Evidence supporting that gut problems are linked to ASD symptoms has been accumulating both in humans and animal models of ASD. Gut microbes and their metabolites may be linked not only to GI problems but also to ASD behavior symptoms. Despite this high interest, most previous studies have looked mainly at microbial structure, and studies on fecal metabolites are rare in the context of ASD. Thus, we aimed to detect fecal metabolites that may be present at significantly different concentrations between 21 children with ASD and 23 neurotypical children and to investigate its possible link to human gut microbiome. Using H-NMR spectroscopy and 16S rRNA gene amplicon sequencing, we examined metabolite profiles and microbial compositions in fecal samples, respectively. Of the 59 metabolites detected, isopropanol concentrations were significantly higher in feces of children with ASD after multiple testing corrections. We also observed similar trends of fecal metabolites to previous studies; children with ASD have higher fecal p-cresol and possibly lower GABA concentrations. In addition, Fisher Discriminant Analysis (FDA) with leave-out-validation suggested that a group of metabolites-caprate, nicotinate, glutamine, thymine, and aspartate-may potentially function as a modest biomarker to separate ASD participants from the neurotypical group (78% sensitivity and 81% specificity). Consistent with our previous Arizona cohort study, we also confirmed lower gut microbial diversity and reduced relative abundances of phylotypes most closely related to Prevotella copri in children with ASD. After multiple testing corrections, we also learned that relative abundances of Feacalibacterium prausnitzii and Haemophilus parainfluenzae were lower in feces of children with ASD. Despite a relatively short list of fecal metabolites, the data in this study support that children with ASD have altered metabolite profiles in feces when compared with neurotypical children and warrant further investigation of metabolites in larger cohorts.

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The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.