Abstract
Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0–4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9–36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution–cardiovascular epidemiological studies.
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References
Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV et al Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010; 121: 2331–2378.
Luttmann-Gibson H, Suh HH, Coull BA, Dockery DW, Sarnat SE, Schwartz J et al Systemic inflammation, heart rate variability and air pollution in a cohort of senior adults. Occup Environ Med 2010.
Pope CA, Hansen ML, Long RW, Nielsen KR, Eatough NL, Wilson WE et al Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects. Environ Health Perspect 2004; 112: 339–345.
Madrigano J, Baccarelli A, Wright RO, Suh H, Sparrow D, Vokonas PS et al Air pollution, obesity, genes and cellular adhesion molecules. Occup Environ Med 2010; 67: 312–317.
Delfino RJ, Staimer N, Tjoa T, Polidori A, Arhami M, Gillen DL et al Circulating biomarkers of inflammation, antioxidant activity, and platelet activation are associated with primary combustion aerosols in subjects with coronary artery disease. Environ Health Perspect 2008; 116: 898–906.
Liu L, Ruddy T, Dalipaj M, Poon R, Szyszkowicz M, You H et al Effects of indoor, outdoor, and personal exposure to particulate air pollution on cardiovascular physiology and systemic mediators in seniors. J Occup Environ Med 2009; 51: 1088–1098.
Sullivan JH, Hubbard R, Liu SL, Shepherd K, Trenga CA, Koenig JQ et al A community study of the effect of particulate matter on blood measures of inflammation and thrombosis in an elderly population. Environ Health 2007; 6: 3.
Ruckerl R, Greven S, Ljungman P, Aalto P, Antoniades C, Bellander T et al Air pollution and inflammation (interleukin-6, C-reactive protein, fibrinogen) in myocardial infarction survivors. Environ Health Perspect 2007; 115: 1072–1080.
Nikolov MC, Coull BA, Catalano PJ, Godleski JJ . An informative Bayesian structural equation model to assess source-specific health effects of air pollution. Biostatistics 2007; 8: 609–624.
McBride SJ, Norris GA, Williams RW, Neas LM . Bayesian hierarchical modeling of cardiac response to particulate matter exposure. J Expo Sci Environ Epidemiol 2011; 21: 74–91.
Sánchez BN, Budtz JÃ, rgensen E, Ryan LM, Hu H . Structural Equation Models. J Am Stat Assoc 2005; 100: 1443–1455.
Grandjean P, Budtz-Jorgensen E, Jorgensen PJ, Weihe P . Umbilical cord mercury concentration as biomarker of prenatal exposure to methylmercury. Environ Health Perspect 2005; 113: 905–908.
Grandjean P, Budtz-Jorgensen E . Total imprecision of exposure biomarkers: implications for calculating exposure limits. Am J Ind Med 2007; 50: 712–719.
Bollen K . Structural Equations with Latent Variables. Wiley New York City: New York. 1989.
Bell B, Rose C, Damon A . The Normative Aging Study: an interdisciplinary and longitudinal study of health and aging. Aging Hum Develop 1972; 3: 4–17.
Kalkstein LS, Valimont KM . An evaluation of summer discomfort in the United State using a relative climatological index. Bull Amer Meteorol Soc 1986; 67: 842–848.
Pearl J . Causal Diagrams for Empirical Research. Biometrika 1995; 82: 669–688.
Wilker EH, Alexeeff SE, Suh H, Vokonas PS, Baccarelli A, Schwartz J . Ambient pollutants, polymorphisms associated with microRNA processing and adhesion molecules: the Normative Aging Study. Environ Health 2011; 10: 45.
Alexeeff SE, Coull BA, Gryparis A, Suh H, Sparrow D, Vokonas PS et al Medium-term exposure to traffic-related air pollution and markers of inflammation and endothelial function. Environ Health Perspect 2011; 119: 481–486.
Zeka A, Sullivan JR, Vokonas PS, Sparrow D, Schwartz J . Inflammatory markers and particulate air pollution: characterizing the pathway to disease. Int J Epidemiol 2006; 35: 1347–1354.
Hoyle RH . The Structural Equation Modeling Approach: Basic concepts and fundamental issues. In Hoyle RH, (Ed.). Structural equation modeling: Concepts, issues, and applications, pp. 1–15 Sage Thousand Oaks, CA. 1995.
Fitzmaurice GM, Laird NM, Ware JH . Applied Longitudinal Analysis. Wiley-Interscience Hoboken, NJ. 2004.
Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P . Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Epidemiology 2005; 16: 385–395.
Acknowledgements
This study was supported in part by grants from the National Institute of Environmental Health Sciences (ES014663-01A2, ES-015172, ES015774 and PO1 ES09825) and from the U.S. Environmental Protection Agency (EPA R827353 and R 83479801).
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Baja, E., Schwartz, J., Coull, B. et al. Structural equation modeling of the inflammatory response to traffic air pollution. J Expo Sci Environ Epidemiol 23, 268–274 (2013). https://doi.org/10.1038/jes.2012.106
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DOI: https://doi.org/10.1038/jes.2012.106
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