Novel Biomarkers for Early Detection and Risk Stratification in Chronic Kidney Disease
DOI:
https://doi.org/10.65327/kidneys.v15i1.628Keywords:
Chronic kidney disease, Biomarkers, Early detection, Risk stratification, Proteomics, Precision nephrologyAbstract
Background: Chronic kidney disease (CKD) is a progressive condition characterized by largely irreversible structural and functional kidney damage, representing a major global public health burden and affecting approximately 13% of the world’s population. Its asymptomatic nature in early stages and close association with cardiovascular morbidity and mortality often lead to delayed diagnosis and poor clinical outcomes.
Methods: This narrative review summarizes current evidence on emerging biomarkers for early detection and risk stratification in CKD. Peer-reviewed studies were examined focusing on biomarkers of tubular injury, inflammation, fibrosis, metabolic dysregulation, multi-omics integration, and the application of artificial intelligence and machine learning in biomarker-based risk prediction.
Results: Novel biomarkers such as neutrophil gelatinase-associated lipocalin, liver-type fatty acid-binding protein, monocyte chemoattractant protein-1, soluble tumor necrosis factor receptors, fibroblast growth factor 23, and uromodulin can detect kidney damage earlier and predict disease progression more accurately than traditional tests. New proteomic and metabolomic techniques allow multiple biomarkers to be combined into panels, improving the prediction of kidney and cardiovascular outcomes. The use of artificial intelligence further supports ongoing risk assessment and personalized monitoring of disease progression.
Conclusions: Biomarker-driven approaches offer significant potential to improve early diagnosis, risk stratification, and individualized management of CKD. Addressing translational barriers through large-scale validation and global collaboration is essential to improve patient outcomes.
Downloads
References
Andrassy KM. Comments on ‘KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease’. Kidney international. 2013 Sep 1;84(3):622-3. https://doi.org/10.1038/ki.2013.243
Hayek SS, Sever S, Ko YA, Trachtman H, Awad M, Wadhwani S, Altintas MM, Wei C, Hotton AL, French AL, Sperling LS. Soluble urokinase receptor and chronic kidney disease. New England journal of medicine. 2015 Nov 12;373(20):1916-25. DOI: 10.1056/NEJMoa1506362
Pontillo C, Mischak H. Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease. Clinical kidney journal. 2017 Apr 1;10(2):192-201.https://doi.org/10.1093/ckj/sfx002
Argiles A, Siwy J, Duranton F, Gayrard N, Dakna M, Lundin U, Osaba L, Delles C, Mourad G, Weinberger KM, Mischak H. CKD273, a new proteomics classifier assessing CKD and its prognosis. Plos one. 2013 May 14;8(5):e62837. https://doi.org/10.1371/journal.pone.0062837
Catanese L, Siwy J, Mischak H, Wendt R, Beige J, Rupprecht H. Recent advances in urinary peptide and proteomic biomarkers in chronic kidney disease: a systematic review. International Journal of Molecular Sciences. 2023 May 23;24(11):9156.
https://doi.org/10.3390/ijms24119156
Neirynck N, Glorieux G, Schepers E, Verbeke F, Vanholder R. Soluble tumor necrosis factor receptor 1 and 2 predict outcomes in advanced chronic kidney disease: a prospective cohort study. PloS one. 2015 Mar 30;10(3):e0122073.https://doi.org/10.1371/journal.pone.0122073
Moroney E, Niewczas MA, Ekinci EI, MacIsaac RJ. Soluble tumor necrosis factor receptors (sTNFRs) as biomarkers for diabetic kidney disease. Journal of Diabetes and its Complications. 2025 Oct 18:109204. https://doi.org/10.1016/j.jdiacomp.2025.109204
Coppolino G, Comi N, Bolignano D, Patella G, Comi A, Provenzano M, Rivoli L, Andreucci M, Fuiano G. Urinary neutrophil gelatinase-associated lipocalin (NGAL) predicts renal function decline in patients with glomerular diseases. Frontiers in Cell and Developmental Biology. 2020 May 29;8:336. https://doi.org/10.3389/fcell.2020.00336
Bienaimé F, Muorah M, Metzger M, Broeuilh M, Houiller P, Flamant M, Haymann JP, Vonderscher J, Mizrahi J, Friedlander G, Stengel B. Combining robust urine biomarkers to assess chronic kidney disease progression. EBioMedicine. 2023 Jul 1;93. https://doi.org/10.1016/j.ebiom.2023.104635
Isakova T, Wahl P, Vargas GS, Gutiérrez OM, Scialla J, Xie H, Appleby D, Nessel L, Bellovich K, Chen J, Hamm L. Fibroblast growth factor 23 is elevated before parathyroid hormone and phosphate in chronic kidney disease. Kidney international. 2011 Jun 2;79(12):1370-8. https://doi.org/10.1038/ki.2011.47
Fliser D, Kollerits B, Neyer U, Ankerst DP, Lhotta K, Lingenhel A, Ritz E, Kronenberg F, MMKD Study Group. Fibroblast growth factor 23 (FGF23) predicts progression of chronic kidney disease: the Mild to Moderate Kidney Disease (MMKD) Study. Journal of the American Society of Nephrology. 2007 Sep 1;18(9):2600-8. DOI: 10.1681/ASN.2006080936
Foster MC, Coresh J, Hsu CY, Xie D, Levey AS, Nelson RG, Eckfeldt JH, Vasan RS, Kimmel PL, Schelling J, Simonson M. Serum β-trace protein and β2-microglobulin as predictors of ESRD, mortality, and cardiovascular disease in adults with CKD in the chronic renal insufficiency cohort (CRIC) study. American journal of kidney diseases. 2016 Jul 1;68(1):68-76. https://doi.org/10.1053/j.ajkd.2016.01.015
Ikeme JC, Scherzer R, Garimella PS, Hallan SI, Katz R, Estrella MM, Ix JH, Shlipak MG. Associations of Plasma and Urine Uromodulin With Kidney Disease Progression in Persons With CKD and Hypertension: The SPRINT Trial. Kidney Medicine. 2025 Sep 19:101118. https://doi.org/10.1016/j.xkme.2025.101118
Puthumana J, Thiessen-Philbrook H, Xu L, Coca SG, Garg AX, Himmelfarb J, Bhatraju PK, Ikizler TA, Siew ED, Ware LB, Liu KD. Biomarkers of inflammation and repair in kidney disease progression. The Journal of clinical investigation. 2021 May 28;131(3). 10.1172/JCI139927
Liu Y, Xu K, Xiang Y, Ma B, Li H, Li Y, Shi Y, Li S, Bai Y. Role of MCP-1 as an inflammatory biomarker in nephropathy. Frontiers in immunology. 2024 Jan 4;14:1303076. https://doi.org/10.3389/fimmu.2023.1303076
Humphreys BD, Xu F, Sabbisetti V, Grgic I, Naini SM, Wang N, Chen G, Xiao S, Patel D, Henderson JM, Ichimura T. Chronic epithelial kidney injury molecule-1 expression causes murine kidney fibrosis. The Journal of clinical investigation. 2013 Sep 3;123(9):4023-35. 10.1172/JCI45361
Eddy AA, Neilson EG. Chronic kidney disease progression. Journal of the American Society of Nephrology. 2006 Nov 1;17(11):2964-6. DOI: 10.1681/ASN.2006070704
Tam FW, Ong AC. Renal monocyte chemoattractant protein-1: an emerging universal biomarker and therapeutic target for kidney diseases?. Nephrology Dialysis Transplantation. 2020 Feb 1;35(2):198-203. https://doi.org/10.1093/ndt/gfz082
Mitsides N, Mitra V, Saha A, Harris S, Kalra PA, Mitra S. Urinary liver-type fatty acid binding protein, a biomarker for disease progression, dialysis and overall mortality in chronic kidney disease. Journal of Personalized Medicine. 2023 Oct 11;13(10):1481.https://doi.org/10.3390/jpm13101481
Xu Y, Xie Y, Shao X, Ni Z, Mou S. L-FABP: A novel biomarker of kidney disease. Clinica chimica acta. 2015 May 20;445:85-90.https://doi.org/10.1016/j.cca.2015.03.017
Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, Winget M, Yasui Y. Phases of biomarker development for early detection of cancer. Journal of the National Cancer Institute. 2001 Jul 18;93(14):1054-61. https://doi.org/10.1093/jnci/93.14.1054
Strimbu K, Tavel JA. What are biomarkers?. Current Opinion in HIV and AIDS. 2010 Nov 1;5(6):463-6. DOI: 10.1097/COH.0b013e32833ed177
Chanda R. Serum Biomarkers and Endothelial Dysfunction as Early Predictors of Cardiovascular Risk in Chronic Kidney Disease: A Multi-Biomarker Meta-Analysis and Clinical Translation Framework. Current Research in Dental, Physiotherapy and Pharmaceutical Sciences (CRDPPS). 2025 Sep 8;1(2):56-79. https://crdpps.nknpub.com/1/article/view/8
Alobaidi S. Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI. Diagnostics. 2025 May 13;15(10):1225. https://doi.org/10.3390/diagnostics15101225
Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. The lancet. 2017 Mar 25;389(10075):1238-52. https://doi.org/10.1016/S0140-6736(16)32064-5
Vaidya VS, Ferguson MA, Bonventre JV. Biomarkers of acute kidney injury. Annu. Rev. Pharmacol. Toxicol.. 2008 Feb 10;48(1):463-93. https://doi.org/10.1146/annurev.pharmtox.48.113006.094615
Levey AS, Coresh J. Chronic kidney disease. The lancet. 2012 Jan 14;379(9811):165-80. https://doi.org/10.1016/S0140-6736(11)60178-5
Chronic Kidney Disease Prognosis Consortium. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. The Lancet. 2010 Jun 12;375(9731):2073-81.https://doi.org/10.1016/S0140-6736(10)60674-5
Shlipak MG, Mattes MD, Peralta CA. Update on cystatin C: incorporation into clinical practice. American Journal of Kidney Diseases. 2013 Sep 1;62(3):595-603.https://doi.org/10.1053/j.ajkd.2013.03.027
Stevens LA, Schmid CH, Greene T, Li L, Beck GJ, Joffe MM, Froissart M, Kusek JW, Zhang YL, Coresh J, Levey AS. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney international. 2009 Mar 2;75(6):652-60.https://doi.org/10.1038/ki.2008.638
Bonventre JV, Yang L. Cellular pathophysiology of ischemic acute kidney injury. The Journal of clinical investigation. 2011 Nov 1;121(11):4210-21. https://doi.org/10.1172/JCI45161
Niewczas MA, Gohda T, Skupien J, Smiles AM, Walker WH, Rosetti F, Cullere X, Eckfeldt JH, Doria A, Mayadas TN, Warram JH. Circulating TNF receptors 1 and 2 predict ESRD in type 2 diabetes. Journal of the American Society of Nephrology. 2012 Mar 1;23(3):507-15. DOI: 10.1681/ASN.2011060627
Pianta TJ, Buckley NA, Peake PW, Endre ZH. Clinical use of biomarkers for toxicant-induced acute kidney injury. Biomarkers in medicine. 2013 Jun 1;7(3):441-56.https://doi.org/10.2217/bmm.13.51
Duffield JS. Cellular and molecular mechanisms in kidney fibrosis. The Journal of clinical investigation. 2014 Jun 2;124(6):2299-306.10.1172/JCI72267
Liu Y. Cellular and molecular mechanisms of renal fibrosis. Nature Reviews Nephrology. 2011 Dec;7(12):684-96.DOI https://doi.org/10.1038/nrneph.2011.149
Perico N, Benigni A, Remuzzi G. Present and future drug treatments for chronic kidney diseases: evolving targets in renoprotection. Nature Reviews Drug Discovery. 2008 Nov;7(11):936-53.DOI https://doi.org/10.1038/nrd2685
Huxley R. The Relationship between Proteinuria and Coronary Risk: A Systematic Review and Meta-Analysis. PLoS Medicine. 2008 Jan 1. https://doi.org/10.1371/journal.pmed.0050207
Wang N, Zhang C. Recent advances in the management of diabetic kidney disease: slowing progression. International Journal of Molecular Sciences. 2024 Mar 7;25(6):3086.https://doi.org/10.3390/ijms25063086
Coca SG, Parikh CR. Urinary biomarkers for acute kidney injury: perspectives on translation. Clinical Journal of the American Society of Nephrology. 2008 Mar 1;3(2):481-90. DOI: 10.2215/CJN.03520807
Mischak H, Ioannidis JP, Argiles A, Attwood TK, Bongcam‐Rudloff E, Broenstrup M, Charonis A, Chrousos GP, Delles C, Dominiczak A, Dylag T. Implementation of proteomic biomarkers: making it work. European journal of clinical investigation. 2012 Sep;42(9):1027-36. https://doi.org/10.1111/j.1365-2362.2012.02674.x
Vaidya VS, Waikar SS, Ferguson MA, Collings FB, Sunderland K, Gioules C, Bradwin G, Matsouaka R, Betensky RA, Curhan GC, Bonventre JV. Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clinical and translational science. 2008 Dec;1(3):200-8. https://doi.org/10.1111/j.1752-8062.2008.00053.x
Rhee EP, Gerszten RE. Metabolomics and cardiovascular biomarker discovery. Clinical chemistry. 2012 Jan 1;58(1):139-47. https://doi.org/10.1373/clinchem.2011.169573
Zoccali C, Vanholder R, Massy ZA, Ortiz A, Sarafidis P, Dekker FW, Fliser D, Fouque D, Heine GH, Jager KJ, Kanbay M. The systemic nature of CKD. Nature Reviews Nephrology. 2017 Jun;13(6):344-58. DOI https://doi.org/10.1038/nrneph.2017.52
Levin A, Tonelli M, Bonventre J, Coresh J, Donner JA, Fogo AB, Fox CS, Gansevoort RT, Heerspink HJ, Jardine M, Kasiske B. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. The Lancet. 2017 Oct 21;390(10105):1888-917. https://doi.org/10.1016/S0140-6736(17)30788-2
Bello AK, Johnson DW, Feehally J, Harris D, Jindal K, Lunney M, Okpechi IG, Salako BL, Wiebe N, Ye F, Tonelli M. Global kidney health atlas (GKHA): design and methods. Kidney international supplements. 2017 Oct 1;7(2):145-53. https://doi.org/10.1016/j.kisu.2017.08.001
Li M, Zhao J, Li X, Chen Y, Feng C, Qian F, Liu Y, Zhang J, He J, Ai B, Ning Z. HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures. Briefings in Bioinformatics. 2020 Jul;21(4):1411-24. https://doi.org/10.1093/bib/bbz078
Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinformatics and biology insights. 2020 Jan;14:1177932219899051. https://doi.org/10.1177/1177932219899051
Beam AL, Kohane IS. Big data and machine learning in health care. Jama. 2018 Apr 3;319(13):1317-8. doi:10.1001/jama.2017.18391
Tomašev, N., Glorot, X., Rae, J.W. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019). https://doi.org/10.1038/s41586-019-1390-1
Gansevoort RT, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, Coresh J, Chronic Kidney Disease Prognosis Consortium. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes in both general and high-risk populations: a collaborative meta-analysis of general and high-risk population cohorts. Kidney international. 2011 Feb 2;80(1): 93–104 doi: 10.1038/ki.2010.531
ISSN 2307-1257
ISSN 2307-1265
