The impact of SGLT2 inhibitors on uric acid levels in patients with heart failure with preserved ejection fraction: integration of mechanisms and clinical evidence.

OBJECTIVE: Hyperuricemia (HUA) is a prevalent comorbidity in patients with heart failure with preserved ejection fraction (HFpEF) and is closely associated with disease progression and adverse outcomes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors can rapidly and sustainably reduce serum uric acid (SUA) levels and lower the incidence of HUA-related clinical events; however, their underlying mechanisms remain unclear. This study employed network pharmacology and molecular docking to systematically investigate the potential mechanisms of SGLT2 inhibitors in HFpEF patients with concomitant hyperuricemia, focusing on uric acid metabolism modulation and inflammation-related pathways. Additionally, a machine learning approach was applied to retrospectively analyze the effects of SGLT2 inhibitors on SUA levels, aiming to clarify the therapy's biological basis and clinical relevance. METHODS: The SMILES structures of eleven SGLT2 inhibitors were retrieved from PubChem. Potential drug targets were predicted via the SwissTargetPrediction platform and intersected with HFpEF- and HUA-related targets obtained from OMIM and GeneCards to identify common targets. Protein-protein interaction (PPI) networks were constructed using STRING to identify hub genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using DAVID. Molecular docking with AutoDock Vina assessed binding affinities. Clinical data of HFpEF patients were collected, and a classification model based on 34 clinical variables was developed using the FLAML automated machine learning framework to predict SGLT2 inhibitor treatment status and changes in SUA. Model interpretability and feature importance were performed using SHAP analysis. RESULTS: Eleven SGLT2 inhibitors acted on 32 targets associated with SUA reduction in HFpEF patients. Key pathways included tumor necrosis factor (TNF), advanced glycation end product-receptor for advanced glycation end product (AGE-RAGE), interleukin-17 (IL-17), and Kaposi's sarcoma-associated herpesvirus (KSHV) signaling. Molecular docking demonstrated favorable binding of SGLT2 inhibitors to core targets, including GAPDH, CASP3, ICAM1, ACE, DPP4, and AGTR1, with canagliflozin showing the strongest predicted binding affinity. Clinical data analyses identified uric acid and creatinine as the most important predictors in the machine learning model, reflecting their importance in model predictions. CONCLUSION: By integrating network pharmacology and retrospective clinical analysis, this study elucidates the potential uric acid-lowering mechanisms of SGLT2 inhibitors in HFpEF patients. The identified targets and pathways provide mechanistic insights into SGLT2 inhibitor pharmacology and support their therapeutic potential for managing hyperuricemia.