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Prenatal Residential Proximity to Agricultural Pesticide Use and IQ in 7-Year-Old Children

Author Affiliations open

1 School of Public Health, University of California, Berkeley, Berkeley, California, USA

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  • Background:
    Residential proximity to agricultural pesticide use has been associated with neural tube defects and autism, but more subtle outcomes such as cognition have not been studied.
    Objectives:
    We evaluated the relationship between prenatal residential proximity to agricultural use of potentially neurotoxic pesticides and neurodevelopment in 7-year-old children.
    Methods:
    Participants included mothers and children (n=283) living in the agricultural Salinas Valley of California enrolled in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study. We estimated agricultural pesticide use within 1 km of maternal residences during pregnancy using a geographic information system, residential location, and California’s comprehensive agricultural Pesticide Use Report data. We used regression models to evaluate prenatal residential proximity to agricultural use of five potentially neurotoxic pesticide groups (organophosphates, carbamates, pyrethroids, neonicotinoids, and manganese fungicides) and five individual organophosphates (acephate, chlorpyrifos, diazinon, malathion, and oxydemeton-methyl) and cognition in 7-year-old children. All models included prenatal urinary dialkyl phosphate metabolite concentrations.
    Results:
    We observed a decrease of 2.2 points [95% confidence interval (CI): <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.5] in Full-Scale IQ and 2.9 points (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.4, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.3) in Verbal Comprehension for each standard deviation increase in toxicity-weighted use of organophosphate pesticides. In separate models, we observed similar decrements in Full-Scale IQ with each standard deviation increase of use for two organophosphates (acephate and oxydemeton-methyl) and three neurotoxic pesticide groups (pyrethroids, neonicotinoids, and manganese fungicides).
    Conclusions:
    This study identified potential relationships between maternal residential proximity to agricultural use of neurotoxic pesticides and poorer neurodevelopment in children. https://doi.org/10.1289/EHP504
  • Received: 08 January 2016
    Revised: 13 May 2016
    Accepted: 14 June 2016
    Published: 25 May 2017

    Address correspondence to R. Gunier, Center for Environmental Research and Children’s Health (CERCH), School of Public Health, University of California at Berkeley, 1995 University Ave., Suite 265, Berkeley, CA 94704. Telephone: (510) 847-3858. E-mail: gunier@berkeley.edu.

    A.B. is a volunteer member of the Board for The Organic Center, a nonprofit organization addressing scientific issues around organic food and agriculture. All other authors declare they have no actual or potential competing financial interests.

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Introduction

In 2007, 311 million kg (684 million pounds) of pesticide active ingredients were used in agriculture in the United States (Grube et al. 2011). California, the state with the largest agricultural output, uses 25% of all U.S. agricultural pesticides, or 84.5 million kg (186 million pounds) annually [California Department of Pesticide Regulation (CDPR) 2014]. Although recent studies have demonstrated widespread organophosphate (OP) pesticide exposures in the general U.S. population, including pregnant women and children [Whyatt et al. 2003; Berkowitz et al. 2003; Lu et al. 2001; Adgate et al. 2001;

Centers for Disease Control and Prevention (CDC) 2009], exposures are often higher in agricultural populations (Lu et al. 2004; Fenske et al. 2002). Among pregnant women in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) study, all of whom lived in an agricultural region, and many of whom either worked in agriculture or lived with people who did, OP urinary metabolites or dialkylphosphate (DAP) levels were ∼40% higher than those in a representative sample of U.S. women of childbearing age (Bradman et al. 2005). We observed adverse associations in the CHAMACOS study between prenatal maternal DAP concentrations and children’s performance on the Bayley Scales of Infant Development at 2 y (Eskenazi et al. 2007), measures of attention at 5 y (Marks et al. 2010), and on the Wechsler Intelligence Scale for Children (WISC) at 7 y (Bouchard et al. 2011a). Several studies in other populations have similarly reported adverse associations of prenatal exposure to OP pesticides and child neurodevelopment (Engel et al. 2011; Rauh et al. 2011), but few studies have examined the effects of other potentially neurotoxic pesticides on child cognitive development.

Populations residing in agricultural areas may be exposed to a complex mixture of neurotoxic pesticides through pesticide drift and para-occupational exposures (Fenske et al. 2002). Some of these pesticide classes<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2014.png" alt="—” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />including OPs as well as carbamates<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2014.png" alt="—” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />share at least one mode of action, depression of acetylcholinesterase (AChE), and there is in vitro evidence that there may be additive inhibitory effects from exposure to certain pesticide mixtures (Mwila et al. 2013). Furthermore, although biomarkers such as DAP metabolites are an important tool for assessing exposures to pesticides, several challenges limit the utility of pesticide biomarkers in epidemiologic analyses. For example, many pesticides have a short half-life in the body, and biomarkers reflect only very recent exposures (on the order of hours to days) (Bradman et al. 2013). In addition, no biomarkers are available for some pesticides, leaving only environmental concentrations or modeling to characterize exposure.

Since 1990, all agricultural pesticide applications in California have been compiled in the uniquely comprehensive Pesticide Use Reporting (PUR) database. In several studies, PUR data have been shown to correlate with pesticide levels in various media. For example, we have shown significant associations between nearby use of specific pesticides based on the PUR data and levels in house dust (Harnly et al. 2009; Gunier et al. 2011), and moderate to strong associations (R2 0.28 − 0.65 between agricultural use of malathion, chlorpyrifos, and diazinon with community air samples (Harnly et al. 2005). In addition, several epidemiologic studies conducted in California have shown that higher nearby agricultural pesticide use is associated with various adverse health outcomes, including OP and fungicide use with Parkinson disease (Costello et al. 2009; Wang et al. 2011), OP and pyrethroid use with autism (Shelton et al. 2014) and birth defects (Carmichael et al. 2014), organochlorine pesticide use with autism (Roberts et al. 2007), and the use of carbamates (benomyl and methomyl) and the neonicotinoid imidacloprid with neural tube defects in children (Rull et al. 2006; Yang et al. 2014). To our knowledge, there have not been any previous studies evaluating residential proximity to reported agricultural pesticide use and more subtle neurodevelopmental outcomes such as cognitive function in children. In the present study, we evaluated the relationship between prenatal residential proximity to agricultural use of a variety of neurotoxic pesticides and neurodevelopment using WISC assessed at 7 y of age because this test provides a more specific and reliable measure of cognition than earlier neurodevelopmental assessments conducted in our cohort and may have greater implications for school performance.

Methods

Study Population

Between October 1999 and October 2000, we enrolled 601 pregnant women as part of the CHAMACOS study. Women were eligible if they were ≥18 y of age, <20wk gestational age, eligible for California’s low-income health care program (MediCal), spoke English or Spanish, and were planning to deliver at the county hospital. We followed the women through the delivery of 537 live-born children. We excluded twins (n=10) and children with medical conditions that could affect neurodevelopmental assessment (n=4, one child each with Down syndrome, autism, deafness, and hydrocephalus). We included children who had a neurodevelopmental assessment at 7 y (n=330) and whose prenatal residential location was known for at least 75 d per trimester during two or more trimesters of pregnancy (n=283). We excluded two participants who did not have prenatal measurements of DAP metabolites. Our final study population for this analysis was 283. The mothers of children included in our analyses were more likely (p<0.05) than those not included to be married (85% vs. 77%), nonsmokers during pregnancy (96% vs. 92%), and older at delivery (mean=26.9<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />vs.<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />24.9 y) than the mothers of children who were not included in these analyses; otherwise, the two populations were similar demographically. Written informed consent was obtained from all women, and oral assent was obtained from all children at 7 y of age; all research was approved by the University of California, Berkeley, Committee for the Protection of Human Subjects before the study began.

Cognitive Assessment

We used the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV) (Wechsler 2003) to assess cognitive abilities when the children were 7 y of age. All assessments were completed by a single bilingual psychometrician, who was trained and supervised by a pediatric neuropsychologist. Scores for four domains were calculated based on the following subtests: Verbal Comprehension (composed of Vocabulary and Similarities subtests), Perceptual Reasoning (Block Design and Matrix Reasoning subtests), Working Memory (Digit Span and Letter–Number Sequencing subtests), and Processing Speed (Coding and Symbol Search subtests). We administered all subtests in the dominant language of the child, which was determined through administration of the oral vocabulary subtest of the Woodcock-Johnson/Woodcock-Muñoz Tests of Cognitive Ability in both English and Spanish (Woodcock and Muñoz-Sandoval 1990) at the beginning of the assessment. Among participants included in these analyses, 68% of the children were tested in Spanish, and 32% of the children were tested in English. The psychometrician was blinded to exposure status. We standardized WISC-IV scores against U.S. population<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />based norms for English- and Spanish-speaking children. We did not administer Letter<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />Number Sequencing or Symbol Search subtests for the first 3 mo of assessments; therefore, 27 participants lacked scores for Processing Speed and Working Memory domains. A Full-Scale IQ was available for 255 children.

Maternal Interviews and Assessments

Bilingual interviewers conducted maternal interviews in Spanish or English twice during pregnancy (∼13 and 26 wk gestation), after delivery, and when the children were 6 mo and 1, 2, 3.5, 5, 7, and 9 y of age. Interviews obtained demographic information including maternal age, education, country of birth, number of years living in the United States, marital status, paternal education, and family income. We collected residential history information by asking participants if they had moved since the last interview and, if so, the dates of all moves. We conducted home visits shortly after enrollment (∼16 wk gestation) and when the child was 6 mo of age. For both visits, the latitude and longitude coordinates of the participant’s home were determined using a handheld global positioning system unit. Residential mobility during pregnancy was common in our cohort, with 53% of all participants moving at least once during pregnancy. For the present analysis, we included women in the sample if their residential location was known for ≥75 d per trimester for at least two trimesters of pregnancy.

Mothers were administered the Peabody Picture Vocabulary Test (PPVT) for English speakers or the Test de Vocabulario en Imagenes Peabody (TVIP) for Spanish speakers at the 6-mo visit to assess verbal intelligence (Dunn and Dunn 1981). If maternal PPVT or TVIP scores were unavailable from the 6-mo visit, we used scores from the re-administration of the test conducted at a 9-y visit (n=5) or assigned the mean score of the sample (n=2). A short version of the HOME (Home Observation for Measurement of the Environment) inventory was completed during the 7-y visit (Caldwell and Bradley 1984).

Geographic-Based Estimates of Agricultural Pesticide Use

We estimated agricultural pesticide use near each woman’s residence during pregnancy using California PUR data from 1999–2001 (CDPR 2015). We selected potentially neurotoxic pesticides with agricultural use in our study area (Monterey County, CA) during the prenatal period, including fifteen OPs and six carbamates (see Table S1), two manganese (Mn)-based fungicides (maneb and mancozeb), eight pyrethroids (permethrin, cypermethrin, tau-fluvalinate, cyfluthrin, fenpropathrin, lambda-cyhalothrin, bifenthrin, and esfenvalerate), and one neonicotinoid (imidacloprid). The PUR data include the amount (in kilograms) of active ingredient applied, the application date, and the location, defined as a 1-mi2 section (1.6km×1.6km) defined by the Public Land Survey System (PLSS). We edited the PUR data to correct for likely outliers that had unusually high application rates by replacing the amount of pesticide applied based on the median application rate for that pesticide and crop combination (Gunier et al. 2001). For each woman, we estimated the amount of all pesticides in each pesticide class used within a 1-km radius of the pregnant woman’s residence using the latitude and longitude coordinates and a geographic information system. In all cases, the 1-km buffer around the home included more than one PLSS section; thus, we weighted the amount of pesticide applied in each section by the proportion of land area that was included in the buffer. We selected a 1-km buffer distance for this analysis because it best captures the spatial scale most strongly correlated with measured agricultural pesticide concentrations in house-dust samples (Harnly et al. 2009; Gunier et al. 2011). Detailed descriptions of the equations and methods that we used to calculate nearby pesticide use have been published previously (Gunier et al. 2011). We estimated pesticide use within 1 km of the maternal residence during each trimester of pregnancy for participants with residential location information available for two or more trimesters (n=283) and computed the average pesticide use during pregnancy by summing the trimester-specific values and dividing by the number of trimesters included. We also created individual variables for nearby use of each of the five individual OP pesticides (acephate, chlorpyrifos, diazinon, malathion, and oxydemeton-methyl) with the highest use in our study area during the prenatal period (Table S1).

Toxicity Weighting and Neurotoxic Pesticide Index

In addition to examining the simple sum of pesticide use in each class, we also used relative potency factors (RPFs) to generate class-specific toxicity-weighted sums to account for differences in the neurotoxicity of individual pesticides in OP and carbamate classes. The RPF of a chemical is the ratio of the relevant toxicological dose of an index chemical to the relevant toxicological dose of the chemical of interest. Currently, RPFs are available for OP and carbamate pesticides [U.S. Environmental Protection Agency (EPA) 2006, 2007], but not for neonicotinoids, pyrethroids, or Mn-fungicides. Thus, we were only able to create toxicity-weighted sum variables for OPs and carbamates. We calculated the toxicity-weighted use for each OP or carbamate pesticide, expressed as kg-equivalents of chlorpyrifos, by multiplying the kilograms of pesticide used within 1 km of the maternal residence during each trimester for each pesticide by the RPF of that pesticide, and summing to create the toxicity-weighted use for the fifteen OP and six carbamate pesticides. Additionally, because these pesticides share a common mechanism of toxicity [acetylcholinesterase (AChE) inhibition], we also generated a toxicity-weighted sum for OPs and carbamates combined (Jensen et al. 2009; U.S. EPA 2002). The RPFs, total kilograms and toxicity-weighted kilograms of use in the Salinas Valley in 2000 for each OP and carbamate pesticide are provided in Table S1.

Finally, we created a rank index of neurotoxic pesticide use that included the five pesticide classes of interest (i.e., OPs, carbamates, neonicotinoids, pyrethroids, and Mn-fungicides) by generating a percentile rank of the participants from lowest to highest use for each neurotoxic pesticide class and then calculating the average percentile rank across the five classes. We also explored principal components analysis (PCA) as a method for combining pesticide use across the five different classes of neurotoxic pesticides.

Data Analysis

We log10-transformed continuous pregnancy average and trimester-specific sums of pesticide use (kilograms/year+1) to reduce heteroscedasticity and the influence of outliers and to improve the linear fit of the model. The scores for Full-Scale IQ and for the four subdomains were normally distributed and were modeled as continuous outcomes.

We selected model covariates a priori based on factors associated with infant neurodevelopment in previous analyses [i.e., child’s exact age at assessment, sex, maternal PPVT score (continuous) and maternal education (<sixth<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />grade<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />vs.≥seventh<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />grade)]. We considered the following variables as additional covariates in our models (Table 1): maternal country of birth, maternal age at delivery, marital status at enrollment, and maternal depression [using the Center for Epidemiologic Studies Depression Scale (CES-D)] (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="≥” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />16<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />on<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />CES-D) at the child’s 7-y visit. In addition, we considered covariates collected at each visit including housing density (number of persons per room), HOME score (continuous), household poverty level (<federal<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />poverty<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />level<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />vs.<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="≥” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />federal<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />poverty<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />level), presence of father in the home (yes/no), maternal work status, location of assessment (field office or recreational vehicle), and season of assessment. We imputed missing values (<10%<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2009.png" alt=" ” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />missing) at a visit point using data from the nearest available visit.

Table 1. CHAMACOS Study cohort characteristics (n=283).

Cohort characteristic n(%)
Maternal country of birth
 Mexico >249 (88.0)
 United States and other >34 (12.0)
Maternal age at delivery (years)
 18–24 >107 (37.8)
 25–29 >99 (35.0)
 30–34 >>49 (17.3)
 35–45 >28 (9.9)
Maternal education
 ≤6th grade >132 (46.6)
 ≥7th grade >151 (53.4)
Marital status at enrollment
 Married/living as married >239 (84.4)
 Married/living as married >239 (84.4)
 Not married >44 (15.6)
Paternal education
 ≤6th grade >129 (60.6)
 ≥7th grade >84 (39.4)
Family income at 7-y visit
#x2002;<Poverty level >203 (71.7)
 ≥Poverty level >80 (28.3)
Maternal depression at 7-y visit
 Yes >78 (27.6)
 No >205 (72.4)
Sex
 Female >152 (53.7)
 Male >131 (46.3)
Language of WISC-IV tests
 Spanish >193 (68.2)
 English >90 (31.8)

Note: WISC-IV, Wechsler Intelligence Scale for Children, fourth edition.

We retained covariates that were significant (p<0.2) at any time point in the multivariate regression models and used the same covariates in all models. We fit separate regression models for each pesticide class or individual pesticide; we also fit models including multiple classes or pesticides.

We used generalized additive models (GAMs) with a three-degrees-of-freedom cubic spline function to test for nonlinearity. None of the digression from linearity tests was significant (p<0.05); therefore, we expressed neurotoxic pesticide use linearly (on the log10 scale) in regression models.

We controlled for DAP metabolites of OP insecticides measured in maternal urine samples (Bradman et al. 2005) collected during prenatal interviews at 13 and 26 wk gestation (n=283) in all models. Prenatal DAPs and agricultural use of OPs were not highly correlated in our cohort (ρ=0.04); therefore, we believe that they provide complementary measures of exposure to OP pesticides. We averaged the two prenatal DAP measurements and used log10-transformed concentrations (nanomoles/liter) in our analyses. In separate sensitivity analyses, we controlled for exposure to other neurotoxicants, which we have previously found to be related to child IQ in our cohort (Eskenazi et al. 2013; Gaspar et al. 2015). Specifically, we considered log10-transformed lipid-adjusted concentrations (nanograms/gram-lipid) measured in prenatal maternal blood samples of p,p′-dichlorodiphenyltrichloroethylene (DDT), p,p′-dichlorodiphenyldichloroethylene (DDE) (n=219) (Bradman et al. 2007), and polybrominated diphenyl ether flame retardants (PBDEs) (n=221) (Castorina et al. 2011). We used the sum of the four major congeners (BDE-47, BDE-99, BDE-100, and BDE-153) to estimate PBDE exposure (Eskenazi et al. 2013).

In other sensitivity analyses, we excluded outliers identified with studentized residuals >3. To control for potential selection bias resulting from loss to follow-up, we ran regression models with weights determined as the inverse probability of inclusion in our analyses at each time point (Hogan et al. 2004). We determined the probability of inclusion using multiple logistic regression models with baseline covariates as potential predictors.

Results

Most mothers were born in Mexico (88.0%), under 30 y of age at delivery (72.8%), and married or living as married (84.4%) at the time of enrollment (Table 1). Nearly half of the mothers (46.6%) and most fathers (60.6%) had a sixth-grade education or less, and most families (71.7%) were living below the poverty level at the time of the 7-y visit. Slightly more than half of the children were girls (53.7%), and most children completed their WISC-IV assessment in Spanish (68.2%). The distributions of agricultural use of neurotoxic pesticides within 1 km of maternal residences during pregnancy are shown in Table 2. The most heavily used pesticide class was OPs, followed by Mn-fungicides. The geometric mean (GM) and geometric standard deviation (GSD) for the cumulative use of 15 OP pesticides within 1 km of maternal residences during pregnancy was 75 (5) kg. For individual OP pesticides, the GM (GSD) ranged from 5 (6) kg for malathion to 23 (3) kg for diazinon, and the use of these five individual OP pesticides was moderately (0.40 for malathion and diazinon) to highly (0.91 for acephate and oxydemeton-methyl) correlated. The GM (GSD) of the other neurotoxic pesticide groups ranged from 4 (2) kg for neonicotinoids to 54 (4) kg for Mn-fungicides. There was moderate to high correlation (0.68− 0.90) between the use of the five different neurotoxic pesticide groups within one kilometer of the maternal residence during pregnancy (Table S2).

Table 2. Total neurotoxic pesticide use in Monterey County in 2000 and distributions of agricultural use within 1 km of the maternal residence during pregnancy (n=283).

Kilograms used (2000) Kilograms used within 1 km of residence during pregnancy
Neurotoxic pesticides Monterey p25 p50 p75 Max GM (GSD)
OPs 244,696 31 93 218 615 75 (5)
 Acephate 34,792 2 10 29 354 9 (4)
 Chlorpyrifos 25,357 1 9 29 331 8 (5)
 Diazinon 56,434 11 22 50 579 23 (3)
 Malathion 37,161 0 2 19 422 5 (6)
 Oxydemeton-methyl 28,767 2 11 24 260 9 (4)
 OPs toxicity weighted 964,130 59 331 891 8,587 175 (9)
Carbamates 59,914 3 16 49 618 16 (5)
 Carbamates toxicity weighted 335,611 14 66 291 9,222 66 (7)
Carbamates and OPs toxicity weighted 1,299,741 95 485 1,379 9,774 270 (9)
Neonicotinoids 7,103 1 3 6 34 4 (2)
Pyrethroids 16,386 1 4 15 79 6 (3)
Mn-fungicides 154,698 25 62 139 960 54 (4)

Notes: GM, geometric mean; GSD, geometric standard deviation; Max, maximum; Mn, manganese; OPs, organophosphates; p25, 25th percentile; p50, 50th percentile; p75, 75th percentile.

OP Pesticides

In general, IQ scores decreased across all domains with increasing use of OP pesticides within 1 km of the maternal residence during pregnancy. Each SD increase in toxicity-weighted OP use during pregnancy was associated with an estimated 2.2-point [95% confidence interval (CI): <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.5] (Table 3) decrease in Full-Scale IQ, which was very similar to but slightly greater than that for the nonweighted use of all OP pesticides combined (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />2.1 points; 95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.8, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3) (Table 4). A 1-SD increase in toxicity-weighted OP pesticide use during pregnancy was also associated with a 2.9-point decrease in Verbal Comprehension scores (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.4, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.3) (Table 3). The results were similar in the unadjusted and adjusted models. For the other WISC domains, there was a nonsignificant decrease of ∼1.4 points per SD increase in toxicity-weighted OP pesticide use. The results were similar whether or not we included the maternal prenatal urinary DAP concentrations in the model. In fact, we observed independent and similar decreases in WISC scores for both a 1-SD increase in prenatal urinary DAPs and a 1-SD increase in toxicity-weighted OP pesticide use when both exposures were included in the same model (Table 3).

Table 3. Unadjusted and adjusted associations between an SD increase in toxicity-weighted OP pesticide use within 1 km of maternal residence and urinary DAPs during pregnancy included in the same model and IQ scales at 7 y of age.

Unadjusted model Adjusteda models
Toxicity-weighted OP pesticides (kg) Toxicity-weighted OP pesticides (kg) Urinary DAPs (nmol/L)
Cognitive test (WISC-IV Scale) n β (95% CI) β (95% CI) β (95% CI)
Working Memory 256 −0.9 (−2.7, 0.8) −1.4 (−3.1, 0.4) −1.7 (−3.3, 0.1)*
Processing Speed 256 −0.9 (−2.6, 0.9) −1.3 (−3.0, 0.5) −1.2 (−2.8, 0.4)
Verbal Comprehension 283 −3.5 (−5.5, 1.5)** −2.9 (−4.4, 1.3)** −2.8 (−4.3, 1.4)**
Perceptual Reasoning 283 −1.2 (−3.1, 0.7) −1.4 (−3.3, 0.5) −1.7 (−3.5, 0.1)
Full-Scale IQ 255 −2.1 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3)* −2.2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.5)* −2.4 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.9)**

Notes: CI, confidence interval; DAPs, dialkylphosphates; OPs, organophosphates; WISC-IV, Wechsler Intelligence Scale for Children, fourth edition.

aAdjusted for child’s age at assessment, sex, language of assessment, maternal education, maternal intelligence, maternal country of birth, maternal depression at 7-year visit, Home Observation for Measurement of the Environment (HOME) Score at 7-year visit and household poverty level at 7-year visit. *p<0.05. ** p<0.01.

Table 4. Adjusted association between a standard deviation increase in neurotoxic pesticide use within one kilometer of residence during pregnancy and IQ scales at 7 y of age from separate models for each exposure.

Full-Scale IQ (n=255) Working Memory (n=256) Processing Speed (n=256) Perceptual Reasoning (n=283) Verbal Comprehension (n=283)
Neurotoxic pesticides β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI)
OPs −2.1 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.8, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3) −1.3 (−3.1, 0.4) −1.1 (−2.9, 0.7) −1.8 (−3.7, 0.1) −2.5 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.1, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.0)**
 Acephate −2.3 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.6)** −1.4 (−3.1, 0.3) −1.4 (−3.1, 0.2) −1.8 (−3.6, 0.1) −2.7 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.3, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.2)**
 Chlorpyrifos −1.4 (−3.0, 0.2) −1.3 (−3.0, 0.3) −0.3 (−1.9, 1.4) −0.8 (−2.6, 1.1) −2.2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.7)**
 Diazinon −1.7 (−3.4, 0.1) −1.3 (−3.0, 0.5) −1 (−2.8, 0.7) −1.7 (−3.6, 0.2) −1.6 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.2, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.1)*
 Malathion −0.8 (−2.5, 0.8) −0.8 (−2.4, 0.9) 0.8 (−0.9, 2.5) −1.2 (−3.0, 0.6) −1.3 (−2.8, 0.2)
 Oxydemeton–methyl −2.3 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.7)** −1.5 (−3.2, 0.2) −1.5 (−3.2, 0.2) −1.5 (−3.4, 0.4) −2.8 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.3, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.3)**
OPs toxicity weighted −2.2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.5)** −1.4 (−3.1, 0.4) −1.3 (−3.0, 0.5) −1.4 (−3.3, 0.5) −2.9 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.4, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.3)**
Carbamates −1.2 (−2.8, 0.4) −0.4 (−2.0, 1.2) −0.2 (−1.8, 1.4) −1.1 (−2.9, 0.7) −2.4 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.0)**
 Carbamates toxicity weighted −1.3 (−2.9, 0.3) −0.6 (−2.2, 1.1) −0.1 (−1.7, 1.5) −1 (−2.8, 0.8) −2.5 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.0)**
Carbamates and OPs toxicity weighted −2.1 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.4)* −1.1 (−2.9, 0.6) −1 (−2.7, 0.7) −1.4 (−3.3, 0.5) −2.9 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.5, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />1.4)**
Neonicotinoids −1.7 (−3.3, 0.0)* −1.1 (−2.8, 0.5) −0.8 (−2.4, 0.9) −1.9 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.8, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.1)* −1.9 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.5, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3)*
Pyrethroids −2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3)* −1.5 (−3.2, 0.2) −1.1 (−2.8, 0.6) −2.1 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.2)* −1.8 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.4, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.3)*
Mn-fungicides −2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.2) −1.2 (−2.9, 0.6) −1.2 (−2.9, 0.6) −1.7 (−3.6, 0.1) −2.1 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.6)**
Rank index of 5 neurotoxic groups −2 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.4)* −1.3 (−3.0, 0.4) −1 (−2.7, 0.7) −1.9 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.8, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.1)* −2.4 (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.9)**

Notes: Mn, manganese. Adjusted for child’s age at assessment, sex, language of assessment, maternal education, maternal intelligence, maternal country of birth, maternal depression at 7-y visit, Home Observation for Measurement of the Environment (HOME) score at 7-y visit, household poverty level at 7-year visit and prenatal urinary dialkylphosphates. *p<0.05. ** p<0.01.

In separate adjusted models for individual OP pesticides (Table 4), there was a 2.3-point decrease in Full-Scale IQ for each SD increase in acephate (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.9, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.6) or oxydemeton-methyl (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.0, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.7). Although they were also negatively related, there was no significant relationship between the use of chlorpyrifos, malathion, or diazinon and Full-Scale IQ. There was a significant negative relationship between agricultural use of acephate, chlorpyrifos, diazinon, and oxydemeton-methyl and Verbal Comprehension. There was no relationship with Working Memory, Processing Speed, or Perceptual Reasoning for any of the individual OPs evaluated. We also included the top 5 OP pesticides in the same model (Model 1, Table S3) and found no association (p<0.05) with Full-Scale IQ for any individual OP pesticide, but the strongest relationship was with oxydemeton-methyl (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />4.2 points; 95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />10.1, 18).

Other Pesticide Groups

We observed a nearly universal trend of lower IQ scores for all domains with greater use of individual OP pesticides and other potentially neurotoxic pesticide groups within 1 km of the maternal residence during pregnancy (Table 4). The combined toxicity-weighted use of OPs and carbamates was associated with decreased Full-Scale IQ, although the unweighted and toxicity-weighted use of carbamates alone was not significantly associated with Full-Scale IQ. In separate models with the other neurotoxic pesticide groups, the use of neonicotinoids, pyrethroids, and Mn-fungicides was each significantly associated (p<0.05) with an approximately 2-point decrease in Full-Scale IQ, Perceptual Reasoning, and Verbal Comprehension (Table 4).

In a single model that included all five neurotoxic pesticide groups, the strongest association with Full-Scale IQ was for toxicity-weighted OP pesticide use with a 2.8-point decrease (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />6.8, 1.3) for each SD increase (Model 2, Table S3). The associations with IQ scales were nearly identical for the first component from PCA, which explained ∼80% of the variance and weighted the five neurotoxic pesticide groups equally, and the average rank index, with a 2.0-point decrease (95% CI: <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3.7, <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.4) in Full-Scale IQ for each SD increase in estimated exposure (Table 4).

Sensitivity Analyses

For all analyses, point estimates were similar but confidence intervals were wider when we restricted the study population to those mothers with residential location known for three trimesters of pregnancy (n=150 with Full-Scale IQ), and associations were weaker when we included all mothers with residential location known for one trimester during pregnancy (n=284 with Full-Scale IQ) (data not shown). Our results were very similar after excluding the relatively few outliers (1–3 participants per exposure/outcome) based on studentized residuals (data not shown). Associations between prenatal neurotoxic pesticide use and WISC scores at 7 y of age became slightly stronger when we used inverse probability weighting to adjust for potential selection bias (data not shown). Including other prenatal exposures that have been related to WISC scores at 7 y of age (DDT/DDE and PBDEs) reduced the number of participants (n=191 with Full-Scale IQ), but the results were very similar for toxicity-weighted OP pesticide use and the other neurotoxic pesticide groups for all WISC scales with and without inclusion of these other prenatal exposures in the models.

Discussion

We observed an inverse relationship between the agricultural use of OP pesticides within 1 km of maternal residences during pregnancy and cognitive development in children at 7 y of age. For each standard deviation increase in agricultural use of total OPs (237 kg) or toxicity-weighted OPs, there was a 2-point decrease (15% of a standard deviation) in Full-Scale IQ. To put these findings in perspective, other authors have estimated that each 1-point decrease in IQ decreases worker productivity by ∼2% (Grosse et al. 2002), reducing lifetime earnings by US$18,000 in 2005 dollars (Nedellec and Rabl 2016). The results were independent of prenatal urinary DAP concentrations in the model, and the effect estimates of nearby OP use and urinary DAPs were of similar magnitude. These independent associations suggest that our previous observation of a relationship between prenatal urinary DAPs and IQ (Bouchard et al. 2011a) did not completely account for exposure to OP pesticides during pregnancy and that using both urinary DAPs and PUR data seems to provide a more complete characterization of OP pesticide exposure. Urinary DAP concentrations provide an estimate of exposure to some, but not all, of the OP pesticides we evaluated using PUR data (Castorina et al. 2010) and primarily reflect dietary exposures (McKone et al. 2007; Bradman et al. 2015). The two individual OP pesticides that had the strongest inverse relationship with Full-Scale IQ were acephate and oxydemeton-methyl, but agricultural use of these two pesticides was highly correlated (r=0.91). It is important to note that although oxydemeton-methyl devolves to urinary DAPs, acephate does not; it is also important to note that oxydemeton-methyl is the most toxic of all the OPs used in the Salinas Valley (∼11 times more toxic than acephate, based on the RPF).

Agricultural use of other potentially neurotoxic pesticide classes was correlated with the use of OPs, and there were also significant inverse associations between Full-Scale IQ and nearby agricultural use of pyrethroid insecticides, Mn-based fungicides (mostly maneb), and a neonicotinoid insecticide (imidacloprid). The combined agricultural use of pesticides from five neurotoxic pesticide classes based on an average rank index and PCA produced similar results to those observed for toxicity-weighted OP pesticide use alone, making it difficult to determine whether OP pesticide use alone is driving the relationship or if the results are caused by the combined use of neurotoxic pesticides that are highly correlated.

This is the first study to evaluate the relationship between cognitive abilities in children and reported agricultural use of neurotoxic pesticides near maternal residences during pregnancy. A recent study conducted in Spain that used residential proximity to agricultural fields as a proxy for pesticide exposure observed an inverse relationship between postnatal, but not prenatal, hectares of crops near the residence and Full-Scale IQ, Verbal Comprehension, and Processing Speed in children 6–11 y of age (González-Alzaga et al. 2015). A study in California utilizing PUR data found that any agricultural use of OPs or pyrethroids within 1.5 km of maternal residences during the third trimester of pregnancy compared with no agricultural use of these pesticides was associated with an approximately doubled risk of autism spectrum disorder (Shelton et al. 2014). Higher concentrations of pyrethroid metabolites in children’s urine have been associated with an increased risk of behavioral problems in school-age children (Oulhote and Bouchard 2013) and with attention deficit/hyperactivity disorder in one study (Wagner-Schuman et al. 2015) but not in another (Quirós-Alcalá et al. 2014). Previous studies have observed inverse associations between children’s cognition and levels of manganese in blood (Riojas-Rodríguez et al. 2010) and hair (Bouchard et al. 2011b; Menezes-Filho et al. 2011). Proximity to agricultural use of the neonicotinoid imidacloprid during pregnancy has been associated with an increased risk of neural tube defects (Rull et al. 2006; Yang et al. 2014), but there are no previously published studies evaluating cognition in children.

The main strength of this study is the use of PUR data, which provide the amount of active ingredients in and the locations of all agricultural pesticide applications, and which represent a major improvement in exposure classification compared with using only crop locations, as in the recent Spanish study (González-Alzaga et al. 2015). The PUR data allowed us to examine pesticides that do not have biomarkers and to assess pesticide mixtures. We also had extensive information on potential confounders and other chemical exposures available for the CHAMACOS cohort. However, there are some limitations of our study. We were able to determine proximity to agricultural pesticide use only at the maternal residence, but not at other locations where the mother may have spent time. Although we used residential proximity to agricultural pesticide use as a proxy for pesticide exposure, previous studies have shown that PUR data are correlated with environmental pesticide concentrations (Harnly et al. 2009; Harnly et al. 2005), suggesting that these data provide a meaningful indicator of pesticide exposure. We did not account for prenatal exposure information from other potential sources of pesticide exposure including home use, occupational take-home, and dietary intake, but our models included prenatal urinary DAPs, which we believe primarily plect both dietary and residential exposures (McKone et al. 2007). In general, these limitations would likely lead to exposure misclassification and would bias our results toward the null.

People living in agricultural communities are exposed to a complex mixture of many individual pesticide active ingredients as well as to potentially neurotoxic adjuvants included in the formulation. Better methods are needed for toxicity weighting across neurotoxic pesticide classes. To improve pesticide exposure assessment based on PUR data, exposure models should be optimized using measured pesticide concentrations in air or house-dust samples. In future analyses, we intend to use more complex statistical methods to address collinearity of exposures, such as weighted quantile sum regression (Gennings et al. 2013) and hierarchical Bayesian models (Kalkbrenner et al. 2010; Rull et al. 2009). The results from the present study need to be replicated in studies that include children living in both agricultural and nonagricultural communities to obtain more variability in exposure to pesticide mixtures.

Conclusions

We observed an inverse association between prenatal residential proximity to agricultural use of OPs and other neurotoxic pesticides and cognition in children at 7 y of age. The results for OPs based on PUR data remained significant when we included prenatal urinary maternal DAP concentrations in the model, and the effect estimates of nearby OP use and urinary DAPs were of similar magnitude. The association also remained after adjustment for prenatal exposure to other neurotoxic chemicals. Agricultural use of individual pesticides and classes of neurotoxic pesticides were highly correlated, making it difficult to identify the specific pesticides that were driving these associations.

Acknowledgments

This study was funded by the National Institute of Environmental Health Sciences grants P01 ES009605 and R56ES023591 and U.S. Environmental Protection Agency grants R82670901 and RD83451301.

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A Case–Control Study of Maternal Polybrominated Diphenyl Ether (PBDE) Exposure and Cryptorchidism in Canadian Populations

Author Affiliations open

1Research Institute of McGill University Health Centre, Montreal, Quebec, Canada

2Department of Pediatrics, McGill University, Montreal, Quebec, Canada

3Department of Pharmacology and Toxicology, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada

4Leslie Dan School of Pharmacy, University of Toronto, Toronto, Ontario, Canada

5Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

6Department of Pediatric Urology, McGill University, Montreal, Quebec, Canada

7Department of Pediatric General and Thoracic Surgery, McGill University, Montreal, Quebec, Canada

8Department of Pediatric Urology, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada

9Division of Pediatric Urology, London Health Sciences Centre, London, Ontario, Canada

10Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada

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  • Background:
    Polybrominated diphenyl ethers (PBDEs) are flame retardants found in North American household products during the past four decades. These chemicals leach out in dust as products age, exposing individuals daily through inhalation and ingestion. Animal studies suggest that PBDEs disrupt sex hormones and adversely affect development of the reproductive system.
    Objectives:
    In the present study, we examined whether there is a link between maternal hair PBDE concentrations and the risk of cryptorchidism (undescended testes) in male infants; testis descent is known to be dependent on androgens.
    Methods:
    Full-term male infants were recruited through clinics in Montreal, Toronto, and London, Canada. Boys with cryptorchidism at 3–18 months of age (n=137) were identified by pediatric urologists and surgeons; similar-aged controls (n=158) had no genitourinary abnormalities as assessed by pediatricians. Eight BDE congeners (BDE-28, -47, -99, -100, -153, -154, -183, -209) were measured by GC-MS (gas chromatography–mass spectrometry) in maternal hair samples collected at the time of recruitment.
    Results:
    The ∑PBDE geometric mean for maternal hair was 45.35 pg/mg for controls and 50.27 pg/mg for cases; the concentrations of three BDEs (BDE-99, -100, and -154) were significantly higher in cases than controls in unadjusted models. In adjusted models, every 10-fold increase in the concentration of maternal hair BDE-99 [OR=2.53 (95% CI: 1.29, 4.95)] or BDE-100 [OR=2.45 (95% CI: 1.31, 4.56)] was associated with more than a doubling in the risk of cryptorchidism. BDE-154 [OR=1.88 (95% CI: 1.08, 3.28)] was also significant.
    Conclusions:
    Our results suggest that maternal exposure to BDE-99, -100, and -154 may be associated with abnormal migration of testes in the male fetus. This may be due to the anti-androgenic properties of the PBDEs. https://doi.org/10.1289/EHP522
  • Received: 17 May 2016
    Revised: 22 September 2016
    Accepted: 08 October 2016
    Published: 26 May 2017

    Address correspondence to C.G. Goodyer, Research Institute of McGill University Health Centre, Centre for Translational Biology, EM0.3211, 1001 Decarie Blvd., Montreal, QC, Canada H4A 3J1. Telephone: (514) 934-1934, ext. 22481. E-mail: cindy.goodyer@muhc.mcgill.ca

    Supplemental Material is available online (https://doi.org/10.1289/EHP522).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Cryptorchidism is the failure of one or both testicles to descend into the scrotum during in utero development of the male fetus (Barteczko and Jacob 2000). This is one of the most common (1.8–9%) urogenital abnormalities observed in normal term male newborns (Virtanen and Toppari 2008). In brief, two stages are involved in testis migration (Barthold 2008). The first occurs between gestational weeks 8 and 15, when the testicles travel from an intra-abdominal perirenal position to the top of the inguinal ring. Late in the third trimester, they then migrate through the inguinal ring and into the scrotal sac. In certain cases, the testes do not undergo the final migration until after birth but by 3 months the majority will have descended, spontaneously reducing the number of cases that require surgery (orchidopexy) to reposition the testes within the scrotum (Kollin and Ritzén 2014). Orchidopexy is recommended between ages of 6 and 12 months to decrease the risk of testicular torsion or trauma, improve fertility and decrease the risk of testicular neoplasm in adulthood.

Animal and clinical studies have demonstrated that normal migration of the testes is dependent on both genetic factors and the in utero hormonal environment (Barthold 2008; Barthold et al. 2015; Huang et al. 2012; Jensen et al. 2010; Virtanen and Toppari 2008). The trans-abdominal phase is linked to expression of two genes: one for the insulin-like peptide-3 (INSL-3) hormone produced by Leydig cells and a second for the INSL-3 receptor, relaxin-family peptide receptor 2 (RXFP2). The second inguinal–scrotal phase is thought to be primarily dependent on androgens produced by the fetal Leydig cells and normal expression of the androgen receptor. Clinical reports have linked cryptorchidism with mutations in the INSL-3, RXFP2, or Androgen Receptor (AR) genes but only in a small number of cases (Bay et al. 2011; Feng et al. 2009; Ferlin et al. 2009). Thus, the etiology of most cases remains unknown. In a study that evaluated the risk contribution from genetic versus intrauterine environmental factors, Jensen et al. (Jensen et al. 2010) found a similar concordance rate in monozygotic and dizygotic twins, providing strong support for an important role of the intrauterine environment.

There is increasing evidence that maternal exposure to certain environmental chemicals may have endocrine disrupting activity at critical stages during testicular development and/or migration due to the ability of these compounds to cross the placenta and enter the fetal environment (Bay et al. 2011; Virtanen and Adamsson 2012). Such chemicals include flame retardants, organochlorine pesticides, fungicides, dioxins, bisphenol A, and phthalates, all of which exhibit estrogenic or anti-androgenic properties in in vitro assays (Balbuena et al. 2013; Christen et al. 2014; Hamers et al. 2006; Harju et al. 2007; Rosenmai et al. 2014; Rouiller-Fabre et al. 2015; Stoker et al. 2005; Yang et al. 2009) and have been linked to genitourinary malformations, including cryptorchidism, in animal studies (Auger et al. 2014; Chen et al. 2015; Christiansen et al. 2009; Christiansen et al. 2010; Christiansen et al. 2014; Emmen et al. 2000; van den Driesche et al. 2012; Welsh et al. 2008). However, evidence for their effects on cryptorchidism in humans remains controversial. Many studies have involved small cohorts and have shown possible, but not significant, associations (Bay et al. 2011; Chevalier et al. 2015; Cook et al. 2011; Jensen et al. 2015; Koskenniemi et al. 2015; Virtanen and Adamsson 2012). One small study (62 cases, 68 controls) of a Danish mother<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />infant cohort reported significant associations between breast milk levels of several polybrominated diphenyl ether (PBDE) flame retardants measured 1<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />3 months after birth with increased risk of cryptorchidism at birth (Main et al. 2007). In a parallel Finnish cohort, no associations were found, despite similar total PBDE levels in breast milk; however, the PBDE congener profile differed (Krysiak-Baltyn et al. 2012; Main et al. 2007).

Because of the contradictory reports in the literature, the goal of the present study was to reexamine the possible association of maternal PBDE exposure and increased risk of undescended testes in male infants. We chose a cohort where the cryptorchid cases were defined by direct observation during orchidopexy. Mother/child pairs were recruited 3<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />18 months after birth because testes may spontaneously descend during the first 3<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />6 postnatal months and surgery only occurs several months after diagnosis of cryptorchidism. Because prior studies examined testes descent at birth, we hypothesized that the previous inconsistent results might be due, in part, to case misclassification. Thus, this study was designed to have an unambiguous cohort of cryptorchid male infants.

Methods and Materials

Participants

A total of 374 mother and child pairs were recruited between the summer of 2011 and summer of 2014. The research study was approved by the ethics boards at the different recruitment centers and all participants signed a written informed consent. The cryptorchidism cases (n=200) were recruited at Pediatric Urology and General Surgery clinics at the Montreal Children’s Hospital (n=80), the Hospital for Sick Children in Toronto (n=115) and the London Health Sciences Centre in London, Ontario (n=5) following diagnosis by pediatric urologists and surgeons. Diagnosis between centers followed accepted standards: all centers reviewed and agreed on the diagnostic criteria (Wein et al. 2016). Infants with retractile testes were excluded from the study. Controls (n=174) were recruited through the Hospital for Sick Children helpline for questions about pregnancy and breastfeeding (n=51) and at a Montreal community pediatric center (n=123): pediatricians verified the lack of urogenital abnormalities. Mothers were eligible to participate if they were <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="≥” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />18 years old, had sufficient hair to provide a sample, and had a child who was between the ages of 3 and 18 months, born full term (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="≥” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />37 weeks gestation) with normal weight (<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2265.png" alt="≥” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />2,500 g), diagnosed with or without cryptorchidism, and with no other genitourinary malformations or genetic syndromes. Participation rate was >95%.

Mothers in both groups filled out a questionnaire with information pertaining to home and work environment, general medical history, reproductive history (including paternal and familial history of cryptorchidism), breastfeeding, diet, alcohol consumption, smoking, medication usage and maternal socio-demographics (age, birth place, ethnicity, education, country of birth, marital status and income). At the time of data analyses, 79 participants were excluded because of missing questionnaire data, leaving 137 cases and 158 controls for a total of 295 participants.

In both case and control groups, standardized genital exams of the infants were conducted by pediatricians soon after birth to obtain information on testicular position (descended vs. undescended), with verification of cryptorchidism by pediatric urologists. Testicles were initially defined as nonpalpable or palpable. Nonpalpable testes were further classified as vanishing, abdominal, or atrophic at the time of surgery. Palpable testes were considered to be inguinal or prescrotal. Ectopic testicles were defined to have a perineal, femoral, prepubic, contralateral scrotal, or superficial inguinal pouch location. The superficial inguinal pouch location was only identified at the time of surgery because this cannot be distinguished from an inguinal testis on physical exam alone. For all cryptorchid children, a chart review was carried out post-surgery by a urologist and the research coordinator to obtain details of the precise location of the testes.

Hair Sample Collection

Hair was used as a matrix to assess the PBDE exposure of mothers and children because previous studies reported a positive correlation between serum and hair PBDE concentrations, especially for tetra- to hexa BDE congeners (Poon et al. 2014; Zheng et al. 2014). Sufficient samples of hair for the PBDE assay were collected at the time of recruitment from all mothers and approximately a third of the babies (many babies had too little or no hair): maternal<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />child paired samples were collected for 57 cases and 50 controls. Using stainless steel scissors, 50<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />100 mg of hair was collected from the mothers and as much hair as possible from the babies: the hair was cut within 1 cm from the scalp at the posterior vertex (Aleksa et al. 2012). The hair samples were stored in sealed envelopes in the dark at 4°C until assayed at the Hospital for Sick Children.

Hair Sample Analyses

To standardize the hair analyses, PBDEs were measured in the first 3–4 cm of hair closest to the root. The methodology for adult and child hair PBDE measurements was established previously (Aleksa et al. 2012; Carnevale et al. 2014; Poon et al. 2014). In brief, samples were rinsed with Milli-Q water and dried with paper towels to remove dust from the hair surface (Poon et al. 2015). The hair was then weighed (5<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />30 mg) and finely cut into 1<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />2 mm pieces. Samples were analyzed by GC-MS for eight PBDE congeners: BDE-28, -47, -99, -100, -153, -154, -183, and -209. Quantification was performed using five-point calibration curves whereby standards (Wellington Laboratories) were added to extracts of a single pool of “blank” hair because it contained negligible levels of the eight PBDEs being measured. The peak area ratios of congeners BDE-28 to -183 to their internal standard (F-BDE-69) and BDE-209 to its standard ( 13 C12-BDE-209) were calculated. The area ratios in blank hair were subtracted from the sample area ratios prior to plotting against the calibration curve to quantify the PBDEs. The PBDE levels were corrected for dry weight of each sample. The limits of detection (LOD) ranged from 1 to 4 pg/mg and the limits of quantification (LOQ) from 3 to 12 pg/mg. The percent recoveries ranged from 100% to 120% with the exception of BDE-47 (135%), and the percent CVs ranged from 13% to 19% with the exception of BDE-209 (33%). Machine-read values were used for concentrations that fell between the LOD and LOQ. Values below the LOD were imputed using multiple imputation (see below). Analyses were limited to those congeners with quantification frequencies >50% (BDE-28, -47, -99, -100, -153, -154, and -209) and their sum. With a quantification frequency of 40.2%, BDE-183 was not considered in statistical analyses.

Statistical Analyses

PBDE concentrations were heavily right-skewed and were thus log10-transformed to reduce the influence of outliers. We used Pearson’s correlations and analysis of variance (ANOVA) to estimate bivariate associations. Multivariable associations between maternal hair PBDE concentrations and cryptorchidism case status were estimated using multiple logistic regression. Because the proportional odds assumption did not hold for all analyses (p<0.05), we used multinomial (rather than ordinal) logistic regression to evaluate associations with cryptorchidism severity based on testis position (i.e., inguinal, ectopic, intra-abdominal) and the number of testes affected (i.e., unilateral, bilateral). Potential confounders were identified based on directed acyclic graphs (DAGs) and included maternal age (continuous), birthplace, ethnicity, marital status, income, education, body mass index (continuous), alcohol and caffeine consumption during pregnancy (yes vs. no), smoking during pregnancy, gestational diabetes, use of assisted reproductive techniques, child age at examination, and family history of cryptorchidism (as shown in Table 1 and below).

Table 1. Geometric means and standard deviations of maternal hair ΣPBDEa concentrations by demographic characteristics and family history of urogenital anomalies among cryptorchidism cases (n=137) and controls (n=158).
Cases Controls
No. (%) GMb (GSDc) No. (%) GM (GSD)
Birthplace
 North America 92 (67) 50.3 (1.9) 124 (78) 43.6 (2.0)
 South America 6 (4) 55.0 (2.0) 5 (3) 79.5 (1.7)
 Europe 12 (9) 48.0 (2.0) 13 (8) 55.1 (2.0)
 Other 27 (20) 52.7 (1.9) 16 (10) 43.9 (1.9)
Ethnic background
 Caucasian 90 (69) 46.2 (1.9) 121 (81) 45.5 (2.0)*
 Asian 8 (6) 48.4 (1.8) 10 (7) 41.2 (1.6)
 Hispanic 12 (9) 52.5 (2.1) 5 (3) 72.1 (1.5)
 Arab 9 (7) 68.0 (1.5) 3 (2) 32.3 (2.2)
 Other 12 (9) 68.7 (1.8) 11 (7) 46.5 (2.2)
Marital status
 Married/living as married 128 (94) 50.0 (1.9) 154 (97) 45.1 (2.0)
 Single (never married) 6 (4) 42.4 (1.9) 1 (1) 31.9 (0.0)
 Other 2 (1) 100.4 (1.0) 3 (2) 66.2 (1.2)
Household income (Canadian dollars)
 0–29,999 10 (9) 59.8 (2.0) 4 (3) 50.2 (1.5)***
 30,000–59,999 20 (18) 54.1 (1.8) 9 (7) 45.8 (2.3)
 60,000–89,999 34 (30) 46.0 (1.9) 31 (20) 40.1 (1.9)
≥90,000 49 (43) 45.0 (2.0) 88 (67) 47.8 (1.9)
Highest education level
 Less than high school 63 (47) 53.4 (2.0) 37 (26) 37.1 (2.2)***
 High school 41 (30) 49.8 (1.8) 62 (43) 45.7 (1.9)
 More than high school 31 (23) 44.5 (1.8) 46 (32) 49.4 (2.0)
Drank during pregnancy
 Yes 10 (9) 49.3 (1.9) 12 (8) 44.4 (1.6)
 No 104 (91) 48.8 (2.0) 143 (92) 45.3 (2.0)
Smoked during pregnancy
 Yes 3 (5) 73.2 (1.3) 4 (3) 45.3 (1.8)
 No 58 (95) 47.8 (1.9) 154 (97) 45.3 (2.0)
Mother’s age (years)
 <25 7 (5) 66.7 (2.0) 1 (1) 11.8 (0.0)**
 25–29 30 (23) 47.8 (1.8) 23 (15) 46.5 (1.9)
 30–34 46 (35) 49.4 (2.1) 74 (47) 47.3 (2.1)
 35–39 41 (32) 49.0 (1.8) 44 (28) 41.2 (1.9)
 ≥40 6 (5) 48.5 (1.6) 14 (9) 49.7 (1.7)
BMI (kg/ m2 )
 <20 13 (11) 45.4 (1.8) 19 (13) 45.6 (2.3)
 20–24.9 61 (50) 49.1 (2.0) 74 (51) 50.8 (1.8)
 25–29.9 32 (26) 57.3 (1.9) 38 (26) 39.2 (1.9)
 30–34.9 13 (11) 39.2 (2.1) 11 (8) 47.0 (2.4)
 ≥35 4 (3) 58.2 (1.5) 3 (2) 32.4 (1.5)
Schooling (years)
 <15 22 (29) 39.9 (2.1) 18 (16) 54.5 (2.3)
 15–19 40 (53) 45.0 (1.8) 77 (68) 44.2 (1.9)
 ≥20 14 (18) 46.0 (1.9) 19 (17) 59.6 (2.1)
Dependents (no.)
 2 13 (11) 67.0 (2.1) 4 (3) 31.3 (2.0)
 3 52 (42) 47.0 (2.1) 67 (49) 45.0 (2.2)
 4 38 (31) 46.8 (1.8) 48 (35) 42.2 (1.8)
 5 16 (13) 53.9 (1.8) 11 (8) 49.2 (2.2)
 ≥6 4 (3) 51.8 (1.8) 7 (5) 47.9 (1.6)
Child’s age (months)
 3–7.9 47 (34) 51.0 (1.8) 64 (42) 45.0 (1.9)
 8–12.9 53 (39) 52.4 (1.9) 65 (42) 47.9 (2.0)
 13–18 37 (27) 46.6 (2.0) 25 (16) 37.3 (2.0)
Paternal history of cryptorchidism/hypospadias
 None 113 (90) 50.2 (1.9) 126 (98) 45.6 (2.0)*
 Cryptorchidism 11 (9) 51.9 (1.8) 2 (2) 80.7 (3.4)
 Hypospadias 1 (1) 67.2 (0.0) 0 (0) 0.0 (0.0)
Family history of cryptorchidism
 Yes 18 (13) 62.3 (1.9) 5 (4) 32.7 (2.6)**
 No 119 (87) 48.7 (1.9) 132 (96) 46.0 (2.0)
Family history of hypospadias
 Yes 1 (1) 48.1 (0.0) 0 (0) 0.0 (0.0)
 No 136 (99) 50.3 (1.9) 136 (100) 45.8 (2.0)
Use of assisted reproductive techniques
 Ovulation induction 3 (2) 43.2 (1.9) 4 (3) 51.2 (1.9)
 Artificial insemination 0 (0) 0.0 (0.0) 3 (2) 34.7 (1.9)
 In vitro fertilization 6 (4) 49.2 (1.3) 6 (4) 43.8 (2.8)
 None 126 (93) 51.0 (1.9) 135 (91) 45.9 (1.9)
Gestational diabetes
 Yes 13 (10) 60.7 (1.5) 11 (7) 40.2 (2.1)
 No 122 (90) 49.7 (1.9) 147 (93) 45.8 (2.0)

aSummed congeners include BDE-28, -47, -99, -100, -153, -154, and -209.

bGeometric mean.

cGeometric standard deviation.

*p<0.05,

**p<0.01,

***p<0.001 based on chi-squared tests comparing frequency distributions between total cases and controls.

ΣPBDE did not significantly differ across demographic characteristics.

Final models included variables that were loosely associated with the outcome (p<0.20) in bivariate analyses (i.e., maternal age, birthplace, ethnicity, marital status, income, highest level of education, and paternal history of cryptorchidism). Missing values were imputed based on multiple imputation by chained equations (MICE) using predictive mean matching for missing covariates and interval-censored regression for PBDE values below the LOD (van Buuren et al. 1999). MICE can use a variety of prediction models that may include variables of any form and with varying levels of missingness to impute missing values. Multiple imputation has been shown to generate valid parameter estimates and, as opposed to single substitution, properly estimates variance by taking into account the uncertainty associated with imputed values (Lubin et al. 2004; Rubin 1976, 1987). Estimates and their variance were estimated by generating 50 imputations and using Rubin’s formula (Rubin 1976, 1987). All analyses were conducted using Intercooled STATA version 13.1 (StataCorp).

Results

Participant Characteristics

The mean maternal age at the time of interview was 33 years (range, 18–48). As shown in Table 1, the majority of the mothers were born in North America (73%) with 71% born in Canada, Caucasian (72%), married or living as married (96%), and with a household income ≥60,000/year (68%). Relative to controls, cases were less likely to be Caucasian, were younger, had a lower family income, and were more likely to have a paternal and family history of cryptorchidism. There were no differences between the geometric mean total hair PBDE levels by demographic characteristics or family history of urogenital anomalies.

Hair PBDE Levels

The geometric means and distribution of the maternal PBDE hair concentrations and their sums for the cases and controls are provided in Tables 2 and 3. Except for BDE-209, PBDE congeners were moderately intercorrelated (r=0.36<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.71; p<0.001; see Table S1). BDE-47 and -209 had the highest levels, followed by BDE-99 and -100, in both cases and controls. The geometric means of the individual PBDEs was significantly higher in the case mothers than the controls for BDE-99 (p<0.002), BDE-100 (p<0.001), and BDE-154 (p<0.04). Tables S2 and S3 provide data for the case and control infants. Again, BDE-47 and -209 were the highest in both groups, followed by BDE-99 and -100. PBDE concentrations in paired maternal and infant hair samples were moderately correlated among both the cases and controls (r=0.34<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.71; p<0.01 <img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2212.png" alt="−” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />0.001) (see Table S4); cases also showed a moderate correlation for the ∑PBDEs (r=0.41; p<0.001). We found no association between hair PBDE concentrations and child age at examination, breastfeeding status or duration, or hair coloring (data not shown).

Table 2. Detection frequencies, geometric means and percentiles of maternal hair PBDE concentrations among cases (n=137).
PBDEs LODa, LOQb (pg/mg) Detect freqc (%) Quant freqd (%) GMe (pg/mg) 95% CIf (pg/mg) Min (pg/mg) 25th percentile (pg/mg) Median (pg/mg) 75th percentile (pg/mg) Max (pg/mg)
∑PBDEsg 50.27 45.12, 56.01 8.92 34.27 53.03 85.99 167.42
BDE-28 1.00, 4.00 74.45 24.09 2.13 1.84, 2.47 <LOD <LOD 2.08 3.77 17.98
BDE-47 3.00, 8.00 85.40 58.39 8.89 7.69, 10.28 <LOD 4.70 10.01 17.03 75.40
BDE-99 2.00, 7.00 86.13 56.20 7.14 6.14, 8.31 <LOD 4.09 7.94 12.71 50.10
BDE-100 1.00, 4.00 89.78 69.34 6.12 5.11, 7.32 <LOD 3.28 7.40 13.47 46.30
BDE-153 2.00, 5.00 71.53 34.31 3.63 3.17, 4.17 <LOD <LOD 3.50 7.06 21.65
BDE-154 1.00, 4.00 92.70 50.36 4.08 3.49, 4.76 <LOD 2.16 4.03 8.00 27.08
BDE-183 4.00, 12.00 40.15 5.11 4.32 3.92, 4.76 <LOD <LOD <LOD 7.38 24.79
BDE-209 1.00, 3.00 93.43 86.86 9.08 7.64, 10.80 <LOD 5.62 11.37 16.67 78.30

aLimit of detection.

bLimit of quantification.

cDetection frequency.

dQuantification frequency.

eGeometric mean.

f95% confidence interval.

gSummed congeners include BDE-28, -47, -99, -100, -153, -154, and -209.

Table 3. Detection frequencies, geometric means and percentiles of maternal hair PBDE concentrations among controls (n=158).
PBDEs LODa, LOQb (pg/mg) Detect freqc (%) Quant freqd (%) GMe (pg/mg) 95% CIf (pg/mg) Min (pg/mg) 25th percentile (pg/mg) Median (pg/mg) 75th percentile (pg/mg) Max (pg/mg)
∑PBDEsg 45.35 40.77, 50.43 8.57 30.76 44.48 74.39 247.52
BDE-28 1.00, 4.00 70.89 31.65 2.42 2.09, 2.81 <LOD <LOD 2.76 4.64 14.46
BDE-47 3.00, 8.00 85.44 60.76 9.03 9.03, 10.26 <LOD 6.07 9.58 15.35 72.70
BDE-99 2.00, 7.00 79.11 37.97 5.11 4.46, 5.86 <LOD 2.66 5.97 8.71 79.40
BDE-100 1.00, 4.00 91.14 49.37 4.09 3.56, 4.70 <LOD 2.33 3.99 7.45 57.97
BDE-153 2.00, 5.00 68.99 23.42 3.10 2.77, 3.47 <LOD <LOD 2.83 4.90 30.20
BDE-154 1.00, 4.00 76.58 46.84 3.28 2.76, 3.90 <LOD 1.08 3.57 6.49 42.59
BDE-183 4.00, 12.00 52.53 14.56 5.20 4.66, 5.79 <LOD <LOD 4.14 9.11 32.06
BDE-209 1.00, 3.00 83.54 74.05 7.07 5.67, 8.81 <LOD 2.77 8.45 18.19 226.65

aLimit of detection.

bLimit of quantification.

cDetection frequency.

dQuantification frequency.

eGeometric mean.

f95% confidence interval.

gSummed congeners include BDE-28, -47, -99, -100, -153, -154, and -209.

Association between Hair PBDE Levels and Cryptorchidism

Figure 1 presents associations between individual BDEs as well as total PBDE levels in maternal hair and the odds of cryptorchidism. Every 10-fold increase in maternal hair BDE-99 [OR=2.53 (95% CI: 1.29. 4.95; p<0.007)], BDE-100 [OR=2.45 (95% CI: 1.31, 4.56; p<0.005)] or BDE-154 [OR=1.88 (95% CI: 1.08, 3.28; p<0.026)] was associated with elevated risk of cryptorchidism in male infants.

Line graph with confidence intervals plotting adjusted and unadjusted odds ratios for the association of eight maternal hair PBDE congeners, namely, BDE-28, BDE-47, BDE-99, BDE-100, BDE-153, BDE-154, BDE-183, and BDE-209 with cryptochidism.

Figure 1. Unadjusted and adjusted association between maternal hair PBDE concentrations and odds of cryptorchidism. *p<0.05, *p<0.01. Error bars represent 95% confidence intervals. Adjusted models included maternal birthplace, ethnicity, marital status, income, age, education, and paternal history of cryptorchidism.

Multinomial Logistic Regression Model

Data on the number of affected testes and site of the testis for all cryptorchid children (as well as the related geometric means of maternal hair PBDEs) are presented in Table S5. Multinomial logistic regression models confirmed an association of BDE-99, -100, and -154 with inguinal localization of the testes (Table 4). Statistical power to detect associations with ectopic (n=7) or intra-abdominal (n=19) cryptorchidism was limited due to the small number of cases. A similar lack of power was observed when associations of PBDEs were evaluated based on whether cryptorchidism was unilateral (one testis undescended; n=108) versus bilateral (both testes undescended; n=17); significant associations were observed only with the unilateral cases (data not shown).

Table 4. Association between maternal hair PBDE concentrations and testis location (n=295).
PBDEs Testis location ORa (95% CI)b
∑PBDEs c Ectopic 3.83 (0.24, 61.23)
Inguinal 1.60 (0.62, 4.09)
Intra-abdominal 1.32 (0.24, 7.33)
BDE-28 Ectopic 0.75 (0.13, 4.23)
Inguinal 0.86 (0.45, 1.64)
Intra-abdominal 0.59 (0.18, 1.87)
BDE-47 Ectopic 1.23 (0.14, 10.68)
Inguinal 0.86 (0.41, 1.80)
Intra-abdominal 0.83 (0.22, 3.14)
BDE-99 Ectopic 5.04 (0.48, 53.47)
Inguinal 2.45 (1.19, 5.04)*
Intra-abdominal 2.64 (0.70, 9.98)
BDE-100 Ectopic 8.37 (0.87, 80.35)
Inguinal 2.42 (1.23, 4.75)**
Intra-abdominal 1.97 (0.57, 6.74)
BDE-153 Ectopic 1.84 (0.26, 12.89)
Inguinal 1.88 (0.93, 3.80)
Intra-abdominal 0.79 (0.23, 2.79)
BDE-154 Ectopic 1.36 (0.27, 6.97)
Inguinal 1.98 (1.08, 3.62)*
Intra-abdominal 1.75 (0.58, 5.32)
BDE-209 Ectopic 2.11 (0.49, 9.14)
Inguinal 1.35 (0.82, 2.25)
Intra-abdominal 1.63 (0.61, 4.33)

Note: Models adjusted for maternal birthplace, ethnicity, marital status, income, age, education, and paternal history of cryptorchidism.

aOdds ratio.

b95% confidence intervals.

cSummed congeners include BDE-28, -47, -99, -100, -153, -154, and -209. *p<0.05, **p<0.01.

Discussion

We report here a significant association between maternal exposure to BDE-99, -100, and -154 and elevated risks of cryptorchidism in male infants. In vivo and in vitro studies of these congeners have demonstrated potent anti-androgenic properties of BDE-100, similar to those of the classical antiandrogen, flutamide, and a 10- to 80-fold lower effect of BDE-99 (Hamers et al. 2006; Harju et al. 2007; Kojima et al. 2009; Lilienthal et al. 2006; Stoker et al. 2005; Yang et al. 2009). One published study that included BDE-154 suggested that it has weak anti-androgenic activity (Stoker et al. 2005). Although BDE-100 has also been predicted to have weak estrogenic activities (Kojima et al. 2009; Meerts et al. 2001; Papa et al. 2010; Yang et al. 2009), these activities are several orders of magnitude lower than observed with estradiol. Overall, these data suggest that the anti-androgenic properties of BDE-99, -100, and -154 may help to explain their association with the disruption of testicular descent in our study.

BDE-47 and -209 were also frequently detected in the hair samples. BDE-47 has been shown to have significant anti-androgenic activities (∼5- to 10-fold less than BDE-100 but higher than BDE-99) in both in vivo and in vitro assays, whereas BDE-209 has little to no activity (Hamers et al. 2006; Stoker et al. 2005). The lack of an association between BDE-47 and risk of cryptorchidism was surprising given our proposed mechanism. This suggests that PBDEs may impact cryptorchidism by additional mechanisms of action. One possibility is that PBDEs are actively metabolized in human tissues leading to the formation of hydroxylated and methoxylated BDEs; some of these metabolites may be more bioactive than the parent compounds (Hamers et al. 2006; Hamers et al. 2008; Kojima et al. 2009; Meerts et al. 2001; Yang et al. 2011). The fact that we measured the parent compounds rather than their metabolites may explain in part why we observed associations with some congeners but not others.

One limitation of our study is the inability to rule out the possible influence of alternative BFRs that may be co-eluting with the PBDEs during the GC-MS analysis. For example, BDE-99 elutes very close to 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB), a component of Firemaster 550 (Fan et al. 2016; Liu et al. 2015; Stapleton et al. 2008). In addition, BDE-154 co-elutes with 2,2<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="′” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />,4,4<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="′” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />,5,5<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2032.png" alt="′” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />-hexabromobiphenyl (BB-153), a major component in previous commercial mixtures of polybrominated biphenyls (Korytár et al. 2005). Given that we were using the same source of hair for the calibration curves, it is possible that, although this hair sample did not contain EH-TBB or BB-153, that other hair samples did.

Two possible mechanisms may underlie the association between exposure to specific PBDEs and cryptorchidism: a) competition at the androgen receptor level, to block the effects of endogenous androgens (e.g., in the gubernaculum muscle or inguinoscrotal fat pad) (Barthold et al. 2016; Kaftanovskaya et al. 2012) and/or b) effects on the Leydig cell in the fetal testes, resulting in endogenous androgen insufficiency (Houk et al. 2004; Wang et al. 2009). Stoker et al. (Stoker et al. 2005) reported that BDE-99 and -100 display antagonistic properties in the rat ventral prostate binding assay (IC50s of 33 and 3 μM, respectively), while BDE-154 has a weak effect; they also found that BDE-100 is a true competitor at the androgen receptor level (Ki=1 μM) and inhibits dihydrotestosterone-induced androgen receptor activation in a concentration-dependent manner. Similar data for BDE-99 and -100 have been obtained by Hamers et al. (2006) using an in vitro androgen receptor CALUX assay. These findings indicate that BDE-100 is a potent competitive inhibitor of the androgen receptor and that the other two BDEs also have antiandrogen receptor properties. Direct effects of these PBDE congeners on the human fetal testes have not been reported. Thus, whether these congeners have effects on endogenous human androgen production is still unknown.

Cryptorchidism studies in animal models have clearly implicated roles for both specific genes (Insl-3, Rxfp2) and the fetal hormonal (androgenic) environment in the migration of testes (Barthold 2008; Gorlov et al. 2002; Huang et al. 2012; Nef and Parada 1999; Virtanen and Toppari 2008). However, similar evidence in the human male is limited despite the fact that cryptorchidism is a relatively common finding in normal term newborn males. Only a few of these infants have been shown to have INSL-3 or RXFP2 gene mutations; in the rare newborns with complete androgen insensitivity due to absence of a functional androgen receptor, the testes remain in the inguinal or groin area (Bay et al. 2011; Feng et al. 2009; Ferlin et al. 2009). Failure to identify genome-wide significant markers associated with nonsyndromic cryptorchidism has led to the recent suggestion that cryptorchidism is the result of a complex multilocus genetic susceptibility with the potential for additional risk from in utero environmental exposures (Barthold et al. 2015).

It is well accepted that the intrauterine environment plays a critical role in fetal development in general and there is increasing evidence that this is true for cryptorchidism as well. A number of epidemiological studies have found significant associations between an increased risk of cryptorchidism and prematurity, low birth weight and maternal gestational diabetes; the roles of smoking, alcohol consumption, acetaminophen use, maternal BMI, and assisted reproduction techniques remain controversial (Zhang et al. 2015). There have also been studies suggesting a specific role for environmental chemicals that readily cross the placenta and have endocrine disruptor properties (e.g., PBDEs, phthalates, tributyltin, bisphenol A, pesticides, perfluorinated compounds, dioxins) but these have been limited to one or two papers per chemical (Agopian et al. 2013; Bay and Anand-Ivell 2014; Christen et al. 2014; Doucet et al. 2009; Main et al. 2007; Rouiller-Fabre et al. 2015; Virtanen and Adamsson 2012).

In general, studies have focused on the anti-androgenic and/or pro-estrogenic properties of endocrine disrupting chemicals because the androgenic in utero environment is crucial in the testicular migration process (Christen et al. 2014; Dean and Sharpe 2013; Jain and Singal 2013; Thankamony et al. 2014). Additional evidence comes from the measurement of anogenital distance (AGD) in humans. AGD is well-known to be related to prenatal hormonal exposure and has been positively correlated with testis size, sperm count, and testosterone levels (Dean and Sharpe 2013; Swan et al. 2005). Mean values of AGD in infant males with cryptorchidism are significantly shorter than in healthy boys, suggesting that global inhibition of androgen production and/or action plays a role in the pathogenesis (Jain and Singal 2013; Thankamony et al. 2014).

We had hypothesized that the severity of the cryptorchidism (abdominal location vs. inguinal/ectopic or unilateral vs. bilateral) might be associated with maternal PBDE exposure. Unfortunately, the numbers of abdominal and bilateral cases were too low to derive a definitive conclusion. Despite this, our study has three major strengths. First, because cryptorchidism can spontaneously resolve in the first 6 months of life, we only considered as cases those infants who were confirmed at the time of orchidopexy; thus, we analyzed an unambiguous case cohort. This population differs from the cases described in the cohort of Danish mothers and infants where Main et al. (Main et al. 2007) found a significant association between the levels of several PBDEs in maternal breast milk (1–3 months postnatal) and cryptorchidism at birth: of n=29 cases, only 4 remained cryptorchid at 3 months. Thus, the Danish study was primarily focused on an association between maternal PBDE exposure and delayed migration of the infant testes. Second, we used multiple imputation to impute PBDE values below the level of detection. This method allowed us to take advantage of the intercorrelation between congeners as well as the predictive power of other covariates to impute undetected PBDE values while accounting for the uncertainty of these values in variance estimates. Finally, we collected data on a large number of potential confounders.

The major advantage of using hair as a biomarker of environmental exposure is that it can be collected in a relatively noninvasive fashion. We have assumed that the PBDE levels in 3–18 months postnatal maternal hair samples are reflective of exposure during the gestational period. This assumption is based on three strong pieces of evidence: a) the lack of an association between maternal hair PBDEs in this study and child age at the time of sample collection or breastfeeding duration; b) the stability of PBDEs over months to years in serum (Castorina et al. 2011; Imm et al. 2009; Makey et al. 2014); and c) the long half-lives of the PBDEs, especially the penta- and hexa-BDEs (1–7 years) that we have found to be associated with risk of cryptorchidism (Thuresson et al. 2006; Trudel et al. 2011).

Unless treated by surgery very early in childhood, consequences of cryptorchidism may include subfertility and testicular cancer in adulthood (Kollin and Ritzén 2014). Recently, a European Union expert panel not only identified “strong toxicological evidence for cryptorchidism due to prenatal PBDE exposure,” but also estimated an annual cost-of-illness at €117–130 million (Hauser et al. 2015). Thus, there are both reproductive health and economic reasons to decrease the present occurrence rate of cryptorchidism, possibly by decreasing maternal exposure to specific environmental chemicals, such as PBDEs, prior to and during gestation.

Conclusions

Our results suggest an association between maternal exposure to BDE-99, -100, and -154, as measured in maternal hair, and abnormal migration of testes in the male fetus; this may be due to the anti-androgenic properties of these PBDEs, especially BDE-100.

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Effects of Prenatal PM10 Exposure on Fetal Cardiovascular Malformations in Fuzhou, China: A Retrospective Case-Control Study

Author Affiliations open

1Department of Obstetrics and Gynecology, Fujian Maternity and Child Health Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, China

2Department of Obstetrics and Gynecology, Fuzhou General Hospital, Fuzhou, Fujian, China

3Fuzhou Clinic Medical College, Fujian Medical University, Fuzhou, Fujian, China

4Dongfang Affiliated Hospital of Xiamen University, Fuzhou, Fujian, China

5China International Science & Technology Cooperation Base for Environmental Factors on Early Development, Fuzhou, Fujian, China

6Central Station of Environmental Monitoring of Fujian Province, Fuzhou, Fujian, China

7Department of Statistics, Fuzhou General Hospital, Fuzhou, Fujian, China

8Department of Neonatology, Fujian Maternity and Child Health Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, China

9Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China

10Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, Ohio, USA

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  • Background:
    Maternal exposure to ambient air pollution has been associated with an increased risk of congenital heart defects in offspring; however, the results are inconsistent.
    Objectives:
    We investigated whether there is an association between prenatal exposure to particulate matter with diameter ≤10μm (PM10) during early pregnancy and fetal cardiovascular malformations.
    Methods:
    The gravidae from a hospital-based case<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />control study in Fuzhou, China, during 2007<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />2013 were assigned 10-d or 1-mo averages of daily PM10 using an air monitor–based inverse distance weighting method during early pregnancy. A total of 662 live-birth or selectively terminated cases and 3,972 live-birth controls were enrolled. The exposure was considered as a categorical variable. A multivariable logistic regression model was constructed to quantify the adjusted odds ratios (aORs) of the exposure to PM10 and the risks of fetal cardiovascular malformations.
    Results:
    PM10 levels were positively associated with the risks of atrial septal defect (aORs ranging from 1.29 to 2.17), patent ductus arteriosus [aORs = 1.54, 1.63; 95% confidence intervals (CIs): 1.17, 2.23; 1.06, 3.24], overall fetal cardiovascular malformations (aOR = 1.28; 95% CI: 1.03, 1.61), ventricular septal defect (aOR = 1.19; 95% CI: 1.00, 1.43), and tetralogy of Fallot (aOR = 1.44; 95% CI: 1.01, 2.19) in the various observed periods scaled by 10 d or 1 mo in the first and second gestation months. The strongest associations were observed for exposure to PM10 in the second quartile, whereas the associations were attenuated when higher concentrations of PM10 in the third and fourth quartiles of the exposure were evaluated. No correlations of PM10 levels with these cardiovascular malformations in the other time periods of gestation were observed.
    Conclusions:
    Our findings suggest some positive associations between maternal exposure to ambient PM10 during the first two months of pregnancy and fetal cardiovascular malformations. https://doi.org/10.1289/EHP289
  • Received: 8 December 2015
    Revised: 30 May 2016
    Accepted: 17 June 2016
    Published: 25 May 2017

    Address correspondence to X.-R. Hong, Department of Obstetrics and Gynecology, Fuzhou General Hospital, 156 North Xihuan Rd., Fuzhou 350025, Fujian, China. Telephone: +86-591-2285-9200. E-mail: hxr0812@163.com or to X.-Q. Chen, Fujian Central Station of Environmental Monitoring, 138 South Fufei Rd., Fuzhou 350003, Fujian, China. Tel: +86-591-8357-0578. E-mail: chenxq63@yahoo.com.cn

    Supplemental Material is available online (https://doi.org/10.1289/EHP289).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

A growing body of epidemiological literature has suggested an association between ambient particulate matter (PM) exposure and fetal anomalies, particularly cardiovascular malformations. Evidence has indicated associations between in utero exposure to PM with an aerodynamic diameter ≤10μm (PM10) and the prevalence of fetal cardiovascular malformations, that is to say, associations between PM10 and ventricular septal defects, pulmonary valve stenosis (Padula et al. 2013), patent ductus arteriosus (Strickland et al. 2009), and multiple congenital heart defects (Agay-Shay et al. 2013). A meta-analysis of air pollutant–anomaly combinations found that PM10 exposure was related to an increased risk of atrial septal defects (Vrijheid et al. 2011). Evidence from a line of studies, however, failed to demonstrate an association between PM10 and fetal cardiovascular malformations (Ritz et al. 2002; Schembari et al. 2014; Stingone et al. 2014), and no definite link was indicated between ambient total suspended particles and congenital heart diseases in a study performed in Brindisi, Italy (Gianicolo et al. 2014). An inverse association was reported between PM10 and ventricular septal defects (Hansen et al. 2009) and between fine particulate matter (PM2.5; with an aerodynamic diameter ≤ 2.5 μm) and atrial septal defects (Stingone et al. 2014), ventricular septal defects (Padula et al. 2013), and isolated patent ductus arteriosus (Agay-Shay et al. 2013). These inconsistent results may be attributed to heterogeneity in the sources, components, and exposure levels of PM, to the methods used, and to differences in demography, topography, meteorology, socioeconomic status, and individual lifestyle of the population surveyed (Gorini et al. 2014).

China has been experiencing exceptionally high levels of air pollution in recent years. A few metropolitan areas are among the most polluted in the world, with daily levels of PM10 averaged at 144.6 μg/m3 during 2004–2008 in Beijing (Guo et al. 2013) and 216–293 μg/m3 in autumn in Shanghai (Zhou et al. 2012). Annual levels of PM10 have exceeded 100 μg/m3 in approximately one-third of the cities covered by the China National Ambient Air Quality Surveillance Network (Yang et al. 2011). PM10 is the only inhalable particulate matter that had been continuously monitored before 2012 within major cities in China. In Fuzhou, the capital city of Fujian Province, daily ambient PM10 levels vary between ∼60–80 μg/m3. These values may represent average levels across the majority of cities in mainland China (Yang et al. 2011).

Rates of birth defects in China have been reported at 5.12%, which is higher than in developed countries (Christianson et al. 2006). Birth defect rates have been higher in the coastal/eastern regions and in urban areas where environmental pollution, particularly ambient air pollution, is more severe than in western regions and in rural areas (Ministry of Health, People’s Republic of China 2011). Cardiovascular malformations, the most frequently occurring anomalies in fetuses and neonates, are the main causes of infant mortality (Dadvand et al. 2011b). In China, a national survey reported a prevalence of 7 to 8 cases of congenital heart diseases per 1,000 live births (China National Center for Cardiovascular Disease 2006); however, there have been few studies regarding associations between particulate air pollution and cardiovascular malformations in China. The air quality in China has been deteriorating over the last several decades because of the booming economy and has had a significant impact globally, particularly in neighboring countries. In the present study, we used a hospital-based retrospective case–control design to investigate whether there was an association between exposure to PM10 and risks of fetal cardiac malformations.

Methods

Study Area

Fuzhou is the capital city of Fujian Province and is located in the coastal area of southeastern China. The city is surrounded on the northeast, northwest, and southwest by hills and mountains and faces southeast to the Taiwan Strait, with an administrative area of 11,968 km2 and a population of 6.24 million. The built-up region of Fuzhou, which is in the central area of the city and has an area of ∼ 200 km2 consists of a population of 1.95 million according to the Sixth National Population Census of the People’s Republic of China for Fuzhou in 2010 (Fuzhou Statistics Bureau 2011) (Figure 1). A built-up region is generally the central area of an urban field with the densest population. The built-up region of Fuzhou is relatively small compared with the entire city area (Figure 1). Fuzhou has a wet, monsoon-influenced, humid marine climate, characterized by a long summer and winter and a short spring and autumn. The temperature averages 19.6°C, with precipitation of 1,348.8 mm between 2007 and 2013 (Fuzhou Water Conservancy Bureau 2015). Ambient air pollution, including airborne PM, is among the average levels in China (Ministry of Environmental Protection, People’s Republic of China 2014).

The map of China shows the location of Fuzhou in relation to Beijing. The map of Fujian highlights the location of Fuzhou. The zoomed map of Fuzhou depicts the locations of the four monitoring stations. The distance scale used is 1 to 100 kilometers.
Figure 1. Location of monitoring stations in Fuzhou, China. Three monitoring stations are distributed within the built-up region of the city, which is in the central area of Fuzhou and has a high population density. One additional monitoring station (Gushan) is located at 450 m above sea level on a mountain outside the built-up region of the city, which serves as a reference for monitoring the background air pollution. Data obtained from the reference station do not enter the estimating system for evaluation of air quality.

Study Population

We collected the data from pregnant women who resided in Fuzhou, China during their pregnancy and were gestationally monitored and delivered at Fujian Maternity and Child Health Hospital or Fuzhou General Hospital. Both hospitals are large, Grade A tertiary hospitals in Fuzhou, Fujian Province, with a total of ∼15,000 births annually for both hospitals, accounting for nearly 2/3 of the total annual births in the built-up region of Fuzhou. All of the data were obtained from prenatal care and parturition records of the pregnant women of who delivered or selected termination at ≥20 gestation weeks, with an estimated date of conception between 1 January 2007 and 31 December 2013. A total of 110,720 births were enrolled for the selection of cases and controls, in which 99,094 births (89.5%) lived in the built-up region of Fuzhou. The date of conception was estimated based on the hospital records (date of birth and gestational age) by assuming that conception occurred 14 d after the last menstrual period date. The Scientific Research Committees and Ethics Committees of Fuzhou General Hospital and Fujian Maternity and Child Health Hospital approved the study involving the use of the hospital records.

Cases consisted of a cohort clinically diagnosed with birth defects. We recorded the details regarding premature delivery, stillbirth, and term birth, ≥20wk late abortion and ≥20wk with antenatal diagnosis of congenital malformations. We also documented the mother’s general condition, including age, gravidity, parity, last menstrual period, residential address, history of abnormal pregnancy and labor, trimester in which prenatal care began, medication, concomitant disease(s) and if any, living habits, psychological status, nutritional status, and diet. The data of infants with any indications of fetal anomalies were also obtained from the hospital records. Fetal malformations were confirmed by echocardiogram, cardiac catheter surgical operation, or autopsy, or by a combination of these methods, with reference to ultrasonic examination, cytogenetic examination, medication administration records, and follow-up materials, whenever available.

Cases were excluded when they met one of the following criteria (Bassili et al. 2000; Jenkins et al. 2007; Strickland et al. 2009): a) missing important records of medical history, including equivocation of conceptus age (nine cases that met the criteria); b) maternal ill conditions, including gestation weeks < 20 or > 44, gestational diabetes or diabetes with pregnancy, congenital cardiopathy with pregnancy, conditions of illness with fever (> 38°C), exposure to pesticides within 3–8 gestation weeks, or history of consanguineous mating or genetic defects (18 cases); c) twin pregnancy (6 cases); and d) isolated patent ductus arteriosus in premature birth (5 cases).

Controls were randomly selected from the total live-born and nonmalformed infants by the date of birth sampling, which were independent of the cases at a ratio of 1:6 (cases:controls) to meet an accepted statistical power. Data for the control samples were derived from the medical records of the gravidae and the birth records from the two hospitals enrolled. All of the newborns in the control group were routinely examined to rule out cardiovascular malformations and other major anomalies.

Data for the numbers of fetal defects, the ages of the pregnant women, their parity, the longitude and latitude of the gravida’s habitation, the season of conception, and diagnoses for fetal malformations were collected and entered manually into an EpiData 3.0 database created using EpiData software (EpiData Association, Odense, Denmark). These data were obtained from the hospital records and the birth records.

Classification of Fetal Cardiovascular Malformations

Cardiovascular malformations were diagnostically categorized in the following nine subtypes: ventricular septal defect, atrial septal defect, patent ductus arteriosus, coarctation of aorta, pulmonary valve stenosis, tetralogy of Fallot, hypoplastic left heart syndrome, transposition of conducting arteries, and other uncommon subtypes such as ectopia cordis, ventricular double outlets, tricuspid atresia, inborn aortic arch anomalies, and others. (Strickland et al. 2009). The concomitance of cardiovascular abnormalities was recognized and counted only if these abnormalities were embryologically independent from each other (Riehle-Colarusso et al. 2007; Strickland et al. 2009). Temporarily isolated cardiovascular conditions in neonates, such as patent oval foramen, were considered to be normal, and neonates with identified trisomies, heterotaxy syndrome, or abnormal cardiac looping were not counted (Riehle-Colarusso et al. 2007; Strickland et al. 2009). Cardiovascular abnormalities combined with simultaneous fetal anomalies other than those in the cardiovascular system were excluded from further analyses because they might have more complicated causes (Agay-Shay et al. 2013).

Ambient PM10 Levels and Estimation of Maternal Exposure to PM10

Ambient PM10 levels were quantified by the Central Station of Environmental Monitoring of Fujian Province. PM10 levels were monitored and recorded hourly from three monitoring stations within the built-up region of Fuzhou, namely Wusibei, Ziyang, and Shida, all of which are part of the China National Ambient Air Quality Surveillance Network (Figure 1). Daily, monthly, and seasonal average levels of PM10 between 1 January 2007 and 31 December 2013 were calculated based on these measurements.

The residential address of each gravida during pregnancy was converted into longitude and latitude using Google Maps software (version 7.6.1) according to a detailed address, including house number, street, and block (Dadvand et al. 2011a). To estimate the individual PM10 exposure level, a station-based inverse distance weighting interpolation method was used to interpolate the daily PM10 concentrations from the three monitoring stations to the predicted residential site across Fuzhou (Padula et al. 2013). For each monitoring site, λ = 1/d2 was used as the weighting factor, where d refers to the distance between the monitoring site and the predicted residential site (Wong et al. 2004). Data from the three monitoring stations were included in each interpolation. When transferring the data from the cases and controls and the corresponding PM10 levels into the database, logistic rectification was performed automatically to identify and correct potential errors in the data entry, which were manually rectified as soon as an error was found.

Statistical Analysis

Twenty-four-hour measurements of PM10, hospital-based birth certificates, and birth defect records were used for our analyses. Maternal exposure and risk factors were classified at the individual level. The risk of fetal cardiovascular malformations versus PM10 exposure was analyzed by incorporating the variables using multivariable logistic regression analyses. Maternal education was coded as one of the three categories: ≤ primary school, junior or senior high school, and ≥college education. Seasons of conception were categorized as warm (May–October) or cool (November–April) based on the local climate conditions. The trimester in which prenatal care began was categorized as the first, second, or third trimester, or no care. Maternal variables, such as psychological status, nutritional status, prenatal folic acid and vitamin use, marital status, and history of abnormal pregnancy and labor, were treated as the dichotomous variables (Rankin et al. 2009). Potential covariates from the hospital records, which included maternal age, parity, educational attainment, prenatal care, prenatal folic acid and vitamin use, season of conception, and marital status, were entered for the final adjustments based on previous studies (Agay-Shay et al. 2013; Myers et al. 2011; Padula et al. 2013) and on the data available in Fuzhou, China.

Potential deviations in the estimation of the conception date may produce greater bias by using a shorter time scale for the analyses. Time lag may exist between the exposure and the body’s response. Considering these factors, we analyzed associations using a 10-d scale, rather than a seven-day (1-wk) scale, over postconception days 11–60 to increase the stability of the results. The association was analyzed by categorizing the exposure duration as gestation days 11–20, 21–30, 31–40, 41–50, and 51–60, and the first and second months of gestation. PM10 exposure estimates were examined as quartiles using the distribution among the controls. Because PM10 exposure estimates were verified to be normally distributed, they were entered into the regression model as categorical variables on the basis of the quartile distribution. The first quartile was used as a reference, and the other (higher) quartiles were compared with the reference to calculate the adjusted odds ratios (aORs) and 95% confidence intervals (CIs).

Comparisons were conducted in the ventricular septal defect, atrial septal defect, patent ductus arteriosus, tetralogy of Fallot, and overall cardiovascular malformations subgroups because they consisted of relatively large sample sizes. The data were analyzed using SPSS 17.0 software (SPSS, Inc., Chicago, IL, USA) and the STATA statistical package (version 10.1; StataCorp LLC, College Station, TX, USA).

Results

Descriptive Statistics

Daily PM10 levels in Fuzhou fluctuated with the average PM10 level at 69.4 μg/m3, between 1 January 2007 and 31 December 2013. The monthly levels were typically higher from February to April within a year, with the highest at 137 μg/m3 in February 2010. The seasonal levels of PM10 ranged between 40 and 99.8 (mostly 44–80) μg/m3, with the highest levels occurring in the spring or summer and the lowest in the autumn; these are delineated in Figure 2.

Line graphs of daily, monthly, and seasonal PM subscript 10 levels measured in micrograms per cubic meter (Y-axis) plotted for the years 2007, 2008, 2009, 2010, 2011, 2012, and 2013 (X-axis). The seasonal PM subscript 10 graph is classified for spring, summer, autumn, and winter.
Figure 2. PM10 levels in Fuzhou, China during 2007–2013. PM10 levels in Fuzhou varied dramatically, with daily levels averaged at 69.4 μg/m3. Daily PM10 levels exceeded 250 µg/m3 on 9 January (263 μg/m3) and 23 February (268 μg/m3), 2008; on 21 March (361 μg/m3) and 23 (522 μg/m3), 2010, peaking on 22 March (1,034 μg/m3), 2010; and were as low as 7 μg/m3 on 10 November 2011 and 16 December 2013. Levels that exceeded 300 μg/m3 are not fully shown in the figure. The average monthly levels ranged between 56.1 and 72.9 μg/m3, and the seasonal levels were generally higher in the spring and lower in the autumn.

All of the enrolled participants were Han, the ethnic majority in China. Before the exclusions, there was a liveborn and stillborn cohort of 110,720 births in Fujian Maternity and Child Health Hospital and Fuzhou General Hospital with the estimated dates of conception between 1 January 2007 and 31 December 2013. Among the 110,720 births, fetal malformations were diagnosed in 1,584 births without the evidence of trisomy. The overall prevalence of fetal malformations was 1.43% with an increasing tendency from 2007 to 2013 (see Table S1).

A total of 700 cases were diagnosed with fetal cardiovascular malformations among the 1,584 cases with fetal malformations (see Table S2). The subtypes of fetal cardiovascular malformations were ranked in a frequency order from high to low as ventricular septal defect, atrial septal defect, patent ductus arteriosus, tetralogy of Fallot, and others (Table 1).

Table 1. Subtype and prevalence of fetal cardiovascular malformation groupings from a total birth population of 110,720 in Fuzhou, China, between 2007 and 2013.

Cardiovascular malformations Number Prevalence (Subtype/total birth population, %) Ratio (Subtype/total, %)
Ventricular septal defect 270 0.244 38.6
Atrial septal defect 145 0.131 20.7
Patent ductus arteriosus 107 0.097 15.3
Tetralogy of Fallot 81 0.073 11.6
Pulmonary valve stenosis 34 0.031 4.9
Transposition of conducting arteries 26 0.023 3.7
Coarctation of aorta 19 0.017 2.7
Hypoplastic left heart syndrome 14 0.013 2.0
Others 4 0.004 0.6
Total 700 0.632 100.0

Correlations of PM10 Exposure Levels and Risks for Fetal Cardiovascular Malformations

After screening for the exclusion criteria, 38 cases that met the exclusion criteria were excluded from the final analyses. Of the 700 cases with fetal cardiovascular malformations, 662 entered the final analyses; of these, 638 cases (96.4%) lived in the built-up region of Fuzhou. The remaining 24 cases lived outside the built-up region, including one living 41 km from the nearest monitoring station and one 33 living km from the nearest monitoring station; the others lived within 15–25 km of the nearest monitoring station. The locations of the pregnant women were geographically stochastically distributed in the city of Fuzhou other than proximal to the two hospitals. The local main emissions sources are the traffic and living sources, which are distributed roughly evenly throughout the observed region, and there are no major emissions sources from heavy industrial sectors in the region and nearby. Therefore, there was no tendency of mothers with different socioeconomic statuses to selectively distribute in places with different levels of PM10. The general conditions of the cases and controls, including age, parity, and distribution of residential sites, are presented in Table S3.

As shown in Table 2, PM10 exposure levels were associated with the risks of atrial septal defect (aOR = 2.07;95%CI: 1.19, 3.22) and patent ductus arteriosus (aOR = 1.59,95%CI: 1.03, 3.00) in the second gestation month. Divided by shorter time scales (10 d), we observed elevated risks for atrial septal defect, with aORs ranging between 1.29 and 2.17, and for patent ductus arteriosus, with aORs at 1.54 (95% CI: 1.17, 2.23) and 1.63 (95% CI: 1.06, 3.24) in the various observed periods; for overall fetal cardiovascular malformations (aOR = 1.28;95%CI: 1.03, 1.61) in gestation days 41–50; for ventricular septal defect (aOR = 1.19;95%CI: 1.00, 1.43) in gestation days 41–50; and for tetralogy of Fallot (aOR = 1.44;95%CI: 1.01, 2.19) in gestation days 31–40. We also observed an increased risk for atrial septal defect (aOR = 1.29; 95%CI: 1.05, 1.74) during gestation days 21–30 despite the absence of an increased risk in the first gestation month. Interestingly, the effect estimates were generally observed to be the highest in the second quartile, with exposure levels ranging from 41.9 to 75.5 μg/m3 and attenuated in the third and fourth quartiles at higher exposure levels.

Table 2. Adjusted odds ratios (95% confidence intervals) for congenital heart malformations by quartiles for PM10 concentrations (μg/m3) during the first 2 mo of gestation during 2007–2013 in Fuzhou, China (total birth population, 110,720).

Time scale/Quartile of exposure level Ventricular septal defect (n=261)a Atrial septal defect (n=137) Patent ductus arteriosus (n=96)b Tetralogy of Fallot (n=75) Overall fetal cardiovascular malformations (n=662)
aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI)
Days 11–20
 <42.9 Reference Reference Reference Reference Reference
 42.9 to<70.5 1.14 (0.90, 1.29) 1.20 (0.78, 1.92) 1.18 (0.72, 1.51) 1.26 (0.65, 2.24) 1.35 (0.78, 2.34)
 70.5 to<118.1 1.09 (0.79, 1.63) 1.09 (0.80, 1.37) 1.10 (0.89, 1.39) 1.07 (0.64, 2.09) 1.11 (0.70, 1.71)
 ≥118.1 0.89 (0.66, 1.07) 0.98 (0.57, 1.61) 1.14 (0.80, 1.46) 1.04 (0.65, 1.59) 1.15 (0.68, 1.78)
Days 21–30
 <41.6 Reference Reference Reference Reference Reference
 41.6 to<74.7 1.19 (0.78, 1.58) 1.29 (1.05, 1.74) 1.27 (0.80, 1.60) 1.29 (0.76, 2.18) 1.19 (0.67, 1.88)
 74.7 to<120.5 1.14 (0.81, 1.53) 1.24 (0.87, 1.55) 1.08 (0.74, 1.37) 1.13 (0.66, 2.17) 1.11 (0.71, 1.49)
 ≥120.5 0.88 (0.67, 1.71) 1.12 (0.90, 1.52) 0.89 (0.67, 1.15) 1.04 (0.77, 1.44) 0.99 (0.70, 1.47)
Days 31–40
 <41.9 Reference Reference Reference Reference Reference
 41.9 to<73.1 1.21 (1.01, 1.53) 2.17 (1.29, 3.64) 1.54 (1.17, 2.23) 1.44 (1.01, 2.19) 1.55 (0.93, 2.47)
 73.1 to<121.3 1.27 (0.88, 1.62) 1.33 (1.07, 1.85) 1.21 (0.78, 1.97) 1.20 (0.76, 1.70) 1.29 (0.75, 2.08)
 ≥121.3 1.23 (0.90, 1.52) 1.04 (0.79, 1.46) 1.07 (0.65, 1.82) 1.07 (0.66, 1.87) 1.11 (0.70, 1.96)
Days 41–50
 <43.1 Reference Reference Reference Reference Reference
 43.1 to<75.5 1.19 (1.00, 1.43) 1.88 (1.22, 2.75) 1.63 (1.06, 3.24) 1.22 (0.94, 1.59) 1.28 (1.03, 1.61)
 75.5 to<121.9 1.19 (0.87, 1.58) 1.18 (0.97, 1.63) 1.26 (0.85, 2.39) 1.25 (0.83, 1.91) 1.12 (0.86, 1.41)
 ≥121.9 1.01 (0.83, 1.44) 1.07 (0.88, 1.48) 1.11 (0.64, 2.06) 0.96 (0.68, 1.55) 1.03 (0.84, 1.36)
Days 51–60
 <43.1 Reference Reference Reference Reference Reference
 43.1 to<74.7 1.11 (0.89, 1.38) 1.33 (0.94, 1.68) 1.30 (0.79, 1.75) 1.39 (0.92, 1.86) 1.20 (0.77, 1.91)
 74.7 to<122.2 0.97 (0.79, 1.42) 0.92 (0.80, 1.35) 1.21 (0.67, 2.27) 1.17 (0.67, 1.60) 1.23 (0.84, 1.76)
 ≥122.2 1.04 (0.90, 1.31) 1.22 (0.95, 1.48) 1.05 (0.70, 1.71) 0.88 (0.65, 1.37) 1.09 (0.73, 1.70)
First month
 <41.1 Reference Reference Reference Reference Reference
 41.1 to<73.8 1.16 (0.82, 1.50) 1.31 (0.87, 1.72) 1.26 (0.95, 1.74) 1.33 (0.70, 2.06) 1.19 (0.78, 1.89)
 73.8 to<122.4 1.10 (0.79, 1.57) 1.08 (0.85, 1.31) 1.12 (0.71, 1.78) 1.21 (0.59, 2.42) 1.16 (0.72, 1.78)
 ≥122.4 0.93 (0.67, 1.41) 1.00 (0.61, 1.54) 0.90 (0.67, 1.31) 0.82 (0.62, 1.27) 1.07 (0.68, 1.55)
Second month
 <41.9 Reference Reference Reference Reference Reference
 41.9 to<74.8 1.17 (0.95, 1.47) 2.07 (1.19, 3.22) 1.59 (1.03, 3.00) 1.32 (0.96, 1.78) 1.33 (0.98, 2.17)
 74.8 to<122.2 1.21 (0.84, 1.60) 1.44 (0.91, 2.05) 1.16 (0.77, 2.11) 1.20 (0.79, 1.71) 1.31 (0.89, 2.20)
 ≥122.2 1.03 (0.86, 1.41) 1.07 (0.87, 1.44) 1.10 (0.72, 1.89) 0.94 (0.71, 1.52) 1.07 (0.78, 1.81)
Notes: aOR, adjusted odds ratio; CI, confidence interval.

an refers to the number of cases. The first quartile was used as a reference, and the higher quartiles were compared with the first quartile. Models were adjusted for maternal age, parity, educational attainment, prenatal care, prenatal folic acid and vitamin use, season of conception, and marital status.

bTerm infants with patent ductus arteriosus persisting for >4wk after birth.

Discussion

In the present study, we estimated associations between maternal exposure to ambient PM10 during the first 2 mo of pregnancy from 2007 to 2013 and the risks of fetal cardiovascular malformations. Our results indicate that exposure to ambient PM10 during early gestation may be associated with increased risk of fetal cardiovascular malformations during the observed time period in Fuzhou, China. The associations between maternal gestational exposure to PM10 and atrial septal defect, fetal patent ductus arteriosus, and overall congenital heart malformations provide further evidence that prenatal exposure to air pollution is associated with risks for fetal heart malformations.

The aORs in the second quartiles were higher than those in the third and fourth quartiles for the trends in most of the results, as shown in Table 2. This finding may suggest a nonmonotonic exposure response relating to other competing birth outcomes, such as spontaneous abortions and stillbirths, which were not considered in the present study (Gianicolo et al. 2014; Padula et al. 2013; Ritz et al. 2002; Vrijheid et al. 2011). Because gestation weeks 3–8 comprise the critical window for the formation of fetal cardiac chambers and inflow and outflow tracks, we intended to collect all the cases that were exposed to PM10 during this time period. However, early PM10 exposure during gestation may increase the risk of fetal abortion before gestation week 20 (Strickland et al. 2009). We therefore might have missed important data from these cases in our analyses. This missing data may be one of the causes of the higher aORs in the second quartiles compared with those in the third and fourth quartiles in our results. Paradoxical reversal of the risks for fetal cardiovascular malformations with excessively high levels of PM10 (Zhang et al. 2016) might also be interpreted as a result of nonmonotonic exposure response, which seems to be coincident with the nonlinearity of our results.

A number of studies have investigated associations between maternal exposure to ambient PM10, combined with other air pollutants, and fetal congenital heart defects. Some original studies have reported increased risks of atrial septal defects (Gilboa et al. 2005; Hwang et al. 2015), patent ductus arteriosus (Strickland et al. 2009), and tetralogy of Fallot (Dolk et al. 2010) associated with maternal PM10 exposure. Our results were consistent with the results of these reports. In addition, our results also suggested a possible inverse association between PM10 exposure and ventricular septal defect, which also coincides with the findings of a previous report (Padula et al. 2013). The correlations that we observed between PM10 exposure and the risk of fetal heart malformations seemed to be comparable to or stronger than, and the exposure levels of PM10 were higher than, those reported by Gilboa et al. (aOR = 2.27; 95%CI: 1.43, 3.60 for isolated atrial septal defects; first quartile < 19.5 μg/m3 and fourth quartile ≥ 29 μg/m3), Strickland et al. (OR = 1.60; 95%CI: 1.11, 2.31 for patent ductus arteriosus; daily PM10 levels between 25.8–43.2 μg/m3), Padula et al. (aOR3rd Quartile = 2.1; 95%CI: 1.1, 3.9 for perimembranous ventricular septal defects; daily PM10 exposure levels 7.9−25.2 μg/m3), and Hwang et al. (OR4th Quartile = 2.52; 95%CI: 1.44, 4.42 for atrial septal defects; averaged PM10 level 56.6 μg/m3). One report indicated an increased risk for tetralogy of Fallot in relation to PM10 exposure (OR = 1.48; 95% CI:0.57,3.84) (Dolk et al. 2010). Our results showed a similar aOR value of 1.44 for PM10 exposure versus tetralogy of Fallot. A meta-analysis supported the correlation between maternal PM10 exposure and the increased risk of atrial septal defects (OR = 1.14; 95%CI: 1.01, 1.28 for each 10 μg/m3 increase) (Vrijheid et al. 2011). However, this finding was not confirmed by another meta-analysis that was conducted more recently (Chen et al. 2014).

Several reports of PM10 exposure and congenital heart defects did not observe a positive association (Dadvand et al. 2011a; Hansen et al. 2009; Ritz et al. 2002; Schembari et al. 2014; Stingone et al. 2014; Vinceti et al. 2016). Most of these reports had low PM10 exposure levels [i.e., levels averaging 18 μg/m3 (Hansen et al. 2009); < 14.9 μg/m3 for <10th centile, and > 40.6 μg/m3 for ≥ 90th centile (Stingone et al. 2014); and with a median of 38.7 μg/m3 (Schembari et al. 2014) and a minimum of 7.1 μg/m3 and a maximum of 50.4 μg/m3 (Dadvand et al. 2011a)] compared with those in the current study, except for one recent report by Zhang et al. (2016) in which the mean PM10 level was 101.7 μg/m3. Our results suggested that adverse associations between PM10 exposure and fetal cardiovascular malformations could also be observed during gestation days 21–30 (atrial septal defect), the earlier stage in which formation of the fetal heart occurs. Agay-Shay et al. (2013) reported an association between maternal PM10 exposure and multiple congenital heart defects, but not isolated atrial and atrial septal defects, ventricular septal defect, or patent ductus arteriosus, at an average PM10 exposure level of 58.8 μg/m3. One study did not report the PM10 exposure level (Ritz et al. 2002). Although there were various factors affecting the results, we considered a level of PM10 exposure that was 2.4–4.1 times higher than the exposure levels reported by most other studies during early pregnancy, which would therefore exert greater toxicities associated with fetal heart malformations. However, exorbitantly high-level exposure to PM10 might have reverse correlations with fetal cardiovascular malformations (Zhang et al. 2016), which is likely to be attributable to a nonlinear correlation between PM10 exposure and its cardiovascular effects.

Ventricular septal defect is the most common subtype of cardiovascular malformations, with a prevalence similar to that reported by Strickland et al. (2009). The proportions for ventricular septal defect and atrial septal defect were comparable to, whereas the proportions for patent ductus arteriosus and tetralogy of Fallot were higher than, those in other reports (Agay-Shay et al. 2013; Gilboa et al. 2005; Schembari et al. 2014; Strickland et al. 2009). The differences between our results and those of similar studies may arise from differences in sample collection and in the disease definition used. We studied term infants with patent ductus arteriosus persisting for ≥ 4 wk after birth compared with 6 wk in the study by Strickland et al. (2009). Furthermore, our hospital-based sample was derived from a relatively small region with limited representatives of the population of congenital heart defects, which might be another cause of the discrepancy in proportions.

We included approximately two-thirds of the regional births in our study. The remaining one-third of births and their mothers who entered other hospitals were not significantly different from those in our hospitals with regard to the mothers’ socioeconomic statuses, and the different residences of the mothers enrolled did not correlate with their socioeconomic statuses. A few pregnant women might transfer to a grade-A hospital from a lower-grade hospital when a complicated pregnancy condition is found. Considering the small percentage of gravidae in lower-grade hospitals and the small percentage of gravidae who incurred abnormal pregnancy including fetal cardiovascular malformations, the discrepancies among the gravidae choosing different grade hospitals, if present, would not significantly alter the outcomes in our analyses.

There are several limitations in the present study. Although we used a geographic technique coupled with an inverse-distance weighting method to evaluate the exposure levels, this model and analyses based on exposure estimates from ambient monitoring networks have been reported to produce somewhat wider 95% CIs than effect estimates based on land-use regression models even with similar estimates of the effect among them (Brauer et al. 2008). The present case–control study was based on cases with pregnancy durations ≥ 20 wk. Some gravidae were excluded from the analyses, which might have led to bias in our results. No maternal residential history during the pregnancy was available. We took only the residential information from the women’s medical records at the early stages of pregnancy regardless of possible residential changes during the pregnancy. Thus, deviation of the exposure assessment with bias in an unknown direction might have occurred, although reports have shown no obvious impact on these assessments resulting from a change in living address during pregnancy (Lupo et al. 2010). Additional unmeasured covariates, such as the socioeconomic status, living habits, and dietary factors of the gravidae, could have affected our results, although we did not evaluate the co-effects from these factors in our analyses because of the limited amount of relevant data. The temporal factors in our study design along with the data obtained from a limited number of monitoring stations might also have compromised our exposure evaluation. We did not investigate other subtypes of cardiovascular malformations beyond the four major subtypes owing to the inadequate sample sizes; thus, we may have missed their possible associations with PM10 exposure. PM10 was found to be associated with pulmonary valve stenosis (Padula et al. 2013). Agay-Shay et al. (2013) reported an association of maternal exposure to increased concentrations of PM10 with multiple congenital heart defects. Associations were found between in utero exposure to PM2.5, but not to coarse particulate matter with an aerodynamic diameter of 10–2.5 μm, and hypoplastic left heart syndrome (Stingone et al. 2014) and transposition of the great arteries (Padula et al. 2013). We did not observe these associations owing to the relatively small sample sizes.

A gravida generally lives inside the house during gestation, and indoor PM10 levels might be very different from outdoor PM10 levels, particularly in developing countries (Ezzati and Kammen 2002); this potentially overestimates, or more likely, underestimates the exposure that could bias our effect estimates. We assigned exposures using only the geographic technique regardless of the impact from local meteorological factors, traffic factors, social geography, and the spatiotemporal activity patterns of individual gravida. More importantly, we did not investigate the co-effects from other air pollutants such as carbon monoxide, nitrogen dioxide, and ozone, which are associated with fetal heart development (Gilboa et al. 2005). Therefore, our data did not represent all of the major air pollutants emitted and transported, which is another limitation of our study. PM2.5 levels were routinely monitored beginning in 2012 in Fuzhou, China. Because we lacked the entire serial monitoring data for PM2.5 during the study period, we could not evaluate the health impact of PM2.5 exposure in the present work.

Conclusions

In conclusion, exposure to ambient levels of PM10 during early pregnancy was shown to be associated with fetal cardiovascular malformations. The most significant association was found in the second quartile of the exposure levels, between 41.9 and 75.5 μg/m3, and was attenuated in the third and fourth quartiles at higher exposure levels. Our findings have potential impact on public health and policy making for emerging countries including China, whose standard criteria were revised in 2012 as daily PM10 levels at 50, 150 μg/m3 and annual PM10 levels at 40, 70 μg/m3 (Grade I, II), respectively (Ministry of Environmental Protection, People’s Republic of China 2012), and stricter criteria may be needed in the near future.

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An Integrated Chemical Environment to Support 21st-Century Toxicology

Author Affiliations open
1Integrated Laboratory Systems, Inc. (ILS), Research Triangle Park, North Carolina, USA; 2Sciome, Research Triangle Park, North Carolina, USA; 3Program Operations Branch, National Toxicology Program (NTP), National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA; 4NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, NTP, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina, USA

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  • Summary: Access to high-quality reference data is essential for the development, validation, and implementation of in vitro and in silico approaches that reduce and replace the use of animals in toxicity testing. Currently, these data must often be pooled from a variety of disparate sources to efficiently link a set of assay responses and model predictions to an outcome or hazard classification. To provide a central access point for these purposes, the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods developed the Integrated Chemical Environment (ICE) web resource. The ICE data integrator allows users to retrieve and combine data sets and to develop hypotheses through data exploration. Open-source computational workflows and models will be available for download and application to local data. ICE currently includes curated in vivo test data, reference chemical information, in vitro assay data (including Tox21<img src="http://usgov.info/wp-content/plugins/wp-o-matic/cache/792128d8b6_2122.png" alt="™” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />/ToxCast<img src="http://usgov.info/wp-content/plugins/wp-o-matic/cache/792128d8b6_2122.png" alt="™” class=”wp-smiley” style=”height: 1em; max-height: 1em;” /> high-throughput screening data), and in silico model predictions. Users can query these data collections focusing on end points of interest such as acute systemic toxicity, endocrine disruption, skin sensitization, and many others. ICE is publicly accessible at https://ice.ntp.niehs.nih.gov. https://doi.org/10.1289/EHP1759

  • Received: 10 February 2017
    Revised: 10 February 2017
    Accepted: 12 February 2017
    Published: 24 May 2017

    Address correspondence to N. Kleinstreuer, NIEHS, 530 Davis Dr., Morrisville, NC 27560, Telephone: (919) 541-7997, E-mail: nicole.kleinstreuer@nih.gov

    S.M.B., C.S., S.Q.M. and D.A. are employed by Integrated Laboratory Systems; J.P., A.S., A.T. and R.S. are employed by Sciome; both are consulting companies.

    Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Introduction

Traditional tests to evaluate chemicals for their potential impact on human and environmental health are performed using animal models, with varying degrees of success in accurately identifying hazards. Advances in science and technology offer the potential to develop more effective approaches based on combining higher-throughput testing methods, human cell–based systems, small model organisms, and computational models. Development and acceptance of new approaches that reduce and replace animal use for better predictivity of human health outcomes require that regulators, test method developers, and computational modelers have access to high-quality toxicity data. Ideally, these data and relevant analysis tools would be available in a user-friendly format that facilitates data integration and exploration, encourages hypothesis generation, and allows for computational processing and modeling. In reality, progress in developing and evaluating new approaches is often hindered by the lack of open-source central access points for curated data on which models can be built or tested. In addition, the data available are often of unknown quality; constructing models or validating new methodologies on such data produces models and test systems with high uncertainty and suboptimal performance. Ultimately, locating and accessing the types of data needed<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2014.png" alt="—” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />data in a structured format with metadata or annotation sufficient to gauge their quality and applicability<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2014.png" alt="—” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />becomes a huge time sink for researchers and a barrier to innovation.

Existing sources of toxicity test data have advantages and limitations. The European Chemical Agency’s registered substances list (ECHA 2017) contains detailed information and test results on thousands of substances obtained from chemical dossiers submitted by manufacturers. Although this resource has depth of information, the data contained in it do not fully support computational analysis owing to the database’s restrictions on data use, to its limited batch query capability, and to formatting variations of the data fields. The U.S. Environmental Protection Agency (EPA) has several web-based resources providing information on chemical properties and toxicity data available in a computational-friendly format without restrictions on use. These include ToxCast™ data available through the tcpl package (Filer et al. 2016), Aggregated Computational Toxicology Resource (ACToR) (Judson et al. 2008, 2012), and the Chemistry Dashboard (U.S. EPA 2017). These U.S. EPA resources are extremely valuable because they provide detailed information on chemical properties and activities from various sources. However, building and evaluating new approaches require different types of data to be combined in a structured format that is informed by relevant biology or toxicity end points. This task is best facilitated by having the data organized by test guidelines or regulatory end points, a feature that is not currently provided by any of these U.S. EPA resources. Similar challenges with respect to organization exist with other resources such as PubChem (Kim et al. 2016; Wang et al. 2014) and the Toxicological Data Network from the U.S. National Library of Medicine (NLM 2016), as well as the Chemical Effects in Biological Systems (CEBS) database from the National Institute of Environmental Health Sciences (NIEHS) (Lea et al. 2017), all of which provide rich content but may not meet data structure needs.

The National Toxicology Program’s Integrated Chemical Environment (ICE) is a web-based resource (https://ice.ntp.niehs.nih.gov) that provides access to curated data and tools that can be used in the development, validation, and implementation of in vitro and in silico approaches that reduce and replace the use of animals in toxicity testing. The ICE data integrator function allows users to query high-quality in vivo and in vitro test results by chemical and by end point. ICE also includes reference chemical lists and supporting data sets, as well as computational predictions for properties such as physicochemical parameters and clearance rates, which are often needed in model development.

ICE Overview

The current release of ICE features in vivo and in vitro data sets covering regulatory test end points such as acute oral and dermal toxicity, skin irritation and sensitization, eye corrosion, and endocrine disruption as well as in silico predictions of test end points and chemical properties (Table 1). These data sets include data assembled and curated by the National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM) during past evaluations of new test methods and reference chemical lists generated from these efforts. ICE also includes a curated version of the high-throughput screening (HTS) data from Tox21TM and ToxCast™ (Huang 2016; Kavlock et al. 2012; Richard et al. 2016; Tice et al. 2013), with data filtered on the basis of chemical quality control information from the National Center for Advancing Translational Sciences and flags generated by the U.S. EPA’s tcpl pipeline (Filer et al. 2016; U.S. EPA 2016;). Additional data sets will be added with future releases.

Table 1. Data types included in 2017 ICE releases and example end points.

Data types Availability Type End point examples
Acute dermal toxicity October 2017 (tentative) In vivo Rodent LD50
Acute inhalation toxicity October 2017 (tentative) In vivo Rodent LC50
Acute oral toxicity March 2017 In vivo Rodent LD50
Acute oral toxicity March 2017 In vitroa Basal cytotoxicity IC50
Androgenic activity March 2017 In vitro Androgen receptor binding and transactivation (agonist and antagonist activity)
Androgenic activity July 2017 (tentative) In vivo Lowest effect level in the rodent Hershberger assay
Androgenic activity March 2017 In silico Androgen receptor pathway model scores
Curated HTS March 2017 In vitro Assay ACC, AC50
Dermal irritation March 2017 In vivo Skin irritation/corrosion classification categories
Dermal sensitization March 2017 In vivo Mouse LLNA EC3 and human patch test lowest effect level
Dermal sensitization March 2017 In vitro KeratinoSens™, DPRA, hCLAT assay results
Dermal sensitization July 2017 (tentative) In silico Binary sensitizer/nonsensitizer call
Estrogenic activity March 2017 In vivo Lowest effect level in the rodent uterotrophic assay
Estrogenic activity March 2017 In silico Estrogen receptor pathway model scores
Ocular irritation March 2017 In vivo Eye irritation/corrosion classification categories
Physicochemical property predictions March 2017 In silico LogP, logVP, logBCF, logS, melting point, boiling point

Notes: AC50, concentration that increases activity by 50%; ACC, activity concentration at cutoff, a measure of the activity threshold for an assay response based on curve-fitting models; EC3, in the LLNA, a test chemical concentration that produces a stimulation index of 3; hCLAT, human cell line activation test; IC50, concentration that inhibits activity (in this context, decreases cell viability) by 50%; LC50, inhalation concentration expected to produce lethality in 50% of animals tested; LD50, dose expected to produce lethality in 50% of animals tested; LLNA, local lymph node assay; physicochemical properties characterized as log values are log 10; logBCF, log of the bioconcentration factor; logP, octanol-water partition coefficient; logVP, the vapor pressure; logS, log of the solubility in water.

aIn vitro data were used to develop a nonanimal method for setting starting doses for in vivo acute oral toxicity studies.

The query function of ICE, the integrator, facilitates combining the different data sets within ICE, allowing users to merge data points from the different data sets to simultaneously examine end points based on all available substance data or by using an input list of chemicals. Upon submission of the query, the user is provided with an overview of the query results along with several exportable data views, supporting both computational manipulation and manual interaction.

To assist the user with developing and interpreting queries, ICE provides a “Help” page that walks the user through building a search and understanding search output. Other user support resources include a “Home” page where updates and other news will be announced, an “About” page with background information, and a “Data Sets” page with information about the sources of the data in ICE. This page includes links that lead to more detailed information about each data set, including references and metadata for context about the data end points. Some information about the studies available in the source data sets is not available in ICE (for example, detailed clinical observations or some study protocol details), but it is available via the provided links and will also be accessible via CEBS (Lea et al. 2017) to facilitate integration with other data resources from the NIEHS.

User Stories

We developed ICE to address needs that have been expressed frequently by NICEATM stakeholders. As such, we tailored the design of ICE around user stories that developed from those needs. Here, we describe stories from three different user groups (method developer, chemical producer, risk assessor) and how ICE can help meet these users’ needs (Figure 1).

Schematic diagram depicting ICE users on the left panel; resources, namely high-quality data, reference chemicals, and computational tools in the center panel; and outcomes, namely identify opportunities to develop new methods, compare method performance, identify data gaps, obtain and examine toxicity and chemical data, and develop testing strategies in the right panel.

Figure 1. Integrated Chemical Environment (ICE) users, resources, and outcomes. ICE was developed to support three main user roles: method developers, chemical producers, and risk assessors. The center panel lists the resources ICE provides to help these user groups complete some of the major tasks listed in the right panel.

Method Developer

Method developers are developing new in vitro or in silico methods to prioritize substances for further toxicity testing, or to test a substance for potential health impacts. The availability of data anchored to regulatory end points of interest from ICE facilitates exploration of the current state of methods, and such curated end point data can be compared with user-generated test data. These capabilities help method developers improve the performance of existing methods or target new method development to areas where the currently available test methods leave room for improvement. Furthermore, by having in vivo data organized with the in vitro and in silico data, it is easier to identify data gaps that may be targeted using in silico approaches.

In vitro test method developers often need reference chemicals, which are chemicals that cause specific, well-characterized biological effects and can therefore be used to assess the performance of a test method designed to measure that effect. These reference chemicals, along with the supporting data, are helpful in both in vitro method development and validation efforts. The reference chemical lists included in ICE provide a starting place to identify test materials and associated data.

For those developing in silico approaches, good quality data are important for training and testing predictive models. Method developers will find that the data in ICE are well annotated, cleaned, and formatted to support in silico modeling, allowing them to focus on method optimization versus data preparation. ICE currently provides many of the parameters needed as input for these models, and open-source workflows for in vitro to in vivo extrapolation and chemical property prediction will be added to ICE later in 2017.

Chemical Producer

For developers of new products for which toxicity testing will be required, typical testing needs include prioritizing substances to move forward through the development process and generating data needed to meet regulatory requirements. The ICE integrator allows the chemical producer to input a list of chemicals and obtain available testing data, which may help with evaluating the potential for adverse impacts during lead agent prioritization.

For chemical producers seeking to replace animals required for testing, the ICE integrator allows end point–oriented searches to compare data sets across all available substances. This capability allows the chemical producer to compare the results coming from different test methods relating to a specific end point. A query that returns in vitro and in silico test results paired with in vivo animal data may allow the chemical producer to better identify which nonanimal methods will best meet their information needs. This information may provide the chemical producer with the confidence in those nonanimal methods that is needed to change in-house testing approaches for ones that use fewer animals, save money, and better predict the end point of interest.

Risk Assessor

Similar to the chemical producer, the risk assessor may want to obtain available animal or human test data on a list of chemicals or review available data from nonanimal methods. Resources currently in ICE can help risk assessors with prioritizing chemicals for further toxicity testing and with identifying the most informative tests for that purpose in addition to providing data on the likely health impacts of a substance. The ICE data explorer view provides user-friendly data interaction and an easy-to-use snapshot of the types of data and end points. This information can be useful in prioritization or in identification of data gaps. The risk assessor may also be interested in comparing the performance of nonanimal test data with existing guideline animal test results across a wide range of chemicals, which is easily done using the ICE integrator.

ICE supports the exporting of data, which facilitates more detailed comparisons of end point variability. Exported data are preformatted and ready for analysis, enabling the risk assessor to move easily from query output into the subsequent analysis workflows. Transparency and reproducibility of data and process are important to the risk assessor; therefore, ICE provides reference details on the Data Sets page and clear referencing from the query output.

Next Steps

Computational tools will be included in ICE by the fall of 2017. These programs and workflows developed by NICEATM staff and external partners will enable researchers to conduct analyses locally using either their own data or data downloaded from ICE. Currently in the development queue are machine learning models for six physicochemical properties (Zang et al. 2017), predictive signatures based on HTS data such as embryonic vascular disruption leading to adverse prenatal outcomes (Knudsen and Kleinstreuer 2011), and in vivo<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />to<img src="https://s.w.org/images/core/emoji/2.2.1/72×72/2013.png" alt="–” class=”wp-smiley” style=”height: 1em; max-height: 1em;” />in vitro extrapolation (IVIVE) workflows (Chang et al. 2015; Wetmore et al. 2012). In addition to downloadable predictions and workflows, interactive tools such as those that facilitate biological pathway–informed IVIVE are needed to help link in vitro activity concentrations to relevant in vivo exposures and outcomes.

Future versions of ICE will also include tutorials aimed at helping users better understand the tools available in ICE and their applications in regulatory safety testing. These tutorials will address questions that frequently arise about appropriate use of computational tools and models on topics such as characterizing the domain of applicability and model uncertainty. Development of these resources is currently underway with a tentative 2018 launch date.

Acknowledgments

This project was funded in part with federal funds from the National Institute of Environmental Health Sciences, National Institutes of Health under contract no. HHSN273201500010C to Integrated Laboratory Systems (ILS) and its subcontractor, Sciome, in support of the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM). The views expressed in this manuscript do not necessarily represent the official positions of any federal agency.

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Dynamic Technical Formulations, LLC. Issues a Voluntary Nationwide Recall of Tri-Ton Due to the Presence of Andarine and Ostarine

Roswell, GA – Dynamic Technical Formulations LLC. is voluntarily recalling all lots of Tri-Ton. This product was sold in 90 count bottles as a dietary supplement and includes all lot number and expiration dates of the product. The US Food and Drug Administration (FDA) lab analysis of Tri-Ton was found to contain andarine and ostarine which are selective androgen receptor modulators (SARMs) that are considered unapproved drugs and anabolic steroid-like substances.

Understanding Bipolar Disorder Caregiver:

Provides caregivers with a general overview of obsessive-compulsive disorder in youth and young adults. Gives guidance on how to provide support. Highlights recommended treatment approaches. Includes a list of helpful resources. Inventory#: SMA16-5007