Heterogeneous Effects of Sodium-Glucose Cotransporter-2 Inhibitors on Acute Kidney Injury: A Causal Learning Approach.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2is) have been associated with lower risk of acute kidney injury (AKI), but existing studies rarely explore heterogeneous treatment effects or underlying causal pathways. We applied a comprehensive causal-learning framework to evaluate both overall and subgroup-specific effects of SGLT2i therapy on AKI. Methods: Using a new-user, active-comparator target trial emulation in the OneFlorida+ data (2014-2023), we estimated individualized and average treatment effects with a doubly robust meta-learner, assessed heterogeneity via subgroup and decision-tree analyses, and used causal structure learning and mediation methods to identify mechanistic pathways linking treatment to AKI. Results: SGLT2 inhibitors were associated with a significant reduction in AKI compared with other second-line glucose-lowering drugs, with an average individual treatment effect of Conclusion: SGLT2 inhibitors reduce AKI risk, but effects vary meaningfully across clinical subgroups and are partially mediated through interconnected cardio-renal pathways. Causal-learning methods provide mechanistic insight beyond average associations and may support more individualized SGLT2i therapy for AKI prevention.