Environmental Health Perspectives最新文献

Background: Some synthetic phenols alter hormonal pathways involved in successful pregnancy and fetal development. Despite high within-subject temporal variability of phenols, previous studies mostly utilized spot urine samples to assess pregnancy exposure. Herein, we investigated associations between pregnancy exposure to eight phenols assessed in multiple pooled urine samples and steroid hormones assessed in maternal hair reflecting cumulative hormone levels over the previous weeks to months.

Methods: We assessed phenol-hormone associations in 928 pregnant women from two pooled cohorts recruited in Spain [Barcelona Life Study Cohort (BiSC), 2018-2021] and France [Assessment of Air Pollution exposure during Pregnancy and Effect on Health (SEPAGES), 2014-2017] using pools of up to 21 samples each, collected in early pregnancy (median gestational age: 18.0 wk), as well as hair collected in late pregnancy (BiSC) or at birth (SEPAGES). We measured two bisphenols, four parabens, benzophenone-3, and triclosan along with metabolites of three adrenal ($\sum \text{cortisol}$, $\sum \text{cortisone}$, and 11-dehydrocorticosterone) and two reproductive (progesterone and testosterone) hormones. We ran adjusted linear regressions for each exposure biomarker-outcome pair and Bayesian kernel machine regression for phenols mixture.

Results: Bisphenol S was associated with higher cortisol and 11-dehydrocorticosterone concentrations. Propylparaben was associated with lower levels of cortisol, cortisone, and 11-dehydrocorticosterone, while methylparaben was linked to a reduction in cortisol levels. Interestingly, associations identified for parabens were stronger for women carrying female fetuses. No associations for phenol mixture were detected.

Conclusions: Our study suggests that pregnancy exposure to bisphenol S and some parabens (propyl- and methylparaben) may affect production of maternal corticosteroid hormones that are important for a successful pregnancy and fetal development. https://doi.org/10.1289/EHP15117.

Background: The biological mechanisms linking early life phthalate exposure with adverse behaviors and cardiometabolic conditions also impact sleep health, but whether early life exposure impacts adolescent sleep is unknown.

Objectives: We evaluated whether gestational and childhood urinary phthalate metabolite mixtures were associated with sleep characteristics during adolescence. We also examined periods of heightened susceptibility to individual phthalates.

Methods: In the Health Outcomes and Measures of the Environment (HOME) Study (Cincinnati, Ohio; 2003-2006; $n=156$), we quantified urinary metabolites of eight parent phthalate diesters during pregnancy (16- and 26-wk) and childhood (ages 1, 2, 3, 4, 5, 8, and 12 years). Using regression calibration approaches, we estimated average measurement error-corrected phthalate metabolite concentrations during pregnancy and childhood. We used wrist actigraphy to assess sleep characteristics for 1 wk among participants at age 12. Using quantile-based g-computation, we estimated covariate-adjusted differences in sleep efficiency (%), sleep fragmentation index scores (%), sleep duration (minutes) per quartile increase in all phthalate metabolite concentrations ($\psi$), and weights indicating the contribution of each metabolite to $\psi$. Using multiple informant models, we examined whether associations between individual phthalate metabolites and sleep characteristics varied by timing of exposure.

Results: Increasing all gestational phthalate metabolites by a quartile was associated with lower sleep efficiency [$\psi =-1.3\%$; 95% confidence interval (CI): $-2.4$, $-0.3$] and higher sleep fragmentation ($\psi =1.6\%$; 95% CI: 0.3, 3.0); mono-n-butyl phthalate (MnBP) and di(2-ethylhexyl) phthalate (DEHP) metabolites contributed most to these relations. Higher childhood phthalate metabolite mixture quartiles were associated with shorter sleep duration ($\psi =-21$ minutes; 95% CI: $-34$, $-9$); monoethyl phthalate (MEP) and monocarboxyoctyl phthalate (MCOP) contributed most to this association. We found that higher DEHP metabolite concentrations during pregnancy were more strongly related to higher sleep fragmentation than childhood concentrations. In contrast, higher MEP and MnBP concentrations during childhood, but not pregnancy, were consistently associated with shorter sleep duration.

Discussion: Phthalate metabolite concentrations during pregnancy and childhood

Background: Ethylene oxide (EtO) is a recognized carcinogen of concern in occupational and environmental settings, but evidence of cancer risks in humans remains limited. Since new EtO emission standards and mitigation measures have been proposed, further investigation of EtO cancer risks is needed to inform quantitative risk assessment.

Objective: Our objective was to estimate the association between cumulative EtO exposure and risk of death from breast cancer.

Methods: We had data on 7,549 women from the largest cohort of EtO-exposed workers who were employed for at least 1 y at one of 13 US facilities, with mortality follow-up from 1 January 1960 to 31 December 2021. We estimated relative rates (RR) of the association between cumulative EtO exposure [parts per million days (ppm-days)] and breast cancer mortality using Cox proportional hazard models, using a matched risk-set sampling design with attained age as the underlying time scale. We further examined a subcohort of women who participated in interviews that contained information about breast cancer risk factors.

Results: Cumulative exposure to EtO was associated with elevated RRs of breast cancer mortality (181 deaths). In a log-log model with a 20-y lag fit, workers who accrued $\mathrm{3,650}\text{\hspace{0.17em}ppm-days}$ of exposure (equivalent to 10 y exposed at a rate of $1\text{\hspace{0.17em}ppm}$) had over three times the rate of breast cancer death in comparison with unexposed workers (RR at 3,650 $\text{ppm-days}=3.15$; 95% CI: 1.78, 5.60). This RR remained elevated for the subset of the cohort with interview data after matching on potential confounders (RR at 3,650 $\text{ppm-days}=3.22$; 95% CI: 1.52, 7.13). We observed evidence of variation in RRs by time since exposure and exposure rate.

Discussion: This updated analysis of an EtO-exposed worker cohort builds on evidence that EtO is a human breast carcinogen and supports recent exposure reduction proposals. Given the high prevalence of breast cancer, the large number of workers exposed to EtO and the potential for widespread environmental exposure increased risks observed even in the low exposure range are of serious public health importance. https://doi.org/10.1289/EHP15566.

Background: Exposure to certain chemicals in disinfectants has been associated with vascular dysfunction in toxicological studies, but the association between disinfectant exposure and clinical cardiovascular disease (CVD) remains unclear.

Objective: The aim of this study was to evaluate the association between occupational exposure to disinfectants and subsequent risk of CVD among US nurses.

Methods: We included 75,675 participants from The Nurses' Health Study II who maintained a nursing job and reported data on occupational disinfectant exposure. We estimated hazard ratios (HR) and 95% confidence intervals (CIs) of incident CVD, including coronary heart disease (CHD) and stroke, using Cox proportional hazard models comparing job types and general disinfection tasks between participants. We also used a job-task-exposure matrix to evaluate the risk of CVD by frequency of cleaning/disinfection tasks and exposure levels of seven specific disinfectants (formaldehyde, glutaraldehyde, hypochlorite bleach, hydrogen peroxide, alcohol, quaternary ammonium compounds, and enzymatic cleaners).

Results: During 10 y of follow-up (2009-2019), we documented 726 incident cases of CVD. In fully adjusted models, the hazard ratio of CVD among nurses who worked in operating rooms was 1.72 [95% confidence interval (CI): 1.25, 2.36], in comparison with those working as educators or administrators. A similar pattern of associations was found when we separately assessed the risk for CHD and stroke [$\text{HR}=1.69$ (95% CI: 1.11, 2.58) and $\text{HR}=1.69$ (95% CI: 1.05, 2.74), respectively] among operating room nurses, in comparison with those working as educators or administrators. Those who used disinfectants weekly had modest elevations in CVD risk ($\text{HR}=1.21$; 95% CI: 1.04, 1.40), in comparison with women who never used disinfectants. The highest CVD risk was observed among nurses using disinfectants or spray or aerosol products 4-7 d/wk and those exposed to the highest levels of the seven specific disinfectants listed above.

Conclusion: Exposure to disinfectants in real-world health care settings was associated with a higher risk of CVD, including CHD and stroke, among US nurses. https://doi.org/10.1289/EHP14945.

Background: The effects of a mixture of exposures on health outcomes are of interest to public health but pose methodological hurdles. These exposures may impact the outcome in opposing ways, which we call the positive and negative partial effects of a mixture. There has been growing interest in estimating these partial effects and their ability to inform public health interventions.

Objectives: Methods like quantile g-computation (QGC) and weighted quantile sums regression (WQSr) were originally developed for estimating an overall mixture effect. These approaches, however, have not been comprehensively evaluated in their ability to estimate partial effects. We study the bias-variance tradeoffs of these approaches in estimating partial effects.

Methods: We compare QGC with sample-splitting (QGCSS) and WQSr with sample-splitting (WQSSS) and new methods including a) QGC a priori (QGCAP) and WQS a priori (WQSAP), which use prior knowledge to determine the positive and negative exposures prior to partial effects estimation; b) model-averaging (QGC-MA); and c) elastic net to determine the split (QGC-Enet). We also considered WQSr with no sample-splitting (WQSNS), repeated holdout sets (RH-WQS), and two-index model with penalized weights (WQS2i). We compared performance under a) exposure correlations, b) varying sample sizes, c) spread in the negative effect across exposures, and d) imbalance in the partial effects.

Results: Our simulation results showed that the estimation of negative and positive partial effects grows in root mean squared error and average bias as correlation among exposures increases, sample sizes shrink, the negative effect is spread over more exposures, or the imbalance between the negative and positive effects increases. Our results are demonstrated in two examples of mixtures in relation to oxidative stress biomarkers and telomere length.

Discussion: Outside of having a priori knowledge, no method is optimally reliable for estimating partial effects across common exposure scenarios. We provide guidance for practitioners of when partial effects might be most accurately estimated under particular settings. https://doi.org/10.1289/EHP14942.