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. 2026 Jun 23;60(26):18480–18493. doi: 10.1021/acs.est.6c01336

Joint Associations of Prenatal Per- and Polyfluoroalkyl Substances and Metal Mixtures with Adiposity in Childhood and Adolescence

Ixel Hernandez-Castro †, Sheryl L Rifas-Shiman ‡,§, Anna Smith †, Pi-I Debby Lin ‡,§, Abby Fleisch ∥,⊥, Diane R Gold #,∇, Mingyu Zhang ○, Izzuddin M Aris ‡,§, Brent Coull ◆, Marie-France Hivert ‡,§,, Emily Oken ‡,§, Andres Cardenas †,▼,*
PMCID: PMC13348671  PMID: 42333904

Abstract

Per- and polyfluoroalkyl substances (PFAS) and metals are ubiquitous environmental contaminants that have been individually linked to childhood adiposity, but their combined effects remain understudied. In the Project Viva cohort (n = 845), we evaluated joint associations of six first-trimester PFAS in plasma and five essential and six nonessential metals in erythrocytes with child and adolescent body mass index (BMI) z-scores and dual-energy X-ray absorptiometry (DXA) total and truncal fat mass indices. We used Bayesian kernel machine regression to evaluate joint associations of PFAS and metals with adiposity. Higher prenatal PFAS and nonessential metal mixture levels were significantly associated with higher mid-childhood and early adolescent BMI z-scores (75th vs 50th percentile: 0.17 [95% Credible Interval (CrI): 0.06, 0.28]; 0.14 [95% CrI: 0.02, 0.25]) and DXA total fat mass (0.17 kg/m2 [0.05, 0.30]; 0.20 kg/m2 [0.07, 0.32]), but not adiposity in late adolescence. Children with lower levels of the prenatal essential metal mixture had higher early and late adolescent DXA total fat mass (25th vs 50th percentile: 0.13 [0.04, 0.22]; 0.08 [0.01, 0.16]). Our findings underscore the importance of considering concurrent prenatal exposures across multiple chemical classes when evaluating environmental influences on child adiposity.

Keywords: PFAS, metals, mixtures, prenatal exposure, adiposity

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Introduction

Childhood adiposity is a key predictor of adult cardiometabolic health, with higher levels of body fat and central fat distribution linked to an elevated risk of hypertension, type 2 diabetes, and cardiovascular disease across the life course. − Over recent decades, the prevalence of adiposity among children and adolescents in the United States has increased substantially, heightening the public health imperative to identify modifiable risk factors. Adiposity develops through a complex interplay of genetic, behavioral, socioeconomic, and environmental factors, with early life exposure to environmental chemicals increasingly recognized as a key contributor to risk. −

Accumulating evidence suggests that endocrine-disrupting chemicals may influence adiposity by disrupting hormonal and metabolic pathways, especially during sensitive developmental windows. Ubiquitous environmental compounds, such as per- and polyfluoroalkyl substances (PFAS) and some nonessential metals, are of particular concern given their endocrine-disrupting properties, − widespread detection in pregnant populations, , and ability to cross the placenta. , Humans are commonly exposed to both PFAS and metals through shared routes, including ingestion of contaminated food, drinking water, inhalation of polluted air or dust, and contact with consumer or industrial products. − Essential metals possess antioxidant properties that support key biochemical and physiological functions at trace levels, , whereas nonessential metals have no known biological functions and can contribute to adverse health outcomes even at low levels. Mechanistic and epidemiologic studies suggest that PFAS and nonessential metals may act on overlapping biological pathways, disrupting lipid metabolism, mitochondrial function, hormonal signaling, and contributing to altered fat accumulation and metabolic disorders, − while essential metals may counteract the harmful effects of other environmental chemicals. ,

Prior cohort studies have reported independent associations of prenatal concentrations of PFAS and nonessential metals with greater adiposity in childhood and adolescence. − We previously reported that higher prenatal exposure to PFAS was associated with small increases in adiposity measures in mid-childhood among girls, and a prenatal PFAS mixture was linked to greater obesity risk in late adolescence in the Project Viva prebirth cohort. Similarly, we found that prenatal exposures to a mixture of nonessential metals were associated with higher adiposity in mid-childhood and early adolescence, while prenatal exposures to a mixture of essential metals were associated with lower early adolescence adiposity measures. However, most epidemiologic studies have evaluated PFAS and metals as separate chemical classes, despite growing recognition that real-world exposures often occur as complex mixtures spanning multiple classes of chemical contaminants. − Emerging evidence suggests that concurrent exposures may interact to modify toxicity and amplify adverse health effects, with previous in vitro studies demonstrating potential synergistic and antagonistic interactions between PFAS and heavy metal mixtures, as well as mixture-specific toxicological pathways.

Therefore, we aimed to assess the joint associations of first-trimester PFAS and metal mixtures on offspring adiposity measures in mid-childhood, early adolescence, and late adolescence among participants in the Project Viva cohort.

Materials and Methods

Study Population

Project Viva is a prebirth cohort of 2,128 mother-infant pairs recruited between 1999 and 2002 from a multispecialty practice in eastern Massachusetts. − Participants were eligible for the study during their first prenatal visit if they were able to participate in English, ≤22 weeks’ gestation, had a singleton pregnancy, and planned to remain in the study area throughout pregnancy. Participants were followed in early and mid-pregnancy visits, and mother-infant pairs were followed throughout delivery, early childhood (median= 3.3 years), mid-childhood (median= 7.7 years), and early (median= 12.9 years) and late adolescence (median= 17.5 years).

Of 2,128 live births, we included in the current analysis participants from the first enrolled pregnancy (n = 2,100), those with complete prenatal PFAS and metal measurements (n = 1,371), and those with at least one adiposity measure in either mid-childhood, early adolescence, or late adolescence (n = 845) (Figure S1). The study protocol was approved by the Institutional Review Board of Harvard Pilgrim Health Care Institute (Approval Number: 235301). Mothers provided written informed consent for themselves and their child at study entry and all postnatal visits, and children provided verbal assent beginning in mid-childhood or written informed consent when they reached age 18 years.

PFAS Concentrations

A detailed description of the analytic methods used to measure prenatal PFAS concentrations in Project Viva has been previously described. , Briefly, we collected maternal plasma samples at study enrollment (median = 9.6 weeks’ gestation) and stored samples in PFAS-free cryovials in liquid nitrogen freezers until shipment to the Centers for Disease Control and Prevention (CDC) laboratory for analysis. We quantified PFAS using online solid-phase extraction coupled with isotope dilution high-performance liquid chromatography-tandem mass spectrometry and only analyzed PFAS with detection frequencies >60% in this study, for consistency with prior analyses. Six PFAS met this criterion, including perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), perfluorohexane sulfonate (PFHxS), perfluorononanoate (PFNA), 2-(N-ethyl-perfluorooctane sulfonamido) acetate (EtFOSAA), and 2-(N-methyl-perfluorooctane sulfonamido) acetate (MeFOSAA). Sample limit of detection (LOD) was 0.2 ng/mL for PFOS and 0.1 ng/mL for all other PFAS. We imputed values below the LOD to the LOD/√2, and we log2-transformed PFAS concentrations prior to analysis.

Metal Concentrations

The analytical methods used to measure prenatal metal concentrations have been previously described. , In brief, we collected maternal blood samples at study enrollment (median= 9.6 weeks’ gestation). We centrifuged samples at 2,000 rpm for 10 min at 4 °C to isolate red blood cells and stored the samples at −70 °C. We measured all metal concentrations using a triple quadrupole inductively coupled plasma mass spectrometry (Agilent 8800), except for prenatal mercury, which was analyzed using a Direct Mercury Analyzer 80 (Milestone Inc.). To ascertain the validity of metal concentrations, we included procedural blanks, initial and continuous calibration verification, and blinded technical replicates.

We included in the current analysis metals with a detection frequency >60% and an intraclass correlation ≥0.6 among duplicates. This criterion was met among 5 nonessential metals (barium (Ba), cadmium (Cd), cesium (Cs), mercury (Hg), and lead (Pb)), 5 essential metals (copper (Cu), magnesium (Mg), manganese (Mn), selenium (Se), zinc (Zn)), and 1 nonessential metalloid (arsenic (As)), from now on referred to as a nonessential metal. All values below the LOD were imputed to the LOD/√2 and metal concentrations were log2-transformed.

Body Composition and Adiposity Measures

We collected body size and composition measures in mid-childhood (median = 7.7 years), early adolescence (median = 13.0 years), and late adolescence (median = 17.5 years). , We measured height using a stadiometer (Shorr Height Board; Shorr Productions) and weight using a calibrated scale (Tanita model TBF-300A). We derived body mass index for each child as weight (in kilograms) divided by height (in meters) squared and age- and sex-adjusted BMI z-scores were calculated using the CDC growth charts. We performed whole-body dual-energy X-ray absorptiometry (DXA) scans using Hologic model Discovery A (Hologic) among a subset of children. We analyzed DXA estimates of total fat mass index and truncal fat mass index, calculated as the total fat mass or truncal fat mass (in kilograms) divided by height (in meters) squared.

Covariates

We collected maternal demographic characteristics from interviews and questionnaires administered at study enrollment. We abstracted child’s assigned sex at birth from medical records. We selected potential confounders based on a priori literature and a directed acyclic graph (Figure S2). ,, Covariates selected included maternal age (years), race and ethnicity (Non-Hispanic Asian, Non-Hispanic Black, Hispanic, Non-Hispanic white, or more than one race or ethnicity), parity (nulliparous or >1), pre-pregnancy BMI, smoking status during pregnancy (never, former, or during pregnancy), maternal educational attainment (<college or college graduate), household income (≤$70,000 per year or >$70,000 per year), and child sex (female or male). We used maternal-reported pre-pregnancy weight and height to calculate pre-pregnancy BMI (kg/m2). We adjusted for maternal race and ethnicity as a potential proxy for unmeasured variables originating from structural racism.

Statistical Analysis

We evaluated descriptive statistics for participant demographic characteristics, first-trimester PFAS and metal concentrations, and adiposity measures in childhood and adolescence. We calculated Spearman correlation coefficients to assess correlations of prenatal PFAS and metal concentrations with adiposity measures at each time point.

To examine the joint associations of prenatal PFAS and metals with adiposity, we employed Bayesian kernel machine regression (BKMR), a flexible and semi-parametric modeling approach that uses a Gaussian kernel function to estimate complex, potentially nonlinear and interactive exposure-response relationships. Since essential metals may exert beneficial impacts, which could be masked by the adverse effects of PFAS and nonessential metals in a single mixture model, we separately modeled prenatal PFAS and nonessential metals with adiposity while adjusting for essential metals, and prenatal essential metals with adiposity while adjusting for prenatal PFAS and nonessential metals. Hierarchical variable selection was implemented to group PFAS and nonessential metals into separate exposure classes, while component-wise variable selection was used in prenatal essential metal mixture models. We specified the Gaussian predictive process at 100 knots, and all continuous covariates were mean-centered and standard deviation scaled prior to modeling. DXA-derived total and truncal fat mass indices were log2-transformed, mean-centered, and standard deviation scaled. We used the Markov chain Monte Carlo (MCMC) sampler to obtain 50,000 posterior samples, and we used half of the iterations as burn-in, with chains thinned to every 10th iteration. We visually assessed convergence using trace plots.

We estimated the overall joint associations of the prenatal PFAS and nonessential metal mixture, as well as the essential metal mixture, on each adiposity measure (BMI z-score, DXA total fat mass index, DXA truncal fat mass index) in mid-childhood, early adolescence, and late adolescence. Additionally, we examined the univariate exposure-response relationship for each chemical and adiposity measure. Exclusion of the null in the 95% Credible Intervals was indicative of mixture associations. We also estimated Posterior Inclusion Probabilities (PIPs) to assess the relative importance of each chemical in the joint mixture association and visually examined pairwise interactions between each exposure while varying a second exposure to its 25th, 50th, and 75th percentiles, holding other exposures at their median. Potential pairwise interactions identified through visual inspection were further evaluated using linear regression models with a multiplicative interaction term. Given prior evidence of potential effect modification by child sex, , we also stratified mixture models by child sex, setting the MCMC sampler to obtain 100,000 posterior samples to account for the smaller sample sizes.

Sensitivity Analyses

We conducted multiple sensitivity analyses to evaluate the robustness of our findings. First, to examine potential pairwise interactions between essential metals, PFAS, and nonessential metals, we used BKMR with hierarchical variable selection, grouping each exposure into a separate class. Additionally, to account for the potential influence of extreme values on BKMR estimates, we performed sensitivity analyses excluding participants with log2-transformed metal concentrations greater than three times the interquartile range (n = 25), consistent with Tukey’s criteria for extreme outliers. As a complementary approach to our BKMR models, we used quantile g-computation, a parametric and generalized linear model approach, to assess the impact of increasing all exposures by one quartile on the outcomes. We used a nonparametric bootstrap (B = 1,000) to obtain the variance and the “qgcomp” package to estimate the models. We replicated quantile g-computation analyses of previously reported single-class chemical mixtures in Project Viva studies to compare the joint estimates in the present study using the same analytical sample and modeling parameters. ,

We also performed linear regression models for associations not previously published in Project Viva studies, which included associations of prenatal EtFOSAA and MeFOSAA with adiposity measures in mid-childhood, prenatal PFAS with adiposity measures in early adolescence, and prenatal metals with adiposity measures in late adolescence. We then assessed for interactions by child sex in those linear regression models by using a multiplicative term, with p-interaction <0.05 used as the criteria for a statistically significant interaction. Finally, for statistically significant interactions, we then stratified linear regression models by child sex.

We conducted BKMR analyses using the “bkmr” package, and all statistical analyses were performed in R (R v.4.3).

Results

Descriptives

Table shows the characteristics of mother-child pairs analyzed in this study across mid-childhood and adolescence, along with adiposity and body composition measures. Overall, maternal participants were primarily non-Hispanic White (72.9%), had an annual household income of > $70,000 USD (63.2%), held a college education (73.4%), and were a mean (SD) of 32.7 (4.8) years old. Participant sample characteristics were similar across analytical samples. BMI z-scores remained consistent across time points, while DXA-derived total and truncal fat mass indices increased over time. Adiposity and body composition measures were generally strongly correlated (ρ>0.76) within time points, but moderately correlated (ρ>0.48) across time points (Figure S3).

1. Participants’ Characteristics of Mother-Child Pairs from Project Viva with Prenatal Metals and PFAS Measured, and at Least One of the Adiposity-Related Outcomes Measurements (N = 845) .

Characteristic Overall mean ±SD/N(%)(N = 845) Mid-childhood mean ± SD/N(%) (N = 736) Early adolescence mean ±SD/N(%) (N = 707) Late adolescence mean ±SD/N(%) (N = 477)
Child age (years) – 8.0 ± 0.8 13.3 ± 1.0 17.7 ± 0.6
Maternal age at enrollment (years) 32.7 ± 4.8 32.6 ± 4.9 32.7 ± 4.7 32.9 ± 4.6
Prepregnancy BMI (kg/m2) 24.8 ± 5.2 24.6 ± 5.2 24.8 ± 5.2 24.6 ± 5.3
Race and ethnicity        
Non-Hispanic White 616 (72.9) 532 (72.3) 516 (73.0) 346 (72.5)
Non-Hispanic Asian 34 (4.0) 32 (4.3) 26 (3.7) 21 (4.4)
Non-Hispanic Black 105 (12.4) 93 (12.6) 91(12.9) 55 (11.5)
Hispanic 54 (6.4) 48 (6.5) 45 (6.4) 30 (6.3)
More than one race or ethnicity 36 (4.3) 31 (4.2) 29 (4.1) 25 (5.2)
Education attainment        
College graduate 620 (73.4) 533 (72.4) 529 (74.8) 364 (76.3)
Annual household income        
>USD $70,000 534 (63.2) 468 (63.6) 452 (63.9) 312 (65.4)
Smoking status        
Never smoked 598 (70.8) 528 (71.7) 496 (70.2) 341 (71.5)
Former smoker 166 (19.6) 142 (19.3) 145 (20.5) 89 (18.7)
Smoked during pregnancy 81 (9.6) 66 (9.0) 66 (9.3) 47 (9.9)
Nulliparous 408 (48.3) 355 (48.2) 347 (49.1) 232 (48.6)
Infant sex        
Female 408 (48.3) 358 (48.6) 350 (49.5) 243 (50.9)
Adiposity outcomes        
Body mass index (z-score) – 0.38 ± 1.00 0.38 ± 1.06 0.39 ± 1.05
DXA total-fat-mass-index (kg/m2) – 4.40 ± 1.98 6.37 ± 3.16 7.15 ± 3.59
DXA trunk-fat-mass-index (kg/m2) – 1.49 ± 0.90 2.44 ± 1.51 3.01 ± 1.87
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a

Abbreviations: PFAS, per- and polyfluoroalkyl substances; BMI, body mass index; SD, standard deviation; USD, United States Dollar; BMI, body mass index; DXA, dual-energy X-ray absorptiometry.

b

N = 590.

c

N = 505.

d

N = 385.

Distributions of first-trimester metals and PFAS are shown in Table S1. Detection frequencies for selected metals and PFAS ranged from 91% to 100% across the study population. Median concentrations of Cd were broadly consistent with levels reported in whole blood among pregnant participants in the National Health and Nutrition Examination Survey (NHANES [1999–2016]), while Hg and Pb concentrations in our study appeared slightly higher. As illustrated in a prior study, median concentrations of common PFAS in this cohort, such as PFOS, PFOA, PFHxS, and PFNA, were similar to those measured in the NHANES population between the 1999 and 2000 cycle. As shown in Figure S4, prenatal PFAS were mildly to moderately correlated with one another, with correlation coefficients ranging from 0.18 (PFNA and EtFOSAA) to 0.74 (PFOA and PFOS). Prenatal metals were generally weakly to moderately correlated with one another, with the strongest correlation observed between Zn and Cu (ρ=0.58) (Figure S4). Although the majority of PFAS and metals were not correlated with one another, some PFAS and metal correlations were observed. Namely, Cd was negatively correlated with PFOS (ρ= −0.11), PFOA (ρ= −0.09), PFNA (ρ= −0.07), and PFHxS (ρ= −0.14), while PNFA was positively correlated with As (ρ = 0.33), Cs (ρ= 0.15), Pb (ρ= 0.10), Hg (ρ= 0.32), and Se (ρ= 0.09). Lastly, Se was negatively correlated with EtFOSAA (ρ = −0.10) and MeFOSAA (ρ = −0.12), while MeFOSAA was negatively correlated with As (ρ = −0.09), Cs (ρ = −0.09), Hg (ρ = −0.08), and Mg (ρ = −0.08).

Mixture Analyses

Associations of the first-trimester PFAS and nonessential metal mixture with adiposity measures across time points are shown in Figure . Higher levels of the prenatal PFAS and nonessential metal mixture were associated with higher mid-childhood BMI z-scores (75th vs 50th percentile: 0.17 [95% CrI: 0.06, 0.28]) and DXA total (0.17 kg/m2 [95% CrI: 0.05, 0.30]) and truncal fat mass indices (0.20 kg/m2 [95% CrI: 0.08, 0.32]). Similarly, we observed higher early adolescent BMI z-scores (75th vs 50th percentile: 0.14 [95% CrI: 0.02, 0.25]) and DXA total (0.20 kg/m2 [95% CrI: 0.07, 0.32]) and truncal fat mass indices (0.25 kg/m2 [95% CrI: 0.11, 0.39]) with higher levels of the prenatal PFAS and nonessential metal mixture (Table S2). A similar positive but attenuated pattern was observed in late adolescence, with estimates including the null. Conversely, lower levels of the essential metal mixture were associated with higher BMI z-scores (25th vs 50th percentile: 0.08 [95% CrI: 0.01, 0.14]) and DXA total (0.13 [95% CrI: 0.04, 0.22]) and truncal fat mass indices (0.12 [95% CrI: 0.03, 0.21]) in early adolescence (Figure and Table S2) and higher DXA total (0.08 [95% CrI: 0.01, 0.16]) and truncal (0.09 [95% CrI: 0.01, 0.18]) fat mass indices in late adolescence.

1.

1

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Overall effect and 95% credible interval of the prenatal PFAS and nonessential metal mixture with BMI z-score and DXA measures in mid-childhood (n = 736 and n = 590) and early (n = 707 and n = 505) and late adolescence (n = 477 and n = 385), using BKMR. Figure illustrates the estimated difference in BMI z-score or DXA measure when setting all exposures to the percentile specified in the x-axis compared to their median values. Models were adjusted for maternal age, race and ethnicity, parity, pre-pregnancy BMI, smoking during pregnancy, educational attainment, household income, and first trimester essential metals, and child sex.

2.

2

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Overall effect and 95% credible interval of the essential metal mixture with BMI z-score and DXA measures in mid-childhood (n = 736 and n = 590) and early (n = 707 and n = 505) and late adolescence (n = 477 and n = 385), using BKMR. Figure illustrates the estimated difference in BMI z-score or DXA measure when setting all exposures to the percentile specified in the x-axis compared to their median values. Models were adjusted for maternal age, race and ethnicity, parity, pre-pregnancy BMI, smoking during pregnancy, educational attainment, household income, child sex, and prenatal PFAS and nonessential metals.

Based on group-level PIPs (Table S3), associations between prenatal PFAS and nonessential metals and mid-childhood adiposity were primarily driven by PFAS chemicals, particularly EtFOSAA and PFOS. In early adolescence, associations between prenatal PFAS and nonessential metals and the BMI z-score models were largely influenced by PFAS, such as EtFOSAA, whereas associations for DXA-derived total and truncal fat mass indices were predominantly driven by nonessential metals, specifically Cd. Associations between essential metal mixtures and adiposity measures were primarily driven by Se and Mn in early and late adolescence. When examining univariate exposure–response plots for the top-ranked conditional PIPs in the prenatal PFAS and nonessential metal mixtures models, we found that patterns were generally consistent with the overall effect plots in mid-childhood and early adolescence (Figure ), but associations between cadmium and DXA total and truncal fat mass indices in early adolescence were approximately J-shaped. In univariate exposure-response plots examining associations between prenatal essential metals and adiposity measures in top-ranked PIPs, Se and Mn were inversely associated with adiposity measures in early and late adolescence, but associations appeared slightly nonlinear, with wide confidence intervals (Figure ). Associations for the other univariate exposure-response plots are shown in Figure S5 and Figure S6. We did not find strong evidence of pairwise interactions between prenatal PFAS and nonessential metal mixtures and any of the adiposity measures and time points. There was some evidence of a potential pairwise interaction between Zn and Mg with DXA total and truncal fat mass indices in early adolescence (Figure S7), with stronger inverse associations when Mg levels were lower. However, we did not observe a statistically significant interaction between Zn and Mg in linear regression models for DXA total (p = 0.60) and truncal fat mass (p = 0.57).

3.

3

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Exposure-response associations with 95% credible intervals of top-ranked conditional Posterior Inclusion Probabilities (PIPs) for prenatal PFAS and nonessential metals with BMI z-scores and DXA measures in mid-childhood (n = 736 and n = 590), early adolescence (n = 707 and n = 505), and late adolescence (n = 477 and n = 385), when all other exposures are set at their median, using BKMR. Models were adjusted for maternal age, race and ethnicity, parity, pre-pregnancy BMI, smoking status, college graduate, household income, child sex, and first trimester essential metals.

4.

4

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Exposure-response associations with 95% credible intervals of top-ranked conditional Posterior Inclusion Probabilities (PIPs) for essential metals with BMI z-scores and DXA measures in mid-childhood (n = 736 and n = 590), early adolescence (n = 707 and n = 505), and late adolescence (n = 477 and n = 385), when all other exposures are set at their median, using BKMR. Models were adjusted for maternal age, race and ethnicity, parity, pre-pregnancy BMI, smoking status, college graduate, household income, child sex, and prenatal PFAS and nonessential metals.

Sex-stratified associations of the first trimester PFAS and nonessential metal mixture were generally consistent with the overall associations for both female (Figure S8) and male-stratified models (Figure S9), but attenuated. Female-stratified models showed a stronger positive association in mid-childhood compared with male-stratified models, whereas male-stratified models had stronger positive associations in adolescence. According to the group-level PIPs in female-stratified models (Table S4), mid-childhood associations of prenatal PFAS and nonessential metals with BMI z-scores and DXA total fat mass index were primarily driven by nonessential metals, specifically Cs, while associations with the DXA trunk fat mass index were primarily driven by PFAS, particularly EtFOSAA. In early and late adolescence, female-stratified associations between the prenatal PFAS and nonessential metal mixture were driven by EtFOSAA across all adiposity measures. In male-stratified models (Table S5), mid-childhood associations of the prenatal PFAS and nonessential metal mixture with BMI z-scores were driven by nonessential metals, such as Hg, and associations with the DXA total and truncal fat mass index were driven by EtFOSAA and MeFOSAA, respectively. Male-stratified associations in early adolescence and late adolescence were led by Cd across all adiposity measures, except for late adolescence BMI z-scores, which were primarily driven by Ba.

Associations of the first-trimester essential metal mixtures were attenuated for both female (Figure S10) and male-stratified models (Figure S11) when compared to the overall associations, but a stronger inverse association was observed among male-stratified models in early and late adolescence relative to female-stratified models. In female-stratified models, associations between the essential metal mixture and BMI z-scores were primarily driven by Cu across all time points, and associations with DXA total and truncal fat mass indices were led by Zn and Se in early adolescence, and Se in late adolescence, respectively. In male-stratified models, Se was the highest ranked PIPs in early adolescence for BMI z-scores and DXA total and truncal fat mass indices, while Mn was the highest ranked PIP in late adolescence.

Sensitivity Analyses

Overall, mixture effects of prenatal PFAS, essential metals, and nonessential metals on adiposity in mid-childhood and early adolescence were similar to the primary results, though slightly attenuated (Figure S12). We did not find evidence of pairwise interactions across time points. Similarly, mixture models excluding extreme outliers were consistent with our primary findings (Figure S13 and Figure S14). In secondary analyses using quantile g-computation (Table S6), results were similar to our BKMR findings. A one-quartile increase in the prenatal PFAS and nonessential metal mixture was associated with higher mid-childhood and early adolescence BMI z-scores (β = 0.33, 95% CI: 0.17, 0.49; β = 0.29, 95% CI: 0.11, 0.46) and DXA total (β = 0.61 kg/m2, 95% CI: 0.27, 0.94; β = 0.63 kg/m2, 95% CI: 0.03, 1.23) and truncal (β = 0.30 kg/m2, 95% CI: 0.15, 0.45; β = 0.32 kg/m2, 95% CI: 0.04, 0.61) fat mass indices. We observed a similar pattern in the late adolescence time point, but 95% confidence intervals included the null. We also found inverse associations between the prenatal essential metal mixture, adjusting for prenatal PFAS and nonessential metals, with BMI z-scores (β = −0.13, 95% CI: −0.24, −0.02) and DXA total (β = −0.52 kg/m2, 95% CI: −0.86, −0.18) and truncal fat (β = −0.24 kg/m2, 95% CI: −0.41, −0.07) mass indices in early adolescence. When compared to the single-class mixtures (nonessential metals or PFAS), the joint PFAS and nonessential metal mixture had stronger effect estimates across adiposity measures and time points, suggesting additive effects.

We did not find any individual associations in adjusted linear regression models examining first-trimester metals and adiposity outcomes in late adolescence (Table S7). In linear models examining associations of prenatal PFAS with adiposity outcomes in mid-childhood (Table S8) and early adolescence (Table S9), we found significant positive associations between EtFOSAA and all adiposity outcomes. Higher MeFOSAA concentrations were also associated with higher BMI z-scores and DXA total and truncal fat mass indices in mid-childhood.

We found various significant interactions between prenatal PFAS and metals with child sex across outcomes in childhood and adolescence (Figure S15). For example, when models were stratified by sex, higher EtFOSAA was associated with higher early adolescence DXA total fat mass index among female models only. In late adolescence, higher Ba was associated with higher BMI z-scores in males only, while females with higher Se had lower DXA total and truncal fat mass indices in late adolescence.

Discussion

In this prospective study, higher prenatal concentrations of a mixture of PFAS and nonessential metals were associated with greater adiposity in childhood and early adolescence, with associations primarily driven by PFAS in mid-childhood and both PFAS and metals in early adolescence. In contrast, lower prenatal concentrations of essential metals were associated with higher adiposity in early and late adolescence, primarily driven by selenium and manganese. We found some evidence of sex-specific interactions for individual chemicals, but sex-stratified mixture associations were largely consistent with the patterns observed in the overall mixture associations. We also found potential pairwise interactions between Zn and Mg with DXA total and truncal fat mass indices in early adolescence mixture models, but these interactions were not supported in traditional regression models. No evidence of pairwise interactions was observed between the prenatal PFAS and nonessential metal mixture or the prenatal PFAS, essential metals, and nonessential metal mixture and adiposity measures. Altogether, our findings suggest that prenatal exposure to PFAS and nonessential metal mixtures may contribute to greater adiposity in mid-childhood and early adolescence, whereas a higher prenatal essential metal mixture may be associated with lower adiposity during adolescence.

Our study is among the first to jointly examine prenatal PFAS and nonessential metal mixtures with child adiposity, contributing to the limited but growing body of research on multi-class chemical mixtures and their combined influence on developmental adiposity. ,, Of these studies, most have evaluated prenatal PFAS in combination with non-persistent chemicals, such as phthalates or phenols, finding adverse associations with adiposity and metabolic outcomes in mid-childhood, but inverse associations in the first 2 years of life. ,,, Other studies have investigated joint prenatal PFAS and metals in relation to other outcomes, such as birthweight, finding inverse associations between prenatal essential metals and larger head circumference. In our study, we found positive associations of prenatal PFAS and nonessential metals with mid-childhood and early adolescent adiposity measures, primarily driven by EtFOSAA and PFOS in mid-childhood and EtFOSAA and Cd in early adolescence, suggesting potential age-specific susceptibility to adiposity.

Most studies to date have focused on the single-class effects of either prenatal PFAS or metals on adiposity outcomes in childhood and adolescence, finding inconsistent associations. ,− Despite this heterogeneity, pooled analyses and studies of prenatal PFAS mixtures generally suggest positive associations with childhood adiposity, consistent with our findings. ,− For instance, a study in the Boston Birth Cohort found that an IQR increase in the prenatal PFAS mixture (MeFOSAA, PFDeA, PFHpS, PFHxS, PFNA, PFOA, PFOS, PFUnA) was associated with higher BMI z-scores (β = 0.09, 95% CI: −0.02, 0.20) and an increased risk of overweight or obesity status among children aged 2 to 18 years. When similarly applying quantile g-computation, we observed stronger associations of the prenatal PFAS and nonessential metal mixture with BMI z-scores in mid-childhood and early adolescence, and a similar pattern in late adolescence. In a prior Project Viva study, a higher prenatal PFAS mixture concentration was associated with an increased risk of obesity in late adolescence. Although we observed similar patterns in late adolescence across our adiposity measures, we did not find significant associations between the prenatal PFAS and nonessential metal mixture and adiposity measures. Differences across these findings may reflect variation in analytic approaches and outcomes used, since the prior Project Viva study primarily reported mixture associations for obesity risk and BMI, whereas their findings for BMI z-scores and DXA-derived adiposity measures were generally consistent with the trends observed in our study.

Similarly, evidence linking prenatal nonessential metal mixtures with childhood adiposity is limited and heterogeneous, though most studies have found positive associations. ,,− Consistent with our findings, a prior Project Viva study similarly found positive associations between the nonessential metal mixture and adiposity measures in mid-childhood and early adolescence, driven by Cd and Cs. However, compared to these findings, we found larger effect estimates between the joint prenatal PFAS and nonessential metal mixture model and higher BMI z-scores, DXA total, and truncal fat mass indices in mid-childhood and early adolescence when using g-computation for comparability. Even under aligned analytic conditions, the current effect estimates for prenatal PFAS and nonessential metal mixtures remained larger than those for single-class chemical mixtures, consistent with additive contributions across exposure classes, suggesting that cumulative joint exposures may play an important role in adiposity in childhood and adolescence.

Higher prenatal essential metal mixtures have generally been inversely associated with adiposity measures in limited prior studies. , For instance, a previous study in a prospective cohort in Mexico found inverse associations of prenatal metal mixtures with cardiometabolic risk factors in children aged 4 to 6 years, suggesting that essential metals, particularly Se and Mn, may play a protective role in metabolic regulation. In a previous analysis within the Project Viva cohort, a prenatal mixture of essential metals (Mg, Mn, Se, Zn) was inversely associated with adiposity measures in early adolescence. Building on this prior analysis, we extended the evaluation of essential mixture associations to late adolescence, incorporating copper into the exposure mixture and additionally adjusting for PFAS. Along with observing a consistent association in both magnitude and inverse direction between the prenatal mixture of essential metals (Mg, Mn, Se, Zn, Cu) and adiposity measures in early adolescence, we additionally observed that these protective associations persisted into late adolescence and were primarily driven by Se and Mn. We also found some evidence of interactions in mixture models, with stronger inverse associations between Zn and DXA total and truncal fat mass indices in early adolescence when Mg levels were lower.

The observed associations of prenatal PFAS and nonessential metal exposures with higher offspring adiposity may be biologically plausible, as these chemicals act through distinct mechanisms that converge on shared metabolic pathways. PFAS can bind to and activate peroxisome proliferator-activated receptors (PPARα and PPARγ), which regulate adipogenesis, adipocyte physiology, and energy homeostasis. − Epidemiologic studies have further linked PFOS and PFOA exposure to reduced DNA cytosine methylation and epigenetic alterations in lipid pathways, which may reflect broader disruptions in metabolic regulation. , Nonessential metals, such as cadmium and mercury, can generate reactive oxygen species, resulting in inflammation, genotoxicity, and oxidative stress, which may impair adipogenesis and adipocytokine secretion. ,− Both chemical classes can also disrupt endocrine signaling and epigenetic regulation during critical windows of development, potentially leading to long-lasting effects on adipocyte differentiation and metabolic programming. ,, Conversely, essential metals, such as Se and Mn, are important antioxidants, which may confer protective effects to metabolic processes at therapeutic levels. ,

Growing evidence suggests that adiposity-based measures provide a more sensitive and physiologically relevant assessment of early metabolic risk than BMI-based obesity classifications alone, capturing variations in body composition that may precede overt disease. − Higher adiposity in children and adolescence is a strong predictor of adult cardiometabolic disease and incident cardiovascular events, − with prenatal exposures to PFAS and metals potentially influencing early life adiposity trajectories during critical periods of metabolic programming. , Given the widespread nature of these exposures, even small shifts in adiposity during early life can translate into meaningful population-level impacts on cardiovascular risk.

Our study had some limitations. The Project Viva cohort is predominantly composed of higher-income, non-Hispanic White participants, which may limit generalizability to pregnant populations in the United States. Moreover, exposure distributions of PFAS in this cohort may be higher than the concentrations more recently observed in the U.S. population, further limiting the generalizability of our findings. Despite adjusting for a comprehensive set of confounders in our analyses, residual confounding by unmeasured factors cannot be ruled out since this was an observational study. Additionally, exposure to higher levels of PFAS and nonessential metals has previously been associated with delayed pubertal development in girls, − and increased adiposity has been linked to earlier pubertal timing. Since pubertal timing may lie on the causal pathway between prenatal PFAS or metals and adiposity, − we did not include pubertal status as a covariate in our models. Future research should further explore the role of puberty timing as a mediator of the associations of prenatal PFAS and metals with adiposity in adolescence. Furthermore, smaller sample sizes at later time points may have limited our ability to detect associations. Lastly, erythrocyte biomarkers are not ideal measures for all metals. For instance, arsenic is better assessed using urinary measures that distinguish between its organic and inorganic forms, while red blood cells are reliable measures of cadmium, lead, magnesium, and manganese. −

Our study also had several notable strengths. First, the use of biomarker-based measures of first-trimester PFAS in plasma and metal exposures in red blood cells provides a reliable estimate of in-utero exposure during a critical period of fetal development. − Second, the prospective design of this study, with repeated, objectively measured DXA-derived adiposity indices from mid-childhood to late adolescence, enabled the assessment of developmental changes in body composition rather than relying on BMI z-scores alone. Finally, the application of advanced mixture modeling allowed for the assessment of both joint and class-specific effects across PFAS and metals.

By integrating multi-class chemical mixtures, our study found that higher prenatal PFAS and nonessential metal mixtures were associated with greater adiposity in mid-childhood and early adolescence but attenuated in late adolescence, with evidence suggesting time-varying and chemical class-specific contributions. Overall, these findings emphasize the importance of considering the additive effect of environmental exposures during early development, as co-exposure to PFAS and nonessential metals may contribute to greater adiposity than captured through single-class chemical mixture models alone. Since both PFAS and metals remain pervasive in the environment, understanding their combined influence is critical for identifying modifiable risk factors for child adiposity. Future studies should replicate these findings in larger and more diverse populations and explore shared mechanistic pathways that may underlie these associations.

Supplementary Material

es6c01336_si_001.pdf (2.3MB, pdf)

Acknowledgments

We would like to thank the Project Viva staff and participants. The TOC graphic was created using BioRender.com.

Glossary

Abbreviations

As

arsenic

Ba

barium

BKMR

Bayesian kernel machine regression

BMI

body mass index

Cd

cadmium

Cs

cesium

Cu

copper

DXA

dual-energy X-ray absorptiometry

EtFOSAA

2-(N-ethyl-perfluorooctane sulfonamido) acetate

Hg

mercury

MCMC

Markov chain Monte Carlo

MeFOSAA

2-(N-methyl-perfluorooctane sulfonamido) acetate

Mg

magnesium

Mn

manganese

Pb

lead

PFAS

per- and polyfluoroalkyl substances

PFDeA

perfluorodecanoic acid

PFHpS

perfluoroheptanesulfonic acid

PFHxS

perfluorohexane sulfonate

PFNA

perfluorononanoate

PFOA

perfluorooctanoate

PFOS

perfluorooctane sulfonate

PFUnA

perfluoroundecanoic acid

Se

selenium

Zn

zinc

Data and materials generated and analyzed for this study will be made available from the corresponding author upon reasonable request and with appropriate permission from the Project Viva team. A detailed description of Project Viva data-sharing policies can be found at: https://www.projectviva.org/for-investigators.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.6c01336.

  • Project Viva participants included in the final analytic sample (Figure S1); Directed Acyclic Graphs (DAGs) for the relationship between A) prenatal PFAS and child adiposity and B) prenatal metals and child adiposity (Figure S2); Spearman correlations of body mass index and dual-energy X-ray absorptiometry adiposity measures (N = 736–385) (Figure S3); Spearman correlations of first-trimester per- and polyfluoroalkyl substances (PFAS) and metals (N = 845) (Figure S4); Univariate plot of the exposure-response association and 95% credible intervals of each PFAS and nonessential metal exposure with BMI z-score and DXA measures in midchildhood (n = 736 and n = 590), early (n = 707 and n = 505), and late adolescence (n = 477 and n = 385), when all other exposures are set at their median (Figure S5); Univariate plot of the exposure-response association and 95% credible intervals of each essential metal exposure with BMI z-score and DXA measures in midchildhood (n = 736 and n = 590), early (n = 707 and n = 505), and late adolescence (n = 477 and n = 385), when all other exposures are set at their median (Figure S6); Bivariate concentration–response curves evaluating associations between each first-trimester essential metal with A) DXA total mass index and B) DXA truncal fat mass index (n = 505) (Figure S7); Female Stratified- Overall effect and 95% credible interval of the prenatal PFAS and nonessential metal mixture with BMI z-score and DXA measures in midchildhood (n = 358 and n = 293), early (n = 350 and n = 257), and late adolescence (n = 243 and n = 205), using BKMR (Figure S8); Male Stratified- Overall effect and 95% credible interval of the prenatal PFAS and nonessential metal mixture with BMI z-score and DXA measures in midchildhood (n = 378 and n = 297), early (n = 357 and n = 248), and late adolescence (n = 234 and n = 180), using BKMR (Figure S9); Female Stratified- Overall effect and 95% credible interval of the essential metal mixture with BMI z-score and DXA measures in midchildhood (n = 358 and n = 293), early (n = 350 and n = 257), and late adolescence (n = 243 and n = 205), using BKMR (Figure S10); Male Stratified- Overall effect and 95% credible interval of the essential metal mixture with BMI z-score and DXA measures in midchildhood (n = 378 and n = 297), early (n = 357 and n = 248), and late adolescence (n = 234 and n = 180), using BKMR (Figure S11); Overall effect and 95% credible interval of the prenatal PFAS and essential and nonessential metal mixture with BMI z-score and DXA measures in midchildhood (n = 736 and n = 590), early (n = 707 and n = 505), and late adolescence (n = 477 and n = 385), using BKMR (Figure S12); Overall effect and 95% credible interval of the prenatal PFAS and nonessential metal mixture with BMI z-score and DXA measures in midchildhood (n = 714 and n = 576), early (n = 683 and n = 490), and late adolescence (n = 463 and n = 373), using BKMR and excluding extreme outliers (Figure S13); Overall effect and 95% credible interval of the prenatal essential metal mixture with BMI z-score and DXA measures in midchildhood (n = 714 and n = 576), early (n = 683 and n = 490), and late adolescence (n = 463 and n = 373), using BKMR and excluding extreme outliers (Figure S14); Associations of prenatal PFAS and metals with adiposity measures in adolescence, stratified by sex (Figure S15); Distributions of first-trimester metals and per- and polyfluoroalkyl substances for participants with all exposure measures and at least one outcome measure (n = 845) (Table S1); BKMR results for the covariate-adjusted joint effects of the prenatal per- and polyfluoroalkyl substances (PFAS) and metal mixture with BMI and DXA adiposity measures (Table S2); Group and conditional posterior inclusion probabilities representing the relative importance of each exposure within each exposure group to the overall mixture effect (Table S3); Female Stratified- Group and conditional posterior inclusion probabilities representing the relative importance of each exposure within each exposure group to the overall mixture effect (Table S4); Male Stratified- Group and conditional posterior inclusion probabilities representing the relative importance of each exposure within each exposure group to the overall mixture effect (Table S5); Quantile g-computation results for the covariate-adjusted joint effects of the prenatal per- and polyfluoroalkyl substances (PFAS) and metal mixture with BMI and DXA adiposity measures (Table S6); Association between first-trimester log2-transformed metals and adiposity outcomes in late adolescence (Table S7); Associations of first-trimester EtFOSAA and MeFOSAA with adiposity outcomes in midchildhood (Table S8); Association between first-trimester per- and polyfluoroalkyl substances (PFAS) and adiposity outcomes in early adolescence (Table S9) (PDF)

This work was supported by the U.S. National Institutes of Health grants R01ES031259, P42ES004705, UG3 OD023286, and P30ES000002. Project Viva is supported by NIH grants R01HD034568 and R24ES030894. Additional financial support for training was provided by the Stanford Propel Fellowship. Dr. Zhang is supported by an American Heart Association Career Development (24CDA1257852). Dr. Fleisch is supported by NIEHS grant R01ES030101.

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

es6c01336_si_001.pdf (2.3MB, pdf)

Data Availability Statement

Data and materials generated and analyzed for this study will be made available from the corresponding author upon reasonable request and with appropriate permission from the Project Viva team. A detailed description of Project Viva data-sharing policies can be found at: https://www.projectviva.org/for-investigators.

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