Long-term exposure to ambient air pollution (including PM1) and metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS)

Individual and joint exposure to PM2.5 constituents and incident risk of metabolic syndrome: A national cohort study

Cohort evidence linking fine particulate matter (PM2.5) constituents to metabolic syndrome (MetS) was extensively scarce. A nationwide MetS-free cohort of 3658 participants aged 45 and above, followed up from 2011 to 2015, were enrolled from 125 cities across China's mainland. Cox proportional hazards models and quantile-based g-computation were adopted to investigate individual and joint effects of exposure to PM2.5 constituents with MetS and its components. Monte Carlo simulations (n = 1000) were utilized to generate quasi-concentration-response (C-R) curve of joint exposure. A total of 633 MetS events occurred during 14,766.5 person-years follow-up (median 4.1 years). An estimated excess risk of 33 %-51 % in MetS incidence was linked to per interquartile range (IQR) increase in individual exposure to PM2.5 constituents. For an IQR-equivalent increase in joint exposure, we estimated a hazard ratio of 1.45 (95 % confidence interval: 1.23-1.69) for MetS, 1.49 (1.31-1.69) for central obesity, 1.19 (1.06-1.34) for high BP, 1.57 (1.34-1.84) for low HDL-C, 1.31 (1.14-1.51) for high TG, and 1.23 (1.02-1.48) for elevated FBG, respectively. Approximately linear or J-shaped C-R curves were consistently observed in individual and joint associations of PM2.5 constituents with MetS and its components. Joint-exposure analyses provided consistent evidence for the greatest contribution of SO42- in triggering PM2.5-associated risks of overall MetS and its components. Stratified analysis suggested higher PM2.5-related MetS risks among older participants and urban residents. These findings added longitudual population-based evidence for increased incident risks of MetS and its components associated with long-term exposures to PM2.5 constituents in middle-aged and older adults.

Correlation between 6PPD-Q and immune along with metabolic dysregulation induced liver lesions in outdoor workers

Outdoor workers who are exposed to traffic-derived pollutants often suffer from a range of diseases, with liver disease being particularly notable. Recently, a rubber stabilizing additive antioxidant N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its transformed-quinone product 6PPD-quinone (6PPD-Q) attracted attention. However, their implication for human health remains inadequately elucidated. In this study, outdoor and indoor workers were recruited to analyze 6PPD and 6PPD-Q distribution in their serum and urine. Simultaneously, blood cell counts, liver function, renal function, blood glucose level, and lipid profile were evaluated by 23 physiological parameters. For the first time, we found that the concentrations of 6PPD (0.54 - 1.66 μg L-1) and 6PPD-Q (0.58 - 4.04 μg L-1) in outdoor group serum were two- and three-fold in the indoor group, respectively. Compared with indoor workers, 18 biochemical parameters, notably total bilirubin and indirect bilirubin, were elevated in outdoor workers (p < 0.05). A computed tomography scan showed liver lesions in 60% of the outdoor group, whereas only 30% of the indoor group. The statistical analysis exhibited that significant positive correlations exist between the serum 6PPD-Q and immune cell counts, total bilirubin, indirect bilirubin, and triglycerides in human beings (p < 0.05). The logistic regression implied that for each 1 μg L-1 increase of 6PPD-Q in serum, the risk of human liver lesions increased by 2.31 times. Our results suggest that outdoor exposure is associated with increased concentrations of 6PPD-Q in serum, which could potentially influence glucose and lipid metabolism, immune cell regulation, and liver health.

Air Pollution and Diabetes Mellitus: Association and Validation in a Desert Area in China

CONTEXT

Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution.

OBJECTIVE

We conducted a cross-sectional study with health examination data from more than 2.9 million adults in 2 regions situated in the southern part of the Taklamakan Desert, China.

METHODS

We assessed 3-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM.

RESULTS

The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI, 1.741-2.070), 1.07 (95% CI, 1.053-1.086), 1.013 (95% CI, 1.008-1.018), 1.009 (95% CI, 1.007-1.011), and 1.337 (95% CI, 1.234-1.449), respectively. Notably, men, individuals aged 50 years or older, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise appeared to be more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings.

CONCLUSION

Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

Association between annual concentration of air pollutants and incidence of metabolic syndrome among Korean adults: Korean Genome and Epidemiology Study (KoGES)

BACKGROUND

Air pollution is a global public health concern and incidence rates of metabolic syndrome (MetS) are increasing. To evaluate the effect of long-term air pollution exposure, we examined the association between long-term exposure to ambient air pollution and the incidences of MetS among Korean adults.

METHODS

We used data from the Korean Genome and Epidemiology Study's Cardiovascular Disease Association Study, a population-based cohort consisting of community-dwelling Korean adults between 2005 and 2011, who were followed up with until 2016 (n = 7,428). Air pollution exposure was estimated using the Congestion Mitigation and Air Quality model based on the participants' addresses. The participants had a physical examination at every visit during follow-up, and MetS was defined based on the National Institute of Health's National Cholesterol Education Program-Adult Treatment Panel III. We used Cox proportional hazard model to analyze the association between long-term air pollution exposure and incidences of MetS per interquartile range (IQR) increment of the annual concentration after adjusting for potential confounders using single and two-pollutant analysis.

RESULTS

The hazard ratios (HR) of MetS per IQR increment in PM2.5, SO2, NO2, and CO were 1.19 (95% CI: 1.12-1.27), 1.57 (95% CI: 1.47-1.68), 1.11 (95% CI: 1.03-1.20), and 1.63 (95% CI: 1.48-1.78), respectively. The incidences of MetS components, which are high blood pressure, elevated fasting glucose, abdominal obesity, high fasting triglyceride (TG), and low fasting high-density lipoprotein (HDL-C), were significantly associated with an IQR increment especially in SO2 and CO. In subgroup analysis, males had higher risk of MetS than females. The HR was the highest in the 60-69 year old age group for all pollutants.

CONCLUSION

In the present study, we found that long-term ambient air pollution exposure increased the incidences of MetS and its components among Korean adults, especially in males and the elderly population.

Associations between anthropogenic heat emissions and serum lipids among adults in northeastern China

Few epidemiological studies have investigated associations between anthropogenic heat emissions (AE) and serum lipids. We recruited 15,477 adults from 33 communities in northeastern China in 2009. We estimated AE flux by using data on energy consumption and socio-economic statistics covering building, transportation, industry, and human metabolism. We assessed the associations between AE and blood lipids and dyslipidemia prevalence using the restricted cubic spline models. The regression coefficients (β) and the 95% CI of total cholesterol for the 75th and 95th percentiles of the exposure were 0.23 mmol/L (95% CI: 0.15, 0.30) and 0.25 mmol/L (95% CI: 0.18, 0.32). We also found AE was positively associated with dyslipidemia. Participants who were female or who had low incomes exhibited more pronounced associations. Our research showed that exposure to AE was significantly associated with serum lipids. These novel, valuable findings are useful to inform policymakers to estimate the risks to human health from anthropogenic heat.

Association between exposure to ambient air pollutants and metabolic syndrome in the vicinity of the Taklamakan Desert

BACKGROUND

Air pollution is a recognized contributor to metabolic syndrome (MetS); but studies in developing regions, including China, remain limited, especially in severely polluted areas near the Taklamakan Desert.

METHODS

Health data from 2,689,455 individuals aged ≥ 18 years in five regions near the Taklamakan Desert were analyzed. MetS diagnosed followed the 2016 Chinese Adult Dyslipidaemias Management Guidelines. Spatio-temporal data from satellite observations were employed to estimate ambient pollution levels, encompassing particulate matter with diameters of up to 1.0 µm (PM1), 2.5 µm (PM2.5), and 10 µm (PM10), along with Ozone (O3) and Carbon monoxide (CO). To investigate the association between air pollutants and the prevalence of MetS and its components, Spatial Generalized Linear Mixed Models were applied, with adjustments made for relevant covariates. Additional stratified and sensitivity analyses were conducted to further investigate these relationships.

RESULTS

The study observed a 20.43 % prevalence of MetS. Non-linear analysis indicated a significant association between all pollutants and MetS prevalence. A 10 μg/m³ increase in concentration was associated with the following respective odds ratios: PM1 (1.341, 95 % CI: 1.331, 1.351), PM2.5 (1.036, 95 % CI: 1.034, 1.037), PM10 (1.006, 95 % CI: 1.005, 1.007), O3 (1.385, 95 % CI: 1.374, 1.396), and CO (1.015,95 %, CI: 1.0147, 1.016). The reliability of these associations was supported by further sensitivity analyses, accounting for variations in age, sex, physical activity, and smoking status. Additional analysis indicated links between pollutants and MetS components, including abdominal obesity, glucose metabolism, and lipid profiles.

CONCLUSIONS

There is an observed association between long-term exposure to air pollution and a heightened risk of MetS, particularly in men, younger individuals, those who are physically inactive, and smokers.

Outdoor light at night is a modifiable environmental factor for metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS)

Metabolic syndrome (MetS) is a significant public health problem and presents an escalating clinical challenge globally. To combat this problem effectively, urgent measures including identify some modifiable environmental factors are necessary. Outdoor artificial light at night (LAN) exposure garnered much attention due to its impact on circadian rhythms and metabolic process. However, epidemiological evidence on the association between outdoor LAN exposure and MetS remains limited. To determine the relationship between outdoor LAN exposure and MetS, 15,477 adults participated the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. Annual levels of outdoor LAN exposure at participants' residential addresses were assessed using satellite data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Generalized linear mixed effect models were utilized to assess the association of LAN exposure with MetS and its components, including elevated waist circumference (WC), triglycerides (TG), blood pressure (BP), fasting blood glucose (FBG), and reduced high-density lipoprotein cholesterol (HDL-C). Effect modification by various social demographic and behavior factors was also examined. Overall, 4701 (30.37 %) participants were defined as MetS. The LAN exposure ranged from 6.03 to 175.00 nW/cm2/sr. The adjusted odds ratio (OR) of MetS each quartile increment of LAN exposure were 1.43 (95 % CI: 1.21-1.69), 1.44 (95 % CI: 1.19-1.74) and 1.52 (95 % CI: 1.11-2.08), respectively from Q2-Q4. Similar adverse associations were also found for the components of MetS, especially for elevated BP, TG and FBG. Interaction analyses indicated that the above associations were stronger in participants without habitual exercise compared with those with habitual exercise (e.g. OR were 1.52 [95 % CI: 1.28-1.82] vs. 1.27 [95 % CI, 1.04-1.55], P-interaction = 0.042 for MetS). These findings suggest that long-term exposure to LAN can have a significant deleterious effect on MetS, potentially making LAN an important modifiable environmental factor to target in future preventive strategies.

Exploring the Interplay Between Climate Change, 24-Hour Movement Behavior, and Health: A Systematic Review

BACKGROUND

Given the emergence of climate change and health risks, this review examined potential relationships between varying indicators of climate change, movement behaviors (ie, physical activity [PA], sedentary behavior, and sleep), and health.

METHODS

Seven databases were searched in March 2020, April 2023, and April 2024. To be included, studies must have examined indicators of climate change and at least one of the movement behaviors as either an exposure or a third variable (ie, mediator/moderator), and a measure of health as outcome. Evidence was summarized by the role (mediator/moderator) that either climate change or movement behavior(s) has with health measures. Relationships and directionality of each association, as well as the strength and certainty of evidence were synthesized.

RESULTS

A total of 79 studies were eligible, representing 6,671,791 participants and 3137 counties from 25 countries (40% low- and middle-income countries). Of 98 observations from 17 studies that examined PA as a mediator, 34.7% indicated that PA mediated the relationship between climate change and health measure such that indicators of adverse climate change were associated with lower PA, and worse health outcome. Of 274 observations made from 46 studies, 28% showed that PA favorably modified the negative association between climate change and health outcome. Evidence was largely lacking and inconclusive for sedentary behavior and sleep, as well as climate change indicators as an intermediatory variable.

CONCLUSIONS

PA may mitigate the adverse impact of climate change on health. Further evidence is needed to integrate PA into climate change mitigation, adaptation, and resilience strategies.

Ambient PMs pollution, blood pressure, potential mediation by short-chain fatty acids: A prospective panel study of young adults in China

BACKGROUND

The concurrent effects of particulate matter (PM) on both blood pressure (BP) and short-chain fatty acids (SCFAs) are insufficiently explored, with limited research on the potential mediating roles of SCFAs.

METHODS

In this prospective panel study with 4 follow-ups, we recruited 40 college students in Hefei, China, to assess the impacts of short-term exposure to PM (aerodynamic diameter ≤10 μm (PM10), ≤2.5 μm (PM2.5), and ≤1 μm (PM1)) on BP and SCFAs, along with potential mechanisms. Real-time PM data, urinary SCFAs levels, and BP indicators were systematically collected. Linear mixed-effects models assessed the relationships between PM, SCFAs, and BP. Mediation analyses explored SCFAs' mediating role in the PM-BP association.

RESULTS

PM exposure was positively linked to BP and negatively associated with SCFAs. For a 10 μg/m3 rise in PM10 at lag 0-72 h, there were notable reductions of 0.0019 % (95 %CI: -0.0028, -0.0010) in Acetic acid, 0.0262 % (-0.0369, -0.0155) in Propionic acid, and 0.0702 % (-0.1025, -0.0378) in Butyric acid. Systolic BP, diastolic BP, and mean arterial pressure (MAP) increased by 2.60 mmHg (0.96, 4.25), 2.24 mmHg (1.18, 3.31), and 2.36 mmHg (1.20, 3.53), respectively, per 10-μg/m3 rise in PM1 at lag 0-24 h. Decreased SCFAs levels explained significant portions (24.69-31.80 %) of the elevated MAP due to PM10. Stronger associations were found in females and individuals with abnormal BMI.

CONCLUSIONS

Our study shows that PM exposure decreases urinary SCFAs levels, which partially mediate the impact of PM on elevated BP. These findings enhance our comprehension of the pathways linking PM exposure to BP changes.

Estimating the burden of diseases attributed to PM2.5 using the AirQ + software in Mashhad during 2016-2021

The study used the AirQ + software developed by the World Health Organization (WHO) to evaluate the health impacts associated with long-term exposure to PM2.5 in Mashhad, Iran. For this purpose, we analyzed the daily average concentrations of PM2.5 (with a diameter of 2.5 micrometers or less) registered by the air quality monitoring stations from 2016 to 2021. The levels of PM2.5 surpassed the Air Quality Guidelines (AQG) limit value of 5 µg/m3 (annual value) established by WHO. The findings revealed that the burden of mortality (from all-natural causes) at people above 30 years old associated with PM2.5 exposures was 2093 [95% confidence interval [CI]: 1627-2314] deaths in 2016 and 2750 [95% CI: 2139-3038] deaths in 2021. In general, the attributable mortality from specific causes of deaths (e.g., COPD (chronic obstructive pulmonary diseases), IHD (ischemic heart diseases) and stroke) in people above 25 years old increased between the years, but the mortality from lung cancer was stable at 46 [95% CI: 33-59] deaths in 2016 and 48 [95% CI: 34-61] deaths in 2021. The attributable mortality from ALRI (Acute Lower Respiratory Infection) in children below 5 years old increased between the years. We also found differences in mortality cases from IHD and stroke among the age groups and between the years 2016 and 2021. It was concluded that burden of disease methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyze the cost-effectiveness of interventions.

Cross-Omics Analyses Reveal the Effects of Ambient PM2.5 Exposure on Hepatic Metabolism in Female Mice

Ambient particulate matter (PM2.5) is a potential risk factor for metabolic damage to the liver. Epidemiological studies suggest that elevated PM2.5 concentrations cause changes in hepatic metabolism, but there is a lack of laboratory evidence. Here, we aimed to evaluate the effects of PM2.5 exposure on liver metabolism in C57BL/6j female mice (10 months old) and to explore the mechanisms underlying metabolic alterations and differential gene expressions by combining metabolomics and transcriptomics analyses. The metabolomics results showed that PM2.5 exposure notably affected the metabolism of amino acids and organic acids and caused hepatic lipid and bile acid accumulation. The transcriptomic analyses revealed that PM2.5 exposure led to a series of metabolic pathway abnormalities, including steroid biosynthesis, steroid hormone biosynthesis, primary bile acid biosynthesis, etc. Among them, the changes in the bile acid pathway might be one of the causes of liver damage in mice. In conclusion, this study clarified the changes in liver metabolism in mice caused by PM2.5 exposure through combined transcriptomic and metabolomic analyses, revealed that abnormal bile acid metabolism is the key regulatory mechanism leading to metabolic-associated fatty liver disease (MAFLD) in mice, and provided laboratory evidence for further clarifying the effects of PM2.5 on body metabolism.

Associations between exposure to various air pollutants and risk of metabolic syndrome: a systematic review and meta-analysis

BACKGROUND

Metabolic Syndrome (MetS) is a widely observed metabolic disorder that is increasingly prevalent worldwide, leading to substantial societal consequences. Previous studies have conducted two separate meta-analyses to investigate the relationship between MetS and air pollutants. However, these studies yielded conflicting results, necessitating a thorough systematic review and meta-analysis to reassess the link between different air pollutants and the risk of developing MetS.

METHODS

We conducted a comprehensive search of relevant literature in databases including PubMed, Embase, Cochrane Library, and Web of Science up to October 9, 2023. The search was specifically restricted to publications in the English language. Following the screening of studies investigating the correlation between air pollution and MetS, we utilized random-effects models to calculate pooled effect sizes along with their respective 95% confidence intervals (CIs). We would like to highlight that this study has been registered with PROSPERO, and it can be identified by the registration number CRD42023484421.

RESULTS

The study included twenty-four eligible studies. The results revealed that an increase of 10 μg/m3 in annual concentrations of PM1, PM2.5, PM10, NO2, SO2, and O3 was associated with a 29% increase in metabolic syndrome (MetS) risk for PM1 (OR = 1.29 [CI 1.07-1.54]), an 8% increase for PM2.5 (OR = 1.08 [CI 1.06-1.10]), a 17% increase for PM10 (OR = 1.17 [CI 1.08-1.27]), a 24% increase for NO2 (OR = 1.24 [CI 1.01-1.51]), a 19% increase for SO2 (OR = 1.19 [CI 1.04-1.36]), and a 10% increase for O3 (OR = 1.10 [CI 1.07-1.13]).

CONCLUSION

The findings of this study demonstrate a significant association between exposure to fine particulate matter (PM1, PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and the incidence of metabolic syndrome (MetS). Moreover, the results suggest that air pollution exposure could potentially contribute to the development of MetS in humans.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China

BACKGROUND

Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods.

AIMS

We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods.

METHODS

We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality.

RESULTS

With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results.

CONCLUSION

This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Association between household and outdoor air pollution and risk for metabolic syndrome among women in Beijing, China

This study explored whether household and outdoor air pollution is associated with a greater risk for metabolic syndrome (MetS) among women. In all 11,860 women who cooked with clean energy were included in the analysis. Cooking frequency, range hood use during cooking, passive smoking exposure, and solid fuel use for heating were used to represent household air pollution. The 2-year average concentration of PM2.5, and face mask usage were used to reflect outdoor air pollution exposure. An index of air pollution exposure was also constructed. Multivariable logistic regression models were used to estimate the association between air pollution and risk for MetS, and a positive correlation was found. Our results indicated that household cooking used clean energy and exposure to a high level of outdoor PM2.5 without face mask usage may contribute to an increased risk for MetS among women.

The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

The role of lifestyle in the association between long-term ambient air pollution exposure and cardiovascular disease: a national cohort study in China

BACKGROUND

Cardiovascular disease (CVD) caused by air pollution poses a considerable burden on public health. We aim to examine whether lifestyle factors mediate the associations of air pollutant exposure with the risk of CVD and the extent of the interaction between lifestyles and air pollutant exposure regarding CVD outcomes.

METHODS

We included 7000 participants in 2011-2012 and followed up until 2018. The lifestyle evaluation consists of six factors as proxies, including blood pressure, blood glucose, blood lipids, body mass index, tobacco exposure, and physical activity, and the participants were categorized into three lifestyle groups according to the number of ideal factors (unfavorable, 0-1; intermediate, 2-4; and favorable, 5-6). Satellite-based spatiotemporal models were used to estimate exposure to ambient air pollutants (including particles with diameters ≤ 1.0 μm [PM1], ≤ 2.5 μm [PM2.5], ≤ 10 μm [PM10], nitrogen dioxide [NO2], and ozone [O3]). Cox regression models were used to examine the associations between air pollutant exposure, lifestyles and the risk of CVD. The mediation and modification effects of lifestyle categories on the association between air pollutant exposure and CVD were analyzed.

RESULTS

After adjusting for covariates, per 10 μg/m3 increase in exposure to PM1 (HR: 1.09, 95% CI: 1.05-1.14), PM2.5 (HR: 1.04, 95% CI: 1.00-1.08), PM10 (HR: 1.05, 95% CI: 1.03-1.08), and NO2 (HR: 1.11, 95% CI: 1.05-1.18) was associated with an increased risk of CVD. Adherence to a healthy lifestyle was associated with a reduced risk of CVD compared to an unfavorable lifestyle (HR: 0.65, 95% CI: 0.56-0.76 for intermediate lifestyle and HR: 0.41, 95% CI: 0.32-0.53 for favorable lifestyle). Lifestyle played a significant partial mediating role in the contribution of air pollutant exposure to CVD, with the mediation proportion ranging from 7.4% for PM10 to 14.3% for PM2.5. Compared to an unfavorable lifestyle, the relative excess risk due to interaction for a healthier lifestyle to reduce the effect on CVD risk was - 0.98 (- 1.52 to - 0.44) for PM1, - 0.60 (- 1.05 to - 0.14) for PM2.5, - 1.84 (- 2.59 to - 1.09) for PM10, - 1.44 (- 2.10 to - 0.79) for NO2, and - 0.60 (- 1.08, - 0.12) for O3.

CONCLUSIONS

Lifestyle partially mediated the association of air pollution with CVD, and adherence to a healthy lifestyle could protect middle-aged and elderly people from the adverse effects of air pollution regarding CVD.

The association of in-utero exposure to air pollution and atherogenic index of plasma in newborns

BACKGROUND

Prenatal exposure to particulate matter (PM) and traffic was associated with the programming of cardiovascular diseases (CVDs) in early life. However, the exact underlying mechanisms are not fully understood. Therefore, we aimed to evaluate the association between in-utero exposure to PMs and traffic indicators with the atherogenic index of plasma (AIP) in newborns, which is a precise index reflecting an enhancement of lipid risk factors for CVDs.

METHODS

In this cross-sectional study, a total of 300 mother-newborn pairs were enrolled in Sabzevar, Iran. Spatiotemporal land-use regression models were used to estimate the level of PM1, PM2.5 and PM10 at the mother's residential address. The total length of streets in different buffers (100,300 and 500m) and proximity to major roads were calculated as indicators of traffic. The AIP of cord blood samples was calculated using an AIP calculator. Multiple linear regression models were used to examine the association of PM concentrations as well as traffic indicators with AIP controlled for relevant covariates.

RESULTS

PM2.5 exposure was significantly associated with higher levels of AIP in newborns. Each interquartile range (IQR) increment of PM2.5 concentration at the mothers' residential addresses was associated with a 5.3% (95% confidence interval (CI): 0.0, 10.6%, P = 0.04) increase in the AIP. Associations between PM1, PM10 and traffic indicators with cord blood level of AIP were positive but not statistically significant.

CONCLUSION

Our findings showed that in utero exposure to PM2.5 may be associated with CVDs programming through the increase of atherogenic lipids.

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from -0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (-0.241 in Romania and -0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.

Exposure to ambient air pollution and chronic bronchitis: Findings from over 6.6 million adults in northwestern China

BACKGROUND

Ambient air pollution increases the risk of respiratory mortality and morbidity, but evidence concerning effects of air pollution on chronic bronchitis (CB) is scarce. This study aimed to evaluate the associations of a set of air pollutants with the burden of CB, and to explore potential modifiers on the associations.

METHODS

In 2020, a total of 6,556,440 adults living in the Northwestern region of China were recruited. The Space-Time Extra-Trees model was employed to assess the annual average concentrations of six air pollutants for the three years (2017-2019) before 2020 , and subsequently allocated to the participants based on the latitude and longitude of their home addresses. We investigated the associations between the levels of various air pollutants and the odds of CB using generalized linear mixed models, and conducted multiple sensitivity analyses and subgroup analyses.

RESULTS

The odds of CB displays an approximately linear association with particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), particulate matter with aerodynamic diameter ≤10 μm (PM10), while it shows a non-linear relationship with gaseous pollutants. In the adjusted model, the odds ratios and 95% confidence intervals for CB per 10 μg/m3 increase in PM2.5, PM10, and sulfur dioxide (SO2) were 1.297 (1.262-1.332), 1.072 (1.064-1.080), and 2.587 (2.186-3.063), respectively. Furthermore, several additional sensitivity analyses demonstrated the stability of these associations. Subgroup analyses found that the aforementioned associations were greater among participants aged below 50 years old and those who smoked and had no leisure time exercise.

CONCLUSION

Long-term exposure to ambient air pollutants may increase the odds of CB, especially among younger people and those with unhealthy lifestyles.

Metabolic syndrome, genetic susceptibility, and risk of chronic obstructive pulmonary disease: The UK Biobank Study

AIM

To investigate the effect of metabolic syndrome (MetS), genetic predisposition, and their interactions, on the risk of developing chronic obstructive pulmonary disease (COPD).

METHODS

Cohort analyses included 287 868 participants from the UK Biobank Study. A genetic risk score for COPD was created using 277 single nucleotide polymorphisms. Cox proportional hazard models were used to evaluate the hazard ratios (HRs) with 95% confidence intervals (CIs) for COPD in relation to exposure factors.

RESULTS

During 2 658 936 person-years of follow-up, 5877 incident cases of COPD were documented. Compared with participants without MetS, those with MetS had a higher risk of COPD (HR 1.24, 95% CI 1.17-1.32). Compared to participants with low genetic predisposition, those with high genetic predisposition had a 17% increased risk of COPD. In the joint analysis, compared with participants without MetS and low genetic predisposition, the HR for COPD for those with MetS and high genetic predisposition was 1.50 (95% CI 1.36-1.65; P < 0.001). However, no significant interaction between MetS and genetic risk was found.

CONCLUSIONS

Metabolic syndrome was found to be associated with an increased risk of COPD, regardless of genetic risk. It is crucial to conduct further randomized control trials to determine whether managing MetS and its individual components can potentially reduce the likelihood of developing COPD.

Cigarette smoking and PM2.5 might jointly exacerbate the risk of metabolic syndrome

Background

Cigarette smoking and particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5) are major preventable cardiovascular mortality and morbidity promoters. Their joint role in metabolic syndrome (MS) pathogenesis is unknown. We determined the risk of MS based on PM2.5 and cigarette smoking in Taiwanese adults.

Methods

The study included 126,366 Taiwanese between 30 and 70 years old with no personal history of cancer. The Taiwan Biobank (TWB) contained information on MS, cigarette smoking, and covariates, while the Environmental Protection Administration (EPA), Taiwan, contained the PM2.5 information. Individuals were categorized as current, former, and nonsmokers. PM2.5 levels were categorized into quartiles: PM2.5 ≤ Q1, Q1 < PM2.5 ≤ Q2, Q2 < PM2.5 ≤ Q3, and PM2.5 > Q3, corresponding to PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3.

Results

The prevalence of MS was significantly different according to PM2.5 exposure (p-value = 0.0280) and cigarette smoking (p-value < 0.0001). Higher PM2.5 levels were significantly associated with a higher risk of MS: odds ratio (OR); 95% confidence interval (CI) = 1.058; 1.014-1.104, 1.185; 1.134-1.238, and 1.149; 1.101-1.200 for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. The risk of MS was significantly higher among former and current smokers with OR; 95% CI = 1.062; 1.008-1.118 and 1.531; 1.450-1.616, respectively, and a dose-dependent p-value < 0.0001. The interaction between both exposures regarding MS was significant (p-value = 0.0157). Stratification by cigarette smoking revealed a significant risk of MS due to PM2.5 exposure among nonsmokers: OR (95% CI) = 1.074 (1.022-1.128), 1.226 (1.166-1.290), and 1.187 (1.129-1.247) for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. According to PM2.5 quartiles, current smokers had a higher risk of MS, regardless of PM2.5 levels (OR); 95% CI = 1.605; 1.444-1.785, 1.561; 1.409-1.728, 1.359; 1.211-1.524, and 1.585; 1.418-1.772 for PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. After combining both exposures, the group, current smokers; PM2.5 > 38.205 μg/m3 had the highest odds (1.801; 95% CI =1.625-1.995).

Conclusion

PM2.5 and cigarette smoking were independently and jointly associated with a higher risk of MS. Stratified analyses revealed that cigarette smoking might have a much higher effect on MS than PM2.5. Nonetheless, exposure to both PM2.5 and cigarette smoking could compound the risk of MS.

Long-term exposure to PM1 is associated with increased prevalence of metabolic diseases: evidence from a nationwide study in 123 Chinese cities

Exposure to particulate matter (PM) has been linked to metabolic diseases. However, the effects of PM with an aerodynamic diameter ≤ 1.0 µm (PM1) on metabolic diseases remain unclear. This study is aimed at assessing the associations of PM1 with metabolic disease risk and quantifying the concentration-response (C-R) relationship of PM1 with metabolic disease risk. A national cross-sectional study was conducted, including 12,495 middle-aged and older adults in 123 Chinese cities. The two-year average concentration of PM1 was evaluated using satellite-based spatiotemporal models. Metabolic diseases, including abdominal obesity, diabetes, hypertension, dyslipidemia, and metabolic syndrome, were identified based on physical examination, blood standard biochemistry examination, and self-reported disease histories. Generalized linear models and C-R curves were used to evaluate the associations of PM1 with metabolic diseases. A total of 12,495 participants were included in this study, with a prevalence of 45.73% for abdominal obesity, 20.22% for diabetes, 42.46% for hypertension, 41.01% for dyslipidemia, and 33.78% for metabolic syndrome. The mean ± standard deviation age of participants was 58.79 ± 13.14 years. In addition to dyslipidemia, exposure to PM1 was associated with increased risks of abdominal obesity, diabetes, hypertension, and metabolic syndrome. Each 10 μg/m3 increase in PM1 concentrations was associated with 39% (odds ratio (OR) = 1.39, 95% confidence interval (CI) 1.33, 1.46) increase in abdominal obesity, 18% (OR = 1.18, 95%CI 1.12, 1.25) increase in diabetes, 11% (OR = 1.11, 95%CI 1.06, 1.16) increase in hypertension, and 25% (OR = 1.25, 95%CI 1.19, 1.31) in metabolic syndrome, respectively. C-R curves showed that the OR values of abdominal obesity, diabetes, hypertension, and metabolic syndrome were increased gradually with the increase of PM1 concentrations. Subgroup analysis indicated that exposure to PM1 was associated with increased metabolic disease risks among participants with different lifestyles and found that solid fuel users were more susceptible to PM1 than clean fuel users. This national cross-sectional study indicated that exposure to higher PM1 might increase abdominal obesity, diabetes, hypertension, and metabolic syndrome risk, and solid fuel use might accelerate the adverse effects of PM1 on metabolic syndrome risk. Further longitudinal cohort studies are warranted to establish a causal inference between PM1 exposure and metabolic disease risk.

Associations between residential greenness and air pollution and the incident metabolic syndrome in a Thai worker cohort

Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM10, CO, SO2, NO2, and O3) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM10 (9.5 μg/m3), SO2 (0.9 ppb), and CO (0.3 ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM10 at concentrations more than 50 μg/m3. In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes.

Development and validation of an age-sex-ethnicity-specific metabolic syndrome score in the Chinese adults

Metabolic syndrome (MetS) is characterized by metabolic dysfunctions and could predict future risk for cardiovascular diseases (CVDs). However, the traditionally defined dichotomous MetS neither reflected MetS severity nor considered demographic variations. Here we develop a continuous, age-sex-ethnicity-specific MetS score based on continuous measures of the five metabolic dysfunctions (waist circumference [WC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], mean arterial pressure [MAP], and fasting blood glucose [FBG]). We find that the weights of metabolic dysfunctions in the score vary across age-sex-ethnicity-specific subgroups, with higher weights for TG, HDL-C, and WC. Each unit increase in the score is associated with increased risks for hyperlipidemia, diabetes, and hypertension, and elevated levels of HbA1c, cholesterol, body mass index, and serum uric acid. The score shows high sensitivity and accuracy for detecting CVD-related risk factors and is validated in different geographical regions. Our study would advance early identification of CVD risks and, more broadly, preventive medicine and sustainable development goals.

Short-Term Ambient Air Ozone Exposure and Components of Metabolic Syndrome in a Cohort of Mexican Obese Adolescents

Ambient air pollution is a major global public health concern; little evidence exists about the effects of short-term exposure to ozone on components of metabolic syndrome in young obese adolescents. The inhalation of air pollutants, such as ozone, can participate in the development of oxidative stress, systemic inflammation, insulin resistance, endothelium dysfunction, and epigenetic modification. Metabolic alterations in blood in components of metabolic syndrome (MS) and short-term ambient air ozone exposure were determined and evaluated longitudinally in a cohort of 372 adolescents aged between 9 to 19 years old. We used longitudinal mixed-effects models to evaluate the association between ozone exposure and the risk of components of metabolic syndrome and its parameters separately, adjusted using important variables. We observed statistically significant associations between exposure to ozone in tertiles in different lag days and the parameters associated with MS, especially for triglycerides (20.20 mg/dL, 95% CI: 9.5, 30.9), HDL cholesterol (-2.56 mg/dL (95% CI: -5.06, -0.05), and systolic blood pressure (1.10 mmHg, 95% CI: 0.08, 2.2). This study supports the hypothesis that short-term ambient air exposure to ozone may increase the risk of some components of MS such as triglycerides, cholesterol, and blood pressure in the obese adolescent population.

Long-term effects of ambient PM2.5 constituents on metabolic syndrome in Chinese children and adolescents

Metabolic syndrome (MetS) is considered a main public health issue as it remarkably adds the risk of cardiovascular disease, leading to a heavy burden of disease. There is growing evidence linking fine particulate matter (PM_2.5) exposure to MetS. However, the influences of PM_2.5 constituents, especially in children and adolescents, remain unclear. Our study was according to a national analysis among Chinese children and adolescents to examine the associations between long-term exposure to PM_2.5 main constituents and MetS. A total of 10,066 children and adolescents aged 10-18 years were recruited in 7 provinces in China, with blood tests, health exams, and questionnaire surveys. We estimated long-term exposures to PM_2.5 mass and its five constituents, containing black carbon (BC), organic matter (OM), inorganic nitrate (NO₃^-), sulfate (SO₄^2-), and soil particles (SOIL) from multi-source data fusion models. Mixed-effects logistic regression models were used with the adjustment of a variety of covariates. In the surveyed populations, 2.9% were classified as MetS. From the single-pollutant models, we discovered that long-term exposures to PM_2.5 mass, BC, OM, NO₃^-, as well as SO₄^2-, were significantly associated with the prevalence of MetS, with odds ratios (ORs) per 1 μg/m³ that were 1.02 (95% confidence interval (CI): 1.01, 1.03) for PM_2.5 mass, 1.24 (95% CI: 1.14, 1.35) for BC, 1.07 (95% CI: 1.04, 1.11) for OM, 1.09 (95% CI: 1.04, 1.13) for NO₃^-, and 1.14 (95% CI:1.04, 1.24) for SO₄^2-. The influence of BC on the prevalence of MetS was robust in both the multi-pollutant model and the PM_2.5-constituent joint model. The paper indicates long-term exposure to PM_2.5 mass and specific PM_2.5 constituents, particularly for BC, was significantly associated with a higher MetS prevalence among children and adolescents in China. Our results highlight the significance of establishing further regulations on PM_2.5 constituents.

Association of residential greenness with the risk of metabolic syndrome in Chinese older adults: a longitudinal cohort study

AIMS

We aimed to investigate the association between residential greenness and MetS in older Chinese adults.

METHODS

Longitudinal data on sociodemographic characteristics and lifestyle were collected from the Shenzhen Healthy Ageing Research (SHARE) cohort. Greenness exposure was assessed through satellite-derived Normalized Difference Vegetation Index (NDVI) values in the 250-m, 500-m, and 1250-m radius around the residential address for each participant. MetS was defined by standard guidelines for the Chinese population.

RESULTS

A total of 49,893 older Chinese adults with a mean age of 70.96 (SD = 5.26) years were included in the study. In the fully adjusted models, participants who lived in the highest quartile of NDVI250-m, NDVI500-m, and NDVI1250-m had a 15% (odds ratio, OR = 0.85, 95% confidence interval, CI: 0.80-0.90), 12% (OR = 0.88, 95% CI: 0.83-0.93), and 11% (OR = 0.89, 95% CI: 0.85-0.95) lower incidence of MetS, respectively, than those living in the lowest quartile (all p-trend < 0.01). Interactions and subgroup analyses showed that age, sex, smoking status, and drinking status were significant effect modifiers (p-interaction for all NDVI < 0.05).

CONCLUSIONS

Residential greenness is associated with a lower risk of MetS in Chinese older adults, especially for young older adults, females, non-smokers, and non-drinkers.

Association of urinary and ambient black carbon, and other ambient air pollutants with risk of prediabetes and metabolic syndrome in children and adolescents

The effects of exposure to black carbon (BC) on various diseases remains unclear, one reason being potential exposure misclassification following modelling of ambient air pollution levels. Urinary BC particles may be a more precise measure to analyze the health effects of BC. We aimed to assess the risk of prediabetes and metabolic syndrome (MetS) in relation to urinary BC particles and ambient BC and to compare their associations in 5453 children from IDEFICS/I. Family cohort. We determined the amount of BC particles in urine using label-free white-light generation under femtosecond pulsed laser illumination. We assessed annual exposure to ambient air pollutants (BC, PM_2.5 and NO₂) at the place of residence using land use regression models for Europe, and we calculated the residential distance to major roads (≤250 m vs. more). We analyzed the cross-sectional relationships between urinary BC and air pollutants (BC, PM_2.5 and NO₂) and distance to roads, and the associations of all these variables to the risk of prediabetes and MetS, using logistic and linear regression models. Though we did not observe associations between urinary and ambient BC in overall analysis, we observed a positive association between urinary and ambient BC levels in boys and in children living ≤250 m to a major road compared to those living >250 m away from a major road. We observed a positive association between log-transformed urinary BC particles and MetS (OR_{per unit increase} = 1.72, 95% CI = 1.21; 2.45). An association between ambient BC and MetS was only observed in children living closer to a major road. Our findings suggest that exposure to BC (ambient and biomarker) may contribute to the risk of MetS in children. By measuring the internal dose, the BC particles in urine may have additionally captured non-residential sources and reduced exposure misclassification. Larger studies, with longitudinal design including measurement of urinary BC at multiple time-points are warranted to confirm 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 (PM_2.5) 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 PM_2.5, 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 short-term exposure to gaseous pollutants on metabolic health indicators of patients with metabolic syndrome in Northwest China

Currently few studies have explored the relationship between exposure to gaseous pollutants and metabolic health indicators in patients, especially in patients with metabolic syndrome (Mets). This study collected 15,520 patients with Mets in a prospective cohort of nearly 50,000 people with 7 years of follow-up from 2011 to 2017, and matched air pollutants and meteorological data during the same period. The mixed effects model was used to analyze the relationship between different short exposure windows (1-week, 1-month, 2-month, and 3-month) of gaseous pollutants (SO2, NO2, and O3) and the metabolic health indicators of patients after controlled the confounding factors. Stratified analysis was performed by demographic characteristics and behavioral factors. The effects of gaseous pollutants on patients with different Met components were also analyzed. The results showed that the short-term exposure to SO2, NO2, and O3 had a certain effect on the metabolic health indicators of patients with Mets in different exposure windows, and with the extension of the exposure window period, the effects increased. The stratified analysis showed that gender, age, and life behaviors might modify these detrimental effects. In addition, the effects of gaseous pollutants on metabolic health indicators in G4 and G7 were more obvious than other Met components, and the effects of gaseous pollutants on the level of LDL-C were found to be statistically significant in most components. Therefore, patients with Mets should pay more attention to the influence of gaseous pollutants to take appropriate protection to reduce potential health risk.

Physical activity attenuated the associations between ambient air pollutants and metabolic syndrome (MetS): A nationwide study across 28 provinces

The independent associations of air pollution and Physical activity (PA) with metabolic syndrome (MetS) were inconsistent, while the joint associations between PA and air pollution with MetS were still unknown. We aimed to (1) further confirm the independent associations of PA and air pollution; (2) examine whether PA would attenuate the positive associations of air pollutants with MetS. We included 13,418 adults above 45 years old in this study. We defined MetS according to the Joint Interim Societies. The concentration of air pollutants (including fine particulate matter (PM_2.5), inhalable particles (PM₁₀), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO)) were estimated by ground-based measurements and satellite remote sensing products. We assessed the level of PA by metabolic equivalent (MET)-hour/week by summing the MET of all activities. We applied logistic regression models with sampling weight to explore the independent and joint associations of PA and air pollutants on MetS. Interaction plots were conducted to exhibit estimates of air pollutants on MetS as a function of PA. We found that all air pollutants were positively associated with the odds of MetS, while PA showed beneficial associations with MetS. The associations of air pollution on MetS decreased accompanied the increase of PA, while the detrimental effects between air pollutants and MetS did not be reversed by PA. In conclusion, PA may attenuate the associations of air pollutants with MetS, although in polluted areas, suggesting that keeping PA might be an effective way to reduce the adverse effects of air pollution with MetS.

Physical activity attenuated the association of ambient ozone with type 2 diabetes mellitus and fasting blood glucose among rural Chinese population

The association of ozone with type 2 diabetes mellitus (T2DM) is uncertain. Moreover, the moderating effect of physical activity on this association is largely unknown. This study aims to evaluate the independent and combined effects of ozone and physical activity on T2DM and fasting blood glucose (FBG) in a Chinese rural adult population. A total of 39,192 participants were enrolled in the Henan Rural Cohort Study. Individual ozone exposure was assessed by using a satellite-based random forest model. The logistic regression and generalized linear models were used to evaluate the associations of ozone and physical activity with T2DM and FBG, respectively. Interaction plots were used to visualize the interaction effects of ozone and physical activity on T2DM or FBG. An interquartile range (IQR) increase in ozone exposure concentration was related to a 53.3% (odds ratio (OR),1.533; 95% confidence interval (CI), 1.426, 1.648) increase in odds of T2DM and a 0.292 mmol/L (95%CI, 0.263, 0.321) higher FBG level, respectively. The effects of ozone on T2DM and FBG generally decreased as physical activity levels increased. Negative additive interactions between ozone and physical activity on T2DM risk were observed (relative excess risk due to interaction (RERI), -0.261; 95%CI, -0.473, -0.048; attributable proportion due to interaction (AP), -0.203; 95%CI, -0.380, -0.027; synergy index (S), 0.520; 95%CI, 0.299, 0.904). The larger effects of ozone were observed among elderly and men on T2DM and FBG than young and women. Long-term exposure to ozone was associated with higher odds of T2DM and higher FBG levels, and these associations might be attenuated by increasing physical activity levels. In addition, there was a negative additive interaction (antagonistic effect) between ozone exposure and physical activity level on T2DM risk, suggesting that physical activity might be an effective method to reduce the burden of T2DM attributed to ozone exposure. Trail registration: The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015, http://www.chictr.org.cn/showproj.aspx?proj=11375.

Systemic inflammation accelerates the adverse effects of air pollution on metabolic syndrome: Findings from the China health and Retirement Longitudinal Study (CHARLS)

Long-term exposure to air pollution and systemic inflammation are associated with increased prevalence of metabolic syndrome (MetS); however, their joint effects in Chinese middle-aged and older adults is unknown. In this cross-sectional study, 11,838 residents aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) Wave 3 in 2015 were included. MetS was diagnosed using the Joint Interim Societies' definition. C-Reactive Protein (CRP) was assessed to reflect systemic inflammation. Individual exposure to air pollutants (particulate matter with a diameter ≤2.5 μm (PM_2.5) or ≤ 10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO)) was evaluated using satellite-based spatiotemporal models according to participant residence at county-level. Generalized linear models (GLMs) were applied to examine the association between air pollution and MetS, and the modification effects of CRP between air pollution and MetS were estimated using interaction terms of CRP and air pollutants in the GLM models. The prevalence of MetS was 32.37%. The adjusted odd ratio (OR) of MetS was 1.192 (95% confidence interval (CI): 1.116, 1.272), 1.177 (95% CI: 1.103, 1.255), 1.158 (95% CI: 1.072, 1.252), 1.303 (95% CI: 1.211,1.403), 1.107 (95% CI: 1.046, 1.171) and 1.156 (95% CI:1.083, 1.234), per inter-quartile range increase in PM_2.5 (24.04 μg/m³), PM₁₀ (39.00 μg/m³), SO₂ (19.05 μg/m³), NO₂ (11.28 μg/m³), O₃ (9.51 μg/m³) and CO (0.46 mg/m³), respectively. CRP was also associated with increased prevalence of MetS (OR = 1.049, 95% CI: 1.035, 1.064; per 1.90 mg/L increase in CRP). Interaction analysis suggested that high CRP levels enhanced the association between air pollution exposure and MetS. Long-term exposure to air pollution is associated with increased prevalence of MetS, which might be enhanced by systemic inflammation. Given the rapidly aging society and heavy burden of MetS, measures should be taken to improve air quality and reduce systemic inflammation.

The Weight of Place: Built Environment Correlates of Obesity and Diabetes

In recent decades, the prevalence of obesity and diabetes has risen substantially in North America and worldwide. To address these dual epidemics, researchers and policymakers alike have been searching for effective means to promote healthy lifestyles at a population level. As a consequence, there has been a proliferation of research examining how the "built" environment in which we live influences physical activity levels, by promoting active forms of transportation, such as walking and cycling, over passive ones, such as car use. Shifting the transportation choices of local residents may mean that more members of the population can participate in physical activity during their daily routine without structured exercise programs. Increasingly, this line of research has considered the downstream metabolic consequences of the environment in which we live, raising the possibility that "healthier" community designs could help mitigate the rise in obesity and diabetes prevalence. This review discusses the evidence examining the relationship between the built environment, physical activity, and obesity-related diseases. We also consider how other environmental factors may interact with the built environment to influence metabolic health, highlighting challenges in understanding causal relationships in this area of research.

Association of PM2.5 and Its Chemical Compositions with Metabolic Syndrome: A Nationwide Study in Middle-Aged and Older Chinese Adults

Studies on the association of PM_2.5 and its compositions with metabolic syndrome (MetS) were limited, and it was unclear which was the most hazardous composition. In this study, we aimed to investigate the association between PM_2.5 and its compositions with MetS and identified the most hazardous composition. In this study, we included 13,418 adults over 45 years across 446 communities from 150 counties of 28 provinces in nationwide China in 2015. MetS was defined based on the five indicators of the Joint Interim Societies, including: blood pressure (SBP (systolic blood pressure) and DBP (diastolic blood pressure)); fasting blood glucose (FBG); fasting triglyceride (FTG); high density lipoprotein cholesterol (HDL-C); and waist circumference (WC). We used chemical transport models to estimate the concentration of PM_2.5 and its compositions, including black carbon, ammonium, nitrate, organic matter, and sulfate. We used a generalized linear regression model to examine the association of PM_2.5 and its compositions with MetS. In this study, we observed that the average age was 61.40 (standard deviation (SD): 9.59). Each IQR (29.76 μg/m³) increase in PM_2.5 was associated with a 1.27 (95% CI: 1.17, 1.37) increase in the odds for MetS. We indicated that black carbon showed stronger associations than other compositions. The higher associations were observed among women, participants aged less than 60 years, who lived in urban areas and in the Northeast, smokers, drinkers, and the obese populations. In conclusion, our findings identified the most harmful composition and sensitive populations and regions that required attention, which would be helpful for policymakers.

Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults

Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM_2.5, O₃) and NDVI for greenness was calculated from satellites data. Ambient temperature data were obtained from European Center for Medium-Range Weather Forecasts (ECMWF). MetS was classified based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) using anthropometric measures and biomarkers. Logistic regression models were utilized to examine the combined relationship of MetS with three-year exposure to air pollutants, temperature and NDVI. Relative excess risk due to interaction (RERI) was calculated to evaluate interaction on an additive scale. We found associations between prevalent MetS and interquartile range (IQR) increases in PM_2.5 (OR: 1.38; 95% confidence interval [95% CI]: 1.23, 1.55) and O₃ (OR: 1.15; 95% CI: 1.09, 1.22). Additive and multiplicative interactions were observed between air pollutants and temperature exposure. Compared to low-temperature level, the relationship between PM_2.5 and MetS attenuated (RERI: 0.22, 95% CI: 0.44, -0.04) at high-temperature level, while the relationship between O₃ and MetS enhanced (RERI: 0.05, 95% CI: 0.02, 0.11). At low NDVI 250 m, the association between PM_2.5 and MetS was stronger (RERI: 0.13, 95% CI: 0.05, 0.19) with high NDVI 250 m as the reference group. Our findings showed that ambient temperature and residential greenness could affect the relationship between air pollutants and MetS.

Healthier Lifestyles Attenuated Association of Single or Mixture Exposure to Air Pollutants with Cardiometabolic Risk in Rural Chinese Adults

There is little research on how long-term exposure to independent and multiple air pollutants changes cardiometabolic risk in adults. In addition, previous studies focused on only the effect of one or two lifestyles on cardiometabolic risk. The evidence on the interactive effects of the lifestyle score and exposure to independent and mixtures of air pollutants on cardiometabolic risk is lacking. A total of 33,638 rural residents were included in the cross-sectional study. The three-year average concentrations of air pollutants for participants were predicted by using a satellite-based prediction. The air pollution score was created to assess the combined exposure of four air pollutants (PM1, PM2.5, PM10, and NO2). A gender−age-specific cardiometabolic risk score was calculated. Multivariable-adjusted linear regression and quantile g-computation were used to investigate the associations between air pollutants and cardiometabolic risk. Interaction plots were applied to describe the interactive effects of air pollution and the healthy lifestyle score on cardiometabolic risk. Per interquartile range (IQR) unit increases in PM1, PM2.5, PM10, or NO2 were associated with 0.162 (95% CI: 0.091, 0.233), 0.473 (95% CI: 0.388, 0.559), 0.718 (95% CI: 0.627, 0.810), and 0.795 (95% CI: 0.691, 0.898) unit increases in cardiometabolic risk score (all p < 0.05), respectively. A 0.854 (95% CI: 0.768, 0.940) unit increase in cardiometabolic risk was associated with each IQR increase in air pollution score. Furthermore, the strengths of associations of PM1, PM2.5, PM10, NO2, and the air pollution score on cardiometabolic risk score were attenuated with the healthy lifestyle score increase. In addition, there was no statistical significance after the lifestyle score equal to four scores for the effect of PM1 on the cardiometabolic risk score. In conclusions, individual or joint air pollutants were associated with an increased cardiometabolic risk. Improving the healthy lifestyle may be an effective method to improve cardiometabolic health in highly polluted rural regions.

Ethnic disparities in the association between ambient air pollution and risk for cardiometabolic abnormalities in China

BACKGROUND

Long-term exposure to ambient air pollution has been associated with cardiometabolic abnormalities (CAs), which, however, may be stronger in vulnerable populations, such as minorities. The variation of the association between ambient air pollution and CAs between the majority (Han) and minority populations in China have been poorly studied.

OBJECTIVES

We aimed to estimate and compare the Hans' and minorities' risks for CAs associated with long-term exposure to ambient air pollution in Southwest China.

METHODS

A cross-sectional study was conducted on the basis of the China Multi-Ethnic Cohort. CAs were defined by the presence of at least three pre-defined metabolic dysfunctions (central obesity, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose). The concentrations of ambient air pollutants, including particulate matters (PM₁, PM_2.5, and PM₁₀) and nitrogen dioxide (NO₂), were generated from random forest models on the basis of multi-source data. One- and two-pollutant regression models were fit to assess associations between air pollutant exposure and CA risks. Sensitivity analyses were performed to examine the robustness of the associations.

RESULTS

The final sample included 51,037 Hans and 28,702 minority participants. The prevalence of CAs was 25.0%, slightly higher in the minorities (25.5%) than the Hans (24.4%). The higher risks for CAs in the overall population were associated with each 10 μg/m³ increase in the exposure to PM₁ (OR = 1.07 [1.05-1.09]), PM_2.5 (OR = 1.11 [1.06-1.17]), PM₁₀ (OR = 1.04 [1.03-1.06]), and NO₂ (OR = 1.04 [1.03-1.07]). Compared to the Hans, the higher risks for CAs were observed in the minorities for PM₁ (OR = 1.35 [1.18-1.53]), PM_2.5 (OR = 1.61 [1.34-1.93]), and PM₁₀ (OR = 1.15 [1.07-1.23]). The associations of metabolic dysfunctions (CA components) with ambient air pollution also varied between the Han and minority populations.

CONCLUSIONS

The associations between exposure to ambient air pollution and CA risks were stronger in the minorities than Hans. Our findings provide a better understanding of ethnic disparities in CA risks when being exposed to ambient air pollution in China, which also have important implications for other low- and middle-income countries where less health resources (e.g., cohort populations) are available to conduct such studies.

Effects of long-term PM2.5 exposure on metabolic syndrome among adults and elderly in Guangdong, China

BACKGROUND

We aimed to explore the association between long-term exposure to particulate matter ≤ 2.5 µm (PM_2.5) and metabolic syndrome (MetS) and its components including fasting blood glucose (FBG), blood pressure, triglyceride (TG), high-density lipoprotein cholesterol (HDL-c) and waist circumference among adults and elderly in south China.

METHODS

We surveyed 6628 participants in the chronic disease and risk factors surveillance conducted in 14 districts of Guangdong province in 2015. MetS was defined based on the recommendation by the Joint Interim Societies' criteria. We used the spatiotemporal land-use regression (LUR) model to estimate the two-year average exposure of ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and O₃) at individual levels. We recorded other covariates by using a structured questionnaire. Generalized linear mixed model was used for analysis.

RESULTS

A 10-μg/m³ increase in the two-year mean PM_2.5 exposure was associated with a higher risk of developing MetS [odd ratio (OR): 1.17, 95% confidence interval (CI): 1.01, 1.35], increased risk of fasting blood glucose level. (OR: 1.18, 95% CI: 1.02, 1.36), and hypertriglyceridemia (OR: 1.36, 95% CI: 1.18, 1.58) in the adjusted/unadjusted models (all P < 0.05). We found significant interaction between PM_2.5 and the region, exercise on the high TG levels, and an interaction with the region, age, exercise and grain consumption on FBG (P _interaction < 0.05).

CONCLUSIONS

Long-term exposure to PM_2.5 was associated with MetS, dyslipidemia and FBG impairment. Efforts should be made for environment improvement to reduce the burden of MetS-associated non-communicable disease.

The neighbourhood environment and profiles of the metabolic syndrome

BACKGROUND

There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components.

METHODS

We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO₂ and PM_2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles.

RESULTS

LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO₂ were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM_2.5 was associated with unhealthier metabolic profiles with MetS.

CONCLUSIONS

This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

PM2.5 exposure during pregnancy is associated with altered placental expression of lipid metabolic genes in a US birth cohort

Inhalation of ambient PM2.5, shown to be able to cross the placenta, has been linked to adverse obstetric and postnatal metabolic health outcomes. The placenta regulates fetal growth and influences postnatal development via fetal programming. Placental gene expression may be influenced by intrauterine exposures to PM2.5. Herein, we explore whether maternal PM2.5 exposure during pregnancy alters placental gene expression related to lipid and glucose metabolism in a U.S. birth cohort, the Rhode Island Child Health Study (RICHS). Average PM2.5 exposure level was estimated linking residential addresses and satellite data across the three trimesters using spatio-temporal models. Based on Gene Ontology annotations, we curated a list of 657 lipid and glucose metabolism genes. We conducted a two-staged analysis by leveraging placental RNA-Seq data from 148 subjects to identify top dysregulated metabolic genes associated with PM2.5 (Phase I) and then validated the results in placental samples from 415 participants of the cohort using RT-qPCR (Phase II). Associations between PM2.5 and placental gene expression were explored using multivariable linear regression models in the overall population and in sex-stratified analyses. The average level of PM2.5 exposure across pregnancy was 8.0μg/m³, which is below the national standard of 12μg/m³. Phase I revealed that expression levels of 32 out of the curated list of 657 genes were significantly associated with PM2.5 exposure (FDR P<0.01), 28 genes showed differential expression modified by sex of the infant. Five of these genes (ABHD3, ATP11A, CLTCL1, ST6GALNAC4 and PSCA) were validated using RT-qPCR. Associations were stronger in placentas from male births compared to females, indicating a sex-dependent effect. These genes are involved in inflammation, lipid transport, cell-cell communication or cell invasion. Our results suggest that gestational PM2.5 exposure may alter placental metabolic function. However, whether it confers long-term programming effects postnatally, especially in a sex-specific matter, warrants further studies.

Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults

Fine particulate matter (PM_2.5) was reported to be associated with metabolic syndrome (MetS), but how PM_2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM_2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects (n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM_2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM_2.5 constituents-MetS" associations. Results indicated PM_2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO₄^2-), calcium ion (Ca²⁺), magnesium ion (Mg²⁺), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM_2.5 and some constituents (SO₄^2-, Ca²⁺, Mg²⁺, Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM_2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO₄^2-, Ca²⁺, Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM_2.5 constituents and MetS risk in China.

Abnormal fasting blood glucose enhances the risk of long-term exposure to air pollution on dyslipidemia: A cross-sectional study

Both long-term exposure to air pollution and abnormal fasting blood glucose (FBG) are linked to dyslipidemia prevalence. However, the joint role of air pollution and FBG on dyslipidemia remains unknown clearly. In this study, we aimed to test whether abnormal FBG could enhance the risks of long-term exposure to air pollutants on dyslipidemia in general Chinese adult population. The present study recruited 8917 participants from 4 cities in Hebei province, China. Participants' individual exposure to air pollutants was evaluated by the Empirical Bayesian Kriging statistical model in ArcGIS10.2 geographic information system. Dyslipidemia was defined according to Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults. Subjects were grouped into normal, prediabetes, diabetes according to FBG level. Generalized linear models were applied to analyze the interaction of air pollutants and FBG on dyslipidemia prevalence. The prevalence of dyslipidemia was 43.83% in our investigation. After adjusting all covariates, we found the risk of four air pollutants (PM_2.5, PM₁₀, NO₂, SO₂) on dyslipidemia prevalence was stronger as higher FBG level, and the adjusted odd ratio of interaction (OR_inter (95% CI)) between PM_2.5, PM₁₀, NO₂, SO₂ and FBG levels on dyslipidemia was 1.171 (1.162, 1.189), 1.119 (1.111, 1.127), 1.124 (1.115, 1.130), 1.107 (1.098, 1.115), respectively. Stratified analyses indicated the modifying effects of FBG on the association of air pollution with dyslipidemia were stronger among male, less than 65 years old, overweight/obesity (all P_inter<0.1). Our study concluded that high FBG levels strengthened the risk of long-term exposure to air pollution on dyslipidemia, especially more noticeable in male, less than 65 years old, overweight.

Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey

BACKGROUND

We hypothesize higher air pollution and fewer greenness exposures jointly contribute to metabolic syndrome (MetS), as mechanisms on cardiometabolic mortality.

METHODS

We studied the samples in the Chinese Longitudinal Healthy Longevity Survey. We included 1755 participants in 2012, among which 1073 were followed up in 2014 and 561 in 2017. We used cross-sectional analysis for baseline data and the generalized estimating equations (GEE) model in a longitudinal analysis. We examined the independent and interactive effects of fine particulate matter (PM_2.5) and Normalized Difference Vegetation Index (NDVI) on MetS. Adjustment covariates included biomarker measurement year, baseline age, sex, ethnicity, education, marriage, residence, exercise, smoking, alcohol drinking, and GDP per capita.

RESULTS

At baseline, the average age of participants was 85.6 (SD: 12.2; range: 65-112). Greenness was slightly higher in rural areas than urban areas (NDVI mean: 0.496 vs. 0.444; range: 0.151-0.698 vs. 0.133-0.644). Ambient air pollution was similar between rural and urban areas (PM_2.5 mean: 49.0 vs. 49.1; range: 16.2-65.3 vs. 18.3-64.2). Both the cross-sectional and longitudinal analysis showed positive associations of PM_2.5 with prevalent abdominal obesity (AO) and MetS, and a negative association of NDVI with prevalent AO. In the longitudinal data, the odds ratio (OR, 95% confidence interval-CI) of PM_2.5 (per 10 μg/m³ increase) were 1.19 (1.12, 1.27), 1.16 (1.08, 1.24), and 1.14 (1.07, 1.21) for AO, MetS and reduced high-density lipoprotein cholesterol (HDL-C), respectively. NDVI (per 0.1 unit increase) was associated with lower AO prevalence [OR (95% CI): 0.79 (0.71, 0.88)], but not significantly associated with MetS [OR (95% CI): 0.93 (0.84, 1.04)]. PM_2.5 and NDVI had a statistically significant interaction on AO prevalence (p_interaction: 0.025). The association between PM_2.5 and MetS, AO, elevated fasting glucose and reduced HDL-C were only significant in rural areas, not in urban areas. The association between NDVI and AO was only significant in areas with low PM_2.5, not under high PM_2.5.

CONCLUSIONS

We found air pollution and greenness had independent and interactive effect on MetS components, which may ultimately manifest in pre-mature mortality. These study findings call for green space planning in urban areas and air pollution mitigation in rural areas.

Indoor solid fuel use and renal function among middle-aged and older adults: A national study in rural China

Solid fuel use is the main source of indoor air pollution, especially in rural areas of developing countries. Nevertheless, the evidence linking indoor solid fuel use and renal function is very limited. Therefore, we investigated the association between indoor solid fuel use and renal function among middle-aged and older adults in rural China. Cystatin C (CysC) concentration of each participant was used to calculate the estimated glomerular filtration rate (eGFR). We used the baseline data to investigate the associations between solid fuel use for cooking and heating and eGFR through a linear-mixed effects model. Then, we applied the generalized linear-mixed effects model with binary distribution to examine the relationship between renal function decline and cooking fuel switching from 2011 to 2015. A total of 4959 participants were included at baseline, and 3536 participants were included in the follow-up analysis. Compared to participants who used clean fuel for both cooking and heating, the eGFR was significantly lower among participants who cooked with solid fuel and heated with clean fuel (β: -2.81; 95% CI: -5.53, -0.09). In the follow-up analysis, the risks of renal function decline for participants using solid fuel for cooking were significantly higher in males (OR: 2.74; 95% CI: 1.68, 4.49), smokers (OR: 5.70; 95% CI: 2.82, 11.55), and drinkers (OR: 7.11; 95% CI: 3.15, 16.02) compared to females, non-smokers, and non-drinkers. Moreover, 45-65 years aged participants (OR: 0.54; 95% CI: 0.33, 0.89) and non-drinkers (OR: 0.61; 95% CI: 0.41, 0.92) who switched from solid to clean cooking fuel had a lower risk of renal function decline. In conclusion, our findings show that household solid fuel use is likely to be an important risk factor for renal function decline in rural China. And switching to cleaner fuel may provide significant public health benefits.

Associations of long-term exposure to ambient air pollutants with metabolic syndrome: The Wuhan Chronic Disease Cohort Study (WCDCS)

BACKGROUND

Evidence on the associations between long-term exposure to ambient air pollutants (including particle with aerodynamic diameter ≤10 μm (PM₁₀), particle with aerodynamic diameter ≤2.5 μm (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂)) and prevalence of metabolic syndrome (MetS) remains inconclusive. This study aimed to determine the associations based on a case-control study nested in the Wuhan Chronic Disease Cohort study (WCDCS), a population-based study with baseline survey in 2019.

METHODS

A total of 10,253 residents living in Wuhan were recruited. The 3-year average concentrations of main pollutants (PM₁₀, PM_2.5, O₃, NO₂, and SO₂) at residences prior to the survey date were estimated to evaluate the long-term exposures. The generalized linear mixed models were used to investigate the changes in MetS prevalence by an IQR increases in each air pollutant exposure concentrations. Interaction effects between air pollutants and demographic, lifestyle, and dietary factors on MetS were evaluated by including an interactive item in the main model.

RESULTS

The prevalence of MetS in Wuhan was 9.8%, and the 3-year exposure concentrations of PM₁₀, PM_2.5, O₃, NO₂, and SO₂ were 84.1 μg/m³, 50.5 μg/m³, 55.7 μg/m³, 46.0 μg/m³, and 9.4 μg/m³, respectively. Higher PM₁₀, PM_2.5 and O₃ exposure concentrations were associated with an elevated MetS prevalence (e.g. an IQR increase in PM_2.5, OR = 1.193, 95% confidence intervals (95%CIs): 1.028, 1.385; for O₃, OR = 1.074, 95%CIs: 1.025, 1.124), whereas NO₂, and SO₂ were negatively or insignificant correlated with odds of Mets (e.g. an IQR increase in NO₂, OR = 0.865, 95%CIs: 0.795, 0.941). Males, smokers, alcohol drinkers and individuals who intake fruits occasionally exposure to PM₁₀ and PM_2.5 were found had a higher risk of developing MetS.

CONCLUSIONS

Long-term exposure to higher concentrations of ambient air pollutants may elevate the prevalence of MetS in populations in Central China. Susceptible individuals especially those with unhealthy lifestyles had a higher risk for MetS.

Associations between metabolic syndrome and anthropogenic heat emissions in northeastern China

BACKGROUND

Recent research attention has been paid to anthropogenic heat emissions (AE), temperature increase generated by human activity such as lighting, transportation, manufacturing, construction, and building climate controls. However, there is no epidemiological data available to investigate the association between anthropogenic heat emissions and metabolic syndrome (MetS), a cluster of conditions that increase risk of stroke, heart disease and diabetes.

OBJECTIVE

To explore the relationships between AE and MetS in China.

METHODS

We recruited 15,477 adults from the 33 Communities Chinese Health Study, a cross-sectional study in northeastern China. We retrieved anthropogenic heat flux by collecting socio-economic and energy consumption data as well as satellite-based nighttime light and Normalized Difference Vegetation Index datasets, including emissions from buildings, transportation, human metabolism, and industries. We also measured MetS components consisting of triglycerides, high density lipoprotein cholesterol, fasting glucose, systolic blood pressure, and diastolic blood pressure, and waist circumference. Restricted cubic spline models were applied to assess the associations between AE and MetS.

RESULTS

The median flux of total AE was 30.98 W/m² and industrial AE was the dominant contributor (87.64%). The adjusted odds ratio and 95% confidence interval (CI) of MetS for the 75th and 95th percentiles of the total AE against the threshold were 1.29 (95% CI: 1.21, 1.38) and 1.65 (95% CI: 1.47, 1.85). Greater AE was associated with higher odds of MetS in a dose-response pattern, and the lowest point of U-shape curve indicated the threshold effect. Participants who are young and middle-aged exhibited stronger associations between AE and MetS.

CONCLUSIONS

Our novel findings reveal that AE are positively associated with MetS and that associations are modified by age. Further investigations into the mechanisms of the effects are needed.

Exposure to air pollution is associated with an increased risk of metabolic dysfunction-associated fatty liver disease

BACKGROUND & AIMS

Accumulating animal studies have demonstrated the harmful contribution of ambient air pollution (AP) to metabolic dysfunction-associated fatty liver disease (MAFLD), but corresponding epidemiological evidence is limited. We examined the associations between long-term AP exposure and MAFLD prevalence in a Chinese population.

METHODS

We conducted a cross-sectional study of 90,086 participants recruited in China from 2018 to 2019. MAFLD was assessed based on radiologically diagnosed hepatic steatosis and the presence of overweight/obese status, diabetes mellitus, or metabolic dysregulation. Residence-specific levels of air pollutants, including particulate matter with aerodynamic diameters of ≤1 μm (PM₁), ≤2.5 μm (PM_2.5), and ≤10 μm (PM₁₀), and nitrogen dioxide (NO₂), were estimated by validated spatiotemporal models. We used logistic regression models to examine the AP-MAFLD associations and further evaluated potential effect modifications by demographics, lifestyle, central obesity, and diabetes status.

RESULTS

Increased exposure levels to all 4 air pollutants were significantly associated with increased odds of MAFLD, with odds ratios (ORs) of 1.13 (95% CI 1.10-1.17), 1.29 (1.25-1.34), 1.11 (1.09-1.14), and 1.15 (1.12-1.17) for each 10 μg/m³ increase in PM₁, PM_2.5, PM₁₀, and NO₂, respectively. Further stratified analyses revealed that individuals who are male, alcohol drinkers, and current and previous smokers, those who consume a high-fat diet, and those with central obesity experience more significant adverse effects from AP exposure than other individuals.

CONCLUSIONS

This study provides evidence that long-term exposure to ambient PM₁, PM_2.5, PM₁₀, and NO₂ may increase the odds of MAFLD in the real world. These effects may be exacerbated by unhealthy lifestyle habits and central obesity.

LAY SUMMARY

We conducted an epidemiological study on the potential effect of ambient air pollution on the risk of metabolic dysfunction-associated fatty liver disease (MAFLD) in approximately 90 thousand adults in China. We found that long-term exposure to ambient air pollution may increase the odds of MAFLD, especially in individuals who are male, smokers, and alcohol drinkers, those who consume a high-fat diet, and those with central obesity.