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

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• 作者：Binta Di茅m茅 ; Sylvie Mavel ; H茅l猫ne Blasco ; Gabriele Tripi ; Fr茅d茅rique Bonnet-Brilhault ; Jo毛lle Malvy ; Cinzia Bocca ; Christian R Andres ; Lydie Nadal-Desbarats ; Patrick Emond
• 刊名：Journal of Proteome Research
• 出版年：2015
• 出版时间：December 4, 2015
• 年：2015
• 卷：14
• 期：12
• 页码：5273-5282
• 全文大小：389K
• ISSN：1535-3907

文摘

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鈥?sup>13C NMR-based approaches and LC鈥揌RMS-based approaches (ESI+ and ESI鈥?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 (R²Y(cum) = 0.88, Q²(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-伪-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.