Particulate Matter (PM) Metals and Racial Disparity in Cardiovascular (CVD) Risk Factors

摘要/摘要 鉴于环境中颗粒物PM等污染物的复杂成分，它是 相信我们接触到的不只是一种化学物质，而是几种可能的混合物 不同的成分和混合比例。这些混合物之所以会形成，是因为它们在 同一时间与潜在因素同源同源。因此，人类更容易患上 暴露在以这些潜在因素为特征的这些混合物中。因子分析是一种用于 定义混合物的文献。不幸的是，识别混合物的统计方法有限。 此外，在怀孕期间接触PM化学品已知对母亲和 孩子。为了解决这一问题，提出了一种基于扩展特征的混合物暴露识别新方法 将采用传统的因子分析和来源分解相结合的方法。这项提议将 检验以下假设：假设1：孕期接触PM混合金属增加 黑人和西班牙裔少数族裔患心血管疾病(CVD)的风险因素 三个月。假设2：心血管疾病危险因素是PM金属暴露与儿童之间的中介变量 发展，通过以下目标。1)对混合物进行量化，并估计母亲在 不同的怀孕三个月。我们将使用新开发的贝叶斯扩展因子分析 (FA)，基于贝叶斯统计的灵活性和误差协方差矩阵的分解 结合SAM模型估计金属混合物。我们将进行时空分析， 混合模型采用广义加性混合模型(GAMM)。我们将使用反距离 权重法估计母亲在怀孕每三个月期间的吸入量。2) 黑白人群心脑血管疾病危险因素差异及空气污染与心脑血管疾病的交互作用 按每三个月的种族/民族分类。我们将使用Logistic回归分析和广义空间线性模型 为了这个目的。3)调查混合金属暴露与婴儿之间的心血管疾病危险因素 死亡率。我们将使用结构方程模型(SEM)来调查哪些风险因素处于 暴露与儿童死亡率之间的路径。拟议的研究结果可能会有 在识别混合物暴露、它们的化学毒性和与心血管疾病相关的含义方面的意义 在怀孕期间。

Summary/Abstract Given the complex composition of pollutants such as particulate matter PM in the environment, it is believed that our exposure is not limited to one chemical at a time but to several possible mixtures with varying composition and mix ratios. These mixtures could be formed because they are emitted at the same time from the same source origin as latent factors. As a result, humans are more susceptible to exposure to these mixtures as characterized by these latent factors. Factor analysis is one tool used in the literature to define mixtures. Unfortunately statistical methods of identifying mixtures are limited. Additionally, exposure to PM chemicals during pregnancy is known to be harmful to both mother and child. To address the problem a new method of identifying mixtures exposure based on extension of the traditional factor analysis combined with source apportionment methods will be used. This proposal will test the following hypothesis: Hypothesis 1: Exposure to PM mixture metals during pregnancy increased the risk of cardiovascular disease (CVD) risk factors among black and Hispanic minorities and vary by trimester. Hypothesis 2: CVD risk factors are mediators between PM metals exposure and child development, through the following Aims. 1) Quantify mixtures and estimate mother's exposure during various trimesters of pregnancy. We will use the newly developed Bayesian extended factor analysis (FA), based on the flexibility of Bayesian statistics and the decomposition of the error covariance matrix combined with SAM models to estimate metal mixtures. We will perform a spatio-temporal analysis of the mixtures using Generalized Additive Mixed Models(GAMM). We will use the inverse distance weighting method to estimate mother's inhaled quantities during each trimester of pregnancy. 2) Investigate CVD risk factors difference among black and white and interaction between air pollution and race/ethnicity by trimester. We will use logistic regression analysis and generalized spatial linear model for this aim. 3) Investigate CVD risk factors moderating between mixture metals exposure and infant mortality. We will use structural equation modeling (SEM) to investigate wich risk factors is in the pathway between exposure and child mortality. Results from the proposed studies could have implications in identifying mixtures exposures, their chemical toxicity and relative implication in CVD during pregnancy.