Development of a MALDI MS-based platform for early detection of acute kidney injury

Carrick, E. et al. (2016) Development of a MALDI MS-based platform for early detection of acute kidney injury. Proteomics Clinical Applications, 10(7), pp. 732-742. (doi: 10.1002/prca.201500117) (PMID:27119821) (PMCID:PMC4950042)

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Purpose: Septic acute kidney injury (AKI) is associated with poor outcome. This can partly be attributed to delayed diagnosis and incomplete understanding of the underlying pathophysiology. Our aim was to develop an early predictive test for AKI based on the analysis of urinary peptide biomarkers by MALDI-MS. Experimental design: Urine samples from 95 patients with sepsis were analyzed by MALDI-MS. Marker search and multimarker model establishment were performed using the peptide profiles from 17 patients with existing or within the next 5 days developing AKI and 17 with no change in renal function. Replicates of urine sample pools from the AKI and non-AKI patient groups and normal controls were also included to select the analytically most robust AKI markers. Results: Thirty-nine urinary peptides were selected by cross-validated variable selection to generate a support vector machine multidimensional AKI classifier. Prognostic performance of the AKI classifier on an independent validation set including the remaining 61 patients of the study population (17 controls and 44 cases) was good with an area under the receiver operating characteristics curve of 0.82 and a sensitivity and specificity of 86% and 76%, respectively. Conclusion and clinical relevance: A urinary peptide marker model detects onset of AKI with acceptable accuracy in septic patients. Such a platform can eventually be transferred to the clinic as fast MALDI-MS test format.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Carrick, Dr Emma and Mullen, Dr Bill and Mansoorian, Dr Bahareh and Husi, Dr Holger and Mischak, Professor Harald
Authors: Carrick, E., Vanmassenhove, J., Glorieux, G., Metzger, J., Dakna, M., Pejchinovski, M., Jankowski, V., Mansoorian, B., Husi, H., Mullen, W., Mischak, H., Vanholder, R., and Van Biesen, W.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:Proteomics Clinical Applications
Publisher:Wiley-VHC Verlag
ISSN (Online):1862-8354
Published Online:17 May 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Proteomics Clinical Applications 10(7): 732-742
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
546181Development of an efficient, robust MS-based platform for early detection of acute kidney injuryHarald MischakMedical Research Council (MRC)G1000791RI CARDIOVASCULAR & MEDICAL SCIENCES