Endocrine disruptors and insulin resistance: quantifying impacts with a novel exposure burden score

内分泌干​​扰物和胰岛素抵抗：用新的暴露负担评分量化影响

• 批准号: 10287815
• 负责人: Shelley Han Liu
• 金额: $ 9.39万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10287815
• 关键词: Address; Adult; Affect; Age; Biological; Biological Assay; Biological Monitoring; Chemical Exposure; Chemicals; Chronic Disease; Clinical; Data; Diabetes Mellitus; Disease; Drops; Education; Endocrine; Endocrine Disruptors; Environmental Exposure; Environmental Health; Ethnic Origin; Exposure to; Future; Glucose; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Health; Hour; Household Products; Individual; Insulin Resistance; Intervention; Literature; Measurement; Measures; Metabolic; Metabolic Pathway; Methods; Modeling; Morbidity - disease rate; National Health and Nutrition Examination Survey; OGTT; Outcome; Parabens; Phenols; Physiological; Plasma; Plastics; Prediabetes syndrome; Principal Component Analysis; Public Health; Race; Research; Research Personnel; Risk; Risk Assessment; Sampling; Socioeconomic Status; Software Tools; Sum; Supervision; Surveys; Testing; Time; base; body system; cohort; college; data framework; data harmonization; diabetes risk; estrogenic; fasting plasma glucose; flexibility; indexing; mortality; novel; phthalates; primary outcome; psychologic; response; risk prediction model; secondary outcome; sex; socioeconomics; theories; web app

PROJECT SUMMARY Over 34 million US adults live with diabetes, a chronic disease associated with high morbidity and mortality. Recently, exposure to endocrine disrupting chemicals (EDCs), both persistent and non-persistent, has been recognized as a potential contributor to insulin resistance and diabetes risk. Evidence suggests that because EDCs affect similar metabolic pathways, the overall effect of EDCs may be greater than effects of individual chemicals on metabolic outcomes. However, researchers lack a simple summary index to quantify exposure burden to EDCs. Because of the large number of EDCs that exist, a summary EDC burden metric could aid in risk assessment, biomonitoring, and be used in diabetes risk prediction models. In this R03, we introduce a flexible class of item response theory (IRT) models to quantify an EDC burden score. We estimate EDC burden as a latent variable that captures the totality of exposures to endocrine disruptors, to both measured and unmeasured chemicals. This summary metric aims to capture the total degree to which the endocrine and other physiological organ systems are perturbed, or burdened, by EDCs. To our knowledge, ours is the first application of IRT models to environmental exposures data. Item response theory is a large set of well- established latent variable models that are commonly used in educational testing (e.g. scoring college entrance exams). Application of these models fill important gaps that are currently missing in mixtures research: 1) They address data harmonization challenges in which different sets of chemicals are measured over time or in different cohorts, 2) They allow us to include infrequently detected chemicals in the burden score calculation without the need for imputation, 3) They are unsupervised so the burden scores will be the same no matter the health outcome, which is needed for biomonitoring purposes. To demonstrate feasibility of this approach, we will leverage multiple years of data from the National Health and Nutrition Examination Survey (NHANES) to gain representative data on endocrine disruptors and insulin resistance for US adults. Over these NHANES survey years, different sets of EDCs were measured, with some common chemicals across all years, which necessitates data harmonization to make full use of all measured chemical data. In Aim 1, we develop three separate burden subscores for PFAS, phthalates, and phenols/parabens, as well as an overall EDC burden score. We will determine if there are disparities in EDC burden for different socio-economic groups (e.g. age, sex, race/ethnicity, socio-economic status). In Aim 2, we will investigate whether EDC burden scores are associated with insulin resistance as measured by the Homeostatic Model Assessment of Insulin Resistance. We will compare our findings with other methods to quantify endocrine disruptor mixtures, such as principal components analysis and molar sum, as well as supervised mixtures approaches. We will create an R package, interactive web application and tutorial to allow environmental health and diabetes researchers to calculate chemical exposure burden scores for their research.

项目摘要 超过3400万美国成年人患有糖尿病，这是一种与高发病率和死亡率相关的慢性疾病。 最近，暴露于内分泌干扰化学品（EDCs），无论是持久性的还是非持久性的， 被认为是胰岛素抵抗和糖尿病风险的潜在因素。有证据表明，由于 内分泌干扰物影响相似的代谢途径，内分泌干扰物的总体影响可能大于单个干扰物的影响。 化学物质对代谢的影响然而，研究人员缺乏一个简单的汇总指数来量化暴露 给EDCs带来负担。由于存在大量EDC，因此汇总EDC负担度量可有助于 风险评估，生物监测，并用于糖尿病风险预测模型。在此R 03中，我们引入了 项目反应理论（IRT）模型的灵活分类，以量化EDC负担评分。我们估计EDC 负担作为一个潜在变量，捕捉暴露于内分泌干扰物的总量， 和无法测量的化学物质该摘要度量旨在捕获内分泌和 其它生理器官系统受到EDCs的干扰或负担。据我们所知， IRT模型在环境暴露数据中的应用。项目反应理论是一套很好的- 建立了教育测试中常用的潜变量模型（例如，大学入学考试评分 考试）。这些模型的应用填补了目前混合物研究中缺少的重要空白：1）它们 应对数据统一方面的挑战，其中不同的化学品是随着时间的推移或 不同的队列，2）它们允许我们在负担评分计算中包括不常检测到的化学物质 不需要插补，3）他们是无人监督的，所以负担分数将是相同的，无论 健康结果，这是生物监测目的所需要的。为了证明这种方法的可行性，我们 将利用国家健康和营养检查调查（NHANES）的多年数据， 获得美国成年人内分泌干扰物和胰岛素抵抗的代表性数据。在这些NHANES 在调查年，测量了不同的EDCs，其中一些常见的化学品在所有年份， 需要统一数据，以充分利用所有测量的化学数据。在目标1中，我们开发了三个 PFAS、邻苯二甲酸酯和酚类/对羟基苯甲酸酯的单独负担子评分，以及总体EDC负担 得分我们将确定不同社会经济群体（例如年龄， 性别、种族/民族、社会经济地位）。在目标2中，我们将研究EDC负担评分是否 与胰岛素抵抗相关，如通过胰岛素抵抗的稳态模型评估所测量的。 我们将比较我们的研究结果与其他方法来量化内分泌干扰物的混合物，如主要 成分分析和摩尔总和，以及监督混合物的方法。我们将创建一个R 软件包、交互式网络应用程序和教程，使环境健康和糖尿病研究人员能够 为他们的研究计算化学品暴露负担分数。