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The effect of ambient PM 10 and PM 2.5 on lung function modified by body muscle and adipose tissue is not fully understood at present. Our aims were to investigate the association between seasonal average air pollutants and lung function in asthmatic patients modified by body composition indicators. In this cross-sectional study, we recruited 914 doctor-diagnosed asthmatic patients, and performed interaction and stratified analysis using the median values of total body muscle (TBM), total body fat (TBF), and percentage body fat (PBF) as well as body mass index (BMI) =25 as the cutoff points of the high/low body composition groups. The adjusted R 2 values of the developed LUR models of PM 2.5 and PM 10 were 91.4% and 90.5% and also verified by cross-validation, respectively. After adjusting for confounding factors, we found that TBM significantly modified the association between PM 10 and lung function among asthma patients (interaction P value <0.05). In the low TBM group, seasonal average concentrations of PM 10 estimated by the LUR model increased by 10 μg/m 3 , and negative associations with lung function indicators were observed. For obese patients with BMI>25 and high TBF, the increase in PM 10 was associated with the decrease in lung function. The asthma patients with obesity and low total body muscle were more susceptible to adverse effects of PM 10 on lung function., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Adult asthma is phenotypically heterogeneous with unclear aetiology. We aimed to evaluate the potential contribution of environmental exposure and its ensuing response to asthma and its heterogeneity., Methods: Environmental risk was evaluated by assessing the records of National Health Insurance Research Database (NHIRD) and residence-based air pollution (particulate matter with diameter less than 2.5 micrometers (PM 2.5 ) and PM 2.5 -bound polycyclic aromatic hydrocarbons (PAHs)), integrating biomonitoring analysis of environmental pollutants, inflammatory markers and sphingolipid metabolites in case-control populations with mass spectrometry and ELISA. Phenotypic clustering was evaluated by t-distributed stochastic neighbor embedding (t-SNE) integrating 18 clinical and demographic variables., Findings: In the NHIRD dataset, modest increase in the relative risk with time-lag effect for emergency (N=209 837) and outpatient visits (N=638 538) was observed with increasing levels of PM 2.5 and PAHs. Biomonitoring analysis revealed a panel of metals and organic pollutants, particularly metal Ni and PAH, posing a significant risk for current asthma (ORs=1.28-3.48) and its severity, correlating with the level of oxidative stress markers, notably Nε-(hexanoyl)-lysine (r=0.108-0.311, p<0.05), but not with the accumulated levels of PM 2.5 exposure. Further, levels of circulating sphingosine-1-phosphate and ceramide-1-phosphate were found to discriminate asthma (p<0.001 and p<0.05, respectively), correlating with the levels of PAH (r=0.196, p<0.01) and metal exposure (r=0.202-0.323, p<0.05), respectively, and both correlating with circulating inflammatory markers (r=0.186-0.427, p<0.01). Analysis of six phenotypic clusters and those cases with comorbid type 2 diabetes mellitus (T2DM) revealed cluster-selective environmental risks and biosignatures., Interpretation: These results suggest the potential contribution of environmental factors from multiple sources, their ensuing oxidative stress and sphingolipid remodeling to adult asthma and its phenotypic heterogeneity., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Exposure to polycyclic aromatic hydrocarbons (PAHs) associated with ambient air particulate matter (PM) poses significant health concerns. Several modeling approaches have been developed for simulating ambient PAHs, but no hourly intra-urban spatial data are currently available. The aim of this study is to develop a new modeling strategy in simulating, on an hourly basis, grid-scale PM2.5-PAH levels. PM and PAHs were collected over a one-year time frame through an established air quality monitoring network within a metropolitan area of Taiwan. Multivariate linear regression models, in combination with correlation analysis and PAH source identification by principal component analysis (PCA), were performed to simulate hourly grid-scale PM2.5-PAH concentrations, taking criteria pollutants and meteorological variables selected as possible predictors. The simulated levels of 72-h personal exposure were found to be significantly (R=0.729**, p<0.01) correlated with those analyzed from portable personal monitors. A geographic information system (GIS) was used to visualize spatially distributed PM2.5-PAH concentrations of the modeling results. This new grid-scale modeling strategy, incorporating the output of simulated data by GIS, provides a useful and versatile tool in personal exposure analysis and in the health risk assessment of air pollution., (Copyright © 2016 Elsevier B.V. All rights reserved.)