Environmental chemical mixtures and metabolomics in autism spectrum disorder

摘要 自闭症谱系障碍(ASD)是一种病因不明的异质性疾病。在全球范围内 ASD的发病率表明，基因本身不太可能是ASD的主要驱动因素，但增加的 患病率可能是由于暴露于环境因素的改变造成的。事实上，我们知道有很多人 环境暴露(营养素、化学物质、压力等)影响儿童健康，通常会产生毒性 通过代谢产物或内源途径中的扰动，使代谢组学分析成为关键 新兴技术来阐明这些暴露与自闭症之间的关系。但我们如何直接 测量这些早期生活中的暴露情况？我们研究的中心是使用新的牙齿基质生物标记物，这 利用牙齿的增量发育生物学(类似于树木的年轮)。这个 我们开发的技术使我们能够在时间上区分怀孕第二个月、第三个月 三个月和出生后阶段，能够识别胎儿和新生儿的敏感生命阶段 发展与自闭症风险最密切相关。对于本应用程序，我们将执行第一个 对ASD牙齿进行有针对性的有机分析，以描述来自不同来源的有毒混合物之间的联系 暴露源(多溴二苯醚、邻苯二甲酸酯和有机氯农药)和 自闭症。这将得到ASD牙齿的首次大规模非靶向代谢组学分析的支持 描绘相应自闭症和非自闭症儿童独特的代谢变化，并产生新的 ASD早期生活病因学假说。由于这两种分析将在相同的牙齿提取中执行，我们将 还进行多因素分析，探索目标毒物暴露之间的关系， 代谢组学和自闭症。我们将承担这项工作，从遗传学和儿童自闭症风险 环境(指控)队列，具有丰富的协调表型、人口统计学、医学、 用于高效分析的遗传和环境数据。我们将使用新的统计方法， 加权分位数和回归(WQS)，它处理高维混合和增加的影响 与传统方法相比，发现与以下相关的生物标记物和生物途径的能力 ASD(n=318)或典型发育(n=190)(无ASD或其他发育迟缓(n=105))。我们的 方法是一种非侵入性的技术进步，可以直接和重复地获得胎儿的 与ASD早期生活病因学相关的生物标志物。

Abstract Autism spectrum disorder (ASD) is a heterogeneous disease with an unknown etiology. The global increase in ASD incidence suggests that genetics alone is unlikely to be the major driver of ASD, but that the increased prevalence is likely due to altered exposures to environmental factors. In fact, we know that numerous environmental exposures (nutrients, chemicals, stress, etc.) impact child health, typically exerting their toxicity through either metabolites or perturbations in endogenous pathways, making metabolomics analysis a key emerging technology to elucidate the relationships between these exposures and ASD. But how do we directly measure these early life exposures? Central to our study is the use of novel tooth matrix biomarkers, which takes advantage of the incremental developmental biology of teeth (similar to tree growth rings). The techniques that we have developed allow us to temporally distinguish exposure between the 2nd trimester, 3rd trimesters, and postnatal periods, enabling identification of the sensitive life stages in fetal and neonatal development most strongly associated with ASD risk. For the present application, we will perform the first targeted organic analysis of ASD teeth to delineate associations between toxicant mixtures from various exposure sources (polybrominated diphenyl ethers (PBDEs), phthalates, and organochlorine pesticides) and autism. This will be supported by the first large-scale untargeted metabolomics analysis of ASD teeth to delineate unique metabolic alterations in corresponding autism and non-autism children, and generate new hypothesis on early life etiology of ASD. As both analyses will be executed in the same tooth extract, we will also perform a multifactorial analysis, exploring the relationships between targeted toxicant exposures, metabolomics profiles, and ASD. We will undertake this work in the Childhood Autism Risks from Genetics and the Environment (CHARGE) cohort, which has a wealth of harmonized phenotypic, demographic, medical, genetic, and environmental data for high efficiency analysis. We will use novel statistical methodology, weighted quantile sum regression (WQS), that addresses effects of high-dimensional mixtures and increases power when compared to traditional methods to discover biomarkers and biological pathways associated with ASD (n=318) or typical development (n=190) (neither ASD nor other developmental delays (n=105)). Our method is a non-invasive advancement in technology to obtain direct and repeated fetal measures of biomarkers associated with early life etiology of ASD.