Siwy J, et al. - Herein, urinary proteome analysis was tested as a tool to distinguish different types of chronic kidney disease (CKD) irrespective of the underlying mechanism of disease. As per findings, urinary proteome analysis displayed the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Furthermore, it was observed that these approaches may offer data to model molecular changes per CKD.

Methods

• This study was performed using 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 and 474 samples were used as the discovery cohort and for independent validation, respectively.
• Using statistical analysis adjusted for multiple testing, peptide biomarkers were defined for each CKD type.
• Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results

• Findings demonstrated that for 7 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.
• Validation of these classifiers was accomplished in an independent cohort and these classifiers displayed good to excellent accuracy for distinguishing one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95).
• Further information was afforded by sequence analysis of the biomarkers that may clarify the underlying pathophysiology.