In this study, we investigated the effect of PM2.5 exposure on BP using the on-going large population follow-up program in China. The estimated association remained robust after adjusting for a wide range of confounders. Nonlinear regression verified that the decreasing trend in BP with decreases in PM2.5 was almost linear. However, there was a threshold at about 70 µg/m3 for the effect of FPM1Y on SBP. Overall, our study supports the hypothesis that reducing PM2.5 was significantly associated with a decrease of BP.
Several cross-sectional studies have been conducted to examine the relationship between PM2.5 exposure and BP. Li et al. reported a positive association of long-term exposure to air pollution with both SBP and DBP using a cross-sectional study of 39,207 participants in Henan Province, China (Li et al., 2020). Xie et al. observed increases of 0.569 (95% CI: 0.564–0.573) mmHg in SBP and 0.384 (95% CI: 0.381–0.388) mmHg in DBP with 10 µg/m3 increase in PM2.5 above 47.9 µg/m3, which was conducted among 39 million people across 2,790 counties of 31 provinces in China (Xie et al., 2018b). These previous cross-sectional studies provide valuable evidence about the relationship between PM2.5 and BP. In addition, some cohort studies have also been conducted to examine this relationship. For example, Zhang et al. reported a positive associations of PM2.5 exposure with both SBP and DBP in their cohort study of 361,560 participants in Taiwan (Zhang et al., 2018). Adar et al. reported decreases in pollution and BP over time among 5,527 older adults in a long-term follow-up prospective cohort (Adar et al., 2018). Overall, these cohort or cross-sectional studies provide certain evidences about the positive associations between PM2.5 and BP. Overall, the findings in our study were consistent with those in the previous reports. However, our study provided more valid evidence in consideration of the study method and exposure scenario. It has been well known that repeated-measurement studies have a stronger ability to verify causality than cross-sectional studies, which has been widely used as a special study design in environmental epidemiology. However, it is difficult to conduct such studies on large-scale populations due to the high cost and requirement for frequent visits. The sample sizes of previous repeated-measurement studies on the relationship between PM2.5 and BP were less than several hundreds of participants to the best of our knowledge (Mu et al., 2014; Huang et al., 2018b; Santos et al., 2019). Our repeated-measurement study from the CHARLS survey including a large sample size of 14,080 participants with at least two valid visits had a great advantage.
Likewise, we chose the exposure scenario of these population with the historically strict enforcement of air pollution prevention and control plan in China. Such quasi-experimental study provides a unique chance to test our hypothesis. Many countries have mitigated air pollution in past decades and the benefits of reducing the population prevalence of hypertension and other outcomes have been documented (Laden et al., 2006; Pope et al., 2009; Bo et al., 2019b). For example, a previous study observed that reducing long-term PM2.5 is associated with a lower risk of hypertension when air pollution is considerably mitigated among adults in Taiwan during 2001–2014 (Bo et al., 2019a). Huang et al. discovered the potential benefits of air pollution control in urban China by assuming different air quality improvement scenarios. They reported that a mean PM2.5 reduction to Beijing Olympic levels by 2030 would gain about 241,000 (95% uncertainty interval, 189,000–293,000) life-years annually (Huang et al., 2017). Wang et al. estimated the PM2.5-associated disease burden using models of virtual scenarios and reported that improving air pollution would reduce the number of PM2.5-related premature deaths in China (Wang et al., 2019b). Our study examined the benefits of improving air quality on BP with a stronger causal association using a natural scenario of policy-driven air quality improvement in China.
We also adopted a nonlinear regression model to verify that BP increased linearly with the increase of FPM1Y when the FPM1Y concentration was < 70 µg/m3. Notably, a threshold was observed for the relationship between FPM1Y and SBP. By contrast, a repeated-measures study conducted in China reported that both SBP and DBP increase linearly with PM2.5 when its concentration is < 50 µg/m3. There is a threshold in the dose-response curve between PM2.5 and SBP, as well as DBP (Chang et al., 2015). Fan et al. reported a “J” shaped concentration-response curve for the relationship between PM2.5 and SBP using a generalized additive mixed model. They observed remarkable increases in SBP when PM2.5 concentrations were > 100 µg/m3, whereas no significant changes in SBP were observed at PM2.5 concentrations < 100 µg/m3 (Fan et al., 2019). A cross-sectional study of 4,121 elderly people conducted in the United States analyzed the dose-response curve between PM2.5 and SBP. They found that SBP increases approximately linearly with the increase of PM2.5 without an obvious threshold (Honda et al., 2018), which was possibly due to the relatively lower average PM2.5 concentration of 10.4 µg/m3. Overall, the thresholds could not be determined in our study, which may due to a relatively lower average FPM1Y of 58.0 µg/m3.
Without adjusting the statistical results by FDR method, the effect of PM2.5 on DBP was stronger in urban participants than rural participants. Also, the effect of PM2.5 on DBP seemed to be stronger in the married population than the single population. The similar phenomenon was ever observed in a nationwide cross-sectional study of among 17,708 participants in China. They reported the effects of PM2.5 on hypertension prevalence were stronger among middle-aged, obese and urban participants (Liu et al., 2017). One possible reason is that the PM2.5 composition in rural and urban areas is different. For example, automobile exhaust and coal combustion generally as contributions in urban areas (Wu et al., 2015), while the residential solid fuel burning was an important source. Meng et al. summarized the contributions of nonresidential and residential activities to ambient PM2.5 concentrations. They reported that for rural areas where PM2.5 levels in the indoor air (95 ± 34 µg/m3) were significantly higher than those in urban indoor air (58 ± 23 µg/m3) because of the strong dependence of rural residents on solid fuels. In contrast, ambient PM2.5, which is mainly from nonresidential sources, was higher in urban (45 ± 19 µg/m3) than in rural areas (22 ± 15 µg/m3) (Meng et al., 2019). Interestingly, the married population was more sensitive to the adverse effects of PM2.5 on BP. Previous studies reported that married and single people have different economic conditions, living habits, and psychosocial stress (Hicken et al., 2014; Weaver et al., 2019). Li et al. determined that the effect of PM2.5 on BP can be modified by behavioral factors, including tobacco smoking, drinking alcohol, high-fat diet, and frequent physical activity (Zhang et al., 2018; Li et al., 2019). In our study, the average age, as well as the proportions of participants with high frequencies of tobacco smoking and drinking alcohol, were larger in single participants than those in married participants. In other words, age and living habits (smoking and drinking alcohol) may play a more important role in single participants than in married participants. It suggested that the BP of married population may be more sensitive to the PM2.5 exposure. However, these results cannot be well explained using the current data and more evidence from additional studies are still needed.
Our study has two important limitations. First, the PM2.5 exposure assessment was based on historical estimates; we did not conduct exact personal exposure measurements, nor did we have information on indoor air quality. This uncertainty in the PM2.5 concentration could lead to exposure misclassifications and bias the results. Similarly, coarseness in the exposure assessment due to the lack of addresses could also lead to exposure misclassification, despite that previous studies used a similar method, e.g., a six U.S. cities prospective cohort study measured air-pollution data in each community at a centrally located air-monitoring station (Dockery et al., 1993). Second, the underlying mechanisms for the modifying effects of population residence and marital status cannot be well explained using the current information. However, to the best of our knowledge, our study examined the largest population to investigate the effect of PM2.5 on BP using the repeated-measurement study design conducted in China. Particularly, our study results provided the direct evidences on the protective effects of the improved air quality on the blood pressure. Above all, our conclusion warrants further studies for confirmation.