GC-MS-based urine metabolic profiling of autism spectrum disorders
GC-MS-based urine metabolic profiling of autism spectrum disorders
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DOI：
10.1007/s00216-013-6934-x
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发表时间：
2013-06-01
影响因子：
4.3
通讯作者：
Andres, Christian R.
中科院分区：
文献类型：
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作者：
Emond, Patrick;Mavel, Sylvie;Andres, Christian R.
自闭症谱系障碍（Autism Spectrum Disorders，ASD）是一组由多种因素引起的神经发育障碍。诊断是基于3岁之前检测到的行为和发育体征，没有可靠的生物标志物。本研究的目的是评估与多元统计模型相关的气相色谱-质谱（GC-MS）的价值，以捕获自闭症个体的整体生化特征。通过液/液提取获得26名自闭症儿童和24名健康儿童的GC-MS尿代谢谱，并进行或不进行肟化步骤，然后进行过甲硅烷基化步骤。然后通过多变量分析，特别是正交偏最小二乘判别分析（OPLS-DA，R Y-2（cum）= 0.97，Q（2）（cum）= 0.88）处理这些代谢谱。鉴别了可区分的代谢物。自闭症儿童琥珀酸盐和乙醇酸盐的相对浓度高于健康儿童，而马尿酸盐、3-羟基苯乙酸盐、香草基羟基丙烯酸盐、3-羟基马尿酸盐、4-羟基苯基-2-羟基乙酸盐、1H-吲哚-3-乙酸盐、磷酸盐、棕榈酸盐、硬脂酸盐和3-甲基己二酸盐的相对浓度则较低。发现两组之间的其他8种代谢物存在差异，这些代谢物未被鉴别，但通过保留时间加定量剂及其限定离子质量进行表征。统计模型的比较得出的结论是，从两种衍生技术获得的数据的组合导致模型最好区分自闭症和健康儿童群体。
Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R Y-2(cum) = 0.97, Q (2)(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children.