Serum Glial Fibrillary Acidic Protein and Neurofilament Light Chain Levels Reflect Different Mechanisms of Disease Progression under B‐Cell Depleting Treatment in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To investigate the longitudinal dynamics of serum glial fibrillary acidic protein (sGFAP) and serum neurofilament light chain (sNfL) levels in people with multiple sclerosis (pwMS) under B-cell depleting therapy (BCDT) and their capacity to prognosticate future progression independent of relapse activity (PIRA) events. METHODS: A total of 362 pwMS (1,480 samples) starting BCDT in the Swiss Multiple Sclerosis (MS) Cohort were included. sGFAP levels in 2,861 control persons (4,943 samples) provided normative data to calculate adjusted Z scores. RESULTS: Elevated sGFAP levels (Z score >1) at 1 year were associated with a higher hazard for PIRA (hazard ratio [HR]: 1.80 [95% CI: 1.17-2.78]; p = 0.0079) than elevated sNfL levels (HR, 1.45 [0.95-2.24], p = 0.0886) in a combined model. Independent of PIRA events, sGFAP levels longitudinally increased by 0.49 Z score units per 10 years follow-up (estimate, 0.49 [0.29, 0.69], p < 0.0001). In patients experiencing PIRA, sGFAP Z scores were 0.52 Z score units higher versus stable patients (0.52 [0.22, 0.83], p = 0.0009). Different sNfL Z score trajectories were found in pwMS with versus without PIRA (interaction p = 0.0028), with an average decrease of 0.92 Z score units per 10 years observed without PIRA (-0.92 [-1.23, -0.60], p < 0.0001), whereas levels in patients with PIRA remained high. INTERPRETATION: Elevated sGFAP and lack of drop in sNfL after BCDT start are associated with increased risk of future PIRA. These findings provide a rationale for combined monitoring of sNfL and sGFAP in pwMS starting BCDT to predict the risk of PIRA, and to use sGFAP as an outcome in clinical trials aiming to impact on MS progressive disease biology. ANN NEUROL 2024.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".