BACKGROUND: Ambient air pollution during pregnancy has been linked with postpartum depression up to 12 months, but few studies have investigated its impact on persistent depression beyond 12 months postpartum. This study aimed to evaluate prenatal ambient air pollution exposure and the risk of persistent depression over 3 years after childbirth and to identify windows of susceptibility.
METHODS: This study included 361 predominantly low-income Hispanic/Latina participants with full-term pregnancies in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort. We estimated daily residential PM2.5, PM10, NO2, and O3 concentrations throughout 37 gestational weeks using inverse-distance squared spatial interpolation from monitoring data and calculated weekly averaged levels. Depression was assessed by the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale at 12, 24, and 36 months postpartum, with persistent postpartum depression defined as a CES-D score ≥16 at any of these timepoints. We performed robust Poisson log-linear distributed lag models (DLM) via generalized estimating equations (GEE) to estimate the adjusted risk ratio (RR).
RESULTS: Depression was observed in 17.8 %, 17.5 %, and 13.4 % of participants at 12, 24, and 36 months, respectively. We found one IQR increase (3.9 ppb) in prenatal exposure to NO2 during the identified sensitive window of gestational weeks 13-29 was associated with a cumulative risk ratio of 3.86 (95 % CI: 3.24, 4.59) for persistent depression 1-3 years postpartum. We also found one IQR increase (7.4 μg/m3) in prenatal exposure to PM10 during gestation weeks 12-28 was associated a cumulative risk ratio of 3.88 (95 % CI: 3.04, 4.96) for persistent depression. No clear sensitive windows were identified for PM2.5 or O3.
CONCLUSIONS: Mid-pregnancy PM10 and NO2 exposures were associated with nearly 4-fold increased risks of persistent depression after pregnancy, which has critical implications for prevention of perinatal mental health outcomes.

The associations of ambient air pollution exposure and low-grade inflammation with lung function remain uncertain. In this study, 276,289 subjects were enrolled in the UK Biobank. Individual exposure to ambient air pollution (including nitrogen dioxide [NO2], nitrogen oxides [NOx]), and particulate matter [PM2.5, PM10, PMcoarse]) were estimated by using the land-use regression model. Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were tested, and low-grade inflammation score (INFLA score) was calculated for each subject. In this cross-sectional study, the median concentrations of air pollution were 9.89 µg/m3 for PM2.5, 15.98 µg/m3 for PM10, 6.09 µg/m3 for PMcoarse, 25.60 µg/m3 for NO2, and 41.46 µg/m3 for NOx, respectively. We observed that PM2.5, PM10, PMcoarse, NO2, NOx was negatively associated with lung function. Besides, significant positive associations between PM exposure and low-grade inflammation were noted. Per interquartile range (IQR) increase in PM2.5, PM10, and PMcoarse was related to higher INFLA score, and the β (95 % CI) was 0.06 (0.03, 0.08), 0.03 (0.02, 0.05), and 0.03 (0.01, 0.04), respectively. Additionally, we found significant negative associations between INFLA scores and lung function. One-unit increase in INFLA score was linked with 12.41- and 11.31-ml decreases in FVC and FEV1, respectively. Compared with individuals with low air pollution exposure and low INFLA scores, participants with high air pollution and high INFLA scores had the lowest FVC and FEV1. Additionally, we observed that INFLA scores could modify the relationships of PM2.5, NO2, and NOx with FVC and FEV1 (Pinteraction <0.05). The negative impact of air pollutants on lung function was more pronounced in subjects with high INFLA scores in comparison to those with low INFLA scores. In conclusion, we demonstrated negative associations between ambient air pollution and lung function, and the observed associations were strengthened and modified by low-grade inflammation.

Accumulating observational studies have linked particulate air pollutants to neurodegenerative diseases (NDDs). However, the causal links and the direction of their associations remain unclear. Therefore, we adopted a two-sample Mendelian randomization (TSMR) design using the GWAS-based genetic instruments of particulate air pollutants (PM2.5 and PM10) from the UK Biobank to explore their causal influence on four common neurodegenerative diseases. Estimates of causative relationships were generated by the Inverse variance weighted (IVW) method with multiple sensitive analyses. The heterogeneity and pleiotropy tests were additionally performed to verify whether our findings were robust. Genetically predicted PM2.5 and PM10 could elevate the occurrence of AD (odds ratio [OR] = 2.22, 95 % confidence interval [CI] 1.53-3.22, PIVW = 2.85×10-5, PFalsediscovery rate[FDR]= 2.85×10-4 and OR = 2.41, 95 % CI: 1.26-4.60, PIVW = 0.008, PFDR=0.039, respectively). The results were robust in sensitive analysis. However, no evidence of causality was found for other NDDs. Our present study suggests that PM2.5 and PM10 have a detrimental effect on AD, which indicates that improving air quality to prevent AD may have pivotal public health implications.

Several studies have documented a relationship between short-term exposure to atmospheric sulfur dioxide (SO2) and chronic obstructive pulmonary disease (COPD). However, findings vary across different regions. This meta-analysis employed a random-effects model to calculate the combined risk estimate for each 10-μg/m3 increase in ambient SO2 concentration. Subgroup analysis aimed to identify sources of heterogeneity. To assess potential bias, studies were evaluated using a domain-based assessment tool developed by the World Health Organization. Sensitivity analyses, based on bias risk, explored how model assumptions influenced associations. An evidence certainty framework was used to evaluate overall evidence quality. The study protocol was registered with PROSPERO (CRD42023446823). We thoroughly reviewed 191 full-text articles, ultimately including 15 in the meta-analysis. The pooled relative risk for COPD was 1.26 (95 % CI 0.94-1.70) per 10-μg/m3 increase in ambient SO2. Eleven studies were deemed high risk due to inadequate handling of missing data. Overall evidence certainty was rated as medium. Given SO2\'s significant public health implications, continuous monitoring is crucial. Future research should include countries in Africa and Oceania to enhance global understanding of atmospheric SO2-related health issues.

The relationships between the exposure to ambient air pollutants during gestation and the incidence of hypertensive disorders in pregnancy (HDPs) or preeclampsia are contradictory. This prospective cohort study enrolled the participants between January 2020 and December 2021 from the Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology. The exposure to ambient air pollutants and daily temperatures were obtained from the ChinaHighAirPollutants dataset and the Big Earth Data Platform for Three Poles, respectively. Logistic regression models were used as single- and two-pollutant models. Restricted cubic splines were applied to each ambient air pollutant exposure to further evaluate the exposure-response relationships. Quantile G-computation approaches were employed to evaluate the cumulative impact of mixed ambient air pollutants on the incidence risk HDPs and preeclampsia. Among 19,325 participants (median age: 30.2 years), 1669 (8.64%) were diagnosed with HDPs and 180 (0.94%) with preeclampsia. While mostly null risk estimates were observed, exposure to PM1, PM2.5, PM10, and NO2 correlated with a decreased incidence risk for HDPs and preeclampsia during most gestational periods. Additionally, our multi-pollutant model presented that an increase by one quartile in the cumulative effect of ambient air pollutants was associated with a significantly decreased incidence risk for HDPs in the trimester before gestation and in the third trimester during gestation, as well as for preeclampsia in the third trimester during gestation. These findings warrant further investigation into the mechanisms underlying these associations.

The infant gut microbiome matures greatly in the first year of life. Ambient air pollution (AAP) exposure is associated with the infant gut microbiome. However, whether time-varying AAP influences infant gut microbiome variation is rarely investigated. This study aimed to investigate the effects of PM2.5, PM10, and O3 on infant gut microbiome variation longitudinally. Demographic information, stool samples, and AAP exposure concentrations were collected at 6, 12, 24 months from infants. Gut microbiome was processed and analyzed using 16S rRNA V3-V4 gene regions. AAP exposure concentrations were calculated using the China High Air Pollutants (CHAP) database. Multiple pollutant models were used to assess the mixed effects of PM2.5, PM10, and O3 on infant gut microbiome variation. Infants\' gut microbiomes at 6, 12, 24 months old had significant differences in alpha diversity, beta diversity, and community composition. PM2.5 and O3 respectively explained 6.3% and 5.3% of the differences in community composition for 24-month-old infants. Single pollutant exposure and multiple pollutant exposure in different periods were both associated with alpha diversity indices and specific gut microbial phyla and genera. AAP was more associated with infant gut microbial alpha diversity indices, phyla variations, and genera variations at 12-24 months than 6-12 months. Multiple pollutant exposure in 0-2 lag months showed negative correlations with 12-24 months variation in Escherichia-Shigella (β = -0.854, 95%CI: 1.398 to -0.310) and Enterococcus (β = -0.979, 95%CI: 1.429 to -0.530). This study highlighted that time-varying PM2.5, PM10, and O3 synergistically influenced the variation of alpha diversity and abundance of gut microbial taxa in infants. Further research is needed to explore the effects and mechanisms of other environmental exposures on infant gut microbiome variation.

UNASSIGNED: Cardiovascular diseases (CVDs) account for 17.9 million deaths annually. Behavioral risk factors increase the risk of dying from CVD. Air pollution is not included in this risk calculation since the appreciation of air pollution as a modifiable risk factor is still limited. The purpose of this study was to analyze CVD mortality attributed to air pollution in all World Health Organization WHO member states and demonstrate the association of CVD mortality with air pollution depending on countries\' income level.
UNASSIGNED: The CVD death rate was calculated by dividing the number of deaths by the total population. The proportion of the population with primary reliance on clean fuels and technologies for cooking was calculated as an indicator of household air pollution. The annual mean concentration of fine particulate matter ≤2.5 µg/m3 and ≤10.0 µg/m3 to which the population is exposed was used as an indicator of ambient air pollution.
UNASSIGNED: There is a gradual increase in CVD mortality attributed to air pollution from high-income countries (HICs) to low-income countries (LICs). Household air pollution is the major cause of CVD mortality in LICs. Ischemic heart disease mortality attributed to ambient air pollution in all countries is higher than stroke mortality attributed to ambient air pollution. In LIC, mortality from stroke is attributed to household air pollution of 39.27 ± 14.47, which is more than twice the stroke mortality attributed to ambient air pollution at 18.60 ± 5.64, t = 7.17, p < 0.01.
UNASSIGNED: Air pollution control should be an essential component of the CVD preventive strategy, along with lifestyle modifications and effective disease management.

BACKGROUND: The carcinogenicity of air pollution and its impact on the risk of lung cancer is well known; however, there are still knowledge gaps and mixed results for other sites of cancer.
METHODS: The current study aimed to evaluate the associations between ambient air pollution [fine particulate matter (PM2.5) and nitrogen oxides (NOx)] and cancer incidence. Exposure assessment was based on historical addresses of >900 000 participants. Cancer incidence included primary cancer cases diagnosed from 2007 to 2015 (n = 30 979). Cox regression was used to evaluate the associations between ambient air pollution and cancer incidence [hazard ratio (HR), 95% CI].
RESULTS: In the single-pollutant models, an increase of one interquartile range (IQR) (2.11 µg/m3) of PM2.5 was associated with an increased risk of all cancer sites (HR = 1.51, 95% CI: 1.47-1.54), lung cancer (HR = 1.73, 95% CI: 1.60-1.87), bladder cancer (HR = 1.50, 95% CI: 1.37-1.65), breast cancer (HR = 1.50, 95% CI: 1.42-1.58) and prostate cancer (HR = 1.41, 95% CI: 1.31-1.52). In the single-pollutant and the co-pollutant models, the estimates for PM2.5 were stronger compared with NOx for all the investigated cancer sites.
CONCLUSIONS: Our findings confirm the carcinogenicity of ambient air pollution on lung cancer and provide additional evidence for bladder, breast and prostate cancers. Further studies are needed to confirm our observation regarding prostate cancer. However, the need for more research should not be a barrier to implementing policies to limit the population\'s exposure to air pollution.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

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