Association of air pollutant levels and blood pressure variability in patients with essential hypertension: a distributed lag analysis study

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) and 1.51 (95% CI: 0.27, 2.75) respectively.Conclusions We observed a lag effect on BPV in patients with essential hypertension following short-term exposure to PM2.5, PM10, SO2 and CO.

Authors

Institutions

Publication Details

Journal
Blood Pressure
Published
2026-07-22
DOI
https://doi.org/10.1080/08037051.2026.2705619
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of air pollutant levels and blood pressure variability in patients with essential hypertension: a distributed lag analysis study

Fang Wang, Shunying Hu, Xinrui Li, Liu Y et al.
Blood Pressure
Air Quality and Health Impacts
article

Association of air pollutant levels and blood pressure variability in patients with essential hypertension: a distributed lag analysis study

Fang Wang, Shunying Hu, Xinrui Li, Liu Y, Yi-Xiang Wang, Jie Liu, Dan Li, Longmei Tang
article en

Abstract

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) and 1.51 (95% CI: 0.27, 2.75) respectively.Conclusions We observed a lag effect on BPV in patients with essential hypertension following short-term exposure to PM2.5, PM10, SO2 and CO.

Blood Pressure
Hebei Medical University (CN), Chinese PLA General Hospital (CN), First Affiliated Hospital of Chinese PLA General Hospital (CN)
Openalex Percentile: Top 9%
Air Quality and Health Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.