Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically‐developing peers: A comparison and validation study. Issue 2 (19th June 2018)

• Record Type: Journal Article
• Authors: Howsmon, Daniel P.; Vargason, Troy; Rubin, Robert A.; Delhey, Leanna; Tippett, Marie; Rose, Shannon; Bennuri, Sirish C.; Slattery, John C.; Melnyk, Stepan; James, S. Jill; Frye, Richard E.; Hahn, Juergen
• Is Part Of: Bioengineering & translational medicine. Volume 3:Issue 2(2018)
• Page Start: 156
• Page End: 165
• Publication Date: 2018-06-19
• Subjects: autism spectrum disorder -- biomarkers -- multivariate statistical analysis; Bioengineering -- Periodicals; Drug development -- Periodicals; Drugs -- Testing -- Periodicals; 660.6
• DOI: 10.1002/btm2.10095
• Languages: English
• ISSNs: 2380-6761

Abstract

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.