# A nationwide population-based study investigating the cardiovascular effects of exposure to particulate matter α-, β-, and γ-activities and individual radionuclides

> **NIH NIH R01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2024 · $673,196

## Abstract

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in the U.S., and numerous studies
have linked CVD to short- and long-term exposures to particulate air pollution (PM), however, the PM properties
responsible for its toxicity are still not fully understood. Recently, we reported associations between PM gross β-
activity and CVD mortality, blood pressure, oxidative stress, and lung and cardiac function. The primary source
of ambient PM radioactivity (PR) in the U.S. is Rn gas, specifically its decay products (radionuclides) which can
attach to ambient PM, and after inhalation, release ionizing radiation (α-, β-, and γ-radiation) inside the human
body (internal dose). Although many studies have reported the effects of Rn decay products on cancer risk, we
know very little about the non-cancer health effects of PR. The goal of this proposal is to investigate the effects
of short- and long-term PR exposures on cardiovascular mortality and morbidity on a national scale and in a
systematic way using a spectrum of PR exposure metrics, and determine whether PR exposure intensifies PM
toxicity. To do this we have put together a large collection of PR measures. Using data from the U.S. EPA
Radiation Network (RadNet), in Aim 1 we will assess the acute effects of short-term PR exposure, as measured
by PM β-activity in 163 cities for the years 1987-2018, and PM γ-activities (35 cities for 2006-2018) on individual
CVD admissions among Medicare enrollees and all ages CVD mortality. In Aim 2 we will examine the chronic
effects of long-term PM β- and γ-activities on CVD admissions. In Aim 3, in the four U.S. cities with U.S. Nuclear
Weapons Non-Proliferation Treaty sites in 2000-2018, we will estimate the acute and chronic effects of exposure
to individual radionuclides on CVD admissions and mortality. In three U.S. cities (Aim 4) we will measure daily
exposures to PM α-activity associated with short-lived radionuclides using a newly developed continuous
monitor, and in one city we will estimate daily exposures to PM α-activity from long-lived radionuclides using
archived and newly collected filters. Using these data we will then assess the acute effects of short-term
exposures to PM2.5 gross α-activity associated with long-lived radionuclides and short-lived radionuclides on
CVD admissions and mortality. In all aims, we will examine seasonal and regional variability, confounding by
PM2.5, exposure-response, timing of exposure, effect modification by individual and area level risk factors and
apply both traditional and causal inference methods to address confounding. This will be the first study to provide
rigorous scientific evidence of the cardiovascular effects of radioactive particles in a national population-based
study, and to describe the CVD effects of PM α-activity. Investigations based on both Medicare and mortality
data will make conclusions generalizable to the entire U.S. population. Because exposures to PR are ubiquitous,
our ...

## Key facts

- **NIH application ID:** 10876616
- **Project number:** 1R01ES035390-01A1
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** PETROS KOUTRAKIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $673,196
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10876616

## Citation

> US National Institutes of Health, RePORTER application 10876616, A nationwide population-based study investigating the cardiovascular effects of exposure to particulate matter α-, β-, and γ-activities and individual radionuclides (1R01ES035390-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10876616. Licensed CC0.

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