Effects of Air Pollution on Heart Rate Variability: The VA Normative Aging Study

1Short- and long-term exposure to air pollution has been associated with increased cardiovascular mortality and morbidity, and individuals with underlying cardiovascular disease, including heart failure, arrhythmia, or diabetes, are at greater risk. Possible mechanisms for these associations include effects on the autonomic nervous system through direct reflexes from airways or through inflammatory response, chemical effects on ion channel function in myocardial cells, ischemic response in the myocardium, and inflammatory responses that trigger endothelial dysfunction, atherosclerosis, and thrombosis.

2Heart rate variability (HRV) is a widely used noninvasive and quantitative marker of cardiac autonomic control. HRV reflects autonomic modulation of the rhythmic activity of the sinus node and is analyzed in the time or frequency domains. Sustained reductions of HRV have been associated with increased risk of mortality in middle-age and elderly subjects, in patients with diabetes, and in survivors of myocardial infarction and other cardiovascular diseases. Air pollution, especially particulate matter < 2.5 μm in aero-dynamic diameter (PM2.5), has been associated with alterations in HRV. However, only two studies explored whether clinical conditions or other subject characteristics modified the association between air pollution and HRV. Little has been reported to date on associations with particle components.

3In this study we examine the relationship between alterations in HRV and ambient air pollutants among community residents. We also investigated modifying effects of hypertension, ischemic heart disease (IHD), diabetes, and use of commonly prescribed antihypertensive medications that increase cardiac vagal activity.

4The Normative Aging Study is a longitudinal study of aging established by the Veterans Administration in 1963, when 2,280 men from the Greater Boston area (21–81 years of age) confirmed to be free of known chronic medical conditions were enrolled. Participants were asked to return for examinations every 3–5 years. Among active cohort members, 603 persons were examined from 14 November 2000 through 30 October 2003.

5Participants visited the study center in the morning, after an overnight fast and abstinence from smoking. Weight and height were measured to compute body mass index (BMI). With the subject seated, heart rate and systolic and diastolic blood pressures were measured by a physician. The mean of the left and right arm measurements was used. For this study, we defined mean arterial blood pressure (MAP) as diastolic pressure plus one-third of the difference between systolic and diastolic blood pressure. Subjects with diabetes were defined by a physician’s diagnosis of type 2 diabetes and/or use of a diabetes medication (e.g., oral hypoglycemic drug, metformin, or insulin). Hypertension was defined as systolic blood pressure of ≥140 mm Hg, diastolic blood pressure of ≥90 mm Hg, or reported use of hypertension medication. Cigarette smoking, alcohol consumption, and subjects’ use of medications were assessed by questionnaire. Medication use was confirmed by a physician interview. Prevalent IHD was identified using the Framingham Heart Study criteria for myocardial infarction and angina pectoris. Temperature of the room where the electrocardiogram (ECG) was taken was recorded.

6We measured HRV between 0600 and 1300 hr using a two-channel (five-lead) ECG monitor (Trillium 3000; Forest Medical, East Syracuse, NY). A detailed description of the HRV measurement protocol is provided elsewhere. Briefly, after the participants had rested for 5 min, the ECG was recorded (sampling rate of 256 Hz per channel) for approximately 7 min with the subject seated. We used the best 4-consecutive-minute interval for the HRV calculations. The ECG digital recordings were processed, and heart rate and HRV measures were calculated using PC-based software (Trillium 3000 PC Companion Software for MS Windows; Forest Medical). Beats were automatically detected and assigned tentative annotations, which were then reviewed by an experienced scanner to correct for any mislabeled beats or artifacts. Only normal-to-normal (NN) beat intervals were included in the analysis. We computed standard deviation of NN intervals (SDNN), the square root of the mean of the squared differences between adjacent NN intervals (r-MSSD), high-frequency power (HF) (0.15–0.4 Hz), low-frequency power (LF) (0.04–0.15 Hz), and LF:HF ratio. Ninety-two subjects with problematic heart rate measurements (atrial fibrillation, atrial bigeminy and trigeminy, pacemakers, irregular rhythm, irregular sinus rhythm, frequent ventricular ectopic activity, ventricular bigeminy, multifocal atrial tachycardia, or measurement time < 3.5 min) were excluded.

7Continuous PM2.5, particle number concentration (PN), and black carbon (BC) were measured at the Harvard School of Public Health monitoring site, 1 km from the exam site, using a Tapered Element Oscillating Microbalance (TEOM) (model 1400A; Rupprecht & Pataschnick Co., East Greenbush, NY), condensation particle counter (model 3022A; TSI Inc., Shoreview, MN), and aethalometer (Magee Scientific, Berkeley, CA), respectively. Because the TEOM sample filter is heated to 50°C, a season-specific correction was used to compensate for the loss of semivolatile mass that occurs at this temperature. Ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, temperature, and dew-point temperature measurements were obtained from the Massachusetts Department of Environmental Protection local monitoring sites. The gaseous pollutants are measured hourly using U.S. Environmental Protection Agency (EPA) reference methods.

8To control for weather, we used apparent temperature, defined as a person’s perceived air temperature. It was calculated with the following formula: −2.653 + (0.994 × air temperature) + (0.0153 × dew-point temperature). We estimated missing PM2.5, PN, and BC measures using a regression model with date, day of week, hour of day, temperature, relative humidity, pressure, and NO2 as predictors (6.4% missing for PM2.5, 7.5% for PN, and 0.5% for BC). To evaluate lagged effects of air pollutants, we used 4-hr, 24-hr, and 48-hr moving averages of air pollution matched on the time of measuring ECG for each subject. These averaging times were chosen based on previous reports in the literature.

9Measures of HRV were log10-transformed to improve normality and stabilize the variance. Linear regression analyses were carried out to evaluate the relation of HRV with each air pollutant. Cardiac medications were categorized as beta-blocker, calcium-channel blocker, and angiotensin-converting enzyme (ACE) inhibitor. After 14 subjects with missing values of the potential confounding factors were excluded, 497 subjects with complete data were available for the analyses.

10The following variables were chosen a priori as clinically important predictors and included in the models: age, BMI, fasting blood glucose (FBG), cigarette smoking, use of cardiac medications, room temperature, season, and the lagged moving average of apparent temperature corresponding to the same moving average period for each air pollutant. MAP was also included because this changed the estimated effect of some air pollutants by more than 10%. To model the nonlinear association of apparent temperature with HRV, we used a cubic spline with 3 degrees of freedom (df).

11We estimated the percent change in each HRV parameter for 1 SD increase for each pollutant as [10(β × SD) – 1] × 100%, with 95% confidence intervals (CI) {10[SD × (β± 1.96 × SE)] – 1} × 100%, where βand SE are the estimated regression coefficient and its standard error. To test whether observed associations in single-pollutant models were robust to inclusion of another pollutant, two-pollutant models were fitted. To assess modifying effects of hypertension, IHD, diabetes or use of cardiac/antihypertensive medications, we ran separate regressions stratified by those variables and compared the percent changes of each pollutant. We also ran regression models including interaction terms.

12Table 1 shows the demographic and clinical characteristics and HRV measurements of the subjects. The study participants were all male, with average age of 72.7 years (SD = 6.6 years). Seventy-two participants had diabetes (14.5%) on the basis of previously mentioned criteria. Hypertension and IHD prevalences were 67.4 and 28.6%, respectively. People with hypertension were older, had higher levels of BMI and FBG, and were more likely to have IHD, have diabetes, and be taking hypertension medications.

13Air pollution and temperature levels are summarized in Table 2. The median time of ECG monitoring was 1000 hr. Levels of all pollutants except O3 decreased after peaking around 0700 hr. All air pollution concentrations during the study period were within the National Ambient Air Quality Standards.

14Of the air pollutants examined, only PM2.5 and O3 showed several significant associations with the HRV outcomes. Table 3 presents the estimated percent changes of HRV in single- and two-pollutant models for various lags of PM2.5 and O3. After adjusting for potential confounders, HF decreased by 13.2% (95% CI, –1.0% to 25.4%) and LF:HF ratio increased by 14.5% (95% CI, 2.9–27.5%) per SD (8.0 μg/m3) increase in the 24-hr moving average of PM2.5. We saw stronger associations with the 48-hr PM2.5: a 20.8% (95% CI, 4.6–34.2%) decrease in HF and an 18.6% (95% CI, 4.1–35.2%) increase in LF:HF ratio per 8.0-μg/m3 increase. We observed a reduction in LF of 11.5% (95% CI, 0.4–21.3%) associated with 1 SD (13 ppb) increment in the 4-hr O3, which was similar in magnitude but only marginally significant with a 24-hr average. In two-pollutant models, the magnitudes of the percent changes for both PM2.5 and O3 diminished slightly.

15We found no significant association of HRV with PN, NO2, SO2, and CO for any of the exposure averaging periods. For brevity and comparability, Table 4 presents the HRV associations using the averaging periods for gaseous pollutants that showed the strongest effect for O3 (4 hr), and the 48-hr averaging period for PN and BC to correspond with the strongest PM2.5 effects. An SD (0.47 μg/m3) elevation in 48-hr BC was associated with a 13.2% (95% CI, –1.1 to 29.6%) increase in the LF:HF ratio. The point estimates for associations between PN and BC, and HRV measures were negative, but gaseous pollutants (SO2 and CO) were positively related.

16We also conducted stratified analyses by IHD, hypertension, and diabetes status (Table 5). The associations of all HRV indices with PM2.5 and O3 were stronger in people with IHD. People with IHD showed 2-fold reductions of SDNN in relation to 48-hr PM2.5 compared with people without IHD. The interaction between 4-hr O3 and IHD was statistically significant for SDNN (p = 0.02 for the interaction term), HF (p = 0.01), and LF (p = 0.004). We also observed consistently stronger associations between all HRV indices and PM2.5 and O3 among people with hypertension. The associations observed in SDNN and LF with PM2.5 were stronger in people with diabetes, with almost 4-fold higher percent changes. However, diabetes did not modify the effect of O3 on HRV.

17We assessed whether each antihypertensive medication modified the effects of PM2.5 and O3 on HRV (Table 6). We found a significant interaction between use of calcium-channel blocker and PM2.5 for LF (p = 0.04). Moreover, subjects who were not taking a calcium-channel blocker showed larger reductions in SDNN and LF in relation to O3. In particular, the association of O3 exposure with reduced LF in the full cohort seems to be driven by the subjects not taking calcium-channel blockers, with a substantial (although imprecisely estimated) increase in LF associated with O3 exposure in subjects on the drug. As a result of this effect on LF as well as HF, a marginally significant association was seen between O3 and SDNN (total HRV) as well.

18We found no significant interaction between PM2.5 and O3, and use of beta-blocker or ACE inhibitor. However, the effect of both pollutants on LF was substantially reduced by beta-blocker drugs. In those taking beta-blockers, the decrease in HF was larger than that in LF in relation to PM2.5, compared with those who had never taken those medications. Thus, a larger increase in LF:HF ratio was observed in participants who were taking beta-blocker. However, the association with ACE inhibitors was opposite: There was a larger increase in LF:HF ratio associated with PM2.5 among those not taking that medication.

19This study is consistent with previous evidence that PM and O3 are associated with decreased HRV, particularly for PM and HF, a parasympathetic (vagal) modulation of the heart. The associations of HRV were strongest with the 48-hr moving averages of particles, but O3 had a shorter-term impact (4 hr and 24 hr). Furthermore, subjects with IHD and hypertension appeared to have larger reductions in HRV measures in relation to both PM2.5 and O3 exposures. People with diabetes had larger decreases of SDNN and LF associated with PM2.5. In addition, we found evidence for an association with BC, a marker of traffic particles.

20When we examined medications, calcium-channel blockers had the most profound effect on the pollution associations, particularly for O3. This modification was primarily on LF, suggesting that this drug is blocking effects of pollution on the sympathetic pathway. If anything, the parasympathetic response was enhanced in these subjects. As expected, beta-blockers seemed to reduce the LF response of both pollutants. By contrast, use of ACE inhibitors did not consistently or significantly modify pollution effects. Drug use patterns in these subgroups are related to underlying conditions, thus limiting the interpretability of these results. Nevertheless, they suggest that air pollution has the ability to affect both sympathetic and parasympathetic pathways. The sympathetic response seems mediated by pathways related to calcium flux into cells, whereas the parasympathetic response seems likely to be due to other mechanisms.

21Previous studies have consistently reported PM associations with decreased HRV in older adults (Table 7). Our results are consistent with those. For example, estimated decreases in HF resulting from an exposure to a PM2.5 increment of 10 μg/m3 in the previous studies were 24.1, 14.9, 19.3, and 5.1%. The last result is for a 10-μg/m3 increase in PM10 and is not directly comparable. We found a 16.2% reduction. Given the CIs (Table 7), these look fairly similar.

22Three studies also evaluated the effect of O3 on cardiac autonomic function, primarily HF. The difference in measuring times used in the studies preclude quantitative comparisons of results, but there was substantial variability. In the present study, O3-related decreases in HF ranged from 2.6 to 11.1% depending on choice of moving averages of O3, but all estimates were insignificant.

23Current knowledge about pathophysiologic mechanisms that connect air pollution exposure and alterations in the autonomic nervous system is limited. One plausible mechanism is that inhalation of PM causes oxidative stress directly or via acute pulmonary inflammation. Oxidative stress in the lungs seems to induce proinflammatory mediators, such as cytokines, to increase extracellular calcium influx possibly through activation of calcium channels in the plasma membrane, and to inactivate nitric oxide. These effects are considered to cause an increase in sympathetic and a reduction in vagal tone, which may be linked with cardiac events, such as ventricular arrhythmias and myocardial infarction. In general, we find air pollution associated with greater reductions in vagal tone than in sympathetic activity.

24A study from the Utah Valley (USA) found positive associations between PM10 and r-MSSD. Additionally, dogs exposed to concentrated ambient air particles showed significantly higher HF and LF compared with filtered air exposure. argued that too much elevation in parasympathetic stimulation may deteriorate cardiac status and result in a fatal bradyarrhythmia. A large follow-up study in Rotterdam, the Netherlands, found that elderly subjects in the highest quartile as well as the lowest quartile of SDNN had significantly increased risks for cardiac mortality, suggesting that in the elderly, alterations in HRV in either direction might be adverse.

25Few previous studies have evaluated modifiers of the air pollution–HRV association. Our results agree with two such studies, which showed larger decreases in HRV among people with hypertension. Although dysregulation of the autonomic nervous system plays a role in the pathogenesis of hypertension, the causal mechanism of modification by hypertension has not been discussed. Hypertension is associated with lower baseline HRV and endothelial dysfunction. Hypertensive people may have higher levels of oxidative-stress–induced inflammatory responses. These existing impairments may make hypertensive people less able to accommodate the additional oxidative stress related to air pollution exposure and therefore could explain the enhanced effect on HRV.

26We observed a larger reduction in HRV among people with diabetes compared with subjects without diabetes. Diabetes is known to be associated with low autonomic function, and has been reported to modify the association of PM with both hospital admissions and deaths. Several epidemiologic studies showed that LF power, which reflects mainly sympathetic modulation, was more influenced by diabetes than any other HRV index. The present study also showed that decreases in LF in relation to PM2.5 exposure were larger in people with diabetes than those in people without diabetes (−19.1 vs. −5.0%). Both diabetes and PM have been associated with oxidative-stress–induced inflammation and endothelial and autonomic dysfunctions. Therefore, susceptible individuals who have preexisting inflammation due to diabetes may be more responsive to airborne particles exposure.

27We found the strongest effects of PM2.5 and O3 in 48-hr and 4-hr moving averages, respectively. The rationale for the moving average model is that air pollution can lead to adverse health events occurring not only on the same day but also on several subsequent days. Hence, the response to an acute pollution exposure could be distributed over a number of days. Because hourly measured concentrations of air pollution were available, we could evaluate several lagged models with end times matched to each participant’s ECG measure, an improvement over traditional approaches using fixed calendar days. We found stronger particle pollutant associations in longer lagged models but stronger O3 associations in shorter ones.

28A potential limitation of this study is that we measured ECG once for each subject, so subject-specific variation of HRV measures may not be ruled out as a potential confounder. However, this variation would have to be correlated with air pollution levels for it to confound the observed associations. We collected information on many possible factors that would affect autonomic function, but the covariates included in the model may not cover all predictors of individual variations of HRV. A longitudinal design would provide for better adjustment of within-subject variation in the observed associations and allow examination of differences in baseline autonomic function over time.

29In this study, many potential confounding factors were included in the model. BMI; blood total cholesterol, high-density lipoprotein, and triglyceride levels; alcohol consumption; and respiratory and cardiovascular disease history did not confound the association between air pollution and HRV. We also measured the ECG at a stable temperature and adjusted for the temperature of the room where the ECG was taken, as well as for ambient meteorologic factors, including apparent temperature and season. Therefore, the observed associations are less likely to reflect bias due to the confounding factors.

30Although we did not conduct personal exposure monitoring during the time of the test, the monitoring site was relatively close (1 km) to the examination site. Moreover, evidence suggests that ambient measures of PM have relatively uniform spatial distribution across urban areas and the longitudinal correlation between daily changes in exposure and daily changes in ambient concentrations are high. Therefore, PM concentrations at the monitoring site should be a good surrogate of PM exposure.

31This study cohort consists of all males and almost all whites. Sex and race may be important determinants of HRV as well as modifiers of the association between air pollution and HRV, as was observed by.

32This population-based study suggests that short-term exposures to PM2.5 and O3 are predictors of alterations in cardiac autonomic function as measured by HRV among older adults. Persons with IHD, hypertension, and diabetes appear to be more susceptible to autonomic dysfunction related to PM2.5 exposure. The consistency of the effect modification observed in this and other studies strengthens evidence that these conditions mark susceptibility to air pollution exposure and provides new information to guide research on underlying biologic mechanisms.