Osteopontin—A Potential Biomarker for IgA Nephropathy: Machine Learning Application

This research was supported by the Department of Clinical Immunology (grant no. N1/20/20) and the Department of Immunology, Transplantology and Internal Diseases (grant no. 1W21/DAR55/2020), Medical University of Warsaw; by Compensa Towarzystwo Ubezpieczen S.A. Vienna Insurance Group; and in part by grant no. 2U01DK100876 from the U.S. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (K.M., B.F., N.K., B.M. and L.P.).

Many potential biomarkers in nephrology have been studied, but few are currently used in clinical practice. One is osteopontin (OPN). We compared urinary OPN concentrations in 80 participants: 67 patients with various biopsy-proven glomerulopathies (GNs)—immunoglobulin A nephropathy (IgAN, 29), membranous nephropathy (MN, 20) and lupus nephritis (LN, 18) and 13 with no GN. Follow-up included 48 participants. Machine learning was used to correlate OPN with other factors to classify patients by GN type. The resulting algorithm had an accuracy of 87% in differentiating IgAN from other GNs using urinary OPN levels only. A lesser effect for discriminating MN and LN was observed. However, the lower number of patients and the phenotypic heterogeneity of MN and LN might have affected those results. OPN was significantly higher in IgAN at baseline than in other GNs and therefore might be useful for identifying patients with IgAN. That observation did not apply to either patients with IgAN at follow-up or to patients with other GNs. OPN seems to be a valuable biomarker and should be validated in future studies. Machine learning is a powerful tool that, compared with traditional statistical methods, can be also applied to smaller datasets.