Abstract
Objectives
This study aimed to estimate the combined effects of long-term fine particulate matter (PM2.5) exposure and physical activity (PA) on cardiovascular disease (CVD) risk and to assess whether the cardiovascular benefits of PA outweigh the potential adverse effects of PM2.5 exposure.
Methods
Data were obtained from the China Health and Retirement Longitudinal Study and the ChinaHighAirPollutants datasets. Cox proportional hazards models were used to assess the independent and combined effects of PA and long-term PM2.5 exposure on CVD. Interaction analyses were conducted to determine whether the cardiovascular effects of PM2.5 or PA were modified by each other.
Results
PA was negatively associated with CVD risk. Each IQR increase in PA significantly reduced the risk of CVD by 10% (HR=0.90, 95% CI 0.83 to 0.98). While PM2.5 exposure was positively associated with CVD, a 10 μg/m3 increase in PM2.5 significantly increased 5% risk of CVD (HR=1.05, 95% CI 1.00 to 1.09). Compared with participants with high PA and low PM2.5 exposure, those with low PA and high PM2.5 exposure had the highest risk of CVD (HR=1.56, 95% CI 1.15 to 2.13). Long-term PM2.5 exposure increased the risk of CVD in participants with low and moderate PAs, but not high PA.
Conclusion
The beneficial effects of high levels of PA may mitigate the detrimental effects of PM2.5 exposure, indicating that PA is an effective strategy for reducing the risk of CVD, even among individuals living in areas with elevated PM2.5 concentrations.
Keywords: Physical activity, Environment, Cardiovascular
WHAT IS ALREADY KNOWN ON THIS TOPIC
Epidemiological evidence on the trade-off between the health benefits of physical activity (PA) and the potentially detrimental effects of fine particulate matter (PM2.5) on cardiovascular disease (CVD) risk is controversial and sparse in areas with high PM2.5 concentrations.
WHAT THIS STUDY ADDS
This study enhances the existing evidence on the combined effects of PA and air pollution on cardiovascular health over a broad range of PM2.5 concentrations. It reveals that high PA levels may mitigate the detrimental effects of PM2.5 exposure on CVD risk, even in high-pollution settings. By providing novel insights from a large, representative sample of middle-aged and older Chinese adults, our findings highlight the importance of considering both environmental and lifestyle factors in CVD prevention.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The beneficial effects of high PA may mitigate the detrimental effects of PM2.5 exposure, indicating that the practice of PA is an effective strategy for reducing the risk of CVD, even among individuals living in areas with elevated PM2.5 concentrations.
Introduction
Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide. According to the Global Burden of Disease Study, the age-standardised incidence rate of CVD in China increased between 1990 and 2019,1 especially among middle-aged and older adults.2 As indicated in the Report on Cardiovascular Health and Diseases in China, the current number of patients with CVD in China is approximately 330 million,3 resulting in a heavy economic burden on society.
Longitudinal studies have demonstrated that long-term exposure to fine particulate matter (PM2.5; airborne particles ≤2.5 µm in aerodynamic diameter) is a significant risk factor for the development of CVD, with the increased risk of CVD ranging from 3% to 25% for each 10 μg/m3 increase in PM2.5 exposure.4,8 Meanwhile, physical activity (PA) is widely recognised as an important health promoter, exhibiting beneficial effects on the prevention of CVD.9 However, it should be noted that when engaging in PA, individuals may transition from nasal to oral breathing, bypassing the natural filtration provided by the nasal passages. This shift can further increase the inhalation of PM2.5, potentially exacerbating its adverse effects.10
To date, only a limited number of studies have investigated the potential trade-off between the health benefits of PA and the potentially detrimental effects of PM2.5 on CVD risk, and their conclusions remain controversial.11,16 Furthermore, the majority of these studies were conducted in high-income countries with relatively low PM2.5 concentrations.11,14 As a rapidly developing middle-income country, China has relatively high PM2.5 concentrations, with a median of 50.4 μg/m3 for middle-aged and older Chinese, and 86% of them have long-term PM2.5 exposure exceeding WHO guidelines (35 μg/m3).17 Therefore, clarifying the cardiovascular benefits of PA in the context of elevated PM2.5 concentrations has become an important public health concern.18
Based on the China Health and Retirement Longitudinal Study (CHARLS), this study aimed to estimate the combined effects of long-term PM2.5 exposure and PA on CVD risk among middle-aged and older Chinese adults (≥45 years) and assess whether the cardiovascular benefits of PA outweigh the potential detrimental effects of PM2.5 exposure.
Methods
Study design and participants
Participants were from the CHARLS, which has been previously documented.19 The CHARLS is a nationally representative longitudinal survey designed to analyse the issue of population ageing in China. Between June 2011 and March 2012, the study enrolled 17 708 participants aged 45 years and older from 150 county-level units using a multistage stratified probability-proportionate-to-size sampling strategy. Participants were followed up at 2-year intervals. All participants provided informed consent.
The present study included participants who were enrolled between June 2011 and March 2012 and were followed up until 2018. We applied specific exclusion criteria to ensure the validity and reliability of our analysis. Participants with a history of heart disease or stroke at baseline (n=2318) were excluded to minimise potential reverse causation bias, as individuals with pre-existing cardiovascular conditions may systematically alter their PA patterns or environmental exposures, thereby confounding the relationship between PM2.5, PA and incident CVD. Additionally, we excluded individuals lost to follow-up (n=1260), younger than 45 years (n=519) and without information on PA (n=9637). Finally, a total of 3974 individuals were included in the analysis (figure 1).
Figure 1. Flow chart for participant selection. PA, physical activity.
PA assessment
Information on PA was collected at the baseline survey using a standardised self-reported questionnaire, a modified version of the validated International Physical Activity Questionnaire (IPAQ).20 In the CHARLS, PA is assessed using a typical week reference period rather than the standard last 7 days, excludes sedentary behaviour metrics and quantifies activity durations through four discrete time categories instead of continuous values.21 22 Participants were asked to report the amount of time they typically spent in different types of PA in a usual week: (1) vigorous PA, such as heavy lifting, digging, ploughing, aerobics, fast bicycling and cycling with a heavy load; (2) moderate PA, such as carrying light loads, bicycling at a regular pace or mopping the floor; (3) mild PA, such as walking. According to the IPAQ, the corresponding metabolic equivalent (MET) values for walking, moderate-intensity PA and vigorous-intensity PA were 3.3, 4 and 8, respectively.23 The MET hours per week (MET·hours/week) were then calculated by multiplying the time spent per week by the MET value for each type of activity. This combines information on the frequency, intensity and duration of PA. The total weekly volume of PA was obtained by summing the MET·hours/week of all the activities together. Referring to previous studies, the amount of PA was divided into three groups based on the tertile cut-off values, which were defined as follows: low PA (≤28.9 MET·hours/week), moderate PA (29.0–69.9 MET·hours/week) and high PA (≥70 MET·hours/week).24 25
PM2.5 exposure assessment
The individuals’ long-term exposure to PM2.5 was determined by linking their residential addresses to the ChinaHighAirPollutants (CHAP) dataset.26 The CHAP dataset estimated the ground-level PM2.5 concentrations in China from 2000 to 2022 using a combination of ground observations, satellite remote-sensing products, atmospheric reanalysis and model simulations. The estimated PM2.5 concentrations exhibited a high quality with a 10-fold cross-validation of R2=0.90.26 The CHARLS did not provide the participants’ residential addresses due to privacy concerns. Therefore, we used city-level PM2.5 concentrations as a proxy for individual exposure. In this study, long-term exposure to PM2.5 was defined as the annual average PM2.5 concentration in the year preceding the baseline.27 For data analysis, the PM2.5 concentrations were divided into three groups based on the tertile cut-off values: low (≤47.4 µg/m3), moderate (47.5–72.1 µg/m3) and high (≥72.2 µg/m3).
Covariates
Covariates were collected using a structured questionnaire at the baseline survey, including age, sex, height, weight, marital status (married and unmarried), educational level (elementary school or below, secondary school, and college and above), occupation (farmer and others), smoking status (never smoking, current smoker and former smoker), drinking status (never drinking, current drinker and former drinker) and self-reported medical history, including hypertension and diabetes. Residence (rural and urban) was also included as a covariate to account for contextual factors that may influence CVD risk, such as differences in lifestyle and access to healthcare between urban and rural areas. Body mass index (BMI) was calculated by dividing weight in kilograms by the square of height in metres (kg/m2), and the resulting value was categorised into three groups: underweight (<18.5 kg/m2), normal weight (18.5–23.9 kg/m2) and overweight (≥24.0 kg/m2). All selected covariates were collected by a structured questionnaire at the baseline survey and were considered to be time independent.
Assessment of outcomes
The outcome of this study was the incidence of CVD, including heart disease and stroke. Consistent with previous studies, information on CVD was collected by asking participants two separate questions: ‘Have you been diagnosed with heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems by a doctor?’ and ‘Have you been diagnosed with stroke?’. Similar to previous studies, participants who reported a history of heart disease or stroke were defined as having incident CVD. If a participant reported both heart disease and stroke, the diagnosis of CVD was recorded based on the earlier occurrence of either condition during the follow-up period. Total CVD events were calculated as the sum of all first-time diagnoses across follow-ups.28 29
Statistical analysis
Continuous variables were expressed as mean±SD or median (IQR) and compared by t-test or one-way analysis of variance. Categorical variables were summarised by frequency with percentage and compared using the χ2 test.
Univariable and multivariable Cox proportional hazards models were established to estimate the associations between PA, PM2.5 and CVD incidence. Model 1 was unadjusted; model 2 was adjusted for age, sex, smoking status, drinking status, educational level, marital status, occupation, residence, BMI, hypertension and diabetes; model 3 was further adjusted for PM2.5 exposure (for the association between PA and CVD incidence) or PA (for the association between PM2.5 and CVD incidence). The proportional hazards assumption for each variable was tested using Schoenfeld residuals, and no violation was identified (p>0.05). HRs with corresponding 95% CIs were examined for each IQR of PA and each 10 μg/m3 increase in PM2.5 concentration. Additionally, the effects of tertile groups were also investigated, with the first group as the reference.
Stratified analyses were conducted to explore the associations of PM2.5 or PA with CVD in each PA or PM2.5 stratum. To comprehensively assess their impact on CVD risk, we also fitted three models. Model 1 was unadjusted. Model 2 was adjusted for non-modifiable biological confounders, including age and sex. Model 3 was fully adjusted. Furthermore, a multiplicative interaction term was included in the model to examine potential interaction effects between PA and PM2.5. To assess the combined effects of PA and long-term PM2.5 exposure on CVD incidence, participants were divided into nine distinct groups based on the categories of PM2.5 concentrations and PA levels, with participants with high PA and low PM2.5 serving as the reference group.
All analyses were performed using Stata (V.14.0) and R (V.3.5.1). A two-sided p value <0.05 was considered statistically significant.
Results
Baseline characteristics
A total of 3974 participants were included in this study. The mean age of the participants was 58.4±9.1 years and 46.6% were male. The mean concentration of PM2.5 was 59.9±17.9 µg/m3, with a range of 21.9–100.5 μg/m3. The median PA was 52.9 MET·hours/week, with an IQR of 58.2 MET·hours/week and a range of 1.1–377.3 MET·hours/week. The baseline characteristics of the participants are shown in table 1. Individuals with high levels of PA were reported to be younger, more likely to be married, residing in rural areas and farmers. The participants were also divided into three groups based on their PM2.5 exposure levels. As shown in online supplemental table S1, individuals with high levels of PM2.5 exposure were more likely to reside in rural areas and to be overweight.
Table 1. Baseline characteristics of the study participants.
| Total(n=3974) | Low PA(n=1565) | Moderate PA(n=1197) | High PA(n=1212) | P value | |
|---|---|---|---|---|---|
| Age (years) | 58.4±9.1 | 59.2±9.9 | 58.3±8.8 | 57.5±8.4 | <0.001 |
| Sex | 0.201 | ||||
| Male | 1852 (46.6) | 756 (48.3) | 550 (46.0) | 546 (45.1) | |
| Female | 2122 (53.4) | 809 (51.7) | 647 (54.0) | 666 (54.9) | |
| Educational level | 0.004 | ||||
| Elementary school or below | 2674 (67.3) | 1053 (67.3) | 789 (65.9) | 832 (68.6) | |
| Secondary school | 1229 (30.9) | 475 (30.3) | 381 (31.8) | 373 (30.8) | |
| College and above | 71 (1.8) | 37 (2.4) | 27 (2.3) | 7 (0.6) | |
| Marital status | <0.001 | ||||
| Married | 3517 (88.5) | 1333 (85.2) | 1069 (89.3) | 1115 (92.0) | |
| Unmarried | 457 (11.5) | 232 (14.8) | 128 (10.7) | 97 (8.0) | |
| Residence | <0.001 | ||||
| Urban | 1525 (38.4) | 669 (42.8) | 474 (39.6) | 382 (31.5) | |
| Rural | 2449 (61.6) | 896 (57.2) | 723 (60.4) | 830 (68.5) | |
| Occupation | <0.001 | ||||
| Farmer | 1669 (42.0) | 484 (30.9) | 500 (41.8) | 685 (56.5) | |
| Others | 2305 (58.0) | 1081 (69.1) | 697 (58.2) | 527 (43.5) | |
| Smoking status | 0.051 | ||||
| Never | 2468 (62.1) | 941 (60.1) | 748 (62.5) | 779 (64.3) | |
| Current | 1204 (30.3) | 496 (31.7) | 384 (29.1) | 360 (29.7) | |
| Former | 302 (7.6) | 128 (8.2) | 101 (8.4) | 73 (6.0) | |
| Drinking status | 0.673 | ||||
| Never | 2315 (58.2) | 922 (58.9) | 687 (57.4) | 706 (58.2) | |
| Current | 1346 (33.9) | 512 (32.7) | 421 (35.2) | 413 (34.1) | |
| Former | 313 (7.9) | 131 (8.4) | 89 (7.4) | 93 (7.7) | |
| BMI | 0.115 | ||||
| Underweight | 253 (6.4) | 99 (6.3) | 69 (5.8) | 85 (7.0) | |
| Normal weight | 2112 (53.1) | 822 (52.5) | 618 (51.6) | 672 (55.5) | |
| Overweight | 1609 (40.5) | 644 (41.2) | 510 (42.6) | 455 (37.5) | |
| PM2.5 level | 0.027 | ||||
| Low (≤47.4 µg/m3) | 1320 (33.2) | 513 (32.8) | 379 (31.7) | 428 (35.3) | |
| Moderate (47.5–72.1 µg/m3) | 1404 (35.3) | 561 (35.8) | 457 (38.2) | 386 (31.9) | |
| High (≥72.2 µg/m3) | 1250 (31.5) | 491 (31.4) | 361 (30.1) | 398 (32.8) | |
| Hypertension | 0.051 | ||||
| No | 3117 (78.4) | 1197 (76.5) | 958 (80.0) | 962 (79.4) | |
| Yes | 857 (21.6) | 368 (23.5) | 239 (20.0) | 250 (20.6) | |
| Diabetes | 0.210 | ||||
| No | 3794 (95.5) | 1483 (94.8) | 1150 (96.1) | 1161 (95.8) | |
| Yes | 180 (4.5) | 82 (5.2) | 47 (3.9) | 51 (4.2) |
Data are mean±SD or frequencies with percentages.
BMI, body mass index; PA, physical activity; PM2.5, fine particulate matter.
Independent associations between PA or PM2.5 and CVD
A total of 731 CVD events were observed over a median follow-up period of 7 years. The independent associations between PA or PM2.5 and the risk of CVD are shown in table 2. In the fully adjusted model, each IQR increase in PA was associated with a 10% reduction in the risk of CVD (HR=0.90, 95% CI 0.83 to 0.98). Compared with participants with low levels of PA, those with high levels of PA exhibited an 18% reduction in the risk of CVD (HR=0.82, 95% CI 0.68 to 0.98). However, there was no significant increase in the risk of CVD among those with moderate PA.
Table 2. Associations between PA, long-term exposure to PM2.5 and the risk of CVD.
| n | Event (%) | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| PA, per IQR | 3974 | 731 (18.4) | 0.89 (0.83 to 0.97) | 0.005 | 0.90 (0.83 to 0.97) | 0.010 | 0.90 (0.83 to 0.98) | 0.011 |
| Low PA | 1565 | 309 (19.7) | Ref | Ref | Ref | |||
| Moderate PA | 1197 | 229 (19.1) | 0.97 (0.82 to 1.15) | 0.722 | 0.98 (0.83 to 1.17) | 0.836 | 0.98 (0.83 to 1.17) | 0.840 |
| High PA | 1212 | 193 (15.9) | 0.80 (0.67 to 0.95) | 0.014 | 0.81 (0.67 to 0.98) | 0.027 | 0.82 (0.68 to 0.98) | 0.031 |
| PM2.5, per 10 μg/m3 | 3974 | 731 (18.4) | 1.07 (1.03 to 1.12) | 0.001 | 1.05 (1.01 to 1.09) | 0.028 | 1.05 (1.00 to 1.09) | 0.031 |
| Low PM2.5 | 1320 | 200 (15.1) | Ref | Ref | Ref | |||
| Moderate PM2.5 | 1404 | 272 (19.4) | 1.31 (1.09 to 1.57) | 0.004 | 1.22 (1.02 to 1.47) | 0.031 | 1.21 (1.01 to 1.46) | 0.040 |
| High PM2.5 | 1250 | 259 (20.7) | 1.38 (1.15 to 1.66) | 0.001 | 1.27 (1.05 to 1.53) | 0.013 | 1.26 (1.05 to 1.52) | 0.015 |
P values, HRs and 95% CIs from Cox regression. Model 1 was unadjusted. Model 2 was adjusted for age, sex, smoking status, drinking status, educational level, marital status, occupation, residence, BMI, hypertension and diabetes. Model 3 was further adjusted for PM2.5 exposure (for the association between PA and CVD incidence) or PA (for the association between PM2.5 and CVD incidence).
BMI, body mass index; CVD, cardiovascular disease; PA, physical activity; PM2.5, fine particulate matter.
Moreover, table 2 shows that long-term PM2.5 exposure was positively associated with the risk of CVD. A 10 μg/m3 increase in PM2.5 exposure was associated with a 5% increase in the risk of CVD (HR=1.05, 95% CI 1.00 to 1.09). Compared with the low level of PM2.5 exposure, the HRs for CVD incidence associated with moderate and high PM2.5 exposures were 1.21 (95% CI 1.01 to 1.46) and 1.26 (95% CI 1.05 to 1.52), respectively.
Combined effects of PA and PM2.5 on CVD
The combined effects of PA and long-term exposure to PM2.5 on the risk of CVD are shown in figure 2 and online supplemental table S2. Participants were divided into nine distinct groups based on the levels of PM2.5 concentrations and PA. Compared with participants with high PA and low PM2.5 exposure, those with low PA and high PM2.5 exposure had the highest risk of CVD (HR=1.56, 95% CI 1.15 to 2.13).
Figure 2. The combined effects of PA and long-term exposure to PM2.5 on the development ofCVD. P values, HRs and 95% CIs from Cox regression were adjusted for age, sex, smoking status, drinking status, educational level, marital status, occupation, residence, BMI, hypertension and diabetes.BMI, body mass index; CVD, cardiovascular disease; PA, physical activity; PM2.5, fine particulate matter.
Stratified analysis
The associations between long-term exposure to PM2.5 and the risk of CVD in each PA level are shown in table 3. Among participants with low and moderate PA levels, high levels of long-term exposure to PM2.5 were associated with 43% and 42% increased risks of CVD, respectively. However, no significant association was observed between PM2.5 exposure and the risk of CVD among participants with the high level of PA.
Table 3. Association between long-term exposure to PM2.5 and the risk of CVD in each stratum of PA.
| Model 1 | Model 2 | Model 3 | |||||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | ||
| Low PA | PM2.5, per 10 μg/m3 | 1.09 (1.03 to 1.16) | 0.004 | 1.10 (1.03 to 1.17) | 0.004 | 1.07 (1.00 to 1.14) | 0.042 |
| Low PM2.5 | Ref | Ref | Ref | ||||
| Moderate PM2.5 | 1.22 (0.91 to 1.62) | 0.181 | 1.22 (0.91 to 1.63) | 0.177 | 1.17 (0.88 to 1.57) | 0.280 | |
| High PM2.5 | 1.58 (1.19 to 2.09) | 0.002 | 1.58 (1.19 to 2.09) | 0.001 | 1.43 (1.07 to 1.90) | 0.015 | |
| Moderate PA | PM2.5, per 10 μg/m3 | 1.10 (1.02 to 1.19) | 0.010 | 1.10 (1.03 to 1.19) | 0.009 | 1.09 (1.01 to 1.18) | 0.021 |
| Low PM2.5 | Ref | Ref | Ref | ||||
| Moderate PM2.5 | 1.54 (1.11 to 2.14) | 0.011 | 1.55 (1.12 to 2.16) | 0.009 | 1.48 (1.06 to 2.07) | 0.021 | |
| High PM2.5 | 1.44 (1.02 to 2.04) | 0.040 | 1.45 (1.03 to 2.06) | 0.035 | 1.42 (1.00 to 2.02) | 0.049 | |
| High PA | PM2.5, per 10 μg/m3 | 1.00 (0.92 to 1.09) | 0.970 | 1.01 (0.93 to 1.09) | 0.840 | 1.00 (0.92 to 1.09) | 0.944 |
| Low PM2.5 | Ref | Ref | Ref | ||||
| Moderate PM2.5 | 1.18 (0.83 to 1.66) | 0.350 | 1.23 (0.87 to 1.74) | 0.238 | 1.22 (0.86 to 1.73) | 0.266 | |
| High PM2.5 | 1.09 (0.77 to 1.54) | 0.634 | 1.12 (0.79 to 1.58) | 0.532 | 1.10 (0.77 to 1.56) | 0.607 | |
P values, HRs and 95% CIs from Cox regression. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Model 3 was further adjusted for smoking status, drinking status, educational level, marital status, occupation, residence, BMI, hypertension and diabetes.
BMI, body mass index; CVD, cardiovascular disease; PA, physical activity; PM2.5, fine particulate matter.
The associations between PA and the risk of CVD in each PM2.5 exposure stratum are shown in table 4. Among participants with a high level of PM2.5, high PA was associated with a 29% reduction in the risk of CVD, whereas the associations between PA and CVD were not significant among participants who were exposed to low to moderate levels of PM2.5. The overall interaction between PA and PM2.5 exposure was not statistically significant (p=0.209).
Table 4. Association between PA and the risk of CVD in each stratum of PM2.5 exposure.
| Model 1 | Model 2 | Model 3 | |||||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | ||
| Low PM2.5 | PA, per IQR | 0.94 (0.82 to 1.09) | 0.426 | 0.95 (0.83 to 1.09) | 0.476 | 0.95 (0.82 to 1.10) | 0.493 |
| Low PA | Ref | Ref | Ref | ||||
| Moderate PA | 0.91 (0.65 to 1.28) | 0.595 | 0.93 (0.66 to 1.31) | 0.665 | 0.98 (0.69 to 1.38) | 0.895 | |
| High PA | 0.92 (0.66 to 1.28) | 0.629 | 0.95 (0.68 to 1.32) | 0.741 | 0.91 (0.65 to 1.29) | 0.607 | |
| Moderate PM2.5 | PA, per IQR | 0.91 (0.80 to 1.05) | 0.187 | 0.93 (0.81 to 1.06) | 0.271 | 0.92 (0.80 to 1.06) | 0.244 |
| Low PA | Ref | Ref | Ref. | ||||
| Moderate PA | 1.15 (0.88 to 1.51) | 0.312 | 1.17 (0.89 to 1.54) | 0.262 | 1.15 (0.87 to 1.52) | 0.315 | |
| High PA | 0.89 (0.66 to 1.22) | 0.474 | 0.93 (0.68 to 1.27) | 0.648 | 0.92 (0.67 to 1.26) | 0.600 | |
| High PM2.5 | PA, per IQR | 0.84 (0.73 to 0.96) | 0.012 | 0.85 (0.74 to 0.97) | 0.019 | 0.87 (0.76 to 1.00) | 0.051 |
| Low PA | Ref | Ref | Ref | ||||
| Moderate PA | 0.83 (0.62 to 1.11) | 0.212 | 0.86 (0.64 to 1.15) | 0.299 | 0.94 (0.70 to 1.27) | 0.697 | |
| High PA | 0.64 (0.47 to 0.86) | 0.004 | 0.66 (0.49 to 0.90) | 0.009 | 0.71 (0.52 to 0.98) | 0.036 | |
P values, HRs and 95% CIs from Cox regression. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Model 3 was further adjusted for smoking status, drinking status, educational level, marital status, occupation, residence, BMI, hypertension and diabetes.
BMI, body mass index; CVD, cardiovascular disease; PA, physical activity; PM2.5, fine particulate matter.
Discussion
In this nationwide cohort of middle-aged and older Chinese adults, we observed a positive association between long-term PM2.5 exposure and CVD risk, while identifying a negative association between PA and CVD risk. Compared with participants with high PA and low PM2.5 exposure, those with low PA and high PM2.5 exposure had a 56% increased risk of CVD. In addition, long-term PM2.5 exposure increased the risk of CVD among participants with low and moderate PAs, but not those with high PA. However, these differences were not statistically significant.
The present study yielded no significant interaction effect between PM2.5 and PA on the risk of CVD incidence, which is consistent with the results of the previous studies.14 15 30 31 For instance, a study using the Korean National Health Insurance Service (NHIS) database, which included 189 771 adults aged 40 years and above, demonstrated that the effect modification of the association between PA and CVD risk by the levels of PM2.5 exposure was not statistically significant.30 Similarly, the Swedish Västerbotten Intervention Programme (VIP), which followed 2221 adults diagnosed with first-time ischaemic heart disease (IHD) and stroke cases for an average of 5.5 years, found no interaction effect between PA and PM2.5 exposure on the recurrence of IHD.31 The Nurses’ Health Study, involving 104 990 US women, also did not yield statistically significant interactions between long-term PM2.5 exposure and PA in relation to CVD risk.14 Furthermore, in areas with high PM2.5 concentrations, a study from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project demonstrated that there was no significant multiplicative interaction between commuting mode and PM2.5 levels on CVD incidence.15
However, several studies have reported contrasting results. Two studies based on the NHIS database revealed a significant effect modification of the association between PA and CVD risk by PM2.5 exposure levels among young adults and cancer survivors.11 13 Additionally, a study from the VIP involving 34 748 adults demonstrated a significant interaction effect between PA and PM2.5 exposure on the incidence of IHD.12 Most recently, the China Kadoorie Biobank, which included 118 274 non-farmers and 201 140 farmers with a median follow-up of 11 years, found that the associations of active commuting and farming activities with CVD differed significantly between those with lower PM2.5 level and those with higher PM2.5 level.16
The discrepancies in these findings may stem from several factors. First, the methods used to estimate PA vary across studies. Due to the absence of detailed information on habitual exercise, some studies used commuting mode as an indicator of PA, which may not fully capture the complexity of PA patterns. Second, the areas in which these studies were conducted exhibit considerable variation in PM2.5 concentrations. For example, the median PM2.5 concentration in the VIP study was 5.48 µg/m3, while it reached 61 µg/m3 in the China-PAR project. Such differences in exposure levels can significantly influence the observed associations. Third, the characteristics of the study populations differ, including variations in age structure and sex ratio, which may affect the generalisability of the findings. In interpreting these findings in the context of our study, we recognise that the lack of a significant interaction effect in our analysis may be influenced by the specific context of our study population and the methods used. Our findings contribute to the existing literature by providing further evidence from a large, representative sample of middle-aged and older Chinese adults. While some studies suggest that PM2.5 exposure may modify the relationship between PA and CVD risk, our results indicate that this interaction is not statistically significant within our specific population and methodological framework. This highlights the importance of considering study-specific factors when interpreting the role of PM2.5 and PA in CVD risk.
Despite the absence of a significant interaction effect between PA levels and PM2.5 exposure, our findings suggest that individuals with high PA levels may have a protective effect against CVD risk even in an environment with high PM2.5 concentrations. A similar finding was observed in a study from the VIP. Among individuals who never actively commuted, a significant positive association was found between residential PM2.5 concentration and the risk of IHD. However, this association was not observed in those who actively commuted.12 This is an important finding because it suggests that lifestyle modifications could potentially offset the adverse effects of air pollution on cardiovascular health. The health benefits of moderate to vigorous PA for health are well established, with reduced risks of hypertension, CVD, diabetes and all-cause mortality.14 24 25 30 32 However, 28% of the global population has an inactive lifestyle,33 and the increased levels of ambient air pollution may be a significant contributor to physical inactivity.34 Furthermore, it is estimated that 99% of the global population currently lives in areas where air quality does not meet the WHO-recommended annual air quality guidelines level (5 μg/m3),35 36 which could have a significant impact on an individual’s motivation to engage in PA. Given the significant public health risks associated with both physical inactivity and exposure to air pollution, which contribute to increased premature morbidity and mortality from non-communicable diseases,37 it is imperative to promote PA and exercise as a primary prevention strategy for CVD, even in polluted settings. Furthermore, our study also found that rural residents have high PM2.5 exposures despite their high levels of PA. This paradox highlights the urgency for exercise professionals and community planners to develop evidence-based guidelines that balance the benefits of exercise with air quality considerations.
The stratified analysis showed that the associations between PA and CVD were not significant among participants exposed to low to moderate levels of PM2.5, which may reflect unmeasured confounding or differential exposure patterns. First, populations exposed to low to moderate levels of PM2.5 were more likely to have higher levels of education and to be predominantly non-agricultural. These populations may spend more time indoors, where unmeasured indoor air pollutants and sedentary behaviours could counteract cardiovascular benefits of PA.38 39 Second, people exposed to high levels of PM2.5 exhibited a higher prevalence of overweight and may derive more benefits from PA due to its dual role in reducing pollution-induced oxidative stress and obesity-related inflammation.40 This synergy could enhance the protective effects of PA against CVD in high-exposure settings. Conversely, in low to moderate PM2.5 environments, where baseline metabolic risks are lower, the incremental benefits of PA may require larger sample sizes or longer follow-up to be detected.
The potential mechanisms through which air pollution and PA exert their combined effects on cardiovascular outcomes remain unclear. PM2.5 exposure has been demonstrated to induce cardiovascular toxicity via multiple pathways, including the enhancement of inflammatory responses, oxidative stress and endothelial injury.41 Conversely, PA has been demonstrated to have beneficial effects on cardiovascular health via reduced systemic inflammation and improved endothelial function.42 In individuals with low PA, the lack of these protective mechanisms may lead to a greater susceptibility to the adverse effects of PM2.5. This could result in exacerbated inflammatory responses and endothelial dysfunction, potentially increasing the risk of cardiovascular events. In conclusion, long-term exposure to PM2.5 and physical inactivity appear to share the same underlying mechanisms and pathways that affect cardiovascular health, resulting in a 56% increased risk of CVD incidence in the present study. Although engaging in PA may result in increased PM2.5 inhalation, PM2.5-related inflammation may be partially offset by PA. Further research is needed to investigate the precise mechanisms underlying these interactions.
The present study has several strengths. It uses a prospective cohort design with a nationally representative sample and a high response rate. Moreover, the study was conducted in an area with a relatively wide range of PM2.5 concentrations, with observed levels ranging from 21.9 μg/m3 to 100.5 μg/m3. This adds to the evidence for the combined effects of PA and air pollution on cardiovascular health over a broad range of PM2.5 concentrations. However, the interpretation of our results is limited by the following factors. First, it should be noted that the diagnosis of heart disease and stroke and the information on PA were self-reported, which could potentially introduce information bias. Second, individual exposure was assigned based on the baseline residential address without consideration of participants’ daily activity patterns or address changes during the follow-up period, which could lead to some exposure misclassification. Furthermore, the use of current residence (rather than historical data) may underestimate lifetime PM2.5 exposure, particularly for individuals with a history of migration. Future studies incorporating lifetime residence histories may better disentangle the interaction between age, cumulative PM2.5 exposure and cardiovascular outcomes. Third, we used city-level PM2.5 concentrations as a proxy for individual exposure. As a result, misclassification of individual exposure may have occurred in our study. Fourth, other pollutants such as ozone, nitrogen dioxide and black carbon were not considered in this study due to lack of data. Future studies should consider the potential confounding effects of other pollutants. Finally, the CHARLS sample was limited to middle-aged and elderly people in China, which limits the generalisability to younger populations and those in other geographical contexts.
Conclusions
In conclusion, our study highlights the importance of considering both environmental and lifestyle factors in the prevention of CVD. While the detrimental effects of long-term PM2.5 exposure persist, our findings demonstrate that regular PA substantially mitigates this risk even in high-pollution settings, indicating that the practice of PA is an effective strategy for reducing the risk of CVD, even among individuals living in areas with elevated PM2.5 concentrations.
Supplementary material
Acknowledgements
We sincerely appreciate the data support provided by the CHARLS team and the CHAP dataset.
Footnotes
Funding: This study was supported by the Beijing Natural Science Foundation (7252036) and the Capital’s Funds for Health Improvement and Research (CFH 2022-1-2062).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Consent obtained directly from patient(s).
Ethics approval: This study involves human participants and the protocol for the CHARLS was approved by the Ethical Review Committee of Peking University (IRB00001052-11015). Participants gave informed consent to participate in the study before taking part.
Data availability free text: The datasets analysed in the current manuscript are available in the CHARLS.
Patient and public involvement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
Data availability statement
Data are available in a public, open access repository.
References
- 1.Wang H, Zhang H, Zou Z. Changing profiles of cardiovascular disease and risk factors in China: a secondary analysis for the Global Burden of Disease Study 2019. Chin Med J. 2023;136:2431–41. doi: 10.1097/CM9.0000000000002741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Huang Q, Jiang Z, Shi B, et al. Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS) BMC Public Health . 2025;25:518. doi: 10.1186/s12889-025-21609-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Hu S-S, The Writing Committee Of The Report On Cardiovascular Health And Diseases In China Report on cardiovascular health and diseases in China 2021: an updated summary. J Geriatr Cardiol. 2023;20:399–430. doi: 10.26599/1671-5411.2023.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lederer AM, Fredriksen PM, Nkeh-Chungag BN, et al. Cardiovascular effects of air pollution: current evidence from animal and human studies. Am J Physiol Heart Circ Physiol . 2021;320:H1417–39. doi: 10.1152/ajpheart.00706.2020. [DOI] [PubMed] [Google Scholar]
- 5.Jalali S, Karbakhsh M, Momeni M, et al. Long-term exposure to PM2.5 and cardiovascular disease incidence and mortality in an Eastern Mediterranean country: findings based on a 15-year cohort study. Environ Health. 2021;20:112. doi: 10.1186/s12940-021-00797-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liang F, Liu F, Huang K, et al. Long-Term Exposure to Fine Particulate Matter and Cardiovascular Disease in China. J Am Coll Cardiol. 2020;75:707–17. doi: 10.1016/j.jacc.2019.12.031. [DOI] [PubMed] [Google Scholar]
- 7.Al-Kindi SG, Brook RD, Biswal S, et al. Environmental determinants of cardiovascular disease: lessons learned from air pollution. Nat Rev Cardiol . 2020;17:656–72. doi: 10.1038/s41569-020-0371-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yang B-Y, Guo Y, Markevych I, et al. Association of Long-term Exposure to Ambient Air Pollutants With Risk Factors for Cardiovascular Disease in China. JAMA Netw Open . 2019;2:e190318. doi: 10.1001/jamanetworkopen.2019.0318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kyu HH, Bachman VF, Alexander LT, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ. 2016;354:i3857. doi: 10.1136/bmj.i3857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Cepeda M, Schoufour J, Freak-Poli R, et al. Levels of ambient air pollution according to mode of transport: a systematic review. Lancet Public Health. 2017;2:e23–34. doi: 10.1016/S2468-2667(16)30021-4. [DOI] [PubMed] [Google Scholar]
- 11.Choi D, Choi S, Kim KH, et al. Combined Associations of Physical Activity and Particulate Matter With Subsequent Cardiovascular Disease Risk Among 5‐Year Cancer Survivors. JAHA . 2022;11:e022806. doi: 10.1161/JAHA.121.022806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Raza W, Krachler B, Forsberg B, et al. Air pollution, physical activity and ischaemic heart disease: a prospective cohort study of interaction effects. BMJ Open . 2021;11:e040912. doi: 10.1136/bmjopen-2020-040912. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kim SR, Choi S, Kim K, et al. Association of the combined effects of air pollution and changes in physical activity with cardiovascular disease in young adults. Eur Heart J. 2021;42:2487–97. doi: 10.1093/eurheartj/ehab139. [DOI] [PubMed] [Google Scholar]
- 14.Elliott EG, Laden F, James P, et al. Interaction between Long-Term Exposure to Fine Particulate Matter and Physical Activity, and Risk of Cardiovascular Disease and Overall Mortality in U.S. Women. Environ Health Perspect . 2020;128:127012. doi: 10.1289/EHP7402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lin Y, Yang X, Liang F, et al. Benefits of active commuting on cardiovascular health modified by ambient fine particulate matter in China: A prospective cohort study. Ecotoxicol Environ Saf. 2021;224:112641. doi: 10.1016/j.ecoenv.2021.112641. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sun D, Liu C, Ding Y, et al. Long-term exposure to ambient PM2·5, active commuting, and farming activity and cardiovascular disease risk in adults in China: a prospective cohort study. Lancet Planet Health. 2023;7:e304–12. doi: 10.1016/S2542-5196(23)00047-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Li W, Tian A, Shi Y, et al. Associations of long-term fine particulate matter exposure with all-cause and cause-specific mortality: results from the ChinaHEART project. Lancet Reg Health West Pac . 2023;41:100908. doi: 10.1016/j.lanwpc.2023.100908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hahad O, Kuntic M, Frenis K, et al. Physical Activity in Polluted Air-Net Benefit or Harm to Cardiovascular Health? A Comprehensive Review. Antioxidants (Basel) 2021;10:1787. doi: 10.3390/antiox10111787. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zhao Y, Hu Y, Smith JP, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS) Int J Epidemiol . 2014;43:61–8. doi: 10.1093/ije/dys203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc . 2003;35:1381–95. doi: 10.1249/01.MSS.0000078924.61453.FB. [DOI] [PubMed] [Google Scholar]
- 21.Huang Y, Lu Z. A cross-sectional study of physical activity and chronic diseases among middle-aged and elderly in China. Sci Rep. 2024;14:30701. doi: 10.1038/s41598-024-78360-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Li X, Zhang W, Zhang W, et al. Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study. BMC Public Health . 2020;20:1682. doi: 10.1186/s12889-020-09671-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lee PH, Macfarlane DJ, Lam TH, et al. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act . 2011;8:115. doi: 10.1186/1479-5868-8-115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Guo C, Yu T, Chang L-Y, et al. Effects of air pollution and habitual exercise on the risk of death: a longitudinal cohort study. CMAJ . 2021;193:E1240–9. doi: 10.1503/cmaj.202729. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Guo C, Yang HT, Chang L-Y, et al. Habitual exercise is associated with reduced risk of diabetes regardless of air pollution: a longitudinal cohort study. Diabetologia . 2021;64:1298–308. doi: 10.1007/s00125-021-05408-4. [DOI] [PubMed] [Google Scholar]
- 26.Wei J, Li Z, Lyapustin A, et al. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sens Environ. 2021;252:112136. doi: 10.1016/j.rse.2020.112136. [DOI] [Google Scholar]
- 27.WHO Guidelines Approved by the Guidelines Review Committee . World Health Organization; 2021. WHO global air quality guidelines: particulate matter (PM(25) and PM(10)), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. [PubMed] [Google Scholar]
- 28.Li F, Wang Y, Shi B, et al. Association between the cumulative average triglyceride glucose-body mass index and cardiovascular disease incidence among the middle-aged and older population: a prospective nationwide cohort study in China. Cardiovasc Diabetol . 2024;23:16. doi: 10.1186/s12933-023-02114-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Gao K, Cao L-F, Ma W-Z, et al. Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine . 2022;44:101264. doi: 10.1016/j.eclinm.2021.101264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kim SR, Choi S, Keum N, et al. Combined Effects of Physical Activity and Air Pollution on Cardiovascular Disease: A Population-Based Study. J Am Heart Assoc. 2020;9:e013611. doi: 10.1161/JAHA.119.013611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Raza W, Krachler B, Forsberg B, et al. Does Physical Activity Modify the Association between Air Pollution and Recurrence of Cardiovascular Disease? Int J Environ Res Public Health . 2021;18:2631. doi: 10.3390/ijerph18052631. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Liu Q, Huang K, Liang F, et al. Long-term exposure to fine particulate matter modifies the association between physical activity and hypertension incidence. J Sport Health Sci. 2022;11:708–15. doi: 10.1016/j.jshs.2022.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Guthold R, Stevens GA, Riley LM, et al. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6:e1077–86. doi: 10.1016/S2214-109X(18)30357-7. [DOI] [PubMed] [Google Scholar]
- 34.An R, Zhang S, Ji M, et al. Impact of ambient air pollution on physical activity among adults: a systematic review and meta-analysis. Perspect Public Health . 2018;138:111–21. doi: 10.1177/1757913917726567. [DOI] [PubMed] [Google Scholar]
- 35.Yu W, Ye T, Zhang Y, et al. Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study. Lancet Planet Health. 2023;7:e209–18. doi: 10.1016/S2542-5196(23)00008-6. [DOI] [PubMed] [Google Scholar]
- 36.Tainio M, Jovanovic Andersen Z, Nieuwenhuijsen MJ, et al. Air pollution, physical activity and health: A mapping review of the evidence. Environ Int . 2021;147:105954. doi: 10.1016/j.envint.2020.105954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Hahad O, Daiber A, Münzel T. Physical activity in polluted air: an urgent call to study the health risks. Lancet Planet Health. 2023;7:e266–7. doi: 10.1016/S2542-5196(23)00055-4. [DOI] [PubMed] [Google Scholar]
- 38.Hu X, Knibbs LD, Zhou Y, et al. The role of lifestyle in the association between long-term ambient air pollution exposure and cardiovascular disease: a national cohort study in China. BMC Med . 2024;22:93. doi: 10.1186/s12916-024-03316-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Gao W, Sanna M, Chen Y-H, et al. Occupational Sitting Time, Leisure Physical Activity, and All-Cause and Cardiovascular Disease Mortality. JAMA Netw Open. 2024;7:e2350680. doi: 10.1001/jamanetworkopen.2023.50680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Pedersen BK, Saltin B. Exercise as medicine - evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports . 2015;25 Suppl 3:1–72. doi: 10.1111/sms.12581. [DOI] [PubMed] [Google Scholar]
- 41.Newby DE, Mannucci PM, Tell GS, et al. Expert position paper on air pollution and cardiovascular disease. Eur Heart J. 2015;36:83–93. doi: 10.1093/eurheartj/ehu458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Lavie CJ, Ozemek C, Carbone S, et al. Sedentary Behavior, Exercise, and Cardiovascular Health. Circ Res . 2019;124:799–815. doi: 10.1161/CIRCRESAHA.118.312669. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available in a public, open access repository.