Objectives/Goals: Ocular adverse events (OAEs) have been associated with GLP-1 use in clinical studies. This study aims to describe the proportion and distribution of ocular adverse events by treatment arm, including serious and non-serious events, in phase 2-4 clinical trials of GLP-1 drugs using publicly available ClincialTrials.gov data. Methods/Study Population: The ClinicalTrials.gov database was searched on 9/11/25 for interventional studies with GLP-1 as treatment (n= 1545). The search was filtered to terminated and completed studies with results (n= 349). Adverse event reports from Clinical Trials Transformation Initiative (CTTI) Aggregate Analysis of ClinicalTrials.gov (AACT) database were restricted to phase 2-4 drug studies (n= 240), of which 167 involved a GLP1-1 drug with 64 studies reporting ocular adverse events. Ocular adverse events were summarized as weighted and unweighted mean proportions by arm and seriousness, with chi-square, Fisher’s exact, and Wilcoxon tests assessing significance. Study characteristics were included in statistical models to further assess differences in ocular adverse event proportions. Results/Anticipated Results: Ocular adverse events ranked 10 among top organ systems affected in GLP-1 drug trials. Among 64 studies reporting ocular adverse events, serious ocular event proportions were higher in GLP-1 arms compared to placebo (weighted: p < 0.01; unweighted: p = 0.02). Most studies (n = 51) systemically assessed ocular events; 10 were non-systematic, and 3 did not classify reporting. The top 10 ocular events highlighted retinal detachment at significantly higher proportions in drug arms than placebo arms (weighted: 0.11% vs. 0.02%, p = 0.04). Cataract and diabetic retinopathy events were observed less frequently in drug arm than placebo arm (weighted: p = 0.02; p = 0.03). Discussion/Significance of Impact: Serious and non-serious ocular adverse events occurred more often in GLP-1 drug arms, driven by specific conditions such as retinal detachment. Using the AACT database enables large-scale evaluation of adverse event patterns, providing broader context than single-study analyses.
Bruhn et al. (Wed,) studied this question.