Autism Metabolomics and Environment (AIME)

 描述（由申请人提供）：胎儿发育期间的基因和环境暴露都可能导致自闭症。我们有兴趣确定哪些风险是可以预防的。在以人群为基础的流行病学研究中，有效的暴露评估方法是实现这一目标的关键因素。在这里，我们建议评估是否可以使用储存的孕妇血清样本中暴露的代谢组学生物标志物来识别自闭症风险因素，并将生物标志物暴露模式与西班牙裔母亲的主要移民人群中的模拟空气污染和农药暴露进行比较，这是一个脆弱和未充分研究的人群，以前没有在加州进行过检查，生活在中央谷的人们以农业相关的农药暴露和柴油、木材燃烧和野火造成的高颗粒空气污染而闻名。我们在加州1-3和研究胎儿发育4-8建立农药和空气污染暴露模型方面有近20年的经验，最近我们的研究扩展到包括自闭症9。加州拥有独特的资源：1）监测数据，以模拟人口水平的常见农药和空气污染物; 2）自2000年以来，储存的产前血清样本来自中央谷人口的产前扩大甲胎蛋白筛查计划（XRAF）; 3）全州范围的区域中心系统，为自闭症和其他发育障碍患者提供服务（CA发育服务部（DDS））。我们建议以一种新的创新方式利用这些资源，将我们在暴露环境建模方面的专业知识与埃默里大学琼斯博士实验室的资源相结合，以确定自闭症的代谢组学暴露标志物和潜在疾病生物标志物。将以靶向和非靶向方式进行代谢组学分析，以确定和定量加州中部200例病例和200例对照妊娠的中期妊娠血清中的大量产前暴露及其代谢物。Jones开发了高分辨率代谢组学，该技术使用质谱和先进的数据提取算法来量化小生物提取物中多达20，000种化学物质。这种新的方法将用于识别暴露和疾病的“签名”，从人体代谢途径和环境暴露的代谢物。这将有助于我们测试现有的和产生新的假设母体暴露（如多环芳烃，农药，内分泌干扰物）和影响后代神经发育的代谢状态;重点是空气污染和神经毒性农药。我们希望这一应用能够为更大规模的自闭症和暴露生物标志物的探索提供必要的证据;鼓励环境政策，并为基于人群的自闭症预防提供机会。

 DESCRIPTION (provided by applicant): Both genes and environmental exposures during fetal development likely contribute to autism. We are interested in identifying risks that are preventable. Valid exposure assessment methods in population-based epidemiologic studies are a key factor for reaching this goal. Here, we propose to assess whether autism risk factors can be identified using metabolomic biomarkers of exposure in stored maternal serum samples from mid-pregnancy and compare biomarker exposure patterns with modelled air pollution and pesticide exposures in a largely immigrant population of Hispanic mothers, a vulnerable and understudied population not previously examined in California, living in the Central Valley known for its farming related pesticide exposures and high particulate air pollution from diesel, wood-burning and wildfires. We have almost two decades of experience building exposure models for pesticides and air pollution in California 1-3 and studying fetal development 4-8 and more recently expanded our research to include autism9. California has unique resources: 1) monitoring data to model common pesticides and air pollutants at the population-level; 2) stored prenatal serum samples from the State's Prenatal Expanded Alphafetoprotein Screening Program (XRAF) for the Central Valley population since the year 2000; and 3) a statewide system of regional centers servicing person with autism and other developmental disabilities (CA Department of Developmental Services (DDS)). We propose to employ these resources in a new and innovative manner combining our expertise in environmental modeling of exposures with the resources in Dr. Jones' laboratory at Emory University to identify metabolomics exposure markers and potential disease biomarkers for autism. Metabolomics analyses will be performed in a targeted as well as untargeted manner to identify and quantify a number of prenatal exposures and their metabolites in mid-pregnancy serum from 200 case and 200 control pregnancies in Central California. Jones has developed high-resolution metabolomics that uses mass spectrometry and advanced data extraction algorithms to quantify up to 20,000 chemicals in small biologic extracts 10-12. This novel approach will be used to identify exposure and disease 'signatures' from metabolites in human metabolic pathways and environmental exposures. This will help us test existing and generate new hypotheses about maternal exposures (e.g. PAHs, pesticides, endocrine disruptors) and metabolic states that affect neurodevelopment in the offspring; focusing on air pollution and neurotoxic pesticides. We expect this application to provide necessary evidence for larger scale explorations of autism and exposure biomarkers; encourage environmental policies and provide opportunities for population based prevention of autism.