Printer Friendly

A Prospective Study of Environmental Exposures and Early Biomarkers in Autism Spectrum Disorder: Design, Protocols, and Preliminary Data from the MARBLES Study.

Introduction

As recently as 2005, very few environmental factors had been studied in relation to risk for autism or related disorders. A substantial new body of literature suggests that noninherited factors, primarily from the prenatal period, are associated with altered risks for autism spectrum disorder (ASD). After a flurry of ecologic studies with inadequate control for group-level confounders, well-conducted epidemiologic studies emerged with intriguing results on several types of exposures: increased ASD risk associated with prenatal exposures to pesticides (Eskenazi et al. 2007; Roberts et al. 2007; Shelton et al. 2014) or sources of air pollution (Becerra et al. 2013; Raz et al. 2014; Suades-Gonzalez et al. 2015; Volk et al. 2012; Windham et al. 2006; Yang et al. 2017); a protective relationship from maternal nutrition (Schmidt et al. 2011; Schmidt et al. 2012; Suren et al. 2013) (Wang et al. 2017); a U-shaped relationship with higher risk from interpregnancy intervals in the range of 0-24 and >60 months (Durkin et al. 2015) (Cheslack-Postava et al. 2014; Coo et al. 2015; Zerbo et al. 2015); and an elevated risk for children whose mothers experienced infection, fever, metabolic or inflammatory conditions during pregnancy (Atladottir et al. 2010a, 2012; Krakowiak et al. 2012; Zerbo et al. 2012) (Hornig et al. 2018; Li et al. 2016; Walker et al. 2015); to name a few.

Various designs have proven useful in pursuit of etiologic factors. Whole population cohorts in Scandinavia, California, and New York assembled by linking birth records to developmental diagnoses reported links between ASD and maternal and paternal age (Quinlan et al. 2015; Sandin et al. 2015; Shelton et al. 2010), interpregnancy interval (Cheslack-Postava et al. 2014), maternal but not childhood infection (Atladottir et al. 2010a, b), and season of conception (Zerbo et al. 2011). These databases also provided the sampling frames for nested case-control designs used to investigate pesticide exposures (Roberts et al. 2007) and air pollution (Becerra et al. 2013). Large sample sizes (thousands to millions of persons; hundreds to tens of thousands with ASD) and complete population enumeration are strengths of these cohorts; however, data are limited or absent for some exposures and potential confounders (e.g., smoking, nutrition, and medical or other factors); standardized confirmation of diagnoses is lacking; diagnostic practices may vary across regions within each database; and generally, biological mechanisms cannot be examined.

Case-control studies have played a major role in both genetic and environmental epidemiology of ASD. The University of California Davis (UC Davis) CHARGE (Childhood ASD Risks from Genetics and Environment) Study, a population-based case-control study nested within California birth cohorts, was launched in 2002 to investigate causes and contributing factors to ASD and developmental delay (DD) (Hertz-Picciotto et al. 2006). Findings from the CHARGE study include evidence of reduced ASD risk in association with maternal prenatal vitamin supplementation during the periconception period (Schmidt et al. 2011, 2012), and evidence of increased risk in association with residential proximity to agricultural pesticide applications (Shelton et al. 2014) or traffic-related and regional air pollution (Volk et al. 2012), parental occupational exposures (McCanlies et al. 2012), maternal metabolic conditions (e.g., obesity) (Krakowiak et al. 2012), preeclampsia (Walker et al. 2015), and fever that went untreated during pregnancy (Zerbo et al. 2012). CHARGE also identified immune dysregulation (Ashwood and Van de Water 2004; Braunschweig et al. 2013; Enstrom et al. 2010) and mitochondrial dysfunction (Giulivi et al. 2010) and mitochondrial DNA damage (Napoli et al. 2013, 2014) as potential underlying mechanisms. The CHARGE Study has also provided population-based data on ASD comorbidities previously reported primarily in clinic-based samples that may overrepresent such conditions; these conditions included an excess of GI symptoms (Chaidez et al. 2014), sleep disturbances (Krakowiak et al. 2008), and minor physical anomalies (Angkustsiri et al. 2011). With each identified environmental, biological, or phenotypic difference between cases and typically developing controls of the same age, the key question was: At what point in early development did the ASD cases diverge from their neurotypically developing counterparts? The case-control design could not answer this question. For example, about 20% of mothers of children with ASD produced antibodies to fetal brain proteins, in comparison with fewer than 2% of mothers of children with typical development (TD) (Braunschweig et al. 2013), but whether this antibody status arose during the pregnancy with the affected child could not be determined. Advancing the field beyond description of differences to investigating etiologic factors, mechanisms, and susceptible windows became an imperative. Thus, the logic of an enriched-risk cohort with enrollment prior to birth emerged organically.

The enriched-risk design is well suited to the study of ASD, given its relatively low prevalence (currently 1 in 59) (Baio et al. 2018). In comparison with a general population cohort, enrollment and follow-up of pregnant women carrying the younger sibling of a child with ASD achieves tremendous efficiencies. This enhanced statistical power is a result of family recurrence risks of ASD, which have long been known to be far greater than incidence in the overall population (Fombonne et al. 1997; Lauritsen et al. 2005). The MARBLES (Markers of Autism Risk in Babies--Learning Early Signs) Study was therefore launched in 2006 by the University of California Davis (UC Davis) Medical Investigations of Neurodevelopmental Disorders (MIND) Institute and Center for Children's Environmental Health. Shortly thereafter, the similarly designed but multisite Early ASD Risk Longitudinal Investigation was initiated (EARLI) (Newschaffer et al. 2012). Although this design was not new, previous cohorts of younger siblings at high risk for ASD had recruited postnatally and did not address environmental etiology. However, five years after the MARBLES study began recruitment, a pooled analysis of twelve 'baby sib cohorts' produced an estimated recurrence risk of 19% in affected families (Ozonoff et al. 2011). Given a ratio of noncases to cases in the high-risk cohort of ~4:1, in comparison with the population-based ratio at that time of 67:1, the younger sibling approach yielded a 13-fold greater risk.

Contrasting with case-control studies, where the greatest challenge is obtaining accurate exposure information from time periods prior to the onset of symptoms, the MARBLES study collects data prospectively. Effort is concentrated during vulnerable periods: preconception, gestation and infancy. With enrollment before or during pregnancy, monitoring of pregnancy and early childhood years provides an opportunity to accurately measure suspect exposures (both exogenous and endogenous), and simultaneously allows a broad search for early biological and environmental markers (Newschaffer et al. 2012).

This paper describes the design, aims, and protocols of the MARBLES study, and presents preliminary statistics describing the demographics of the study population and the ASD recurrence rate.

Aims

The overarching goals of the MARBLES study are to uncover environmental etiological agents contributing to ASD, investigate their mechanisms of action, and identify early biological markers of elevated risk. Using an integrated approach that includes exposure measurements, biological markers from specimens obtained in the study, and experimental research on tissues not obtained from MARBLES study participants (P01-ES011269; R01-ES020392), our initial aims were: a) to determine whether in utero or infant exposures to pyrethroid pesticides or polybrominated diphenyl ethers (PBDEs) were associated with ASD diagnosis or early indicators of developmental delays; b) to experimentally describe neurotoxicity within these chemical classes; c) to evaluate associations of immune responses with both the environmental chemicals and the neurodevelopmental outcomes; and d) to examine alterations in mitochondrial DNA for relationships with both child outcomes and chemical exposures. The second aim (b) differs from the others in its focus on in vitro neurotoxic outcomes. These molecular toxicology experiments exposed mouse neural cells in vitro to pyrethroids to define the structure-activity relationships for specific compounds (Cao et al. 2014; Kim et al. 2011).

A conceptual framework underlying this study is shown in Figure 1, with the upstream environmental exposures on the left, biological changes in the middle, and clinical outcomes on the right; the examples of each type of variable include many that are measured in the MARBLES study. Additional work recently completed or currently underway is to examine phthalate exposures in relation to ASD and other developmental outcomes (R21ES025551); placental DNA methylation and other epigenetic markers (R01ES029213, DOD AR110194); maternal nutrition (R01ES025574); mitochondrial aberrations (R21HD086769), and the intestinal microbiome (R01ES028089) and builds the MARBLES study to maintain the cohort and facilitate data and sample sharing (R24ES028533). The cohort design provides an efficient framework for assessing, in the future, a broad array of compounds, lifestyle factors, mechanisms, nonchemical stressors, biomarkers, and interactions.

Methods

Study Design

The MARBLES study is a prospective observational study, launched in 2006, that enrolls women who already have a child diagnosed with an ASD and who are pregnant or planning an additional pregnancy. We also enrolled pregnant women whose offspring would be either a half-sibling, or an equivalent or closer blood relative, to a child with ASD. The MARBLES study follows the mother and the child(ren) longitudinally to accurately characterize the early life exposures both pre- and postnatally, to measure prediagnostic biological markers, and to document the development of the child to 36 months of age. Prospective collections encompass: a) extensive lifestyle, home environmental information and samples, behavioral, medical, nutritional and other exposure data; b) biological specimens from the mother, father, and children; and c) detailed psychometric assessments of neurocognitive, social, and behavioral development from birth to 3 years of age.

Study Population, Eligibility, and Recruitment

The study population for MARBLES is defined as mothers who already have a child with autism and who are pregnant or planning a pregnancy. We recruit primarily through the California Department of Developmental Services (DDS), which is the state agency that coordinates services for persons with developmental disabilities, regardless of race, religion, citizenship, or financial status. Estimates from the early 2000s indicate that about 75-80% of affected children are enrolled in DDS services (Croen et al. 2002). Milder cases will be underrepresented because of the state requirement beginning in 2003 that those individuals receiving services must have three significant functional limitations. The UC Davis study team periodically obtains an updated list of families receiving state-funded services for a child with an ASD. Families on the DDS list are mailed a letter notifying them that the MARBLES study will be contacting them, unless they opt out by calling or emailing their response. If, within two weeks, they do not opt out, study staff members call the family and screen for eligibility. The MARBLES study also enrolls a small percent of families who are referred by other research studies at the UC Davis MIND Institute or by health and service providers, or who learn about the study at outreach events, or by word of mouth.

Inclusion criteria. The inclusion criteria for the full study are: a) mother or father has one or more biological child(ren) with ASD and/ or the gestating younger child has an older half-sibling, or an equivalent or closer blood relative, with ASD; b) mother is 18 y or older; c) mother is pregnant; d) mother speaks, reads, and understands English sufficiently to complete the protocol (see Supplemental Material, "Understanding English and Mental Competence" for details) and the younger sibling will be taught to speak English; e) mother lives within 2.5 h of the Davis/Sacramento region at time of enrollment. As part of the recruitment screening phone call, study staff members obtain verbal consent and administer the Social-Communication Questionnaire (SCQ) to confirm the first criterion, i.e., a diagnosis of ASD in the proband (older sibling). Staff members also request the report from the proband's prior diagnoses, and in particular we seek ASD Diagnostic Observation Schedule (ADOS) scores. If no ADOS has been done or if no scores were included in the report, we schedule a visit to administer the ADOS to the older sibling, for diagnostic confirmation. Ideally, all of the probands would have ADOS scores; however, in a few cases the family did not come in and we defaulted to the SCQ to confirm.

Women who have one or more biological children with ASD but who are not pregnant, are pre-menopausal, and have no known biological condition that would prevent pregnancy, are designated as "active, pre-pregnant" for as long as they are not using contraception and are in a sexual relationship with a nonsterile male partner or are planning assisted reproduction. These women are contacted monthly or bimonthly to check on their pregnancy status. We conduct home visits for a subset of prepregnant women to collect specimens and related blood draw and stress forms (Table 1). Women who could become pregnant but are currently using contraception or not in a sexual relationship with a male partner are scheduled for less frequent recruitment calls ranging from 1 month to 1 y, based on their likelihood of becoming pregnant.

Preenrollment. Before the first home visit, mothers receive a welcome packet with a detailed description of the study, consent forms for the mother and father, instructions for collection of the 24-h urine specimen and the materials needed, and several self-administered forms. These forms include the Early Developmental Questionnaire (EDQ) (Ozonoff et al. 2005) for the proband, which is a parent reporting instrument that obtains information related to onset of ASD symptoms and potential regression; Social Responsiveness Scale (SRS) (Constantino 2002) for the mother, father, and proband, a standardized instrument for assessing social skills in a general population at any age; and 24-h and 48-h recalls to be completed with the 24-h urine collection kit (see below for details). All of these materials are collected from the mothers at the first visit, after informed consent has been completed.

The MARBLES study complies with all applicable requirements regarding human subjects and is approved by both UC Davis and State of California institutional review boards. No data are collected without informed consent.

Fieldwork: Exposure, Covariate Data, and Specimen Collection

Overview. Home visits are conducted at enrollment, throughout pregnancy, and during the first year of the younger sibling's life at 2 wk and 3, 6, and 12 months of age. Visits at 24 and 36 months take place at the UC Davis MIND Institute. Participation involves collection of biological specimens from the younger sibling, the proband (affected older sibling), and both parents (for both proband and younger sibling), as well as environmental samples taken in the home. The mother completes structured self-administered questionnaires, is interviewed by telephone at scheduled time points, and completes diaries related to exposures and illnesses. Maternal and child medical records are obtained and abstracted. Table 1 shows the schedule for specimen collections and data related to exposures, not including medical information.

Biospecimen collection (Table 1). Prenatally, the first visit occurs as early as possible during pregnancy. During this visit, study staff members collect from all mothers a blood sample, vaginal swabs, hair samples, and saliva samples and instruct mothers on collection of three first-morning spot urine samples and one 24-h sample, each collected a week apart, per trimester. The woman then collects the urine samples on her own, providing them to study staff at subsequent visits. Throughout pregnancy, blood samples are collected at each visit, for up to five, or occasionally six, total samples. We also collect saliva samples from the affected older sibling(s) and biological father of the younger sibling for genetic analyses.

At delivery, nonstudy hospital personnel assist in collecting maternal peripheral blood, vaginal swab, cord blood, placenta, and meconium samples; a MARBLES study staff member attends to ensure all samples are taken, and then transports them to the laboratory for processing and storage. These arrangements are made through our clinician-investigator (CW) outreach to obstetricians and delivery hospitals for all participants, beginning well before the mother's due date.

Postnatally, maternal biological samples are collected at home visits, including hair (3-month visit) and saliva (3- and 6-month visits); and for breastfeeding mothers, breast milk, urine (first-morning void spot samples) and blood. A single urine sample is collected from mothers who are not breastfeeding at 24 months. Younger sibling biospecimens include blood, saliva, hair, urine (a clean catch sample captured in a pediatric urine bag (U-bag) applied to the infant the day of the visit as well as multiple diapers from infants collected by the parent and stored in the participant's home freezer), and, for a portion of the study, fecal samples. See Supplemental Material, "Sample collection, containment, processing and storage" for details.

Forms linked to specimen collections. When blood specimens are collected, information is also obtained using the Blood Draw Form, which is completed by the parent participant, on recent food intake, household (e.g., pesticides, cleaning agents) and personal care products (cosmetics, lotions, shampoo), medication use, illness and stress, and other factors that could affect blood measurements. Other forms are timed with the collection of urine samples: a self-administered 24-h food recall and a 48-h product recall. The food recall was designed by our team to capture consumption of foods potentially contaminated with high levels of pesticides or phthalates, as well as food preparation and cooking methods, and water sources. The product recall focuses on those foods containing chemicals for which the metabolites can be measured in urine. The mother completes these recalls for the younger sibling before each urine or diaper sample and for her own urine collections if she is breastfeeding.

Home walkthrough and environmental sample collection. Study personnel conduct an observational "Home Walkthrough," at two home visits--one in pregnancy and one 6 months postnatally--to characterize the building and assess heating and cooling systems, cookware, furnishings, and other sources of environmental exposures. Cleaning and household products are surveyed, and participants are asked about lead testing and other questions regarding home characteristics. Dust samples from the main living area are taken using a standardized vacuum (Eureka Mighty-Mite Model 3670) (Allen et al. 2008; Rudel et al. 2003), and another dust sample is collected from the household vacuum.

Environmental Exposure Questionnaire (EEQ). Beginning shortly after enrollment, a trained study staff member interviews mothers by telephone in the first part of the EEQ, which documents general demographic, residential, occupational, and lifestyle (e.g., tobacco, drug use) information for both parents, and mother's pregnancy and medical history. Participants also complete a set of self-administered forms to record their and the child's residential history and use of a broad range of household (pesticides, cleaning products) and personal care products (shampoos, antibacterial soaps), with a focus on the 6 months before conception of the younger sibling to the time of first interview. The interviews and self-administered EEQ forms are repeated in the third trimester, and at 6 months and 30 months postpartum, each covering the intervening period since prior interview. The mother is also interviewed regarding the pregnancy and early life exposures for the proband, usually around her midpregnancy with the younger sibling. A single interviewer conducts all MARBLES study interviews throughout the pregnancy and at 6 months postnatally, allowing establishment of rapport. Only the 30-month interview is conducted by other interviewers.

Diaries. During pregnancy, each mother is asked to complete a short Weekly Symptom Diary, covering health symptoms, medication, and supplement usage; interactions with health-care providers: and experiences and environmental exposures from a selected set of household or personal care products for which we want to closely track changes throughout the time period. Following delivery, women complete the Monthly Symptom Diaries for a year, and Quarterly Symptom Diaries thereafter; both of these diaries are focused on the younger sibling. Questions document diaper rash, fever, allergic reactions, medications, what the child is eating, sun exposure, and personal care products used on the child. Participants can choose paper or online completion. The purpose of the diaries is to capture rapidly changing exposures or experiences, whereas the EEQ obtains a broad picture for longer time periods. The three study questionnaires are available at http://marbles.ucdavis.edu/research.html.

Child and Family Health and Development

Table 2 presents the schedule of child developmental evaluations, medical record collection, treatment information, and assessments or reports of health (physical and mental) and developmental conditions in family members. Child developmental batteries include autism-related instruments as well as measures of cognitive and adaptive function and behavioral domains. Additionally, study personnel periodically measure head circumference and photograph the infant' s palm along with frontal and profile views of the head and face for characterizing morphology.

Autism-related instruments. At each visit, beginning at 6 months, ASD symptomatology is assessed in the younger sibling by trained psychologists or other study staff, except where indicated below (see Supplemental Material, "Neurodevelopmental Assessments" for additional details). These instruments vary by the child's age: the Autism Observation Scale for Infants (AOSI) (Bryson et al. 2008) and parent/caregiver report forms such as the Infant-Toddler Checklist (Wetherby et al. 2002) at 6 and 12 months; the Examiner Ratings of Social Engagement (Ozonoff et al. 2010) at 6, 12, 24, and 36 months; the Modified Checklist for ASD in Toddlers (Robins et al. 2001) at 24 months; and at 24 and 36 months, the gold standard ADOS) (Lord et al. 2000a; 2000b). The ADOS is a semistructured, standardized assessment in which the examiner observes the child's social interaction, communication, play, and imaginative use of materials. The examiner chooses one of three possible ADOS modules to best match the age and expressive language level of the individual child. At 36 months, parents complete the Social Communication Questionnaire (SCQ) (Rutter et al. 2003) for the younger sibling. If the child shows evidence of ASD on the ADOS, the primary caregiver is interviewed with the ASD Diagnostic Interview-Revised (ADI-R) (Lord et al. 1994), a standardized instrument that documents the child' s history of symptoms in social, communication, and repetitive behavior domains required for an ASD diagnosis. Clinicians administering these instruments are trained and have formally attained research reliability on the ADI-R, ADOS, and AOSI.

Other assessment domains for the younger sibling. Trained staff members test for cognitive, language, and motor development [(Mullen Scales of Early Learning (MSEL)] (Mullen 1995); parents report the child's adaptive behavior and self-help skills (Vineland Adaptive Behavior Scales) (Sparrow et al. 1984) and internalizing and externalizing behavior problems (Child Behavior Checklist) (Achenbach 2000).

Parental concern. Parents are asked at each visit, in an open-ended question, if they have concerns about their child's behavior or development; previous work has demonstrated that parent concerns as early as 6-12 months about sensory behavior or motor, social, or communicative development predict a later ASD outcome, though lack of concerns is not a reliable predictor of later typical development (Ozonoff et al. 2009; Sacrey et al. 2015). When developmental concerns have been reported by a parent, two senior clinicians are present at the next appointment; one to administer the developmental protocol and another to observe and provide clinical support and diagnostic confirmation. If a second clinician is not available during the visit, clinical confirmation is conducted by the second clinician reviewing the videotape. Finally, at the end of each visit, the examiner reports on the child' s overall social-communicative behavior, rating eye contact, shared social affect, and overall social responsiveness using the Examiner Ratings of Social Engagement (Ozonoff et al. 2010).

Outcome classification. Two methods are used for defining the neurodevelopmental outcome of each participant based on information from the 36-month visit: an algorithmic, data-driven method and a clinical best-estimate (CBE) method.

The outcome algorithm classifies children as ASD, TD, and NonTypical Development (NonTD), based on ADOS and MSEL scores using previously published methods from the Baby Siblings Research Consortium (Ozonoff et al. 2014). Children with ASD have scores over the ADOS cutoff and meet DSM-5 criteria for ASD. NonTD children have low MSEL scores (i.e., two or more MSEL subscales that are more than 1.5 SD below the normative mean or at least one MSEL subscale that is more than 2 SD below the normative mean), elevated ADOS scores (i.e., within 3 points of the ASD cutoff), or both. Children with TD outcomes have all MSEL scores above 2.0 SD below the normative mean, no more than one MSEL subscale that is between 1.5 and 2.0 SD below the normative mean, and scores on the ADOS more than three points below the ASD cutoff.

To determine CBE, two clinicians review the ADOS and ADI-R results, discuss their clinical impressions based on direct observations, and reach a consensus on the final study diagnosis. Initially, nine outcome categories are used: DSM-5 ASD, DSM-5 Social (Pragmatic) Communication Disorder, Broader Autism Phenotype (BAP: social communication difficulties below the ASD threshold), ADHD Concerns (developmentally atypical levels of hyperactivity, inattention, and/or impulsivity), Other Externalizing Behavior Problems (oppositional, defiant, or disruptive behavior that is extreme for the child' s age), Anxiety or Mood Problems (anxious, depressed, or emotionally dysregulated behavior), Learning Difficulties/Global Developmental Delay (low scores across multiple cognitive and motor domains), Speech-Language Problems (immature speech patterns or low language levels), or Typically Developing (TD: none of the above). Children classified into the ASD and Social (Pragmatic) Communication Disorder categories meet full DSM-5 criteria. The CBE outcome categories were not intended to map on to specific DSM categories, but rather to capture categories of clinical concern based on clinician judgment. For analyses using the CBE, we group the six (nonASD nontypical) categories as "Other Developmental Concerns."

Other health and developmental data. Each participant gives contact information and signs waivers for MARBLES study staff to obtain her pregnancy and delivery records as well as the child's neonatal and pediatric charts. Study staff members seek to obtain these four types of records (see Supplemental Material, "Health Information") and are trained to abstract variables from the records and enter them into a structured electronic system. Supervisors review key aspects of each abstraction, and validation is achieved by blinded reabstraction of over 10% of charts. Because we do not receive 100% of requested records, field staff who attend each MARBLES study birth collect key clinical information from the delivery and neonatal hospital records into structured maternal and infant abstraction forms with the help of hospital personnel. MARBLES staff administer a family medical history, including an autoimmune questionnaire. Staff members systematically attempt to obtain pregnancy, labor and delivery, neonatal, and child' s medical records. If the participant saw multiple providers of a given type, all those providers are contacted. On an as-needed basis, specific variables are abstracted from each of these four main record types and entered into electronic databases. Supervisors review key aspects of each abstraction, and validation is achieved by blinded reabstraction of over 10% of charts.

For the younger sibling, we collect information on treatments and services received, including the type (examples: behavioral therapy, physical therapy, medications), the duration of the treatment, and the age at which the child received it. For information on other family members, MARBLES staff administers a family medical history with an emphasis on some health and developmental disorders and an autoimmune disorders questionnaire in first- and second-degree relatives. Another form that we created documents family members' developmental and learning disabilities (Family Characteristics Questionnaire). Mothers self-report their level of stress on the abbreviated version of the Perceived Stress Scale (Cohen et al. 1983). The Social Responsiveness Scale (SRS) (Constantino 2002; Constantino et al. 2003), a continuous measure of social impairment in natural social settings for the general population, is completed by each parent for their partner. A parent also completes the EDQ and SRS for the proband, and the SRS for the younger sibling.

Environmental Chemical Exposures

The MARBLES study was designed with a broad goal to evaluate various chemical compounds with potential for influencing brain development, either via direct effects on neural tissue (synapse formation, signaling) (Cao et al. 2014; Vester and Caudle 2016), or through other mechanisms, such as epigenetic (Feinberg and Fallin 2015; Schroeder et al. 2011, 2016; Teh et al. 2014; Tylee et al. 2016), immune (Ashwood et al. 2009; Atladottir et al. 2010a; Lyall et al. 2014a), endocrine (Adibi et al. 2015; Majewska et al. 2014; Park et al. 2017; Swan et al. 2015), or mitochondrial (Wong and Giulivi 2016) pathways. Table 3 provides a candidate, but not comprehensive, list of chemicals, each with a literature in humans, experimental animals or in vitro systems supporting potential neurodevelopmental toxicity and hence a hypothesized contribution to ASD.

For example, phthalates, a class of plasticizers used widely in consumer products, building materials, and medical applications, have been associated with a range of behavioral and developmental outcomes in human observational and experimental animal or in vitro studies (Ejaredar et al. 2015; Jeddi et al. 2016; Miodovnik et al. 2014; Zarean et al. 2016). These reviews also discuss potential mechanistic pathways. A second example is the polybrominated diphenyl ethers, which were widely used as flame retardants in household furnishings and electronics during the 1990s and early 2000s, are highly persistent both in the environment and in the human body and have been well studied both in toxicological and epidemiological studies. According to recent reviews and a meta-analysis, neurodevelopmental impacts, and IQ in particular, are among the most consistently observed outcomes, and additional evidence suggests thyroid disruption as one of several potentially contributing biological pathways (Costa et al. 2014; Gibson et al. 2018; Lam et al. 2017; Linares et al. 2015; Vuong et al. 2018). Although fewer studies have directly examined the hydroxylated metabolites (OH-PBDEs), research in experimental animals, humans, and wildlife has supported a greater transplacental transfer than the parent compounds and/or greater binding affinity to neurotransmitter receptors and to thyroid-binding proteins, including transthyretin, as well as thyroid-binding globulin, the primary human thyroid transport protein (Chen et al. 2013; Dingemans et al. 2011).

The pyrethroid and pyrethrin insecticides have received less attention than the above-mentioned chemical classes, despite known neurotoxicity with key targets of dopamine and GABA neurotransmission systems, which are implicated in autism (Meyer et al. 2008; Shelton et al. 2012; Vester and Caudle 2016). Exposure is widespread, resulting from agricultural, household, garden, and pet pest control (U.S. EPA 2010). Previous reviews have emphasized how sparse the research has been, especially for neurotoxic effects from chronic low-level human exposures, i.e., chronic exposures at levels that do not induce acute effects (Koureas et al. 2012; Ray and Fry 2006). In the last few years, associations of preor postnatal pyrethroid exposures with suboptimal child development have been reported in cross-sectional (Quiros-Alcala et al. 2014) (Oulhote and Bouchard 2013; Viel et al. 2015) (Viel et al. 2017); longitudinal (Eskenazi et al. 2018; Watkins et al. 2016) (Furlong et al. 2017); and case-control studies (Roberts et al. 2007) (Shelton et al. 2014), with these latter two specifically addressing ASD risk. Overall, based on this literature, pyrethroids are a candidate exposure for the MARBLES study.

Exposure Assessment

Environmental chemicals. The sample collection and questionnaires were developed to allow exposure assessment for a wide range of compounds. Chemical exposures occur through inhalation; ingestion of food, water, and nondietary material; and dermal absorption. For many compounds, measurements in biological specimens effectively integrate exposures over all these pathways of intake. For compounds with a long half-life in the body (PBDEs, e.g.), biological samples provide robust exposure measures over substantial time periods. However, for compounds with short half-lives, measurements in biological samples are best supplemented with other information. Because pyrethroid pesticides are rapidly metabolized and excreted, urinary pyrethroid pesticide metabolites, which represent recent exposures, are considered the best biomarkers. However, precisely because of this rapid elimination and our interest in more extended exposures, we additionally collect house dust, a longer-term reservoir for home chemicals; residential address history, which can be linked to commercial pesticide applications through Geographic Information Systems; and participant responses in questionnaires and diaries about household pesticide use and consumption of foods likely to contain pesticide residues. Additionally, repeated sampling of urine specimens can provide integrated metrics of exposures. Table 3 summarizes biological and environmental sampling and other sources of exposure information for a few select chemical classes and some biological markers of potential mechanistic relevance.

Multiple chemicals will be assayed in the course of the MARBLES study. With regard to the chemicals currently under investigation, overall strategies to achieve quality assurance and quality control include analysis of both internal and external standards, as well as duplicates in each batch; participation in the Artic Monitoring and Assessment Program for persistent pollutants, including PBDEs; verification of detection limits; establishing percent recovered for low level compounds; and use of calibration curves. Methods for the PBDE analyses in blood, for pesticide metabolite determinations in urine, and for details on all the quality assurance, quality-control protocols have been published (Lin et al. 2013; Olsson et al. 2004). Additionally, we have described the within-woman variability across pregnancy in pesticide metabolite concentrations (Barkoski et al. 2018).

Food intake and nutritional assessment. Recall of food intake allows assessment of nutritional status of the mother and child for various time periods, estimation of intake of pesticides and other chemicals in food, and correlation of food intake with nutrient metabolite and environmental toxicant measurements in urine. Mothers are asked to complete food frequency questionnaires (FFQs) to provide a comprehensive history of her usual dietary and supplemental intake across three time-frames: gestational weeks 1-20, gestational weeks 20 through delivery, and the postnatal period from delivery until the end of breastfeeding. The MARBLES study uses the Block 2005 FFQ that was designed to estimate usual and customary intake of a wide array of nutrients and food groups using approximately 110 food items. This Block FFQ underwent extensive validation, including in pregnant women, Californian women, and across multiple ethnicities (Block et al. 1986, 1990) (Block et al. 1992, 2006; Horn-Ross et al. 2008; Johnson et al. 2007). Other questionnaires developed by MARBLES staff and investigators obtain vitamin and supplement intake, including types, brands (open-ended), frequency, dose, and timing for the younger sibling, and for the mother during pregnancies with both younger sibling and proband.

Data Management, Cleaning, and Sharing

All data from child assessments are double-entered, and discrepancies are flagged and reviewed by hand. All scoring that is done prior to data entry is checked by a second study team member. Initial checks of range and distributions, as well as consistency across variables, are done every time new data are entered and added to the main study files. Analysis variables are constructed in consultation with investigators planning to publish using those variables. All data are stored on secure servers in protected files and backed up regularly. Data are deposited in the National Database for Autism Research (NDAR; ndar.nih.gov). Both data and specimens are shared with investigators outside the study team. Outside investigators can make requests using a standardized form. Requests are reviewed by study investigators for relevance of aims, consideration of limited volumes of specimens, and a determination of whether the goals of the proposed project are compatible with, yet not redundant of, those of the MARBLES study investigators.

Results

Enrollment, Retention, Descriptive Statistics, and Diagnoses

Our goal during the current funding cycle was to recruit 450 eligible pregnant mothers, and we have now surpassed that target, with 463 participants enrolled to date. Of these, 40% were enrolled in the first trimester, 36% in the second trimester, and 24% in the third trimester. The vast majority of these were recruited by MARBLES staff contacting households of children who were receiving services for ASD through the California DDS system, with a few also recruited from MIND Institute clinics and other research studies.

As of 30 June 2018, eligible women with 463 separate pregnancies (carrying 471 fetuses) had enrolled in the study; 2 other pregnancies were determined ineligible. Of these 463, 24 pregnancies resulted in loss, 26 dropped out during pregnancy (including one twin pregnancy), and 6 participants are still pregnant. From the remaining 407 pregnancies known to have ended in livebirth (s), 46 families dropped out after delivery (includes 1 set of twins), leaving 361 (87%), which included 6 sets of twins. Thus, 367 children were still actively being followed or have completed the protocol. Of these, 160 (44%) were females and 207 (56%) males. Among families that enrolled and for which the pregnancy did not end in miscarriage (439), the participants from 367 pregnancies (6 still pregnant, 361 already delivered) are currently active in the study. Thus, excluding the miscarriages, who are no longer at risk for ASD, this total corresponds to an overall retention of 84%.

We have also successfully contacted over 2,000 nonpregnant mothers, and for those who are eligible (see eligibility section above), we maintain a call-back list to check for changes in pregnancy status. Additionally, 94 prepregnant women who were actively attempting to become pregnant had a prepregnancy visit. Of these, 44 women did become pregnant and enrolled.

Table 4 presents demographic characteristics of the parents who have completed or are expected to complete the MARBLES study protocol. At conception, three-fourths of the mothers were 30 years of age or older. Fathers were older. Over half the mothers and the fathers were nonHispanic white, 23% were Hispanic, and one-fourth to one-fifth were born in a country other than the United States or Mexico. About half of the mothers and fathers had a bachelor's or higher degree, and 18% of fathers had a high school diploma or less.

Among those enrolled, thousands of urine and blood samples have been collected during pregnancy, and of those who delivered, we have obtained placental tissue from 92%, and cord blood from 86%. Beginning at delivery and including postpartum visits at 2 wk, 3, 6, 12, 24, and 36 months, our average visit completion rate is 89%. Of MARBLES younger siblings who passed their third birthday, 93% completed the 36-month visit, providing a final diagnosis for 273 of them using the CBE, or due to missing scores on the MSEL, 266 using the algorithm.

Using the algorithmic diagnosis, 64 met criteria for ASD, for a recurrence risk of 24%, consistent with previous results from similar enhanced-risk sibling cohorts (Ozonoff et al. 2011) (Messinger et al. 2013). An additional 66 (25%) were classified as having nonASD nontypical development, as described above. In a preliminary study of organophosphate pesticides, neither ASD nor nontypical development was associated with prenatal concentrations of these compounds measured in urine. Although statistical power was limited, in stratified analysis we did find a suggestive association between OP pesticides and ASD risk among girls, but not boys (Philippat et al. 2018).

Discussion and Conclusion

The MARBLES study has successfully recruited a cohort of pregnant women at elevated risk to deliver a child who develops ASD and has achieved a high rate of retention (84%). The MARBLES study comprises mother-child pairs with detailed characterizations of both the prenatal and postnatal environmental milieu and the child' s behavioral and developmental profile. Biospecimens collected from mother, father, proband, and younger sibling provide a resource to investigate early exposures, including potentially modifiable factors, that influence the child' s trajectory; to study mechanistic pathways that may compromise neurodevelopment in ASD; and to search for early molecular changes of relevance to autistic behaviors.

Of particular significance is initiation of the cohort in the prenatal period. Neuroanatomic findings, imaging, molecular studies, and epidemiology (Liu 2011; Rapin 1995; Rodier et al. 1996; Schmidt et al. 2012) all place origins of ASD in early gestation. A summary of published time windows of vulnerability to environmental factors suggests, however, that these critical periods of susceptibility to exposure vary by specific agent (Lyall et al. 2014b). Moreover, insults to neurodevelopment could occur not only during, but also before and after gestation.

In comparison with other families, parents participating in MARBLES have a heightened concern about their child's developmental outcome. Some parents may also be more aware or concerned about environmental factors, which may affect their responses to exposure-related questions, or may alter their behaviors, for instance, to decrease their use of certain products. However, all of the MARBLES study's mothers are in this category of parents who are concerned because they all have an older child with ASD, and none knows the future outcome for their children, which may reduce the likelihood of reporting bias.

The high-risk pregnancy cohort has a few limitations, the primary one being small sample sizes due to the costs of enrolling, managing specimens, and conducting successful follow-up to the age at which definitive diagnoses are made. For this reason, pooling across studies of similar design would be highly desirable. Secondly, heterogeneity of endophenotypes (genetically related behavioral phenotypes) may translate into wide variation in susceptibility to environmental agents. If environmental influences on ASD do differ for children with versus children without an older affected sibling, findings from the MARBLES study might not be generalizable to children who develop ASD in the population as a whole. For example, if susceptibility to environmental factors that contribute to ASD is determined by the presence of rare, highly penetrant gene variants that are more common in families with a history of ASD, then the influence of the environmental factors might differ in our high-risk population in comparison with the general population. On the other hand, if a contribution from environmental exposures depends on the presence of certain common gene variants that confer greater susceptibility [as was observed for folic acid and one-carbon metabolism genes (Schmidt et al. 2011, 2012)], then we might expect children in high- and low-risk families to be similarly affected. Thus, for both null and positive findings in the MARBLES study, generalizability to families without a history of ASD (the majority of ASD cases) may be difficult to predict. To date, genetic research on ASD has focused primarily on rare gene variants, so the potential influence of common variants, either alone or in combination with environmental exposures, is largely unexplored.

Some research supports a role of common genetic variants interacting with environmental agents in autism etiology, including the MTHFR677A allele, MET-C allele, and total burden of copy number (repeated segments of DNA) (Kim et al. 2017; Schmidt et al. 2011, 2012; Volk et al. 2014). For example, ASD was associated with total copy number burden (summing the number of extra base pairs across the genome), and this association remained significant after excluding known pathogenic (often large and rare) copy number variants (Girirajan et al. 2013). Subsequent work showed an air pollutant that had no impact on risk of ASD when considered alone, but was associated with increased risk of ASD when exposure occurred in those with an elevated copy number burden. This result may indicate an elevated susceptibility from global genetic instability, either because of the interaction with air pollution and/or because increased copy number burden could itself be a consequence of multiple hits from environmental factors. Similarly, the observation that specific de novo single-nucleotide polymorphism (SNP) variants are strongly linked to ASD (Neale et al. 2012; Sanders et al. 2012) raises the possibility that these new mutations may themselves have environmental origins. Either of these mechanisms, depending on prevalence, could lead to either a low or high likelihood that associations with xenobiotic exposures are generalizable beyond the enhanced-risk cohort.

MARBLES and other cohort studies are essential to progress in understanding ASD etiology. With the exception of exposures documented in existing records (such as medical charts or air pollutant monitoring), case-control studies have few sources other than parental reports. Unfortunately, many exposures predominantly depend on individual behaviors and cannot be retrospectively determined from existing records or exposure databases. Additionally, identification of early biological or xenobiotic markers, requires, with few exceptions, prospective specimen collection. Cohort studies fill all these gaps, and when the outcomes are relatively rare, as for ASD, their statistical power can be greatly increased through the design of an "enhanced-risk" population, such as families who already have a child with ASD. The MARBLES study therefore provides an excellent platform for prospectively assessing environmental risk and protective factors, identifying biological markers that may serve as early signs of ASD, and providing clues about underlying mechanistic pathways.

Acknowledgments

The MARBLES Study has been supported by NIH grants: P01-ES011269, 1R01-ES020392, R01-ES025574, P30-ES023513, and R01-ES028089 from the National Institute of Environmental Health Sciences; U54-HD079125 from the Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Centers Network; NIH-UL1-TR000002 for the UC Davis Clinical and Translational Sciences Center and 2K12-HD051958; by the following U.S. EPA (U.S. Environmental Protection Agency) grants #R-829388 & R833292; by DOD #AR110194; and by the UC Davis MIND Institute.

Additionally, the authors thank the Whiteley Center for providing subsidized accommodations for a scholarly retreat during which this manuscript was revised. The authors express our gratitude to B. Elms and E. Guerrero-Angel for their data cleaning, management, and analysis, and to the entire MARBLES staff, past and current, for their invaluable and meticulous work in protocol development, recruitment, data collection, and specimen collection, processing, and storage.

I.H.P., C.W., R.S., D.T., and S.O. have received funding for research, travel or meeting costs from Autism Speaks. C.W. has received honoraria and travel support from the Merck Speaker's Bureau. J.L. has received funding to cover travel costs as a member of the Dup15q Alliance Scientific Advisory Board. D.T. has received research funding from the Merck Investigators Study Program; travel support and consulting fees from Pfizer Consumer Healthcare; and travel support and honoraria from the International Scientific Association of Probiotics and Prebiotics. I.P. has received travel support and compensation for expert testimony from Sher Leff, PC, and funding to cover travel costs as a member of the Scientific Advisory Board of IUF (Leibniz-Institut fur Umweltmedizinische Forschung), Dusseldorf, Germany. J.V de W. has received funding for travel to present at the Autism Research Institute.

References

Achenbach TM. Child Behavior Checklist and related instruments. In: Maruish ME, ed. The Use of Psychological Testing for Treatment Planning and Outcome Assessment. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.; 1994:517-549.

Achenbach TM. 1991. Integrative Guide to the 1991 CBCL/4-18, Ysr, and Trf Profiles. Burlington, VT: University of Vermont, Department of Psychiatry.

Achenbach TM, Rescorla LA. 2000. Manual for the ASEBA Preschool Forms & Profiles. Burlington, VT:University of Vermont, Research Center for Children, Youth, & Families. http://www.aseba.org/research/basicreferences.html.

Adibi JJ, Lee MK, Naimi AI, Barrett E, Nguyen RH, Sathyanarayana S, et al. 2015. Human chorionic gonadotropin partially mediates phthalate association with male and female anogenital distance. J Clin Endocrinol Metab 100(9):E1216E1224, PMID: 26200238, https://doi.org/10.1210/jc.2015-2370.

Allen JG, McClean MD, Stapleton HM, Webster TF. 2008. Critical factors in assessing exposure to PBDEs via house dust. Environ Int 34(8):1085-1091, PMID: 18456330, https://doi.org/10.1016/j.envint.2008.03.006.

Angkustsiri K, Krakowiak P, Moghaddam B, Wardinsky T, Gardner J, Kalamkarian N, et al. 2011. Minor physical anomalies in children with autism spectrum disorders. Autism 15(6):746-760, PMID: 21610186, https://doi.org/10.1177/1362361310397620.

Ashwood P, Van de Water J. 2004. Is autism an autoimmune disease? Autoimmun Rev 3(7-8):557--562, PMID: 15546805, https://doi.org/10.1016/j.autrev.2004.07.036.

Ashwood P, Schauer J, Pessah IN, Van de Water J. 2009. Preliminary evidence of the in vitro effects of BDE-47 on innate immune responses in children with autism spectrum disorders. J Neuroimmunol 208(1-2):130--135, PMID: 19211157, https://doi.org/10.1016/j.jneuroim.2008.12.012.

Atladottir HO, Thorsen P, Ostergaard L, Schendel DE, Lemcke S, Abdallah M, et al. 2010a. Maternal infection requiring hospitalization during pregnancy and autism spectrum disorders. J Autism Dev Disord 40(12):1423-1430, PMID: 20414802, https://doi.org/10.1007/s10803-010-1006-y.

Atladottir HO, Thorsen P, Schendel DE, 0stergaard L, Lemcke S, Parner ET. 2010b. Association of hospitalization for infection in childhood with diagnosis of autism spectrum disorders: a Danish cohort study. Arch Pediatr Adolesc Med 164:470-477, PMID: 20439799, https://doi.org/10.1001/archpediatrics.2010.9.

Atladottir HO, Henriksen TB, Schendel DE, Parner ET. 2012. Autism after infection, febrile episodes, and antibiotic use during pregnancy: an exploratory study. Pediatrics 130(6):e1447-e1454, PMID: 23147969, https://doi.org/10.1542/peds. 2012-1107.

Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, et al. 2018. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill Summ 67(6):1-23, PMID: 29701730, https://doi.org/10. 15585/mmwr.ss6706a1.

Barkoski J, Bennett D, Tancredi D, Barr DB, Elms W, Hertz-Picciotto I. 2018. Variability of urinary pesticide metabolite concentrations during pregnancy in the MARBLES Study. Environ Res 165:400-409, PMID: 29860212, https://doi.org/ 10.1016/j.envres.2018.05.002.

Barr DB, Olsson AO, Wong L-Y, Udunka S, Baker SE, Whitehead RD, et al. 2010. Urinary concentrations of metabolites of pyrethroid insecticides in the general U.S. population: National Health and Nutrition Examination Survey 1999-2002. Environ Health Perspect 118(6):742-748, PMID: 20129874, https://doi.org/10. 1289/ehp.0901275.

Bauman WA, Shaw S, Jayatilleke E, Spungen AM, Herbert V. 2000. Increased intake of calcium reverses vitamin B12 malabsorption induced by metformin. Diabetes Care 23(9):1227-1231, PMID: 10977010, https://doi.org/10.2337/diacare. 23.9.1227.

Becerra TA, Wilhelm M, Olsen J, Cockburn M, Ritz B. 2013. Ambient air pollution and autism in Los Angeles county, California. Environ Health Perspect 121(3):380-386, PMID: 23249813, https://doi.org/10.1289/ehp.1205827.

Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. 1986. A databased approach to diet questionnaire design and testing. Am J Epidemiol 124(3):453-469, PMID: 3740045, https://doi.org/10.1093/oxfordjournals.aje.a114416.

Block G, Woods M, Potosky A, Clifford C. 1990. Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol 43(12):1327-1335, PMID: 2254769, https://doi.org/10.1016/0895-4356(90)90099-B.

Block G, Thompson FE, Hartman AM, Larkin FA, Guire KE. 1992. Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. J Am Diet Assoc 92(6):686-693, PMID: 1607564.

Block G, Wakimoto P, Jensen C, Mandel S, Green RR. 2006. Validation of a food frequency questionnaire for Hispanics. Prev Chronic Dis 3(3):A77, PMID: 16776878.

Braun JM, Smith KW, Williams PL, Calafat AM, Berry K, Ehrlich S, et al. 2012. Variability of urinary phthalate metabolite and bisphenol A concentrations before and during pregnancy. Environ Health Perspect 120(5):739-745, PMID: 22262702, https://doi.org/10.1289/ehp.1104139.

Braunschweig D, Krakowiak P, Duncanson P, Boyce R, Hansen RL, Ashwood P, et al. 2013. Autism-specific maternal autoantibodies recognize critical proteins in developing brain. Transl Psychiatry 3:e277, PMID: 23838888, https://doi.org/ 10.1038/tp.2013.50.

Bryson SE, Zwaigenbaum L, McDermott C, Rombough V, Brian J. 2008. The Autism Observation Scale for Infants: scale development and reliability data. J Autism Dev Disord 38(4):731-738, PMID: 17874180, https://doi.org/10.1007/s10803-0070440-y.

Cao Z, Cui Y, Nguyen HM, Jenkins DP, Wulff H, Pessah IN. 2014. Nanomolar bifenthrin alters synchronous Ca2+ oscillations and cortical neuron development independent of sodium channel activity. Mol Pharmacol 85(4):630-639, PMID: 24482397, https://doi.org/10.1124/mol.113.090076.

Chaidez V, Hansen RL, Hertz-Picciotto I. 2014. Gastrointestinal problems in children with autism, developmental delays or typical development. J Autism Dev Disord 44(5):1117--1127, PMID: 24193577, https://doi.org/10.1007/s10803-013-1973-x.

Chen A, Park JS, Linderholm L, Rhee A, Petreas M, DeFranco EA, et al. 2013. Hydroxylated polybrominated diphenyl ethers in paired maternal and cord sera. Environ Sci Technol 47(8):3902-3908, PMID: 23506475, https://doi.org/10. 1021/es3046839.

Cheslack-Postava K, Suominen A, Jokiranta E, Lehti V, McKeague IW, Sourander A, et al. 2014. Increased risk of autism spectrum disorders at short and long interpregnancy intervals in Finland. J Am Acad Child Adolesc Psychiatry 53(10):1074-1081. e1074., PMID: 25245351, https://doi.org/10.1016/j.jaac.2014.06. 009.

Cohen S, KamarckT, Mermelstein R. 1983. A global measure of perceived stress. J Health Soc Behav 24(4):386-396, https://doi.org/10.2307/2136404.

Constantino JN. 2002. The Social Responsiveness Scale. Los Angeles, CA: Western Psychological Services.

Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, et al. 2003. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord 33(4):427-433, PMID: 12959421.

Coo H, Ouellette-Kuntz H, Lam YM, Brownell M, Flavin MP, Roos LL. 2015. The association between the interpregnancy interval and autism spectrum disorder in a Canadian cohort. Can J Public Health 106:e36-e42, PMID: 25955670, https://doi.org/10.17269/cjph.106.4667.

Costa LG, de Laat R, Tagliaferri S, Pellacani C. 2014. A mechanistic view of polybrominated diphenyl ether (PBDE) developmental neurotoxicity. Toxicol Lett 230(2):282-294, PMID: 24270005, https://doi.org/10.1016/j.toxlet.2013.11.011.

Croen LA, Grether JK, Hoogstrate J, Selvin S. 2002. The changing prevalence of autism in California. J Autism Dev Disord 32(3):207-215, PMID: 12108622, https://doi.org/10.1023/a:1015453830880.

Dingemans MM, van den Berg M, Westerink RH. 2011. Neurotoxicity of brominated flame retardants: (in)direct effects of parent and hydroxylated polybrominated diphenyl ethers on the (developing) nervous system. Environ Health Perspect 119(7):900-907, PMID: 21245014, https://doi.org/10.1289/ehp.1003035.

Durkin MS, DuBois LA, Maenner MJ. 2015. Inter-pregnancy intervals and the risk of autism spectrum disorder: results of a population-based study. J Autism Dev Disord 45(7):2056-2066, PMID: 25636677, https://doi.org/10.1007/s10803-0152368-y.

Ejaredar M, Nyanza EC, Ten Eycke K, Dewey D. 2015. Phthalate exposure and childrens neurodevelopment: a systematic review. Environ Res 142:51-60, PMID: 26101203, https://doi.org/10.1016/j.envres.2015.06.014.

Enstrom AM, Onore CE, Van de Water JA, Ashwood P. 2010. Differential monocyte responses to TLR ligands in children with autism spectrum disorders. Brain Behav Immun 24(1):64-71, PMID: 19666104, https://doi.org/10.1016/j.bbi.2009.08. 001.

Eskenazi B, Marks AR, Bradman A, Harley K, Barr DB, Johnson C, et al. 2007. Organophosphate pesticide exposure and neurodevelopment in young Mexican-American children. Environ Health Perspect 115(5):792-798, PMID: 17520070, https://doi.org/10.1289/ehp.9828.

Eskenazi B, An S, Rauch SA, Coker ES, Maphula A, Obida M, et al. 2018. Prenatal exposure to DDT and pyrethroids for malaria control and child neurodevelopment: the VHEMBE Cohort, South Africa. Environ Health Perspect 126(4):047004, PMID: 29648420, https://doi.org/10.1289/EHP2129.

Feinberg AP, Fallin MD. 2015. Epigenetics at the crossroads of genes and the environment. JAMA 314(11):1129--1130, PMID: 26372577, https://doi.org/10.1001/ jama.2015.10414.

Fombonne E, Du Mazaubrun C, Cans C, Grandjean H. 1997. Autism and associated medical disorders in a French epidemiological survey. J Am Acad Child Adolesc Psychiatry 36(11):1561-1569, PMID: 9394941, https://doi.org/10.1097/ 00004583-199711000-00019.

Furlong MA, Barr DB, Wolff MS, Engel SM. 2017. Prenatal exposure to pyrethroid pesticides and childhood behavior and executive functioning. Neurotoxicology 62:231-238, PMID: 28811173, https://doi.org/10.1016/j.neuro.2017.08.005.

Geisser P, Burckhardt S. 2011. The pharmacokinetics and pharmacodynamics of iron preparations. Pharmaceutics 3(1):12-33, PMID: 24310424, https://doi.org/ 10.3390/pharmaceutics3010012.

Gibson EA, Siegel EL, Eniola F, Herbstman JB, Factor-Litvak P. 2018. Effects of polybrominated diphenyl ethers on child cognitive, behavioral, and motor development. Int J Environ Res Public Health 15:1636, PMID: 30072620, https://doi.org/ 10.3390/ijerph15081636.

Girirajan S, Johnson RL, Tassone F, Balciuniene J, Katiyar N, Fox K, et al. 2013. Global increases in both common and rare copy number load associated with autism. Hum Mol Genet 22(14):2870-2880, PMID: 23535821, https://doi.org/10. 1093/hmg/ddt136.

Giulivi C, Zhang Y-F, Omanska-Klusek A, Ross-Inta C, Wong S, Hertz-Picciotto I, et al. 2010. Mitochondrial dysfunction in autism. JAMA 304(21):2389-2396, PMID: 21119085, https://doi.org/10.1001/jama.2010.1706.

Haid M, Muschet C, Wahl S, Romisch-Margl W, Prehn C, Moller G, et al. 2018. Long-term stability of human plasma metabolites during storage at -80 [degrees]C. J Proteome Res 17(1):203-211, PMID: 29064256, https://doi.org/10.1021/acs. jproteome.7b00518.

Hertz-Picciotto I, Croen LA, Hansen R, Jones CR, van de Water J, Pessah IN. 2006. The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environ Health Perspect 114(7):1119-1125, PMID: 16835068, https://doi.org/10.1289/ehp.8483.

Horn-Ross PL, Lee VS, Collins CN, Stewart SL, Canchola AJ, Lee MM, et al. 2008. Dietary assessment in the California Teachers Study: reproducibility and validity. Cancer Causes Control 19(6):595-603, PMID: 18256894, https://doi.org/10. 1007/s10552-008-9124-y

Hornig M, Bresnahan MA, Che X, Schultz AF, Ukaigwe JE, Eddy ML, et al. 2018. Prenatal fever and autism risk. Mol Psychiatry 23(3):759-766, PMID: 28607458, https://doi.org/10.1038/mp.2017.119.

Jeddi MZ, Janani L, Memari AH, Akhondzadeh S, Yunesian M. 2016. The role of phthalate esters in autism development: a systematic review. Environ Res 151:493-504, PMID: 27567353, https://doi.org/10.1016/j.envres.2016.08.021.

Johnson BA, Herring AH, Ibrahim JG, Siega-Riz AM. 2007. Structured measurement error in nutritional epidemiology: applications in the Pregnancy, Infection, and Nutrition (PIN) Study. J Am Stat Assoc 102(479):856-866, PMID: 18584067, https://doi.org/10.1198/016214506000000771.

Jones G. 2008. Pharmacokinetics of vitamin D toxicity. Am J Clin Nutr 88:582S586S, PMID: 18689406, https://doi.org/10.1093/ajcn/88.2.582S.

Kader F, Ghai M. 2015. DNA methylation and application in forensic sciences. Forensic Sci Int 249:255-265, PMID: 25732744, https://doi.org/10.1016/j.forsciint. 2015.01.037.

Kim D, Volk H, Girirajan S, Pendergrass S, Hall MA, Verma SS, et al. 2017. The joint effect of air pollution exposure and copy number variation on risk for autism. Autism Res 10(9):1470-1480, PMID: 28448694, https://doi.org/10.1002/aur.1799.

Kim KH, Bose DD, Ghogha A, Riehl J, Zhang R, Barnhart CD, et al. 2011. Para- and ortho-substitutions are key determinants of polybrominated diphenyl ether activity toward ryanodine receptors and neurotoxicity. Environ Health Perspect 119(4):519-526, PMID: 21106467, https://doi.org/10.1289/ehp.1002728.

Koch HM, Angerer J. 2007. Di-iso-nonylphthalate (DINP) metabolites in human urine after a single oral dose of deuterium-labelled DINP. Int J Hyg Environ Health 210(1):9-19, PMID: 17182279, https://doi.org/10.1016/j.ijheh.2006.11.008.

Koch HM, Preuss R, Angerer J. 2006. Di(2-ethylhexyl)phthalate (DEHP): human metabolism and internal exposure--an update and latest results. Int J Androl 29(1):155--165. discussion 181-155, PMID: 16466535, https://doi.org/10.111Vj. 1365-2605.2005.00607.x.

Koureas M, Tsakalof A, Tsatsakis A, Hadjichristodoulou C. 2012. Systematic review of biomonitoring studies to determine the association between exposure to organophosphorus and pyrethroid insecticides and human health outcomes. Toxicol Lett 210(2):155-168, PMID: 22020228, https://doi.org/10.1016/j.toxlet.2011.10.007.

Krakowiak P, Goodlin-Jones B, Hertz-Picciotto I, Croen LA, Hansen RL. 2008. Sleep problems in children with autism spectrum disorders, developmental delays, and typical development: a population-based study. J Sleep Res 17(2):197-206, PMID: 18482108, https://doi.org/10.1111/j.1365-2869.2008.00650.x.

Krakowiak P, Walker CK, Bremer AA, Baker AS, Ozonoff S, Hansen RL, et al. 2012. Maternal metabolic conditions and risk for autism and other neurodevelopmental disorders. Pediatrics 129(5):e1121-e1128, PMID: 22492772, https://doi.org/10. 1542/peds.2011 -2583.

Krumdieck CL, Fukushima K, Fukushima T, Shiota T, Butterworth CE. 1978. A long-term study of the excretion of folate and pterins in a human subject after ingestion of 14C folic acid, with observations on the effect of diphenylhydantoin administration. Am J Clin Nutr 31(1):88-93, PMID: 413430, https://doi.org/10.1093/ajcn/31.1.88.

Lam J, Lanphear BP, Bellinger D, Axelrad DA, McPartland J, Sutton P, et al. 2017. Developmental PBDE exposure and IQ/ADHD in childhood: a systematic review and meta-analysis. Environ Health Perspect 125(8):086001, PMID: 28799918, https://doi.org/10.1289/EHP1632.

Laskowski DA. 2002. Physical and chemical properties of pyrethroids. Rev Environ Contam Toxicol 174:49-170, PMID: 12132343, https://doi.org/10.1007/978-1-47574260-2_3.

Lauritsen MB, Pedersen CB, Mortensen PB. 2005. Effects of familial risk factors and place of birth on the risk of autism: a nationwide register-based study. J Child Psychol Psychiatry 46(9):963-971, PMID: 16108999, https://doi.org/10.1111/ j.1469-7610.2004.00391.x.

Lebedeva IA, Kulikov EE, Ivanova NV, Poltaraus AB. 2000. [Mitochondrial DNA sequencing of human hair shafts stored for long time]. [In Russian] Sud Med Ekspert 43(3):9-14, PMID: 11186957.

Li M, Fallin MD, Riley A, Landa R, Walker SO, Silverstein M, et al. 2016. The association of maternal obesity and diabetes with autism and other developmental disabilities. Pediatrics 137:1-10, PMID: 26826214, https://doi.org/10.1542/peds.2015-2206.

Lin YP, Pessah IN, Puschner B. 2013. Simultaneous determination of polybrominated diphenyl ethers and polychlorinated biphenyls by gas chromatographytandem mass spectrometry in human serum and plasma. Talanta 113:41-48, PMID: 23708622, https://doi.org/10.1016/j.talanta.2013.04.001.

Linares V, Belles M, Domingo JL. 2015. Human exposure to PBDE and critical evaluation of health hazards. Arch Toxicol 89(3):335-356, PMID: 25637414, https://doi.org/ 10.1007/s00204-015-1457-1.

Liu JS. 2011. Molecular genetics of neuronal migration disorders. Curr Neurol Neurosci Rep 11(2):171 -178, PMID: 21222180, https://doi.org/10.1007/s11910010-0176-5.

Loew D, Eberhardt A, Heseker H, Kubler W. 1987. [Plasma kinetics and elimination of folic acid] [In German]. Klin Wochenschr 65(11):520-524, PMID: 3613465, https://doi.org/10.1007/BF01721039.

Lord C, Rutter M, Le Couteur A. 1994. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24(5):659-685, PMID: 7814313, https://doi.org/10.1007/BF02172145.

Lord C, Risi S, Lambrecht L, Cook EH Jr., Leventhal BL, DiLavore PC, et al. 2000a. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30:205-223, PMID: 11055457, https://doi.org/10.1023/A: 1005592401947.

Lord C, Rutter M, DiLavore PC, Risi S. 2000b. The Autism Diagnostic Observation Schedule (ADOS). Los Angeles, CA: Western Psychological Services.

Lyall K, Ashwood P, Van de Water J, Hertz-Picciotto I. 2014a. Maternal immune-mediated conditions, autism spectrum disorders, and developmental delay. J Autism Dev Disord 44:1546-1555, PMID: 24337796, https://doi.org/10.1007/ s10803-013-2017-2.

Lyall K, Schmidt RJ, Hertz-Picciotto I. 2014b. Maternal lifestyle and environmental risk factors for autism spectrum disorders. Int J Epidemiol 43(2):443-464, PMID: 24518932, https://doi.org/10.1093/ije/dyt282.

Majewska MD, Hill M, Urbanowicz E, Rok-Bujko P, Bienkowski P, Namystowska I, et al. 2014. Marked elevation of adrenal steroids, especially androgens, in saliva of prepubertal autistic children. Eur Child Adolesc Psychiatry 23(6):485-498, PMID: 24043498, https://doi.org/10.1007/s00787-013-0472-0.

McCanlies EC, Fekedulegn D, Mnatsakanova A, Burchfiel CM, Sanderson WT, Charles LE, et al. 2012. Parental occupational exposures and autism spectrum disorder. J Autism Dev Disord 42(11):2323-2334, PMID: 22399411, https://doi.org/ 10.1007/s10803-012-1468-1.

Meeker JD, Calafat AM, Hauser R. 2012. Urinary phthalate metabolites and their biotransformation products: predictors and temporal variability among men and women. J Expo Sci Environ Epidemiol 22(4):376-385, PMID: 22354176, https://doi.org/10.1038/jes.2012.7.

Messinger D, Young GS, Ozonoff S, Dobkins K, Carter A, Zwaigenbaum L, et al. 2013. Beyond autism: a Baby Siblings Research Consortium study of high-risk children at 3 years of age. J Am Acad Child Adolesc Psychiatry 52(3):300-308.e1, PMID: 23452686, https://doi.org/10.1016/j.jaac.2012.12.011.

Meyer DA, Carter JM, Johnstone AF, Shafer TJ. 2008. Pyrethroid modulation of spontaneous neuronal excitability and neurotransmission in hippocampal neurons in culture. Neurotoxicology 29(2):213-225, PMID: 18243323, https://doi.org/ 10.1016/j.neuro.2007.11.005.

Miodovnik A, Edwards A, Bellinger DC, Hauser R. 2014. Developmental neurotoxicity of ortho-phthalate diesters: review of human and experimental evidence. Neurotoxicology 41:112-122, PMID: 24486776, https://doi.org/10.1016/j.neuro. 2014.01.007.

Mullen EM. 1995. Mullen Scales of Early Learning. Circle Pines, MN:American Guidance Services, Inc.

Napoli E, Wong S, Giulivi C. 2013. Evidence of reactive oxygen species-mediated damage to mitochondrial DNA in children with typical autism. Mol Autism 4(1):2, PMID: 23347615, https://doi.org/10.1186/2040-2392-4-2.

Napoli E, Wong S, Hertz-Picciotto I, Giulivi C. 2014. Deficits in bioenergetics and impaired immune response in granulocytes from children with autism. Pediatrics 133(5):e1405-e1410, PMID: 24753527, https://doi.org/10.1542/peds.2013-1545.

Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, Sabo A, et al. 2012. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485(7397):242-245, PMID: 22495311, https://doi.org/10.1038/nature11011.

Newschaffer CJ, Croen LA, Fallin MD, Hertz-Picciotto I, Nguyen DV, Lee NL, et al. 2012. Infant siblings and the investigation of autism risk factors. J Neurodev Disord 4(1):7, PMID: 22958474, https://doi.org/10.1186/1866-1955-4-7.

Olsson AO, Baker SE, Nguyen JV, Romanoff LC, Udunka SO, Walker RD, et al. 2004. A liquid chromatography-tandem mass spectrometry multiresidue method for quantification of specific metabolites of organophosphorus pesticides, synthetic pyrethroids, selected herbicides, and deet in human urine. Anal Chem 76(9):2453-2461, PMID: 15117183, https://doi.org/10.1021/ac0355404.

Oulhote Y, Bouchard MF. 2013. Urinary metabolites of organophosphate and pyrethroid pesticides and behavioral problems in Canadian children. Environ Health Perspect 121(11-12):1378-1384, PMID: 24149046, https://doi.org/10.1289/ehp.1306667.

Owens JA, Spirito A, McGuinn M. 2000. The Children's Sleep Habits Questionnaire (CSHQ): psychometric properties of a survey instrument for school-aged children. Sleep. Sleep 23(8):1043-1051, PMID: 11145319, https://doi.org/10.1093/sleep/23.8.1d.

Ozonoff S, Williams BJ, Landa R. 2005. Parental report of the early development of children with regressive autism: the delays-plus-regression phenotype. Autism 9(5):461-486, PMID: 16287700, https://doi.org/10.1177/1362361305057880.

Ozonoff S, Young GS, Steinfeld MB, Hill MM, Cook I, Hutman T, et al. 2009. How early do parent concerns predict later autism diagnosis? J Dev Behav Pediatr 30(5):367-375, PMID: 19827218, https://doi.org/10.1097/DBP.0b013e3181ba0fcf.

Ozonoff S, Iosif AM, Baguio F, Cook IC, Hill MM, Hutman T, et al. 2010. A prospective study of the emergence of early behavioral signs of autism. J Am Acad Child Adolesc Psychiatry 49(3):256-266, PMID: 20410715, https://doi.org/10. 1016/j.jaac.2009.11.009.

Ozonoff S, Young GS, Carter A, Messinger D, Yirmiya N, Zwaigenbaum L, et al. 2011. Recurrence risk for autism spectrum disorders: a Baby Siblings Research Consortium study. Pediatrics 128(3):e488-e495, PMID: 21844053, https://doi.org/10.1542/peds.2010-2825.

Ozonoff S, Young GS, Belding A, Hill M, Hill A, Hutman T, et al. 2014. The broader autism phenotype in infancy: when does it emerge? J Am Acad Child Adolesc Psychiatry 53(4):398-407.e2, PMID: 24655649, https://doi.org/10.1016/j.jaac.2013. 12.020.

Park BY, Lee BK, Burstyn I, Tabb LP, Keelan JA, Whitehouse AJO, et al. 2017. Umbilical cord blood androgen levels and ASD-related phenotypes at 12 and 36 months in an enriched risk cohort study. Mol Autism 8:3, PMID: 28163867, https://doi.org/10.1186/s13229-017-0118-z.

Philippat C, Barkoski J, Tancredi DJ, Elms B, Barr DB, Ozonoff S, et al. 2018. Prenatal exposure to organophosphate pesticides and risk of autism spectrum disorders and other non-typical development at 3 years in a high-risk cohort. Int J Hyg Environ Health 221(3):548-555, PMID: 29478806, https://doi.org/10. 1016/j.ijheh.2018.02.004.

Quinlan CA, McVeigh KH, Driver CR, Govind P, Karpati A. 2015. Parental age and autism spectrum disorders among New York City children 0-36 months of age. Matern Child Health J 19(8):1783-1790, PMID: 25776271, https://doi.org/10.1007/ s10995-015-1692-3.

Quiros-Alcala L, Mehta S, Eskenazi B. 2014. Pyrethroid pesticide exposure and parental report of learning disability and attention deficit/hyperactivity disorder in U.S. children: NHANES 1999-2002. Environ Health Perspect 122(12):1336-1342, PMID: 25192380, https://doi.org/10.1289/ehp.1308031.

Rapin I. 1995. Autistic regression and disintegrative disorder: how important the role of epilepsy? Semin Pediatr Neurol 2(4):278-285, PMID: 9422256, https://doi.org/10.1016/S1071-9091 (95)80007-7.

Ray DE, Fry JR. 2006. A reassessment of the neurotoxicity of pyrethroid insecticides. Pharmacol Ther 111(1):174-193, PMID: 16324748, https://doi.org/10.1016/j. pharmthera.2005.10.003.

Raz R, Roberts AL, Lyall K, Hart JE, Just AC, Laden F, et al. 2014. Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case-control analysis within the Nurses' Health Study II Cohort. Environ Health Perspect 123(3):264-270, PMID: 25522338, https://doi.org/10.1289/ ehp.1408133.

Roberts EM, English PB, Grether JK, Windham GC, Somberg L, Wolff C. 2007. Maternal residence near agricultural pesticide applications and autism spectrum disorders among children in the California Central Valley. Environ Health Perspect 115(10):1482-1489, PMID: 17938740, https://doi.org/10.1289/ehp.10168.

Robins DL, Fein D, Barton ML, Green JA. 2001. The modified checklist for autism in toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders. J Autism Dev Disord 31(2):131-144, PMID: 11450812, https://doi.org/10.1023/A:1010738829569.

Rodier PM, Ingram JL, Tisdale B, Nelson S, Romano J. 1996. Embryological origin for autism: developmental anomalies of the cranial nerve motor nuclei. J Comp Neurol 370(2):247-261, PMID: 8808733, https://doi.org/10.1002/(SICI)1096-9861 (19960624)370:2<247::AID-CNE8>3.0.CO;2-2.

Rudel RA, Camann DE, Spengler JD, Korn LR, Brody JG. 2003. Phthalates, alkylphenols, pesticides, polybrominated diphenyl ethers, and other endocrine-disrupting compounds in indoor air and dust. Environ Sci Technol 37(20):45434553, PMID: 14594359, https://doi.org/10.1021/es0264596.

Rutter M, Bailey A, Lord C. 2003. SCQ: Social Communication Questionnaire. Los Angeles, CA:Western Psychological Services.

Sacrey LA, Zwaigenbaum L, Bryson S, Brian J, Smith IM, Roberts W, et al. 2015. Can parents' concerns predict autism spectrum disorder? A prospective study of high-risk siblings from 6 to 36 months of age. J Am Acad Child Adolesc Psychiatry 54(6):470-478, PMID: 26004662, https://doi.org/10.1016/jjaac.2015.03.014.

Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. 2012. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485(7397):237-241, PMID: 22495306, https://doi.org/10. 1038/nature10945.

Sandin S, Schendel D, Magnusson P, Hultman C, Suren P, Susser E, et al. 2015. Autism risk associated with parental age and with increasing difference in age between the parents. Mol Psychiatry 21(5):693-700, PMID: 26055426, https://doi.org/10.1038/mp.2015.70.

Saude E, Sykes B. 2007. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics 3:19-27, https://doi.org/10.1007/s11306006-0042-2.

Schmidt RJ, Hansen RL, Hartiala J, Allayee H, Schmidt LC, Tancredi DJ, et al. 2011. Prenatal vitamins, one-carbon metabolism gene variants, and risk for autism. Epidemiology 22(4):476-485, PMID: 21610500, https://doi.org/10. 1097/EDE.0b013e31821d0e30.

Schmidt RJ, Tancredi DJ, Ozonoff S, Hansen RL, Hartiala J, Allayee H, et al. 2012. Maternal periconceptional folic acid intake and risk of autism spectrum disorders and developmental delay in the CHARGE (CHildhood Autism Risks from Genetics and Environment) case-control study. Am J Clin Nutr 96(1):80-89, PMID: 22648721, https://doi.org/10.3945/ajcn.110.004416.

Schroeder DI, Lott P, Korf I, LaSalle JM. 2011. Large-scale methylation domains mark a functional subset of neuronally expressed genes. Genome Res 21(10):1583--1591, PMID: 21784875, https://doi.org/10.1101/gr.119131.110.

Schroeder DI, Schmidt RJ, Crary-Dooley FK, Walker CK, Ozonoff S, Tancredi DJ, et al. 2016. Placental methylome analysis from a prospective autism study. Mol Autism 7:51, PMID: 28018572, https://doi.org/10.1186/s13229-016-0114-8.

Shabihkhani M, Lucey GM, Wei B, Mareninov S, Lou JJ, Vinters HV, et al. 2014. The procurement, storage, and quality assurance of frozen blood and tissue biospecimens in pathology, biorepository, and biobank settings. Clin Biochem 47(4-5):258-266, PMID: 24424103, https://doi.org/10.1016/j.clinbiochem.2014.01.002.

Shelton JF, Tancredi DJ, Hertz-Picciotto I. 2010. Independent and dependent contributions of advanced maternal and paternal ages to autism risk. Autism Res 3(1):30-39, PMID: 20143326, https://doi.org/10.1002/aur.116.

Shelton JF, Hertz-Picciotto I, Pessah IN. 2012. Tipping the balance of autism risk: potential mechanisms linking pesticides and autism. Environ Health Perspect 120(7):944-951, PMID: 22534084, https://doi.org/10.1289/ehp.1104553.

Shelton JF, Geraghty EM, Tancredi DJ, Delwiche LD, Schmidt RJ, Ritz B, et al. 2014. Neurodevelopmental disorders and prenatal residential proximity to agricultural pesticides: the CHARGE study. Environ Health Perspect 122(10):1103-1109, PMID: 24954055, https://doi.org/10.1289/ehp.1307044.

Shin H-M, McKone TE, Tulve NS, Clifton MS, Bennett DH. 2013. Indoor residence times of semivolatile organic compounds: model estimation and field evaluation. Environ SciTechnol 47(2):859-867, PMID: 23244175, https://doi.org/10.1021/es303316d.

Sparrow S, Balla D, Cicchetti D. 1984. Vineland Adaptive Behavior Scales Interview Edition Expanded Form Manual. Circle Pines, MN:American Guidance Service.

Suades-Gonzalez E, Gascon M, Guxens M, Sunyer J. 2015. Air pollution and neuropsychological development: a review of the latest evidence. Endocrinology 156(10):3473-3482, PMID: 26241071, https://doi.org/10.1210/en.2015-1403.

Suren P, Roth C, Bresnahan M, Haugen M, Hornig M, Hirtz D, et al. 2013. Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children. JAMA 309(6):570-577, PMID: 23403681, https://doi.org/10.1001/jama.2012.155925.

Swan SH, Sathyanarayana S, Barrett ES, Janssen S, Liu F, Nguyen RH, et al. 2015. First trimester phthalate exposure and anogenital distance in newborns. Hum Reprod 30(4):963-972, PMID: 25697839, https://doi.org/10.1093/humrep/deu363.

Teh AL, Pan H, Chen L, Ong ML, Dogra S, Wong J, et al. 2014. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res 24(7):1064-1074, PMID: 24709820, https://doi.org/10. 1101/gr.171439.113.

Thuresson K, Hoglund P, Hagmar L, Sjodin A, Bergman A, Jakobsson K, et al. 2006. Apparent half-lives of hepta- to decabrominated diphenyl ethers in human serum as determined in occupationally exposed workers. Environ Health Perspect 114(2):176-181, PMID: 16451851, https://doi.org/10.1289/ehp.8350.

Tylee DS, Hess JL, Quinn TP, Barve R, Huang H, Zhang-James Y, et al. 2016. Blood transcriptomic comparison of individuals with and without autism spectrum disorder: a combined-samples mega-analysis. Amer J Med Genet Part B 9999:1-21, PMID: 27862943, https://doi.org/10.1002/ajmg.b.32511.

U.S. EPA (U.S. Environmental Protection Agency). 2010. "Reevaluation: Pyrethrins and pyrethroids." http://www.epa.gov/oppsrrd1/reevaluation/pyrethroids-pyrethrins.html [accessed 24 October 2010].

Vester A, Caudle WM. 2016. The synapse as a central target for neurodevelopmental susceptibility to pesticides. Toxics 4(3):18, PMID: 29051423, https://doi.org/ 10.3390/toxics4030018.

Viel JF, Warembourg C, Le Maner-Idrissi G, Lacroix A, Limon G, Rouget F, et al. 2015. Pyrethroid insecticide exposure and cognitive developmental disabilities in children: the PELAGIE mother-child cohort. Environ Int 82:69-75, PMID: 26057254, https://doi.org/10.1016/j.envint.2015.05.009.

Viel JF, Rouget F, Warembourg C, Monfort C, Limon G, Cordier S, et al. 2017. Behavioural disorders in 6-year-old children and pyrethroid insecticide exposure: The PELAGIE mother-child cohort. Occup Environ Med 74(4):275-281, PMID: 28250046, https://doi.org/10.1136/oemed-2016-104035.

Volk HE, Lurmann F, Penfold B, Hertz-Picciotto I, McConnell R. 2012. Traffic-related air pollution, particulate matter, and autism. JAMA Psychiatry 70(1):71-77, https://doi.org/10.1001/jamapsychiatry.2013.266.

Volk HE, Kerin T, Lurmann F, Hertz-Picciotto I, McConnell R, Campbell DB. 2014. Autism spectrum disorder: interaction of air pollution with the met receptor tyrosine kinase gene. Epidemiology 25(1):44-47, PMID: 24240654, https://doi.org/ 10.1097/EDE.0000000000000030.

Vuong AM, Yolton K, Dietrich KN, Braun JM, Lanphear BP, Chen A. 2018. Exposure to polybrominated diphenyl ethers (PBDEs) and child behavior: current findings and future directions. Horm Behav 101:94-104, PMID: 29137973, https://doi.org/ 10.1016/j.yhbeh.2017.11.008.

Walker CK, Krakowiak P, Baker A, Hansen RL, Ozonoff S, Hertz-Picciotto I. 2015. Preeclampsia, placental insufficiency, and autism spectrum disorder or developmental delay. JAMA Pediatr 169(2):154-162, PMID: 25485869, https://doi.org/10.1001/jamapediatrics.2014.2645.

Wang M, Li K, Zhao D, Li L. 2017. The association between maternal use of folic acid supplements during pregnancy and risk of autism spectrum disorders in children: a meta-analysis. Mol Autism 8:51, PMID: 29026508, https://doi.org/10. 1186/s13229-017-0170-8.

Watkins DJ, Fortenberry GZ, Sanchez BN, Barr DB, Panuwet P, Schnaas L, et al. 2016. Urinary 3-phenoxybenzoic acid (3-PBA) levels among pregnant women in Mexico City: distribution and relationships with child neurodevelopment. Environ Res 147:307-313, PMID: 26922411, https://doi.org/10.1016/j.envres.2016.02.025.

Wetherby AM, Allen L, Cleary J, Kublin K, Goldstein H. 2002. Validity and reliability of the communication and symbolic behavior scales developmental profile with very young children. J Speech Lang Hear Res 45(6):1202-1218, PMID: 12546488, https://doi.org/10.1044/1092-4388(2002/097).

Windham G, Zhang L, Gunier R, Croen L, Grether J. 2006. Autism spectrum disorders in relation to distribution of hazardous air pollutants in the San Francisco bay area. Environ Health Perspect 114(9):1438-1444, PMID: 16966102, https://doi.org/10. 1289/ehp.9120.

Wong S, Giulivi C. 2016. Autism, mitochondria and polybrominated diphenyl ether exposure. CNS Neurol Disord Drug Targets 15(5):614-623, PMID: 27071785, https://doi.org/10.2174/1871527315666160413122624.

Yang C, Zhao W, Deng K, Zhou V, Zhou X, Hou Y. 2017. The association between air pollutants and autism spectrum disorders. Environ Sci Pollut Res Int 24(19):15949-15958, PMID: 28540549, https://doi.org/10.1007/s11356-017-8928-2.

Zarean M, Keikha M, Poursafa P, Khalighinejad P, Amin M, Kelishadi R. 2016. A systematic review on the adverse health effects of di-2-ethylhexyl phthalate. Environ Sci Pollut Res Int 23(24):24642-24693, PMID: 27714658, https://doi.org/ 10.1007/s11356-016-7648-3.

Zempleni J. 1995. Pharmacokinetics of vitamin B6 supplements in humans. J Am Coll Nutr 14(6):579-586, PMID: 8598418, https://doi.org/10.1080/07315724. 1995.10718546.

Zerbo O, Iosif AM, Delwiche L, Walker C, Hertz-Picciotto I. 2011. Month of conception and risk of autism. Epidemiology 22(4):469-475, PMID: 21543984, https://doi.org/10.1097/EDE.0b013e31821d0b53.

Zerbo O, Iosif AM, Walker C, Ozonoff S, Hansen RL, Hertz-Picciotto I. 2012. Is maternal influenza or fever during pregnancy associated with autism or developmental delays? Results from the CHARGE (CHildhood Autism Risks from Genetics and Environment) study. J Autism Dev Disord 43(1):25-33, PMID: 22562209, https://doi.org/10.1007/s10803-012-1540-x.

Zerbo O, Yoshida C, Gunderson EP, Dorward K, Croen LA. 2015. Interpregnancy interval and risk of autism spectrum disorders. Pediatrics 136(4):651-657, PMID: 26371204, https://doi.org/10.1542/peds.2015-1099.

Irva Hertz-Picciotto, (1,2) Rebecca J. Schmidt, (1,2) Cheryl K. Walker, (3,2) Deborah H. Bennett, (1) McKenzie Oliver, (1,2) Kristine M. Shedd-Wise, (1) Janine M. LaSalle, (5,2) Cecilia Giulivi, (4,2) Birgit Puschner, (4,2) Jennifer Thomas, (1,2) Dorcas L. Roa, (2) Isaac N. Pessah, (4,2) Judy Van de Water, (6,2) Daniel J. Tancredi, (7) and Sally Ozonoff (8,2)

(1) Department of Public Health Sciences, School of Medicine, University of California Davis (UC Davis), Davis, California, USA

(2) UC Davis MIND (Medical Investigations of Neurodevelopmental Disorders) Institute, UC Davis, Davis, California, USA

(3) Department of Obstetrics & Gynecology, School of Medicine, UC Davis, Davis, California, USA

(4) Department of Molecular Biosciences, School of Veterinary Medicine, UC Davis, Davis, California, USA

(5) Department of Medical Microbiology, School of Medicine, UC Davis, Davis, California, USA

(6) Department of Rheumatology and Allergy, School of Medicine, UC Davis, Davis, California, USA

(7) Department of Pediatrics, School of Medicine, UC Davis, Davis, California, USA

(8) Department of Psychiatry, School of Medicine, UC Davis, Davis, California, USA

Address correspondence to I. Hertz-Picciotto, Dept. of Public Health Sciences, Building Med Sciences 1c. Parking lot 56, University of California Davis, Davis, CA, USA 95616. Telephone: (530) 752-3025. Email: ihp@ucdavis.edu

Supplemental Material is available online (https://doi.org/10.1289/EHP535).

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

Received 26 May 2016; Revised 21 September 2018; Accepted 22 September 2018; Published 22 November 2018.

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.

Please Note: Illustration(s) are not available due to copyright restrictions.

Caption: Figure 1. Conceptual overview of environmental influence on health and development. The upper part of each box indicates a category of measurement, progressing from external exposure to internal dose, to the effective dose based on what reaches the target site--organ or specific tissue/region. If the dose is sufficient, it may result in an early biological effect, which, if strong or persistent enough over time, may lead to altered function. Finally, functional deficits may progress to an outright disease or diagnosable condition. For each box, one or more examples is given, to illustrate the types of measurements that may be feasible in a prospective pregnancy cohort that follows children postnatally, highlighting a few of the variables being assessed in the MARBLES study.
Table 1. Collection of specimens and Exposure-Related data by time
point.

                                                        Prenatal
                                                      (trimester)

Specimen or Data                      Preconception      1st

Maternal
Blood and blood draw form                 x (a)         x (a)
Urine                                     x (a)         x (a)
Hair                                      x (a)         x (a)
Saliva                                    x (a)         x (a)
Vaginal secretions                                      x (a)
Breast milk
Exposure diaries [c]                                  weekly (a)
24h food recalls (d)                                    x (a)
48h product used                                        x (a)
Maternal Block FFQ
EEQ: Current Pregnancy (f)                              x (a)
EEQ: Proband Pregnancy (f)
Younger Sibling
Cord blood
Placenta
Meconium
Blood and blood draw form
Urine
Hair
Saliva
Head circumference
Dysmorphology pictures
EEQ: postnatal
Diet & supplements
Paternal
Saliva
Proband (affected older sibling)
Saliva
Environmental Exposure
Home walkthrough questionnaire (k)
Dust sample

                                       Prenatal (trimester)

Specimen or Data                         2nd          3rd

Maternal
Blood and blood draw form               x (a)          x
Urine                                   x (a)          x
Hair                                                   x
Saliva                                  x (a)          x
Vaginal secretions                      x (a)          x
Breast milk
Exposure diaries [c]                  weekly (a)   weekly (a)
24h food recalls (d)                    x (a)          x
48h product used                        x (a)          x
Maternal Block FFQ                      x (a)          x
EEQ: Current Pregnancy (f)                           x (a)
EEQ: Proband Pregnancy (f)              x (h)
Younger Sibling
Cord blood
Placenta
Meconium
Blood and blood draw form
Urine
Hair
Saliva
Head circumference
Dysmorphology pictures
EEQ: postnatal
Diet & supplements
Paternal
Saliva                                  x (j)
Proband (affected older sibling)
Saliva                                  x (j)
Environmental Exposure
Home walkthrough questionnaire (k)      x (m)        x (m)
Dust sample                               x

                                        Perinatal         Postnatal

Specimen or Data                      Labor/Delivery    2wk      3mo

Maternal
Blood and blood draw form                   x                   x (b)
Urine                                                  x (b)    x (b)
Hair                                                              x
Saliva                                                            x
Vaginal secretions                          x
Breast milk                                            x (b)    x (b)
Exposure diaries [c]                                           monthly
24h food recalls (d)                                     x        x
48h product used                                         x        x
Maternal Block FFQ
EEQ: Current Pregnancy (f)
EEQ: Proband Pregnancy (f)
Younger Sibling
Cord blood                                  x
Placenta                                    x
Meconium                                    x
Blood and blood draw form                                         x
Urine                                                    x        x
Hair                                      x (i)        x (i)
Saliva                                    x (i)        x (i)      x
Head circumference                                       x        x
Dysmorphology pictures                                   x        x
EEQ: postnatal
Diet & supplements
Paternal
Saliva
Proband (affected older sibling)
Saliva
Environmental Exposure
Home walkthrough questionnaire (k)
Dust sample

                                                 Postnatal

Specimen or Data                        6mo      12mo       24mo

Maternal
Blood and blood draw form              x (b)     x (b)        x
Urine                                  x (b)     x (b)      x (b)
Hair
Saliva                                   x
Vaginal secretions
Breast milk                            x (b)     x (b)      x (b)
Exposure diaries [c]                  monthly   monthly   quarterly
24h food recalls (d)                     x         x          x
48h product used                         x         x          x
Maternal Block FFQ                     x [e]
EEQ: Current Pregnancy (f)               x                  x (g)
EEQ: Proband Pregnancy (f)
Younger Sibling
Cord blood
Placenta
Meconium
Blood and blood draw form                x         x          x
Urine                                    x         x          x
Hair                                               x          x
Saliva                                   x         x          x
Head circumference                       x         x          x
Dysmorphology pictures                   x         x          x
EEQ: postnatal                           x                  x (g)
Diet & supplements                       x
Paternal
Saliva
Proband (affected older sibling)
Saliva
Environmental Exposure
Home walkthrough questionnaire (k)       x         x
Dust sample                              x

                                       Postnatal

Specimen or Data                        36mo

Maternal
Blood and blood draw form
Urine                                   x (b)
Hair
Saliva
Vaginal secretions
Breast milk                             x (b)
Exposure diaries [c]                  quarterly
24h food recalls (d)                      x
48h product used                          x
Maternal Block FFQ
EEQ: Current Pregnancy (f)
EEQ: Proband Pregnancy (f)
Younger Sibling
Cord blood
Placenta
Meconium
Blood and blood draw form                 x
Urine                                     x
Hair
Saliva                                    x
Head circumference                        x
Dysmorphology pictures                    x
EEQ: postnatal
Diet & supplements
Paternal
Saliva
Proband (affected older sibling)
Saliva
Environmental Exposure
Home walkthrough questionnaire (k)
Dust sample

Note: EEQ, Environmental Exposure Questionnaire; FFQ, Food Frequency
Questionnaire.

(a) If possible, depending on timing of enrollment.

(b) If breastfeeding.

(c) Mother completes Weekly Symptom Diary during pregnancy, Monthly
Diary for Babies & Breastfeeding Mothers during the first year, and
Quarterly Diary for Children over 1 Year.

(d) Mother completes food and product use recalls to coincide with
urine collections. These pertain to her own exposures during the
prenatal period, to the younger sibling during the postnatal period,
and to her own postnatal exposures if/when breastfeeding.

(e) Administered when mother stops breastfeeding, regardless of when
that is, and covers the entire breastfeeding period.

(f) Telephone administered.

(g) Completed at 30 months postpartum.

(h) Environmental exposure questionnaire for the pregnancy with the
affected older sibling is completed once by the mother during pregnancy
with the younger sibling, timing based on enrollment.

(i) Completed once at the hospital following delivery or at the 2-week
postpartum visit.

(j) Collected once at first visit when father/proband are present.

(k) Completed or collected by staff, with additional home walkthrough
questions answered by the mother.

(l) Completed once in the first year-generally at the earliest time
point during infancy when the baby has hair.

(m) Completed once in pregnancy.

Table 2. Collection of developmental, behavioral, and medical
information on child and family by time point.

Forms and Source

Forms                                   Source

Family Developmental/Mental Health
Social Responsiveness Scale             Constantino 2002;
                                        Constantino et al. 2003
Early Developmental Questionnaire       Ozonoff et al. 2005
Social Communication Questionnaire      Rutter et al. 2003

Maternal Perceived Stress Scale         Cohen et al. 1983
Family Characteristics Questionnaire    UC Davis study team

Medical
Family Medical History (includes        UC Davis study team
physical and mental health)
Autoimmune survey                       UC Davis study team

Medical records                         Health care providers

Autism-Related
Autism Observation Scale for Infants    Bryson et al. 2008
Infant Toddler Checklist--              Wetherby et al. 2002
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in        Robins et al. 2001
Toddlers
Autism Diagnostic Observation           Lord et al. 2000a,b
Schedule
Autism Diagnostic Interview Revised     Lord et al. 1994
Examiner Ratings of Social              Ozonoff et al. 2010
Engagement
Social Responsiveness Scale             Constantino 2002;
                                        Constantino et al. 2003
Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning         Mullen 1995
Vineland Adaptive Behavior Scales       Sparrow et al. 1984
Child Behavior Checklist                Achenbach 1991. 1994
Children's Sleep Habits                 Owens et al. 2000
Questionnaire
Intervention History                    UC Davis study team
Parent Concerns                         Ozonoff et al. 2009

Forms and Source                            Prenatal (Trimesters)

Forms                                   1st          2nd

Family Developmental/Mental Health
Social Responsiveness Scale                          mother, father.
                                                     & proband (a)
Early Developmental Questionnaire                    proband (a)
Social Communication Questionnaire

Maternal Perceived Stress Scale         mother (c)   mother (c)
Family Characteristics Questionnaire

Medical
Family Medical History (includes
physical and mental health)
Autoimmune survey

Medical records                                      mother, sibling.
                                                     & proband (ad)
Autism-Related
Autism Observation Scale for Infants
Infant Toddler Checklist--
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in
Toddlers
Autism Diagnostic Observation
Schedule
Autism Diagnostic Interview Revised
Examiner Ratings of Social
Engagement
Social Responsiveness Scale

Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning
Vineland Adaptive Behavior Scales
Child Behavior Checklist
Children's Sleep Habits
Questionnaire
Intervention History
Parent Concerns

Forms and Source
                                       Prenatal          Postnatal
                                       (Trimesters)

Forms                                   3rd              2 Weeks

Family Developmental/Mental Health
Social Responsiveness Scale

Early Developmental Questionnaire
Social Communication Questionnaire

Maternal Perceived Stress Scale         mother           mother
Family Characteristics Questionnaire

Medical
Family Medical History (includes
physical and mental health)
Autoimmune survey

Medical records                                          mother.
                                                         sibling (e)
Autism-Related
Autism Observation Scale for Infants
Infant Toddler Checklist--
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in
Toddlers
Autism Diagnostic Observation
Schedule
Autism Diagnostic Interview Revised
Examiner Ratings of Social
Engagement
Social Responsiveness Scale

Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning
Vineland Adaptive Behavior Scales
Child Behavior Checklist
Children's Sleep Habits
Questionnaire
Intervention History
Parent Concerns

Forms and Source                              Postnatal

Forms                                   3 Months         6 Months

Family Developmental/Mental Health
Social Responsiveness Scale

Early Developmental Questionnaire
Social Communication Questionnaire

Maternal Perceived Stress Scale         mother           mother
Family Characteristics Questionnaire    all first
                                        degree
                                        relatives
Medical
Family Medical History (includes        mother
physical and mental health)
Autoimmune survey                       first and
                                        second degree
                                        relatives
Medical records

Autism-Related
Autism Observation Scale for Infants                     sibling
Infant Toddler Checklist--                               sibling
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in
Toddlers
Autism Diagnostic Observation
Schedule
Autism Diagnostic Interview Revised
Examiner Ratings of Social                               sibling
Engagement
Social Responsiveness Scale

Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning                          sibling
Vineland Adaptive Behavior Scales
Child Behavior Checklist
Children's Sleep Habits
Questionnaire
Intervention History
Parent Concerns                         sibling          sibling

Forms and Source                             Postnatal

Forms                                   12 Months     24   Months

Family Developmental/Mental Health
Social Responsiveness Scale

Early Developmental Questionnaire
Social Communication Questionnaire

Maternal Perceived Stress Scale         mother       mother
Family Characteristics Questionnaire

Medical
Family Medical History (includes
physical and mental health)
Autoimmune survey

Medical records                         sibling      sibling

Autism-Related
Autism Observation Scale for Infants    sibling
Infant Toddler Checklist--              sibling
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in                     sibling
Toddlers
Autism Diagnostic Observation                        sibling
Schedule
Autism Diagnostic Interview Revised
Examiner Ratings of Social              sibling      sibling
Engagement
Social Responsiveness Scale

Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning         sibling      sibling
Vineland Adaptive Behavior Scales
Child Behavior Checklist
Children's Sleep Habits                              sibling
Questionnaire
Intervention History                                 sibling
Parent Concerns                         sibling      sibling

Forms and Source                        Postnatal

Forms                                   36 Months

Family Developmental/Mental Health
Social Responsiveness Scale             sibling

Early Developmental Questionnaire       sibling (b)
Social Communication Questionnaire      sibling,
                                        proband
Maternal Perceived Stress Scale         mother
Family Characteristics Questionnaire

Medical
Family Medical History (includes
physical and mental health)
Autoimmune survey

Medical records                         sibling

Autism-Related
Autism Observation Scale for Infants
Infant Toddler Checklist--
Communication and Symbolic
Behaviors Scale Developmental
Profile
Modified Checklist for Autism in
Toddlers
Autism Diagnostic Observation           sibling
Schedule
Autism Diagnostic Interview Revised     sibling (b)
Examiner Ratings of Social              sibling
Engagement
Social Responsiveness Scale             sibling

Cognitive, Adaptive & Behavioral
Mullen Scales of Early Learning         sibling
Vineland Adaptive Behavior Scales       sibling
Child Behavior Checklist                sibling
Children's Sleep Habits                 sibling
Questionnaire
Intervention History                    sibling
Parent Concerns                         sibling

Note: Shaded items are self-administered by the primary caregiver
(generally the mother), others are administered by, or with assistance
from staff. Proband is the affected older sibling; Sibling is the
younger sibling born during the study.

(a) Administered once at some point during the pregnancy (could be any
trimester).

(b) Only for younger siblings showing signs of ASD.

(c) If possible, depending on timing of enrollment.

(d) All records for proband obtained upon study entry.

(e) Maternal is labor and delivery.

Table 3. Some chemical classes of interest, biomarkers, and source
materials used in MARBLES study.

Compound Class                  Half-lives

Environmental xenobiotics       In the human body and environment (a)

Pyrethroid Pesticides           Hours to a few days in human body
                                (Barr et al. 2010); days to a year in
                                sunlight outdoors; longer indoors
                                (Laskowski 2002; Shin et al. 2013)

Brominated Flame Retardants     Typically months in human body;
(PBDEs)                         persistent in the environment
                                (Thuresson et al. 2006)

PBDE Hydroxy-metabolites        Little data found in humans regarding
                                OH-PBDE half-lives, or their
                                variability (b)

Phthalates                      Hours to days in human body (Braun et
                                al. 2012; Koch and Angerer 2007; Koch
                                et al. 2006; Meeker et al. 2012)
Nutrients
Folic acid or folate            1.5 to 31.5 hours for newly absorbed
                                folic acid. and about 100 days for
                                folate pool in human body (Krumdieck et
                                 al. 1978; Loew et al. 1987)

Vitamin B6                      Minutes to an hour in human body
                                (Zempleni 1995)

Vitamin B12                     In human body, minutes for
                                holotranscobalamin II (bioavailable
                                form); 2 weeks for transcobalaminl and
                                II (storage protein) (Bauman et al.
                                2000)

Iron                            Differs by type from hours to days in
                                the human body (Geisser and Burckhardt
                                2011)

Vitamin D                       15 hours for calcitriol (active form)
                                and 15 days for calcidiol (Jones 2008)

Biomarkers                      In stored samples

Maternal antibodies to          Unknown
fetal brain

Mitochondrial DNA               Mean [+ or -] SD: 62 [+ or -] 28 years
                                (c) (Lebedeva et al. 2000)

DNA methylation (whole genome   DNA and its modifications are stable
bisulfite sequencing)           for years if dried or frozen (Kader and
                                 Ghai 2015; Shabihkhani et al. 2014)

RNA/Gene Expression             RNA is stable for 5 years if human
                                samples are frozen promptly and RNA is
                                isolated directly from frozen samples
                                (Shabihkhani et al. 2014)

Metabolomics                    Differs by compound and medium (Saude
                                and Sykes 2007; Haid et al. 2018) with
                                about half of compounds stable up to 5
                                years at -80[degrees]C in blood
                                (Haid et al. 2018).

Compound Class                       Urine   Blood   Dust   Placenta

Environmental xenobiotics

Pyrethroid Pesticides                X               X

Brominated Flame Retardants                  X       X
(PBDEs)

PBDE Hydroxy-metabolites                     X

Phthalates                           X               X

Nutrients
Folic acid or folate                         X              X

Vitamin B6                                   X

Vitamin B12                                  X

Iron                                         X

Vitamin D

Biomarkers

Maternal antibodies to fetal brain           X

Mitochondrial DNA                            X              X

DNA methylation (whole genome                               X
bisulfite sequencing)

RNA/Gene Expression                          X

Metabolomics                         X       X              X

                                                       Food
                                     Cord    Breast    Frequency
Compound Class                       Blood   Milk      Questionnaires

Environmental xenobiotics

Pyrethroid Pesticides

Brominated Flame Retardants                  X
(PBDEs)

PBDE Hydroxy-metabolites

Phthalates

Nutrients
Folic acid or folate                 X                 X

Vitamin B6                           X                 X

Vitamin B12                          X                 X

Iron                                 X                 X

Vitamin D

Biomarkers

Maternal antibodies to fetal brain

Mitochondrial DNA

DNA methylation (whole genome        X
bisulfite sequencing)

RNA/Gene Expression                  X

Metabolomics                         X       X

                                     Food      Environmental
Compound Class                       Recalls   Questionnaires

Environmental xenobiotics

Pyrethroid Pesticides                   X         X

Brominated Flame Retardants
(PBDEs)

PBDE Hydroxy-metabolites

Phthalates                                        X

Nutrients
Folic acid or folate

Vitamin B6

Vitamin B12

Iron

Vitamin D

Biomarkers

Maternal antibodies to fetal brain

Mitochondrial DNA

DNA methylation (whole genome
bisulfite sequencing)

RNA/Gene Expression

Metabolomics

                                     GIS-Linked
                                     California
                                     Pesticide
                                     Use Report   Medical
Compound Class                       Database     Records

Environmental xenobiotics

Pyrethroid Pesticides                   X

Brominated Flame Retardants
(PBDEs)

PBDE Hydroxy-metabolites

Phthalates                                            X

Nutrients
Folic acid or folate

Vitamin B6

Vitamin B12

Iron

Vitamin D

Biomarkers

Maternal antibodies to fetal brain

Mitochondrial DNA

DNA methylation (whole genome
bisulfite sequencing)

RNA/Gene Expression

Metabolomics

Note: Table includes analytes for which past or current funding covers
measurements in at least one medium; other sources and media are also
shown, whether or not funded for assays. Additional media collected in
MARBLES include: hair, liva, maternal vaginal secretions, newborn
meconium, and fecal specimens.

(a) Half-lives in both the environment and the body vary by specific
compound or congener within a class: ranges are provided.

(b) Some OH-PBDEs in the body are formed through transformation of
PBDEs, and therefore temporal variation also depends on half-lives of
PBDEs, as well as metabolic rates to form and remove OH-PBDEs.

(c) To ensure the rigor and scientific reproducibility of data, the
reported half-life for mtDNA integrity in PBMCs and placenta (expressed
as mean [+ or -] SD; n = 50) was experimentally determined in Giulivi's
laboratory assuming a pseudo-first order kinetics. The value is within
that reported for mtDNA in hair shafts. Upon receipt of frozen
samples, DNA was always extracted within a day. The extracted DNA was
stored at -20[degrees]C in 0.5 mM EDTA, 10mM Tris-CI (pH 9.0) at a
concentration of 200 ng/[micro]l. After outcomes are determined by
qRTPCR, the mtDNA is stored at -80[degrees]C.

Table 4. Demographic characteristics of families enrolled and retained
through 30 June 2018 in the MARBLES study.

                                        Mother           Father

Characteristic                          N       %      N      %

Parents' age at conception of
younger sibling (years) (ab)
<24                                    12      3.3     9    2.51
25-29                                  69      18.8   40    11.14
30-34                                  129     35.2   103   28.69
35-39                                  128     34.9   122   33.98
40-44                                  25      6.8    65    18.11
[greater than or equal to]45 years      4      1.1    20    5.57
Missing                                                8
Parents' Race/Ethnicity (c)
Caucasian/White (nonHispanic)          184     51.5   182   51.1
Hispanic (any race)                    84      23.5   82     23
Asian                                  59      16.5   59    16.6
African American/Black                 18       5     15     4.2
Multiracial or Other Race              12      3.4    18     5.1
Missing                                 4              5
Parents' birth location6
United States--California              200     58.5   170   50.9
United States--other state             57      16.7   60     18
United States--Unspecified              3      0.8     4     1.2
Mexico                                 14      4.1    22     6.6
Other                                  68      19.9   78    23.4
Missing                                19             27
Parents' education (c)
No high school diploma                  9      2.5    17     4.8
High school diploma                    19      5.4    48    13.6
Some college, Associate's degree       144     40.7   105   29.8
or Vocational training
Bachelor' s degree                     111     31.4   108   30.7
Master's, PhD, or other                71      20.1   74     21
professional degree
Missing                                 7              9

(a) All families actively enrolled (either pregnant or postdelivery)
through 30 June 2018. MARBLES Study, for N = 367 total children
(postnatal) or pregnancies not yet delivered (excludes miscarriages,
and drop-outs).

(b) Date of conception calculated by taking the child's date of birth
(DOB), subtracting the gestational age (GA) and adding 14 days:
Conception Date = DOB-GA + 14. The parent's age is then calculated on
that date.

(c) Among families of younger siblings born alive, and actively
enrolled through 30 June 2018. MARBLES Study, N = 361.
COPYRIGHT 2018 National Institute of Environmental Health Sciences
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research
Author:Hertz-Picciotto, Irva; Schmidt, Rebecca J.; Walker, Cheryl K.; Bennett, Deborah H.; Oliver, McKenzie
Publication:Environmental Health Perspectives
Article Type:Report
Date:Nov 1, 2018
Words:16314
Previous Article:Erratum: "Prenatal Exposure to DDT and Pyrethroids for Malaria Control and Child Neurodevelopment: The VHEMBE Cohort, South Africa".
Next Article:Native American Perspectives on Health and Traditional Ecological Knowledge.
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |