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. Author manuscript; available in PMC: 2025 Mar 15.
Published in final edited form as: Sci Total Environ. 2024 Jan 23;916:170344. doi: 10.1016/j.scitotenv.2024.170344

Phthalate exposure increases interferon-γ during pregnancy: The Atlanta African American Maternal-Child Cohort

Kaitlin R Taibl 1, Anne L Dunlop 2, Dana Boyd Barr 1, P Barry Ryan 1, Parinya Panuwet 1, Elizabeth J Corwin 3, Jasmin A Eatman 1,4, Youran Tan 1, Donghai Liang 1, Stephanie M Eick 1
PMCID: PMC10922519  NIHMSID: NIHMS1961984  PMID: 38266723

Abstract

Background:

The immune system undergoes unique adaptations during pregnancy and is particularly sensitive to environmental chemicals, such as phthalates, which are associated with acute and chronic inflammatory medical conditions. However, current knowledge of how phthalate exposures are associated with systemic inflammation in pregnant people is limited by cross-sectional study designs and single chemical models. Our objective was to estimate the association between repeated measures of prenatal phthalate exposures, examined individually and collectively, and a panel of clinical inflammatory biomarkers.

Methods:

In the Atlanta African American Maternal-Child Cohort, biospecimens were collected at mean 11 and 26 weeks gestation (N=126). Concentrations of eight urinary phthalate metabolites and five serum inflammatory biomarkers, including CRP, IFN-γ, IL-6, IL-10, and TNF-α, were measured. Linear mixed effect regression and quantile g-computation models were used to estimate the associations for single phthalates and their exposure mixture, respectively.

Results:

Participants who self-reported any use of alcohol, tobacco, or marijuana in the month prior to pregnancy had increased MEP, MBP, MiBP, and TNF-α, relative to those with no substance use. IFN-γ was elevated in response to MECPP (% change=11.73, 95% confidence interval [CI]=0.22, 24.56), MEHHP (% change= 8.67, 95% CI= 1.54, 16.31), MEOHP (% change=7.92, 95% CI=0.84, 15.51), and their parent phthalate, ΣDEHP (% change=10.19, 95% CI=0.19, 21.18). The phthalate mixture was also associated with an increase in IFN-γ (% change=10.19, 95% CI=4.25, 16.47).

Conclusions:

Our findings suggest DEHP metabolites induce systemic inflammation during pregnancy. The pro-inflammatory cytokine IFN-γ may play an important role in the relationship between prenatal phthalate exposures and adverse pregnancy outcomes.

Graphical Abstract

graphic file with name nihms-1961984-f0001.jpg

I. INTRODUCTION

Pregnant people undergo a series of immunological adaptations, including an increase in anti-inflammatory responses via neutrophils, monocytes, and Th2-type cytokines and a decrease in pro-inflammatory responses via B and T lymphocytes, natural killer (NK) cells, and Th1-type cytokines.1 The dynamic sequence of signals and processes is tightly controlled by the immune system, which if perturbed, poses a major health risk to the mother, placenta, and fetus. For example, biomarkers of acute inflammation [i.e., interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6)] and chronic inflammation [i.e., C-reactive protein (CRP)] are associated with preeclampsia, gestational diabetes, and preterm birth.2-4 Low anti-inflammatory responses, such as IL-10 deficiency, have also been linked to several adverse pregnancy outcomes.5-7 While the exact etiology of immune changes in pregnant people is believed to be multifactorial, systemic inflammation during pregnancy may be related to prenatal exposure to environmental chemicals, such as phthalates.8,9

Phthalates improve the flexibility and longevity of products, like plastics, and hold color, shine, and fragrance in cosmetics, which are the main human exposure sources.10,11 These endocrine-disrupting chemicals (EDCs) affect human reproduction, development, and immune function.12 Likewise, the health effects of phthalates are amplified during pregnancy. We have previously observed that prenatal exposure to a mixture of phthalates is associated with a significant reduction in birthweight for gestational age z-scores.13 Studies also find that prenatal phthalate exposure is associated with an elevated risk of miscarriage, gestational hypertension, preterm birth, and fetal growth restriction.14-17 Inflammatory signals and processes may provide mechanistic insight into how phthalates disrupt the maternal-placental-fetal immune system and their association with adverse pregnancy outcomes.8,18-21

Our objective was to estimate the association between urinary phthalate metabolites and their mixture with serum inflammatory biomarkers at two timepoints among pregnant people in the Atlanta African American Maternal-Child Cohort. We hypothesized that repeated measures of phthalate exposure would be associated with increased pro-inflammatory biomarkers and decreased anti-inflammatory biomarkers in the maternal immune milieu during pregnancy. The results may lend to potential mechanisms by which prenatal phthalate exposures are associated with systemic inflammation in the mother, as each biomarker is produced through a different biological pathway.

II. METHODS

Atlanta African American Maternal-Child Cohort

The Atlanta African American Maternal-Child Cohort is an ongoing, prospective birth cohort that began enrollment in 2014 and performs follow-up with maternal-child dyads until the child reaches six years of age. Participants are recruited at Emory University Hospital Midtown, an academic/teaching hospital, or Grady Memorial Hospital, a county hospital, and eligible to participate if pregnant with a singleton between 8-14 weeks gestation, self-identify as an African American or Black female, between 18-40 years of age, were born in the United States (US), fluent in English, and do not have any chronic medical conditions. Additional details about the study design and population characteristics are available elsewhere.22,23 As part of the study, participants complete up to two prenatal visits at early pregnancy (8-14 weeks gestation, enrollment) and late pregnancy (24-30 weeks gestation, follow-up). Our analytic sample was restricted to 126 participants with urinary phthalate metabolite and serum inflammatory biomarker measurements available at both study visits (eFigure 1). On average, urine and venous blood were collected at 11 weeks gestation (SD=2.2) and 26 weeks gestation (SD=2.4). All participants provided written, informed consent and the Institutional Review Board at Emory University (reference #: 68441) approved the study. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.24

Quantification of Urinary Phthalate Metabolites

Urine samples were stored at −80°C then analyzed for creatinine and phthalate metabolites by the Laboratory of Exposure Assessment and Development for Environmental Research (LEADER) at Emory University using an established protocol.20,25 Quality assurance and quality control (QA/QC) was satisfied by the inclusion of bench QC materials, National Institute of Standards and Technology (NIST) reference materials and internal standards.20

Eight phthalate metabolites were measured in participants enrolled in the Atlanta African American Maternal-Child Cohort. We quantified these eight phthalate metabolites, including mono-n-butyl phthalate (MBP), mono-ethyl phthalate (MEP), mono-benzyl phthalate (MBzP), mono-isobutyl phthalate (MiBP), mono-2-ethlyhexyl phthalate (MEHP), mono-2-ethyl-5-oxohexyl phthalate (MEOHP), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP), and mono-2-ethly-5-carboxypentyl phthalate (MECPP), and the parent phthalate, di-2-ethylhexyl phthalate (DEHP), with the molar sum of MEHP, MEHHP, MEOHP, and MECPP. The parent phthalate, abbreviation, and chemical structure for each metabolite is presented in eTable 1. Urinary concentrations detected below the limit of detection (LOD) were imputed with the LOD/√2. All urinary concentrations (detected and imputed) were natural log-transformed to address right skewness.26

Quantification of Serum Inflammatory Biomarkers

We quantified levels of five inflammatory biomarkers: CRP, IFN-γ, IL-6, IL-10, and TNF-α. The venous blood samples were centrifuged for serum then stored at −80°C until analysis at Emory University by the Multiplexed Immunoassay Core. To measure the four cytokines, we used the MesoScale assay platform (MesoScale Diagnostics, Rockville, Maryland). To measure CRP, we used enzyme-linked immunosorbent assay (ELISA) kits (R&D systems, cat# SCRP00) with the manufacturer protocol. The concentration of CRP was calculated based on the four parameter calibration curves generated using the BioTek Gen5 software. All inflammatory biomarkers were natural log-transformed to address right skewness.

Statistical Analysis

All statistical tests were performed in R (Boston, MA, USA, Version 4.1.0). A p-value<0.05 was considered statistically significant. Only participants with complete data for urinary phthalate metabolites, serum inflammatory biomarkers, and covariates were included in the analyses. Covariates were ascertained at enrollment and the exposures and outcomes were measured at enrollment and follow-up.

Descriptors of Maternal Phthalate Exposure and Inflammatory Biomarkers:

Maternal characteristics were ascertained via a self-reported questionnaire at enrollment and medical record abstraction at delivery. Pregnant people self-reported their age, marital status, educational attainment, any personal use of alcohol, tobacco, or marijuana in the previous month, annual household income, and number of household members. Division of annual household income by household size was used to calculate income-poverty ratio, which was categorized below or above the Federal Poverty Level (i.e., 132%), a criterion used to determine eligibility for federal assistance programs, such as Medicaid, Children’s Health Insurance Program (CHIP), and Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Medical insurance type, infant sex assigned at birth, parity, and pre-pregnancy body mass index (BMI), the ratio of weight (kg) to height (m2), were based on clinical data. We constructed a directed acyclic graph (DAG) to identify potential confounders for the association between phthalate exposure and systemic inflammation during pregnancy (eFigure 2).

The distributions of urinary phthalate metabolites and serum inflammatory biomarkers were examined with detection frequencies and select percentiles. We also used intraclass correlation coefficients (ICC) and generalized additive models to characterize the concentration changes during pregnancy. Correlations among phthalate metabolites and inflammatory biomarkers were estimated with Spearman correlation coefficients. To identify descriptors of phthalate exposure and inflammation during pregnancy, we fit linear mixed effect regression models between the biomarkers and maternal characteristics, which were treated as separate predictors in individual models. We included maternal characteristics for which there is evidence of an association in the literature.27,28 All models were adjusted for gestational age at sample collection to account for different timing in measurement and included a random intercept for participant ID to account for intraindividual correlation between the repeated measures. The models for phthalate exposure also included urinary creatinine as a covariate to account for urine dilution.29 Finally, effect estimates (β) and 95% confidence intervals (CIs) were converted to percent change using the equation, (eβ- 1)*100 , for ease of interpretation.

Maternal Exposure to Single Phthalates and Inflammatory Biomarkers:

The association between a urinary phthalate metabolite and a serum inflammatory biomarker, both measured at enrollment and follow-up, was estimated using linear mixed effect regression. A random intercept was included for participant ID to account for the intraindividual correlation between the two timepoints. All models were minimally adjusted for urinary creatinine and gestational age at sample collection. Fully adjusted models also included maternal age, education, pre-pregnancy BMI, and recent personal use of alcohol, tobacco, or marijuana. The effect estimates (β) and 95% CIs were converted to percent change using the equation, (2*eβ- 1)*100 and interpreted as the change in an inflammatory biomarker for a 100% increase in phthalate concentrations.

Maternal Exposure to Multiple Phthalates and Inflammatory Biomarkers:

The association between the exposure mixture containing MEP, MBP, MBzP, MiBP, MECPP, MEHHP, MEHP, and MEOHP and a serum inflammatory biomarker was estimated with quantile g-computation.30 We opted to use quantile g-computation over other common mixture methods because it does not require directional homogeneity or a large sample size, and is capable of integrating repeated measures. Quantile g-computation estimates an “overall mixture” effect by re-coding all phthalate metabolites in the mixture index as “score” variables based on quartile cut points, which are then included in the model for the outcome. The effect estimate (ψ) quantifies the percent change in the serum level of an inflammatory biomarker for a simultaneous 1-quartile increase in all phthalate metabolites in the exposure mixture. To account for clustering of repeated biomarker measures within each participant, models were parameterized with a random intercept for participant ID to account for intraindividual correlation between the two timepoints.31 Under the assumptions of exchangeability, causal consistency, positivity, no interference, and correct model specification, ψ estimates obtained from quantile g-computation can be interpreted as a causal effect for an intervention on the mixture. All models were adjusted for the same set of covariates as the linear mixed effect models (maternal age, education, pre-pregnancy BMI, recent personal use of alcohol, tobacco, or marijuana, urinary creatinine, and gestational age of sample collection).

Sensitivity Analyses:

We restricted the analytic sample to participants with repeated measures of the urinary phthalate metabolites and serum inflammatory biomarkers during pregnancy (N=126). To ensure this inclusion criterion did not bias our results, we repeated the main analyses among participants with at least one measure of urinary phthalate metabolites and serum inflammatory biomarkers (n=342).

III. RESULTS

Study Population

A total of 126 pregnant people with repeated measures of urinary phthalate metabolites and serum inflammatory biomarkers were included in this analysis; on average, pregnant people were 26 years of age at enrollment and had a pre-pregnancy BMI of 27 kg/m2, plus the majority had attained a high school education or less (54.8%) and did not report recent personal use of alcohol, marijuana, or tobacco (54.8%). Characteristics of the study population are presented in Table 1.

Table 1.

Sociodemographic and clinical characteristics of pregnant people in the Atlanta African American Maternal-Child Cohort, 2016 – 2020.

Characteristic Analytic Sample Overall Cohort
N 126 640
Gestational Weeks at Enrollment 11 ± 2.2 11 ± 2.2
  Missing - 93 (14.5%)
Gestational Weeks at Follow-up 26 ± 2.4 27 ± 2.5
  Missing - 235 (36.7%)
Age (years) 26 ± 5.1 25 ± 4.9
  Missing - 93 (14.5%)
Body Mass Index (kg/m2) 27 ± 6.9 29 ± 7.9
Missing - 93 (14.5%)
Marital Status
  Married or Cohabitating 49 (38.9%) 263 (41.1%)
  Single 77 (61.1%) 284 (44.4%)
  Missing - 93 (14.5%)
Education Level
  <High School 17 (13.5%) 87 (13.6%)
  High School 52 (41.3%) 213 (33.3%)
  Some College 35 (27.8%) 158 (24.7%)
  College or Graduate School 22 (17.5%) 89 (13.9%)
  Missing - 93 (14.5%)
Income-to-poverty Ratio
  >132% 49 (38.9%) 230 (35.9%)
  ≤132% 77 (61.1%) 317 (49.5%)
  Missing - 93 (14.5%)
Recent Personal Use of Alcohol Tobacco, or Marijuana
  None 69 (54.8%) 306 (47.8%)
  Any 57 (45.2%) 241 (37.7%)
  Missing - 93 (14.5%)
Parity
  0 54 (42.9%) 254 (39.7%)
  1 36 (28.6%) 149 (23.3%)
  ≥2 36 (28.6%) 144 (22.5%)
  Missing - 93 (14.5%)
Hospital
  Emory University Hospital Midtown 52 (41.3%) 221 (34.5%)
  Grady Memorial Hospital 74 (58.7%) 326 (50.9%)
  Missing - 93 (14.5%)
Medical Insurance
  Medicaid 100 (79.4%) 433 (67.7%)
  Private 26 (20.6%) 114 (17.8%)
  Missing - 93 (14.5%)
Infant Sex
  Male 63 (50.0%) 257 (40.2%)
  Female 63 (50.0%) 272 (42.5%)
  Missing - 111 (17.3%)
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Abbreviations: N, number; SD, standard deviation.

Notes: Statistics presented as N (%) or mean ± SD. Maternal age, marital status, education level, parity, and medical insurance were ascertained at enrollment. Pre-pregnancy body mass index (BMI) abstracted from the medical record at the enrollment visit. Personal use of alcohol, tobacco, or marijuana in the month prior to enrollment used to categorize as none or any.

Distributions of Urinary Phthalate Metabolites and Serum Inflammatory Biomarkers

The concentration percentiles and ICCs of urinary phthalate metabolites and serum inflammatory biomarkers by study visit are reported in eTable 2. We also show the visual distributions of each phthalate metabolite and inflammatory biomarker across pregnancy in eFigure 3 and eFigure 4, respectively. In 100% of the study population, we detected MEP, MBzP, and MEHHP. The phthalate metabolite concentrations decreased from early pregnancy to late pregnancy, except for MEP and MBzP, which increased between the two study visits. Out of the eight phthalate metabolites analyzed, the strongest positive correlation observed was between MEOHP and MEHHP (ρ=0.97) (eFigure 5). Further, median concentrations of the phthalate metabolites were consistent between the two study visits, except for MEP and MEHP, which were lower at late pregnancy. The median concentrations of the cytokines and CRP did not substantially change between study visits either. Across the five inflammatory biomarkers, IL-10 was weakly and negatively correlated with CRP (ρ=−0.02) while moderately and positively correlated with IFN-γ (ρ=0.38) (eFigure 6).

Associations of Maternal Characteristics with Urinary Phthalate Metabolites and Serum Inflammatory Biomarkers

Urinary concentrations of phthalate metabolites did not differ across strata of marital status, BMI, or infant sex (Table 2). Pregnant people aged 26-30 years had lower MiBP, MEHHP, and MEHP compared to those aged 18-25 years. A high school or some college education was associated with lower MBP relative to less than a high school education. We additionally observed lower MiBP and MECCP among those with private insurance compared to Medicaid. Participants with any recent consumption of alcohol, tobacco, or marijuana also had higher MiBP, MBP, and MEP.

Table 2.

Percent changes in repeated urinary phthalate metabolite concentrations by maternal characteristics, estimated using linear mixed effect models in the Atlanta African American Maternal-Child Cohort, 2016 – 2020 (N=126).

Characteristic N MEP MBP MBzP MiBP MECPP MEHHP MEHP MEOHP ΣDEHP
Age at Enrollment (years)
18 - 25 73 Referent Referent Referent Referent Referent Referent Referent Referent Referent
26 - 30 21 −2.96 (−40.55, 58.41) −20.55 (−45.25, 15.30) 2.02 (−26.89, 42.36) −39.95 (−60.98, −7.58) −29.53 (−54.21, 8.46) −46.74 (−61.83, −25.68) −37.50 (−55.21, −12.79) −21.34 (−39.03, 1.49) −28.82 (−45.90, −6.35)
31 - 35 19 15.03 (−30.90, 91.48) 0 (−32.43, 47.99) 15.03 (−19.17, 63.69) −18.94 (−48.36, 27.23) 46.23 (−6.84, 129.52) 29.69 (−7.06, 80.98) 29.69 (−7.06, 80.98) 11.63 (−13.48, 44.02) 25.86 (−4.34, 65.6)
36 - 40 13 −14.79 (−52.67, 53.42) −20.55 (−49.38, 24.71) −2.96 (−35.70, 46.46) −49.34 (−70.16, −14.00) 15.03 (−32.24, 95.27) 3.05 (−30.37, 52.50) 5.13 (−28.97, 55.58) 0 (−26.92, 36.83) 3.05 (−26.15, 43.79)
Marital Status
Married or Cohabitating 49 Referent Referent Referent Referent Referent Referent Referent Referent Referent
Single 77 −6.76 (−35.75, 35.31) 6.18 (−19.30, 39.71) 7.25 (−16.87, 38.38) 37.71 (−1.31, 92.17) 23.37 (−11.59, 72.15) 15.03 (−10.85, 48.41) 15.03 (−10.85, 48.41) 13.88 (−6.39, 38.54) 13.88 (−6.39, 38.54)
Education Level
<Higli School 17 Referent Referent Referent Referent Referent Referent Referent Referent Referent
High School 52 −4.88 (−45.05, 64.67) −42.31 (−61.02, −14.62) 7.25 (−27.53, 58.72) −2.96 (−40.55, 58.41) −18.13 (−49.84, 33.64) −15.63 (−42.99, 24.86) −16.47 (−42.44, 21.22) −6.76 (−29.14, 22.68) −11.31 (−33.90, 19.01)
Some College 35 −1.00 (−45.01, 78.25) −39.35 (−60.59, −6.65) 2.02 (−32.40, 53.97) −17.30 (−50.32, 37.66) 6.18 (−37.45, 80.25) 4.08 (−31.04, 57.08) 12.75 (−23.81, 66.86) 3.05 (−23.20, 38.26) 8.33 (−20.83, 48.23)
College or Graduate School 22 −1.00 (−48.15, 89.04) −56.83 (−73.03, −30.90) 1.01 (−35.65, 58.53) −44.57 (−68.60, −2.14) −25.92 (−58.04, 30.79) −35.60 (−58.97, 1.09) −31.61 (−55.57, 5.25) −24.42 (−45.84, 5.46) −28.11 (−49.48, 2.31)
Income-to-poverty Ratio
>132% 49 Referent Referent Referent Referent Referent Referent Referent Referent Referent
≤132% 77 −4.88 (−33.16, 35.36) 32.31 (0.56, 74.09) 1.01 (−21.71, 30.32) 29.69 (−5.22, 77.46) 20.92 (−11.63, 65.47) 13.88 (−11.73, 46.93) 10.52 (−14.34, 42.59) 7.25 (−11.84, 30.47) 10.52 (−9.15, 34.45)
Recent Personal Use of Alcohol, Tobacco, or Marijuana
None 69 Referent Referent Referent Referent Referent Referent Referent Referent Referent
Any 57 43.33 (0.72, 103.97) 46.23 (13.34, 88.66) 7.25 (−15.23, 35.69) 40.49 (2.68, 92.24) 27.12 (−7.10, 73.95) 10.52 (−14.34, 42.59) 15.03 (−9.08, 45.53) 10.52 (−7.36, 31.84) 13.88 (−6.39, 38.54)
Body Mass Index (kg/m2)
<18.5 3 Referent Referent Referent Referent Referent Referent Referent Referent Referent
18.5 – 24.99 53 24.61 (−61.56, 303.9) 34.99 (−45.21, 232.54) 73.33 (−22.40, 287.13) −39.35 (−78.54, 71.39) −43.45 (−80.38, 62.97) −26.66 (−68.42, 70.37) −26.66 (−67.80, 67.06) −41.14 (−67.94, 8.07) −34.30 (−66.26, 27.94)
25.0 – 29.99 32 18.53 (−64.14, 291.81) 31.00 (−47.86, 229.1) 118.15 (−4.23, 396.89) −10.42 (−68.91, 158.16) −33.63 (−77.42, 95.03) −22.89 (−67.45, 82.65) −19.75 (−64.77, 82.80) −39.95 (−67.93, 12.43) −32.29 (−65.23, 31.84)
≥30 38 11.63 (−66.23, 268.99) 36.34 (−44.66, 235.89) 69.89 (−23.94, 279.47) −13.93 (−70.13, 148.04) −53.70 (−83.93, 33.43) −33.63 (−71.43, 54.16) −33.63 (−70.86, 51.16) −42.31 (−68.58, 5.93) −39.35 (−68.85, 18.10)
Parity
0 54 Referent Referent Referent Referent Referent Referent Referent Referent Referent
1 36 −18.13 (−45.75, 23.57) −25.92 (−45.86, 1.37) 24.61 (−7.13, 67.20) 6.18 (−26.83, 54.10) −15.63 (−41.86, 22.43) −28.82 (−46.95, −4.50) −28.11 (−46.42, −3.54) −18.94 (−34.66, 0.56) −19.75 (−36.57, 1.53)
≥2 36 −36.87 (−60.56, 1.05) −3.92 (−33.79, 39.43) 5.13 (−24.66, 46.70) 40.49 (−8.72, 116.24) 49.18 (−3.07, 129.61) 19.72 (−14.20, 67.06) 22.14 (−12.47, 70.44) 13.88 (−11.73, 46.93) 23.37 (−6.24, 62.32)
Hospital
Emory University Hospital Midtown 52 Referent Referent Referent Referent Referent Referent Referent Referent Referent
Grady Memorial Hospital 74 16.18 (−18.36, 65.33) 44.77 (12.21, 86.79) 11.63 (−13.48, 44.02) 49.18 (9.02, 104.13) 33.64 (−2.33, 82.87) 22.14 (−5.33, 57.59) 17.35 (−9.04, 51.41) 7.25 (−11.84, 30.47) 12.75 (−7.32, 37.16)
Medical Insurance
Medicaid 100 Referent Referent Referent Referent Referent Referent Referent Referent Referent
Private 26 10.52 (−28.19, 70.10) −25.17 (−46.38, 4.41) 2.02 (−23.97,36.89) −46.21 (−62.93, −21.93) −34.30 (−55.60, −2.76) −15.63 (−38.34, 15.44) −17.30 (−38.37, 10.96) −10.42 (−29.19, 13.34) −15.63 (−33.32, 6.74)
Infant Sex
Male 63 Referent Referent Referent Referent Referent Referent Referent Referent Referent
Female 63 −1.00 (−30.43, 40.89) −2.96 (−26.24, 27.69) 7.25 (−16.87, 38.38) 4.08 (−23.94, 42.42) 5.13 (−23.17, 43.85) 12.75 (−12.61, 45.47) 6.18 (−17.70, 37.00) 10.52 (−7.36, 31.84) 8.33 (−10.95, 31.78)
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Abbreviations: GM, geometric mean; GSD, geometric standard deviation; mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MBP), mono-benzyl phthalate (MBzP), mono-iso-butyl phthalate (MiBP), mono-2-ethyl-5-carboxypentyl phthalate (MECPP), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP), mono-2-ethylhexyl phthalate (MEHP), mono-2-ethyl-5-oxohexyl phthalate (MEOHP), di-2-ethylhexyl phthalate (DEHP).

Note: DEHP calculated as the molar sum of MEHP, MEHHP, MECPP, and MEOHP. All models adjusted for urinary creatinine and gestational age at sample collection, and included a random intercept for participant ID.

We found few significant associations between inflammatory biomarkers and maternal characteristics (Table 3). IL-10 was lower among pregnant people carrying a female compared to a male infant. Participants who self-reported any recent use of alcohol, marijuana, or tobacco had lower CRP, versus those that reported no substance use. In contrast, CRP was higher among high school or college graduates, relative to those who did not graduate from high school. We also observed an adverse association between IL-6 and obesity.

Table 3.

Percent change in repeated serum inflammatory biomarker concentrations by maternal characteristics, estimated using linear mixed effect models in Atlanta African American Maternal-Child Cohort, 2016 – 2020 (N=126).

Characteristic N C-Reactive Protein Interferon-γ Interleukin-6 Interleukin-10 Tumor Necrosis Factor-α
Age at Enrollment (years)
18 - 25 73 Referent Referent Referent Referent Referent
26 - 30 21 −11.31 (−49.76, 56.58) 16.18 (−18.36, 65.33) 1.01 (−24.72, 35.53) −15.63 (−34.61, 8.85) −1.00 (−17.01, 18.10)
31 - 35 19 49.18 (−17.14, 168.59) 23.37 (−14.99, 79.03) 18.53 (−13.38, 62.19) −10.42 (−31.91, 17.87) 17.35 (−1.63, 39.99)
36 - 40 13 2.02 (−48.62, 102.59) 37.71 (−10.52, 111.95) 31.00 (−9.73, 90.10) −5.82 (−31.17, 28.87) 7.25 (−13.55, 33.06)
Marital Status
Married or Cohabitating 49 Referent Referent Referent Referent Referent
Single 77 −9.52 (−40.05, 36.56) −19.75 (−39.01, 5.59) −18.13 (−34.01, 1.57) 16.18 (−4.50, 41.34) 6.18 (−5.60, 19.43)
Education Level
<High School 17 Referent Referent Referent Referent Referent
High School 52 153.45 (38.04, 365.34) −4.88 (−36.97, 43.56) 9.42 (−21.59, 52.68) 1.01 (−24.72, 35.53) −1.00 (−18.62, 20.44)
Some College 35 37.71 (−27.88, 162.95) −3.92 (−37.57, 47.88) 6.18 (−25.38, 51.10) −16.47 (−38.96, 14.29) −1.00 (−18.62, 20.44)
College or Graduate School 22 185.77 (41.11, 478.69) 10.52 (−30.95, 76.90) 32.31 (−10.60, 95.81) 4.08 (−25.41, 45.24) −1.00 (−20.20, 22.83)
Income-to-poverty Ratio
>132% 49 Referent Referent Referent Referent Referent
≤132% 77 9.42 (−27.50, 65.14) 3.05 (−21.68, 35.58) −10.42 (−27.79, 11.14) 8.33 (−10.95, 31.78) −1.00 (−11.98, 11.36)
Recent Personal Use of Alcohol, Tobacco, or Marijuana
None 69 Referent Referent Referent Referent Referent
Any 57 −40.55 (−59.83, −12.01) −6.76 (−27.73, 20.30) −9.52 (−27.06, 12.25) −10.42 (−24.90, 6.87) 2.02 (−9.30, 14.75)
Body Mass Index (kg/m2)
<18.5 3 Referent Referent Referent Referent Referent
18.5 – 24.99 53 −3.92 (−72.59, 236.83) 37.71 (−42.99, 232.68) 37.71 (−32.00, 178.88) 69.89 (−7.47, 211.93) 32.31 (−12.33, 99.69)
25.0 – 29.99 32 97.39 (−44.79, 605.69) 11.63 (−53.79, 169.66) 55.27 (−23.33, 214.43) 47.70 (−21.12, 176.54) 50.68 (−0.16, 127.41)
≥30 38 191.54 (−16.84, 922.05) 32.31 (−45.23, 219.63) 111.70 (4.54, 328.71) 27.12 (−30.76, 133.40) 31.00 (−13.20, 97.70)
Parity
0 54 Referent Referent Referent Referent Referent
1 36 6.18 (−34.95, 73.33) 6.18 (−22.40, 45.30) 12.75 (−12.61, 45.47) 1.01 (−20.16, 27.79) −1.00 (−15.36, 15.81)
≥2 36 −6.76 (−47.19, 64.61) 12.75 (−22.31, 63.62) −18.13 (−38.98, 9.86) −3.92 (−25.53, 23.96) −3.92 (−19.46, 14.61)
Hospital
Emory University Hospital Midtown 52 Referent Referent Referent Referent Referent
Grady Memorial Hospital 74 −20.55 (−47.36, 19.91) −9.52 (−31.23, 19.05) −16.47 (−32.67, 3.62) 11.63 (−8.24, 35.80) 8.33 (−3.69, 21.85)
Medical Insurance
Medicaid 100 Referent Referent Referent Referent Referent
Private 26 44.77 (−11.31, 136.32) 25.86 (−8.02, 72.22) 13.88 (−13.45, 49.84) 1.01 (−20.16, 27.79) −4.88 (−18.68, 11.27)
Infant Sex
Male 63 Referent Referent Referent Referent Referent
Female 63 −9.52 (−40.05, 36.56) 15.03 (−10.85, 48.41) −4.88 (−23.33, 18.01) −19.75 (−32.73, −4.27) −2.96 (−13.72, 9.16)
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Abbreviations: GM, geometric mean; GSD, geometric standard deviation.

Note: Associations estimated with linear mixed effect regression and interpreted as the percent change in a serum inflammatory biomarker by maternal characteristic. All models adjusted for urinary creatinine and gestational age at sample collection, and included a random intercept for participant ID.

Gestational Changes in Serum Inflammatory Biomarkers from Single Phthalate Metabolites

Associations between phthalate metabolites and inflammatory biomarkers in minimally adjusted linear mixed effect models (eTable 3) were similar to those in models fully adjusted for all covariates (eTable 4; Figure 1). In the fully adjusted models, we found a significant association between MEHP and IL-6 (% change=−4.74, 95% CI=−8.54, −0.77). MBzP was associated with a comparable increase in TNF-α (% change=4.25, 95% CI=0.08, 8.58). Prenatal concentrations of ΣDEHP and its individual metabolites, except for MEHP, were also positively associated with IFN-γ. The greatest, positive change was observed between MECPP and IFN-γ (% change=11.73, 95% CI=0.22, 24.56). Finally, none of the fully adjusted models yielded a strong, significant association between urinary phthalate metabolites and their exposure mixture with CRP or IL-10.

Figure 1.

Figure 1.

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Associations between repeated measures of phthalate metabolites and inflammatory biomarkers during pregnancy in the Atlanta African American Maternal-Child Cohort, 2016 – 2020 (N=126).

Abbreviations: Confidence interval (CI), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MBP), mono-benzyl phthalate (MBzP), mono-iso-butyl phthalate (MiBP), mono-2-ethyl-5-carboxypentyl phthalate (MECPP), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP), mono-2-ethylhexyl phthalate (MEHP), mono-2-ethyl-5-oxohexyl phthalate (MEOHP), di-2-ethylhexyl phthalate (DEHP).

Note: Metabolites of a phthalate parent other than DEHP color coded as purple. Metabolites of DEHP color coded as pink. Molar sum of DEHP color coded as orange. Phthalate exposure mixture color coded as gold. Single chemical associations estimated with linear mixed effect regression and interpreted as the percent change in a serum inflammatory biomarker for a 100% increase in a urinary phthalate metabolite. Multi-chemical associations estimated with quantile g-computation and interpreted as the percent change in a serum inflammatory biomarker for every 1-quartile increase in the urinary phthalate metabolite mixture. All models are adjusted for urinary creatinine, gestational age at sample collection, maternal age, education, body mass index, and recent use of any alcohol, marijuana, or tobacco use, and included a random intercept included for participant ID. Phthalate mixture included MEP, MBP, MBzP, MiBP, MECPP, MEHHP, MEHP, MEOHP. DEHP is the molar sum of MEHP, MEHHP, MEOHP, and MECPP.

Gestational Changes in Serum Inflammatory Biomarkers from Multiple Phthalate Metabolites

Associations between the mixture of phthalate metabolites and inflammatory biomarkers are shown in Figure 1 and eTable 5. For every 1-quartile increase in the exposure mixture containing MBP, MEP, MBzP, MiBP, MEHP, MEOHP, MEHHP, and MECPP, there was an increase in IFN-γ by 10.19% (95% CI=4.25, 16.47), after adjusting for covariates. We also found a modest mixture effect of the eight phthalate metabolites on TNF-α (% change=7.18; 95% CI=−0.69, 15.67), which aligned with the trends in single chemical models. CRP was the only inflammatory biomarker lower in relation to the phthalate exposure mixture, but not statistically significant.

Sensitivity Analyses

The associations estimated in the analytic sample with repeated measures (N=126) were comparable to those estimated in the sample with at least one measure during pregnancy (N=342) (eTable 6 and eTable 7). In the sensitivity analyses, there were fewer statistically significant associations, though magnitudes and directions of the estimates were similar to those in the primary analyses.

IV. DISCUSSION

In this repeated measures analysis of 126 pregnant African Americans, we identified several sociodemographic and clinical predictors of urinary phthalate metabolites and serum inflammatory biomarkers. Further, prenatal concentrations of ΣDEHP and the overall phthalate mixture were associated with changes in the circulating panel of cytokines. There were consistent and biologically relevant patterns of increased IFN-γ and TNF-α, yet decreased CRP and IL-6, which mirror concentrations among pregnant people in an inflammatory state/32,33 The simultaneous fluctuation in pro- and anti-inflammatory processes, as indicated here and supported by other work, may explain how phthalate exposure disrupts the maternal immune system and is related to adverse pregnancy outcomes.20

Across our panel of five inflammatory biomarkers, IFN-γ was most associated with prenatal phthalate exposure. This pro-inflammatory, Th1-type cytokine mediates the intracellular immune response to xenobiotics, a possible explanation as to why we observed strong associations in relation to phthalate parent compounds and metabolites.32 Specifically, we found the phthalate exposure mixture was associated with increased IFN-γ by the same magnitude as ΣDEHP in single chemical models, suggesting this high-molecular weight phthalate drives the adverse association. Alternatively, 3rd trimester concentrations of low-molecular weight phthalates, including di-methyl phthalate (DMP) and di-n-butyl phthalate (DBP), were associated with lower cord blood concentrations of IFN-γ in the Barwon Infant Study.34 No significant correlations between individual phthalate metabolites and IFN-γ during early pregnancy were found in the Michigan Mother-Infant Pairs (MMIP) cohort.19 While this finding is in contrast to our observations, the findings presented here align with laboratory work, as a study conducted in zebrafish embryos found the genetic transcription for IFN-γ was upregulated following exposure to DMP, DBP, and the mixture of both phthalates.35 The different results in human populations and animal models warrants future research on maternal exposure to existing and emerging phthalates, systemic inflammation, and health outcomes in the perinatal period. Future laboratory studies may also help to identify potential threshold amounts by which phthalate exposure leads to elevated inflammation.

In the US, African American and non-Hispanic Black women are exposed to phthalates more so than any other racial and ethnic group/36,37 These exposure disparities are compounded by institutionalized discrimination and racialized stress; racist beauty standards perpetuate contact with products that contain phthalates, including vaginal douches and feminine wipes targeted towards women of color, plus enhance vulnerability to immunogenic chemicals via non-chemical stress.13 Correspondingly, we found higher educational attainment was associated with lower MiBP and MBP concentrations. The only biologic characteristic related to the maternal immune milieu was infant sex; in this study, pregnant people carrying a female had lower interleukins, TNF-α, and CRP. These findings are consistent with already published work on the sexual dimorphism of immunity and inflammation across the life course.38 There was also an association between a BMI indicative of obesity prior to pregnancy and higher IL-6. As suggested by others, perhaps existing obesity primes people for systemic inflammation when they become pregnant.39-41

This analysis is not without limitations. While there were no overt systematic differences between the participants included in the analytic sample compared to those enrolled in the overall cohort, we cannot rule out the possibility of selection bias. Our modest sample size may have also limited the statistical power needed to detect subtle associations between phthalate exposure and systemic inflammation, and thus are subject to chance. Further, there is a potential to observe different associations with more than once repeated measures for urinary phthalate metabolites and serum inflammatory biomarkers. Both are known to vary between measurements and therefore, replicate studies are needed to validate the findings reported here. In the future, investigators should consider incorporating at least one sample per trimester to explore the temporal dynamics of the exposure-outcome relationships. Another future direction is to analyze the association between recent phthalate replacements, such as di-2-ethylhexyl terephthalate (DEHTP) and di(isononyl) cyclohexane-1,2-dicaroxylate (DINCH), with inflammatory biomarkers during pregnancy. Finally, while multiple imputation with the LOD/2 is standard practice in environmental health, we acknowledge that such procedure may influence the distribution for phthalate metabolites with a lower detection frequency, such as MECPP and MEHP in this study.

Conclusions:

A major strength of our study was leveraging linear mixed effect regression with a novel extension of quantile g-computation, which enabled us to compare the association of single versus multiple phthalates with inflammatory biomarkers during pregnancy. To our knowledge, a similar modeling approach has been undertaken in only one other study that focused on oxylipins – a large family of bioactive lipid mediators of inflammation – and included Black women as a minority racial group.31 Herein, we analyzed circulating cytokines and an acute phase protein measured in routine lab tests ordered by clinicians that are indicative of anti-inflammatory, Th2 responses (e.g., IL-10) and pro-inflammatory, Th1 responses (e.g., IFN-γ). Therefore, our results have the potential to be scaled and translated to healthcare settings for improved maternal-child health. An additional strength was the study population comprised exclusively of pregnant African Americans, who experience disproportionate rates of phthalate exposures and health outcomes characterized by inflammation. We acknowledge, however, that racial homogeneity may be perceived to limit the generalizability of our results to pregnant people from other races/ethnicities. Taken altogether, the findings presented here may inform future strategies to identify immunological targets of phthalates and people at an increased risk of adverse pregnancy outcomes.

Supplementary Material

1

Highlights.

  • Phthalates are human-made chemicals that may cause systemic inflammation

  • Inflammation is linked with numerous adverse human health conditions and disorders

  • We analyzed repeated measures of phthalate metabolites and inflammatory biomarkers

  • Prenatal exposure to a phthalate mixture was associated with increased IFN-γ

  • IFN-γ is a cytokine that plays a key role in activation of cellular immunity

Acknowledgements:

We would like to thank the study participants who participated in the Atlanta African American Maternal-Child Cohort and the clinical health care providers and staff at the prenatal recruiting sites for helping with data and sample collection, logistics, and chemical analyses in the laboratory, especially Nathan Mutic, Priya D’Souza, Estefani Ignacio Gallegos, Nikolay Patrushev, Kristi Maxwell Logue, Castalia Thorne, Shirleta Reid, and Cassandra Hall. The authors report no conflicts of interest.

Funding Statement:

This research was supported by the Environmental influences on Child Health Outcomes (ECHO) program, Office of the Director, National Institutes of Health (NIH), under award numbers UG3/UH3OD023318 as well as NIH research grants [R21ES032117, R01NR014800, R01MD009064, R24ES029490, R01MD009746], NIH center grants [P50ES02607, P30ES019776, UH3OD023318, U2CES026560, U2CES026542], NIH training grants [T32 ES012870], and Environmental Protection Agency (USEPA) center grant [83615301]. Funding for Stephanie M Eick was provided by the JPB Environmental Health Fellowship.

Footnotes

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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.

Disclosure Statement:

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

CRediT Author Statement

Kaitlin R. Taibl was responsible for Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization.

Anne L. Dunlop contributed to Investigation, Resources, Data curation, Writing – review & editing, Project administration, Funding acquisition.

Dana Boyd Barr contributed to Methodology, Validation, Resources, Data curation, Writing – review & editing, Supervision, Funding acquisition.

P. Barry Ryan contributed to Methodology, Validation, Resources, Data curation, Writing – review & editing, Supervision, Funding acquisition.

Parinya Panuwet contributed to Data curation, Methodology, Writing – review & editing.

Elizabeth Corwin contributed to Data curation, Methodology, Writing – review & editing.

Jasmin Eatman contributed to Methodology, Resources, Writing – review & editing.

Youran Tan contributed to Data curation, Methodology, Resources, Writing – review & editing.

Donghai Liang contributed to Conceptualization, Methodology, Investigation, Writing – review & editing, Supervision, Funding acquisition.

Stephanie M. Eick was responsible for Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Supervision.

All authors read and approved the final manuscript.

Access to Data and Data Sharing:

The datasets for this manuscript are not publicly available because, per the National Institutes of Health (NIH)-approved Environmental influences on Child Health Outcomes (ECHO) Data Sharing Policy, the entirety of the ECHO-wide cohort data has not yet been made available to the public for review/analysis. Requests to access the datasets should be directed to the ECHO Data Analysis Center, ECHO-DAC@rti.org.

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Associated Data

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Supplementary Materials

1
NIHMS1961984-supplement-1.docx (515.8KB, docx)

Data Availability Statement

The datasets for this manuscript are not publicly available because, per the National Institutes of Health (NIH)-approved Environmental influences on Child Health Outcomes (ECHO) Data Sharing Policy, the entirety of the ECHO-wide cohort data has not yet been made available to the public for review/analysis. Requests to access the datasets should be directed to the ECHO Data Analysis Center, ECHO-DAC@rti.org.

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