Serum periostin as a biomarker in eosinophilic granulomatosis with polyangiitis
Bibliographic record
Abstract
OBJECTIVE: Identification of a biomarker for disease activity in eosinophilic granulomatosis with polyangiitis (EGPA; Churg-Strauss) remains an unmet need. This study examined the value of serum periostin, a marker of type 2 inflammation, as a measure of disease activity in patients with EGPA. METHODS: Participants enrolled in a multicenter, prospective cohort of patients with EGPA were included in this study if they had disease activity (defined as Birmingham Vasculitis Activity Score [BVAS] > 0) during follow-up. Serum levels of periostin were measured at flare visit as well as two pre- and two post-flare visits, if available. The outcome of disease activity was assessed either with BVAS or Physician Global Assessment (PGA). Mixed-effect models were used to examine the association between periostin levels and disease activity. Comparisons were made with a historical cohort of healthy individuals and patients with asthma. RESULTS: In the 49 patients included in the study, the median periostin level was 60 ng/ml (IQR 50 to 73) in all visits and did not significantly change across visits. Multivariate analyses found no association between periostin level and presence or absence of flare according to the BVAS (adjusted OR 1.00 [95% CI 0.98 to 1.02], p = 0.98) but an increase in periostin level was significantly associated with greater disease severity during a flare according to the PGA (adjusted beta-coefficient 0.02 [95% CI 0.004 to 0.03], p = 0.01). Periostin levels in EGPA were significantly higher than previously studied healthy controls and patients with asthma. CONCLUSION: In EGPA serum periostin level is modestly associated with greater disease severity during a flare but does not discriminate active from inactive disease. Periostin levels in EGPA are higher than in other previously studied cohorts, including healthy populations and patients with asthma, and are relatively stable over time.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".