[1. Jha V, Garcia-Garcia G, Iseki K et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013; 382(9888): 260-272.10.1016/S0140-6736(13)60687-X]Search in Google Scholar
[2. Pontillo C and Mischak H. Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease. Clinical Kidney Journal. 2017; 10(2): 192-201.10.1093/ckj/sfx002]Search in Google Scholar
[3. Albalat A, Mischak H, and Mullen W. Urine proteomics in clinical applications: technologies, principal considerations and clinical implementation. Prilozi. 2011; 32(1): 13-44.]Search in Google Scholar
[4. Thongboonkerd V. Proteomic analysis of renal diseases: unraveling the pathophysiology and biomarker discovery. Expert Rev Proteomics. 2005; 2(3): 349-366.10.1586/14789450.2.3.349]Open DOISearch in Google Scholar
[5. Andersen S, Mischak H, Zürbig P et al. Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria. BMC Nephrol. 2010; 11(1): 29-10.1186/1471-2369-11-29]Search in Google Scholar
[6. Stalmach A, Albalat A, Mullen W, and Mischak H. Recent advances in capillary electrophoresis coupled to mass spectrometry for clinical proteomic applications. Electrophoresis. 2013; 34(11): 1452-1464.10.1002/elps.201200708]Open DOISearch in Google Scholar
[7. Pontillo C, Filip S, Borras DM et al. CE-MS-based proteomics in biomarker discovery and clinical application. Proteomics Clin Appl. 2015; 9(3-4): 322-334.10.1002/prca.201400115]Search in Google Scholar
[8. Kaiser T, Hermann A, Kielstein J.T. et al. Capillary electrophoresis coupled to mass spectrometry to establish polypeptide patterns in dialysis fluids. J Chromatogr A. 2003; 1013(1-2): 157-171.10.1016/S0021-9673(03)00712-X]Search in Google Scholar
[9. Wittke S, Fliser D, Haubitz M et al. Determination of peptides and proteins in human urine with capillary electrophoresis–mass spectrometry, a suitable tool for the establishment of new diagnostic markers. J Chromatogr A. 2003; 1013(1-2): 173-181.10.1016/S0021-9673(03)00713-1]Search in Google Scholar
[10. Mischak H, Vlahou A, and Ioannidis JP. Technical aspects and inter-laboratory variability in native peptide profiling: The CE-MS experience. Clin Biochem. 2013; 46(6): 432-443.10.1016/j.clinbiochem.2012.09.02523041249]Search in Google Scholar
[11. Metzger J, Kirsch T, Schiffer E et al. Urinary excretion of twenty peptides forms an early and accurate diagnostic pattern of acute kidney injury. Kidney Int. 2010; 78(12): 1252-1262.10.1038/ki.2010.32220827258]Open DOISearch in Google Scholar
[12. Delles C, Schiffer E, von Zur MC et al. Urinary proteomic diagnosis of coronary artery disease: identification and clinical validation in 623 individuals. J Hypertens. 2010; 28(11): 2316-2322.10.1097/HJH.0b013e32833d81b720811296]Search in Google Scholar
[13. Lankisch TO, Metzger J, Negm AA et al. Bile proteomic profiles differentiate cholangiocarcinoma from primary sclerosing cholangitis and choledocholithiasis. Hepatology. 2011; 53(3): 875-884.10.1002/hep.2410321374660]Open DOISearch in Google Scholar
[14. Alkhalaf A, Zürbig P, Bakker SJ et al. Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy. PLoS One. 2010; 5(10): e13421-10.1371/journal.pone.0013421295811220975990]Search in Google Scholar
[15. Rossing K, Mischak H, Dakna M et al. Urinary proteomics in diabetes and CKD. J Am Soc Nephrol. 2008; 19(7): 1283-1290.10.1681/ASN.2007091025244030118448586]Open DOISearch in Google Scholar
[16. 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(11): 2424-2437.10.1074/mcp.M110.001917298424120616184]Search in Google Scholar
[17. Dakna M, Harris K, Kalousis A et al. Addressing the challenge of defining valid proteomic biomarkers and classifiers. BMC Bioinformatics. 2010; 11: 594-10.1186/1471-2105-11-594301784521208396]Open DOISearch in Google Scholar
[18. Mischak H, Delles C, Klein J, and Schanstra JP. Urinary proteomics based on capillary electrophoresis-coupled mass spectrometry in kidney disease: discovery and validation of biomarkers, and clinical application. Adv Chronic Kidney Dis. 2010; 17(6): 493-506.10.1053/j.ackd.2010.09.00421044772]Search in Google Scholar
[19. Zürbig P, Jerums G, Hovind P et al. Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy. Diabetes. 2012; 61(12): 3304-3313.10.2337/db12-0348350187822872235]Search in Google Scholar
[20. 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(2): 259-267.10.1007/s00125-012-2755-223086559]Search in Google Scholar
[21. Argiles A, Siwy J, Duranton F et al. CKD273, a New Proteomics Classifier Assessing CKD and Its Prognosis. PLoS One. 2013; 8(5): e62837-10.1371/journal.pone.0062837365390623690958]Search in Google Scholar
[22. Ovrehus MA, Zurbig P, Vikse BE, and Hallan SI. Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria. Clin Proteomics. 2015; 12(1): 21-10.1186/s12014-015-9092-7452884826257595]Search in Google Scholar
[23. 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(18): 5888-5897.10.1016/j.jprot.2012.07.02422842158]Open DOISearch in Google Scholar
[24. Nkuipou-Kenfack E, Duranton F, Gayrard N et al. Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease. PLoS ONE. 2014; 9(5): e96955-10.1371/journal.pone.0096955401619824817014]Search in Google Scholar
[25. 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. Nephrol Dial Transplant. 2014; 29(8): 1563-1570.10.1093/ndt/gfu039411814024589724]Search in Google Scholar
[26. 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(3): e010310-10.1136/bmjopen-2015-010310478532826936907]Search in Google Scholar
[27. Nielsen SE, Persson F, Frandsen E et al. Spironolactone diminishes urinary albumin excretion in patients with type 1 diabetes and microalbuminuria: a randomized placebo-controlled crossover study. Diabet Med. 2012; 29(8): e184-e190.10.1111/j.1464-5491.2012.03585.x22268920]Search in Google Scholar
[28. Lindhardt M, Persson FI, Oxlund C. et al. Predicting albuminuria response to spironolactone treatment with urinary proteomics in patients with type 2 diabetes and hypertension. Nephrol Dial Transplant. 2016; in press10.1093/ndt/gfw40628064163]Search in Google Scholar
[29. 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(8): 1999-2010.10.1681/ASN.2014050423452016525589610]Search in Google Scholar
[30. Gu YM, Thijs L, Liu YP et al. The urinary proteome as correlate and predictor of renal function in a population study. Nephrol Dial Transplant. 2014;10.1093/ndt/gfu23424981581]Search in Google Scholar
[31. Kuznetsova T, Mischak H, Mullen W, and Staessen JA. Urinary proteome analysis in hypertensive patients with left ventricular diastolic dysfunction. Eur Heart J. 2012; 33(18): 2342-2350.10.1093/eurheartj/ehs185370516122789915]Search in Google Scholar
[32. Lindhardt M, Persson F, Zurbig P et al. Urinary proteomics predict onset of microalbuminuria in normoalbuminuric type 2 diabetic patients, a substudy of the DIRECT-Protect 2 study. Nephrol Dial Transplant. 2016; in press10.1093/ndt/gfw29227507891]Search in Google Scholar
[33. 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; in press10.1093/ndt/gfw23927387473]Search in Google Scholar
[34. Schievink B, Kropelin T, Mulder S et al. Early renin-angiotensin system intervention is more beneficial than late intervention in delaying endstage renal disease in patients with type 2 diabetes. Diabetes Obes Metab. 2016; 18(1): 64-71.10.1111/dom.1258326434564]Open DOISearch in Google Scholar
[35. Critselis E and Lambers HH. Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression. Nephrol Dial Transplant. 2016; 31(2): 249-254.10.1093/ndt/gfv062]Search in Google Scholar
[36. Siwy J, Zürbig P, Argiles A et al. Non-invasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol Dial Transplant. 2016; in press10.1093/ndt/gfw337583730127984204]Search in Google Scholar
[37. Papale M, Di Paolo S, Magistroni R et al. Urine Proteome Analysis May Allow Noninvasive Differential Diagnosis of Diabetic Nephropathy. Diabetes Care. 2010; 33(11): 2409-2415.10.2337/dc10-0345296350420671095]Open DOISearch in Google Scholar
[38. Piyaphanee N, Ma Q, Kremen O et al. Discovery and initial validation of alpha 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome. Proteomics Clinical Applications. 2011; 5(5-6): 334-342.10.1002/prca.201000110703930621591266]Open DOISearch in Google Scholar
[39. Wu TF, Fu YY, Brekken D et al. Urine Proteome Scans Uncover Total Urinary Protease, Prostaglandin D Synthase, Serum Amyloid P, and Superoxide Dismutase as Potential Markers of Lupus Nephritis. Journal of Immunology. 2010; 184(4): 2183-2193.10.4049/jimmunol.0900292292785820065116]Search in Google Scholar
[40. Kalantari S, Rutishauser D, Samavat S et al. Urinary Prognostic Biomarkers and Classification of IgA Nephropathy by High Resolution Mass Spec Coupled with Liquid Chromatography. PLoS One. 2013; 8(12)10.1371/journal.pone.0080830385505424339887]Search in Google Scholar
[41. Graterol F, Navarro-Munoz M, Ibernon M et al. Poor histological lesions in IgA nephropathy may be reflected in blood and urine peptide profiling. BMC Nephrology. 2013; 1410.1186/1471-2369-14-82363749023577616]Search in Google Scholar
[42. Pesic I, Stefanovic V, Muller GA et al. Identification and validation of six proteins as marker for endemic nephropathy. Journal of Proteomics. 2011; 74(10): 1994-2007.10.1016/j.jprot.2011.05.02021635978]Search in Google Scholar
[43. Lim SC, Liying DQ, Toy WC et al. Adipocytokine zinc alpha(2) glycoprotein (ZAG) as a novel urinary biomarker for normo-albuminuric diabetic nephropathy. Diabetic Medicine. 2012; 29(7): 945-949.10.1111/j.1464-5491.2011.03564.x22211921]Open DOISearch in Google Scholar
[44. ito-Martin A, Ucero AC, Zubiri I et al. Osteoprotegerin in Exosome-Like Vesicles from Human Cultured Tubular Cells and Urine. PLoS One. 2013; 8(8)10.1371/journal.pone.0072387375194924058411]Search in Google Scholar
[45. Gonzalez-Calero L, Martin-Lorenzo M, de la Cuesta F et al. Urinary alpha-1 antitrypsin and CD59 glycoprotein predict albuminuria development in hypertensive patients under chronic renin-angiotensin system suppression. Cardiovascular Diabetology. 2016; 1510.1186/s12933-016-0331-7471531126772976]Search in Google Scholar
[46. Gold L, Ayers D, Bertino J et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010; 5(12): e15004-]Search in Google Scholar
[47. Holzer M, Birner-Gruenberger R, Stojakovic T et al. Uremia Alters HDL Composition and Function. Journal of the American Society of Nephrology. 2011; 22(9): 1631-1641.10.1681/ASN.2010111144317193521804091]Open DOISearch in Google Scholar
[48. Weichhart T, Kopecky C, Kubicek M et al. Serum Amyloid A in Uremic HDL Promotes Inflammation. Journal of the American Society of Nephrology. 2012; 23(5): 934-947.10.1681/ASN.2011070668333829122282592]Open DOISearch in Google Scholar
[49. Takahashi K, Wall SB, Suzuki H et al. Clustered O-Glycans of IgA1. Molecular & Cellular Proteomics. 2010; 9(11): 2545-2557.10.1074/mcp.M110.001834298423720823119]Search in Google Scholar
[50. Piroddi M, Palmese A, Pilolli F et al. Plasma nitroproteome of kidney disease patients. Amino Acids. 2011; 40(2): 653-667.10.1007/s00726-010-0693-120676907]Open DOISearch in Google Scholar
[51. Antonelou MH, Kriebardis AG, Velentzas AD et al. Oxidative stress-associated shape transformation and membrane proteome remodeling in erythrocytes of end stage renal disease patients on hemodialysis. Journal of Proteomics. 2011; 74(11): 2441-2452.10.1016/j.jprot.2011.04.00921515423]Open DOISearch in Google Scholar
[52. varez-Llamas G, Zubiri I, Maroto AS et al. A role for the membrane proteome in human chronic kidney disease erythrocytes. Translational Research. 2012; 160(5): 374-383.10.1016/j.trsl.2012.06.00422814359]Search in Google Scholar
[53. Luczak M, Formanowicz D, Pawliczak E et al. Chronic kidney disease-related atherosclerosis - proteomic studies of blood plasma. Proteome Science. 2011; 910.1186/1477-5956-9-25311237621569504]Search in Google Scholar
[54. Luczak M, Formanowicz D, Marczak L et al. Deeper insight into chronic kidney disease-related atherosclerosis: comparative proteomic studies of blood plasma using 2DE and mass spectrometry. Journal of Translational Medicine. 2015; 1310.1186/s12967-014-0378-8431665725622820]Search in Google Scholar
[55. Luczak M, Suszynska-Zajczyk J, Marczak L et al. Label-Free Quantitative Proteomics Reveals Differences in Molecular Mechanism of Atherosclerosis Related and Non-Related to Chronic Kidney Disease. International Journal of Molecular Sciences. 2016; 17(5)10.3390/ijms17050631488145727144566]Search in Google Scholar
[56. Glorieux G, Mullen W, Duranton F et al. New insights in molecular mechanisms involved in chronic kidney disease using high-resolution plasma proteome analysis. Nephrol Dial Transplant. 2015; 30(11): 1842-1852.10.1093/ndt/gfv25426160894]Search in Google Scholar
[57. Antonelou MH, Georgatzakou HT, Tzounakas VL et al. Blood modifications associated with end stage renal disease duration, progression and cardiovascular mortality: a 3-year follow-up pilot study. Journal of Proteomics. 2014; 101: 88-101.10.1016/j.jprot.2014.02.00924549005]Open DOISearch in Google Scholar
[58. Butte AJ, Sigdel TK, Wadia PP et al. Protein Microarrays Discover Angiotensinogen and PRKRIP1 as Novel Targets for Autoantibodies in Chronic Renal Disease. Molecular & Cellular Proteomics. 2011; 10(3)10.1074/mcp.M110.000497304714121183621]Search in Google Scholar
[59. Fechete R, Heinzel A, Perco P et al. Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy. Proteomics Clinical Applications. 2011; 5(5-6): 354-366.10.1002/prca.20100013621491608]Search in Google Scholar
[60. Krochmal M, Fernandes M, Filip S et al. PeptiCKDdb- peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease. Database-the Journal of Biological Databases and Curation. 2016;10.1093/database/baw128500932427589965]Search in Google Scholar
[61. Dominiczak AF, Herget-Rosenthal S, Delles C et al. Systems biology to battle vascular disease. Nephrol Dial Transplant. 2010; 25(4): 1019-1022.10.1093/ndt/gfq02520133282]Search in Google Scholar
[62. Molina F, Dehmer M, Perco P et al. Systems biology: opening new avenues in clinical research. Nephrol Dial Transplant. 2010; 25(4): 1015-1018.10.1093/ndt/gfq03320139409]Search in Google Scholar
[63. Cisek K, Krochmal M, Klein J, and Mischak H. The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease. Nephrol Dial Transplant. 2015.10.1093/ndt/gfv36426487673]Search in Google Scholar
[64. EMA. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/06/WC500169469.pdf. 2014;]Search in Google Scholar
[65. 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 press10.1016/j.ekir.2017.06.004566928529130072]Search in Google Scholar
[66. Mischak H, Delles C, Vlahou A, and Vanholder R. Proteomic biomarkers in kidney disease: issues in development and implementation. Nat Rev Nephrol. 2015; 11(4): 221-232.10.1038/nrneph.2014.24725643662]Search in Google Scholar
[67. Stepczynska A, Schanstra JP, and Mischak H. Implementation of CE-MS-identified proteome-based biomarker panels in drug development and patient management. Bioanalysis. 2016; 8(5): 439-455.10.4155/bio.16.826891752]Search in Google Scholar
[68. Andresdottir G, Jensen ML, Carstensen B et al. Improved survival and renal prognosis of patients with type 2 diabetes and nephropathy with improved control of risk factors. Diabetes Care. 2014; 37(6): 1660-1667.10.2337/dc13-203624623028]Open DOISearch in Google Scholar
[69. Critselis E, Vlahou A, Stel VS, and 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 press10.1093/ndt/gfx06829106632]Search in Google Scholar
[70. Klein J, Ramirez-Torres A, Ericsson A et al. Urinary peptidomics provides a noninvasive humanized readout of diabetic nephropathy in mice. Kidney Int. 2016; in press10.1016/j.kint.2016.06.02327528550]Open DOISearch in Google Scholar
[71. Siwy J, Zoja C, Klein J. et al. Evaluation of the Zucker Diabetic Fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles. PLoS One. 2012; 7(12): e51334-10.1371/journal.pone.0051334351741623236474]Search in Google Scholar
[72. 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(8): 1563-1570.10.1093/ndt/gfu039411814024589724]Search in Google Scholar
[73. Mischak H. Pro: Urine proteomics as a liquid kidney biopsy: no more kidney punctures! Nephrol Dial Transplant. 2015; 30(4): 532-537.10.1093/ndt/gfv046]Search in Google Scholar