The association of annual air pollution exposure with ...

Science of the Total Environment 543 (2016) 61–66

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The association of annual air pollution exposure with blood pressure among patients with sleep-disordered breathing Wen-Te Liu a,b,c,d,e, Kang-Yun Lee a,f, Hsin-Chien Lee d,g, Hsiao-Chi Chuang a,b, Dean Wu g,h,i, Jer-Nan Juang c, Kai-Jen Chuang j,k,⁎ a

Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan d Sleep Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan e Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan f Department of Internal Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan g Department of Psychiatry, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan h Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan i Department of Neurology, School of Medicine, Taipei Medical University, Taipei, Taiwan j Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan k School of Public Health, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Annual exposure to air pollution was associated with blood pressure changes. • Annual air pollution-induced effect was associated with autonomic imbalance. • Patients with severe sleep apnea were sensitive to fine particle-induced effects.

a r t i c l e

i n f o

Article history: Received 21 July 2015 Received in revised form 27 October 2015 Accepted 27 October 2015 Available online 12 November 2015

a b s t r a c t While sleep-disordered breathing (SDB), high blood pressure (BP) and air pollution exposure have separately been associated with increased risk of cardiopulmonary mortality, the association linking air pollution exposure to BP among patients with sleep-disordered breathing is still unclear. We collected 3762 participants' data from the Taipei Medical University Hospital's Sleep Center and air pollution data from the Taiwan Environmental Protection Administration. Associations of 1-year mean criteria air pollutants [particulate matter with aerodynamic

Abbreviations: BP, Blood pressure; DBP, Diastolic blood pressure; NO2, Nitrogen dioxide; O3, Ozone; PM10, Particulate matter with aerodynamic diameters ≤10 μm; PM2.5, Particulate matter with aerodynamic diameters ≤2.5 μm; SBP, Systolic blood pressure; SDB, Sleep-disordered breathing. ⁎ Corresponding author at: Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wu-Hsing Street, Taipei 110, Taiwan. E-mail address: [email protected] (K.-J. Chuang).

http://dx.doi.org/10.1016/j.scitotenv.2015.10.135 0048-9697/© 2015 Elsevier B.V. All rights reserved.

62 Editor: D. Barcelo Keywords: Air pollution Blood pressure Apnea–hypopnea index Sleep-disordered breathing Epidemiology

W.-T. Liu et al. / Science of the Total Environment 543 (2016) 61–66

diameters ≤10 μm (PM10), particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5), nitrogen dioxide (NO2) and ozone (O3)] with systolic BP (SBP) and diastolic BP (DBP) were investigated by generalized additive models. After controlling for age, sex, body mass index (BMI), temperature and relative humidity, we observed that increases in air pollution levels were associated with decreased SBP and increased DBP. We also found that patients with apnea–hypopnea index (AHI) ≥30 showed a stronger BP response to increased levels of air pollution exposure than those with AHI b 30. Stronger effects of air pollution exposure on BP were found in overweight participants than in participants with normal BMI. We concluded that annual exposure to air pollution was associated with change of BP among patients with sleep-disordered breathing. The association between annual air pollution exposure and BP could be modified by AHI and BMI. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The association of short-term and long-term air pollution exposure with cardiovascular morbidity and mortality has been demonstrated in previous studies (Brook et al., 2010; Mustafic et al., 2012; Shah et al., 2013). Such epidemiological association has been linked to the effects of air pollution on elevated blood pressure (BP), a possible mechanism linking air pollution exposure to cardiovascular effects (Brook et al., 2010) in several panel studies (Chuang et al., 2005; Ibald-Mulli et al., 2001; Lin et al., 2009; Zanobetti et al., 2004). Sleep-disordered breathing (SDB) is a bunch of physiopathologic conditions that are characterized by an abnormal respiratory pattern during sleep, such as snoring, sleep apneas, etc. It affects as much as 17% of U.S. adults (Young et al., 1993), 59% of Taiwanese adults (Chuang et al., 2008) and may be more prevalent in Asian adults (Mirrakhimov et al., 2013). The severity of sleep apnea can be indicated by using the apnea–hypopnea index (AHI). The value of AHI is represented by the number of apnea and hypopnea events per hour of sleep and categorized as normal (0–4) or severe (≥30) (Ruehland et al., 2009). SDB has been reported to be associated with increased risk of autonomic dysfunction (Wang et al., 2008), increased BP (Peppard et al., 2000) and cardiovascular mortality (Yumino et al., 2009). It is biologically possible that patients with SDB might be susceptible to air pollution exposure. However, the association of air pollution exposure with BP in patients with SDB is still unclear. Therefore, we designed this study to investigate whether annual exposure to ambient air pollution was associated with BP change among patients with SDB. Moreover, we also examined our hypothesis that patients with severe sleep apnea had greater air pollution-induced effects on BP change compared to those with AHI in normal range.

and its related software (Somnologica, Medcare, Iceland) with standard techniques. Sleep stages and arousals were scored according to the AASM criteria (Ruehland et al., 2009). Respiratory efforts were measured by respiratory plethysmography, and arterial oxygen saturation was measured by pulse oximetry. Based on the polysomnography results, SDB was categorized as AHI 0–4 was normal, 5–14 was mild sleep apnea, 15–29 was moderate sleep apnea, and ≥30 was severe sleep apnea. Systolic BP (SBP) and diastolic BP (DBP) were calculated as the average of two seated blood pressure readings (taken about 1 min apart using a mercury sphygmomanometer) after polysomnography examination in the following morning. 2.3. Air pollution and weather data Eighteen fixed-site monitoring stations operated by the Taiwan Environmental Protection Administration (TEPA) throughout Taipei metropolitan (Taipei City and New Taipei City) measured hourly air pollution and weather data. Yearly concentrations of particulate matter with aerodynamic diameters ≤10 μm (PM10), particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2) and ozone (O3) were used to represent 3762 participants' annual air pollution exposure. Each participant can be assigned to one monitoring station which is the nearest one to his or her residence. All air pollution and weather data were matched with the measuring date of blood pressure. The data averaged over 365 days before the blood pressure measuring date were used to estimate yearly pollution effects on blood pressure. A map of the study area and distribution of the location of monitoring stations is presented in Fig. 1 (TEPA (Taiwan Environmental Protection Administration), 2015). 2.4. Statistical analyses

2. Materials and methods 2.1. Study design and participants This prospective, observational study was designed to monitor changes in BP and annual air pollution exposure in patients with SDB. Outpatients were enrolled from the sleep center of Taipei Medical University Hospital from November 2005 to November 2012. The exclusion criteria were an age younger than 20 years or older than 80, coronary artery disease, heart failure, diabetes mellitus, bronchiectasis, acute exacerbation within 1 month of the study or chronic respiratory failure. All patients underwent polysomnography at night to identify SDB and conducted BP measurement in the next day morning. Air pollution data from the Taiwan Environmental Protection Administration were used to measure patients' annual air pollution exposure. All patients received regular rehabilitation programs, and medical care did not change during the study period. The joint institutional review board at the Taipei Medical University in Taipei, Taiwan approved this study (TMU-JIRB No.: 201412036). 2.2. Polysomnography and blood pressure Polysomnography was performed on all participants by using a polysomnography digital system (Embla N7000, Medcare, Iceland)

We applied generalized additive models to investigate the relationship between air pollution and blood pressure (Hastie and Tibshirani, 1989) among 3762 participants. Previous studies have reported that yearly air pollution exposure is associated with cardiovascular effects (Chen and Schwartz, 2008; Chuang et al., 2011). Therefore, the exposure variables were 1-year mean PM10, PM2.5, NO2 and O3, and the outcome variables were SBP and DBP. Each regression model included age, sex and BMI. The models also adjusted for smooth function terms as fit by penalized cubic regression spline to reflect non-linear effects of continuous covariates, including yearly mean temperature with 6° of freedom and yearly mean relative humidity with 5° of freedom (Schwartz, 1994). We further conducted subgroup analysis by using AHI to split the data in to three groups which AHI were 0–4, 5–29 and ≥30. The model applied in subgroup analysis was the same model used in all participants. The sensitivity analyses were performed by excluding patients with unreasonable high AHI, BMI and air pollution exposure. Effect modification by AHI {≥30 (severe) vs. 0–4 (normal)} and BMI {N25 (overweight) vs. ≤ 25 (normal)} (WHO (World Health Organization), 2009) was assessed in a separate generalized additive mixed model by including interaction terms between PM2.5 effect and each potential effect modifier among all participants. Model selections were based on minimizing Akaike's Information Criterion (AIC).All


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Fig. 1. Map of the study area, distribution of the location of the monitoring stations in Taipei City and New Taipei City. For a more detailed description, please visit the website of the Taiwan Air Quality Monitoring Network (TAQMN) operated by the Taiwan Environmental Protection Administration (http://taqm.epa.gov.tw/taqm/en/default.aspx).

statistical analyses were performed using R Statistical Software, V.3.1.1 (R Development Core Team, 2008). 3. Results The participants recruited in this study were mostly female, overweight adults. The mean age of them was 46.7 years (standard deviation, SD = 13.3). Their mean BMI was 26.7 kg/m2 (SD = 5.2), and the male:female ratio was approximately 1:3. 39.2% of them had severe sleep apnea and 23.9% of them were normal. The mean (SD) values of SBP and DBP were 120.1 mmHg (17.2) and 81.5 (11.4), which were within the normal range (20) (Table 1). The summary of air pollution and weather data are shown in Table 2. The annual concentration of PM2.5 was not only higher than National Ambient Air Quality Standard in Taiwan (15 μg/m3) (21) but also in US (12 μg/m3) (22). The rest of air pollutants' annual levels were under air quality standard. The weather conditions were warm and humid. Table 3 lists the associations of SBP and DBP with yearly PM10, PM2.5, NO2 and O3 among all participants or the participants in the three different AHI groups estimated by generalized additive models. In general, the models showed that decrease in SBP was significantly associated with increased levels in all air pollutants while increase in DBP was mainly associated with increased levels in particles and NO2. Changes in BP were more strongly associated with NO2 and PM2.5 than with PM10 and O3. Moreover, the greatest effect of NO2 on DBP was observed among the participants in the group with AHI ≥ 30, whereas small increase in DBP with NO2 exposure was observed among the participants in the group with AHI 0-4. A similar pattern was found in the association between PM2.5 exposure and elevated DBP among the participants in the group with AHI ≥ 30 or AHI 0–4.

As shown in Table 4, we found effect modifications of NO2 and PM2.5 on elevated DBP by AHI and BMI. Participants with AHI ≥ 30 showed significant increases in DBP associated with air pollution exposure. In contrast, participants with AHI b 30 showed no significant change in DBP. We also found stronger effects of NO2 and PM2.5 on elevated DBP in overweight participants (BMI N 25) than in participants with a normal BMI. No effect modification of air pollution exposure on BP changes by sex was found in this study (data not shown).

Table 1 Basic characteristics of 3762 study participants. Variables Sex (no) Female Male Age (years) Mean Range Body mass index (kg/m2) Mean Range Systolic blood pressure (mm/Hg) Mean Range Diastolic blood pressure (mm/Hg) Mean Range Apnea–hypopnea index (no) 0–4 5–29 ≥30

No (%)/mean ± standard deviation 2734 (72.7) 1028 (27.3) 46.7 ± 13.3 20–80 26.7 ± 5.2 14.8–49.8 120.1 ± 17.2 77.0–205.0 81.5 ± 11.4 40.0–132.0 900 (23.9) 1387 (36.9) 1475 (39.2)

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W.-T. Liu et al. / Science of the Total Environment 543 (2016) 61–66 Table 2 Long-term air pollution exposure and weather conditions of 3762 study participants. Variables

Mean ± standard deviation

1-year mean PM10 (μg/m3) Mean Range 1-year mean PM2.5 (μg/m3) Mean Range 1-year mean NO2 (ppb) Mean Range 1-year mean O3 (ppb) Mean Range 1-year mean temperature (°C) Mean Range 1-year mean humidity (%) Mean Range

Apnea–hypopnea index ≥30 Yes No P-value for interaction Body mass index N25 Yes No P-value for interaction

48.3 ± 2.0 37.6–52.2 27.1 ± 1.2 22.3–30.5 21.2 ± 1.1 18.0–23.7

PM2.5

NO2

0.49 (0.01, 0.97) −0.35 (−0.94, 0.24) 0.03

0.58 (0.03, 1.14) −0.27 (−0.88, 0.34) 0.02

0.57 (0.12, 1.02) 0.04 (−0.36, 0.40) 0.04

0.73 (0.24, 1.22) −0.10 (−0.46, 0.26) 0.03

NO2, nitrogen dioxide; PM2.5, particles with aerodynamic diameters less than 2.5 μm. All models were adjusted for sex, age, body mass index, temperature, relative humidity.

27.0 ± 1.0 24.3–28.7 23.3 ± 0.2 22.7–23.7 74.2 ± 0.8 72.4–76.1

NO2, nitrogen dioxide; O3, ozone; PM10, particles with aerodynamic diameters less than 2.5 μm; PM2.5, particles with aerodynamic diameters less than 2.5 μm.

4. Discussion The present study is the first one to demonstrate the association between annual air pollution exposure and BP changes among patients with SDB. Although increased level of air pollution was associated with elevated DBP, opposed effect of air pollution exposure on SBP was observed in this study. Moreover, estimated effects of air pollution on BP were stronger in overweight subjects and subjects with severe sleep apnea. The association between short-term air pollution exposure and BP changes has been demonstrated in several epidemiological studies (Chen et al., 2012; Chuang et al., 2005; Hoffmann et al., 2012; Ibald-Mulli et al., 2001; Lin et al., 2009; Zanobetti et al., 2004) and controlled studies (Brook et al., 2002; Gong et al., 2003; Törnqvist et al., 2007). Only a few studies explored the association of annual air pollution exposure with BP changes. The observed increase in DBP with annual air pollution exposure in the present study is consistent with previous findings among human subjects. Dong et al. (2013) investigated the association between residential long-term exposure to air Table 3 Beta coefficients (95% confidence interval) for increases in long-term air pollution exposure with changes in blood pressure among patients with sleep-disordered breathing.

All study participants 1-year mean PM10 (μg/m3) 1-year mean PM2.5 (μg/m3) 1-year mean NO2 (ppb) 1-year mean O3 (ppb) Apnea–hypopnea index 0–4 1-year mean PM10 (μg/m3) 1-year mean PM2.5 (μg/m3) 1-year mean NO2 (ppb) 1-year mean O3 (ppb) Apnea–hypopnea index 5–29 1-year mean PM10 (μg/m3) 1-year mean PM2.5 (μg/m3) 1-year mean NO2 (ppb) 1-year mean O3 (ppb) Apnea–hypopnea index ≥30 1-year mean PM10 (μg/m3) 1-year mean PM2.5 (μg/m3) 1-year mean NO2 (ppb) 1-year mean O3 (ppb)

Table 4 Beta coefficients (95% confidence interval) for effect modification of the association between diastolic blood pressure and 1-year mean air pollution exposure.

Systolic blood pressure

Diastolic blood pressure

−1.47 (−1.76, −1.19)⁎ −2.39 (−2.81, −1.97)⁎ −2.43 (−2.90, −1.96)⁎ −1.54 (−2.11, −0.98)⁎

0.21 (0.01, 0.41)⁎ 0.37 (0.08, 0.67)⁎ 0.45 (0.12, 0.78)⁎ 0.27 (−0.12, 0.66)

−2.10 (−2.64, −1.57)⁎ −3.33 (−4.11, −2.56)⁎ −3.28 (−4.11, −2.46)⁎ −1.92 (−2.96, −0.87)⁎

−0.41 (−0.80, −0.02)⁎ −0.32 (−0.89, 0.26) −0.33 (−0.94, 0.28) −0.10 (−0.85, 0.65)

−1.56 (−2.04, −1.09)⁎ −2.86 (−3.55, −2.17)⁎ −2.91 (−3.68, −2.13)⁎ −1.20 (−2.12, −0.27)⁎

0.23 (−0.09, 0.54) 0.27 (−0.19, 0.72) 0.31 (−0.21, 0.82) 0.70 (0.10, 1.30)⁎

−1.17 (−1.63, −0.71)⁎ −1.75 (−2.44, −1.05)⁎ −1.85 (−2.65, −1.05)⁎ −1.54 (−2.48, −0.61)⁎

0.38 (0.06, 0.69)⁎ 0.52 (0.04, 1.00)⁎ 0.66 (0.11, 1.22)⁎ 0.19 (−0.46, 0.84)

All models were adjusted for sex, age, body mass index, temperature, relative humidity. ⁎ p-value b 0.05.

pollution and blood pressure in 24,845 Chinese adults and found DBP increased by 0.32 mmHg (95% CI, 0.08–0.56) per 19 μg/m3 interquartile increase in PM10 and 0.37 mmHg (95% CI, 0.14–0.61) per 22 μg/m3 interquartile increase in O3. Foraster et al. (2014) analyzed cohort data from 1926 participants in Girona, Spain and found the marginal significant association between annual average outdoor NO2 and increased DBP (beta coefficient = 0.56; 95% confidence interval: − 0.03, 1.14). Chen et al. (2015) analyzed cross-sectional data from 27,752 Taipei City residents over 65 years of age and found one-year exposures to PM10, PM2.5 absorbance, and NO2 were associated with higher DBP, with estimates 0.73 [95% confidence interval (CI): 0.44, 1.03], 0.62 (95% CI: 0.24, 0.99), and 0.65 (95% CI: 0.44, 0.85) mmHg for PM10 (10 μg/m3), PM2.5 absorbance (10−5m−1), and NO2 (10 μg/m3), respectively. Our previous study also reported the association of increased DBP with annual average air pollutants among 1023 elderly in Taipei, Taiwan. The estimated increases in DBP were 14.87 mmHg (95% CI, 12.73–17.02) per 48 μg/m3 interquartile increase in PM10, 31.29 mmHg (95% CI, 25.43– 37.14) per 20.42 μg/m3 interquartile increase in PM2.5, and 12.43 mmHg (95% CI, 10.63–14.23) per 12.83 μg/m3 interquartile increase in NO2 (Chuang et al., 2011). Taken together, the effect magnitudes of air pollutants on DBP among these studies are similar and comparable to our study findings. Our study results further confirm previous epidemiological evidence for chronic, air pollution-induced effects on BP changes and support the views of the American Heart Association's expert panel on biological mechanisms of air pollution-induced effects on BP through autonomic imbalance and endothelial dysfunction (Brook et al., 2010). We found opposing BP responses with annual air pollution exposure, with decreases in SBP and increases in DBP. Although the potential biological mechanisms that mediate these responses remain unclear, such findings may imply the complicated physiologic balance of hemodynamic systems in charge of BP regulation in response to air pollution exposure. A decrease in SBP may reflect a decrease in cardiac contractility. Previous studies have reported the association between air pollution exposure and increase in the square root of the mean of squared differences between adjacent NN intervals (r-MSSD) or high-frequency power, the parameters influenced by parasympathetic nervous system (Peretz et al., 2008; Fakhri et al., 2009; Davoodi et al., 2010). A shift of autonomic nervous system (ANS) to increased parasympathetic nervous system may reduce cardiac contractility, heart rate and then blood pressure. Our findings suggest that patients with severe sleep apnea may be susceptible to air pollution-associated BP changes. Previous studies have demonstrated that patients with cardiopulmonary diseases are at an increased risk of morbidity (Zanobetti et al., 2000) and mortality (Bateson and Schwartz, 2004; Sunyer et al., 2000) associated with air pollution exposure. To our best knowledge, no study explore whether patients with severe sleep apnea are a risk group for air pollutioninduced BP changes. Previous studies have reported that SDB is a risk factor for autonomic dysfunction (Wang et al., 2008), increased BP (Peppard et al., 2000) and cardiovascular morbidity and mortality (Jordan et al., 2014; Yumino et al., 2009). Air pollution, especially particulate air pollutant has been demonstrated to translocate from the nose


up the olfactory nerve into the brain (Elder et al., 2006), induce brain inflammation (Campbell et al., 2005) and neurotransmitter changes (Kleinman et al., 2008), and then influence sleep quality. Particulate air pollution has also been reported to interact with lung receptors or nerves, influence systemic ANS balance and heart rhythm and then result in BP changes (Brook et al., 2010). Therefore, patients with sleep apnea are vulnerable to air pollution is biologically plausible. Our study also found that the effect of air pollution was modified by BMI. The results showed that having a BMI over 25 may put subjects at higher risk of air pollution-induced BP changes than subjects with normal BMI. Such findings are consistent with previous studies showing that people with abnormal BMIs may be vulnerable to air pollution exposure (Chahine et al., 2007; Schikowski et al., 2013). We recognize that our study has a major limitation. Exposure misclassification and measurement error may bias our findings toward either positive or null results due to the lack of information regarding household air pollution exposure, the number of air monitoring stations, air conditioning status, environmental tobacco smoke and subjects' daily activity patterns. Furthermore, we only adjusted sex, age and BMI for individual-level confounders. Therefore, we could not rule out the possibility of unmeasured confounding due to various physical and psychosocial stressors associated with both cardiovascular risks and air pollution levels such as socio-economic status, physical activity, diet, alcohol consumption, complete smoking status, environmental tobacco smoke exposure, noise exposure, diabetes, medication and family history of cardiovascular diseases. 5. Conclusion We found changes in BP after annual air pollution exposure that resulted from both decreases in SBP and increases in DBP suggesting that annual air pollution exposure might decrease stroke volume by decreasing cardiac contractility or increasing vascular resistance. AHI and BMI could modify the effect of air pollution exposure on increased DBP implying that annual air pollution mediated hemodynamic changes might be important for vulnerable subjects, especially those were overweight and with SDB. Although the observed BP changes after annual air pollution exposure are small, the subsequent autonomic system imbalance, vasomotor dysfunction, reduced cardiac flow and increased risk of cardiovascular diseases among general and vulnerable population may be considerable. Future studies are needed to clarify the possible mechanisms between the air pollutants exposure and the regulation of BP changes. Acknowledgments This study was supported by grants (MOST 103-2621-M-038-001 and 104TMU-SHH-26) from the Ministry of Science and Technology of Taiwan and Taipei Medical University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Bateson, T.F., Schwartz, J., 2004. Who is sensitive to the effects of particulate air pollution on mortality? A case-crossover analysis of effect modifiers. Epidemiology 15, 143–149. Brook, R.D., Rajagopalan, S., Pope III, C.A., Brook, J.R., Bhatnagar, A., Diez-Roux, A.V., et al., 2010. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation 121, 2331–2378. Brook, R.D., Brook, J.R., Urch, B., Vincent, R., Rajagopalan, S., Silverman, F., 2002. Inhalation of fine particulate air pollution and ozone causes acute arterial vasoconstriction in healthy adults. Circulation 105, 1534–1536. Campbell, A., Oldham, M., Becaria, A., Bondy, S.C., Meacher, D., Sioutas, C., et al., 2005. Particulate matter in polluted air may increase biomarkers of inflammation in mouse brain. Neurotoxicology 26, 133–140.

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