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Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

(2017) NEPHROLOGY DIALYSIS TRANSPLANTATION. 32(12). p.2079-2089
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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 474samples 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 modelmolecular changes per CKD.
Keywords
biomarkers, chronic kidney disease, peptides, proteome analysis, urine, DIABETIC-NEPHROPATHY, RENAL-DISEASES, CAPILLARY-ELECTROPHORESIS, BIOMARKER DISCOVERY, MASS-SPECTROMETRY, IGA NEPHROPATHY, PATTERNS, BIOPSY, GLOMERULONEPHRITIS, PROGRESSION

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Chicago
Siwy, Justyna, Petra Zürbig, Angel Argiles, Joachim Beige, Marion Haubitz, Joachim Jankowski, Bruce A Julian, et al. 2017. “Noninvasive Diagnosis of Chronic Kidney Diseases Using Urinary Proteome Analysis.” Nephrology Dialysis Transplantation 32 (12): 2079–2089.
APA
Siwy, Justyna, Zürbig, P., Argiles, A., Beige, J., Haubitz, M., Jankowski, J., Julian, B. A., et al. (2017). Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. NEPHROLOGY DIALYSIS TRANSPLANTATION, 32(12), 2079–2089.
Vancouver
1.
Siwy J, Zürbig P, Argiles A, Beige J, Haubitz M, Jankowski J, et al. Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. NEPHROLOGY DIALYSIS TRANSPLANTATION. 2017;32(12):2079–89.
MLA
Siwy, Justyna, Petra Zürbig, Angel Argiles, et al. “Noninvasive Diagnosis of Chronic Kidney Diseases Using Urinary Proteome Analysis.” NEPHROLOGY DIALYSIS TRANSPLANTATION 32.12 (2017): 2079–2089. Print.
@article{8502614,
  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 474samples 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 modelmolecular changes per CKD.},
  author       = {Siwy, Justyna and Z{\"u}rbig, Petra and Argiles, Angel and Beige, Joachim and Haubitz, Marion and Jankowski, Joachim and Julian, Bruce A and Linde, Peter G and Marx, David and Mischak, Harald and Mullen, William and Novak, Jan and Ortiz, Alberto and Persson, Frederik and Pontillo, Claudia and Rossing, Peter and Rupprechts, Harald and Schanstra, Joost P and Vlahou, Antonia and Vanholder, Raymond},
  issn         = {0931-0509},
  journal      = {NEPHROLOGY DIALYSIS TRANSPLANTATION},
  keyword      = {biomarkers,chronic kidney disease,peptides,proteome analysis,urine,DIABETIC-NEPHROPATHY,RENAL-DISEASES,CAPILLARY-ELECTROPHORESIS,BIOMARKER DISCOVERY,MASS-SPECTROMETRY,IGA NEPHROPATHY,PATTERNS,BIOPSY,GLOMERULONEPHRITIS,PROGRESSION},
  language     = {eng},
  number       = {12},
  pages        = {2079--2089},
  title        = {Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis},
  url          = {http://dx.doi.org/10.1093/ndt/gfw337},
  volume       = {32},
  year         = {2017},
}

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