Identification and Characterization of Potential Environmental Risk Factors for ALS Using the ATSDR ALS Registry Cases and a Control Population

肌萎缩侧索硬化症（ALS）是成人中最常见的运动神经元疾病， 每年发病率为1-2.6/10万。已知的风险因素包括年龄、某些基因突变和男性 在老年之前。由于病因基本未知，确定ALS的可改变的危险因素是一个重要的 最大限度地减少这种疾病的负担的方法;然而，研究结果与环境 ALS的危险因素，如金属、溶剂和农药暴露，过去的研究局限于其 检查基因突变与ALS和基因的个体水平风险因素的存在的能力， 环境的相互作用。尽管环境空气污染物已经被检查了其他神经系统疾病， 帕金森病和阿尔茨海默病等疾病，迄今为止只有一项研究探讨了 与ALS的潜在关系这项研究旨在调查环境和职业 暴露包括持久性环境毒物和环境空气污染物和ALS。病例对照 研究提出，并将涉及从国家ALS登记处识别ALS患者， 可用的生物储存库样本和风险因素数据（病例，n=330），以及从相同样本中招募对照 按年龄、种族和性别匹配的县或州（n=330）。调查问卷将收集以下信息： 社会人口因素、吸烟史、兵役史、环境/职业暴露 （农药、金属、溶剂）和终生居住和职业史（如有）。 环境/职业暴露（即，自我报告和环境空气污染物）将进行审查， ALS风险与血液样本中有毒物质水平的验证。居住历史将用于计算 2002-2012年期间PM2.5和臭氧的时间加权平均暴露量。关于35种神经毒性空气的数据 通过美国环保署国家级空气毒性评估（NATA）确定的污染物将用于 计算可用NATA年份（1999年， 2002年、2005年和2011年）。我们将测量血液中溶剂和杀虫剂的浓度， 病例组（n=330）和对照组（n=330），使用一组持久性环境污染物检测 （有机氯农药、多氯联苯和溴化阻燃剂）。这将有助于验证 对估计暴露的反应。将使用条件logistic估计比值比和95% CI 经人口统计学、混杂因素和吸烟校正的回归分析。此外，在ALS病例中， （n=330），我们将研究环境毒物在人体生物学中的功能关系， 样品和关键生物学途径和共同基因（例如，C9 ORF 72，SOD 1，NEK 1）与 ALS的发展和/或进展。这项拟议中的研究将是第一个检查血液中 环境毒物和基因突变的存在，并结合环境和 来自访谈数据的职业暴露（即，自我报告和环境空气污染物）与ALS风险的关系。

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease in adults and has a global incidence of 1-2.6/100,000 annually. Known risk factors include age, certain genetic mutations, and male sex prior to older age. With largely unknown etiology, identifying modifiable risk factors for ALS is an important approach to minimizing the burden of this disease; however, findings have been inconsistent for environmental risk factors for ALS, such as metal, solvent, and pesticide exposure and past studies have been limited in their ability to examine the presence of genetic mutations with individual level risk factors for ALS and gene- environment interactions. Although ambient air pollutants have been examined with regard to other neurologic conditions, such as Parkinson’s disease and Alzheimer’s disease, only one study to date has explored the potential relationship with ALS. The study aims to investigate associations of environmental and occupational exposures including persistent environmental toxicants and ambient air pollutants and ALS. A case control study is proposed and will involve identification of persons with ALS from the National ALS Registry with available biorepository samples and risk factor data (cases, n=330), and recruitment of controls from the same county or state matched by age, race, and sex (n=330). The questionnaire will obtain information on sociodemographic factors, smoking history, military service history, environmental/occupational exposures (pesticides, metals, solvents), and lifetime residential and occupational history, as available. Environmental/occupational exposures (i.e., self-report and ambient air pollutants) will be examined in relation to ALS risk with verification of toxicant levels in blood samples. Residential history will be used to calculate a time weighted average exposure of PM2.5 and ozone for the period 2002-2012. Data on 35 neurotoxic air pollutants identified through the US EPA National-Scale Air Toxics Assessment (NATA) will be used to calculate average annual average residential exposure for cases and controls for available NATA years (1999, 2002, 2005, and 2011). We will measure exposures to solvents and pesticides in blood concentrations of cases (n=330) and controls (n=330) using a battery of tests for persistent environmental pollutants (organochlorine pesticides, polychlorinated biphenyls, and brominated flame retardants). This will help verify the responses for estimated exposures. Odds ratios and 95% CIs will be estimated using conditional logistic regression with adjustment for demographics, confounders, and smoking. Additionally, among ALS cases (n=330), we will examine the functional relationship between environmental toxicants in human biological samples and key biological pathways and common genes (e.g., C9ORF72, SOD1, NEK1) associated with the development and/or progression of ALS. The proposed study will be one of the first to examine blood levels of environmental toxicants and presence of genetic mutations in combination with data on environmental and occupational exposures from interview data (i.e., self-report and ambient air pollutants) in relation to ALS risk.