1 Department of Health Evaluation Sciences, Pennsylvania State University College of Medicine, Hershey, PA.
2 Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
3 Division of Adult and Community Health, Cardiovascular Health Branch, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
Received for publication April 7, 2003; accepted for publication November 21, 2003.
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ABSTRACT |
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air pollution; cardiovascular diseases; heart rate
Abbreviations: Abbreviations: AIRS, Aerometric Information Retrieval System; ARIC, Atherosclerosis Risk in Communities; HRV, heart rate variability; PM10, particulate matter less than 10 µm in aerodynamic diameter; ppm, parts per million; SD, standard deviation.
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INTRODUCTION |
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MATERIALS AND METHODS |
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The HRV data collected during the fourth cohort examination were used in combination with the air pollution data gathered during the same period for this study (9 years after study entry). Since there was no air quality monitor in Washington County, Maryland, participants from that center (n = 3,126) were excluded from this study. Additionally, because of small numbers, persons of ethnicities other than European or African American (n = 31) were excluded. Because ambient pollutant levels were not measured daily in all field centers, nitrogen dioxide data for Jackson, Mississippi, were not available for the entire study period, and a small number of persons had no HRV data (n = 58), the effective sample sizes for this report were 4,899, 5,431, 6,232, 4,390, and 6,784 for analyses involving PM10, ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide, respectively.
Air pollution data
We obtained data on levels of criteria pollutants at the ARIC Study field centers for 19961998 from the Environmental Protection Agencys Aerometric Information Retrieval System (AIRS) database. AIRS is a computer-based repository of information on airborne pollution in the United States. The AIRS system is administered by the Environmental Protection Agencys Office of Air Quality Planning and Standards. The AIRS database contains measurements of ambient concentrations of air pollutants from thousands of monitoring stations operated by the Environmental Protection Agency or by state or local agencies. These monitoring sites conform to uniform criteria of site selection, instrumentation, and quality assurance. The directly measured daily ambient air pollution data are sent to the AIRS system for storage and analysis. The AIRS database also contains descriptive information about each monitoring station, including its location and operator (2022).
The PM10 data obtained from the AIRS database were monitor-specific daily 24-hour averages. From these monitor-specific 24-hour averages, we calculated a county-specific daily average PM10 value by averaging all available PM10 measures from all operating monitors within a county on any calendar date. The gaseous pollutant data obtained from the AIRS database were hourly monitor-specific measures. From these hourly monitor-specific measures, we calculated monitor-specific daily concentrations as either the 8-hour average (10 a.m.6 p.m.) for ozone or 24-hour averages for carbon monoxide, sulfur dioxide, and nitrogen dioxide. Then, from these monitor-specific daily concentrations, we calculated county-specific daily average concentrations of each gaseous pollutant by averaging all available monitor-specific daily concentrations from all operating monitors within a county on any calendar date.
From the National Weather Center, we obtained data on relative humidity (percentage), temperature (degrees Kelvin), and sky cloud cover (fraction of the celestial dome covered by clouds on a scale of 0 to 10, where 0 indicates a very clear sky and 10 indicates a totally obscured sky), with the calendar date and county/state identifiable. In this report, "daily meteorologic variables" were defined as the relative humidity, temperature, and sky cloud cover in the county at 2:00 p.m.
We linked the individual-level cardiovascular disease risk factor data and HRV data with the county-specific daily average air pollution data and daily meteorologic data, according to the clinical examination date and the state and county of each participants residence. Thus, the individual-residence-level air pollution and meteorologic parameters 1, 2, and 3 days prior to clinical examination (HRV measurement date) were combined with individual-level information on cardiovascular disease risk factors and HRV data to form the analytical database.
HRV data
Study participants were asked to fast for 12 hours and abstain from smoking prior to examination. Following venipuncture, a light snack (with caffeine-free beverages) was provided, followed within 1 hour by the HRV data collection. Participants had three electrocardiographic electrodes placed on the epigastrium. Resting, supine, 5-minute beat-to-beat R-R interval data were collected between 8:30 a.m. and 12:30 p.m. after the participant had rested comfortably for 15 minutes in the supine position in a quiet, semidark room with a constant temperature of 24°C. A dedicated computer and specialized software (PREDICT II HRVECG; Arrhythmia Research Technology, Inc., Austin, Texas) were used for continuous detection and recording of the electrocardiographic R waves and R-R intervals, at a sampling frequency of 1,000 Hz (9).
Measurement of beat-to-beat HRV for assessment of cardiac autonomic control was performed on all study participants in the ARIC HRV reading center by trained and certified technicians. Overall, 95 percent of the records were "artifact-free," defined as having fewer than 1 percent artifactual QRS complexes in the entire record. Details on the data processing and analysis have been published previously (9, 23). Briefly, 5-minute raw heart rate data were first subjected to a filter program for identification and removal of any artifacts under visual control by a single, trained operator. A plot of the smoothed version of the heart rate data over time was then superimposed on the plot of the raw data, to confirm a good fit of any segment of smoothed data. The procedure could be repeated until a satisfactory plot was obtained. After the above smoothing, each R wave in a record was labeled as either a normal R wave or an artifactual R wave. These labeled R-R interval data were then analyzed by means of PREDICT II HRVECG for further processing and power spectral analysis and time domain analysis. During the data processing phase, a data-editing program was used to remove any R-R intervals labeled as artifactual from the HRV analysis. Segments with such artifacts were imputed, and R-R intervals in these segments were recalculated using an algorithm developed by Arrhythmia Research Technology, Inc. Fast Fourier transformation was performed for estimation of the power spectral density. An example of 5-minute time domain heart rate data is shown in figure 1, and an example of a power spectral density curve following fast Fourier transformation of the time domain heart rate data for one participant is shown in figure 2. From the power spectral density curve, the high-frequency spectral power (0.150.40 Hz) and low-frequency spectral power (0.040.15 Hz) were calculated. Following the recommendation of the Task Force on HRV Research (24), high-frequency power and low-frequency power were defined as the power (area) between the 0.15- and 0.40-Hz bands and the 0.04- and 0.15-Hz bands under the power spectral density curve, respectively. The standard deviation (SD) of all normal R-R intervals and heart rate were calculated from the time domain data after replacement of artifacts.
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Statistical analysis
Data on population characteristics were obtained as means and SDs or proportions. Multivariable linear regression models were used to assess the associations between each individual pollutant measured 13 days prior to the HRV measurement and each HRV index and to adjust for relevant confounding factors. Following convention (24), logarithmically transformed high-frequency power and low-frequency power were used in the analysis. Statistical interactions between each pollutant and major covariates were evaluated via the inclusion of an interaction term in the regression models, and p 0.10 was used to identify statistically significant interaction terms. In the presence of a statistical interaction, stratum-specific regression coefficients were calculated. To elucidate the time course of PM10 and HRV, we fitted lagged regression models by including in the models the primary measure of PM10 (PM10 1 day prior to HRV measurement) and 1- and 2-day lags (PM10 measured 2 and 3 days prior to HRV measurement, respectively). In the regression models, we adjusted for individual cardiovascular disease risk factors known to be significantly associated with HRV in this population (22) and the meteorologic factors (humidity, temperature, and season) that were significantly associated with pollution levels in our preliminary analysis. All statistical computations were performed using SAS software, version 8.2 (SAS Institute, Inc., Cary, North Carolina).
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RESULTS |
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To highlight potentially confounding factors, we have also presented in table 1 the mean values or proportions of major covariates by quartile of HRV high-frequency power (representing the outcome variable). On the basis of these data and our previous experience, we adjusted all of our statistical models for age, ethnicity-center, sex, current smoking, body mass index, heart rate, use of cardiovascular medication, hypertension, prevalent coronary heart disease, and diabetes. When analyzing the PM10-HRV association, we also adjusted for season, temperature, humidity, and total sky cover, because our exploratory data analysis identified these meteorologic variables as important confounders for the PM10-HRV association but not for the associations between gaseous pollutants and HRV.
Multivariable-adjusted regression coefficients, standard errors, and p values for the association of PM10 with HRV indices are presented in table 2. As is indicated in the column for all participants, ambient PM10 concentrations measured 1 day prior to the HRV measurement were inversely associated with both frequency and time domain HRV indices and were positively associated with heart rate. Since the interactions between PM10 and hypertension were statistically significant (p < 0.05) for all HRV indices analyzed, we have also presented in table 2 the regression coefficients, standard errors, and p values stratified by hypertension status. These stratified results suggest consistently more pronounced associations between PM10 and HRV among persons with a history of hypertension.
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In tables 24, the regression models were adjusted for ethnicity and center. To further confirm that the significant associations presented in tables 24 were not due to an effect of study center, we performed two additional analyses (data not shown). We first tested center x pollutant interactions in relation to each of the HRV indices, and none were found to be statistically significant at p < 0.10. We also stratified the main models in tables 24 by center, and the patterns of association were similar across the three centers.
To elucidate the time course of PM10 and HRV, we also analyzed 1- and 2-day lags (PM10 measured 2 and 3 days prior to HRV measurement, respectively) between PM10 concentrations and HRV. Several conventional diagnostic tests for collinearity (variance inflation factor, condition index, condition numbers, and variance proportion) were performed, and no significant collinearity was indicated in these lagged models. The findings from these lagged models are presented in table 5. PM10 concentrations measured either 2 or 3 days prior to HRV measurement were not significantly associated with HRV indices. Furthermore, adjustment for 2- and 3-day PM10 simultaneously did not change the pattern of association between PM10 1 day prior to examination and HRV indices. Similar lagged analysis was performed for each gaseous pollutant, and the results (data not shown) were consistent with those for the PM10 lagged analysis.
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DISCUSSION |
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Recently, the relations of several potentially important arrhythmogenic mechanisms to ambient air pollution have been investigated. One of these is the acute adverse effect of air pollution on cardiac autonomic control. It was hypothesized that increased air pollution levels, specifically increased levels of fine particulate matter, stimulate the autonomic nervous system and lead to an imbalance of cardiac autonomic control characterized by sympathetic activation unopposed by parasympathetic control (913). Such an imbalance of cardiac autonomic control may predispose people, especially those who are more susceptible to particulate matter exposure, to greater risk of life-threatening arrhythmias and acute cardiac events. These associations may be due to: 1) the direct actions of particles that are hematogenously translocated from the lungs to the heart and vasculature; 2) the reflexive responses of the cardiac autonomic system to direct particulate activation of chemosensitive pulmonary afferents; 3) nonspecific responses of the cardiac autonomic system to noxious pulmonary stress mediated by sympathetic efferents; and/or 4) longer-term, perhaps cumulative responses to stimulus-evoked production and release of inflammatory cytokines (including certain interleukins and tumor necrosis factor) from pulmonary macrophages, epithelial cells, or fibroblasts (11, 1618). Although the particulate matter-HRV pathway is biologically plausible, no study has yet examined the air pollution-cardiac autonomic control association in a large population-based sample. Few studies have reported associations between gaseous pollutants and cardiac autonomic control.
Results from this large population-based study, which to our knowledge is the first in this field, suggest that higher levels of PM10, ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide, even at levels far below the current Environmental Protection Agency standards, have adverse effects on cardiac autonomic control. Our findings were cross-sectionally derived from population-based samples and reflect only the short-term effects of air pollution on HRV. To our knowledge, this is the first population-based study to confirm the findings of the previous panel studies (913), and it has better generalizability than the panel studies because of the population-based sample. This study evaluated the association of short-term exposures measured 1 day prior to HRV assessment with cardiac autonomic control, and its findings are suggestive of short-term effects of air pollution on HRV. Thus, if the association is real, the findings suggest that air pollution has an impact on cardiovascular disease by way of an "acute" increase in air pollution levels, even within traditionally low ranges, and such an acute increase in pollution levels may lead to an immediate (short-term) decrease in HRV. Such a decrease in HRV may increase the risk of acute cardiovascular disease events or trigger the onset of a cardiovascular disease event. When the regression coefficients from each individual pollutant model are compared, the effect size for PM10 is considerably larger than the effect sizes for gaseous pollutants.
The observed effect modifications by existing cardiovascular conditions (modification by hypertension for PM10 and by prevalent coronary heart disease for sulfur dioxide) are suggestive of differential susceptibility to pollutant exposures. This is consistent with findings from studies of the association between ambient fine particle concentrations and cardiac autonomic control (9) and with the observation of a stronger association between air pollutant exposure and cardiopulmonary mortality among elderly persons with a history of cardiopulmonary disease (1, 2). Of the findings in tables 2 and 4, the interactions of hypertension with PM10 were the most consistent across all of the HRV indices. The interactions of prevalent coronary heart disease with sulfur dioxide and of ethnicity with ozone were only significant for one of the HRV indices. No other interactions were statistically significant. When making interpretations regarding an interaction or the lack of one, caution should be exercised, since we tested interactions between each of the pollutants and hypertension, history of coronary heart disease, diabetes, chronic pulmonary diseases, age, sex, education, and ethnicity in relation to each of the HRV indices. Testing of multiple interactions was motivated by previous studies that indicated several comorbid conditions as effect modifiers for air pollution and cardiovascular disease risk. The statistically significant interactions we have identified in these data may be chance findings, while the lack of statistical significance for some potential effect modifiers may be due to limited statistical power. Replication of these interactions in other studies is needed before any conclusion of differential susceptibility by comorbid conditions can be made.
Lagged analysis in our data, represented by the results of the lagged PM10 analysis presented in table 5, indicated that pollutant concentrations measured 2 or 3 days prior to HRV measurement were not significantly associated with HRV indices. Furthermore, adjusting for 2- and 3-day exposures simultaneously did not change the pattern of association between exposures 1 day prior to HRV measurement and HRV indices. These results are consistent with our previous findings (9) and are indicative of an acute effect of PM10 on cardiac autonomic control. Because of the lack of a biologically plausible hypothesis justifying further investigations of lag functions, no additional lags were considered.
In summary, these data are supportive of the hypothesized air pollution-HRV-cardiovascular disease pathway at the population level. The magnitudes of the estimated effects shown in tables 24 that is, the regression coefficients associated with a 1-SD difference in levels of each of the pollutantsare generally small, indicating weak associations. Although results were not adjusted for measurement error, these weak associations suggest that exposures to these pollutants are "minor" risk factors for cardiovascular disease. For example, in this population, a 5-year increment in age, male sex, and a positive history of cardiovascular disease were associated with 0.11-, 0.24-, and 0.15-unit decreases in the log-transformed high-frequency power index, respectively, in comparison with a 0.06-unit decrease associated with a 1-SD increment of PM10. By contrast, from a public health perspective, one could argue that estimates of the magnitude observed in this study would have a significant impact on the health of the population because of its widespread, long-term exposure to low levels of ambient air pollutants. In this regard, the pollutant levels for this study were derived as daily averages from ambient air monitors such as those used in different locations in the United States and are reflective of the low ambient levels to which most of the population is exposed on a daily basis.
This was a cross-sectional study, which precluded consideration of a temporal relation between the air pollutants and cardiac autonomic control, although we were able to assess short-term, prior exposure over the days preceding the HRV measurement. We assessed ambient exposures to five criteria pollutants by calculating the daily averages from measured data available from several monitors within a county. Although this approach provides the technically most feasible measures of exposure for individual residents, we cannot rule out misclassification of the exposures. However, there is no evidence suggesting that such misclassification might be systematic with regard to levels of individual HRV measures and other cardiovascular disease risk factors, because clinical examination (and HRV measurement) dates were assigned at random to all study participants. Following this argument, the associations we observed in this study would have been underestimated because of nondifferential misclassification of exposure. This was a large, population-based study; as such, the skewed distribution of the exposure variables had less of an impact on the overall results than it would have in studies with small sample sizes and panel studies. We performed sensitivity analysis by excluding persons with extremely high levels of pollution exposure prior to their clinical examination, and the results were not meaningfully changed (data not shown).
We emphasize that this study was designed to investigate the short-term association between air pollution and cardiac autonomic control in data obtained 1, 2, and 3 days prior to the HRV measurements and that we are unable to rule out other patterns of exposure-outcome association, such as subacute or long-term cumulative effects. Most of the published literature validating the use of HRV as a measure of cardiac autonomic control and the prediction of incident cardiac events from HRV measures was based on studies of longer duration. It is biologically plausible that chronic air pollution can also impair cardiac autonomic control. In this study, we statistically adjusted for individual-level risk factors for cardiovascular disease, such as age, sex, smoking, body mass index, use of cardiovascular medication, prevalent coronary heart disease, diabetes, hypertension, demographic and socioeconomic status, ethnicity-center, educational level, and meteorologic factors such as season, temperature, humidity, and total sky cover. Thus, the results are less likely to reflect bias due to these confounding factors. Although residual confounding by other factors cannot be totally ruled out, we do not believe that minor residual confounding factors could have yielded the consistent findings observed. Finally, we had to exclude a large number of persons from our analysis, mostly because of unavailability of exposure data. This reduces the generalizability of the findings, but it is unlikely to have introduced selection bias, because of the manner in which the four study cohorts were chosen. Our analysis indicated that persons included were similar to those excluded with regard to major cardiovascular disease risk factors.
Particulate matter is a complex mixture of suspended particles that vary in size and composition. In this study, we assessed the acute effects of five criteria pollutants; the ability to generalize these findings to other pollutants, such as particulate matter less than 2.5 µm in diameter, may be limited. Similarly, information on the composition of particles was not available for this analysis; thus, inferences from our data can only be made for the mass concentration of PM10, not its chemical composition or proportions of components in the mixture.
In conclusion, the data from this population-based, cross-sectional study suggest that cardiac autonomic control as measured by HRV and heart rate is adversely associated with higher levels of environmentally relevant ambient pollutants, with the strongest associations being observed for PM10 and in persons with positive histories of hypertension and coronary heart disease. Given the established and consistent associations between lower HRV, higher heart rate, and the development of cardiovascular disease, our findings suggest an injury mechanism and a potential underlying pathway by which air pollution could affect the risk of cardiovascular disease morbidity and mortality.
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ACKNOWLEDGMENTS |
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The authors thank Douglas Gray, Joanne Caulfield, and Barbara Hynum of the Pennsylvania State University College of Medicine (Hershey, Pennsylvania) and Phyllis Johnson of the University of North Carolina at Chapel Hill (Chapel Hill, North Carolina) for their assistance in carrying out this study. The authors also thank the staff of the ARIC Study for their important contributions.
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NOTES |
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REFERENCES |
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