Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR)

Association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, China

To date, a limited number of studies have assessed the impact of individual and combined PM2.5 components on mumps incidence. Between 2013 and 2019, we collected data on 6270 mumps cases in Lanzhou, along with corresponding PM2.5 and its components, to analyze their temporal and spatial distributions. A generalized additive mixed model was constructed to examine the association between PM2.5 components and mumps incidence. Additionally, Bayesian kernel machine regression was used to evaluate the combined and interactive effects of co-exposure to PM2.5 components on mumps incidence and to identify key contributing components. A significant linear correlation was found between PM2.5 and mumps incidence at lag 1 month, with a relative risk (RR) of 1.85 (95 % CI: 1.14, 3.02) for each unit increase in PM2.5 (log-transformed PM2.5 concentration). Organic matter (OM) at lag 0 and 1 month, as well as black carbon (BC) at lag 1 month, were significantly positively correlated with mumps incidence. Furthermore, the joint exposure-effect curve for PM2.5 components and mumps incidence displayed an approximate V-shape. The effects of PM2.5 and its components on mumps incidence were more pronounced during the warm season. These findings suggest a significant association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, with potential variations in effect depending on the specific components of PM2.5.

Relationship between air pollution exposure and insulin resistance in Chinese middle-aged and older populations: evidence from Chinese cohort

Aims

This study aimed to determine the relationships between mixed exposure to six air pollutants, namely, particulate matter with an aerodynamic diameter of 2.5 micrometers or less (PM2.5), PM with an aerodynamic diameter of 10 micrometers or less (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), cobalt (CO) and ozone (O3), and insulin resistance (IR) indices in Chinese middle-aged and older populations.

Methods

A total of 2,219 participants from the China Health and Retirement Longitudinal Study (CHARLS), who are followed from 2011 to 2015, were included. Surface air pollutant concentration data were obtained from the China High Air Pollutants (CHAP) database. Multivariable linear regression analysis was used to examine the longitudinal associations between different air pollutants and various IR indices. Additionally, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g computation (Qgcomp) were further utilized to assess the mixed effects of the six air pollutants.

Results

Fully adjusted linear models revealed that increases in the levels of the six air pollutants (in μg/m3) were associated with higher triglyceride-glucose-body mass index (TyG-BMI; Beta = 0.027-0.128), triglyceride-glucose-waist circumference (TyG-WC; Beta = 0.155-0.674), and metabolic score for insulin resistance (METS-IR; Beta = 0.001-0.029) values during the four-year follow-up period. Further mixture analysis indicated that combined exposure to the six air pollutants had a significant cumulative effect on the increases in these three IR indices. Among the pollutants, NO2 and O3 were identified as the primary contributor to the cumulative effect. The result of mediation analysis supported the mediating role of BMI in the relationship between air pollution and IR (mediation proportion: 49.1%-93.5%). The results from both subgroup analysis and sensitivity analysis supported the detrimental effects of air pollution on IR.

Conclusion

Both individual and mixed exposures to air pollution were significantly associated with IR in Chinese middle-aged and older individuals, with our study providing new evidence.

The effect of long-term exposure to a mixture of air pollutants on chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major global cause of morbidity and mortality; however, evidence on the effects of air pollutant mixtures on COPD remains limited. This study assessed the impact of long-term exposure to multiple pollutants on COPD prevalence and identified vulnerable subgroups. We analyzed Korea National Health and Nutrition Examination Survey (2010-2017) data linked to 5-year moving average concentrations of CO, NO₂, SO₂, O₃, PM₂.₅, and PM₁₀. Bayesian kernel machine regression (BKMR) estimated the combined effects of pollutants on COPD prevalence, with subgroup analyses performed according to sex, smoking status, and airflow limitation. Adjustments included age, sex, BMI, smoking status, and household income. Among 21,804 participants, 3515 had COPD. BKMR analysis showed that long-term exposure to a pollutant mixture was associated with increased COPD prevalence. O₃ and NO₂ were identified as the most influential pollutants (posterior inclusion probabilities > 0.50). Further analysis showed a significant increase in COPD risk with higher NO₂ and O₃ concentrations, particularly when other pollutants were at lower or median levels. Significant interactions were observed, particularly between SO₂ and CO, CO and O₃, and NO₂ and O₃. Subgroup analyses identified vulnerable populations, indicating stronger associations among females and never smokers and more pronounced effects in individuals with GOLD 2-4. These findings suggest that long-term exposure to multiple air pollutants could increase COPD risk, particularly for females, never smokers, and individuals with more severe COPD. Targeted interventions and policy measures are needed to reduce exposure, especially for these at-risk populations.

Coexposure to ambient air pollution and temperature and its associations with birth outcomes in women undergoing assisted reproductive technology in Fujian, China: A retrospective cohort study

BACKGROUND

The interactions between pollutants and temperature coexposure, the mixing effects and their potential mechanisms remain uncertain.

METHODS

This retrospective cohort study included 11,766 women with infertility who received treatment at Fujian Hospital between 2015 and 2024. The daily mean concentrations of the six pollutants and the relative humidity and temperature data were acquired from the Fujian region. Data on genes were obtained from the Comparative Toxicogenomics Database.

RESULTS

O3 (aOR=0.80, 95 % CI=0.725--0.891) and temperature (aOR=0.936, 95 % CI=0.916--0.957) were negatively correlated with live birth rates. Moreover, PM10 (aOR=1.135, 95 % CI=1.028--1.252) and PM2.5 (aOR=1.146, 95 % CI=1.03--1.274) were positively associated with preterm birth. Among the effects on live births, PM2.5, PM10, NO2, CO, and SO2 had significant synergistic effects with temperature; in addition, O3 had significant antagonistic effects with temperature. A notable trend toward declining live birth rates with elevated concentrations of mixed pollutants was observed. Different infertility patients have different sensitivities to coexposure. Gene enrichment and cell experiments are associated mainly with cellular life activities.

CONCLUSIONS

Individual effects, interactions, and mixed effects between temperature and air pollutants and birth outcomes persist when air pollutant levels are relatively low. AAP may trigger miscarriage through cytotoxic effects.

Ozone exposure and cardiovascular disease: A narrative review of epidemiology evidence and underlying mechanisms

Ozone (O3) poses a significant global public health concern as it exerts adverse effects on human cardiovascular health. Nevertheless, there remains a lack of comprehensive understanding regarding the relationships between O3 exposure and the risk of cardiovascular diseases (CVD), as well as the underlying biological mechanisms. To address this knowledge gap, this narrative review meticulously summarizes the existing epidemiological evidence, susceptibility, and potential underlying biological mechanisms linking O3 exposure with CVD. An increasing body of epidemiological studies has demonstrated that O3 exposure heightens the incidence and mortality of CVD, including specific subtypes such as ischemic heart disease, hypertension, and heart failure. Certain populations display heightened vulnerability to these effects, particularly children, the elderly, obese individuals, and those with pre-existing conditions. Proposed biological mechanisms suggest that O3 exposure engenders respiratory and systemic inflammation, oxidative stress, disruption of autonomic nervous and neuroendocrine systems, as well as impairment of coagulation function, glucose, and lipid metabolism. Ultimately, these processes contribute to vascular dysfunction and the development of CVD. However, some studies have reported the absence of associations between O3 and CVD, or even potentially protective effects of O3. Inconsistencies among the literature may be attributed to inaccurate assessment of personal O3 exposure levels in epidemiologic studies, as well as confounding effects stemming from co-pollutants and temperature. Consequently, our findings underscore the imperative for further research, including the development of reliable methodologies for assessing personal O3 exposure, exploration of O3 exposure's impact on cardiovascular health, and elucidation of its biological mechanisms. These endeavors will consolidate the causal relationship between O3 and cardiovascular diseases, subsequently aiding efforts to mitigate the risks associated with O3 exposure.

Can greenspace modify the combined effects of multiple air pollutants on pulmonary tuberculosis treatment outcomes? An empirical study conducted in Zhejiang Province, China

BACKGROUND

Evidence on the combined effects of air pollutants and greenspace exposure on pulmonary tuberculosis (PTB) treatment is limited, particularly in developing countries with high levels of air pollution.

OBJECTIVE

We aimed to examine the individual and combined effects of long-term exposure to air pollutants on PTB treatment outcomes while also investigating the potential modifying effect of greenspace.

METHODS

This population-based study included 82,784 PTB cases notified in Zhejiang Province, China, from 2015 to 2019. The 24-month average concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2) before PTB diagnosis were estimated using a dataset derived from satellite-based machine learning models and monitoring stations. Greenspace exposure was assessed using the annual China Land Cover Dataset. We conducted analyses using time-varying Cox proportional hazards models and cumulative risk indices.

RESULTS

In individual effect models, each 10 µg/m3 increase in PM2.5, NO2, O3, and SO2 concentrations was associated with hazard ratios for PTB treatment success of 0.95 (95% confidence interval (CI): 0.93-0.97), 0.92 (95% CI: 0.91-0.94), 0.98 (95% CI: 0.97-0.99), and 1.52 (95% CI: 1.49-1.56), respectively. In combined effect models, long-term exposure to the combination of air pollutants was negatively associated with PTB treatment success, with a joint hazard ratio (JHR) of 0.79 (95% CI: 0.63-0.96). Among the pollutants examined, O3 contributed the most to the increased risks, followed by PM2.5 and NO2. Additionally, areas with moderate levels of greenspace showed a reduced risk (JHR = 0.81, 95% CI: 0.62-0.98) compared with the estimate from the third quantile model (JHR = 0.68, 95% CI: 0.52-0.83).

CONCLUSIONS

Combined air pollutants significantly impede successful PTB treatment outcomes, with O3 and PM2.5 accounting for nearly 75% of this detrimental effect. Moderate levels of greenspace can mitigate the adverse effects associated with combined air pollutants, leading to improved treatment success for patients with PTB.

The Effect of Physical Activity on Combined Cadmium, Lead, and Mercury Exposure

Background/Objective: Environmental exposures, such as heavy metals, can significantly affect physical activity, an important determinant of health. This study explores the effect of physical activity on combined exposure to cadmium, lead, and mercury (metals), using data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES). Methods: Physical activity was measured with ActiGraph GT3X+ devices worn continuously for 7 days, while blood samples were analyzed for metal content using inductively coupled plasma mass spectrometry. Descriptive statistics and multivariable linear regression were used to assess the impact of multi-metal exposure on physical activity. Additionally, Bayesian Kernel Machine Regression (BKMR) was applied to explore nonlinear and interactive effects of metal exposures on physical activity. Using a Gaussian process with a radial basis function kernel, BKMR estimates posterior distributions via Markov Chain Monte Carlo (MCMC) sampling, allowing for robust evaluation of individual and combined exposure-response relationships. Posterior Inclusion Probabilities (PIPs) were calculated to quantify the relative importance of each metal. Results: The linear regression analysis revealed positive associations between cadmium and lead exposure and physical activity. BKMR analysis, particularly the PIP, identified lead as the most influential metal in predicting physical activity, followed by cadmium and mercury. These PIP values provide a probabilistic measure of each metal's importance, offering deeper insights into their relative contributions to the overall exposure effect. The study also uncovered complex relationships between metal exposures and physical activity. In univariate BKMR exposure-response analysis, lead and cadmium generally showed positive associations with physical activity, while mercury exhibited a slightly negative relationship. Bivariate exposure-response analysis further illustrated how the impact of one metal could be influenced by the presence and levels of another, confirming the trends observed in univariate analyses while also demonstrating the complexity varying doses of two metals can have on either increased or decreased physical activity. Additionally, the overall exposure effect analysis across different quantiles revealed that higher levels of combined metal exposures were associated with increased physical activity, though there was greater uncertainty at higher exposure levels as the 95% credible intervals were wider. Conclusions: Overall, this study fills a critical gap by investigating the interactive and combined effects of multiple metals on physical activity. The findings underscore the necessity of using advanced methods such as BKMR to capture the complex dynamics of environmental exposures and their impact on human behavior and health outcomes.

Endocrine disruptors and bladder function: the role of phthalates in overactive bladder

Background

Phthalates, widely used as plasticizers, are pervasive environmental contaminants and endocrine disruptors. Their potential role in overactive bladder (OAB) pathogenesis is underexplored, necessitating further investigation into their impact on OAB using large-scale epidemiological data.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2011 to 2018. A weighted multivariable logistic regression model was employed to examine the relationship between urinary phthalate concentrations and OAB. Subgroup analyses were conducted to explore differences in associations across various subgroups. Restricted cubic spline (RCS) analysis was used to investigate the potential non-linear relationship between urinary phthalate concentrations and OAB. Additionally, Bayesian Kernel Machine Regression (BKMR) analysis was performed to explore the overall effects and interactions of phthalate mixtures.

Results

In the multivariable logistic regression model fully adjusted for confounding variables, higher concentrations of MBzP and MiBP were associated with an increased risk of OAB, particularly in the highest tertiles (MBzP: OR = 1.401, 95% CI: 1.108-1.771; MiBP: OR = 1.050, 95% CI: 1.045-1.056). Subgroup analysis found that subgroup characteristics did not have a significant moderating effect on the association between phthalates and OAB. RCS analysis revealed a linear relationship between both MBzP and MiBP and OAB. BKMR analysis confirmed a positive overall effect of phthalate mixtures on OAB risk, with MBzP identified as the major contributing factor.

Conclusion

In our study cohort, a positive correlation between urinary phthalate concentrations and OAB was observed, necessitating further research to validate and refine this conclusion.

Combined health effects of air pollutant mixtures on respiratory mortality using BKMR in Hangzhou, China

Previous research on respiratory system mortality primarily focused on understanding their combined effects and have neglected the fact that air pollution mixtures are interrelated. This study used Bayesian kernel machine regression (BKMR) to analyze the relationship between air pollutant mixtures and respiratory mortality in Hangzhou, China from 2014 to 2018. The results showed a significant association between pollutant mixtures and respiratory system mortality primarily driven by PM2.5 and SO2. The joint exposure of air pollutants was positively correlated with respiratory system mortality at lag 01 and lag 02 days. The estimated joint effects of log-transformed mixture air pollution exposure on log-transformed respiratory system mortality increased from -0.02 (95% CI: -0.08-0.02) and -0.01 (95% CI: -0.05-0.04) at the 25th percentile to 0.06 (95% CI: 0.01-0.12) and 0.04 (95% CI: -0.001, 0.09) at the 75th percentile. Additionally, there was evidence of an interaction between O3 and PM10. This study confirms that exposure to multiple pollutants is a significant public health problem facing the Hangzhou population given the compounded effect proven with regression analysis, while furthermore, the control of PM2.5 and SO2 also represents a serious concern.Implications: Evidence indicates interactions between O3 and PM10. This study demonstrates that exposure to multiple pollutants exerts combined effects on the public health of the Hangzhou population, highlighting the importance of controlling PM2.5 and SO2.

The individual and combined effects of polycyclic aromatic hydrocarbons on asthma among US children: evidence from the NHANES study

Polycyclic Aromatic Hydrocarbons (PAHs) exposure has been linked to asthma, but their individual and combined effects in children remain unclear. Using data from the 2003-2012 National Health and Nutrition Examination Survey (NHANES), we investigated the associations between PAHs and asthma through logistic regression, Weighted Quantile Sum (WQS) regression, Quantile g Computation (qg computation), and Bayesian Kernel Machine Regression (BKMR). Subgroup analyses revealed a significant impact of PAHs on asthma, particularly in males. The WQS model showed a marginally significant combined effect of 9 PAHs on asthma (Odds Ratio = 1.37, 95% Confidence Interval: 1.06-1.75). BKMR also indicated a positive association between combined PAH exposure and asthma. 2-Hydroxyfluorene and 1-Hydroxypyrene were identified as the most significant contributors. These findings suggest that mixed PAH exposure is associated with asthma risk in children.

Interactive and lag effects of environmental factors on the density of schistosome-transmitting Oncomelania hupensis: A twelve-year monthly repeated survey

Schistosomiasis is a significant public health threat, and Oncomelania hupensis is the only intermediate host for schistosoma japonicum. We conducted 12-year monthly repeated surveys to explore the interactive and lag effects of environmental factors on snail density and to monitor their long-term and seasonal trends in a bottomland around the Dongting Lake region in China. Relevant environmental data were obtained from multiple sources. A Bayesian kernel machine regression model and a Bayesian temporal model combined with a distributed lag model were constructed to analyze interactive and lag effects of environmental factors on snail density. The results indicated the average annual snail density in the study site exhibited an increasing and then decreasing trend, peaking in 2013. Snail densities were the highest in October and the lowest in January in a year. Normalized Difference Vegetation Index (NDVI) and water level were the most effective predictors of snail density, with potential interactions among temperature, precipitation, and NDVI. The mean minimum temperature in January, water level, precipitation and NDVI were positively correlated with snail density at lags ranging from 1 to 4 months. These findings could serve as references for relevant authorities to monitor the changing trend of snail density and implement control measures, thereby reducing the occurrence of schistosomiasis.

Associations of co-exposure to polycyclic aromatic hydrocarbons and lead (Pb) with IGF1 methylation in peripheral blood of preschool children from an e-waste recycling area

BACKGROUND

Childhood exposure to polycyclic aromatic hydrocarbons (PAHs) or lead (Pb) is associated with epigenetic modifications. However, the effects of their co-exposures on IGF1 (Insulin-like growth factor 1) methylation and the potential role in child physical growth are unclear.

METHODS

From our previous children study (N = 238, ages of 3-6), 75 children with higher total concentrations of urinary ten hydroxyl PAH metabolites (∑10OH-PAHs) from an e-waste recycling area, Guiyu, and 75 with lower ∑10OH-PAHs from Haojiang (reference area) were included. Pb and IGF1 P2 promoter methylation in peripheral blood were also measured. Multivariable linear regression analyses were performed to estimate individual associations, overall effects and interactions of co-exposure to OH-PAHs and Pb on IGF1 methylation were further explored using Bayesian kernel machine regression.

RESULTS

Methylation of IGF1 (CG-232) was lower (38.00 vs. 39.74 %, P < 0.001), but of CG-207 and CG-137 were higher (59.94 vs. 58.41 %; 57.60 vs. 56.28 %, both P < 0.05) in exposed children than the reference. The elevated urinary 2-OHPhe was associated with reduced methylation of CG-232 (B = -0.051, 95 % CI: -0.096, -0.005, P < 0.05), whereas blood Pb was positively associated with methylation of CG-108 (B = 0.106, 95 %CI: 0.013, 0.199, P < 0.05), even after full adjustment. Methylations of CG-224 and 218 significantly decreased when all OH-PAHs and Pb mixtures were set at 35th - 40th and 45th - 55th percentile compared to when all fixed at 50th percentile. There were bivariate interactions of co-exposure to the mixtures on methylations of CG-232, 224, 218, and 108. Methylations correlated with height, weight, were observed in the exposed children.

CONCLUSIONS

Childhood co-exposure to high PAHs and Pb from the e-waste may be associated with IGF1 promoter methylation alterations in peripheral blood. This, in turn, may interrupt the physical growth of preschool children.

Air quality and kidney health: Assessing the effects of PM10, PM2.5, CO, and NO2 on renal function in primary glomerulonephritis

BACKGROUND

While extensive studies have elucidated the relationships between exposure to air pollution and chronic diseases, such as cardiovascular disorders and diabetes, the intricate effects on specific kidney diseases, notably primary glomerulonephritis (GN)-an immune-mediated kidney ailment-are less well understood. Considering the escalating incidence of GN and conspicuous lack of investigative focus on its association with air quality, investigation is dedicated to examining the long-term effects of air pollutants on renal function in individuals diagnosed with primary GN.

METHODS

This retrospective cohort analysis was conducted on 1394 primary GN patients who were diagnosed at Seoul National University Bundang Hospital and Seoul National University Hospital. Utilizing time-varying Cox regression and linear mixed models (LMM), we examined the effect of yearly average air pollution levels on renal function deterioration (RFD) and change in estimated glomerular filtration rate (eGFR). In this context, RFD is defined as sustained eGFR of less than 60 mL/min per 1.73 m2.

RESULTS

During a mean observation period of 5.1 years, 350 participants developed RFD. Significantly, elevated interquartile range (IQR) levels of air pollutants-including PM10 (particles ≤10 micrometers, HR 1.389, 95 % CI 1.2-1.606), PM2.5 (particles ≤2.5 micrometers, HR 1.353, 95 % CI 1.162-1.575), CO (carbon monoxide, HR 1.264, 95 % CI 1.102-1.451), and NO2 (nitrogen dioxide, HR 1.179, 95 % CI 1.021-1.361)-were significantly associated with an increased risk of RFD, after factoring in demographic and health variables. Moreover, exposure to PM10 and PM2.5 was associated with decreased eGFR.

CONCLUSIONS

This study demonstrates a substantial link between air pollution exposure and renal function impairment in primary GN, accentuating the significance of environmental determinants in the pathology of immune-mediated kidney diseases.

Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

BACKGROUND

Extensive evidence indicates that both lifestyle factors and air pollution are strongly associated with all-cause mortality. However, little studies in this field have integrated these two factors in order to examine their relationship with mortality and explore potential interactions.

METHODS

A cohort of 271,075 participants from the UK Biobank underwent analysis. Lifestyles in terms of five modifiable factors, namely smoking, alcohol consumption, physical activity, diet, and sleep quality, were classified as unhealthy (0-1 score), general (2-3 score), and healthy (4-5 score). Air pollution, including particle matter with a diameter ≤ 2.5 μm (PM2.5), particulate matter with a diameter ≤ 10 μm (PM10), particulate matter with a diameter 2.5-10 μm (PM2.5-10), nitrogen dioxide (NO2), and nitrogen oxides (NOx), was divided into three levels (high, moderate, and low) using Latent Profile Analysis (LPA). Cox proportional hazard regression analysis was performed to examine the links between lifestyle, air pollution, and all-cause mortality before and after adjustment for potential confounders. Restricted cubic spline curves featuring three knots were incorporated to determine nonlinear relationships. The robustness of the findings was assessed via subgroup and sensitivity analyses.

RESULTS

With unhealthy lifestyles have a significantly enhanced risk of death compared to people with general lifestyles (HR = 1.315, 95% CI, 1.277-1.355), while people with healthy lifestyles have a significantly lower risk of death (HR = 0.821, 95% CI, 0.785-0.858). Notably, the difference in risk between moderate air pollution and mortality risk remained insignificant (HR = 0.993, 95% CI, 0.945-1.044). High air pollution, on the other hand, was independently linked to increased mortality risk as compared to low air pollution (HR = 1.162, 95% CI, 1.124-1.201). The relationship between NOx, PM10, and PM2.5-10 and all-cause mortality was found to be nonlinear (p for nonlinearity < 0.05). Furthermore, no significant interaction was identified between lifestyle and air pollution with respect to all-cause mortality.

CONCLUSIONS

Exposure to ambient air pollution elevated the likelihood of mortality from any cause, which was impacted by individual lifestyles. To alleviate this hazard, it is crucial for authorities to escalate environmental interventions, while individuals should proactively embrace and sustain healthy lifestyles.

Air pollution mixture associated with oxidative stress exacerbation and symptoms deterioration in allergic rhinitis patients: Evidence from a panel study

With allergic rhinitis (AR) on the rise globally, there has been a growing focus on the role of environmental pollutants in the onset of AR. However, the potential mechanisms by how and which these pollutants exacerbate AR conditions remain unknown. This panel study of 49 patients diagnosed with AR over one year aimed to assess the individual and combined effects of short-term exposure to multiple ambient pollutants on oxidative stress, symptoms, and quality of life among patients with AR. All participants underwent four repeated assessments of health conditions and personal environmental exposures (PM2.5, O3, SO2, and NO2) over warm and cold seasons during 2017-2018. We evaluated two oxidative stress biomarkers (malondialdehyde [MDA], and superoxide dismutase [SOD]) via nasal lavage. We collected information on self-reported symptoms and quality of life using the Rhinitis Symptom Scale (SRS), the Visual Analog Scale (VAS), and the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) through in-person interviews. Bayesian kernel machine regression (BKMR) was used to evaluate the joint effects of pollutant mixture and identify key contributors. The results revealed a significant association of the pollutant mixture when all four pollutants were at or above their median levels, with increased oxidative stress. This was evidenced by elevated MDA and reduced SOD. We found a joint detrimental effect of the pollutant mixture on AR symptoms with a strong association with increased SRS scores, but a non-significant positive association with VAS and RQLQ scores. PM2.5, O3, and SO2 presented as the potentially primary contributors to the adverse health effects associated with the pollutant mixture in Taiyuan city. Patients with AR exposed to short-term air pollutant mixture are more likely to have greater nasal symptoms and worse quality of life from increased oxidative stress and reduced antioxidant capacity. Further research is warranted to better elucidate the underlying mechanisms.

Air pollution and survival in patients with malignant mesothelioma and asbestos-related lung cancer: a follow-up study of 1591 patients in South Korea

BACKGROUND

Despite significant advancements in treatments such as surgery, radiotherapy, and chemotherapy, the survival rate for patients with asbestos-related cancers remains low. Numerous studies have provided evidence suggesting that air pollution induces oxidative stress and inflammation, affecting acute respiratory diseases, lung cancer, and overall mortality. However, because of the high case fatality rate, there is limited knowledge regarding the effects of air pollution exposures on survival following a diagnosis of asbestos-related cancers. This study aimed to determine the effect of air pollution on the survival of patients with malignant mesothelioma and asbestos-related lung cancer.

METHODS

We followed up with 593 patients with malignant mesothelioma and 998 patients with lung cancer identified as asbestos victims between 2009 and 2022. Data on five air pollutants-sulfur dioxide, carbon monoxide, nitrogen dioxide, fine particulate matter with a diameter < 10 μm, and fine particulate matter with a diameter < 2.5 μm-were obtained from nationwide atmospheric monitoring stations. Cox proportional hazard models were used to estimate the association of cumulative air pollutant exposure with patient mortality, while adjusting for potential confounders. Quantile-based g-computation was used to assess the combined effect of the air pollutant mixture on mortality.

RESULTS

The 1-, 3-, and 5-year survival rates for both cancer types decreased with increasing exposure to all air pollutants. The estimated hazard ratios rose significantly with a 1-standard deviation increase in each pollutant exposure level. A quartile increase in the pollutant mixture was associated with a 1.99-fold increase in the risk of malignant mesothelioma-related mortality (95% confidence interval: 1.62, 2.44). For lung cancer, a quartile increase in the pollutant mixture triggered a 1.87-fold increase in the mortality risk (95% confidence interval: 1.53, 2.30).

CONCLUSION

These findings support the hypothesis that air pollution exposure after an asbestos-related cancer diagnosis can negatively affect patient survival.

Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study

BACKGROUND

Blood homocysteine (Hcy) level has become a sensitive indicator in predicting the development of cardiovascular disease. Studies have shown an association between individual mineral intake and blood Hcy levels. The effect of mixed minerals' intake on blood Hcy levels is unknown.

METHODS

Data were obtained from the baseline survey data of the Shanghai Suburban Adult Cohort and Biobank(SSACB) in 2016. A total of 38273 participants aged 20-74 years met our inclusion and exclusion criteria. Food frequency questionnaire (FFQ) was used to calculate the intake of 10 minerals (calcium, potassium, magnesium, sodium, iron, zinc, selenium, phosphorus, copper and manganese). Measuring the concentration of Hcy in the morning fasting blood sample. Traditional regression models were used to assess the relationship between individual minerals' intake and blood Hcy levels. Three machine learning models (WQS, Qg-comp, and BKMR) were used to the relationship between mixed minerals' intake and blood Hcy levels, distinguishing the individual effects of each mineral and determining their respective weights in the joint effect.

RESULTS

Traditional regression model showed that higher intake of calcium, phosphorus, potassium, magnesium, iron, zinc, copper, and manganese was associated with lower blood Hcy levels. Both Qg-comp and BKMR results consistently indicate that higher intake of mixed minerals is associated with lower blood Hcy levels. Calcium exhibits the highest weight in the joint effect in the WQS model. In Qg-comp, iron has the highest positive weight, while manganese has the highest negative weight. The BKMR results of the subsample after 10,000 iterations showed that except for sodium, all nine minerals had the high weights in the joint effect on the effect of blood Hcy levels.

CONCLUSION

Overall, higher mixed mineral's intake was associated with lower blood Hcy levels, and each mineral contributed differently to the joint effect. Future studies are available to further explore the mechanisms underlying this association, and the potential impact of mixed minerals' intake on other health indicators needs to be further investigated. These efforts will help provide additional insights to deepen our understanding of mixed minerals and their potential role in health maintenance.

Association between exposure to a mixture of organochlorine pesticides and hyperuricemia in U.S. adults: A comparison of four statistical models

The association between the exposure of organochlorine pesticides (OCPs) and serum uric acid (UA) levels remained uncertain. In this study, to investigate the combined effects of OCP mixtures on hyperuricemia, we analyzed serum OCPs and UA levels in adults from the National Health and Nutrition Examination Survey (2005-2016). Four statistical models including weighted logistic regression, weighted quantile sum (WQS), quantile g-computation (QGC), and bayesian kernel machine regression (BKMR) were used to assess the relationship between mixed chemical exposures and hyperuricemia. Subgroup analyses were conducted to explore potential modifiers. Among 6,529 participants, the prevalence of hyperuricemia was 21.15%. Logistic regression revealed a significant association between both hexachlorobenzene (HCB) and trans-nonachlor and hyperuricemia in the fifth quintile (OR: 1.54, 95% CI: 1.08-2.19; OR: 1.58, 95% CI: 1.05-2.39, respectively), utilizing the first quintile as a reference. WQS and QGC analyses showed significant overall effects of OCPs on hyperuricemia, with an OR of 1.25 (95% CI: 1.09-1.44) and 1.20 (95% CI: 1.06-1.37), respectively. BKMR indicated a positive trend between mixed OCPs and hyperuricemia, with HCB having the largest weight in all three mixture analyses. Subgroup analyses revealed that females, individuals aged 50 years and above, and those with a low income were more vulnerable to mixed OCP exposure. These results highlight the urgent need to protect vulnerable populations from OCPs and to properly evaluate the health effects of multiple exposures on hyperuricemia using mutual validation approaches.

Associations between co‑exposure to heavy metals and vertebral compression fracture, as well as femoral neck bone mineral density: A cross-sectional study from NHANES data

OBJECTIVE

Accumulating evidence showed that exposure to heavy metals was harmful to human health. Little is known regarding the mixing effects of multiple metal exposures on vertebral compression fracture (VCF) and femoral neck bone mineral density (BMD). This study aimed to explore the individual and joint effects of four heavy metals [manganese (Mn), lead (Pb), cadmium (Cd) and mercury (Hg)] on VCF risk and femoral neck BMD.

METHODS

This cross-sectional study included 1,007 eligible individuals with vertebral fractures from National Health and Nutrition Examination Survey 2013-2014. The outcome was the risk of VCF and femoral neck BMD. Weighted multivariate logistic regression was used to explore the individual effect of four heavy metals on the VCF risk, separately. Weighted multivariate linear regression was used to explore the individual effect of four heavy metals on the femoral neck BMD, separately. Adopted bayesian kernel machine regression (BKMR) model and quantile-based g computation (qgcomp) to examine the joint effects of four heavy metals on the VCF risk and femoral neck BMD.

RESULTS

Among the population, 57 individuals developed VCF. After adjusting covariates, we found no statistical differences regarding the individual effects of four heavy metals on the risk of VCF. BKMR model and qgcomp indicated that there were no statistical differences regarding the joint effects between four heavy metals on the VCF risk. In addition, we found that Cd was associated with femoral neck BMD, and an increase in the mixture of heavy metal exposures was associated with a decreased risk of femoral neck BMD.

CONCLUSION

No significant correlation was observed between co-exposure to Mn, Pb, Cd and Hg and VCF risk. But co-exposure to Mn, Pb, Cd and Hg may be associated with femoral neck BMD.

The association between multi-heavy metals exposure and lung function in a typical rural population of Northwest China

BACKGROUND

Heavy metal exposure is acknowledged to be associated with decrease of lung function, but the relationship between metals co-exposure and lung function in rural areas of Northwest China remains unclear, particularly in an area famous for heavy metal pollution and solid fuel use. Therefore, the purpose of this study is to explore the effects of heavy metal exposure on lung function and the potential impacts of living habit in a rural cohort of Northwest China.

METHODS

The study area included five villages of two regions in Northwestern China-Gansu province. All participants were recruited from the Dongdagou-Xinglong (DDG-XL) rural cohort in the study area. Urine levels of 10 common and representative heavy metals were detected by ICP-MS, including Cobalt (Co), Nickel (Ni), Molybdenum (Mo), Cadmium (Cd), Stibium (Sb), Copper (Cu), Zinc (Zn), Mercury (Hg), Lead (Pb), and Manganese (Mn). The lung function was detected by measuring percentages of predicted forced vital capacity (FVC%) and predicted forced expiratory volume in one second (FEV1%) as well as the ratio of FEV1/FVC. We also analyzed the association between heavy metals and pulmonary ventilation dysfunction (PVD). Restricted cubic spline, logistic regression, linear regression, and bayesian kernel machine regression (BKMR) model were used to analyze the relationship between heavy metal exposure and lung function.

RESULTS

Finally, a total of 382 participants were included in this study with an average age of 56.69 ± 7.32 years, and 82.46% of them used solid fuels for heating and cooking. Single metal exposure analysis showed that the higher concentration of Hg, Mn, Sb, and lower Mo may be risk factors for PVD. We also found that FEV1% and FVC% were negatively correlated with Sb, Hg, and Mn, but positively correlated with Mo. The effect of mixed heavy metals exposure could be observed through BKMR model, through which we found the lung function decreased with the increase of heavy metal concentration. Furthermore, the males, BMI ≥ 24 kg/m2 and who used solid fuels showed a higher risk of PVD when exposed to Co, Zn, and Hg.

CONCLUSIONS

Our results suggested that heavy metal exposure was associated with decrease of lung function regardless of single exposure or mixed exposure, particularly for Sb, Hg, Mn and those who use solid fuels.

Associations between Poorer Mental Health with Work-Related Effort, Reward, and Overcommitment among a Sample of Formal US Solid Waste Workers during the COVID-19 Pandemic

Background

Effort-reward imbalance (ERI) and overcommitment at work have been associated poorer mental health. However, nonlinear and nonadditive effects have not been investigated previously.

Methods

The association between effort, reward, and overcommitment with odds of poorer mental health was examined among a sample of 68 formal United States waste workers (87% male). Traditional, logistic regression and Bayesian Kernel machine regression (BKMR) modeling was conducted. Models controlled for age, education level, race, gender, union status, and physical health status.

Results

The traditional, logistic regression found only overcommitment was significantly associated with poorer mental health (IQR increase: OR = 6.7; 95% CI: 1.7 to 25.5) when controlling for effort and reward (or ERI alone). Results from the BKMR showed that a simultaneous IQR increase in higher effort, lower reward, and higher overcommitment was associated with 6.6 (95% CI: 1.7 to 33.4) times significantly higher odds of poorer mental health. An IQR increase in overcommitment was associated with 5.6 (95% CI: 1.6 to 24.9) times significantly higher odds of poorer mental health when controlling for effort and reward. Higher effort and lower reward at work may not always be associated with poorer mental health but rather they may have an inverse, U-shaped relationship with mental health. No interaction between effort, reward, or overcommitment was observed.

Conclusion

When taking into the consideration the relationship between effort, reward, and overcommitment, overcommitment may be most indicative of poorer mental health. Organizations should assess their workers' perceptions of overcommitment to target potential areas of improvement to enhance mental health outcomes.

Serum Nutritional Biomarkers and All-Cause and Cause-Specific Mortality in U.S. Adults with Metabolic Syndrome: The Results from National Health and Nutrition Examination Survey 2001-2006

BACKGROUND

There is limited research on the associations between serum nutritional biomarkers and mortality risk in patients with metabolic syndrome (MetS). Existing studies merely investigated the single-biomarker effect. Thus, this study aimed to investigate the combined effect of nutritional biomarker mixtures and mortality risk using the Bayesian kernel machine regression (BKMR) model in patients with MetS.

METHODS

We included the MetS patients, defined according to the 2018 Guideline on the Management of Blood Cholesterol from the National Health and Nutrition Examination Survey (NHANES) 2001-2006. A total of 20 serum nutritional biomarkers were measured and evaluated in this study. The Cox proportional hazard model and restricted cubic spline models were used to evaluate the individual linear and non-linear association of 20 nutritional biomarkers with mortality risk. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixture of nutritional biomarkers and mortality risk.

RESULTS

A total of 1455 MetS patients had a median age of 50 years (range: 20-85). During a median of 17.1-year follow-up, 453 (24.72%) died: 146 (7.20%) caused by CVD and 87 (5.26%) by cancer. Non-linear and linear analyses indicated that, in total, eight individual biomarkers (α-carotene, β-carotene, bicarbonate, lutein/zeaxanthin, lycopene, potassium, protein, and vitamin A) were significantly associated with all-cause mortality (all p-values < 0.05). Results from BKMR showed an association between the low levels of the mixture of nutritional biomarkers and high risk of all-cause mortality with the estimated effects ranging from 0.04 to 0.14 (referent: medians). α-Carotene (PIP = 0.971) and potassium (PIP = 0.796) were the primary contributors to the combined effect of the biomarker mixture. The nutritional mixture levels were found to be negatively associated with the risk of cardiovascular disease (CVD) mortality and positively associated with the risk of cancer mortality. After it was stratified by nutrients, the mixture of vitamins showed a negative association with all-cause and CVD mortality, whereas the mixture of mineral-related biomarkers was positively associated with all-cause and cancer mortality.

CONCLUSION

Our findings support the evidence that nutritional status was associated with long-term health outcomes in MetS patients. It is necessary for MetS patients to be concerned with certain nutritional status (i.e., vitamins and mineral elements).

Are there joint effects of different air pollutants and meteorological factors on mental disorders? A machine learning approach

Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM_2.5, PM₁₀, O₃, SO₂, and NO₂), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10 μg/m³ increase in O₃, PM_2.5 and PM₁₀ exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O₃ and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O₃ and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.

Association between combined exposure to plasma heavy metals and dyslipidemia in a chinese population

BACKGROUND

Exposure to heavy metals in the environment is widespread, while the relationship between combined exposure to heavy metals and dyslipidemia is unclear.

METHODS

A cross-sectional study was performed, and 3544 participants aged 30 years or older were included in the analyses. Heavy metal concentrations in plasma were based on inductively coupled plasma‒mass spectrometry. The relationship between heavy metals and dyslipidemia was estimated by logistic regression. BKMR was used to evaluate metal mixtures and their potential interactions.

RESULTS

In logistic regression analysis, participants in the fourth quartile of Fe and Zn (Fe > 1352.38 µg/L; Zn > 4401.42 µg/L) had a relatively higher risk of dyslipidemia (Fe, OR = 1.13, 95% CI: 0.92,1.38; Zn, OR = 1.30, 95% CI: 1.03,1.64). After sex stratification, females in the third quartile of plasma Zn (1062.05-4401.42 µg/L) had a higher relative risk of dyslipidemia (OR = 1.75, 95% CI: 1.28, 2.38). In BKMR analysis, metal mixtures were negatively associated with dyslipidemia in females when all metal concentrations were above the 50th percentile. In the total population (estimated from 0.030 to 0.031), As was positively associated with dyslipidemia when other metals were controlled at the 25th, 50th, or 75th percentile, respectively, and As was below the 75th percentile. In females (estimated from - 0.037 to -0.031), Zn was negatively associated with dyslipidemia when it was above the 50th percentile.

CONCLUSION

This study indicated that As was positively associated with dyslipidemia and that Zn may be negatively associated with dyslipidemia in females. Combined metal exposure was negatively associated with dyslipidemia in females. Females with low plasma Zn levels are more likely to develop dyslipidemia and should receive more clinical attention in this population.

Association between Perfluoroalkyl and Polyfluoroalkyl Substances and Women's Infertility, NHANES 2013-2016

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are widely used in consumer products. However, the role of PFAS in infertility is still poorly understood. A total of 788 women from the 2013-2016 nationally representative NHANES were included to explore the association between PFAS exposure and self-reported infertility. Six PFAS, including PFDE, PFNA, PFHxS, n-PFOA, n-PFOS, and Sm-PFOS, were detected by online SPE-HPLC-TIS-MS/MS. We used the generalized linear regression model (GLM), generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) to assess the single effects, non-linear relationships, and mixed effects on women's infertility, respectively. The prevalence of self-reported infertility was 15.54% in this study. In GLM, n-PFOA showed a negative association with self-reported infertility in women for the Q3 (OR: 0.396, 95% CI: 0.119, 0.788) and Q4 (OR: 0.380, 95% CI: 0.172-0.842) compared with Q1 (p for trend = 0.013). A negative trend was also observed in n-PFOS and ∑PFOS (p for trend < 0.05). In GAM, a non-linear relationship was revealed in Sm-PFOS, which exhibits a U-shaped relationship. The BKMR model indicated that there might be a joint effect between PFAS and women's infertility, to which PFNA contributed the highest effect (PIP = 0.435). Moreover, age stratification analysis showed a different dose-response curve in under and above 35 years old. Women under the age of 35 have a more noticeable U-shaped relationship with infertility. Therefore, the relatively low level of mixed PFAS exposure was negatively associated with self-reported infertility in women in general, and the impact of PFAS on infertility may vary among women of different age groups. Further studies are needed to determine the etiological relationship.

Co-exposure to multiple air pollutants and sleep disordered breathing in patients with or without obstructive sleep apnea: A cross-sectional study

BACKGROUND

Air pollution may be a contributing risk factor for obstructive sleep apnea (OSA). However, the health effects of co-exposure to multiple air pollutants on OSA patients remain unclear.

OBJECTIVES

To assess the joint effect of multi-pollutant on sleep disordered breathing (SDB) parameters in patients with or without OSA and identify the dominant pollutants.

METHODS

A total of 2524 outpatients from April 2020 to May 2021 were recruited in this cross-sectional study. Ambient air pollutant data were obtained from the nearest central monitoring stations to participants' residential address. SDB parameters were measured by the ApneaLink devices, including apnea-hypopnea index (AHI), hypopnea index (HI), oxygen desaturation index (ODI), average oxygen saturation (SpO₂), percentage sleep time with <90% saturation (T90), and desaturation. Bayesian kernel machine regression (BKMR) was applied to evaluate the effects of multiple pollutants.

RESULTS

Significant associations were observed between air pollutants and SDB parameters (including increases in AHI, HI, ODI, and desaturation) among patients with OSA. Co-exposure to air pollutants was positively correlated with AHI, HI, and ODI. PM₁₀ and O₃ dominated the effects of pollutant mixtures on OSA, with the highest posterior inclusion probability (PIP) values of 0.592 and 0.640, respectively. Stratified analysis showed that, compared to male patients with OSA, stronger effects on the SDB parameters were observed in female patients. Stronger associations were also found in the warm season than those in the cold season.

CONCLUSION

Co-exposure to air pollutants was associated with SDB parameters among patients with OSA, PM₁₀ and O₃ might play the dominant roles.

Network Dynamics in Elemental Assimilation and Metabolism

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.