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Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides
JP. Schanstra, P. Zürbig, A. Alkhalaf, A. Argiles, SJ. Bakker, J. Beige, HJ. Bilo, C. Chatzikyrkou, M. Dakna, J. Dawson, C. Delles, H. Haller, M. Haubitz, H. Husi, J. Jankowski, G. Jerums, N. Kleefstra, T. Kuznetsova, DM. Maahs, J. Menne, W....
Jazyk angličtina Země Spojené státy americké
Typ dokumentu klinické zkoušky, časopisecké články, multicentrická studie, práce podpořená grantem
NLK
Free Medical Journals
od 1990 do Před 1 rokem
PubMed Central
od 2008 do Před 1 rokem
Europe PubMed Central
od 2008 do Před 1 rokem
Open Access Digital Library
od 1990-07-01
PubMed
25589610
DOI
10.1681/asn.2014050423
Knihovny.cz E-zdroje
- MeSH
- biologické markery moč MeSH
- chronická renální insuficience moč MeSH
- dospělí MeSH
- hodnoty glomerulární filtrace MeSH
- kohortové studie MeSH
- lidé středního věku MeSH
- lidé MeSH
- peptidy moč MeSH
- progrese nemoci MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- klinické zkoušky MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.
2nd Department of Internal Medicine 3rd Faculty of Medicine Charles University Prague Czech Republic
Austin Health University of Melbourne Heidelberg Australia
Barbara Davis Center for Childhood Diabetes University of Colorado Denver Aurora Colorado
Department of Cardiovascular Sciences University of Leuven Leuven Belgium
Department of Internal Medicine 4 Charity Medical University of Berlin Berlin Germany
Department of Nephrology and Hypertension Medical School of Hanover Hanover Germany
Department of Nephrology and Hypertension University Hospital of Magdeburg Magdeburg Germany
Department of Nephrology Klinikum Fulda gAG Fulda Germany
Diabetes Centre Isala Clinics Zwolle The Netherlands
Division of Biotechnology Biomedical Research Foundation Academy of Athens Athens Greece
Faculty of Health University of Aarhus Aarhus Denmark
Faculty of Health University of Copenhagen Copenhagen Denmark
Institute for Molecular Cardiovascular Research RWTH Aachen University Hospital Aachen Germany
KfH Renal Unit Department Nephrology Leipzig and Martin Luther University Halle Wittenberg Germany
Mario Negri Institute of Pharmacology Research Bergamo Italy
mosaiques diagnostics GmbH Hanover Germany
Nephrology Section Department of Internal Medicine Ghent University Hospital Ghent Belgium
Paul Sabatier University Toulouse France
RD Néphrologie Montpellier France
School of Biomedical and Healthcare Sciences Plymouth University Plymouth United Kingdom
Steno Diabetes Center Gentofte Denmark
University Department of Nephrology Medical Faculty University of Skopje Skopje Macedonia
University Medical Center Groningen and University of Groningen Groningen The Netherlands
Citace poskytuje Crossref.org
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