Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology
Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology
复制标题
DOI：
10.1021/acs.jproteome.5b00699
复制
发表时间：
2015-12-01
影响因子：
4.4
通讯作者：
Emond, Patrick
中科院分区：
文献类型：
--
作者：
Dieme, Binta;Mavel, Sylvie;Emond, Patrick
自闭症谱系障碍（ASD）是一种没有临床生物标志物的神经发育障碍。本研究的目的是表征ASD的代谢特征，并评估多平台分析方法，以开发用于诊断和疾病随访的预测工具。使用基于H-1和H-1-C-13 NMR的方法和基于LC-HRMS的方法（HILIC和C18色谱柱上的ESI+和ESI-）分析尿样。对46个尿样（22名自闭症儿童和24名对照）的训练集从六种分析模式中获得的数据表进行多变量分析（正交偏最小二乘判别分析，OPLS-DA）。然后使用16个样本（8名自闭症儿童和8名对照）的预测集和受试者工作特征曲线对这些OPLS-DA模型中的每一个的预测进行评估。此后，数据融合块缩放OPLS-DA模型生成的6个最佳模型获得的每一个模态。与每种分析模态模型相比，该融合的OPLS-DA模型显示出增强的性能（（RY）-Y-2（cum）= 0.88，Q（2）（cum）= 0.75），以及更好的预测能力（AUC = 0.91，p值= 0.006）。自闭症儿童和对照儿童之间差异最显著的代谢物（p < 0.05）是硫酸吲哚酚、N-α-乙酰基-L-精氨酸、甲基胍和苯乙酰谷氨酰胺。这种多模态方法有可能有助于找到强大的生物标志物和表征ASD人群的代谢表型。
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 H-1 and H-1-C-13 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 ((RY)-Y-2(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-alpha-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.