Treatment of Muckle-Wells syndrome: analysis of two IL-1-blocking regimens
Bibliographic record
Abstract
OBJECTIVES: Muckle-Wells syndrome (MWS) is an autoinflammatory disease characterized by excessive interleukin-1 (IL-1) release, resulting in recurrent fevers, sensorineural hearing loss, and amyloidosis. IL-1 inhibition with anakinra, an IL-1 receptor antagonist, improves clinical symptoms and inflammatory markers. Subclinical disease activity is commonly observed. Canakinumab, a fully human IgG1 anti-IL-1β monoclonal antibody, can abolish excess IL-1β. The study aim was to analyze the efficacy and safety of these two anti-IL-1 therapies. METHODS: Two cohorts of patients with severe MWS and confirmed NLRP3 mutation were treated with anakinra and/or canakinumab. Clinical and laboratory features including ESR, CRP, SAA, and the neutrophil marker S100A12 were determined serially. Disease activity was captured by MWS disease activity scores (MWS-DAS). Remission was defined as MWS-DAS ≤5 plus normal CRP and SAA. Treatment efficacy and safety were analyzed. RESULTS: The study included 12 anakinra- and 14 canakinumab-treated patients; the median age was 33.5 years (3.0 years to 72.0 years); 57% were female patients. Both treatment regimens led to a significant reduction of clinical disease activity and inflammatory markers. At last follow-up, 75% of anakinra-treated and 93% of canakinumab-treated patients achieved remission. During follow-up, S100A12 levels mirrored recurrence of disease activity. Both treatment regimens had favorable safety profiles. CONCLUSIONS: IL-1 blockade is an effective and safe treatment in MWS patients. MWS-DAS in combination with MWS inflammatory markers provides an excellent monitoring tool set. Canakinumab led to a sustained control of disease activity even after secondary failure of anakinra therapy. S100A12 may be a sensitive marker to detect subclinical disease activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".