Novel Biomarkers for Early Detection and Prognosis of Diabetic Kidney Disease

Abstract

Diabetic kidney disease (DKD) is a common microvascular complication of diabetes mellitus and a major cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) in the world. While traditional markers, such as urinary albumin-to-creatinine ratio, serum creatinine, and estimated glomerular filtration rate remain pivotal in clinical evaluation, they often fall short in identifying subclinical renal damage and in predicting the trajectory of a particular disease. This study assesses newer markers of early detection, prognostic stratification and therapeutic monitoring of DKD. It covers various biomarkers of glomerular and tubular injury, inflammation, oxidative stress, fibrosis and metabolic dysregulation such as nephrin, podocin, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, tumour necrosis factor receptors, CCL20, trimethylamine N-oxide and fibrosis-related mediators. The study also discusses genomic, epigenomic, transcriptomic, proteomic, and metabolomic methods, highlighting the potential of combination of multi-omics, systems biology, artificial intelligence, and machine learning to enhance personalized risk prediction. The use of a panel of biomarkers including molecular, clinical, imaging, and digital more accurate for diagnosis and prognosis than a single-marker approach. But challenges of external validation, assay variability, high assay costs, regulatory demands and the ability to integrate advanced testing into routine workflows have limited clinical translation. Thus, large-scale prospective studies, common analytical procedures and cost-effectiveness analyses are needed. The use of validated molecular markers in conjunction with current tools leads to earlier intervention, tailored treatment choices, more effective therapeutic monitoring, and better long-term renal and cardiovascular outcomes for DKD.