Pediatric chronic kidney disease (CKD) is a clinical syndrome characterized by renal hypofunction occurring due to gradual and irreversible kidney damage that can further progress over time. New biomarkers may help early diagnosis of pediatric patients suffering from CKD and improve the outcome. Untargeted metabolomics based on LC-QTOF-MS has been used to find new biomarkers for the early diagnosis of CKD in plasma from pediatric patients. In order to avoid any bias in the determination of statistically significant entities as a consequence of the data analysis method followed, two different chemometric approaches have been used, Mass Profiler Professional (MPP) software and Matlab R2015a software. Metabolic fingerprints of control and CKD pediatric patients were compared and five metabolites which showed a significant change common to both data analysis procedures were identified. Sphingosine-1-phosphate, n-butyrylcarnitine, cis-4-decenoylcarnitine and an unidentified feature with 126.0930 m/z were found to be increased in plasma from pediatric patients with CKD, whereas bilirubin was significantly decreased. A partial least squares discriminant analysis model built with these 5 entities classified correctly 96% of the samples. In addition, when considering only early CKD patients against controls, a performance of 97% was obtained. Thus, these promising metabolites could be suitable biomarkers for the early diagnosis of pediatric CKD in a clinical setting.
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.
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