Use of the Adaptive LASSO Method to Identify PM2.5 Components Associated with Blood Pressure in Elderly Men: The Veterans Affairs Normative Aging Study

Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study

BACKGROUND

Ventilator-associated pneumonia (VAP) is one of the most important hospital acquired infections in patients requiring mechanical ventilation (MV) in the intensive care unit, but the effective and robust predictable tools for VAP prevention were relatively lacked.

METHODS

This study aimed to establish a weighted risk scoring system to examine VAP risk among a two-stage VAP case-control study, and to evaluate the diagnostic performance of risk factor score (RFS) for VAP. We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. Finally, the diagnostic performance of the RFS was testified and further validated in another 177 pairs of VAP case-control study.

RESULTS

LASSO, RF and XGBoost consistently revealed significant associations of length of stay before MV, MV time, surgery, tracheotomy, multiple drug resistant organism infection, C-reactive protein, PaO2, and APACHE II score with VAP. RFS was significantly linearly associated with VAP risk [odds ratio and 95 % confidence interval = 2.699 (2.347, 3.135)], and showed good discriminations for VAP both in discovery stage [area under the curve (AUC) = 0.857] and validation stage (AUC = 0.879).

CONCLUSIONS

Results of this study revealed co-occurrence of multiple predictors for VAP risk. The risk factor scoring system proposed is a potentially useful predictive tool for clinical targets for VAP prevention.

Meeting clean air targets could reduce the burden of hypertension among women of reproductive age in India

BACKGROUND

Air pollution is one of the leading risk factors for hypertension globally. However, limited epidemiological evidence exists in developing countries, specifically with indigenous health data and for fine particulate matter (PM2.5) composition. Here, we addressed this knowledge gap in India.

METHODS

Using a logistic regression model, we estimated the association between hypertension (systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg) prevalence among women of reproductive age (WRA, 15-49 years) from the fifth round of the National Family Health Survey and long-term exposure to PM2.5 and its composition, after adjusting for confounders. We also explored the moderating effects of socioeconomic indicators through a multiplicative interaction with PM2.5.

RESULTS

Hypertension prevalence increased by 5.2% (95% uncertainty interval: 4.8%-5.7%) for every 10 μg/m3 increase in ambient PM2.5 exposure. Significant moderating effects were observed among smokers against nonsmokers and for various sociodemographic parameters. Among PM2.5 species, every interquartile range increase in black carbon (BC) and sulphate exposure was significantly associated with higher odds of hypertension than for organic carbon and dust. We estimated that achieving the National Clean Air Program target and World Health Organization air quality guidelines can potentially reduce hypertension prevalence by 2.42% and 4.21%, respectively.

CONCLUSION

Our results demonstrate that increasing ambient PM2.5 exposure is associated with a higher prevalence of hypertension among WRA in India. The risk is not uniform across various PM2.5 species and is higher with BC and sulphate. Achieving clean air targets can substantially reduce the hypertension burden in this population.

Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation

BACKGROUND

The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear.

METHOD

A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively.

RESULT

PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP.

CONCLUSION

Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

New findings on the risk of hypertension from organophosphorus exposure under different glycemic statuses: The key role of lipids?

BACKGROUND AND OBJECTIVE

Considering the widespread use of organophosphorus pesticides (OPs) and the global prevalence of hypertension (HTN), as well as studies indicating that different glycemic statuses may respond differently to the biological effects of OPs. Therefore, this study, based on the Henan rural cohort, aims to investigate the association between OPs exposure and HTN, and further explores whether lipids mediate these associations.

METHODS

We measured the plasma levels of OPs in 2730 participants under different glycemic statuses using gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS). A generalized linear model, Quantile g-computation (QGC), adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) models were used to assess the impact of OPs exposure on HTN, with least absolute shrinkage and selection operator (LASSO) penalty regression identifying main OPs. Mediation models were used to evaluate the intermediary role of blood lipids in the OPs-HTN relationship.

RESULTS

The detection rates for all OPs were high, ranging from 76.35 % to 99.17 %. In the normal glucose tolerance (NGT) population, single exposure models indicated that malathion and phenthoate were associated with an increased incidence of HTN (P-FDR < 0.05), with corresponding odds ratios (ORs) and 95 % confidence intervals (CIs) of 1.624 (1.167,2.260) and 1.290 (1.072,1.553), respectively. QGC demonstrated a positive association between OP mixtures and HTN, with malathion and phenthoate being the primary contributors. Additionally, the AENET model's Exposure Response Score (ERS) suggested that the risk of HTN increases with higher ERS (P < 0.001). Furthermore, BKMR revealed that co-exposure to OPs increases HTN risk, with phenthoate having a significant impact. Furthermore, triglycerides (TG) mediated 6.55 % of the association between phenthoate and HTN. However, no association was observed in the impaired fasting glucose (IFG) and type 2 diabetes mellitus (T2DM) populations.

CONCLUSIONS

Our findings suggest that in the NGT population, OPs may significantly contribute to the development of HTN, proposing TG as a potential novel target for HTN prevention.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules

With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.

Long-term exposure to ambient PM2.5 and its constituents is associated with MAFLD

Background & Aims

Existing evidence suggests that long-term exposure to ambient fine particulate pollution (PM2.5) may increase metabolic dysfunction-associated fatty liver disease (MAFLD) risk. However, there is still limited evidence on the association of PM2.5 constituents with MAFLD. Therefore, this study explores the associations between the five main chemical constituents of PM2.5 and MAFLD to provide more explicit information on the liver exposome.

Methods

A total of 76,727 participants derived from the China Multi-Ethnic Cohort, a large-scale epidemic survey in southwest China, were included in this study. Multiple linear regression models were used to estimate the pollutant-specific association with MAFLD. Weighted quantile sum regression was used to evaluate the joint effect of the pollutant-mixture on MAFLD and identify which constituents contribute most to it.

Results

Three-year exposure to PM2.5 constituents was associated with a higher MAFLD risk and more severe liver fibrosis. Odds ratios for MAFLD were 1.480, 1.426, 1.294, 1.561, 1.618, and 1.368 per standard deviation increase in PM2.5, black carbon, organic matter, ammonium, sulfate, and nitrate, respectively. Joint exposure to the five major chemical constituents was also positively associated with MAFLD (odds ratio 1.490, 95% CI 1.360-1.632). Nitrate contributed most to the joint effect of the pollutant-mixture. Further stratified analyses indicate that males, current smokers, and individuals with a high-fat diet might be more susceptible to ambient PM2.5 exposure than others.

Conclusions

Long-term exposure to PM2.5 and its five major chemical constituents may increase the risk of MAFLD. Nitrate might contribute most to MAFLD, which may provide new clues for liver health. Males, current smokers, and participants with high-fat diets were more susceptible to these associations.

Impact and implications

This large-scale epidemiologic study explored the associations between constituents of fine particulate pollution (PM2.5) and metabolic dysfunction-associated fatty liver disease (MAFLD), and further revealed which constituents play a more important role in increasing the risk of MAFLD. In contrast to previous studies that examined the effects of PM2.5 as a whole substance, this study carefully explored the health effects of the individual constituents of PM2.5. These findings could (1) help researchers to identify the specific particles responsible for hepatotoxicity, and (2) indicate possible directions for policymakers to efficiently control ambient air pollution, such as targeting the sources of nitrate pollution.

Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

BACKGROUND

Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources.

METHODS

We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000-2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95% of the variation of the DNA methylation data. We also performed regional and pathway analyses to identify significant regions and pathways.

RESULTS

We included 669 men with 1,178 visits between 2000-2013. The subjects had a mean age of 75 years. The identified probes, regions, and pathways varied by PMCs and their sources. For example, iron was associated with 6 probes and 6 regions, whereas nitrate was associated with 15 probes and 3 regions. The identified pathways from biomass burning, coal burning, and heavy fuel oil combustion sources were associated with cancer, inflammation, and cardiovascular diseases, whereas there were no pathways associated with all traffic.

CONCLUSIONS

Our findings showed that the effects of PM2.5 on DNAm varied by its PMCs and sources.

The role of systemic inflammation and oxidative stress in the association of particulate air pollution metal content and early cardiovascular damage: A panel study in healthy college students

Exposure to fine particulate matter (PM_2.5) has been associated with adverse cardiovascular outcomes. However, the effects of toxic metals in PM_2.5 on cardiovascular health remain unknown. To investigate the early cardiovascular effects of specific PM_2.5 metal constituents at the personal level, we conducted a panel study on 45 healthy college students in Caofeidian, China. Personal exposure concentrations and cardiovascular effect markers were monitored simultaneously within one year in four study periods. Four linear mixed-effects models were used to analyze the relationship between personal exposure to PM_2.5 and 15 metal fractions (Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, and Pb) with soluble CD36 (sCD36), C-reactive protein (CRP), and oxidized low-density lipoprotein (OX-LDL) levels, heart rate, and blood pressure. The concentrations of most individual metals (Mn, Cu, Zn, As, Se, Mo, Cd, Sb and Pb) were the highest in winter. Meanwhile, there were significant differences in inflammatory (sCD36 and CRP) and oxidative stress (OX-LDL) markers in the serum of participants over the four seasons. In particular, the estimated effects of personal metal exposure (such as V, As, Se, Cd, and Pb) on sCD36 and pulse pressure (PP) levels were consistently significant across the four LME models. A significant mediating role of sCD36 was also found in the relationship between personal exposure to Zn and Cr and changes in PP levels. Our findings provide clues and potential mechanisms regarding the cardiovascular effects of specific toxic constituents of PM_2.5 in healthy young adults.

Using exterior housing conditions to predict elevated pediatric blood lead levels

Housing-based lead paint dust is the most common source of lead exposure for US-born children. Although year of housing construction is a critical indicator of the lead hazard to US children, not all housing of the same age poses the same risk to children. Additional information about housing condition is required to differentiate the housing-based lead risk at the parcel level. This study aimed to identify and assess a method for gathering and using observations of exterior housing conditions to identify active housing-based lead hazards at the parcel level. We used a dataset of pediatric blood lead observations (sample years 2000-2013, ages 6-72 months, n = 6,589) to assess associations between observations of exterior housing conditions and housing-based lead risk. We used graphical and Lasso regression methods to estimate the likelihood of an elevated blood lead observation (≥3.5 μg/dL). Our methods estimate a monotonic increase in the likelihood of an elevated blood lead observation as housing conditions deteriorate with the largest changes associated with homes in the greatest disrepair. Additionally we estimate that age of home construction works in consort with housing conditions to amplify risks among those houses built before 1952. Our analysis indicates that a survey of external housing conditions can be used in combination with age of housing in the identification process, at the parcel level, of homes that pose a housing-based lead hazard to children.

Protective effect of methionine on the intestinal oxidative stress and microbiota change induced by nickel

Nickel is a common heavy metal pollutant in industrial areas and can cause oxidative damage to human and animal organs. As an essential amino acid with antioxidant function, methionine (Met) may protect the body from the oxidative stress induce by nickel, however, there is not enough research to study in this aspect. The study aims at investigating the effect of Met on the nickel-induced intestinal oxidative stress and further detected the gut microbiota changes. Mice were gavaged with quantitative NiCl₂ (1.6 mg/ml, 0.25 ml) and fed with different doses of methionine in each group. The contents of intestinal oxidation product and antioxidant enzymes were determined by different biochemical quantitative methods, and the data showed that NiCl₂ increased the content of intestinal oxidation product (MDA), and the antioxidant enzymes (GSH-Px, GR, SOD and CAT) were decreased. But this situation was alleviated in the group fed with additional methionine solution (0.5 mg/ml). In addition, we detected changes in the gut microbiota using high-throughput sequencing, the results showed that the structure of intestinal flora was disturbed by NiCl₂, but methionine restored the germs with antioxidant capacity. Based on the results, we speculate that methionine can alleviate the impact of NiCl₂ on the intestinal by enhancing the activity of antioxidant enzymes and the number of gut bacteria with anti-oxidation, suggesting that methionine as a nutritional additive may have the potential to treat nickel poisoning.

Residential greenness attenuated association of long-term air pollution exposure with elevated blood pressure: Findings from polluted areas in Northern China

Background

Evidence on the hypertensive effects of long-term air pollutants exposure are mixed, and the joint hypertensive effects of air pollutants are also unclear. Sparse evidence exists regarding the modifying role of residential greenness in such effects.

Methods

A cross-sectional study was conducted in typically air-polluted areas in northern China. Particulate matter with diameter < 1 μm (PM₁), particulate matter with diameter < 2.5 μm (PM_2.5), particulate matter with diameter < 10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) were predicted by space-time extremely randomized trees model. We used the Normalized Difference Vegetation Index (NDVI) to reflect residential green space. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were examined. We also calculated the pulse pressure (PP) and mean arterial pressure (MAP). Generalized additive model and quantile g-computation were, respectively, conducted to investigate individual and joint effects of air pollutants on blood pressure. Furthermore, beneficial effect of NDVI and its modification effect were explored.

Results

Long-term air pollutants exposure was associated with elevated DBP and MAP. Specifically, we found a 10-μg/m³ increase in PM_2.5, PM₁₀, and SO₂ were associated with 2.36% (95% CI: 0.97, 3.76), 1.51% (95% CI: 0.70, 2.34), and 3.54% (95% CI: 1.55, 5.56) increase in DBP; a 10-μg/m³ increase in PM_2.5, PM₁₀, and SO₂ were associated with 1.84% (95% CI: 0.74, 2.96), 1.17% (95% CI: 0.52, 1.83), and 2.43% (95% CI: 0.71, 4.18) increase in MAP. Air pollutants mixture (one quantile increase) was positively associated with increased values of DBP (8.22%, 95% CI: 5.49, 11.02) and MAP (4.15%, 95% CI: 2.05, 6.30), respectively. These identified harmful effect of air pollutants mainly occurred among these lived with low NDVI values. And participants aged ≥50 years were more susceptible to the harmful effect of PM_2.5 and PM₁₀ compared to younger adults.

Conclusions

Our study indicated the harmful effect of long-term exposure to air pollutants and these effects may be modified by living within higher green space place. These evidence suggest increasing residential greenness and air pollution control may have simultaneous effect on decreasing the risk of hypertension.

Do urinary metals associate with the homeostasis of inflammatory mediators? Results from the perspective of inflammatory signaling in middle-aged and older adults

OBJECTIVE

We aimed to investigate whether urinary metal mixtures are associated with the homeostasis of inflammatory mediators in middle-aged and older adults.

METHODS

A four-visit repeated-measures study was conducted with 98 middle-aged and older adults from five communities in Beijing, China. Only one person was lost to follow-up at the third visit. Ultimately, 391 observations were included in the analysis. The urinary concentrations of 10 metals were measured at each visit using inductively coupled plasma mass spectrometry (ICP-MS) with a limit of detection (LOD) ranging from 0.002 to 0.173 µg/L, and the detection rates were all above 84%. Similarly, 14 serum inflammatory mediators were measured using a Beckman Coulter analyzer and the Bio-Plex MAGPIX system. A linear mixed model (LMM), LMM with least absolute shrinkage and selection operator regularization (LMMLASSO), and Bayesian kernel machine regression (BKMR) were adopted to explore the effects of urinary metal mixtures on inflammatory mediators.

RESULTS

In LMM, a two-fold increase in urinary cesium (Cs) and chromium (Cr) was statistically associated with -35.22% (95% confidence interval [CI]: -53.17, -10.40) changes in interleukin 6 (IL-6) and -11.13% (95 %CI: -20.67, -0.44) in IL-8. Urinary copper (Cu) and selenium (Se) was statistically associated with IL-6 (88.10%, 95%CI: 34.92, 162.24) and tumor necrosis factor-alpha (TNF-α) (22.32%, 95%CI: 3.28, 44.12), respectively. Similar results were observed for the LMMLASSO and BKMR. Furthermore, Cr, Cs, Cu, and Se were significantly associated with other inflammatory regulatory network mediators. For example, urinary Cs was statistically associated with endothelin-1, and Cr was statistically associated with endothelin-1 and intercellular adhesion molecule 1 (ICAM-1). Finally, the interaction effects of Cu with various metals on inflammatory mediators were observed.

CONCLUSION

Our findings suggest that Cr, Cs, Cu, and Se may disrupt the homeostasis of inflammatory mediators, providing insight into the potential pathophysiological mechanisms of metal mixtures and chronic diseases.

Urinary phthalate metabolites and arterial stiffness: A panel study

The link between phthalates exposure and arterial stiffness in adults remains unclear. We aimed to investigate the associations of urinary phthalate metabolites with arterial stiffness in a longitudinal panel study involving 3 repeated visits among 127 Chinese adults. Urine samples were collected once a day for 4 consecutive days and 10 urinary phthalate metabolites were measured by gas chromatography-tandem mass spectrometry (GC-MS/MS). Brachial ankle pulse wave velocity (baPWV) and ankle-brachial index (ABI) were determined using an oscillometric device (BP-203RPEIII; Omron) in physical examinations during each visit. Linear mixed-effect (LME) models with the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) method were applied to assess the associations between urinary phthalate metabolites and arterial stiffness parameters. The odds ratio (OR) for peripheral arterial disease (PAD) was estimated using generalized estimating equations. For ABI, mono-methyl phthalate (MMP) and mono-n-butyl phthalate (MBP) at lag 0 day were selected by the adaptive LASSO, whereas no phthalates were selected for baPWV. After adjusting for potential covariates and other metabolites, we found ABI reduction was associated with one-unit increase of ln-transformed urinary MBP at lag 0 day [β = 0.013 (SE = 0.006), P = 0.003)]. Stratified analysis revealed that the inverse association was more evident in males (P_interaction = 0.025). In addition, we observed a borderline risk of PAD in relation to MBP exposure at lag 0 day (P = 0.06). Our data suggested that environmental exposure to MBP may contribute to arterial stiffness, and the effect seems to be sex-specific.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

BACKGROUND

School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.

OBJECTIVE

We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States.

METHODS

Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.

RESULTS

LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively.

DISCUSSION

The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Long-term effects of PM2.5 components on blood pressure and hypertension in Chinese children and adolescents

Growing evidence has linked fine particulate matter (PM_2.5) exposure to elevated blood pressure, but the effects of PM_2.5 components are unclear, particularly in children and adolescents. Based on a cross-sectional investigation in China, we analyzed the associations between long-term exposure to PM_2.5 and its major components with elevated blood pressure in children and adolescents. A representative sample (N = 37,610) of children and adolescents with age 7-18 years was collected in seven Chinese provinces. Exposures to PM_2.5 and five of its major components, including black carbon (BC), organic matter (OM), inorganic nitrate (NO₃^-), sulfate (SO₄^2-), and soil particles (SOIL), were estimated using satellite-based spatiotemporal models. The associations between long-term exposures to PM_2.5 and its components and diastolic blood pressure (DBP), systolic blood pressure (SBP), and hypertension were investigated using mixed-effects logistic and linear regression models. Within the populations, 11.5 % were classified as hypertension. After adjusting for a variety of covariates, per interquartile range (IQR) increment in PM_2.5 mass and BC levels were significantly associated with a higher hypertension prevalence with odds ratios (ORs) of 1.56 (95% confidence interval (CI): 1.08, 2.25) for PM_2.5 and 1.19 (95% CI: 1.04, 1.35) for BC. Long-term exposures to PM_2.5 and BC have also been associated with elevated SBP and DBP. Additionally, OM and NO₃^- were significantly associated with increased SBP, while SOIL was significantly associated with increased DBP. In the subgroup analysis, the associations between long-term exposures to BC and blood pressure vary significantly by urbanicity of residential area and diet habits. Our study suggests that long-term exposure to PM_2.5 mass and specific PM_2.5 components, especially for BC, are significantly associated with elevated blood pressure and a higher hypertension prevalence in Chinese children and adolescents.

The influences of ambient fine particulate matter constituents on plasma hormones, circulating TMAO levels and blood pressure: A panel study in China

Considerable investigations have been carried out to address the relationship between ambient fine particulate matter (PM_2.5) and blood pressure (BP) in patients with hypertension. However, few studies have explored the influence of PM_2.5 and its constituents on Trimethylamine N-oxide (TMAO), an established risk factor for hypertension and cardiovascular disease (CVD), particularly in severely air-polluted areas. To explore the potential impact of PM_2.5 constituents on BP, plasma hormones, and TMAO, a panel study was conducted to investigate changes in BP, plasma hormones, and TMAO in response to ambient air pollution exposure in stage 1 hypertensive young adults. Linear mixed effect models were used to estimate the cumulative effects of fine particulate matters (PM_2.5) and its constituents on BP, plasma hormones and TMAO. We found that one interquartile range (IQR) (35 μg/m³) increase in 0-1 day moving-average PM_2.5 concentrations was statistically significantly associated with elevated systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with estimated values of 0.13 (95% confidence interval (CI): 0.03 to 0.23) mmHg, 0.18 (95% CI: 0.08 to 0.28) mmHg, and 0.17 (95% CI: 0.09 to 0.26) mmHg, respectively. Hormone disturbance in the renin-angiotensin-aldosterone system was also associated with PM_2.5 exposure. Elevated TMAO levels with an IQR increase for 0-4, 0-5, 0-6 moving-average concentrations of PM_2.5 were found, and the increased values ranged from 26.28 (95% CI: 2.92 to 49.64) to 60.78 (31.95-89.61) ng/ml. More importantly, the PM_2.5-bound metal constituents, such as manganese (Mn), titanium (Ti), and selenium (Se) showed robust associations with elevated BP and plasma TMAO levels. This study demonstrates associations between PM_2.5 metal constituents and increased BP, changes in plasma hormones and TMAO, in stage 1 hypertensive young adults. Source control, aiming to reduce the emission of PM_2.5-bound metals should be implemented to reduce the risk of hypertension and CVD.

Association of indoor and outdoor short-term PM2.5 exposure with blood pressure among school children

The association between particulate matter and children's increased blood pressure is inconsistent, and few studies have evaluated indoor exposure, accounting for time-activity. The present study aimed to examine the association between personal short-term exposure to PM_2.5 and blood pressure in children. We conducted a panel study with up to three physical examinations during different seasons of 2018 (spring, summer, and fall) among 52 children. The indoor PM_2.5 concentration was continuously measured at home and classroom of each child using indoor air quality monitors. The outdoor PM_2.5 concentration was measured from the nearest monitoring station. We constructed a mixed effect model to analyze the association of short-term indoor and outdoor PM_2.5 exposure accounting for time-activity of each participant with blood pressure. The average PM_2.5 concentration was 34.3 ± 9.2 μg/m³ and it was highest in the spring. The concentration measured at homes was generally higher than that measured at outdoor monitoring station. A 10-μg/m³ increment of the up to previous 3-day mean (lag0-3) PM_2.5 concentration was associated with 2.7 mmHg (95%CI = 0.8, 4.0) and 2.1 mmHg (95%CI = 0.3, 4.0) increases in systolic and diastolic blood pressure, respectively. In a panel study comprehensively evaluating both indoor and outdoor exposures, which enabled more accurate exposure assessment, we observed a statistically significant association between blood pressure and PM_2.5 exposure in children.

Exposure to Metal Mixtures in Association with Cardiovascular Risk Factors and Outcomes: A Scoping Review

Since the National Institute of Environmental Health Sciences (NIEHS) declared conducting combined exposure research as a priority area, literature on chemical mixtures has grown dramatically. However, a systematic evaluation of the current literature investigating the impacts of metal mixtures on cardiovascular disease (CVD) risk factors and outcomes has thus far not been performed. This scoping review aims to summarize published epidemiology literature on the cardiotoxicity of exposure to multiple metals. We performed systematic searches of MEDLINE (PubMed), Scopus, and Web of Science to identify peer-reviewed studies employing statistical mixture analysis methods to evaluate the impact of metal mixtures on CVD risk factors and outcomes among nonoccupationally exposed populations. The search was limited to papers published on or after 1998, when the first dedicated funding for mixtures research was granted by NIEHS, through 1 October 2021. Twenty-nine original research studies were identified for review. A notable increase in relevant mixtures publications was observed starting in 2019. The majority of eligible studies were conducted in the United States (n = 10) and China (n = 9). Sample sizes ranged from 127 to 10,818. Many of the included studies were cross-sectional in design. Four primary focus areas included: (i) blood pressure and/or diagnosis of hypertension (n = 15), (ii) risk of preeclampsia (n = 3), (iii) dyslipidemia and/or serum lipid markers (n = 5), and (iv) CVD outcomes, including stroke incidence or coronary heart disease (n = 8). The most frequently investigated metals included cadmium, lead, arsenic, and cobalt, which were typically measured in blood (n = 15). The most commonly utilized multipollutant analysis approaches were Bayesian kernel machine regression (BKMR), weighted quantile sum regression (WQSR), and principal component analysis (PCA). To our knowledge, this is the first scoping review to assess exposure to metal mixtures in relation to CVD risk factors and outcomes. Recommendations for future studies evaluating the associations of exposure to metal mixtures with risk of CVDs and related risk factors include extending environmental mixtures epidemiologic studies to populations with wider metals exposure ranges, including other CVD risk factors or outcomes outside hypertension or dyslipidemia, using repeated measurement of metals to detect windows of susceptibility, and further examining the impacts of potential effect modifiers and confounding factors, such as fish and seafood intake.

The Association Between Long-Term Exposure to Particulate Matter and Incidence of Hypertension Among Chinese Elderly: A Retrospective Cohort Study

Background and Objectives: Studies that investigate the links between particulate matter ≤2. 5 μm (PM_2.5) and hypertension among the elderly population, especially those including aged over 80 years, are limited. Therefore, we aimed to examine the association between PM_2.5 exposure and the risk of hypertension incidence among Chinese elderly. Methods: This prospective cohort study used 2008, 2011, 2014, and 2018 wave data from a public database, the Chinese Longitudinal Healthy Longevity Survey, a national survey investigating the health of those aged over 65 years in China. We enrolled cohort participants who were free of hypertension at baseline (2008) from 706 counties (districts) and followed up in the 2011, 2014, and 2018 survey waves. The annual PM_2.5 concentration of 706 counties (districts) units was derived from the Atmospheric Composition Analysis Group database as the exposure variable, and exposure to PM_2.5 was defined as 1-year average of PM_2.5 concentration before hypertension event occurrence or last interview (only for censoring). A Cox proportional hazards model with penalized spline was used to examine the non-linear association between PM_2.5 concentration and hypertension risk. A random-effects Cox proportional hazards model was built to explore the relationship between each 1 μg/m³, 10 μg/m³ and quartile increment in PM_2.5 concentration and hypertension incidence after adjusting for confounding variables. The modification effects of the different characteristics of the respondents were also explored. Results: A total of 7,432 participants aged 65-116 years were enrolled at baseline. The median of PM_2.5 exposure concentration of all the participants was 52.7 (inter-quartile range, IQR = 29.1) μg/m³. Overall, the non-linear association between PM_2.5 and hypertension incidence risk indicated that there was no safe threshold for PM_2.5 exposure. The higher PM_2.5 exposure, the greater risk for hypertension incidence. Each 1 μg/m³ [adjusted hazard ratio (AHR): 1.01; 95% CI: 1.01-1.02] and 10 μg/m³ (AHR: 1.12; 95% CI: 1.09-1.16) increments in PM_2.5, were associated with the incidence of hypertension after adjusting for potential confounding variables. Compared to first quartile (Q1) exposure, the adjusted HRs of hypertension incidence for the Q2, Q3 and Q4 exposure of PM_2.5 were 1.31 (95% CI: 1.13-1.51), 1.35 (95% CI: 1.15-1.60), and 1.83 (95% CI: 1.53-2.17), respectively. The effects appear to be stronger among those without a pension, living in a rural setting, and located in central/western regions. Conclusion: We found no safe threshold for PM_2.5 exposure related to hypertension risk, and more rigorous approaches for PM_2.5 control were needed. The elderly without a pension, living in rural and setting in the central/western regions may be more vulnerable to the effects of PM_2.5 exposure.

Association between the urinary nickel and the diastolic blood pressure in general population

AIM

To investigate the association of the level of nickel and blood pressure (BP) level in a general population.

METHODS

A total of 2201 participants were enrolled from the National Health and Nutrition Examination Surveys (NHANES) 2017-2018. Urinary nickel level was measured using inductively coupled-plasma mass spectrometry. Multivariable linear regressions were performed to explore the associations between nickel and systolic BP and diastolic BP. Restricted cubic splines were used to explore the nonlinearity.

RESULTS

Per one-fold increase of nickel was associated with a 0.67-unit decrease of diastolic BP (β -0.67, 95 % confidence interval [CI] [-1.15, -0.18]; p = 0.007). Comparing with the lowest quartile, the highest quartile decreased 2.21-unit diastolic BP (β -2.21, 95 % CI [-3.84, -0.59]; p = 0.007). Restricted cubic spline confirmed the relationship was linear. Subgroup analysis found that the association was only significant in population without hypertension.

CONCLUSIONS

The urinary nickel, as a long-term exposure biomarker, was associated with the diastolic BP in individuals without hypertension.

Short- and intermediate-term exposure to ambient fine particulate elements and leukocyte epigenome-wide DNA methylation in older men: the Normative Aging Study

BACKGROUND

Several epigenome-wide association studies (EWAS) of ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5) have been reported. However, EWAS of PM_2.5 elements (PEs), reflecting different emission sources, are very limited.

OBJECTIVES

We performed EWAS of short- and intermediate-term exposure to PM_2.5 and 13 PEs. We hypothesized that significant changes in DNAm may vary by PM_2.5 mass and its elements.

METHODS

We repeatedly collected blood samples in the Normative Aging Study and measured leukocyte DNA methylation (DNAm) with the Illumina HumanMethylation450K BeadChip. We collected daily PM_2.5 and 13 PEs at a fixed central site. To estimate the associations between each PE and DNAm at individual cytosine-phosphate-guanine (CpG) sites, we incorporated a distributed-lag (0-27 d) term in the setting of median regression with subject-specific intercept and examined cumulative lag associations. We also accounted for selection bias due to loss to follow-up and mortality prior to enrollment. Significantly differentially methylated probes (DMPs) were identified using Bonferroni correction for multiple testing. We further conducted regional and pathway analyses to identify significantly differentially methylated regions (DMRs) and pathways.

RESULTS

We included 695 men with 1,266 visits between 1999 and 2013. The subjects had a mean age of 75 years. The significant DMPs, DMRs, and pathways varied by to PM_2.5 total mass and PEs. For example, PM_2.5 total mass was associated with 2,717 DMPs and 10,470 DMRs whereas Pb was associated with 3,173 DMPs and 637 DMRs. The identified pathways by PM_2.5 mass were mostly involved in mood disorders, neuroplasticity, immunity, and inflammation, whereas the pathways associated with motor vehicles (BC, Cu, Pb, and Zn) were related with cardiovascular disease and cancer (e.g., "PPARs signaling").

CONCLUSIONS

PM_2.5 and PE were associated with methylation changes at multiple probes and along multiple pathways, in ways that varied by particle components.

Combustion-derived particulate organic matter associated with hemodynamic abnormality and metabolic dysfunction in healthy adults

Epidemiological evidence on cardiometabolic health of particulate organic matter (POM) and its sources is sparse. In a panel of 73 healthy adults in Beijing, China, daily concentrations of ambient fine particulate matter-bound polycyclic aromatic hydrocarbons (PAHs) and n-alkanes were measured throughout the study period, and Positive Matrix Factorization approach was used to identity PAHs sources. Linear mixed-effect models and mediation analyses were applied to examine the associations and potential interlink pathways between POM and biomarkers indicative of hemodynamics, insulin resistance, vascular calcification and immune inflammation. We found that significant alterations in cardiometabolic measures were associated with POM exposures. In specific, interquartile range increases in PAHs concentrations at prior up to 9 days were observed in association with significant elevations of 2.6-2.9% in diastolic blood pressure, 6.6-8.1% in soluble ST2, 10.5-14.5% in insulin, 40.9-45.7% in osteoprotegerin, and 36.3-48.7% in interleukin-17A. Greater associations were generally observed for PAHs originating from traffic emissions and coal burning. Mediation analyses revealed that POM exposures may prompt the genesis of hemodynamic abnormalities, possibly via worsening insulin resistance and calcification potential. These findings suggested that cardiometabolic health benefits would be achieved by reducing PM from combustion emissions.

Long-Term Exposures to Air Pollution and the Risk of Atrial Fibrillation in the Women's Health Initiative Cohort

BACKGROUND

Atrial fibrillation (AF) is associated with substantial morbidity and mortality. Short-term exposures to air pollution have been associated with AF triggering; less is known regarding associations between long-term air pollution exposures and AF incidence.

OBJECTIVES

Our objective was to assess the association between long-term exposures to air pollution and distance to road on incidence of AF in a cohort of U.S. women.

METHODS

We assessed the association of high resolution spatiotemporal model predictions of long-term exposures to particulate matter (PM 10 and PM 2.5 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and distance to major roads with incidence of AF diagnosis, identified through Medicare linkage, among 83,117 women in the prospective Women's Health Initiative cohort, followed from enrollment in Medicare through December 2012, incidence of AF, or death. Using time-varying Cox proportional hazards models adjusted for age, race/ethnicity, study component, body mass index, physical activity, menopausal hormone therapy, smoking, diet quality, alcohol consumption, educational attainment, and neighborhood socioeconomic status, we estimated the relative risk of incident AF in association with each pollutant.

RESULTS

A total of 16,348 incident AF cases were observed over 660,236 person-years of follow-up. Most exposure-response associations were nonlinear. NO 2 was associated with risk of AF in multivariable adjusted models [Hazard Ratio  ( HR ) = 1.18 ; 95% confidence interval (CI): 1.13, 1.24, comparing the top to bottom quartile, p -for-trend = < 0.0001 ]. Women living closer to roadways were at higher risk of AF (e.g., HR = 1.07 ; 95% CI: 1.01, 1.13 for living within 50 m of A3 roads, compared with ≥ 1,000  m , p -for-trend = 0.02 ), but we did not observe adverse associations with exposures to PM 10 , PM 2.5 , or SO 2 . There were adverse associations with PM 10 (top quartile HR = 1.10 ; 95% CI: 1.05, 1.16, p -for-trend = < 0.0001 ) and PM 2.5 (top quartile HR = 1.09 ; 95% CI: 1.03, 1.14, p -for-trend = 0.002 ) in sensitivity models adjusting for census region.

DISCUSSION

In this study of postmenopausal women, NO 2 and distance to road were consistently associated with higher risk of AF. https://doi.org/10.1289/EHP7683.

Effects of polycyclic aromatic hydrocarbons and multiple metals co-exposure on the mosaic loss of chromosome Y in peripheral blood

Mosaic loss of chromosome Y (mLOY) is an indicator of genome instability, but the environmental stressors of mLOY remained largely unknown. In this study, we detected the internal exposure levels of 11 polycyclic aromatic hydrocarbon (PAH) metabolites and 22 metals among 888 coke-oven workers, and calculated their blood mLOY based on genome-wide SNP genotyping data and presented as median log R ratio (mLRR-Y). The generalized linear model (GLM), LASSO, and Bayesian kernel machine regression (BKMR), were used to select mLOY-relevant chemicals. The results of these models consistently suggested the negative dose-response relationships of urinary 1-hydroxynaphthalene (1-OHNa), antimony (Sb), and molybdenum (Mo) with mLRR-Y. There were no pairwise interactions between these three chemicals (P_interaction > 0.05), but subjects with high exposure to ≥ 2 kinds of these chemicals showed reducing mLRR-Y [β(95%CI) = - 0.015(- 0.023, - 0.008)], increasing oxidative DNA damage (marked by 8-hydroxydeoxyguanosine) [β(95%CI) = 0.625(0.454, 0.796)] and chromosome damage (marked by micronucleus frequency in lymphocytes) [frequency ratio (FR) and 95%CI = 1.146(1.047, 1.225)] than those with low exposure to all these chemicals. The combined effects of 1-OHNa, Sb, and Mo on elevating DNA damage may partly explain their joint effects on increased blood mLOY. These results provided a new insight into environmental hazards co-exposure on chromosome-Y deletions.

Associations Between Serum Multiple Metals Exposures and Metabolic Syndrome: a Longitudinal Cohort Study

Although many studies have confirmed metabolic syndrome (MetS) is correlated with metal exposures, few studies have elucidated the associations of multiple metals with MetS risk. We aim to explore the relationship between serum 22 metals and MetS. We determined serum 22 metals using ICP-MS and used LASSO regression to select metals independently related with MetS to construct multiple-metals model. We further explored the dose-response relationship between positive metals and MetS by the restricted cubic spline regression. After screening by LASSO regression, serum 11 metals were selected to construct multiple-metals model in cross-sectional analysis, while 5 metals in longitudinal analysis. In the 11-metal model, only tin and zinc were associated with MetS in cross-sectional analysis (OR_tin = 2.22, 95% CI:1.43, 3.45; OR_zinc = 2.17, 95% CI: 1.42, 3.32; both P_trend < 0.05). Besides, the same results were found in the 5-metal model in longitudinal analysis (HR_tin = 1.66, 95% CI: 0.87, 3.17; HR_zinc = 1.83, 95% CI: 1.07, 3.14; both P_trend < 0.05). Moreover, there were positive linear relationships between serum tin and zinc concentrations and the increasing risk of MetS (both P_overall < 0.05, P_{non-linearity} > 0.05). Furthermore, the interaction between high tin and high zinc was also associated with increasing MetS risk (P_interaction < 0.05). We found that serum tin and zinc were independently and interactively associated with MetS in the southern Chinese men. Our results suggested that high tin and zinc may be the risk factors of MetS.

The short-term association of ambient fine particulate air pollution with hypertension clinic visits: A multi-community study in Guangzhou, China

BACKGROUND

The association of ambient fine particulate pollution with daily outpatient clinic visits (OCV) for hypertension in China remains to be investigated.

OBJECTIVES

This study aimed to examine short-term impacts of exposure to fine particulate matter of aerodynamic diameter < 2.5μm (PM_2.5) on daily OCV for hypertension, using a large-scale multi-center community database in Guangzhou, one of the most densely-populated cities in Southern China.

METHODS

We collected a total of 28,548 individual records of OCV from 22 community healthcare facilities in Guangzhou from January 1st to May 7th 2020. Hourly data on air pollutants and daily information on meteorological factors were obtained. According to the World Health Organization air-quality guidelines, daily excessive concentration hours (DECH) was calculated. PM_2.5 daily mean, hourly-peak concentration and DECH were used as the exposure variables. Based on a case-time-control design, the Cox regression model was applied to evaluate the short-term relative risks (RR) of daily OCV for hypertension. Sensitivity analyses were conducted, with nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone being adjusted.

RESULTS

Daily mean and hourly-peak of PM_2.5 were significantly associated with daily OCV for hypertension, while weaker associations were observed for DECH. The estimated RRs at lag day 0 were 1.039 (95% confidence interval [CI]: 1.037, 1.040), 1.851 (95%CI: 1.814, 1.888), and 1.287 (95%CI: 1.276, 1.298), respectively, in association with a 1-unit increase in DECH, daily mean, and hourly-peak concentration of PM_2.5. For the lagged effect, lag₄ models estimated the greatest RRs for PM_2.5 DECH and hourly-peak, whereas a lag₂ model produced the highest for PM_2.5 daily mean.

DISCUSSION

This study consolidates the evidence for a positive correlation between ambient PM_2.5 exposure and risks of hypertensive OCV. It also provides profound insight regarding planning for health services needs and establishing early environmental responses to the worsening air pollution in the communities.

Mediated relationships between multiple metals exposure and fasting blood glucose by reproductive hormones in Chinese men

Previous studies have reported metals exposure contribute to the change of fasting blood glucose (FBG) level. However, the roles of reproductive hormones in their associations have not been fully elucidated. The aim of the study is to investigate the associations of multiple serum metals with reproductive hormones, and to further explore potential roles of reproductive hormones in relationships between metals exposure and FBG level. A total of 1911 Chinese Han men were analyzed by a cross-sectional study. We measured serum levels of 22 metals by inductively coupled plasma mass spectrometer (ICP-MS). FBG, total testosterone (TT), estradiol (E2), follicle stimulating hormone (FSH), and sex hormone-binding globulin (SHBG) levels were determined. Least absolute shrinkage and selection operator (LASSO) regression models were conducted to select important metals, and restricted cubic spline models were then used to estimate dose-response relationships between selected metals and reproductive hormones. We also conducted mediation analyses to evaluate whether reproductive hormones played mediating roles in the associations between metals and FBG. We found significant inverse dose-dependent trends of copper, tin and zinc with E2; zinc with SHBG; copper and nickel with TT, while significant positive dose-dependent trend of iron with E2, respectively. Moreover, approximately inverted U-shaped associations existed between lead and SHBG, iron and TT. In addition, E2, SHBG and TT were negatively associated with FBG level. In mediation analyses, the association of copper with FBG was mediated by E2 and TT, with a mediation ratio of 10.4% and 22.1%, respectively. Furthermore, E2 and SHBG mediated the relationship of zinc with FBG, with a mediation ratio of 7.8% and 14.5%, respectively. E2 mediated 11.5% of positive relationship between tin with FBG. Our study suggested that the associations of metals exposure with FBG may be mediated by reproductive hormones.

Associations between PM2.5 metal components and QT interval length in the Normative Aging Study

BACKGROUND

Several studies have found associations between increases in QT interval length, a marker of cardiac electrical instability, and short-term fine particulate matter (PM2.5) exposures. To our knowledge, this is the first study to examine the association between specific PM2.5 metal components and QT interval length.

METHODS

We measured heart-rate corrected QT interval (QTc) duration among 630 participants in the Normative Aging Study (NAS) based in Eastern Massachusetts between 2000 and 2011. We utilized time-varying linear mixed-effects regressions with a random intercept for each participant to analyze associations between QTc interval and moving averages (0-7 day moving averages) of 24-h mean concentrations of PM2.5 metal components (vanadium, nickel, copper, zinc and lead) measured at the Harvard Supersite monitoring station. Models were adjusted for daily PM2.5 mass estimated at a 1 km × 1 km grid cell from a previously validated prediction model and other covariates. Bayesian kernel machine regression (BKMR) was utilized to assess the overall joint effect of the PM2.5 metal components.

RESULTS

We found consistent results with higher lead (Pb) associated with significant higher QTc intervals for both the multi-pollutant and the two pollutant (PM2.5 mass and a PM2.5 component) models across the moving averages. The greatest effect of lead on QTc interval was detected for the 4-day moving average lead exposure. In the multi-pollutant model, each 2.72 ng/m3 increase in daily lead levels for a 4-day moving average was associated with a 7.91 ms (95% CI: 3.63, 12.18) increase in QTc interval. In the two-pollutant models with PM2.5 mass and lead, each 2.72 ng/m3 increase in daily lead levels for a 4-day moving average was associated with an 8.50 ms (95% CI: 4.59, 12.41) increase in QTc interval. We found that 4-day moving average of copper has a negative association with QTc interval when compared to the other PM2.5 metal components. In the multi-pollutant model, each 1.81 ng/m3 increase in daily copper levels for a 4-day moving average was associated with an -3.89 ms (95% CI: -6.98, -0.79) increase in QTc interval. Copper's essential function inside the human body could mediate its cardiotoxicity on cardiac conductivity and explain why we found that copper in comparison to the other metals was less harmful for QTc interval.

CONCLUSIONS

Exposure to metals contained in PM2.5 are associated with acute changes in ventricular repolarization as indicated by QT interval characteristics.

Internal metal(loid)s are potentially involved in the association between ambient fine particulate matter and blood pressure: A repeated-measurement study in north China

The effects of ambient fine particulate matter (PM_2.5) exposure on blood pressure have been widely reported. However, there remains uncertainty regarding the underlying roles of particulate matter components. We aimed to investigate the association between ambient PM_2.5 exposure and blood pressure, as well as the potential effects of trace metal(loid)s, in a repeated-measurement study that enrolled women of childbearing age. Our study included 35 participants from Hebei Province, China, each of whom was visited for five times. During each visit, we conducted questionnaire surveys, measured blood pressure, and collected blood. The daily PM_2.5 exposure of participants was estimated according to their residential addresses using a spatiotemporal model that combined monitoring data with satellite measurements and chemical-transport model simulations. This model was used to calculate average PM_2.5 concentrations in 1, 3, 7, 15, 30, and 60 days prior to each visit. Serum concentrations of various trace metal(loid)s were measured. A linear mixed-effects model was used to investigate associations among study variables. Overall, the mean (standard deviation) 60 days PM_2.5 concentration over all five visits was 108.1(43.3) μg/m³. PM_2.5 concentration was positively associated with both systolic and diastolic blood pressures. Likewise, ambient PM_2.5 concentration was positively associated with serum concentrations of manganese and arsenic, and negatively associated with serum concentrations of nickel, tin, and chromium. Only the serum concentration of molybdenum was negatively associated with systolic blood pressure. We concluded that ambient PM_2.5 exposure may contribute to elevated blood pressure, potentially by interfering with internal intake of various metal(loid)s in the human body.

Association between ambient fine particulate matter with blood pressure levels among Iranian individuals admitted for cardiac and respiratory diseases: Data from CAPACITY study

BACKGROUND

The relation between air pollution and cardiovascular diseases (CVDs) risk factors, especially blood pressure (BP) levels, has been less frequently assessed. The aim of this study was evaluating the association between air pollutants of less than 2.5 µm [particulate matter (PM2.5)] and BP indices among individuals admitted with CVDs and pulmonary diseases.

METHODS

This cross-sectional study was in context of air pollution associated with hospitalization and mortality of CVDs and respiratory diseases (CAPACITY) study. Data of 792 Iranian patients referring to two hospitals in Isfahan, Iran, for cardiovascular or respiratory problems from March 2011 to March 2012 were used for analysis. BP indices including systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) were obtained from patients’ medical forms and mean PM2.5 concentrations during 24 hours prior to admission of each patient were obtained from Isfahan Department of Environment (DOE).

RESULTS

Mean ± standard deviation (SD) of participants’ age were 62.5 ± 15.9 years. All BP indices on admission were significantly higher in women compared with men. Adjustment of all potential confounders including age, sex, temperature, wind speed, and dew point revealed that increasing one quartile in PM2.5 concentrations had been associated with 1.98 mmHg raising in SBP at the time of admission [95% confidence interval (CI) = 0.41-3.54, P = 0.010]. Women with cardiac diseases had higher all BP indices with increased PM2.5 concentration [SBP: β: 4.30, 95% CI = 0.90-7.70, P = 0.010; DBP: β: 1.89, 95% CI = 0.09-3.69, P = 0.040; MAP: β: 3.09, 95% CI = 0.68-5.51, P= 0.010, respectively).

CONCLUSION

Our findings suggest that increasing PM2.5 concentration has been positively associated with raising SBP in total population and all BP indices among women with cardiac problems at admission time. Several comprehensive studies are required for confirming these relations.

Ambient Particle Components and Newborn Blood Pressure in Project Viva

Background

Both elemental metals and particulate air pollution have been reported to influence adult blood pressure (BP). The aim of this study is to examine which elemental components of particle mass with diameter ≤2.5 μm (PM_2.5) are responsible for previously reported associations between PM_2.5 and neonatal BP.

Methods and Results

We studied 1131 mother‐infant pairs in Project Viva, a Boston‐area prebirth cohort. We measured systolic BP (SBP) and diastolic BP (DBP) at a mean age of 30 hours. We calculated average exposures during the 2 to 7 days before birth for the PM_2.5 components—aluminum, arsenic, bromine, sulfur, copper, iron, zinc, nickel, vanadium, titanium, magnesium, potassium, silicon, sodium, chlorine, calcium, and lead—measured at the Harvard supersite. Adjusting for covariates and PM_2.5, we applied regression models to examine associations between PM_2.5 components and median SBP and DBP, and used variable selection methods to select which components were more strongly associated with each BP outcome. We found consistent results with higher nickel associated with significantly higher SBP and DBP, and higher zinc associated with lower SBP and DBP. For an interquartile range increase in the log Z score (1.4) of nickel, we found a 1.78 mm Hg (95% CI, 0.72–2.84) increase in SBP and a 1.30 (95% CI, 0.54–2.06) increase in DBP. Increased zinc (interquartile range log Z score 1.2) was associated with decreased SBP (−1.29 mm Hg; 95% CI, −2.09 to −0.50) and DBP (−0.85 mm Hg; 95% CI: −1.42 to −0.29).

Conclusions

Our findings suggest that prenatal exposures to particulate matter components, and particularly nickel, may increase newborn BP.

Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty

BACKGROUND

Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods.

METHODS

For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation.

RESULTS

Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%.

CONCLUSIONS

This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

[Combined effects of different environmental factors on health: air pollution, temperature, green spaces, pollen, and noise]

Environmental factors affect the health and wellbeing of urban residents. However, they do not act individually on humans, but instead show potential synergistic or antagonistic effects. Questions that arise from this are: How does a combination of air pollutants with other environmental factors impact health? How well are these associations evidenced? What methods can we use to look at them? In this article, methodical approaches regarding the effects of a combination of various environmental factors are first described. Environmental factors are then examined, which together with different air pollutants, have an impact on human health such as ambient temperature, noise, and pollen as well as the effect of green spaces. Physical activity and nutrition are addressed regarding the attenuation of health effects from air pollution.While there is often clear evidence of health effects of single environmental stressors, there are still open questions in terms of their interaction. The research methods required for this still need to be further developed. The interrelationship between the different environmental factors make it clear that (intervention) measures for reducing single indicators are also interlinked. Regarding traffic, switching from passive to active transport (e.g., due to safe cycle paths and other measures) leads to less air pollutants, smaller increases in temperature in the long term, and at the same time improved health of the individual. As a result, sensible planning of the built environment has great potential to reduce environmental stressors and improve people's health and wellbeing.

The Gender-Based Differences in Vulnerability to Ambient Air Pollution and Cerebrovascular Disease Mortality: Evidences Based on 26781 Deaths

The gender-based differences in the vulnerability to ambient air pollution have not been widely explored. This study aimed to investigate vulnerability differences to the short-term effects of PM2.5, PM10 and O3 between cerebrovascular diseases (CEVD) deaths of men and women. The general additive models (GAMs) and distributed lag non-linear models (DLNMs) were adopted, and both single-pollutant and two-pollutant models were performed to analyze the associations between ambient air pollution and daily CEVD deaths. Both models indicated that O3 was the most suspicious pollutant that could induce excess CEVD deaths, and women tended to be more vulnerable to O3. These results were confirmed by seasonal analysis, in which we also found both genders were more vulnerable to O3 in winter. The exposure-response relationships revealed that women were usually more vulnerable to ambient air pollution than men, and the exposure-response curves differed significantly between genders. Our findings suggested that more attention should be paid on the adverse effects of ambient O3, and the protection of women CEVD population against air pollution should be emphasized.

Geographic variations in the blood pressure responses to short-term fine particulate matter exposure in China

Results from recent studies on associations between blood pressure (BP) and short-term exposure to fine particulate matter (PM_2.5) have been inconsistent. Most studies have been evaluations of small geographic areas, with no national study in China. This study aimed to examine the acute BP responses to ambient PM_2.5 among the general population of Chinese adults. During 2012-2015, systolic and diastolic BP levels were obtained from a large national representative sample, the China Hypertension Survey database (n = 479,842). Daily PM_2.5 average exposures with a spatial resolution of 0.1° were estimated using a data assimilation that combines satellite measurements, air model simulations, and monitoring values. Overall, a 10-μg/m³ increase in daily PM_2.5 was associated with a 0.035 (95% confidence interval: 0.020, 0.049) mmHg change in systolic BP and 0.001 (-0.008, 0.011) mmHg in diastolic BP after adjustments. Stratified by geographic regions, the systolic and diastolic BP levels varied from -0.050 (-0.109, 0.010) to 0.242 (0.176, 0.307) mmHg, and from -0.026 (-0.053, 0.001) to 0.051 (0.020, 0.082) mmHg, respectively. Statistically significant positive BP-PM_2.5 associations were only found in South and North China for systolic levels and in Southwest China for diastolic levels. We further explored the regional study population characteristics and exposure-response curves, and found that the geographic variations in BP-PM_2.5 associations were probably due to different population compositions or different PM_2.5 exposure levels. Our study provided national-level evidence on the associations between ambient PM_2.5 exposure and elevated BP levels. The magnitude of the estimated associations varied substantially by geographic location in China. CLINICAL TRIAL REGISTRATION: The Clinical trial registration name was Survey on prevalence of hypertension in China; the registration number was ChiCTR-ECS-14004641. http://www.chictr.org.cn/showproj.aspx?proj=4932.

Long-term exposure to airborne particulate matter of 1 μm or less and blood pressure in healthy young adults: A national study with 1.2 million pregnancy planners

No evidence exists concerning the effect of airborne particulate matter of 1 μm or less (PM₁) on blood pressure of young adults planning for pregnancy. We collected health examination information of about 1.2 million couples (aged 18-45 years) from a national birth cohort in China from Jan 1, 2013 to Oct 1, 2014 and matched their home address to daily PM₁ and PM_2.5 concentrations, which were predicted by remote sensing information. Generalized additive mixed models were used to analyze associations between long-term exposure to PM and blood pressure, after controlling for individual factors. A 10 μg/m³ increase in PM₁ was associated with increased systolic blood pressure (SBP) for 0.26 (95%CI: 0.24, 0.29) mmHg in females and 0.29 (95%CI: 0.26, 0.31) mmHg in males, respectively. PM₁ was also associated with increased DBP for 0.22 (95%CI: 0.20, 0.23) mmHg in females and 0.17 (95%CI: 0.15, 0.19) mmHg in males, respectively. Similar effects on blood pressure were found for PM_2.5, meanwhile, the effect of PM_2.5 on SBP increased with the scale of PM₁ included in PM_2.5 (p for interaction term <0.01). In summary, long-term exposure to PM₁ as well as PM_2.5 was associated with increased SBP and DBP of Chinese young adults planning for pregnancy.

Evaluating associations between early pregnancy trace elements mixture and 2nd trimester gestational glucose levels: A comparison of three statistical approaches

OBJECTIVE

Studies have shown that individual trace element levels might be associated with abnormal glycemic status, with implications for diabetes. Few studies have considered these trace elements as a mixture and their impact on gestational glucose levels. Comparing three statistical approaches, we assessed the associations between essential trace elements mixture and gestational glucose levels.

METHODS

We used data from 1720 women enrolled in the Eunice Kennedy Shriver National Institute of Child Health and Human Development's Fetal Growth Study, for whom trace element concentrations (zinc, selenium, copper, molybdenum) were measured by inductively coupled plasma mass spectrometry (ICP-MS) using plasma collected during the 1st trimester. Non-fasting glucose levels were measured during the gestational diabetes mellitus (GDM) screening test in the 2nd trimester. We applied (1) Bayesian Kernel Machine Regression (BKMR); (2) adaptive Least Absolute Shrinkage and Selection Operator (LASSO) in a mutually adjusted linear regression model; and (3) generalized additive models (GAMs) to evaluate the joint associations between trace elements mixture and glucose levels adjusting for potential confounders.

RESULTS

Using BKMR, we observed a mean 2.7 mg/dL higher glucose level for each interquartile increase of plasma copper (95% credible interval: 0.9, 4.5). The positive association between plasma copper and glucose levels was more pronounced at higher quartiles of zinc. Similar associations were detected using adaptive LASSO and GAM. In addition, results from adaptive LASSO and GAM suggested a super-additive interaction between molybdenum and selenium (both p-values = 0.04).

CONCLUSION

Employing different statistical methods, we found consistent evidence of higher gestational glucose levels associated with higher copper and potential synergism between zinc and copper on glucose levels.

Fine Particulate Matter Leads to Unfolded Protein Response and Shortened Lifespan by Inducing Oxidative Stress in C. elegans

Oxidative stress has been proven as one of the most critical regulatory mechanisms involved in fine Particulate Matter- (PM_2.5-) mediated toxicity. For a better understanding of the underlying mechanisms that enable oxidative stress to participate in PM_2.5-induced toxic effects, the current study explored the effects of oxidative stress induced by PM_2.5 on UPR and lifespan in C. elegans. The results implicated that PM_2.5 exposure induced oxidative stress response, enhanced metabolic enzyme activity, activated UPR, and shortened the lifespan of C. elegans. Antioxidant N-acetylcysteine (NAC) could suppress the UPR through reducing the oxidative stress; both the antioxidant NAC and UPR inhibitor 4-phenylbutyric acid (4-PBA) could rescue the lifespan attenuation caused by PM_2.5, indicating that the antioxidant and moderate proteostasis contribute to the homeostasis and adaptation to oxidative stress induced by PM_2.5.

Short-term effects of ambient particulate matter on blood pressure among children and adolescents:A cross-sectional study in a city of Yangtze River delta, China

Several studies have demonstrated associations between short-term exposure to particulate matter (PM) and blood pressure (BP) among various adults groups, but evidence in children and adolescents is still rare. In 2016, a cross-sectional survey was conducted among 194 104 participants aged 6-17 years in Suzhou, China. Daily concentrations of particulate matters with an aerodynamic diameter of ≤10 μg/m³ (PM₁₀) and aerodynamic diameter ≤2.5 μg/m³ (PM_2.5) on 0-6 days preceding BP examination were collected from nearby air monitoring stations. Using generalized linear mixed-effects models, short-term effects of PM on personal BP were estimated. A 10 μg/m³ increment in the 0-6 day mean of PM_2.5 was significantly associated with elevation of 0.20 mmHg [95% confidence interval (95% CI) 0.16-0.23] in systolic BP (SBP), 0.49 mmHg (95% CI 0.45-0.53) in diastolic BP (DBP), respectively. Similarly, 0.14 mmHg (95% CI 0.12-0.16) higher SBP and 0.32 mmHg (95% CI 0.30-0.34) higher DBP were found for each 10 μg/m³ increase in 0-6 day mean of PM₁₀. More apparent associations were observed in females than in males. Odds ratio (95%CI) of for PM_2.5 exposure at 0-6 d mean was 1.06 (1.03-1.08) in females, while it was 1.01 (0.99-1.03) in males. Participants with young ages, underweight and obesity were also associated with increased susceptibility to PM-induced BP effects. Short-term exposure in PM was significantly associated with elevated BP in children, indicating a need to control PM levels and protect children from PM exposure in China.

Refining the World Health Organization Definition: Predicting Autopsy-Defined Sudden Arrhythmic Deaths Among Presumed Sudden Cardiac Deaths in the POST SCD Study

BACKGROUND

Conventional definitions of sudden cardiac death (SCD) presume cardiac cause. We studied the World Health Organization-defined SCDs autopsied in the POST SCD study (Postmortem Systematic Investigation of SCD) to determine whether premortem characteristics could identify autopsy-defined sudden arrhythmic death (SAD) among presumed SCDs.

METHODS

Between January 2, 2011, and January 4, 2016, we prospectively identified all 615 World Health Organization-defined SCDs (144 witnessed) 18 to 90 years in San Francisco County for medical record review and autopsy via medical examiner surveillance. Autopsy-defined SADs had no extracardiac or acute heart failure cause of death. We used 2 nested sets of premortem predictors-an emergency medical system set and a comprehensive set adding medical record data-to develop Least Absolute Selection and Shrinkage Operator models of SAD among witnessed and unwitnessed cohorts.

RESULTS

Of 615 presumed SCDs, 348 (57%) were autopsy-defined SAD. For witnessed cases, the emergency medical system model (area under the receiver operator curve 0.75 [0.67-0.82]) included presenting rhythm of ventricular tachycardia/fibrillation and pulseless electrical activity, while the comprehensive (area under the receiver operator curve 0.78 [0.70-0.84]) added depression. If only ventricular tachycardia/fibrillation witnessed cases (n=48) were classified as SAD, sensitivity was 0.46 (0.36-0.57), and specificity was 0.90 (0.79-0.97). For unwitnessed cases, the emergency medical system model (area under the receiver operator curve 0.68 [0.64-0.73]) included black race, male sex, age, and time since last seen normal, while the comprehensive (area under the receiver operator curve 0.75 [0.71-0.79]) added use of β-blockers, antidepressants, QT-prolonging drugs, opiates, illicit drugs, and dyslipidemia. If only unwitnessed cases <1 hour (n=59) were classified as SAD, sensitivity was 0.18 (0.13-0.22) and specificity was 0.95 (0.90-0.97).

CONCLUSIONS

Our models identify premortem characteristics that can better specify autopsy-defined SAD among presumed SCDs and suggest the World Health Organization definition can be improved by restricting witnessed SCDs to ventricular tachycardia/fibrillation or nonpulseless electrical activity rhythms and unwitnessed cases to <1 hour since last normal, at the cost of sensitivity.

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

Road proximity influences indoor exposures to ambient fine particle mass and components

Exposure to traffic-related PM_2.5 mass and its components can affect human health. Meanwhile, indoor concentrations are better exposure predictors as compared to outdoor concentrations because individuals spend the majority of their time indoors. We estimated the impact of traffic emissions on indoor PM_2.5 mass and its species as a function of road proximity in Massachusetts. A linear regression model was built using 662 indoor samples and 580 ambient samples. Analysis shows that indoor exposures to traffic-related particles increased dramatically with road proximity. We defined relative concentration decrease, R(α), as the ratio of the indoor concentration at perpendicular distance α in meters from the closest major road to the indoor concentration at 1800 m from the major road. R(13) values for PM_2.5 mass and Black Carbon (BC) were 1.3 (95%CI: 1.4, 1.6) and 2.1 (95%CI: 1.3, 2.8) for A12 roads, and 1.3 (95%CI: 1.2, 1.4) and 1.2 (95%CI: 1.1, 1.3) for A3 roads. R(α) values were also estimated for Fe, Mn, Mo, Sr and Ti for A12 roads, and Ca, Cu, Fe, Mn, Mo, Ni, Si, Sr, V and Zn for A3 roads. R(α) values for species associated mainly with brakes, tires or road dust (e.g., Mn, Mo and Sr) were higher than others. For A12 roads, R(13) values for Mn and Mo were 10.9 (95%CI: 0.9, 20.9) and 6.5 (95%CI: 1.4, 11.5), and ranged from 1.3 to 2.1 for other species; for A3 roads, R(13) values for Mn, Mo and Sr were 1.9 (95%CI: 1.1, 2.9), 1.8 (95%CI: 1.1, 2.4), and 8.5 (95%CI: 5.9, 10.9), and ranged from 1.2 to 1.6 for others. Our results indicate a significant impact of local traffic emissions on indoor air, which depends on road proximity. Thus road proximity which has been used in many epidemiological studies is a reasonable exposure metric.

Shipping pollution emission associated with increased cardiovascular mortality: A time series study in Guangzhou, China

Substantial evidence has linked short-term exposure to ambient fine particulate matter (PM_2.5) with increased cardiovascular mortality, however, the specific chemical constituent and emission source responsible for this effect remained largely unclear. A time series Poisson model was employed to quantify the association of cardiovascular mortality with two sets of shipping pollution emission: nickel (Ni), vanadium (V) (the indices of shipping emission) and estimated shipping emission using a source apportionment approach in Guangzhou, China in 2014. We observed that Ni, V, and estimated shipping emission in PM_2.5 were associated with increased cardiovascular mortality, an inter-quartile range (IQR) increase in lag₂ Ni was associated with 4.60% (95% CI: 0.14%, 9.26%) increase in overall cardiovascular mortality, and 13.35% (95% CI: 5.54%, 21.75%) increase in cerebrovascular mortality; each IQR increase of lag₁ V was correlated with 6.01% (95% CI: 1.83%, 10.37%) increase in overall cardiovascular mortality, and 11.02% (95% CI: 3.15%, 19.49%) increase in cerebrovascular mortality; and each IQR increase in lag₁ shipping emission was associated with 5.55% (95% CI: 0.78%, 10.54%) increase in overall cardiovascular mortality, and 10.39% (95% CI: 1.43%, 20.14%) increase in cerebrovascular mortality. The results remained robust to adjustment for PM_2.5 mass and gaseous air pollutants. This study suggests that shipping emission is an important detrimental factor of cardiovascular mortality, and should be emphasized in air pollution control and management in order to protect the public health in Guangzhou, China.

Assessing the cumulative health effect following short term exposure to multiple pollutants: An evaluation of methodological approaches using simulations and real data

BACKGROUND

Assessment of the cumulative effect of correlated exposures is an open methodological issue in environmental epidemiology. Most previous studies have applied regression models with interaction terms or dimension reduction methods. The combined effect of pollutants has been also evaluated through the use of exposure scores that incorporate weights based on the strength of the component-specific associations with health outcomes.

METHODS

We compared three approaches addressing multi-pollutant exposures in epidemiological models: main effects models, the adaptive least absolute shrinkage and selection operator (LASSO) and a weighted exposure score. We assessed the performance of the methods by simulations under various scenarios for the pollutants' correlations. We further applied these methods to time series data from Athens, Greece in 2007-12 to investigate the combined effect of short-term exposure to six regulated pollutants on all-cause and respiratory mortality.

RESULTS

The exposure score provided the least biased estimate under all correlation scenarios for both mortality outcomes. The adaptive LASSO performed well in the case of low and medium correlation between exposures while the main effect model resulted in severe bias. In the real data application, the cumulative effect estimate was similar between approaches for all-cause mortality ranging from 0.7% increase per interquartile range (IQR) (score) to 1.1% (main effects), while for respiratory mortality conclusions were contradictive and ranged from - 0.6% (adaptive LASSO) to 2.8% (score).

CONCLUSIONS

Τhe use of a weighted exposure score to address cumulative effects of correlated metrics may perform well under different exposure correlation and variability in the health outcomes.

Associations of Annual Ambient Fine Particulate Matter Mass and Components with Mitochondrial DNA Abundance

BACKGROUND

Fine particulate matter (PM2.5) represents a mixture of components with potentially different toxicities. However, little is known about the relative effects of PM2.5 mass and PM2.5 components on mitochondrial DNA (mtDNA) abundance, which may lie on the pathway of PM2.5-associated disease.

METHODS

We studied 646 elderly male participants in the Normative Aging Study from Greater Boston to investigate associations of long-term exposure to PM2.5 mass and PM2.5 components with mtDNA abundance. We estimated concentrations of pollutants for the 365-day preceding examination at each participant's address using spatial- and temporal-resolved chemical transport models. We measured blood mtDNA abundance using RT-PCR. We applied a shrinkage and selection method (adaptive LASSO) to identify components most predictive of mtDNA abundance, and fit multipollutant linear mixed-effects models with subject-specific intercept to estimate the relative effects of individual PM component.

RESULTS

MtDNA abundance was negatively associated with PM2.5 mass in the previous year and-after adjusting for PM2.5 mass-several PM2.5 components, including organic carbon, sulfate (marginally), and nitrate. In multipollutant models including as independent variables PM2.5 mass and PM2.5 components selected by LASSO, nitrate was associated with mtDNA abundance. An SD increase in annual PM2.5-associated nitrate was associated with a 0.12 SD (95% confidence intervals [CI] = -0.18, -0.07) decrease in mtDNA abundance. Analyses restricted to PM2.5 annual concentration below the current 1-year U.S. Environmental Protection Agency standard produced similar results.

CONCLUSIONS

Long-term exposures to PM2.5-associated nitrate were related to decreased mtDNA abundance independent of PM2.5 mass. Mass alone may not fully capture the potential of PM2.5 to oxidize the mitochondrial genome.See video abstract at, http://links.lww.com/EDE/B274.

Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis

Although numerous studies have investigated the association of ambient air pollution with hypertension and blood pressure (BP), the results were inconsistent. We performed a comprehensive systematic review and meta-analysis of these studies. Seven international and Chinese databases were searched for studies examining the associations of particulate (diameter<2.5 μm (PM_2.5), 2.5-10 μm (PM_2.5-10) or >10 μm (PM₁₀)) and gaseous (sulfur dioxide (SO₂), nitrogen dioxide (NO₂), nitrogen oxides (NO_x), ozone (O₃), carbon monoxide (CO)) air pollutants with hypertension or BP. Odds ratios (OR), regression coefficients (β) and their 95% confidence intervals were calculated to evaluate the strength of the associations. Subgroup analysis, sensitivity analysis, and meta-regression analysis were also conducted. The overall meta-analysis showed significant associations of long-term exposures to PM_2.5 with hypertension (OR = 1.05), and of PM₁₀, PM_2.5, and NO₂ with DBP (β values: 0.47-0.86 mmHg). In addition, short-term exposures to four (PM₁₀, PM_2.5, SO₂, NO₂), two (PM_2.5 and SO₂), and four air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂), were significantly associated with hypertension (ORs: 1.05-1.10), SBP (β values: 0.53-0.75 mmHg) and DBP (β values: 0.15-0.64 mmHg), respectively. Stratified analyses showed a generally stronger relationship among studies of men, Asians, North Americans, and areas with higher air pollutant levels. In conclusion, our study indicates a positive association between ambient air pollution and increased BP and hypertension. Geographical and socio-demographic factors may modify the pro-hypertensive effects of air pollutants.

Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science

PURPOSE OF REVIEW

The purpose of this review is to describe the most recent statistical approaches to estimate the effect of multi-pollutant mixtures or multiple correlated exposures on human health.

RECENT FINDINGS

The health effects of environmental chemicals or air pollutants have been widely described. Often, there exists a complex mixture of different substances, potentially highly correlated with each other and with other (environmental) stressors. Single-exposure approaches do not allow disentangling effects of individual factors and fail to detect potential interactions between exposures. In the last years, sophisticated methods have been developed to investigate the joint or independent health effects of multi-pollutant mixtures or multiple environmental exposures. A classification of the most recent methods is proposed. A non-technical description of each method is provided, together with epidemiological applications and operational details for implementation with standard software.

Neonatal exposure to environmental pollutants and placental mitochondrial DNA content: A multi-pollutant approach

BACKGROUND

Placental mitochondrial DNA (mtDNA) content can be indicative of oxidative damage to the placenta during fetal development and is responsive to external stressors. In utero exposure to environmental pollutants that may influence placental mtDNA needs further exploration.

OBJECTIVES

We evaluated if placental mtDNA content is altered by environmental pollution in newborns and identified pollutants independently associated to alterations in placental mtDNA content.

METHODS

mtDNA content was measured in placental tissue of 233 newborns. Four perfluoroalkyl compounds and nine organochlorine compounds were quantified in cord blood plasma samples and six toxic metals in whole cord blood. We first applied a LASSO (least absolute shrinkage and selection operator) penalized regression model to identify independent associations between environmental pollutants and placental mtDNA content, without penalization of several covariates. Then adjusted estimates were obtained using an ordinary least squares (OLS) regression model evaluating the pollutants' association with placental mtDNA content, adjusted for several covariates.

RESULTS

Based on LASSO penalized regression, oxychlordane, p,p'-dichlorodiphenyldichloroethylene, β-hexachlorocyclohexane, perfluorononanoic acid, arsenic, cadmium and thallium were identified to be independently associated with placental mtDNA content. The OLS model showed a higher placental mtDNA content of 2.71% (95% CI: 0.3 to 5.2%; p=0.03) and 1.41% (0.1 to 2.8%, p=0.04) for a 25% concentration increase of respectively cord blood β-hexachlorocyclohexane and arsenic. For a 25% concentration increase of cord blood thallium, a 4.88% lower placental mtDNA content (95% CI: -9.1 to -0.5%, p=0.03) was observed.

CONCLUSION

In a multi-pollutant approach, low fetal exposure levels of environmental organic and inorganic pollutants might compromise placental mitochondrial function as exemplified in this study by alterations in mtDNA content.