Environment International 80 (2015) 19–25

Contents lists available at ScienceDirect

Environment International journal homepage: www.elsevier.com/locate/envint

Associations between size-fractionated particulate air pollution and blood pressure in a panel of type II diabetes mellitus patients Ang Zhao a,1, Renjie Chen a,b,1, Cuicui Wang a, Zhuohui Zhao a, Changyuan Yang a, Jianxiong Lu c, Xuan Chen c, Haidong Kan a,b,⁎ a b c

School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, & Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, China Tianping Community Health Service Center, Xuhui District, Shanghai, China

a r t i c l e

i n f o

Article history: Received 21 July 2014 Received in revised form 9 February 2015 Accepted 2 March 2015 Available online xxxx Keywords: Air pollution Particulate matter Diameter Blood pressure Diabetes Panel study

a b s t r a c t Little is known regarding how the size distribution of particulate matter (PM) air pollution influences its effect on blood pressure (BP), especially among patients with diabetes. The objective of this study was to explore the short-term associations between size-fractionated PM and BP among diabetes patients. We scheduled 6 repeated BP examinations every 2 weeks from 13 April 2013 to 30 June 2013 in a panel of 35 type 2 diabetes mellitus patients recruited from an urban community in Shanghai, China. We measured real-time PM concentrations in the size range of 0.25 to 10 μm. We used linear mixed-effect models to examine the short-term association of sizefractionated PM and BP after controlling for individual characteristics, mean temperature, relative humidity, day of the week, years with diabetes and use of antihypertensive medication. The association with systolic BP and pulse pressure strengthened with decreasing diameter. The size fractions with the strongest associations were 0.25 to 0.40 μm for number concentrations and ≤2.5 μm for mass concentrations. Furthermore, these effects occurred immediately even after 0–2 h and lasted for up to 48 h following exposure. An interquartile range increase in 24-h average number concentrations of PM0.25–0.40 was associated with increases of 3.61 mm Hg in systolic BP and 2.96 mm Hg in pulse pressure. Females, patients younger than 65 years of age and patients without antihypertensive treatment were more susceptible to these effects. Our results revealed important size and temporal patterns of PM in elevating BP among diabetes patients in China. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction According to the Global Burden of Metabolic Risk Factors for Chronic Diseases, in 2010, high blood pressure (BP) was the leading risk factor for cardiovascular diseases, chronic kidney disease, and diabetes throughout the world, leading to more than 40% of deaths from these diseases worldwide. The mortality burden of cardiometabolic risk factors has shifted from high-income to low- and middle-income countries (The Global Burden of Metabolic Risk Factors for Chronic Diseases, 2014). As the largest developing country, China has witnessed a gradual increase in the prevalence of both cardiometabolic diseases and risk determinants (Yang et al., 2010). For example, the estimated prevalence of diabetes among a representative sample of adults in China was 11.6% and the prevalence of pre-diabetes was 50.1% (Xu et al., 2013). Therefore, identifying the potential risk factors for diabetes is important to reduce the disease burden. ⁎ Corresponding author at: School of Public Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. E-mail address: [email protected] (H. Kan). 1 These authors contributed equally to the work.

http://dx.doi.org/10.1016/j.envint.2015.03.003 0160-4120/© 2015 Elsevier Ltd. All rights reserved.

Numerous epidemiological studies have demonstrated the shortterm associations between elevated ambient air pollution and increased risks of adverse cardiovascular events (Koulova and Frishman, 2014; Mustafic et al., 2012). However, the underlying mechanisms have not been well established. Previous studies have suggested that increased arterial BP may be largely responsible for the cardiovascular effects associated with PM exposure (Brook and Rajagopalan, 2009). Diabetes itself increases the risk of hypertension because of chronic autonomic dysregulation, endothelial dysfunction and the systematic inflammatory state, and PM exposure also leads to higher BP through the same pathway (Hoffmann et al., 2012). Therefore, PM-mediated BP elevation may be involved in the development of diabetes and increases the vulnerability of diabetes patients to the hazardous exposure to PM (O'Neill et al., 2005; Zanobetti and Schwartz, 2002). PM consists of discrete particles that range in size over several orders of magnitude, including inhalable particles (≤10 μm in aerodynamic diameter, PM10), coarse particles (PM2.5–10), fine particles (≤2.5 μm in aerodynamic diameter, PM2.5), and ultrafine particles (≤0.1 μm in aerodynamic diameter, PM0.1). Particle size is an important determinant of the site and efficiency of deposition in the respiratory tract, and an indicator of chemical composition and source (Araujo and Nel, 2009; Peng

20

A. Zhao et al. / Environment International 80 (2015) 19–25

et al., 2009). Therefore, the effect of PM on BP varies considerably by its diameter. However, among the size fractions of PM, only the effects of PM2.5 and PM10 have been widely examined in previous studies. Little is known about how the size distribution of PM influences its effect on BP, especially for PM ≤1 μm in aerodynamic diameter. Therefore, the objective of this longitudinal panel study was to explore the associations between short-term exposures to size-fractionated PM and arterial BP among a panel of type II diabetes mellitus (T2DM) patients in Shanghai, China. 2. Materials and methods 2.1. Study design Shanghai comprises urban and sub-urban districts and counties, with a total area of 6341 square kilometers (km2), and had a population of 23.8 million at the end of 2013. In this study, we recruited 35 T2DM patients from Tianping Community, which is located in the central urban area (Xuhui District) of Shanghai with a total area of 2.68 km2 and a population of 86,000. We measured both the environmental and health data from the Tianping Community Health Service Center (TCHSC). The inclusion criteria for this study included: doctor-diagnosed T2DM, permanent residents of Tianping Community, more than 40 years of age, and no history of smoking, alcoholism or severe chronic cardiopulmonary diseases. We scheduled 6 follow-up visits every 2 weeks from 13 April 2013 to 30 June 2013. The subjects were randomly divided into 4 subgroups and were invited to take part in BP examinations on one day of two weekends at a 2-week interval to capture dayto-day variations in levels of PM and BP. Each examination was conducted at the same time of the same day of week to exclude any circadian rhythms. We used a self-administered questionnaire to collect personal information including name, address, age, sex, education status, income level, blood glucose level, recent history of medication, and activity patterns 3 days before the scheduled body examination. Height and weight were measured at the first follow-up to calculate the body mass index (BMI). This study was approved by the Institutional Review Board of the School of Public Health, Fudan University, and informed consent was provided by each participant. 2.2. BP measurement A physician of TCHSC performed standardized resting BP measurements during each visit. Briefly, participants rested in a sitting position in a quiet room for at least 10 min before left upper arm BP was measured using a mercury sphygmomanometer at least three times with a 2-min minimum interval between measurements. In most cases the second and third sets of readings were averaged to calculate the systolic BP (SBP) and diastolic BP (DBP) (Rioux et al., 2010). However, if the difference between the SBP or DBP values of the second and third measurements was N5 mm Hg, the BP was considered unstable, and another 1 to 3 measurements were taken until the difference between the last two measurements was ≤ 5 mm Hg. The pulse pressure (PP) was calculated as the difference between the average SBP and DBP values. 2.3. Environmental data Real-time (one value per 5 min) ambient particle number concentrations (PNCs) were measured using the Environmental Dust Monitor 365 (GRIMM; Grimm Aerosol Technik GmbH & Co. KG, Ainring, Germany) installed on the rooftop of the building of TCHSC (approximately 20 m high). There were no apparent emission sources (including arterial streets) or tall buildings around TCHSC. This instrument allows for continuous measurements of PNC with 30 size channels ranging from 0.25 to 32 μm. Ultrafine particles (PM smaller than 0.1 μm) were

not measured in this study due to the limitation of our instruments. Considering the very small number concentrations of particles N 1 μm in diameter, we only analyzed PNCs in the range of 0.25 to 1.0 μm in this analysis. To avoid any issues associated with multiple comparisons, we combined the size fractions into larger strata. These PNC strata included 0.25–0.40, 0.40–0.65 and 0.65–1.0 μm. To evaluate the effects of PM above 1 μm in diameter, we obtained real-time particle mass concentrations (PMC) of PM10 and PM2.5 and concentrations of four gaseous pollutants from the nearest governmentowned monitoring station in Huangpu District, which was approximately 2.5 km away from the TCHSC. We also calculated PM10–2.5 by subtracting PM2.5 from PM10. Methods based on tapered element oscillating microbalance (TEOM), ultraviolet fluorescence, chemiluminescence, infrared absorption and ultraviolet absorption were used to measure PMC, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3), respectively. The TEOM™ Series 1400ab Continuous Ambient Particulate Monitor (Thermo Fisher Scientific Inc.) was used to measure PM2.5 and PM10. Models 100A, 200A, 300, and 400A made by API Inc. were used to continuously monitor SO2, NO2, CO, and O3, respectively. We obtained daily mean temperature and mean relative humidity data from a weather station approximately 2 km from TCHSC.

2.3.1. Statistical analysis Environmental and health data were linked by the time of the subject's BP measurements. We used linear mixed-effect models to examine the association of size-fractionated PM and BP. This model allows each subject to serve as his or her own control over time, and adjustment for within-subject covariates that do not change over time. BP measurements were entered as dependent variables without logtransformation because they almost followed the normal distribution via initial inspection. Fixed-effect independent variables include air pollutants, as well as covariates to adjust for their potential confounding effects. These covariates include individual characteristics (age, sex, BMI, income, antihypertensive medication and years with T2DM), daily mean temperature, daily mean relative humidity, and an indicator variable of “day of the week”. The variable of antihypertensive medication includes three levels: “none”, “calcium antagonists”, and “angiotensin converting enzyme inhibitor”. Finally, we incorporated a random intercept for each subject to account for the correlation among multiple BP measurements collected for the same participant. After the main model was established, we introduced PNC or PMC at each size range in single-pollutant models. To fully explore the lag structures for PM's short-term effects on BP, we examined the models using multiple periods preceding BP measurements, i.e., single lags of 0–2 hour (h), 3–6 h, 7–12 h, 13–24 h, 25–48 h and 49–72 h. We did not evaluate lags of longer than 72 h because few short-term studies found that air pollution's effects could last longer than 3 days (Dominici et al., 2006; Mustafic et al., 2012; Urch et al., 2005). We adjusted a priori for the confounding effects of ambient temperature and relative humidity using the moving average of the same day of BP examination and the previous 3 days. We performed stratification analyses to explore the effect modifications by individual characteristics including age, sex, BMI, income, antihypertensive treatment and years with T2DM. To allow for stratification analyses, we dichotomized these variables. As a sensitivity analysis, we fitted two-pollutant models to evaluate the robustness of our results after controlling for simultaneous exposure to gaseous pollutants (SO2, NO2, CO and O3). Statistical tests were two-sided, and p-values of ≤0.05 were considered statistically significant. All analyses were conducted in R software (Version 2.15.3, R Foundation for Statistical Computing, Vienna, Austria) using “lme4” package. The results were presented as the change of BP and its 95% confidence intervals (CIs) associated with an interquartile range (IQR) increase of PM concentrations.

A. Zhao et al. / Environment International 80 (2015) 19–25

3. Results

Table 2 Summary statistics of blood pressure measurements, 24-h average air pollution levels and 24-h average weather conditions in this study.

3.1. Descriptive statistics

Variables

There were no missing data in this study. Table 1 provides the summary statistics of these participants. Sixty percent of the subjects were hyperpietics and 40% were under regular medication of antihypertensive during our study period. The results from the self-administered questionnaires showed that they did not smoke and were not exposed to environmental tobacco smoke at home or the workplace. They also did not drink alcohol, participate in strenuous physical activity, or leave the urban areas 3 days before the scheduled body examination. Table 2 provides the summary statistics of BP measurements. In total, we obtained 210 measurements of BP throughout the 6 followups. On average, the range of variation in SBP for the same participant was 25 mm Hg, 17 mm Hg in DBP and 22 mm Hg in PP. SBP was strongly correlated with PP (r = 0.82), and moderately correlated with DBP (r = 0.34). The correlations between PP and DBP were relatively weak (r = −0.26). According to our study design, we performed 24 body examinations (6 times for 4 subgroups). Table 2 provides summary statistics of the 24-h averages of air pollutant concentrations and weather variables prior to each follow-up. PNCs decreased greatly with increasing diameter, and were dominated by particles b 0.40 μm which accounted for 86% of the total PNCs. There were 1615 particles b 1 μm per 1 cm3 air and only 5 for particles of 1.0–10 μm; therefore, we excluded PNC for size 1.0– 10 μm. Alternatively, we used PMC for 1.0–10 μm in this analysis. The average PMC ≤ 2.5 μm was 60 μg/m3 during the follow-ups, much higher than the range of 10–20 μg/m3 commonly reported in North America and Western Europe (Dominici et al., 2006). The average temperature and relative humidity were 22 °C and 72%, respectively, reflecting the subtropical climate in Shanghai. Generally, size-fractionated PNCs were strongly correlated with each other and with mass concentrations of PM2.5 (Pearson r = 0.8– 0.9), and moderately correlated with PM2.5–10 (r = 0.4–0.6); PNCs were moderately correlated with gaseous pollutants and weakly correlated with weather conditions (r = 0.1–0.2).

3.2. Regression results In single-pollutant models, we estimated the associations between BP and size-fractionated PM over multiple single lags from 0 to 72 h (see Fig. 1). Overall, particles of all the fractions other than 2.5–10 μm are significantly associated with SBP and PP using at least one lag. However, there was not any statistically significant associations between DBP and PM in all of the size ranges using all lag structures. Table 1 Basic characteristics of the study participants. Characteristic

Measure

Age (years) Male (%) BMI (kg/m2) Incomea b50,000 CNY 50,000–100,000 CNY N100,000 CNY Antihypertensive medication None Calcium antagonist Angiotensin converting enzyme inhibitor History of diabetes mellitus (years) Fasting blood glucose (mmol/L) Postprandial blood glucose (mmol/L) Glycosylated hemoglobin (%)

65.0 ± 8.7 18(50%) 26.0 ± 3.4

Abbreviations: BMI: body mass index; CNY: Chinese Yuan. a Annual household income per capita.

21

22(63%) 12(34%) 1(3%) 21(60%) 7(20%) 7(20%) 10.0 ± 8.0 7.2 ± 1.6 10.1 ± 3.8 7.5 ± 1.0

Blood pressure (mm Hg) Systolic Diastolic Pulse pressure PNC (per cm3) PNC0.25–0.40 PNC0.40–0.65 PNC0.65–1.00 Mass concentrations (μg/m3) PM2.5 PM10–2.5 NO2 SO2 O3 CO (mg/m3) Weather condition Temperature (°C) Relative humidity (%)

Mean

SD

Min

Median

Max

IQR

130 74 56

13 8 13

96 56 28

130 74 54

180 98 92

17 11 16

1385 211 19

841 184 19

90 10 1

1201 148 14

5436 1125 150

1048 195 17

60 19 46 12 77 0.8

31 14 23 15 41 0.3

15 2 1 1 4 0.1

52 15 42 9 74 0.7

22 72

4 14

10 40

22 73

173 74 169 440 276 2.0 32 93

43 17 26 11 51 0.5 5 18

Abbreviations: SD, standard deviation; PNC, particle number concentration; PM, particulate matter; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; IQR, interquartile range.

As shown in Fig. 1, the magnitude of the associations strengthens as the particle diameter decreases. PNC0.25–0.40 accounts for the largest increase in BP among PNC0.25–1.0. PMC2.5, but not PMC2.5–10, is significantly associated with BP elevation. The corresponding increases for PNC0.25–0.40 are similar to those for PMC2.5. As for lag structure, the effects of PM on BP occur as early as 0 to 2 h. The effects of PNC increase modestly, become strongest at lags of 25 to 48 h, and are statistically insignificant at lags of 49–72 h. The effect estimates of PMC remain stable over 24 h and then attenuate substantially at lags of 25 to 48 h. For example, an IQR increase in 24-h average PNC0.25–0.40 is associated with increases of 3.61 mm Hg (95% CI: 1.48, 5.74) in SBP, 0.53 mm Hg (95% CI: − 0.94, 2.00) in DBP and 2.96 mm Hg (95% CI: 1.04, 4.87) in PP; for PM2.5, the corresponding increases are 2.71 mm Hg (95% CI: 0.50, 4.91), 0.55 mm Hg (95% CI: −0.95,2.05) and 2.15 mm Hg (95% CI: 0.18,4.13). Fig. 2 presents the estimates on effect modification. PNC0.25–0.40 has larger effects among females than males. The significant association between PNC0.25–0.40 and BP was restricted among those without antihypertensive treatment. The effects were stronger among those participants younger than 65 years of age. The results for the modification analyses for PMC2.5 were the same as PNC0.25–0.40. The effects were almost similar in different strata of BMI, income and years with T2DM (data not shown). Fig. 3 shows the results from sensitivity analyses in two-pollutant models using 24-h average concentrations. Generally, the size distribution in the effects of PM on BP is not substantially changed but the magnitude and significance differ in different models. After controlling for SO2, NO2 and CO, the effects of PNC are not changed on PP, but are substantially attenuated on SBP. Our results are robust to the adjustment of O3. 4. Discussion This study suggested that short-term exposure to PM air pollution was significantly associated with elevated SBP and BP, but not DBP, among a panel of T2DM patients in Shanghai, China. For PNC, the association strengthened with decreasing diameter in a wide range of 0.25 to 1 μm. For PMC, PM2.5, rather than coarse PM, was significantly associated with BP. The size fractions with the strongest associations were 0.25–0.40 μm for PNC and ≤ 2.5 μm for PMC. Furthermore, these effects occurred immediately even after 0–2 h and lasted up to 48 h. Females, patients younger than 65 years of age and patients without

22

A. Zhao et al. / Environment International 80 (2015) 19–25 8 6

Changes in SBP

4 2 0 -2 -4

0-2 h

3-6 h

7-12 h

13-24 h

25-48 h

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PMC 2.5–10

PNC 0.25-0.40

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

Lag Diamete

-6

49-72 h

4 3

Changes in DBP

2 1 0 -1 -2

0-2 h

3-6 h

7-12 h

13-24 h

25-48 h

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PMC 2.5–10

PNC 0.25-0.40

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

Lag Diamete

-3

49-72 h

8 6

2 0 -2

Lag

0-2 h

3-6 h

7-12 h

13-24 h

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

25-48 h

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PMC 2.5–10

PNC 0.25-0.40

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5–10

PMC 2.5

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

-4

Diamete

Changes in PP

4

49-72 h

Fig. 1. Change in blood pressure (mean and 95% confidence interval, mm Hg) associated with an interquartile range increase of size-fractionated particle concentrations using different lag structures in Shanghai, China. The X-axis refers to different size distributions; the Y-axis refers to the changes in blood pressure; the blocks refer to different lag periods. SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; PNC, particle number concentration; PMC, particulate mass concentration.

antihypertensive treatment were more susceptible to these effects. To our knowledge, this was one of the few studies that examined the association between PM and BP in T2DM patients (Hoffmann et al., 2012; Liu et al., 2007). Our findings contributed to the scientific evidence that linked air pollution and BP in a country with high air pollution levels. The previous epidemiological findings on the associations between PM and SBP were quite inconsistent. Consistent with some previous studies, we found that PM was significantly associated with increased SBP (Dvonch et al., 2009; Hoffmann et al., 2012; Liu et al., 2007; Wu

et al., 2013). For example, Dvonch et al. found that a 10 μg/m3 increase in daily PM2.5 was associated with a 3.2 mm Hg increase in SBP (Dvonch et al., 2009). Hoffmann et al. estimated an increase of 1.4 mm Hg in SBP corresponding to an IQR increase of PM2.5 (3.54 μg/m3) in a panel of T2DM patients (Hoffmann et al., 2012). SBP was elevated by 0.43 mm Hg per 10 μg/m3 increase in personal exposure to PM10 among 25 diabetic patients (Liu et al., 2007). A study conducted in Beijing, China showed that an IQR increase of 51.2 μg/m3 in PM2.5 was associated with a 1.08 mm Hg (95% CI: 0.17, 1.99) increase in SBP on the following day (Wu et al., 2013). However, there were still other

A. Zhao et al. / Environment International 80 (2015) 19–25

23

8

6

Changes in BP

4

2

0

-2

-4

SBP

PP

SBP

PP

SBP

> 65 years

≤65 years

PP

SBP

Male

PP

SBP

Female

PP

SBP

Without antihypertensive treatment

PP

With antihypertensive treatment

Fig. 2. Age, gender and antihypertensive treatment-specific increase in blood pressure (mean and 95% confidence interval, mm Hg) associated with an interquartile range increase of the 24-hour moving average of PNC0.25–0.40 in single-pollutant models. The X-axis refers to different BP indicators; the Y-axis refers to the changes in blood pressure; the blocks refer to modification factors. SBP, systolic blood pressure; PP, pulse pressure.

studies that found null or inverse associations (Harrabi et al., 2006; Ibald-Mulli et al., 2004; Madsen and Nafstad, 2006). For example, Ibald-Mulli et al. failed to detect a significant association between PM2.5 and SBP among 131 adults with coronary heart disease (IbaldMulli et al., 2004). We did not find a significant association between PM for all of the size fractions and DBP, which was consistent with some previous studies (Auchincloss et al., 2008; Jacobs et al., 2012). For example, Auchincloss et al. found that both PP and SBP were positively associated with PM2.5, but not DBP (Auchincloss et al., 2008). Conversely, some other studies still found positive association with DBP (de Paula Santos et al., 2005; Urch et al., 2005; Wu et al., 2013). For example, a panel study conducted in Beijing found that an IQR increase of 51.2 μg/m3 in PM2.5 was associated with a 0.96 mm Hg (95% CI: 0.31, 1.61) increase in DBP on the following day (Wu et al., 2013). PP is an established index of arterial stiffness. Arterial stiffness reflects the age-related deterioration of the elastic properties of the aorta, and was known to be a risk factor for cardiovascular disease (Nawrot et al., 2003; Safar et al., 2003). Hence, increased PP can be used as an intermediate factor involved in the PM-related cardiovascular

Adjusted for SO2

Without adjustment

8

morbidity and mortality (Jacobs et al., 2012). In this study, we found that all size fractions of PM other than coarse PM were associated with PP, which was consistent with previous studies (Auchincloss et al., 2008; Jacobs et al., 2012). Jacobs et al. further found that the positive associations were limited in those taking antihypertensive medications (Jacobs et al., 2012). Given the broad inconsistency in the literature, explaining the heterogeneous or negative findings was quite difficult because a number of factors may be involved, including: indicators of BP, PM levels and size/chemical characteristics, exposure assessment methods, copollutants, comorbidities, individual characteristics, sample size, number of follow-ups, weather patterns, and study periods. Limited by the data availability, we did not know which specific factors contributed most to the inconsistency or were mainly responsible for the negative findings on DBP. Few studies have examined the modifications by age, sex, BMI, income, antihypertensive treatment and years with T2DM in the PMmediated effects on BP. The susceptibility of females in this study may be explained by their relatively frail physique, increased deposition of PM in the lung, higher airway responsiveness, and possible unfavorable

Adjusted for NO2

Adjusted for CO

Adjusted for O3

6

Changes in BP

4 2 0 -2 -4 -6

SBP

PP

SBP

PP

SBP

PP

SBP

PP

SBP

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

PMC 2.5

PMC 2.5–10

PNC 0.65-1.0

PNC 0.40-0.65

PNC 0.25-0.40

Diameter

-8

PP

Fig. 3. Change in blood pressure (mean and 95% confidence interval, mm Hg) associated with an interquartile range increase of the 24-hour moving average size-fractionated particle concentrations after adjustment for 4 gaseous pollutants using two-pollutant models. The X-axis refers to different size distributions; the Y-axis refers to the changes in blood pressure; the block refers to BP. SBP: systolic blood pressure; PP: pulse pressure. PNC: particle number concentration; PMC: particulate mass concentration; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone.

24

A. Zhao et al. / Environment International 80 (2015) 19–25

socioeconomic status in China (Kan et al., 2008). We found that the PMmediated increases in BP were observed only among patients without antihypertensive medication, suggesting a potentially protective effect of antihypertensive treatment (Huang and Ghio, 2009). The increases of BP associated with PM were stronger among those younger than 65 years of age. This might simply be because almost all patients older than 65 years took antihypertensive medications during our study period, alleviating the BP change due to exposure to PM. The precise pathophysiologic mechanisms of PM-associated BP increase remain speculative. First, PM-induced oxidative stress and systemic inflammation may play an important role in the pathophysiologic process of cardiovascular diseases such as hypertension (Lassegue and Griendling, 2004). Second, an imbalance in the autonomic nervous system may cause vasoconstriction (Brook and Rajagopalan, 2009; Devlin et al., 2003); this pathway might be provoked by the interaction of inhaled particles with nerve endings and receptors in the airways (Widdicombe and Lee, 2001), which might help explain the very rapid changes of BP in response to a short-term exposure in our study. Third, short-term exposure to PM may lead to increased release of the endothelium-dependent vasoconstrictor (endothelin-1) (Peretz et al., 2008). Diabetes patients were vulnerable to the detrimental effects of PM (Goldberg et al., 2006; Zanobetti and Schwartz, 2002). Through a comparison on diabetics and non-diabetics, Zanobetti et al. found that PM10-associated cardiovascular hospital admission among diabetics was twice as high as that among non-diabetics (Zanobetti and Schwartz, 2002). Some mechanisms might explain this susceptibility. First, the inherent inflammation/oxidative stress may increase the vulnerability of people with type II diabetes to the hazardous effects of particles (Resnick and Howard, 2002). Second, the abnormal levels of plasma endothelin and other hyperglycemia-related protein factors among diabetics may lead to endothelium dysfunction (O'Neill et al., 2005). Third, an upregulation of inflammatory activity by PM exposure has been observed on diabetics (Zanobetti and Schwartz, 2002). It has been observed that particles' health effects increased with smaller size because smaller particles generally had a higher pulmonary deposition efficiency, easier vascular penetration, larger surface area, as well as more toxic components adhered (Delfino et al., 2005). Thus, it has been of great interest, both from a scientific viewpoint and from a regulatory perspective, as to what specific size fractions of PM are more toxic than others. The size-dependent pattern of PM's effects on BP was not extensively reported (Bellavia et al., 2013). We found that smaller particles had larger effects on BP, especially those with size less than 0.40 μm and coarse PM were not associated with BP. Our results were also supported by a double-blind randomized placebocontrolled crossover study, which demonstrated that coarse PM had weaker effects on systolic BP (Bellavia et al., 2013). The study has two strengths. First, this study was conducted over a short period from April to June to avoid inherent seasonal changes of BP. Second, we extensively examined the size-dependent effects of PM on BP changes in a wide range of 0.25–10 μm in a region with much higher air pollution levels than the developed countries. The limitations should also be addressed. First, we used community air monitoring rather than personal exposure measurements to represent the true exposure of all participants, thus exposure measurement error cannot be fully excluded in this study. Second, data on mass concentrations of PM2.5, PM2.5–10, and gaseous pollutants were obtained from a nearby fixed-sited station, which may further add to the measurement error and may make the results of two-pollutant models unstable. Third, limited by our monitoring capability, we were unable to evaluate the associations between particles smaller than 0.25 μm (including ultrafine particles) and BP. Fourth, given the strong correlations among different particle size fractions, we cannot separate the independent effects for each size range. Fifth, the multi-collinearity between PM and gaseous pollutants made our results from two-pollutant models more instable.

In summary, our results revealed important size and temporal patterns of PM in elevating BP among diabetes patients in China. Our findings may have important implications for the control of particulate air pollution and management of BP (especially for T2DM patients) in China. Acknowledgments The study was supported by the National Basic Research Program (973 program) of China (2011CB503802), National Natural Science Foundation of China (81222036), China Medical Board Collaborating Program (13–152), Consulting service for center of excellence in Global Health Policy Development and Governance in China (GHSP-CS-OP302), and Cyrus Tang Foundation (CTF2013001). The authors acknowledge the contributions of the staff of Tianping Community Health Service Center in participant recruitment and field organization. We also thank all of the participating subjects. The authors declare that they have no competing financial interests. References Araujo, J.A., Nel, A.E., 2009. Particulate matter and atherosclerosis: role of particle size, composition and oxidative stress. Part. Fibre Toxicol. 6, 24. Auchincloss, A.H., Diez Roux, A.V., Dvonch, J.T., Brown, P.L., Barr, R.G., Daviglus, M.L., Goff, D.C., Kaufman, J.D., O'Neill, M.S., 2008. Associations between recent exposure to ambient fine particulate matter and blood pressure in the Multi-ethnic Study of Atherosclerosis (MESA). Environ. Health Perspect. 116, 486–491. Bellavia, A., Urch, B., Speck, M., Brook, R.D., Scott, J.A., Albetti, B., Behbod, B., North, M., Valeri, L., Bertazzi, P.A., Silverman, F., Gold, D., Baccarelli, A.A., 2013. DNA hypomethylation, ambient particulate matter, and increased blood pressure: findings from controlled human exposure experiments. J. Am. Heart Assoc. 2, e000212. Brook, R.D., Rajagopalan, S., 2009. Particulate matter, air pollution, and blood pressure. J. Am. Soc. Hypertens. 3, 332–350. de Paula Santos, U., Braga, A.L., Giorgi, D.M., Pereira, L.A., Grupi, C.J., Lin, C.A., Bussacos, M.A., Zanetta, D.M., do Nascimento Saldiva, P.H., Filho, M.T., 2005. Effects of air pollution on blood pressure and heart rate variability: a panel study of vehicular traffic controllers in the city of Sao Paulo, Brazil. Eur. Heart J. 26, 193–200. Delfino, R.J., Sioutas, C., Malik, S., 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environ. Health Perspect. 113, 934–946. Devlin, R.B., Ghio, A.J., Kehrl, H., Sanders, G., Cascio, W., 2003. Elderly humans exposed to concentrated air pollution particles have decreased heart rate variability. Eur. Respir. J. Suppl. 40, 76s–80s. Dominici, F., Peng, R.D., Bell, M.L., Pham, L., McDermott, A., Zeger, S.L., Samet, J.M., 2006. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295, 1127–1134. Dvonch, J.T., Kannan, S., Schulz, A.J., Keeler, G.J., Mentz, G., House, J., Benjamin, A., Max, P., Bard, R.L., Brook, R.D., 2009. Acute effects of ambient particulate matter on blood pressure: differential effects across urban communities. Hypertension 53, 853–859. Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration, 2014. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 8, 634–647. Goldberg, M.S., Burnett, R.T., Yale, J.F., Valois, M.F., Brook, J.R., 2006. Associations between ambient air pollution and daily mortality among persons with diabetes and cardiovascular disease. Environ. Res. 100, 255–267. Harrabi, I., Rondeau, V., Dartigues, J.F., Tessier, J.F., Filleul, L., 2006. Effects of particulate air pollution on systolic blood pressure: a population-based approach. Environ. Res. 101, 89–93. Hoffmann, B., Luttmann-Gibson, H., Cohen, A., Zanobetti, A., de Souza, C., Foley, C., Suh, H.H., Coull, B.A., Schwartz, J., Mittleman, M., Stone, P., Horton, E., Gold, D.R., 2012. Opposing effects of particle pollution, ozone, and ambient temperature on arterial blood pressure. Environ. Health Perspect. 120, 241–246. Huang, Y.C., Ghio, A.J., 2009. Controlled human exposures to ambient pollutant particles in susceptible populations. Environ. Health 8, 33. Ibald-Mulli, A., Timonen, K.L., Peters, A., Heinrich, J., Wolke, G., Lanki, T., Buzorius, G., Kreyling, W.G., de Hartog, J., Hoek, G., ten Brink, H.M., Pekkanen, J., 2004. Effects of particulate air pollution on blood pressure and heart rate in subjects with cardiovascular disease: a multicenter approach. Environ. Health Perspect. 112, 369–377. Jacobs, L., Buczynska, A., Walgraeve, C., Delcloo, A., Potgieter-Vermaak, S., Van Grieken, R., Demeestere, K., Dewulf, J., Van Langenhove, H., De Backer, H., Nemery, B., Nawrot, T.S., 2012. Acute changes in pulse pressure in relation to constituents of particulate air pollution in elderly persons. Environ. Res. 117, 60–67. Kan, H., London, S.J., Chen, G., Zhang, Y., Song, G., Zhao, N., Jiang, L., Chen, B., 2008. Season, sex, age, and education as modifiers of the effects of outdoor air pollution on daily mortality in Shanghai, China: the Public Health and Air Pollution in Asia (PAPA) study. Environ. Health Perspect. 116, 1183–1188. Koulova, A., Frishman, W.H., 2014. Air pollution exposure as a risk factor for cardiovascular disease morbidity and mortality. Cardiol. Rev. 22, 30–36.

A. Zhao et al. / Environment International 80 (2015) 19–25 Lassegue, B., Griendling, K.K., 2004. Reactive oxygen species in hypertension; an update. Am. J. Hypertens. 17, 852–860. Liu, L., Ruddy, T.D., Dalipaj, M., Szyszkowicz, M., You, H., Poon, R., Wheeler, A., Dales, R., 2007. Influence of personal exposure to particulate air pollution on cardiovascular physiology and biomarkers of inflammation and oxidative stress in subjects with diabetes. J. Occup. Environ. Med. 49, 258–265. Madsen, C., Nafstad, P., 2006. Associations between environmental exposure and blood pressure among participants in the Oslo Health Study (HUBRO). Eur. J. Epidemiol. 21, 485–491. Mustafic, H., Jabre, P., Caussin, C., Murad, M.H., Escolano, S., Tafflet, M., Perier, M.C., Marijon, E., Vernerey, D., Empana, J.P., Jouven, X., 2012. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. JAMA 307, 713–721. Nawrot, T., Den Hond, E., Thijs, L., Staessen, J.A., 2003. Isolated systolic hypertension and the risk of vascular disease. Curr. Hypertens. Rep. 5, 372–379. O'Neill, M.S., Veves, A., Zanobetti, A., Sarnat, J.A., Gold, D.R., Economides, P.A., Horton, E.S., Schwartz, J., 2005. Diabetes enhances vulnerability to particulate air pollutionassociated impairment in vascular reactivity and endothelial function. Circulation 111, 2913–2920. Peng, R.D., Bell, M.L., Geyh, A.S., McDermott, A., Zeger, S.L., Samet, J.M., Dominici, F., 2009. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environ. Health Perspect. 117, 957–963. Peretz, A., Sullivan, J.H., Leotta, D.F., Trenga, C.A., Sands, F.N., Allen, J., Carlsten, C., Wilkinson, C.W., Gill, E.A., Kaufman, J.D., 2008. Diesel exhaust inhalation elicits acute vasoconstriction in vivo. Environ. Health Perspect. 116, 937–942. Resnick, H.E., Howard, B.V., 2002. Diabetes and cardiovascular disease. Annu. Rev. Med. 53, 245–267.

25

Rioux, C.L., Tucker, K.L., Mwamburi, M., Gute, D.M., Cohen, S.A., Brugge, D., 2010. Residential traffic exposure, pulse pressure, and C-reactive protein: consistency and contrast among exposure characterization methods. Environ. Health Perspect. 118, 803–811. Safar, M.E., Levy, B.I., Struijker-Boudier, H., 2003. Current perspectives on arterial stiffness and pulse pressure in hypertension and cardiovascular diseases. Circulation 107, 2864–2869. Urch, B., Silverman, F., Corey, P., Brook, J.R., Lukic, K.Z., Rajagopalan, S., Brook, R.D., 2005. Acute blood pressure responses in healthy adults during controlled air pollution exposures. Environ. Health Perspect. 113, 1052–1055. Widdicombe, J., Lee, L.Y., 2001. Airway reflexes, autonomic function, and cardiovascular responses. Environ. Health Perspect. 109 (Suppl. 4), 579–584. Wu, S., Deng, F., Huang, J., Wang, H., Shima, M., Wang, X., Qin, Y., Zheng, C., Wei, H., Hao, Y., 2013. Blood pressure changes and chemical constituents of particulate air pollution: results from the Healthy Volunteer Natural Relocation (HVNR) study. Environ. Health Perspect. 121, 66–72. Xu, Y., Wang, L., He, J., Bi, Y., Li, M., Wang, T., Jiang, Y., Dai, M., Lu, J., Xu, M., Li, Y., Hu, N., Li, J., Mi, S., Chen, C.S., Li, G., Mu, Y., Zhao, J., Kong, L., Chen, J., Lai, S., Wang, W., Zhao, W., Ning, G., 2013. Prevalence and control of diabetes in Chinese adults. JAMA 310, 948–959. Yang, W.Y., Lu, J.M., Weng, J.P., Jia, W.P., Ji, L.N., Xiao, J.Z., Shan, Z.Y., Liu, J., Tian, H.M., Ji, Q.H., Zhu, D.L., Ge, J.P., Lin, L.X., Chen, L., Guo, X.H., Zhao, Z.G., Li, Q., Zhou, Z.G., Shan, G.L., He, J., Disorders, C.N.D.M., 2010. Prevalence of diabetes among men and women in China. N. Engl. J. Med. 362, 1090–1101. Zanobetti, A., Schwartz, J., 2002. Cardiovascular damage by airborne particles: are diabetics more susceptible? Epidemiology 13, 588–592.