Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically- developing peers: A comparison and validation study

Daniel P. Howsmon1,2 | Troy Vargason2,3 | Robert A. Rubin4 | Leanna Delhey5,6 |
Marie Tippett5,6 | Shannon Rose5,6 | Sirish C. Bennuri5,6 | John C. Slattery6 |
Stepan Melnyk6 | S. Jill James6 | Richard E. Frye7 | Juergen Hahn1,2,3

1
Dept. of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
2
Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180
3
Dept. of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180
4
Dept. of Mathematics, Whittier College, Whittier, CA 90602
5
Arkansas Children’s Research Institute, Little Rock, AR 72202
6
Dept. of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205
7
Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85013 and University of Arizona College of Medicine, Phoenix, AZ 85004

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 trans-
sulfuration (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 diag-
nostic 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 dis-
ease, applicable to at least a subset of the ASD population.

Keywords

autism spectrum disorder, biomarkers, multivariate statistical analysis

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