Long-term exposure to ambient air pollutants and their interaction with physical activity on insomnia: A prospective cohort study

Associations of non‑essential metals and their mixture with non-alcoholic fatty liver disease in Chinese older adults

BACKGROUND

Research investigating the impact of the non-essential metal (NEM) mixture on non-alcoholic fatty liver disease (NAFLD) among the elderly is presently insufficient. This study investigated the relationships between individual NEMs, their mixtures, and NAFLD in elderly individuals residing in Chinese communities.

METHODS

The analysis included 2741 participants drawn from the baseline survey of a longitudinal study. Urinary concentrations of aluminum (Al), gallium (Ga), arsenic (As), cesium (Cs), barium (Ba), thallium (Tl), uranium (U), and cadmium (Cd) were quantified using inductively coupled plasma mass spectrometry (ICP-MS). NAFLD diagnosis was determined using abdominal ultrasound imaging. Logistic regression and restricted cubic spline (RCS) models were utilized to evaluate the relationships between individual NEMs and NAFLD. Additionally, Bayesian kernel machine regression (BKMR) and quantile-based computation regression (QGC) models were employed to assess the impact of the NEM mixture on NAFLD.

RESULTS

After adjusting for covariates, Tl was significantly associated with an increased likelihood of NAFLD (OR 1.26, 95% CI 1.10-1.44). Both RCS and BKMR models confirmed a linear relationship between urine Tl and the risk of NAFLD. Additionally, both BKMR and QGC models highlighted a significant connection between the NEMs mixture and NAFLD, identifying Tl as the primary driver. Significant interactions were observed between Tl and Ba, as well as between Tl and hypertension (Pinteraction = 0.055) and Tl and central obesity (Pinteraction = 0.008), collectively demonstrating synergistic impacts on NAFLD risk.

CONCLUSIONS

The NEM mixture is associated with a higher risk of NAFLD in Chinese old adults, with Tl as the primary contributor. Additional investigation is required to validate these findings and shed light on underlying biological pathways through which co-exposure to NEMs contribute to NAFLD.

Impact of gene-environment interactions on atrial fibrillation and cardiac structure

Environmental pollution is a major burden of cardiovascular disease. The aim of the study was to investigate the interactions between combined environmental factors and genetic susceptibility on atrial fibrillation (AF) and cardiac structures. The study included 374,495 participants from the UK Biobank, utilizing genetic data and environmental variables (including air pollution, noise, greenspace and water quality). Polygenic risk score (PRS) was calculated to estimate individual genetic risk. Cox proportional hazard model was applied to estimate the impact of exposure factors on the risk of AF occurrence. The mediation analysis was applied to assess the relationship among environmental scores, AF and cardiac structures. Population attributable fraction (PAF) was employed to assess potential influence of mitigating unfavorable environment characteristics on AF. The results showed that the highest group of four domain scores exhibited 3.38-16.83% higher AF risk than the lowest. Individuals with higher scores in four domains and high PRS had hazard ratio (95%CI) of 2.76 (2.62, 2.91), 2.61 (2.47, 2.75), 2.86 (2.71, 3.02) and 2.84 (2.66, 3.02). Environmental factors could indirectly affect cardiac structures through AF. Up to 7.37% of AF cases could be preventable through environmental interventions. Our findings pointed that gene-environment interaction can increase AF risk, which further affect cardiac structures.

Association between exposure to air pollution and risk of Dyssomnia: a systematic review and meta-analysis

BACKGROUND

Typically affecting people's quality of life, dyssomnia is among the most prevalent chronic illnesses worldwide. A positive correlation between air pollution and dyssomnia has been demonstrated by epidemiological research. However, no meta-analyses evaluating the relationship between the prevalence of dyssomnia in people of all ages and air pollutants (PM2.5, PM10, NO2, SO2, and O3) were found.

OBJECTIVES

Conduct a meta-analysis utilizing data from current studies (until 2024) to provide reliable insights into the relationship between air pollution exposure and the likelihood of dyssomnia prevalence.

METHODS

We systematically searched three databases for studies on air pollution and dyssomnia up to January 15, 2024. Random-effects models were used to estimate the pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). Subgroup analyses, funnel plots, and meta-regression analyses were also performed.

RESULTS

There were 11 studies from 4 different nations that involved 3,328,183 participants in total. The odds ratios (ORs) for PM2.5 and PM10 were 1.29 (1.16-1.44) and 1.13 (1.03-1.23) per 10 µg/m3 increase in pollutants, respectively. The OR per 10 µg/m3 increment of gaseous pollutants were 1.06 (1.00-1.12) for NO2 and 1.16 (1.04-1.31) for O3. No significant association was observed between SO2 and dyssomnia. Adults are more sensitive to air pollution than children or adolescents for that the effects of PM2.5、PM10 and SO2 were significantly stronger in adults than children or adolescents. The effect of air pollution on dyssomnia was more significant in developed countries than in developing countries. There was a difference in the subgroup test for PM10 between developed and developing countries.

CONCLUSION

This meta-analysis implies the relationship between the air pollution and dyssomnia. Economic status and age may influence the effect. It was suggested to provide guidance for disease prevention and explored potential avenues for further research.

Association of air pollution with incidence of late-onset seborrhoeic dermatitis: a prospective cohort study in UK Biobank

BACKGROUND

Late-onset seborrhoeic dermatitis seriously affects patients' quality of life. Studies have shown an association between air pollution and other inflammatory skin diseases. However, associations between air pollution exposures and the incidence of late-onset seborrhoeic dermatitis have not been elucidated.

OBJECTIVES

To investigate air pollution's role in the incidence of late-onset seborrhoeic dermatitis.

METHODS

We engaged a prospective cohort analysis utilizing the UK Biobank database. Exposure data spanning various years for specific air pollutants, namely particulate matter [PM; with an aerodynamic diameter of ≤ 2.5 µm (PM2.5), between 2.5 and 10 μm (PM2.5-10), ≤ 10 μm (PM10)] along with nitrogen oxides (NO plus NO2, denoted NOx) and NO2, were incorporated. Through a composite air pollution score constructed from five pollutants and employing Cox proportional hazards models, the relationship between air pollution and seborrhoeic dermatitis was delineated.

RESULTS

Our examination of 193 995 participants identified 3363 cases of seborrhoeic dermatitis. Higher concentrations of specific pollutants, particularly in the upper quartile (Q4), were significantly linked to an elevated risk of seborrhoeic dermatitis. Notably, PM2.5, PM10, NO2 and NOx exhibited hazard ratios of 1.11, 1.15, 1.22 and 1.15, respectively. The correlation was further solidified with a positive association between air pollution score increments and onset of seborrhoeic dermatitis. Intriguingly, this association was accentuated in certain demographics, including younger men, socioeconomically deprived people, smokers, daily alcohol consumers, and those engaging in regular physical activity.

CONCLUSIONS

Our findings revealed that air pollution exposures were associated with incidence of late-onset seborrhoeic dermatitis. These results emphasize the importance of preventing environmental air pollution exposures to mitigate the risk of developing the condition.

Co-exposure to multiple air pollutants, genetic susceptibility, and the risk of myocardial infarction onset: a cohort analysis of the UK Biobank participants

AIMS

The relationship between the long-term joint exposure to ambient air pollution and incidence of myocardial infarction (MI) and modification by genetic susceptibility remain inconclusive.

METHODS AND RESULTS

We analysed 329 189 UK Biobank participants without MI at baseline. Exposure concentrations to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were obtained. Air pollution score assessing the joint exposure was calculated, and its association with MI was evaluated via Cox model under the P value aggregation framework. Genetic susceptibility to MI was evaluated by incorporating polygenic risk score (PRS) into models. Risk prediction models were also established. During a median follow-up of 13.4 years, 9993 participants developed MI. Per interquartile range increase of PM2.5, PM10, NO2, and NOx resulted in 74% [95% confidence intervals (CIs) 69%-79%], 67% (63%-72%), 46% (42%-49%), and 38% (35%-41%) higher risk of MI. Compared with the lowest quartile (Q1) of air pollution score, the multivariable adjusted hazard ratio (HR) (95%CIs) of Q4 (the highest cumulative air pollution) was 3.50 (3.29-3.72) for MI. Participants with the highest PRS and air pollution score possessed the highest risk of incident MI (HR = 4.88, 95%CIs 4.35-5.47). Integrating PRS, air pollution exposure, and traditional factors substantially improved risk prediction of MI.

CONCLUSION

Long-term joint exposure to air pollutants including PM2.5, PM10, NO2, and NOx is substantially associated with increased risk of MI. Genetic susceptibility to MI strengthens such adverse joint association. Air pollutions together with genetic and traditional factors enhance the accuracy of MI risk prediction.

Exploring pollutant joint effects in disease through interpretable machine learning

Identifying the impact of pollutants on diseases is crucial. However, assessing the health risks posed by the interplay of multiple pollutants is challenging. This study introduces the concept of Pollutants Outcome Disease, integrating multidisciplinary knowledge and employing explainable artificial intelligence (AI) to explore the joint effects of industrial pollutants on diseases. Using lung cancer as a representative case study, an extreme gradient boosting predictive model that integrates meteorological, socio-economic, pollutants, and lung cancer statistical data is developed. The joint effects of industrial pollutants on lung cancer are identified and analyzed by employing the SHAP (Shapley Additive exPlanations) interpretable machine learning technique. Results reveal substantial spatial heterogeneity in emissions from CPG and ILC, highlighting pronounced nonlinear relationships among variables. The model yielded strong predictions (an R of 0.954, an RMSE of 4283, and an R2 of 0.911) and emphasized the impact of pollutant emission amounts on lung cancer responses. Diverse joint effects patterns were observed, varying in terms of patterns, regions (frequency), and the extent of antagonistic and synergistic effects among pollutants. The study provides a new perspective for exploring the joint effects of pollutants on diseases and demonstrates the potential of AI technology to assist scientific discovery.

Physical activity attenuates the association of long-term exposure to nitrogen dioxide with sleep quality and its dimensions in Chinese rural older adults

BACKGROUND

Joint impacts of air pollution and physical activity (PA) on sleep quality remain unaddressed. We aimed to investigate whether PA attenuates the association of long-term exposure to nitrogen dioxide (NO2) with sleep quality and its dimensions in older adults.

METHODS

This study included 3408 Chinese rural older adults. Annual NO2 was estimated using the Space-Time Extra-Trees model. PA was assessed by International Physical Activity Questionnaire. Sleep quality was evaluated using Pittsburgh Sleep Quality Index (PSQI) scale. Linear regression models were used to assess the associations of long-term NO2 exposure and PA with sleep quality and its dimensions, and interaction plots were used to depict the attenuating effect of PA on associations of NO2 with sleep quality and its dimensions.

RESULTS

Three-year (3-y) average NO2 (per 0.64-μg/m3 increment) was positively associated with global PSQI (β = 0.41, 95 % CI: 0.23, 0.59), sleep duration (β = 0.16, 95 % CI: 0.11, 0.21), and habitual sleep efficiency (β = 0.22, 95 % CI: 0.17, 0.27), while PA was negatively associated with global PSQI (β = -0.33, 95 % CI: -0.46, -0.20) and five domains of PSQI other than sleep duration and sleep disturbances. The associations of NO2 with global PSQI, sleep duration, and habitual sleep efficiency were attenuated with increased PA (Pinteraction were 0.037, 0.020, and 0.079, respectively).

CONCLUSIONS

PA attenuates the adverse impacts of long-term NO2 exposure on sleep quality, especially on sleep duration, and habitual sleep efficiency, in Chinese rural elderly people. Participating in PA should be encouraged in this population, and continued efforts are still needed to reduce air pollution.

Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank

BACKGROUND

Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility.

METHODS

This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS.

RESULTS

Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score.

CONCLUSIONS

Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.