To the Editor: Air pollution can lead to respiratory and cardiovascular diseases (CVDs), and its impact on human health is very significant. Air particles less than 0.1 µm can pass through the blood brain barrier, reach the brain, and enter the central nervous system, causing physiological and pathological changes. A study shows that long-term exposure to fine particulate matter (PM2.5) will increase the risk of CVDs among Chinese residents.[1] Another study found that a short time exposure to low concentrations of air pollutants may also lead to the onset of acute coronary syndrome within one hour.[2] A meta-analysis based on seven databases showed that environmental air pollution was positively correlated with elevated blood pressure and hypertension.[3] Mounting evidence indicates that air pollution can cause cardiovascular and cerebrovascular diseases and affect human health furtherly.

Guizhou province is located in the southwest of China. Its unique geoimagedataal location, lifestyle, and economic development level contribute to lower prevalence of hypertension in Guizhou province compared to other provinces. The air pollution in Guizhou province is relatively light, with a large coverage of green plants, resulting in good air quality. However, exposure to low concentrations of air pollutants still have a certain impact on health. This study aims to explore the relationship between the prevalence of hypertension and the four main air pollutants (sulfur dioxide [SO2], nitrogen dioxide [NO2], PM2.5, and particulate matter [PM10]) in Guizhou province, clarify the impact of air pollutants on hypertension, and provide a foundation for the prevention and control of hypertension related to air pollution.

The monthly concentrations of SO2, NO2, PM2.5, and PM10 in Guizhou province from January 2016 to December 2021 were collected through the Monthly Report on Environmental Quality of Guizhou Province, issued by the Department of Ecological Environment of Guizhou province. The selected observation cities were Guiyang, Zunyi, Tongren, Bijie, Qiannan, and Qianxinan of Guizhou province. A special project called the “Prevalence Survey of Important CVDs in China and Research on Key Technologies” gathered the CVD status of residents of the above six cities through a questionnaire survey. Finally, 13,476 subjects were recruited and the related data, including information on residents suffering from hypertension and data on four types of air pollutants (SO2, NO2, PM2.5, and PM10), were collected, collated, and analyzed.

SPSS software (Version 25.0, IBM Corp., Chicago, IL, USA) was used for general descriptive analysis, chi-squared test, and a multiple linear regression model to explore the impact of air pollutants on blood pressure. According to previous studies, age, sex, nation, marital status, employment status, education level, body mass index (BMI) and location were controlled in the basic model. Seasonal factors variables were further added to the final model to illustrate the potential seasonal changes in blood pressure. In multivariate unconditional logistic regression analyses, hypertension was used as the dependent variable, the covariates included age, gender, marriage, education level, occupation, employment status, ethnicity, region, and BMI, and the quartile concentrations of NO2, SO2, PM2.5, and PM10 in cold and warm months were used as the independent variables. The first quartile of each pollutant was used as the reference value. Trend test was used to calculate the trend of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with the change of pollutant concentration. Graphpad Prism (version 6, GraphPad Software, California, SD, USA) was used to make a box chart to show the summary of pollutants. Hypertension was defined as either SBP ≥140 mmHg or DBP ≥90 mmHg. The warm months, from April to October, and cold months, from November to March, were defined by the average monthly temperatures of ≥16°C and <16°C, respectively.

A total of 13,476 residents in Guizhou province were finally included in this study. The prevalence rate of hypertension was 25.9% (3488/13,476). There were statistically significant difference in age, marital status, education level, occupation, employment status, ethnicity, region, and BMI between residents with and without hypertension (all P <0.05) [Supplementary Table 1, https://links.lww.com/CM9/B822].

The average concentrations of PM2.5, PM10, SO2, and NO2 were 32.65 µg/m3, 59.00 µg/m3, 24.98 µg/m3, and 21.48 µg/m3, respectively, which are lower than the national secondary standard for ambient air quality [Supplementary Figure 1, https://links.lww.com/CM9/B822]. As shown in Supplementary Figure 2, https://links.lww.com/CM9/B822, the monthly average concentrations of the four pollutants obviously higher in cold months than in warm months, especially the particulate matter PM2.5 and PM10, which were both highest in December, and then decreased in the warm months, falling to the lowest concentrations in June and July. NO2 and SO2 concentrations were higher in the cold months, and had a relatively small fluctuation all year.

In the warm months, the third quartile and fourth quartile of NO2 were risk factors for elevated SBP of Guizhou residents, and the odds ratio (OR) values were 3.55 (95% confidence interval [CI]: 2.17, 5.80) and 1.94 (95% CI: 1.17, 3.21), respectively. When assessing the risk of elevated DBP, we found that the OR value of the third quartile of NO2 was 1.98 (95% CI: 1.04, 3.76), which caused an increase in the risk of hypertension [Table 1].

Table 1 - Effects of air pollutants on SBP and DBP in different seasons using multivariate unconditional logistic regression analyses. Items Warm months (from April to October) Cold months (from November to March) SBP DBP SBP DBP OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value NO2(µg/m3) Q1 (<16) Reference – Reference – Reference – Reference – Q2 (16–22) – – – – 1.51 (1.28, 1.78) <0.01 1.30 (1.02, 1.65) 0.03 Q3 (23–27) 3.55 (2.17, 5.80) 0.01 1.98 (1.04, 3.76) 0.04 1.39 (1.21, 1.59) <0.01 1.39 (1.14, 1.69) <0.01 Q4 (>27) 1.94 (1.17, 3.21) 0.01 1.61 (0.83, 10.00) 0.16 – – – – P for trend 0.14 0.90 0.32 0.38 SO2 (µg/m3) Q1 (<12) Reference – Reference – Reference Reference – Q2 (12–17) – – – – 1.16 (0.96, 1.42) 0.13 1.37 (1.05, 1.76) 0.02 Q3 (18–41) – – – – 0.36 (0.30, 0.45) <0.01 – – Q4 (>41) 0.89 (0.74, 1.08) 0.24 0.99 (0.70, 1.41) 0.97 – – – – P for trend – – 0.37 – PM2.5 (µg/m3) Q1 (<16) Reference – Reference – Reference – Reference – Q2 (16–31) – – – – 1.25 (1.08, 1.43) <0.01 1.13 (0.94, 1.38) 0.18 Q3 (32–41) – – – – 1.09 (0.96, 1.26) 0.19 – – Q4 (>41) 0.51 (0.41, 0.61) 0.01 – – 1.08 (0.95, 1.24) 0.25 2.09 (1.64, 2.65) <0.01 P for trend – – 0.14 0.25 PM10 (µg/m3) Q1 (<32) Reference – Reference – Reference – Reference – Q2 (32–65) – – – – 0.67 (0.50, 0.90) <0.01 – – Q3 (66–81) 0.75 (0.56, 1.01) 0.06 0.71 (0.48, 1.05) 0.08 0.48 (0.36, 0.65) <0.01 0.91 (0.73, 1.12) 0.37 Q4 (>81) – – 0.69 (0.47, 1.01) 0.06 – – – – P for trend – 0.90 <0.05 –

CI: Confidence interval; DBP: Diastolic blood pressure; NO2: Niteogen dioxide; OR: Odds ratio; PM2.5: Fine particulate matter; PM10: Particulate matter; SBP: Systolic blood pressure; SO2: Sulfur dioxide; –: Not available.

In cold months, the second and third quartiles of NO2 were associated with increased risk of SBP, with OR values of 1.51 (95% CI: 1.28, 1.78) and 1.39 (95% CI: 1.21, 1.59), respectively. And the second and third quartiles of NO2 increased risk for DBP, with OR values of 1.30 (95% CI: 1.02, 1.65) and 1.39 (95% CI: 1.14, 1.69), respectively. The second quartile of SO2 was associated with risk factor of DBP, with OR value of 1.37 (95% CI: 1.05, 1.76). The OR value of the second quartile of PM2.5 was 1.25 (95% CI: 1.08, 1.43) for SBP in cold months. For DBP, the fourth quartile of PM2.5 was associated with increased risk factor, with OR value of 2.09 (95% CI: 1.64, 2.65) [Table 1]. These results suggest that air pollutants can increase the risk of hypertension to some extent. Besides, trend test found that changes in PM10 concentrations may have an impact on SBP in Guizhou residents.

The changes of SBP and DBP with increased pollutant concentration (per 10µg/m3) under different multiple linear regression models are presented in the Supplementary Table 2, https://links.lww.com/CM9/B822. When assessing the impact of pollutants on SBP, PM2.5 and NO2 were statistically significant in the original and final models, while PM10 and SO2 were statistically significant in unadjust, basic, and final models. When assessing the impact of pollutants on DBP, PM2.5 and SO2 had statistical significance in all three models, while PM10 and NO2 had statistical significance only in the final model.

According to survey responses, the prevalence of hypertension among residents is 25.9% in Guizhou province. It is found that except for gender, the prevalence of hypertension among residents of different ages, marital status, education, occupation, employment status, ethnic group, region, and BMI is statistically significant. Air pollutants in Guizhou province were at a low level throughout the year and the monthly variation characteristics of pollutants in Guizhou province showed a characteristic with higher level in cold weather and lower level in warm weather, which may due to the heavy use of coal in cold weather.[4]

According to the results of multi-factor unconditional logistic regression, in the warm months, NO2 has statistical significance impacting SBP and DBP. SBP data indicated that the third and fourth quartiles of NO2 are related to a higher risk of hypertension, and the concentration of NO2 in the third quartile accounted for a part of the increase of DBP. NO2 and SO2 are the air pollutants that had the greatest influence on SBP and DBP in the cold months. Guizhou residents are used to burning coal for heating in winter, and the increased content of SO2 discharged into the air is another air pollutant that may cause high blood pressure. PM2.5 has a stronger risk of causing hypertension than PM10. This study confirms our hypothesis that exposure to air pollutants will influence blood pressure. In the fully adjusted final model, DBP and SBP are positively correlated to air pollutants (NO2, SO2, PM10, PM2.5). This study suggests that it is very important to control the concentration of pollutants in the air.

In summary, the concentrations of the main air pollutants in Guizhou province are at low level, and the monthly average concentration of pollutants shows a trend of “high cold and low warm.” Although the main air pollutants in Guizhou province are at a low concentration all the year, they will also affect SBP and DBP, and thus affecting the incidence of hypertension. This study suggests that air pollutants are associated with hypertension among Guizhou residents. This is a problem that cannot be ignored. Focusing on the impact of low concentrations of air pollutants on blood pressure is the first step toward reducing the prevalence of hypertension among Guizhou residents.

Funding

This study was funded by grants from the Science and Technology Support Program of Guizhou Province (No. Qiankehe Zhicheng [2020]4Y171), Science & Technology Program of Guizhou Province (No. QKHPTRC-CXTD [2022]014), and Key Project of Postgraduate Teaching Reform of Zunyi Medical University (No. ZYK011).

Conflicts of interest

None.

References 1. Liang F, Liu F, Huang K, Yang X, Li J, Xiao Q, et al. Long-term exposure to fine particulate matter and CVDs in China. J Am Coll Cardiol 2020;75:707–717. doi: 10.1016/j.jacc.2019.12.031. 2. Chen R, Jiang Y, Hu J, Chen H, Li H, Meng X, et al. Hourly air pollutants and acute coronary syndrome onset in 1.29 million patients. Circulation 2022;145:1749–1760. doi: 10.1161/CIRCULATIONAHA.121.057179. 3. Yang BY, Qian Z, Howard SW, Vaughn MG, Fan SJ, Liu KK, et al. Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis. Environ Pollut 2018;235:576–588. doi: 10.1016/j.envpol.2018.01.001. 4. Xing L, Jiang Z, Lu L, Chuan L. Analysis on the current situation and change trend of ambient air quality in Guizhou province (in Chinese). Sci Technol 2018;27:137–152. doi: 10.19392/j.cnki.1671-7341.201827123.