Serum and Histologic Eosinophilia as a Predictive Biomarker of Response to Mirikizumab in Ulcerative Colitis
Bibliographic record
Abstract
INTRODUCTION: Eosinophils may contribute to ulcerative colitis (UC) pathogenesis through inflammation and epithelial injury. Mirikizumab, a selective IL-23 inhibitor, has shown efficacy in moderate-to-severe UC. However, the relationship between eosinophils and treatment outcomes with IL-23 inhibition remains unclear. OBJECTIVE: This post-hoc analysis of the LUCENT-1 trial evaluated trends in serum and histologic eosinophils as biomarkers for early response to mirikizumab induction therapy in UC. DESIGN: Data from LUCENT-1 (NCT03518086) were analyzed to assess serum eosinophil counts and histologic eosinophil presence at baseline and week 12. Clinical response, remission, and endoscopic improvement were defined by modified Mayo score criteria. Logistic regression models, adjusted for factors including steroid use, prior biologic failure, disease severity, and albumin levels, assessed associations between baseline eosinophil levels and treatment outcomes. RESULTS: Among 928 participants, 66.6% achieved clinical response. Responders had higher baseline serum eosinophils (0.23 × 109/L vs 0.19 × 109/L, P = .001) and showed greater reductions at week 12. Elevated baseline eosinophils (≥0.57 × 109/L) were associated with higher rates of clinical response (80% vs 65.7%, aOR: 2.31, P = .013), remission (33.3% vs 25.1%, aOR: 1.91, P = .044), and endoscopic improvement (46.7% vs 33.8%, aOR: 2.33, P = .003). Reductions in histologic eosinophils were also linked to improved outcomes, especially in those with moderate/marked baseline presence. CONCLUSIONS AND RELEVANCE: Baseline eosinophil levels may predict response to mirikizumab and guide early treatment decisions. These findings support a potential role for eosinophils as biomarkers in UC management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".