The association between glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and suicidality: reports to the Food and Drug Administration Adverse Event Reporting System (FAERS)
Bibliographic record
Abstract
INTRODUCTION: Recently, the European Medicines Agency (EMA) received reports of suicidal thoughts and self-injury associated with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) liraglutide and semaglutide. RESEARCH DESIGN AND METHODS: Herein, we sought to evaluate suicidality associated with all GLP-1 RAs relative to other glucose-lowering agents currently approved by the United States Food and Drug Administration (FDA). Reports of suicidal ideation, "depression/suicidal", suicidal behavior, suicidal attempts, and completed suicide associated with GLP-1 RA exposure reported to the FDA between 2005 and October 2023 were obtained from the FDA Adverse Event Reporting System (FAERS). We present data using the reporting odds ratio (ROR). The ROR was considered significant when the lower limit of the 95% confidence interval (CI) was greater than 1.0. RESULTS: Disproportionate reporting of suicidal ideation and "depression/suicidal" was observed with semaglutide and liraglutide. Disproportionate reporting of suicidal behavior, suicide attempts, and completed suicide was not observed for any of the FDA-approved GLP-1 RAs. CONCLUSIONS: Using the Bradford Hill criteria, however, and taking into consideration confounders, no causal link between GLP-1 RAs and suicidality exists.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".