Glucagon-like peptide-1 receptor agonists for the treatment of opioid use disorders: a systematic review
Bibliographic record
Abstract
Abstract Introduction: Extant literature indicated that glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may potentially reduce risk of opioid overdose in persons with opioid use disorders (OUDs). Herein, we conducted a comprehensive synthesis of the effects of GLP-1 and GLP-1 RAs on OUDs. Methods: We examined preclinical and clinical paradigms examining the effects of GLP-1 and GLP-1 RAs on OUD and OUD-associated behaviours (i.e. opioid self-administration, opioid-seeking behaviour). Relevant articles were retrieved from OVID (MedLine, Embase, AMED, PsychINFO, and JBI EBP Database), PubMed, and Web of Science from database inception to 1 May 2025. Primary studies (n = 10) examining the aforementioned effects associated with GLP-1 and GLP-1 RA administration were retrieved for analysis. Results: GLP-1 RAs (i.e. exenatide, liraglutide) reduced opioid-seeking behaviour (p < 0.05) and self-administration of opioid drugs (p < 0.05) in preclinical paradigms. In addition, results from human studies indicate that GLP-1 administration was associated with reducing the risk of opioid overdose in human studies (aIRR = 0.60, 95% CI [0.43, 0.83]). Conclusion: GLP-1 RAs may affect opioid self-administration as well as the risk for overdose as evidenced by both preclinical and clinical data. There is a need for adequate well-controlled studies to determine whether GLP-1 RAs may provide clinically meaningful improvement and risk reduction in persons living with OUDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".