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; William Mullen; Jan Novak; Alberto Ortiz; Frederik Persson; Claudia Pontillo; Peter Rossing; Harald Rupprecht; Joost P. Schanstra; Antonia Vlahou; Raymond Vanholder


Nephrol Dial Transplant. 2017;32(12):2079-2089. 

In This Article

Abstract and Introduction


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.


The prevalence of chronic kidney disease (CKD), defined as structural kidney damage or significant loss of glomerular filtration rate (GFR) (<60 mL/min/1.73 m2 for at least 3 months),[1] is estimated to be 8–16% worldwide, with an increasing trend.[2,3] Therefore, CKD is now recognized as a global public health problem. The frequencies of the various types of CKD vary between countries, likely due to differences in genetically determined mechanisms of disease, environmental influences and criteria for performance of a kidney biopsy. A correct assessment of a CKD patient requires a precise diagnosis to guide the most appropriate treatment.

The diagnostic workup comprises assessment of clinical features (e.g. nephritic syndrome, isolated hematuria, rapidly progressive glomerulonephritis), histological findings [e.g. IgA nephropathy (IgAN)], biological mechanisms (e.g. hemolytic uremic syndrome) and possibly genetic factors (e.g. mutation in Col4a5). However, kidney biopsy is usually not applied to diagnose CKD in patients with diabetes and isolated hypertension as it is an invasive procedure with inherent risk and likely to provide no additional information for the clinical management.[4] As a consequence, misdiagnosis may occur. As an example, the existence of hypertensive nephropathy (nephrosclerosis) has been called into question.[5] With the exception of diabetes-associated CKD, the determination of the cause of renal disease is necessary and becomes more challenging. A variety of diagnostic tests may be pursued to refine the clinical diagnosis, with biopsy remaining the gold standard to assess diagnostic and prognostic histological features. However, kidney biopsy is an invasive procedure, and its diagnostic accuracy is sometimes limited.[6] Moreover, characterization of the urinary proteome may provide useful information about response to treatment. Recommendations for development of biomarkers using proteomics applicable for clinical care have recently been published.[7,8]

We have demonstrated before that analysis of the urinary proteome using capillary electrophoresis coupled to mass spectrometry (CE-MS) enables discrimination between patients with and without CKD,[9] as well as prediction of progression of CKD, irrespective of the underlying disease mechanism.[10] The CE-MS technology allows the analysis of naturally occurring peptides (without tryptic digestion). This approach is often also called peptidomics, which is a subfield of proteomics. Using CE-MS, specific biomarkers for different types of CKD, such as ANCA-associated vasculitis, IgAN and diabetic nephropathy (DN), were defined.[11–13] In the present study, we assessed the value of the urinary proteome, as defined by CE-MS analysis, for the noninvasive discrimination of various types of CKD. Our results support the presence of urinary peptides with discriminatory power for different types of CKD. If the findings are validated in additional studies, such an approach to define CKD based on urinary profiles may be especially helpful at early stages of clinical disease, when a biopsy is not feasible due to small kidneys or comorbidities, or if a patient declines biopsy.