Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

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.

FIGURE 1

ROC analysis of the classification results for each developed classifier applied to independent samples from the validation set, for each type of CKD.

FIGURE 2

Regulation of the specific proteins in individual types of CKD. The mean fold change and standard deviation (when more than one fragment was observed) of proteins with a mean fold change of >2 based on discovery data for the DN&N group (A), FSGS (B), IgAN (C), LN (D), MCD (E), MN (F) and vasculitis-induced kidney disease (G) are shown. The four proteins (except panel A where five proteins are marked because two proteins have equal P-values) for which the fragments had the lowest adjusted P-values are marked in red.

FIGURE 2

Regulation of the specific proteins in individual types of CKD. The mean fold change and standard deviation (when more than one fragment was observed) of proteins with a mean fold change of >2 based on discovery data for the DN&N group (A), FSGS (B), IgAN (C), LN (D), MCD (E), MN (F) and vasculitis-induced kidney disease (G) are shown. The four proteins (except panel A where five proteins are marked because two proteins have equal P-values) for which the fragments had the lowest adjusted P-values are marked in red.