Association of long-term exposure to ambient PM2.5 and its constituents with gut microbiota: Evidence from a China cohort

Abstract

Accumulating animal experiments and epidemiological studies have found that exposure to fine particulate matter (PM_2.5) is associated with altered gut microbiota (GM). However, it is unclear what kind of role the PM_2.5 constituents play in the PM_2.5-GM association. Therefore, this study aimed to investigate the association of long-term exposure to PM_2.5 and its constituents (PM_cons) with GM. This study included 1583 participants from a cohort in Southwest China. Satellite remote sensing and chemical transport modelling were used to determine the yearly average concentrations of PM_cons. GM data were derived from 16 s sequencing based on stool samples. Generalized propensity score weighting regression and Bayesian Kernel Machine Regression (BKMR) were used to estimate the individual and joint association of exposure to PM_cons with the Shannon index. The weighted correlation analysis was used to estimate the association of PM_cons with the composition of GM. The result showed that an interquartile range increase of 3-year average black carbon (BC), ammonium, nitrate, organic matter (OM), sulfate, and soil particles (SOIL) were negatively associated with Shannon index with mean difference (95 % confidence interval) being -0.144 (-0.208, -0.080), -0.141 (-0.205, -0.078), -0.126 (-0.184, -0.068), -0.117 (-0.172, -0.062), -0.153 (-0.221, -0.085), and - 0.153 (-0.222, -0.085). BKMR indicated joint exposure to PM_cons was associated with decreased Shannon index, and BC had the largest posterior inclusion probability (0.578). Weighted correlation analyses indicated PM_cons were associated with decreased Bacteroidetes (r = -0.204, P < 0.001 for PM_2.5) and increased Proteobacteria (r = 0.273, P < 0.001 for PM_2.5). These results revealed that long-term exposure to PM_cons was associated with GM. BC was the most important constituent in the association, indicating that the source of BC should be controlled to mitigate the negative effects of PM_2.5 on GM.

Keywords: Bayesian kernel machine regression; Epidemiological study; Generalized propensity score weighting regression; Gut microbiota; PM(2.5) constituent.