Literature DB >> 30844626

LC-QQQ-MS routine analysis method for new biomarker quantification in plasma aimed at early chronic kidney disease diagnosis.

S Benito1, A Sánchez-Ortega2, N Unceta1, M A Goicolea1, R J Barrio3.   

Abstract

Pediatric chronic kidney disease (CKD) is currently assessed using glomerular filtration rate (GFR), which is calculated from different equations based on serum creatinine concentration. However, serum creatinine is affected by several factors and is not reliable enough for the diagnosis of CKD, especially at early stages. Recent targeted and untargeted metabolomics studies found 7 new potential biomarkers that could be useful for early pediatric chronic kidney disease diagnosis. Thus, a new LC-QQQ-MS analytical method has been developed and validated aimed at routine analysis of these 7 potential biomarkers: NG,NG'-dimethyl-l-arginine di(p-hydroxyazobenzene-p'-sulfonate) (SDMA), S-adenosyl-l-methionine (SAM), n-butyryl-l-carnitine (nC4), iso-butyryl-l-carnitine (iC4), citrulline (CIT), creatinine (CNN) and d-erytro-sphingosine-1-phosphate (S1P), in addition to creatinine, the classical biomarker for CKD diagnosis. Then, this analytical method has been used for the quantification of plasma samples from a heterogeneous group of CKD pediatric patients and a control pediatric population. Data analysis of these results showed that it is possible to differentiate between CKD and control populations based on the metabolite concentration in plasma.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarker; Chronic kidney disease; LC-QQQ-MS routine analysis; Metabolomics

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Year:  2019        PMID: 30844626     DOI: 10.1016/j.jpba.2019.02.042

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  1 in total

1.  Plasma metabolites associated with chronic kidney disease and renal function in adults from the Baltimore Longitudinal Study of Aging.

Authors:  Yuko Yamaguchi; Marta Zampino; Ruin Moaddel; Teresa K Chen; Qu Tian; Luigi Ferrucci; Richard D Semba
Journal:  Metabolomics       Date:  2021-01-11       Impact factor: 4.747

  1 in total

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