# Improving inferences on health effects of chemical exposures

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $407,611

## Abstract

Adverse effects of environmental contaminants on human health are a major public health concern.
We are all exposed to a complex mixture of different chemical contaminants through the air we breathe,
the water we drink, the food we eat, and the products we use. As new industrial products are produced,
leading to new direct and indirect exposures, there is a pressing need for new tools for assessing the
adverse health effects in humans associated with exposure to chemical mixtures. Challenges include huge
numbers of different possible mixtures, the curse of dimensionality in multivariate nonparametric
regression and moderate to high correlation in different exposures. Building on compelling preliminary
results from a highly successful NIEHS PRIME program R01, we develop a transformative statistical
toolbox for inferences on health effects of chemical exposures, both in the high throughput screening
context and for better disentangling health effects of chemical mixtures in epidemiology studies. The
research proceeds through the following Aims. Aim 1 develops methods for inferring synergistic and
antagonistic interactions from epidemiologic data, including for data collected longitudinally motivated
by studies of exposure effects on childhood neurodevelopment. We improve substantially over current
nonparametric regression approaches in interpretability and power to detect interactions; synergistic
interactions in which chemicals amplify each other’s effects are particularly important. Aim 2 develops
clustering methods to improve understanding of variation in exposure in relation to health. These
methods will have broad impact in dramatically improving practical performance over current model-
based clustering approaches. In addition, easily interpretable results are provided, adding additional
insights over state-of-the-art regression-based methods. Aim 3 develops new methods for inferring
relationships between chemical molecular structure and biologic activity. Given the sheer number
of chemicals lacking any direct in vivo or in vitro data, it becomes crucial to use molecular structure to
predict biologic activity. Leveraging on ToxCast/Tox21 and other data sources, we develop improved
statistical models for relating chemical structure to activity, for inferring low-dimensional summaries of
chemical activity based on molecular structure, and for optimally choosing the next chemicals to be tested.
These methods can be used to predict effects of chemicals lacking any direct in vivo or in vitro data
through targeted borrowing of information across related chemicals in the database. Aim 4 develops
user-friendly and reproducible software, while using the methods to thoroughly analyze data from the
motivating epidemiology studies, with a particular focus on the Mount Sinai Children’s Environmental
Health Study and the UNC Early Life Factors Study, which both focus on assessing exposure effects on
neurodevelopment in early childhood. We expect our method...

## Key facts

- **NIH application ID:** 10896299
- **Project number:** 5R01ES035625-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** David Brian Dunson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $407,611
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896299

## Citation

> US National Institutes of Health, RePORTER application 10896299, Improving inferences on health effects of chemical exposures (5R01ES035625-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10896299. Licensed CC0.

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