Fetal window of vulnerability to airborne polycyclic aromatic hydrocarbons on proportional intrauterine growth restriction

Background

Although the entire duration of fetal development is generally considered a highly susceptible period, it is of public health interest to determine a narrower window of heightened vulnerability to polycyclic aromatic hydrocarbons (PAHs) in humans. We posited that exposure to PAHs during the first trimester impairs fetal growth more severely than a similar level of exposure during the subsequent trimesters.

Methods

In a group of healthy, non-smoking pregnant women with no known risks of adverse birth outcomes, personal exposure to eight airborne PAHs was monitored once during the second trimester for the entire cohort (n = 344), and once each trimester within a subset (n = 77). Both air monitoring and self-reported PAH exposure data were used in order to statistically estimate PAH exposure during the entire gestational period for each individual newborn.

Results

One natural-log unit increase in prenatal exposure to the eight summed PAHs during the first trimester was associated with the largest decrement in the Fetal Growth Ratio (FGR) (−3%, 95% Confidence Interval (CI), −5 to −0%), birthweight (−105 g, 95% CI, −188 to −22 g), and birth length (−0.78 cm, 95% CI, −1.30 to −0.26 cm), compared to the unit effects of PAHs during the subsequent trimesters, after accounting for confounders. Furthermore, a unit exposure during the first trimester was associated with the largest elevation in Cephalization Index (head to weight ratio) (3 μm/g, 95% CI, 1 to 5 μm/g). PAH exposure was not associated with evidence of asymmetric growth restriction in this cohort.

Conclusion

PAH exposure appears to exert the greatest adverse effect on fetal growth during the first trimester. The present data support the need for the protection of pregnant women and the embryo/fetus, particularly during the earliest stage of pregnancy.     

Semiparametric regression for periodic longitudinal hormone data from multiple menstrual cycles

We consider semiparametric regression for periodic longitudinal data. Parametric fixed effects are used to model the covariate effects and a periodic nonparametric smooth function is used to model the time effect. The within-subject correlation is modeled using subject-specific random effects and a random stochastic process with a periodic variance function. We use maximum penalized likelihood to estimate the regression coefficients and the periodic nonparametric time function, whose estimator is shown to be a periodic cubic smoothing spline. We use restricted maximum likelihood to simultaneously estimate the smoothing parameter and the variance components. We show that all model parameters can be easily obtained by fitting a linear mixed model. A common problem in the analysis of longitudinal data is to compare the time profiles of two groups, e.g., between treatment and placebo. We develop a scaled chi-squared test for the equality of two nonparametric time functions. The proposed model and the test are illustrated by analyzing hormone data collected during two consecutive menstrual cycles and their performance is evaluated through simulations.     

Penalized spline smoothing using Kaplan–Meier weights with censored data

In this paper, we consider the problem of nonparametric curve fitting in the specific context of censored data. We propose an extension of the penalized splines approach using Kaplan–Meier weights to take into account the effect of censorship and generalized cross‐validation techniques to choose the smoothing parameter adapted to the case of censored samples. Using various simulation studies, we analyze the effectiveness of the censored penalized splines method proposed and show that the performance is quite satisfactory. We have extended this proposal to a generalized additive models (GAM) framework introducing a correction of the censorship effect, thus enabling more complex models to be estimated immediately. A real dataset from Stanford Heart Transplant data is also used to illustrate the methodology proposed, which is shown to be a good alternative when the probability distribution for the response variable and the functional form are not known in censored regression models.     

Prenatal exposure to polycyclic aromatic hydrocarbons could increase the risk of low birth weight by affecting the DNA methylation states in a Chinese cohort

Polycyclic aromatic hydrocarbons (PAHs), as a kind of endocrine disruptors, can enter the fetus body cross the placental barrier from prenatal PAHs exposure to cause adverse birth outcomes. However, it is controversial association between prenatal PAHs exposure and low birth weight (LBW) of their infants. So the present study aimed to estimate the effects of prenatal PAHs exposure during the pregnancy on the risk of LBW in a Chinese cohort through modifying the DNA methylation states. A longitudinal prospective study with 407 pregnant women was established from May to October 2019. The prenatal PAHs exposure during the pregnancy was assessed using the internal dose such as the PAHs metabolites and PAH-DNA adducts in the umbilical cord blood. The methylation levels of genomic DNA and growth-related genes (IGF1 and IGF2) were assessed, while the expressions of these genes were both determined by RT-PCR and Elisa methods. The growth outcomes and relevant Z-scores were recorded at birth. The correlations between the DNA methylation status and concentrations of PAHs, expression levels of growth-related genes and body weight/WAZ were investigated as the measures. According to the PAH-DNA adducts, the subjects were divided into two groups: PAHs-exposed group (PAH-DNA adducts>0, n = 55) and non-exposed group (PAH-DNA adducts = 0, n = 352). Compared with the non-exposed group, it displayed marked decreased birth weight, and increased concentrations of PAHs and DNA methylation levels of the global genomic, IGF1 and IGF2 with their lower expressions in the PAHs-exposed group. These hypermethylation (global genomic, CpG14 and CpG15 of IGF1, and CpG14 of IGF2) were positively correlated with the contents of PAHs in the umbilical cord blood, and negatively correlated with the growth outcomes and their expressions. Totally, prenatal PAHs exposures may contribute to an increased risk of LBW of their infants by modulating the DNA methylation states of genomic DNA and growth-related genes (IGF1 and IGF2) in the umbilical cord blood, which could provide the prenatal prevention of PAHs exposure from possible environmental media except from the occupation and tobacco usage to ensure the health of their infants.     

Maternal occupational exposure to polycyclic aromatic hydrocarbons and congenital heart defects among offspring in the national birth defects prevention study

BACKGROUND: There is evidence in experimental model systems that exposure to polycyclic aromatic hydrocarbons (PAHs) results in congenital heart defects (CHDs); however, to our knowledge, this relationship has not been examined in humans. Therefore, we conducted a case‐control study assessing the association between estimated maternal occupational exposure to PAHs and CHDs in offspring. METHODS: Data on CHD cases and control infants were obtained from the National Birth Defects Prevention Study for the period of 1997 to 2002. Exposure to PAHs was assigned by industrial hygienist consensus, based on self‐reported maternal occupational histories from 1 month before conception through the third month of pregnancy. Logistic regression was used to evaluate the association between maternal occupational PAH exposure and specific CHD phenotypic subtypes among offspring. RESULTS: The prevalence of occupational PAH exposure was 4.0% in CHD case mothers (76/1907) and 3.6% in control mothers (104/2853). After adjusting for maternal age, race or ethnicity, education, smoking, folic acid supplementation, and study center, exposure was not associated with conotruncal defects (adjusted odds ratio [AOR], 0.98; 95% confidence interval [CI], 0.58–1.67), septal defects (AOR, 1.28; 95% CI, 0.86–1.90), or with any isolated CHD subtype. CONCLUSIONS: Our findings do not support an association between potential maternal occupational exposure to PAHs and various CHDs in a large, population‐based study. For CHD phenotypic subtypes in which modest nonsignificant associations were observed, future investigations could be improved by studying populations with a higher prevalence of PAH exposure and by incorporating information on maternal and fetal genotypes related to PAH metabolism. Birth Defects Research (Part A), 2012. © 2012 Wiley Periodicals, Inc.     

Evaluating Human Risk from Exposure to Alkylated PAHs in an Aquatic System

Polycyclic aromatic hydrocarbons (PAHs) are a large class of organic chemicals typically found as mixtures in the aquatic environment from natural, petrogenic, and pyrogenic sources. People can be exposed to PAHs through ingestion or dermal contact with contaminated sediments or through ingestion of finfish and shellfish exposed to contaminated sediments. Although more than 100 PAHs have been identified, human exposure and risk are commonly evaluated for 18 individual PAHs. Other PAHs, such as alkylated PAHs, likely contribute to biological activity of environmental PAH mixtures; however, insufficient toxicity data are available to quantify their potential risk. This article presents an initial evaluation of the potential for human health risk from exposure to alkylated PAHs in sediment and fish. Individual alkylated PAHs have been observed to have potentially mutagenic, tumor-promoting, or carcinogenic activity. However, except for 1-and 2-methylnaphthalene, insufficient toxicity data are available to quantify toxicity or cancer risk from exposure to individual alkylated PAHs or mixtures of alkylated PAHs. This article describes a proposed strategy to better understand the potential human health risk from exposure to alkylated PAHs. Implementation of this strategy will contribute to evaluations of human exposure to complex PAH mixtures in the environment.     

Smoothing spline-based score tests for proportional hazards models

We propose "score-type" tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial.     

Hypothesis testing in semiparametric additive mixed models

We consider testing whether the nonparametric function in a semiparametric additive mixed model is a simple fixed degree polynomial, for example, a simple linear function. This test provides a goodness-of-fit test for checking parametric models against nonparametric models. It is based on the mixed-model representation of the smoothing spline estimator of the nonparametric function and the variance component score test by treating the inverse of the smoothing parameter as an extra variance component. We also consider testing the equivalence of two nonparametric functions in semiparametric additive mixed models for two groups, such as treatment and placebo groups. The proposed tests are applied to data from an epidemiological study and a clinical trial and their performance is evaluated through simulations.     

Smoothness selection for penalized quantile regression splines

Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental biomarkers. When such a large set of analyses are carried out by penalized spline smoothing, reliable automatic selection of the smoothing parameter is particularly important. We show that two popular methods for smoothness selection may tend to overfit when estimating extreme quantiles as a smooth function of a predictor such as age; and that improved results can be obtained by multifold cross-validation or by a novel likelihood approach. A simulation study, and an application to a functional magnetic resonance imaging data set, demonstrate the favorable performance of our methods.     

Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t‐Statistics,with Applications to False Discovery Rate Estimation in Microarray Data Analysis

Summary Given a large number of t‐statistics, we consider the problem of approximating the distribution of noncentrality parameters (NCPs) by a continuous density. This problem is closely related to the control of false discovery rates (FDR) in massive hypothesis testing applications, e.g., microarray gene expression analysis. Our methodology is similar to, but improves upon, the existing approach by Ruppert, Nettleton, and Hwang (2007, Biometrics, 63, 483–495). We provide parametric, nonparametric, and semiparametric estimators for the distribution of NCPs, as well as estimates of the FDR and local FDR. In the parametric situation, we assume that the NCPs follow a distribution that leads to an analytically available marginal distribution for the test statistics. In the nonparametric situation, we use convex combinations of basis density functions to estimate the density of the NCPs. A sequential quadratic programming procedure is developed to maximize the penalized likelihood. The smoothing parameter is selected with the approximate network information criterion. A semiparametric estimator is also developed to combine both parametric and nonparametric fits. Simulations show that, under a variety of situations, our density estimates are closer to the underlying truth and our FDR estimates are improved compared with alternative methods. Data‐based simulations and the analyses of two microarray datasets are used to evaluate the performance in realistic situations.     

Effects of co-occurring aromatic hydrocarbons on degradation of individual polycyclic aromatic hydrocarbons in marine sediment slurries

Rates of polycyclic aromatic hydrocarbon (PAH) degradation and mineralization were influenced by preexposure to alternate PAHs and a monoaromatic hydrocarbon at relatively high (100 ppm) concentrations in organic-rich aerobic marine sediments. Prior exposure to three PAHs and benzene resulted in enhanced [14C]naphthalene mineralization, while [14C]anthracene mineralization was stimulated only by benzene and anthracene preexposure. Preexposure of sediment slurries to phenanthrene stimulated the initial degradation of anthracene. Prior exposure to naphthalene stimulated the initial degradation of phenanthrene but had no effect on either the initial degradation or mineralization of anthracene. For those compounds which stimulated [14C]anthracene or [14C]naphthalene mineralization, longer preexposures (2 weeks) to alternative aromatic hydrocarbons resulted in an even greater stimulation response. Enrichment with individual PAHs followed by subsequent incubation with one or two PAHs showed no alteration in degradation patterns due to the simultaneous presence of PAHs. The evidence suggests that exposure of marine sediments to a particular PAH or benzene results in the enhanced ability of these sediments to subsequently degrade that PAH as well as certain other PAHs. The enhanced degradation of a particular PAH after sediments have been exposed to it may result from the selection and proliferation of specific microbial populations capable of degrading it. The enhanced degradation of other PAHs after exposure to a single PAH suggests that the populations selected have either broad specificity for PAHs, common pathways of PAH degradation, or both.     

On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components

Summary We examine a generalized F ‐test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying‐coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two‐way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F ‐test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome‐wide critical value and p ‐value of a genetic association test in a genome‐wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 10⁸ simulations) and asymptotic approximation may be unreliable and conservative.     

Effects of co-occurring aromatic hydrocarbons on degradation of individual polycyclic aromatic hydrocarbons in marine sediment slurries.

Rates of polycyclic aromatic hydrocarbon (PAH) degradation and mineralization were influenced by preexposure to alternate PAHs and a monoaromatic hydrocarbon at relatively high (100 ppm) concentrations in organic-rich aerobic marine sediments. Prior exposure to three PAHs and benzene resulted in enhanced [14C]naphthalene mineralization, while [14C]anthracene mineralization was stimulated only by benzene and anthracene preexposure. Preexposure of sediment slurries to phenanthrene stimulated the initial degradation of anthracene. Prior exposure to naphthalene stimulated the initial degradation of phenanthrene but had no effect on either the initial degradation or mineralization of anthracene. For those compounds which stimulated [14C]anthracene or [14C]naphthalene mineralization, longer preexposures (2 weeks) to alternative aromatic hydrocarbons resulted in an even greater stimulation response. Enrichment with individual PAHs followed by subsequent incubation with one or two PAHs showed no alteration in degradation patterns due to the simultaneous presence of PAHs. The evidence suggests that exposure of marine sediments to a particular PAH or benzene results in the enhanced ability of these sediments to subsequently degrade that PAH as well as certain other PAHs. The enhanced degradation of a particular PAH after sediments have been exposed to it may result from the selection and proliferation of specific microbial populations capable of degrading it. The enhanced degradation of other PAHs after exposure to a single PAH suggests that the populations selected have either broad specificity for PAHs, common pathways of PAH degradation, or both.     

Varying coefficient model with unknown within-subject covariance for analysis of tumor growth curves

SUMMARY: In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback-Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors.     

Environmental polycyclic aromatic hydrocarbon (PAH) exposure and DNA damage in Mexican children

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants presenting a public health risk, particularly to children, a vulnerable population. PAHs have genotoxic and carcinogenic properties, which depend on their metabolism. Many enzymes involved in PAH metabolism, including CYP1A1, CYP1B1, GSTM and GSTT are polymorphic, which may modulate the activation/deactivation of these compounds. We evaluated PAH exposure and DNA damage in children living in the vicinity of the main petrochemical complex located in the Gulf of Mexico, and explored the modulation by genetic polymorphisms of PAH excretion and related DNA damage. The participants (n=82) were children aged 6-10y attending schools near the industrial area. Urinary 1-hydroxypyrene (1-OHP; a biomarker of PAH exposure) was determined by reverse-phase-HPLC; DNA damage by the comet assay (Olive Tail Moment (OTM) parameter); CYP1A1*2C and CYP1B1*3 polymorphisms by real time-PCR; and GSTM1*0 and GSTT1*0 by multiplex PCR. The median value of 1-OHP was 0.37μmol/mol creatinine; 59% of children had higher 1-OHP concentrations than those reported in environmentally exposed adults (0.24μmol/mol creatinine). A stratified analysis showed increased DNA damage in children with 1-OHP concentrations greater than the median value. We observed higher 1-OHP concentrations in children with CYP1A1*2C or GSTM1*0 polymorphisms, and a positive influence of CYP1A1*2C on OTM values in children with the highest PAH exposure. The data indicate that children living in the surroundings of petrochemical industrial areas are exposed to high PAH levels, contributing to DNA damage and suggesting an increased health risk; furthermore, data suggest that polymorphisms affecting activation enzymes may modulate PAH metabolism and toxicity.     

Incorporating marginal covariate information in a nonparametric regression model for a sample of R x C tables

SUMMARY: Nonparametric regression models are proposed in the framework of ecological inference for exploratory modeling of disease prevalence rates adjusted for variables, such as age, ethnicity/race, and socio-economic status. Ecological inference is needed when a response variable and covariate are not available at the subject level because only summary statistics are available for the reporting unit, for example, in the form of R x C tables. In this article, only the marginal counts are assumed available in the sample of R x C contingency tables for modeling the joint distribution of counts. A general form for the ecological regression model is proposed, whereby certain covariates are included as a varying coefficient regression model, whereas others are included as a functional linear model. The nonparametric regression curves are modeled as splines fit by penalized weighted least squares. A data-driven selection of the smoothing parameter is proposed using the pointwise maximum squared bias computed from averaging kernels (explained by O'Sullivan, 1986, Statistical Science 1, 502-517). Analytic expressions for bias and variance are provided that could be used to study the rates of convergence of the estimators. Instead, this article focuses on demonstrating the utility of the estimators in a study of disparity in health outcomes by ethnicity/race.     

Robust estimation for ordinary differential equation models

Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data.     

Fitting mixture models to birth weight data: a case study.

Birth weights by gestational age are compared in two birth cohorts from Northern Finland, the first from 1966 and the second from 1985-1986. A curious fact in the data is that mean birth weight before the 39th week was lower in the latter series although the mean birth weight for the total series was higher. Similar findings have been reported in other series. A mixture model with the nonparametric regression function is proposed for studying the hypothesis that the difference was caused by more frequent gross errors in gestational assessment in the earlier cohort. The probability of an error in gestational assessment then greatly depends on the observed gestational age, which makes the mixture model nonstandard. Maximum likelihood solutions to the parameters in the proposed model were computed employing the general expectation-maximization (EM) algorithm. A technique for studying the effect of errors on the intrauterine weight gain curve is proposed and applied to our two birth cohorts. The risk of underestimation of gestational age seems to be larger in the previous series and the differences between the growth curves almost totally vanish when "corrected" by means of the mixture model.     

Preclinical Reproductive and Developmental Toxicity Profile of a Glycine Transporter Type 1 (Glyt1) Inhibitor

Bitopertin is a glycine type 1 (GlyT1) inhibitor intended for the treatment of psychiatric disorders. The principle adverse effect in the regulatory reproductive toxicity studies was peri‐natal pup death when rat dams were treated during parturition at a dose resulting in five‐times the human therapeutic exposure (AUC). Cessation of dosing two days before parturition prevented the pup deaths. Investigatory experiments and pharmacokinetic modelling suggested that the neonatal mortality was related to transplacental passage of bitopertin leading to high systemic levels in the newborn pups. Brain levels of bitopertin in the rat fetus and neonate were two‐fold higher than in the mother. As illustrated by knock‐out mice models, GlyT1 function is essential for neonatal pup survival in rodents, but is not necessary for normal prenatal morphological development. The glycine transport systems are immature at birth in the rat, but are functionally well‐developed in the human newborn. While the relevance to humans of the neonatal mortality seen in rats following late gestational exposure is unknown, bitopertin would not be recommended for use during late pregnancy unless the anticipated benefit for the mother outweighs the potential risk to the newborn.     

Male‐mediated prenatal loss: Functions and mechanisms

Sexually selected infanticide has been the subject of intense empirical and theoretical study for decades; a related phenomenon, male‐mediated prenatal loss, has received much less attention in evolutionary studies. Male‐mediated prenatal loss occurs when inseminated or pregnant females terminate reproductive effort following exposure to a nonsire male, either through implantation failure or pregnancy termination. Male‐mediated prenatal loss encompasses two sub‐phenomena: sexually selected feticide and the Bruce effect. In this review, we provide a framework that explains the relationship between feticide and the Bruce effect and describes what is known about the proximate and ultimate mechanisms involved in each. Using a simple model, we demonstrate that male‐mediated prenatal loss can provide greater reproductive benefits to males than infanticide. We therefore suggest that, compared to infanticide, male‐mediated prenatal loss may be more prevalent in mammalian species and may have played a greater role in their social evolution than has previously been documented.