# Methods for Data Integration and Risk Assessment for Environmental Mixtures

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $448,039

## Abstract

Project Summary
Humans are routinely exposed to mixtures of chemical and other environmental factors, making the
quantification of health effects associated with environmental mixtures a critical goal for establishing
environmental policy sufficiently protective of human health. Advancing research on mixtures science requires
innovation across a span of disciplines in environmental health: exposure science, statistical methods for risk
estimation in toxicology and epidemiology, and risk assessment. Accordingly, this proposal structures three
specific aims spanning the primary needs in mixtures science: exposure biology (the development of good
biomarkers for environmental mixtures), estimation of risk associated with pre- and post-natal exposures to
environmental mixtures in children's health, and methods for improving guidance values in risk assessment of
mixtures to improve environmental policy. Specifically, incorporating the critical aspect of exposure timing, in
Aim 1 we will (1) develop methods that integrate information from studies with highly temporally resolved
information on exposure into studies with more temporally targeted biomarker measures; and (2) develop
methods that incorporate multiple biomarkers of exposure at varying temporal scales within the same study.
Armed with new temporally resolved data on exposure mixtures, in Aim 2, we will develop new classes of
models that can assess whether either (1) exposure at one time can “prime” an individual to be more
susceptible to a concurrent or subsequent chemical exposure, or (2) exposure to a nutrient or other “protective”
exposure at a given time can buffer an individual's tolerance to chemical exposures experienced at other
times. However, simply identifying chemicals that are bad actors evidenced through epidemiology data does
not adequately inform public health risk assessors about “acceptable ranges” of environmental exposures from
consumer products, which is fundamental to regulatory guidelines. In Aim 3, we will develop new classes of
models that incorporate and evaluate regulatory guideline values into analyses of health effects of exposure to
chemical and nutritional mixtures. Essential to this project is access to motivating data from two on-going
pregnancy cohort studies of child development. The PROGRESS study is a cohort in Mexico City. Teeth
biomarkers from these children provide a high temporal-resolution record of perinatal exposures to metals. The
SELMA study is a large pregnancy cohort in Sweden with prenatal endocrine disrupting chemicals (EDC)
exposure and dietary data available, making it possible to test for the potential mitigating effect of good
nutrition on health effects from EDCs.

## Key facts

- **NIH application ID:** 10155484
- **Project number:** 5R01ES028811-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Brent Andrew Coull
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $448,039
- **Award type:** 5
- **Project period:** 2018-05-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10155484

## Citation

> US National Institutes of Health, RePORTER application 10155484, Methods for Data Integration and Risk Assessment for Environmental Mixtures (5R01ES028811-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10155484. Licensed CC0.

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