Blood pressure · 2026
Association of air pollutant levels and blood pressure variability in patients with essential hypertension: a distributed lag analysis study.
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Liu Jie, Wang Fei, Liu Yatian, Li Xinrui, Li Dan, Wang Jing, Hu Shunying, Tang Longmei
Аннотация
BACKGROUND: Studies on the effect of atmospheric pollutant levels on blood pressure variability (BPV) are limited. However, the effects of atmospheric pollutant levels on blood pressure variability (BPV) in hypertensive patients remain unknown. People who are exposed to air pollutants in the short term have elevated blood pressure. The objective of this study was to explore the effects of air pollutant levels on BPV in patients with essential hypertension.
METHODS: A total of 1,658 patients diagnosed with essential hypertension were included. We collected BPV indicators from the patients and pollutant data, including particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulphur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone 8-h mean (O3-8 h). We used the daily averages of PM2.5 and PM10, CO, NO2, SO2 from monitoring stations near the patients' homes. We constructed a distributed lag model for the median regression of the distribution, to explore the lag effect and correlation between air pollution components and BPV in patients with essential hypertension.
RESULTS: For every 10-μg/m³ increase in PM2.5, the maximum lag effect on dSBPSD (day time systolic blood pressure standard deviation) and dSBPCV (daytime systolic blood pressure coefficient of variation) was observed at lag 0, while that on dDBPCV (daytime diastolic blood pressure coefficient of variation) was observed at lag 4, with increments of 1.08 (95% CI: 0.15, 2.02), 0.80 (95% CI: 0.11, 1.50), and 0.53 (95% CI: 0.04, 1.02), respectively. For every 10-μg/m³ increase in PM10, the maximum lag effect on nDBPSD (night-time diastolic blood pressure standard deviation) and nDBPCV (night-time diastolic blood pressure coefficient of variation) was observed at lag 5, with increments of 0.57 (95% CI: 0.16, 0.99) and 1.00 (95% CI: 0.19, 1.8), respectively. For every 10-μg/m³ increase in SO2, the maximum lag effect on dSBPSD and dSBPCV were observed at lag 0, with increments of 4.35 (95% CI: 1.21, 7.48) and 3.54 (95% CI: 0.87, 6.21), respectively. For every 1-g/m³ increase in CO, the maximum lag effect on dSBPCV was observed at lag 0, with increments of 1.21 (95% CI: 0.25, 2.18) .
CONCLUSIONS: We observed a lag effect on BPV in patients with essential hypertension following short-term exposure to PM2.5, PM10, SO2 and CO.
METHODS: A total of 1,658 patients diagnosed with essential hypertension were included. We collected BPV indicators from the patients and pollutant data, including particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulphur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone 8-h mean (O3-8 h). We used the daily averages of PM2.5 and PM10, CO, NO2, SO2 from monitoring stations near the patients' homes. We constructed a distributed lag model for the median regression of the distribution, to explore the lag effect and correlation between air pollution components and BPV in patients with essential hypertension.
RESULTS: For every 10-μg/m³ increase in PM2.5, the maximum lag effect on dSBPSD (day time systolic blood pressure standard deviation) and dSBPCV (daytime systolic blood pressure coefficient of variation) was observed at lag 0, while that on dDBPCV (daytime diastolic blood pressure coefficient of variation) was observed at lag 4, with increments of 1.08 (95% CI: 0.15, 2.02), 0.80 (95% CI: 0.11, 1.50), and 0.53 (95% CI: 0.04, 1.02), respectively. For every 10-μg/m³ increase in PM10, the maximum lag effect on nDBPSD (night-time diastolic blood pressure standard deviation) and nDBPCV (night-time diastolic blood pressure coefficient of variation) was observed at lag 5, with increments of 0.57 (95% CI: 0.16, 0.99) and 1.00 (95% CI: 0.19, 1.8), respectively. For every 10-μg/m³ increase in SO2, the maximum lag effect on dSBPSD and dSBPCV were observed at lag 0, with increments of 4.35 (95% CI: 1.21, 7.48) and 3.54 (95% CI: 0.87, 6.21), respectively. For every 1-g/m³ increase in CO, the maximum lag effect on dSBPCV was observed at lag 0, with increments of 1.21 (95% CI: 0.25, 2.18) .
CONCLUSIONS: We observed a lag effect on BPV in patients with essential hypertension following short-term exposure to PM2.5, PM10, SO2 and CO.