Adult air pollution exposure and risk of infertility in the nurses' health study II

BACKGROUND: Exposures to air pollution has been associated with lower conception and fertility rates. However, the impact of pollution on infertility is unknown. OBJECTIVES: To examine the associations of roadway proximity (a measure of traffic exposure) and particulate matter (PM) air pollution and incidence of infertility. METHODS: Proximity to major roadways and ambient exposures to particulate matter less than 10 microns (PM10), between 2.5 and 10 microns (PM2.5-10), and less than 2.5 microns (PM2.5) were determined for all residential addresses for 36,294 members of the prospective Nurses' Health Study II cohort from 1993 to 2003. Infertility was defined by report of attempted conception for ≥12 months without success. Participants were able to report if evaluation was sought and if so, offer multiple clinical indications for infertility. Multivariable adjusted Cox proportional hazard models were used to estimate the relation between each exposure and infertility risk. RESULTS: Over 213,416 person-years, there were 2,508 incident reports of infertility. Results for overall infertility were inconsistent across exposure types. We observed a small increased risk in those living closer to compared to farther from a major road, multivariable adjusted hazard ratio (HR)=1.11(95% confidence interval (CI) = 1.02-1.20). Among those reporting primary infertility, risk was greater with closer distance to road and for all PM size fractions and exposure time windows. The multivariable adjusted HR (95%CI) for women living closer to compared to farther from a major road for primary infertility was 1.37 (1.22-1.52), while for secondary infertility HR=1.07 (0.95-1.21). In addition, the HR for every 10 mcg increase in cumulative PM2.5 among women with primary infertility was 1.61 (1.35-1.92), while it was 1.1 (0.91-1.33) for those with secondary infertility. CONCLUSIONS: This study suggests exposures to traffic and PM may be associated with a small increased risk of infertility, especially primary infertility

Air Pollution Exposures During Adulthood and Risk of Endometriosis in the Nurses’ Health Study II

Background: Particulate matter and proximity to large roadways may promote disease mechanisms, including systemic inflammation, hormonal alteration, and vascular proliferation, that may contribute to the development and severity of endometriosis. Objective: Our goal was to determine the association of air pollution exposures during adulthood, including distance to road, particulate matter < 2.5 μm, between 2.5 and 10 μm, and < 10 μm, (PM2.5, PM10–2.5, PM10), and timing of exposure with risk of endometriosis in the Nurses’ Health Study II. Methods: Proximity to major roadways and outdoor levels of PM2.5, PM10–2.5, and PM10 were determined for all residential addresses from 1993 to 2007. Multivariable-adjusted time-varying Cox proportional hazard models were used to estimate the relation between these air pollution exposures and endometriosis risk. Results: Among 84,060 women, 2,486 incident cases of surgically confirmed endometriosis were identified over 710,230 person-years of follow-up. There was no evidence of an association between endometriosis risk and distance to road or exposure to PM2.5, PM10–2.5, or PM10 averaged over follow-up or during the previous 2- or 4-year period. Conclusions: Traffic and air pollution exposures during adulthood were not associated with incident endometriosis in this cohort of women. Citation: Mahalingaiah S, Hart JE, Laden F, Aschengrau A, Missmer SA. 2014. Air pollution exposures during adulthood and risk of endometriosis in the Nurses’ Health Study II. Environ Health Perspect 122:58–64; http://dx.doi.org/10.1289/ehp.130662

Temporal Variability and Predictors of Urinary Bisphenol A Concentrations in Men and Women

Background: Bisphenol A (BPA) is used to manufacture polymeric materials, such as polycarbonate plastics, and is found in a variety of consumer products. Recent data show widespread BPA exposure among the U.S. population.Objective Our goal in the present study was to determine the temporal variability and predictors of BPA exposure. Methods: We measured urinary concentrations of BPA among male and female patients from the Massachusetts General Hospital Fertility Center. Results: Between 2004 and 2006, 217 urine samples were collected from 82 subjects: 45 women (145 samples) and 37 men (72 samples). Of these, 24 women and men were partners and contributed 42 pairs of samples collected on the same day. Ten women became pregnant during the follow-up period. Among the 217 urine samples, the median BPA concentration was 1.20 μg/L, ranging from below the limit of detection (0.4 μg/L) to 42.6 μg/L. Age, body mass index, and sex were not significant predictors of urinary BPA concentrations. BPA urinary concentrations among pregnant women were 26% higher (–26%, +115%) than those among the same women when not pregnant (p > 0.05). The urinary BPA concentrations of the female and male partner on the same day were correlated (r = 0.36; p = 0.02). The sensitivity of classifying a subject in the highest tertile using a single urine sample was 0.64. Conclusion: We found a nonsignificant increase in urinary BPA concentrations in women while pregnant compared with nonpregnant samples from the same women. Samples collected from partners on the same day were correlated, suggesting shared sources of exposure. Finally, a single urine sample showed moderate sensitivity for predicting a subject’s tertile categorization

Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records

The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /- 0.01%. A parsimonious predictive model utilizing age (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /- 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.IIS-1914792 - National Science Foundation; N00014-19-1-2571 - Office of Naval Research Global; GM135930 - Office of Extramural Research, National Institutes of HealthPublished versio

PBRM1 Regulates Stress Response in Epithelial Cells

Polybromo1 (PBRM1) is a chromatin remodeler subunit highly mutated in cancer, particularly clear cell renal carcinoma. PBRM1 is a member of the SWI/SNF subcomplex, PBAF (PBRM1-Brg1/Brm-associated factors), and is characterized by six tandem bromodomains. Here we establish a role for PBRM1 in epithelial cell maintenance through the expression of genes involved in cell adhesion, metabolism, stress response, and apoptosis. In support of a general role for PBRM1 in stress response and apoptosis, we observe that loss of PBRM1 results in an increase in reactive oxygen species generation and a decrease in cellular viability under stress conditions. We find that loss of PBRM1 promotes cell growth under favorable conditions but is required for cell survival under conditions of cellular stress

Serum Concentrations of Polychlorinated Biphenyls in Relation to in Vitro Fertilization Outcomes

Background: Human exposure to polychlorinated biphenyls (PCBs) remains widespread. PCBs have been associated with adverse reproductive health outcomes including reduced fecundability and increased risk of pregnancy loss, although the human data remain largely inconclusive

Association of Hexachlorobenzene (HCB), Dichlorodiphenyltrichloroethane (DDT), and Dichlorodiphenyldichloroethylene (DDE) with in Vitro Fertilization (IVF) Outcomes

Background: Hexachlorobenzene (HCB), dichlorodiphenyltrichloroethane (DDT), and dichlorodiphenyldichloroethylene (DDE) are persistent chlorinated pesticides with endocrine activity that may adversely affect the early stages of human reproduction

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records

INTRODUCTION: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. METHODS: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. RESULTS: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. CONCLUSION: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.R01 GM135930 - NIGMS NIH HHS; 000000000000000000000000000000000000000000000000000007726917 - Lawrence Berkeley National Laboratory; CCF-2200052 - National Science Foundation; IIS-1914792 - National Science FoundationAccepted manuscrip

Bisphenol A exposure in Mexico City and risk of prematurity: a pilot nested case control study

Abstract Background Presence of Bisphenol A (BPA) has been documented worldwide in a variety of human biological samples. There is growing evidence that low level BPA exposure may impact placental tissue development and thyroid function in humans. The aim of this present pilot study was to determine urinary concentrations of BPA during the last trimester of pregnancy among a small subset of women in Mexico City, Mexico and relate these concentrations to risk of delivering prematurely. Methods A nested case-control subset of 60 participants in the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study in Mexico City, Mexico were selected based on delivering less than or equal to 37 weeks of gestation and greater than 37 weeks of gestation. Third trimester archived spot urine samples were analyzed by online solid phase extraction coupled with high performance liquid chromatography isotope dilution tandem mass spectrometry. Results BPA was detected in 80.0% (N = 48) of the urine samples; total concentrations ranged from &lt; 0.4 &#956;g/L to 6.7 &#956;g/L; uncorrected geometric mean was 1.52 &#956;g/L. The adjusted odds ratio of delivering less than or equal to 37 weeks in relation to specific gravity adjusted third trimester BPA concentration was 1.91 (95%CI 0.93, 3.91, p-value = 0.08). When cases were further restricted to births occurring prior to the 37th week (n = 12), the odds ratio for specific-gravity adjusted BPA was larger and statistically significant (p &lt; 0.05). Conclusions This is the first study to document measurable levels of BPA in the urine of a population of Mexican women. This study also provides preliminary evidence, based on a single spot urine sample collected during the third trimester, that pregnant women who delivered less than or equal to 37 weeks of gestation and prematurely (&lt; 37 weeks) had higher urinary concentrations of BPA compared to women delivering after 37 weeks.http://deepblue.lib.umich.edu/bitstream/2027.42/78251/1/1476-069X-9-62.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78251/2/1476-069X-9-62.pdfPeer Reviewe

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species