Glial Fibrillary Acidic Protein as a Marker of Disease in Relapsing Multiple Sclerosis: Post Hoc Analysis of Phase 3 Ozanimod Trials
Bibliographic record
Abstract
BACKGROUND: This post hoc analysis investigated relationships between baseline plasma glial fibrillary acidic protein (GFAP), a potential biomarker for multiple sclerosis (MS), and baseline characteristics and on-treatment outcomes in participants with relapsing MS (RMS) from two Phase 3 trials that randomly assigned ozanimod 0.46 mg or 0.92 mg or interferon β-1a 30 μg. METHODS: In the Phase 3 trials (SUNBEAM [ClinicalTrials.gov: NCT02294058; EudraCT: 2014-002320-27], duration: ≥ 12 months; and RADIANCE [ClinicalTrials.gov: NCT02047734; EudraCT: 2012-002714-40], duration: 24 months) baseline plasma GFAP was measured via Simoa (Quanterix). Adjusted regression models were used to determine relationships between baseline GFAP and baseline participant characteristics and to predict on-treatment outcomes. RESULTS: Baseline GFAP was associated with sex and had inverse associations with baseline body mass index and whole brain volume (WBV). Baseline GFAP had positive associations with baseline age, neurofilament light chain, numbers of gadolinium-enhancing (GdE) and T2 lesions, and Expanded Disability Status Scale (EDSS) score. Baseline GFAP had inverse associations with on-treatment WBV and the proportion of participants with no evidence of disease activity-3. Baseline GFAP had positive associations with on-treatment number of relapses through Months 12 and 24, number of GdE lesions at Month 12, number of new/enlarging T2 lesions over 12 months, and Month 12 EDSS score. In a multivariable lasso model, baseline GFAP concentration independently predicted only the number of relapses through Month 12. CONCLUSIONS: These data suggest that plasma GFAP is a relapse-independent metric of baseline disease severity and a predictor of treatment response in participants with RMS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".