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

基本信息

批准号：
10456248
负责人：
Shelley Han Liu
金额：
$ 9.15万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10456248
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

项目摘要

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.

项目概要超过 3400 万美国成年人患有糖尿病，这是一种发病率和死亡率较高的慢性疾病。最近，接触内分泌干扰化学物质（EDC），无论是持久性还是非持久性，已成为人们关注的焦点。被认为是导致胰岛素抵抗和糖尿病风险的潜在因素。有证据表明，因为 EDC影响相似的代谢途径，EDC的整体影响可能大于个体影响化学物质对代谢结果的影响。然而，研究人员缺乏一个简单的汇总指数来量化暴露程度 EDC 的负担。由于存在大量 EDC，汇总 EDC 负担指标可以帮助风险评估、生物监测，并可用于糖尿病风险预测模型。在这个R03中，我们介绍了一个灵活的项目反应理论 (IRT) 模型类别，用于量化 EDC 负担分数。我们估计EDC 负担作为一个潜在变量，捕获了内分泌干扰物暴露的总量，无论是测量还是和未测量的化学品。该汇总指标旨在捕捉内分泌和内分泌失调的总程度。其他生理器官系统受到 EDC 的干扰或负担。据我们所知，我们是第一 IRT 模型在环境暴露数据中的应用。项目反应理论是一个大集合建立了教育测试中常用的潜变量模型（例如大学入学评分）考试）。这些模型的应用填补了目前混合物研究中缺失的重要空白：1）它们解决数据协调挑战，其中不同组的化学品随着时间或时间的推移进行测量不同的群体，2）它们允许我们在负担分数计算中包含不常检测到的化学物质无需插补，3）他们不受监督，因此无论情况如何，负担分数都是相同的健康结果，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) 的多年数据来获取美国成年人内分泌干扰物和胰岛素抵抗的代表性数据。超过这些 NHANES 调查年份中，测量了不同组的 EDC，其中包括所有年份中的一些常见化学物质，需要数据协调，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、邻苯二甲酸盐和苯酚/对羟基苯甲酸酯的单独负担子分数，以及总体 EDC 负担分数。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，我们将调查 EDC 负担分数是否与通过胰岛素抵抗稳态模型评估测量的胰岛素抵抗相关。我们将把我们的发现与量化内分泌干扰物混合物的其他方法进行比较，例如主要成分分析和摩尔和，以及监督混合物方法。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计算他们的研究的化学品暴露负担分数。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.

DOI：
10.1038/s41380-020-00963-5
发表时间：
2021-08
期刊：
Molecular psychiatry
影响因子：
11
作者：
Norbury A;Liu SH;Campaña-Montes JJ;Romero-Medrano L;Barrigón ML;Smith E;MEmind Study Group;Artés-Rodríguez A;Baca-García E;Perez-Rodriguez MM
通讯作者：
Perez-Rodriguez MM

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.

DOI：
10.2196/30833
发表时间：
2021-09-15
期刊：
JMIR mental health
影响因子：
5.2
作者：
Ryu J;Sükei E;Norbury A;H Liu S;Campaña-Montes JJ;Baca-Garcia E;Artés A;Perez-Rodriguez MM
通讯作者：
Perez-Rodriguez MM

The dynamics of being homebound over time: A prospective study of Medicare beneficiaries, 2012-2018.