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

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, thu...

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Autores: Rudnicki, Michael, Siwy, Justyna, Wendt, Ralph, Lipphardt, Mark, Koziolek, Michael J., Maixnerova, Dita, Peters, Bjorn, Kerschbaum, Julia, Leierer, Johannes, Neprasova, Michaela, Banasik, Miroslaw, Sanz Bartolomé, Ana Belén, Pérez Gómez, María Vanessa, Ortiz Arduán, Alberto, Stegmayr, Bernd, Tesar, Vladimir, Mischak, Harald, Beige, Joachim, Reich, Heather N.
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/708402
Acceso en línea:http://hdl.handle.net/10486/708402
https://dx.doi.org/10.1093/ndt/gfaa307
Access Level:acceso abierto
Palabra clave:biomarker
glomerulonephritis
IgAN
progression
urine proteomics
Medicina
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spelling Urine proteomics for prediction of disease progression in patients with IgA nephropathyRudnicki, MichaelSiwy, JustynaWendt, RalphLipphardt, MarkKoziolek, Michael J.Maixnerova, DitaPeters, BjornKerschbaum, JuliaLeierer, JohannesNeprasova, MichaelaBanasik, MiroslawSanz Bartolomé, Ana BelénPérez Gómez, María VanessaOrtiz Arduán, AlbertoStegmayr, BerndTesar, VladimirMischak, HaraldBeige, JoachimReich, Heather N.biomarkerglomerulonephritisIgANprogressionurine proteomicsMedicinaRisk 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 aloneOxford University PressDepartamento de MedicinaFacultad de Medicina20222022-01-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/708402https://dx.doi.org/10.1093/ndt/gfaa307reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7084022026-06-23T12:46:27Z
dc.title.none.fl_str_mv Urine proteomics for prediction of disease progression in patients with IgA nephropathy
title Urine proteomics for prediction of disease progression in patients with IgA nephropathy
spellingShingle Urine proteomics for prediction of disease progression in patients with IgA nephropathy
Rudnicki, Michael
biomarker
glomerulonephritis
IgAN
progression
urine proteomics
Medicina
title_short Urine proteomics for prediction of disease progression in patients with IgA nephropathy
title_full Urine proteomics for prediction of disease progression in patients with IgA nephropathy
title_fullStr Urine proteomics for prediction of disease progression in patients with IgA nephropathy
title_full_unstemmed Urine proteomics for prediction of disease progression in patients with IgA nephropathy
title_sort Urine proteomics for prediction of disease progression in patients with IgA nephropathy
dc.creator.none.fl_str_mv Rudnicki, Michael
Siwy, Justyna
Wendt, Ralph
Lipphardt, Mark
Koziolek, Michael J.
Maixnerova, Dita
Peters, Bjorn
Kerschbaum, Julia
Leierer, Johannes
Neprasova, Michaela
Banasik, Miroslaw
Sanz Bartolomé, Ana Belén
Pérez Gómez, María Vanessa
Ortiz Arduán, Alberto
Stegmayr, Bernd
Tesar, Vladimir
Mischak, Harald
Beige, Joachim
Reich, Heather N.
author Rudnicki, Michael
author_facet Rudnicki, Michael
Siwy, Justyna
Wendt, Ralph
Lipphardt, Mark
Koziolek, Michael J.
Maixnerova, Dita
Peters, Bjorn
Kerschbaum, Julia
Leierer, Johannes
Neprasova, Michaela
Banasik, Miroslaw
Sanz Bartolomé, Ana Belén
Pérez Gómez, María Vanessa
Ortiz Arduán, Alberto
Stegmayr, Bernd
Tesar, Vladimir
Mischak, Harald
Beige, Joachim
Reich, Heather N.
author_role author
author2 Siwy, Justyna
Wendt, Ralph
Lipphardt, Mark
Koziolek, Michael J.
Maixnerova, Dita
Peters, Bjorn
Kerschbaum, Julia
Leierer, Johannes
Neprasova, Michaela
Banasik, Miroslaw
Sanz Bartolomé, Ana Belén
Pérez Gómez, María Vanessa
Ortiz Arduán, Alberto
Stegmayr, Bernd
Tesar, Vladimir
Mischak, Harald
Beige, Joachim
Reich, Heather N.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Medicina
Facultad de Medicina
dc.subject.none.fl_str_mv biomarker
glomerulonephritis
IgAN
progression
urine proteomics
Medicina
topic biomarker
glomerulonephritis
IgAN
progression
urine proteomics
Medicina
description 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
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/708402
https://dx.doi.org/10.1093/ndt/gfaa307
url http://hdl.handle.net/10486/708402
https://dx.doi.org/10.1093/ndt/gfaa307
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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