Effect of oral non-peptide GLP-1 receptor agonist orforglipron (LY3502970) in participants with obesity or overweight: A Phase 2 study
Bibliographic record
Abstract
Does treatment with Orforglipron (OFG) achieve significant greater change in body weight (BW) and waist circumference (WC) from baseline compared to placebo (PBO)? Methods : In this phase 2 study, participants (N=272) were randomized to placebo or OFG (12, 24, 36, or 45 mg) maintenance treatment. OFG doses were increased to target using different dose escalation schemes in each arm. The primary endpoint was to compare percent body weight (BW) change from baseline in OFG vs placebo at Week 26, with a secondary endpoint at 36 weeks. Secondary endpoints included also change from baseline in waist circumference (WC) and BMI, and the percentage of participants achieving ≥5 or ≥10% weight loss. Results : At baseline, mean BW was 108.7 kg, BMI was 37.9 kg/m2, and 94% of participants had a BMI ≥30 kg/m2. Mean percentage BW loss, mean change in BMI and WC, and the percentage of participants achieving ≥5% or ≥10% weight loss were significantly greater with all OFG doses vs placebo. The AE profile was similar to other GLP-1 RAs; most were GI-related and mild to moderate in severity. Conclusion : The novel non-peptide GLP-1 receptor agonist OFG led to greater reductions in BW, BMI, and WC compared with placebo. These promising data support continued development of OFG as an oral treatment for obesity. Publication History Article published online: 18 April 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".