Air pollution and metabolic syndrome risk: Evidence from nine observational studies

Association between long-term exposure to PM2.5 chemical components and metabolic syndrome in middle-aged and older adults

Background

Previous studies indicated that exposure to ambient fine particulate matter (PM2.5) could increase the risk of metabolic syndrome (MetS). However, the specific impact of PM2.5 chemical components remains uncertain.

Methods

A national cross-sectional study of 12,846 Chinese middle-aged and older adults was conducted. Satellite-based spatiotemporal models were employed to determine the 3-year average PM2.5 components exposure, including sulfates (SO4 2-), nitrates (NO3 -), ammonia (NH4 +), black carbon (BC), and organic matter (OM). Generalized linear models were used to investigate the associations of PM2.5 components with MetS and the components of MetS, and restricted cubic splines curves were used to establish the exposure-response relationships between PM2.5 components with MetS, as well as the components of MetS.

Results

MetS risk increased by 35.1, 33.5, 33.6, 31.2, 32.4, and 31.4% for every inter-quartile range rise in PM2.5, SO4 2-, NO3 -, NH4 +, OM and BC, respectively. For MetS components, PM2.5 chemical components were associated with evaluated risks of central obesity, high blood pressure (high-BP), high fasting glucose (high-FBG), and low high-density lipoprotein cholesterol (low-HDL).

Conclusion

This study indicated that exposure to PM2.5 components is related to increased risk of MetS and its components, including central obesity, high-BP, high-FBG, and low-HDL. Moreover, we found that the adverse effect of PM2.5 chemical components on MetS was more sensitive to people who were single, divorced, or widowed than married people.

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.

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.

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.

Mediation of metabolic syndrome in the association between long-term exposure to particulate matter and incident cardiovascular disease: Evidence from a population-based cohort in Chengdu

BACKGROUND

Particulate matter (PM) exposure has been linked with cardiovascular disease (CVD) and metabolic syndrome (MetS), the latter characterized by concurrent multiple metabolic disorders. As a result, the mechanisms assumption from PM to CVD through MetS have emerged, thus requiring further epidemiological evidence. This cohort study aimed to assess whether MetS mediates the associations of PM with CVD risk.

METHODS

This study included 14,195 participants from the Chengdu cohort of the China Multi-Ethnic Cohort (CMEC) study in 2018. The primary outcome of incident CVD diagnoses was identified using matched hospital records from the Health Information Center of Sichuan Province. Residence-specific levels of PM with aerodynamic diameters of ≤ 1 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10) were estimated by spatiotemporal models. Causal mediation analyses were applied to evaluate the indirect effect of MetS.

RESULTS

Increased exposure levels to PM were significantly associated with MetS and CVD. Mediation analyses indicated that the associations between PM exposure and CVD were mediated by MetS, with the proportion of multiple mediations being 19.3%, 12.1%, and 13.5% for PM1, PM2.5, and PM10, respectively. Further moderated mediation analyses suggested that male, overweight individuals, alcohol drinkers, and those suffering from indoor air pollution may experience more significant adverse effects from PM exposure on CVD via MetS than others.

CONCLUSIONS

Our findings suggest that MetS partially mediates the association between long-term exposure to PM and CVD. These mediation effects appear to be amplified by demographic characteristics and unhealthy lifestyles.

Differences in the components of metabolic syndrome by age and sex: a cross-sectional and longitudinal analysis of a cohort of middle-aged and older Japanese adults

BACKGROUND

The prevalence of metabolic syndrome (MetS) in Japan, a super-aged society, is increasing and poses a major public health issue. Several studies have reported sex differences in the association between age and MetS prevalence. This study aimed to examine the association between age and the prevalence of MetS based on multiple screening criteria and MetS components by sex.

METHODS

We used 6 years of individual-level longitudinal follow-up data (June 2012 to November 2018; checkup year: 2012-2017) of middle-aged and older adults aged 40-75 years in Japan (N = 161,735). The Joint Interim Statement criteria, International Diabetes Federation criteria, and another set of criteria excluding central obesity were used as the screening criteria for MetS. The prevalence of MetS and MetS components was cross-sectionally analyzed according to sex and age. A longitudinal association analysis of age, MetS, and MetS components by sex was performed using a multilevel logistic model, adjusted for lifestyle- and regional-related factors.

RESULTS

Sex differences were observed in the prevalence and association of MetS and MetS components. In all age groups, the prevalence of central obesity was higher among women, and the prevalence of high blood pressure and fasting glucose was higher among men (P < 0.001). The prevalence of high triglyceride and low high-density lipoprotein cholesterol was higher among women aged > 60 years (P < 0.05). Based on the criteria of the Joint Interim Statement and International Diabetes Federation, the prevalence of MetS was higher among women than in men aged > 55 years (P < 0.001). Men had a higher prevalence of MetS without central obesity than women in all age groups (P < 0.001). The odds ratio for MetS and MetS components with aging was greater among women than in men.

CONCLUSIONS

Medical management should be based on the prevalence of MetS and its components according to sex and age. In particular, the high prevalence of MetS without central obesity in middle-aged and older Japanese men suggests that the adoption of the Joint Interim Statement criteria, which do not precondition central obesity, should be considered.

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.

Association between short-term ambient nitrogen dioxide and type 2 diabetes outpatient visits: A large hospital-based study

Type 2 diabetes (T2DM) as a non-communicable disease imposes heavy disease burdens on society. Limited studies have been conducted to assess the effects of short-term air pollution exposure on T2DM, especially in Asian regions. Our research aimed to determine the association between short-term exposure to ambient nitrogen dioxide (NO₂) and outpatient visits for T2DM in Chongqing, the largest city in western China, based on the data collected from November 28, 2013 to December 31, 2019. A generalized additive model (GAM) was applied, and stratified analyses were performed to investigate the potential modifying effects by age, gender, and season. Meanwhile, the disease burden was revealed from attributable risk. Positive associations between short-term NO₂ and daily T2DM outpatient visits were observed. The strongest association was observed at lag 04, with per 10 μg/m³ increase of NO₂ corresponded to increased T2DM outpatient visits at 1.57% [95% confidence interval (CI): 0.48%, 2.65%]. Stronger associations were presented in middle-aged group (35-64 years old), male group, and cool seasons (October to March). Moreover, there were 1.553% (8664.535 cases) of T2DM outpatient visits attributable to NO₂. Middle-aged adults, males, and patients who visited in cool seasons suffered heavier burdens. Conclusively, short-term exposure to NO₂ was associated with increased outpatient visits for T2DM. Attention should be paid to the impact of NO₂ on the burden of T2DM, especially for those vulnerable groups.

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.

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.

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.

Nicotinamide Mononucleotide and Coenzyme Q10 Protects Fibroblast Senescence Induced by Particulate Matter Preconditioned Mast Cells

Particulate matter (PM) pollutants impose a certain degree of destruction and toxicity to the skin. Mast cells in the skin dermis could be activated by PMs that diffuse across the blood vessel after being inhaled. Mast cell degranulation in the dermis provides a kind of inflammatory insult to local fibroblasts. In this study, we evaluated human dermal fibroblast responses to conditioned medium from KU812 cells primed with PM. We found that PM promoted the production of proinflammatory cytokines in mast cells and that the cell secretome induced reactive oxygen species and mitochondrial reactive oxygen species production in dermal fibroblasts. Nicotinamide mononucleotide or coenzyme Q10 alleviated the generation of excessive ROS and mitochondrial ROS induced by the conditioned medium from PM-activated KU812 cells. PM-conditioned medium treatment increased the NF-κB expression in dermal fibroblasts, whereas NMN or Q10 inhibited p65 upregulation by PM. The reduced sirtuin 1 (SIRT 1) and nuclear factor erythroid 2-related Factor 2 (Nrf2) expression induced by PM-conditioned medium was reversed by NMN or Q10 in HDFs. Moreover, NMN or Q10 attenuated the expression of senescent β-galactosidase induced by PM-conditioned KU812 cell medium. These findings suggest that NMN or Q10 ameliorates PM-induced inflammation by improving the cellular oxidative status, suppressing proinflammatory NF-κB, and promoting the levels of the antioxidant and anti-inflammatory regulators Nrf2 and SIRT1 in HDFs. The present observations help to understand the factors that affect HDFs in the dermal microenvironment and the therapeutic role of NMN and Q10 as suppressors of skin aging.

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.

Long-term PM2.5 exposure and various health outcomes: An umbrella review of systematic reviews and meta-analyses of observational studies

Adverse effects from exposure to particulate matter <2.5 μm in diameter (PM_2.5) on health-related outcomes have been found in a range of experimental and epidemiological studies. This study aimed to assess the significance, validity, and reliability of the relationship between long-term PM_2.5 exposure and various health outcomes. The Embase, PubMed, Web of Science, CNKI, WANFANG, VIP, and SinoMed databases and reference lists of the retrieved review articles were searched to obtain meta-analysis and systematic reviews focusing on PM_2.5-related outcomes as of August 31, 2021. Random-/fixed-effects models were applied to estimate summary effect size and 95% confidence intervals (CIs). The quality of included meta-analyses was evaluated based on the AMSTAR 2 tool. Small-study effect and excess significance bias studies were conducted to further assess the associations. Registered PROSPERO number: CRD42020200606. This included 24 articles involving 71 associations between PM_2.5 exposure and the health outcomes. The evidence for the positive association of 10 μg/m³ increments of long-term exposure to PM_2.5 and stroke incidence in Europe was convincing (effect size = 1.07, 95% CI: 1.05-1.10). There was evidence that was highly suggestive of a positive association between 10 μg/m³ increments of long-term exposure to PM_2.5 and the following health-related outcomes: mortality of lung cancer (effect size = 1.11, 95% CI: 1.08-1.13) and Alzheimer's disease (effect size = 4.79, 95% CI: 2.79-8.21). There was highly suggestive evidence that chronic obstructive pulmonary disease risk is positively associated with higher long-term PM_2.5 exposure versus lower long-term PM_2.5 exposure (effect size = 2.32, 95% CI: 1.88-2.86). In conclusion, the positive association of long-term exposure to PM_2.5 and stroke incidence in Europe was convincing. Given the validity of numerous associations of long-term exposure to PM_2.5 and health-related outcomes is subject to biases, more robust evidence is urgently needed.