Pub Date : 2018-09-24DOI: 10.1289/isesisee.2018.p02.1390
Jamie L. Humphrey, E. Kinnee, L. Kubzansky, C. Reid, L. McClure, Lucy F. Robinson, J. Clougherty
INTRODUCTION Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions. The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority. This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities. METHODS In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following. 1. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have cit
{"title":"Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease.","authors":"Jamie L. Humphrey, E. Kinnee, L. Kubzansky, C. Reid, L. McClure, Lucy F. Robinson, J. Clougherty","doi":"10.1289/isesisee.2018.p02.1390","DOIUrl":"https://doi.org/10.1289/isesisee.2018.p02.1390","url":null,"abstract":"INTRODUCTION\u0000Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions.\u0000The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority.\u0000This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities.\u0000\u0000\u0000METHODS\u0000In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following.\u00001. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have cit","PeriodicalId":21038,"journal":{"name":"Research report","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79597382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Rich, A. Peters, A. Schneider, W. Zareba, S. Breitner, D. Oakes, Jelani Wiltshire, Cathleen Kane, M. Frampton, Regina Hampel, P. Hopke, J. Cyrys, M. Utell
INTRODUCTION Previous studies have examined changes in heart rate variability (HRV*) and repolarization associated with increased particulate matter (PM) concentrations on the same and previous few days. However, few studies have examined whether these health responses to PM occur within a few hours or even less. Moreover, it is not clear whether exposure of subjects to ambient or-controlled PM concentrations both lead to similar health effects or whether any of the subjects' individual characteristics modify any of their responses to PM. The aims of the cur- rent study were to investigate whether exposure to PM was associated with rapid changes (< 60 minutes or con- current hour up to a delay of 6 hours) in markers of car- diac rhythni or changes in total antioxidant capacity (a marker of protection against oxidative stress) and whether any PM effects on cardiac rhythm markers were modified by total antioxidant capacity, age, obesity, smoking, hypertension, exertion, prior myocardial infarction (MI), or medication. METHODS We obtained data from a completed study in Augsburg, Germany (a panel study in N= 109 subjects, including a group with type 2 diabetes or impaired glucose tolerance [IGT; also known as prediabetes]) and a group of other- wise healthy subjects with a potential genetic susceptibil- ity to detoxifying and inflammatory pathways (Hampel et al. 2012b), as well as three completed studies in Rochester, New York (the REHAB panel study of N= 76 postinfarction patients in a cardiac rehabilitation pro- gram [Rich et al. 2012b]; the UPDIABETES study of con- trolled exposure to ultrafine particles [UFPs, particles with an aerodynamic diameter < 100 nm] of N = 19 patients with type 2 diabetes [Stewart et al. 2010; Vora et al. 2014j; and the UPCON controlled-exposure study of concentrated UFP exposure in N = 20 young, healthy, life- time nonsmokers). Data included 5-minute and 1-hour values for HRV and repolarization parameters from elec- trocardiogram (ECG) recordings and total antioxidant capacity measured in stored blood samples. Ambient con- centrations of UFPs, accumulation-mode particles (AMP, particles with an aerodynamic diameter of 100-500 nm), fine PM (PM2.5, particles with an aerodynamic diameter 2.5 pm), and black carbon (BC) were also available. We first conducted factor analyses in each study to find subgroups of correlated ECG outcomes and to reduce the number of outcomes examined in our statistical models. We then restricted the statistical analyses to the factors and representative.outcomes that were common to all four studies, including total HRV (measured as the standard deviation of normal-to-normal [NN] beat intervals [SDNNj), parasympathetic modulation (measured as the root mean square of the successive differences [RMSSD between adjacent NN beat intervals), and T-wave morphol- ogy (measured as T-wave complexity). Next, we used addi- tive mixed models to estimate the change in each outcome associated with increased pol
{"title":"Ambient and Controlled Particle Exposures as Triggers for Acute ECG Changes.","authors":"D. Rich, A. Peters, A. Schneider, W. Zareba, S. Breitner, D. Oakes, Jelani Wiltshire, Cathleen Kane, M. Frampton, Regina Hampel, P. Hopke, J. Cyrys, M. Utell","doi":"10.1289/isee.2014.o-030","DOIUrl":"https://doi.org/10.1289/isee.2014.o-030","url":null,"abstract":"INTRODUCTION\u0000Previous studies have examined changes in heart rate variability (HRV*) and repolarization associated with increased particulate matter (PM) concentrations on the same and previous few days. However, few studies have examined whether these health responses to PM occur within a few hours or even less. Moreover, it is not clear whether exposure of subjects to ambient or-controlled PM concentrations both lead to similar health effects or whether any of the subjects' individual characteristics modify any of their responses to PM. The aims of the cur- rent study were to investigate whether exposure to PM was associated with rapid changes (< 60 minutes or con- current hour up to a delay of 6 hours) in markers of car- diac rhythni or changes in total antioxidant capacity (a marker of protection against oxidative stress) and whether any PM effects on cardiac rhythm markers were modified by total antioxidant capacity, age, obesity, smoking, hypertension, exertion, prior myocardial infarction (MI), or medication.\u0000\u0000\u0000METHODS\u0000We obtained data from a completed study in Augsburg, Germany (a panel study in N= 109 subjects, including a group with type 2 diabetes or impaired glucose tolerance [IGT; also known as prediabetes]) and a group of other- wise healthy subjects with a potential genetic susceptibil- ity to detoxifying and inflammatory pathways (Hampel et al. 2012b), as well as three completed studies in Rochester, New York (the REHAB panel study of N= 76 postinfarction patients in a cardiac rehabilitation pro- gram [Rich et al. 2012b]; the UPDIABETES study of con- trolled exposure to ultrafine particles [UFPs, particles with an aerodynamic diameter < 100 nm] of N = 19 patients with type 2 diabetes [Stewart et al. 2010; Vora et al. 2014j; and the UPCON controlled-exposure study of concentrated UFP exposure in N = 20 young, healthy, life- time nonsmokers). Data included 5-minute and 1-hour values for HRV and repolarization parameters from elec- trocardiogram (ECG) recordings and total antioxidant capacity measured in stored blood samples. Ambient con- centrations of UFPs, accumulation-mode particles (AMP, particles with an aerodynamic diameter of 100-500 nm), fine PM (PM2.5, particles with an aerodynamic diameter 2.5 pm), and black carbon (BC) were also available. We first conducted factor analyses in each study to find subgroups of correlated ECG outcomes and to reduce the number of outcomes examined in our statistical models. We then restricted the statistical analyses to the factors and representative.outcomes that were common to all four studies, including total HRV (measured as the standard deviation of normal-to-normal [NN] beat intervals [SDNNj), parasympathetic modulation (measured as the root mean square of the successive differences [RMSSD between adjacent NN beat intervals), and T-wave morphol- ogy (measured as T-wave complexity). Next, we used addi- tive mixed models to estimate the change in each outcome associated with increased pol","PeriodicalId":21038,"journal":{"name":"Research report","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84755108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this study was to predict the lung burden in rats and humans of diesel exhaust particles from automobile emissions by means of a mathematical model. We previously developed a model to predict the deposition of diesel exhaust particles in the lungs of these species. In this study, the clearance and retention of diesel exhaust particles deposited in the lung are examined. A diesel particle is composed of a carbonaceous core (soot) and adsorbed organics. These materials can be removed from the lung after deposition by two mechanisms: (1) mechanical clearance, provided by mucociliary transport in the ciliated airways as well as macrophage phagocytosis and migration in the nonciliated airways, and (2) clearance by dissolution. To study the clearance of diesel exhaust particles from the lung, we used a compartmental model consisting of four anatomical compartments: nasopharyngeal, tracheobronchial, alveolar, and the lung-associated lymph node compartments. We also assumed a particle model made up of material components according to the characteristics of clearance: (1) a carbonaceous core of about 80 percent of particle mass, (2) slowly cleared organics of about 10 percent of particle mass, and (3) fast-cleared organics accounting for the remaining 10 percent of particle mass. The kinetic equations of the retention model were first developed for Fischer-344 rats. The transport rates of each material component of diesel exhaust particles (soot, slowly cleared organics, and fast-cleared organics) were derived using available experimental data and several mathematical approximations. The lung burden results calculated from the model showed that although the organics were cleared at nearly constant rates, the alveolar clearance rate of diesel soot decreased with increasing lung burden. This is consistent with existing experimental observations. At low lung burdens, the alveolar clearance rate of diesel soot was a constant, equal to the normal clearance rate controlled by macrophage migration to the mucociliary escalator, whereas at high lung burdens, the clearance rate was determined principally by transport to the lymphatic system. The retention model of diesel exhaust particles for rats was extrapolated to humans of different age groups, from birth to adulthood. To derive the transport rates for the human model, the mechanical clearance from the alveolar region of the lung was assumed to be dependent on the specific particulate burden on the alveolar surface. The reduction in the mechanical clearance in adult humans caused by exposure to high concentrations of diesel exhaust was found to be much less than that observed in rats. The reduction in children was greater than that in adults.(ABSTRACT TRUNCATED AT 400 WORDS)
{"title":"Retention modeling of diesel exhaust particles in rats and humans.","authors":"C. P. Yu, K. J. Yoon, Yu-Kang Chen","doi":"10.1089/JAM.1991.4.79","DOIUrl":"https://doi.org/10.1089/JAM.1991.4.79","url":null,"abstract":"The objective of this study was to predict the lung burden in rats and humans of diesel exhaust particles from automobile emissions by means of a mathematical model. We previously developed a model to predict the deposition of diesel exhaust particles in the lungs of these species. In this study, the clearance and retention of diesel exhaust particles deposited in the lung are examined. A diesel particle is composed of a carbonaceous core (soot) and adsorbed organics. These materials can be removed from the lung after deposition by two mechanisms: (1) mechanical clearance, provided by mucociliary transport in the ciliated airways as well as macrophage phagocytosis and migration in the nonciliated airways, and (2) clearance by dissolution. To study the clearance of diesel exhaust particles from the lung, we used a compartmental model consisting of four anatomical compartments: nasopharyngeal, tracheobronchial, alveolar, and the lung-associated lymph node compartments. We also assumed a particle model made up of material components according to the characteristics of clearance: (1) a carbonaceous core of about 80 percent of particle mass, (2) slowly cleared organics of about 10 percent of particle mass, and (3) fast-cleared organics accounting for the remaining 10 percent of particle mass. The kinetic equations of the retention model were first developed for Fischer-344 rats. The transport rates of each material component of diesel exhaust particles (soot, slowly cleared organics, and fast-cleared organics) were derived using available experimental data and several mathematical approximations. The lung burden results calculated from the model showed that although the organics were cleared at nearly constant rates, the alveolar clearance rate of diesel soot decreased with increasing lung burden. This is consistent with existing experimental observations. At low lung burdens, the alveolar clearance rate of diesel soot was a constant, equal to the normal clearance rate controlled by macrophage migration to the mucociliary escalator, whereas at high lung burdens, the clearance rate was determined principally by transport to the lymphatic system. The retention model of diesel exhaust particles for rats was extrapolated to humans of different age groups, from birth to adulthood. To derive the transport rates for the human model, the mechanical clearance from the alveolar region of the lung was assumed to be dependent on the specific particulate burden on the alveolar surface. The reduction in the mechanical clearance in adult humans caused by exposure to high concentrations of diesel exhaust was found to be much less than that observed in rats. The reduction in children was greater than that in adults.(ABSTRACT TRUNCATED AT 400 WORDS)","PeriodicalId":21038,"journal":{"name":"Research report","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1991-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79377984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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