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

BackgroundSodium-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. MethodsUsing 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. ResultsSGLT2 inhibitors were associated with a significant reduction in AKI compared with other second-line glucose-lowering drugs, with an average individual treatment effect of -0.0039 (95% CI: -0.0065 to -0.0014). Kaplan-Meier curves demonstrated consistently lower cumulative AKI incidence among SGLT2i users. Subgroup analyses revealed substantial heterogeneity: protective effects were strongest in younger adults, males, and non-Hispanic Black patients, whereas benefits were attenuated in older adults, females, and those with baseline CKD. Decision tree-based heterogeneity modeling further identified atrial fibrillation, anemia, and antiparkinson agent use as key effect modifiers. Causal structure learning highlighted atrial fibrillation, anemia, chronic kidney disease, and eGFR as central intermediating nodes. Mediation analyses showed that most of the benefit operated through direct pathways (ADE {approx} -0.0034 to -0.0035), while anemia and heart failure contributed small but statistically significant indirect effects. ConclusionSGLT2 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.