Long-term Exposure to Ambient PM2.5 and Its Components Associated With Diabetes: Evidence From a Large Population-Based Cohort From China.

OBJECTIVE: Association between particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) components and diabetes remains unclear. We therefore aimed to investigate the associations of long-term exposure to PM2.5 components with diabetes. RESEARCH DESIGN AND METHODS: This study included 69,210 adults with no history of diabetes from a large-scale epidemiologic survey in Southwest China from 2018 to 2019. The annual average concentrations of PM2.5 and its components were estimated using satellite remote sensing and chemical transport modeling. Diabetes was identified as fasting plasma glucose ≥7.0 mmol/L (126 mg/dL) or hemoglobin A1c ≥48 mmol/mol (6.5%). The logistic regression model and weighted quantile sum method were used to estimate the associations of single and joint exposure to PM2.5 and its components with diabetes, respectively. RESULTS: Per-SD increases in the 3-year average concentrations of PM2.5 (odds ratio [OR] 1.08, 95% CI 1.01-1.15), black carbon (BC; 1.07, 1.01-1.15), ammonium (1.07, 1.00-1.14), nitrate (1.08, 1.01-1.16), organic matter (OM; 1.09, 1.02-1.16), and soil particles (SOIL; 1.09, 1.02-1.17) were positively associated with diabetes. The associations were stronger in those ≥65 years. Joint exposure to PM2.5 and its components was positively associated with diabetes (OR 1.04, 95% CI 1.01-1.07). The estimated weight of OM was the largest among PM2.5 and its components. CONCLUSIONS: Long-term exposure to BC, nitrate, ammonium, OM, and SOIL is positively associated with diabetes. Moreover, OM might be the most responsible for the relationship between PM2.5 and diabetes. This study adds to the evidence of a PM2.5-diabetes association and suggests controlling sources of OM to curb the burden of PM2.5-related diabetes.

Increased allostatic load associated with ambient air pollution acting as a stressor: Cross-sectional evidence from the China multi-ethnic cohort study.

BACKGROUND: Allostatic load measures the cumulative biological burden imposed by chronic stressors. Emerging experimental evidence supports that air pollution acting as a stressor activates the neuroendocrine system and then produces multi-organ effects, leading to allostatic load. However, relevant epidemiological evidence is limited. OBJECTIVES: We aim to explore the relationships between chronic exposure to ambient air pollution (PM1, PM2.5, PM10, and O3) and allostatic load in Chinese adults. METHODS: This cross-sectional study included 85,545 participants aged 30-79 from the baseline data of the China Multi-Ethnic Cohort (CMEC). Ambient air pollution levels were evaluated by a satellite-based random forest approach. The previous three-year average exposure concentrations were calculated for each participant based on the residential address. The outcome allostatic load was identified through the sum of the sex-specific scores of twelve biomarkers belonging to four major categories: cardiovascular, metabolic, anthropometric, and inflammatory parameters. We performed statistical analysis using a doubly robust approach which relies on inverse probability weighting and outcome model to adjust for confounding. RESULTS: Long-term exposure to ambient air pollution was significantly associated with an increased risk of allostatic load, with relative risk (95% confidence interval) of 1.040 (1.024, 1.057), 1.029 (1. 018, 1. 039), and 1.087 (1.074, 1.101) for each 10 µg/m3 increase in ambient PM2.5, PM10, and O3, respectively. No significant relationship was observed between chronic exposure to PM1 and allostatic load. The associations between air pollution and allostatic load are modified by some intrinsic factors and non-chemical stressors. The people with older, minority, lower education, and lower-income levels had a significantly higher allostatic load induced by air pollution. CONCLUSIONS: Chronic exposure to ambient PM2.5, PM10, and O3 may increase the allostatic load. This finding provides epidemiological evidence that air pollution may be a chronic stressor, leading to widespread physiological burdens.

Association of long-term exposure to ambient air pollutants with blood lipids in Chinese adults: The China Multi-Ethnic Cohort study.

BACKGROUND: Dyslipidemia is a crucial risk factor for cardiovascular diseases. Previous studies have suggested that air pollution is associated with blood lipids. However, little evidence exists in low- and middle-income regions. We aimed to investigate the association between air pollution and blood lipids in southwestern China. METHODS: We included 67,305 participants aged 30-79 years from the baseline data of the China Multi-Ethnic Cohort (CMEC) study. Three-year average concentrations of particles with diameters ≤1 µm (PM1), particles with diameters ≤ 2.5 µm (PM2.5), particles with diameters ≤ 10 µm (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using satellite-based spatiotemporal models. Individual serum lipids, including cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), were measured. Linear, logistic, and quantile regression models were used to evaluate the association between ambient air pollution and blood lipids. RESULTS: All five air pollutants in our study were associated with lipid levels. Increased air pollution exposure was associated with a high risk of dyslipidemia. Each 10 µg/m3 increase in PM2.5 was associated with 0.92% (95% confidence interval (CI): 0.64%, 1.20%), 2.23% (95% CI: 1.44%, 3.02%), and 3.04% (95% CI: 2.61%, 3.47%) increases in TC, TG, and LDL-C levels, respectively, and a 2.03% (95% CI: 1.69%, 2.37%) decrease in HDL-C levels, and high risks of dyslipidemia (OR = 1.14, 95% CI: 1.10, 1.18). Stronger associations of air pollution with blood lipids were found in participants with high lipid levels than in those with low lipid levels. CONCLUSION: Long-term exposure to air pollutants was associated with blood lipid levels and the risk of dyslipidemia. People with high lipid levels were more susceptible to air pollution. Therefore, air pollution prevention and control may help reduce the incidence of dyslipidemia and the burden of CVDs.

Determinants of self-rated health among an older Tibetan population in a Chinese plateau area: analysis based on the conceptual framework for determinants of health.

BACKGROUND: Self-rated health (SRH) has been frequently used in population health surveys. However, most of these studies only focus on specific factors that might directly affect SRH, so only partial or confounding information about the determinants of SRH is potentially obtained. Conducted in an older Tibetan population in a Chinese plateau area, the aim of our study is to assess interrelationships between various factors affecting SRH based on the conceptual framework for determinants of health. METHODS: Between May 2018 and September 2019, 2707 Tibetans aged 50 years or older were recruited as part of the China Multi-Ethnic Cohort Study (CMEC) from the Chengguan District of Lhasa city in Tibet. The information included SRH and variables based on the conceptual framework for determinants of health (i.e., socioeconomic status, health behaviors, physical health, mental health, and chronic diseases). Structural equation modeling (SEM) was used to estimate the direct and indirect effects of multiple factors in the conceptual framework. RESULTS: Among all participants, 5.54% rated their health excellent, 51.16% very good, 33.58% good, 9.12% fairly poor and 0.59% poor. Physical health (ß = - 0.23, P <  0.001), health behaviors (ß = - 0.44, P <  0.001), socioeconomic status (ß = - 0.29, P <  0.001), chronic diseases (ß = - 0.32, P <  0.001) and gender (ß = 0.19, P <  0.001) were directly associated with SRH. Socioeconomic status, physical health and gender affected SRH both directly and indirectly. In addition, there are potential complete mediator effects in which age and mental health affect SRH through mediators, such as physical health, health behaviors and chronic diseases. CONCLUSIONS: The findings suggested that interventions targeting behavioral changes, health and chronic disease management should be attached to improve SRH among older populations in plateau areas without ignoring gender and socioeconomic disparities.