Targeted metabolomic analysis of plasma samples for the diagnosis of inherited metabolic disorders
Language English Country Netherlands Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
22018716
DOI
10.1016/j.chroma.2011.09.074
PII: S0021-9673(11)01465-8
Knihovny.cz E-resources
- MeSH
- Principal Component Analysis MeSH
- Child MeSH
- Adult MeSH
- Humans MeSH
- Metabolome MeSH
- Metabolomics methods MeSH
- Adolescent MeSH
- Child, Preschool MeSH
- Flow Injection Analysis MeSH
- Reproducibility of Results MeSH
- Cluster Analysis MeSH
- Tandem Mass Spectrometry MeSH
- Amino Acid Metabolism, Inborn Errors blood diagnosis MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Metabolomics has become an important tool in clinical research and diagnosis of human diseases. In this work we focused on the diagnosis of inherited metabolic disorders (IMDs) in plasma samples using a targeted metabolomic approach. The plasma samples were analyzed with the flow injection analysis method. All the experiments were performed on a QTRAP 5500 tandem mass spectrometer (AB SCIEX, U.S.A.) with electrospray ionization. The compounds were measured in a multiple reaction monitoring mode. We analyzed 50 control samples and 34 samples with defects in amino acid metabolism (phenylketonuria, maple syrup urine disease, tyrosinemia I, argininemia, homocystinuria, carbamoyl phosphate synthetase deficiency, ornithine transcarbamylase deficiency, nonketotic hyperglycinemia), organic acidurias (methylmalonic aciduria, propionic aciduria, glutaric aciduria I, 3-hydroxy-3-methylglutaric aciduria, isovaleric aciduria), and mitochondrial defects (medium-chain acyl-coenzyme A dehydrogenase deficiency, carnitine palmitoyltransferase II deficiency). The controls were distinguished from the patient samples by principal component analysis and hierarchical clustering. Approximately 80% of patients were clearly detected by absolute metabolite concentrations, the sum of variance for first two principle components was in the range of 44-55%. Other patient samples were assigned due to the characteristic ratio of metabolites (the sum of variance for first two principle components 77 and 83%). This study has revealed that targeted metabolomic tools with automated and unsupervised processing can be applied for the diagnosis of various IMDs.
References provided by Crossref.org
Cellwise outlier detection and biomarker identification in metabolomics based on pairwise log ratios
