Periostin is a systemic biomarker of eosinophilic airway inflammation in asthma
Bibliographic record
Abstract
Background: Eosinophilic airway inflammation is heterogeneous in asthma. We recently described a distinct subtype of asthma defined by the expression of genes inducible by Th2 cytokines in bronchial epithelium. This gene signature, which includes periostin, is present in approximately half of asthmatics, and correlates with eosinophilic airway inflammation. However, identification of this subtype depends on invasive airway sampling, hence non-invasive biomarkers of this phenotype are desirable. Objective: Identify systemic biomarkers of eosinophilic airway inflammation. Methods: We measured fractional exhaled nitric oxide (FeNO) and peripheral blood eosinophil, periostin, YKL-40, and IgE levels and compared these biomarkers to airway eosinophilia in 5 cohorts of asthmatics across a range of severity (N=150). Results: We replicated our previous finding of a three-gene bronchial epithelial Th2 signature in a subset of asthmatics and found that peripheral blood periostin levels were highly correlated to the gene signature. Blood periostin is significantly elevated in asthmatics with evidence of eosinophilic airway inflammation relative to those with minimal eosinophilic airway inflammation despite inhaled corticosteroid (ICS) treatment across a range of disease severity. A logistic regression model including sex, age, body mass index (BMI), IgE, blood eosinophils, FeNO, and serum periostin in 59 severe asthmatics showed that, of these indices, serum periostin was the single best predictor of airway eosinophilia (p=0.007). Conclusions: Periostin is a systemic biomarker of airway eosinophilia in asthma and has potential utility in patient selection for emerging asthma therapeutics targeting Th2 inflammation.
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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 | 0.000 |
| 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".