Urine proteomics for prediction of disease progression in patients with IgA nephropathy.
IgAN
biomarker
glomerulonephritis
progression
urine proteomics
Journal
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
ISSN: 1460-2385
Titre abrégé: Nephrol Dial Transplant
Pays: England
ID NLM: 8706402
Informations de publication
Date de publication:
31 12 2021
31 12 2021
Historique:
received:
21
07
2020
pubmed:
15
12
2020
medline:
23
3
2022
entrez:
14
12
2020
Statut:
ppublish
Résumé
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. 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. 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). A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone.
Sections du résumé
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.
Identifiants
pubmed: 33313853
pii: 6033100
doi: 10.1093/ndt/gfaa307
pmc: PMC8719618
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
42-52Subventions
Organisme : CIHR
Pays : Canada
Investigateurs
Joachim Beige
(J)
Ralph Wendt
(R)
Justyna Siwy
(J)
Petra Zürbig
(P)
Harald Mischak
(H)
Annika Durban
(A)
Julia Raad
(J)
Igor Golovko
(I)
Heather Reich
(H)
Ping Lam
(P)
Stuart Yang
(S)
Jiménez Díaz
(J)
Ana Belen Sanz
(AB)
Beatriz Fernandez-Fernandez
(B)
Jorge Enrique Rojas-Rivera
(JE)
Maria Vanessa Perez-Gomez
(MV)
Alberto Ortiz
(A)
Maria Dolores Sanchez-Niño
(MD)
Jinny Sanchez-Rodriguez
(J)
Michael Rudnicki
(M)
Julia Kerschbaum
(J)
Johannes Leierer
(J)
Gert Mayer
(G)
Bernd Stegmayr
(B)
Björn Peters
(B)
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA.
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