Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using ¹H and ¹H–¹³C NMR-based approaches and LC–HRMS-based approaches (ESI+ and ESI– on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (<i>R</i>²<i>Y</i>(cum) = 0.88, <i>Q</i>²(cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, <i>p</i>-value = 0.006). Metabolites that are most significantly different between autistic and control children (<i>p</i> < 0.05) are indoxyl sulfate, <i>N</i>-α-acetyl-l-arginine, methyl guanidine, and phenyl­acetyl­glutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population