Sputum Antineutrophil Cytoplasmic Antibodies in Serum Antineutrophil Cytoplasmic Antibody–Negative Eosinophilic Granulomatosis with Polyangiitis
Bibliographic record
Abstract
Abstract Rationale Eosinophilic granulomatosis with polyangiitis (eGPA) is a small-vessel vasculitis where 40% of patients present with serum antineutrophil cytoplasmic antibodies (ANCAs). We examined the presence and clinical relevance of sputum ANCAs in the serum ANCA− patients with eGPA. Methods ANCA was investigated in matched sputum and blood samples collected from 23 patients with eGPA (n = 10, serum ANCA+), 19 patients with eosinophilic asthma (prednisone dependent), and 13 healthy volunteers. IgG reactivity to common target antigens and cytokine profiles in sputum samples were examined. Pathogenicity of detected sputum ANCA was assessed using in vitro degranulation assays. Measurements and Main Results Most patients with eGPA (17 of 23, 74%) showed significantly increased sputum ANCAs compared with patients with eosinophilic asthma (P = 0.002) and healthy controls (P < 0.0001), irrespective of their serum ANCA status. In addition, 16 of 17 (94%) of sputum ANCA+ patients had clinical manifestations of severe asthma compared with 3 of 6 (50%) in the sputum ANCA− subset (P = 0.04). Microarray analysis of 123 common antigens failed to reveal a specific target for the ANCA IgG. However, immunoprecipitated immunoglobulins from ANCA+ sputum allowed extensive extracellular trap formations from both neutrophils and eosinophils in vitro, indicating pathogenicity of detected IgG autoantibodies. Cytokine analysis showed lung-localized increases in CXCL8 (neutrophil/eosinophil chemotaxis), CCL24 (eosinophil recruitment), and CXCL12 (lymphocyte recruitment) in the sputa from ANCA+ patients (P < 0.01). Conclusions We report a novel finding of ANCA reactivity in the sputa of patients with eGPA in whom disease severity is driven by respiratory complications. Investigating localized autoimmunity may lead to the discovery of novel pathomechanisms, therapeutic targets, and optimal biomarkers for diagnosing and managing eGPA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".