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      The Interaction Effects of Meteorological Factors and Air Pollution on the Development of Acute Coronary Syndrome

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          Abstract

          This study investigated the interaction effects of meteorological factors and air pollutants on the onset of acute coronary syndrome (ACS). Data of ACS patients were obtained from the Taiwan ACS Full Spectrum Registry and comprised 3164 patients with a definite onset date during the period October 2008 and January 2010 at 39 hospitals. Meteorological conditions and air pollutant concentrations at the 39 locations during the 488-day period were obtained. Time-lag Poisson and logistic regression were used to explore their association with ACS incidence. One-day lag atmospheric pressure (AP), humidity, particulate matter (PM 2.5, and PM 10), and carbon monoxide (CO) all had significant interaction effects with temperature on ACS occurrence. Days on which high temperatures (>26 °C) and low AP (<1009 hPa) occurred the previous day were associated with a greater likelihood of increased incidence of developing ACS. Typhoon Morakot was an example of high temperature with extremely low AP associated with higher ACS incidence than the daily average. Combinations of high concentrations of PM or CO with low temperatures (<21 °C) and high humidity levels with low temperatures were also associated with increased incidence of ACS. Atmospheric pollution and weather factors have synergistic effects on the incidence of ACS.

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          Most cited references46

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          Akaike's information criterion in generalized estimating equations.

          W. Pan (2001)
          Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
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            Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health

            Numerous epidemiologic time-series studies have shown generally consistent associations of cardiovascular hospital admissions and mortality with outdoor air pollution, particularly mass concentrations of particulate matter (PM) ≤2.5 or ≤10 μm in diameter (PM2.5, PM10). Panel studies with repeated measures have supported the time-series results showing associations between PM and risk of cardiac ischemia and arrhythmias, increased blood pressure, decreased heart rate variability, and increased circulating markers of inflammation and thrombosis. The causal components driving the PM associations remain to be identified. Epidemiologic data using pollutant gases and particle characteristics such as particle number concentration and elemental carbon have provided indirect evidence that products of fossil fuel combustion are important. Ultrafine particles < 0.1 μm (UFPs) dominate particle number concentrations and surface area and are therefore capable of carrying large concentrations of adsorbed or condensed toxic air pollutants. It is likely that redox-active components in UFPs from fossil fuel combustion reach cardiovascular target sites. High UFP exposures may lead to systemic inflammation through oxidative stress responses to reactive oxygen species and thereby promote the progression of atherosclerosis and precipitate acute cardiovascular responses ranging from increased blood pressure to myocardial infarction. The next steps in epidemiologic research are to identify more clearly the putative PM casual components and size fractions linked to their sources. To advance this, we discuss in a companion article (Sioutas C, Delfino RJ, Singh M. 2005. Environ Health Perspect 113:947–955) the need for and methods of UFP exposure assessment.
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              Ambient particulate pollutants in the ultrafine range promote early atherosclerosis and systemic oxidative stress.

              Air pollution is associated with significant adverse health effects, including increased cardiovascular morbidity and mortality. Exposure to particulate matter with an aerodynamic diameter of <2.5 microm (PM(2.5)) increases ischemic cardiovascular events and promotes atherosclerosis. Moreover, there is increasing evidence that the smallest pollutant particles pose the greatest danger because of their high content of organic chemicals and prooxidative potential. To test this hypothesis, we compared the proatherogenic effects of ambient particles of <0.18 microm (ultrafine particles) with particles of <2.5 microm in genetically susceptible (apolipoprotein E-deficient) mice. These animals were exposed to concentrated ultrafine particles, concentrated particles of <2.5 microm, or filtered air in a mobile animal facility close to a Los Angeles freeway. Ultrafine particle-exposed mice exhibited significantly larger early atherosclerotic lesions than mice exposed to PM(2.5) or filtered air. Exposure to ultrafine particles also resulted in an inhibition of the antiinflammatory capacity of plasma high-density lipoprotein and greater systemic oxidative stress as evidenced by a significant increase in hepatic malondialdehyde levels and upregulation of Nrf2-regulated antioxidant genes. We conclude that ultrafine particles concentrate the proatherogenic effects of ambient PM and may constitute a significant cardiovascular risk factor.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                09 March 2017
                2017
                : 7
                : 44004
                Affiliations
                [1 ]Division of Cardiology, Department of Internal Medicine, Changhua Christian Hospital , Changhua, Taiwan
                [2 ]Institute of Statistics and Information Science, National Changhua University of Education , Changhua, Taiwan
                [3 ]School of Medicine, College of Medicine, Kaohsiung Medical University , Kaohsiung, Taiwan
                [4 ]Department of Beauty Science and Graduate Institute of Beauty Science Technology, Chienkuo Technology University , Changhua, Taiwan
                [5 ]Division of Nephrology, Department of Internal Medicine, Changhua Christian Hospital , Changhua, Taiwan
                [6 ]School of Medicine, Chung Shan Medical University , Taichung, Taiwan
                [7 ]Medical Research Center, Department of Internal Medicine, Changhua Christian Hospital , Changhua, Taiwan
                Author notes
                Article
                srep44004
                10.1038/srep44004
                5343658
                28276507
                caa8bd78-921a-4fd4-8b30-3bf7cfb8f56d
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 20 July 2016
                : 02 February 2017
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