Environmental chemical mixtures and metabolomics in autism spectrum disorder

自闭症谱系障碍中的环境化学混合物和代谢组学

• 批准号: 10515646
• 负责人: Lauren Petrick
• 金额: $ 66.51万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-25 至 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10515646
• 关键词: 1 year old; Address; Affect; Animals; Autopsy; Behavioral; Biochemical; Biological; Biological Markers; Biology; Biometry; Birth; Blood; Brain; California; Case/Control Studies; Chemical Models; Chemicals; Child; Child Health; Childhood Autism Risks from Genetics and the Environment; Clinical; Data; Dental; Development; Developmental Biology; Developmental Delay Disorders; Diagnosis; Disease; Emerging Technologies; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiology; Etiology; Event; Exposure to; Foundations; Funding; Genetic; Growth; Health; In Vitro; Incidence; Individual; Late pregnancy; Life; Link; Measurement; Measures; Medical; Metabolic; Metabolism; Metals; Methodology; Methods; Neonatal; Newborn Infant; Nutrient; Onset of illness; Outcome; Participant; Pathogenesis; Pathway interactions; Phenotype; Pregnancy; Prevalence; Public Health; Questionnaires; Research; Research Design; Sample Size; Sampling Studies; Second Pregnancy Trimester; Source; Statistical Methods; Stress; Sum; Symptoms; Techniques; Technology; Therapeutic; Third Pregnancy Trimester; Tooth Diseases; Tooth structure; Toxic effect; Toxicant exposure; Toxin; Trees; Twin Multiple Birth; Umbilical Cord Blood; United States National Institutes of Health; Work; autism spectrum disorder; biological systems; cohort; disorder risk; early life exposure; environmental chemical; environmental chemistry; environmental stressor; fetal; high dimensionality; in utero; infancy; metabolomics; metal metabolism; mother nutrition; novel; organochlorine pesticide; phthalates; polybrominated diphenyl ether; population based; postnatal; postnatal period; prenatal; prenatal exposure; prevent; response; study population; temporal measurement; toxicant; treatment strategy

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.

抽象的 自闭症谱系障碍（ASD）是一种具有未知病因的异质疾病。全球增长 ASD发病率表明，仅遗传学不太可能是ASD的主要驱动力，但增加了 患病率可能是由于对环境因素的暴露变化所致。实际上，我们知道很多 环境暴露（营养，化学物质，压力等）影响儿童健康，通常会施加毒性 通过代谢物或内源途径中的扰动，使代谢组学分析成为关键 新兴技术以阐明这些暴露与ASD之间的关系。但是我们如何直接 衡量这些早期生活暴露？我们研究的中心是使用新型牙齿矩阵生物标志物， 利用牙齿的增量发育生物学（类似于树木生长环）。这 我们已经开发的技术使我们能够在第二个孕期之间暂时区分暴露 孕期和产后时期，可以鉴定胎儿和新生儿的敏感生命阶段 发展与ASD风险最密切相关。对于本申请，我们将执行第一个 对ASD牙齿的有机有机分析，以从各种有毒混合物之间描述相关 暴露源（多溴二苯基醚（PBDES），邻苯二甲酸酯和有机氯农药）和 自闭症。这将由ASD牙齿的第一个大规模的非靶向代谢组学分析来支持 描述相应的自闭症和非自闭症儿童的独特代谢改变，并产生新的 关于ASD早期病因的假设。由于两个分析都将在同一牙齿提取物中执行，我们将 还进行多因素分析，探索目标有毒物体暴露之间的关系， 代谢组学轮廓和ASD。我们将在遗传学的童年自闭症风险和 环境（充电）队列，具有大量协调的表型，人口，医学， 用于高效分析的遗传和环境数据。我们将使用新颖的统计方法论， 加权分位数和回归（WQS），该回归解决了高维混合物的影响并增加 与传统方法发现生物标志物和与之相关的生物途径相比 ASD（n = 318）或典型发展（n = 190）（ASD和其他发育延迟（n = 105））。我们的 方法是技术的非侵入性进步，以获得直接和重复的胎儿测量 与ASD的早期病因相关的生物标志物。