Associations of Long‐Term Exposure to Fine Particulate Constituents With Cardiovascular Diseases and Underlying Metabolic Mediations: A Prospective Population‐Based Cohort in Southwest China

Abstract

Cardiovascular disease (CVD) remains a predominant threat to public health, being the leading cause of mortality. ¹ In 2019, ischemic heart disease (IHD) accounted for an aggregate of 182 million disability‐adjusted life‐years, while for stroke it was 143 million. ² As a ubiquitous risk factor, ambient air pollution contributes heavily to the CVD‐related disease burden for the entire population. ^{3 , 4} Recent research indicates that disease burden attributed to particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) has notably shifted from developed to developing countries. ² Therefore, research on PM_2.5‐related topics to combat CVD is both necessary and urgent.

The composition of PM_2.5 encompasses a range of chemical constituents. These include black carbon (BC), organic matter (OM), nitrate (NO3−), chloride (Cl⁻), sulfate (SO42−), and ammonium (NH4+), among others. ⁵ While the PM_2.5 concentration, known as PM_2.5 mass, is undeniably important, the chemical constituents of PM_2.5 are of greater significance. This is because the composition can differ depending on the environment in which one resides, which, in turn, can cause varying health effects of PM_2.5. ⁶ Most of the current studies focusing on PM_2.5 and CVD have used PM_2.5 mass concentration as exposure. However, the toxicity and underlying mechanisms associated with the chemical components of PM_2.5 remain to be fully understood. ⁷ In addition, given that multiple chemical constituents coexisted in PM_2.5, exposure to PM_2.5 essentially equates to simultaneous exposure to these various constituents. Hence, it is reasonable to evaluate the joint associations between these constituents and CVD risks and determine the most detrimental one. However, current research used each PM_2.5 constituent as a single exposure, ⁸ and more evidence on joint exposure is required.

Further, based on existing research, it is theorized that metabolic disorders mediate the health impacts of PM_2.5 on CVD. ^{5 , 9 , 10} Specifically, the mechanisms of PM_2.5 on CVD have involved endothelial damage, vascular dysfunction, and thrombosis. Especially under long‐term exposure, these changes may accumulate and not be resolved, leading to metabolic disorders, possibly metabolic syndrome (MetS), and even CVD events. ⁵ However, detailed epidemiological evidence about PM_2.5 chemical constituents on this topic remains lacking. Quantifying the mediation effect of MetS in these associations and further identifying the most responsible constituent to CVD could help clinicians refine treatment schedules and the public mitigate specific polluted sources.

Therefore, this study used a prospective population‐based cohort to: (1) evaluate associations between PM_2.5 chemical constituents and CVD and identify which constituents contribute most to the association with CVD risk; and (2) explore the potential mediated roles of MetS in the association between the most contributable constituent and CVD risk. Research efforts focused on uncovering the potential mechanisms linking PM_2.5 chemical constituents to CVD could shed new light on cardioprotective action for reducing air pollution risk.

Methods

Results

Table 1 summarizes the baseline characteristics of the participants. Among 14 427 adults, 1837 were diagnosed with CVD, 416 with IHD, 393 with CARs, and 916 with CBVD during 43 895 person‐year follow‐up (average, 3.04 years). In this study, participants were an average age of 50.3±11.8 years at baseline, and over half were women. The mean DASH score was 20.2 (SD, 4.4), and physical activity was 21.9 metabolic equivalent in hours per day (SD, 15.0 metabolic equivalent in hours per day). Of the participants, 52.9% were secondhand smokers, while 53.3% were drinkers. A total of 2744 adults were diagnosed with MetS at baseline.

Table 2 presents the distributions of pollutants' concentrations. The mean 3‐year average concentration of PM_2.5 was 55.2 μg/m³ (SD, 5.2 μg/m³). For constituents, the mean exposure levels ranged from 2.27 μg/m³ (interquartile range, 0.42 μg/m³) for Cl⁻ to 21.60 μg/m³ (6.60 μg/m³) for OM. Further, the correlations and spatial distributions of PM_2.5 chemical constituents are shown in Figures S3 and S4. The correlations among chemical constituents were positive except for Cl⁻ (r_s=−0.06 for BC, r_s=−0.15 for OM).

Figure and Figure S5 display the associations between ambient PM_2.5 chemical constituents and CVD risks. We found ambient PM_2.5 (per interquartile range 8.11 μg/m³) was positively and significantly associated with total CVD (HR, 1.67 [95% CI, 1.52–1.84]), IHD (HR, 1.26 [95% CI, 1.03–1.54]), CAR (HR, 1.49 [95% CI, 1.21–1.83]), and CBVD (HR, 2.11 [95% CI, 1.82–2.43]). For constituents, NO3−, NH4+, and BC were associated with all CVD outcomes. On the other hand, OM and SO42− showed similar associations with CVD risks when compared with PM_2.5 mass, except for associations with IHD. In addition, no significant association was found between Cl⁻ and CAR. In the sensitivity analyses, the associations were not materially changed (Tables S2–S8).

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Figure 1

Hazard ratios with 95% CIs for subcategories of cardiovascular diseases, associated with per interquartile range increase in exposure to particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) chemical constituents.

All models were adjusted for age, sex, education, marital status, annual household income, urbanicity, family history of hypertension, body mass index, smoke, second‐hand smoke, alcohol, indoor air pollution, physical activity, the Dietary Approaches to Stop Hypertension score, 3‐year average temperature, hypertension, and diabetes. BC indicates black carbon; CAR, cardiac arrest; CBVD, cerebrovascular disease; Cl⁻, chloride; CVD, cardiovascular disease; IHD, ischemic heart disease; NH4+, ammonium; NO3−, nitrate; OM, organic matter; and SO42−, sulfate. *P<0.05; **P<0.01; ***P<0.001.

The mixed pollutants derived by QGC models were positively associated with total CVD (HR, 1.79 [95% CI, 1.63–1.97]), IHD (HR, 1.48 [95% CI, 1.21–1.81]), CAR (HR, 1.70 [95% CI, 1.35–2.14]), and CBVD (HR, 2.25 [95% CI, 1.94–2.62]) (Figure). The highest weighted pollutants in QGC models were OM for total CVD and CAR and Cl⁻ for IHD and CBVD (Figure S6). Sensitivity analyses also show robustness (Table S9).

Figure S7 presents the associations between PM_2.5 chemical constituents and MetS and its components. We observed significant associations between PM_2.5 and MetS (odds ratio [OR], 1.37 [95% CI, 1.23–1.53]), as well as specific components such as obesity (OR, 1.48 [95% CI, 1.33–1.65]), raised triglycerides (OR, 1.14 [95% CI, 1.06–1.23]), and raised BP (OR, 1.42 [95% CI, 1.31–1.55]). Among associations of constituents with MetS and its components, NO3− showed the highest estimates for MetS (OR, 1.38 [95% CI, 1.26–1.52]) and obesity (OR, 1.54 [95% CI, 1.40–1.68]), whereas Cl⁻ and OM had the highest estimates for raised triglycerides (OR, 1.17 [95% CI, 1.07–1.28]) and raised BP (OR, 1.48 [95% CI, 1.34–1.64]), respectively. In addition, Cl⁻ was negatively associated with raised BP (OR, 0.90 [95% CI, 0.82–0.99]). In the sensitivity analyses, the associations remained robust (Tables S10–S16).

The results of the subgroup analysis are shown in Tables S17 and S18. The heterogeneity tests for the associations between PM_2.5 mass and CVD in these subgroups were not significant. However, the associations of BC with total CVD (HR, 1.58 [95% CI, 1.39–1.79]) and NO3− with CBVD (HR, 1.53 [95% CI, 1.35–1.73]) were stronger in the female group.

Mediation analyses were performed to assess the potential mediating roles of MetS and its components on the associations of OM with total CVD and CAR risks, Cl⁻ with IHD and CBVD risks, and PM_2.5 mass with 4 types of CVD risks (Table 3 and Tables S19–S23). MetS mediated the associations between PM_2.5 mass and total CVD, IHD, and CBVD risks. The mediation proportions for these associations ranged from 0.3% to 1.6%. For constituents, only the association between OM and total CVD risk was significantly mediated by MetS, with a mediation proportion of 1.3%. Further, among the components of MetS, obesity, raised triglycerides, and BP were found to have significant mediation effects in these associations. Because some constituents showed positive associations with CVD risks, we also performed mediation analyses for these constituents (Tables S24–S29). These constituents showed similar results with PM_2.5. Further, results remain robust in the sensitivity analysis (Tables S30–S35).

Discussion

In the current study, we found that NO3−, NH4+, OM, BC, Cl⁻, and SO42− may be positively associated with CVD risks. Among these chemical constituents, OM and Cl⁻ might play vital roles in the positive associations between joint exposure to PM_2.5 chemical constituents and CVD risks. Moreover, we observed that associations of OM with CVD risks were partially mediated by MetS. To the best of our knowledge, this population‐based epidemiological study may be the first to analyze the joint associations of PM_2.5 chemical constituents with CVD and its subtypes and further evaluate the mediated role of metabolic mechanisms in the associations. This large‐scale cohort provides a promising direction for exploring the cause underlying the associations of these pollutants with CVD risks.

Over the past decades, long‐term PM_2.5 exposure has been linked to CVD by numerous studies. ⁴ Typically, current epidemiological studies use PM_2.5 mass or a single PM_2.5 constituent (ie, BC) as exposures. In contrast, little research has focused on the long‐term associations of PM_2.5 chemical constituents on both CVD and its subtypes, especially in relatively highly polluted countries. Despite a few pieces of evidence suggesting the long‐term associations of constituents on CVD mortality, ²⁵ only 2 studies reported significant associations from constituents on incident CVD based on cohort design. ^{8 , 26} The first study involved 14 331 participants from 25 provincial regions in China, using self‐reported total CVD as the outcome. ⁸ They found significant associations between CVD and PM_2.5 chemical constituents such as NO3−, NH4+, BC, and SO42−. Similarly, the second study included 26 851 individuals from 3 highly polluted (PM_2.5 ranged from 67.7 to 77.9 μg/m³) regions in Northern China, also using self‐reported CVD as the outcome. ²⁶ They reported BC and OM were significantly associated with incident CVD. Consistent with these studies, our findings indicate similar positive associations between constituents and total CVD. Further, we reported that the most responsible pollutant for the associations of mixture pollutants with total CVD was OM, which coincides with the findings of the second study. On the other hand, several recent studies have reported similar results concerning the associations between PM_2.5 chemical constituents and MetS. For example, a cross‐sectional study included 13 418 older adults from 28 provinces in China that reported NO3−, NH4+, OM, BC, and SO42− were associated with MetS. ²⁷ Another study, also cross‐sectional, with 10 066 children and adolescents in China, found significant associations between NO3−, OM, BC, and SO42− and MetS. ²⁸ Furthermore, a cohort study including 5453 children from Europe reported a significant association between BC and MetS. ²⁹ Consistent with these findings, we reported similar results of the associations between constituents and MetS. By comparison, in addition to more outcome types and individual exposure assessments, we applied mediation analysis to further elucidate the potential causal relationships among PM_2.5 chemical constituents, MetS, and CVD. By focusing on ICD‐10–based CVD diagnoses, this study may provide more precise and plausible evidence.

As secondary inorganic aerosols, NO3−, SO42−, and NH4+ were found that might have detrimental impacts on CVD in this study. Previous research has reported similar associations of these constituents with CVD incidence and mortality. ^{8 , 25 , 30 , 31} Similarly, we found significant associations between BC and OM with CVD, which echoed with most existing evidence. ^{32 , 33} For example, Liang et al. reported the significant associations of BC and OM with CVD mortality risk. ³⁴ Kazemiparkouhi et al provided evidence that coal combustion–related chemical constituents (such as organic carbon) were strongly associated with CVD mortality. ³⁵

However, mechanistic evidence underpinning these associations remains sparse. A panel study suggested that SO42− might contribute to inflammation, oxidative stress, blood coagulation, and autonomic dysfunction. ³⁶ Another panel study proved that elemental carbon, organic carbon, NO3−, and SO42− might induce systematic inflammation (eg, fibrinogen and C‐reactive protein) and coagulation (soluble CD40 ligand and plasminogen activator inhibitor 1). ^{37 , 38} A repeated‐measure study reported positive associations of NO3− with antioxidant enzymes extracellular superoxide dismutase and glutathione peroxidase 1, supporting the activation of circulating antioxidant enzymes. ³⁹

Considering the real‐world scenario that exposure to PM_2.5 is actual joint exposure to multiple chemical constituents, ⁶ QGC was applied to estimate the joint associations. The results showed the same direction as PM_2.5, and the maximum weights of OM and Cl⁻ suggested the vital contribution of these constituents in chronic associations of PM_2.5 on CVD risks. This finding coincided with other evidence that reported OM might mainly account for the associations of PM_2.5 with CVD risk factors. ⁴⁰ Moreover, for some components of OM, such as polycyclic aromatic hydrocarbons (PAHs), the primary source is typically the incomplete combustion of organic materials. ^{41 , 42} Animal studies indicate that PAHs, such as benzo[a]pyrene, may initiate the aryl hydrocarbon receptor, which leads to lipid peroxidation and tissue damage. ^{43 , 44 , 45} In addition, the aryl hydrocarbon receptor has been suggested to interact with TCF21, a gene related to coronary artery disease, activating a gene expression program related to inflammation. ⁴⁶ This process can be enhanced by PAHs and contribute to the risk of CVD. Research also suggests that PAHs are associated with oxidative stress, ⁴⁷ inflammation, ⁴⁸ atherosclerosis, ⁴⁹ endothelial dysfunction, ⁵⁰ and hypertension. ⁵¹ On the other hand, the epidemiological evidence also indicates that PAHs are associated with inflammatory markers such as 1‐hydroxypyrene levels ⁵² and elevated systolic BP. ⁵³ Further, while the CIs for the association between mixture pollutant and IHD and that between PM_2.5 mass and IHD overlap, the lower bound for PM_2.5 mass approaches one. This observation may be explained by the nonsignificant associations of specific constituents, such as OM and SO42−, with IHD. Moreover, consistent with previous studies, ^{26 , 54} the results of our subgroup analyses suggested that sex might be a potential effect modifier in the association between PM_2.5 chemical constituents and CVD. However, we found no significant heterogeneity among the associations of PM_2.5 mass with CVD among these groups, which further emphasizes the necessity of considering chemical constituents.

Another interesting finding was that some constituents, such as BC, NH4+, and SO42−, were positively associated with CVD risks, yet in multiple pollutant models, they showed substantial negative weights. Similar results have also been observed in other studies. For example, in 2‐pollutant models, the estimated associations between NH4+ and CVD were negative but not statistically significant, ²⁶ which is also the case for the associations between SO42− and CVD. ³⁷ This may be explained by high correlations among these constituents. The correlations can lead to some constituents being nonsignificant in QGC, resulting in the overall negative effect being close to zero and negative weights being substantive. ²¹ Further, some constituents showed positive but low weights in QGC. The high correlation with OM may explain the relatively low weights of BC. A previous review suggested that because of the high correlation between OM and BC, their effects on cardiovascular end points may overlap, making it difficult to distinguish between their respective impacts. ³² Other constituents of low positive weights, such as NO3−, were also found to have a positive association with fasting glucose or cardiac electrophysiological properties in the 2‐pollutant model by the previous study. ⁴⁰ These findings emphasize the importance of key constituents in the associations between PM_2.5 and CVD. There is an urgent need to turn attention to the joint exposure mechanisms of chemical constituents in order to develop targeted strategies, such as personal protection. ⁵⁵

To explain the importance of OM and Cl⁻ on CVD, our research interest further focused on metabolic disorders as increasing studies emphasized the associations between PM_2.5 and MetS. ⁵⁶ Given that MetS is an important risk factor of CVD, and considering the associations identified by recent studies between PM_2.5 and MetS, it is plausible that MetS may mediate the associations between PM_2.5 and CVD. Our results show that the association of OM with CVD was mediated by MetS, suggesting that OM may be involved in the metabolic mechanisms of CVD. This is consistent with afore evidence demonstrating the associations between OM and several metabolic risk factors, including fasting glucose, ECG, and hypertension, among others. ^{8 , 40} Further, recent studies have shown that components of MetS, such as obesity, ⁵⁷ dyslipidemia, ⁵⁸ fasting plasma glucose, ⁵⁹ and BP, ⁶⁰ are associated with PM_2.5. Our research identified obesity, triglycerides, and raised BP as mediators in the associations of PM_2.5 and OM with CVD. These findings may indicate the vital role these components potentially play in mediating the association between PM_2.5 and CVD. While our study did not find a mediating role for raised fasting plasma glucose, recent research has reported associations between PM_2.5 and its constituents and diabetes, ^{61 , 62 , 63 , 64} suggesting that further exploring these mechanisms is warranted. These findings not only indicate potential benefits from targeting metabolic mechanisms such as obesity, dyslipidemia, and hypertension in interventions but also highlight the urgent need to reduce pollutant sources such as industry‐related combustion to mitigate the cardiovascular impact of OM. On the other hand, our findings indicate that while Cl⁻ was positively associated with CVD, it was marginally negatively associated with BP, as well as in the mediation analyses. Such seemingly contradictory results could also be found in existing studies. For example, Wu et al found Cl⁻ might be involved in the antioxidant process, ³⁹ whereas Feng et al reported a negative association of Cl⁻ with inflammation biomarkers (monocyte chemoattractant protein 1). ⁶⁵ Another explanation for this inconsistency may be the variety of study designs and regions. Further, Cl⁻ was closely related to sea salt and wind‐blown dust. ^{37 , 66 , 67 , 68} While sea salt was sourced from natural processes, dust contains metal elements such as calcium and potassium ions, which have been associated with adverse health effects. ^{69 , 70} Further, a panel study involving 30 patients with chronic obstructive pulmonary disease reported negative associations between sodium and calcium ions and biomarkers of inflammation, such as monocyte chemoattractant protein, as well as biomarkers of coagulation such as plasminogen activator inhibitor 1. ³⁷ Nevertheless, biological mechanisms of Cl⁻ in the airway remain rare, and further research is needed to clarify the long‐term effects.

Our study has several strengths in addition to the large population of the cohort design, including a rich set of covariates based on questionnaires with fewer measurement errors and precise laboratory examinations, ICD‐10–based outcome, and a series of methods that are broadly accepted in the realm of environmental joint exposure assessment, yielding consistent and robust results. In addition, we performed a series of sensitivity analyses to confirm the robustness of our findings because the associations may vary with exposure windows. The robustness of our results performed well, and the assumption of proportional hazards was not violated. Nevertheless, there are still some limitations in this study. First, because the health condition of participants with no hospital records cannot be identified after the baseline survey, results may be underestimated. Second, the follow‐up time for our cohort might be considered relatively short for CVD outcomes due to the availability of medical records. Third, we assessed the exposure levels of individuals by their residential addresses without detailed information on activity patterns. However, considering that living houses and workplaces were not distant, measurement error in the results due to residential addresses may be limited.

Conclusions

Our study indicates that among the PM_2.5 chemical constituents that are positively associated with CVD risks, OM and Cl⁻ might be significant contributors to the associations between PM_2.5 and CVD risks. Further mediation analysis showed that metabolic mechanisms might be involved in the associations between OM and CVD risks. This study provided brand new evidence on which chemical constituents are attributed most in the association of PM_2.5 with increased CVD risks and their underlying metabolic mechanisms. Therefore, more individualized treatment strategies concerning the main constituent (eg, OM) in the area where patients live are needed. The action to control the source of specific PM_2.5 chemical constituents (ie, OM) is urgent. Further, the mechanisms of chemical constituents still require in‐depth research.

Sources of Funding

This work was supported by the National Key Research and Development Program of China (grant number 2017YFC0907305), the National Natural Science Foundation of China (grant numbers 82103943, 82073667, and 81973151), and Sichuan Science and Technology Program (grant number 2021YFS0129).

Disclosures

None.

Supporting information

Acknowledgments

Notes

This article was sent to Mahasin S. Mujahid, PhD, MS, Associate Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.033455

For Sources of Funding and Disclosures, see page 10.

Contributor Information

Jing Wei, Email: moc.361@sr_gnijiew.

Juying Zhang, Email: moc.361@901gniyuj.

Xing Zhao, Email: nc.ude.ucs@oahzgnix.

References