Autism Spectrum Disorders (ASD) are a group of neurological disorders that present with limited social communication/interaction and restricted, repetitive behaviors/interests. The current estimate is that approximately 1.9% of children in the US are diagnosed with ASD. While this is a high prevalence and the economic burden by ASD is significant, there is still considerable debate regarding the underlying pathophysiology of ASD. Because of this lack of biological knowledge, autism diagnoses are restricted to observational behavioral and psychometric tools. This work takes a step towards the goal of incorporating bio-chemical data into ASD diagnosis by analyzing measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways. Unlike traditional approaches that are based upon comparing differences in individual metabolite concentrations between children with and without an ASD diagnosis, we made use of multivariate classification via Fisher Discriminant Analysis and used Kernel Partial Least Squares regression to predict adaptive behavior. Although these results need to be replicated in independent studies, these analyses suggest combinations of metabolites in these path-ways as potential biomarkers for ASD.