Glucagon-like peptide-1 receptor agonists (GLP-1RAs) as treatment for nicotine cessation in psychiatric populations: a systematic review
Bibliographic record
Abstract
BACKGROUND: Nicotine use and nicotine use disorder (NUD) are the leading causes of preventable death in the United States. Persons with mental disorders (e.g., bipolar disorder) are differentially susceptible to nicotine use. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are indicated for type 2 diabetes mellitus (T2DM) and obesity and show preliminary evidence of efficacy in addiction-related behaviours. Herein, we synthesize extant preclinical and clinical evidence evaluating the effect of GLP-1RAs on neurobiological systems and behaviours salient to nicotine consumption and cessation. METHODS: Online databases (MedLine, Embase, AMED, PsychINFO, JBI EBP Database, PubMed, Web of Science, Google Scholar) were searched from inception to May 21, 2024. Relevant studies were also extracted from the reference lists of the obtained articles. All articles were screened against inclusion and exclusion criteria. RESULTS: Administration of GLP-1RAs reduced nicotine self-administration and nicotine-seeking behaviour in animal models that, in some cases, is sustained beyond exposure to the agent. GLP-1RAs also mitigated post-nicotine cessation weight gain, craving, withdrawal, and hyperphagia. The preceding effects are attributable to modulation of reward-related brain regions (e.g., mesolimbic dopamine system), resulting in nicotine aversion. GLP-1RAs were also efficacious as adjunctive therapies [e.g., in combination with nicotine replacement therapies (NRTs)]. CONCLUSION: The multi-effect characteristics in NUD paradigms provide a compelling rationale for large, adequately powered, long-term, randomized controlled trials of GLP-1RAs in the treatment and prevention of NUD. The replicated effect on mitigating post-nicotine cessation weight gain is a differentiating feature of GLP-1RAs from extant proven therapies for NUD.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 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".