Evidence map›Paper›PMID 27984204›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2017

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis.

Justyna Siwy, Petra Zürbig, Angel Argiles, Joachim Beige, Marion Haubitz, Joachim Jankowski, Bruce A Julian, Peter G Linde, David Marx, Harald Mischak and 10 more

Open access · bronzeAbstract read
In one paragraph

Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 2 pooled it
6.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Review
  5. Review
  6. Article
  7. Urinary peptide analysis to predict the response to blood pressure medication.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
    Article
  8. Review
  9. Review
  10. Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
    Article
  11. Article
  12. Dynamics of urine proteomics biomarker and disease progression in patients with IgA nephropathy.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023
    Article
  13. Article
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  19. Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023
    Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 17 institutions in 10 countries.

Justyna SiwyMosaiques Diagnostics GmbH, Hanover, Germany.
Petra ZürbigMosaiques Diagnostics GmbH, Hanover, Germany.
Angel ArgilesRD Néphrologie, Montpellier, France.
Joachim BeigeKfH Renal Unit, Department Nephrology, Leipzig and Martin Luther University, Halle/Wittenberg, Germany.
Marion HaubitzDepartment of Nephrology, Klinikum Fulda gAG, Fulda, Germany.
Joachim JankowskiInstitute for Molecular Cardiovascular Research, RWTH Aachen University Hospital, Aachen, Germany.
Bruce A JulianUniversity of Alabama at Birmingham, Birmingham, AL, USA.
Peter G LindeAbbvie, North Chicago, IL, USA.
David MarxDepartment of Nephrology and Kidney Transplantation, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
Harald MischakMosaiques Diagnostics GmbH, Hanover, Germany.
William MullenBHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
Jan NovakUniversity of Alabama at Birmingham, Birmingham, AL, USA.
Alberto OrtizSchool of Medicine, Jimenez Diaz Foundation Institute for Health Research, Autonomous University of Madrid, Madrid, Spain.
Frederik PerssonSteno Diabetes Center, Gentofte, Denmark.
Claudia PontilloMosaiques Diagnostics GmbH, Hanover, Germany.
Peter RossingSteno Diabetes Center, Gentofte, Denmark.
Harald RupprechtDepartment of Nephrology, Klinikum Bayreuth, Bayreuth, Germany.
Joost P SchanstraInstitute of Cardiovascular and Metabolic Disease, French Institute of Health and Medical Research U1048, Toulouse, France.
Antonia VlahouDivision of Biotechnology, Biomedical Research Foundation, Academy of Athens, Athens, Greece.
Raymond VanholderNephrology Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium.
Mosaiques Diagnostics and Therapeutics (Germany) · DESteno Diabetes Centers · DKUniversity of Alabama at Birmingham · USAbbVie (United States) · USBayreuth Medical Center · DEBiomedical Research Foundation of the Academy of Athens · GRBritish Heart Foundation · GBCharité - Universitätsmedizin Berlin · DEGhent University Hospital · BEHôpitaux Universitaires de Strasbourg · FRKlinikum Fulda · DELuther University · KRMaastricht University · NLMontpellier Business School · FRUniversidad Autónoma de Madrid · ESUniversité Toulouse III - Paul Sabatier · FRUniversity of Glasgow · GB

Funding

Molecular Basis of Pathogenicity of IgA1-containing Immune ComplexesR01DK078244 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI JAN NOVAK · 2007 to 2026
$6.0M
Molecular Basis of Pathogenicity of IgA1-containing Immune ComplexesR56DK078244 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI NOVAK, JAN · 2012 to 2012
$92k
NIDDK NIH HHS R01 DK078244NIDDK NIH HHS R56 DK078244
6 · The paper itself

Abstract

backgroundIn 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.

methodsWe 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.

resultsFor 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.

conclusionsOur 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.

Indexed as

AdultAgedBiomarkersFemaleHumansMaleMiddle AgedProteomeProteomicsRenal Insufficiency, ChronicROC CurveUrinalysisBiomarkersProteomebiomarkerschronic kidney diseasepeptidesproteome analysisurine

Identifiers

PMID27984204
PMCPMC5837301
OpenAlexW2529160875

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.