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Serial Quantification of Urinary Protein Biomarkers to Predict Drug-induced Acute Kidney Injury

[ Vol. 20 , Issue. 8 ]

Author(s):

Yi Da, K. Akalya, Tanusya Murali, Anantharaman Vathsala, Chuen-Seng Tan, Sanmay Low, Hui-Ning Lim, Boon-Wee Teo, Titus Lau, Lizhen Ong and Horng-Ruey Chua*   Pages 656 - 664 ( 9 )

Abstract:


Background: Drug-induced Acute Kidney Injury (AKI) develops in 10-15% of patients who receive nephrotoxic medications. Urinary biomarkers of renal tubular dysfunction may detect nephrotoxicity early and predict AKI.

Methods: We prospectively studied patients who received aminoglycosides, vancomycin, amphotericin, or calcineurin inhibitors, and collected their serial urine while on therapy. Patients who developed drug-induced AKI (fulfilling KDIGO criteria) were matched with non-AKI controls in a 1:2 ratio. Their urine samples were batch-analyzed at time-intervals leading up to AKI onset; the latter benchmarked against the final day of nephrotoxic therapy in non- AKI controls. Biomarkers examined include clusterin, beta-2-microglobulin, KIM1, MCP1, cystatin-C, trefoil-factor- 3, NGAL, interleukin-18, GST-Pi, calbindin, and osteopontin; biomarkers were normalized with corresponding urine creatinine.

Results: Nine of 84 (11%) patients developed drug-induced AKI. Biomarkers from 7 AKI cases with pre-AKI samples were compared with those from 14 non-AKI controls. Corresponding mean ages were 55(±17) and 52(±16) years; baseline eGFR were 99(±21) and 101(±24) mL/min/1.73m2 (all p=NS). Most biomarker levels peaked before the onset of AKI. Median levels of 5 biomarkers were significantly higher in AKI cases than controls at 1-3 days before AKI onset (all μg/mmol): clusterin [58(8-411) versus 7(3-17)], beta-2-microglobulin [1632(913-3823) versus 253(61-791)], KIM1 [0.16(0.13-0.76) versus 0.07(0.05-0.15)], MCP1 [0.40(0.16-1.90) versus 0.07(0.04-0.17)], and cystatin-C [33(27-2990) versus 11(7-19)], all p<0.05; their AUROC for AKI prediction were >0.80 (confidence intervals >0.50), with average accuracy highest for clusterin (86%), followed by beta-2-microglobulin, cystatin-C, MCP1, and KIM1 (57%) after cross-validation.

Conclusion: Serial surveillance of these biomarkers could improve the lead time for nephrotoxicity detection by days.

Keywords:

Antimicrobials, beta-2-microglobulin, biomarkers, calcineurin inhibitors, clusterin, drug-induced acute kidney injury, nephrotoxicity.

Affiliation:

Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Saw Swee Hock School of Public Health, National University of Singapore, Singapore 119077, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Department of Pharmacy, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074, Department of Laboratory Medicine, National University Hospital, Singapore 119074, Division of Nephrology, University Medicine Cluster, National University Hospital, Singapore 119074



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