Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine

Diabetic kidney disease (DKD) remains a leading cause of global morbidity and mortality. While current therapies like sodium-glucose cotransporter 2 (<i>SGLT2</i>) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have improved outcomes, significant challenges persist in early detection and halting progression. This review synthesizes recent transformative advances in DKD research. We highlight how single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have unveiled unprecedented cellular heterogeneity, delineated pathogenic trajectories like maladaptive tubular cell states, and established immune dysregulation as central to disease progression. Concurrently, metabolomics provides a window into early metabolic disturbances, identifying novel biomarkers that reflect mitochondrial dysfunction and oxidative stress. Furthermore, artificial intelligence (AI) is revolutionizing clinical practice, with deep learning models like DeepDKD demonstrating high accuracy in non-invasive screening using retinal images and enabling refined risk stratification. These multi-omics insights are paralleled by the development of novel therapeutic agents targeting inflammation, fibrosis, and metabolic pathways beyond traditional targets. The integration of high-resolution molecular profiling, AI-driven analytics, and mechanism-based therapeutics is paving the way for a new era of precision nephrology, offering hope for earlier intervention and personalized management strategies for DKD.