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National Institute of Environmental Health Sciences

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Your Environment. Your Health.

Publication Detail

Title: Individual-level concentrations of fine particulate matter chemical components and subclinical atherosclerosis: a cross-sectional analysis based on 2 advanced exposure prediction models in the multi-ethnic study of atherosclerosis.

Authors: Kim, Sun-Young; Sheppard, Lianne; Kaufman, Joel D; Bergen, Silas; Szpiro, Adam A; Larson, Timothy V; Adar, Sara D; Diez Roux, Ana V; Polak, Joseph F; Vedal, Sverre

Published In Am J Epidemiol, (2014 Oct 01)

Abstract: Long-term exposure to outdoor particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) has been associated with cardiovascular morbidity and mortality. The chemical composition of PM2.5 that may be most responsible for producing these associations has not been identified. We assessed cross-sectional associations between long-term concentrations of PM2.5 and 4 of its chemical components (sulfur, silicon, elemental carbon, and organic carbon (OC)) and subclinical atherosclerosis, measured as carotid intima-media thickness (CIMT) and coronary artery calcium, between 2000 and 2002 among 5,488 Multi-Ethnic Study of Atherosclerosis participants residing in 6 US metropolitan areas. Long-term concentrations of PM2.5 components at participants' homes were predicted using both city-specific spatiotemporal models and a national spatial model. The estimated differences in CIMT associated with interquartile-range increases in sulfur, silicon, and OC predictions from the spatiotemporal model were 0.022 mm (95% confidence interval (CI): 0.014, 0.031), 0.006 mm (95% CI: 0.000, 0.012), and 0.026 mm (95% CI: 0.019, 0.034), respectively. Findings were generally similar using the national spatial model predictions but were often sensitive to adjustment for city. We did not find strong evidence of associations with coronary artery calcium. Long-term concentrations of sulfur and OC, and possibly silicon, were associated with CIMT using 2 distinct exposure prediction modeling approaches.

PubMed ID: 25164422 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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