multicenter Spinal fluid Consortium for MS (SPINCOMS). In Aim 1, each of the 3 defined tests will be validated in 100 new SPINCOMS patients. To validate the prognostic test, 100 MS patients with CSF collected at least 3 years ago will be evaluated at follow-up examination with standardized clinical outcomes. CSF will be analyzed blinded using pre-defined statistical models.
AIM 2. To explore whether collected CSF-biomarkers point towards pathogenic heterogeneity that may predict patient-specific efficacy for different disease-modifying treatments (DMTs) or identify pathogenic mechanisms not targeted by current DMTs. In Aim 2, clustering analysis will assess pathogenic heterogeneity and explore potential predictors of response to therapy.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Relapsing Remitting Multiple Sclerosis |
| ||
| Progressive Multiple Sclerosis |
| ||
| Non-Inflammatory Neurological Diseases |
| ||
| Other Non-Inflammatory Neurological Diseases |
|
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| Measure | Description | Time Frame |
|---|---|---|
| Biomarker Predicted Outcomes against NeurEx-based outcomes | CSF-biomarker-predicted outcomes against measured NeurEx-based outcomes, considering a Bonferroni-adjusted significance level 0.05/3 = 0.017 (to adjust for 3 tests). | 3 years |
| MS Severity Model Analyses | As secondary analyses of MS severity model,assessment of correlations between CSF-biomarker-predicted outcomes and more traditional MS outcomes. Specifically, correlate CSF-biomarker-based scores of MS severity and MSSS, ARMSSS and by MS-DSS, calculated from the follow-up visit scores. Based on power calculations, 100 relevant patients/classifier will provide > 90% power to externally validate all 3 Somascan CSF-biomarker-based models. | 3 years |
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MS Patients selection criteria
Non-MS Patients selection criteria Required: 25 Non-Inflammatory Neurological Disease (NIND), 25 Other Inflammatory Neurological Disease (OIND)
NIND: e.g., ischemic-gliotic changes, CADASIL and other leukodystrophies, migraines, ischemic spinal cord lesions etc OIND: e.g. CNS Sjogren's, SLE, vasculitis, CNS infections, MOG-associated disorders, NMO spectrum disorders (NMOSD)
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Relapsing-remitting multiple sclerosis patients
Progressive multiple sclerosis patients
Patients with non-inflammatory neurological diseases (NIND): e.g., ischemic-gliotic changes, CADASIL and other leukodystrophies, migraines, ischemic spinal cord lesions.
Patients with other inflammatory neurological diseases (OIND):e.g. CNS Sjogren's, SLE, vasculitis, CNS infections, MOG-associated disorders, NMO spectrum disorders (NMOSD)
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Washington University in St Louis | St Louis | Missouri | 63110 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29922952 | Background | Kidd DP. Sarcoidosis of the central nervous system: clinical features, imaging, and CSF results. J Neurol. 2018 Aug;265(8):1906-1915. doi: 10.1007/s00415-018-8928-2. Epub 2018 Jun 19. | |
| 29059494 | Background | Barbour C, Kosa P, Komori M, Tanigawa M, Masvekar R, Wu T, Johnson K, Douvaras P, Fossati V, Herbst R, Wang Y, Tan K, Greenwood M, Bielekova B. Molecular-based diagnosis of multiple sclerosis and its progressive stage. Ann Neurol. 2017 Nov;82(5):795-812. doi: 10.1002/ana.25083. |
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Will be determined based whether or not recruitment numbers will be sufficient to power outcome analyses.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 28, 2022 | Nov 27, 2024 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D009103 | Multiple Sclerosis |
| ID | Term |
|---|---|
| D020278 | Demyelinating Autoimmune Diseases, CNS |
| D020274 | Autoimmune Diseases of the Nervous System |
| D009422 | Nervous System Diseases |
| D003711 | Demyelinating Diseases |
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| OTHER |
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Cerebrospinal fluids
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| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |