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Author Notes:

Corresponding author at: Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA. donghai.liang@emory.edu (D. Liang).

The field study conducted as part of this study benefitted greatly from the assistance of many students, staff, and faculty at both Georgia Tech and Emory.

Specific thanks go to C. Cornwell, K. Parada, S. Shim, K. Johnson and E. Yang for their tremendous help in conducting the field study.

We want to thank Dr. R. Weber, Dr. V. Verma, and Ms. D. Gao for their measurements of oxidative potential of ambient fine particles via DTT assay.

We are indebted to Dr. J. Schauer (U. Wisconsin) for loaning us several instruments to supplement our sampling network.

We would also like to thank Dr. Seung-Hyun Cho from RTI, Inc. for her collaboration on this project.

The Georgia EPD allowed us access to their roadside monitoring site and helped provide data from those monitors, and we particularly thank Ken Buckley for his assistance with this.

We also like to thank Ms. V. Tran for conducting the LC/MS on the DRIVE biosamples.

We owe a debt of gratitude to the administrators at Georgia Tech for allowing us to conduct this study on campus and in their residence hall facilities.

Declaration of interests: None.


Research Funding:

upport for this project were provided through a contract with the Health Effects Institute (RFA #4942-RFA13–1/14–3).

The study used on the instrumentation assembled for field studies conducted as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE), which was funded by a US Environmental Protection Agency STAR grant R834799.

Dr. Golan gratefully acknowledges support by a post-doctoral fellowship from the Environment and Health Fund, Jerusalem, Israel.

We acknowledge NSF for providing a fellowship to JM (DGE-1650044), and Dr. Russell made use of funds provided by a generous gift from Howard T. Tellepson.

Dr. Walker gratefully acknowledges the following grants supported by NIH (NIMH R01MH107205, NIEHS T32ES012870, and NIH S10OD018006).

We acknowledge the support from the HERCULES exposome research center, supported by the National Institute of Environmental Health Sciences of the National Institutes of Health (P30ES019776).


  • Science & Technology
  • Life Sciences & Biomedicine
  • Environmental Sciences
  • Environmental Sciences & Ecology
  • High resolution metabolomics
  • Traffic related air pollution
  • Metabolic perturbations
  • Oxidative stress
  • Inflammation
  • Biomarkers

Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution

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Journal Title:

Environment International


Volume 120


, Pages 145-154

Type of Work:

Article | Final Publisher PDF


Background: High-resolution metabolomics (HRM) is emerging as a sensitive tool for measuring environmental exposures and biological responses. The aim of this analysis is to assess the ability of high-resolution metabolomics (HRM) to reflect internal exposures to complex traffic-related air pollution mixtures. Methods: We used untargeted HRM profiling to characterize plasma and saliva collected from participants in the Dorm Room Inhalation to Vehicle Emission (DRIVE) study to identify metabolic pathways associated with traffic emission exposures. We measured a suite of traffic-related pollutants at multiple ambient and indoor sites at varying distances from a major highway artery for 12 weeks in 2014. In parallel, 54 students living in dormitories near (20 m) or far (1.4 km) from the highway contributed plasma and saliva samples. Untargeted HRM profiling was completed for both plasma and saliva samples; metabolite and metabolic pathway alternations were evaluated using a metabolome-wide association study (MWAS) framework with pathway analyses. Results: Weekly levels of traffic pollutants were significantly higher at the near dorm when compared to the far dorm (p < 0.05 for all pollutants). In total, 20,766 metabolic features were extracted from plasma samples and 29,013 from saliva samples. 45% of features were detected and shared in both plasma and saliva samples. 1291 unique metabolic features were significantly associated with at least one or more traffic indicator, including black carbon, carbon monoxide, nitrogen oxides and fine particulate matter (p < 0.05 for all significant features), after controlling for confounding and false discovery rate. Pathway analysis of metabolic features associated with traffic exposure indicated elicitation of inflammatory and oxidative stress related pathways, including leukotriene and vitamin E metabolism. We confirmed the chemical identities of 10 metabolites associated with traffic pollutants, including arginine, histidine, γ‑linolenic acid, and hypoxanthine. Conclusions: Using HRM, we identified and verified biological perturbations associated with primary traffic pollutant in panel-based setting with repeated measurement. Observed response was consistent with endogenous metabolic signaling related to oxidative stress, inflammation, and nucleic acid damage and repair. Collectively, the current findings provide support for the use of untargeted HRM in the development of metabolic biomarkers of traffic pollution exposure and response.

Copyright information:

© 2018 Elsevier Ltd

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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