Real‑World Safety Signals of Tirzepatide Versus GLP‑1RAs: A Machine Learning Analysis of FDA FAERS Data

Background: Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist, has rapidly expanded in clinical use. However, its real-world safety profile relative to established GLP-1 receptor agonists (GLP-1RAs) remains incompletely characterized. Methods: We conducted a retrospective pharmacovigilance analysis of U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) data from 2015 to 2025. Adverse events associated with tirzepatide and other GLP-1RAs were categorized into clinically meaningful groups. We applied interpretable machine-learning models, including logistic regression and random forest classifiers, to identify factors associated with serious adverse events and to compare agent-specific safety patterns. Results: Among 479,921 GLP-1RA-related adverse event reports, gastrointestinal disorders, dosing and administration issues, and injection-site reactions were most common. Tirzepatide accounted for a high volume of reports but showed comparatively lower proportions of pancreatic, thyroid, and gallbladder events than several legacy GLP-1RAs. Across both models, seriousness classification was driven primarily by adverse-event type and reporting context rather than by drug identity. Tirzepatide demonstrated mid-range feature importance and did not independently drive serious adverse-event classification. Conclusions: In this large real-world pharmacovigilance analysis, tirzepatide did not exhibit disproportionate serious safety signals compared with other GLP-1RAs. These findings highlight that absolute reporting volume in FAERS may reflect uptake and reporting behavior rather than intrinsic drug risk, underscoring the importance of interpretable analytic approaches when evaluating post-marketing safety.