Extreme Air Pollution Conditions Adversely Affect Blood Pressure and Insulin Resistance: The Air Pollution and Cardiometabolic Disease Study

Abstract

Mounting evidence supports that fine particulate matter adversely affects cardiometabolic diseases particularly in susceptible individuals; however, health effects induced by the extreme concentrations within megacities in Asia are not well described. We enrolled 65 nonsmoking adults with metabolic syndrome and insulin resistance in the Beijing metropolitan area into a panel study of 4 repeated visits across 4 seasons since 2012. Daily ambient fine particulate matter and personal black carbon levels ranged from 9.0 to 552.5 µg/m(3) and 0.2 to 24.5 µg/m(3), respectively, with extreme levels observed during January 2013. Cumulative fine particulate matter exposure windows across the prior 1 to 7 days were significantly associated with systolic blood pressure elevations ranging from 2.0 (95% confidence interval, 0.3-3.7) to 2.7 (0.6-4.8) mm Hg per SD increase (67.2 µg/m(3)), whereas cumulative black carbon exposure during the previous 2 to 5 days were significantly associated with ranges in elevations in diastolic blood pressure from 1.3 (0.0-2.5) to 1.7 (0.3-3.2) mm Hg per SD increase (3.6 µg/m(3)). Both black carbon and fine particulate matter were significantly associated with worsening insulin resistance (0.18 [0.01-0.36] and 0.22 [0.04-0.39] unit increase per SD increase of personal-level black carbon and 0.18 [0.02-0.34] and 0.22 [0.08-0.36] unit increase per SD increase of ambient fine particulate matter on lag days 4 and 5). These results provide important global public health warnings that air pollution may pose a risk to cardiometabolic health even at the extremely high concentrations faced by billions of people in the developing world today.

Keywords: air pollution; diabetes mellitus; hypertension; insulin resistance; metabolism.

Figure 1. Changes in Seated Clinic Blood Pressure Levels Associated with Previous Single Day Lag Concentrations of PM_2.5 and Black Carbon

SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; AI.75, augmentation index at a heart rate of 75 beats/min. Data points and error bars represent the estimated changes and 95% confidence intervals associated with a 3.61 μg/m³ (standard deviation of 1-day lag of personal BC) increase in 1-day to 7-day single day lags of personal and ambient BC concentration, as well as a 67.2 μg/m³ (standard deviation of 1-day lag of ambient PM_2.5) increase in 1-day to 7-day single day lags of ambient PM_2.5 concentration using linear mixed models. Significant associations (p-value≤0.05) were shown in red. All models controlled for age, sex, body mass index, temperature, relative humidity, hypertension, diabetes mellitus, and study visit.

Figure 2. Changes in Seated Clinic Blood Pressure Levels Associated with Previous 1 to 7 day Long Cumulative PM_2.5 and Black Carbon Concentrations

SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; AI.75, augmentation index at a heart rate of 75 beats/min. Data points and error bars represent the estimated changes of the health endpoints and 95% confidence intervals associated with a 3.61 μg/m³ increase in 1-day to 7-day long cumulative personal and ambient BC concentrations, as well as a 67.2 μg/m³ increase in 1-day to 7-day long cumulative ambient PM_2.5 concentrations using linear mixed models. Significant associations (p-value ≤ 0.05) were shown in red. All models controlled for age, sex, body mass index, temperature, relative humidity, hypertension, diabetes mellitus, and study visit.

Figure 3. Changes in Metabolic Biomarkers Associated with Previous Single Day Lag Concentrations of PM_2.5 and Black Carbon

HOMA-IR, homeostasis model assessment of insulin resistance. Data points and error bars represent the estimated changes of the health endpoints and 95% confidence intervals associated with a 3.61 μg/m³ increase in 1-day to 7-day single day lags of personal and ambient BC concentration, as well as a 67.2 μg/m³ increase in 1-day to 7-day single day lags of ambient PM_2.5 concentration using linear mixed models. Significant associations (p-value ≤ 0.05) were shown in red. All models controlled for age, sex, body mass index, temperature, relative humidity, hypertension, diabetes mellitus, and study visit.