High-Throughput Safety Signal Detection for Sodium-Glucose Cotransporter-2 Inhibitors in Type 2 Diabetes

Abstract

Objective: To systematically identify potential safety signals associated with sodium-glucose cotransporter-2 inhibitors (SGLT2i) in patients with type 2 diabetes (T2D) using a high-throughput target trial emulation pipeline applied to real-world data. Methods: We utilized electronic health record (EHR) data from the OneFlorida+ Data Trust (2012-2023). Using a retrospective new-user cohort design, we compared SGLT2i initiators to initiators of other second-line glucose-lowering drugs (GLDs). We evaluated risk across all ICD-10-CM 4-digit diagnosis codes. A semi-Bayesian shrinkage method was employed to adjust hazard ratios (HR) and p-values to account for multiple testing and stabilize estimates for rare outcomes. Results: The analysis identified several statistically significant safety signals. Validating our method, we observed known adverse events such as candidiasis of the vulva and vagina (HR=1.64) and other urogenital fungal infections. We also detected potential signals for conditions such as viral conjunctivitis and melanocytic nevi. Conclusion: This high-throughput screening effectively identified both known and potential new safety signals for SGLT2i. The use of semi-Bayesian shrinkage provides a robust framework for post-marketing surveillance in large healthcare databases.