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

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. Aims of this study were to characterize a metabolic signature of ASD, and to evaluate multi-platform analytical methodologies in order to develop predictive tools for diagnosis and disease follow up. Urines were analyzed using: 1H- and 1 H-13C-NMR-based approaches and LC-HRMS-based approaches (ESI+ and ESI- on a HILIC and C18 chromatography column). Data tables obtained from the six analytical modalities on a training set of 46 urines (22 autistic children and 24 controls) were processed by multivariate analysis (OPLS-DA). Prediction of each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and ROC curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLSDA model showed an enhanced performance (R 2Y(cum)=0.88, Q 2 (cum)=0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC=0.91, p-value 0.006). Metabolites that are most significantly different between autistic and control children (p<0.05) are indoxyl sulfate, N-〈-Acetyl-L-arginine, methyl guanidine and phenylacetylglutamine. This multi-modality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.