The cardiopulmonary effects of ambient air pollution and mechanistic pathways: a comparative hierarchical pathway analysis

• Published in: PloS one 2014-12, Vol.9 (12), p.e114913
• Main Authors: Roy, Ananya, Gong, Jicheng, Thomas, Duncan C, Zhang, Junfeng, Kipen, Howard M, Rich, David Q, Zhu, Tong, Huang, Wei, Hu, Min, Wang, Guangfa, Wang, Yuedan, Zhu, Ping, Lu, Shou-En, Ohman-Strickland, Pamela, Diehl, Scott R, Eckel, Sandrah P
• Format: Article
• Language: English
• Subjects: Air Pollutants - adverse effects; Air Pollutants - analysis; Air pollution; Atmospheric pollution; Autonomic Agents - metabolism; Biomarkers; Biomarkers - metabolism; China - epidemiology; Comparative analysis; Engineering and Technology; Hemostasis - physiology; Humans; Inflammation; Inflammation - chemically induced; Inflammation - metabolism; Medicine and Health Sciences; Models, Biological; Nitrogen dioxide; Oxidative stress; Oxidative Stress - physiology; Particulate matter; Physical Sciences; Physiological aspects; Pneumonia - chemically induced; Pneumonia - epidemiology; Pneumonia - metabolism; Pollutants; Research and Analysis Methods; Roy, Ananya; Sulfates; Time Factors; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0114913

Previous studies have investigated the associations between exposure to ambient air pollution and biomarkers of physiological pathways, yet little has been done on the comparison across biomarkers of different pathways to establish the temporal pattern of biological response. In the current study, we aim to compare the relative temporal patterns in responses of candidate pathways to different pollutants. Four biomarkers of pulmonary inflammation and oxidative stress, five biomarkers of systemic inflammation and oxidative stress, ten parameters of autonomic function, and three biomarkers of hemostasis were repeatedly measured in 125 young adults, along with daily concentrations of ambient CO, PM2.5, NO2, SO2, EC, OC, and sulfate, before, during, and after the Beijing Olympics. We used a two-stage modeling approach, including Stage I models to estimate the association between each biomarker and pollutant over each of 7 lags, and Stage II mixed-effect models to describe temporal patterns in the associations when grouping the biomarkers into the four physiological pathways. Our results show that candidate pathway groupings of biomarkers explained a significant amount of variation in the associations for each pollutant, and the temporal patterns of the biomarker-pollutant-lag associations varied across candidate pathways (p