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Urinary Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy

  • Microvascular Complications—Nephropathy (AP Maxwell, Section Editor)
  • Published:
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Abstract

The last decade has seen a surge in publications describing novel biomarkers for early detection of diabetic nephropathy (DN), but as yet none have outperformed albuminuria in well-designed prospective studies. This is partially attributable to our incomplete understanding of the many complex interrelated mechanisms underlying DN development, a heterogeneous process unlikely to be captured by a single biomarker. Proteomics offers the advantage of simultaneously analysing the entire protein content of a biological sample, and the technique has gained attention as a potential tool for a more accurate diagnosis of disease at an earlier stage as well as a means by which to unravel the pathogenesis of complex diseases such as DN using an untargeted approach. This review will discuss the potential of proteomics as both a clinical and research tool, evaluating exploratory work in animal models as well as diagnostic potential in human subjects.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. International Diabetes Federation. IDF Atlas, 7th Edition. Brussels, Belgium 2015.

  2. Collins AJ, Foley RN, Herzog C, Chavers B, Gilbertson D, Herzog C, et al. US Renal Data System 2012 annual data report. Am J Kidney Dis. 2013;61(1 Suppl 1):A7. doi:10.1053/j.ajkd.2012.11.031. e1-476.

    Article  PubMed  Google Scholar 

  3. Mogensen CE, Christensen CK, Vittinghus E. The stages in diabetic renal disease. With emphasis on the stage of incipient diabetic nephropathy. Diabetes. 1983;32 Suppl 2:64–78.

    Article  PubMed  Google Scholar 

  4. Ziyadeh FN, Sharma K. Overview: combating diabetic nephropathy. J Am Soc Nephrol. 2003;14(5):1355–7.

    Article  PubMed  Google Scholar 

  5. Stewart JH, McCredie MR, Williams SM, Jager KJ, Trpeski L, McDonald SP, et al. Trends in incidence of treated end-stage renal disease, overall and by primary renal disease, in persons aged 20-64 years in Europe, Canada and the Asia-Pacific region, 1998-2002. Nephrology (Carlton). 2007;12(5):520–7. doi:10.1111/j.1440-1797.2007.00830.x.

    Article  Google Scholar 

  6. Fox CS, Matsushita K, Woodward M, Bilo HJ, Chalmers J, Heerspink HJ, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet. 2012;380(9854):1662–73. doi:10.1016/S0140-6736(12)61350-6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ninomiya T, Perkovic V, de Galan BE, Zoungas S, Pillai A, Jardine M, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009;20(8):1813–21. doi:10.1681/ASN.2008121270.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tuttle KR, Bakris GL, Bilous RW, Chiang JL, de Boer IH, Goldstein-Fuchs J, et al. Diabetic kidney disease: a report from an ADA consensus conference. Diabetes Care. 2014;37(10):2864–83. doi:10.2337/dc14-1296.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Haller H, Ito S, Izzo Jr JL, Januszewicz A, Katayama S, Menne J, et al. Olmesartan for the delay or prevention of microalbuminuria in type 2 diabetes. N Engl J Med. 2011;364(10):907–17. doi:10.1056/NEJMoa1007994.

    Article  CAS  PubMed  Google Scholar 

  10. Ruggenenti P, Perna A, Ganeva M, Ene-Iordache B, Remuzzi G, Group BS. Impact of blood pressure control and angiotensin-converting enzyme inhibitor therapy on new-onset microalbuminuria in type 2 diabetes: a post hoc analysis of the BENEDICT trial. J Am Soc Nephrol. 2006;17(12):3472–81. doi:10.1681/ASN.2006060560.

    Article  CAS  PubMed  Google Scholar 

  11. Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95. doi:10.1067/mcp.2001.113989.

    Article  Google Scholar 

  12. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–16. doi:10.1161/CIRCULATIONAHA.109.192278.

    Article  PubMed  PubMed Central  Google Scholar 

  13. KDIGO. Clinical practice guideline for evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:73–90.

    Article  Google Scholar 

  14. Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20(11):2305–13. doi:10.1681/ASN.2009020171.

    Article  PubMed  Google Scholar 

  15. Stevens LA, Coresh J, Feldman HI, Greene T, Lash JP, Nelson RG, et al. Evaluation of the modification of diet in renal disease study equation in a large diverse population. J Am Soc Nephrol. 2007;18(10):2749–57. doi:10.1681/ASN.2007020199.

    Article  PubMed  Google Scholar 

  16. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Camargo EG, Soares AA, Detanico AB, Weinert LS, Veronese FV, Gomes EC, et al. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is less accurate in patients with type 2 diabetes when compared with healthy individuals. Diabet Med. 2011;28(1):90–5. doi:10.1111/j.1464-5491.2010.03161.x.

    Article  CAS  PubMed  Google Scholar 

  18. Guidone C, Gniuli D, Castagneto-Gissey L, Leccesi L, Arrighi E, Iaconelli A, et al. Underestimation of urinary albumin to creatinine ratio in morbidly obese subjects due to high urinary creatinine excretion. Clin Nutr. 2012;31(2):212–6. doi:10.1016/j.clnu.2011.10.007.

    Article  CAS  PubMed  Google Scholar 

  19. Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med. 1984;310(6):356–60. doi:10.1056/NEJM198402093100605.

    Article  CAS  PubMed  Google Scholar 

  20. Parving HH, Oxenboll B, Svendsen PA, Christiansen JS, Andersen AR. Early detection of patients at risk of developing diabetic nephropathy. A longitudinal study of urinary albumin excretion. Acta Endocrinol (Copenh). 1982;100(4):550–5.

    CAS  Google Scholar 

  21. de Zeeuw D, Remuzzi G, Parving HH, Keane WF, Zhang Z, Shahinfar S, et al. Albuminuria, a therapeutic target for cardiovascular protection in type 2 diabetic patients with nephropathy. Circulation. 2004;110(8):921–7. doi:10.1161/01.CIR.0000139860.33974.28.

    Article  PubMed  Google Scholar 

  22. Hillege HL, Fidler V, Diercks GF, van Gilst WH, de Zeeuw D, van Veldhuisen DJ, et al. Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population. Circulation. 2002;106(14):1777–82.

    Article  CAS  PubMed  Google Scholar 

  23. Lambers Heerspink HJ, Kropelin TF, Hoekman J, de ZD. Drug-induced reduction in albuminuria is associated with subsequent renoprotection: a meta-analysis. J Am Soc Nephrol. 2014. doi:10.1681/ASN.2014070688.

  24. Rossing P, Hougaard P, Parving HH. Progression of microalbuminuria in type 1 diabetes: ten-year prospective observational study. Kidney Int. 2005;68(4):1446–50. doi:10.1111/j.1523-1755.2005.00556.x.

    Article  PubMed  Google Scholar 

  25. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med. 2003;348(23):2285–93. doi:10.1056/NEJMoa021835.

    Article  CAS  PubMed  Google Scholar 

  26. Perkins BA, Ficociello LH, Roshan B, Warram JH, Krolewski AS. In patients with type 1 diabetes and new-onset microalbuminuria the development of advanced chronic kidney disease may not require progression to proteinuria. Kidney Int. 2010;77(1):57–64. doi:10.1038/ki.2009.399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pavkov ME, Knowler WC, Lemley KV, Mason CC, Myers BD, Nelson RG. Early renal function decline in type 2 diabetes. Clin J Am Soc Nephrol. 2012;7(1):78–84. doi:10.2215/CJN.07610711.

    Article  CAS  PubMed  Google Scholar 

  28. Macisaac RJ, Jerums G. Diabetic kidney disease with and without albuminuria. Curr Opin Nephrol Hypertens. 2011;20(3):246–57. doi:10.1097/MNH.0b013e3283456546.

    Article  CAS  PubMed  Google Scholar 

  29. Fioretto P, Steffes MW, Mauer M. Glomerular structure in nonproteinuric IDDM patients with various levels of albuminuria. Diabetes. 1994;43(11):1358–64.

    Article  CAS  PubMed  Google Scholar 

  30. Babazono T, Nyumura I, Toya K, Hayashi T, Ohta M, Suzuki K, et al. Higher levels of urinary albumin excretion within the normal range predict faster decline in glomerular filtration rate in diabetic patients. Diabetes Care. 2009;32(8):1518–20. doi:10.2337/dc08-2151.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Currie G, McKay G, Delles C. Biomarkers in diabetic nephropathy: present and future. World J Diabetes. 2014;5(6):763–76. doi:10.4239/wjd.v5.i6.763.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gohda T, Niewczas MA, Ficociello LH, Walker WH, Skupien J, Rosetti F, et al. Circulating TNF receptors 1 and 2 predict stage 3 CKD in type 1 diabetes. J Am Soc Nephrol. 2012;23(3):516–24. doi:10.1681/ASN.2011060628. Circulating TNF receptors are among the most promising predictive biomarkers for CKD. This paper explores their use to predict stage 3 CKD in people with type 1 diabetes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Niewczas MA, Gohda T, Skupien J, Smiles AM, Walker WH, Rosetti F, et al. Circulating TNF receptors 1 and 2 predict ESRD in type 2 diabetes. J Am Soc Nephrol. 2012;23(3):507–15. doi:10.1681/ASN.2011060627. Circulating TNF receptors are among the most promising predictive biomarkers for CKD. This paper explores their use to predict ESRD in people with type 2 diabetes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schutte E, Gansevoort RT, Benner J, Lutgers HL, Lambers Heerspink HJ. Will the future lie in multitude? A critical appraisal of biomarker panel studies on prediction of diabetic kidney disease progression. Nephrol Dial Transplant. 2015;30 Suppl 4:iv96–104. doi:10.1093/ndt/gfv119. This is an important statement on the use of multimarker approaches in research and clinical practice.

    Article  PubMed  Google Scholar 

  35. Schievink B, Mol PG, Lambers Heerspink HJ. Surrogate endpoints in clinical trials of chronic kidney disease progression: moving from single to multiple risk marker response scores. Curr Opin Nephrol Hypertens. 2015;24(6):492–7. doi:10.1097/MNH.0000000000000159.

    Article  PubMed  Google Scholar 

  36. Wilkins MR, Pasquali C, Appel RD, Ou K, Golaz O, Sanchez JC, et al. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y). 1996;14(1):61–5.

    Article  CAS  Google Scholar 

  37. Van Eyk JE. Overview: the maturing of proteomics in cardiovascular research. Circ Res. 2011;108(4):490–8. doi:10.1161/CIRCRESAHA.110.226894.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lindsey ML, Mayr M, Gomes AV, Delles C, Arrell DK, Murphy AM, et al. Transformative impact of proteomics on cardiovascular health and disease: a scientific statement from the American Heart Association. Circulation. 2015;132(9):852–72. doi:10.1161/CIR.0000000000000226. A contemporary overview of proteomics with a focus on cardiovascular diseases. Principles, opportunities and challenges apply also to CKD and DN.

    Article  CAS  PubMed  Google Scholar 

  39. Tunon J, Martin-Ventura JL, Blanco-Colio LM, Lorenzo O, Lopez JA, Egido J. Proteomic strategies in the search of new biomarkers in atherothrombosis. J Am Coll Cardiol. 2010;55(19):2009–16. doi:10.1016/j.jacc.2010.01.036.

    Article  CAS  PubMed  Google Scholar 

  40. Gerszten RE, Asnani A, Carr SA. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. Circ Res. 2011;109(4):463–74. doi:10.1161/CIRCRESAHA.110.225003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kalantari S, Jafari A, Moradpoor R, Ghasemi E, Khalkhal E. Human urine proteomics: analytical techniques and clinical applications in renal diseases. Int J Proteomics. 2015;2015:782798. doi:10.1155/2015/782798. This review describes the available techniques for urinary proteomic analyses. We have not systematically reviewed technical aspects in our paper and refer the reader for example to this review.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Albalat A, Franke J, Gonzalez J, Mischak H, Zurbig P. Urinary proteomics based on capillary electrophoresis coupled to mass spectrometry in kidney disease. Methods Mol Biol. 2013;919:203–13. doi:10.1007/978-1-62703-029-8_19.

    Article  CAS  PubMed  Google Scholar 

  43. Stalmach A, Albalat A, Mullen W, Mischak H. Recent advances in capillary electrophoresis coupled to mass spectrometry for clinical proteomic applications. Electrophoresis. 2013;34(11):1452–64. doi:10.1002/elps.201200708.

    Article  CAS  PubMed  Google Scholar 

  44. Fisher WG, Lucas JE, Mehdi UF, Qunibi DW, Garner HR, Rosenblatt KP, et al. A method for isolation and identification of urinary biomarkers in patients with diabetic nephropathy. Proteomics Clin Appl. 2011;5(11-12):603–12. doi:10.1002/prca.201000156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Afkarian M, Bhasin M, Dillon ST, Guerrero MC, Nelson RG, Knowler WC, et al. Optimizing a proteomics platform for urine biomarker discovery. Mol Cell Proteomics. 2010;9(10):2195–204. doi:10.1074/mcp.M110.000992.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Klein J, Papadopoulos T, Mischak H, Mullen W. Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine. Electrophoresis. 2014;35(7):1060–4. doi:10.1002/elps.201300327.

    Article  CAS  PubMed  Google Scholar 

  47. Robertson GL, Norgaard JP. Renal regulation of urine volume: potential implications for nocturia. BJU Int. 2002;90 Suppl 3:7–10.

    Article  PubMed  Google Scholar 

  48. Delles C, Schiffer E, von Zur MC, Peter K, Rossing P, Parving HH, et al. Urinary proteomic diagnosis of coronary artery disease: identification and clinical validation in 623 individuals. J Hypertens. 2010;28(11):2316–22. doi:10.1097/HJH.0b013e32833d81b7.

    Article  CAS  PubMed  Google Scholar 

  49. Andersen S, Mischak H, Zurbig 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. doi:10.1186/1471-2369-11-29.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Nakatani S, Kakehashi A, Ishimura E, Yamano S, Mori K, Wei M, et al. Targeted proteomics of isolated glomeruli from the kidneys of diabetic rats: sorbin and SH3 domain containing 2 is a novel protein associated with diabetic nephropathy. Exp Diabetes Res. 2011;2011:979354. doi:10.1155/2011/979354.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Zhang D, Yang H, Kong X, Wang K, Mao X, Yan X, et al. Proteomics analysis reveals diabetic kidney as a ketogenic organ in type 2 diabetes. Am J Physiol Endocrinol Metab. 2011;300(2):E287–95. doi:10.1152/ajpendo.00308.2010.

    Article  CAS  PubMed  Google Scholar 

  52. Thongboonkerd V, Barati MT, McLeish KR, Benarafa C, Remold-O’Donnell E, Zheng S, et al. Alterations in the renal elastin-elastase system in type 1 diabetic nephropathy identified by proteomic analysis. J Am Soc Nephrol. 2004;15(3):650–62.

    Article  CAS  PubMed  Google Scholar 

  53. Fugmann T, Borgia B, Revesz C, Godo M, Forsblom C, Hamar P, et al. Proteomic identification of vanin-1 as a marker of kidney damage in a rat model of type 1 diabetic nephropathy. Kidney Int. 2011;80(3):272–81. doi:10.1038/ki.2011.116.

    Article  CAS  PubMed  Google Scholar 

  54. Tsai PY, Chen SM, Chen HY, Li YC, Imai K, Hsu KY, et al. Proteome analysis of altered proteins in streptozotocin-induced diabetic rat kidney using the fluorogenic derivatization-liquid chromatography-tandem mass spectrometry method. Biomed Chromatogr. 2013;27(3):382–9. doi:10.1002/bmc.2803.

    CAS  PubMed  Google Scholar 

  55. Betz BB, Jenks SJ, Cronshaw AD, Lamont DJ, Cairns C, Manning JR, et al. Urinary peptidomics in a rodent model of diabetic nephropathy highlights epidermal growth factor as a biomarker for renal deterioration in patients with type 2 diabetes. Kidney Int. 2016;89(5):1125–35. doi:10.1016/j.kint.2016.01.015. An interesting study that shows the potential of urinary proteomics to detect functionally relevant dysregulated proteins in experimental models that can be subsequently confirmed in human disease.

    Article  CAS  PubMed  Google Scholar 

  56. Siwy J, Zoja C, Klein J, Benigni A, Mullen W, Mayer B, 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. doi:10.1371/journal.pone.0051334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. van der Pol E, Boing AN, Harrison P, Sturk A, Nieuwland R. Classification, functions, and clinical relevance of extracellular vesicles. Pharmacol Rev. 2012;64(3):676–705. doi:10.1124/pr.112.005983.

    Article  PubMed  Google Scholar 

  58. Pitt JM, Kroemer G, Zitvogel L. Extracellular vesicles: masters of intercellular communication and potential clinical interventions. J Clin Invest. 2016;126(4):1139–43. doi:10.1172/JCI87316.

    Article  PubMed  Google Scholar 

  59. Xu R, Greening DW, Zhu HJ, Takahashi N, Simpson RJ. Extracellular vesicle isolation and characterization: toward clinical application. J Clin Invest. 2016;126(4):1152–62. doi:10.1172/JCI81129.

    Article  PubMed  Google Scholar 

  60. Wang D, Sun W. Urinary extracellular microvesicles: isolation methods and prospects for urinary proteome. Proteomics. 2014;14(16):1922–32. doi:10.1002/pmic.201300371.

    Article  CAS  PubMed  Google Scholar 

  61. Rood IM, Deegens JK, Merchant ML, Tamboer WP, Wilkey DW, Wetzels JF, et al. Comparison of three methods for isolation of urinary microvesicles to identify biomarkers of nephrotic syndrome. Kidney Int. 2010;78(8):810–6. doi:10.1038/ki.2010.262.

    Article  CAS  PubMed  Google Scholar 

  62. Zubiri I, Posada-Ayala M, Benito-Martin A, Maroto AS, Martin-Lorenzo M, Cannata-Ortiz P, et al. Kidney tissue proteomics reveals regucalcin downregulation in response to diabetic nephropathy with reflection in urinary exosomes. Transl Res. 2015;166(5):474–84 e4. doi:10.1016/j.trsl.2015.05.007. This paper describes a proteomic approach in urinary exosomes. Exosomes provide information beyond the soluble features in urine.

    Article  CAS  PubMed  Google Scholar 

  63. Zubiri I, Posada-Ayala M, Sanz-Maroto A, Calvo E, Martin-Lorenzo M, Gonzalez-Calero L, et al. Diabetic nephropathy induces changes in the proteome of human urinary exosomes as revealed by label-free comparative analysis. J Proteomics. 2014;96:92–102. doi:10.1016/j.jprot.2013.10.037.

    Article  CAS  PubMed  Google Scholar 

  64. Raimondo F, Corbetta S, Morosi L, Chinello C, Gianazza E, Castoldi G, et al. Urinary exosomes and diabetic nephropathy: a proteomic approach. Mol Biosyst. 2013;9(6):1139–46. doi:10.1039/c2mb25396h.

    Article  CAS  PubMed  Google Scholar 

  65. Jin J, Ku YH, Kim Y, Kim Y, Kim K, Lee JY, et al. Differential proteome profiling using iTRAQ in microalbuminuric and normoalbuminuric type 2 diabetic patients. Exp Diabetes Res. 2012;2012:168602. doi:10.1155/2012/168602.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lewandowicz A, Bakun M, Kohutnicki R, Fabijanska A, Kistowski M, Imiela J, et al. Changes in urine proteome accompanying diabetic nephropathy progression. Pol Arch Med Wewn. 2015;125(1-2):27–38.

    PubMed  Google Scholar 

  67. Papale M, Di Paolo S, Magistroni R, Lamacchia O, Di Palma AM, De Mattia A, et al. Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy. Diabetes Care. 2010;33(11):2409–15. doi:10.2337/dc10-0345.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wu J, Chen YD, Yu JK, Shi XL, Gu W. Analysis of urinary proteomic patterns for type 2 diabetic nephropathy by ProteinChip. Diabetes Res Clin Pract. 2011;91(2):213–9. doi:10.1016/j.diabres.2010.11.036.

    Article  CAS  PubMed  Google Scholar 

  69. Gu W, Zou LX, Shan PF, Chen YD. Analysis of urinary proteomic patterns for diabetic nephropathy by ProteinChip. Proteomics Clin Appl. 2008;2(5):744–50. doi:10.1002/prca.200780083.

    Article  CAS  PubMed  Google Scholar 

  70. Jain S, Rajput A, Kumar Y, Uppuluri N, Arvind AS, Tatu U. Proteomic analysis of urinary protein markers for accurate prediction of diabetic kidney disorder. J Assoc Physicians India. 2005;53:513–20.

    PubMed  Google Scholar 

  71. Sharma K, Lee S, Han S, Lee S, Francos B, McCue P, et al. Two-dimensional fluorescence difference gel electrophoresis analysis of the urine proteome in human diabetic nephropathy. Proteomics. 2005;5(10):2648–55. doi:10.1002/pmic.200401288.

    Article  CAS  PubMed  Google Scholar 

  72. Lapolla A, Seraglia R, Molin L, Williams K, Cosma C, Reitano R, et al. Low molecular weight proteins in urines from healthy subjects as well as diabetic, nephropathic and diabetic-nephropathic patients: a MALDI study. J Mass Spectrom. 2009;44(3):419–25. doi:10.1002/jms.1520.

    Article  CAS  PubMed  Google Scholar 

  73. Bellei E, Rossi E, Lucchi L, Uggeri S, Albertazzi A, Tomasi A, et al. Proteomic analysis of early urinary biomarkers of renal changes in type 2 diabetic patients. Proteomics Clin Appl. 2008;2(4):478–91. doi:10.1002/prca.200780109.

    Article  CAS  PubMed  Google Scholar 

  74. Dihazi H, Muller GA, Lindner S, Meyer M, Asif AR, Oellerich M, et al. Characterization of diabetic nephropathy by urinary proteomic analysis: identification of a processed ubiquitin form as a differentially excreted protein in diabetic nephropathy patients. Clin Chem. 2007;53(9):1636–45. doi:10.1373/clinchem.2007.088260.

    Article  CAS  PubMed  Google Scholar 

  75. Thrailkill KM, Nimmo T, Bunn RC, Cockrell GE, Moreau CS, Mackintosh S, et al. Microalbuminuria in type 1 diabetes is associated with enhanced excretion of the endocytic multiligand receptors megalin and cubilin. Diabetes Care. 2009;32(7):1266–8. doi:10.2337/dc09-0112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Jiang H, Guan G, Zhang R, Liu G, Cheng J, Hou X, et al. Identification of urinary soluble E-cadherin as a novel biomarker for diabetic nephropathy. Diabetes Metab Res Rev. 2009;25(3):232–41. doi:10.1002/dmrr.940.

    Article  CAS  PubMed  Google Scholar 

  77. Caseiro A, Ferreira R, Quintaneiro C, Pereira A, Marinheiro R, Vitorino R, et al. Protease profiling of different biofluids in type 1 diabetes mellitus. Clin Biochem. 2012;45(18):1613–9. doi:10.1016/j.clinbiochem.2012.08.027.

    Article  CAS  PubMed  Google Scholar 

  78. Good DM, Zurbig P, Argiles A, Bauer HW, Behrens G, Coon JJ, et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics. 2010;9(11):2424–37. doi:10.1074/mcp.M110.001917.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Meier M, Kaiser T, Herrmann A, Knueppel S, Hillmann M, Koester P, et al. Identification of urinary protein pattern in type 1 diabetic adolescents with early diabetic nephropathy by a novel combined proteome analysis. J Diabetes Complicat. 2005;19(4):223–32. doi:10.1016/j.jdiacomp.2004.10.002.

    Article  PubMed  Google Scholar 

  80. Rossing K, Mischak H, Dakna M, Zurbig P, Novak J, Julian BA, et al. Urinary proteomics in diabetes and CKD. J Am Soc Nephrol. 2008;19(7):1283–90. doi:10.1681/ASN.2007091025.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Schanstra JP, Zurbig P, Alkhalaf A, Argiles A, Bakker SJ, Beige J, et al. Diagnosis and prediction of CKD progression by assessment of urinary peptides. J Am Soc Nephrol. 2015;26(8):1999–2010. doi:10.1681/ASN.2014050423.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Jantos-Siwy J, Schiffer E, Brand K, Schumann G, Rossing K, Delles C, et al. Quantitative urinary proteome analysis for biomarker evaluation in chronic kidney disease. J Proteome Res. 2009;8(1):268–81. doi:10.1021/pr800401m.

    Article  CAS  PubMed  Google Scholar 

  83. Siwy J, Schanstra JP, Argiles A, Bakker SJ, Beige J, Boucek P, 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):1563–70. doi:10.1093/ndt/gfu039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Alkhalaf A, Zurbig P, Bakker SJ, Bilo HJ, Cerna M, Fischer C, et al. Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy. PLoS One. 2010;5(10):e13421. doi:10.1371/journal.pone.0013421.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Otu HH, Can H, Spentzos D, Nelson RG, Hanson RL, Looker HC, et al. Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy. Diabetes Care. 2007;30(3):638–43. doi:10.2337/dc06-1656.

    Article  CAS  PubMed  Google Scholar 

  86. Bhensdadia NM, Hunt KJ, Lopes-Virella MF, Michael Tucker J, Mataria MR, Alge JL, et al. Urine haptoglobin levels predict early renal functional decline in patients with type 2 diabetes. Kidney Int. 2013;83(6):1136–43. doi:10.1038/ki.2013.57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Merchant ML, Perkins BA, Boratyn GM, Ficociello LH, Wilkey DW, Barati MT, et al. Urinary peptidome may predict renal function decline in type 1 diabetes and microalbuminuria. J Am Soc Nephrol. 2009;20(9):2065–74. doi:10.1681/ASN.2008121233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Zürbig P, Jerums G, Hovind P, Macisaac RJ, Mischak H, Nielsen SE, et al. Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes. 2012;61(12):3304–13. doi:10.2337/db12-0348.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Roscioni SS, de Zeeuw D, Hellemons ME, Mischak H, Zurbig P, Bakker SJ, et al. A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus. Diabetologia. 2013;56(2):259–67. doi:10.1007/s00125-012-2755-2. Roscioni et al. describe how the CKD273 urinary proteomic classifier can predict progression of DN from normoalbuminuria to microalbuminuria and from microalbuminuria to macroalbuminuria. The sample size is, however, small.

    Article  CAS  PubMed  Google Scholar 

  90. Argiles A, Siwy J, Duranton F, Gayrard N, Dakna M, Lundin U, et al. CKD273, a new proteomics classifier assessing CKD and its prognosis. PLoS One. 2013;8(5):e62837. doi:10.1371/journal.pone.0062837.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Lindhardt M, Persson F, Currie G, Pontillo C, Beige J, Delles C, 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. doi:10.1136/bmjopen-2015-010310. This paper describes the design of the PRIORITY study, the first large-scale stratified randomised clinical trial based on urinary proteomics.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Our work is supported by collaborative project grants from the European Commission: ‘iMODE-CKD’ (grant agreement 608332), ‘sysVASC’ (grant agreement 603288), ‘HOMAGE’ (grant agreement 305507) and ‘PRIORITY’ (grant agreement 279277).

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  1. Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK

    G. Currie & C. Delles

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  1. G. Currie
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  2. C. Delles
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Correspondence to G. Currie.

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G. Currie and C. Delles declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Microvascular Complications—Nephropathy

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Currie, G., Delles, C. Urinary Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy. Curr Diab Rep 16, 104 (2016). https://doi.org/10.1007/s11892-016-0798-3

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