Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort

Exposure to PM2.5 and its constituents is associated with metabolic dysfunction-associated fatty liver disease: a cohort study in Northwest of China

Accumulating animal studies have demonstrated associations between ambient air pollution (AP) and metabolic dysfunction-associated fatty liver disease (MAFLD), but relevant epidemiological evidence is limited. We evaluated the association of long-term exposure to AP with the risk of incident MAFLD in Northwest China. The average AP concentration between baseline and follow-up was used to assess individual exposure levels. Cox proportional hazard models and restricted cubic spline functions (RCS) were used to estimate the association of PM2.5 and its constituents with the risk of MAFLD and the dose-response relationship. Quantile g-computation was used to assess the joint effects of mixed exposure to air pollutants on MAFLD and the weights of the various pollutants. We observed 1516 cases of new-onset MAFLD, with an incidence of 10.89%. Increased exposure to pollutants was significantly associated with increased odds of MAFLD, with hazard ratios (HRs) of 2.93 (95% CI: 1.22, 7.00), 2.86 (1.44, 5.66), 7.55 (3.39, 16.84), 4.83 (1.89, 12.38), 3.35 (1.35, 8.34), 1.89 (1.02, 1.62) for each interquartile range increase in PM2.5, SO42-, NO3-, NH4+, OM, and BC, respectively. Stratified analyses suggested that females, frequent exercisers and never-drinkers were more susceptible to MAFLD associated with ambient PM2.5 and its constituents. Mixed exposure to SO42-, NO3-, NH4+, OM and BC was associated with an increased risk of MAFLD, and the weight of BC had the strongest effect on MAFLD. Exposure to ambient PM2.5 and its constituents increased the risk of MAFLD.

Prenatal exposure to polycyclic aromatic hydrocarbons and phthalate acid esters and gestational diabetes mellitus: A prospective cohort study

BACKGROUND

Polycyclic aromatic hydrocarbons and phthalate acid esters (PAHs & PAEs), known as endocrine disrupting chemicals (EDCs), widely exist in daily life and industrial production. Previous studies have suggested that PAHs & PAEs may modify the intrauterine homeostasis and have adverse effects on fetal development. However, epidemiological evidence on the associations between PAHs & PAEs and gestational diabetes mellitus (GDM) is still limited.

OBJECTIVE

To investigate the effects of prenatal PAHs &PAEs exposure on the risk of GDM and hyperglycemia in pregnant women.

METHODS

The study population was a total of 725 pregnant women from a prospective birth cohort study conducted from December 2019 to December 2021. Blood glucose levels were collected by the hospital information system. Urinary PAHs & PAEs concentrations were determined by gas chromatography tandem mass spectrometry. The Poisson regression in a generalized linear model (GLM), multiple linear regression, quantile-based g-computation method (qgcomp), and Bayesian kernel machine regression (BKMR) were applied to explore and verify the individual and overall effects of PAHs & PAEs on glucose homeostasis. Potential confounders were adjusted in all statistical models.

RESULTS

A total of 179 (24.69%) women were diagnosed with GDM. The Poisson regression suggested that a ln-unit increment of 4-OHPHE (4-hydroxyphenanthrene) (adjusted Risk Ratio (aRR) = 1.13; 1.02-1.26) was associated with the increased GDM risk. Mixed-exposure models showed similar results. We additionally found that MBZP (mono-benzyl phthalate) (aRR = 1.19; 1.02-1.39) was positively related to GDM risk in qgcomp model. Although neither model demonstrated that 2-OHNAP (2-hydroxynaphthalene) and 9-OHFLU (9-hydroxyfluorene) increased the risk of GDM, 2-OHNAP and 9-OHFLU exposure significantly increased blood glucose levels. BKMR model further confirmed that overall effects of PAHs & PAEs were significantly associated with the gestational hyperglycemia and GDM risk.

CONCLUSIONS

Our study presents that environmental exposure to PAHs & PAEs was positively associated with gestational glucose levels and the risks of developing GDM. In particular, 2-OHNAP, 9-OHFLU, 4-OHPHE and MBZP may serve as important surveillance markers to prevent the development of GDM.

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.

Increased Risk of Gestational Hypertension by Periconceptional Exposure to Ambient Air Pollution and Effect Modification by Prenatal Depression

BACKGROUND

Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear.

METHODS

We ascertained GH and preeclampsia cases in a prospective pregnancy cohort in Los Angeles, CA. Daily levels of ambient particulate matters (with a diameter of ≤10 μm [PM10] or ≤2.5 μm [PM2.5]), nitrogen dioxide, and ozone were averaged for each week from 12 weeks preconception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability.

RESULTS

Among 619 participants, 60 developed preeclampsia and 42 developed GH. We identified a susceptible window for exposure to PM2.5 from 1 week preconception to 11 weeks postconception: higher exposure (5 µg/m3) within this window was associated with an average of 8% (95% CI, 1%-15%) higher risk of GH. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks postconception with increased risk of GH (PM2.5, 16% [95% CI, 3%-31%]; PM10, 39% [95% CI, 13%-72%]; nitrogen dioxide, 65% [95% CI, 17%-134%]; and ozone, 45% [95% CI, 9%-93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with preeclampsia in any analyses.

CONCLUSIONS

We identified periconception through early pregnancy as a susceptible window of air pollution exposure with an increased risk of GH. Prenatal depression increases vulnerability to air pollution exposure and GH.

A cohort study of the multipollutant effects of PM2.5, NO2, and O3 on C-reactive protein levels during pregnancy

Background

PM2.5, NO2, and O3 contribute to the development of adverse pregnancy complications. While studies have investigated the independent effects of these exposures, literature on their combined effects is limited. Our objective was to study the multipollutant effects of PM2.5, NO2, and O3 on maternal systemic C-reactive protein (CRP) levels.

Methods

We used data from 1170 pregnant women enrolled in the Maternal-Infant Research on Environmental Chemicals Study (MIREC) study in Canada. Air pollution exposures were assigned to each participant based on residential location. CRP was measured in third-trimester blood samples. We fit multipollutant linear regression models and evaluated the effects of air pollutant mixtures (14-day averages) using repeated-holdout Weighted Quantile Sum (WQS) regression and by calculating the Air Quality Health Index (AQHI).

Results

In multipollutant models adjusting for NO2, O3, and green space, each interquartile range (IQR) increase in 14-day average PM2.5 (IQR: 6.9 µg/m3) was associated with 27.1% (95% confidence interval [CI] = 6.2, 50.7) higher CRP. In air pollution mixture models adjusting for green space, each IQR increase in AQHI was associated with 37.7% (95% CI = 13.9, 66.5) higher CRP; and an IQR increase in the WQS index was associated with 78.6% (95% CI = 29.7, 146.0) higher CRP.

Conclusion

PM2.5 has the strongest relationship of the individual pollutants examined with maternal blood CRP concentrations. Mixtures incorporating all three pollutants, assessed using the AQHI and WQS index, showed stronger relationships with CRP compared with individual pollutants and illustrate the importance of conducting multipollutant analyses.

Maternal Exposure to Environmental Air Pollution and Premature Rupture of Membranes: Evidence from Southern China

BACKGROUND Exposure to air pollution (AP) during pregnancy is associated with pre-labor rupture of membranes (PROM). However, there is limited research on this topic, and the sensitive exposure windows remain unclear. The present study assessed the association between AP exposure and the risk of PROM, as well as seeking to identify the sensitive time windows. MATERIAL AND METHODS This retrospective study analyzed 4276 pregnant women's data from Tongling Maternal and Child Health Hospital from 2020 to 2022. We obtained air pollution data, including particulate matter (PM) with an aerodynamic diameter of ≤2.5 μm (PM₂․₅), particulate matter with an aerodynamic diameter of ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃), from the Tongling Ecology and Environment Bureau. Demographic information was extracted from medical records. We employed a distributed lag model to identify the sensitive exposure windows of prenatal AP affecting the risk of PROM. We conducted a sensitivity analysis based on pre-pregnancy BMI. RESULTS We found a significant association between prenatal exposure to AP and increased PROM risk after adjusting for confounders, and the critical exposure windows of AP were the 6th to 7th months of pregnancy. In the underweight group, an increase of 10 µg/m³ in PM₂․₅ was associated with a risk of PROM, with an odds ratio (OR) of 1.48 (95% CI: 1.16, 1.89). Similarly, a 10 µg/m³ increase in PM₁₀ was associated with a risk of PROM, with an OR of 1.45 (95% CI: 1.05, 1.77). CONCLUSIONS Prenatal exposure to AP, particularly during months 6-7 of pregnancy, is associated with an increased risk of PROM. This study extends and strengthens the evidence on the association between prenatal exposure to AP and the risk of PROM, specifically identifying the critical exposure windows.

Gestational and Postpartum Exposure to PM2.5 Components and Glucose Metabolism in Chinese Women: A Prospective Cohort Study

Pregnant women are physiologically prone to glucose intolerance, while the puerperium represents a critical phase for recovery. However, how air pollution disrupts glucose homeostasis during the gestational and early postpartum periods remains unclear. This prospective cohort study conducted an oral glucose tolerance test and measured the insulin levels of 834 pregnant women in Guangzhou, with a follow-up for 443 puerperae at 6-8 weeks postpartum. Residential PM2.5 and five chemical components were estimated by an established spatiotemporal model. The adjusted linear model showed that an IQR increase in gestational PM2.5 exposure was associated with an increase of 0.17 mmol/L (95% CI: 0.06, 0.28) in fasting plasma glucose (FPG) and 0.24 (95% CI: 0.05, 0.42) in the insulin resistance index. Postpartum PM2.5 exposure was linked to a 0.17 mmol/L (95% CI: 0.05, 0.28) elevation in FPG per IQR, with a strengthened association found in women with gestational diabetes (Pinteraction = 0.003). In the quantile-based g-computation model, NO3- consistently contributed to the combined effect of PM2.5 components on gestational and postpartum FPG. This study was the first to suggest that PM2.5 components were associated with exacerbated gestational insulin resistance and elevated postpartum FPG. Targeted interventions reducing the emissions of toxic PM2.5 components are essential to improving maternal glucose metabolism.

The role of liver enzymes in the association between ozone exposure and diabetes risk: a cross-sectional study of Zhuang adults in China

Background: Growing evidence has demonstrated the role of ambient air pollutants in driving diabetes incidence. However, epidemiological evidence linking ozone (O3) exposure to diabetes risk has been scarcely studied in Zhuang adults in China. We aimed to investigate the associations of long-term exposure to O3 with diabetes prevalence and fasting plasma glucose (FPG) and estimate the mediating role of liver enzymes in Zhuang adults. Methods: We recruited 13 843 ethnic minority adults during 2018-2019 based on a cross-sectional study covering nine districts/counties in Guangxi. Generalized linear mixed models were implemented to estimate the relationships between O3 exposure and diabetes prevalence and FPG. Mediation effect models were constructed to investigate the roles of liver enzymes in the associations of O3 exposure with diabetes prevalence and FPG. Subgroup analyses were conducted to identify potential effect modifications. Results: Long-term exposure to O3 was positively associated with diabetes prevalence and FPG levels in Zhuang adults, with an excess risk of 7.32% (95% confidence interval [CI]: 2.56%, 12.30%) and an increase of 0.047 mmol L-1 (95% CI: 0.032, 0.063) for diabetes prevalence and FPG levels, respectively, for each interquartile range (IQR, 1.18 μg m-3) increment in O3 concentrations. Alanine aminotransferase (ALT) significantly mediated 8.10% and 29.89% of the associations of O3 with FPG and diabetes prevalence, respectively, and the corresponding mediation proportions of alkaline phosphatase (ALP) were 8.48% and 30.00%. Greater adverse effects were observed in females, obese subjects, people with a low education level, rural residents, non-clean fuel users, and people with a history of stroke and hypertension in the associations of O3 exposure with diabetes prevalence and/or FPG levels (all P values for interaction < 0.05). Conclusion: Long-term exposure to O3 is related to an increased risk of diabetes, which is partially mediated by liver enzymes in Chinese Zhuang adults. Promoting clean air policies and reducing exposure to environmental pollutants should be a priority for public health policies geared toward preventing diabetes.

Association between particulate air pollution and hypertensive disorders in pregnancy: A retrospective cohort study

BACKGROUND

Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy.

METHODS AND FINDINGS

A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP.

CONCLUSIONS

Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Association of prenatal exposure to PM2.5 and NO2 with gestational diabetes in Western New York

BACKGROUND

Although many studies have examined the association between prenatal air pollution exposure and gestational diabetes (GDM), the relevant exposure windows remain inconclusive. We aim to examine the association between preconception and trimester-specific exposure to PM2.5 and NO2 and GDM risk and explore modifying effects of maternal age, pre-pregnancy body mass index (BMI), smoking, exercise during pregnancy, race and ethnicity, and neighborhood disadvantage.

METHODS

Analyses included 192,508 birth records of singletons born to women without pre-existing diabetes in Western New York, 2004-2016. Daily PM2.5 and NO2 at 1-km2 grids were estimated from ensemble-based models. We assigned each birth with exposures averaged in preconception and each trimester based on residential zip-codes. We used logistic regression to examine the associations and distributed lag models (DLMs) to explore the sensitive windows by month. Relative excess risk due to interaction (RERI) and multiplicative interaction terms were calculated.

RESULTS

GDM was associated with PM2.5 averaged in the first two trimesters (per 2.5 μg/m3: OR = 1.08, 95% CI: 1.01, 1.14) or from preconception to the second trimester (per 2.5 μg/m3: OR = 1.10, 95% CI: 1.03, 1.18). NO2 exposure during each averaging period was associated with GDM risk (per 10 ppb, preconception: OR = 1.10, 95% CI: 1.06, 1.14; first trimester: OR = 1.12, 95% CI: 1.08, 1.16; second trimester: OR = 1.10, 95% CI: 1.06, 1.14). In DLMs, sensitive windows were identified in the 5th and 6th gestational months for PM2.5 and one month before and three months after conception for NO2. Evidence of interaction was identified for pre-pregnancy BMI with PM2.5 (P-for-interaction = 0.023; RERI = 0.21, 95% CI: 0.10, 0.33) and with NO2 (P-for-interaction = 0.164; RERI = 0.16, 95% CI: 0.04, 0.27).

CONCLUSION

PM2.5 and NO2 exposure may increase GDM risk, and sensitive windows may be the late second trimester for PM2.5 and periconception for NO2. Women with higher pre-pregnancy BMI may be more susceptible to exposure effects.

Ambient air pollutants and breast cancer stage in Tehran, Iran

This study aimed to examine the impacts of single and multiple air pollutants (AP) on the severity of breast cancer (BC). Data of 1148 diagnosed BC cases (2008-2016) were obtained from the Cancer Research Center and private oncologist offices in Tehran, Iran. Ambient PM10, SO2, NO, NO2, NOX, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene, and BTEX data were obtained from previously developed land use regression models. Associations between pollutants and stage of BC were assessed by multinomial logistic regression models. An increase of 10 μg/m3 in ethylbenzene, o-xylene, m-xylene, and 10 ppb of NO corresponded to 10.41 (95% CI 1.32-82.41), 4.07 (1.46-11.33), 2.89 (1.08-7.73) and 1.08 (1.00-1.15) increase in the odds of stage I versus non-invasive BC, respectively. Benzene (OR, odds ratio = 1.16, 95% CI 1.01-1.33) and o-xylene (OR = 1.18, 1.02-1.38) were associated with increased odds of incidence of BC stages III & IV versus non-invasive stages. BC stage I and stage III&IV in women living in low SES areas was associated with significantly higher levels of benzene, ethylbenzene, o-xylene, and m-xylene. The highest multiple-air-pollutants quartile was associated with a higher odds of stage I BC (OR = 3.16) in patients under 50 years old. This study provides evidence that exposure to AP is associated with increased BC stage at diagnosis, especially under premenopause age.

[Association analysis between mixed exposure to phenols and semen quality]

Phenols such as bisphenols, parabens, and triclosan are common environmental endocrine disruptors. Previous epidemiological studies have suggested that phenols may affect semen quality, but the results were inconsistent. In addition, most existing studies have been limited to the effects of a single chemical compound, ignoring the health effects of mixed exposure to multiple chemicals. Thus, we aimed to explore the associations between individual and mixed exposure to phenols and various semen quality parameters. In this study, a rapid and sensitive method was used to determine 18 phenolic compounds in urine samples of 799 volunteers who donated sperm samples to the Shanghai Human Sperm Bank. A spot urine sample was collected from each subject on the day of their clinic visit and stored at -20 ℃ until testing. Urine samples (200 μL) were extracted and added with 20 μL of an internal standard and 50 μL of β-glucuronidase solution. The mixtures were then incubated for 12 h at 37 ℃. After hydrolysis, the samples were extracted twice using ethyl acetate (500 μL). The concentrations of the 18 phenolic compounds were measured using high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Semen quality parameters were analyzed using a computer-aided semen analyzer. Multiple linear regressions were used to detect the associations between individual phenol exposure and semen quality parameters. In addition, weighted quantile sum (WQS) models were used to explore the associations between mixed-phenol exposure and semen quality parameters. After adjusting for potential covariates, the results of multiple linear regressions showed that exposure to ethyl paraben (EtP) was significantly negatively associated with sperm concentration and total sperm count (P<0.05). In addition, exposure to mixed phenols was significantly associated with decreased sperm concentration; methyl paraben (MeP) and EtP were identified as the main contributors to this decrease. Thus, phenol exposure may be associated with decreased semen quality in young males, particularly with respect to sperm concentration and total sperm count.

A prediction model for classifying maternal pregnancy smoking using California state birth certificate information

BACKGROUND

Systematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates.

OBJECTIVE

To develop a prediction model for maternal tobacco smoking in late pregnancy based on birth certificate information using a combination of self- or provider-reported smoking and biomarkers (smoking metabolites) in neonatal blood spots as the alloyed gold standard.

METHODS

We designed a case-control study where childhood cancer cases were identified from the California Cancer Registry and controls were from the California birth rolls between 1983 and 2011 who were cancer-free by the age of six. In this analysis, we included 894 control participants and performed high-resolution metabolomics analyses in their neonatal dried blood spots, where we extracted cotinine [mass-to-charge ratio (m/z) = 177.1023] and hydroxycotinine (m/z = 193.0973). Potential predictors of smoking were selected from California birth certificates. Logistic regression with stepwise backward selection was used to build a prediction model. Model performance was evaluated in a training sample, a bootstrapped sample, and an external validation sample.

RESULTS

Out of seven predictor variables entered into the logistic model, five were selected by the stepwise procedure: maternal race/ethnicity, maternal education, child's birth year, parity, and child's birth weight. We calculated an overall discrimination accuracy of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval [CI] 0.77, 0.84) in the training set. Similar accuracies were achieved in the internal (AUC 0.81, 95% CI 0.77, 0.84) and external (AUC 0.69, 95% CI 0.64, 0.74) validation sets.

CONCLUSIONS

This easy-to-apply model may benefit future birth registry-based studies when there is missing maternal smoking information; however, some smoking status misclassification remains a concern when only variables from the birth certificate are used to predict maternal smoking.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Effect modification of dietary diversity on the association of air pollution with incidence, complications, and mortality of type 2 diabetes: Results from a large prospective cohort study

BACKGROUND

It remains unknown whether the dietary diversity score (DDS) could modify the association of long-term exposure to individual air pollutants and the mixture of various pollutants with the incidence, complications, and mortality of type 2 diabetes (T2D).

METHODS

We included 162,579 participants from the UK Biobank who had ≥ one 24-h dietary assessment and were free of diabetes or diabetes complications before their last response date of the 24-h dietary assessment. Exposure to benzene, NOx, NO2, SO2, PM10, and PM2.5 was estimated at each participant's residential location using a bilinear interpolation algorithm based on air dispersion models on a 1 km × 1 km grid. The DDS was calculated based on repeated 24-h dietary assessments. The outcomes were the incidence, complications, and mortality of T2D. Associations of individual pollutants and multiple pollutants mixtures with outcomes were assessed using Cox proportional hazards regression models and the quantile g-computation approach, respectively. We further stratified these analyses by DDS.

RESULTS

During a median of 10.1 years of follow-up, 2978 participants developed incident T2D, 1181 developed T2D complications, and 242 died due to T2D. Long-term single-pollutant and multi-pollutant exposure were associated with elevated risk of incidence, complications, and mortality of T2D. For example, for incident T2D, the hazard ratio and 95 % confidence interval for each quantile increase were 1.155 (1.095, 1.215) for the air pollution mixture. We observed significant interactions between air pollution (benzene, NOx, NO2, PM10, PM2.5, and the air pollution mixture) and DDS (P-interaction <0.05), with the corresponding associations being significantly weaker in adults with high DDS than in those with low DDS.

CONCLUSION

Higher dietary diversity may attenuate the harmful impacts of air pollution on T2D-related outcomes. A higher diversity diet could be used to prevent the onset and progression of T2D induced by long-term exposure to various air pollutants.

Assessment of potential risk factors associated with gestational diabetes mellitus: evidence from a Mendelian randomization study

Background

Previous research on the association between risk factors and gestational diabetes mellitus (GDM) primarily comprises observational studies with inconclusive results. The objective of this study is to investigate the causal relationship between 108 traits and GDM by employing a two-sample Mendelian randomization (MR) analysis to identify potential risk factors of GDM.

Methods

We conducted MR analyses to explore the relationships between traits and GDM. The genome-wide association studies (GWAS) for traits were primarily based on data from the UK Biobank (UKBB), while the GWAS for GDM utilized data from FinnGen. We employed a false discovery rate (FDR) of 5% to account for multiple comparisons.

Results

The inverse-variance weighted (IVW) method indicated that the genetically predicted 24 risk factors were significantly associated with GDM, such as "Forced expiratory volume in 1-second (FEV1)" (OR=0.76; 95% CI: 0.63, 0.92), "Forced vital capacity (FVC)" (OR=0.74; 95% CI: 0.64, 0.87), "Usual walking pace" (OR=0.19; 95% CI: 0.09, 0.39), "Sex hormone-binding globulin (SHBG)" (OR=0.86; 95% CI: 0.78, 0.94). The sensitivity analyses with MR-Egger and weighted median methods indicated consistent results for most of the trats.

Conclusion

Our study has uncovered a significant causal relationship between 24 risk factors and GDM. These results offer a new theoretical foundation for preventing or mitigating the risks associated with GDM.

The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China

Emerging research findings suggest that airborne particulate matter might be a risk factor for gestational diabetes mellitus (GDM). However, the concentration-response relationships and the susceptible time windows for different types of particulate matter may vary. In this retrospective analysis, we employ a novel robust approach to assess the crucial time windows regarding the prevalence of GDM and to distinguish the susceptibility of three GDM subtypes to air pollution exposure. This study included 16,303 pregnant women who received routine antenatal care in 2018-2021 at the Maternal and Child Health Hospital in Chongqing, China. In total, 2482 women (15.2%) were diagnosed with GDM. We assessed the individual daily average exposure to air pollution, including PM2.5, PM10, O3, NO2, SO2, and CO based on the volunteers' addresses. We used high-accuracy gridded air pollution data generated by machine learning models to assess particulate matter per maternal exposure levels. We further analyzed the association of pre-pregnancy, early, and mid-pregnancy exposure to environmental pollutants using a generalized additive model (GAM) and distributed lag nonlinear models (DLNMs) to analyze the association between exposure at specific gestational weeks and the risk of GDM. We observed that, during the first trimester, per IQR increases for PM10 and PM2.5 exposure were associated with increased GDM risk (PM10: OR = 1.19, 95%CI: 1.07~1.33; PM2.5: OR = 1.32, 95%CI: 1.15~1.50) and isolated post-load hyperglycemia (GDM-IPH) risk (PM10: OR = 1.23, 95%CI: 1.09~1.39; PM2.5: OR = 1.38, 95%CI: 1.18~1.61). Second-trimester O3 exposure was positively correlated with the associated risk of GDM, while pre-pregnancy and first-trimester exposure was negatively associated with the risk of GDM-IPH. Exposure to SO2 in the second trimester was negatively associated with the risk of GDM-IPH. However, there were no observed associations between NO2 and CO exposure and the risk of GDM and its subgroups. Our results suggest that maternal exposure to particulate matter during early pregnancy and exposure to O3 in the second trimester might increase the risk of GDM, and GDM-IPH is the susceptible GDM subtype to airborne particulate matter exposure.

Association of Antepartum and Postpartum Air Pollution Exposure With Postpartum Depression in Southern California

Importance

Women are especially vulnerable to mental health matters post partum because of biological, emotional, and social changes during this period. However, epidemiologic evidence of an association between air pollution exposure and postpartum depression (PPD) is limited.

Objective

To examine the associations between antepartum and postpartum maternal air pollution exposure and PPD.

Design, Setting, and Participants

This retrospective cohort study used data from Kaiser Permanente Southern California (KPSC) electronic health records and included women who had singleton live births at KPSC facilities between January 1, 2008, and December 31, 2016. Data were analyzed between January 1 and May 10, 2023.

Exposures

Ambient air pollution exposures were assessed based on maternal residential addresses using monthly averages of particulate matter less than or equal to 2.5 μm (PM2.5), particulate matter less than or equal to 10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) from spatial interpolation of monitoring station measurements. Constituents of PM2.5 (sulfate, nitrate, ammonium, organic matter, and black carbon) were obtained from fine-resolution geoscience-derived models based on satellite, ground-based monitor, and chemical transport modeling data.

Main Outcomes and Measures

Participants with an Edinburgh Postnatal Depression Scale score of 10 or higher during the 6 months after giving birth were referred to a clinical interview for further assessment and diagnosis. Ascertainment of PPD was defined using a combination of diagnostic codes and prescription medications.

Results

The study included 340 679 participants (mean [SD] age, 30.05 [5.81] years), with 25 674 having PPD (7.54%). Increased risks for PPD were observed to be associated with per-IQR increases in antepartum and postpartum exposures to O3 (adjusted odds ratio [AOR], 1.09; 95% CI, 1.06-1.12), PM10 (AOR, 1.02; 95% CI, 1.00-1.04), and PM2.5 (AOR, 1.02; 95% CI, 1. 00-1.03) but not with NO2; PPD risks were mainly associated with PM2.5 organic matter and black carbon. Overall, a higher risk of PPD was associated with O3 during the entire pregnancy and postpartum periods and with PM exposure during the late pregnancy and postpartum periods.

Conclusions and Relevance

The study findings suggest that long-term exposure to antepartum and postpartum air pollution was associated with higher PPD risks. Identifying the modifiable environmental risk factors and developing interventions are important public health issues to improve maternal mental health and alleviate the disease burden of PPD.

Exposure to parabens and semen quality in reproductive-aged men

BACKGROUND

Parabens are common preservatives in personal care products, cosmetics, and medical goods. In the past few years, animal studies showed the male reproductive toxicity associated with some parabens. Yet, epidemiological studies have generated inconsistent findings and research rarely has focused on the mixture effects of the parabens. We aimed to explore the associations between individual paraben exposure as well as the mixture and semen quality parameters.

METHODS

A total of 795 male partners from preconception couples were included in the study. Their urine samples were analyzed for the concentrations of six parabens, namely methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP), butyl paraben (BuP), benzyl paraben (BzP) and heptyl paraben (HeP). Multiple linear regression models and weighted quantile sum regression (WQS) models were utilized to assess the relationships between individual paraben exposure and paraben mixture with semen quality parameters, respectively.

RESULTS

After adjusting for covariates, exposure to a paraben mixture was significantly associated with declining sperm concentration, total sperm count, and progressive motility, among which BuP was identified as the main contributor to sperm concentration and total sperm count while MeP to progressive motility. Results from multiple linear regression models were generally in line with the WQS analysis.

CONCLUSIONS

Our results suggest negative associations between paraben mixture and sperm concentration, total sperm count, and sperm motility among reproductive-aged men.

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity

Importance

The rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking.

Objective

To examine associations between long- and short-term maternal heat exposure and SMM.

Design, Setting, and Participants

This retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included.

Exposures

Moderate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week.

Main Outcomes and Measures

The primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms.

Results

There were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P < .001) and 28% (95% CI, 17%-41%; P < .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature >75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P < .001) to the most severe heatwave exposure (HWD9: daily maximum temperature >95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P < .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P < .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P < .001).

Conclusions and Relevance

In this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.

Preconceptional and prenatal exposure to air pollutants and risk of gestational diabetes in the MADRES prospective pregnancy cohort study

Background

Air pollution has been associated with gestational diabetes mellitus (GDM). We aim to investigate susceptible windows of air pollution exposure and factors determining population vulnerability.

Methods

We ascertained GDM status in the prospective Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort from Los Angeles, California, USA. We calculated the relative risk of GDM by exposure to ambient particulate matter (PM10; PM2.5), nitrogen dioxide (NO2), and ozone (O3) in each week from 12 weeks before to 24 weeks after conception, adjusting for potential confounders, with distributed lag models to identify susceptible exposure windows. We examined effect modification by prenatal depression, median-split pre-pregnancy BMI (ppBMI) and age.

Findings

Sixty (9.7%) participants were diagnosed with GDM among 617 participants (mean age: 28.2 years, SD: 5.9; 78.6% Hispanic, 11.8% non-Hispanic Black). GDM risk increased with exposure to PM2.5, PM10, and NO2 in a periconceptional window ranging from 5 weeks before to 5 weeks after conception: interquartile-range increases in PM2.5, PM10, and NO2 during this window were associated with increased GDM risk by 5.7% (95% CI: 4.6-6.8), 8.9% (8.1-9.6), and 15.0% (13.9-16.2), respectively. These sensitive windows generally widened, with greater effects, among those with prenatal depression, with age ≥28 years, or with ppBMI ≥27.5 kg/m2, than their counterparts.

Interpretation

Preconception and early-pregnancy are susceptible windows of air pollutants exposure that increased GDM risk. Prenatal depression, higher age, or higher ppBMI may increase one's vulnerability to air pollution-associated GDM risk.

Funding

National Institutes of Health, Environmental Protection Agency.

Effects of Air Pollution Exposure during Preconception and Pregnancy on Gestational Diabetes Mellitus

This study aimed to investigate the association between air pollution and gestational diabetes mellitus (GDM) in small- and medium-sized cities, identify sensitive periods and major pollutants, and explore the effects of air pollution on different populations. A total of 9820 women who delivered in Handan Maternal and Child Health Hospital in the Hebei Province from February 2018 to July 2020 were included in the study. Logistic regression and principal component logistic regression models were used to assess the effects of air pollution exposure during preconception and pregnancy on GDM risk and the differences in the effects across populations. The results suggested that each 20 μg/m3 increase in PM2.5 and PM10 exposure during preconception and pregnancy significantly increased the risk of GDM, and a 10 μg/m3 increase in NO2 exposure during pregnancy was also associated with the risk of GDM. In a subgroup analysis, pregnant women aged 30-35 years, nulliparous women, and those with less than a bachelor's education were the most sensitive groups. This study provides evidence for an association between air pollution and the prevalence of GDM, with PM2.5, PM10, and NO2 as risk factors for GDM.

Independent and interactive effects of ozone and thermal inversion exposure on the risk of gestational diabetes mellitus in Wuhan, China

The adverse effects of exposure to thermal inversion (TI) and ozone (O3) on human health have been reported; however, there are few studies have explored the independent and potential interactive effects of them on gestational diabetes mellitus (GDM). A total of 31,262 pregnant women from the Wuhan Children's Hospital covering the period from 2017 to 2021 were included in this study. The logistic regression adjusted for the covariates was applied to explore the independent effect of exposure to O3 and TI on GDM. The relative excess risk due to the interaction (RERI) analysis was applied to assess the possible interactive effect. Per 10 μg/m3 increased in O3 (OR = 1.069, 95% CI: 1.049, 1.089) during the first trimester and per 10 days increased in TI (OR = 1.041, 95% CI: 1.005, 1.080) in the second trimester were significantly associated with the risk of GDM. The synergistic effect of exposure to TI and O3 was larger than their sum effect (RERI = 0.330, 95% CI: 0.170, 0.476). This study added further support for public health-related policy to improve maternal health by curbing TI and O3.

Combined exposure to PM2.5 and PM10 in reductions of physiological development among preterm birth: a retrospective study from 2014 to 2017 in China

Background

Preterm birth (PTB) has been linked with ambient particulate matter (PM) exposure. However, data are limited between physiological development of PTB and PM exposure.

Methods

Trimester and season-specific PM exposure including PM2.5 and PM10 was collected from Jiaxing between January 2014 and December 2017. Information about parents and 3,054 PTB (gestational age < 37 weeks) outcomes such as weight (g), head circumference (cm), chest circumference (cm), height (cm) and Apgar 5 score were obtained from birth records. We used generalized linear models to assess the relationship between PTB physiological developmental indices and PM2.5, PM10 and their combined exposures. A binary logistic regression model was performed to assess the association between exposures and low birth weight (LBW, < 2,500 g).

Results

Results showed that there were 75.5% of low birth weight (LBW) infants in PTB. Decreased PM2.5 and PM10 levels were found in Jiaxing from 2014 to 2017, with a higher PM10 level than PM2.5 each year. During the entire pregnancy, the highest median concentration of PM2.5 and PM10 was in winter (61.65 ± 0.24 vs. 91.65 ± 0.29 μg/m3) followed by autumn, spring and summer, with statistical differences in trimester-specific stages. After adjusting for several potential factors, we found a 10 μg/m3 increase in joint exposure of PM2.5 and PM10 during the entire pregnancy associated with reduced 0.02 week (95%CI: -0.05, -0.01) in gestational age, 7.9 g (95%CI: -13.71, -2.28) in birth weight, 0.8 cm in height (95%CI: -0.16, -0.02), 0.05 cm (95%CI: -0.08, - 0.01) in head circumference, and 0.3 (95%CI: -0.04, -0.02) in Apgar 5 score, except for the chest circumference. Trimester-specific exposure of PM2.5 and PM10 sometimes showed an opposite effect on Additionally, PM2.5 (OR = 1.37, 95%CI: 1.11, 1.68) was correlated with LBW.

Conclusion

Findings in this study suggest a combined impact of fine particulate matter exposure on neonatal development, which adds to the current understanding of PTB risk and health.

The Combined Effects of Short-Term Exposure to Multiple Meteorological Factors on Unintentional Drowning Mortality: Large Case-Crossover Study

BACKGROUND

Drowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear.

OBJECTIVE

We aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality.

METHODS

Unintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors.

RESULTS

A total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and -9.5% to the overall effects, respectively.

CONCLUSIONS

This study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Associations of air pollution mixtures with ambulatory blood pressure: The MobiliSense sensor-based study

Air pollution is acknowledged as a determinant of blood pressure (BP), supporting the hypothesis that air pollution, via hypertension and other mechanisms, has detrimental effects on human health. Previous studies evaluating the associations between air pollution exposure and BP did not consider the effect that air pollutant mixtures may have on BP. We investigated the effect of exposure to single species or their synergistic effects as air pollution mixture on ambulatory BP. Using portable sensors, we measured personal concentrations of black carbon (BC), nitrogen dioxide (NO2), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O3), and particles with aerodynamic diameters below 2.5 μm (PM2.5). We simultaneously collected ambulatory BP measurements (30-min intervals, N = 3319) of 221 participants over one day of their lives. Air pollution concentrations were averaged over 5 min to 1 h before each BP measurement, and inhaled doses were estimated across the same exposure windows using estimated ventilation rates. Fixed-effect linear models as well as quantile G-computation techniques were applied to associate air pollutants' individual and combined effects with BP, adjusting for potential confounders. In mixture models, a quartile increase in air pollutant concentrations (BC, NO2, NO, CO, and O3) in the previous 5 min was associated with a 1.92 mmHg (95% CI: 0.63, 3.20) higher systolic BP (SBP), while 30-min and 1-h exposures were not associated with SBP. However, the effects on diastolic BP (DBP) were inconsistent across exposure windows. Unlike concentration mixtures, inhalation mixtures in the previous 5 min to 1 h were associated with increased SBP. Out-of-home BC and O3 concentrations were more strongly associated with ambulatory BP outcomes than in-home concentrations. In contrast, only the in-home concentration of CO reduced DBP in stratified analyses. This study shows that exposure to a mixture of air pollutants (concentration and inhalation) was associated with elevated SBP.

Joint effect of ambient PM2.5 exposure and vitamin B12 during pregnancy on the risk of gestational diabetes mellitus

BACKGROUND

Evidence has indicated that the risk of gestational diabetes mellitus (GDM) was linked to PM_2.5 exposure during pregnancy, but findings on susceptible exposure windows are inconsistent. Further, previous studies have not paid attention to B₁₂ intake in the relationship between PM_2.5 exposure and GDM. The study is aimed to identify the strength and exposure periods for associations of PM_2.5 exposure with GDM, followed by exploring the potential interplay of gestational B₁₂ levels and PM_2.5 exposure on the risk of GDM.

METHODS

The participants were recruited in a birth cohort between 2017 and 2018, and 1396 eligible pregnant women who completed a 75-g oral glucose tolerance test (OGTT) were included. Prenatal PM_2.5 concentrations were estimated using an established spatiotemporal model. Logistic and linear regression analyses were used to test associations of gestational PM_2.5 exposure with GDM and OGTT-glucose levels, respectively. The joint associations of gestational PM_2.5 exposure and B₁₂ level on GDM were examined under crossed exposure combinations of PM_2.5 (high versus low) and B₁₂ (insufficient versus sufficient).

RESULTS

In the 1396 pregnant women, the median levels of PM_2.5 exposure during the 12 weeks before pregnancy, the 1st trimester, and the 2nd trimesters were 59.33 μg/m³, 63.44 μg/m³, and 64.39 μg/m³, respectively. The risk of GDM was significantly associated with a 10 μg/m³ increase of PM_2.5 during the 2nd trimester (RR = 1.44, 95 % CI: 1.01, 2.04). The percentage change in fasting glucose was also associated with PM_2.5 exposure during the 2nd trimester. A higher risk of GDM was observed among women with high PM_2.5 exposure and insufficient B₁₂ levels than those with low PM_2.5 and sufficient B₁₂.

CONCLUSION

The study supported higher PM_2.5 exposure during the 2nd trimester is significantly associated with GDM risk. It first highlighted insufficient B₁₂ status might enhance adverse effects of air pollution on GDM.

Examining the Relationship Between Extreme Temperature, Microclimate Indicators, and Gestational Diabetes Mellitus in Pregnant Women Living in Southern California

Few studies have assessed extreme temperatures' impact on gestational diabetes mellitus (GDM). We examined the relation between GDM risk with weekly exposure to extreme high and low temperatures during the first 24 weeks of gestation and assessed potential effect modification by microclimate indicators.

Methods

We utilized 2008-2018 data for pregnant women from Kaiser Permanente Southern California electronic health records. GDM screening occurred between 24 and 28 gestational weeks for most women using the Carpenter-Coustan criteria or the International Association of Diabetes and Pregnancy Study Groups criteria. Daily maximum, minimum, and mean temperature data were linked to participants' residential address. We utilized distributed lag models, which assessed the lag from the first to the corresponding week, with logistic regression models to examine the exposure-lag-response associations between the 12 weekly extreme temperature exposures and GDM risk. We used the relative risk due to interaction (RERI) to estimate the additive modification of microclimate indicators on the relation between extreme temperature and GDM risk.

Results

GDM risks increased with extreme low temperature during gestational weeks 20--24 and with extreme high temperature at weeks 11-16. Microclimate indicators modified the influence of extreme temperatures on GDM risk. For example, there were positive RERIs for high-temperature extremes and less greenness, and a negative RERI for low-temperature extremes and increased impervious surface percentage.

Discussion

Susceptibility windows to extreme temperatures during pregnancy were observed. Modifiable microclimate indicators were identified that may attenuate temperature exposures during these windows, which could in turn reduce the health burden from GDM.

Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California

Background

Little research exists regarding the relationships between green space and postpartum depression (PPD). We aimed to investigate the relationships between PPD and green space exposure, and the mediating role of physical activity (PA).

Methods

Clinical data were obtained from Kaiser Permanente Southern California electronic health records in 2008-2018. PPD ascertainment was based on both diagnostic codes and prescription medications. Maternal residential green space exposures were assessed using street view-based measures and vegetation types (i.e., street tree, low-lying vegetation, and grass), satellite-based measures [i.e., Normalized Difference Vegetation Index (NDVI), land-cover green space, and tree canopy cover], and proximity to the nearest park. Multilevel logistic regression was applied to estimate the association between green space and PPD. A causal mediation analysis was performed to estimate the proportion mediated by PA during pregnancy in the total effects of green space on PPD.

Findings

In total, we included 415,020 participants (30.2 ± 5.8 years) with 43,399 (10.5%) PPD cases. Hispanic mothers accounted for about half of the total population. A reduced risk for PPD was associated with total green space exposure based on street-view measure [500 m buffer, adjusted odds ratio (OR) per interquartile range: 0.98, 95% CI: 0.97-0.99], but not NDVI, land-cover greenness, or proximity to a park. Compared to other types of green space, tree coverage showed stronger protective effects (500 m buffer, OR = 0.98, 95% CI: 0.97-0.99). The proportions of mediation effects attributable to PA during pregnancy ranged from 2.7% to 7.2% across green space indicators.

Interpretation

Street view-based green space and tree coverage were associated with a decreased risk of PPD. The observed association was primarily due to increased tree coverage, rather than low-lying vegetation or grass. Increased PA was a plausible pathway linking green space to lower risk for PPD.

Funding

National Institute of Environmental Health Sciences (NIEHS; R01ES030353).

Ambient air pollution and gestational diabetes mellitus: An updated systematic review and meta-analysis

OBJECTIVE

We aimed to evaluate the relationship between the composition of particulate matter (PM) and gestational diabetes mellitus (GDM) by a comprehensively review of epidemiological studies.

METHODS

We systematically identified cohort studies related to air pollution and GDM risk before February 8, 2023 from six databases (PubMed, Embase, Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and Chongqing VIP Chinese Science and Technology Periodical databases). We calculated the relative risk (RR) and its 95% confidence intervals (CIs) to assess the overall effect by using a random effects model.

RESULTS

This meta-analysis of 31 eligible cohort studies showed that exposure to PM_2.5, PM₁₀, SO₂, and NO₂ was associated with a significantly increased risk of GDM, especially in preconception and first trimester. Analysis of the components of PM_2.5 found that the risk of GDM was strongly linked to black carbon (BC) and nitrates (NO₃^-). Specifically, BC exposure in the second trimester and NO₃^- exposure in the first trimester elevated the risk of GDM, with the RR of 1.128 (1.032-1.231) and 1.128 (1.032-1.231), respectively. The stratified analysis showed stronger correlations of GDM risk with higher levels of pollutants in Asia, except for PM_2.5 and BC, which suggested that the specific composition of particulate pollutants had a greater effect on the exposure-outcome association than the concentration.

CONCLUSIONS

Our study found that ambient air pollutant is a critical factor for GDM and further studies on specific particulate matter components should be considered in the future.

The effect of ambient ozone exposure on three types of diabetes: a meta-analysis

BACKGROUND

Ozone as an air pollutant is gradually becoming a threat to people's health. However, the effect of ozone exposure on risk of developing diabetes, a fast-growing global metabolic disease, remains controversial.

OBJECTIVE

To evaluate the impact of ambient ozone exposure on the incidence rate of type 1, type 2 and gestational diabetes mellitus.

METHOD

We systematically searched PubMed, Web of Science, and Cochrane Library databases before July 9, 2022, to determine relevant literature. Data were extracted after quality evaluation according to the Newcastle Ottawa Scale (NOS) and the agency for healthcare research and quality (AHRQ) standards, and a meta-analysis was used to evaluate the correlation between ozone exposure and type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D), and gestational diabetes mellitus (GDM). The heterogeneity test, sensitivity analysis, and publication bias were performed using Stata 16.0.

RESULTS

Our search identified 667 studies from three databases, 19 of which were included in our analysis after removing duplicate and ineligible studies. Among the remaining studies, three were on T1D, five were on T2D, and eleven were on GDM. The result showed that ozone exposure was positively correlated with T2D [effect size (ES) = 1.06, 95% CI: 1.02, 1.11] and GDM [pooled odds ratio (OR) = 1.01, 95% CI: 1.00, 1.03]. Subgroup analysis demonstrated that ozone exposure in the first trimester of pregnancy might raise the risk of GDM. However, no significant association was observed between ozone exposure and T1D.

CONCLUSION

Long-term exposure to ozone may increase the risk of T2D, and daily ozone exposure during pregnancy was a hazard factor for developing GDM. Decreasing ambient ozone pollution may reduce the burden of both diseases.

Critical windows of exposure to air pollution and gestational diabetes: assessing effect modification by maternal pre-existing conditions and environmental factors

BACKGROUND

Ambient air pollution has been associated with gestational diabetes (GD), but critical windows of exposure and whether maternal pre-existing conditions and other environmental factors modify the associations remains inconclusive.

METHODS

We conducted a retrospective cohort study of all singleton live birth that occurred between April 1^st 2006 and March 31^st 2018 in Ontario, Canada. Ambient air pollution data (i.e., fine particulate matter with a diameter ≤ 2.5 μm (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃)) were assigned to the study population in spatial resolution of approximately 1 km × 1 km. The Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI) were also used to characterize residential exposure to green space as well as the Active Living Environments (ALE) index to represent the active living friendliness. Multivariable Cox proportional hazards regression models were used to evaluate the associations.

RESULTS

Among 1,310,807 pregnant individuals, 68,860 incident cases of GD were identified. We found the strongest associations between PM_2.5 and GD in gestational weeks 7 to 18 (HR = 1.07 per IQR (2.7 µg/m³); 95% CI: 1.02 - 1.11)). For O₃, we found two sensitive windows of exposure, with increased risk in the preconception period (HR = 1.03 per IQR increase (7.0 ppb) (95% CI: 1.01 - 1.06)) as well as gestational weeks 9 to 28 (HR 1.08 per IQR (95% CI: 1.04 -1.12)). We found that women with asthma were more at risk of GD when exposed to increasing levels of O₃ (p- value for effect modification = 0.04). Exposure to air pollutants explained 20.1%, 1.4% and 4.6% of the associations between GVI, NDVI and ALE, respectively.

CONCLUSION

An increase of PM_2.5 exposure in early pregnancy and of O₃ exposure during late first trimester and over the second trimester of pregnancy were associated with gestational diabetes whereas exposure to green space may confer a protective effect.

Identification of Postpartum Depression in Electronic Health Records: Validation in a Large Integrated Health Care System

BACKGROUND

The accuracy of electronic health records (EHRs) for identifying postpartum depression (PPD) is not well studied.

OBJECTIVE

This study aims to evaluate the accuracy of PPD reporting in EHRs and compare the quality of PPD data collected before and after the implementation of the International Classification of Diseases, Tenth Revision (ICD-10) coding in the health care system.

METHODS

Information on PPD was extracted from a random sample of 400 eligible Kaiser Permanente Southern California patients' EHRs. Clinical diagnosis codes and pharmacy records were abstracted for two time periods: January 1, 2012, through December 31, 2014 (International Classification of Diseases, Ninth Revision [ICD-9] period), and January 1, 2017, through December 31, 2019 (ICD-10 period). Manual chart reviews of clinical records for PPD were considered the gold standard and were compared with corresponding electronically coded diagnosis and pharmacy records using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Kappa statistic was calculated to measure agreement.

RESULTS

Overall agreement between the identification of depression using combined diagnosis codes and pharmacy records with that of medical record review was strong (κ=0.85, sensitivity 98.3%, specificity 83.3%, PPV 93.7%, NPV 95.0%). Using only diagnosis codes resulted in much lower sensitivity (65.4%) and NPV (50.5%) but good specificity (88.6%) and PPV (93.5%). Separately, examining agreement between chart review and electronic coding among diagnosis codes and pharmacy records showed sensitivity, specificity, and NPV higher with prescription use records than with clinical diagnosis coding for PPD, 96.5% versus 72.0%, 96.5% versus 65.0%, and 96.5% versus 65.0%, respectively. There was no notable difference in agreement between ICD-9 (overall κ=0.86) and ICD-10 (overall κ=0.83) coding periods.

CONCLUSIONS

PPD is not reliably captured in the clinical diagnosis coding of EHRs. The accuracy of PPD identification can be improved by supplementing clinical diagnosis with pharmacy use records. The completeness of PPD data remained unchanged after the implementation of the ICD-10 diagnosis coding.

BIOMARKERS OF MATERNAL SMOKING AND THE RISK OF RETINOBLASTOMA IN OFFSPRING

PURPOSE

Previous studies examining the risk of retinoblastoma with maternal smoking were inconclusive, likely due in part to the reliance on self-reported maternal smoking. This study uses biomarkers of tobacco smoking in neonatal dried blood spots to investigate associations between maternal smoking and retinoblastoma in offspring.

METHODS

The authors randomly selected 498 retinoblastoma cases and 895 control subjects born between 1983 and 2011 from a population-based case-control study in California. Maternal pregnancy-related smoking was measured using the following three metrics: provider or self-reported smoking during pregnancy, cotinine, and hydroxycotinine in neonatal blood. The authors used multivariable logistic regression to estimate the effects of maternal tobacco smoking on retinoblastoma.

RESULTS

Using all metrics (biomarkers or self-report), maternal smoking late in pregnancy or early postpartum was related to retinoblastoma (all types; odds ratio = 1.44, 95% confidence interval: 1.00-2.09). Relying on cotinine or hydroxycotinine to ascertain smoking, maternal smoking was related to unilateral retinoblastoma (odds ratio = 1.66, 95% confidence interval: 1.08-2.57).

CONCLUSION

The results indicate that maternal smoking during pregnancy may be a risk factor for retinoblastoma, particularly among unilateral cases.

Associations of ozone exposure with gestational diabetes mellitus and glucose homeostasis: Evidence from a birth cohort in Shanghai, China

BACKGROUND

Associations between individual exposure to ozone (O₃) and gestational diabetes mellitus (GDM) have rarely been investigated, and critical windows of O₃ exposure for GDM have not been identified.

OBJECTIVES

We aimed to explore the associations of gestational O₃ exposure with GDM and glucose homeostasis as well as to identify the potential critical windows.

METHODS

A total of 7834 pregnant women were included. Individual O₃ exposure concentrations were evaluated using a high temporal-spatial resolution model. Each participant underwent an oral glucose tolerance test (OGTT) to screen for GDM between 24 and 28 gestational weeks. Multiple logistic and multiple linear regression models were used to estimate the associations of O₃ with GDM risks and with blood glucose levels of OGTT, respectively. Distributed lag nonlinear models (DLNMs) were used to estimate the critical windows of O₃ exposure for GDM.

RESULTS

Nearly 13.29 % of participants developed GDM. After controlling for covariates, we observed increased GDM risks per IQR increment of O₃ exposure in the first trimester (OR = 1.738, 95 % CI: 1.002-3.016) and the first two trimesters (OR = 1.576, 95 % CI: 1.005-2.473). Gestational O₃ exposure was positively associated with increased fasting blood glucose (the first trimester: β = 2.964, 95 % CI: 1.529-4.398; the first two trimesters: β = 1.620, 95 % CI: 0.436-2.804) and 2 h blood glucose (the first trimester: β = 6.569, 95 % CI: 1.775-11.363; the first two trimesters: β = 6.839, 95 % CI: 2.896-10.782). We also observed a concentration-response relationship of gestational O₃ exposure with GDM risk, as well as fasting and 2 h blood glucose levels. Additionally, 5-10 gestational weeks was identified as a critical window of O₃ exposure for GDM development.

CONCLUSION

In summary, we found that gestational O₃ exposure disrupts glucose homeostasis and increases the risk of GDM in pregnant women. Furthermore, 5-10 gestational weeks could be a critical window for the effects of O₃ exposure on GDM.

Association of air pollution and fine particulate matter (PM2.5) exposure with gestational diabetes: a systematic review and meta-analysis

Background

The association between air pollution (AP) and gestational diabetes mellitus (GDM), especially between different pollutants and GDM, remains controversial and debatable. Hence, we conducted this systematic review and meta-analysis to provide comprehensive evidence-based support for the association between AP and GDM.

Methods

The databases of the Cochrane Library, Embase, PubMed, and Web of Science were searched from inception to 1 April 2022, in combination with manual retrieval. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of case-control studies and cohort studies, while the Joana Brigg's Institute (JBI) critical appraisal checklist was used for the quality assessment of cross-sectional studies.

Results

We identified 35 epidemiological studies (including 33 cohort studies, 1 cross-sectional study, and 1 case-control study) covering 6,939,725 pregnant women, of whom 865,460 were GDM patients. The NOS score of all included case-control studies and cohort studies was higher than six, and one of the included cross-sectional studies was rated as high quality according to the JBI assessment. Meta-analysis showed that fine particulate matter and air pollutants [PM2.5, odds ratio (OR) =1.06, 95% confidence interval (CI): 1.05-1.08, Z =7.76, P<0.001; PM10, OR =1.06, 95% CI: 1.01-1.11, Z =2.62, P=0.009; sulfur dioxide (SO₂), OR =1.18, 95% CI: 1.10-1.26, Z = 4.69, P<0.001; nitric oxide (NO), OR =1.04, 95% CI: 1.03-1.06,Z =3.33, P=0.001; nitrogen oxides (NO_X), OR =1.07, 95% CI: 1.04-1.11, Z =3.93, P<0.001; black carbon (BC), OR =1.08, 95% CI: 1.06-1.10, Z =7.58, P<0.001] was associated with GDM. Furthermore, no significant association was observed between O₃, CO, and nitrogen dioxide (NO₂) exposure and GDM.

Conclusions

Exposure to PM2.5, PM10, SO₂, NO, NO_X, and BC significantly increases the risk of GDM. AP is a remediable environmental trigger that can be prevented by human interventions, such as lowering AP levels or limiting human exposure to air pollutants. The government should strengthen the supervision of air quality and make air quality information more transparent. Besides, living conditions are crucial during pregnancy. Living in a place with more green areas is recommended, and indoor air purification should also be enhanced.

Long-term Exposure to Ambient PM2.5 and Its Components Associated With Diabetes: Evidence From a Large Population-Based Cohort From China

OBJECTIVE

Association between particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) components and diabetes remains unclear. We therefore aimed to investigate the associations of long-term exposure to PM2.5 components with diabetes.

RESEARCH DESIGN AND METHODS

This study included 69,210 adults with no history of diabetes from a large-scale epidemiologic survey in Southwest China from 2018 to 2019. The annual average concentrations of PM2.5 and its components were estimated using satellite remote sensing and chemical transport modeling. Diabetes was identified as fasting plasma glucose ≥7.0 mmol/L (126 mg/dL) or hemoglobin A1c ≥48 mmol/mol (6.5%). The logistic regression model and weighted quantile sum method were used to estimate the associations of single and joint exposure to PM2.5 and its components with diabetes, respectively.

RESULTS

Per-SD increases in the 3-year average concentrations of PM2.5 (odds ratio [OR] 1.08, 95% CI 1.01-1.15), black carbon (BC; 1.07, 1.01-1.15), ammonium (1.07, 1.00-1.14), nitrate (1.08, 1.01-1.16), organic matter (OM; 1.09, 1.02-1.16), and soil particles (SOIL; 1.09, 1.02-1.17) were positively associated with diabetes. The associations were stronger in those ≥65 years. Joint exposure to PM2.5 and its components was positively associated with diabetes (OR 1.04, 95% CI 1.01-1.07). The estimated weight of OM was the largest among PM2.5 and its components.

CONCLUSIONS

Long-term exposure to BC, nitrate, ammonium, OM, and SOIL is positively associated with diabetes. Moreover, OM might be the most responsible for the relationship between PM2.5 and diabetes. This study adds to the evidence of a PM2.5-diabetes association and suggests controlling sources of OM to curb the burden of PM2.5-related diabetes.

Air Pollution: Possible Interaction between the Immune and Nervous System?

Exposure to environmental pollutants is a serious and common public health concern associated with growing morbidity and mortality worldwide, as well as economic burden. In recent years, the toxic effects associated with air pollution have been intensively studied, with a particular focus on the lung and cardiovascular system, mainly associated with particulate matter exposure. However, epidemiological and mechanistic studies suggest that air pollution can also influence skin integrity and may have a significant adverse impact on the immune and nervous system. Air pollution exposure already starts in utero before birth, potentially causing delayed chronic diseases arising later in life. There are, indeed, time windows during the life of individuals who are more susceptible to air pollution exposure, which may result in more severe outcomes. In this review paper, we provide an overview of findings that have established the effects of air pollutants on the immune and nervous system, and speculate on the possible interaction between them, based on mechanistic data.

Adaptive-mixture-categorization (AMC)-based g-computation and its application to trace element mixtures and bladder cancer risk

Several new statistical methods have been developed to identify the overall impact of an exposure mixture on health outcomes. Weighted quantile sum (WQS) regression assigns the joint mixture effect weights to indicate the overall association of multiple exposures, and quantile-based g-computation is a generalized version of WQS without the restriction of directional homogeneity. This paper proposes an adaptive-mixture-categorization (AMC)-based g-computation approach that combines g-computation with an optimal exposure categorization search using the F statistic. AMC-based g-computation reduces variance within each category and retains the variance between categories to build more powerful predictors. In a simulation study, the performance of association analysis was improved using categorizing by AMC compared with quantiles. We applied this method to assess the association between a mixture of 12 trace element concentrations measured from toenails and the risk of non-muscle invasive bladder cancer. Our findings suggested that medium-level (116.7-145.5 μg/g) vs. low-level (39.5-116.2 μg/g) of toenail zinc had a statistically significant positive association with bladder cancer risk.

Risk of thyroid cancer and benign nodules associated with exposure to parabens among Chinese adults in Wuhan, China

Parabens are widely used as preservatives, which have been found to affect thyroid function in toxicological studies. However, population studies on whether they are associated with thyroid tumors remain unclear. This study aims to investigate the relationship between environmental paraben exposure and thyroid cancer and benign nodules. We recruited participants from the Department of Thyroid and Breast Surgery at Wuhan Central Hospital, Wuhan, China. The detectable percentages of methyl paraben, ethyl paraben, and propyl paraben in the urinary samples of 425 study subjects were 99.1%, 95.3%, and 92.0%, respectively. All uncorrected and creatinine-corrected parabens were moderately correlated with one another. After adjusting for possible confounders, all three parabens were associated with an increased risk of thyroid cancer. Furthermore, the mixture pollutant analysis of parabens found positive associations with risk of thyroid cancer (OR = 0.24, 95% CI: 0.18, 0.31) and benign nodules (OR = 1.33, 95% CI: 0.86, 1.80). We observed that individual exposure to paraben mixtures may be associated with the risk of thyroid cancer and benign nodules.

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.

Risk of maternal exposure to mixed air pollutants during pregnancy for congenital heart diseases in offspring

OBJECTIVE

To explore the risk of maternal exposure to mixed air pollutants of particulate matter 1 (PM ₁), particulate matter 2.5 (PM _2.5), particulate matter 10 (PM ₁₀) and NO ₂ for congenital heart disease (CHD) in offspring, and to estimate the ranked weights of the above pollutants.

METHODS

6038 CHD patients and 5227 healthy controls from 40 medical institutions in 21 cities in Guangdong Registry of Congenital Heart Disease (GRCHD) from 2007 to 2016 were included. Logistic regression model was used to estimate the effect of maternal exposure to a single air pollutant on the occurrence of CHD in offspring. Spearman correlation coefficient was used to analyze the correlation between various pollutants, and Quantile g-computation was used to evaluate the joint effects of mixed exposure of air pollutants on CHD and the weights of various pollutants.

RESULTS

The exposure levels of PM ₁, PM _2.5, PM ₁₀ and NO ₂ in the CHD group were significantly higher than those in the control group (all P<0.01). The correlation coefficients among PM ₁, PM _2.5, PM ₁₀ and NO ₂ were greater than 0.80. PM ₁, PM _2.5, PM ₁₀ and NO ₂ exposure were associated with a significantly increased risk of CHD in offspring. Mixed exposure of these closely correlated pollutants presented much stronger effect on CHD than exposure of any single pollutants. There was a monotonic increasing relationship between mixed exposure and CHD risk. For each quantile increase in mixed exposure, the risk of CHD increased by 47% ( OR=1.47, 95% CI: 1.34-1.61). Mixed exposure had greater effect on CHD in the early pregnancy compared with middle and late pregnancy, but the greatest effect was the exposure in the whole pregnancy. The weight of PM ₁₀ is the highest in the mixed exposure (81.3%).

CONCLUSIONS

Maternal exposure to the mixture of air pollutants during pregnancy increases the risk of CHD in offspring, and the effect is much stronger than that of single exposure of various pollutants. PM ₁₀ has the largest weights and the strongest effect in the mixed exposure.

Analysis of changes in air pollution quality and impact of COVID-19 on environmental health in Iran: application of interpolation models and spatial autocorrelation

In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO₂), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

Association of exposure to air pollutants with gestational diabetes mellitus in Chiayi City, Taiwan

INTRODUCTION

We investigated the associations of exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) and several gaseous pollutants with risk of gestational diabetes mellitus (GDM) in Taiwan.

METHODS

We retrospectively identified pregnant women who underwent a two-step approach to screen for GDM between 2006 and 2014. Information on concentrations of air pollutants (including PM_2.5, sulfur dioxide [SO₂], nitrogen oxides [NO_x], and ozone [O₃]) were collected from a single fixed-site monitoring station. We conducted logistic regression analyses to determine the associations between exposure to air pollutants and risk of GDM.

RESULTS

A total of 11210 women were analyzed, and 705 were diagnosed with GDM. Exposure to PM_2.5 during the second trimester was associated with a nearly 50% higher risk of GDM (odds ratio [OR] 1.47, 95% CI 0.96 to 2.24, p=0.077). The associations were consistent in the two-pollutant model (PM_2.5 + SO₂ [OR 1.73, p=0.038], PM_2.5 + NO_x [OR 1.52, p=0.064], PM_2.5 + O₃ [OR 1.96, p=0.015]), and were more prominent in women with age <30 years and body mass index <25 kg/m² (interaction p values <0.01).

DISCUSSION

Exposure to PM_2.5 was associated with risk of GDM, especially in women who were younger or had a normal body mass index.