Urine proteomics for prediction of disease progression in patients with IgA nephropathy
© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association - 37(2021), 1 vom: 31. Dez., Seite 42-52 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rudnicki, Michael [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarker |
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| Anmerkungen: |
Date Completed 22.03.2022 Date Revised 05.04.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/ndt/gfaa307 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM318791285 |
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| 100 | 1 | |a Rudnicki, Michael |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Urine proteomics for prediction of disease progression in patients with IgA nephropathy |
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| 500 | |a Date Revised 05.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a CONCLUSIONS: A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a IgAN | |
| 650 | 4 | |a biomarker | |
| 650 | 4 | |a glomerulonephritis | |
| 650 | 4 | |a progression | |
| 650 | 4 | |a urine proteomics | |
| 700 | 1 | |a Siwy, Justyna |e verfasserin |4 aut | |
| 700 | 1 | |a Wendt, Ralph |e verfasserin |4 aut | |
| 700 | 1 | |a Lipphardt, Mark |e verfasserin |4 aut | |
| 700 | 1 | |a Koziolek, Michael J |e verfasserin |4 aut | |
| 700 | 1 | |a Maixnerova, Dita |e verfasserin |4 aut | |
| 700 | 1 | |a Peters, Björn |e verfasserin |4 aut | |
| 700 | 1 | |a Kerschbaum, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Leierer, Johannes |e verfasserin |4 aut | |
| 700 | 1 | |a Neprasova, Michaela |e verfasserin |4 aut | |
| 700 | 1 | |a Banasik, Miroslaw |e verfasserin |4 aut | |
| 700 | 1 | |a Sanz, Ana Belen |e verfasserin |4 aut | |
| 700 | 1 | |a Perez-Gomez, Maria Vanessa |e verfasserin |4 aut | |
| 700 | 1 | |a Ortiz, Alberto |e verfasserin |4 aut | |
| 700 | 1 | |a Stegmayr, Bernd |e verfasserin |4 aut | |
| 700 | 1 | |a Tesar, Vladimir |e verfasserin |4 aut | |
| 700 | 1 | |a Mischak, Harald |e verfasserin |4 aut | |
| 700 | 1 | |a Beige, Joachim |e verfasserin |4 aut | |
| 700 | 1 | |a Reich, Heather N |e verfasserin |4 aut | |
| 700 | 0 | |a PERSTIGAN working group |e verfasserin |4 aut | |
| 700 | 1 | |a Beige, Joachim |e investigator |4 oth | |
| 700 | 1 | |a Wendt, Ralph |e investigator |4 oth | |
| 700 | 1 | |a Siwy, Justyna |e investigator |4 oth | |
| 700 | 1 | |a Zürbig, Petra |e investigator |4 oth | |
| 700 | 1 | |a Mischak, Harald |e investigator |4 oth | |
| 700 | 1 | |a Durban, Annika |e investigator |4 oth | |
| 700 | 1 | |a Raad, Julia |e investigator |4 oth | |
| 700 | 1 | |a Golovko, Igor |e investigator |4 oth | |
| 700 | 1 | |a Reich, Heather |e investigator |4 oth | |
| 700 | 1 | |a Lam, Ping |e investigator |4 oth | |
| 700 | 1 | |a Yang, Stuart |e investigator |4 oth | |
| 700 | 1 | |a Díaz, Jiménez |e investigator |4 oth | |
| 700 | 1 | |a Sanz, Ana Belen |e investigator |4 oth | |
| 700 | 1 | |a Fernandez-Fernandez, Beatriz |e investigator |4 oth | |
| 700 | 1 | |a Rojas-Rivera, Jorge Enrique |e investigator |4 oth | |
| 700 | 1 | |a Perez-Gomez, Maria Vanessa |e investigator |4 oth | |
| 700 | 1 | |a Ortiz, Alberto |e investigator |4 oth | |
| 700 | 1 | |a Sanchez-Niño, Maria Dolores |e investigator |4 oth | |
| 700 | 1 | |a Sanchez-Rodriguez, Jinny |e investigator |4 oth | |
| 700 | 1 | |a Rudnicki, Michael |e investigator |4 oth | |
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| 700 | 1 | |a Stegmayr, Bernd |e investigator |4 oth | |
| 700 | 1 | |a Peters, Björn |e investigator |4 oth | |
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