Reanalysis of metabolomics data reveals that microbiota transfer therapy modulates important fecal and plasma metabolite profiles in children with autism spectrum disorders

While Autism Spectrum Disorder (ASD) is diagnosed through behavioral symptoms and psychometric evaluations, it has also been associated with distinct metabolomic patterns. A previous clinical trial of Microbiome Transplant Therapy (MTT) in children with ASD and gastrointestinal (GI) issues revealed significant differences in plasma metabolomics between children with ASD and their typically developing (TD) counterparts, which diminished after MTT. The objective of this study was to reanalyze the plasma and fecal samples using updated metabolomics libraries at Metabolon, and applying a comprehensive panel of statistical methods. This approach aimed to provide deeper insights into ASD-related metabolic differences and the impact of MTT. The reanalysis identified more statistically significant metabolites and highlighted specific metabolites whose relative peak intensities differed between the ASD and TD groups, as well as metabolites with significant changes in peak intensity post-MTT. Several plasma metabolites, including sarcosine, iminodiacetate, caproate, and caprylate, initially showed significant differences between the ASD and TD groups but shifted to resemble TD levels after MTT treatment. In fecal samples, p-cresol sulfate and sphingolipids emerged as metabolites with altered intensities following MTT treatment. Multivariate Fishers Discriminant Analysis (FDA) with leave-one-out cross-validation revealed that a set of metabolites--including p-cresol sulfate, hydroxyproline, and caprylate--could robustly classify the ASD and TD cohorts pre-treatment. However, after treatment, the same FDA model could not distinguish the two groups, as the FDA scores became similar to those of the TD cohort. Our findings enhance the understanding of ASD-associated metabolic changes and highlight the potential of MTT to influence these profiles. This underscores the importance of reanalysis using updated databases and robust statistical methods for comparative analysis. Further studies with larger cohorts and placebo-controlled trials are necessary to validate these results and explore the biochemical pathways involved, paving the way for personalized treatment approaches.