Plasma biomarker discovery for early chronic kidney disease diagnosis based on chemometric approaches using LC-QTOF targeted metabolomics data

Chronic kidney disease (CKD) is a progressive pathological condition in which renal function deteriorates in time. The first diagnosis of CKD is often carried out in general care attention by general practitioners by means of serum creatinine (CNN) levels. However, it lacks sensitivity and thus, there is a need for new robust biomarkers to allow the detection of kidney damage particularly in early stages. Multivariate data analysis of plasma concentrations obtained from LC-QTOF targeted metabolomics method may reveal metabolites suspicious of being either up-regulated or down-regulated from urea cycle, arginine methylation and arginine-creatine metabolic pathways in CKD pediatrics and controls. The results show that citrulline (CIT), symmetric dimethylarginine (SDMA) and S-adenosylmethionine (SAM) are interesting biomarkers to support diagnosis by CNN: early CKD samples and controls were classified with an increase in classification accuracy of 18% when using these 4 metabolites compared to CNN alone. These metabolites together allow classification of the samples into a definite stage of the disease with an accuracy of 74%, being the 90% of the misclassifications one level above or below the CKD stage set by the nephrologists. Finally, sex-related, age-related and treatment-related effects were studied, to evaluate whether changes in metabolite concentration could be attributable to these factors, and to correct them in case a new equation is developed with these potential biomarkers for the diagnosis and monitoring of pediatric CKD. (C) 2017 Elsevier B.V. All rights reserved.