Untargeted metabolomic analysis of urine samples in the diagnosis of some inherited metabolic disorders
Language English Country Czech Republic Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
25482736
DOI
10.5507/bp.2014.048
Knihovny.cz E-resources
- Keywords
- inherited metabolic disorders, mass spectrometry, untargeted metabolomics,
- MeSH
- Adenylosuccinate Lyase deficiency MeSH
- Principal Component Analysis MeSH
- Autistic Disorder diagnosis MeSH
- Biomarkers urine MeSH
- Cystinuria diagnosis MeSH
- Child MeSH
- Adult MeSH
- Galactosemias diagnosis MeSH
- Mass Spectrometry methods MeSH
- Infant MeSH
- Humans MeSH
- Metabolic Diseases diagnosis MeSH
- Metabolomics methods MeSH
- Adolescent MeSH
- Young Adult MeSH
- Maple Syrup Urine Disease diagnosis MeSH
- Purine-Pyrimidine Metabolism, Inborn Errors diagnosis MeSH
- Case-Control Studies MeSH
- Chromatography, High Pressure Liquid methods MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Infant MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Adenylosuccinate Lyase MeSH
- Biomarkers 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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