Date of Award

Spring 5-11-2018

Degree Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

Public Health

First Advisor

Roby Greenwald

Second Advisor

Christina Hemphill Fuller

Third Advisor

Matt Hayat

Abstract

INTRODUCTION: Many studies have examined the positive effects of physical activity on mortality and morbidity, as well as the negative impact of exposure to air pollutants. Few, however, have investigated the complex interaction between physical activity and air pollution exposure. One promising method for analyzing this interaction is using metabolomics to identify the resulting metabolites produced from each exposure. This study will provide insight into the modification by physical activity of health biomarkers associated with acute exposure to air pollutants.

AIM: The aim of this study is to identify novel biomarkers of pollutant exposures using metabolomics analyses. Specifically, this study will examine differences in exposures to air pollutants (black carbon, ozone, particulate matter, particle number concentration) and corresponding metabolic changes in subjects before and after outdoor physical activity.

METHODS: Sixty-five saliva samples for metabolomics analysis were collected from a sample of 57 individuals in Atlanta, GA from June through July 2016. The ambient outdoor concentrations of PM2.5 (PM), ozone (O3), black carbon (BC), and particle number concentration (PNC) were measured throughout each 2-hr sampling event. Each participant’s physical activity levels were monitored as well. Each pollutant (above) was analyzed per individual via four separate metrics: ambient outdoor concentration, exposure, cumulative inhaled dose, and maximum one-minute dose. Samples were processed via ultra-high resolution liquid chromatography-mass spectrometry. Various mixed regression models used pollutant metrics to predict changes in metabolic feature intensity from pre- to post-exposure. Metabolite identification and pathway analysis for each significant feature was calculated using Mummichog version 1.0.3 with a level of significance of .05.

RESULTS: All pollutant metrics were significantly associated with changes in metabolic features. PNC max showed the highest number of significant associations with 172 significantly associated metabolic features. Additionally, Mummichog analysis yielded significant pathway predictions for all feature metrics, with a total of 48 significant metabolic pathway predictions

DISCUSSION: We identified 48 metabolic pathways that showed significant correlation with air pollution exposure. However, the lack of consistency in pathway predictions suggests that acute pollution exposure may take more than 2 hours to have a measurable effect on the salivary metabolome. Our research suggests that while high-resolution metabolomics shows potential for reliably identifying biomarkers of pollutant-related stress, there is still much room for further development in this field.

Available for download on Friday, April 26, 2019

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