Abstract 4365415: Comparative Efficacy of Glucagon-Like Peptide-1 Receptor Agonists and Co-Agonists for Weight Loss Among Patients Without Diabetes: A Network Meta-Analysis
Bibliographic record
Abstract
Background: Multiple glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and newer dual/triple co-agonists promote weight loss in adults without diabetes, yet comparative efficacy across agents remains uncertain. Aim: To compare and rank the efficacy of GLP-1 RAs and co-agonists for weight loss using network meta-analysis (NMA). Methods: We searched MEDLINE, EMBASE and Cochrane CENTRAL from inception to May 2025 for randomized controlled trials (RCTs) enrolling adults with overweight/obesity without diabetes. Each agent was analyzed at its highest tested (pre-market) or approved dose. We synthesized relative weight change using a frequentist random-effects NMA at approximately 6 months and 1-1.5 years, and ranked treatments using surface under the cumulative ranking curves (SUCRA; the probability that an agent is the best, scaled 0-1). Results: We identified 25 RCTs (n=15 913) evaluating 11 agents (3 commercially available for weight management [liraglutide, weekly semaglutide, and tirzepatide] and 8 pre-market). At 6 months, all agents significantly reduced weight versus placebo; retatrutide ranked highest (mean difference [MD]: -15.8%, 95% confidence interval [CI] -17.6 to -14.1; SUCRA 1.00), followed by mazdutide (MD: -12.3%, 95% CI -14.1 to -10.5; SUCRA 0.89) and orforglipron (MD: -10.6%, 95% CI -12.8 to -8.4; SUCRA 0.75). Among the commercially available agents, tirzepatide ranked highest (MD: -9.6%, 95% CI -10.1 to -9.1; SUCRA 0.64). No head-to-head trials were available at this timepoint, so all active-to-active estimates were indirect. At 1-1.5 years, retatrutide remained top-ranked (SUCRA 0.99) and outperformed all currently marketed agents, achieving 3.2% greater weight loss than tirzepatide (95% CI -0.4 to 6.8; SUCRA 0.86), 9.9% more than weekly semaglutide (95% CI 6.5 to 13.3; SUCRA 0.47), and 17.2% more than liraglutide (95% CI 13.8 to 20.7; SUCRA 0.14). Conclusions: Among adults without diabetes, dual- and triple-agonists, particularly tirzepatide and retatrutide, achieve the greatest weight reductions, while conventional single GLP-1 RAs yield smaller effects. These findings can guide clinicians and policymakers as novel agents progress toward regulatory approval.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".