Association of inflammation and lung function decline caused by personal PM2.5 exposure: a machine learning approach in time-series data.

Abstract

Numerous studies focused on the association between lung function impairment and inflammation caused by fine particulate matter (PM2.5), but the causal relationships are difficult to clarify. In the current study, twenty healthy Chinese young adults who participated in 7 days of observation every four seasons were enrolled, and autoregression models (AM) and classification and regression trees (CART) in a machine learning framework were applied to analyze the association among PM2.5 exposure, inflammation, and lung function from a data structure perspective. There were strong cross-correlations between personal dose of PM2.5 (Dw) and lung functions (vital capacity (VC), forced vital capacity (FVC), etc.). These cross-correlation coefficients were associated with inflammatory indicators (uteroglobin (UG), serum amyloid (SAA), and fractional exhaled nitric oxide (FeNO)). CART reported that inflammatory indicators UG and SAA had the predictive ability of the directional association between Dw and FVC at 1-day lag and that high levels of UG and SAA predicted that PM2.5 exposure induced lung function decline. Consistently, lower lung function indicators at a 2-day lag after personal PM2.5 exposure predicted the high value of inflammatory indicator FeNO. Taken together, we applied machine learning algorithms to analyze repeated measurement data, finding that inflammation and lung function decline caused by PM2.5 could affect each other.

• Authors: Hao Yu, Tian Xu, Juan Chen, Wenjun Yin, Fang Ye
• Journal: Environmental science and pollution research international (Environ Sci Pollut Res Int) Vol. 29 Issue 53 Pg. 80436-80447 (Nov 2022) ISSN: 1614-7499 [Electronic] Germany
• PMID: 35716299 (Publication Type: Journal Article)
• Copyright: © 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Chemical References

• Air Pollutants
• Uteroglobin
• Particulate Matter

Topics

• Young Adult
• Humans
• Air Pollutants (analysis)
• Uteroglobin (analysis)
• Particulate Matter (analysis)
• Inflammation (chemically induced)
• Lung (chemistry)
• Machine Learning
• Environmental Exposure (analysis)
• Air Pollution