Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more system...

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Autores principales: Esther Nkuipou-Kenfack, Flore Duranton, Nathalie Gayrard, Àngel Argilés, Ulrika Lundin, Klaus M Weinberger, Mohammed Dakna, Christian Delles, William Mullen, Holger Husi, Julie Klein, Thomas Koeck, Petra Zürbig, Harald Mischak
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spelling oai:doaj.org-article:3a637c8923964918aad48ef242dfe71b2021-11-18T08:19:53ZAssessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.1932-620310.1371/journal.pone.0096955https://doaj.org/article/3a637c8923964918aad48ef242dfe71b2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24817014/?tool=EBIhttps://doaj.org/toc/1932-6203Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.Esther Nkuipou-KenfackFlore DurantonNathalie GayrardÀngel ArgilésUlrika LundinKlaus M WeinbergerMohammed DaknaChristian DellesWilliam MullenHolger HusiJulie KleinThomas KoeckPetra ZürbigHarald MischakPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e96955 (2014)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Esther Nkuipou-Kenfack
Flore Duranton
Nathalie Gayrard
Àngel Argilés
Ulrika Lundin
Klaus M Weinberger
Mohammed Dakna
Christian Delles
William Mullen
Holger Husi
Julie Klein
Thomas Koeck
Petra Zürbig
Harald Mischak
Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
description Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.
format article
author Esther Nkuipou-Kenfack
Flore Duranton
Nathalie Gayrard
Àngel Argilés
Ulrika Lundin
Klaus M Weinberger
Mohammed Dakna
Christian Delles
William Mullen
Holger Husi
Julie Klein
Thomas Koeck
Petra Zürbig
Harald Mischak
author_facet Esther Nkuipou-Kenfack
Flore Duranton
Nathalie Gayrard
Àngel Argilés
Ulrika Lundin
Klaus M Weinberger
Mohammed Dakna
Christian Delles
William Mullen
Holger Husi
Julie Klein
Thomas Koeck
Petra Zürbig
Harald Mischak
author_sort Esther Nkuipou-Kenfack
title Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
title_short Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
title_full Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
title_fullStr Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
title_full_unstemmed Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
title_sort assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/3a637c8923964918aad48ef242dfe71b
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