Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology.

The complex phenotype of diabetic nephropathy from type 2 diabetes complicates diagnosis and prognosis of patients. Kidney histology may help overcome this difficult situation, particularly if it further suggests molecular profiles. This study describes a method using panoptic segmentation and deep learning to interrogate both urinary proteomics and histomorphometric image features to predict whether patients progress to end-stage kidney disease since biopsy date. A subset of urinary proteomics had the most predictive power in identifying progressors, which could annotate significant tubular and glomerular features related to outcomes. This computational method, which aligns molecular profiles and histology, may improve our understanding of pathophysiological progression of diabetic nephropathy as well as carry clinical implications in histopathological evaluation.