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Fine particulate matter constituents and blood pressure in patients with chronic obstructive pulmonary disease: A panel study in Shanghai, China
Environmental Research 159 (2017) 291–296
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Fine particulate matter constituents and blood pressure in patients with chronic obstructive pulmonary disease: A panel study in Shanghai, China
MARK
Zhijing Lina,1, Yue Niua,1, Renjie Chena, Wenxi Xub, Huichu Lia, Cong Liua, Jing Caia, ⁎ ⁎⁎ Zhuohui Zhaoa, Haidong Kana,c, , Liping Qiaod, a School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China b Huangpu District Center for Disease Control and Prevention, Shanghai 200023, China c Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Parenthood Research, Institute of Reproduction and Development, Fudan University, Shanghai 200032, China d State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Fine particulate matter Chemical constituent Blood pressure Panel study
Objective: The evidence is limited about the potentially different health effects of various chemical constituents of fine particulate matter (PM2.5). We thus assessed the acute effects of various chemical constituents of PM2.5 on blood pressure (BP). Methods: We performed a longitudinal panel study with six repeated visits in 28 urban residents with chronic obstructive pulmonary disease in Shanghai, China from May to July, 2014. Twelve (43%) of them took antihypertensive medications. We measured resting BP by using a mercury sphygmomanometer and monitored realtime concentrations of PM2.5 constituents at a nearby site. Based on the linear mixed-effects model, we evaluated the effects of 10 major constituents in PM2.5 on BP, using a single-constituent model and a constituent-residual model after accounting for the multicollinearity. Results: We obtained a total of 168 pairs of effective BP measurements during the study period. There are moderate or high correlations among various PM2.5 constituents. An interquartile range increase of PM2.5 (19.1 μg/m3) was associated with increments of 1.90 mmHg [95% confidence interval (CI): 0.66, 3.13] in systolic BP, 0.68 mmHg (95%CI: −0.02, 1.37) in diastolic BP and 1.23 mmHg (95%CI: 0.19, 2.29) in pulse pressure. Some constituents of PM2.5, including organic carbon, elemental carbon, nitrate and ammonium, were robustly associated with elevated BP after controlling for total PM2.5 mass and accounting for multi-collinearity. Two constituents (magnesium and calcium) were associated with decreased BP. Conclusions: Organic carbon, elemental carbon, nitrate and ammonium may be mainly responsible for elevated BP from a short-term exposure to PM2.5.
1. Introduction Ambient particulate matter (PM2.5) air pollution is a main contributing risk factor for global disease burden, and it has been causally related to morbidity and mortality from cardiovascular diseases (CVD) (Lim et al., 2012). Potential mechanisms underlying these associations may include altered autonomic function, oxidative stress and inflammation, vasomotor dysfunction, and/or the release of
hemodynamically active mediators into systemic circulation (Brook et al., 2010; Giorgini et al., 2016). High blood pressure (BP) is another contributing risk factor for global disease burden and is also a strong risk factor for the development of CVD (Danaei et al., 2014; Lim et al., 2012). An increasing number of human studies have attempted to elucidate the association between particulate air pollution and BP, but the results were controversial. For example, some studies found a significant
⁎ Corresponding author at: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. ⁎⁎ Corresponding author at: State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, 508 Qingzhou Road, Shanghai 200233, China. E-mail addresses: [email protected] (H. Kan), [email protected] (L. Qiao). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.envres.2017.08.024 Received 20 April 2017; Received in revised form 3 August 2017; Accepted 11 August 2017 0013-9351/ © 2017 Elsevier Inc. All rights reserved.
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elemental carbon (EC) in PM2.5 were measured by a semi-continuous OC/EC analyzer (model RT-4, Sunset Laboratory Inc.). The concentrations of eight major water-soluble inorganic ions in PM2.5, including chlorine (Cl−), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), sodium (Na+), potassium (K+), magnesium (Mg2+), and calcium (Ca2+) were measured by a commercial instrument for online Monitoring of Aerosols and Gases (MARGA, model ADI 2080, Applikon Analytical B.V.). The Quality Assurance/Quality Control procedures, including maintenance and cleaning for this instrument as well as calibrations for air flow rate, mass foil, and temperature/pressure, were conducted according to the Technical Guideline of Automatic Stations of Ambient Air Quality in Shanghai based on the national specification HJ/T193–2005. To allow for the adjustment of weather conditions, we collected daily mean temperature and mean relative humidity from a meteorological station (Xujiahui station) of the Shanghai Meteorological Bureau. To allow for the adjustment of simultaneous exposures to gaseous pollutants, daily concentrations of ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were collected from a nearby fixed-site monitor. Daily 24-h mean concentrations for SO2, NO2 and CO, and maximum 8-h mean concentration of O3 were averaged from a monitoring station (Luwan station), which was located in the urban area of Shanghai.
association (Brook et al., 2011a; Delfino et al., 2010; Hoffmann et al., 2012; Jacobs et al., 2012), but others showed inverse (S.Y. Chen et al., 2012; Harrabi et al., 2006) or non-significant associations (Bilenko et al., 2015; Brook et al., 2011b; Madsen and Nafstad, 2006). There is a relative lack of studies examining the PM2.5-BP associations in developing countries with high particulate air pollution levels. The effects of PM2.5 on BP also constitute one of the key biological pathways whereby particulate air pollution adversely affects the cardiovascular system (Mustafic et al., 2012). Furthermore, PM2.5 is mainly composed of carbonaceous components and water-soluble ions, which may have different effects on the cardiovascular system (Brook et al., 2010). Thus, it is crucial to determine which constituents dominate the effects of PM2.5 on BP, but the evidence is scarce or limited to a small fraction of constituents (Mordukhovich et al., 2009; Urch et al., 2005; Wu et al., 2013). Shanghai, as the largest city in China, is now facing serious air pollution problems and a heavy burden of CVDs. Therefore, we conducted a longitudinal panel study in Shanghai, China, to evaluate the acute effects of various PM2.5 chemical constituents on BP. Chronic obstructive pulmonary disease (COPD) is a disease state characterized by persistent airflow limitation that is not fully reversible (Vestbo et al., 2013). COPD patients were selected because their cardiovascular system is hypothesized to be vulnerable to the adverse effects of PM2.5 (Giorgini et al., 2016; Vestbo et al., 2013).
2.3. Blood pressure 2. Methods After sitting in a quiet room for at least 5 min, participants had their left upper arm resting BP measured by professional technicians using a mercury sphygmomanometer (Yuwell type A) with a cuff-size adjustment based on the air circumference. Three consecutive measurements, separated by at least 2 min between each, were taken and the mean of the 2nd and 3rd readings was used for data analysis. If the differences of the 2nd and 3rd measurements were > 5 mmHg, a new round of measurements was arranged until the differences were within 5 mmHg (Houston and Harper, 2008). We evaluated systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP, the difference of SBP and DBP).
2.1. Design and population The study was carried out in Shanghai, China, from May 27 to July 5, 2014. Details on the subject recruitment and study design were described in our previous publication (Chen et al., 2015). Briefly, we initially recruited 30 nonsmoking patients with mild-to-moderate COPD according to the classifications of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) (Vestbo et al., 2013). The diagnoses for all COPD patients were confirmed by physicians before they were enrolled in the current study. During the study period, two patients quit the follow-ups because of an exacerbation of COPD symptoms. To reduce the inherent seasonal variations of BP, six rounds of weekly follow-ups for 28 subjects were scheduled. To control for possible circadian rhythm, day-of-week and subjective effects, BP measurements for each subject were scheduled at the same time (1:30 p.m. to 2:30 p.m.) on the same day of the week (Tuesday, Thursday, and Saturday) by the same professional technician. Data on individual and medical characteristics, including age, gender, height, weight, education attainment, income, medication use, duration of COPD, and chronic comorbidities, were collected by self-administered questionnaires at enrollment. Besides, study subjects were asked to record any use of medications, acute exacerbation of COPD, and whether they went out of the central urban areas during the study period. The study was approved by the Institutional Review Board of School of Public Health, Fudan University, and informed written consents were provided by all participants at enrollment.
2.4. Statistical analysis Environmental and health data were linked by the time (hour) of physical examinations. We used the linear mixed-effect (LME) model to assess the associations between PM2.5 constituents and BP (Chen et al., 2015; Zhao et al., 2015), which has the advantage of accounting for within-subject correlations due to repeated measurements by simply including a random intercept for each subject. The measurements of BP indicators followed approximate normal distributions and were considered response variables one at a time. PM2.5 or one of its constituents was introduced as a fixed-effect independent variable and a randomeffect intercept was introduced for each subject. We also included several covariates in the LME model: (1) individual characteristics, including age, gender, body mass index, educational attainment, duration of COPD and chronic comorbidities, to account for betweensubject variability; (2) weather conditions, including temperature and relative humidity, to adjust for their potential confounding effects; and (3) an indicator variable of “day of the week” to adjust for temporal variation and traffic load (Chen et al., 2015; Cosselman et al., 2012). To explore the lag structure in the acute effects of PM2.5, we examined this model by using multiple periods preceding BP measurements, that is, single-day lags of 0–24 h (0 d), 25–48 h (1 d), 2 d, 3 d, 4 d, and 5 d (Hoffmann et al., 2012). The daily concentrations of PM2.5 and its components (lag 0, lag 1, etc.) were calculated as 24-h averages. The lag interval with the largest effect estimate was introduced into constituent-specific analyses. In addition to the aforementioned single-constituent LME model, we also established a constituent-residual model to assess the robustness in
2.2. Environmental data Real-time concentrations of PM2.5 and its chemical constituents were measured by a fixed-site monitor located at the Shanghai Academy of Environmental Sciences, which was about 4 km away from the center of the community where the subjects resided. It is a representative urban monitoring site that was mostly surrounded by commercial properties and residential dwellings. The details of principles and operation of the instrument have been described elsewhere (Du et al., 2011; Wang et al., 2013). In brief, the mass concentration of PM2.5 was measured by an online particulate monitor (FH 62 C14 series, Thermo Fisher Scientific Inc.). Organic carbon (OC) and 292
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the effect estimation of a constituent (Chen et al., 2015; Wu et al., 2013, 2012). To be specific, we replaced the constituent with its residual, which was obtained by establishing a linear regression model between total PM2.5 and this constituent. This can be considered a crude measure of the “independent” contribution of a constituent to the observed effects of PM2.5 after accounting for multi-collinearity (Urch et al., 2005). To test the robustness of our results to the adjustment for the simultaneous exposure to gaseous pollutants, we conducted a sensitivity analysis by including O3, SO2, NO2, and CO in single-constituent models one at a time (i.e., two-pollutant models). We also conducted stratified analyses by gender status and the prevalence of hypertension. We further tested the statistical significance of differences between effect estimates of the strata of a potential effect modifier (for example, the difference between males and females) by calculating the 95% confidence interval (95% CI) as
Table 1 Descriptive statistics on blood pressure and the 24-h averaging environmental variables during the study.a
BP, mmHg SBP DBP PP PM2.5, μg/m3 Total mass ClNO3SO42Na+ NH4+ K+ Mg2+ Ca2+ OC EC Gaseous pollutant, μg/m3 O3 SO2 NO2 CO (mg/m3) Weatherb Temperature, °C Relative humidity, %
2 2 Qˆ 1 − Qˆ 2± 1.96 SEˆ1 + SEˆ2 ,
Where Qˆ 1 and Qˆ 2 are the effect estimates in the 2 strata and SEˆ1 and SEˆ2 are their respective standard errors (R. Chen et al., 2012). All models were conducted in R software with the “lme4” package (Version 3.3.1, R Foundation for Statistical Computing, Vienna, Austria). The effect estimates were expressed as the absolute changes in BP [in millimeters mercury (mmHg)] with their 95% CIs in association with each interquartile range (IQR) increase in PM2.5 constituent concentrations. All statistical tests were two-sided with a significant level at p < 0.05.
Mean
SD
Min
Median
Max
IQR
123 74 49
15.2 7.6 11.5
91 58 27
120 74 49
170 100 96
21.5 11.0 16.0
38.4 0.70 9.6 12.2 0.06 6.1 0.15 0.21 1.6 6.9 1.8
20.8 0.35 5.4 6.5 0.11 3.2 0.24 0.13 1.1 2.9 0.7
10. 6 0.11 2.4 2.8 0.00 0.9 0.00 0.08 0.6 3.8 0.8
34.1 0.66 8.7 10.7 0.05 5.4 0.08 0.19 1.2 5.6 1.6
105.1 1.34 25.0 34.3 0.43 15.1 0.95 0.50 4.7 13.3 3.3
19.1 0.54 5.4 6.1 0.06 4.3 0.21 0.14 0.51 4.5 0.8
79.2 7.1 46.6 0.8
27.4 4.2 12.9 0.2
29.4 3.2 27.6 0.5
74.8 5.1 42.4 0.8
139.5 16.9 70.9 1.2
35.8 4.7 22.7 0.2
23.8 71.7
1.6 12.7
21.5 43.0
23.3 72.0
27.2 90.0
2.2 19.0
a Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, difference between the average systolic blood pressure and diastolic blood pressure values; PM2.5, particulate matter with an aerodynamic diameter less than 2.5 μm; Cl-, chloride; NO3-, nitrate; SO42-, sulfate; Na+, sodium; NH4+, ammonium; K+, potassium; Mg2+, magnesium; Ca2+, calcium; OC, organic carbon; EC, elemental carbon; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; SD, standard deviation; IQR, interquartile range. b Data are presented as the average of weather conditions on the present day and previous 3 days.
3. Results 3.1. Descriptive statistics The mean age of the subjects (n = 28) was 64 years and the mean body mass index was 24.7 kg/m2. Females accounted for nearly 78.6%. Table 1 summarizes the descriptive statistics on BP, PM2.5 constituents and weather conditions. Overall, we obtained 168 pairs of effective BP measurements during the study period with six effective measurements per subject. The average SBP was 123 mmHg, and the average DBP was 74 mmHg, suggesting that the majority of the subjects had adequate BP levels. Results from self-administered questionnaires showed that none of them participated in strenuous physical activity and took anti-COPD medications 3 days before scheduled physical examinations. During our study period, none of them went out of the central urban areas of Shanghai. According to self-administered questionnaires, 12 patients had the comorbidity of hypertension and they all had a stable and regular intake of hypotensors. During the study period, the 24 h-mean concentrations of PM2.5 varied from 10.6 μg/m3 to 105.1 μg/m3 with a mean of 38.4 μg/m3. There are no missing hourly data for PM2.5 but a small fraction (about 5%) of missing data for constituents. For PM2.5 constituents, SO42- accounts for the largest proportion of PM2.5 (32% on average), followed by NO3- (25%), OC (18%) and NH4+ (16%). Generally, there are moderate or high correlations among various PM2.5 constituents, and weak or moderate correlations between PM2.5 constituents and gaseous pollutants (see Table S1 in Supplementary material). The 24 h-mean concentrations of gaseous pollutants were 79.2 μg/m3 for O3 (maximal 8-h), 7.1 μg/m3 for SO2, 46.6 μg/m3 for NO2 and 0.8 mg/m3 for CO.
(lags of 0–24 h), and were substantially attenuated, and even became negative and statistically insignificant at longer lags. The association between PM2.5 and DBP was marginally insignificant. An IQR increase in PM2.5 concentrations (lag 0 day) corresponded to increments of 1.90 mmHg (95%CI: 0.66, 3.13), 0.68 mmHg (95%CI: −0.02, 1.37) and 1.23 mmHg (95%CI: 0.19, 2.29) in SBP, DBP and PP, respectively. The lag patterns in the constituent-specific associations were similar (data not shown). Then, the exposure at lag 0 day was used in further analyses. Fig. 2 illustrates the changes in SBP associated with an IQR increase in various PM2.5 constituents. In the single-constituent model, all constituents other than Mg2+ were significantly associated with higher SBP. Notably, the effects of EC and NO3- remained robust in the constituent-residual model with an IQR increase leading to increments of 4.70 mmHg (95%CI: 0.77, 8.63) and 2.93 mmHg (95%CI: 0.08, 5.78) in SBP. The associations of all PM2.5 constituents other than Cl- and Mg2+ with DBP were marginally significant or insignificant in the singleconstituent model (see Fig. 3). OC and EC were significantly associated with higher DBP in the constituent-residual model with corresponding increments equaling to 3.08 mmHg (95%CI: 0.14, 6.01) and 2.96 mmHg (95%CI: 0.62, 5.31). As presented in Fig. 4, the associations between all PM2.5 constituents (other than Mg2+, Na+, K+, and Ca2+) and PP were marginally significant or insignificant in the single-constituent model. There were no significant associations in the constituent-PM2.5 model. NO3- and NH4+ were significantly associated with increases of 2.85 mmHg (95%CI: 0.32, 5.39) and 3.72 mmHg (95%CI: 0.22, 7.22) in PP, respectively in the constituent-residual model. However, based on constituent-residual models, we found negative associations between magnesium and calcium and BP. In the sensitivity analyses, the adjustment for SO2, NO2, and CO decreased many estimates of PM2.5 constituents whereas increased some estimates; the adjustment for O3 did not substantially change the
3.2. Regression results Fig. 1 presents the lag patterns in the effects of PM2.5 on SBP, DBP and PP. We reported results by using the averaged concentrations over lag 0–3 days due to insignificant associations with BP in longer lag periods. There are moderate correlations among different lags of PM2.5 (see Table S2 in Supplementary material). The associations between PM2.5 and SBP and PP were statistically significant only at lag 0 day 293
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Fig. 1. The lag structure of changes in SBP, DBP and PP associated with an IQR increase in PM2.5. Data are presented as point estimates and their 95% CIs. Definition of abbreviations as in Table 1.
Fig. 3. Changes in DBP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
Fig. 2. Changes in SBP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
(19.1 μg/m3) in 24-h mean PM2.5 (total mass) was significantly associated with increments of 1.90 mmHg in SBP and 1.23 mmHg in PP. Similarly, a panel study among healthy young adults in Beijing, China reported that an IQR increase (51.2 μg/m3) in PM2.5 during the preceding day was associated with a 1.08-mmHg increase in SBP and a 0.96-mmHg increase in DBP (Wu et al., 2013). Jacobs et al. estimated a larger increase of PP (4.0 mmHg) corresponding to an IQR increase (20.8 μg/m³) in 24-h mean PM2.5 among a group of elderly people (Jacobs et al., 2012). Hoffmann et al. estimated a 1.4-mmHg increase in SBP per IQR increase of PM2.5 (3.54 μg/m3) in the previous 5 days in a panel of Type 2 diabetes mellitus patients (Hoffmann et al., 2012). However, there were still other studies that found non-significant or null associations (Harrabi et al., 2006; Zhang et al., 2016). The heterogeneity in these studies could be partly explained by the differences in study populations (healthy subjects vs. high risk patients; young vs. elderly), lags of exposure 0–5 days), characteristics of PM2.5 mixtures, and the methods to measure BP (clinic, home and 24-h ambulatory monitoring; automatic or mercurial sphygmomanometers; single or multiple measurements; left or right upper arms). Several PM2.5 constituents, including OC, EC, nitrate, and ammonium, had robust and positive associations with SBP, DBP, or PP in
estimates for each constituent (see Fig. S1-S3 in Supplementary material). Besides, the associations between PM2.5 and BP parameters were significant in females but not in males, although the between-gender difference was not statistically significant (see Table S3 in Supplementary material). The associations of PM2.5 with BP parameters were in similar magnitude between subgroups with and without hypertension (see Table S3 in Supplementary material). 4. Discussion The present longitudinal panel study provided a relatively comprehensive analysis of the acute effects of various PM2.5 chemical constituents on BP in a context of high PM2.5 pollution levels. The findings suggested that 24-h mean exposure to PM2.5 was significantly associated with elevated BP levels. Our results further demonstrated that some constituents, including OC, EC, NO3- and NH4+, were robustly associated with BP increments. Previous findings on the associations between PM2.5 and BP varied considerably by directions, magnitude and measures of BP (Zhang et al., 2016). In the present study, we found that an IQR increase 294
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vasculo-active molecules (i.e. endothelin-1) and angiotensin-converting enzyme into the circulation system, consequently resulting in detrimental vascular effects and further increasing BP levels (Haddy et al., 2006; Miller, 2014). Nevertheless, there was little human evidence linking PM2.5 constituents with these biological pathways. An experimental study reported that SO42- and NO3- could significantly shorten coagulation time (Sangani et al., 2010). Soluble constituents (e.g., ions) may access the bloodstream and directly influence vascular endothelium by translocating across epithelial membranes (Furuyama et al., 2009). For example, the mechanisms underlying Na+-induced increments in BP may involve alterations in nitric oxide synthesis, vasculature, cardiac function, autonomic nervous system, and plasma asymmetric dimethyl-L-arginine (Farquhar et al., 2015). K+ may lower BP by hyperpolarizing endothelial cells through stimulation of Na+ pump, opening K+ channels, reducing vasoconstrictive sensitivity to norepinephrine and angiotensin II, and proliferating the vascular smooth muscle and sympathetic nervous system cells (Haddy et al., 2006; Houston and Harper, 2008). Mg2+ may decrease BP by inducing endothelial-dependent vasodilation, competing with Na+ for binding sites on vascular smooth muscle cells, and binding to K+ in a cooperative manner (Jee et al., 2002). Several limitations still existed in our study, First, data on personal exposure and time-location activity were unavailable, which may lead to exposure measurement errors (Zeger et al., 2000). Second, we did not evaluate the effects of metal elements due to the limitations of our monitoring instruments, which have been associated with higher BP levels in previous investigations (Cakmak et al., 2014; Lippmann et al., 2006). Third, we failed to record the detailed information on antihypertensive medication, which may modify the subjects’ susceptibility to the BP-increasing effects of PM2.5 (Delfino et al., 2010; Dvonch et al., 2009; Giorgini et al., 2016; Tobin et al., 2005). Fourth, our sample size is relatively small and some important associations might have been underestimated. Finally, the extrapolation of our results may be limited because this study was based on a susceptible population.
Fig. 4. Changes in PP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
different statistical models, whereas sodium, potassium, magnesium, and calcium had negative or non-significant associations after accounting for multi-collinearity. The robust effects of OC and EC on BP in our study were supported by some previous studies. For example, a similar panel study among healthy young adults also found significant associations of OC and EC with BP measures (Wu et al., 2013). OC was strongly associated with increased BP in a panel with coronary artery diseases (Delfino et al., 2010). Another panel study among cardiac rehabilitation patients showed that ambient black carbon (approximate to EC) were significantly associated with resting DBP (Zanobetti et al., 2004). However, other studies have not shown a link between carbonaceous particles and BP (Jansen et al., 2005; Williams et al., 2012). For example, a panel study among elderly subjects with respiratory diseases found null association between BC and BP (Jansen et al., 2005). The heterogeneity may due to different study samples (healthy subjects vs. patients; young vs. elderly), measurement methods (personal vs. ambient-level exposure), and statistical models. As for the effects of soluble ions, a previous study in healthy adults only found robust associations between chloride and increased levels of SBP and DBP (Wu et al., 2012). In our study, most soluble ions could elevate BP levels in the single-constituent models, but only NO3- and NH4+ were robustly associated with elevated BP after accounting for multi-collinearity. In addition, the inverse associations of Mg2+ and Ca2+ with SBP or PP in our study were consistent with a recent epidemiologic study in healthy adults (Wu et al., 2013). Because the two ions were not inversely correlated with other constituents in our study (see Table S1), their negative associations with BP remained to be elucidated in further investigations. It is biologically plausible that PM2.5 and its components have the potential to elevate BP. First, an imbalance in the autonomic nervous system may provoke vasoconstriction through the interaction of inhaled particles with nerve endings and receptors in airways (Bartoli et al., 2009; Brook et al., 2010, 2009; Giorgini et al., 2016; Jacobs et al., 2012), leading to an acute increase of BP in response to PM2.5exposure. Second, the activation of oxidative stress and systemic inflammation may play a potential role in elevating BP levels (Brook and Rajagopalan, 2009; Brook et al., 2010; Giorgini et al., 2016; Miller, 2014). Third, short-term exposure to PM2.5 may cause a release of
5. Conclusions In summary, this study showed short-term associations between 24h mean PM2.5 and elevated BP levels in COPD patients in Shanghai, China. Among the 10 major PM2.5 constituents examined in this analysis, OC, EC, NO3- and NH4+ might be mainly responsible for the elevated BP by PM2.5. Magnesium and calcium were associated with decreased BP. Further investigations with larger sample size, personal exposure measurements and comprehensive measurements of PM2.5 compositions are needed to confirm our findings and characterize the biological pathways linking PM2.5 constituents with BP increments. Conflict of interest The authors declare no competing financial interest. Acknowledgements This work was funded by the National Natural Science Foundation of China (91643205), the Public Welfare Research Program of National Health and Family Planning Commission of China (201502003), the Shanghai 3-Year Public Health Action Plan (GWTD2015S04 and 15GWZK0202) and the Research Program of Shanghai Environmental Protection Bureau (2016-11). Appendix A. Supplementary material Supplementary material associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2017.08.024. 295
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Fine particulate matter constituents and blood pressure in patients with chronic obstructive pulmonary disease: A panel study in Shanghai, China
MARK
Zhijing Lina,1, Yue Niua,1, Renjie Chena, Wenxi Xub, Huichu Lia, Cong Liua, Jing Caia, ⁎ ⁎⁎ Zhuohui Zhaoa, Haidong Kana,c, , Liping Qiaod, a School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China b Huangpu District Center for Disease Control and Prevention, Shanghai 200023, China c Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Parenthood Research, Institute of Reproduction and Development, Fudan University, Shanghai 200032, China d State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Fine particulate matter Chemical constituent Blood pressure Panel study
Objective: The evidence is limited about the potentially different health effects of various chemical constituents of fine particulate matter (PM2.5). We thus assessed the acute effects of various chemical constituents of PM2.5 on blood pressure (BP). Methods: We performed a longitudinal panel study with six repeated visits in 28 urban residents with chronic obstructive pulmonary disease in Shanghai, China from May to July, 2014. Twelve (43%) of them took antihypertensive medications. We measured resting BP by using a mercury sphygmomanometer and monitored realtime concentrations of PM2.5 constituents at a nearby site. Based on the linear mixed-effects model, we evaluated the effects of 10 major constituents in PM2.5 on BP, using a single-constituent model and a constituent-residual model after accounting for the multicollinearity. Results: We obtained a total of 168 pairs of effective BP measurements during the study period. There are moderate or high correlations among various PM2.5 constituents. An interquartile range increase of PM2.5 (19.1 μg/m3) was associated with increments of 1.90 mmHg [95% confidence interval (CI): 0.66, 3.13] in systolic BP, 0.68 mmHg (95%CI: −0.02, 1.37) in diastolic BP and 1.23 mmHg (95%CI: 0.19, 2.29) in pulse pressure. Some constituents of PM2.5, including organic carbon, elemental carbon, nitrate and ammonium, were robustly associated with elevated BP after controlling for total PM2.5 mass and accounting for multi-collinearity. Two constituents (magnesium and calcium) were associated with decreased BP. Conclusions: Organic carbon, elemental carbon, nitrate and ammonium may be mainly responsible for elevated BP from a short-term exposure to PM2.5.
1. Introduction Ambient particulate matter (PM2.5) air pollution is a main contributing risk factor for global disease burden, and it has been causally related to morbidity and mortality from cardiovascular diseases (CVD) (Lim et al., 2012). Potential mechanisms underlying these associations may include altered autonomic function, oxidative stress and inflammation, vasomotor dysfunction, and/or the release of
hemodynamically active mediators into systemic circulation (Brook et al., 2010; Giorgini et al., 2016). High blood pressure (BP) is another contributing risk factor for global disease burden and is also a strong risk factor for the development of CVD (Danaei et al., 2014; Lim et al., 2012). An increasing number of human studies have attempted to elucidate the association between particulate air pollution and BP, but the results were controversial. For example, some studies found a significant
⁎ Corresponding author at: School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. ⁎⁎ Corresponding author at: State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, 508 Qingzhou Road, Shanghai 200233, China. E-mail addresses: [email protected] (H. Kan), [email protected] (L. Qiao). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.envres.2017.08.024 Received 20 April 2017; Received in revised form 3 August 2017; Accepted 11 August 2017 0013-9351/ © 2017 Elsevier Inc. All rights reserved.
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elemental carbon (EC) in PM2.5 were measured by a semi-continuous OC/EC analyzer (model RT-4, Sunset Laboratory Inc.). The concentrations of eight major water-soluble inorganic ions in PM2.5, including chlorine (Cl−), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), sodium (Na+), potassium (K+), magnesium (Mg2+), and calcium (Ca2+) were measured by a commercial instrument for online Monitoring of Aerosols and Gases (MARGA, model ADI 2080, Applikon Analytical B.V.). The Quality Assurance/Quality Control procedures, including maintenance and cleaning for this instrument as well as calibrations for air flow rate, mass foil, and temperature/pressure, were conducted according to the Technical Guideline of Automatic Stations of Ambient Air Quality in Shanghai based on the national specification HJ/T193–2005. To allow for the adjustment of weather conditions, we collected daily mean temperature and mean relative humidity from a meteorological station (Xujiahui station) of the Shanghai Meteorological Bureau. To allow for the adjustment of simultaneous exposures to gaseous pollutants, daily concentrations of ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were collected from a nearby fixed-site monitor. Daily 24-h mean concentrations for SO2, NO2 and CO, and maximum 8-h mean concentration of O3 were averaged from a monitoring station (Luwan station), which was located in the urban area of Shanghai.
association (Brook et al., 2011a; Delfino et al., 2010; Hoffmann et al., 2012; Jacobs et al., 2012), but others showed inverse (S.Y. Chen et al., 2012; Harrabi et al., 2006) or non-significant associations (Bilenko et al., 2015; Brook et al., 2011b; Madsen and Nafstad, 2006). There is a relative lack of studies examining the PM2.5-BP associations in developing countries with high particulate air pollution levels. The effects of PM2.5 on BP also constitute one of the key biological pathways whereby particulate air pollution adversely affects the cardiovascular system (Mustafic et al., 2012). Furthermore, PM2.5 is mainly composed of carbonaceous components and water-soluble ions, which may have different effects on the cardiovascular system (Brook et al., 2010). Thus, it is crucial to determine which constituents dominate the effects of PM2.5 on BP, but the evidence is scarce or limited to a small fraction of constituents (Mordukhovich et al., 2009; Urch et al., 2005; Wu et al., 2013). Shanghai, as the largest city in China, is now facing serious air pollution problems and a heavy burden of CVDs. Therefore, we conducted a longitudinal panel study in Shanghai, China, to evaluate the acute effects of various PM2.5 chemical constituents on BP. Chronic obstructive pulmonary disease (COPD) is a disease state characterized by persistent airflow limitation that is not fully reversible (Vestbo et al., 2013). COPD patients were selected because their cardiovascular system is hypothesized to be vulnerable to the adverse effects of PM2.5 (Giorgini et al., 2016; Vestbo et al., 2013).
2.3. Blood pressure 2. Methods After sitting in a quiet room for at least 5 min, participants had their left upper arm resting BP measured by professional technicians using a mercury sphygmomanometer (Yuwell type A) with a cuff-size adjustment based on the air circumference. Three consecutive measurements, separated by at least 2 min between each, were taken and the mean of the 2nd and 3rd readings was used for data analysis. If the differences of the 2nd and 3rd measurements were > 5 mmHg, a new round of measurements was arranged until the differences were within 5 mmHg (Houston and Harper, 2008). We evaluated systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP, the difference of SBP and DBP).
2.1. Design and population The study was carried out in Shanghai, China, from May 27 to July 5, 2014. Details on the subject recruitment and study design were described in our previous publication (Chen et al., 2015). Briefly, we initially recruited 30 nonsmoking patients with mild-to-moderate COPD according to the classifications of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) (Vestbo et al., 2013). The diagnoses for all COPD patients were confirmed by physicians before they were enrolled in the current study. During the study period, two patients quit the follow-ups because of an exacerbation of COPD symptoms. To reduce the inherent seasonal variations of BP, six rounds of weekly follow-ups for 28 subjects were scheduled. To control for possible circadian rhythm, day-of-week and subjective effects, BP measurements for each subject were scheduled at the same time (1:30 p.m. to 2:30 p.m.) on the same day of the week (Tuesday, Thursday, and Saturday) by the same professional technician. Data on individual and medical characteristics, including age, gender, height, weight, education attainment, income, medication use, duration of COPD, and chronic comorbidities, were collected by self-administered questionnaires at enrollment. Besides, study subjects were asked to record any use of medications, acute exacerbation of COPD, and whether they went out of the central urban areas during the study period. The study was approved by the Institutional Review Board of School of Public Health, Fudan University, and informed written consents were provided by all participants at enrollment.
2.4. Statistical analysis Environmental and health data were linked by the time (hour) of physical examinations. We used the linear mixed-effect (LME) model to assess the associations between PM2.5 constituents and BP (Chen et al., 2015; Zhao et al., 2015), which has the advantage of accounting for within-subject correlations due to repeated measurements by simply including a random intercept for each subject. The measurements of BP indicators followed approximate normal distributions and were considered response variables one at a time. PM2.5 or one of its constituents was introduced as a fixed-effect independent variable and a randomeffect intercept was introduced for each subject. We also included several covariates in the LME model: (1) individual characteristics, including age, gender, body mass index, educational attainment, duration of COPD and chronic comorbidities, to account for betweensubject variability; (2) weather conditions, including temperature and relative humidity, to adjust for their potential confounding effects; and (3) an indicator variable of “day of the week” to adjust for temporal variation and traffic load (Chen et al., 2015; Cosselman et al., 2012). To explore the lag structure in the acute effects of PM2.5, we examined this model by using multiple periods preceding BP measurements, that is, single-day lags of 0–24 h (0 d), 25–48 h (1 d), 2 d, 3 d, 4 d, and 5 d (Hoffmann et al., 2012). The daily concentrations of PM2.5 and its components (lag 0, lag 1, etc.) were calculated as 24-h averages. The lag interval with the largest effect estimate was introduced into constituent-specific analyses. In addition to the aforementioned single-constituent LME model, we also established a constituent-residual model to assess the robustness in
2.2. Environmental data Real-time concentrations of PM2.5 and its chemical constituents were measured by a fixed-site monitor located at the Shanghai Academy of Environmental Sciences, which was about 4 km away from the center of the community where the subjects resided. It is a representative urban monitoring site that was mostly surrounded by commercial properties and residential dwellings. The details of principles and operation of the instrument have been described elsewhere (Du et al., 2011; Wang et al., 2013). In brief, the mass concentration of PM2.5 was measured by an online particulate monitor (FH 62 C14 series, Thermo Fisher Scientific Inc.). Organic carbon (OC) and 292
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the effect estimation of a constituent (Chen et al., 2015; Wu et al., 2013, 2012). To be specific, we replaced the constituent with its residual, which was obtained by establishing a linear regression model between total PM2.5 and this constituent. This can be considered a crude measure of the “independent” contribution of a constituent to the observed effects of PM2.5 after accounting for multi-collinearity (Urch et al., 2005). To test the robustness of our results to the adjustment for the simultaneous exposure to gaseous pollutants, we conducted a sensitivity analysis by including O3, SO2, NO2, and CO in single-constituent models one at a time (i.e., two-pollutant models). We also conducted stratified analyses by gender status and the prevalence of hypertension. We further tested the statistical significance of differences between effect estimates of the strata of a potential effect modifier (for example, the difference between males and females) by calculating the 95% confidence interval (95% CI) as
Table 1 Descriptive statistics on blood pressure and the 24-h averaging environmental variables during the study.a
BP, mmHg SBP DBP PP PM2.5, μg/m3 Total mass ClNO3SO42Na+ NH4+ K+ Mg2+ Ca2+ OC EC Gaseous pollutant, μg/m3 O3 SO2 NO2 CO (mg/m3) Weatherb Temperature, °C Relative humidity, %
2 2 Qˆ 1 − Qˆ 2± 1.96 SEˆ1 + SEˆ2 ,
Where Qˆ 1 and Qˆ 2 are the effect estimates in the 2 strata and SEˆ1 and SEˆ2 are their respective standard errors (R. Chen et al., 2012). All models were conducted in R software with the “lme4” package (Version 3.3.1, R Foundation for Statistical Computing, Vienna, Austria). The effect estimates were expressed as the absolute changes in BP [in millimeters mercury (mmHg)] with their 95% CIs in association with each interquartile range (IQR) increase in PM2.5 constituent concentrations. All statistical tests were two-sided with a significant level at p < 0.05.
Mean
SD
Min
Median
Max
IQR
123 74 49
15.2 7.6 11.5
91 58 27
120 74 49
170 100 96
21.5 11.0 16.0
38.4 0.70 9.6 12.2 0.06 6.1 0.15 0.21 1.6 6.9 1.8
20.8 0.35 5.4 6.5 0.11 3.2 0.24 0.13 1.1 2.9 0.7
10. 6 0.11 2.4 2.8 0.00 0.9 0.00 0.08 0.6 3.8 0.8
34.1 0.66 8.7 10.7 0.05 5.4 0.08 0.19 1.2 5.6 1.6
105.1 1.34 25.0 34.3 0.43 15.1 0.95 0.50 4.7 13.3 3.3
19.1 0.54 5.4 6.1 0.06 4.3 0.21 0.14 0.51 4.5 0.8
79.2 7.1 46.6 0.8
27.4 4.2 12.9 0.2
29.4 3.2 27.6 0.5
74.8 5.1 42.4 0.8
139.5 16.9 70.9 1.2
35.8 4.7 22.7 0.2
23.8 71.7
1.6 12.7
21.5 43.0
23.3 72.0
27.2 90.0
2.2 19.0
a Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, difference between the average systolic blood pressure and diastolic blood pressure values; PM2.5, particulate matter with an aerodynamic diameter less than 2.5 μm; Cl-, chloride; NO3-, nitrate; SO42-, sulfate; Na+, sodium; NH4+, ammonium; K+, potassium; Mg2+, magnesium; Ca2+, calcium; OC, organic carbon; EC, elemental carbon; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; SD, standard deviation; IQR, interquartile range. b Data are presented as the average of weather conditions on the present day and previous 3 days.
3. Results 3.1. Descriptive statistics The mean age of the subjects (n = 28) was 64 years and the mean body mass index was 24.7 kg/m2. Females accounted for nearly 78.6%. Table 1 summarizes the descriptive statistics on BP, PM2.5 constituents and weather conditions. Overall, we obtained 168 pairs of effective BP measurements during the study period with six effective measurements per subject. The average SBP was 123 mmHg, and the average DBP was 74 mmHg, suggesting that the majority of the subjects had adequate BP levels. Results from self-administered questionnaires showed that none of them participated in strenuous physical activity and took anti-COPD medications 3 days before scheduled physical examinations. During our study period, none of them went out of the central urban areas of Shanghai. According to self-administered questionnaires, 12 patients had the comorbidity of hypertension and they all had a stable and regular intake of hypotensors. During the study period, the 24 h-mean concentrations of PM2.5 varied from 10.6 μg/m3 to 105.1 μg/m3 with a mean of 38.4 μg/m3. There are no missing hourly data for PM2.5 but a small fraction (about 5%) of missing data for constituents. For PM2.5 constituents, SO42- accounts for the largest proportion of PM2.5 (32% on average), followed by NO3- (25%), OC (18%) and NH4+ (16%). Generally, there are moderate or high correlations among various PM2.5 constituents, and weak or moderate correlations between PM2.5 constituents and gaseous pollutants (see Table S1 in Supplementary material). The 24 h-mean concentrations of gaseous pollutants were 79.2 μg/m3 for O3 (maximal 8-h), 7.1 μg/m3 for SO2, 46.6 μg/m3 for NO2 and 0.8 mg/m3 for CO.
(lags of 0–24 h), and were substantially attenuated, and even became negative and statistically insignificant at longer lags. The association between PM2.5 and DBP was marginally insignificant. An IQR increase in PM2.5 concentrations (lag 0 day) corresponded to increments of 1.90 mmHg (95%CI: 0.66, 3.13), 0.68 mmHg (95%CI: −0.02, 1.37) and 1.23 mmHg (95%CI: 0.19, 2.29) in SBP, DBP and PP, respectively. The lag patterns in the constituent-specific associations were similar (data not shown). Then, the exposure at lag 0 day was used in further analyses. Fig. 2 illustrates the changes in SBP associated with an IQR increase in various PM2.5 constituents. In the single-constituent model, all constituents other than Mg2+ were significantly associated with higher SBP. Notably, the effects of EC and NO3- remained robust in the constituent-residual model with an IQR increase leading to increments of 4.70 mmHg (95%CI: 0.77, 8.63) and 2.93 mmHg (95%CI: 0.08, 5.78) in SBP. The associations of all PM2.5 constituents other than Cl- and Mg2+ with DBP were marginally significant or insignificant in the singleconstituent model (see Fig. 3). OC and EC were significantly associated with higher DBP in the constituent-residual model with corresponding increments equaling to 3.08 mmHg (95%CI: 0.14, 6.01) and 2.96 mmHg (95%CI: 0.62, 5.31). As presented in Fig. 4, the associations between all PM2.5 constituents (other than Mg2+, Na+, K+, and Ca2+) and PP were marginally significant or insignificant in the single-constituent model. There were no significant associations in the constituent-PM2.5 model. NO3- and NH4+ were significantly associated with increases of 2.85 mmHg (95%CI: 0.32, 5.39) and 3.72 mmHg (95%CI: 0.22, 7.22) in PP, respectively in the constituent-residual model. However, based on constituent-residual models, we found negative associations between magnesium and calcium and BP. In the sensitivity analyses, the adjustment for SO2, NO2, and CO decreased many estimates of PM2.5 constituents whereas increased some estimates; the adjustment for O3 did not substantially change the
3.2. Regression results Fig. 1 presents the lag patterns in the effects of PM2.5 on SBP, DBP and PP. We reported results by using the averaged concentrations over lag 0–3 days due to insignificant associations with BP in longer lag periods. There are moderate correlations among different lags of PM2.5 (see Table S2 in Supplementary material). The associations between PM2.5 and SBP and PP were statistically significant only at lag 0 day 293
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Fig. 1. The lag structure of changes in SBP, DBP and PP associated with an IQR increase in PM2.5. Data are presented as point estimates and their 95% CIs. Definition of abbreviations as in Table 1.
Fig. 3. Changes in DBP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
Fig. 2. Changes in SBP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
(19.1 μg/m3) in 24-h mean PM2.5 (total mass) was significantly associated with increments of 1.90 mmHg in SBP and 1.23 mmHg in PP. Similarly, a panel study among healthy young adults in Beijing, China reported that an IQR increase (51.2 μg/m3) in PM2.5 during the preceding day was associated with a 1.08-mmHg increase in SBP and a 0.96-mmHg increase in DBP (Wu et al., 2013). Jacobs et al. estimated a larger increase of PP (4.0 mmHg) corresponding to an IQR increase (20.8 μg/m³) in 24-h mean PM2.5 among a group of elderly people (Jacobs et al., 2012). Hoffmann et al. estimated a 1.4-mmHg increase in SBP per IQR increase of PM2.5 (3.54 μg/m3) in the previous 5 days in a panel of Type 2 diabetes mellitus patients (Hoffmann et al., 2012). However, there were still other studies that found non-significant or null associations (Harrabi et al., 2006; Zhang et al., 2016). The heterogeneity in these studies could be partly explained by the differences in study populations (healthy subjects vs. high risk patients; young vs. elderly), lags of exposure 0–5 days), characteristics of PM2.5 mixtures, and the methods to measure BP (clinic, home and 24-h ambulatory monitoring; automatic or mercurial sphygmomanometers; single or multiple measurements; left or right upper arms). Several PM2.5 constituents, including OC, EC, nitrate, and ammonium, had robust and positive associations with SBP, DBP, or PP in
estimates for each constituent (see Fig. S1-S3 in Supplementary material). Besides, the associations between PM2.5 and BP parameters were significant in females but not in males, although the between-gender difference was not statistically significant (see Table S3 in Supplementary material). The associations of PM2.5 with BP parameters were in similar magnitude between subgroups with and without hypertension (see Table S3 in Supplementary material). 4. Discussion The present longitudinal panel study provided a relatively comprehensive analysis of the acute effects of various PM2.5 chemical constituents on BP in a context of high PM2.5 pollution levels. The findings suggested that 24-h mean exposure to PM2.5 was significantly associated with elevated BP levels. Our results further demonstrated that some constituents, including OC, EC, NO3- and NH4+, were robustly associated with BP increments. Previous findings on the associations between PM2.5 and BP varied considerably by directions, magnitude and measures of BP (Zhang et al., 2016). In the present study, we found that an IQR increase 294
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vasculo-active molecules (i.e. endothelin-1) and angiotensin-converting enzyme into the circulation system, consequently resulting in detrimental vascular effects and further increasing BP levels (Haddy et al., 2006; Miller, 2014). Nevertheless, there was little human evidence linking PM2.5 constituents with these biological pathways. An experimental study reported that SO42- and NO3- could significantly shorten coagulation time (Sangani et al., 2010). Soluble constituents (e.g., ions) may access the bloodstream and directly influence vascular endothelium by translocating across epithelial membranes (Furuyama et al., 2009). For example, the mechanisms underlying Na+-induced increments in BP may involve alterations in nitric oxide synthesis, vasculature, cardiac function, autonomic nervous system, and plasma asymmetric dimethyl-L-arginine (Farquhar et al., 2015). K+ may lower BP by hyperpolarizing endothelial cells through stimulation of Na+ pump, opening K+ channels, reducing vasoconstrictive sensitivity to norepinephrine and angiotensin II, and proliferating the vascular smooth muscle and sympathetic nervous system cells (Haddy et al., 2006; Houston and Harper, 2008). Mg2+ may decrease BP by inducing endothelial-dependent vasodilation, competing with Na+ for binding sites on vascular smooth muscle cells, and binding to K+ in a cooperative manner (Jee et al., 2002). Several limitations still existed in our study, First, data on personal exposure and time-location activity were unavailable, which may lead to exposure measurement errors (Zeger et al., 2000). Second, we did not evaluate the effects of metal elements due to the limitations of our monitoring instruments, which have been associated with higher BP levels in previous investigations (Cakmak et al., 2014; Lippmann et al., 2006). Third, we failed to record the detailed information on antihypertensive medication, which may modify the subjects’ susceptibility to the BP-increasing effects of PM2.5 (Delfino et al., 2010; Dvonch et al., 2009; Giorgini et al., 2016; Tobin et al., 2005). Fourth, our sample size is relatively small and some important associations might have been underestimated. Finally, the extrapolation of our results may be limited because this study was based on a susceptible population.
Fig. 4. Changes in PP associated with an IQR increase in 24 h-average (lag 0 day) concentrations of PM2.5 constituents in different models. Data are presented as effect estimates and their 95% CIs. Definition of abbreviations as in Table 1.
different statistical models, whereas sodium, potassium, magnesium, and calcium had negative or non-significant associations after accounting for multi-collinearity. The robust effects of OC and EC on BP in our study were supported by some previous studies. For example, a similar panel study among healthy young adults also found significant associations of OC and EC with BP measures (Wu et al., 2013). OC was strongly associated with increased BP in a panel with coronary artery diseases (Delfino et al., 2010). Another panel study among cardiac rehabilitation patients showed that ambient black carbon (approximate to EC) were significantly associated with resting DBP (Zanobetti et al., 2004). However, other studies have not shown a link between carbonaceous particles and BP (Jansen et al., 2005; Williams et al., 2012). For example, a panel study among elderly subjects with respiratory diseases found null association between BC and BP (Jansen et al., 2005). The heterogeneity may due to different study samples (healthy subjects vs. patients; young vs. elderly), measurement methods (personal vs. ambient-level exposure), and statistical models. As for the effects of soluble ions, a previous study in healthy adults only found robust associations between chloride and increased levels of SBP and DBP (Wu et al., 2012). In our study, most soluble ions could elevate BP levels in the single-constituent models, but only NO3- and NH4+ were robustly associated with elevated BP after accounting for multi-collinearity. In addition, the inverse associations of Mg2+ and Ca2+ with SBP or PP in our study were consistent with a recent epidemiologic study in healthy adults (Wu et al., 2013). Because the two ions were not inversely correlated with other constituents in our study (see Table S1), their negative associations with BP remained to be elucidated in further investigations. It is biologically plausible that PM2.5 and its components have the potential to elevate BP. First, an imbalance in the autonomic nervous system may provoke vasoconstriction through the interaction of inhaled particles with nerve endings and receptors in airways (Bartoli et al., 2009; Brook et al., 2010, 2009; Giorgini et al., 2016; Jacobs et al., 2012), leading to an acute increase of BP in response to PM2.5exposure. Second, the activation of oxidative stress and systemic inflammation may play a potential role in elevating BP levels (Brook and Rajagopalan, 2009; Brook et al., 2010; Giorgini et al., 2016; Miller, 2014). Third, short-term exposure to PM2.5 may cause a release of
5. Conclusions In summary, this study showed short-term associations between 24h mean PM2.5 and elevated BP levels in COPD patients in Shanghai, China. Among the 10 major PM2.5 constituents examined in this analysis, OC, EC, NO3- and NH4+ might be mainly responsible for the elevated BP by PM2.5. Magnesium and calcium were associated with decreased BP. Further investigations with larger sample size, personal exposure measurements and comprehensive measurements of PM2.5 compositions are needed to confirm our findings and characterize the biological pathways linking PM2.5 constituents with BP increments. Conflict of interest The authors declare no competing financial interest. Acknowledgements This work was funded by the National Natural Science Foundation of China (91643205), the Public Welfare Research Program of National Health and Family Planning Commission of China (201502003), the Shanghai 3-Year Public Health Action Plan (GWTD2015S04 and 15GWZK0202) and the Research Program of Shanghai Environmental Protection Bureau (2016-11). Appendix A. Supplementary material Supplementary material associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2017.08.024. 295
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