Long-term PM0.1 exposure and human blood lipid metabolism: New insight from the 33-community study in China

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.

Mechanistic insights into cardiovascular effects of ultrafine particle exposure: A longitudinal panel study

BACKGROUND

Ultrafine particle (UFP) has been linked with higher risks of cardiovascular diseases; however, the biological mechanisms remain to be fully elucidated.

OBJECTIVES

This study aims to investigate the cardiovascular responses to short-term UFP exposure and the biological pathways involved.

METHODS

A longitudinal panel study was conducted among 32 healthy, non-smoking young adults in Shanghai, China, who were engaged in five rounds of follow-ups between December 2020 and November 2021. Individual exposures were calculated based on the indoor and outdoor real-time measurements. Blood pressure, arterial stiffness, targeted biomarkers, and untargeted proteomics and metabolomics were examined during each follow-up. Linear mixed-effect models were applied to analyze the exposure and health data. The differential proteins and metabolites were used for pathway enrichment analyses.

RESULTS

Short-term UFP exposure was associated with significant increases in blood pressure and arterial stiffness. For example, systolic blood pressure increased by 2.10 % (95 % confidence interval: 0.63 %, 3.59 %) corresponding to each interquartile increase in UFP concentrations at lag 0-3 h, while pulse wave velocity increased by 2.26 % (95 % confidence interval: 0.52 %, 4.04 %) at lag 7-12 h. In addition, dozens of molecular biomarkers altered significantly. These effects were generally present within 24 h after UFP exposure, and were robust to the adjustment of co-pollutants. Molecular changes detected in proteomics and metabolomics analyses were mainly involved in systemic inflammation, oxidative stress, endothelial dysfunction, coagulation, and disturbance in lipid transport and metabolism.

DISCUSSION

This study provides novel and compelling evidence on the detrimental subclinical cardiovascular effects in response to short-term UFP exposure. The multi-omics profiling further offers holistic insights into the underlying biological pathways.

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.

Influence of Air Pollution Exposures on Cardiometabolic Risk Factors: a Review

PURPOSE OF REVIEW

The increasing prevalence of cardiometabolic risk factors (CRFs) contributes to the rise in cardiovascular disease. Previous research has established a connection between air pollution and both the development and severity of CRFs. Given the ongoing impact of air pollution on human health, this review aims to summarize the latest research findings and provide an overview of the relationship between different types of air pollutants and CRFs.

RECENT FINDINGS

CRFs include health conditions like diabetes, obesity, hypertension etc. Air pollution poses significant health risks and encompasses a wide range of pollutant types, air pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O2). More and more population epidemiological studies have shown a positive correlation between air pollution and CRFs. Although various pollutants have diverse effects on specific cellular molecular pathways, their main influence is on oxidative stress, inflammation response, and impairment of endothelial function. More and more studies have proved that air pollution can promote the occurrence and development of cardiovascular and metabolic risk factors, and the research on the relationship between air pollution and CRFs has grown intensively. An increasing number of studies are using new biological monitoring indicators to assess the occurrence and development of CRFs resulting from exposure to air pollution. Abnormalities in some important biomarkers in the population (such as homocysteine, uric acid, and C-reactive protein) caused by air pollution deserve more attention. Further research is warranted to more fully understand the link between air pollution and novel CRF biomarkers and to investigate potential prevention and interventions that leverage the mechanistic link between air pollution and CRFs.

Association between Air Pollution and Lipid Profiles

Dyslipidemia is a critical factor in the development of atherosclerosis and consequent cardiovascular disease. Numerous pieces of evidence demonstrate the association between air pollution and abnormal blood lipids. Although the results of epidemiological studies on the link between air pollution and blood lipids are unsettled due to different research methods and conditions, most of them corroborate the harmful effects of air pollution on blood lipids. Mechanism studies have revealed that air pollution may affect blood lipids via oxidative stress, inflammation, insulin resistance, mitochondrial dysfunction, and hypothalamic hormone and epigenetic changes. Moreover, there is a risk of metabolic diseases associated with air pollution, including fatty liver disease, diabetes mellitus, and obesity, which are often accompanied by dyslipidemia. Therefore, it is biologically plausible that air pollution affects blood lipids. The overall evidence supports that air pollution has a deleterious effect on blood lipid health. However, further research into susceptibility, indoor air pollution, and gaseous pollutants is required, and the issue of assessing the effects of mixtures of air pollutants remains an obstacle for the future.

Long-term effect of submicronic particulate matter (PM1) and intermodal particulate matter (PM1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study

BACKGROUND

There is insufficient evidence of associations between incident dyslipidemia with PM₁ (submicronic particulate matter) and PM_1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM₁ and PM_1-2.5 on incident dyslipidemia respectively.

METHODS

We studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia.

RESULTS

After a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 μg/m³ uptick in four-year average concentrations of PMs (PM₁ and PM_1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01-1.23)] and 1.23 (95% CI: 1.06-1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM₁ on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98-1.32) vs. 1.04 (95% CI: 0.89-1.21)], the elderly [1.23 (95% CI: 1.04-1.45) vs. 1.03 (95% CI: 0.91-1.17)], people with less than primary school education [1.12 (95% CI: 0.94-1.33) vs. 1.08 (95% CI: 0.94-1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00-1.36) vs. 1.06 (95% CI: 0.93-1.21)], however, the difference was not statistically significant (Z = -0.82, P = 0.413; Z = -1.66, P = 0.097; Z = 0.32, P = 0.752; Z = -0.89, P = 0.372).

CONCLUSIONS

Long-term exposure to PM₁ and PM_1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area

Background

Although the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited.

Method

A total of 68,416 people in South China were enrolled and followed up during 2019-2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM₁₀ and O₃ exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated.

Results

Overall, each 10 μg/m³ increase in PM₁₀ concentration was associated with a 3.1% (95%CI: 1.3%-4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O₃ exposure (6.8%, 5.5%-8.2%). Furthermore, each 10 μg/m³ increase in PM₁₀ was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O₃ was relevant to a 4.7%-22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM₁₀ exposure (P _interaction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O₃ (P _interaction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O₃ exposure (P _interaction: 0.032).

Conclusion

We provide comprehensive evidence on the hospitalization hazard of monthly PM₁₀ and O₃ exposure and their interaction with individual factors.

Exposure to ultrafine particles and childhood obesity: A cross-sectional analysis of the Seven Northeast Cities (SNEC) Study in China

BACKGROUND

Studies on the obesogenic effect of air pollution on children have been mixed and sparse. Moreover, due to insufficient air monitoring, few studies have investigated the role of more tiny but unregulated particles (ambient particles with a diameter of 0.1 μm or less, ultrafine particles).

OBJECTIVE

We sought to explore the associations between long-term exposure to ambient ultrafine particles (UFPs) and childhood obesity in Chinese children.

METHODS

In this cross-sectional study, we randomly recruited 47,990 children, aged 6-18 years, from seven cities in Northeastern China between 2012 and 2013. Child age- and sex-specific z-scores for body mass index (BMI Z-score) and weight status were generated using the World Health Organization growth reference. Four-year average concentrations of UFPs and airborne particulates of diameter ≤ 1 μm (PM₁), ≤2.5 μm (PM_2.5), and ≤10 μm (PM₁₀) were estimated at home, using neural network simulated WRF-Chem model and spatiotemporal model, respectively. Confounder-adjusted generalized linear mixed models examined the associations between air pollution and BMI Z-score and the prevalence of childhood obesity.

RESULT

We found that UFPs exposure was associated with greater childhood BMI Z-score and a higher likelihood of obesity. Compared with the lowest quartile, higher quartiles of UFPs were associated with greater odds for obesity prevalence in children (i.e., the adjusted OR was 1.25; 95 % CI, 1.12-1.39; 1.43; 95 % CI, 1.27-1.61; and 1.41; 95 % CI, 1.25-1.58 for the second, third, and fourth quartile, respectively). Similar associations were observed for PM₁, PM_2.5, and PM₁₀, and were greater in boys and children living close to roadways.

CONCLUSIONS

Long-term UFPs exposure was associated with a greater likelihood of childhood obesity, and stronger associations on BMI Z-score were observed in boys and children living close to roadways. This study indicates that more attention should be paid to the health effects of UFPs, and routinely monitoring of UFPs should be considered.