Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children

• Published in: PloS one Vol. 9; no. 11; p. e112445
• Main Authors: West, Paul R, Amaral, David G, Bais, Preeti, Smith, Alan M, Egnash, Laura A, Ross, Mark E, Palmer, Jessica A, Fontaine, Burr R, Conard, Kevin R, Corbett, Blythe A, Cezar, Gabriela G, Donley, Elizabeth L R, Burrier, Robert E
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.11.2014; Public Library of Science (PLoS)
• Subjects: Age; Amino acids; Autism; Autism Spectrum Disorder - blood; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - metabolism; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - blood; Blood; Blood plasma; Cardiac patients; Child; Child, Preschool; Children; Chromatography; Chromatography, Liquid; Clinical trials; Diagnosis; Diagnostic systems; Diagnostic tests; Female; Gas Chromatography-Mass Spectrometry; Genetic counseling; Humans; Learning algorithms; Machine Learning; Male; Mass Spectrometry; Mass spectroscopy; Measurement methods; Medical research; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Multivariate Analysis; Physical Sciences; Physiology; Precision Medicine - methods; Psychiatry; Reproducibility of Results; Scientific imaging; Sensitivity and Specificity; Statistical analysis; Statistical methods; Studies; Therapy; Urine; United States > US; Wisconsin; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0112445

The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.