<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0"><Article><Journal><PublisherName>yemenjmed</PublisherName><JournalTitle>Yemen Journal of Medicine</JournalTitle><PISSN>c</PISSN><EISSN>o</EISSN><Volume-Issue>Volume 4 Issue 3</Volume-Issue><IssueTopic>Multidisciplinary</IssueTopic><IssueLanguage>English</IssueLanguage><Season>September- December 2025</Season><SpecialIssue>N</SpecialIssue><SupplementaryIssue>N</SupplementaryIssue><IssueOA>Y</IssueOA><PubDate><Year>2025</Year><Month>12</Month><Day>31</Day></PubDate><ArticleType>Article</ArticleType><ArticleTitle>Novel Biomarkers for Acute Kidney Injury: Diagnostic Profiles, Etiology-Specific Applications, Prognostic Impacts, and the Path to Precision Medicine: Bridging the Clinical Action Gap—Review</ArticleTitle><SubTitle/><ArticleLanguage>English</ArticleLanguage><ArticleOA>Y</ArticleOA><FirstPage>514</FirstPage><LastPage>525</LastPage><AuthorList><Author><FirstName>Elmukhtar</FirstName><LastName>Habas1</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>N</CorrespondingAuthor><ORCID/><FirstName>Aml</FirstName><LastName>Habas2</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Amnna</FirstName><LastName>Rayani3</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Ala</FirstName><LastName>Habas4</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Khaled</FirstName><LastName>Alarbi5</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Eshrak</FirstName><LastName>Habas6</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Mohamed</FirstName><LastName>Baghi5</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Mohammad</FirstName><LastName>Babikir7</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Abdelrahaman</FirstName><LastName>Hamad8</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/><FirstName>Almehdi</FirstName><LastName>Errayes9</LastName><AuthorLanguage>English</AuthorLanguage><Affiliation/><CorrespondingAuthor>Y</CorrespondingAuthor><ORCID/></Author></AuthorList><DOI>10.63475/yjm.v4i3.0226</DOI><Abstract>Acute kidney injury (AKI) remains a substantial clinical challenge, affecting approximately half of all critically ill patients and is associated with a high risk of mortality, need for dialysis, and progression to chronic kidney disease (CKD). Traditional diagnostic methods, which rely on urine output and serum creatinine (sCr), are non-specific and delayed, thus missing the crucial window for early intervention. New urine and plasma biomarkers, such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), tissue inhibitor of metalloproteinase-2, insulin-like growth factor-binding protein 7 ([TIMP-2] × [IGFBP7]), and C-C motif chemokine ligand 14 (CCL14), have become effective tools for risk assessment and early detection over the past decade. With diagnostic accuracy superior to creatinine, these biomarkers allow for the identification of AKI within 2 to 12 hours, as they represent tubular stress, damage, and healing processes. Multi-marker panels further enhance diagnostic performance, particularly in complex clinical scenarios such as sepsis and heart surgery. Etiology-specific biomarker patterns are now well-delineated: minimal elevations in prerenal conditions may guide safe fluid management, whereas sustained increases in intrinsic AKI suggest poor recovery and may necessitate renal replacement therapy (RRT). Biomarker-guided interventions have been shown to reduce the incidence of severe AKI by 15% to 30% in high-risk populations. Emerging biomarker types that have the potential to improve early detection and prognosis accuracy include filtration surrogates, oxidative stress indicators, microRNAs (e.g., miR-21, exosomal panels), and inflammation/repair biomarkers. Despite these advancements, difficulties remain, including inconsistencies in testing, high costs, limited data on juvenile and postrenal AKI, and a “clinical action gap” where biomarker findings have not been reliably linked to evidence-based therapies. The integration of artificial intelligence with point-of-care diagnostics has significant potential for future clinical applications. This review consolidates current data to illustrate how emerging biomarkers are transforming thetreatment of AKI from a reactive diagnosis to a proactive, precision-oriented strategy.</Abstract><AbstractLanguage>English</AbstractLanguage><Keywords>Acute kidney injury, biomarkers, NGAL, TIMP-2, IGFBP7, prognosis, precision medicine, artificial intelligence</Keywords><URLs><Abstract>https://yemenjmed.com/admin/abstract?id=290</Abstract></URLs><References><ReferencesarticleTitle>References</ReferencesarticleTitle><ReferencesfirstPage>16</ReferencesfirstPage><ReferenceslastPage>19</ReferenceslastPage><References>1. Hoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10): 607-625.2. Susantitaphong P, Cruz DN, Cerda J, Abulfaraj M, Alqahtani F, Koulouridis I, et al. World incidence of AKI: A metaanalysis. Clin J Am Soc Nephrol. 2013;8(9):1482-1493.3. Chawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, Bagshaw SM, et al; Acute Disease Quality Initiative Workgroup 16. Acute kidney disease and renal recovery: Consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol. 2017;13(4): 241-257.4. Ostermann M, Joannidis M. Acute kidney injury 2016: Diagnosis and diagnostic workup. Crit Care. 2016;20(1):299.5. See EJ, Jayasinghe K, Glassford N, Bailey M, Johnson DW, Polkinghorne KR, et al. Long-term risk of adverse outcomes after acute kidney injury: A systematic review and meta-analysis of cohort studies using consensus definitions of exposure. Kidney Int. 2019;95(1):160-172.6. Ostermann M, Zarbock A, Goldstein S, Kashani K, Macedo E, Murugan R, et al. Recommendations on acute kidney injury biomarkers from the Acute Disease Quality Initiative consensus conference: A consensus statement. JAMA Netw Open. 2020;3(10):e2019209.7. Haase M, Bellomo R, Devarajan P, Schlattmann P, Haase-Fielitz A; NGAL Meta-analysis Investigator Group. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: A systematic review and meta-analysis. Am J Kidney Dis. 2009;54(6):1012-1024.8. Matarneh A, Akkari A, Sardar S, Salameh O, Dauleh M, Matarneh B, et al. Beyond creatinine: Diagnostic accuracy of emerging biomarkers for AKI in the ICU - A systematic review. Ren Fail. 2025;47(1):2556295.9. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guidelines for acute kidney injury. Kidney Int Suppl. 2012;2(1):1-138.10. Yang H, Chen Y, He J, Li Y, Feng Y. Advances in the diagnosis of early biomarkers for acute kidney injury: A literature review. BMC Nephrol. 2025;26(1):115.11. Murray PT, Mehta RL, Shaw A, Ronco C, Endre Z, Kellum JA, et al. Potential use of biomarkers in acute kidney injury: Report and summary of recommendations from the 10th Acute Dialysis Quality Initiative consensus conference. Kidney Int. 2014;85(3):513-521.12. Kashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw SM, Bell M, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013;17(1):R25.13. Albert C, Haase M, Albert A, Kropf S, Bellomo R, Westphal S, et al. Biomarker-guided risk assessment for acute kidney injury: Time for clinical implementation? Ann Lab Med. 2021;41(1):1-15.14. Haase M, Devarajan P, Haase-Fielitz A, Bellomo R, Cruz DN, Wagener G, et al. The outcome of neutrophil gelatinase-associated lipocalin-positive subclinical acute kidney injury: A multicenter pooled analysis of prospective studies. J Am Coll Cardiol. 2011;57(17):1752-1761.15. Han WK, Waikar SS, Johnson A, Betensky RA, Dent CL, Devarajan P, et al. Urinary biomarkers in the early diagnosis of acute kidney injury. Kidney Int. 2008;73(7): 863-869.16. Kellum JA, Chawla LS. Cell-cycle arrest and acute kidney injury: The light and the dark sides. Nephrol Dial Transplant. 2016;31(1):16-22.17. Xu Y, Xie Y, Shao X, Ni Z, Mou S. L-FABP: A novel biomarker of kidney disease. Clin Chim Acta. 2015;445:85-90.18. Lin X, Yuan J, Zhao Y, Zha Y. Urine interleukin-18 in prediction of acute kidney injury: A systematic review and meta-analysis. J Nephrol. 2015;28(1):7-16.19. Bagshaw SM, Al-Khafaji A, Artigas A, Davison D, Haase M, Lissauer M, et al. External validation of urinary C-C motif chemokine ligand 14 (CCL14) for prediction of persistent acute kidney injury. Crit Care. 2021;25(1):185.20. Koozi H, Engstrand;ouml;m J, Friberg H, Frigyesi A. Explainable AI identifies key biomarkers for acute kidney injury prediction in the ICU. Intensive Care Med Exp. 2025;13(1):106. 21. Horie R, Hayase N, Asada T, Yamamoto M, Matsubara T, Doi K. Trajectory pattern of serially measured acute kidney injury biomarkers in critically ill patients: A prospective observational study. Ann Intensive Care. 2024;14(1):84.22. Parikh CR. A point-of-care device for acute kidney injury: A fantastic, futuristic, or frivolous and;lsquo;measureand;rsquo;? Kidney Int. 2009;76(1):8-10.23. Nguyen MT, Devarajan P. Biomarkers for the early detection of acute kidney injury. Pediatr Nephrol. 2008; 23(12):2151-2157.24. Sandokji I, Greenberg JH. Plasma and urine biomarkers of CKD: A review of findings in the CKiD study. Semin Nephrol. 2021;41(5):416-426.25. Makris K, Spanou L. Acute kidney injury: Diagnostic approaches and controversies. Clin Biochem Rev. 2016;37(4):153-175.26. Kellum JA, Prowle JR. Paradigms of acute kidney injury in the intensive care setting. Nat Rev Nephrol. 2018;14(4):217-230.27. Bhatraju PK, Zelnick LR, Chinchilli VM, Moledina DG, Coca SG, Parikh CR, et al. Association between early recovery of kidney function after acute kidney injury and long-term clinical outcomes. JAMA Netw Open. 2020;3(4):e202682.28. Legrand M, Rossignol P. Cardiovascular consequences of acute kidney injury. N Engl J Med. 2020;382(23): 2238-2248.29. Nejat M, Pickering JW, Walker RJ, Endre ZH. Urinary cystatin C is diagnostic of acute kidney injury and sepsis, and predicts mortality in the intensive care unit. Crit Care. 2010;14(3):R85.30. Gambino C, Piano S, Stenico M, Tonon M, Brocca A, Calvino V, et al. Diagnostic and prognostic performance of urinary neutrophil gelatinase-associated lipocalin in patients with cirrhosis and acute kidney injury. Hepatology. 2023;77(5):1630-1638.31. Erstad BL. Usefulness of the biomarker TIMP-2and;bull;IGFBP7 for acute kidney injury assessment in critically ill patients: A narrative review. Ann Pharmacother. 2022;56(1):83-92.32. Doi K, Noiri E, Maeda-Mamiya R, Ishii T, Negishi K, Hamasaki Y, et al. Urinary L-type fatty acid-binding protein as a new biomarker of sepsis complicated with acute kidney injury. Crit Care Med. 2010;38(10): 2037-2042.33. Koyner JL, Garg AX, Coca SG, Sint K, Thiessen-Philbrook H, Patel UD, et al. Biomarkers predict recovery from acute kidney injury: The TRIBE-AKI consortium. Clin J Am Soc Nephrol. 2015;10(12):2151-2159.34. James MT, Levey AS, Tonelli M, Tan Z, Barry R, Pannu N, et al. Incidence and prognosis of acute kidney injuries in hospitalized patients with heart failure. Circulation. 2023;147(11):867-879.35. von Groote T, Albert F, Meersch M, Koch R, Porschen C, Hartmann O, et al. Proenkephalin A 119-159 predicts early and successful liberation from renal replacement therapy in critically ill patients with acute kidney injury: A post hoc analysis of the ELAIN trial. Crit Care. 2022;26(1):333.36. Zarbock A, Nadim MK, Pickkers P, Gomez H, Bellomo R, Legrand M, et al. Sepsis-associated acute kidney injury: Consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol. 2023;19(6): 401-417.37. de Geus HR, Bakker J, Lesaffre EM, le Noble JL. Neutrophil gelatinase-associated lipocalin at ICU admission predicts for acute kidney injury in adult patients. Am J Respir Crit Care Med. 2011;183(7):907-914.38. Moledina DG, Wilson FP, Pober JS, Perazella MA, Singh N, Luciano RL, et al. Urine TNF-and;alpha; and IL-9 for clinical diagnosis of acute interstitial nephritis. JCI Insight. 2019;4(10):e127456.39. Pannu N, James M, Hemmelgarn B, Klarenbach S; Alberta Kidney Disease Network. Association between AKI, recovery, and long-term outcomes. Clin J Am Soc Nephrol. 2016;11(1):21-28.40. Coca SG, Nadkarni GN, Garg AX, Koyner J, ThiessenPhilbrook H, McArthur E, et al. Urinary, plasma, and composite biomarkers for AKI after cardiac surgery. J Am Soc Nephrol. 2023;34(8):1378-1390.41. Ostermann M, Legrand M, Meersch M, Srisawat N, Zarbock A, Kellum JA. Biomarkers in acute kidney injury. Ann Intensive Care. 2024;14(1):145.42. Hoste EA, Bagshaw SM, Bellomo R, Cely CM, Colman R, Cruz DN, et al. Epidemiology of acute kidney injury in critically ill patients: The multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411-1423.43. Chen JJ, Kuo G, Hung CC, Lin YF, Chen YC, Wu VC, et al. Furosemide stress test and biomarkers in predicting acute kidney injury outcomes. Crit Care Med. 2024;52(7): 1045-1055.44. Xu K, Rosenstiel P, Paragas N, Hinze C, Gao X, Tian S, et al. Unique transcriptional programs identify subtypes of AKI. J Am Soc Nephrol. 2023;34(6):991-1006.45. Singer E, Schrezenmeier EV, Elger A, Seelow ER, Krannich A, Luft FC, et al. Urinary NGAL-positive acute kidney injury and poor long-term outcomes in hospitalized patients. Kidney Int Rep. 2016;1(3):114-124.46. Qian BS, Jia HM, Weng YB, Li XC, Chen CD, Guo FX, et al. Analysis of urinary C-C motif chemokine ligand 14 (CCL14) and first-generation urinary biomarkers for predicting renal recovery from acute kidney injury: A prospective exploratory study. J Intensive Care. 2023;11(1):11.47. Allegretti AS, Ortiz G, Wenger J, Deferio JJ, Wibecan J, Kalim S, et al. Prognosis of acute kidney injury and hepatorenal syndrome in patients with cirrhosis: A prospective cohort study. Int J Nephrol. 2015;2015: 108139.48. Wiersema R, Jukarainen S, Vaara ST, Poukkanen M, Lakkisto P, Wong H, et al. Two subphenotypes of septic acute kidney injury are associated with different 90-day mortality and renal recovery. Crit Care. 2020;24(1):150.49. von Groote T, Meersch M, Romagnoli S, Ostermann M, Ripolland;eacute;s-Melchor J, Schneider AG, et al. Biomarkerguided intervention to prevent acute kidney injury after major surgery (BigpAK-2 trial): Study protocol for an international, prospective, randomised controlled trial. BMJ Open. 2023;13(3):e070240.50. Guzzi LM, Bergler T, Binnall B, Engelman DT, Forni L, Germain MJ, et al. Clinical use of [TIMP-2]and;bull;[IGFBP7] biomarker testing to assess risk of acute kidney injury in critical care: Guidance from an expert panel. Crit Care. 2019;23(1):225.51. Vijayan A, Faubel S, Askenazi DJ, Cerda J, Fissell WH, Heung M, et al. Clinical use of the urine biomarker [TIMP2] and;times; [IGFBP7] for acute kidney injury risk assessment. Am J Kidney Dis. 2016;68(1):19-28.52. Liu Y, Zhao X, Liao M, Ke G, Zhang XB. Point-of-care biosensors and devices for diagnostics of chronic kidney disease. Sens Diagn. 2024;3:1789-1806.53. Baker TM, Bird CA, Broyles DL, Klause U. Determination of urinary neutrophil gelatinase-associated lipocalin (uNGAL) reference intervals in healthy adult and pediatric individuals using a particle-enhanced turbidimetric immunoassay. Diagnostics (Basel). 2025;15(1):95.54. Meersch M, Schmidt C, Van Aken H, Rossaint J, Gand;ouml;rlich D, Stege D, et al. Validation of cell-cycle arrest biomarkersand;nbsp;for acute kidney injury after pediatric cardiac surgery. PLoS One. 2014;9(10):e110865.55. Stanski N, Menon S, Goldstein SL, Basu RK. Integration of urinary neutrophil gelatinase-associated lipocalin with serum creatinine delineates acute kidney injury phenotypes in critically ill children. J Crit Care. 2019;53:1-7.56. Meena J, Thomas CC, Kumar J, Mathew G, Bagga A. Biomarkers for prediction of acute kidney injury in pediatric patients: A systematic review and meta-analysis of diagnostic test accuracy studies. Pediatr Nephrol. 2023;38(10):3241-3251.57. Gerhardt LMS, McMahon AP. Multi-omic approaches to acute kidney injury and repair. Curr Opin Biomed Eng. 2021;20:100344.58. Samek W, Wiegand T, Mand;uuml;ller KR. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models [preprint]. arXiv. 2017;1708. 08296.59. Amann J, Blasimme A, Vayena E, Frey D, Madai VI; Precise4Q consortium. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.</References></References></Journal></Article></article>
