Metabolism-Disrupting Chemical Mixtures during Pregnancy, Folic Acid Supplementation, and Liver Injury in Mother-Child Pairs.

Background and Aims: Scarce knowledge about the impact of metabolism-disrupting chemicals (MDCs) on liver injury limits opportunities for intervention. We evaluated pregnancy MDC-mixture associations with liver injury and effect modification by folic acid (FA) supplementation in mother-child pairs. Methods: We studied ∼200 mother-child pairs from the Mexican PROGRESS cohort, with measured 43 MDCs during pregnancy (estimated air pollutants, blood/urine metals or metalloids, urine high- and low-molecular-weight phthalate [HMWPs, LMWPs] and organophosphate-pesticide [OP] metabolites), and serum liver enzymes (ALT, AST) at ∼9 years post-parturition. We defined liver injury as elevated liver enzymes in children, and using established clinical scores for steatosis and fibrosis in mothers (i.e., AST:ALT, FLI, HSI, FIB-4). Bayesian Weighted Quantile Sum regression assessed MDC-mixture associations with liver injury outcomes. We further examined chemical-chemical interactions and effect modification by self-reported FA supplementation. Results: In children, many MDC-mixtures were associated with liver injury outcomes. Per quartile HMWP-mixture increase, ALT increased by 10.1% (95%CI: 1.67%, 19.4%) and AST by 5.27% (95% CI: 0.80%, 10.1%). LMWP-mixtures and air pollutant-mixtures were associated with higher AST and ALT, respectively. Air pollutant and non-essential metal/element associations with liver enzymes were attenuated by maternal cobalt blood concentrations ( p -interactions<0.05). In mothers, only the LMWP-mixture was associated with liver injury [OR=1.53 (95%CI: 1.01, 2.28) for HSI>36, and OR=1.62 (95%CI: 1.05, 2.49) for AST:ALT<1]. In mothers and children, most associations were attenuated (null) at FA supplementation≥600mcg/day ( p -interactions<0.05). Conclusions: Pregnancy MDC exposures may increase liver injury risk, particularly in children. These associations may be attenuated by higher FA supplementation and maternal cobalt levels.

Associations of prenatal exposure to phthalates and their mixture with lung function in Mexican children.

Early life phthalates exposure has been associated with adverse respiratory outcomes. However, evidence linking prenatal phthalates exposure and childhood lung function has been inconclusive. Additionally, few studies have examined phthalates exposure as a mixture and explored sexually dimorphic associations. We aimed to investigate sex-specific associations of prenatal phthalates mixtures with childhood lung function using the PROGRESS cohort in Mexico (N = 476). Prenatal phthalate concentrations were measured in maternal urine collected during the 2nd and 3rd trimesters. Children's lung function was evaluated at ages 8-13 years. Individual associations were assessed using multivariable linear regression, and mixture associations were modeled using repeated holdout WQS regression and hierarchical BKMR; data was stratified by sex to explore sex-specific associations. We identified significant interactions between 2nd trimester phthalates mixture and sex on FEV1 and FVC z-scores. Higher 2nd trimester phthalate concentrations were associated with higher FEV1 (ß = 0.054, 95 %CI: 0.005, 0.104) and FVC z-scores (ß = 0.074, 95 % CI: 0.024, 0.124) in females and with lower measures in males (FEV1, ß = -0.017, 95 %CI: -0.066, 0.026; FVC, ß = -0.014, 95 %CI: -0.065, 0.030). This study indicates that prenatal exposure to phthalates is related to childhood lung function in a sex-specific manner.

Green Space and Internalizing or Externalizing Symptoms Among Children.

Importance: Evidence suggests that living near green space supports mental health, but studies examining the association of green space with early mental health symptoms among children are rare. Objective: To evaluate the association between residential green space and early internalizing (eg, anxiety and depression) and externalizing (eg, aggression and rule-breaking) symptoms. Design, Setting, and Participants: Data for this cohort study were drawn from the Environmental Influences on Child Health Outcomes cohort; analysis was conducted from July to October 2023. Children born between 2007 and 2013 with outcome data in early (aged 2-5 years) and/or middle (aged 6-11 years) childhood who resided in 41 states across the US, drawing from clinic, hospital, and community-based cohorts, were included. Cohort sites were eligible if they recruited general population participants and if at least 30 children had outcome and residential address data to measure green space exposure. Nine cohorts with 13 sites met these criteria. Children diagnosed with autism or developmental delay were excluded, and 1 child per family was included. Exposures: Green space exposure was measured using a biannual (ie, summer and winter) Normalized Difference Vegetation Index, a satellite image-based indicator of vegetation density assigned to monthly residential history from birth to outcome assessment. Main Outcome and Measures: Child internalizing and externalizing symptoms were assessed using the Child Behavior Checklist for Ages 1½ to 5 or 6 to 18. The association between green space and internalizing and externalizing symptoms was modeled with multivariable linear regression using generalized estimating equations, adjusting for birthing parent educational level, age at delivery, child sex, prematurity, and neighborhood socioeconomic vulnerability. Models were estimated separately for early and middle childhood samples. Results: Among 2103 children included, 1061 (50.5%) were male; 606 (29.1%) identified as Black, 1094 (52.5%) as White, 248 (11.9%) as multiple races, and 137 (6.6%) as other races. Outcomes were assessed at mean (SD) ages of 4.2 (0.6) years in 1469 children aged 2 to 5 years and 7.8 (1.6) years in 1173 children aged 6 to 11 years. Greater green space exposure was associated with fewer early childhood internalizing symptoms in fully adjusted models (b = -1.29; 95% CI, -1.62 to -0.97). No associations were observed between residential green space and internalizing or externalizing symptoms in middle childhood. Conclusions and Relevance: In this study of residential green space and children's mental health, the association of green space with fewer internalizing symptoms was observed only in early childhood, suggesting a sensitive period for nature exposure. Policies protecting and promoting access to green space may help alleviate early mental health risk.

Sensitive development windows of prenatal air pollution and cognitive functioning in preschool age Mexican children.

Introduction: Neurotoxicity resulting from air pollution is of increasing concern. Considering exposure timing effects on neurodevelopmental impairments may be as important as the exposure dose. We used distributed lag regression to determine the sensitive windows of prenatal exposure to fine particulate matter (PM2.5) on children's cognition in a birth cohort in Mexico. Methods: Analysis included 553 full-term (≥37 weeks gestation) children. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatiotemporal model. McCarthy Scales of Children's Abilities (MSCA) were used to assess children's cognitive function at 4-5 years old (lower scores indicate poorer performance). To identify susceptibility windows, we used Bayesian distributed lag interaction models to examine associations between prenatal PM2.5 levels and MSCA. This allowed us to estimate vulnerable windows while testing for effect modification. Results: After adjusting for maternal age, socioeconomic status, child age, and sex, Bayesian distributed lag interaction models showed significant associations between increased PM2.5 levels and decreased general cognitive index scores at 31-35 gestation weeks, decreased quantitative scale scores at 30-36 weeks, decreased motor scale scores at 30-36 weeks, and decreased verbal scale scores at 37-38 weeks. Estimated cumulative effects (CE) of PM2.5 across pregnancy showed significant associations with general cognitive index (CE^ = -0.35, 95% confidence interval [CI] = -0.68, -0.01), quantitative scale (CE^ = -0.27, 95% CI = -0.74, -0.02), motor scale (CE^ = -0.25, 95% CI = -0.44, -0.05), and verbal scale (CE^ = -0.2, 95% CI = -0.43, -0.02). No significant sex interactions were observed. Conclusions: Prenatal exposure to PM2.5, particularly late pregnancy, was inversely associated with subscales of MSCA. Using data-driven methods to identify sensitive window may provide insight into the mechanisms of neurodevelopmental impairment due to pollution.

The Association of Air Pollution Exposure With Glucose and Lipid Levels: The Role of an Extreme Air Pollution Event Alongside 2 Decades of Moderate Exposure.

Extreme air pollution events and moderate exposure to fine particulate matter (PM2.5) are associated with increased cardiometabolic risk. The World Trade Center (WTC) Health Program general responder cohort includes responders to the WTC disaster. We investigated whether their exposure to this extreme air pollution event (2001) was associated with long-term metabolic outcomes, independently from the associations of intermediate-term PM2.5 exposure later in life (2004-2019). We included 22,447 cohort members with cholesterol (n = 96,155) and glucose (n = 81,599) laboratory results. Self-reported WTC exposure was derived from a questionnaire. PM2.5 exposure was derived from a satellite-based model. We observed an increase of 0.78 mg/dL (95% confidence interval (CI): 0.30, 1.26) in glucose and 0.67 mg/dL (95% CI: 1.00, 2.35) in cholesterol levels associated with an interquartile range increase in PM2.5 averaged 6 months before the study visit. Higher WTC-exposure categories were also associated with higher cholesterol (0.99 mg/dL, 95% CI: 0.30, 1.67, for intermediate exposure) and glucose (0.82 mg/dL, 95% CI: 0.22, 1.43, for high exposure) levels. Most associations were larger among people with diabetes. Extreme air pollution events and intermediate PM2.5 exposure have independent metabolic consequences. These exposures contributed to higher glucose and lipids levels among WTC responders, which may be translated into increased cardiovascular risk.

Short-term exposure to PM2.5 and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area.

BACKGROUND: Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in low and middle income countries. We aimed to explore the association between short-term exposure to PM2.5 with broad-category and cause-specific mortality outcomes in the Mexico City Metropolitan Area (MCMA), and potential effect modification by age, sex, and SES characteristics in such associations. METHODS: We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from the MCMA for the period of 2004-2019. Daily 1 × 1 km PM2.5 (median = 23.4 µg/m3; IQR = 13.6 µg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Odds ratios were converted into percent increase for ease of interpretation. RESULTS: PM2.5 exposure was associated with broad-category mortality outcomes, including all non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-µg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between age, sex and SES strata. CONCLUSIONS: Exposure to PM2.5 was associated with non-accidental, broad-category and cause-specific mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indication of effect modification by individual-level characteristics.

Place-Based Strategies Addressing Neighborhood Environments to Improve Perinatal and Preterm Infant Outcomes.

Preterm birth (defined as birth <37 weeks of gestation) is a significant health concern globally, with lasting implications for individuals, families, and society. In the United States, high preterm birth rates among Black and low-income populations likely result from differences in environmental exposures. Structural racism and economic disadvantage have led to unequal distribution of polluting industrial sites and roadways across society as well as differential access to health-promoting resources which contribute to preterm birth risk. Once born, preterm infants remain at risk for numerous environmentally responsive adverse health outcomes that affect growth and development throughout childhood and adulthood. In this commentary, we describe associations of neighborhood environments with pregnancy and preterm infant health outcomes and propose strategies to address harmful exposures that affect families across the lifespan.

Associations of neighborhood social vulnerability with emergency department visits and readmissions among infants with bronchopulmonary dysplasia.

OBJECTIVES: To characterize associations of the CDC Social Vulnerability Index (SVI) with medically attended acute respiratory illness among infants with bronchopulmonary dysplasia (BPD). STUDY DESIGN: Retrospective cohort of 378 preterm infants with BPD from a single center. Multivariable logistic regression quantified associations of SVI with medically attended acute respiratory illness, defined as emergency department (ED) visits or hospital readmissions within a year after first hospital discharge. Mediation analysis quantified the extent to which differences in SVI may explain known Black-White disparities in medically attended acute respiratory illness. RESULTS: SVI was associated with medically attended respiratory illness (per SVI standard deviation increment, aOR 1.44, 95% CI: 1.17-1.78). Adjustment for race and ethnicity attenuated the association (aOR 1.27, 95% CI: 0.97-1.64). SVI significantly mediated 31% of the Black-White disparity in ED visits (p = 0.04). CONCLUSIONS: SVI was associated with, and may partially explain racial disparities in, medically attended acute respiratory illness among infants with BPD.

Impact of exposure to air pollution on cervicovaginal microbial communities.

PURPOSE: Vaginal microbial communities can be dominated by anaerobic (community state type IV, CST IV) or Lactobacillus (other CSTs) species. CST IV is a risk factor for spontaneous preterm birth (sPTB) and is more common among Black than White populations. In the US, average air pollution exposures are higher among Black compared to White people and exert systemic health effects. We sought to (1) quantify associations of air pollution, specifically particulate matter <2.5 µm in diameter (PM2.5), with CST IV and (2) explore the extent to which racial disparities in PM2.5 exposure might explain racial differences in the prevalence of CST IV. DESIGN: Methods: We performed a secondary analysis of 566 participants of the Motherhood & Microbiome study. PM2.5 exposures were derived from a machine learning model integrating NASA satellite and EPA ground monitor data. Previously, cervicovaginal swabs from 15 to 20 weeks' gestation were analyzed using 16 S rRNA sequencing and hierarchical clustering assigned CSTs. Multivariable logistic regression models calculated adjusted odds ratios of CST IV (vs. other CSTs) per interquartile range (IQR) increment of PM2.5. Race-stratified and mediation analyses were performed. RESULTS: Higher PM2.5 exposure was associated with CST IV (aOR 1.39, 95% CI 1.02-1.91). Further adjustment for race/ethnicity attenuated the association (aOR 1.34, 95% CI: 0.97-1.83). Black participants (vs. White) had higher median PM2.5 exposure (10.6 vs. 9.6 µg/m3, P < 0.001) and higher prevalence of CST IV (47% vs. 11%, P < 0.001). Mediation analysis revealed that higher PM2.5 exposure may explain 3.9% (P = 0.038) and 3.3% (P = 0.15) of the Black-White disparity in CST IV in unadjusted and adjusted models, respectively. CONCLUSION: PM2.5 was associated with CST IV, a risk factor for sPTB. Additionally, PM2.5 exposure may partially explain racial differences in the prevalence of CST IV. Further research is warranted to discover how environmental exposures affect microbial composition and perpetuate racial health disparities.

Temperature and socioeconomic vulnerability: associations with cardiac event-induced posttraumatic stress symptoms.

Background: Posttraumatic stress symptoms (PTSS) are common after acute coronary syndrome (ACS) and predict increased morbidity and mortality. Climate change contributes to worse mental and cardiovascular health outcomes, thus, PTSS represent a potential mechanism linking climate change to adverse cardiovascular outcomes. Because people living in areas with lower socioeconomic status (SES) experience greater climate vulnerability, have worse cardiovascular health, and may be more susceptible to PTSS, any effect of temperature on PTSS could be amplified in this population. Methods: Spatial regression models were estimated to test the association of temperature and temperature variability (within-day variability, directed change over time, and absolute change over time), census tract-level SES, and their interaction with PTSS 1 month post-hospital discharge in a longitudinal cohort study comprising 956 patients evaluated for ACS at an urban U.S. academic medical center between November 2013-May 2017. PTSS were self-reported in relation to the ACS event that brought the patient to the hospital. Census tract-level was computed as a composite score from the CDC Social Vulnerability Index, with higher values indicating lower SES. Results: No temperature or temperature variability metrics were associated with PTSS. Lower census tract-level SES was associated with greater PTSS at 1 month. There was a marginally significant interaction of SES with ACS status, such that we only observed evidence of an association among those with ACS. Conclusion: Temperature exposures were not associated with acute CVD-induced PTSS, which could be a result of a small sample size, mismatched timescale, or lack of a true effect. Conversely, lower census tract-level SES was associated with developing worse PTSS 1 month after evaluation for an ACS. This association appeared stronger in individuals with a true ACS. Early interventions to prevent PTSS could promote better mental and CVD outcomes in this at-risk population.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach.

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 µg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

Associations between early-life exposure to PM2.5 and reductions in childhood lung function in two North American longitudinal pregnancy cohort studies.

Data integration of epidemiologic studies across different geographic regions can provide enhanced exposure contrast and statistical power to examine adverse respiratory effects of early-life exposure to particulate matter <2.5 microns in diameter (PM2.5). Methodological tools improve our ability to combine data while more fully accounting for study heterogeneity. Methods: Analyses included children enrolled in two longitudinal birth cohorts in Boston, Massachusetts, and Mexico City. Propensity score matching using the 1:3 nearest neighbor with caliper method was used. Residential PM2.5 exposure was estimated from 2 months before birth to age 6 years using a validated satellite-based spatiotemporal model. Lung function was tested at ages 6-11 years and age, height, race, and sex adjusted z scores were estimated for FEV1, FVC, FEF25-75%, and FEV1/FVC. Using distributed lag nonlinear models, we examined associations between monthly averaged PM2.5 levels and lung function outcomes adjusted for covariates, in unmatched and matched pooled samples. Results: In the matched pooled sample, PM2.5 exposure between postnatal months 35-44 and 35-52 was associated with lower FEV1 and FVC z scores, respectively. A 5 µg/m3 increase in PM2.5 was associated with a reduction in FEV1 z score of 0.13 (95% CI = -0.26, -0.01) and a reduction in FVC z score of 0.13 (95% CI = -0.25, -0.01). Additionally PM2.5 during postnatal months 23-39 was associated with a reduction in FEF25-75% z score of 0.31 (95% CI = -0.57, -0.05). Conclusions: Methodological tools enhanced our ability to combine multisite data while accounting for study heterogeneity. Ambient PM2.5 exposure in early childhood was associated with lung function reductions in middle childhood.

Daily exposure to PM2.5 and 1.5 million deaths: A time-stratified case-crossover analysis in the Mexico City Metropolitan Area.

Background: Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in Latin America. Methods: We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from Mexico City Metropolitan Area for the period of 2004-2019. Daily 1×1 km PM2.5 (median=23.4 µg/m3; IQR=13.6 µg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Results: PM2.5 exposure was associated with higher total non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-µg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between SES strata. Conclusions: Exposure to PM2.5 was associated with mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indications of effect modification by individual SES-related characteristics.

Ambient PM2.5 exposure and salivary cortisol output during pregnancy in a multi-ethnic urban sample.

OBJECTIVES: Evidence from murine research supports that fine particulate matter (PM2.5) may stimulate the hypothalamic-pituitary-adrenal axis, leading to elevated circulating glucocorticoid levels. Epidemiologic research examining parallel associations document similar associations. We examined these associations among a diverse sample of pregnant individuals exposed to lower levels of ambient PM2.5. MATERIALS AND METHODS: Participants included pregnant individuals enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) pre-birth cohort. Daily residential PM2.5 exposure was estimated using a satellite-based spatial-temporal hybrid model. Maternal 3rd trimester salivary cortisol levels were used to calculate several features of the diurnal cortisol rhythm. We used multivariable linear regression to examine PM2.5 during the pre-conception period and during each trimester in relation to cortisol awakening rise (CAR), slope, and area under the curve relative to ground (AUCG). RESULTS AND DISCUSSION: The average PM2.5 exposure level across pregnancy was 8.13 µg/m3. PM2.5 in each exposure period was positively associated with AUCG, a measure of total cortisol output across the day. We also observed an inverse association between PM2.5 in the 3rd trimester and diurnal slope, indicating a steeper decline in cortisol throughout the day with increasing exposure. We did not detect strong associations between PM2.5 and slope for the other exposure periods or between PM2.5 and CAR for any exposure period. CONCLUSIONS: In this sample, PM2.5 exposure across the preconception and pregnancy periods was associated with increased cortisol output, even at levels below the U.S. National Ambient Air Quality Annual Standard for PM2.5 of 12.0 µg/m3.

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models.

BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM2.5 concentrations (mean PM2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM2.5). OBJECTIVE: Our goal was to develop a machine-learning model to predict mean PM2.5 and max PM2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. METHODS: We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization. RESULTS: Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 µg/m3, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 µg/m3. In 2010, everybody in the study region was exposed to unhealthy levels of PM2.5. Hotter days had greater PM2.5 concentrations. Finally, we found similar exposure to PM2.5 across levels of social marginalization. SIGNIFICANCE: Machine learning algorithms can be used to predict highly spatiotemporally resolved PM2.5 concentrations even in regions with sparse monitoring. IMPACT: Our PM2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Prenatal exposure to PM2.5 and childhood cognition: Accounting for between-site heterogeneity in a pooled analysis of ECHO cohorts in the Northeastern United States.

BACKGROUND: Emerging studies have investigated the adverse health effects of PM2.5 using data from multiple cohorts, and results often are not generalizable across cohorts. We aimed to assess associations between prenatal PM2.5 and childhood cognition in two U.S. cohorts while accounting for between-site heterogeneity. METHODS: Analyses included 348 mother-child dyads enrolled in the dual site (New York City and Boston) PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort and in the First Thousand Days of Life (FTDL) study (Northern Virginia) participating in the Environmental influences on Child Health Outcomes (ECHO) national consortium. Residential prenatal PM2.5 exposure was estimated using a validated satellite-based model and childhood cognition was measured using the NIH Toolbox Cognition Battery at three to eight years of age. We used a log-linear model applied to contingency tables formed by cross-classifying covariates by site to examine between-site heterogeneity using 3rd trimester PM2.5 exposure, age-corrected cognition scores, and covariates potentially causing heterogeneities. Multivariable linear regression models informed by the combinability analysis were used to estimate the coefficients and 95% confidence intervals (CIs) for the association between 3rd trimester PM2.5 exposure and age-corrected cognition scores (mean = 100, SD = 15). RESULTS: The log-linear model indicated that inter-study associations were similar between PRISM-NYC and FTDL, which were different from those in PRISM-Boston. Accordingly, we combined the data of PRISM-NYC and FTDL cohorts. We observed associations between 3rd trimester PM2.5 and cognition scores, findings were varying by site, childsex, and test. For example, a 1 µg/m3 increase of 3rd trimester PM2.5 was associated with -4.35 (95% CI = -8.73, -0.25) mean early childhood cognition scores in females in PRISM-Boston. In the pooled NYC + FTDL site, the association between PM2.5 and childhood cognition may be modified by maternal education and urbanicity. CONCLUSIONS: We found associations between prenatal PM2.5 and impaired childhood cognition. Since multi-site analyses are increasingly conducted, our findings suggest the needed awareness of between-site heterogeneity.

Street-view greenspace exposure and objective sleep characteristics among children.

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007-2010 (mid-childhood, mean age 7.9 years) and 2012-2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep.

Maternal steroids during pregnancy and their associations with ambient air pollution and temperature during preconception and early gestational periods.

Hormones play critical roles in facilitating pregnancy progression and the onset of parturition. Several classes of environmental contaminants, including fine particulate matter (PM2.5) and ambient temperature, have been shown to alter hormone biosynthesis or activity. However, epidemiologic research has not considered PM2.5 in relation to a broader range of steroid hormones, particularly in pregnant women. Using metabolomics data collected within 20-40 weeks of gestation in an ethnically diverse pregnancy cohort study, we identified 42 steroid hormones that we grouped into five classes (pregnenolone, androgens, estrogens, progestin, and corticosteroids) based on their biosynthesis type. We found that exposure to PM2.5 during the pre-conception and early prenatal periods was associated with higher maternal androgen concentrations in late pregnancy. We also detected a positive association between early pregnancy PM2.5 exposure and maternal pregnenolone levels and a marginal positive association between early pregnancy PM2.5 exposure and progestin levels. When considering each hormone metabolite individually, we found positive associations between early pregnancy PM2.5 exposure and five steroids, two of which survived multiple comparison testing: 11beta-hydroxyandrosterone glucuronide (a pregnenolone steroid) and adrosteroneglucuronide (a progestin steroid). None of the steroid classes were statistically significant associated with ambient temperature. In sex-stratified analyses, we did not detect any sex differences in our associations. This is the first study showing that exposure to fine particulate matter during the pre-conception and early prenatal periods can lead to altered steroid adaptation during the state of pregnancy, which has been shown to have potential consequences on maternal and child health.

Intermediate- and long-term associations between air pollution and ambient temperature and glycated hemoglobin levels in women of child bearing age.

BACKGROUND: Air pollution has been linked to obesity while higher ambient temperatures typically reduce metabolic demand in a compensatory manner. Both relationships may impact glucose metabolism, thus we examined the association between intermediate- and long-term exposure to fine particulate matter (PM2.5) and ambient temperature and glycated hemoglobin(HbA1c), a longer-term marker of glucose control. METHODS: We assessed 3-month, 6-month, and 12-month average air pollution and ambient temperature at 1-km2 spatial resolution via satellite remote sensing models (2013-2019), and assessed HbA1c at four, six, and eight years postpartum in women enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort based in Mexico City. PM2.5 and ambient temperature were matched to participants' addresses and confirmed by GPS tracker. Using linear mixed-effects models, we examined the association between 3-month, 6-month, and 12-month average PM2.5 and ambient temperature with repeated log-transformed HbA1c values. All models included a random intercept for each woman and were adjusted for calendar year, season, and individual-level confounders (age, marital status, smoking, alcohol consumption level, and education level). RESULTS: We analyzed 1,265 HbA1c measurements of 484 women. Per 1 µg/m3 increase in 3-month and 6-month PM2.5, HbA1c levels increased by 0.28% (95% confidence interval (95 %CI): 0.14, 0.42%) and 0.28% (95 %CI: 0.04, 0.52%) respectively. No association was seen for 12-month average PM2.5. Per 1 °C increase in ambient temperature, HbA1c levels decreased by 0.63% (95 %CI: -1.06, -0.21%) and 0.61% (95 %CI: -1.08, -0.13%), while the 12-month average again is not associated with HbA1c. CONCLUSIONS: Intermediate-term exposure to PM2.5 and ambient temperature are associated with opposing changes in HbA1c levels, in this region of high PM2.5 and moderate temperature fluctuation. These effects, measurable in mid-adult life, may portend future risk of type 2 diabetes and possible heart disease.