Body weight management and safety with efpeglenatide in adults without diabetes: A phase II randomized study
Bibliographic record
Abstract
AIM: To evaluate the safety of efpeglenatide, a long-acting glucagon-like peptide-1 receptor agonist (GLP-1RA), and its effects on body weight management in adults without diabetes. MATERIALS AND METHODS: with comorbidity were randomized 1:1:1:1:1 to efpeglenatide (4 mg once weekly, 6 mg once weekly, 6 mg once every 2 wk, or 8 mg once every 2 wk; n = 237) or placebo (n = 60) in combination with a hypocaloric diet. The primary endpoint was body weight change from baseline after 20 wk of treatment, assessed using a mixed-effect model with repeated measures with an unstructured covariance matrix over all post-screening visits; treatment comparisons were based on least squares mean estimates. RESULTS: Over 20 wk, all doses of efpeglenatide significantly reduced body weight from baseline versus placebo (P < 0.0001), with placebo-adjusted reductions ranging between -6.3 kg (6 mg once every 2 wk) and -7.2 kg (6 mg once weekly). Greater proportions of efpeglenatide-treated participants had body weight loss of ≥5% or ≥10% versus placebo (P < 0.01, all comparisons). Efpeglenatide led to significant improvements in glycaemic variables (fasting plasma glucose and glycated haemoglobin) and lipid profiles (cholesterol, triglycerides) versus placebo. Rates of study discontinuations as a result of adverse events ranged from 5% to 19% with efpeglenatide. Gastrointestinal effects were the most common treatment-emergent adverse events. CONCLUSIONS: Efpeglenatide once weekly and once every 2 wk led to significant body weight reduction and improved glycaemic and lipid variables versus placebo. It was also well tolerated for weight management in adults without diabetes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".