Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders
Jazyk angličtina Země Česko Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
25482736
DOI
10.5507/bp.2014.048
Knihovny.cz E-zdroje
- Klíčová slova
- inherited metabolic disorders, mass spectrometry, untargeted metabolomics,
- MeSH
- adenylsukcinátlyasa nedostatek MeSH
- analýza hlavních komponent MeSH
- autistická porucha diagnóza MeSH
- biologické markery moč MeSH
- cystinurie diagnóza MeSH
- dítě MeSH
- dospělí MeSH
- galaktosemie diagnóza MeSH
- hmotnostní spektrometrie metody MeSH
- kojenec MeSH
- lidé MeSH
- metabolické nemoci diagnóza MeSH
- metabolomika metody MeSH
- mladiství MeSH
- mladý dospělý MeSH
- nemoc s močí javorového sirupu diagnóza MeSH
- poruchy metabolismu purinů a pyrimidinů diagnóza MeSH
- studie případů a kontrol MeSH
- vysokoúčinná kapalinová chromatografie metody MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- kojenec MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- adenylsukcinátlyasa MeSH
- biologické markery MeSH
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.
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