Environment International 128 (2019) 95–102

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Environment International

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Associations of long-term exposure to ambient PM1 with hypertension and blood pressure in rural Chinese population: The rural cohort study T

Na Lia,1, Gongbo Chena,1, Feifei Liua, Shuyuan Maoa, Yisi Liub, Yitan Houa, Yuanan Luc, ⁎ ⁎ Suyang Liua, Chongjian Wangd, , Hao Xianga, , Yuming Guoe,d,2, Shanshan Lie,2 a Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, b Department of Environmental and Occupational Health Sciences, University of Washington, 1959 NE Pacific Street, Seattle, USA c Environmental Health Laboratory, Department of Public Health Sciences, University Hawaii at Manoa, 1960 East West Rd, Biomed Bldg, D105, Honolulu, USA d Department of Epidemiology and Biostatistics, School of Public Health, University, Zhengzhou, Henan, China e Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

ARTICLE INFO ABSTRACT

Handling Editor: Hanna Boogaard Background: The epidemiological evidence on relationships between long-term exposure to particulate matter Keywords: and hypertension and blood pressure has been inconclusive. Limited evidence was available for particulate Air pollution matter with an aerodynamic diameter ≤ 1 μm (PM1) in rural areas of developing countries.

PM1 Objective: This study aimed to investigate the associations between long-term exposure to PM1 and hypertension Hypertension and blood pressure among rural Chinese population. Blood pressure Methods: This study included 39,259 participants who had completed the baseline survey from Henan Rural Rural China Cohort. Participants' exposure to PM1 was assessed by a satellite-based spatiotemporal model. The binary logistic

regression model was used to examine the association between long-term PM1 exposure and hypertension, and

multivariable linear regression model was used to investigate the associations between long-term PM1 exposure and systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP). Moreover, we examined potential effect modifications by demographic, lifestyle and diet factors.

Results: The mean concentration of PM1 for all participants during the 3-year before baseline survey was 3 3 59.98 μg/m . Each 1 μg/m increase in PM1 concentration was significantly associated with an increase of 4.3% [Odds ratio(OR) = 1.043, 95% confidence interval(CI): 1.033, 1.053] in odds for hypertension, an increase of 0.401 mm Hg (95% CI, 0.335, 0.467), 0.328 mm Hg (95% CI, 0.288, 0.369), 0.353 mm Hg (95% CI, 0.307, 0.399) and 0.073 mm Hg (95% CI, 0.030, 0.116) in SBP, DBP, MAP and PP, respectively. Further stratified

analyses showed that the effect of PM1 on hypertension and blood pressure could be modified by sex, lifestyle and diet.

Conclusions: This study suggests that long-term exposure to ambient PM1 increases the risk of hypertension and is associated with elevations in blood pressure in rural Chinese adults, especially in male and those with un- healthy habits.

1. Introduction life lost years (DALYs) in 2017 (GBD, 2018). During the past four decades, the number of people with high blood pressure has been rising Hypertension is a well-established risk factor for cardiovascular worldwide, especially in low-income and middle-income countries disease that contributed to 17.8 million deaths worldwide in 2017 (Zhou et al., 2017). In China, blood pressure control is a national public (Brook et al., 2010; GBD, 2018). Among all risk factors, high systolic health priority (Chen, 2009), as nearly half of adults aging 35–75 years blood pressure (SBP) has been identified as the largest contributor of are suffering from hypertension and the prevalence has been rising (Lu all-cause deaths worldwide during the period from 1990 through 2017, et al., 2017). In addition to genetic factors, changes in lifestyle and diet accounting for 10.4 million deaths and 218 million disability-adjusted habits, deteriorating environmental condition has been recognized as a

⁎ Corresponding authors. E-mail addresses: [email protected] (C. Wang), [email protected] (H. Xiang). 1 These authors contributed equally to this work. 2 Yuming Guo and Shanshan Li are senior authors. https://doi.org/10.1016/j.envint.2019.04.037 Received 13 March 2019; Received in revised form 16 April 2019; Accepted 16 April 2019 Available online 03 May 2019 0160-4120/ © 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). N. Li, et al. Environment International 128 (2019) 95–102

Fig. 1. The locations of five survey sites in the Henan Rural Cohort Study. Note: The map of China was down- loaded from the national administra- tion of surveying, mapping and geoin- formation (Available at: http://bzdt. nasg.gov.cn/index.jsp. Accessed on 27 January, 2018), and the serial number was 8012790168. The map of Henan Province was generated by Map in- stitution of Henan Province.

risk factor for increased blood pressure as well (Argacha et al., 2018). China, in July 2015. The detailed descriptions of cohort design and In recent years, mounting evidence contributed to a better under- study population were previously published elsewhere (Liu et al., 2019; standing of the associations between exposure to ambient air pollutants Tian et al., 2018a; Tian et al., 2018b). Briefly, the cohort recruited and elevated BP and hypertension. A comprehensive meta-analysis re- participants from the general population using multistage stratified viewed the existing evidence and reported globally significant asso- cluster sampling method. The target population were 18- to 79-year-old ciations of long-term exposure to fine particulate matter (particle permanent residents without severe physical or mental disease. In the matter with an aerodynamic diameter ≤ 2.5 μm, PM2.5) with hy- first stage, five rural counties in different geographical regions (south, pertension, and of particulate matter with an aerodynamic central, north, east, and west) of Henan Province were selected by diameter ≤ 10 μm (PM10), PM2.5, and nitrogen dioxide (NO2) with in- simple cluster sampling. In the second stage, in view of the compliance creased diastolic blood pressure (DBP) (Yang et al., 2018a). However, of the residents, population stability and local medical conditions, one high heterogeneity was found for this meta-analysis and the existing to three rural communities (referred to as a “township”) in each county literature on long-term exposure to air pollution and blood pressure were selected by the local Centre for Disease Control and Prevention. In mainly focused on pollutants such as PM10,PM2.5 and NO2. Few re- the final stage, the eligible candidates who signed informed consent in searches have evaluated the cardiovascular effects of long-term ex- each administrative unit (rural village) of the selected township were posure to PM1 that is the important component of PM2.5 and may have included in the study sample. Overall, a total of 41,893 invitations were more extensive toxic effects than PM2.5 (Chen et al., 2017). In addition sent out to those who met the inclusion criteria and 39,259 participants to SBP and diastolic blood pressure (DBP), increased mean arterial responded and completed baseline survey, with a response rate of pressure (MAP) and pulse pressure (PP), the steady component and 93.7% (Liu et al., 2018). In this study, 52 participants were excluded pulsatile component of blood pressure respectively, have been reported due to missing data about blood pressure measurement, hypertension to be related to higher cardiovascular risk as well (Darne et al., 1989; status, and other key covariates. Therefore, this analysis included Madhavan et al., 1994). However, compared with SBP and DBP, far less 39,207 (99.9%) of the recruited participants completing the baseline evidence has been available for air pollution and MAP/PP. Moreover, survey. Data on demographic characteristics, socioeconomic char- the majority of previous studies were conducted in urban areas. They acteristics, health behaviors, physician-diagnosed diseases, medication paid little attention to rural areas in developing countries, where hy- history, and family history of diseases were collected via face-to-face pertension is becoming much more prevalent and air pollution is wor- interviews by well-trained local investigators. sening. The study complied with the 1975 Declaration of Helsinki and was In this context, to fill in the gap and add to the evidence for adverse approved by the ethics committee of Zhengzhou University. Written effect of long-term exposure to PM1, we investigated the associations informed consent was obtained from each participant at their enroll- between long-term exposure to PM1 and hypertension and four blood ment. pressure component measurements in rural Chinese population. We ff fi also examined several potential e ect modi cations by demographic, 2.2. Air pollution exposure assessment lifestyle and diet factors.

Daily PM1 concentrations were predicted at a 0.1° × 0.1° spatial 2. Methods resolution, using satellite remote sensing, meteorology, and land use information. The detailed description of the prediction has been pre- 2.1. Study population viously published (Chen et al., 2018a; Chen et al., 2018b). In brief, we combined two types of Moderate Resolution Imaging Spectro- We studied participants from The Henan Rural Cohort Study radiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, (Registration number: ChiCTR-OOC-15006699), which was established Dark Target (DT) and Deep Blue (DB). A random forests model based on in five rural areas ( of city, Yima county of machine learning algorithms was employed to model ground-monitored

Sanmenxia city, of city, county of PM1 data with AOD data and other spatial and temporal predictors Xinxiang city and Yuzhou county of city) of Henan Province, (e.g., urban cover, forest cover and calendar month). A 10-fold cross-

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Table 1 geocoded address of natural village through AutoNavi Map (https://lbs. Basic characteristics of study participants. amap.com/api/webservice/guide/api/georegeo). This is a Chinese web

Characteristicsa Non-hypertension Hypertension P value mapping, navigation and location-based services provider and has (n = 26,384) (n = 12,823) provided mapping data to Google since 2006. Then we calculated the average during the three-year before the baseline survey for each par- Age, years 53.27 ± 12.43 60.39 ± 10.09 < 0.001 3 ticipant as long-term exposure concentration of PM1. PM1, μg/m 57.31 ± 2.67 57.72 ± 2.62 < 0.001 Body mass index, kg/ 24.25 ± 3.36 26.04 ± 3.67 < 0.001 3 m 2.3. Outcome assessment Systolic blood 115.82 ± 11.91 146.81 ± 16.86 < 0.001 pressure, mm Hg Diastolic blood 72.58 ± 8.14 88.22 ± 10.67 < 0.001 Blood pressure was measured using an electronic sphygmoman- pressure, mm Hg ometer (HEM-770A Fuzzy, Omron, Kyoto, Japan) in the sitting position Mean arterial pressure, 87.00 ± 8.64 107.75 ± 11.12 < 0.001 for three times according to the American Heart Association's standar- mm Hg dized protocol (Perloff et al., 1993). Participants were advised not to Pulse pressure, mm Hg 43.23 ± 8.68 58.59 ± 14.50 < 0.001 ff Sex 0.170 smoke, drink alcohol, co ee, or tea, and to abstain from exercising for Male 10,348 (39.2) 5127 (39.9) at least 30 min before measurement. Additionally, they were not al- Female 16,036 (60.8) 7709 (60.1) lowed to talk during the measurement. The average value of three Educational level < 0.001 measurements was used as the blood pressure measurement for this Low 10,722 (40.6) 6834 (53.2) study. Mean arterial pressure was calculated as DBP + 1/3 Medium 11,210 (42.5) 4418 (34.4) − ff High 4452(16.9) 1584 (12.3) (SBP DBP); pulse pressure was calculated as the di erence between Marital status < 0.001 SBP and DBP (Darne et al., 1989). According to the 2010 Chinese Married or 24,023 (91.1) 11,185 (87.1) guidelines for the management of hypertension (Liu, 2011), hyperten- cohabiting sion in our analysis was defined as having a measured SBP Widowed/single/ 2361 (8.9) 1651 (12.9) ≥ ≥ divorced/ 140 mm Hg or DBP 90 mm Hg, or having a self-report of either separation physician-diagnosed hypertension or anti-hypertension treatment. Income < 0.001 ≤500 RMB 8924 (33.8) 5078 (39.6) 500–1000 RMB 8667 (32.8) 4226 (32.9) 2.4. Covariates ≥1000 RMB 8793 (33.3) 3532 (27.5) Smoking 0.131 We controlled for potential confounders based on the previous lit- Never 19,145 (72.6) 9407 (73.2) erature on air pollution and blood pressure. Demographic covariates Ever 7239 (27.4) 3429 (26.7) Drinking 0.010 included age and sex. Socioeconomic covariates included education Never 20,496 (77.7) 9823 (76.5) level (“low”, “medium” or “high”), marital status (“married/cohabi- Ever 5888 (22.3) 3013 (23.5) tating” or “widowed/single/divorced/separation”), average monthly Physical activity < 0.001 income (“≤500 RMB”, “500–1000 RMB” or “≥1000 RMB”). Health Low 7856 (29.8) 4841 (37.7) behavior covariates included smoking status (“never smoking” or “ever Moderate 10,409 (39.5) 4386 (34.2) ” “ ” “ High 8119 (30.8) 3609 (28.1) smoking ), alcohol drinking status ( never drinking or ever High fat diet 5461 (20.7) 2012 (15.7) < 0.001 drinking”), high fat diet (“yes” or “no”), more vegetables and fruits More vegetables and 11,748 (44.5) 4625 (36.0) < 0.001 intake (“yes” or “no”), physical activity (“low level”, “moderate level” fruits intake or “high level”). Health status covariates included body mass index Family history of 4055 (15.4) 3535 (27.5) < 0.001 hypertension (BMI), family history of hypertension, and type 2 diabetes. Regarding Type 2 diabetes 1808 (6.9) 1896 (14.8) < 0.001 education level, participants with no schooling or participants who attained up to primary school were considered as low education level, a Note: Data are the mean ± standard deviation for continuous variables while those who graduated from junior school were considered as and number (percentage) for categorical variables. medium and those who graduated from senior high school or above were considered as high education level. For smoking status, the re- validation was performed to assess the predictive ability. The results of sponses of “former smoking” and “current smoking” were merged into 2 10-fold cross-validation showed that R and Root Mean Squared Error the variable of “ever smoking” in order to further analysis and make μ 3 (RMSE) for daily prediction was 64% and 17 g/m . For annual pre- comparison with other studies. This is also the case for drinking status. 2 μ 3 diction, R and RMSE was 82% and 9 g/m , respectively. We assigned According to Chinese dietary guidelines (SS Wang et al. 2016), “More PM1 concentration estimates for each participant according to the vegetables and fruits intake” was defined as average intake of 500 g or

Table 2 3 Odds ratios of hypertension and changes in blood pressure (mm Hg) associated with per 1 μg/m increment in long-term exposure to PM1.

Effect estimates Crude modela Adjusted model 1b Adjusted model 2c

OR (95%CI) 1.059 (1.051, 1.068) 1.075 (1.066, 1.084) 1.043 (1.033, 1.053) Changes in mm Hg (95% CI) SBP 0.633 (0.559, 0.707) 0.749 (0.680, 0.818) 0.401 (0.335, 0.467) DBP 0.539 (0.496, 0.582) 0.553 (0.510, 0.596) 0.328 (0.288, 0.369) MAP 0.570 (0.520, 0.621) 0.618 (0.569, 0.667) 0.353 (0.307, 0.399) PP 0.094 (0.045, 0.143) 0.196 (0.154, 0.238) 0.073 (0.030, 0.116)

Abbreviations: PM1, particle matter with aerodynamic diameter ≤ 1.0 μm; OR, odds ratio; CI, confidence interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure. a Crude Model: no adjustment. b Adjusted Model 1: adjusted for age, sex, marital status, education level, income. c Adjusted Model 2: also adjusted for smoking, alcohol drinking, vegetables and fruits intake, high fat diet, physical activity, family history of hypertension, body mass index, type 2 diabetes and hypertension medicine use (not in logistic regression).

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Fig. 2. Odds ratio (95% confidence intervals) of hypertension in association with an increment of 3 1 μg/m in PM1 concentration, stratified by po- tential modifiers. Notes: Adjusted for sex, age, marital status, education level, income, smoking, alcohol drinking, physical activity, vegetables and fruits intake, high fat diet, family history of hy- pertension, body mass index, type 2 diabetes; P denotes the p-value for interaction term.

more vegetables and fruits per day. High fat diet was defined as con- Cohort Study. The basic characteristics of all participants are shown in sumptions of 75 g or more meat from livestock and poultry per day. Table 1. Additionally, we also presented basic characteristic of parti- Physical activity was divided into three levels according to the inter- cipants by five survey sites in Table S1.The mean age of all participants national physical activity questionnaire (IPAQ) (Lee et al., 2011): low, was 55.6 (SD:12.19) years, and the majority were female (60.1%). In moderate, and high. Diabetes was defined as having a fasting plasma total, 12,823 hypertension cases were identified with the prevalence of glucose (FPG) ≥ 7.0 mmol/L, and/or diagnosed as diabetes by a phy- 32.7%. Among the hypertension cases, 7879 (61.4%) were self-reported sician (American Diabetes, 2013). and 4955 (38.6%) were diagnosed as hypertension in the survey, 6319 (49.2%) had taken antihypertensive medication during the past two 2.5. Statistical analysis weeks. Participants with hypertension tended to be older (60.39 versus 53.27, P < 0.001), had a higher level of BMI (26.02 versus 24.21, Descriptive analyses were conducted for all variables. Continuous P < 0.001) than normotensive participants. Also, they were more variables were described as mean ± standard deviations (SD) and ca- likely to have family history of hypertension (27.5% versus 15.4% tegorical variables were expressed as counts and percentage. P < 0.001) and diabetes (14.8% versus 6.9%, P < 0.001) than nor- Differences in the distribution of baseline characteristics between motensive participants. The 3-year average concentration of PM1 for 3 groups were tested using Mann-Whitney U test for continuous variables overall cohort was 55.99 μg/m (SD:2.06), and the hypertensive parti- and chi-square test for categorical variables. We employed the logistic cipants had similar PM1 exposure levels with normotensive participants μ 3 μ 3 regression model to examine the association between long-term PM1 (57.72 g/m and 57.31 g/m , respectively). 3 exposure and hypertension as a dichotomous outcome. And multi- For all participants, the OR of hypertension associated with 1 μg/m variate linear regression models were employed to investigate asso- increment in PM1 concentration was 1.059 (95%CI: 1.051, 1.068) in ciations between PM1 exposure and blood pressure component as a crude model and 1.043 (95%CI: 1.033, 1.053) after fully adjusting for continuous measure. The effect estimates were presented as odds ratios potential confounders (Adjusted model 2). Based on the final adjusted μ 3 (ORs) for hypertension and changes in mm Hg for blood pressure model (Adjusted model 2), each 1 g/m increase in PM1 concentration 3 measures per 1 μg/m increment in PM1 concentration, with corre- was associated with an elevation of 0.401 (95% CI: 0.335, 0.467) sponding 95% confidence intervals (CIs). mm Hg in SBP, 0.328 (95% CI: 0.288, 0.369) mm Hg in DBP, 0.353

We also examined whether the PM1-hypertension and PM1-BP as- (95% CI: 0.307, 0.399) mm Hg in MAP and 0.073 (95% CI: 0.030, sociations were potentially modified by sex, age, smoking status, 0.116) mm Hg in PP (Table 2). drinking status, high-fat diet, more vegetables and fruits intake and The results of stratified analysis for hypertension are shown in Fig. 2 3 physical activity. We did subgroup analyses by each potential modifier, and Table S2. The ORs of hypertension associated with each 1 μg/m fi and a cross-product term was added into separate models to assess the increase in PM1 were signi cantly higher among males, ever-smokers, significance of interaction terms (Liu et al., 2017). The sensitivity ever drinkers, those who had more vegetables and fruits intake and analysis was performed to examine the robustness of results. We did those with a high-fat diet (Fig. 2 and Table S2). Fig. 3 and Table S3 fi sensitivity analyses using average concentrations of PM1 for 1, 2, 4, and presents the results of strati ed analysis for blood pressure measure- 5 years before the survey to evaluate the long-term effects of PM1 ex- ments. The associations between PM1 and four blood pressure compo- posure, and by adjusting for survey site as a covariate. nent measurements were modified by sex, smoking status, drinking All the statistical analyses were completed using R 3.5.0 (R status and physical activity (P for interaction < 0.05). We observed Foundation for Statistical Computing, 2004 (ISBN 3-900051-00-3), stronger positive associations in males, ever-smokers, ever-drinkers and Vienna, Austria). participants with high level of physical activity. Except for PP, the effect modification by more vegetables and fruits intake and high-fat diet 3. Results were also observed for SBP, DBP and MAP (Fig. 3 and Table S3). The results of sensitivity analysis are presented in Table S4. The ff Fig. 1 shows the locations of five survey sites in the Henan Rural e ect estimates of associations between PM1 exposure and

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3 Fig. 3. Changes in four blood pressure components (mm Hg) associated with an increment of 1 μg/m in long-term PM1 concentration, stratified by potential modifiers. Notes: Adjusted for sex, age, marital status, education level, income, smoking, alcohol drinking, physical activity, vegetables and fruits intake, high fat diet, family history of hypertension, body mass index, type 2 diabetes, hypertension medicine use; P denotes the p-value for interaction term.

3 hypertension did not change substantially after using different average range increase (IQR, 41.7 μg/m )inPM2.5 concentration was associated concentration of PM1 but adjusting for the survey site. The results of with an OR of 1.11 (95% CI: 1.05, 1.17) for hypertension and an in- PM1 and blood pressure measurements remained robust in all types of crement of 0.60 mm Hg (95% CI: 0.05, 1.15) in SBP (Liu et al., 2017). A sensitivity analysis (comparing Table S4 with Table 2). study conducted among older Chinese adults showed that each 10 μg/ 3 m increase in PM2.5 concentration was related to an OR of 1.14 (95% CI:1.07, 1.22) for hypertension, an increment of 1.30 mm Hg (95% CI: 4. Discussion 0.04, 3.56) in SBP and an increment of 1.04 mm Hg (95% CI: 0.31, 1.78) in DBP (Lin et al., 2017b). A recent meta-analysis pooled 20 This study suggested that long-term exposure to PM1 was sig- studies globally and reported a significant association between long- fi ni cantly associated with increased prevalence of hypertension and term exposure to PM2.5 and hypertension (OR = 1.05; 95% CI: 1.01, elevated blood pressure in rural Chinese adults. To the best of our 1.09) (Yang et al., 2018a). However, far less information is available fi knowledge, this is the rst study to investigate associations between regarding the effect of long-term exposure to PM1 on hypertension and exposure to PM1 and prevalence of hypertension and blood pressure in blood pressure. In fact, some current researches have indicated that the rural China. We used baseline data from a large cohort study with strict size and surface area were critical characteristics of particulate matter quality control and standardized assessment of outcome and con- that determine their potential to elicit biological effects (Nel, 2005; fi founders. Our ndings can add further to the growing body of evidence Valavanidis et al., 2008). Smaller particles, such as PM1 and ultrafine that particulate matter can be an important risk factor for hypertension particles, have higher surface-to-volumes ratios and can penetrate and perturbations in blood pressure. Considering the large population deeper into the airways of the respiratory tract. Also, they are more of hypertensive patients and the severity of air pollution, our findings likely to persist in the lung parenchyma. Thus, smaller particles can are of remarkable significance for public health. elicit stronger biological effects like oxidative damage and in- There is an increasing number of studies evaluating long-term ef- flammatory injury (Valavanidis et al., 2008). Additionally, it has been fects of exposure to particulate air pollutants on hypertension and blood reported that most of the toxic metals tended to accumulate in the pressure in China. For example, Liu et al. reported that an inter quartile

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smaller particles (PM2.5 or less) and therefore PM1 may contain more hypertension and higher elevations in blood pressure for participants toxins from anthropogenic emissions, which may even lead to gene who were ever smokers and ever drinkers compared with never-smo- damage (Izhar et al., 2016; Ravindra et al., 2008). However, neither kers and never-drinkers. Existing evidence has indicated that both China nor other countries has set air quality standards and guidelines smoking and drinking can promote inflammatory response and oxida- for PM1 because of insufficiency of scientific evidence on this pollutant tive stress (Bazzano et al., 2003; O'Keefe and Gheewala, 2008; Piano, and unavailability of technologies for PM1 measurements (Wang and 2017), which was identified as main biological mechanism of health Hao, 2012). Along with other studies on health effects of PM1 (Chen damage induced by air pollution. Also, drinking can promote mi- et al., 2017; Lin et al., 2016; Yang et al., 2018b), the findings from this tochondrial dysfunction, programmed cell death and anatomical da- study can provide evidence for policy makers when promulgating mage to the cardiovascular system, especially to heart itself (Piano, standards and guidelines for the control of PM1 pollution. 2017). Our study found that long-term exposure to ambient PM1 was po- Previous epidemiological studies found that higher consumption of sitively associated with all four blood pressure component measure- fruits and vegetables may mitigate the cardiovascular effects of parti- ments, while previous studies showed inconsistent results regarding air culate matter (Lin et al., 2017a; Lin et al., 2017b), which agreed with pollution and blood pressure. A recent study conducted among re- hypothesized mechanisms that air pollution induced health damage productive-age adults in China reported that long-term exposure to mainly through oxidative stress. More intake of fruits and vegetables

PM2.5 was in association with increment of both SBP and DBP (Xie improved the status of anti-oxidants and anti-inflammatory micro- et al., 2018). Similar results have also been reported in a cross-sectional nutrient status such as vitamin C, E, carotenoids, B group vitamins and study in Taiwanese adults (Zhang et al., 2018). However, some studies flavonoids, which is in relation to an effect reduction in oxidative stress, showed different results. Liu et al. observed a significant association of inflammation and other adverse reactions induced by ambient air pol-

PM2.5 and SBP, whereas a null association for DBP, in a nation-wide lutants (Bowler and Crapo, 2002; Lin et al., 2017b; Romieu et al., study of Chinese adults aging over 35 years old (Liu et al., 2017). The 1998). However, higher ORs of hypertension and larger elevations in findings from a cohort of older Americans suggested that long-term blood pressure were observed among those who had more intake of exposure to PM2.5 was correlated with SBP but not with DBP (Honda fruits and vegetables (≥500 g per day) in this study, which should be et al., 2018). And yet, Chen et al. observed isolated elevations in DBP, interpreted cautiously. Such inconsistence might be due to exposure but not SBP, with 1-year exposure to air pollution among elderly re- misclassification about intake of vegetables and fruits. Firstly, we sidents of Taipei City (Chen et al., 2015). These mixed results may be considered vegetables consumption and fruits consumption together attributed to differences in characteristics of study population, sources whereas it was categorized as vegetables intake and fruits intake se- or compositions of air pollutants, and measurements of outcomes, as parately in other studies. Secondly, the information on type of vege- well as different statistical methods (Cai et al., 2016). tables and fruits, cooking method and frequency of intake are in- Each blood pressure component measurement quantifies an aspect sufficient, and theses information is critical in examining the effect of of cardiovascular function, and elevations in any component may lead fruits and vegetables intake on air pollution exposure. Because Chinese to increased risk for cardiovascular outcomes. (Darne et al., 1989; accustomed to eating cooked vegetables and there may be destruction Franklin et al., 2001; Honda et al., 2018; Kannel et al., 1981). Our re- and reduction in antioxidant components during the process of cooking. sults showed significant associations of long-term PM1 exposure with all (Lin et al., 2017b). four blood pressure component measurements, which could be relevant In addition, those who had a high-fat diet were found to have for public health initiatives. According to a series of studies, diastolic greater risks of hypertension and larger increases in SBP, DBP and MAP. blood pressure was found to be the strongest predictor of coronary This could be explained that high-fat diet played an important role in heart disease (CHD) development (HR per 10 mm Hg increment, 1.34; the development of obesity (Schrauwen and Westerterp, 2000), which 95% CI, 1.18–1.51) in individuals younger than 50 years old (Franklin may substantially amplify the associations between air pollution and et al., 2001), whereas systolic blood pressure elevations were found to increased blood pressure, even the susceptibility to the adverse health be the greatest risk for incident congestive heart failure (HR per effects of air pollutants (Bray and Popkin, 1998; Zhao et al., 2013). A

10 mm Hg increment, 1.56; 95% CI,1.37, 1.77) among adults aging significantly larger PM1-induced increase in blood pressure was ob- 50–79 years (Haider et al., 2003). Furthermore, the existing research served among participants who had high-level physical activity com- showed that a “small” reduction of 2 mm Hg in the mean of SBP has pared with those who had low-level physical activity. Similar results been estimated to reduce 25% stroke events in the population (Girerd were also reported in studies on the associations between PM2.5 ex- and Giral, 2004). The possible biological mechanism by which parti- posure and other cardiovascular outcomes (Lin et al., 2017a). The culate matter raised blood pressure includes the elicitation of oxidative possible mechanisms might be that people may have a higher level of stress, systemic inflammation, endothelial dysfunction and DNA me- exposure to air pollutants during the outdoor physical activities because thylation (Brook et al., 2010; Pope and Dockery, 2006; C Wang et al. of the increased breathing rates and intensity (Weichenthal et al., 2016). The activation of pulmonary reflexes induced by inhalation of 2014). Thus physically active people had an increased inhalation and particulate matter may cause autonomic nervous system imbalance and deposition of air pollutants in the body, leading to the amplification of arterial remodeling (Brook et al., 2009). Hypertrophic remodeling of harmful effects of air pollution (Strak et al., 2010). resistance vessels may lead to medial thickness, resulting in BP eleva- Several limitations in our study should also be noted. First, this tions (Valavanidis et al., 2008). study used the baseline survey data of the cohort and the onset date of Health effects of particulate air pollution can be modified by many hypertension for each case was unknown. The cross-sectional design of factors. It is of great significance to identify subgroups who are po- study restricted us to a non-causal relationship between PM1 exposure tentially susceptible to the adverse effects of particulate air pollution. and hypertension and blood pressure. Second, the individual exposure

Our findings showed a significantly larger effect of PM1 exposure on to ambient PM1 showed very small differences among the overall co- hypertension and blood pressure in males than females. As prior evi- hort, for the reason that all of five study sites were seriously polluted dence proposed, this sexual discrepancy could be attributed to some areas. Third, demographic information and lifestyle characteristics biological characteristics such as hormones, function of smooth muscle were collected using a questionnaire, thus recall bias may not be and vascular, airway and lung size (citation). Also, this sex-based dif- avoided. Forth, traffic noise was another possible uncontrolled con- ference in effect estimates can be related to some behavioral char- founder in this study, which has been suggested to associate with ele- acteristics (e.g. outdoor activity, smoking and drinking). Men may ac- vated blood pressure and other cardiovascular events (Chang et al., cumulate greater PM1 exposures through long periods of physical 2007; Dzhambov and Dimitrova, 2018; Sears et al., 2018). However, activity (Clougherty, 2010). In addition, we observed larger ORs of considering the cohort study was conducted in rural areas and roads in

100 N. Li, et al. Environment International 128 (2019) 95–102 rural areas are mostly village roads or country lanes, there is no heavy Chen, S.-Y., Wu, C.F., Lee, J.H., Hoffmann, B., Peters, A., Brunekreef, B., et al., 2015. traffic and severe noise in rural areas compared with urban areas. Thus Associations between long-term air pollutant exposures and blood pressure in elderly ffi residents of Taipei City: a cross-sectional study. Environ. Health Perspect. 123, we suspected that rural people may not be impacted by the tra c noise 779–784. and this may not have considerable impact on our results. In addition, Chen, G., Li, S., Zhang, Y., Zhang, W., Li, D., Wei, X., et al., 2017. Effects of ambient pm 1 road condition data in rural areas are relatively scarce in China. Finally, air pollution on daily emergency hospital visits in China: an epidemiological study. fi Lancet Planet. Health 1, e221–e229. there potentially exists larger spatial error of misclassi cation when Chen, G., Knibbs, L.D., Zhang, W., Li, S., Cao, W., Guo, J., et al., 2018a. Estimating geocoding rural address compared with geocoding address in urban spatiotemporal distribution of pm1 concentrations in China with satellite remote environments. sensing, meteorology, and land use information. Environ. Pollut. 233, 1086–1094. Chen, G., Morawska, L., Zhang, W., Li, S., Cao, W., Ren, H., et al., 2018b. Spatiotemporal variation of pm 1 pollution in China. Atmos. Environ. 178, 198–205. 5. Conclusion Clougherty, J.E., 2010. A growing role for gender analysis in air pollution epidemiology. Environ. Health Perspect. 118, 167–176. Darne, B., Girerd, X., Safar, M., Cambien, F., Guize, L., 1989. Pulsatile versus steady This study demonstrated that long-term exposure to PM1 was sig- fi component of blood-pressure - a cross-sectional analysis and a prospective analysis on ni cantly associated with increased risk of hypertension and elevated cardiovascular mortality. Hypertension 13, 392–400. blood pressure measurements in rural Chinese adults. Males, ever- Dzhambov, A.M., Dimitrova, D.D., 2018. Residential road traffic noise as a risk factor for hypertension in adults: systematic review and meta-analysis of analytic studies smokers, ever-drinkers, those with high-fat diet and high-level physical – ff published in the period 2011-2017. Environ. Pollut. 240, 306 318. activity might be more susceptible to the adverse e ect of long-term Franklin, S.S., Larson, M.C., Khan, S.A., Wong, N.D., Leip, E.P., Kannel, W.B., et al., 2001. PM1 exposure. Our findings added further to evidence that PM1 ex- Does the relation of blood pressure to coronary heart disease risk change with aging? posure could be an important risk factor for hypertension and pertur- The Framingham heart study. Circulation 103, 1245–1249. fi GBD, 2018. Global, regional, and national comparative risk assessment of 84 behavioural, bations in blood pressure. In addition, these ndings also suggests a environmental and occupational, and metabolic risks or clusters of risks for 195 necessity for including an index for the concentration of PM1 in Chinese countries and territories, 1990–2017. Lancet 392, 1923–1994. air quality standards for the purpose of more effective mitigation of air Girerd, X., Giral, P., 2004. Risk stiratification for the prevention of cardiovascular com- plications of hypertension. Curr. Med. Res. Opin. 20, 1137–1142. pollution in China. Haider, A.W., Larson, M.G., Franklin, S.S., Levy, D., 2003. Systolic blood pressure, dia- stolic blood pressure, and pulse pressure as predictors of risk for congestive heart Declarations of interest failure in the Framingham heart study. Ann. Intern. Med. 138, 10–16. Honda, T., Pun, V.C., Manjourides, J., Suh, H., 2018. Associations of long-term fine particulate matter exposure with prevalent hypertension and increased blood pres- The authors declare no conflicts of interest. sure in older Americans. Environ. Res. 164, 1–8. Izhar, S., Goel, A., Chakraborty, A., Gupta, T., 2016. Annual trends in occurrence of Funding submicron particles in ambient air and health risk posed by particle bound metals. Chemosphere 146, 582–590. Kannel, W.B., Wolf, P.A., McGee, D.L., Dawber, T.R., McNamara, P., Castelli, W.P., 1981. The authors acknowledge the cooperation of all the participants and Systolic blood-pressure, arterial rigidity, and risk of stroke - the Framingham-study. – administrators in this study. This research was supported by the JAMA, J. Am. Med. Assoc. 245, 1225 1229. Lee, P.H., Macfarlane, D.J., Lam, T., Stewart, S.M., 2011. Validity of the international Foundation of National Key Research and Development Program of physical activity questionnaire short form (ipaq-sf): a systematic review. Int. J. China (Grant NO: 2016YFC0900803) and the Natural Science Fund of Behav. Nutr. Phys. Act. 8, 115. Hubei Province (Granter number: 2018CFB634). Dr. Guo is supported Lin, H., Tao, J., Du, Y., Liu, T., Qian, Z., Tian, L., et al., 2016. Particle size and chemical constituents of ambient particulate pollution associated with cardiovascular mor- by Career Development Fellowship APP1107107 from the Australian tality in Guangzhou, China. Environ. Pollut. 208, 758–766. National Health and Medical Research Council (NHMRC). Dr. S. Li is Lin, H., Guo, Y., Di, Q., Zheng, Y., Kowal, P., Xiao, J., et al., 2017a. 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