Multivariate Techniques Enable a Biochemical Classification of Children with Autism Spectrum Disorder versus Typically-Developing Peers: A Comparison and Validation Study

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 paper 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 disorder, applicable to at least a subset of the ASD population.

Reference:
D.P. Howsmon, T. Vargason, R.A. Rubin, L. Delhey, M. Tippett, S. Rose, S.C. Bennuri, J.C. Slattery, S. Melnyk, S.J. James, R.E. Frye, and J. Hahn. Multivariate Techniques Enable a Biochemical Classification of Children with Autism Spectrum Disorder versus Typically-Developing Peers: A Comparison and Validation Study.

Bioengineering & Translational Medicine 3, No. 2, pp. 156-165 (2018)