First, artificial intelligence-driven multi-omics integration—incorporating urinary exosomal RNA profiles, single-cell epigenetic data, and wearable device metrics—will enable prediction of individual channelopathies (e.g., concurrent TRPC6/Piezo1 dysregulation) and optimization of SGLT2 inhibitor therapeutic responses. The gene discussed is SLC5A2; the disease is channelopathy.