Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder that results in impairments in social communications and interactions as well as repetitive behaviours. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its aetiology, the diagnosis of ASD is performed exclusively based on an individual's behaviour assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transsulfuration (FOCM/TS) pathways analyzed via both Fisher discriminant analysis and support vector machine showed a strong capability to distinguish between ASD and typically developing peers (TD) cohorts. Furthermore, using kernel partial least squares regression it was possible to assess some degree of behavioural severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

中文翻译：

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开发自闭症谱系障碍诊断测试：大数据与代谢组学的结合


自闭症谱系障碍（ASD）被定义为一种神经发育障碍，会导致社交沟通和互动以及重复行为障碍。尽管目前的估计显示，美国约有 2.2% 的儿童受到影响，但由于该疾病的表现高度异质，人们对 ASD 病理生理学的了解相对较少。鉴于对控制其病因的生物学机制的了解有限，自闭症谱系障碍的诊断完全基于临床医生通过心理测量工具评估的个人行为来进行。尽管目前还没有现成的用于 ASD 诊断的生化测试，但多元统计方法显示出有效利用多种生化测量进行分类和表征的巨大潜力。在这项工作中，通过 Fisher 判别分析和支持向量机分析与叶酸依赖性一碳代谢和转硫 (FOCM/TS) 途径相关的标记物，显示出区分 ASD 和典型发育同龄人 (TD) 群体的强大能力。此外，使用核偏最小二乘回归可以从代谢组数据评估某种程度的行为严重程度。虽然所提出的结果需要在未来的独立研究中重复，但它们代表了发现临床相关 ASD 生物标志物的一个有前途的途径。