Effects of short- and long-term exposures to multiple air pollutants on depression among the labor force: A nationwide longitudinal study in China.

BACKGROUND: Depression prevalence has surged within the labor force population in recent years. While links between air pollutants and depression were explored, there was a notable scarcity of research focusing on the workforce. METHODS: This nationwide longitudinal study analyzed 27,457 workers aged 15-64. We estimated monthly mean concentrations of fine particulate matter (PM2.5), its primary components, and Ozone (O3) at participants' residences using spatiotemporal models. To assess the relationship between short- (1 to 3 months) and long-term (1 to 2 years) exposure to various air pollutants and depressive levels and occurrences, we employed linear mixed-effects models and mixed-effects logistic regression. We considered potential occupational moderators, such as labor contracts, overtime compensation, and total annual income. RESULTS: We found significant increases in depression risks within the workforce linked to both short- and long-term air pollution exposure. A 10 µg/m3 rise in 2-year average PM2.5, black carbon (BC), and O3 concentrations correlated with increments in depressive scores of 0.009, 0.173, and 0.010, and a higher likelihood of depression prevalence by 0.5 %, 12.6 %, and 0.7 %. The impacts of air pollutants and depression were more prominent in people without labor contracts, overtime compensation, and lower total incomes. CONCLUSION: Exposures to air pollutants could increase the risk of depression in the labor force population. The mitigating effects of higher income, benefits, and job security against depression underscore the need for focused mental health interventions.

DNA methylation mediates the effects of PM2.5 and O3 on ceramide metabolism: A novel mechanistic link between air pollution and insulin resistance.

Insulin resistance (IR), linked to air pollution, is an initial stage of early-onset Type 2 diabetes mellitus (T2DM). While ceramide metabolism plays an important role in IR pathogenesis, the effects of air pollution on this process and its mechanisms remain unclear. We recruited young adults aged 18-30 years to a panel study in Wuhan, China. Using personal portable devices and stationary monitoring stations, we tracked particulate matter with aerodynamic diameters≤ 2.5 µm (PM2.5) and Ozone (O3) levels. Liquid chromatography/mass spectrometry (LC-MS) based metabolomics quantified ceramide metabolism, and Illumina Infinium Human Methylation 850 kBeadChip assay measured deoxyribonucleic acid (DNA) methylation. Linear mixed-effects models assessed relationships of air pollution with i) IR indexes, ii) ceramide metabolism, and iii) DNA methylation. Mediation analysis was subsequently performed to evaluate the potential mediating effect of DNA methylation in the association between air pollution and ceramide metabolism. PM2.5 and O3 were associated with elevated IR. Specifically, each 10 µg/m3 increase in PM2.5 and O3 at lag0-12 h significantly increased triglyceride­glucose index (TyG index) and TyG-BMI (TyG - Body mass index) by 0.88%, 0.89% and 0.26%, 0.26%, respectively. Furthermore, levels of eight ceramides were altered by air pollution exposure, and nine methylated CpG sites in inflammation genes mediated the effects of air pollution on ceramide metabolism. Our findings imply the existence of a novel mechanism connecting air pollution to IR.

The Impact of Air Pollution on Physical Functioning Decline and the Benefits of Greenness: Evidence From a Nationwide Cohort Study.

BACKGROUND: Physical functional limitations (PFLs) increase the vulnerability of adults, but their pathogenesis remains unclear. METHODS: We conducted a nationwide longitudinal study on 62 749 records from 18 878 adults (aged ≥45) from 28 provinces in China. Risk of PFLs was assessed using a validated 9-item questionnaire. Exposure levels of air pollutants (PM10, PM2.5, and PM1) and greenness (normalized difference vegetation index, NDVI) were estimated using a satellite-based spatiotemporal model. We used the cumulative link mixed effects model to estimate the associations between short-term and long-term exposure to air pollutants, greenness, and risk of PFLs. We employed the interaction effect model to evaluate interactions between air pollutants and greenness. RESULTS: Participants were 60.9 ±â€…9.6 years, with an average follow-up of 5.87 (1.65) years. Exposure to air pollution was significantly associated with a higher risk of PFLs. For instance, the odds ratio (OR) associated with each 10 µg/m3 higher in 6-month averaged PM10, PM2.5, and PM1 were 1.025 (95% CI: 1.015-1.035), 1.035 (95% CI: 1.018-1.054), and 1.029 (95% CI: 1.007-1.050), respectively. Conversely, exposure to greenness was associated with decreased risk of PFLs; the OR associated with each 1-unit higher in 1-year averaged NDVI was 0.724 (95% CI: 0.544-0.962). Furthermore, higher greenness levels were found to mitigate the adverse effects of 1-year, 6-month, 1-month averaged PM10, and 1-year averaged PM2.5 on the risk of PFLs. CONCLUSIONS: Air pollution raises the risk of PFLs, whereas greenness could mitigate the adverse effects. Reducing air pollution and enhancing greenness could prevent physical functioning.

Exposures to ambient air pollutants increase prevalence of sleep disorder in adults: Evidence from Wuhan Chronic Disease Cohort Study (WCDCS).

BACKGROUND: Sleep disorder contributes to memory dysfunction and chronic diseases. Clear evidence of environment disturbance, such as residential noise, are associated with an increased risk of sleep disorder. However, not enough studies have been conducted on association between residential air pollutants and sleep disorder. We sought to determine whether exposures to residential air pollutants associated with risk of sleep disorder among adults. METHODS: Using the dataset of the Wuhan Chronic Disease Cohort Study (WCDCS), we investigated the prevalence of sleep disorder and five sleep disorder symptoms in the study. The data of air pollutants (including PM10, PM2.5, NO2, SO2 and O3) were obtained from 10 air quality monitoring stations in Wuhan. We utilized logistic regression model to evaluate the associations of five types of air pollutants with odds ratio (OR) of sleep disorder and symptoms. The potential moderating effects of socio-demographic factors in the associations were explored using the interaction effects model. RESULTS: Of the study participants, 52.1 % had sleep disorder. Exposures to higher concentrations of air pollutants were associated with increased prevalence of sleep disorder. For example, per interquartile range (IQR) increases in concentrations of PM10, PM2.5 or SO2 corresponded to the increase of sleep disorder increased prevalence at 14.7 % (adjusted odds ratio (aOR) = 1.147, 95 %CI:1.062, 1.240), 8.9 % (aOR = 1.089, 95 %CI: 1.003, 1.182) and 15.8 % (aOR = 1.158, 95 %CI: 1.065, 1.260). For symptoms specific analyses, significant linkages of PM10, PM2.5, SO2 with difficulty in falling asleep, wake up after falling asleep and early awaken were observed. Moderating effects of age and place of residence on the linkages of PM10 with increased prevalence of sleep disorder were identified. CONCLUSION: Higher level of air pollution exposure could increase the prevalence of sleep disorder. Middle-aged and elderly population, as well as the rural residents are more likely to suffer from sleep disorder.

Independent and interactive associations between greenness and ambient pollutants on novel glycolipid metabolism biomarkers: A national repeated measurement study.

This study aims to investigate the independent and interactive effects of greenness and ambient pollutants on novel glycolipid metabolism biomarkers. A repeated national cohort study was conducted among 5085 adults from 150 counties/districts across China, with levels of novel glycolipid metabolism biomarkers of TyG index, TG/HDL-c, TC/HDL-c, and non-HDL-c measured. Exposure levels of greenness and ambient pollutants (including PM1, PM2.5, PM10, and NO2) for each participant were determined based on their residential location. Linear mixed-effect and interactive models were used to evaluate the independent and interactive effects between greenness and ambient pollutants on the four novel glycolipid metabolism biomarkers. In the main models, the changes [ß (95% CIs)] of TyG index, TG/HDL-c, TC/HDL-c, and non-HDL-c were -0.021 (-0.036, -0.007), -0.120 (-0.175, -0.066), -0.092 (-0.122, -0.062), and -0.445 (-1.370, 0.480) for every 0.1 increase in NDVI, and were 0.004 (0.003, 0.005), 0.014 (0.009, 0.019), 0.009 (0.006, 0.011), and 0.067 (-0.019, 0.154) for every 1 µg/m3 increase in PM1. Results of interactive analyses demonstrated that individuals living in low-polluted areas could get greater benefits from greenness than those living in highly-polluted areas. Additionally, the results of mediation analyses revealed that PM2.5 mediated 14.40% of the association between greenness and the TyG index. Further research is needed to validate our findings.

Exposure to air pollution and prevalence of metabolic syndrome: A nationwide study in China from 2011 to 2015.

BACKGROUND: Evidence concerning the influence of air pollution on metabolic syndrome (MetS) is still limited. We aimed to investigate whether sustained exposure to air pollutants are associated with increased prevalence of MetS and its individual components. METHODS: We conducted a cross-sectional study comprised of 14,097 individuals participated in the first or third survey of the CHARLS. The personal cumulative (3-year averaged) exposure concentrations of nitrogen dioxide (NO2), particulate matter (PM) with a diameter of 1.0 µm or less (PM1), PM with a diameter of 10 µm or less (PM10) and PM with a diameter of 2.5 µm or less (PM2.5) were estimated using a spatiotemporal random forest model at 0.1° × 0.1° spatial resolution based on residential address of each participant provided. We utilized logistic regression models to estimate the associations of the four air pollutants with the prevalence of MetS and its individual components, and performed interaction analyses to evaluate potential effect modifications by gender, health status, age and drinking status. RESULTS: Sustained exposure to air pollutants is associated with increased prevalence of MetS. For every 10 µg/m3 increase in NO2, PM1, PM10 and PM2.5, the adjusted odds ratio (OR) of MetS was 2.276 (95 % CI: 2.148, 2.412), 1.207 (95 % CI: 1.155, 1.263), 1.027 (95 % CI: 1.006, 1.048) and 1.027 (95 % CI: 0.989, 1.066), respectively. For MetS components, we observed significant associations between NO2, PM1, PM10 and central obesity, high blood pressure, elevated fasting glucose and low high-density lipoprotein cholesterol. For example, the adjusted OR of low high-density lipoprotein cholesterol for every 10 µg/m3 increase in NO2 was 1.855 (95 % CI: 1.764, 1.952). We also identified that age could significantly modified the association between NO2 and prevalence of MetS. CONCLUSIONS: Chinese adults sustained exposure to higher concentrations of air pollutants are associated with increased prevalence of MetS and its components.

Effects of air pollution and residential greenness on sleep disorder: A 8-year nationwide cohort study.

BACKGROUND: Sleep disorder influencing the quality of life, however, its contributing factors have not been fully identified yet. Recently the potential effects of environmental exposures like air pollution and greenness on sleep disorder have attracted attention, but the evidence in China is limited, particularly in the middle-aged and elderly. METHODS: We conducted a nationwide prospective study that included 21,878 Chinese citizens aged 45 years or above. For each participant, the 3-year averaged exposure concentrations of air pollutants (including PM10, PM2.5, PM1, NO2) and greenness (assessed by NDVI) were estimated based on residential address. We used mixed-effects logistic models to examine the associations of sustained air pollutants and greenness exposures with the occurrence of sleep disorder, and used linear mixed-effects models to assess the associations with sleep duration. Specifically, interaction effects models were employed to identify potential modificators of the above associations. RESULTS: A total of 39,580 survey responses were received, with the overall occurrence rate of sleep disorder was 25.7%. A 10 µg/m3 increment in PM10 and PM2.5 were associated with increased occurrence of sleep disorder at 2% (aOR = 1.02, 95%CI:1.01, 1.04) and 7% (aOR = 1.07, 95%CI: 1.04, 1.11), and were associated with reduced sleep duration by 0.07 (95% CI: 0.08, 0.05) and 0.04 (95% CI: 0.05, 0.03) hours, respectively. Residential greenness appears to the potential protective factor for sleep disorder, that a 0.1 higher of the NDVI was associated a 9% (aOR = 0.91, 95%CI: 0.86, 0.96) decreased occurrence of sleep disorder and 0.09 h (95% CI: 0.05, 0.13) longer of sleep duration. Age and residence were identified as modificators of the above significant associations. CONCLUSION: Sustained exposure to air pollutants can increase the occurrence of sleep disorder and can reduce sleep duration, while exposure to higher levels of greenness can protect sleep health from the side effects of air pollutants.

Sustained air pollution exposures, fasting plasma glucose, glycated haemoglobin, prevalence and incidence of diabetes: a nationwide study in China.

BACKGROUND: Evidence remains limited and inconsistent for the associations between sustained air pollution exposures and diabetes development. This study aimed to determine the potential effects of particulate matter with a diameter of ≤10 micrometres (PM10), particulate matter with a diameter of ≤2.5 micrometres (PM2.5) and nitrogen dioxide (NO2) on alterations of fasting plasma glucose (FPG), glycated haemoglobin (HbA1c), in particular, on prevalence and incidence of diabetes. METHODS: Cross-sectional analyses were conducted based on 9628 participants aged ≥45 years from the baseline survey (2011) of the China Health and Retirement Longitudinal Study (CHARLS), whereas cohort analyses were based on 3510 individuals without diabetes at baseline in the third survey (2015). Residences of participants were geocoded and the air pollution exposures were estimated using a satellite-based spatiotemporal model. Linear, logistic and modified Poisson regression models, adjusting for multiple confounders, were applied to assess the associations between air pollution and FPG, HbA1c, prevalence and incidence of diabetes, respectively. RESULTS: Associations between PM10, PM2.5 and increased levels of FPG and HbA1c were identified. The levels of FPG and HbA1c increased by 0.025 mmol/L (95% CI: 0.007, 0.044) and 0.011 mmol/L (95% CI: 0.002, 0.019), respectively, for a 10-µg/m3 increase in PM10, and the levels of FPG and HbA1c increased by 0.061 mmol/L (95% CI: 0.028, 0.096) and 0.016 mmol/L (95% CI: 0.000, 0.031), respectively, for a 10-µg/m3 increase in PM2.5. There were also positive associations between diabetes prevalence and PM2.5 and PM10. In the cohort analyses, PM10, PM2.5 and NO2 were associated with a higher incidence of diabetes. CONCLUSION: Air pollution was allied to diabetes development in elderly Chinese populations. Considering the impact of the dramatic increase in the incidence and prevalence of diabetes in China, interventions to improve air quality are urgently needed.