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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: Int J Hyg Environ Health. 2024 Feb 13;257:114334. doi: 10.1016/j.ijheh.2024.114334

Associations of Parental Preconception and Maternal Pregnancy Urinary Phthalate Biomarker and Bisphenol-A Concentrations with Child Eating Behaviors

Jordana Leader 1, Lidia Mínguez-Alarcón 2, Paige L Williams 3, Jennifer B Ford 1, Ramace Dadd 1, Olivia Chagnon 1, Emily Oken 4, Antonia M Calafat 5, Russ Hauser 6,7, Joseph M Braun 8
PMCID: PMC10939723  NIHMSID: NIHMS1966637  PMID: 38350281

Abstract

Background:

Eating behaviors are controlled by the neuroendocrine system. Whether endocrine disrupting chemicals have the potential to affect eating behaviors has not been widely studied in humans. We investigated whether maternal and paternal preconception and maternal pregnancy urinary phthalate biomarker and bisphenol-A (BPA) concentrations were associated with children’s eating behaviors.

Methods:

We used data from mother-father-child triads in the Preconception Environmental exposure And Childhood health Effects (PEACE) Study, an ongoing prospective cohort study of children aged 6-13 years whose parent(s) previously enrolled in a fertility clinic-based prospective preconception study. We quantified urinary concentrations of 11 phthalate metabolites and BPA in parents’ urine samples collected preconceptionally and during pregnancy. Parents rated children’s eating behavior using the Child Eating Behavior Questionnaire (CEBQ). Using multivariable linear regression, accounting for correlation among twins, we estimated covariate-adjusted associations of urinary phthalate biomarkers and BPA concentrations with CEBQ subscale scores.

Results:

This analysis included 195 children (30 sets of twins), 160 mothers and 97 fathers; children were predominantly non-Hispanic white (84%) and 53% were male. Paternal and maternal preconception monobenzyl phthalate (MBzP) concentrations and maternal preconception mono-n-butyl phthalate (MnBP) were positively associated with emotional overeating, food responsiveness, and desire to drink scores in children (β′s = 0.11 [95% CI: 0.01, 0.20]–0.21 [95% CI 0.10, 0.31] per loge unit increase in phthalate biomarker concentration). Paternal preconception BPA concentrations were inversely associated with scores on food approaching scales. Maternal pregnancy MnBP, mono-isobutyl phthalate (MiBP) and MBzP concentrations were associated with increased emotional undereating scores. Maternal pregnancy monocarboxy-isononyl phthalate concentrations were related to decreased food avoiding subscale scores.

Conclusions:

In this cohort, higher maternal and paternal preconception urinary concentrations of some phthalate biomarkers were associated with increased food approaching behavior scores and decreased food avoiding behavior scores, which could lead to increased adiposity in children.

Keywords: prenatal exposure, endocrine disrupting chemicals, eating behavior, child health, preconception exposure, phthalate

1. Background

Eating behaviors are controlled by the neuroendocrine system. Appetite and eating behaviors are primarily controlled by a complex circuit of nuclei in the hypothalamus that incorporates afferent and efferent signals (Ramamoorthy et al., 2015; Ross and Desai, 2013; Steculorum et al., 2013; Walley and Roepke, 2018). Hormones, like leptin, act to promote formation of hypothalamic pathways during development and affect the hypothalamic network to regulate eating behaviors later in life (McMillen et al., 2005; Ross and Desai, 2013; Walley and Roepke, 2018). Environmental factors during pregnancy can lead to perturbations in the hypothalamic appetite regulatory systems that are established during early development (Ramamoorthy et al., 2015). Additionally, the environment experienced by either parent before conception may also contribute to the child’s risk of metabolic disease (Rando and Simmons, 2015). Interconnected epigenetic information carriers such as RNAs, chromatin, and DNA modifications may transmit information about the preconception environment to the offspring (Rando, 2012).

Phthalates and phenols are EDCs used in many consumer and personal care products (Ghassabian et al., 2022; Witorsch and Thomas, 2010). There is widespread exposure in American, European, and Canadian populations to both phthalates and phenols during the preconception and prenatal periods (Arbuckle et al., 2014; Dewalque et al., 2014; Haines et al., 2017; Silva et al., 2004; Wang et al., 2019; Woodruff et al., 2011). Phthalates have the potential to influence child eating behavior by impacting hormonally driven programming of energy homeostasis, in particular the development and organization of hypothalamic and extrahypothalamic centers that control eating behavior (Walley and Roepke, 2018). Additionally, bisphenol A (BPA) has a similar structure to endogenous factors (e.g., hormones and neuropeptides) that play a role in eating behaviors and activation of receptors such as peroxisome proliferator-activated receptor gamma (PPARγ), estrogen receptor (ER) α/β, and estrogen-related receptor gamma (ERRγ). Preconception and pregnancy maternal exposure to BPA has an adverse effect on offspring eating behavior in mice (Anderson et al., 2013; Walley and Roepke, 2018). Additionally, both BPA-exposed male and female rodents have been found to be resistant to the effects of leptin injection on body weight gain (Walley and Roepke, 2018) which helps to regulate feeding and glucose metabolism (Steculorum et al., 2013). However, few studies have specifically evaluated the impact of endocrine disrupting chemicals (EDCs) on eating behaviors.

While numerous human studies suggest that prenatal exposure to some EDC impacts adiposity and risk of obesity (Kim et al., 2019; Wassenaar et al., 2017; Wu et al., 2020), we are unaware of any examining associations between EDCs and child eating behaviors. We hypothesize that eating behaviors may be sensitive to the obesogenic effects of EDCs given that eating behaviors are developmentally programmed and sensitive to hormones that EDCs can affect (Heindel and Blumberg, 2018). Additionally, there is a lack of epidemiology studies examining the association of preconception EDC exposure with child eating behaviors or adiposity despite experimental studies in rodents observing that preconception exposures may impact offspring obesity and metabolic disease (Rando and Simmons, 2015). Both phthalates and BPA can promote adipogenesis by activating PPARs, binding to estrogen receptors, and interfering with thyroid hormone receptors (Stojanoska et al., 2016). This, as well as other EDC-induced hormonal activity on leptin, ghrelin and insulin may impact eating behavior to increase or decrease food intake (Murray et al., 2014). Some epidemiologic studies support an association between early-life phthalate exposure and child adiposity (Deierlein et al., 2016; Teitelbaum et al., 2008; Trasande et al., 2013), while others do not (Braun, 2017).

Certain eating behaviors are associated with obesity and related cardiometabolic traits (Carnell and Wardle, 2007; Kininmonth et al., 2021; Zhang et al., 2023), thus being one biological pathway by which obesogens act. However, the link between EDCs and eating behaviors has not been widely studied in humans. Thus, we investigated whether both maternal and paternal preconception and maternal pregnancy phthalate biomarkers and BPA urinary concentrations were associated with parent-reported child eating behavior among mother-father-child triads in the Preconception Environmental exposure And Childhood health Effects (PEACE) Study.

2. Methods

2.1. Study Participants

The PEACE study enrolls children aged 6-13 years whose parent(s) previously enrolled in the Environment and Reproductive Health (EARTH) study. The EARTH study was a prospective cohort that enrolled over 900 women between the ages of 18 and 45 years and 500 of their male partners who sought care at the Massachusetts General Hospital (MGH) Fertility Center. Participants eligible to enroll in the PEACE study and considered for this analysis were 438 mothers whose pregnancy resulted in a live birth between 2005 and 2017, along with 273 fathers and their 501 children (including siblings and multiples) (Figure 1). Children were excluded from PEACE study enrollment if they were conceived using donor gametes or had a gestational carrier. Additionally, children were excluded from this analysis if their parent did not complete behavioral questionnaires, or if they lacked measurements of maternal or paternal phthalate biomarkers and BPA urinary concentrations.

Figure 1:

Figure 1:

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Flow Chart of PEACE Study Participation

We conducted follow-up with study mothers, fathers, and children, beginning in July 2018. In-person assessments were offered until March 2020, and then virtually thereafter due the restrictions resulting from the COVID-19 pandemic. In the summer of 2022, a shortened in person study visit was offered to participants in addition to virtual participation. The PEACE study was approved by the Harvard T.H. Chan School of Public Health institutional review board. The EARTH study was approved by the Human Subject Committees of the Harvard T.H. Chan School of Public Health, MGH, and the Centers for Disease Control and Prevention (CDC). Verbal (for children <7 years of age) or written (children >7 years of age) consent was obtained from all child participants and written consent for their guardians.

2.2. Phthalates and BPA Exposure Assessment

We collected urine samples in polypropylene specimen cups multiple times in the preconception period and during pregnancy from mothers. Mothers provided up to two preconception urine samples (collected on different days) for each fertility treatment cycle. Fathers provided one preconception urine sample on the day that their partner underwent the fertility procedure. Only preconception samples associated with the cycle that resulted in the pregnancy of the enrolled child were used. A cycle was defined as the start of a new in vitro fertilization (IVF) cycle or intrauterine insemination (IUI). Additionally, mothers provided up to three urine samples during pregnancy (one per trimester).

Research staff measured the specific gravity (SG) of each urine sample using a handheld refractometer (National Instrument Company Inc). They then divided the urine samples into aliquots in polypropylene cryovials, froze them for long-term storage at −80 °C, and shipped them on dry ice overnight to the Centers for Disease Control and Prevention (CDC, Atlanta, Georgia). Chemists quantified the urinary concentrations of BPA and 11 phthalate monoester metabolites using solid-phase extraction coupled with high-performance liquid chromatography–isotope dilution tandem mass spectrometry as described before.(Silva et al., 2007a; Ye et al., 2005) The limits of detection (LOD) ranged from 0.2 to 0.75 ng/mL, depending on the target biomarker (Table 1). Precision was 1.8-17%, depending on the biomarker and concentration.(Silva et al., 2007b; Ye et al., 2005) The concentrations of the following urinary phthalate metabolites were quantified: monoethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), monobenzyl phthalate (MBzP), mono(3-carboxypropyl) phthalate (MCPP), monocarboxyoctyl phthalate (MCOP), monocarboxy-isononyl phthalate (MCNP), and four metabolites of di(2-ethylhexyl) phthalate (DEHP), namely mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono(2-ethyl-5-carboxypentyl) phthalate (MECPP). We calculated the molar sum of the four DEHP metabolites by dividing each metabolite concentration by its molecular weight as follows: ∑DEHP = [(MEHP*(1/278.34)) + (MEHHP*(1/294.34)) + (MEOHP*(1/292.33)) + (MECPP*(1/308.33))]. We then multiplied the molar sum by the molecular weight of MECPP (308.33) to express −DEHP in ng/ml of MECPP. BPA and phthalate metabolite urinary concentrations were adjusted for urine dilution by SG using the following formula: Ps = Pi[(SGm − 1) / (SGi − 1)],where Ps is the SG-standardized chemical biomarker concentration (μg/L), Pi is the measured biomarker concentration (μg/L), SGi is measured specific gravity, and SGm is the mean SG concentration for the maternal (1.016) or paternal (1.015) preconception or maternal pregnancy (1.014) samples.(Boeniger et al., 1993) Biomarker concentrations below the LOD were assigned a value equal to the LOD divided by the square root of 2 prior to SG adjustment.(Hornung and Reed, 2011)

Table 1:

Univariate Statistics of Specific-Gravity Standardized Urinary Phthalate Biomarker and BPA Concentrations (ng/mL) among PEACE Study Mothers and Fathers (2005-2017)a

Biomarker Paternal Preconception Maternal Preconception Maternal Pregnancy
N LOD (% >LOD) Geometric Mean (25th, 75th Percentile) N LOD (% > LOD) Geometric Mean (25th, 75th Percentile) N LOD (% > LOD) Geometric Mean (25th, 75th Percentile)
MEP 97 0.75 (100%) 40 (14, 105) 151 0.75 (100%) 70 (28, 134) 160 0.75 (100%) 45 (18, 99)
MnBP 97 0.40 (97%) 11 (5.9, 18) 151 0.40 (98%) 14 (8.3, 25) 160 0.40 (98%) 12 (7.3, 19)
MiBP 97 0.53 (97%) 5.7 (3.5, 11) 151 0.53 (97%) 7.5 (4.2, 15) 160 0.53 (96%) 6.6 (4.1, 11)
MBzP 97 0.25 (92%) 2.7 (1.4, 6.5) 151 0.25 (94%) 3.7 (1.9, 7.2) 160 0.25 (95%) 3.4 (2.1, 4.9)
MCPP 97 0.35 (96%) 4.2 (1.7, 7.0) 151 0.35 (97%) 4.3 (2.0, 8.4) 160 0.35 (98%) 3.9 (2.0, 6.5)
MCOP 88 0.70 (100%) 28 (7.4, 89) 140 0.70 (100%) 27 (8.9, 81) 144 0.70 (98%) 25 (13, 49)
MCNP 88 0.33 (99%) 4.2 (2.1, 7.7) 140 0.33 (97%) 5.0 (2.4, 8.4) 144 0.33 (96%) 3.9 (2.4, 5.8)
MEHP 97 0.72 (80%) 3.2 (1.3, 6.3) 151 0.72 (78%) 2.7 (1.4, 5.0) 160 0.72 (74%) 3.0 (1.4, 6.0)
MEHHP 97 0.40 (97%) 19 (8.8, 42) 151 0.40 (99%) 15 (7.0, 28) 160 0.40 (98%) 14 (7.5, 20)
MEOHP 97 0.20 (97%) 12 (4.7, 23) 151 0.20 (99%) 10 (5.2, 18) 160 0.20 (98%) 11 (5.6, 19)
MECPP 97 0.40 (100%) 31 (12, 64) 151 0.40 (100%) 27 (13, 45) 160 0.40 (99%) 23 (13, 42)
∑DEHP 97 - 69 (33, 139) 151 - 58 (25, 142) 160 - 55 (29, 105)
BPA 97 0.40 (94%) 1.8 (0.89, 3.0) 151 0.40 (90%) 1.6 (0.98, 2.3) 160 0.40 (86%) 1.2 (0.80, 1.8)
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a

Monoethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), monobenzyl phthalate (MBzP), mono(3-carboxypropyl) phthalate (MCPP), monocarboxyoctyl phthalate (MCOP), monocarboxy-isononyl phthalate (MCNP), mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono(2-ethyl-5-carboxypentyl) phthalate (MECPP; molar sum of the four DEHP metabolites (∑DEHP), bisphenol A (BPA)

2.3. Child Eating Behavior Assessment

Eating behaviors were measured using the Child Eating Behavior Questionnaire (CEBQ), a valid and reliable parent-rated instrument used to assess eight dimensions of eating style in children (Domoff et al., 2015; Quah et al., 2019; Wardle et al., 2001). The CEBQ is comprised of 35 questions that measure four food approaching dimensions: food responsiveness (FR), food enjoyment (FE), desire to drink (DD), emotional overeating (EOE) and four food avoiding dimensions: food fussiness (FF), slowness in eating (SE), satiety responsiveness (SR), emotional undereating (EUE). All questions are scored on a 1–5 Likert scale ranging from ‘never’ to ‘always’. Subscale scores representing these dimensions were calculated by averaging responses on questions that mapped to each dimension. The CEBQ was completed by parents online via RedCap and then scored using SAS Statistical software (Cary, NC) by PEACE study staff. In addition, we calculated composite scales for food approaching and food avoiding behaviors as the sum of subscales of that domain (range: 1-20). Higher scores indicate greater behavior reported in that dimension. There are not any published cut off scores for problematic eating behaviors as measured by the CEBQ, however, children with overweight or obesity have substantially higher scores than children of normal weight (Kimin et al., 2022; Wahlqvist et al., 2015).

The CEBQ has been validated in several populations (Carnell and Wardle, 2007; Domoff et al., 2015; Quah et al., 2019; Sleddens et al., 2008; Svensson et al., 2011) and its subscales have been found to be correlated with laboratory measurements of children’s eating behaviors,(Ashcroft et al., 2008) child BMI,(Kininmonth et al., 2021; Webber et al., 2009) and cardiometabolic outcomes.(Jansen et al., 2012; Sleddens et al., 2008; Vafeiadi et al., 2016; Viana et al., 2008) Overall, food approaching scales were positively related to weight/BMI z-scores and food avoiding scales were negatively related to weight/BMI z-scores. Food approach dimensions have been validated against direct observations of eating behaviors (Carnell and Wardle, 2007).

2.4. Covariates

Covariates were considered as potential confounders or predictors of our outcomes of interest based on substantive knowledge and a directed acyclic graph (DAG) (Supplemental figure 1). Child and parental sociodemographic information were collected electronically through RedCap by parent report at the time of the PEACE study enrollment. These included child age, sex assigned at birth, and race, parental race, and education, and presence of a sibling in the home. Child pubertal status, height, and weight were parent-reported. Additionally, study staff measured child height and weight during in person study visits, otherwise parental report was used (61%). Additional parental covariate information collected at enrollment in the EARTH study and during the study were used including parental age at conception, pre-pregnancy height and weight and smoking status. Use of in vitro fertilization (IVF) and intrauterine insemination (IUI) was abstracted from medical records.

2.5. Statistical Analysis

We averaged loge-transformed SG-adjusted urinary phthalate biomarkers and BPA concentrations measured during a cycle or pregnancy (up to two samples for maternal preconception and up to three for samples during pregnancy). If only one urine sample was available for that period, then the concentrations for the single sample were used. We calculated descriptive statistics and detection frequencies for phthalate biomarkers and BPA concentrations for the three time periods. Spearman’s rho was calculated to assess the correlation between individual urinary phthalate biomarkers and BPA during each of the three time periods. We also calculated the mean, standard deviation, and range (minimum and maximum) of each CEBQ subscale and composite scale scores as well as the correlation between subscales and composite scales. We also calculated correlations between CEBQ subscale and composite scale scores with child BMI z-scores. We examined the mean and standard deviation of each CEBQ subscale and composite scale stratified by covariates.

We estimated associations of paternal and maternal preconception and maternal pregnancy SG-adjusted loge-transformed urinary phthalate biomarker and BPA concentrations with CEBQ subscale scores using multivariable generalized estimating equations (GEE) linear models, accounting for correlation among multiple fetal gestations under an exchangeable correlation structure. Based on a priori knowledge and the aforementioned DAG, all analyses were adjusted for child sex assigned at birth, child age (continuous, years), child race (white vs non-white), if the child has a sibling, maternal age, maternal/paternal baseline BMI (<25 kg/m2, between 25 and 30 kg/m2, >30 kg/m2), maternal/paternal education (High school and/or college education vs post graduate education), smoking in the house (ever or current vs. never), and mode of conception (ART and IUI vs natural conception). Very few covariates had any missing values and those that did (less than 3% missing) were replaced by the average or most frequent covariate value. Each phthalate biomarker and BPA was evaluated separately in relation to each CEBQ subscale.

2.6. Sensitivity Analyses

We conducted a sensitivity analysis further adjusting all models for child BMI z-score calculated using the 2000 CDC growth charts (Wei et al., 2023). Additionally, for specific phthalate biomarkers or BPA showing at least marginal trends (p-value < 0.10) with a CEBQ subscale in the main analysis, we conducted additional analyses adjusting for urinary concentrations in the other time periods (i.e., maternal preconception and pregnancy, maternal and paternal preconception, or pregnancy and paternal preconception) to reduce potential residual confounding by exposures in other windows.

3. Results

The present analysis included data from 160 mothers, 97 fathers and 195 children (30 sets of twins). Parents were predominantly non-Hispanic white (87% for mothers, 85% for fathers), highly educated (mothers, 71% had a post graduate degree; fathers, 47% had a post graduate degree), and on average mothers and fathers were 34 and 35 years old at conception, respectively (Table 2). Similarly, children were predominately white (84%), an average age of 9.7 years at follow-up and 53% were male. As this is a cohort recruited from a fertility clinic, 64% of children were conceived through IVF, 12% of children were conceived using IUI, and 24% of children were conceived without medical assistance (Table 3).

Table 2:

Demographic Characteristics of PEACE Study Participants: Parents

Characteristic Maternal (n=160) Paternal (n=97)
Mean Age at conception (years) [SD] 34 [3.6] 35 [4.4]
Baseline BMI (kg/m2)
 < 25 110 (69%) 27 (28%)
 25 - ≤30 34 (21%) 52 (54%)
 > 30 15 (10%) 18 (18%)
Education
 Less than college education 47 (30%) 51 (53%)
 College education or greater 110 (70%) 45 (47%)
Race
 White 135 (87%) 80 (85%)
 Non-White 21 (13%) 14 (15%)
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Paternal education missing 1, paternal race missing 3.

Maternal BMI missing 1, maternal education missing 3, maternal race missing 4

Table 3:

Demographic Characteristics of PEACE Study Participants: Children

Characteristics Child (n=195)
Mean Age at Visit (years) [SD] 9.4 [1.7]
Sibling
 No siblings 35 (18%)
 At least one sibling 160 (82%)
Child Sex Assigned at Birth
 Boy 104 (53%)
 Girl 91 (41%)
Child Race
 White 163 (84%)
 Other 32 (16%)
Parent Smoking Status
 Current or Ever Smoker 127 (65%)
 Never Smoker 68 (35%)
Mode of Conception
 Unassisted conception 46 (24%)
 Intrauterine Insemination (IUI) 24 (12%)
 Invitro Fertilization (IVF)* 125 (64%)
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Note: all entries are N (%) except age at conception

*

Includes IVF with or without ICSI

In the preconception period, most mothers (84%) provided two urine samples. During pregnancy, 5%, 25%, and 70% mothers provided 1, 2, or 3 samples, respectively. All fathers provided 1 preconception urine sample. The distribution of urinary phthalate biomarker and BPA concentrations (Table 1) were representative of the larger study cohort (Braun et al., 2012; James-Todd et al., 2018; Messerlian et al., 2017). Loge-transformed phthalate biomarker and BPA urinary concentrations were weakly to moderately correlated within couples in the preconception period (Spearman rho ranged from −0.36 to 0.52) (see Supplementary Figure 2) and within maternal periods (maternal preconception and pregnancy) (Spearman rho ranged from −0.19 to 0.46) (see Supplementary Figure 3).

Mean CEBQ subscale scores ranged from 1.8 (SD: 0.74) to 3.8 (SD: 0.79) (Table 4) and did not differ substantially across levels of covariates (Supplemental Table 1). As expected, the subscales related to the food approaching domain (food enjoyment, emotional overeating, food responsiveness and desire to drink) were weakly to moderately correlated with each other (Spearman rho 0.20 to 0.63), while subscales related to the food avoiding domain (emotional undereating, satiety responsiveness, slow eating, and food fussiness) were also weakly to moderately correlated with each other (Spearman rho 0.12 to 0.42). Emotional undereating was also weakly to moderately positively correlated with three food approaching domain subscales (emotional overeating, food responsiveness and desire to drink) (Spearman rho 0.15 to 0.43). Subscales related to the food approaching domain were positively correlated with child BMI z-score while subscales related to the food avoiding domain were negatively correlated with child BMI z-scores (Supplemental Figure 4).

Table 4:

Univariate Statistics of Child Eating Behavior Questionnaire (CEBQ) for PEACE Study Children (n=195).

CEBQ Subscale Mean (SD) Min-Max
Food Enjoyment 3.8 (0.79) 1.75 - 5.00
Emotional Overeating 1.8 (0.74) 1.00 - 4.25
Food Responsiveness 2.3 (0.78) 1.00 - 5.00
Desire to Drink 2.2 (0.80) 1.00 - 4.67
Emotional Undereating 2.5 (0.85) 1.00 - 4.75
Satiety Responsiveness 2.7 (0.70) 1.20 - 4.60
Slow Eating 2.5 (0.86) 1.00 - 5.00
Food Fussiness 2.9 (0.99) 1.00 - 5.00
Food Approaching Behavior Composite* 10 (2.2) 5.90 - 16.37
Food Avoiding Behavior Composite** 11 (2.3) 5.43 - 16.92
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*

Food approaching behavior composite is the sum of food enjoyment, emotional overeating, food responsiveness and desire to drink scores.

**

Food avoiding behavior composite is the sum of emotional undereating, satiety responsiveness, slow eating and food fussiness scores.

3.1. Paternal Preconception Period

Higher paternal preconception concentrations of MEP, MBzP, MCPP and ∑DEHP were associated with higher mean scores on some food approaching subscales. For instance, children whose fathers had higher preconception urinary MEP had higher mean emotional overeating scores (β = 0.11 per 1 loge unit increase, 95% CI: 0.01, 0.20), food responsiveness scores (β = 0.14; 95% CI: 0.06, 0.22) and desire to drink scores (β = 0.14; 95% CI: 0.02, 0.26). In contrast, higher paternal preconception urinary BPA was related to lower mean food approaching subscale scores (Figure 2 and Supplemental Table 3). Higher paternal preconception concentrations of MiBP, MCOP, MCPP, and ∑DEHP were associated with lower mean scores on some food avoiding subscales (Figure 2 and Supplemental Table 1). For instance, higher mean paternal preconception urinary concentrations of MiBP (β = −0.16; 95% CI: −0.31, −0.01) and ∑DEHP (β = −0.13; 95% CI: −0.23, −0.03) were related to a lower mean slow eating score in children (Figure 2 and Supplemental Table 4).

Figure 2:

Figure 2:

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Adjusted1 Average Difference in CEBQ Subscale Scores per loge Increase in Paternal Preconception Urinary Phthalate Biomarker and BPA Concentration (n=125 children)

1Adjusted for child sex assigned at birth, child age, child race (white vs non-white), whether the child has any sibling, smoking in the home (never vs. ever or current), maternal age (continuous, years), paternal education (high school and/or college education vs post-graduate education), paternal baseline BMI (<25, ≤25<30, >30), mode of conception (medically assisted conception vs natural conception).

3.2. Maternal Preconception Period

Overall, maternal preconception urinary concentrations of MnBP, MBzP, ∑DEHP and BPA were associated with higher mean scores on some food approaching subscales. For instance, children whose mothers had higher preconception urinary MnBP had higher emotional overeating scores (β = 0.14, 95% CI: 0.01, 0.27), food responsiveness scores (β = 0.21; 95% CI: 0.10, 0.31) and higher desire to drink scores (β = 0.16; 95% CI: 0.05, 0.28) (Figure 3 and Supplemental Table 5). Higher maternal urinary concentrations of MnBP and MiBP were related to lower slow eating (MnBP: β = −0.16; 95% CI: −0.27, −0.05, MIBP: β = −0.15; 95% CI: −0.27, −0.04) and lower food fussiness scores (MnBP: β =−0.12; 95% CI: −0.24, 0.01, MIBP: β = −0.13 95% CI: −0.27, 0.01).

Figure 3:

Figure 3:

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Adjusted1 Average Difference in CEBQ Subscale Scores per loge Increase in Maternal Preconception Urinary Phthalate Biomarker and BPA Concentration (n=185 children)

1Adjusted for child sex assigned at birth, child age, child race (white vs non-white), whether the child has any sibling, smoking in the home (never vs. ever or current), maternal age (continuous, years), maternal education (high school and/or college education vs post-graduate education), maternal pre-pregnancy BMI (<25, ≤25<30, >30), mode of conception (medically assisted conception vs natural conception).

3.3. Maternal Pregnancy Period

The patterns of association observed in regard to maternal pregnancy concentrations were not as consistent as they were for maternal preconception concentrations. While higher urinary pregnancy concentrations of some biomarkers (MEP, MCPP, MCNP and ∑DEHP) were associated with higher mean food enjoyment scores, others (MCPP and MCOP) were related to lower mean food approaching subscale scores (Figure 4 and Supplemental Table 7). Similarly, some maternal pregnancy urinary biomarker concentrations (MnBP, MiBP and MBzP) were related to higher mean scores for various food avoiding behaviors while higher maternal pregnancy urinary concentrations of MCNP were related to lower mean food avoiding subscale scores (Figure 4 and Supplemental Table 8).

Figure 4:

Figure 4:

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Adjusted1 Average Difference in CEBQ Subscale Scores per loge Increase in Maternal Pregnancy Urinary Phthalate Biomarker and BPA Concentration (n=195 children)

1Adjusted for child sex assigned at birth, child age, child race (white vs non-white), whether the child has any sibling, smoking in the home (never vs. ever or current), maternal age (continuous, years), maternal education (high school and/or college education vs post-graduate education), maternal pre-pregnancy BMI (<25, ≤25<30, >30), mode of conception (medically assisted conception vs natural conception).

3.4. Sensitivity Analysis

When additionally adjusting all models for child BMI z-score, the samples sizes were reduced (n children in the paternal preconception period = 104, n children in the maternal preconception period = 149 and n children in the maternal pregnancy period = 159) due to either missing (n=22) or implausible (n=17) BMI z-scores (Wei et al., 2023). Most relations remained in all three periods (paternal and maternal preconception and maternal pregnancy) when adjusting for child BMI z-scores (Supplemental Tables 9–14). However, when additionally adjusting for child BMI z-scores, almost all relationships between maternal pregnancy phthalate biomarkers (MEP, MCPP, MCNP) and food enjoyment scores were attenuated (Supplemental Table 13). As well, in both the paternal and maternal preconception periods, new significant or marginally significant trends were found. Paternal preconception urinary concentrations of MiBP were associated with decreased mean slow eating scores and MEP concentrations were associated with decreased food fussiness scores (Supplemental Table 10). New relationships between maternal preconception biomarker concentrations and food avoiding subscale scores were found when additionally adjusted for child BMI z-scores (Supplemental Table 12).

In the paternal preconception period, most of the relationships remained after additionally adjusting the main models for maternal preconception urinary concentrations (Supplemental Table 15a, 15b and 16). However, the relationship of paternal preconception urinary MBzP with food responsiveness was attenuated. In the maternal preconception period, additionally adjusting for paternal preconception urinary concentrations, some associations were attenuated (Supplemental Tables 17 and 18). It is important to note that when additionally adjusted for paternal preconception concentrations the sample size was reduced (from 169 to 104 children). For the maternal pregnancy period, after additionally adjusted for maternal preconception urinary concentrations, all relationships between maternal pregnancy phthalate biomarker concentrations and all CEBQ subscales remained (Supplemental Table 19 and 20). When maternal pregnancy period was additionally adjusted for paternal preconception urinary concentrations, a number of associations were attenuated (Supplemental Table 19 and 20).

4. Discussion

In this cohort, maternal and paternal preconception urinary concentrations of certain phthalate biomarkers were related to increased food approaching and decreased food avoiding behavior subscale scores. Specifically, greater maternal preconception urinary MnBP concentrations and paternal preconception urinary MEP concentrations were related to increased appetite for food or desire to eat, which is predictive of greater consumption of food. Greater maternal and paternal preconception urinary MCPP and ∑DEHP concentrations were associated with decreases in behaviors related to ceasing eating or choosing not to initiate eating based on the perception of being full. In the paternal preconception period, higher concentrations of BPA showed opposite associations as compared to phthalate biomarker concentrations (decreased food approaching behavior scores). We did not see consistent evidence that maternal pregnancy phthalate biomarkers or BPA concentrations were associated with child eating behaviors.

While some aforementioned associations were attenuated after adjusting for the other parent’s urinary concentrations of the same phthalate biomarker or BPA, overall, we still saw greater food approaching behaviors and lower levels of food avoiding behaviors associated with higher maternal and paternal preconception urinary phthalate biomarker concentrations. This finding suggests that higher maternal and paternal preconception urinary concentrations of phthalate biomarkers may be associated with increased food consumption in their children.

Prior toxicological studies suggest that exposure preconceptionally or during pregnancy to some phthalates and BPA may impact biological pathways underlying appetitive behavior. Offspring of mice exposed to BPA during pregnancy were more insensitive to the effects of leptin (a known mediator of eating behaviors) on the expression of hypothalamic neurons (hypothalamic pro-opiomelanocortin) compared to controls (Desai et al., 2018; MacKay et al., 2017). Leptin acts on the developing hypothalamus to govern its development (Steculorum et al., 2013) which suggests that BPA exposure during pregnancy in mice can reduce the ability for appetite suppression in offspring. Additionally, perinatal exposure to BPA in rats was shown to influence pre- and post-synaptic connections at the hypothalamic level, leading to increased food consumption by stimulating compulsive eating behaviors (Bernal et al., 2022; Hu et al., 2016). In zebrafish, BPA can bind to the CB1 receptor which could lead to an increased appetite (Tian et al., 2021). Another experimental study found that prenatal exposure to DEHP in mice resulted in increased food intake and increase in body weight in both mothers as well as their offspring (Schmidt et al., 2012). While another study in mice examining maternal perinatal exposure to a mixture of phthalates found no association with food intake in offspring (Neier et al., 2019).

A toxicology study using 3T3-L1 cells found that the expression of mRNA and proteins in the Notch pathway, which plays a role in body and energy homeostasis, were increased by MEHP exposure (Qi et al., 2021). A study in mice found that preconception paternal dicyclohexyl phthalate exposure was associated impaired insulin signaling and altered hepatic gene expression in offspring (Liu et al., 2023). While there are limited studies examining paternal EDC exposure and child eating behavior, a number of rodent studies have found effects of paternal diet on offspring metabolism (Rando and Simmons, 2015). A study in mice suggested that that paternal prediabetes increases an offspring’s susceptibility of diabetes through gametic epigenetic alterations (Wei et al., 2014).

We are unaware of other epidemiologic studies examining the relationship of preconception or pregnancy phthalate biomarker or BPA concentrations with child eating behavior. An alternative explanation for the impact of preconception exposure on child’s behavior could be that it is a surrogate for exposure of the child after birth. That is, men and women with higher preconception exposure to phthalates and BPA may also have higher exposed children after birth (Shoaff et al., 2017). A study by Shoaff et al found that maternal pregnancy MEP and MBzP concentrations were associated child urinary phthalate metabolite concentrations at 1, 2, 3, 4, 5, and 8 y of age (Shoaff et al., 2017). Although not the preconception window, we could extrapolate that maternal (and perhaps paternal) exposures peri-conception are also related to children’s exposures.

Several studies have examined the associations between eating behaviors and childhood obesity. A systematic review and meta-analysis of 11 studies reported prospective relationships between six measures of appetite and adiposity measures (Kininmonth et al., 2021). The systematic review supports the hypothesis that appetitive traits are one behavioral mechanism that explain an individual’s susceptibility to gain excess weight (or not) (Kininmonth et al., 2021). A study examining two United Kingdom based cohorts additionally found that higher satiety responsiveness/slowness in eating and food fussiness scores showed inverse association with weight, whereas higher food responsiveness, enjoyment of food, emotional overeating and desire to drink scores were positively associated with an increase in weight (Webber et al., 2009).

Several pathways may link parental EDC exposure to child eating behaviors. Firstly, EDC-induced alterations in epigenetic mechanisms during development may affect the development or maintenance of appetitive traits or metabolism (Heindel et al., 2022). For instance, EDCs could interfere with orexigenic or anorexic signaling pathways, and provide incorrect information about food availability to the gamete or fetus (Heindel et al., 2024). Alternatively, child eating behavior could be in part due to factors related to both the food environment, eating behaviors, and EDC exposure. Prior research shows that children’s diet and food preferences are influenced by their food environments, including the eating behaviors of their parents (Patel et al., 2018; Savage et al., 2007; Wardle and Cooke, 2008). Moreover, some EDCs are used in food processing and packaging, and families with more obesogenic eating behaviors may also consume more processed and packaged food (Menichetti et al., 2023). Thus, parent’s eating behaviors may have influenced both their preconception/prenatal EDC exposure and child’s eating behaviors.

Toxicological studies have examined the effects of perinatal BPA and phthalate exposure on offspring adiposity/obesity. In animal studies, exposure to MEHP (a metabolite of DEHP) directly activates proliferator-activated receptors which promote adipogenesis (Feige et al., 2007). Another animal study found that perinatal exposure to DEHP was associated with increased body weight in offspring (Hao et al., 2013). A systematic review and meta-analysis highlighted the impact of early-life exposure to BPA on obesity-related outcomes in rodents (Wassenaar et al., 2017). The pooled analysis of 13 studies showed that pregnancy or early life BPA concentrations were associated with greater fat weight in rodents. An additional animal study showed that maternal rat exposure to BPA was related to offspring adiposity, and increased expression of pro-adipogenic factors in adipose tissue (Desai et al., 2018). Our epidemiologic study found that higher paternal preconception BPA concentrations were associated with a decrease in food approaching behaviors; however, we did see a relationship between higher maternal preconception BPA concentrations and emotional overeating scores, a food approaching subscale.

There are a number of epidemiologic studies examining relationships of pregnancy urinary phthalate biomarker concentrations with child weight. Systematic reviews on this topic concluded there were inconclusive results (Gao et al., 2022; Ribeiro et al., 2019; Thayer et al., 2012). Two studies based in the United States found an association of increased prenatal phthalate concentrations with higher BMI and waist circumference in their children (Harley et al., 2017; Teitelbaum et al., 2012). A third United States based study found associations between prenatal phthalate concentrations and higher adiposity between the ages of 3 and 4 years (Ferguson et al., 2022). In contrast, a Spanish population-based cohort study found that prenatal high-molecular-weight urinary phthalate metabolite concentrations were associated with lower weight between birth and six months and lower BMI at later ages in boys but with higher weight and BMI in girls (Valvi et al., 2015). Lastly, two United States cohorts found no associations between higher maternal prenatal phthalate metabolite concentrations and higher body fat percentage or excess childhood adiposity (Buckley et al., 2016; Shoaff et al., 2017). Inconsistencies in study results warrant the need for further research in this field in a larger sample size because potential impacts on human health could be significant.

Prior studies have also examined the relationship between pregnancy concentrations of BPA and child adiposity. Two systematic reviews found a positive association with higher prenatal urine BPA concentrations and risk of childhood obesity (Kim et al., 2019; Wu et al., 2020). A cohort study from Mexico found increased maternal third-trimester BPA concentrations were positively associated with BMI z-score in girls (Yang et al., 2017). In a New York City study, prenatal urinary BPA concentrations were positively associated with fat mass and waist circumference at age 7 years (Hoepner et al., 2016). However, other studies found contradictory results. An Ohio study found that higher prenatal and early-childhood BPA exposures were not associated with increased BMI at 2–5 years of age, but higher early-childhood BPA exposures were associated with accelerated growth during this period (Braun et al., 2014). A California based cohort found that increasing BPA concentrations in mothers during pregnancy were associated with decreased BMI, body fat, and overweight/obesity among their daughters at 9 years of age (Harley et al., 2013).

Our study has several strengths and limitations. First, we had a modest sample size in part due to challenges associated with conducting follow-up during the COVID-19 pandemic, which affected our ability to conduct in person study visits and obtain anthropometric measurements. The modest sample size precluded us from exploring sex-stratified associations which have been seen in the animal and human literature and therefore may have added to our further understanding of the relationships. A significant strength of the study is that we were able to use the rich database of parental covariates and were able to explore paternal preconception period. Another strength of our study is that we used a valid and reliable measure of children’s eating behaviors (Quah et al., 2019; Wardle et al., 2001). The cohort was homogenous in terms of race and socio-economic status, which may reduce the generalizability of our findings, but it also reduced the potential for confounding from these factors. Although we adjusted for the correlation between twins using a generalized linear mixed effects model, given the size of our study we were not able to additionally adjust for the correlation between siblings (6 additional sets of siblings). As well, this is a sub-fertile cohort and studies have explored if IVF treatments have been associated with increased weight in children in some studies (Elhakeem et al., 2023, 2022; Guo et al., 2017; Hart and Norman, 2013). While the results are inconclusive, we adjusted for the use of ART in all models. Assessing phthalate and BPA exposure can be a challenge because of their short half-lives and the frequent and episodic nature of exposures. Thus, single urinary phthalate biomarker and BPA concentrations may not be indicative of a person’s average exposure over days, weeks, or months. However, a strength of our study was that we had multiple urine samples for both maternal periods of exposure for most women and multiple samples may have helped reduced exposure misclassification. An additional potential limitation of looking at multiple periods of exposure for 10 metabolites is spurious findings from multiple comparisons. However, the fact that we observed similar patterns of association for metabolites across CEBQ domains reduces the likelihood of this. The majority of associations between preconception phthalate biomarker and BPA concentrations and CEBQ subscale scores remained even after controlling for urinary concentrations of that phthalate biomarker or BPA from the other parent or the other period of exposure, suggesting that the other parent’s phthalate biomarker and BPA concentrations are not likely a large source of residual confounding.

5. Conclusion

In this preconception prospective cohort, higher maternal and paternal preconception urinary concentrations of some phthalate biomarkers and BPA were associated with eating behaviors that have been related to increased adiposity. These results suggest that eating behaviors may be a link between early life EDC exposures and childhood adiposity. While our results add to the literature examining the potential importance of eating behaviors in the context of chemical obesogens, additional research in larger prospective cohorts will help to understand the links between preconception EDC exposures, eating behaviors, and risk of obesity and related sequelae.

Supplementary Material

1

Highlights.

  • We explored preconception and pregnancy phthalates in relation to eating behaviors

  • Parental preconception phthalates were associated with less food avoiding behaviors

  • Preconception phthalates were associated with more food approaching behaviors

Acknowledgments

The authors would like to thank all PEACE study participants for their invaluable contributions. Additionally, we would like to thank Alex Azevedo for their contribution to study recruitment and data collection. The authors extend their appreciation to the National Institute of Environmental Health Sciences (NIEHS) for funding this work through the grants numbered R01 ES027408 and R01 ES009718.

Disclaimer:

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC). Use of trade name is for identification only and does not imply endorsement by the CDC, the Public Health Service, or the U.S. Department of Health and Human Services.

Abbreviations:

EDCs

Endocrine Disrupting Chemicals

MEP

Monoethyl phthalate

MnBP

mono-n-butyl phthalate

MiBP

mono-isobutyl phthalate

MBzP

monobenzyl phthalate

MCPP

mono (3-carboxypropyl) phthalate

MCOP

monocarboxyoctyl phthalate

MCNP

monocarboxy-isononyl phthalate

MEHP

mono(2-ethylhexyl) phthalate

MEHHP

mono(2-ethyl-5-hydroxyhexyl) phthalate

MEOHP

mono(2-ethyl-5-oxohexyl) phthalate

MECPP

mono(2-ethyl-5-carboxypentyl) phthalate

∑DEHP

molar sum of di(2-ethylhexyl) phthalate metabolites MEHP MEHHP, MEOHP and MECPP

BPA

bisphenol A

CEBQ

Child Eating Behavior Questionnaire

EARTH

Environment and Reproductive Health

PEACE

Preconception Environmental exposure And Childhood health Effects

Footnotes

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