Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study
Bioengineering & Translational Medicine, 14 May 2018 https://doi.org/10.1002/btm2.10095 Funding information National Institutes of Health, Grant/Award Number: 1R01AI110642
Daniel P. Howsmon Troy Vargason Robert A. Rubin Leanna Delhey Marie Tippett Shannon Rose Sirish C. Bennuri John C. Slattery Stepan Melnyk S. Jill James Richard E. Frye Juergen Hahn
Abstract
Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate‐dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort. The comparison results indicate a high sensitivity and specificity for the original data set and up to a 88% correct classification of the ASD cohort at an expected 5% misclassification rate for typically‐developing controls. These results form the foundation for the development of a biochemical test for ASD which promises to aid diagnosis of ASD and provide biochemical understanding of the disease, applicable to at least a subset of the ASD population.
