Associations between ambient particulate matter exposure and the prevalence of arthritis: Findings from the China Health and Retirement Longitudinal Study.
<h4>Background</h4>Arthritis is a leading cause of global disability, but its etiology is complex and has not yet been fully understood. Recent studies suggest that air pollution is potentially linked to arthritis onset, although current research predominantly focuses on the effects of p...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327695 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <h4>Background</h4>Arthritis is a leading cause of global disability, but its etiology is complex and has not yet been fully understood. Recent studies suggest that air pollution is potentially linked to arthritis onset, although current research predominantly focuses on the effects of particulate matter (PM2.5). There is a lack of comprehensive analysis regarding the influence of various particulate pollutants and their key constituents on arthritis prevalence. This research used China Health and Retirement Longitudinal Study (CHARLS) data and explored the associations between arthritis prevalence and different particulate matters (PM2.5, PM10, and PM1, ≤ 2.5 micrometers, ≤ 10 micrometers, and ≤ 1 micrometer in diameter, respectively), as well as ammonium (NH4) and nitrate (NO3), with gaseous pollutants [such as sulfate (SO4) and ozone (O3)] as a reference.<h4>Methods</h4>This study was conducted based on the 2015 CHARLS cross-sectional data, and 3,802 participants were included. The levels of air pollution exposure were estimated using a spatial-temporal extreme random forest model, integrating ground monitoring, remote sensing data, and model simulations, encompassing PM1, PM2.5, PM10, NH4, NO3, O3, and SO4 concentrations. The association of air pollution with arthritis prevalence was assessed utilizing generalized linear models (GLM), while adjusting for various confounding variables.<h4>Results</h4>Long-term exposure to PM₁, PM₂.₅, PM₁₀, NH₄ ⁺ , and NO₃ ⁻ was positively associated with self-reported arthritis prevalence. Specifically, each interquartile range (IQR) increase in PM₁ corresponded to 4.4% higher odds of arthritis (odds ratio [OR] per IQR: 1.044; 95% confidence interval [CI]: 1.011-1.070), indicating a modest association. Subsequent ORs per IQR for PM₂.₅ and PM₁₀ were 1.019 (95% CI: 1.004-1.036) and 1.012 (95% CI: 1.002-1.021), respectively, reflecting similar but smaller positive associations, whereas NH₄⁺ and NO₃ ⁻ showed moderate associations with ORs per IQR of 1.143 (95% CI: 1.017-1.285) and 1.082 (95% CI: 1.011-1.159), respectively. These findings were robust to sensitivity analyses.<h4>Conclusion</h4>To our knowledge, this is the first study to identify a significant association between long-term exposure to PM₁, PM₂.₅, PM₁₀ and the secondary inorganic aerosol constituents NH₄ and NO₃, and the prevalence of self-reported arthritis in middle-aged and older adults. However, owing to its cross-sectional design, the absence of subtype differentiation and reliance on self-reported diagnoses, these findings may be influenced by reverse causation and measurement error, and should therefore be interpreted with caution. |
|---|---|
| ISSN: | 1932-6203 |