Median values of percent change in outcome measure across temperature lags, for the daily lag(s) of PM2.5, ozone and AQHI exhibiting the strongest association with each measure and least sensitivity to temperature lag, are shown in Fig. 3. Pooled estimates combining 2013 and 2014 indicated that the AQHI was associated with a significant increase in heart rate (2.1%, 95% confidence interval [CI] 0.8%, 3.3% per interquartile range [IQR]), significant decreases in HRV measures (HF, −19.1%, 95% CI −29.1%, −7.7%; RMSSD, −9.5%, 95% CI −14.5%, −4.3%), as well as decreased RHI (−6.5%, 95% CI −10.1%, −2.7%). Significant positive associations with FeNO were observed in both 2013 and 2014, but there was significant heterogeneity in effect between years (P = 0.02). Significant associations were also observed of several measures with PM2.5 and ozone. The AQHI, ozone and PM2.5 exhibited consistent negative associations with daily PEFR and oxygen saturation, and positive associations with 8-hydroxy-2′-deoxyguanosine (8OHdG) while associations with other cardio-respiratory measures and oxidative stress markers were less consistent (see Figures, Supplemental Digital Content 3 and 4, http://links.lww.com/JOM/A330, which show associations with other cardio-respiratory measures, and urinary oxidative stress markers, respectively).
In sensitivity analyses, associations of the AQHI with HRV were generally reduced in magnitude and statistical significance when daily pre-exercise heart rate was included as a covariate. In some instances, associations of oxidative stress markers with the AQHI were somewhat larger in magnitude when non creatinine-corrected values were employed with creatinine as a covariate, but they remained non significant (not shown).
Changes Over Study Duration
Associations between cardio-respiratory measures and day of study were insensitive to lag of air pollution and temperature. Median values across lags of air pollution and temperature, expressed as percent of mean observed values of each outcome measure per 70 days (the study duration), are shown in Fig. 4. Significant improvements (opposite in direction to effects observed in association with air pollution) were observed over the course of the study for most measures. Pooled estimates of effects in 2013 and 2014 indicated a significant reduction in heart rate (−6.1%, 95% CI −8.4%, −3.8%), significant increases in HRV measures (HF, 31.3%, 95% CI 6.1%, 62.5%, RMSSD, 15.1%, 95% CI 4.8%, 26.3%), and RHI (11.9%, 95% CI 5.2%, 19.1%) over the course of the study. FeNO increased significantly over the study duration in both 2013 and 2014, but there was significant heterogeneity in effect between years (P < 0.0001). Significant positive associations were also observed between study duration and PEFR, systolic and diastolic blood pressure and oxygen saturation (see Figures, Supplemental Digital Content 5 and 6, http://links.lww.com/JOM/A330, which show associations with other cardio-respiratory measures, and urinary oxidative stress markers, respectively).
Few significant differences were observed between men and women in associations with HRV measures, FeNO, or RHI in 2013 or 2014 (see Tables, Supplemental Digital Content 7 and 8, http://links.lww.com/JOM/A330, which show differences in associations by sex in 2013 and 2014, respectively). In 2013, significant differences were observed in associations between the AQHI and HRV measures by statin use (see Table, Supplemental Digital Content 7, http://links.lww.com/JOM/A330, which shows differences in associations by statin use in 2013); associations were generally negative and significant among those not taking statins, and positive or null among statin users. Similarly, in 2013, significant positive associations were observed between study duration and HRV measures, FeNO and RHI in those not taking statins, while they were null in statin users, but the difference in effect was only significant for LF. Differences in effects by statin use were not consistently observed in 2014 (see Table, Supplemental Digital Content 8, http://links.lww.com/JOM/A330, which shows differences in associations by statin use in 2014).
We found significant associations between air pollution and subclinical adverse changes in cardio-respiratory physiological measures among older adults in a rural area characterized by moderate concentrations of regional pollutants—ozone and PM2.5—and low concentrations of traffic and industrial air pollutants. Associations were more consistent across multiple outcomes in 2013 compared with 2014, and in 2013 there were significant differences in observed associations between statin users and non-users. The reduced associations with air pollution observed in 2014 may be attributable to overall reduced time spent outdoors in 2014 due to poorer weather. Although air pollution concentrations and temperature were similar, there was more than twice as much rain between June 1 and August 31of 2014 compared with 2013 (166 mm vs. 71 mm). Associations of air pollution with subclinical adverse effects exhibited coherence among several measures, strengthening the likelihood of a causal association.
Subclinical cardiovascular measures represent responses to air pollution exposure which do not result in overt events such as myocardial infarction, heart failure, stroke or death, but provide evidence of possible pathophysiological mechanisms which could underlie these observable events.28 Reduced heart rate variability, for example, may indicate cardiovascular autonomic imbalance28 and provide information on future mortality risk independent of that provided by traditional risk factors,33 while impaired endothelial function may directly trigger cardiovascular events.28 Similarly, increased FeNO reflects local eosinophilic pulmonary inflammation,24 which may be associated with other pulmonary or extrapulmonary effects. Increased levels of systemic markers of inflammation or oxidative stress, or of vasoactive substances, could reflect pathways for diverse systemic adverse effects.19
Numerous previous panel studies have examined links between air pollution and cardio-respiratory measures among older adults (see Table, Supplemental Digital Content 9, http://links.lww.com/JOM/A330, which summarizes results from earlier studies). Our observations of a 2% increase in heart rate and 10% to 20% decrease in heart rate variability parameters in association with an interquartile range change in AQHI are consistent with the direction and magnitude of effect observed in elsewhere.1,4–11 However, other studies reported no or positive associations,2–3,12 and a recent review concluded that the totality of evidence did not support the existence of an association between PM2.5 and HRV.37 Several other studies also found that associations with air pollution occurred at short lag times (hours to less than 1 day).1,4–8,10,11 Our observation that associations of the AQHI with HRV were reduced in magnitude and statistical significance when daily pre-exercise heart rate was included as a covariate is not surprising in that heart rate (both pre- and post-exercise) exhibited a positive association with the AQHI. Heart rate has not been consistently included as a covariate in HRV analyses in other studies. Similar to our findings, one study reported that reduced HRV in response to air pollution was only observed in those not taking statins, and more specifically in those with the glutathione s-transferase M1 null genotype not taking statins.38 Increased FeNO in healthy adults has also been previously reported within 6 hours of exposure to coarse particles.24 Studies of O2 saturation have been inconsistent,13–15,21 but of those that have detected significant associations with air pollution, they have been small in magnitude, similar to our findings, at lags of 0 or 1 day.13,14 Previous studies have reported significant increases in MDA26 and 8-OHdG27 within days of air pollution exposure, similar to our results for 8-OHdG. Our findings from 2013 of an approximately 1 mm Hg increase in systolic and diastolic blood pressure per IQR increase in AQHI at lag 2 days are also consistent with the magnitude of effect observed in other studies.16–22 Lag times observed in these studies have varied from 0 to 5 days.
As an incidental finding, we also observed significant improvements in several outcomes over the duration of the study (in the opposite direction to associations with air pollution). These effects were somewhat more consistent between the 2 study years than effects of air pollutants. While the absence of a control group prevents us from conclusively attributing these effects to the daily regimen of light outdoor activity, the health benefits of physical activity are well-established39 and outdoor physical activity in particular may have additional mental health benefits compared with indoor activity.40 Previous studies have demonstrated improvements in cardio-respiratory physiological measures following aerobic training in older adults, but generally after longer training periods of up to 6 to 12 months. Study designs have included cross-sectional studies, prospective observational studies, and randomized controlled trials. Similar to our findings, effects have included reduced resting heart rate,41,42 increased HRV,41–43 increased endothelial function,44,45 and increased pulmonary flow and volume measures.24,46 Increased FeNO, similar to our findings, has also been reported.24,47 Small increases (less than 10 ppb or 20%) in FeNO would not be considered clinically important relative to criteria for individuals with chronic airway disease,48 but could nonetheless signify a subclinical negative impact of exercise training. Our findings of small increases in blood pressure over the duration of the study differ from results of a recent meta-analysis, which reported pooled estimates of decreases in systolic and diastolic blood pressure,49 although a small number of individual studies included in the meta-analysis also reported increases in blood pressure. Whether our findings could result from some other factor like calibration drift is not clear. Findings regarding markers of oxidative stress have been mixed. Consistent with our findings, reduced urine 8-OHdG50,51 has been previously reported in relation to exercise programs.
Strengths and Limitations
A key strength of our study is the relatively long duration and large sample size compared with most previous panel studies of air pollution, combined with daily measurements and prescribed daily activity. Although some previous studies were of similar duration to ours, none involved daily prescribed activity, and only one examined whether there was a trend in cardio-respiratory parameters over the duration of the study.47 To our knowledge, our study is also only the second of its kind conducted in a rural area.47 Conducting the study over two summers also allowed us to evaluate the consistency of results over two time periods, and examination of several cardio-respiratory physiological measures permitted us to evaluate coherence among a variety of effects.
We lacked personal monitoring data, but we deployed a dedicated monitor close to the site where weekly health measures were conducted, which would tend to reduce exposure measurement error with respect to weekly health measures. While greater error might be present with respect to daily measures when subjects were further from the monitoring site, the community is small and there are no major local pollution sources, suggesting that concentrations of PM2.5 and ozone would be expected to be relatively homogeneous over the study area. Pollutant concentrations measured at the study site were highly correlated with those at a government monitor 15 km away. We also lacked data on daily activity other than prescribed outdoor exercise undertaken as part of the study protocol. The repeated measures design has the advantage that subjects served as their own controls for the purpose of evaluating the impact of temporal changes in air pollution exposure. However, since we did not have a control group which did not engage in prescribed daily outdoor activity, we cannot conclusively attribute the incidental finding of improvements in several measures over time to a cardio-respiratory training effect. In particular, improvement in PEFR could represent improved technique rather than improved fitness. Daily physical activity and health measures at home were unsupervised, thus we have no objective data on the actual duration and intensity of outdoor activity. However, health measure data exhibited plausible distributions consistent with previous studies and mean post-exercise heart rate was greater than mean pre-exercise heart rate. We conducted numerous hypothesis tests which increases the probability of false positive findings, however coherence among multiple measures strengthens the likelihood that our findings reflect true associations.
Our findings of associations between the AQHI and subclinical adverse cardio-respiratory effects provide support for the applicability of the AQHI as a predictor of health effects in rural areas, even though the AQHI is based on the association between air pollution and mortality in large urban centres. Associations of air pollution with adverse effects exhibited coherence among several measures, strengthening the likelihood of a causal association. Differences in response by statin use were observed consistently in 2013 but not 2014 and require replication in other studies. Our findings suggest that older adults living in rural areas may benefit from reducing outdoor activity when air pollution levels are particularly high in order to reduce acute adverse cardio-respiratory effects. However, additional research is needed to determine at what values of the AQHI or pollutant concentrations outdoor activity should be avoided, and whether short term risks could be averted while preserving longer term benefits of outdoor physical activity. Future research is also needed to examine possible alternative pollutant weightings for the AQHI based on observed associations.
The authors are extremely grateful to the dedicated study participants, who generously gave of their time during the study, and to the conscientious field staff who collected the data: Mackenzie Clements, Adil Khan, Jenna Mason, Shirley Ngai, Tor Oiamo, Tawn Rellinger, Nandini Thogarapalli, and Chad Walker. The authors also thank Ryan Kulka, Hongyu You, and Keith Van Ryswyk for operating the air monitoring equipment during the study and providing the air quality data, and Gail Millar and Melanie Noullet for their advice on data collection procedures.
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