# Identification of environmental chemicals capable of inducing health impairments acutely and across generations

> **NIH NIH R21** · VAN ANDEL RESEARCH INSTITUTE · 2021 · $285,000

## Abstract

SUMMARY
Epidemiological evidence suggests that parental environmental exposure correlates with
increased risk of chronic diseases in children and even grandchildren. Animal studies provide
clear evidence that ancestral exposure to chemicals such as endocrine disruptors, dioxins, or
pesticides can induce non-Mendelian (but heritable) health impairments across generations.
Most research thus far has focused on rodents, which imposes limits of a few compounds at a
time in small numbers of animals and statistically sound experiments covering multiple
generations require years. Thus, comprehensive attempts to identify chemicals capable of
inducing intergenerational effects have been completely lacking. Importantly, the mechanisms
by which parental exposure leads to heritable health effects in subsequent generations
are still poorly understood. The objectives of this project are to 1) develop and verify a new
high-throughput fruit fly (Drosophila) model for chemical exposure and intergenerational health
effects, and 2) identify reprograming signatures to help uncover potential biological mechanisms
for transmitting those non-genetic effects from parent to offspring.

## Key facts

- **NIH application ID:** 10217832
- **Project number:** 1R21ES032060-01A1
- **Recipient organization:** VAN ANDEL RESEARCH INSTITUTE
- **Principal Investigator:** Heidi Lempradl
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $285,000
- **Award type:** 1
- **Project period:** 2021-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10217832

## Citation

> US National Institutes of Health, RePORTER application 10217832, Identification of environmental chemicals capable of inducing health impairments acutely and across generations (1R21ES032060-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10217832. Licensed CC0.

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