Objective: A supervised multivariate model to classify the metabolome alterations between autistic spectrum disorders (ASD) patients and controls, siblings of autistic patients, has been realized and used to realize a network model of the ASD patients' metabolome.
Methods: In our experiment we propose a quantification of urinary metabolites with the Mass Spectroscopy technique couple to Gas Chromatography. A multivariate model has been used to extrapolate the variables of importance for a network model of interaction between metabolites. In this way we are able to propose a network-based approach to ASD description.
Results: Children with autistic disease composing our studied population showed elevated concentration of several organic acids and sugars. Interactions among diet, intestinal flora and genes may explain such findings. Among them, the 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid has been previously described as altered in autistic subjects. Other metabolites increased are 3,4-dihydroxybutyric acid, glycolic acid and glycine, cis-aconitic acid; phenylalanine, tyrosine, p-hydroxyphenylacetic acid, and homovanillic acid are all involved in the tyrosine pathway leading to neurotransmitter cathecolamine.
Conclusion: GC-MS-based metabolomic analysis of the urinary metabolome suggests to have the required sensitivity and specificity to gain insight into ASD phenotypes and aid a personalized network-based medicine approach.
Keywords: ASD; GC-MS; network-driven medicine.