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

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


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.


autism spectrum disorder, biomarkers, multivariate statistical analysis

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