Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease
Marta Kordalewska , Szymon Macioszek , Renata Wawrzyniak , Małgorzata Sikorska-Wiśniewska , Tomasz Śledziński , Michał Chmielewski , Adriana Mika , Michał Markuszewski
AbstractChanges in metabolites composition can reflect currently present pathological processes in a living organism and constitute a basis for diagnosis and treatment improvements. Thus, the multiplatform metabolomics approach was applied for the investigation of molecular mechanisms of chronic kidney disease (CKD) progression. The high-performance liquid chromatography coupled with time-of-flight mass spectrometry (HPLC-TOF-MS) and gas chromatography coupled with triple quadrupole mass spectrometry (GC-QqQ/MS) serum metabolic fingerprinting followed by uni- and multivariate statistical analysis was carried out to determine metabolic pattern differentiating CKD patients and healthy controls. Furthermore, metabolites changes between stage 3 and 4 of the disease, as well as health status were investigated. The progression of the disease was found to be related to alterations in acylcarnitine, amino acid, lysophospholipid and carbohydrate metabolism. Elevated levels of serum acylcarnitines, sugar alcohols, and organic acids, as well as decreased levels of lysophospholipids, and amino acids, were found to be statistically significant for CKD progression. The obtained results confirm the utility of metabolomics approach as a tool for an explanation of molecular processes underlying CKD development.
|Journal series||Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences, ISSN 1570-0232, (A 30 pkt)|
|Publication size in sheets||0.5|
|Keywords in English||Chronic kidney disease, untargeted metabolomics, LC-MS, GC-MS, potential diagnostic indicators|
|ASJC Classification||; ; ; ;|
|Score|| = 30.0, 18-07-2019, ArticleFromJournal|
= 30.0, 18-07-2019, ArticleFromJournal
|Publication indicators||: 2017 = 0.969; : 2017 = 2.441 (2) - 2017=2.526 (5)|
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