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

Paul R. West, David G Amaral, Preeti Bais, Alan M. Smith, Laura A. Egnash, Mark E. Ross, Jessica A. Palmer, Burr R. Fontaine, Kevin R. Conard, Blythe A. Corbett, Gabriela G. Cezar, Elizabeth L R Donley, Robert E. Burrier

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Background: 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.

Objectives: 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.

Conclusions: 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.

Funding: Stemina funded this study. The funder provided support in the form of salaries for authors (PRW, LAE, AMS, MER, JAP, BRF, KRC, GGC, ELRD, and REB), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Methods: 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.

Results: 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.

Original languageEnglish (US)
Article numbere112445
JournalPLoS One
Issue number11
StatePublished - Nov 7 2014

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)


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