GC-MS-based urine metabolic profiling of autism spectrum disorders

• DOI: 10.1007/s00216-013-6934-x
• Publication Date: 2013-04-09T03:00:07Z
• Authors: Patrick Emond; Sylvie Mavel; Nacima Aïdoud; Lydie Nadal-Desba...; Frédéric Montigny; Frédérique Bonnet...; Catherine Barthélémy; Marc Merten; Pierre Sarda; Frédéric Laumonnier; Patrick Vourc’h; Hélène Blasco; Christian R. Andres
• Keywords: Male; 0301 basic medicine; Adolescent; Chemical Fractionation; MESH: Child Development Disorders, Pervasive; Gas Chromatography-Mass Spectrometry; Clinical; MESH: Chemical Fractionation; 03 medical and health sciences; MESH: Child; Humans; Metabolomics; Chemometrics; MESH: Metabolomics; Child; GC; MESH: Adolescent; Biomedical analysis; MESH: Humans; Mass spectrometry; Statistics; MESH: Case-Control Studies; MESH: Male; 6. Clean water; MESH: Gas Chromatography-Mass Spectrometry; 3. Good health; Child Development Disorders, Pervasive; Case-Control Studies; Bioanalytical methods; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; Female; MESH: Female

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R(2)Y(cum) = 0.97, Q(2)(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children.