A novel urinary proteomics classifier for non-invasive evaluation of interstitial fibrosis and tubular atrophy in chronic kidney disease

Abstract: (1) Background: Non-invasive urinary peptides biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree interstitial fibrosis and tubular atrophy (IFTA) associated urinary peptides; (2) Methods: The urinary peptides profiles of 435 patients enrolled in this study were analysed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n=200) and a test (n=135) cohort. The fibrosis group was defined as IFTA ≥15% and no fibrosis as IFTA<10%.; (3) Results: Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides associated with IFTA. When combined into a classifier (FPP_BH29), the FPP_BH29 separated the fibrosis and no fibrosis patients from an independent test set (n=186) with area under the curve (AUC) of 0.84 (95%CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed (rho=-0.5, p<0.0001; (4) Urinary proteomics analysis serves as a non-invasive tool to evaluate the degree of renal fibrosis and in contrast to kidney biopsy allows repeat measurements during the disease course.