The traditional chronic kidney disease (CKD) biomarkers (eGFR based on serum creatinine, sex and age and albuminuria) cannot predict a patient’s individual risk for developing progressive CKD. For this reason, it is necessary to identify novel CKD biomarkers that will be able to predict which patients are prone to develop progressive disease and discriminate between disease processes in different parts of the nephron (glomeruli or tubules).
A good biomarker should change before or simultaneously with lesion development and its changes should correlate strongly with lesion development. Also, there should be a close relationship between severity of injury and amount of detectable biomarker and its levels should decrease with diminishing injury.
Among the large number of molecules under investigation, we have reviewed the most promising ones: NGAL and KIM-1, MCP-1, MMP-9, clusterin, MMP-9, TIMP-1, Procollagen I alpha 1 and suPAR. All these, have been studied as biomarkers for prediction of CKD progression in cohorts of patients with chronic kidney disease of different stages and various aetiologies (proteinuric and non-proteinuric, glomerulonephritides, diabetic, hypertensive and polycystic kidney disease). There is evidence that these molecules could be useful as biomarkers for progressive chronic kidney disease, however, the available data are not enough to draw final conclusions. Further studies with large cohorts and long follow-up are required to identify appropriate biomarkers, that will be able to accurately and reliably define the risk for progressive chronic kidney disease.
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1. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation. 2006; 113: 2335–62
2. Biomarkers Definitions Working Group. Bio-markers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 2001; 69: 89–95
3. Coresh J, Selvin E, Stevens LA et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007; 298(17): 2038–47
4. Goodsaid FM, Blank M, Dieterle F et al. Novel biomarkers of acute kidney toxicity. Clin Pharmacol Ther. 2009; 86(5): 490–6.
5. Goumenos DS, Tsamandas AC, Oldroyd S et al. Transforming growth factor-beta (1) and myofibroblasts: a potential pathway towards renal scarring in human glomerular disease. Nephron. 2001; 87(3): 240–8.
6. Goumenos DS, Kalliakmani P, Tsakas S, Papachristou E, Vlachojannis JG. Growth factors and apoptosis-related protein expression in human crescentic nephritis. Med Sci Monit. 2008; 14: 243–48.
7. Papasotiriou M, Kalliakmani P, Huang L et al. Does treatment with corticosteroids and cyclosporine reduce transglutaminase type 2 expression in the renal tissue of patients with membranous nephropathy? Nephron Clin Pract. 2012; 121: 60–7.
8. Goumenos DS, Brown CB, Shortland J, el Nahas AM. (1994) Myofibroblasts, predictors of progression of mesangial IgA nephropathy? Nephrol. Dial. Transplant. 1994; 9: 1418–25.
9. Ichimura T, Bonventre JV, Bailly V et al. Kidney injury molecule-1 (KIM-1), a putative epithelial cell adhesion molecule containing a novel immunoglobulin domain, is up-regulated in renal cells after injury. J. Biol. Chem. 1998; 273: 4135–42.
10. Ismail O, Zhang X, Bonventre JV, Gunaratnam L. G protein α12 (Gα12) is a negative regulator of kidney injury molecule-1-mediated efferocytosis. Am. J. Phys. Renal Phys. 2016; 310: 607–20
11. Yin C, Wang N. Kidney injury molecule-1 in kidney disease, Ren. Fail. 2016; 38: 1567–73.
12. Zhang Z, Humphreys BD, Bonventre JV. Shedding of the urinary biomarker kidney injury molecule-1 (KIM-1) is regulated by MAP kinases and juxtamembrane region. J. Am. Soc. Nephrol. 2007; 18: 2704–14.
13. Prozialeck WC, Vaidya VS, Liu J et al. Kidney injury molecule-1 is an early biomarker of cadmium nephrotoxicity. Kidney Int. 2007; 72: 985–93.
14. Lim AI, Chan LY, Lai KN et al. Distinct role of matrix metalloproteinase-3 in kidney injury molecule-1 shedding by kidney proximal tubular epithelial cells. Int. J. Biochem. Cell Biol. 2012; 44: 1040–50.
15. Bailly V, Zhang Z, Meier W et al. Shedding of kidney injury molecule-1, a putative adhesion protein involved in renal regeneration. J. Biol. Chem. 2002; 277: 39739–48.
16. Lim AI, Tang SC, Lai KN, Leung JC. Kidney injury molecule-1: more than just an injury marker of tubular epithelial cells? J. Cell. Physiol. 2013; 228: 917–24.
17. Han WK, Bailly V, Abichandani R et al. Kidney Injury Molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int. 2002; 62: 237–44.
18. Ichimura T, Hung CC, Yang SA et al. Kidney injury molecule-1: a tissue and urinary biomarker for nephrotoxicant-induced renal injury. Am. J. Phys. Renal Phys. 2004; 286: 552–63.
19. Bonventre JV, Yang L. Kidney injury molecule-1. Curr. Opin. Crit. Care. 2010; 16: 556–61.
20. Vaidya VS, Ozer JS, Dieterle F et al. Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies. Nat. Biotechnol. 2010; 28: 478–85.
21. van Timmeren MM, van den Heuvel MC, Bailly V, Bakker SJ, van Goor H, Stegeman CA. Tubular kidney injury molecule-1 (KIM-1) in human renal disease. J. Pathol. 2007; 212: 209–17.
22. Schröppel B, Krueger B, Walsh L et al. Tubular expression of KIM-1 does not predict delayed function after transplantation. J. Am. Soc. Nephrol. 2010; 21: 536–42.
23. Wasung ME, Chawla LS, Madero M. Biomarkers of renal function, which and when? Clin. Chim. Acta. 2015; 438: 350–57.
24. De Silva PMCS, Mohammed Abdul KS, Eakanayake EM et al. Urinary biomarkers KIM-1 and NGAL for detection of chronic kidney disease of uncertain etiology (CKDu) among agricultural communities in Sri Lanka. PLoS Negl. Trop. Dis. 2016;10:e0004979
25. Castillo-Rodriguez E, Fernandez-Prado R, Martin-Cleary C et al. Kidney injury marker 1 and neutrophil gelatinase associated lipocalin in chronic kidney disease. Nephron. 2017; 136: 263–67.
26. Nasioudis D, Witkin SS: Neutrophil gelatinase-associated lipocalin and innate immune responses to bacterial infections. Med Microbiol Immunol. 2015; 204: 471–79.
27. Kuncio, G.S.; Neilson, E.G.; Haverty, T. Mechanisms of tubulointerstitial fibrosis. Kidney Int. 1991; 39: 550–56.
28. Viau A, Karoui KE, Laouari D et al. Lipocalin 2 is essential for chronic kidney disease in mice and human. J. Clin. Investig. 2010; 120: 4065–76.
29. Dubin RF, Judd S, Scherzer R et al. Urinary Tubular Injury Biomarkers Are Associated With ESRD and Death in the REGARDS Study. Kidney Int Rep. 2018; 3(5): 1183–92.
30. Seibert FS, Sitz M, Passfall J et al. Prognostic Value of Urinary Calprotectin, NGAL and KIM-1. Chronic Kidney Disease. Kidney Blood Press Res. 2018; 43(4): 1255–62.
31. Ding Y, Nie LM, Pang Y et al. Composite urinary biomarkers to predict pathological tubulointerstitial lesions in lupus nephritis. Lupus. 2018; 27(11): 1778–89.
32. Alderson HV, Ritchie JP, Pagano S et al. The Associations of Blood Kidney Injury Molecule-1 and Neutrophil Gelatinase–Associated Lipocalin with Progression from CKD to ESRD. Clin J Am Soc Nephrol. 2016; 11(12): 2141–49.
33. De Carvalho JA, Tatsch E, Hausen BS et al. Urinary kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin as indicators of tubular damage in normoalbuminuric patients with type 2 diabetes. Clinical Biochemistry. 2016; 49(3): 232–36.
34. Satirapoj B, Aramsaowapak K, Tangwonglert T, Supasyndh O. Novel tubular biomarkers predict renal progression in type 2 diabetes mellitus: a prospective cohort study. J Diabetes Res. 2016; 2016: 3102962.
35. Nielsen SE, Reinhard H, Zdunek D et al. Tubular markers are associated with decline in kidney function in proteinuric type 2 diabetic patients. Diabetes Res Clin Pract. 2012; 97(1): 71–6.
36. Panduru NM, Sandholm N, Forsblom C et al. Kidney injury molecule-1 and the loss of kidney function in diabetic nephropathy: a likely causal link in patients with type 1 diabetes. Diabetes Care. 2015; 38(6): 1130–37.
37. Smith ER, Lee D, Cai M et al. Urinary neutro-phil gelatinase-associated lipocalin may aid prediction of renal decline in patients with non-proteinuric stages 3 and 4 chronic kidney disease. Nephrol Dial Transplant. 2013; 28: 1569–79.
38. Bolignano D, Lacquaniti A, Coppolino G et al. Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol. 2009; 4: 337–44.
39. Mitsnefes MM, Kathman TS, Mishra J et al. Serum neutrophil gelatinase-associated lipocalin as a marker of renal function in children with chronic kidney disease. Pediatr Nephrol. 2007; 22(1): 101–8.
41. Van Coillie E, Van Damme J, Opdenakker G. The MCP/eotaxin subfamily of CC chemokines. Cytokine Growth Factor Rev. 1999; 10: 61–86.
42. Cushing SD, Berliner JA, Valente AJ et al. Minimally modified low density lipoprotein induces monocyte chemotactic protein 1 in human endothelial cells and smooth muscle cells. Proc Natl Acad Sci USA. 1990; 87: 5134–38.
43. Leonard EJ, Yoshimura T. Human monocyte chemoattractant protein-1 (MCP-1). Immunol Today. 1990; 11: 97–101.
44. Morii T, Fujita H, Narita T et al. Increased urinary excretion of monocyte chemoattractant protein-1 in proteinuric renal diseases. Ren Fail. 2003; 25(3): 439–44.
45. Segarra-Medrano A, Carnicer-Caceres C, Valtierra-Carmeno N et al. Value of urinary levels of interleukin-6, epidermal growth factor, monocyte chemoattractant protein type1 and transforming growth factor β1 in predicting the extent of fibrosis lesions in kidney biopsies of patients with IgA nephropathy. Nefrologia. 2017; 37(5): 531–38.
46. Worawichawong S, Worawichawong S, Radinahamed P et al. Urine Epidermal Growth Factor, Monocyte Chemoattractant Protein-1 or their Ratio as Biomarkers for Interstitial Fibrosis and Tubular Atrophy in Primary Glomerulonephritis. Kidney Blood Press Res. 2016; 41(6): 997–1007.
47. Dantas M, Romão EA, Costa RS et al. Urinary excretion of monocyte chemoattractant protein-1: a biomarker of active tubulointerstitial damage in patients with glomerulopathies. Kidney Blood Press Res. 2007; 30(5): 306–13.
48. Wang X, Lieske JC, Alexander MP et al. Tubulointerstitial fibrosis of living donor kidneys associates with urinary monocyte chemoattractant protein. Am J Nephrol. 2016; 43(6): 454–59.
49. Ho J, Rush DN, Gibson IW et al. Early urinary CCL2 is associated with the later development of interstitial fibrosis and tubular atrophy in renal allografts. Transplantation. 2010; 90(4): 394–400.
50. Ho J, Wiebe C, Gibson IW et al. Elevated Urinary CCL2: Cr at 6 months is associated with renal allograft interstitial fibrosis and inflammation at 24 months. Transplantation. 2014; 98(1): 39–46
51. de Boer IH, Gao X, Bebu I et al. Biomarkers of tubulointerstitial damage and function in type 1 diabetes. BMJ Open Diabetes Res Care. 2017; 5(1):e000461.
52. Zeng XF, Lu DX, Li JM et al. Performance of urinary neutrophil gelatinase-associated lipocalin, clusterin, and cystatin C in predicting diabetic kidney disease and diabetic microalbuminuria: a consecutive cohort study. BMC Nephrol. 2017; 18(1): 233.
53. Hidaka S, Kränzlin B, Gretz N, Witzgall R. Urinary clusterin levels in the rat correlate with the severity of tubular damage and may help to differentiate between glomerular and tubular injuries. Cell and Tissue Research, 2002; 310(3): 289–96.
54. Dvergsten J, Manivel JC, Correa-Rotter R, Rosenberg ME. Expression of clusterin in human renal diseases. Kidney Int. 1994; 45(3): 828–35.
56. Askenazi DJ, Koralkar R, Patil N, Halloran B, Ambalavanan N, Griffin R. Acute Kidney Injury urine biomarkers in very low-birth-weight infants. Clin J Am Soc Nephrol. 2016; 11(9): 1527–35.
57. Rouse RL, Zhang J, Stewart SR, Rosenzweig BA, Espandiari P, Sadrieh NK.. Comparative profile of commercially available urinary biomarkers in preclinical drug-induced kidney injury and recovery in rats. Kidney Int. 2011; 79(11): 1186–97.
58. Cho Y, Johnson DW, Vesey DA, Hawley CM, Clarke M, Topley N; balANZ Trial Investigators. Utility of urinary biomarkers in predicting loss of residual renal function: The balANZ Trial. Perit Dial Int. 2015; 35(2): 159–71.
59. Singhal MK, Bhaskaran S, Vidgen E, Bargman JM, Vas SI, Oreopoulos DG. Rate of decline of residual renal function in patients on continuous peritoneal dialysis and factors affecting it. Perit Dial Int. 2000; 20(4): 429–38.
60. Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol 2009; 20(5): 1069–77.
61. Kim SS, Song SH, Kim JH et al. Urine clusterin/apolipoprotein J is linked to tubular damage and renal outcomes in patients with type 2 diabetes mellitus. Clin Endocrinol (Oxf). 2017; 87(2): 156–64.
62. Zeng XF, Lu DX, Li JM et al. Performance of urinary neutrophil gelatinase-associated lipocalin, clusterin, and cystatin C in predicting diabetic kidney disease and diabetic microalbuminuria: a consecutive cohort study. BMC Nephrol. 2017; 18(1): 233.
63. Lindsey ML, Iyer RP, Jung M, DeLeon-Pennell KY, Ma Y. Matrix Metalloproteinases as input and output signals for post-myocardial infarction remodeling. J Mol Cell Cardiol. 2016; 91: 134–40.
64. Tan RJ, Liu Y. Matrix metalloproteinases in kidney homeostasis and diseases. Am J Physiol Renal Physiol. 2012; 302(11): 1351–61.
65. Ke B, Fan C, Yang L, Fang X. Matrix Metalloproteinases-7 and kidney fibrosis. Front Physiol. 2017; 8: 21.
66. Urushihara M, Kagami S, Kuhara T, Tamaki T, Kuroda Y. Glomerular distribution and gelatinolytic activity of matrix metalloproteinases in human glomerulonephritis. Nephrol Dial Transplant. 2002; 17(7): 1189–96.
67. Erol M, Yigit O, Tasdemir M et al. Potential of serum and urinary Matrix Metalloproteinase-9 levels for the early detection of renal involvement in children with Henoch-Schönlein Purpura. Iran J Pediatr. 2016; 26(4): 6129.
68. Musiał K, Bargenda A, Zwolińska D. Urine matrix metalloproteinases and their extracellular inducer EMMPRIN in children with chronic kidney disease. Ren Fail. 2015; 37(6): 980–4.
69. van der Zijl NJ, Hanemaaijer R, Tushuizen ME et al. Urinary matrix metalloproteinase-8 and -9 activities in type 2 diabetic subjects: A marker of incipient diabetic nephropathy? Clin Biochem. 2010; 43(7-8): 635–9.
70. Sanders JS, Huitema MG, Hanemaaijer R, van Goor H, Kallenberg CG, Stegeman CA. Urinary matrix metalloproteinases reflect renal damage in anti-neutrophil cytoplasm autoantibody-associated vasculitis. Am J Physiol Renal Physiol. 2007; 293(6): 1927–34.
71. Korzeniecka-Kozerska A, Wasilewska A, Tenderenda E, Sulik A, Cybulski K. Urinary MMP-9/NGAL ratio as a potential marker of FSGS in nephrotic children. Dis Markers. 2013; 34(5): 357–62.
72. Hultström M, Leh S, Skogstrand T, Iversen BM. Upregulation of tissue inhibitor of metallopro-teases-1 (TIMP-1) and procollagen-N-peptidase in hypertension-induced renal damage. Nephrol Dial Transplant. 2008; 23(3): 896–903.
73. Catania JM, Chen G, Parrish AR. Role of matrix metalloproteinases in renal pathophysiologies. Am J Physiol Renal Physiol. 2007; 292(3): 9 05–11.
74. Duymelinck C, Dauwe SE, De Greef KE, Ysebaert DK, Verpooten GA, De Broe ME. TIMP-1 gene expression and PAI-1 antigen after unilateral ureteral obstruction in the adult male rat. Kidney Int. 2000; 58(3): 1186–201.
75. Han SY, Jee YH, Han KH et al. An imbalance between matrix metalloproteinase-2 and tissue inhibitor of matrix metalloproteinase-2 contributes to the development of early diabetic nephropathy. Nephrol Dial Transplant. 2006; 21(9): 2406–16.
76. Kwiatkowska E, Domanski L, Bober J et al. Urinary Metalloproteinases-9 and -2 and Their Inhibitors TIMP-1 and TIMP-2 are Markers of Early and Long-Term Graft Function After Renal Transplantation. Kidney Blood Press Res. 2016; 41(3): 288–97.
77. Vanden Heuvel GB, Abrahamson DR. Quantitation and localization of laminin A, B1, and B2 chain RNA transcripts in developing kidney. Am J Physiol. 1993; 265(2 Pt 2): 293–9.
78. Hörstrup JH, Gehrmann M, Schneider B et al. Elevation of serum and urine levels of TIMP-1 and tenascin in patients with renal disease. Nephrol Dial Transplant. 2002; 17(6): 1005–13.
79. Bieniaś B, Sikora P. Urinary metalloproteinases and tissue inhibitors of metalloproteinases as potential early biomarkers for renal fibrosis in children with nephrotic syndrome. Medicine (Baltimore). 2018; 97(8): e9964.
80. Kanauchi M, Nishioka H, Nakashima Y, Hashimoto T, Dohi K. Role of tissue inhibitors of metalloproteinase in diabetic nephropathy. Nihon Jinzo Gakkai Shi. 1996; 38(3): 124–8.
81. Li L, Shen Y, Ding Y, Liu Y, Su D, Liang X. Hrd1 participates in the regulation of collagen I synthesis in renal fibrosis. Mol Cell Biochem. 2014; 386(1–2): 35–44.
82. Myllyharju J, Kivirikko KI. Collagens, modifying enzymes and their mutations in humans, flies and worms. Trends Genet. 2004; 20(1): 33–43.
84. Tharaux PL, Chatziantoniou C, Casellas D, Fouassier L, Ardaillou R, Dussaule JC. Vascular endothelin-1 gene expression and synthesis and effect on renal type I collagen synthesis and nephroangiosclerosis during nitric oxide synthase inhibition in rats. Circulation. 1999; 99(16): 2185–91.
85. Nast CC, Adler SG, Artishevsky A, Kresser CT, Ahmed K, Anderson PS. Cyclosporine induces elevated procollagen alpha 1 (I) mRNA levels in the rat renal cortex. Kidney Int. 1991; 39(4): 631–8.
86. Wolf G, Killen PD, Neilson EG. Cyclosporin A stimulates transcription and procollagen secretion in tubulointerstitial fibroblasts and proximal tubular cells. J Am Soc Nephrol. 1990; 1(6): 918–22.
87. Hultström M, Leh S, Skogstrand T, Iversen BM. Upregulation of tissue inhibitor of metallopro-teases-1 (TIMP-1) and procollagen-N-peptidase in hypertension-induced renal damage. Nephrol Dial Transplant. 2008; 23(3): 896–903.
88. Park M, Katz R, Shlipak MG et al. Urinary markers of fibrosis and risk of cardiovascular events and death in kidney transplant recipients: The FAVORIT Trial. Am J Transplant. 2017; 17(10): 2640–49.
89. Wada T, Nangaku M. A circulating permeability factor in focal segmental glomerulosclerosis: the hunt continues. Clin Kidney J. 2015; 8(6): 708–15.
90. Wei C, Trachtman H, Li J et al.. Circulating suPAR in two cohorts of primary FSGS. J Am Soc Nephrol. 2012; 23(12): 2051–9.
91. Hayek SS, Sever S, Ko YA et al. Soluble urokinase receptor and chronic kidney disease. N Engl J Med. 2015; 373(20): 1916–25.
92. Hayek SS, Koh KH, Grams ME et al. A tripartite complex of suPAR, APOL1 risk variants and αvβ3 integrin on podocytes mediates chronic kidney disease. Nat Med. 2017; 23(8): 945–53.
93. Hayek SS, Ko YA, Awad M et al. Cardiovascular disease biomarkers and suPAR in predicting decline in renal function: A Prospective Cohort Study. Kidney Int Rep. 2017; 2(3): 425–32.
94. Zhao Y, Liu L, Huang J et al. Plasma soluble urokinase receptor level is correlated with podocytes damage in patients with IgA nephropathy. PLoS One. 2015; 10(7): e0132869
95. Lv L, Wang F, Wu L et al. Soluble urokinase-type plasminogen activator receptor and incident end-stage renal disease in Chinese patients with chronic kidney disease. Nephrol Dial Transplant. 2018 Aug 13. doi: 10.1093/ndt/gfy265.
96. Theilade S, Lyngbaek S, Hansen TW et al. Soluble urokinase plasminogen activator receptor levels are elevated and associated with complications in patients with type 1 diabetes. J Intern Med. 2015; 277(3): 362–71.
97. 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): 2424–37.
98. Pejchinovski M., Mischak H. Clinical proteomics in kidney disease: from discovery to clinical application. Prilozi. 2017; 38(3): 39–54.
99. Zürbig P, Jerums G, Hovind P et al. Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy. Diabetes. 2012; 61(12): 3304–13.
100. 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): 259–67.
101. Argiles A, Siwy J, Duranton F et al. CKD273, a New Proteomics Classifier Assessing CKD and Its Prognosis. PLoS One. 2013; 8(5): e62837
102. 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; 32(9): 1510–16.
103. Kramer H, Boucher RE, Leehey D et al. Increasing mortality in adults with diabetes and low estimated glomerular filtration rate in the absence of albuminuria. Diabetes Care. 2018; 41(4): 775–81.
104. Zürbig P, Mischak H, Menne J, Haller H. CKD273 enables efficient prediction of diabetic nephropathy in nonalbuminuric patients. Diabetes Care. 2019; 42(1): e4-e5. doi: 10.2337/dc18-1322.