# Innovative Methodologic Advances for Mixtures Research in Epidemiology

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2020 · $459,807

## Abstract

Abstract
Human biomonitoring for chemical exposures has generated large amounts of data. Analysis of those data
presents a challenging problem to epidemiologists and biostatisticians. One prominent characteristic of these
environmental data is that exposures are always mixtures of chemicals and the chemicals in a mixture are
often moderately or highly correlated. The adverse effect of an individual chemical on any health outcome is
usually small due to the low exposure level. However, effects of exposure to chemicals in mixtures can
accumulate and act synergistically on health outcomes. The overarching goal of this project is to develop better
statistical methods for understanding the detrimental health impacts of exposure to mixtures of chemicals. To
accomplish this goal, we propose improvements over the existing genome-wide complex trait analysis
approach so that the accumulative effects and the total interaction effects of exposure to chemical mixtures
can be estimated with minimal bias. We further propose to estimate the individual chemical effects as the
average causal effect through the propensity score adjustment. The estimates will serve as the basis for
toxicity assessment of chemicals. Lastly, we propose a flexible network analysis approach to understand the
potential causal pathways from exposure to mixtures to health outcomes. The methods will be applied to a
number of datasets on which the research team has been working to answer important scientific questions with
regards to the associations of persistent organic pollutant exposures with endocrine and cardio-metabolic
outcomes, and biological pathways and nutrients relevant to these associations. The datasets also serve as
testing formats for developing and using the software package implementing the proposed methods. The
software package will be made freely available to environmental research community. The results of this
project are expected to substantially improve our ability to understand complex relationships among the many
chemical exposures found in human populations and detrimental health outcomes. Our development of
innovative methods will potentially facilitate the investigation of biological pathways mediating these
relationships and enhance our understanding of nutritional and other factors that may in part ameliorate
adverse effects of toxicants.

## Key facts

- **NIH application ID:** 9854957
- **Project number:** 5R01ES028790-03
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** HUA YUN CHEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $459,807
- **Award type:** 5
- **Project period:** 2018-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9854957

## Citation

> US National Institutes of Health, RePORTER application 9854957, Innovative Methodologic Advances for Mixtures Research in Epidemiology (5R01ES028790-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9854957. Licensed CC0.

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