The association between long-term air pollution exposure and Chinese visceral adiposity index: A nationwide study of middle-aged and older adults
Abstract
Air pollution has been closely linked to diabetes, metabolic disorders, and cardiovascular diseases; visceral adiposity is a common high-risk factor for these conditions. However, the potential role of air pollution on visceral adiposity remains unclear. In this study, we investigated the association between exposure to multiple air pollutants and visceral adiposity using the Chinese visceral adiposity index (cVAI) in middle-aged (45 - 60 years) and older (> 60 years) Chinese adults. We conducted a cross-sectional study using data from 7,552 participants aged ≥45 years from the 2015 China Health and Retirement Longitudinal Study 2015. Data related to air pollution exposure levels, including particulate matter with an aerodynamic diameters ≤ 2.5 μm (PM2.5), particulate matter with an aerodynamic diameters ≤ 10 μm (PM10), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), were acquired from the ChinaHighAirPollution dataset. Restricted cubic spline analysis was then used to investigate potential non-linear associations. Weighted quantile sum (WQS) regression was also used to address co-exposure to multiple pollutants and identify the relative contributions of each pollutant. Compared to the lowest quantile, exposure to the highest quartile levels of PM2.5, PM10, NO2, O3, and SO2 was associated with a significant increase in cVAI (all p for trend <0.0001). Males and smokers exhibited stronger associations between air pollutant exposure and cVAI(p for interaction < 0.05). Specifically, smokers in the highest quartile of PM2.5 exposure had a β coefficient of 16.89(95%CI:11.00,22.78), while males had a β coefficient of 14.38(95%CI:9.68,19.07), indicating significantly higher risks of increased visceral adiposity in these groups. WQS analysis identified NO2 and PM2.5 as the primary contributors to increased cVAI. This study is the first to reveal that air pollution, particularly PM2.5 and NO2, was significantly associated with increased visceral adiposity in middle-aged and older Chinese adults, especially for high-risk groups, such as males and smokers. It highlights the urgent need for public health policies to reduce air pollution exposure to mitigate the risk of visceral adiposity and its associated metabolic disorders.
https://doi.org/10.5061/dryad.fqz612k3j
Description of the data and file structure
Data from the CHARLS database https://charls.pku.edu.cn/
Files and variables
File: data.csv
Description: Variables in the dataset
Variables
- sequence number
- CO: Carbon Monoxide (μg/m³)
- NO2: Nitrogen Dioxide (μg/m³)
- O3: Ozone (μg/m³)
- PM10: Particulate Matter ≤ 10 micrometers (μg/m³)
- PM2.5: Particulate Matter ≤ 2.5 micrometers (μg/m³)
- SO2: Sulfur Dioxide (μg/m³)
- wave: Number of rounds of participation in the survey
- Sex: Gender of participants
- Region: area of residence of participants (south or north)
- Drinking: Alcohol consumption by participants (no or >1m or <1m)
- Smoke: Participants' smoking status (yes or no)
- Health_status: Participants' health (good or poor)
- Hypertension: Whether the participant had high blood pressure (yes or no)
- Physical_disability: Whether the participant has Physical disability (yes or no)
- cVAI: Chinese visceral adiposity index
- NO2Q.median: The median of each quartile of Nitrogen Dioxide (μg/m³)
- PM2.5Q.median: The median of each quartile of Particulate Matter ≤ 2.5 micrometers (μg/m³)
- O3Q.median: The median of each quartile of Ozone (μg/m³)
- PM10Q.median: The median of each quartile of Particulate Matter ≤ 10 micrometers (μg/m³)
- COQ.median: The median of each quartile of Carbon Monoxide (μg/m³)
- SO2Q.median: The median of each quartile of Sulfur Dioxide (μg/m³)
- Age_group: Age groupings (<=60,>60 years)
Code/software
R software (Version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/)
Access information
Other publicly accessible locations of the data:
- CHARLS database
Data was derived from the following sources:
- All datasets are available from the CHARLS database (https://charls.charlsdata.com/users/profile/index/zh-cn.html)
