What this is
- This research examines the link between long-term exposure to ambient air pollution and () in China.
- It focuses on how lifestyle factors may mediate this association, particularly in middle-aged and elderly populations.
- The study utilizes data from 7000 participants, assessing their lifestyle choices and exposure to various air pollutants.
Essence
- Long-term exposure to ambient air pollution increases the risk of (), with lifestyle factors partially mediating this association. A healthy lifestyle can significantly reduce the incidence of , especially among those exposed to high pollution levels.
Key takeaways
- Exposure to () and nitrogen dioxide (NO) is associated with an increased risk of . Specifically, a 10 μg/m increase in exposure correlates with a hazard ratio (HR) of 1.09 (95% CI: 1.05-1.14) for .
- Adhering to a healthy lifestyle significantly reduces risk. Compared to an unfavorable lifestyle, the HR for an intermediate lifestyle is 0.65 (95% CI: 0.56-0.76) and for a favorable lifestyle is 0.41 (95% CI: 0.32-0.53).
- Lifestyle factors mediate 7.4% to 14.3% of the association between air pollution exposure and . This indicates that improving lifestyle can mitigate some adverse effects of pollution on cardiovascular health.
Caveats
- The study relies on self-reported diagnoses of , which may introduce bias. Despite this, consistency with other studies supports the validity of the findings.
- Exposure assessment was based on district-level data rather than individual measurements, potentially leading to misclassification of pollution exposure.
- The lifestyle assessment was static and based on baseline levels, which may not accurately reflect long-term lifestyle changes among participants.
Definitions
- Cardiovascular disease (CVD): A group of disorders affecting the heart and blood vessels, leading to conditions like heart attacks and strokes.
- Particulate matter (PM): A mixture of tiny solid and liquid particles suspended in the air, categorized by size (e.g., PM10, PM2.5).
Simplified
Background
Cardiovascular disease (CVD) is the leading cause of mortality worldwide [1]. In the updated 2019 Global Burden of Disease, the number of patients with CVD in 2019 reached 55.4 million worldwide, an increase of 77.12% compared to 1990, with the largest increases occurring in South and East Asia [1]. Therefore, considering the poor prognosis, attention should be given to the management of risk factors for CVD.
Ambient air pollution, as the most important environmental risk factor for health globally, is a major cause of CVD and CVD-related mortality [2]. Particulate matter (PM) (including PM ≤ 10 μm [PM10], PM ≤ 2.5 μm [PM2.5], which also includes PM ≤ 1 μm [PM1]), nitrogen dioxide (NO2), and ozone (O3) are well-documented outdoor air pollutants in studies of CVD [3–6]. Curbing the disease burden related to air pollution exposure is a long-term endeavor. In addition to encouraging the control of emissions of major air pollutants at their source, modifiable individual lifestyle factors are also important [7].
Accumulating evidence has shown that unhealthy lifestyle factors, i.e., physiological and behavioral risk factors, contribute to a large burden of CVD [7, 8]. Conversely, adherence to a healthy lifestyle for CVD prevention may reduce the adverse effects of air pollution exposure [9–12]. Although studies have investigated the association of lifestyle factors between air pollution exposure and CVD risk, numerous other unresolved questions remain [8–12]. First, despite lifestyle factors being interrelated, few studies have established a composite lifestyle assessment to reflect and assess its impact on the adverse effect of ambient air pollution exposure. In addition, the extent to which adherence to a healthy lifestyle modifies and mediates the link between ambient air pollution exposure and CVD incidence is still unknown. Third, it is unclear whether these effects are consistent across different levels of healthy lifestyles, different types of ambient air pollutants, and different levels of air pollution exposure. More importantly, evidence for protective measures against CVD in the context of high levels of air pollution is still lacking.
Hence, a study that evaluates the associations between air pollutant exposure, lifestyles and the risk of CVD is crucial to develop targeted strategies for the control of CVD. We used a large national database from China to determine the effect of a healthy lifestyle on the association between ambient air pollution exposure and CVD.
Methods
Study population
The China Health and Retirement Longitudinal Study (CHARLS) is an ongoing national longitudinal study conducted by Peking University that began in 2011. In short, the CHARLS employed a probability proportionated to size sampling, covering more than 17,000 representative respondents aged 45 years and above from 28 provinces across China, with a response rate of about 85% (Additional file 1: Fig. S1), as detailed elsewhere [13]. In a face-to-face setting, high-quality data were collected via standardized questionnaires about demographic information, health conditions, and medical history. The participants underwent a physical examination and blood biomarker detection. All participants were followed up every 2 years after the baseline survey.
Maps of the distribution of studied ambient air pollutants and enrolled participant locations across China. NO, nitrogen dioxide; O, ozone; PM, particulate matter with an aerodynamic diameter less than 1 μm; PM, particulate matter with an aerodynamic diameter less than 2.5 μm; PM, particulate matter with an aerodynamic diameter < 10 μm 2 3 1 2.5 10
Ambient air pollution exposure acquisition
Details of the measurement of ambient air pollution exposure are presented in the appendix (Additional file 1: Method S1) [14–23]. Briefly, daily concentrations of PM1 and PM2.5 were estimated at a high spatial resolution (0.1° × 0.1°) by satellite-based spatiotemporal models. The concentrations of PM10, NO2, and O3 were derived from appropriately located air monitors in the district where each study participant lived. Geographic matching was performed based on the residence where each enrolled participant lived, accurate to urban or rural level (as defined by the 2013 urban and rural statistical division codes of the National Bureau of Statistics of China). We geocoded the addresses where each participant’s residence was located and superimposed a grid to predict monthly ambient air pollutant concentrations. We then averaged the ambient air pollutant (PM1, PM2.5, PM10, NO2, and O3) concentrations for each participant over the 1-year period before the interview day to assign exposure. Other exposure averaging periods were assessed, as outlined in the Sensitivity analyses section that follows.
Healthy lifestyle measurement
Based on the Life’s Simple 7 proposed by the American Heart Association and previous studies [9, 24, 25], we constructed a healthy lifestyle score to generalize modifiable risk factors, including blood pressure, blood glucose, blood lipids, body mass index (BMI), smoking, and physical activity. Blood pressures were taken as the mean of three measurements at 45-s intervals recorded on the right upper arm after 5 min of sitting rest using an OmronTM HEM-7112 Blood Pressure Monitor by trained staff. Fasting blood glucose and lipid profiles were measured by enzymatic colorimetric tests. Height and weight were measured with light clothing and without shoes to calculate BMI. Smoking status was categorized as either current smoking or not. Ideal physical activity was defined as 30 min of vigorous exercise (including heavy lifting, digging, ploughing, aerobics, fast bicycling, and cycling with a heavy load) or moderate physical activity (including carrying light loads, bicycling at a regular pace, or mopping the floor) at least three times a week. We did not include dietary factors since the CHARLS did not collect specific dietary information. The correlation between the various lifestyle factors is shown in Additional file 1: Fig. S3. Each of these six factors was assigned a score of 0 to 1 (Additional file 1: Table S1), and we calculated the total score for the modifiable lifestyle factors on a scale from 0 to 6, with higher scores indicating greater adherence to a healthy lifestyle. Participants were divided into three categories of lifestyle according to the scores (unfavorable, 0–1 point; intermediate, 2–4 points; favorable, 5–6 points) [12]. The proportions of ideal factors in different lifestyle groups are shown in Additional file 1: Fig. S4.
Cardiovascular disease definition
CVD was defined by medical diagnosis as reported in response to the following questionnaire items: “Have you ever been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, other heart problems by a doctor since the last interview?”; “Have you ever been diagnosed with stroke by a doctor since the last interview?” [26]. Participants were considered to have CVD if they gave a positive response to at least one question.
Covariates
Data for age (years), sex (male/female), education (primary school and below/junior high school/high school and above), residence (rural/urban), per capita household annual income (¥, hereafter referred to as income), and alcohol consumption (drink more than once a month/drink less than once a month/never drink) were obtained from standard questionnaires.
Blood test
Participants were requested to fast for 8–12 h before blood sample collection. Blood samples were stored at – 70 °C, and bioassays were performed at uniform quality control by the National Centers for Disease Control. Blood lipids (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides) and blood glucose were measured by enzymatic colorimetric tests. Hypersensitive C-reactive protein was detected by immunoturbidimetric assay.
Statistical analyses
Participants were divided into three groups according to the extent of healthy lifestyle as mentioned above. P values for trends were calculated with each type of healthy lifestyle taken as a unit by using linear regression analyses and the Wald chi-square test for trend analysis of continuous and categorical variables across the three groups. We developed a directed acyclic graph (DAG) to guide covariate selection (Additional file 1: Fig. S5). A Cox regression model was used to examine the associations between air pollutant exposure (per 10 μg/m3 increase) and CVD and between lifestyles and CVD (expressed as hazard ratio [HR] and 95% confidence interval [CI]). The results of the proportional hazards assumption in all models were satisfactory. Participants were divided into five groups based on the levels of different air pollutant exposure, with quintile 1 (Q1) representing low level exposure and Q2–Q5 representing high level exposure to air pollution. Stratification analysis was conducted to examine the effect of lifestyle and CVD in different air pollutant exposure (low: Q1 and high: Q2–Q5) subgroups. Potential confounders that were significant in the baseline comparison or considered clinically important were selected for the multivariate adjusted models. Two main models were employed for clinically significant covariate adjustment: model 1 (crude model) was unadjusted, and model 2 (maximally adjusted model) was adjusted for age, sex, education level, residence, alcohol consumption, and income.
To fully identify the modification effect of a healthy lifestyle on the association between air pollutant exposure and CVD, we employed three methods. First, the modification on a multiplicative scale (using HR for the term between the healthy lifestyle category [unfavorable, intermediate, favorable] and air pollutant exposure) was tested using the likelihood ratio test, with statistical significance indicated by a confidence interval of HR that did not include 1. Second, the marginal effects using the means were calculated in the dimension of healthy lifestyle categories and the dimension of different air pollutant concentrations. Third, we assessed the modification effect on an additive scale (using HR for the term between the lifestyle categories and air pollutant exposure) by calculating the relative excess risk due to interaction (RERI), and the additive interaction was considered statistically significant when its confidence interval did not include 0. Participants were divided into two categories of lifestyle (unfavorable: 0–1 scores and intermediate and favorable: 2–6 scores) for the potential additive interaction analysis.
Given that there are associations of air pollutant exposure and healthy lifestyle with incident CVD (Additional file: Fig. S5), we investigated whether the association between air pollution exposure and incident CVD was mediated by different lifestyle categories using the “mediation” R package with 10,000 bootstraps. 1
Several sensitivity analyses were then performed to test the robustness of the studied association. First, the effects of adherence to a single lifestyle factor on CVD at different levels of air pollutant exposure were identified. Second, subgroup analyses (age and sex) of the effect of a healthy lifestyle on the risk of CVD in different air pollutant exposures were explored. Then, we developed a series of models to verify the robustness of the main results as follows: (1) as the prevalence of depressive symptoms evaluated by a 10-question version of the Center for Epidemiologic Studies-Depression (CES-D) scale is considered a novel CVD risk factor, we removed participants without CES-D scores (n = 6881) and repeated the main analysis with further adjustment for depressive symptoms. Depressive symptoms were defined by a CES-D score of ≥ 12 [27]; (2) to account for the impact of household solid fuel use on the main outcome, we developed a new model that accounts for household solid fuel use (n = 6947). Domestic solid fuel use (coal, crop residue or wood burning) for cooking (yes/no) were obtained from standard questionnaires; (3) due to possible measurement bias associated with dynamic changes in air pollutant exposure during the follow-up period, we used a time-varying model to calculate averaged cumulative air pollutant exposure from the year prior to the baseline visit to the year of outcome instead of 1-year exposure before enrolment; (4) to check the representativeness of the 1-year exposure analysis, a 3-year average air pollution exposure before baseline survey was calculated; (5) as sleep is included by the AHA as a new component of Life’s Essential 8, we included nighttime sleep duration in the lifestyle score and reclassified participants into three new lifestyle categories (unfavorable, 0–1 point; intermediate, 2–4 points; favorable, 5–7 points) (n = 6951). According to a previous meta-analysis on the association between sleep duration and cardiovascular outcomes, sleep disorder was defined by a self-reported night sleep duration of < 6 h or > 8 h [28]; (6) each of these six factors was assigned a score of 0 to 2 (Additional file 1: Table S1) instead of 0 to 1, and participants were divided into three lifestyle categories according to their lifestyle scores (unfavorable, 0–5 points; intermediate, 6–9 points; favorable, 10–12 points); (7) we replicated the main analysis by excluding patients without covariates (n = 6235) or who changed their residence (n = 5519); (8) since lung function could affect the effects of air pollution on disease, we performed the analysis with inclusion restricted to participants without chronic lung disease in all the above explored correlations (n = 5689). A history of chronic lung disease was self-reported; (9) considering participants who died during the follow-up period, a competing risk model was employed; and (10) we replaced the death by free of CVD and repeated the analyses (n = 7605). The method of the last observation carried forward or the means and medians were used to interpolate the missing data. P value < 0.05 (two-sided) was considered statistical significance. The Stata (StataCorp LLC, version 15.0) and R software (version 4.2.2) were used for the data analyses.
Results
Baseline characteristics
| Total= 7000N | Unfavorable lifestyle= 818N | Intermediate lifestyle= 5308N | Favorable lifestyle= 874N | valueP | |
|---|---|---|---|---|---|
| Age, years | 58.43 ± 8.80 | 58.87 ± 8.27 | 58.79 ± 8.90 | 55.85 ± 8.16 | < 0.001 |
| Male sex | 3247 (46.39%) | 511 (62.47%) | 2484 (46.80%) | 252 (28.83%) | < 0.001 |
| Educationa | < 0.001 | ||||
| Primary school and below | 5647 (70.47%) | 518 (63.33%) | 3700 (69.72%) | 641 (73.34%) | |
| Junior high school | 1592 (19.87%) | 200 (24.45%) | 1087 (20.48%) | 141 (16.13%) | |
| High school and above | 774 (9.66%) | 100 (12.22%) | 520 (9.80%) | 92 (10.53%) | |
| Residence | < 0.001 | ||||
| Rural | 4662 (66.60%) | 467 (57.09%) | 3580 (67.45%) | 615 (70.37%) | |
| Urban | 2338 (33.40%) | 351 (42.91%) | 1728 (32.55%) | 259 (29.63%) | |
| Per capita household annual income, RMBa | 5513.33 (1899.38–13,500.00) | 6867.67 (2333.33–15,333.33) | 5245.71 (1815.83–13,265.00) | 5920.00 (2056.25–13,500.00) | < 0.001 |
| Smoking | 2678 (38.26%) | 540 (66.01%) | 2020 (38.06%) | 118 (13.50%) | < 0.001 |
| Alcohol consumption | < 0.001 | ||||
| Drink more than once a month | 1829 (26.13%) | 296 (36.19%) | 1377 (25.94%) | 156 (17.85%) | |
| Drink less than once a month | 575 (8.21%) | 62 (7.58%) | 440 (8.29%) | 73 (8.35%) | |
| Never drink | 4596 (65.66%) | 460 (56.23%) | 3491 (65.77%) | 645 (73.80%) | |
| Physical activity | 2016 (28.80%) | 33 (4.03%) | 1389 (26.17%) | 594 (67.96%) | < 0.001 |
| Nighttime sleep durationa | 6.39 ± 1.89 | 6.40 ± 1.86 | 6.39 ± 1.90 | 6.39 ± 1.85 | 0.892 |
| Solid fuel use for cookinga | 4012 (57.75%) | 407 (50.12%) | 3095 (58.73%) | 510 (58.96%) | < 0.001 |
| CES-D scorea | 8.04 ± 6.12 | 7.11 ± 5.82 | 8.04 ± 6.09 | 8.95 ± 6.42 | < 0.001 |
| BMI, kg/ma2 | 23.49 ± 3.81 | 26.48 ± 3.82 | 23.32 ± 3.73 | 21.75 ± 2.59 | < 0.001 |
| SBP, mmHg | 128.32 ± 20.84 | 141.60 ± 18.29 | 128.96 ± 20.50 | 112.01 ± 13.37 | < 0.001 |
| DBP, mmHg | 74.98 ± 12.00 | 82.53 ± 11.12 | 75.22 ± 11.65 | 66.40 ± 9.35 | < 0.001 |
| TC, mg/dL | 194.01 ± 38.40 | 226.35 ± 40.33 | 192.59 ± 36.77 | 172.39 ± 24.96 | < 0.001 |
| Triglycerides, mg/dL | 104.43 (74.34–152.22) | 147.79 (102.66–230.10) | 103.55 (74.34–148.68) | 83.19 (62.84–114.17) | < 0.001 |
| LDL-C, mg/dL | 116.76 ± 34.53 | 137.77 ± 40.05 | 115.98 ± 33.73 | 101.93 ± 22.56 | < 0.001 |
| HDL-C, mg/dL | 51.48 ± 15.32 | 47.31 ± 15.78 | 51.80 ± 15.40 | 53.46 ± 13.60 | < 0.001 |
| Blood glucose, mg/dL | 109.64 ± 34.94 | 128.59 ± 47.92 | 109.27 ± 33.61 | 94.16 ± 13.87 | < 0.001 |
| Hs-CRP, mg/dL | 0.98 (0.53–2.02) | 1.46 (0.77–2.88) | 0.97 (0.54–1.99) | 0.67 (0.41–1.31) | < 0.001 |
| Chronic lung diseasea | 595 (8.51%) | 68 (8.31%) | 463 (8.73%) | 64 (7.33%) | 0.449 |
| Lipids lowering therapy | 243 (3.47%) | 60 (7.33%) | 166 (3.13%) | 17 (1.95%) | < 0.001 |
| Ambient air pollutants | |||||
| PM, μg/m13 | 39.97 ± 13.80 | 42.01 ± 13.64 | 39.87 ± 13.82 | 38.69 ± 13.64 | < 0.001 |
| PM, μg/m103 | 93.44 ± 28.05 | 97.49 ± 27.98 | 93.32 ± 27.99 | 90.33 ± 28.05 | < 0.001 |
| PM, μg/m2.53 | 52.61 ± 15.86 | 54.57 ± 15.61 | 52.61 ± 15.82 | 50.84 ± 16.08 | < 0.001 |
| NO, μg/m23 | 29.23 ± 10.79 | 31.15 ± 10.74 | 29.20 ± 10.76 | 27.57 ± 10.74 | < 0.001 |
| O, μg/m33 | 95.33 ± 6.49 | 95.77 ± 7.05 | 95.26 ± 6.46 | 95.30 ± 6.11 | 0.145 |
Associations of air pollution exposure and lifestyle with incident CVD
Joint effects of lifestyle and air pollutant exposure on the incidence of CVD. Unfavorable lifestyle as reference. Model adjusted for age, sex, education, residence, alcohol consumption, and income. CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; NO, nitrogen dioxide; O, ozone; PM, particulate matter with an aerodynamic diameter less than 1 μm; PM, particulate matter with an aerodynamic diameter less than 2.5 μm; PM, particulate matter with an aerodynamic diameter < 10 μm 2 3 1 2.5 10
| Unadjusted for lifestyle | Adjusted for lifestylea | |
|---|---|---|
| PM1 | 1.09 (1.05–1.14) | 1.08 (1.04–1.13) |
| PM2.5 | 1.04 (1.00–1.08) | 1.04 (1.00–1.07) |
| PM10 | 1.05 (1.03–1.08) | 1.05 (1.03–1.07) |
| NO2 | 1.11 (1.05–1.18) | 1.10 (1.04–1.16) |
| O3 | 1.04 (0.95–1.14) | 1.03 (0.94–1.13) |
Mediation analysis of lifestyle in the association between air pollution exposure and incident CVD
| UnfavorableHR (95% CI) | IntermediateHR (95% CI) | FavorableHR (95% CI) | Mediation proportion (%) (95% CI) | |
|---|---|---|---|---|
| PM1 | 8.0 (3.6–17.8) | |||
| Model 1 | 1.15 (1.04–1.28) | 1.09 (1.04–1.14) | 0.97 (0.84–1.13) | |
| valueP | 0.008 | 0.001 | 0.697 | |
| Model 2 | 1.17 (1.05–1.31) | 1.08 (1.03–1.13) | 0.97 (0.83–1.14) | |
| valueP | 0.005 | 0.003 | 0.762 | |
| PM2.5 | 14.3 (5.6–54.1) | |||
| Model 1 | 1.12 (1.02–1.23) | 1.04 (1.00–1.09) | 0.97 (0.85–1.10) | |
| valueP | 0.02 | 0.058 | 0.591 | |
| Model 2 | 1.13 (1.02–1.24) | 1.03 (0.98–1.07) | 0.96 (0.84–1.10) | |
| valueP | 0.016 | 0.238 | 0.552 | |
| PM10 | 7.4 (3.8–13.3) | |||
| Model 1 | 1.09 (1.03–1.14) | 1.05 (1.02–1.07) | 1.01 (0.94–1.08) | |
| valueP | 0.001 | < 0.001 | 0.798 | |
| Model 2 | 1.09 (1.04–1.15) | 1.04 (1.02–1.07) | 1.01 (0.94–1.09) | |
| valueP | 0.001 | < 0.001 | 0.751 | |
| NO2 | 12.0 (6.3–24.6) | |||
| Model 1 | 1.21 (1.06–1.38) | 1.09 (1.03–1.16) | 0.97 (0.80–1.17) | |
| valueP | 0.004 | 0.005 | 0.751 | |
| Model 2 | 1.25 (1.09–1.44) | 1.08 (1.01–1.15) | 0.98 (0.80–1.20) | |
| valueP | 0.001 | 0.017 | 0.819 | |
| O3 | - | |||
| Model 1 | 1.21 (0.98–1.50) | 1.03 (0.93–1.14) | 0.83 (0.59–1.16) | |
| valueP | 0.056 | 0.92 | 0.419 | |
| Model 2 | 1.24 (0.99–1.54) | 1.00 (0.90–1.11) | 0.87 (0.62–1.22) | |
| valueP | 0.071 | 0.556 | 0.279 |
Interaction analysis of lifestyle and air pollution with incident CVD
Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between ambient air pollutant exposure and CVD. Model adjusted for age, sex, education, residence, alcohol consumption, and income. CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; NO, nitrogen dioxide; O, ozone; PM, particulate matter with an aerodynamic diameter less than 1 μm; PM, particulate matter with an aerodynamic diameter less than 2.5 μm; PM, particulate matter with an aerodynamic diameter < 10 μm; RERI, relative excess risk due to interaction 2 3 1 2.5 10
Sensitivity analyses
The results of the association between air pollutant exposure, lifestyle and risk of CVD were not substantially changed when we considered depressive symptoms and solid fuel use as covariates (Additional file: Tables S8-9). The replacement of diverse air pollutant exposure time frames or different definitions of lifestyle scores and categories also reached the same findings (Additional file: Tables S8-9). Moreover, the results were similar to the main findings in the subgroups of participants who had all covariates, were free of chronic lung disease, or did not change residence (Additional file: Tables S8-9). The findings of the interaction between lifestyle and exposure to air pollutants were stable across different time frames of air pollution exposure, various definitions of lifestyle scoring and categorization, and even when considering the competing risk of mortality (Additional file: Tables S10-14). The baseline information, exposure, and outcome between the included and excluded participants were similar (Additional file: Tables S15-16). 1 1 1 1 1
Discussion
In this national cohort study in China, exposure to higher ambient air pollution (mainly for PM1, PM2.5, PM10, and NO2) was associated with a higher risk of CVD, with 7.4% to 14.3% of the association being mediated by lifestyle factors. Healthy lifestyles mitigated the adverse effect of exposure to high levels of air pollutant exposure on CVD, especially for exposure to PM1, PM2.5, NO2, and O3. Furthermore, the protective effect of a healthy lifestyle on CVD was stronger as the levels of air pollutant exposure increased. This national representative cohort study provides compelling evidence of getting closer to a healthy lifestyle to decrease the risk of air pollution on CVD. As the influence of air pollution in most countries will continue for some time to come, our findings provide some relief for middle-aged and elderly people who are exposed to air pollution and may have substantial implications for other countries that are developing appropriate responses to air pollution.
A systematic review reported that exposure to all major air pollutants except O3 was associated with an increased risk of myocardial infarction [29], similar to our findings. Modifiable risk factors have attracted much more attention in recent years and have gradually turned into a healthy lifestyle that is closely related to the individual. Many clinical trials or observational studies have explored individual-level ways to reduce the harmful cardiovascular effect caused by air pollution [10–12, 30–32], but the conclusions were limited. First, these studies estimating the impact of a single lifestyle factor on CVD often could not reflect the comprehensive lifestyle [10, 11, 30, 31]. Additionally, the mediating and interactive roles of a comprehensive lifestyle in the relationship between air pollutant exposure and CVD were also unclear. Our study employed a more comprehensive way of assessing lifestyle and a more quantitative approach to evaluating the role of lifestyle and its interaction with air pollution in incident CVD. To some extent, the results of the study could fill the gaps of previous studies and provide supporting evidence for addressing the cardiovascular harms of air pollution.
In our study, we found that the adverse effect of air pollutant exposure on CVD was alleviated when lifestyle factors were accounted for. In the mediation analysis, we found that 7.4% to 14.3% of the association between air pollutant exposure and CVD risk among Chinese adults could be explained by lifestyle factors. This effect was less than the previous mediating effect of hypertension on the effect of air pollution on CVD [9], probably because the comprehensive lifestyle assessment was more objective with a balance of each lifestyle factor than the individual assessment. The mediating role of lifestyle in the association between cardiovascular risk factors and CVD has been verified in many studies [33–35]. Our results further confirm that CVD events are attributed to air pollution mainly through deteriorating lifestyle factors, such as high blood pressure, elevated blood glucose, and blood lipids. It is also suggested that promoting healthy lifestyles alone is still insufficient to avoid the adverse effects of air pollution on CVD, and other protective measures are also needed. In the sensitivity analyses, we further adjusted for novel CVD risk factors, depressive status, and indoor air pollution, and the results were consistent. Attention to emotional health and indoor air pollution may also contribute to reducing the harmful impact of air pollution on CVD [36, 37]. In addition, we also considered nighttime sleep duration as one of the lifestyle factors, which further enriched the meaning of healthy lifestyles protecting against the negative effects of air pollutant exposure.
Another novel finding of our study is that significant interactions were found between air pollution exposure and healthy lifestyle regarding incident CVD, which was supported in previous studies [4, 32]. The results of marginal effect analyses indicated that the protective effect of a healthy lifestyle on the reduction of air pollution-related CVD was stronger as the healthy lifestyle score increased, and this effect was also more effective as the air pollution level increased. This is an interesting finding, since it provided the dose–response relationship between air pollution exposure and risk of CVD that can be protected by a healthy lifestyle. A similar study focused on the cardiovascular effect of physical activity as well as outdoor air pollution exposure and pointed out that physical activity may result in high CVD risk in participants with high levels of air pollution exposure compared to those exposed at low levels [10]. The reason for this inconsistency with the results of our study may be the monotony of lifestyle assessment. In our study, in addition to being more physically active, people with a healthy lifestyle also had an advantage in other cardiovascular health indicators, such as target blood pressure, blood glucose, and blood lipids. The results of the interaction between lifestyle and air pollution remain consistent across different air pollutant exposure time frames or across differently defined lifestyle scores and categories. Therefore, people with healthy lifestyle could also resist the adverse effects of air pollution on CVD even under high exposure to air pollution.
Several studies have focused on the impacts of air pollutant inhalation and deposition on the cardiovascular system [38–40]. In a direct manner, air pollutants can pass directly through the alveoli into the bloodstream and eventually deposit on blood vessel walls, leading to endothelial dysfunction, vasoconstriction, and thrombosis [38, 39]. In an indirect way, air pollutants may induce oxidative stress and systemic inflammatory responses, leading to autonomic dysfunction and exacerbating the progression of CVD [40]. The cardiovascular benefits of adhering to a healthy lifestyle are due to its ability to reduce cardiovascular risk factors across multiple dimensions. There is evidence that regular physical activity can increase tolerance to the adverse effects of air pollution exposure [41]. Moreover, overall lifestyle improvement can achieve cardiovascular benefits by reducing cardiovascular metabolic risks [32] and mitigating systemic inflammation, thereby resisting the harm caused by air pollution [42].
Our results support public health efforts aimed at reducing the burden of CVD. A healthy lifestyle can effectively reduce the risk of CVD, even among those exposed to high levels of air pollution. This has significant strategic implications for the prevention and control of CVD in countries with high exposure to air pollution, in addition to public health policies that encourage the reduction of air pollutant intake by reducing air pollutant emissions and increasing air filtration systems, on the other hand, and, more importantly, focus on lifestyle improvements at the individual level.
Our study has several advantages. First, we used a cohort with a median follow-up of 7 years to clarify the mediating role of a comprehensive healthy lifestyle in the relationship between exposure to various air pollutants and CVD risk. We also identified the interaction between a healthy lifestyle and exposure to different air pollutants. Additionally, we conducted a series of sensitivity analyses, including different air pollution exposure time frames and classification methods for lifestyle factors, to strengthen our conclusions. There are several limitations to our study. First, given the protection policies of the CHARLS for sensitive information such as an individual’s residential address, we can only estimate exposure based on the district in which the participant lives, which may lead to misclassification of exposure. Nevertheless, we employed the accurate measurement of air pollution as much as possible and introduced indoor air pollution as a covariate in the sensitivity analysis to supplement this deficiency. Second, the diagnosis of CVD was based on self-reported data derived from the physician diagnosed, which may also have some degree of bias. However, high consistency between self-reported coronary artery disease and medical records was confirmed by the English Longitudinal Study of Ageing researchers [43]. Third, the assessment of lifestyle in this study was based on baseline levels rather than being dynamic and therefore may not represent the long-term status. However, the lifestyles of middle-aged and older adults tend to be more fixed, with less likelihood of significant changes, thus causing minimal fluctuation in the groupings. Finally, previous studies have shown that nutrient intake also affects the relationship between air pollution exposure and CVD [30, 31], but due to the lack of dietary data in the CHARLS, the effect of dietary factors could not be estimated in this study. Nevertheless, differences in diet can also be partially reflected in lifestyle factors, such as lipid profiles, blood glucose, blood pressure, and BMI. Future studies are needed to further explore the impact of diet on the relationship between air pollution exposure and CVD.
Conclusions
Based on a national cohort in China, we found that the association between ambient air pollutant exposure and CVD was partially mediated by lifestyle. Adherence to a healthy lifestyle can significantly reduce the incidence of CVD, especially for people exposed to high levels of air pollution. This study highlights the importance of lifestyle improvement in reducing the burden of CVD disease, providing an effective way to mitigate the impact of air pollution. Proactive policies are needed to address the health problems caused by air pollution, and in addition to tackling the source of air pollution, individual-level protective measures to maintain a healthy lifestyle are also needed.
Supplementary Information
Additional file 1: Method S1. Ambient air pollution exposure acquisition. Figure S1. Sampling procedure. Figure S2. Study flowchart. Figure S3. The association of different lifestyle factors. Figure S4. (a) The proportion of single ideal factor in different lifestyle groups. (b) The proportion of ideal factors in different lifestyle groups. Figure S5. Directed acyclic graph. Figure S6. The marginal effect of lifestyle on CVD and in the relationship between ambient air pollutant exposure and CVD. Table S1. The score criteria of different lifestyle factors. Table S2. The exposure level of different air pollutants among the study population. Table S3. The exposure level by quintile of air pollutant. Table S4. The HRs (95% CIs) of the associations between lifestyle and CVD with and without adjustment for ambient air pollutant exposure. Table S5. Joint effects of lifestyle and air pollutant exposure on the incidence of CVD. Table S6. The HRs (95% CIs) of incident CVD associated with each lifestyle factor at different levels of air pollutant exposure. Table S7. Subgroup analysis of the additive interactions analysis of the effect of dichotomized lifestyle on the association between ambient air pollutant exposure and CVD in high air pollutant exposure levels (Q2–Q5). Table S8. The HRs (95% CIs) of associations between air pollutant exposure (per 10 μg/m3 increase) and incident CVD, and the mediation effect of lifestyle categories on air pollution and CVD in different sensitivity analysis models. Table S9. The HRs (95% CIs) of the association between ambient air pollutant exposure (per 10 μg/m3 increase) and CVD in different lifestyle categories in different sensitivity analysis models. Table S10. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between time-varying ambient air pollutant exposure and CVD. Table S11. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between 3 years of ambient air pollutant exposure and CVD. Table S12. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new categories and nighttime sleep duration on the association between ambient air pollutant exposure and CVD. Table S13. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new assignment of lifestyle categories on the association between ambient air pollutant exposure and CVD. Table S14. The subdistribution HRs (sHRs, 95% CI) of the associations between ambient air pollutant exposure (per 10 μg/m3) and CVD in different lifestyle categories. Table S15. Baseline characteristics of included and excluded participants. Table S16. Baseline characteristics of included participants and those without lifestyle scores.