Urine proteomics for prediction of disease progression in patients with IgA nephropathy. Issue 1 (14th December 2020)

• Record Type: Journal Article
• Authors: Rudnicki, Michael; Siwy, Justyna; Wendt, Ralph; Lipphardt, Mark; Koziolek, Michael J; Maixnerova, Dita; Peters, Björn; Kerschbaum, Julia; Leierer, Johannes; Neprasova, Michaela; Banasik, Miroslaw; Sanz, Ana Belen; Perez-Gomez, Maria Vanessa; Ortiz, Alberto; Stegmayr, Bernd; Tesar, Vladimir; Mischak, Harald; Beige, Joachim; Reich, Heather N
• Is Part Of: Nephrology dialysis transplantation. Volume 37:Issue 1(2022)
• Page Start: 42
• Page End: 52
• Publication Date: 2020-12-14
• Subjects: biomarker -- glomerulonephritis -- IgAN -- progression -- urine proteomics; Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfaa307
• Languages: English
• ISSNs: 0931-0509

Abstract

Abstract: Background: Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification. Methods: In this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models. Results: Of the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m 2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83–0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64–0.81). Conclusions: A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone. Graphical Abstract: