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 m2 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.

Department of Internal Medicine 4 Nephrology and Hypertension Medical University Innsbruck Innsbruck Austria

Department of Medicine Division of Nephrology University Health Network University of Toronto Toronto Canada

Department of Nephrology 1st School of Medicine and General University Hospital Charles University Prague Czech Republic

Department of Nephrology and Rheumatology University Medical Centre Göttingen Göttingen Germany

Department of Nephrology and Transplantation Medicine Wroclaw Medical University Wroclaw Poland

Department of Nephrology Skaraborg Hospital Skövde Sweden

Department of Public Health and Clinical Medicine Umeå University Umeå Sweden

Division of Nephrology and KfH Renal Unit Hospital St Georg Leipzig Germany

Martin Luther University Halle Wittenberg Halle Saale Germany

Mosaiques Diagnostics GmbH Hannover Germany

Nephrology Research University of Toronto Toronto Ontario Canada

Research Health Institute Fundación Jiménez Díaz University Madrid Spain

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