Submission 22 July 2025
Acceptance 27 Aug 2025
Publication 25 October 2025
Volume 13 Issue 10
Emerging Trends in Diabetic Nephropathy: Integrating Biomarkers, Artificial Intelligence, and Precision Medicine for Early Detection
1Sarah, 2Jonathan, 3Emily, 4Robert Grace
1Division of Endocrinology, Stanford University School of Medicine, Stanford, CA 94305, USA
2Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
3Stanford Diabetes Research Center, Stanford, CA 94305, USA
4Department of Pathology, Stanford Health Care, Stanford, CA 94305, USA
Abstract
Diabetic nephropathy remains a leading cause of chronic kidney disease and end-stage renal failure worldwide. Despite significant therapeutic advances, early detection remains a major challenge. This study explores the integration of serum and urinary biomarkers with artificial intelligence (AI)-based predictive models for improved risk stratification in patients with type 2 diabetes mellitus. Using a multi-omic dataset from 1,200 patients, we developed a machine learning model capable of identifying subclinical nephropathy with 89% accuracy. Integration of genetic markers and precision medicine tools further enhanced diagnostic specificity. Early intervention guided by AI predictions demonstrated significant reduction in renal decline over a three-year follow-up.
Keywords: diabetic nephropathy; biomarkers; precision medicine; artificial intelligence; chronic kidney disease
