[The effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease].

<b>Objective:</b> To assess the effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease (COPD), identify key air pollutants, and analyze potential influencing factors. <b>Methods:</b> In this panel study, 92 stable COPD patients were recruited. From March 2021 to September 2023 in Beijing, all participants completed 254 nights of sleep monitoring. The total sleep duration, light sleep duration, deep sleep duration and rapid eye movement sleep duration and their respective proportions in total sleep duration were recorded. The exposure levels of fine particulate matter (PM<sub>2.5</sub>), inhalable particulate matter (PM<sub>10</sub>), nitrogen dioxide (NO<sub>2</sub>), ozone (O<sub>3</sub>), sulfur dioxide (SO<sub>2</sub>), and carbon monoxide (CO) were estimated based on the infiltration factor method and time-activity logs of participants. To assess the lag effect of air pollutants, moving average concentrations of air pollutants from 0-1 day to 0-3 months were calculated. The linear mixed-effect model and Bayesian kernel machine regression (BKMR) model were used to assess the single and joint effects of air pollutants on sleep structure parameters in COPD patients, respectively. <b>Results:</b> All six types of air pollutants were associated with changes in sleep structure, manifesting as an increase in total sleep duration and light sleep proportion and a reduction in deep sleep proportion. The effects of O<sub>3</sub> were strongest at lag 0-6 days, while other air pollutants were at lag 0-3 months. Joint exposure to multiple air pollutants exerted significant joint effects on sleep structure, and NO<sub>2</sub> was identified as the dominant pollutant. NO<sub>2</sub> had a posterior inclusion probability (PIP) greater than 0.5 for light sleep proportion (PIP=0.691) and deep sleep proportion (PIP=0.957). With an interquartile range (IQR) increase of 8.6 μg/m<sup>3</sup> in NO<sub>2</sub> at lag 0-3 months, the light sleep proportion increased by 10.5% (95%<i>CI</i>: 2.2%-19.4%), and the deep sleep proportion decreased by 19.5% (95%<i>CI</i>:-30.6%- -6.8%). <b>Conclusion:</b> Joint exposure to air pollutants is associated with changes in sleep structure in stable COPD patients, and NO<sub>2</sub> may be a key pollutant.

Authors

Institutions

Publication Details

Journal
PubMed
Published
2025-05-06
DOI
https://doi.org/10.3760/cma.j.cn112150-20240910-00722
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

[The effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease].

Bingqiu Chen, Y H Chen, Z H Zhang, Ming J. Zuo et al.
PubMed
Chronic Obstructive Pulmonary Disease (COPD) Research
article

[The effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease].

Bingqiu Chen, Y H Chen, Z H Zhang, Ming J. Zuo, F R Deng, Lijing Zhao, Xiaowen Ji, Chao Zhao, Xinbiao Guo, Wenli Zhang
article en

Abstract

<b>Objective:</b> To assess the effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease (COPD), identify key air pollutants, and analyze potential influencing factors. <b>Methods:</b> In this panel study, 92 stable COPD patients were recruited. From March 2021 to September 2023 in Beijing, all participants completed 254 nights of sleep monitoring. The total sleep duration, light sleep duration, deep sleep duration and rapid eye movement sleep duration and their respective proportions in total sleep duration were recorded. The exposure levels of fine particulate matter (PM<sub>2.5</sub>), inhalable particulate matter (PM<sub>10</sub>), nitrogen dioxide (NO<sub>2</sub>), ozone (O<sub>3</sub>), sulfur dioxide (SO<sub>2</sub>), and carbon monoxide (CO) were estimated based on the infiltration factor method and time-activity logs of participants. To assess the lag effect of air pollutants, moving average concentrations of air pollutants from 0-1 day to 0-3 months were calculated. The linear mixed-effect model and Bayesian kernel machine regression (BKMR) model were used to assess the single and joint effects of air pollutants on sleep structure parameters in COPD patients, respectively. <b>Results:</b> All six types of air pollutants were associated with changes in sleep structure, manifesting as an increase in total sleep duration and light sleep proportion and a reduction in deep sleep proportion. The effects of O<sub>3</sub> were strongest at lag 0-6 days, while other air pollutants were at lag 0-3 months. Joint exposure to multiple air pollutants exerted significant joint effects on sleep structure, and NO<sub>2</sub> was identified as the dominant pollutant. NO<sub>2</sub> had a posterior inclusion probability (PIP) greater than 0.5 for light sleep proportion (PIP=0.691) and deep sleep proportion (PIP=0.957). With an interquartile range (IQR) increase of 8.6 μg/m<sup>3</sup> in NO<sub>2</sub> at lag 0-3 months, the light sleep proportion increased by 10.5% (95%<i>CI</i>: 2.2%-19.4%), and the deep sleep proportion decreased by 19.5% (95%<i>CI</i>:-30.6%- -6.8%). <b>Conclusion:</b> Joint exposure to air pollutants is associated with changes in sleep structure in stable COPD patients, and NO<sub>2</sub> may be a key pollutant.

PubMedVol. 59(5)
Shanxi Medical University (CN), Peking University (CN)
Chronic Obstructive Pulmonary Disease (COPD) Research
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.