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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".