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 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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".