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Nephrol Dial Transplant. 2017 Dec 1;32(12):2079-2089. doi: 10.1093/ndt/gfw337.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis.

Author information

1
Mosaiques Diagnostics GmbH, Hanover, Germany.
2
RD Néphrologie, Montpellier, France.
3
KfH Renal Unit, Department Nephrology, Leipzig and Martin Luther University, Halle/Wittenberg, Germany.
4
Department of Nephrology, Klinikum Fulda gAG, Fulda, Germany.
5
Institute for Molecular Cardiovascular Research, RWTH Aachen University Hospital, Aachen, Germany.
6
School for Cardiovascular Diseases (CARIM), University of Maastricht, Maastricht, The Netherlands.
7
University of Alabama at Birmingham, Birmingham, AL, USA.
8
Abbvie, North Chicago, IL, USA.
9
Department of Nephrology and Kidney Transplantation, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
10
BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
11
School of Medicine, Jimenez Diaz Foundation Institute for Health Research, Autonomous University of Madrid, Madrid, Spain.
12
Steno Diabetes Center, Gentofte, Denmark.
13
Charite-Universitätsmedizin, Berlin, Germany.
14
Faculty of Health, University of Aarhus, Aarhus, Denmark.
15
Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
16
Department of Nephrology, Klinikum Bayreuth, Bayreuth, Germany.
17
Institute of Cardiovascular and Metabolic Disease, French Institute of Health and Medical Research U1048, Toulouse, France.
18
Université Toulouse III Paul-Sabatier, Toulouse, France.
19
Division of Biotechnology, Biomedical Research Foundation, Academy of Athens, Athens, Greece.
20
Nephrology Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium.

Abstract

Background:

In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease.

Methods:

We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results:

For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology.

Conclusions:

Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

KEYWORDS:

biomarkers; chronic kidney disease; peptides; proteome analysis; urine

PMID:
27984204
PMCID:
PMC5837301
DOI:
10.1093/ndt/gfw337
[Indexed for MEDLINE]
Free PMC Article

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