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. Author manuscript; available in PMC: 2021 Sep 1.
Published in final edited form as: Environ Int. 2020 Jun 26;142:105858. doi: 10.1016/j.envint.2020.105858

Prenatal maternal organophosphorus pesticide exposures, paraoxonase 1, and childhood adiposity in the Mount Sinai Children’s Environmental Health Study

Taylor M Etzel a, Stephanie M Engel b, Lesliam Quirós-Alcalá a, Jia Chen c, Dana B Barr d, Mary S Wolff c, Jessie P Buckley a
PMCID: PMC7340581  NIHMSID: NIHMS1601996  PMID: 32599353

Abstract

Background:

Animal studies suggest that organophosphorus pesticides (OPs) may be environmental obesogens. While prenatal OP exposures have been associated with altered infant glucose metabolism, associations with pediatric adiposity remain unknown.

Methods:

We summed concentrations of three dimethylphosphate (ΣDMP) and three diethylphosphate (ΣDEP) metabolites of OPs measured in third trimester spot urine samples collected from pregnant women enrolled in New York City, 1998–2002. We measured percent fat mass using bio-electrical impedance analysis and calculated age- and sex-standardized body mass index (BMI) z-scores from anthropometric measurements collected at approximately 4, 6, and 7–9 years of age (166 children, 333 observations). We assessed covariate-adjusted associations of OPs with repeated adiposity measures using linear mixed models and evaluated effect measure modification (EMM) by sex and paroxonase (PON) 1 –108C/T and Q192R polymorphisms measured in maternal peripheral blood samples.

Results:

The geometric mean urinary concentration of ΣDMP metabolites (29.9 nmol/L, IQR: 105.2 nmol/L) was higher than ΣDEP metabolites (8.8 nmol/L, IQR: 31.2 nmol/L). Adjusted associations were null, with differences in fat mass per 10-fold increase in prenatal ΣDMP and ΣDEP concentrations of 0.7% (95% CI: −0.6, 2.0) and 0.8% (95% CI: −0.4, 2.0), respectively. Maternal PON1-108C/T polymorphisms modified relationships of prenatal ΣDMP with percent fat mass (EMM p-value=0.18) and ΣDEP with BMI z-scores (EMM p-value=0.12). For example, ΣDMP was modestly associated with increased percent fat mass among children of mothers with the at-risk CT or TT genotype (β=1.2%, 95% CI: −0.6, 3.0) but not among those whose mothers had the CC genotype (β= −0.4%, 95% CI: −2.4, 1.5). Associations were not modified by sex or maternal PON1 Q192R polymorphisms.

Conclusions:

We observed little evidence of a relationship between prenatal OP exposures and child adiposity, although there was some suggestion of increased risk among offspring of mothers who were slow OP metabolizers. Larger studies are warranted to further evaluate possible associations of prenatal OP exposures with child adiposity and differences by maternal PON1 genotype, which regulates OP metabolism and may increase susceptibility to exposure.

Keywords: Maternal Exposures, Organophosphorus Compounds, Children’s Health, Adiposity, Paraoxonase-1

1. Introduction

Childhood overweight and obesity dramatically increased in the United States from the 1970’s to 2010’s and obesity prevalence has remained high.1,2 Obese children have worse psychosocial health3,4 and are more likely to be overweight or obese as adults with an increased risk of developing cardiovascular disease or type II diabetes.5,6 Well-known causes of obesity such as poor diet and physical inactivity do not fully account for the increase in childhood obesity prevalence.7 Obesogens are chemicals that inappropriately alter lipid homeostasis and may increase susceptibility to weight gain across the lifespan, particularly when exposures occur during susceptible periods of development such as the prenatal period.8 Organophosphorus pesticides (OPs) are a class of insecticides that may act as chemical obesogens.9 Residential application of OPs, such as chlorpyrifos and diazinon, was common in the United States until most were voluntarily phased out by registrants from indoor and outdoor residential uses by 2004. This phase-out went into effect in order to meet safety standards set forth by the 1996 Food Quality Protection Act and emerging human health concerns, particularly in children.10,11 However, OP pesticides remain widely used in agriculture and consumption of conventionally grown produce is an important exposure pathway for the general population.12,13

Previous toxicological research suggests that OPs may affect metabolism and cause excess weight gain in adipose tissues.14,15 OPs target cyclic AMP, which is controlled by adenylyl cyclase.16 Adenylyl cyclase and cyclic AMP regulate metabolic, hepatic, cardiovascular, and hormonal functions.14,16,17 Additionally, animal studies suggest that pre- and postnatal exposure to OPs may be associated with obesity and type 2 diabetes by acting on pancreas, liver, and endocrine organs.9 In animal models, exposure to OPs during important developmental periods, such as gestation, can permanently alter cellular responses and increase the risk of obesity and type 2 diabetes during adulthood.9,14

Paroxonase-1 (PON1), an antioxidant, is a key enzyme involved in lipid and OP metabolism.18,19 Several well-characterized polymorphisms in the PON1 gene have been shown to affect enzyme expression levels (−108 C/T, T having reduced enzyme expression), or substrate-specific catalytic efficiency (Q192R, Q having slower catalytic efficiency).20,21 These polymorphisms have previously been shown to differentiate individuals on their susceptibility to the adverse effects of OP exposure.18,20,22,23

While epidemiological research on prenatal OP exposures and adiposity are limited, studies of adults suggest an association between increased OP exposures and hypothyroidism24,25 and diabetes.26–28 One previous epidemiologic study found an association between prenatal OP exposure and increased insulin levels in infants at birth.29 Another epidemiological study found that children born to women who were occupationally exposed to pesticides had increased body fat percentages compared to children born to non-occupationally exposed mothers.30 Additionally, prenatal exposure to organochlorine pesticides, such as hexachlorobenzene and polychlorinated biphenyls (PCBs), have been associated with childhood and adult obesity.31–33 However, no studies to date have assessed prenatal OP exposure in relation to child adiposity outcomes. To fill this critical data gap, we sought to examine associations between prenatal maternal OP exposures and childhood adiposity in the Mount Sinai Children’s Environmental Health Study. Because previous epidemiological studies have reported differences in health effects of prenatal OP exposures by child’s sex34 or PON1 genotype,22,23 we also explored effect measure modification (EMM) of associations by these factors.

2. Materials and Methods

2.1. Study Population

The Mount Sinai Children’s Environmental Health Study is a prospective multiethnic pregnancy cohort. The study enrolled primiparous women with singleton pregnancies between 1998 and 2002 from the Mount Sinai Diagnostic and Treatment Center and two adjacent private practices in New York City who delivered at the Mount Sinai Medical Center.34,35 Of the 1,450 eligible women who were approached, 479 consented to participate (33%). Detailed exclusion criteria have been described elsewhere.36 Briefly, 75 women (8.4%) were excluded due to medical complications, very premature birth delivery (delivery before 32 completed weeks or at less than 1,500 g), delivery of an infant with genetic abnormalities or malformations, inability of staff to collect samples in time, and change of hospital or residence outside of New York City. The final birth cohort consists of 404 singleton, live born infants for whom birth data were available. Children were invited to return for three follow-up visits scheduled at approximately ages 4–5.5, 6, and 7–9 years.

Written informed consent was provided by women before study participation and children ≥ 7 years of age provided assent. The study was approved by the Mount Sinai School of Medicine Institutional Review Board. The involvement of the Centers for Disease Control and Prevention (CDC) laboratory and Johns Hopkins University Bloomberg School of Public Health was determined not to constitute engagement in human subjects research for the measurement of biological samples and this present analysis, respectively.

2.2. Organophosphorus Pesticide Exposures

Prenatal maternal spot urine samples were collected in the third trimester of pregnancy (mean=31.2 weeks, SD=3.7). Maternal urine samples were analyzed at the Centers for Disease Control and Prevention (CDC) in Atlanta, GA for six dialkylphosphate (DAP) metabolites, including three diethylphosphate metabolites (DEP) (diethyldithiophosphate, diethylphosphate, and diethylthiophosphate) and three dimethylphosphate metabolites (DMP) (dimethyldithiophosphate, dimethylphosphate, and dimethylthiophosphate), with a validated laboratory method using gas chromatography–tandem mass spectrometry (GC-MS/MS). Creatinine was measured in each sample using a commercially available diagnostic enzyme method (Vitros CREA slides; Ortho Clinical Diagnostics, Raritan, NJ) to account for urinary dilution. Laboratory and quality control methods have been described in detail elsewhere.37,38

Some samples were missing a DEP or DMP metabolite value due to analytic interference. As previously described, these values were imputed using the other non-missing values within their respective class.36,39 OP metabolite concentrations below the limit of detection (LOD) were given a random value below the LOD that was imputed using maximum likelihood estimation based on a log-normal distribution that was truncated at the LOD.22 Metabolites were then summed on a molar basis to obtain total diethylphosphate (ΣDEP) and total dimethylphosphate (ΣDMP) biomarker concentrations. To adjust for urinary dilution, we used covariate-adjusted standardization.40 Covariates in our creatinine prediction model included maternal age, race, education level, and BMI. Then, due to their non-normal distributions, we log10–transformed OP biomarker concentrations for analysis to reduce the influence of outlying values.22,23,36,41 We also created tertile variables using covariate-adjusted creatinine standardized OP biomarker concentrations.

2.3. Outcome Assessment

Weight and body composition were assessed at each follow-up visit using bioelectrical impedance analysis with a pediatric Tanita scale (Model TBF-300; Tanita Corporation of America). Unlike BMI, bioelectrical impedance analysis estimates of body composition can differentiate between lean and fat mass, particularly among children.42,43 We used the Tanita fat mass estimates to calculate percent fat mass as (fat mass/weight) x100.44 Although body composition equations built into the Tanita scale are not validated for children <7 years of age, previous research in this cohort demonstrated a strong correlation between percent fat mass values estimated using Tanita body composition equations and other equations developed and validated for this age group.44 Height was assessed as the average of measurements collected using a stadiometer; two measures were collected for each child and a third measure was taken if the difference between the first two measures exceeded 2.0 cm. Body mass index (BMI) was calculated as weight(kilograms)/height(meters)2 and age- and sex- standardized z-scores and percentiles were determined using a CDC SAS macro.45 We classified children as overweight/obese at each follow-up visit if their age- and sex-standardized BMI percentile was ≥85, which corresponds to a BMI z-score of 1, and used this variable to categorize children as ever being overweight/obese at any follow-up visit. Body composition and anthropometric assessments were taken while children were barefoot and wearing light clothing.

2.4. Covariates

Mothers completed a structured interview administered by trained interviewers at enrollment to ascertain demographic and other maternal characteristics. Pregnancy and delivery information was ascertained from a perinatal database at Mount Sinai Hospital. PON1 –108C/T and Q192R polymorphisms were determined from maternal peripheral blood; DNA extraction and genotyping methods have been previously described.46–48 Physical activity at each follow-up visit was assessed using parental or caregiver report of whether the child was active “most of the time” (active) or “hardly at all” (inactive).

Potential confounders were selected a priori based on a directed acyclic graph (DAG) (Supplemental Figure 1). Demographic and socioeconomic characteristics included in our final adjusted model were maternal race/ethnicity, age at delivery, education, work status during pregnancy, and housing type. We also adjusted for pre-pregnancy BMI, maternal smoking status during pregnancy, child’s sex, and age (months) of child at follow-up visit. We included child inactivity at follow-up, a strong predictor of childhood adiposity, in our final model to increase precision of our estimates. Our final model was also adjusted for year of birth to account for temporal trends in OP exposure due to the phase out of chlorpyrifos and diazinon as well as trends in childhood adiposity over time.2 Finally, in addition to covariate-adjusted creatinine standardization, we included log-transformed urinary creatinine concentrations in our final models to account for urinary dilution.

2.5. Statistical Analysis

Of the 404 mother-infant pairs in the birth cohort, 54 (13.4%) mothers were missing third trimester urinary OP or creatinine concentrations. Among those with complete prenatal OP and creatinine concentrations, we excluded individuals with no outcome measurements during follow-up (n=183) and one participant who was missing information on home ownership and education. Our analysis includes 166 children, with 333 total visits for percent fat mass and 334 total visits for BMI analysis. Of the 166 children, 61, 43, and 62 had one, two, or three follow-up visits, respectively. Children did not need to complete all visits in order to be included in our study as by adjusting for covariates we assume exchangeability among children at each visit.49

First, we conducted descriptive statistics to assess distributions of exposures, covariates, and outcomes. We compared baseline characteristics and child characteristics of participants in the birth cohort (n=404) with our analysis population (n=166) and determined statistical differences by either two-sided t-tests (continuous variables) or a chi-square test (categorical variables). We also calculated average percent fat mass and BMI z-score by follow-up visit and child sex.

In separate models for ΣDMP and ΣDEP, we estimated differences in percent fat mass and BMI z-score per 10-fold increase in third trimester maternal urinary metabolite concentrations using linear mixed models to account for repeated outcome measures. We used likelihood ratio tests to compare models that 1) were fit with maximum likelihood versus restricted maximum likelihood, 2) included linear, quadratic, or cubic terms for age in months to account for potential non-linear growth trajectories, and 3) included a random intercept only versus a random intercept and a random slope for child age in months. Based on likelihood ratio tests our final models used a random intercept only, a linear term for age in months, an unstructured covariance matrix, and fit with maximum likelihood estimation. We assessed associations of covariate-adjusted creatinine standardized log10 OP concentrations with outcomes in models adjusted only for urinary creatinine and in models additionally adjusted for the covariates described above. We examined potential non-linear dose-response relationships by fitting models using tertiles of third trimester covariate-adjusted creatinine corrected ΣDMP and ΣDEP maternal metabolite concentrations and comparing estimates for the second and third tertile of ΣDMP and ΣDEP metabolite exposure to the lowest tertile of exposure.

We examined EMM by PON1 genotype, child’s age, and child’s sex using an augmented product-term approach to account for potential modifier-depending confounding.50 Our models were adjusted for the same covariates as used int he adjusted linear mixed models. Maternal PON1 genotypes were dichotomized. For PON1 –108C/T the T allele is considered the at-risk allele and indicates lower PON1 enzyme activity, therefore women were classified by the presence of the T allele in their genotypes (CT or TT vs. CC). Similarly, the PON1 Q192R QQ genotype is considered to be at-risk and women were dichotomized by genotype (QQ vs. QR or RR).

Finally, we assessed clinically-relevant outcomes by estimating the relative risk of ever being overweight/obese at follow-up per 10-fold increase in third trimester ΣDMP and ΣDEP maternal urinary OP metabolite concentrations using Poisson regression with robust error variance.

We considered main effects to be statistically significant at an alpha of 0.05. For EMM by child sex and maternal PON1 genotype, we considered modification to be present when the product term p-value was ≤0.20. We used SAS software, version 9.4. (SAS Institute, Inc., Cary, North Carolina) and RStudio, version 1.1.383 (RStudio: Integrated Development Environment for R) for data analyses.

2.6. Sensitivity Analyses

We conducted several sensitivity analyses to assess the robustness of our results. First, we adjusted for adequacy of gestational weight gain, which has been associated with increased risk of childhood overweight or obesity51,52 and may be associated with dietary predictors of OP exposure, but was missing for 29 participants. Additionally, gestational weight gain may be a mediator. Adequacy of gestational weight gain was calculated as the ratio of observed gestational weight gain (last pregnancy weight minus self-reported pre-pregnancy weight) to expected gestational weight gain based on the 2009 Institute of Medicine recommendations × 100.53 We also explored potential bias from loss to follow-up by applying inverse probability weights in our final models.54 We calculated weights for each follow-up visit by taking the inverse of the probability of having complete exposure and outcome information at the visit, based on prenatal OP exposure concentrations, maternal race/ethnicity, education, marital status, housing type, maternal age, and smoking during pregnancy.

3. Results

Women in our study population were predominantly of Hispanic or other race/ethnicity (54.8%), did not have a college degree (78.3%), identified as a homemaker (52.7%), did not smoke during pregnancy (83.7%), rented their home (56.0%), gave birth in 2000 (38.9%), and had an average age at delivery of 24.5 (SD:6.5) years and pregnancy BMI (kg/m2) of 23.8 (SD:4.6) (Table 1). Baseline maternal and child characteristics were similar between those individuals included in our study (N=166) and those who were not (N=238) (Supplemental Table 1). ΣDMP urinary metabolite concentrations were higher (32.4 vs. 29.9 nmol/L) and ΣDEP urinary metabolite concentrations were lower (9.58 vs. 12.5 nmol/L) in the baseline cohort among those who had measured OP concentrations (N=356) compared to our study population. There were 70 children (41.9%) who were overweight or obese at any follow-up visit.

Table 1.

Characteristics of participants in our study sample from the Mount Sinai Children’s Environmental Health Study (1998–2002).

Characteristic N (%)
Total (N) 166
Maternal Race/Ethnicity
 Non-Hispanic White 31 (18.7)
 Non-Hispanic Black 44 (26.5)
 Hispanic or Other 91 (54.8)
Maternal Age at Delivery (years) (mean, SD) 24.5 (6.5)
Maternal Education (≥college degree) 36 (21.7)
Marital Working Status
 Employed 40 (24.0)
 Student 39 (23.4)
 Homemaker 88 (52.7)
Maternal Smoking during Pregnancy 27 (16.3)
Maternal pre-pregnancy BMI (kg/m2) (mean, SD) 23.8 (4.6)
Housing type
 Public 56 (33.7)
 Rental 93 (56.0)
 Owner Occupied 17 (10.2)
 Child’s Sex (male) 94 (56.6)
Year of Birth
 1998 27 (16.2)
 1999 56 (33.5)
 2000 65 (38.9)
 2001 19 (11.4)
 2002 N/A
Maternal PON1 −108 C/T
 CC (high) 81 (48.5)
 CT/TT (low) 82 (49.1)
 Missing 4 (2.4)
Maternal PON1 Q192R
 QR/RR (high) 115 (68.9)
 QQ (low) 48 (28.7)
 Missing 4 (2.4)
Adequacy of Gestational Weight Gaina
 Less than recommended 13 (7.8)
 Recommended 36 (21.6)
 More than recommended 97 (58.1)
 Missing 21 (12.6)
Physical Activity at Follow-Upb
 Inactive 73 (43.7)
 Active most of the time 91 (54.5)
 Missing 3 (1.8)
Child Ever Overweight or Obesec
 Yes 70 (42.1)
 No 96 (57.8)
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a

Ratio of observed gestational weight gain (last pregnancy weight minus self-reported pre-pregnancy weight) to expected gestational weight gain based on the 2009 Institute of Medicine recommendations × 100.53

b

Proportion classified as inactive at any follow-up visit.

c

Proportion classified as overweight or obese at any follow-up visit.

The proportion of individual DMP and DEP metabolites that were below the LOD ranged from 10.8 to 89.2% (Table 2). There were 12 (7.2%) and 20 (12.0%) individuals with all metabolites <LOD for ΣDMP and ΣDEP, respectively. The geometric mean urinary concentration of ΣDMP metabolites (29.9 nmol/L) was higher than ΣDEP metabolites (8.8 nmol/L) (Table 2). We found no statistically significant differences in geometric mean urinary concentrations of ΣDMP and ΣDEP metabolites by maternal race/ethnicity, overweight/obesity during follow-up, or maternal PON1 status (all p-values >0.12) (Supplemental Table 2). The average child age in years at each visit was 4.8 (SD: 0.4) at visit 1, 6.2 (SD: 0.1) at visit 2, and 7.8 (SD: 0.8) at visit 3 (Supplemental Table 3). The average percent fat mass was 18.8% (SD: 8.5%) and increased at each study visit (Supplemental Table 3). The average age- and sex-standardized BMI z-score was 0.6 (SD: 1.2) and was similar at each study visit (Supplemental Table 3).

Table 2.

Distributions of organophosphorus pesticide metabolite concentrations (nmol/L) in third trimester maternal urine samples, Mount Sinai Children’s Environmental Health Study (1998–2002).a,b

Metabolite (nmol/L) Geometric Meana Minimum 25th Percentile Median 75th Percentile Maximum
Dimethylphosphate (ΣDMP) 29.9 <LOD 10.7 29.9 115.9 4870.0
Diethylphosphate (ΣDEP) 8.80 <LOD 6.40 12.5 37.6 342.0
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a

Percentage of dimethylphosphate metabolites <LOD: DMDP (76.7%), DMP (39.5%), and DMTP (10.7%); Percentage of diethylphosphate metabolites <LOD: DEDP (89.2%), DEP (56.9%), and DETP (19.8%)

*

LOD for dimethylphosphate metabolites (nmol/L): DMDP (0.3), DMP (0.5), and DMTP (0.4); LOD for diethylphosphate metabolites (nmol/L): DEDP (0.2), DEP (0.3) and DETP (0.4)

Results from unadjusted and adjusted linear mixed models were similar and all associations of third trimester ΣDMP and ΣDEP urinary metabolite concentrations with fat mass and BMI z-scores were null (Table 3). For example, a 10-fold increase in third trimester ΣDMP and ΣDEP urinary metabolite concentrations was non-significantly associated with a 0.7% (95% CI: −0.6%, 2.0%) and 0.8% (95% CI: −0.4%, 2.0%) greater percent fat mass in childhood, respectively (Table 3). Results were also null in tertile analyses, with some evidence of a potential threshold effect (Supplemental Table 4). For example, mothers in the second and third tertile of ΣDEP urinary metabolite concentrations had children with a 0.3% (95% CI: −−2.7%, 3.3%) and 2.4% (95% CI: −0.5%, 5.3%) greater percent fat mass compared to women in the first tertile, respectively (Supplemental Table 4).

Table 3.

Difference in percent fat mass and BMI z-score per 10-fold increase in third trimester maternal urinary organophosphorus pesticide metabolite concentrations (nmol/L) among children ages 4–9 years in the Mount Sinai Children’s Environmental Health Study.

Unadjusted β (95% CI) Adjusteda β (95% CI)
Percent Fat Mass
Dimethylphosphate (ΣDMP) 0.7 (−0.7, 2.1) 0.7 (−0.6, 2.0)
Diethylphosphate (ΣDEP) 0.7 (−0.6, 2.1) 0.8 (−0.4, 2.0)
BMI Z-score
Dimethylphosphate (ΣDMP) 0.1 (−0.1, 0.3) 0.1 (−0.1, 0.3)
Diethylphosphate (ΣDEP) 0.1 (−0.1, 0.3) 0.1 (−0.1, 0.3)
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a.

Final model adjusted for creatinine concentration, maternal race/ethnicity, maternal age at delivery, maternal education, maternal work status, maternal smoking status during pregnancy, maternal pre-pregnancy BMI, maternal creatinine, year of birth, housing type, child age (months), child sex, and physical activity during follow-up.

Maternal PON1–108C/T genotype modified relationships between third trimester ΣDMP urinary metabolite concentrations and percent fat mass (EMM p-value=0.18) and third trimester ΣDEP urinary metabolite concentrations and BMI z-score (EMM p-value=0.12) (Figure, Supplemental Table 5). On average, each 10-fold increase in ΣDMP urinary metabolite concentrations was associated with 1.2% (95% CI: −0.6%, 3.0%) greater fat mass among offspring of mothers who carried the PON1 −108 T allele (CT and TT genotypes) but 0.4% (95% CI: −2.4%, 1.5%) lower fat mass among offspring of mothers with the PON1–108 CC genotype. Similarly, each 10-fold increase in ΣDEP urinary metabolite concentrations was associated with slightly higher BMI z-scores (β: 0.2, 95% CI: −0.2, 0.5) among offspring of mothers with the at-risk PON1–108 CT and TT genotypes but slightly lower BMI z-scores (β: −0.1, 95% CI: −0.4, 0.2) among offspring of mothers without the at-risk genotype. Child age modified the relationships between third trimester ΣDEP urinary metabolite concentrations and BMI z-score (EMM p-value=0.10) but not other associations (Supplemental Figure 2). The relationship between ΣDEP and BMI-z score was stronger among older children. There was no evidence of EMM of BMI z-scores by maternal PON1 Q192R genotype (p-values >0.58) (Supplemental Table 5) or child sex (p-values >0.42) (Supplemental Table 6).

Figure.

Figure.

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Adjusted difference in percent fat mass and BMI z-score per 10-fold increase in third trimester maternal urinary organophosphorus pesticide metabolite concentrations (nmol/L) among children ages 4–9 years in the Mount Sinai Children’s Environmental Health Study: Effect measure modification by maternal PON1 –108C/T genotype.

*For the PON1–108 CT/TT genotype there were 82 mothers with 153 and 154 child visits for Fat Mass% and BMI z-score, respectively. For the PON1–108 CC genotype there were 81 mothers with 148 child visits.

*EMM p-values are 0.16, 0.33, 0.26, and 0.11, respectively.

*Final model adjusted for maternal race/ethnicity, maternal age at delivery, maternal education, maternal work status, maternal smoking status during pregnancy, maternal pre-pregnancy BMI, maternal creatinine, housing type, year of birth, child age (months), child sex, and physical activity during follow-up.

Unadjusted and adjusted results for associations of ΣDMP and ΣDEP urinary metabolite concentrations with risk of ever being overweight or obese during follow-up were similar to our main analysis of continuous adiposity outcomes. Risk ratios per 10-fold increase in third trimester ΣDMP and ΣDEP urinary metabolite concentrations were 1.19 (95% CI: 0.94, 1.52) and 1.11 (95% CI: 0.90, 1.37), respectively.

Sensitivity analysis with adjustment for adequacy of maternal gestational weight gain did not appreciably change our results (data not shown). Findings were also similar when we assessed possible bias from loss to follow-up by including unstabilized inverse probability weights in our final models (Supplemental Table 7).

4. Discussion

In this prospective study of a New York City cohort, we found no statistically significant associations between third trimester maternal urinary OP metabolite concentrations and child adiposity at ages 4 to 9 years. While there was some suggestion that prenatal OP exposure was associated with greater adiposity at follow-up among those whose mothers carried the slow enzyme activity genotypes for PON1–108, magnitudes of association were small, and estimates were imprecise.

Previous toxicological research suggests that early-life exposure to OPs may promote obesity through several pathways.9,14 The first potential mechanism of action targets the pancreatic endocrine system and alters the secretion of insulin and glucagon, which is important for glucose homeostasis.9 Second, early-life exposure to OPs have been shown to alter the hepatic adenylyl cyclase and cyclic AMP signaling which results in a hyperresponsiveness to gluconeogenic stimuli.14,16,17 OPs may also target the liver altering lipid metabolism and lipid accumulation.9 Third, OPs may alter the metabolism and increase the amount of adipose tissue or induce oxidative stress.9 Next, OP exposures may alter the hypothalamic-pituitary-adrenal axis leading to an increase in corticosterone levels which in turn affects endocrine organs.14,15 Finally, OPs may alter the intestinal barrier and gut microbiome, which plays a key role in human health.55,56

We are not aware of any previous studies that have examined associations between prenatal OP exposure and adiposity in children or adults. One previous epidemiologic study found an association between gestational OP exposure and increased insulin levels in the cord blood of infants at birth,29 suggesting that prenatal OP exposure may disrupt the metabolic activity of offspring. Additionally, we hypothesized that the association between prenatal OP exposures and reduced intra-uterine growth previously observed in this cohort would be related to greater postnatal BMI. This pattern of low birth weight followed by subsequent excess adiposity has been observed for other environmental exposures such as gestational cigarette smoke exposure.57 While we did not observe strong evidence for our hypothesis, our results were consistent with previous findings that, among mothers with decreased maternal PON1 Q192R activity, higher concentrations of prenatal urinary ΣDEP metabolites were associated with lower birthweight and higher concentrations of prenatal urinary ΣDMP metabolites were associated with reduced birth length.41,58

While there are no other studies of OPs and adiposity, two previous studies of adult farmworker populations found associations between occupational OP exposures and diabetes, a metabolic outcome related to adiposity. In the Agricultural Health Study, a large prospective cohort study of licensed pesticide applicators and their spouses in North Carolina and Iowa between 1993–2003, greater self-reported OP pesticide use at baseline was significantly associated with self-reported incident diabetes during a 10-year follow-up period.27,28 Similarly, a population based case-control study of 2,000 Thai farm workers showed an association between self-reported lifetime exposures to all pesticides, particularly mevinphos, and the prevalence of type 2 diabetes.26

We observed some evidence that associations differed by maternal PON1–108 C/T status, with a larger increase in percent fat mass and BMI z-scores among those who had the PON1–108 T allele, a marker of lower maternal PON1 expression and decreased OP metabolism. PON1 detoxifies OPs through the cleavage of toxic oxons preventing cholinesterase inhibition.47 PON1 polymorphisms have been shown to be a biomarker of susceptibility to adverse health outcomes from chemical exposures.21 In particular, the PON1 – 108C/T SNP is a binding site for an important transcription factor that influences levels of PON1 expression.59 Individuals with the PON1 –108 T-allele express less PON1 than the C allele.60 The PON1 Q192R SNP has also been used to determine PON1 expression levels, with the Q allele having a slower substrate specific catalytic efficiency compared to the R allele.20 While we hypothesized EMM of associations based on PON1 status due to altered OP metabolism, PON1 may be independently related to metabolism and obesity.19. In addition to being an antioxidant, PON1 also has anti-inflammatory properties and is essential for lipid and glucose regulation. PON1 that is expressed in the liver is secreted into the bloodstream and binds to high-density lipoproteins (HDL) and PON1 expression in the muscle regulates glucose uptake.19 Variability in PON1 genotypes may lead to reduced PON1 activity which has been associated with a higher prevalence of obesity, diabetes, or cardiovascular disease and thus the mechanism(s) by which PON1 modifies these relationships remains unclear.19 Additionally, we assessed maternal rather than offspring PON1 status because we were interested in the role of PON1 in detoxifying OP metabolites before they crossed the placenta. The potential role of maternal versus child PON1 status could be explored in future studies.

Our null findings may be attributed to exposure misclassification. We only had one OP measure during pregnancy and previous studies suggest that OP metabolites have poor reproducibility among pregnant women.61–64 Estimating associations based on a single exposure measurement can lead to bias towards the null.65 In addition to only having one measure during third trimester of pregnancy, we had no measures of OP concentrations during childhood. If the critical period of susceptibility to OP exposure is during early pregnancy or postnatal life, then our third trimester measurement may have been a misclassified proxy for the relevant period, likely also resulting in bias towards the null. However, previous research suggests that fetal growth and adipocyte replication is rapid during the third trimester of pregnancy, indicating our measurement during the third trimester is a relevant exposure period for fat development.66 Furthermore, while DAP metabolites are the most commonly reported internal measure of OP exposure, they are nonspecific. DAP metabolites can derive from multiple parent OPs, which may differ in toxicity and potency, precluding us from linking our outcome to specific OPs. DAP metabolites can also overestimate exposure to parent pesticide compounds because hydrolysis or photolysis of parent compounds present in food and the environment generates preformed DAPs that are thought to be not toxic, but are excreted unchanged in urine.67–69 Additionally, some individual DAP metabolites had a large proportion of samples below the LOD. However, the sum of DMP and DEP metabolites used in our analyses is largely driven by the metabolites that were detected. Next, our study had substantial loss to follow-up which resulted in a limited sample size that reduced our power to detect potential associations and evaluate EMM. However, there were few differences between characteristics of our sample and the full cohort and our formal sensitivity analysis for loss to follow-up found no evidence of bias from study attrition. Also, there is the potential for unmeasured confounding by factors such as maternal diet during pregnancy. Although we adjusted for several measured confounders to at least partially block non-causal pathways between prenatal OP exposure and child adiposity such as maternal education, marital status, and pre-pregnancy BMI (Supplemental Figure 1), we cannot rule out the potential for bias in our estimates. However, mothers who eat more produce would have higher concentrations of OP concentrations and reduced adiposity, suggesting a bias towards the null. Finally, as suggested by our EMM analyses by child age, we may have missed an association between prenatal OP exposure and child adiposity if the effects do not manifest until adolescence or later in life. Puberty may be a critical period.70

5. Conclusion

OPs were not associated with child adiposity in the overall population or when stratified by PON1 genotype. However, larger studies with repeated exposure measurements during pregnancy are warranted to further evaluate differences in associations by maternal PON1 genotype, which regulates OP metabolism, and may increase susceptibility to exposure, as well as to identify potential periods of susceptibility.

Supplementary Material

5

Highlights.

  • Organophosphorus pesticides may act as obesogens

  • First study of associations between prenatal OP exposures and child adiposity

  • No associations observed between prenatal ΣDMP and ΣDEP and child fat mass or BMI

  • Maternal PON1 genotype may modify associations with child adiposity

Acknowledgments:

We gratefully acknowledge the contributions of Dr. Gertrud Berkowitz in developing the Mount Sinai Children’s Environmental Health cohort and Dr. James Wetmur for the PON1 genotyping. We also acknowledge Dr. Ellen Silbergeld and Dr. Meghan F. Davis for their thoughtful and helpful feedback on the manuscript.

Sources of Financial Support: This work was supported by the National Institute of Environmental Health Sciences (NIEHS, T32 ES007141, R01 ES030078, P30 ES010126, P30ES023515) and the National Institute of Diabetes and Digestive and Kidney Diseases (P30 DK072488). The Mount Sinai Children’s Environmental Health Study was supported by grants from the NIEHS (ES009584), the U.S. Environmental Protection Agency (R827039 and RD831711), the Agency for Toxic Substances and Disease Registry, and The New York Community Trust. LQA was supported by a NHLBI K01 Career Development Award (K01HL138124).

Footnotes

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Conflicts of Interest: There are no conflicts of interest

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References:

  • 1.Kumar S, Kelly AS. Review of Childhood Obesity: From Epidemiology, Etiology, and Comorbidities to Clinical Assessment and Treatment. Mayo Clin Proc. 2017;92(2):251–265. [DOI] [PubMed] [Google Scholar]
  • 2.Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. Jama. 2014;311(8):806–814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord. 1999;23 Suppl 2:S2–11. [DOI] [PubMed] [Google Scholar]
  • 4.Daniels SR, Arnett DK, Eckel RH, et al. Overweight in children and adolescents: pathophysiology, consequences, prevention, and treatment. Circulation. 2005;111(15):1999–2012. [DOI] [PubMed] [Google Scholar]
  • 5.Wright CM, Parker L, Lamont D, Craft AW. Implications of childhood obesity for adult health: findings from thousand families cohort study. BMJ (Clinical research ed). 2001;323(7324):1280–1284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Skinner AC, Perrin EM, Moss LA, Skelton JA. Cardiometabolic Risks and Severity of Obesity in Children and Young Adults. 2015;373(14):1307–1317. [DOI] [PubMed] [Google Scholar]
  • 7.Heindel JJ, vom Saal FS. Role of nutrition and environmental endocrine disrupting chemicals during the perinatal period on the aetiology of obesity. Mol Cell Endocrinol. 2009;304(1–2):90–96. [DOI] [PubMed] [Google Scholar]
  • 8.Grun F, Blumberg B. Endocrine disrupters as obesogens. Mol Cell Endocrinol. 2009;304(1–2):19–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Czajka M, Matysiak-Kucharek M, Jodlowska-Jedrych B, et al. Organophosphorus pesticides can influence the development of obesity and type 2 diabetes with concomitant metabolic changes. Environmental research. 2019;178:108685. [DOI] [PubMed] [Google Scholar]
  • 10.U.S. EPA. Diazinon Revised Risk Assessment and Agreement with Registrants In: Office of Pesticide Programs, ed. Washington, DC: US EPA; 2001. [Google Scholar]
  • 11.U.S. EPA. Chlorpyrifos. Revised Risk Assessment and Agreement with Registrants In: Office of Pesticide Programs, ed. Washington, DC:: U.S. EPA; 2000. [Google Scholar]
  • 12.Grube A, Donaldson D, Kiely T, Wu L. Pesticides Industry Sales and Usage: 2006 and 2007 Market Estimates In: US Environmental Protection Agency, ed. Vol EPA 733-R-11–001. Washington, DC: 2011. [Google Scholar]
  • 13.Barr DB, Wong LY, Bravo R, et al. Urinary concentrations of dialkylphosphate metabolites of organophosphorus pesticides: National Health and Nutrition Examination Survey 1999–2004. Int J Environ Res Public Health. 2011;8(8):3063–3098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Slotkin TA. Does early-life exposure to organophosphate insecticides lead to prediabetes and obesity? Reprod Toxicol. 2011;31(3):297–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Meggs WJ, Brewer KL. Weight gain associated with chronic exposure to chlorpyrifos in rats. Journal of Medical Toxicology. 2007;3(3):89–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Meyer A, Seidler FJ, Slotkin TA. Developmental effects of chlorpyrifos extend beyond eurotoxicity: critical periods for immediate and delayed-onset effects on cardiac and hepatic cell signaling. Environmental Health Perspectives. 2004;112(2):170–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Adigun AA, Seidler FJ, Slotkin TA. Disparate developmental neurotoxicants converge on the cyclic AMP signaling cascade, revealed by transcriptional profiles in vitro and in vivo. Brain Research. 2010;1316:1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Costa LG, Li WF, Richter RJ, Shih DM, Lusis A, Furlong CE. The role of paraoxonase (PON1) in the detoxication of organophosphates and its human polymorphism. Chemico-biological interactions. 1999;119–120:429–438. [DOI] [PubMed] [Google Scholar]
  • 19.Meneses MJ, Silvestre R, Sousa-Lima I, Macedo MP. Paraoxonase-1 as a Regulator of Glucose and Lipid Homeostasis: Impact on the Onset and Progression of Metabolic Disorders. Int J Mol Sci. 2019;20(16). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Costa LG, Richter RJ, Li WF, Cole T, Guizzetti M, Furlong CE. Paraoxonase (PON 1) as a biomarker of susceptibility for organophosphate toxicity. Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals. 2003;8(1):1–12. [DOI] [PubMed] [Google Scholar]
  • 21.Eskenazi B, Huen K, Marks A, et al. PON1 and neurodevelopment in children from the CHAMACOS study exposed to organophosphate pesticides in utero. Environ Health Perspect. 2010;118(12):1775–1781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Engel SM, Bradman A, Wolff MS, et al. Prenatal Organophosphorus Pesticide Exposure and Child Neurodevelopment at 24 Months: An Analysis of Four Birth Cohorts. Environ Health Perspect. 2016;124(6):822–830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Engel SM, Wetmur J, Chen J, et al. Prenatal exposure to organophosphates, paraoxonase 1, and cognitive development in childhood. Environ Health Perspect. 2011;119(8):1182–1188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Shrestha S, Parks CG, Goldner WS, et al. Pesticide Use and Incident Hypothyroidism in Pesticide Applicators in the Agricultural Health Study. Environmental health perspectives. 2018;126(9):97008–97008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Goldner WS, Sandler DP, Yu F, et al. Hypothyroidism and pesticide use among male private pesticide applicators in the agricultural health study. J Occup Environ Med. 2013;55(10):1171–1178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Juntarawijit C, Juntarawijit Y. Association between diabetes and pesticides: a case-control study among Thai farmers. Environ Health Prev Med. 2018;23(1):3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Montgomery MP, Kamel F, Saldana TM, Alavanja MC, Sandler DP. Incident diabetes and pesticide exposure among licensed pesticide applicators: Agricultural Health Study, 1993–2003. American journal of epidemiology. 2008;167(10):1235–1246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Starling AP, Umbach DM, Kamel F, Long S, Sandler DP, Hoppin JA. Pesticide use and incident diabetes among wives of farmers in the Agricultural Health Study. Occupational and environmental medicine. 2014;71(9):629–635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Debost-Legrand A, Warembourg C, Massart C, et al. Prenatal exposure to persistent organic pollutants and organophosphate pesticides, and markers of glucose metabolism at birth. Environmental research. 2016;146:207–217. [DOI] [PubMed] [Google Scholar]
  • 30.Tinggaard J, Wohlfahrt-Veje C, Husby S, et al. Prenatal pesticide exposure and PON1 genotype associated with adolescent body fat distribution evaluated by dual X-ray absorptiometry (DXA). Andrology. 2016;4(4):735–744. [DOI] [PubMed] [Google Scholar]
  • 31.Valvi D, Mendez MA, Martinez D, et al. Prenatal concentrations of polychlorinated biphenyls, DDE, and DDT and overweight in children: a prospective birth cohort study. Environ Health Perspect. 2012;120(3):451–457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Smink A, Ribas-Fito N, Garcia R, et al. Exposure to hexachlorobenzene during pregnancy increases the risk of overweight in children aged 6 years. Acta Paediatr. 2008;97(10):1465–1469. [DOI] [PubMed] [Google Scholar]
  • 33.Karmaus W, Osuch JR, Eneli I, et al. Maternal levels of dichlorodiphenyl dichloroethylene (DDE) may increase weight and body mass index in adult female offspring. Occupational and environmental medicine. 2009;66(3):143–149. [DOI] [PubMed] [Google Scholar]
  • 34.Berkowitz GS, Wetmur JG, Birman-Deych E, et al. In utero pesticide exposure, maternal paraoxonase activity, and head circumference. Environ Health Perspect. 2004;112(3):388–391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Landrigan PJ, Claudio L, Markowitz SB, et al. Pesticides and inner-city children: exposures, risks, and prevention. Environmental health perspectives. 1999;107 Suppl 3(Suppl 3):431–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Engel SM, Berkowitz GS, Barr DB, et al. Prenatal organophosphate metabolite and organochlorine levels and performance on the Brazelton Neonatal Behavioral Assessment Scale in a multiethnic pregnancy cohort. American journal of epidemiology. 2007;165(12):1397–1404. [DOI] [PubMed] [Google Scholar]
  • 37.Barr DB, Allen R, Olsson AO, et al. Concentrations of selective metabolites of organophosphorus pesticides in the United States population. Environmental research. 2005;99(3):314–326. [DOI] [PubMed] [Google Scholar]
  • 38.Bravo R, Caltabiano LM, Weerasekera G, et al. Measurement of dialkyl phosphate metabolites of organophosphorus pesticides in human urine using lyophilization with gas chromatography-tandem mass spectrometry and isotope dilution quantification. Journal of exposure analysis and environmental epidemiology. 2004;14(3):249–259. [DOI] [PubMed] [Google Scholar]
  • 39.Eskenazi B, Harley K, Bradman A, et al. Association of in utero organophosphate pesticide exposure and fetal growth and length of gestation in an agricultural population. Environ Health Perspect. 2004;112(10):1116–1124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.O’Brien KM, Upson K, Cook NR, Weinberg CR. Environmental Chemicals in Urine and Blood: Improving Methods for Creatinine and Lipid Adjustment. Environ Health Perspect. 2016;124(2):220–227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Wolff MS, Engel S, Berkowitz G, et al. Prenatal pesticide and PCB exposures and birth outcomes. Pediatr Res. 2007;61(2):243–250. [DOI] [PubMed] [Google Scholar]
  • 42.Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. The Relation of Childhood BMI to Adult Adiposity: The Bogalusa Heart Study. Pediatrics. 2005;115(1):22–27. [DOI] [PubMed] [Google Scholar]
  • 43.Boeke CE, Oken E, Kleinman KP, Rifas-Shiman SL, Taveras EM, Gillman MW. Correlations among adiposity measures in school-aged children. BMC pediatrics. 2013;13:99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Buckley JP, Engel SM, Mendez MA, et al. Prenatal Phthalate Exposures and Childhood Fat Mass in a New York City Cohort. Environ Health Perspect. 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.CDC. SAS Program for the CDC Growth Charts (Ages 0 to < 20 Years). http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. Published 2004. Accessed January 7 2013.
  • 46.Chen J, Chan W, Wallenstein S, Berkowitz G, Wetmur JG. Haplotype-phenotype relationships of paraoxonase-1. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2005;14(3):731–734. [DOI] [PubMed] [Google Scholar]
  • 47.Chen J, Kumar M, Chan W, Berkowitz G, Wetmur JG. Increased influence of genetic variation on PON1 activity in neonates. Environ Health Perspect. 2003;111(11):1403–1409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Wetmur JG, Kumar M, Zhang L, Palomeque C, Wallenstein S, Chen J. Molecular haplotyping by linking emulsion PCR: analysis of paraoxonase 1 haplotypes and phenotypes. Nucleic acids research. 2005;33(8):2615–2619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Hernan MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman&Hall/CRC; 2020. [Google Scholar]
  • 50.Buckley JP, Doherty BT, Keil AP, Engel SM. Statistical Approaches for Estimating Sex-Specific Effects in Endocrine Disruptors Research. Environ Health Perspect. 2017;125(6):067013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Hillier TA, Pedula KL, Vesco KK, Oshiro CE, Ogasawara KK. Impact of Maternal Glucose and Gestational Weight Gain on Child Obesity over the First Decade of Life in Normal Birth Weight Infants. Maternal and child health journal. 2016;20(8):1559–1568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Lau EY, Liu J, Archer E, McDonald SM, Liu J. Maternal weight gain in pregnancy and risk of obesity among offspring: a systematic review. Journal of obesity. 2014;2014:524939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.IOM/NRC. Weight Gain during Pregnancy: Reexamining the Guidelines. Washington, DC: National Academies Press; 2009. [PubMed] [Google Scholar]
  • 54.Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstet Gynecol Scand. 2018;97(4):407–416. [DOI] [PubMed] [Google Scholar]
  • 55.Liang Y, Zhan J, Liu D, et al. Organophosphorus pesticide chlorpyrifos intake promotes obesity and insulin resistance through impacting gut and gut microbiota. Microbiome. 2019;7(1):19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Tirelli V, Catone T, Turco L, Di Consiglio E, Testai E, De Angelis I. Effects of the pesticide clorpyrifos on an in vitro model of intestinal barrier. Toxicology in Vitro. 2007;21(2):308–313. [DOI] [PubMed] [Google Scholar]
  • 57.Oken E, Levitan EB, Gillman MW. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. International journal of obesity (2005). 2008;32(2):201–210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Harley KG, Engel SM, Vedar MG, et al. Prenatal Exposure to Organophosphorous Pesticides and Fetal Growth: Pooled Results from Four Longitudinal Birth Cohort Studies. Environmental health perspectives. 2016;124(7):1084–1092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Kim DS, Burt AA, Ranchalis JE, et al. Dietary cholesterol increases paraoxonase 1 enzyme activity. Journal of lipid research. 2012;53(11):2450–2458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Furlong CE, Suzuki SM, Stevens RC, et al. Human PON1, a biomarker of risk of disease and exposure. Chemico-biological interactions. 2010;187(1–3):355–361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Hioki K, Ito Y, Oya N, et al. Intra-individual variations of organophosphate pesticide metabolite concentrations in repeatedly collected urine samples from pregnant women in Japan. Environmental health and preventive medicine. 2019;24(1):7–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Lewis RC, Cantonwine DE, Anzalota Del Toro LV, et al. Distribution and determinants of urinary biomarkers of exposure to organophosphate insecticides in Puerto Rican pregnant women. Sci Total Environ. 2015;512–513:337–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Casas M, Basagana X, Sakhi AK, et al. Variability of urinary concentrations of nonpersistent chemicals in pregnant women and school-aged children. Environ Int. 2018;121(Pt 1):561–573. [DOI] [PubMed] [Google Scholar]
  • 64.Spaan S, Pronk A, Koch HM, et al. Reliability of concentrations of organophosphate pesticide metabolites in serial urine specimens from pregnancy in the Generation R Study. Journal of exposure science & environmental epidemiology. 2015;25(3):286–294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Perrier F, Giorgis-Allemand L, Slama R, Philippat C. Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies. Epidemiology. 2016;27(3):378–388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Dietz WH. Critical periods in childhood for the development of obesity. The American Journal of Clinical Nutrition. 1994;59(5):955–959. [DOI] [PubMed] [Google Scholar]
  • 67.Chen L, Zhao T, Pan C, Ross JH, Krieger RI. Preformed biomarkers including dialkylphosphates (DAPs) in produce may confound biomonitoring in pesticide exposure and risk assessment. Journal of agricultural and food chemistry. 2012;60(36):9342–9351. [DOI] [PubMed] [Google Scholar]
  • 68.Zhang X, Driver JH, Li Y, Ross JH, Krieger RI. Dialkylphosphates (DAPs) in fruits and vegetables may confound biomonitoring in organophosphorus insecticide exposure and risk assessment. Journal of agricultural and food chemistry. 2008;56(22):10638–10645. [DOI] [PubMed] [Google Scholar]
  • 69.Quiros-Alcala L, Bradman A, Smith K, et al. Organophosphorous pesticide breakdown products in house dust and children’s urine. J Expo Sci Environ Epidemiol. 2012;22(6):559–568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Heindel JJ, vom Saal FS, Blumberg B, et al. Parma consensus statement on metabolic disruptors. Environ Health. 2015;14:54. [DOI] [PMC free article] [PubMed] [Google Scholar]

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