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.
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.

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)
- is associated with mortality18
- attains high evidence levels19
- shows cost-effectiveness compared to urinary albumin excretion20 (Figure 3E)
- is used for pilot studies with pharmaceutical companies
- got “letter of support” from American Food and Drug Administration (FDA)

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).

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.
References:
1. Good DM, Zürbig P, Argiles A et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics 2010; 9: 2424-2437
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; 32(11): 1866-1873
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 2018; 33(2): 296-303
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(1): 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 2017; 2(6): 1066-1075
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. Tofte N, Lindhardt M, Adamova K et al. Characteristics of high- and low-risk individuals in the PRIORITY study: urinary proteomics and mineralocorticoid receptor antagonism for prevention of diabetic nephropathy in Type 2 diabetes. Diabet Med. 2018; 35(10): 1375-1382
18. Currie GE, von Scholten BJ, Mary S et al. Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovasc Diabetol. 2018; 17(1): 50
19. 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
20. 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 2018; 33(3): 441-449
21. Siwy J, Zurbig P, Argiles A et al. Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol Dial Transplant 2017; 33(12): 2079-2089