GLP-1R Agonists for Weight Loss in Psychiatric Disorders: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Context: Individuals with psychiatric disorders have a higher prevalence of obesity, partly because of the use of psychotropic medications. Safe and effective pharmacological interventions for weight management in this population are needed. Objective: To assess the efficacy and safety of glucagon-like peptide-1 receptor agonists (GLP-1Ras) in individuals with psychiatric disorders and obesity/overweight through a systematic review and meta-analysis, focusing on weight and metabolic outcomes. Data Sources: We searched PubMed, Embase, Cochrane CENTRAL, and ClinicalTrials.gov until May 2025 for observational studies that evaluated GLP-1RAs in this population. Study Selection: Studies including adults with psychiatric disorders and obesity treated with GLP-1RAs were eligible; 10 randomized controlled trials met the inclusion criteria. Data Extraction: Two reviewers independently extracted the data and assessed the risk of bias using the Cochrane RoB 2 tool. Outcomes included weight, body mass index (BMI), waist circumference, glucose level, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, triglycerides, and adverse events. Data Synthesis: GLP-1RAs significantly reduced body weight [mean difference (MD) -5.03 kg; 95% confidence interval (CI): -6.04 to -4.01)], BMI (MD -1.59 kg/m²; 95% CI: -2 to -1.18), waist circumference (MD -3.4 cm 95% CI: -4.83 to-1.97), and fasting glucose (MD -0.29 mmol/L; 95% CI: -0.53 to -0.05) compared to controls. Gastrointestinal side effects were more frequent but generally mild and did not increase discontinuation rates. Conclusion: GLP-1RAs are effective and well-tolerated for managing obesity in psychiatric populations, offering significant weight and metabolic benefits. Further studies are needed to evaluate newer agents, such as semaglutide and tirzepatide, particularly in longer trials with standardized protocols.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".