Relative humidity, temperature, and hypertensive disorders of pregnancy: Findings from the Project Viva cohort.

BACKGROUND: Preeclampsia is a multi-system hypertensive disorder of pregnancy that is a leading cause of maternal and fetal morbidity and mortality. Prior studies disagree on the cause and even the presence of seasonal patterns in its incidence. Using unsuitable time windows for seasonal exposures can bias model results, potentially explaining these inconsistencies. OBJECTIVES: We aimed to investigate humidity and temperature as possible causes for seasonal trends in preeclampsia in Project Viva, a prebirth cohort in Boston, Massachusetts, considering only exposure windows that precede disease onset. METHODS: Using the Parameter-elevation Relationships on Independent Slopes Model (PRISM) Climate Dataset, we estimated daily residential temperature and relative humidity (RH) exposures during pregnancy. Our primary multinomial regression adjusted for person-level covariates and season. Secondary analyses included distributed lag models (DLMs) and adjusted for ambient air pollutants including fine particulates (PM2.5). We used Generalized Additive Mixed Models (GAMMs) for systolic blood pressure (SBP) trajectories across hypertensive disorder statuses to confirm exposure timing. RESULTS: While preeclampsia is typically diagnosed late in pregnancy, GAMM-fitted SBP trajectories for preeclamptic and non-preeclamptic women began to diverge at around 20 weeks' gestation, confirming the need to only consider early exposures. In the primary analysis with 1776 women, RH in the early second trimester, weeks 14-20, was associated with significantly higher odds of preeclampsia (OR per IQR increase: 1.81, 95% CI: 1.10, 2.97). The DLM corroborated this window, finding a positive association from weeks 12-20. There were no other significant associations between RH or temperature and preeclampsia or gestational hypertension in any other time period. DISCUSSION: The association between preeclampsia and RH in the early second trimester was robust to model choice, suggesting that RH may contribute to seasonal trends in preeclampsia incidence. Differences between these results and those of prior studies could be attributable to exposure timing differences.

Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study.

BACKGROUND: Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures. METHODS: Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables. RESULTS: We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr < 60 mL/min/1.73m2. NO2 was inversely associated with a first-time diagnosis of CKD with aOR of 0.77 (95% CI: 0.66, 0.90). CONCLUSIONS: One-year average PM2.5 was associated with reduced eGFRcr, while O3 and NO2 were inversely associated. Neither PM2.5 or O3 were associated with a first-time identification of CKD, NO2 was inversely associated. We recommend future research examining the relationship between air pollution and impaired renal function.

Health benefits of decreases in on-road transportation emissions in the United States from 2008 to 2017.

Decades of air pollution regulation have yielded enormous benefits in the United States, but vehicle emissions remain a climate and public health issue. Studies have quantified the vehicle-related fine particulate matter (PM2.5)-attributable mortality but lack the combination of proper counterfactual scenarios, latest epidemiological evidence, and detailed spatial resolution; all needed to assess the benefits of recent emission reductions. We use this combination to assess PM2.5-attributable health benefits and also assess the climate benefits of on-road emission reductions between 2008 and 2017. We estimate total benefits of $270 (190 to 480) billion in 2017. Vehicle-related PM2.5-attributable deaths decreased from 27,700 in 2008 to 19,800 in 2017; however, had per-mile emission factors remained at 2008 levels, 48,200 deaths would have occurred in 2017. The 74% increase from 27,700 to 48,200 PM2.5-attributable deaths with the same emission factors is due to lower baseline PM2.5 concentrations (+26%), more vehicle miles and fleet composition changes (+22%), higher baseline mortality (+13%), and interactions among these (+12%). Climate benefits were small (3 to 19% of the total). The percent reductions in emissions and PM2.5-attributable deaths were similar despite an opportunity to achieve disproportionately large health benefits by reducing high-impact emissions of passenger light-duty vehicles in urban areas. Increasingly large vehicles and an aging population, increasing mortality, suggest large health benefits in urban areas require more stringent policies. Local policies can be effective because high-impact primary PM2.5 and NH3 emissions disperse little outside metropolitan areas. Complementary national-level policies for NOx are merited because of its substantial impacts-with little spatial variability-and dispersion across states and metropolitan areas.

Effect of extreme high temperature on cognitive function at different time scales: A national difference-in-differences analysis.

BACKGROUND: Mounting evidence has demonstrated that high temperature was associated with adverse health outcomes, especially morbidity and mortality. Nonetheless, the impact of extreme high temperature on cognitive performance, which is the fundamental capacity for interpreting one's surroundings, decision-making, and acquiring new abilities, has not been thoroughly investigated. METHODS: We aimed to assess associations between extreme high temperature at different time scales and poor cognitive function. We used longitudinal survey data from the three waves of data from China Family Panel Study, providing an 8-year follow-up of 53,008 participants from China. We assessed temperature and extreme high temperature exposure for each participant based on the residential area and date of cognitive test. We defined the proportion of days/hours above 32 °C as the metric of the exposure to extreme high temperature. Then we used generalized additive model and difference-in-differences approach to explore the associations between extreme high temperature and cognitive function. RESULTS: Our results demonstrated that either acute exposure or long-term exposure to extreme high temperature was associated with cognitive decline. At hourly level, 0-1 hour acute exposure to extreme high temperature would induce -0.93 % (95 % CI: -1.46 %, -0.39 %) cognitive change. At annual level, 10 percentage point increase in the hours proportion exceeding 32 °C in the past two years induced -9.87 % (95 % CI: -13.99 %, -5.75 %) cognitive change. Furthermore, subgroup analyses indicated adaptation effect: for the same 10 percentage increase in hours proportion exceeding 32 °C, people in warmer areas had cognitive change of -6.41 % (-11.22 %, -1.61 %), compared with -15.30 % (-21.07 %, -9.53 %) for people in cool areas. CONCLUSION: Our results demonstrated that extreme high temperature was associated with reduced cognitive function at hourly, daily and annual levels, warning that people should take better measures to protect the cognitive function in the context of climate change.

Impact of ground-level ozone exposure on sleep quality and electroencephalogram patterns at different time scales.

Ozone exposure is associated with various adverse health outcomes, but its impact on sleep quality is uncertain. Here we assessed the causal effect of long-term (yearly and monthly) exposure to ozone on nocturnal workday sleep time in a national representative sample from the China Family Panel Study, using a difference-in-differences approach. We further followed ninety healthy Chinese young adults four times in four seasons from September 2020 to June 2021, measured their daily sleep architecture using accelerometers, ascertained daily ozone exposure, recorded 5-min eye-closed resting-state electroencephalogram (EEG) signals at the last day of each one-week-long measurement session, and explored the effect of ozone exposure on objectively-measured sleep architecture. In the national sample, we found that every 1 interquartile range (IQR) µg/m3 increase in yearly and monthly ozone exposure was causally associated with 7.31 (p = 0.0039) and 4.19 (p = 0.040) minutes decline in nocturnal workday sleep time; the dose-response curve represented a quasi-linear pattern with no safety threshold, and plateaued at higher concentrations. In the small-scale study with objectively-measured sleep architecture, we found that every 1 IQR µg/m3 increase in the weekly ozone exposure was associated with 5.33 min decrease in night-time total sleep time (p = 0.031), 1.63 percentage points decrease in sleep efficiency (p < 0.001), 1.99 min increase in sleep latency (p = 0.0070), and 5.34 min increase in wake after sleep onset time (p = 0.0016) in a quasi-linear pattern. Notably, we found the accumulating trend of ozone exposure on sleep quality during both the short-term and long-term periods. We also found that short-term ozone exposure was associated with altered EEG patterns, mediated by sleep quality. This study indicates that long-term and short-term ozone exposures have negative and accumulating impacts on sleep quality and might impair brain functioning. More hidden health burdens of ozone are worth exploring.

Causal effects of prenatal and chronic PM2.5 exposures on cognitive function.

Growing evidence indicated an association between PM2.5 exposure and cognitive function, but the causal effect and the cognitive effect of prenatal PM2.5 exposure remain elusive. We obtained 15,099 subjects from a nationally representative sample of China and measured their cognitive performance. We ascertained subjects' prenatal PM2.5 exposure and chronic PM2.5 exposure of the recent two years. Using this national sample, we found that PM2.5 exposure during the mid- to late-pregnancy was significantly associated with declined cognition and income; chronic PM2.5 exposure was also independently associated with cognition and income measured at adulthood with greater magnitude. Negative effect modification was observed between prenatal and chronic PM2.5 exposure. Instrumental variable approach and difference-in-difference study verified causal effects: every 1 µg/m3 increase in prenatal and chronic PM2.5 exposures were causally associated with -0.22% (-0.38%, -0.06%) and -0.17% (-0.31%, -0.03%) changes in cognitive function, respectively. People with low cognition and low income were more vulnerable to PM2.5 exposure with greater cognitive and income decline. In the future, although China's improved air quality continues to benefit people and reduce cognitive decline induced by chronic PM2.5 exposure, high prenatal PM2.5 exposure will continue to hurt the overall cognition of Chinese population, since in total 360 million people were born during the 2000-2020 polluted era. Prenatal PM2.5-induced cognitive decline would remain largely unchanged before 2050 and gradually reduce after 2065, regardless of environmental policy scenarios. The long-lasting cognitive impact of PM2.5 is worth considering while enacting environmental policies.

Intermediate and long-term exposure to air pollution and temperature and the extracellular microRNA profile of participants in the normative aging study (NAS).

BACKGROUND: The molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences. OBJECTIVES: To assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles. METHODS: Our study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis. RESULTS: We found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease. CONCLUSION: Our results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model.

BACKGROUND: Studies examining the nonfatal health outcomes of exposure to air pollution have been limited by the number of pollutants studied and focus on short-term exposures. METHODS: We examined the relationship between long-term exposure to fine particulate matter with an aerodynamic diameter <2.5 micrometers (PM2.5), NO2, and tropospheric ozone and hospital admissions for 4 cardiovascular and respiratory outcomes (myocardial infarction, ischemic stroke, atrial fibrillation and flutter, and pneumonia) among the Medicare population of the United States. We used a doubly robust method for our statistical analysis, which relies on both inverse probability weighting and adjustment in the outcome model to account for confounding. The results from this regression are on an additive scale. We further looked at this relationship at lower pollutant concentrations, which are consistent with typical exposure levels in the United States, and among potentially susceptible subgroups. RESULTS: Long-term exposure to fine PM2.5 was associated with an increased risk of all outcomes with the highest effect seen for stroke with a 0.0091% (95% CI, 0.0086-0.0097) increase in the risk of stroke for each 1-µg/m3 increase in annual levels. This translated to 2536 (95% CI, 2383-2691) cases of hospital admissions with ischemic stroke per year, which can be attributed to each 1-unit increase in fine particulate matter levels among the study population. NO2 was associated with an increase in the risk of admission with stroke by 0.00059% (95% CI, 0.00039-0.00075) and atrial fibrillation by 0.00129% (95% CI, 0.00114-0.00148) per ppb and tropospheric ozone was associated with an increase in the risk of admission with pneumonia by 0.00413% (95% CI, 0.00376-0.00447) per parts per billion. At lower concentrations, all pollutants were consistently associated with an increased risk for all our studied outcomes. CONCLUSIONS: Long-term exposure to air pollutants poses a significant risk to cardiovascular and respiratory health among the elderly population in the United States, with the greatest increase in the association per unit of exposure occurring at lower concentrations.

A fast divide-and-conquer sparse Cox regression.

We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$. The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square approximation to the partial likelihood (PL). These sequences of linearization enable us to maximize the PL with only a small subset and perform penalized estimation via a fast approximation to the PL. The algorithm is applicable for the analysis of both time-independent and time-dependent survival data. Simulations suggest that the proposed DAC algorithm substantially outperforms the full sample-based estimators and the existing DAC algorithm with respect to the computational speed, while it achieves similar statistical efficiency as the full sample-based estimators. The proposed algorithm was applied to extraordinarily large survival datasets for the prediction of heart failure-specific readmission within 30 days among Medicare heart failure patients.

Short-term PM2.5 exposure and early-readmission risk: a retrospective cohort study in North Carolina heart failure patients.

BACKGROUND: Short-term changes in ambient fine particulate matter (PM2.5) increase the risk for unplanned hospital readmissions. However, this association has not been fully evaluated for high-risk patients or examined to determine if the readmission risk differs based on time since discharge. Here we investigate the relation between ambient PM2.5 and 30-day readmission risk in heart failure (HF) patients using daily time windows and examine how this risk varies with respect to time following discharge. METHODS: We performed a retrospective cohort study of 17,674 patients with a recorded HF diagnosis between 2004 and 2016. The cohort was identified using the EPA CARES electronic health record resource. The association between ambient daily PM2.5 (µg/m3) concentration and 30-day readmissions was evaluated using time-dependent Cox proportional hazard models. PM2.5 associated readmission risk was examined throughout the 30-day readmission period and for early readmissions (1-3 days post-discharge). Models for 30-day readmissions included a parametric continuous function to estimate the daily PM2.5 associated readmission hazard. Fine-resolution ambient PM2.5 data were assigned to patient residential address and hazard ratios are expressed per 10 µg/m3 of PM2.5. Secondary analyses examined potential effect modification based on the time after a HF diagnosis, urbanicity, medication prescription, comorbidities, and type of HF. RESULTS: The hazard of a PM2.5-related readmission within 3 days of discharge was 1.33 (95% CI 1.18-1.51). This PM2.5 readmission hazard was slightly elevated in patients residing in non-urban areas (1.43, 95%CI 1.22-1.67) and for HF patients without a beta-blocker prescription prior to the readmission (1.35; 95% CI 1.19-1.53). CONCLUSION: Our findings add to the evidence indicating substantial air quality-related health risks in individuals with underlying cardiovascular disease. Hospital readmissions are key metrics for patients and providers alike. As a potentially modifiable risk factor, air pollution-related interventions may be enacted that might assist in reducing costly and burdensome unplanned readmissions.

Do temporal trends of associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalizations differ by sub-populations and urbanicity-a study of 968 U.S. counties and the Medicare population.

While associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalization are well documented and evidence suggests that such associations change over time, it is unclear whether these temporal changes exist in understudied less-urban areas or differ by sub-population. We analyzed daily time-series data of 968 continental U.S. counties for 2000-2016, with cause-specific hospitalization from Medicare claims and population-weighted PM2.5 concentrations originally estimated at 1km × 1 km from a hybrid model. Circulatory and respiratory hospitalizations were categorized based on primary diagnosis codes at discharge. Using modified Bayesian hierarchical modelling, we evaluated the temporal trend in association between PM2.5 and hospitalizations and whether disparities in this trend exist across individual-level characteristics (e.g., sex, age, race, and Medicaid eligibility as a proxy for socio-economic status) and urbanicity. Urbanicity was categorized into three levels by county-specific percentage of urban population based on urban rural delineation from the U.S. Census. In this cohort with understudied less-urban areas without regulatory monitors, we still found positive association between circulatory and respiratory hospitalization and short-term exposure to PM2.5, with higher effect estimates towards the end of study period. Consistent with current literature, we identified significant disparity in associations by race, socioeconomic status and urbanicity. We found that the percentage change in circulatory hospitalization rate per 10 µg/m3 increase in PM2.5 was higher in the 2008-2016 time period compared to the 2000-2007 period by 0.33% (95% posterior credible interval 0.22, 0.44%), 0.52% (0.33, 0.69%), and 0.67% (0.53, 0.83%) for low, medium and high tertiles of urban areas, respectively. We also observed significant differences in temporal trends of associations across socioeconomic status, sex, and age, indicating a possible widening in disparity of PM2.5-related health burden. This study raises the importance of considering environmental justice issues in PM2.5-related health impacts with respect to how associations may change over time.

Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration.

BACKGROUND: Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. METHODS: We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. RESULTS: Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. CONCLUSION: We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

A Direct Estimate of the Impact of PM2.5, NO2, and O3 Exposure on Life Expectancy Using Propensity Scores.

BACKGROUND: Many studies have reported associations of air pollutants and death, but fewer examined multiple pollutants, or used causal methods. We present a method for directly estimating changes in the distribution of age at death using propensity scores. METHODS: We included all participants in Medicare from 2000 to 2016 (637,207,589 person-years of follow-up). We fit separate logistic regressions modeling the probability of death at each year of age from 65 to 98 or older as a function of exposure to particulate matter less tha 2.5 µM in diameter (PM2.5), NO2, and O3, using separate propensity scores for each age. We estimated the propensity score using gradient boosting. We estimated the distribution of life expectancy at three counterfactual exposures for each pollutant. RESULTS: The estimated increase in mean life expectancy had the population been exposed to 7 versus 12 µg/m3 PM2.5 was 0.29 years (95% CI = 0.28, 0.30). The change in life expectancy had the population been exposed to 10 versus 20 ppb of NO2 was -0.01 years (95% CI = -0.015, -0.006). The increase in mean life expectancy had the population been exposed to 35 versus 45 ppb of O3 was 0.15 years (95% CI = 0.14, 0.16). Each of these effects was independent and additive. CONCLUSIONS: We estimated that reducing PM2.5 and O3 concentrations to levels below current standards would increase life expectancy by substantial amounts compared with the recent increase of life expectancy at age 65 of 0.7 years in a decade. Our results are not consistent with the hypothesis that exposure to NO2 decreases life expectancy.

Long-term Exposure to PM2.5 and Mortality for the Older Population: Effect Modification by Residential Greenness.

BACKGROUND: Although many studies demonstrated reduced mortality risk with higher greenness, few studies examined the modifying effect of greenness on air pollution-health associations. We evaluated residential greenness as an effect modifier of the association between long-term exposure to fine particles (PM2.5) and mortality. METHODS: We used data from all Medicare beneficiaries in North Carolina (NC) and Michigan (MI) (2001-2016). We estimated annual PM2.5 averages using ensemble prediction models. We estimated mortality risk per 1 µg/m3 increase using Cox proportional hazards modeling, controlling for demographics, Medicaid eligibility, and area-level covariates. We investigated health disparities by greenness using the Normalized Difference Vegetation Index with measures of urbanicity and socioeconomic status. RESULTS: PM2.5 was positively associated with mortality risk. Hazard ratios (HRs) were 1.12 (95% confidence interval (CI) = 1.12 to 1.13) for NC and 1.01 (95% CI = 1.00 to 1.01) for MI. HRs were higher for rural than urban areas. Within each category of urbanicity, HRs were generally higher in less green areas. For combined disparities, HRs were higher in low greenness or low SES areas, regardless of the other factor. HRs were lowest in high-greenness and high-SES areas for both states. CONCLUSIONS: In our study, those in low SES and high-greenness areas had lower associations between PM2.5 and mortality than those in low SES and low greenness areas. Multiple aspects of disparity factors and their interactions may affect health disparities from air pollution exposures. Findings should be considered in light of uncertainties, such as our use of modeled PM2.5 data, and warrant further investigation.

A national difference in differences analysis of the effect of PM2.5 on annual death rates.

Many studies have reported that PM2.5 was associated with mortality, but these were criticized for unmeasured confounding, not using causal modeling, and not focusing on changes in exposure and mortality rates. Recent studies have used propensity scores, a causal modeling approach that requires the assumption of no unmeasured confounders. We used differences in differences, a causal modeling approach that focuses on exposure changes, and controls for unmeasured confounders by design to analyze PM2.5 and mortality in the U.S. Medicare population, with 623, 036, 820 person-years of follow-up, and 29, 481, 444 deaths. We expanded the approach by clustering ZIP codes into 32 groups based on racial, behavioral and socioeconomic characteristics, and analyzing each cluster separately. We controlled for multiple time varying confounders within each cluster. A separate analysis examined participants whose exposure was always below 12 µg/m3. We found an increase of 1 µg/m3 in PM2.5 produced an increased risk of dying in that year of 3.85 × 10-4 (95% CI 1.95 × 10-4, 5.76 × 10-4). This corresponds to 14,000 early deaths per year per 1 µg/m3. When restricted to exposures below 12 µg/m3, the increased mortality risk was 4.26 × 10-4 (95% CI 1.43 × 10-4, 7.09 × 10-4). Using a causal modeling approach robust to omitted confounders, we found associations of PM2.5 with increased death rates, including below U.S. and E.U. standards.

A Co-Twin control study of fine particulate matter and the prevalence of metabolic syndrome risk factors.

The relationship between ambient fine particulate matter (PM2.5) and metabolic syndrome (MetS) is understudied. It also remains unknown whether familial factors play a role in this relationship. In a study of 566 middle-aged twins, we examined the association of PM2.5 with MetS risk factors, measured by a MetS score as a summation of individual risk factors (range, 0 to 5). High-resolution PM2.5 estimates were obtained through previously validated models that incorporated monitor and satellite derived data. We estimated two-year average PM2.5 concentrations based on the ZIP code of each twin's residence. We used ordinal response models adapted for twin studies. When treating twins as individuals, the odds ratio of having 1-point higher MetS score was 1.78 for each 10 µg/m3-increase in exposure to PM2.5 (confidence interval [CI]: 1.01, 3.15), after adjusting for potential confounders. This association was mainly between pairs; the odds ratio was 1.97 (CI: 1.01, 3.84) for each 10 µg/m3-increase in the average pairwise exposure level. We found no significant difference in MetS scores within pairs who were discordant for PM2.5 exposure. In conclusion, higher PM2.5 in residence area is associated with more MetS risk factors. This association, however, is confounded by shared familial factors.

Examining PM2.5 concentrations and exposure using multiple models.

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 µg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 µg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 µg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 µg m-3 in 2011 and PM2.5 improvements of about 2 µg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Emulating causal dose-response relations between air pollutants and mortality in the Medicare population.

BACKGROUND: Fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are major air pollutants that pose considerable threats to human health. However, what has been mostly missing in air pollution epidemiology is causal dose-response (D-R) relations between those exposures and mortality. Such causal D-R relations can provide profound implications in predicting health impact at a target level of air pollution concentration. METHODS: Using national Medicare cohort during 2000-2016, we simultaneously emulated causal D-R relations between chronic exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and all-cause mortality. To relax the contentious assumptions of inverse probability weighting for continuous exposures, including distributional form of the exposure and heteroscedasticity, we proposed a decile binning approach which divided each exposure into ten equal-sized groups by deciles, treated the lowest decile group as reference, and estimated the effects for the other groups. Binning continuous exposures also makes the inverse probability weights robust against outliers. RESULTS: Assuming the causal framework was valid, we found that higher levels of PM2.5, O3, and NO2 were causally associated with greater risk of mortality and that PM2.5 posed the greatest risk. For PM2.5, the relative risk (RR) of mortality monotonically increased from the 2nd (RR, 1.022; 95% confidence interval [CI], 1.018-1.025) to the 10th decile group (RR, 1.207; 95% CI, 1.203-1.210); for O3, the RR increased from the 2nd (RR, 1.050; 95% CI, 1.047-1.053) to the 9th decile group (RR, 1.107; 95% CI, 1.104-1.110); for NO2, the DR curve wiggled at low levels and started rising from the 6th (RR, 1.005; 95% CI, 1.002-1.018) till the highest decile group (RR, 1.024; 95% CI, 1.021-1.027). CONCLUSIONS: This study provided more robust evidence of the causal relations between air pollution exposures and mortality. The emulated causal D-R relations provided significant implications for reviewing the national air quality standards, as they inferred the number of potential early deaths prevented if air pollutants were reduced to specific levels; for example, lowering each air pollutant concentration from the 70th to 60th percentiles would prevent 65,935 early deaths per year.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Aryl hydrocarbon receptor (AhR) regulates adipocyte differentiation by assembling CRL4B ubiquitin ligase to target PPARγ for proteasomal degradation.

Peroxisome proliferator-activated receptor γ (PPARγ) is the central regulator of adipogenesis, and its dysregulation is linked to obesity and metabolic diseases. Identification of the factors that regulate PPARγ expression and activity is therefore crucial for combating obesity. Aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor with a known role in xenobiotic detoxification. Recent studies have suggested that AhR also plays essential roles in energy metabolism. However, the detailed mechanisms remain unclear. We previously reported that experiments with adipocyte-specific Cullin 4b (Cul4b)-knockout mice showed that CUL4B suppresses adipogenesis by targeting PPARγ. Here, using immunoprecipitation, ubiquitination, real-time PCR, and GST-pulldown assays, we report that AhR functions as the substrate receptor in CUL4B-RING E3 ubiquitin ligase (CRL4B) complex and is required for recruiting PPARγ. AhR overexpression reduced PPARγ stability and suppressed adipocyte differentiation, and AhR knockdown stimulated adipocyte differentiation in 3T3-L1 cells. Furthermore, we found that two lysine sites on residues 268 and 293 in PPARγ are targeted for CRL4B-mediated ubiquitination, indicating cross-talk between acetylation and ubiquitination. Our findings establish a critical role of AhR in regulating PPARγ stability and suggest that the AhR-PPARγ interaction may represent a potential therapeutic target for managing metabolic diseases arising from PPARγ dysfunction.