Body Mass Index Did Not Affect Efficacy and Safety of Etrasimod: A Post Hoc Analysis of the ELEVATE Ulcerative Colitis Clinical Program
Bibliographic record
Abstract
INTRODUCTION: High body mass index (BMI) may reduce ulcerative colitis (UC) treatment efficacy. Etrasimod is an oral, once-daily (QD), selective sphingosine 1-phosphate 1,4,5 receptor modulator for the treatment of moderately to severely active UC. This post hoc analysis assessed treatment outcomes according to BMI in ELEVATE UC 52 and ELEVATE UC 12. METHODS: Patients receiving etrasimod 2 mg QD or placebo were stratified by BMI: <25, 25-30, and >30 kg/m 2 . Efficacy and safety were assessed at weeks 12 (pooled data) and 52 (ELEVATE UC 52 only) in addition to biomarkers assessments from week 0-12. RESULTS: For BMI <25 (N = 443) and 25-30 kg/m 2 (N = 217) subgroups, more patients receiving etrasimod vs placebo achieved clinical remission at weeks 12 (BMI <25 kg/m 2 : 27.9% vs 13.3%; 25-30 kg/m 2 : 28.5% vs 10.0%; all P < 0.001) and 52 (BMI <25 kg/m 2 : 30.8% vs 10.0%; 25-30 kg/m 2 : 33.3% vs 6.3%; all P < 0.0001), and also for all other efficacy end points ( P < 0.05). In the BMI >30 kg/m 2 subgroup (N = 127), more patients receiving etrasimod vs placebo achieved clinical remission at week 52 (36.5% vs 3.9%; P < 0.0001) and most other efficacy end points at weeks 12 and 52 (all P < 0.05). Overall, regardless of baseline BMI, numerically lower fecal calprotectin levels were observed with etrasimod vs placebo in treatment responders. The safety profile of etrasimod was consistent between BMI subgroups. DISCUSSION: A high BMI did not significantly affect efficacy and safety outcomes of etrasimod (NCT03945188; NCT03996369).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".