Uranium exposure in drinking water and cardiovascular disease in the Strong Heart Study

项目摘要 铀（U）是一种天然存在的元素，可能与心血管健康结果有关。跨 在美国，铀通过风和水的侵蚀被释放到环境中， 人类开采、碾磨和其他形式的铀加工。重要的是，U在美国印第安人中无处不在 (AI)社区，心血管疾病（CVD）负担仍然很高， 通过饮用水接触仍然是一个令人关切的问题。而公共场所铀的最高污染水平（MCL） 水系统（30 µg/L）于2008年生效，在此之前，美国没有对MCL进行监管，AI 依赖私人威尔斯水井提供饮用水的社区没有受到管制。强心研究（SHS） 在北达科他州、南达科他州、亚利桑那州和俄克拉荷马州的AI社区中进行的CVD前瞻性研究， 代表了一个重要的人口，了解的程度和影响的U暴露对健康的结果。这 需要对水和体内铀暴露剂量进行全面评估，包括在 尿然而，尿U数据仅在SHS的基于亲属关系的扩展中可用，这些扩展包括 Strong Heart Family Study（SHS），需要估计整个SHS社区的暴露量 以确定那些高U暴露和确定长期健康结果。这一目标 研究是：1）根据现有的全国范围内的数据，估计SHS和SHS参与者的水铀暴露 地下水测量，2）估计SHS参与者的尿U浓度， 饮用水U和尿U浓度之间的关系，以及3）调查是否估计 SHS和SHS中的U暴露（水和尿液）与CVD结局相关，无论是总体还是根据 参与区域和其他特征。水暴露估计（目标1）将依赖于社区水 来自美国环保署的铀浓度系统估算，国家铀资源评估 水文地球化学和河流沉积物勘测数据库，其中包括335，547个采样点 美国地质勘探局国家水信息系统， 提供了美国190多万个地点的水资源数据;强心水研究，一个试点 家庭用水U测量研究（n = 441）;和美国地质勘探局勘测研究。这些数据集将 用于分配水U措施，以家庭的位置。对于目标2，估计水量与 SHS中的U暴露和尿U浓度将用于预测SHS参与者的尿U值 用机器学习的方法。对于目标3，我们将研究估算水量与 SHS和SHS患者尿U浓度与临床和亚临床心血管结局的关系 参与者这项工作将估计整个SHS的铀暴露，确定风险增加的人群， 与铀相关的心血管疾病结果，并为减少人工智能社区和其他社区的水铀暴露提供信息 美国各地同样受到铀暴露影响的社区。

PROJECT SUMMARY Uranium (U) is a naturally occurring element potentially associated with cardiovascular health outcomes. Across the United States (US), U is released into the environment through wind and water erosion, as well as anthropogenic mining, milling and other forms of U processing. Importantly, U is ubiquitous in American Indian (AI) communities, where the cardiovascular disease (CVD) burden remains disproportionally high, and U exposure via drinking water remains a concern. While the maximum contaminant level (MCL) for U in public water systems (30 µg/L) took effect in 2008, no MCL was regulated in the US prior to this time, and AI communities relying on private wells for drinking water are not regulated. The Strong Heart Study (SHS), a prospective study of CVD among AI communities in North Dakota, South Dakota, Arizona and Oklahoma, represents an important population to understand the extent and impact of U exposure on health outcomes. This requires a comprehensive assessment of both water and internal dose U exposure, including U measured in urine. However, urinary U data is only available in a kinship-based extension of the SHS, those comprising the Strong Heart Family Study (SHFS), and there is need to estimate this exposure across the entire SHS community to identify those with elevated U exposure and determine the long-term health outcomes. The objectives of this study are to 1) estimate water U exposure in SHS and SHFS participants relying on existing nationwide groundwater measurements, 2) estimate urinary U concentrations in SHS participants leveraging the associations between drinking water U and urinary U concentrations in the SHFS, and 3) investigate if estimated U exposure (water and urinary) in the SHS and SHFS is linked to CVD outcomes, both overall, and according to participant region and other characteristics. Water U exposure estimations (Aim 1) will rely on community water system estimations of U concentrations derived from the US EPA, the National Uranium Resource Evaluation Hydrogeochemical and Stream Sediment Reconnaissance database, which includes 335,547 sampling locations across the contiguous US collected from 1975 to 1980; the USGS National Water Information System, which provides water-resources data on over 1.9 million sites across the US; the Strong Heart Water Study, a pilot study of household water U measures (n = 441); and the USGS Reconnaissance study. These datasets will be used to assign water U measures to household locations. For Aim 2, the relationship between estimated water U exposure and urinary U concentrations in SHFS will be used to predict urinary U values in SHS participants with machine learning approaches. For Aim 3, we will investigate the association between estimated water and urinary U concentrations with both clinical and subclinical cardiovascular outcomes in SHS and SHFS participants. This work will estimate U exposure across the entire SHS, identify populations at increased risk of U-related CVD outcomes, and inform interventions to reduce water U exposure in AI communities and other communities equally affected by U exposure across the US.