Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders 

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F15%3AN0000007" target="_blank" >RIV/00098892:_____/15:N0000007 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15310/15:33158013 RIV/61989592:15110/15:33158013

Result on the web

<a href="https://biomed.papers.upol.cz/pdfs/bio/2015/04/11.pdf" target="_blank" >https://biomed.papers.upol.cz/pdfs/bio/2015/04/11.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5507/bp.2014.048" target="_blank" >10.5507/bp.2014.048</a>

Result language

angličtina

Original language name

Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders 

Original language description

Background. Metabolomics is becoming an important tool in clinical research and the diagnosis of human diseases. It has been used in the diagnosis of inherited metabolic disorders with pronounced biochemical abnormalities. The aim of this study was to determine if it could be applied in the diagnosis of inherited metabolic disorders (IMDs) with less clear biochemical profiles from urine samples using an untargeted metabolomic approach. Methods. A total of 14 control urine samples and 21 samples from infants with cystinuria, maple syrup urine disease, adenylosuccinate lyase deficiency and galactosemia were tested. Samples were analyzed by liquid chromatography on aminopropyl column in aqueous normal phase separation system using gradient elution of acetonitrile/ammonium acetate. Detection was performed by time-of-flight mass spectrometer fitted with electrospray ionisation in positive mode. The data were statistically processed using principal component analysis (PCA), principal component discriminant function analysis (PCA-DFA) and partial least squares (PLS) regression. Results. All patient samples were first distinguished from controls using unsupervised PCA. Discrimination of the patient samples was then unambiguously verified using supervised PCA-DFA. Known markers of the diseases in question were successfully confirmed and a potential new marker emerged from the PLS regression. Conclusion. This study showed that untargeted metabolomics can be applied in the diagnosis of mild IMDs with less clear biochemical profiles.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30202 - Endocrinology and metabolism (including diabetes, hormones)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biomedical Papers-Olomouc  

ISSN

1213-8118

e-ISSN

1804-7521

Volume of the periodical

159

Issue of the periodical within the volume

4

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

4

Pages from-to

582-585

UT code for WoS article

000366566700011

EID of the result in the Scopus database

2-s2.0-84949651155