Green space and its types can attenuate the associations of PM2.5 and its components with prediabetes and diabetes-- a multicenter cross-sectional study from eastern China.

BACKGROUND: The effect of fine particulate matter (PM2.5) components on prediabetes and diabetes is of concern, but the evidence is limited and the specific role of different green space types remains unclear. This study aims to investigate the relationship of PM2.5 and its components with prediabetes and diabetes as well as the potential health benefits of different types and combinations of green spaces. METHODS: A multicenter cross-sectional study was conducted in eastern China by using a multi-stage random sampling method. Health screening and questionnaires for 98,091 participants were performed during 2017-2020. PM2.5 and its five components were estimated by the inverse distance weighted method, and green space was reflected by the Normalized Difference Vegetation Index (NDVI), percentages of tree or grass cover. Multivariate logistic regression and quantile g-computing were used to explore the associations of PM2.5 and five components with prediabetes and diabetes and to elucidate the potential moderating role of green space and corresponding type combinations in these associations. RESULTS: Each interquartile range (IQR) increment of PM2.5 was associated with both prediabetes (odds ratio [OR]: 1.15, 95%CI [confidence interval]: 1.10-1.20) and diabetes (OR: 1.18, 95% CI: 1.11-1.25), respectively. All five components of PM2.5 were related to prediabetes and diabetes. The ORs of PM2.5 on diabetes were 1.49 (1.35-1.63) in the low tree group and 0.90 (0.82-0.98) in the high tree group, respectively. In the high tree-high grass group, the harmful impacts of PM2.5 and five components were significantly lower than in the other groups. CONCLUSION: Our study suggested that PM2.5 and its components were associated with the increased risk of prediabetes and diabetes, which could be diminished by green space. Furthermore, the coexistence of high levels of tree and grass cover provided greater benefits. These findings had critical implications for diabetes prevention and green space-based planning for healthy city.

Effects of socioeconomic status and regional inequality on the association between PM2.5 and its components and cardiometabolic multimorbidity: A multicenter population-based survey in eastern China.

BACKGROUND: Despite evidence linking fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM), the impact of its components remains unclear. Socioeconomic status (SES) and regional disparities may confound their association. We aim to evaluate the associations between PM2.5 components and CMM and explore how socioeconomic status and regional disparities affect these relationships. METHODS: We recruited 108,941 participants aged 35-76 years from ten cities in eastern China. Individual exposure was assessed using Tracking Air Pollution in China (TAP) data, including PM2.5 and five components: ammonium (NH4+), black carbon (BC), nitrates (NO3-), organic matter (OM), and sulfates (SO42-). Generalized linear models and quantile g-computation models were employed to quantify the effects of PM2.5 components on CMM and to identify key components. Stratified analyses were performed to investigate the modifying effect of SES and regional disparities. RESULTS: For each increase in interquartile range (IQR), BC (odds ratio [OR] 1.37, 95 % CI 1.29-1.47), OM (1.38, 1.29-1.48), NH4+ (1.31, 1.21-1.40), NO3- (1.34, 1.25-1.44), and SO42- (1.28, 1.20-1.38) were positively associated with CMM. Joint exposure to five components was significantly positively associated with CMM (OR: 1.27, 95 % CI: 1.21-1.33), with SO42- having the highest estimated weight, followed by NO3- and BC. These associations were stronger for participants from low socio-economic status and poor regions. CONCLUSION: In summary, we found a stronger hazard effect of PM2.5 and its components on CMM, compared to those suffering from CMDs, particularly among participants with low socioeconomic status and in poor regions. SO42- may be a primary contributor to the association between PM2.5 components and CMM. These findings underscore the importance of prioritizing CMM and targeting SO42-related pollution sources in health policies, particularly amid China's aging population, reducing environmental health inequalities is critical.