Export to Word
(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R03ES033374&format=word)
| Principal Investigator: Liu, Shelley Han | |
|---|---|
| Institute Receiving Award | Icahn School Of Medicine At Mount Sinai |
| Location | New York, NY |
| Grant Number | R03ES033374 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 01 Aug 2021 to 31 Jul 2024 |
| DESCRIPTION (provided by applicant): | 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. |
| Science Code(s)/Area of Science(s) |
Primary: 15 - Exposure Assessment/Exposome Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | See publications associated with this Grant. |
| Program Officer | Bonnie Joubert |