Inflammatory and oxidative stress markers associated with decreased cervical length in pregnancy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134275/1/aji12545_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134275/2/aji12545.pd

Associations between repeated ultrasound measures of fetal growth and biomarkers of maternal oxidative stress and inflammation in pregnancy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146468/1/aji13017_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146468/2/aji13017.pd

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Abstract Background As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. Methods In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Results Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. Conclusions There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.http://deepblue.lib.umich.edu/bitstream/2027.42/112386/1/12940_2013_Article_691.pd

A quantile-based g-computation approach to addressing the effects of exposure mixtures

Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components.We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate effects of mixtures in common scenarios. We examine the bias, confidence interval coverage, and bias-variance tradeoff of quantile g-computation and WQS regression, and how these quantities are impacted by the presence of non-causal exposures, exposure correlation, unmeasured confounding, and non-linear effects. Quantile g-computation, unlike WQS regression allows inference on mixture effects that is unbiased with appropriate confidence interval coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted. Unlike inferential approaches that examine effects of individual exposures, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously.Comment: Main manuscript (3 figures, 4 tables, 7000 words) + appendi

Associations between mixtures of urinary phthalate metabolites with gestational age at delivery: a time to event analysis using summative phthalate risk scores

Abstract Background Preterm birth is a significant public health concern and exposure to phthalates has been shown to be associated with an increased odds of preterm birth. Even modest reductions in gestational age at delivery could entail morbid consequences for the neonate and analyzing data with this additional information may be useful. In the present analysis, we consider gestational age at delivery as our outcome of interest and examine associations with multiple phthalates. Methods Women were recruited early in pregnancy as part of a prospective, longitudinal birth cohort at the Brigham and Women’s Hospital in Boston, Massachusetts. Urine samples were collected at up to four time points during gestation for urinary phthalate metabolite measurement, and birth outcomes were recorded at delivery. From this population, we selected all 130 cases of preterm birth (< 37 weeks of gestation) as well as 352 random controls. We conducted analysis with both geometric average of the exposure concentrations across the first three visits as well as using repeated measures of the exposure. Two different time to event models were used to examine associations between nine urinary phthalate metabolite concentrations and time to delivery. Two different approaches to constructing a summative phthalate risk score were also considered. Results The single-pollutant analysis using a Cox proportional hazards model showed the strongest association with a hazard ratio (HR) of 1.21 (95% confidence interval (CI): 1.09, 1.33) per interquartile range (IQR) change in average log-transformed mono-2-ethyl-5-carboxypentyl phthalate (MECPP) concentration. Using the accelerated failure time model, we observed a 1.19% (95% CI: 0.26, 2.11%) decrease in gestational age in association with an IQR change in average log-transformed MECPP. We next examined associations with an environmental risk score (ERS). The fourth quartile of ERS was significantly associated with a HR of 1.44 (95% CI: 1.19, 1.75) and a reduction of 2.55% (95% CI: 0.76, 4.30%) in time to delivery (in days) compared to the first quartile. Conclusions On average, pregnant women with higher urinary metabolite concentrations of individual phthalates have shorter time to delivery. The strength of the observed associations are amplified with the risk scores when compared to individual pollutants.https://deepblue.lib.umich.edu/bitstream/2027.42/144517/1/12940_2018_Article_400.pd

A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group LASSO (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.Comment: 29 pages, 6 figure

Fetal Organophosphate Pesticide Exposure and Child Adiposity Measures at 10 Years of Age in the General Dutch Population

BACKGROUND: Fetal exposure to organophosphate (OP) pesticides might lead to fetal metabolic adaptations, predisposing individuals to adverse metabolic profiles in later life. OBJECTIVE: We examined the association of maternal urinary OP pesticide metabolite concentrations in pregnancy with offspring body mass index (BMI) and fat measures at 10 years of age. METHODS: Between 2002 and 2006, we included 642 mother–child pairs from the Generation R Study, a population-based prospective cohort study in Rotterdam, the Netherlands. We measured maternal urinary concentrations of OP pesticide metabolites, namely, dialkyl phosphates, including three dimethyl and three diethyl phosphates in early-, mid-and late-pregnancy. At 10 years of age, child total and regional body fat and lean mass were measured through dual energy X-ray absorptiometry, and abdominal and organ fat through magnetic resonance imaging. RESULTS: Higher maternal urinary pregnancy-average or trimester-specific dialkyl, dimethyl, or diethyl phosphate concentrations were not associated with childhood BMI and the risk of overweight. In addition, we did not observe any association of dialkyl, dimethyl, or diethyl phosphate concentrations with total and regional body fat, abdominal visceral fat, liver fat, or pericardial fat at child age of 10 y. CONCLUSION: We observed no associations of maternal urinary dialkyl concentrations during pregnancy with childhood adiposity measures at 10 years of age. Whether these associations develop at older ages should be further studied.</p

Caucasian Infants Scan Own- and Other-Race Faces Differently

Young infants are known to prefer own-race faces to other race faces and recognize own-race faces better than other-race faces. However, it is entirely unclear as to whether infants also attend to different parts of own- and other-race faces differently, which may provide an important clue as to how and why the own-race face recognition advantage emerges so early. The present study used eye tracking methodology to investigate whether 6- to 10-month-old Caucasian infants (N = 37) have differential scanning patterns for dynamically displayed own- and other-race faces. We found that even though infants spent a similar amount of time looking at own- and other-race faces, with increased age, infants increasingly looked longer at the eyes of own-race faces and less at the mouths of own-race faces. These findings suggest experience-based tuning of the infant's face processing system to optimally process own-race faces that are different in physiognomy from other-race faces. In addition, the present results, taken together with recent own- and other-race eye tracking findings with infants and adults, provide strong support for an enculturation hypothesis that East Asians and Westerners may be socialized to scan faces differently due to each culture's conventions regarding mutual gaze during interpersonal communication

Urinary Concentrations of Bisphenol A and Phthalate Metabolites Measured during Pregnancy and Risk of Preeclampsia

Background: Preeclampsia represents a major cause of maternal mortality and morbidity worldwide. Although it is known that the placenta plays a central role in development of preeclampsia, investigation into the contribution of environmental toxicants to the risk of preeclampsia has been sparse. Objectives: In the present study we examined the relationship between longitudinally measured urinary BPA and phthalate metabolite concentrations during gestation and preeclampsia. Methods: A nested case–control study of preterm birth was performed in 2011 from women enrolled in a prospective birth cohort study at Brigham and Women’s Hospital in Boston. There were 50 cases of preeclampsia as part of this study. Urine samples were analyzed for concentrations of BPA and nine phthalate metabolites several times during pregnancy. Adjusted Cox proportional hazard models were used to calculate hazard ratios of preeclampsia in association with an interquartile range increase in BPA and phthalate concentrations and were weighted to reflect results generalizable to the base population. Results: Adjusted hazard ratios indicated that an interquartile range increase of urinary concentrations of BPA (1.53; 95% CI: 1.04, 2.25) and MEP (monoethyl phthalate) (1.72; 95% CI: 1.28, 2.30) at 10 weeks gestation was associated with onset of preeclampsia, whereas significantly elevated hazard ratios were found across gestation for all DEHP [di(2-ethylhexyl) phthalate] metabolites. These relationships differed based on infant sex. Conclusions: Urinary concentrations of BPA and several phthalate metabolites were significantly associated with increased risk of preeclampsia. If validated, these results indicate an environmental contribution of endocrine-disrupting chemicals to preeclampsia and suggest a modifiable means to reduce the mortality and morbidity associated with this condition. Citation: Cantonwine DE, Meeker JD, Ferguson KK, Mukherjee B, Hauser R, McElrath TF. 2016. Urinary concentrations of bisphenol A and phthalate metabolites measured during pregnancy and risk of preeclampsia. Environ Health Perspect 124:1651–1655; http://dx.doi.org/10.1289/EHP18

Urinary BPA and Phthalate Metabolite Concentrations and Plasma Vitamin D Levels in Pregnant Women: A Repeated Measures Analysis

Background: In addition to its well-established role in maintaining skeletal health, vitamin D has essential regulatory functions in female reproductive and pregnancy outcomes. Phthalates and bisphenol A (BPA) are endocrine disruptors, and previous research has suggested that these chemical agents may disrupt circulating levels of total 25(OH)D in adults. Objectives: We investigated the relationships between repeated measures of urinary phthalate metabolites and BPA and circulating total 25(OH)D in a prospective cohort of pregnant women. Methods: The present study population includes participants (n=477) in a nested case–control study of preterm birth drawn from a prospective birth cohort of pregnant women at Brigham and Women’s Hospital in Boston, Massachusetts. Urine and blood samples were collected for biomarker measurements at median 10 wk and 26 wk of gestation. Results: In repeated measures analysis, we observed that an interquartile range (IQR) increase in urinary mono-3-carboxypropyl phthalate (MCPP) was associated with a 4.48% decrease [95% confidence interval (CI): −7.37, −1.58] in total 25(OH)D. We also detected inverse associations for metabolites of di(2-ethylhexyl) phthalate (DEHP) [percent difference (%Δ)=−2.83 to −2.16]. For BPA, we observed a nonsignificant inverse association with total 25(OH)D in the overall population. Our sensitivity analysis revealed that the associations for some metabolites (e.g., MEHP) varied by race/ethnicity, which may reflect potential differences in susceptibility. In agreement with findings from repeated measures analysis, we reported that DEHP metabolites and BPA were significantly associated with an approximate 20% increase in the odds of vitamin D deficiency (≤20 ng/mL) [odds ratio (95% CI): 1.19 (1.06, 1.35) for molar sum of DEHP metabolites and 1.22 (1.01, 1.47) for BPA] at median 10 wk and 26 wk, respectively. Conclusions: Our results provide suggestive evidence of the potential for environmental exposure to phthalates and/or BPA to disrupt circulating vitamin D levels in pregnancy. https://doi.org/10.1289/EHP117