Length of PM<inf>2.5</inf>exposure and alterations in the serum metabolome among women undergoing infertility treatment

• Type of Material: Article
• Authors: Robert B. Hood, Emory University; Donghai Liang, Emory University; Ziyin Tang, Emory University; Itai Kloog, Icahn School of Medicine at Mount Sinai; Joel Schwartz, Harvard T.H. Chan School of Public Health; Francine Laden, Harvard T.H. Chan School of Public Health; Dean Jones, Emory University; Audrey Gaskins, Emory University
• Language: English
• Date: 2022-02-04
• Publisher: ISEL
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1097/EE9.0000000000000191
• Title of Journal or Parent Work: Environmental Epidemiology
• Volume: 6
• Issue: 1
• Start Page: E191
• End Page: E191
• Grant/Funding Information: This work was supported by the following grants from the NIEHS (P30-ES019776, R01-ES009718, R01-ES022955, P30-ES000002, and R00-ES026648) and the United States Environmental Protection Agency (RD-834798 and RD-83587201).
• Keywords: anti-inflammatory; untargeted metabolomics; lipid metabolism; PM2.5; amino acid metabolism; pro-inflammatory; energy metabolism

Abstract

Background: Both acute and chronic exposure to fine particulate matter (PM2.5) have been linked to negative health outcomes. Studies have used metabolomics to describe the biological pathways linking PM2.5 with disease but have focused on a single exposure window. We compared alterations in the serum metabolome following various short- and long-term PM2.5 exposures. Methods: Participants were women undergoing in vitro fertilization at a New England fertility clinic (n = 200). Women provided their residential address and provided a blood sample during controlled ovarian stimulation. PM2.5 exposure was estimated in the 1, 2, and 3 days, 2 weeks, and 3 months prior to blood collection using a validated spatiotemporal model. We utilized liquid chromatography with high-resolution mass spectrometry. We used generalized linear models to test for associations between metabolomic features and PM2.5 exposures after adjusting for potential confounders. Significant features (P < 0.005) were used for pathway analysis and metabolite identification. Results: We identified 17 pathways related to amino acid, lipid, energy, and nutrient metabolism that were solely associated with acute PM2.5 exposure. Fifteen pathways, mostly, pro-inflammatory, anti-inflammatory, amino acid, and energy metabolism, were solely associated with long-term PM2.5 exposure. Seven pathways were associated with the majority of exposure windows and were mostly related to anti-inflammatory and lipid metabolism. Among the significant features, we confirmed seven metabolites with level-1 evidence. Conclusions: We identified serum metabolites and metabolic pathways uniquely associated with acute versus chronic PM2.5 exposure. These different biologic pathways may help explain differences in disease states when investigating different lengths of PM2.5 exposure.