A systematic review on the efficacy of GLP-1 receptor agonists in mitigating psychotropic drug-related weight gain
Bibliographic record
Abstract
OBJECTIVE: Many psychotropic drugs are highly associated with related weight gain. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are established anti-obesity and glucose-lowering agents. Preliminary evidence also indicates they are fit for purpose in mitigating psychotropic drug-related weight gain (PDWG). This systematic review aims to synthesize the extant evidence from randomized controlled trials (RCTs) on the effects of GLP-1RAs on weight change in persons experiencing PDWG. METHODS: Online databases (ie, PubMed, OVID Medline, Google Scholar) were searched to identify relevant studies from inception to January 1, 2024. Articles were screened by title, abstract, and full-text by three independent reviewers against inclusion and exclusion criteria. RESULTS: We identified six studies with participants aged ≥18 (n=374) that were eligible for inclusion in our systematic review. Most studies reported a significant and clinically meaningful effect of GLP-1RAs on anthropometrics and/or metabolics. All RCTs replicated the finding of modest or greater effects of GLP-1RAs; the most studied agents were liraglutide and exenatide. There was insufficient literature to conduct a meta-analysis. CONCLUSION: Evidence suggests that GLP-1RAs are effective in mitigating weight gain in persons prescribed psychiatric medication. It is hypothesized that GLP-1RAs may moderate weight change in persons prescribed psychiatric medication through direct effects on metabolism and cognitive processes implicated in hunger/satiety. Future studies should aim to explore the long-term safety, tolerability, and efficacy profiles of various GLP-1RAs in the treatment and prevention of abnormal weight and metabolic homeostasis in psychiatric populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".