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.

Ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5) consists of various toxic constituents. However, the health effect of PM_2.5 may differ depending on its constituents, but the joint effect of PM_2.5 constituents remains incompletely understood. Our goal was to evaluate the joint effect of long-term PM_2.5 constituent exposures on dyslipidemia and identify the most hazardous chemical constituent. This study included 67,015 participants from the China Multi-Ethnic Cohort study. The average yearly levels of PM_2.5 constituents for all individuals at their residences were assessed through satellite remote sensing and chemical transport modeling. Dyslipidemia was defined as one or more following abnormal blood lipid concentrations: total cholesterol (TC) ≥ 6.22 mmol/L, triglycerides (TG) ≥ 2.26 mmol/L, high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L, and low-density lipoprotein cholesterol (LDL-C) ≥ 4.14 mmol/L. The logistic regression model was utilized to examine the single effect of PM2.5 constituents on dyslipidemia, while the weighted quantile sum regression model for the joint effect. The odds ratio with a 95 % confidence interval for dyslipidemia positively related to per-SD increase in the three-year average was 1.29 (1.20-1.38) for PM_2.5 mass, 1.25 (1.17-1.34) for black carbon, 1.24 (1.16-1.33) for ammonium, 1.33 (1.24-1.43) for nitrate, 1.34 (1.25-1.44) for organic matter, 1.15 (1.08-1.23) for sulfate, 1.30 (1.22-1.38) for soil particles, and 1.12 (1.05-1.92) for sea salt. Stronger associations were observed in individuals < 65 years of age, males, and those with low physical activity. Joint exposure to PM_2.5 constituents was positively related to dyslipidemia (OR: 1.09, 95 %CI: 1.05-1.14). Nitrate was identified as the constituent with the largest weight (weighted at 0.387). Long-term exposure to PM_2.5 constituents poses a significant risk to dyslipidemia and nitrate might be the most responsible for the risk. These findings indicate that reducing PM_2.5 constituent exposures, especially nitrate, could be beneficial to alleviate the burden of disease attributed to PM_2.5-related dyslipidemia.

Wild birds are significant vectors in global pathogen transmission, but the diversity and spatial distribution of the pathogens detected in them remain unclear. Understanding the transmission dynamics and hotspots of wild-bird-associated pathogens (WBAPs) is crucial for early disease prevention. We compiled an up-to-date dataset encompassing all WBAPs by conducting an extensive search of publications from 1959 to 2022, mapped their diversity and global distribution, and utilized three machine learning algorithms to predict geospatial hotspots where zoonotic and emerging WBAPs were prevalent. Based on 1,834 selected studies, a total of 760 pathogens associated with 1,438 wild bird species were identified, including 387 emerging and 212 zoonotic pathogens. Migratory birds exhibited higher pathogen richness (593 species) but a lower proportion of zoonotic pathogens (27.2%) compared to resident birds (303 species and 39.3%, both p < 0.01). When comparing different ecological groups, waterfowl had the highest richness of zoonotic pathogens (128 species), followed by songbirds (76 species). The distribution of WBAPs was significantly influenced by the habitat suitability index of wild birds, mammalian richness, and climatic factors. The potential geographical hotspots of zoonotic and emerging WBAPs were widely distributed in tropical areas of Asia, Africa, and South America, with zoonotic WBAPs having a wider distribution in South America. Our study illustrates that the geographical hotspots of WBAPs are more widespread than reported, especially in low-income areas, and that the identification, surveillance, and prevention of WBAP infections should be prioritized. This work was funded by the National Key Research and Development Program of China.

BACKGROUND: Daily exposure to ambient air pollution is associated with stroke morbidity and mortality; however, the association between hourly exposure to air pollutants and risk of emergency hospital admissions for stroke and its subtypes remains relatively unexplored. METHODS: We obtained hourly concentrations of fine particulate matter (PM _2.5 ), respirable particulate matter (PM ₁₀ ), nitrogen dioxide (NO ₂ ), sulfur dioxide (SO ₂ ), ozone (O ₃ ), and carbon monoxide (CO) from the China National Environmental Monitoring Center. We conducted a time-stratified case-crossover study among 86 635 emergency hospital admissions for stroke across 10 hospitals in 3 cities (Jinhua, Hangzhou, and Zhoushan) in Zhejiang province, China, between January 1, 2016 and December 31, 2021. Using a conditional logistic regression combined with a distributed lag linear model, we estimated the association between hourly exposure to multiple air pollutants and risk of emergency hospital admissions for total stroke, ischemic stroke, hemorrhagic stroke, and undetermined type. RESULTS: Hourly exposure to PM _2.5 , PM ₁₀ , NO ₂ , and SO ₂ was associated with an increased risk of hospital admissions for total stroke and ischemic stroke. The associations were most pronounced during the concurrent hour of exposure and lasted for ≈2 hours. We found that the risk was more pronounced among male patients or those aged <65 years old. CONCLUSIONS: Our findings suggest that exposure to PM _2.5 , PM ₁₀ , NO ₂ , and SO ₂ , but not CO and O ₃ , is associated with emergency hospital admissions for total stroke or ischemic stroke shortly after exposure. Implementing targeted pollution emission reduction measures may have significant public health implications in controlling and reducing the burden of stroke.