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Urine proteomics for prediction of disease progression in patients with IgA nephropathy

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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719618/
https://www.ncbi.nlm.nih.gov/pubmed/33313853
http://dx.doi.org/10.1093/ndt/gfaa307
Descripción
Sumario: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.