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Biomedicines
Volume 10
Issue 4
10.3390/biomedicines10040734
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Article

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

by
Barbara Moszczuk
1,2,3,
Natalia Krata
1,2,
Witold Rudnicki
4,5,
Bartosz Foroncewicz
1,2,
Dominik Cysewski
6,
Leszek Pączek
1,2,6,
Beata Kaleta
3,*,† and
Krzysztof Mucha
1,2,6,*,†
1
Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, 02-006 Warsaw, Poland
2
ProMix Center (ProteogenOmix in Medicine), Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, 02-006 Warsaw, Poland
3
Department of Clinical Immunology, Medical University of Warsaw, 02-006 Warsaw, Poland
4
Computational Centre and Institute of Computer Science, University of Białystok, 15-245 Białystok, Poland
5
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, 02-630 Warsaw, Poland
6
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editors: Giuseppe Cappellano and Marie Černá
Biomedicines 2022, 10(4), 734; https://doi.org/10.3390/biomedicines10040734
Received: 31 January 2022 / Revised: 8 March 2022 / Accepted: 18 March 2022 / Published: 22 March 2022
(This article belongs to the Special Issue 30 Years of OPN Milestones and Future Avenues)
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Abstract

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. View Full-Text
Keywords: biomarkers; IgA nephropathy; lupus nephritis; machine learning; membranous nephropathy; osteopontin; peroxiredoxins biomarkers; IgA nephropathy; lupus nephritis; machine learning; membranous nephropathy; osteopontin; peroxiredoxins
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MDPI and ACS Style

Moszczuk, B.; Krata, N.; Rudnicki, W.; Foroncewicz, B.; Cysewski, D.; Pączek, L.; Kaleta, B.; Mucha, K. Osteopontin—A Potential Biomarker for IgA Nephropathy: Machine Learning Application. Biomedicines 2022, 10, 734. https://doi.org/10.3390/biomedicines10040734

AMA Style

Moszczuk B, Krata N, Rudnicki W, Foroncewicz B, Cysewski D, Pączek L, Kaleta B, Mucha K. Osteopontin—A Potential Biomarker for IgA Nephropathy: Machine Learning Application. Biomedicines. 2022; 10(4):734. https://doi.org/10.3390/biomedicines10040734

Chicago/Turabian Style

Moszczuk, Barbara, Natalia Krata, Witold Rudnicki, Bartosz Foroncewicz, Dominik Cysewski, Leszek Pączek, Beata Kaleta, and Krzysztof Mucha. 2022. "Osteopontin—A Potential Biomarker for IgA Nephropathy: Machine Learning Application" Biomedicines 10, no. 4: 734. https://doi.org/10.3390/biomedicines10040734

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