Long-term ambient PM_2.5 exposure associated with cardiovascular risk factors in Chinese less educated population

Despite the improvements in the air quality of China, air pollution still plays an important role in public health issues during the past decades. The ambient fine particulate matter (particles with an aerodynamic diameter ≤ 2.5 μm, PM_2.5) might contribute to 1.1 million (95%CI: 1.0 million to 1.8 million) deaths in China [1], with the most common reason as cardiovascular disease (CVD) mortality [2, 3]. As strong predictors for 10-year risk of CVD [4], the prevalence of hypertension, diabetes, dyslipidemia and overweight was 24.3, 4.3, 49.3 and 32.0%, respectively [5], according to the analysis of 23,010 subjects from Chinese Physiological Constant and Health Condition (CPCHC) conducted from 2007 to 2011. Intervention of risk factors mentioned above would undoubtedly decrease the burden of cardiovascular disease.

Association between the cardiovascular risk factors and PM_2.5 was vastly investigated, but the result remained inconsistent, with some reporting positive association and some reporting non-association even negative association [6‐13]. Moreover, most research was conducted in North America and Europe where annual PM_2.5 is below 35μg/m³, with only a few studies conducted in developing countries like China, which often suffer a much higher concentration and greater domestic variability [14]. Even those conducted in developing countries were region-based [5], hardly covering the full PM_2.5 spectrum and effects of ethnicity. Meanwhile, previous research suggested that education was a strong modifier of health effects of air pollution [15, 16], and studies exploring the association between PM_2.5 and cardiovascular risk factors in China mainly focused on population with preliminary or below education attainment [12, 17‐20]. Considering evidence for the increasing prevalence of diabetes, hypertension, hyperlipidemia and overweight in those newly industrialized countries were emerging [21‐24], it was of great significance to explore the potential associations between PM_2.5 and these metabolic disorders in a nationwide population-based database.

Hence, we aimed to investigate the association between long-term exposure to PM_2.5 and the prevalence of major cardiovascular risk factors, including hypertension, diabetes, hyperlipidemia and overweight. Because of the air pollution exposure connected with the social-economic status of the population, the modification effect of education level as an indicator of social-economic status was also analyzed.

82,806 participants were in enrolled in the CPCHC survey (Fig. 1). Among 36,623 participants who were selected to participate in biochemical tests, 29,919 of them completed a standard questionnaire surveying demographic information (age, sex, education, ethnicity, etc.) and lifestyles (smoking status, drinking status, intensity of physical activity, diet types, etc.). Excluding 1091 participants with missing data on BMI, WC, SBP or DBP and 322 participants without data on FBG, TG, TC, LDL-C or HDL-C, the remaining 19,236 participants whose age older than 18 years old were selected for the present study.

The mean age was 42.8 ± 16.1 years (Table 1). 53.0% of the studied population were female. 22.2% of the participants attained preliminary school or below education, and 58.6% of the studied population attained at least a high school education. Most of the participants were Han ethnicity (62.9%), nonsmoker (78.9%), nondrinker (76.9%), and moderate physical activity (53.8%). A small proportion of the participants reported diet types as spicy diet (22.0%), light diet (18.7%), sugar-rich diet (13.0%), salt-rich diet (10.7%), or fat-rich diet (9.0%). Nearly half of the participants were urban residents (50.8%) and came from southern China (53.2%). In general, 755 (3.9%), 4383 (22.8%), 5391 (28.0%) and 7727 (40.2%) of the participants had diabetes, hypertension, hyperlipidemia, and overweight, respectively. The median concentration of PM_2.5 was 33.4 (IQR 14.8–53.4) μg/m³ in the surveyed cities, covering northern, southern, eastern and western China (Fig. S2). Chengdu was reported with the highest concentration (94.7 μg/m³), while the lowest level of PM_2.5 exposure was documented in Xiwuqi (8.0 μg/m³) (Fig. S3). The prevalence of hypertension and hyperlipidemia were significantly lower in the urban region (Table S1). The prevalence of hypertension and overweight were significantly lower in southern China, but the prevalence of hyperlipidemia was higher (Table S2).

In general, each 10 μg/m³ increment of 5-year PM_2.5 exposure was associated with hypertension (OR = 1.022, 95%CI: 1.001, 1.043), hyperlipidemia (OR = 0.939, 95%CI: 0.921, 0.957), and overweight (OR = 0.926, 95%CI: 0.910, 0.942), after adjustment of age, sex, education, ethnicity, smoking, drinking, intensity of physical activity and diet types (Fig. 2). Education was the only covariate that significantly interacted with PM_2.5 on association with all the interesting risk factors (P for interaction: diabetes< 0.001, hypertension < 0.001, hyperlipidemia = 0.004, overweight < 0.001, respectively.) (Fig. 3). In general, PM_2.5 was associated with increased prevalence of diabetes (OR = 1.118, 95%CI: 1.037, 1.206), hypertension (OR = 1.101, 95%CI: 1.056, 1.147) and overweight (OR = 1.071, 95%CI: 1.030, 1.114) in population with preliminary or below education attainment, but decreased prevalence of hyperlipidemia (OR = 0.915, 95%CI: 0.878, 0.953). The exposure-response analysis revealed that PM_2.5 had near-linear relationship with diabetes, hypertension and hyperlipidemia in less-educated population, but the prevalence of overweight was firstly increased and then decreased with the increase of PM_2.5 exposure (Fig. 4). The corresponding relationship in the general population, as well as in the well-educated population, was different from the less-educated, especially the association between PM_2.5 and diabetes in the less-educated, the general, and the well-educated population, which were positive, null and inverse respectively (Figs. S4, S5).

In sensitivity analyses, we introduced the covariates stepwise in the regression models, and the regression result tended to be stable after being adjusted by age, sex, education and ethnicity, and model 7 was used in this study which included age, sex, education, ethnicity, smoking status, drinking status, intensity of physical activity and diet types (Fig. S6). The PM_2.5 exposure values defined by different time duration were highly correlated, and the model stayed stable after changing the PM_2.5 exposure used in the regression model (Fig. S7). Meanwhile, the association between binary PM_2.5 exposure and cardiovascular risk factors was similar to the results of continuous PM_2.5 exposure (Fig. S8). Stratified by disease status, we found that the general trend of PM_2.5 association with each risk factor was generally stable. However, hypertension (P for interaction < 0.001 for hyperlipidemia; P for interaction = 0.009 for overweight) and overweight (P for interaction = 0.039 for diabetes; P for interaction = 0.041 for hypertension; P for interaction < 0.001 for hyperlipidemia) were the risk factors that interacted with PM_2.5 (Fig. 5). Residence strongly interacted with PM_2.5 on all the risk factors (Fig. S9), but region significantly interacted with PM_2.5 only on hypertension (Fig. S10). Besides, in models adjusted for age, sex, education, ethnicity, smoking, drinking, the intensity of physical activity, diet types, and residence, the results remained robust compared to models without residence adjustment (Figs. S11, S12, S13, S14).

In this study, we firstly used a multi-provincial and cross-ethnical study in China to demonstrate that long-term high concentration PM_2.5 exposure (8.0–94.7 μg/m³) was positively associated with diabetes, hypertension, and overweight in the participants with poor education, but not in the well-educated population of Chinese adults. After adjusting for several potential confounders, the negative association between PM_2.5 exposure and hyperlipidemia remained robust and significant among 19,236 multiethnic Chinese adults.

Our findings suggest that higher long-term PM_2.5 exposure was positively associated with diabetes, hypertension, and overweight in the participants with poor education. Significant positive association between PM_2.5 and diabetes, hypertension and overweight was also reported in several cohort studies, including a few Chinese cohorts [7, 9, 31‐33], and regions with lower PM_2.5 concentration, like American [34, 35], Canadian [36, 37], and Korean [38]. Our results were also consistent with several cross-sectional studies among the Chinese population [12, 17‐20]. Participants with preliminary or below education attainment accounted for 63.23, 63.5, 59.5, 69.2 and 68.1% on Yang’s study on diabetes [20], Liu’s study on diabetes [19], Lin’s study on hypertension [17], Liu’s study hypertension [18], and Yang’s study on overweight [12].

However, we did not observe significant association between PM_2.5 and diabetes or hypertension in participants with middle school or above education attainment. This discrepancy may partly be due to education was a strong modifier of the health effects of air pollution [15]. As an important indicator of socio-economic status (SES), education may modify the health effects of air pollution in several ways. First, the less-educated were more associated with higher pollution exposure in their working environment, such as mining, heavy industrial, construction and other outdoor jobs. Second, smoke produced by traditional biofuel burning and insufficient ventilation in kitchen may aggravate household pollution, which is also more common in the low SES population. Evidence also exists suggesting that individuals with lower education are generally more exposed to more PM_2.5 pollution than those with higher education in the workday, or the workplace, even at home [39]. Third, lower education groups might lack access to medical care and awareness of disease, resulting in worse health outcomes [40]. Fourth, lower SES was related to higher stress levels, thus affecting the neuroendocrine dysfunction and leading to development of diabetes and hypertension [16, 41]. Several other cohort studies also investigated the association between diabetes and hypertension but made null conclusions, including American cohorts [6, 8], and German cohort [42], which may partly be due to modification of education attainment. We also reported that PM_2.5 would decrease the prevalence of overweight in better-educated groups, which was consistent with a previous study revealing populations with high SES were less likely to be obese and vice versa [43]. Income-level and peer effects can partly explain the modification of education on obesity [44]. Dose-response curve revealed a non-linear effect of PM_2.5 on overweight, which was also reported in previous literature. According to de Bont et al. [45], the association between PM_2.5 and obesity was most evident for PM_2.5 from 20 to 28 μg/m³ but not higher, a range lower than the median value than our research. This could be attributed to both the dosage saturation effect and other social factors. In the past few decades, air pollution, especially PM_2.5 has drawn extensive attention in China due to information spread by social media, television, newspaper, etc. Corresponding initiatives against air pollution might thus result in better health conditions, especially those who perceived earlier and took it carefully, which coincided with the high-educated population in this study. Nonetheless, such a protective effect might be absent in lower PM2.5 exposure region, which might explain the non-linear effect. Similar effect could be observed for hypertension, which also showed a protective trend at the high end of the PM_2.5 spectrum. However, caution should be taken as this subgroup has smaller sample size. More investigation should be taken before any definite conclusion.

We also observed that health effect of PM_2.5 was different between the male and the female. The difference between males and female was explored in previous studies [15, 46, 47], but the patterns remained inconsistent. Region interacted with PM_2.5 on the prevalence of hypertension, which might be explained by the association between region and temperature, while the lower outdoor temperature is strongly associated with a higher prevalence of hypertension [48, 49]. Urban-rural disparities in the prevalence of diabetes [50], hypertension [51], dyslipidemia [52] and overweight [53] in China have been reported. Urban or rural residence strongly interacted with PM_2.5 on the prevalence of each cardiovascular risk factor. Residence was also an indicator of SES as education, and educational attainment and residence were also highly correlated in this study (data not shown). The rural-to-urban migration trend [54] and urbanization degree would change the risk of acquiring chronic diseases [55], but relevant data were not recorded in CPCHC survey, which may result in biased estimation of rural-urban effect. These results may have occurred by chance or may reveal underlying challenges in analyses of air pollution where multiple, highly correlated attributes of air pollutions of an individual’s environment can potentially play a role.

Only a few studies investigated the relationship between long-term PM_2.5 exposure and cholesterol or hyperlipidemia, and the findings remained inconsistent [10, 11, 13, 56‐58]. In this study, we demonstrated that PM_2.5 exposure was associated with decrease in the prevalence of hyperlipidemia in both low and high educational groups. However, no significant association was reported in a cross-sectional study in the USA [58], and a 12-month PM_2.5 exposure cohort study of midlife American women [10]. Meanwhile, previous studies conducted in different provinces of China revealed a significant positive association between PM_2.5 exposure and cholesterol level [11, 56, 57]. The inconsistency among those studies may result from the following reasons. Firstly, annual PM_2.5 was below 35μg/m³ in most research mentioned above, much lower than our research. Even previous studies conducted in China were province-based and had not covered the full spectrum of PM_2.5 concentrations considered in our analyses. This may suggest the association between PM_2.5 and lipid might be concentration-dependent. Secondly, sex may influence the health effect of PM_2.5, and our analysis for female participants revealing null association as in the midlife-American women study [10]. Thirdly, ethnicity may be a strong mediator of metabolic syndrome [59], but previous Chinese studies included only participants in eastern China, including Taiwan [56], Liaoning [57], and Henan [11], and the majority of participants were Han ethnicity. Health-related behaviors and lifestyle factors of different ethnicity would shape different lipid profiles, thus modifying the effect of air pollution [60]. Mao et al. also reported that increased PM_2.5 pollution was associated with decreased TG in the rural population of China [11], which also indicated the difficulty to explore the health effect of PM_2.5.

The mechanisms underlying PM_2.5 and cardiovascular risk factors have been vastly studied. Inhalation of particulate matter by lung can lead to autonomous nervous system imbalance, lung inflammation and alveolocapillary translocation. It was also followed by releasing of catecholamines and systemic inflammation, resulting in various subclinical and clinical disorders, including diabetes and hypertension, as indicated in our study focusing on participants with preliminary or below school education [61]. Recent experimental evidence suggested that long-term and heavy PM_2.5 exposure would reduce total cholesterol level and triglyceride level, leading to lipid peroxidation and dyslipidemia, which may partly explain the discrepancy between our study and previous conclusions [62]. Another recent study on mice revealed that female mice were more susceptible to ambient PM_2.5 exposure-induced hepatic cholesterol accumulation, indicating that health effect of PM_2.5 may be sex-dependent [63]. The mechanisms underlying the association between PM_2.5 and cholesterol and body weight should be furthered explored.

To our best knowledge, this is the largest multi-provincial and multi-ethnic study in developing countries to date and covered the broadest PM_2.5 spectrum, providing evidence for health effect of PM_2.5 in both high concentration and low concentration. Previous studies conducted in American and European countries fail to cover the impact of high PM_2.5 concentration (>35μg/m³) on CVD risk factors. This research also demonstrated population with lower education might be especially susceptible to PM_2.5 association with CVD risk factors.

Long-term PM_2.5 exposure was associated with higher prevalence of diabetes, hypertension, and overweight in the less-educated general population, which contributed to the cardiovascular disease. The robust protection effect of high-concentration PM_2.5 exposure in hyperlipidemia needs more studies.