Cluster Analysis of Inflammatory Biomarker Expression in the International Severe Asthma Registry
Bibliographic record
Abstract
BACKGROUND: Allergy, eosinophilic inflammation, and epithelial dysregulation are implicated in severe asthma pathogenesis. OBJECTIVE: We characterized biomarker expression in adults with severe asthma. METHODS: Within the International Severe Asthma Registry (ISAR), we analyzed data from 10 countries in North America, Europe, and Asia, with prespecified thresholds for biomarker positivity (serum IgE ≥ 75 kU/L, blood eosinophils ≥ 300 cells/μL, and FeNO ≥ 25 ppb), and with hierarchical cluster analysis using biomarkers as continuous variables. RESULTS: ) predicted 72% ± 20%. By prespecified thresholds, 59% were IgE positive, 57% eosinophil positive, and 58% FeNO positive. There was substantial inflammatory biomarker overlap; 59% were positive for either 2 or 3 biomarkers. Five distinct clusters were identified: cluster 1 (61%, low-to-medium biomarkers) comprised highly symptomatic, older females with elevated BMI and frequent exacerbations; cluster 2 (18%, elevated eosinophils and FeNO) older females with lower BMI and frequent exacerbations; cluster 3 (14%, extremely high FeNO) older, highly symptomatic, lower BMI, and preserved lung function; cluster 4 (6%, extremely high IgE) younger, long duration of asthma, elevated BMI, and poor lung function; cluster 5 (1.2%, extremely high eosinophils) younger males with low BMI, poor lung function, and high burden of sinonasal disease and polyposis. CONCLUSIONS: There is significant overlap of biomarker positivity in severe asthma. Distinct clusters according to biomarker expression exhibit unique clinical characteristics, suggesting the occurrence of discrete patterns of underlying inflammatory pathway activation and providing pathogenic insights relevant to the era of monoclonal biologics.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".