Abstract Background and Aims Although IgA nephropathy (IgAN) is the most common primary glomerulonephritis in many parts of the world, risk of progressive kidney function decline is significant. Furthermore, the effect of immunosuppressive treatment in IgAN currently remains uncertain. There is no validated tool to predict disease progression or response to treatment. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts fast disease progression in patients with IgAN, thus enabling a personalized risk stratification and potentially improving patient management. Method In this multicenter study in 7 centers in Europe and in Canada urine samples were collected as part of clinical routine at time of biopsy in n= 300 patients (63% male, age 42±14 years) with biopsy proven IgAN. The follow-up data were collected for at least one year. Progressive disease was defined as an annual loss of kidney function (estimated glomerular filtration rate, eGFR) of more than -5ml/min/1.73m2 per year or when end-stage kidney disease (ESKD) was reached. The institutional review boards of each of the participating centers approved this study. Urine samples were analyzed using capillary electrophoresis coupled mass spectrometry (CE-MS). Whole proteome/peptidome profile was obtained for each sample. This study received funding from the European Union´s ERA PerMed program. Results Urine proteome/peptidome profiles were obtained from n=294 patients. On average, 2383 peptides were detected per sample. The data were subsequently divided into a discovery (n=154) and validation cohort (n=140). The comparison of the progressors (n=35) and non-progressors (n=119) in the discovery cohort resulted in the definition of more than 100 significant peptides. These included mainly fragments from collagen, mostly type 1 (decreased in progressors) and from different blood derived proteins like alpha-1-antitrypsin, alpha-2-HS-glycoprotein and apolipoproteins (increased in progressors). The distribution of the 100 most significant peptides in the progressor and non-progressor group is shown in the figure. The peptides were combined into a classifier using support vector machine. After optimizing the classifier employing a take-one-out procedure combined with n-1 cross-validation, the urine-peptide based algorithm enabled separation of progressors versus no progressors with an accuracy of 90% in take-one-out cross-validation. This classifier was subsequently applied to the validation cohort and resulted in highly significant separation of progressive from non-progressive IgAN patients. Furthermore, this classifier will be further applied blinded in an independent well characterized multicenter cohort of 267 IgAN patients. Conclusion We identified a urinary proteome profile which was associated with progressive loss of GFR in patients with IgAN. Further validation of this profile in an independent cohort is ongoing. The data indicate that CE-MS-based urinary proteomics enables identifying IgAN patients at high risk of disease progression. These patients may benefit from aggressive immunosuppressive treatment. Upon validation of the classifier in an independent cohort, its value in predicting response to immunosuppression will be assessed, aiming at establishing an innovative strategy that could improve patient management and personalize treatment of IgAN patients. Figure: Distribution of the 100 most significant peptides in the progressor and no progressor group of IgAN patients. The molecular mass (on a logarithmic scale) is plotted against normalized migration time. Signal intensity is encoded by peak height. In the lower panel the most intensive peptides were omitted and the other peptides showed in fivefold magnification