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。全球更多地 ASD发病率表明，遗传学本身不太可能是ASD的主要驱动因素，但增加的遗传因素可能是ASD的主要驱动因素。 流行可能是由于改变了对环境因素的接触。事实上，我们知道， 环境暴露（营养物质、化学物质、压力等）影响儿童健康，特别是发挥其毒性 通过代谢物或内源性途径的扰动，使代谢组学分析成为关键 新兴技术来阐明这些暴露与ASD之间的关系。但我们如何直接 测量这些早期的暴露量我们研究的中心是使用新的牙齿基质生物标志物， 利用牙齿的渐进发育生物学（类似于树木的年轮）。的 我们开发的技术使我们能够在时间上区分第二个三个月，第三个三个月和第四个三个月之间的暴露。 妊娠期和产后期，能够识别胎儿和新生儿的敏感生命阶段 发展与ASD风险密切相关。对于本申请，我们将执行第一个 ASD牙齿的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关联 接触源（多溴联苯醚、邻苯二甲酸盐和有机氯农药）， 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， 描述相应自闭症和非自闭症儿童的独特代谢改变，并产生新的 ASD早期病因假说由于这两种分析将在相同的牙齿提取物中执行，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ASD我们将在《遗传学带来的儿童自闭症风险》中开展这项工作， 环境（CHARGE）队列，具有丰富的协调表型，人口统计学，医学， 基因和环境数据，用于高效分析。我们将使用新的统计方法， 加权分位数和回归（WQS），解决高维混合物的影响，并增加 与传统方法相比，发现生物标志物和与之相关的生物途径的能力 ASD（n=318）或典型发育（n=190）（非ASD或其他发育迟缓（n=105））。我们 方法是一种非侵入性的技术进步，可以获得直接和重复的胎儿测量， 与ASD早期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.