Long-Term Exposure to Fine Particulate Matter, Blood Pressure, and Incident Hypertension in Taiwanese Adults

Abstract

Background: Long-term exposure to particulate matter (PM) air pollution may increase blood pressure and the risk of hypertension. However, epidemiological evidence is scarce and inconsistent.

Objectives: We investigated the associations between long-term exposure to PM with an aerodynamic diameter <2.5μm (PM_2.5), blood pressure, and incident hypertension in a large Taiwanese cohort.

Methods: We studied 361,560 adults ≥18y old from a large cohort who participated in a standard medical examination program during 2001 to 2014. Among this group, 125,913 nonhypertensive participants were followed up. A satellite-based spatiotemporal model was used to estimate the 2-y average PM_2.5 concentrations at each participant's address. Multivariable linear regression was used in the cross-sectional data analysis with the 361,560 participants to investigate the associations between PM_2.5 and systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP), and Cox proportional hazard regression was used in the cohort data analysis with the 125,913 participants to investigate the associations between PM_2.5 and incident hypertension.

Results: Each 10-μg/m³ increment in the 2-y average PM_2.5 concentration was associated with increases of 0.45 mmHg [95% confidence interval (CI): 0.40, 0.50], 0.07 mmHg (95% CI: 0.04, 0.11), and 0.38 mmHg (95% CI: 0.33, 0.42) in SBP, DBP, and PP, respectively, after adjusting for a wide range of covariates and possible confounders. Each 10-μg/m³ increment in the 2-y average PM_2.5 concentration was associated with an increase of 3% in the risk of developing hypertension [hazard ratio=1.03 (95% CI: 1.01, 1.05)]. Stratified and sensitivity analyses yielded similar results.

Conclusions: Long-term exposure to PM_2.5 air pollution is associated with higher blood pressure and an increased risk of hypertension. These findings reinforce the importance of air pollution mitigation strategies to reduce the risk of cardiovascular disease. https://doi.org/10.1289/EHP2466.

Figure 1.

Location map of the study participants. (A) 361,560 participants in blood pressure–${\mathrm{PM}}_{2.5}$ analysis. (B) 125,913 participants in incident hypertension–${\mathrm{PM}}_{2.5}$ analysis. Circles represent the locations of the study participants.

Figure 2.

Stratified analysis on associations between long-term ${\mathrm{PM}}_{2.5}$ exposure ($10{-\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ increments) and systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) at baseline. Effect estimates (coefficients) are derived from multivariable linear regression analysis, and bars cover 95% confidence intervals. Results were adjusted for age (not in age-stratified analysis), sex (not in sex-stratified analysis), education level (not in education level–stratified analysis), smoking status (not in smoking-stratified analysis), alcohol drinking, leisure-time physical activity, occupational exposure to dust or organic solvents in the workplace, season, body mass index (BMI; not in BMI-stratified analysis), hypertension (not in hypertension-stratified analysis), diabetes (not in diabetes-stratified analysis), hyperlipidemia, and self-reported cardiovascular disease, stroke, or cancer. The lines with hollow circles from left to right represent the participants who were $<65\mathrm{y}\text{old}$, were males, had a high education level, were nonsmokers, had no hypertension, had no diabetes, and had BMI $<25{\mathrm{kg}/\mathrm{m}}^2$, respectively. The lines with solid circles from left to right represent the participants who were $\ge 65\mathrm{y}\text{old}$, were females, had a low education level, were smokers, had hypertension, had diabetes, and had $\text{BMI}\ge 25{\mathrm{kg}/\mathrm{m}}^2$, respectively. p-Values for interaction terms between ${\mathrm{PM}}_{2.5}$ (continuous variable) and each potential modifier (dichotomous variable) are presented in the figure.

Figure 3.

Stratified analysis on associations between long-term ${\mathrm{PM}}_{2.5}$ exposure ($10-\mathrm{\mu }\mathrm{g}/{\mathrm{m}}^3$ increments) and incident hypertension. Effect estimates (hazard ratios) are derived from Cox proportional hazard regression analysis, and bars cover 95% confidence intervals. Results were adjusted for age (not in age-stratified analysis), sex (not in sex-stratified analysis), education level (not in education level–stratified analysis), smoking status (not in smoking-stratified analysis), alcohol drinking, leisure-time physical activity, occupational exposure to dust or organic solvents in the workplace, season, body mass index (BMI; not in BMI-stratified analysis), diabetes (not in diabetes-stratified analysis), hyperlipidemia, and self-reported cardiovascular disease, stroke, or cancer. The lines with hollow circles from left to right represent the participants who were $<65\mathrm{y}\text{old}$, were males, had a high education level, were nonsmokers, had no diabetes, and had BMI $<25{\mathrm{kg}/\mathrm{m}}^2$, respectively. The lines with solid circles from left to right represent the participants who were $\ge 65\mathrm{y}\text{old}$, were females, had a low education level, were smokers, had diabetes, and had $\text{BMI}\ge 25{\mathrm{kg}/\mathrm{m}}^2$, respectively. p-Values for interaction terms between ${\mathrm{PM}}_{2.5}$ (continuous variable) and each potential modifier (dichotomous variable) are calculated and presented in the figure.