Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China

BACKGROUND

Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions.

OBJECTIVES

To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases.

METHODS

We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence.

RESULTS

PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively).

CONCLUSIONS

Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals.

ENVIRONMENTAL IMPLICATION

PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

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

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.