Associations of residential greenness, ambient air pollution, biological sex, and glucocorticoids levels in rural China

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Residential greenness and cardiac conduction abnormalities: epidemiological evidence and an explainable machine learning modeling study

BACKGROUND

Previous studies indicated the beneficial influence of residential greenness on cardiovascular disease (CVD), however, the association of residential greenness with cardiac conduction performance remains unclear. This study aims to examine the epidemiological associations between residential greenness and cardiac conduction abnormalities in rural residents, simultaneously exploring the role of residential greenness for cardiac health in an explainable machine learning modeling study.

METHODS

A total of 27,294 participants were derived from the Henan Rural Cohort. Two satellite-based indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. Independent and combined associations of residential greenness indices and physical activities with electrocardiogram (ECG) parameter abnormalities were evaluated using the logistic regression model and generalized linear model. The Gradient Boosting Machine (GBM) and the SHapely Additive exPlanations (SHAP) were employed in the modeling study.

RESULTS

The odds ratios (OR) and 95% confidence interval (CI) for QRS interval, heart rate (HR), QTc interval, and PR interval abnormalities with per interquartile range in NDVI were 0.896 (0.873-0.920), 0.955 (0.926-0.986), 1.015 (0.984-1.047), and 0.986 (0.929-1.045), respectively. Furthermore, the participants with higher physical activities plus residential greenness (assessed by EVI) were related to a 1.049-fold (1.017-1.081) and 1.298-fold (1.245-1.354) decreased risk for abnormal QRS interval and HR. Similar results were also observed in the sensitivity analysis. The NDVI ranked fifth (SHAP mean value 0.116) in the analysis for QRS interval abnormality risk in the modeling study.

CONCLUSION

A higher level of residential greenness was significantly associated with cardiac conduction abnormalities. This effect might be strengthened in residents with more physical activities. This study indicated the cruciality of environmental greenness to cardiac functions and also contributed to refining preventive medicine and greenness design strategies.

Green space, air pollution and gestational diabetes mellitus: A retrospective cohort study in central China

Emerging evidence suggests residential surrounding green space is beneficial for human health. The association between green space and GDM showed inconsistent results, and potential effect modification of green space with air pollution is still unclear. This study aims to evaluate the association between green space and GDM, and further explore potential interaction and medication effects. Participants were recruited from a retrospective cohort study between 2015 and 2020 in Henan, China. Residential green space based on normalized difference vegetation index (NDVI) and air pollution exposure were estimated using spatial-statistical models. Multivariate logistic regression was applied to evaluate the association between per 0.1 unit increase in NDVI with 4 buffer sizes (250 m, 500 m, 1000 m, 2000 m) and GDM. We examined potential interaction of green space and air pollutants on GDM. Mediating effects of air pollution associated with green space exposure on GDM were also investigated by causal mediation analyses. A total of 46,665 eligible pregnant women were identified. There were 4092 (8.8 %) women diagnosed with GDM according to the IADPSG criteria. We found that per 0.1-unit increment in NDVI250 m, NDVI500 m, NDVI1000 m and NDVI2000 m in second trimester were associated with the decreased risk of GDM, with adjusted OR of 0.921(95 %CI: 0.890-0.953), 0.922 (95 %CI: 0.891-0.953), 0.921 (95 %CI: 0.892-0.952) and 0.921 (95 %CI: 0.892-0.951), respectively. We identified significant interactions between second trimester PM2.5 and O3 exposure and NDVI for GDM (Pinteraction < 0.001). The causal mediation analysis showed that PM2.5 mediated approximately 2.5-5.5 % of the association between green space and GDM, while the estimated mediating effect of O3 was approximately 30.1-38.5 %. In conclusion, our study indicates that residential green space was associated with a reduced risk of GDM, particularly second trimester. Green space may benefit to GDM partly mediated by a reduction in PM2.5 and O3.