Chronic kidney disease

Proteins are responsible for structure and signalling in living organisms and in every organ. Based on this consideration, it appears only logical investigate proteomic changes in the context of chronic kidney disease (CKD), aiming at identifying molecular changes associated with CKD onset and progression that can be linked to molecular pathophysiology and that could serve as more appropriate biomarkers or even as therapeutic targets. Figure 1 illustrates this concept of how early diagnosis and/or prognosis of diseases, based on proteomic changes involved in pathology, improves chances for better outcomes for patients.

FIGURE 1. Early diagnosis and/or prognosis of diseases improves chances for a better outcome for the patient. The initiation of molecular processes that result in (chronic) diseases can be detected based on molecular changes, using proteomic technologies, prior to advanced organ damage. This could allow earlier intervention where drugs are most effective (Stepczynska et al.).


Diagnosis and Prognosis of CKD

The urinary peptides-based classifier CKD273 is a general classifier for the diagnosis of all CKD types. It was developed in 2010 by Good et al. 1. The rational was to identify biomarkers associated with CKD in general and enable the early detection of molecular changes that predict the development or progression of CKD. In the above study, 273 urinary peptides were identified that significantly differed between CKD and healthy controls. The first validation of the CKD273 using 144 samples showed a sensitivity of 85% and specificity of 100% with an area under the curve (AUC) of 0.96 for the diagnosis of CKD. To establish an added value in patient management, CKD273 was assessed in several studies2-14. A graphic depiction of the studies published to date using CKD273 is presented in Figure 2.

FIGURE 2: Graphical distribution of the studies evaluating the performance of the CKD273-classifier in the diagnosis and prognosis of CKD according to disease stage (Critselis at al.). 


Characteristics of CKD273 classifier:

  • is validated in independent multicentre cohorts1;7;13 (Figure 3A)
  • is validated in longitudinal cohorts12;14
  • shows better results than currently used biomarkers10;12;14 (Figure 3B)
  • can predict endpoints of CKD3;10;11
  • is validated concerning its stability, intra- and intermediate precision, reproducibility, and interference1;15
  • shows treatment effects2;5;6 (Figure 3C)
  • is currently used in an interventional trial (PRIORITY)16, (Figure 3D)
  • attains high evidence levels17
  • shows cost-effectiveness compared to urinary albumin excretion18 (Figure 3E)
  • is used for pilot studies with pharmaceutical companies
  • got “letter of support” from American Food and Drug Administration (FDA)


FIGURE 3. The CKD273 highlights. A. validation of the CKD273 in nine independent cohorts shoved consistent high diagnostics accuracy with area under curve above (AUC) 0.95 (Siwy et al.). B. Comparison of CKD273 with clinical parameters with significant higher AUC for the CKD273 classifier (AUC=0.82, black line) than for albuminuria (AUC=0.76, red line) using patients with fast-progressing CKD (eGFR slope decline of >−5% per year). C. The classification results of microalbuminuric patients before (visit 2) and after two years (visit 9) treatment with 200 mg Irbesartan with significant (p=0.024) decline of classification scores after Irbesartan treatment indicating an improvement of kidney phisiology (Andersen et al.). D. Ongoing interventional study in which CKD273 is used for the patient stratification and participants with a high-risc CKD273 score are included in the randomised intervention study with active drug or placebo in addition to standard care. E. One-way sensitivity analyses for the cost-effectiveness from an European prospective of screaning T2D patients using CKD273 as compared to screening annually with UAE over 40-year time frame that indicates that with increasing probability of annual CKD progression, the CKD273 screening was associated with a greater gain in QALYs in diabetic patients (Critselis at al.).


Non-invasive discrimination of various types of CKD

Currently, our CE-MS technology is capable of detecting over 80% of all CKDs using CKD273 classifier:

  • Diabetic nephropathy (DN)
  • Focal-segmental glomerulosclerosis (FSGS)
  • IgA nephropathy (IgAN)
  • Minimal-change disease (MCD)
  • Vasculitis
  • Lupus nephritis (LN)
  • Membranous nephropathy (MN)

In addition, the urinary proteome analysis clearly differentiated various types of CKD. The urinary peptides shows different expression, occurrence dependent on the underlying disease (Figure 4).


FIGURE 4. Urinary proteome map focused on the differential diagnosis of renal diseases. For each CE-MS-defined polypeptide of a given pattern with mass plotted against CE migration time (1), the biomarker-defining parameters (mass, CE migration time, protein ID (2), determined polypeptide sequence (4), and fragment information) can be displayed. In addition, the amplitude distribution of the biomarker presuming a Gaussian distribution (3) and statistical data (5) available for selected specific diseases are shown. MS/MS spectrum of the biomarker (6) completes the biomarker information. NC = Normal Control, DN = Diabetic Nephropathy, FSGS = Focal-segmental Glomerulosclerosis, IgAN = IgA Nephropathy, MCD = Minimal-change Disease, SLE = Lupus Nephritis, MGN = Membranous Nephropathie.


We could also define specific urinary peptide classifiers19 which are composites multiple peptides specific for a single CKD type. These classifiers enable a differential diagnosis of certain types of CKD (Figure 5). They may also serve as an excellent basis for the assessment of the different types of CKD, to understand molecular pathophysiology and to identify the best-suited therapeutic targets. In contrast to kidney biopsy, urinary proteome analysis offers the possibility of being applied early in the course of the disease when the benefit of intervention is optimal and of being repeatable without any risk for the patient and, thus, can be used to monitor progression of disease and/or treatment response.


FIGURE 5. Discrimination of the individual CKD types from all others CKD types based on 474 samples resulted in high accuracy with AUC-values ≥ 0.77 (Siwy et al.).



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   2.   Andersen S, Mischak H, Zürbig P, Parving HH, Rossing P. Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria. BMC Nephrol 2010; 11: 29

   3.   Argiles A, Siwy J, Duranton F et al. CKD273, a new proteomics classifier assessing CKD and its prognosis. PLoS One 2013; 8: e62837

   4.   Gu YM, Thijs L, Liu YP et al. The urinary proteome as correlate and predictor of renal function in a population study. Nephrology Dialysis Transplantation 2014; 29: 2260-2268

   5.   Lindhardt M, Persson F, Zurbig P et al. Urinary proteomics predict onset of microalbuminuria in normoalbuminuric type 2 diabetic patients, a sub-study of the DIRECT-Protect 2 study. Nephrol Dial Transplant 2016; in press:

   6.   Lindhardt M, Persson F, Oxlund C et al. Predicting albuminuria response to spironolactone treatment with urinary proteomics in patients with type 2 diabetes and hypertension. Nephrol Dial Transplant 2017;

   7.   Molin L, Seraglia R, Lapolla A et al. A comparison between MALDI-MS and CE-MS data for biomarker assessment in chronic kidney diseases. J Proteomics 2012; 75: 5888-5897

   8.   Ovrehus MA, Zurbig P, Vikse BE, Hallan SI. Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria. Clin Proteomics 2015; 12: 21

   9.   Pontillo C, Jacobs L, Staessen JA et al. A urinary proteome-based classifier for the early detection of decline in glomerular filtration. Nephrol Dial Transplant 2016;

10.   Pontillo C, Zhang Z, Schanstra J et al. Prediction of chronic kidney disease stage 3 by CKD273, a urinary proteomic biomarker. Kidney International Reports 2017; in press:

11.   Roscioni SS, de ZD, Hellemons ME et al. A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus. Diabetologia 2012; 56: 259-267

12.   Schanstra JP, Zurbig P, Alkhalaf A et al. Diagnosis and prediction of CKD progression by assessment of urinary peptides. J Am Soc Nephrol 2015; 26: 1999-2010

13.   Siwy J, Schanstra JP, Argiles A et al. Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy. Nephrology Dialysis Transplantation 2014; 29: 1563-1570

14.   Zürbig P, Jerums G, Hovind P et al. Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes 2012; 61: 3304-3313

15.   Mischak H, Vlahou A, Ioannidis JP. Technical aspects and inter-laboratory variability in native peptide profiling: The CE-MS experience. Clin Biochem 2013; 46: 432-443

16.   Lindhardt M, Persson F, Currie G et al. Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy in TYpe 2 diabetic patients with normoalbuminuria (PRIORITY): essential study design and rationale of a randomised clinical multicentre trial. BMJ Open 2016; 6: e010310

17.   Critselis E, Heerspink HJ. Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression: A systematic review of the current evidence. Nephrol Dial Transplant 2014; 31: 249-254

18.   Critselis E, Vlahou A, Stel V, Morton RL. Cost-effectiveness of screening type 2 diabetes patients for chronic kidney disease progression with the CKD273 urinary peptide classifier as compared to urinary albumin excretion. Nephrol Dial Transplant 2017; in press:

19.   Siwy J, Zurbig P, Argiles A et al. Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol Dial Transplant 2016;