Gastrointestinal Safety Assessment of GLP-1 Receptor Agonists in the US: A Real-World Adverse Events Analysis from the FAERS Database
Bibliographic record
Abstract
Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used to treat obesity and diabetes but are linked to a variety of gastrointestinal (GI) adverse events (AEs). Real-world data on GLP-1 RA-related GI AEs and outcomes are limited. This study assessed GI AEs and adverse outcomes using the US FDA Adverse Event Reporting System (FAERS). Methods: This retrospective pharmacovigilance study used the US FDA FAERS database (2007–2023). We searched GLP-1 RA medications, AEs, and adverse outcomes. Demographic, treatment indication, and AE data were collected. Descriptive analysis involved frequencies and percentages, while reporting odds ratio (ROR), proportional reporting ratio, Bayesian confidence propagation neural network, and multivariate logistic regression were used to analyze GLP-1 RA-related GI AEs and outcomes. Results: From 2007 to 2023, a total of 187,757 AEs were reported with GLP-1 RAs, and 16,568 were GLP-1 RA-associated GI AEs in the US. Semaglutide was linked to higher odds of nausea (IC025: 0.151, βCoeff: 0.314), vomiting (IC025: 0.334, βCoeff: 0.495), and delayed gastric emptying (IC025: 0.342, βCoeff: 0.453). Exenatide was associated with pancreatitis (IC025: 0.601, βCoeff: 0.851) and death (ROR: 4.50, IC025: 1.101). Overall, semaglutide had a broader range of notable adverse effects; by comparison, dulaglutide and liraglutide use was associated with fewer significant GI AEs. Conclusions: Analysis of the FAERS data reveals that GLP-1 RAs, particularly semaglutide and exenatide, are significantly associated with specific GI AEs, such as nausea, vomiting, delayed gastric emptying, and pancreatitis. Clinicians should be aware of these potential risks to ensure optimal monitoring and patient safety. This study demonstrated the utility of pharmacovigilance data in identifying safety signals, which can inform future pharmacoepidemiological investigations to confirm causal relationships. Clinicians should be aware of these potential risks to ensure optimal monitoring and patient safety.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".