Prenatal PFAS exposure and outcomes related to maternal gut microbiome composition in later pregnancy

Stephanie Morgan; Sayed Hassan Raza Shah; Sarah S Comstock; Jaclyn M Goodrich; Donghai Liang; Youran Tan; Kimberly McKee; Douglas Ruden; Alexandra R Sitarik; Andrea E Cassidy-Bushrow; Anne L Dunlop; Michael C Petriello

doi:10.1016/j.envres.2025.121709

. Author manuscript; available in PMC: 2026 Aug 15.

Published in final edited form as: Environ Res. 2025 Apr 29;279(Pt 1):121709. doi: 10.1016/j.envres.2025.121709

Prenatal PFAS exposure and outcomes related to maternal gut microbiome composition in later pregnancy

Stephanie Morgan ¹, Sayed Hassan Raza Shah ², Sarah S Comstock ³, Jaclyn M Goodrich ⁴, Donghai Liang ⁵, Youran Tan ⁵, Kimberly McKee ⁶, Douglas Ruden ¹, Alexandra R Sitarik ^7,⁸, Andrea E Cassidy-Bushrow ^7,^9,¹⁰, Anne L Dunlop ¹¹, Michael C Petriello ^1,¹², on behalf of program collaborators for Environmental influences on Child Health Outcomes

PMCID: PMC12136995 NIHMSID: NIHMS2080631 PMID: 40311903

Abstract

The composition of the gut microbiome is dependent on factors including diet, lifestyle, and exposure to environmental chemicals, and has implications for human health. Per- and polyfluoroalkyl substances (PFAS), a class of man-made chemicals that have nonstick and flame-retardant properties may impact on gut microbiome composition. Our objective was to elucidate links between PFAS and maternal gut microbiome composition in two geographically diverse sites of the Environmental Influences on Child Health Outcomes program. The present analysis includes participants in the Atlanta African American Maternal Child Cohort;ATL AA and a predominately non-Hispanic White subsample of the Michigan Archive for Research on Child Health Cohort;MARCH with serum or plasma PFAS concentrations measured in early or late pregnancy and 16s rRNA sequencing from maternal gut microbiome samples available primarily in later pregnancy (2^nd-3^rd trimester). Linear regression models tested associations between prenatal PFAS levels (separately for the 1^st/3^rd trimesters) and measures of alpha diversity, bacterial composition differences, and differential taxonomic abundance. Bayesian Kernel Machine Regression and Elastic net regression mixture modeling were also incorporated. In both cohorts, multiple PFAS were significantly associated with the relative abundance of specific microbiome taxa even after adjustment for covariates including maternal diet, age, race, BMI, and smoking; A total of 16 significant family-level associations were identified for ATL AA (e.g., PFOA with Clostridiaceae; natural log fold change=0.94) and 13 significant family-level associations identified for MARCH e.g., PFOS with Desulfovibrionaceae; natural log fold change=−1.53 (p_FDR<0.05), but similarities between cohorts were lacking. Mixture analyses did not identify interactive or combined effects but did provide modest evidence of inclusion of individual PFAS in beta diversity models in both cohorts. In 2 distinct cohorts, there were significant associations between prenatal PFAS and the relative abundance of several bacterial taxa, but these differences were cohort-specific. This work suggests that PFAS may modulate the gut microbiome during pregnancy.

Keywords: PFAS, Microbiome, Maternal Exposures, Pregnancy, Environmental Health

Introduction

The gut microbiota, a diverse and extensive collection of commensal bacteria within the digestive tract, is a well-characterized site of metabolism in which the products have a sizable impact on overall host health. It functions to gather nutrients, detoxify xenobiotics, renew and maintain the intestinal barrier, and protect against pathogens through competitive inhibition of colonization.^1–5 The symbiotic relationship between gut microbiota and the host is mediated through neural, endocrinologic, immunological, and metabolic pathways. ^6–9 The gut microbiota shifts in composition throughout the lifespan, and in particular, during pregnancy.¹ As a result of physiological changes in pregnancy, the rate of metabolism increases, hormones directly and indirectly alter gut microbiome composition, and the immune system produces a state of low-grade inflammation, leading to increased gut permeability^{1, 3, 10, 11} in order to support energy storage in fat to provide for fetal growth and lactation and to educate the fetal immune system.^{1, 3, 12} A diverse gut microbiota during pregnancy indicates a balanced microbial community that can support optimal fetal development and maternal well-being.¹³ Dysbiosis (i.e., any change in composition of resident commensal communities relative to that found in healthy individuals, including loss of diversity) during pregnancy has been associated with several complications, including gestational diabetes mellitus (GDM), preterm labor, and fetal growth restriction. ^{3, 14, 15} Dysbiosis can be identified by examining changes in specific taxa of bacteria or by looking at broader diversity metrics (e.g., alpha diversity measures summarize the structure of a microbiome with respect to the number of taxonomic groups and/or distribution of abundances of the groups, and beta diversity measures summarize which samples differ with consideration of sequence abundances or the presence–absence of sequences).¹⁶ A diverse gut microbiota has been associated with a more robust immune system and more efficient digestion, while reduced diversity has been associated with impaired ability to metabolize nutrients and xenobiotics, which can lead to increased systemic inflammation and oxidative stress.¹⁷ Thus, understanding factors that can perturb microbiome form and function may be especially important during times of microbiome flux including pregnancy.

The health of the gut microbiome is dependent on a variety of factors including diet, lifestyle, infections, medication use, and environmental factors. ^{3, 18–22} Recently, animal studies have shown that environmental chemicals such as persistent organic pollutants (POPs) can alter microbiome composition. ^{21, 23} Many studies have focused on legacy pollutants including polychlorinated biphenyls (PCBs), ^23–27 but emerging contaminants with similarly long biological half-lives such as per- and polyfluoroalkyl substances (PFAS) may also directly or indirectly impact microbiome composition. PFAS are a class of thousands of man-made chemicals that have nonstick, surfactant, and flame-retardant properties; they are used in a variety of industrial processes and consumer products, including cookware, paints, cosmetics, and food packaging. Although the manufacturing of long chain legacy PFAS including PFOA and PFOS has been phased out in the United States beginning around 2002, short chain replacements such as GenX have been readily produced since ^28–32 and many have long biological and environmental half-lives that bioaccumulate in biological tissues, including the gut lining. ^{33, 34} PFAS exposure has been previously related to diseases of the cardiovascular, immune, endocrine, reproductive, and metabolic systems, and recent literature has shown the role PFAS play in modulating the gut microbiome. ^35–40 PFAS exposures have been shown to be associated with reduced gut microbiome diversity and increased ratio of pathogenic to commensal bacteria, as well as a decrease in production of microbiota-derived short-chain fatty acids (SCFAs). ^{37, 41–43} Animal, and some human studies, have also demonstrated that exposure to PFAS during critical developmental periods can lead to permanent changes in gut microbiome composition. ^43–46 Epidemiological studies focusing on associations between PFAS concentrations during pregnancy and maternal microbiome composition are lacking. As prenatal PFAS exposures have also been associated with adverse pregnancy outcomes such as preterm labor and fetal growth restriction, ^{40, 47, 48} it is important to elucidate links between PFAS exposures and maternal microbiome composition in diverse pregnancy cohorts. We hypothesize that higher maternal blood PFAS levels during pregnancy will be associated with differential gut microbiome composition and decreased diversity measures. Here, we leveraged the Environmental Influences on Child Health Outcomes (ECHO) platform and present results from subsamples of the Atlanta African American Maternal Child Cohort and Michigan Archive for Research on Child Health Cohort where maternal gut microbiome sequencing and PFAS concentrations were available. Significant associations between PFAS and specific gut microbiota taxa were identified, but these were cohort-specific. This analysis can provide further insight into the microbiome as a target of PFAS toxicity and may implicate a possible mechanism linking vertical exposures to offspring health.

Methods:

Cohort Descriptions:

This work leverages extant samples from two sites of the Environmental Influences on Child Health Outcomes (ECHO) program.^{49, 50} Associations between maternal PFAS exposure and maternal microbiome composition are reported for each cohort separately and datasets were not combined due to differences in sample collection, microbiome sequencing, and ASV vs. OTU-specific bioinformatics workflows. Cohort descriptions and pertinent methods are described below and summarized in Table 1.

Table 1.

Characteristics and comparisons of study cohorts

Variable	ATL AA N=101	MARCH N=61	p-value^a
Age			<0.001
Median [Q1; Q3]	25.0 [21.0; 28.0]	36.0 [33.0; 39.0]
N (%) Missing	0 (0%)	0 (0%)
Race			<0.001
White, N (Column %)	0 (0.00%)	55 (90.2%)
Black and African American, N (Column %)	101 (100%)	5 (8.20%)
Asian Indian, N (Column %)	0 (0.00%)	<5 (1.64%)^c
N (%) Missing	0 (0%)	0 (0%)
BMI			0.12
Median [Q1; Q3]	25.5 [22.8; 32.3]	23.9 [21.8; 28.9]
N (%) Missing	0 (0%)	0 (0%)
Smoked prior to pregnancy			0.083
No, N (Column %)	79 (78.2%)	55 (90.2%)
Yes, N (Column %)	22 (21.8%)	6 (9.84%)
N (%) Missing	0 (0%)	0 (0%)
Dairy (servings)			0.27
Median [Q1; Q3]	1.13 [0.51; 1.57]	1.24 [0.83; 1.50]
N (%) Missing	28 (27.7%)	6 (9.8%)
Fruits and Vegetables (cups)			<0.001
Median [Q1; Q3]	7.09 [5.96; 9.02]	2.77 [2.15; 3.88]
N (%) Missing	25 (24.8%)	5 (8.2%)
Added Sugar (teaspoons)			<0.001
Median [Q1; Q3]	7.92 [4.28; 11.1]	11.3 [9.10; 13.3]
N (%) Missing	28 (27.7%)	7 (11.5%)
Fiber (grams)			0.40
Median [Q1; Q3]	16.2 [10.6; 24.6]	15.0 [11.7; 18.9]
N (%) Missing	27 (26.7%)	5 (8.2%)
Meat, fish, poultry, beans, eggs (servings)			N/A
Median [Q1; Q3]	2.01 [1.10; 3.43]	N/A^b
N (%) Missing	28 (27.7%)	61 (100%)

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calculated using the wilcoxon rank sum test, the chi-square test, or fisher’s exact test.

not measured in cohort.

<5 is used due to privacy concerns.

ATL AA, Atlanta African American cohort; MARCH, Michigan Archive for Research on Child Health Cohort

Atlanta African American Maternal Child Cohort.

The present analysis included participants in the Atlanta African American Maternal Child Cohort (ATL AA cohort hereafter) which is an ongoing prospective birth cohort that began enrollment in 2014; samples for the analysis described herein were collected pregnant participants during 2014-2019.^{51, 52} Participants were recruited from two prenatal clinics in Atlanta, Georgia, spanning a socioeconomically diverse array of patients. Inclusion criteria included: 1) self-identifying as African American or Black; 2) being born in the US; 3) presenting with a singleton pregnancy between 8-14 weeks gestation; 4) ability to communicate in English; 5) between 18-40 years; 6) absence of chronic medical conditions. ATL AA excluded (from enrollment), women who were taking medications chronically for a condition but retained women in the cohort who were given a prescription medication once enrolled. The research protocol was reviewed and approved by the Emory University Institutional Review Board; all participants provided written informed consent. The ATL AA cohort and the collection of the relevant covariates has been well described in^{48, 51, 53–55} and the sub-cohort used for these analyses is summarized below. Participants with either a 1^st or 3^rd trimester PFAS measurement as well as a 3^rd trimester gut microbiota sample were included in the current analysis. Rectal swabs were self-collected as part of a clinic visit between 28- and 39-weeks gestation (median [Q1, Q3] = 30.1 [28.7, 32.3]). At the time of statistical analysis, a total of 108 women had a 3^rd trimester rectal swab sequenced and 101 of those had PFAS quantified during the 1^st and/or 3^rd trimesters (N=76 with 1^st trimester PFAS data; N=77 with 3^rd trimester PFAS data).

The Michigan Archive for Research on Child Health Cohort.

The Michigan Archive for Research on Child Health (MARCH) cohort began recruitment of pregnant women in 2016 and closed recruitment in 2023⁵⁶. The MARCH study enrolled pregnant women from 21 prenatal clinics serving 11 birthing hospitals in the lower peninsula of the state of Michigan. The analysis described herein included a subset of pregnant women who gave written informed consent for providing fecal samples and health data. This study was approved by the human subjects’ research protection program at Michigan State University (IRB numbers:16-1429, 17-1352, 00005020). Inclusion criteria for these analyses were limited to pregnant women recruited at their first prenatal appointment from ten sites in the lower peninsula, (more than 50% of the women included in the current sample were primarily recruited at the Ann Arbor, Michigan sites), 18 years of age or older, as well as English speaking. A total of 61 women were included in analyses. These participants had a pregnancy plasma sample available for PFAS analysis (N=52 from 2^nd trimester, N=9 from 3^rd trimester) as well as a pregnancy stool sample available for 16S analysis (N=2 from 2^nd trimester, N=56 from 3^rd trimester, N=3 with gestational age unknown). Gestational ages of PFAS samples ranged from 16 to 34 weeks (median [Q1, Q3] = 26.3 [25.1, 27.1]), while gestational age at stool sample collection ranged from 25 to 41 weeks (median [Q1, Q3] = 34.2 [31.9, 36.5]). Note that in covariate adjusted models, Asian Indian women were excluded due to small sample size and ECHO restrictions related to anonymity. Relevant covariates were derived from birth certificates or questionnaire. Additionally, unlike in ATL AA, we were unable to stratify results by trimester of PFAS measurement due to insufficient sample sizes.

Microbiome Datasets:

ATL AA cohort.

Maternal rectal swab samples were collected (rather than voided stool) at prenatal visits at 28 weeks and beyond (third trimester; late pregnancy) (with most collected between 28-36 weeks due to timing the research visits with a standard prenatal blood collection in that window). The initial dataset included a total of 108 women with 3^rd trimester stool community composition characterized using 16S sequencing. Of these 108, 101 had prenatal PFAS data (described in detail below). DNA was extracted from participant swab samples using the DNeasy PowerSoil Kit (cat# 12888-100, Qiagen). Amplification of the V3-V4 regions of the 16S rRNA gene was performed using a two-step-PCR previously described.⁵⁷ Amplicons were visualized on a 2% agarose gel, quantified, pooled in equimolar concentration, and purified prior to sequencing on an Illumina HiSeq 2500 (University of Maryland Genome Institute) modified to generate 300 bp paired-end reads.⁵⁸ Bioinformatic processing of the sequence data has been described previously,⁵⁹ with reads assembled and chimeras removed per dada2 protocol and sequences clustered into amplicon sequence variants (ASVs). Taxonomy was assigned to each amplicon sequence variant (ASV) generated using PECAN (version 1.0). The sequencing depths of the 101 samples remaining for analysis ranged from 1,184 to 109,109 (Q1=18,785, median=24,205, Q3=33,934).

MARCH cohort.

Stool samples were collected in plastic stool hats at home by participants during the third trimester of pregnancy similar to previously described methods.⁶⁰ Resulting samples were returned to the lab via US mail allowing the sample to remain at non-ideal temperatures for an average of 4 days before storage at −80°C (Omnigene tubes are manufactured to preserve the sample for up to 2 weeks at room temperature). Genomic DNA was extracted from the stool samples using MoBio Powersoil DNA Isolation kit (Qiagen MoBio, Carlsbad, CA) and 16S amplicon libraries assembled as described previously.⁶¹ MARCH used a 250bp region of the V4 region of the 16S gene for amplification. Operational taxonomic unit (OTU) taxonomies were assigned by phylotype using the SILVA reference taxonomy (release 128). The dataset included a total of 101 MARCH women with 3^rd trimester stool community composition characterized using 16S sequencing. The majority of stool samples were packed in Omnigene tubes; however, one sample used a ParaPak tube, while another sample had an unknown tube type. These two samples were dropped prior to analysis. Also, one sample had a sequencing depth of 1 and was dropped prior to analysis. A threshold of 97% sequencing similarity was used for operational taxonomic units (OTU) clustering. The dataset included a total of 98 MARCH women with later pregnancy (2^nd and 3^rd trimester) stool community composition characterized using 16S sequencing; of these, 61 had prenatal PFAS data (described in detail below). The sequencing depths of these 61 samples ranged from 19,191 to 120,778 (Q1=59,716, median=70,726, Q3=86,212). None of the 61 women had recent antibiotic exposure prior to stool sample collection. (Harmonization of ATL AA datasets which utilize ASV pipelines and MARCH 16S datasets which use OTU pipelines was not attempted here due to many differences between cohorts including sample collection and sequencing primer choice. Some studies have shown community compositions can differ up to 10% depending on which pipeline is chosen.⁶²)

PFAS Datasets:

ATL AA cohort.

A total of 15 PFAS compounds were quantified in 1^st and 3^rd trimester serum; only 7 of the 15 compounds had a detection rate >50% averaged between the two time points; we focused statistical analyses on PFAS available at both collections for ATL AA. PFAS compounds and detection rates are listed in Table S1 with PFAS compounds included in analyses indicated with a superscript. PFAS compounds were reported in ng/mL, and values <LOD imputed as $LOD/ \sqrt{2}$ This was selected rather than quantile regression imputation of left-censored data (QRILC), because LODs of some PFAS differed between analytical runs for this dataset. Serum PFAS were analyzed at two laboratories for ATL AA. These two laboratories were part of the Children’s Health Exposure Analysis Resource (CHEAR) and the Human Health Exposure Analysis Resource (HHEAR) laboratories, including Wadsworth Center/New York University Laboratory Hub (Wadsworth/NYU) and the Laboratory of Exposure Assessment and Development for Environmental Research (LEADER) at Emory University. For both sites, LODs were defined as the lowest concentrations in the calibration curve, where the signal to noise ratio of the observed signal was ≥3 with an accuracy within 100 ± 20%. ⁶⁷ CHEAR and HHEAR are exposure assessment consortia of the National Institute of Environmental Health Studies and the core laboratories of CHEAR/HHEAR implemented harmonized quality control procedures for delivering comparable data.^{63, 64} Briefly, serum PFAS were extracted and measured using hybrid-solid-phase extraction (SPE) and ultra-performance liquid chromatography (LC) coupled to tandem mass spectrometry (MS) (Wadsworth) and online SPE coupled with high-performance LC-MS/MS (Emory). Quantification was completed using isotope dilution with appropriate mass labeled PFAS standards and calibration curves. Further details of the solid phase extraction techniques and analytical methods for PFAS quantification have been described previously.^{47, 65}

MARCH cohort.

PFAS in the plasma of MARCH women during the 2^nd and 3^rd trimester was quantified by the Wadsworth HHEAR Targeted Analysis Laboratory using solid phase extraction techniques as described previously and above ^{66, 67} and included 13 PFAS (Table S1),Of these 13 compounds, 5 had a detection rate ~50% and were retained for analysis (PFHxS: 100%, PFOS: 100%, PFNA: 100%, PFOA: 96.9%, and PFDA: 47.7%). Quantile regression imputation of left-censored data (QRILC) was performed using the R Package imputeLCMD for values less than the limit of detection. This is our preferred method for handling mass spectrometry-based data below LODs based off past reports comparing upwards of 8 different imputation methods (QRILC had the best performance for left-censored missing not at random data).⁶⁸ All PFAS data were log-transformed, resulting in data on the log(ng/mL) scale. PFAS included in statistical analyses for both cohorts are summarized in Figure 1 and Table S1.

Figure 1: — Log concentration of PFHxS, PFOS, PFOA, PFNA, PFDA, PFUNDA, and NMFOSAA compared between ATL AA and Michigan MARCH cohorts. Trimester differences within ATL AA women were tested using generalized estimating equations, while cohort differences in late pregnancy were tested using Analysis of Variance. p<0.05 was considered significant. N includes both detectable and non-detectable (after imputing <LOD values). Bar represents median, the top of the box is Q1, and the bottom of the box is Q3. PFAS were measured in serum for ATL AA and plasma for MARCH. PFUNDA and NMFOSAA were not included in Michigan analyses due to low detection rates and are therefore not shown in the plot. Values <LOD have been imputed (see methods for further detail). PFAS, per- and polyfluoroalkyl substances; ATL AA, Atlanta African American cohort; NMFOSAA, N-methyl Perfluorooctane sulfonamido acetic acid; PFDA, perfluorodecanoic acid; PFHxS, perfluorohexane sulfonic acid; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctanesulfonic acid; PFUnDA, Perfluoroundecanoic acid.

Statistical Analyses

Analyses were performed in R version 4.4.0; statistical significance was pre-specified at level 0.05. Basic cohort characteristics were compared using the Wilcoxon rank sum test, the chi-square test, or fisher’s exact test, as appropriate. Trimester differences in PFAS levels within ATL AA women were tested using generalized estimating equations (GEE), while cohort differences in PFAS levels were tested using Analysis of variance (ANOVA). Bacterial relative abundances at the phylum level were compared between cohorts using Wilcoxon rank sum tests. For PFAS/microbiota associations, statistical analyses for both cohorts were generally similar, taking into account differences in data availability, and were completed separately with no data harmonization between cohorts. For the ATL AA cohort, linear regression was used to test associations between prenatal PFAS levels (separately for the 1^st and 3^rd trimesters) and measures of gut alpha diversity (richness,⁶⁹ Pielou’s evenness,⁷⁰ Shannon’s diversity⁷¹) adjusting the technical variable sequencing depth (i.e., “minimally adjusted model”) which is common for studies of the microbiome. Full covariate selection was completed using a directed acyclic graph methodology taking into account what covariates were available for both cohorts, covariates known to impact the exposure, outcome, or both, and the total number of variables in the model (Figure S1). Fully adjusted models were fitted, and included sequencing depth, maternal age, maternal BMI at first prenatal visit, maternal diet (described in further detail below), and tobacco use since the month prior to pregnancy (per medical record or self-report). For MARCH, fully adjusted models were similar and also included sequencing depth, maternal age at birth, maternal race, maternal pre-pregnancy BMI, maternal diet, and smoking prior to pregnancy. Note that, Asian Indian women were excluded in adjusted models due to ECHO restrictions on small sample sizes (n<5). Complete-case analysis was used for adjusted models. Stool beta diversity was calculated using the R vegan package⁷² and was defined using both Canberra⁷³ and Bray-Curtis dissimilarity⁷⁴ indices to capture different types of abundance shifts (Bray-Curtis gives more weight to abundant species while Canberra treats all species pairs equally). Although beta-diversity associations are directionless, it is possible to examine which taxa load onto each Principle Component (PCO). In ATL AA, Canberra PCO₁ was positively correlated with Ruminococcaceae, while PCO₂ was positively correlated with Streptococcaceae. Bray-Curtis PCO₁ was positively correlated with Lachnospiraceae, while PCO₂ was positively correlated with Lactobacillaceae (Figures S2–5). In MARCH, Canberra PCO₁ was positively correlated with Firmicutes_unclassified, while PCO₂ was positively correlated with Bacillales_Incertae_Sedis_XI. Bray-Curtis PCO₁ was positively correlated with Bacteroidaceae, while PCO₂ was positively correlated with Firmicutes_unclassified (Figures S-9). The adonis2 function of vegan⁷² was used to perform PERMANOVA tests for differential stool bacterial composition by PFAS levels using 1,000 permutations, fitting using both minimally and fully adjusted models. To test for differential taxonomic abundance between exposure to different PFAS, taxa tests were performed using two different methods: Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), with the R package ANCOMBC, as well as DESeq, using the R package DESeq2.^{75, 76} ANCOM-BC2 was considered the primary testing method (used for both ASV/OTU and family-level testing), as this has been shown to produce consistent results across studies and testing methods,⁷⁷ while DESeq was used for across and within cohort comparisons at the family level. Sequencing depth adjustment was not necessary for taxa testing, as this is inherently accounted for in both methods. A prevalence cutoff (i.e., the percentage of samples that had the taxa present at any abundance; non-zero) of 1% was used to avoid testing overly sparse taxa; Benjamini & Hochberg (1995) false discovery rate (FDR) correction was performed, with p_FDR<0.05 considered significant. We considered each PFAS (and within trimester for ATL AA) as one set of exposure and FDR corrected within that exposure set. Taxa testing was performed at both the family level to summarize potentially large shifts in composition due to PFAS exposure as well as the individual ASV/OTU level to capture more subtle and specific variations.

To examine the extent to which later pregnancy PFAS mixtures associate with microbiota composition, Bayesian Kernel Machine Regression (BKMR) modeling was implemented using the R package bkmr.⁷⁸ PFAS measurements were normalized prior to BKMR (mean=0, SD=1) for interpretation purposes. Each alpha diversity metric was fit as BKMR outcomes, as well as the first two PCOs of both beta diversity metrics. All default parameters were used, except that 10,000 iterations were performed (instead of 1,000), and variable selection was performed. Sequencing depth was used as an adjustment covariate. Trace plots were used to investigate model convergence. Elastic net regression (ENR) was also utilized as an alternative approach to BKMR also focusing on each alpha diversity metric as well as the first two principal coordinates of both beta diversity metrics as outcomes. The R glmnet package ⁷⁹ was used to fit elastic net models, while the caret package⁸⁰ was used to perform parameter tuning (with best values selected based on minimum Root Mean Square Error). Models were trained using a simple random sample of 80% of the data and tested using the remaining 20% to calculate R² values. Sequencing depth was forced into all models (i.e., no shrinkage applied). BKMR can identify the largest contributing variables and their interactions as well as their combined association with outcomes, while ENR is useful for large datasets by reducing model overfitting. ENR handles datasets with large numbers of parameters of unclear significance and creates a model by using principles from Lasso and Ridge regression.^{81, 82}

Maternal Diet;

As maternal diet is a known modulator of gut microbiome diversity and abundance⁸³ as well as a possible source of PFAS,⁸⁴ efforts were made to incorporate extant maternal dietary information in to fully adjusted models. In ATL AA, a semi-quantitative food frequency questionnaire was administered at prenatal visits 1 and 2.⁸⁵ If more than one dietary survey was completed by a participant, the one closest to the time of stool sample collection was retained for analysis. Estimated daily intake of fiber (grams), added sugar (teaspoons), fruits and vegetables (cups), dairy (servings), as well as meat, fish, poultry, beans, eggs (servings) was used for analysis purposes. In MARCH, prenatal survey data was available which included a dietary questionnaire administered using National Cancer Institute’s Five-Factor Screener, available from PhenX Nutrition and Dietary Supplement protocols.⁸⁶ Estimated daily intake of fiber (grams), added sugar (teaspoons), fruits and vegetables (cups), and dairy (servings) was used for analysis purposes. In both cohorts, any observations >2 SDs above or below the mean were set to missing. To reduce the number of dietary variables needed for adjustment, principal component analysis (PCA) was performed in both cohorts. The first two principal components explained 81% of the variance in diet variables in ATL AA and 76% of the variance in MARCH; these were therefore used in adjusted models (referred to simply as “maternal diet” throughout manuscript). For both cohorts, we also report Pearson correlations between each PFAS and each individual dietary variable (Figures S10–S11).

Results:

Cohort demographics, PFAS concentrations, and microbiome composition.

Demographics for both cohorts are summarized in Table 1. For ATL AA, gestational age at PFAS measurement during the early pregnancy sample (1^st trimester) ranged from 6.9 to 12.9 weeks (median [Q1, Q3] = 10.9 [9.0, 12.0]), while gestational age at PFAS measurement during the later time point (3^rd trimester) ranged from 28.0 to 36.4 weeks (median [Q1, Q3] = 29.4 [28.4, 30.4]. Michigan mothers were typically older than ATL AA mothers (p<0.001); all ATL AA mothers were Black/African American (by study design), while 90% were White in the Michigan sub-cohort (p<0.001). Further, fruit and vegetable intake was lower, while added sugar was higher in Michigan mothers (both p<0.001). In ATL AA mothers, PFHxS, PFOS, PFNA, and PFUNDA significantly increased from early to later pregnancy (Figure 1). Between ECHO sites, ATL AA mothers had higher PFHxS, PFNA, and PFDA than MARCH mothers during later pregnancy (Figure 1). In ATL AA, fiber significantly inversely correlated with PFOA, but there were no other significant correlations between PFAS and maternal dietary variables in ATL AA or any observed in MARCH. Finally, we compared microbiome composition between the two cohorts (Figure 2). At the phylum level, relative abundances of seven of eight phyla tested (all but Firmicutes, p_FDR =0.46) were significantly different by cohort at p_FDR <0.05. Bacteroidetes (p_FDR =0.001), Proteobacteria (p_FDR <0.001), and Verrucomicrobia (p_FDR <0.001) had significantly higher abundances in the Michigan cohort, while Actinobacteria (p_FDR =0.001), Synergistetes (p_FDR <0.001), Fusobacteria (p_FDR <0.001), and Campilobacterota (p_FDR <0.001) had significantly higher abundances in the ATL AA cohort.

Figure 2: — Differences in later pregnancy maternal gut microbiome of ATL AA (N=101) and MARCH (N=61) women. Phyla relative abundances by cohort were compared using Wilcoxon rank sum tests for the top 8 phyla plotted above. Seven of the eight phyla tested (all but Firmicutes, p_FDR=0.46) were significantly different by cohort at p_FDR<0.05. Bacteroidetes (p_FDR=0.001), Proteobacteria (p_FDR<0.001), and Verrucomicrobia (p_FDR<0.001) had significantly higher abundances in the Michigan cohort, while Actinobacteria (p_FDR=0.001), Synergistetes (p_FDR<0.001), Fusobacteria (p_FDR<0.001), and Campilobacterota (p_FDR<0.001) had significantly higher abundances in the ATL AA cohort. * Denotes statistical significance by Wilcoxon rank sum tests.

ATL AA, Atlanta African American cohort

Differences in stool diversity metrics by PFAS levels in Atlanta (ATL AA) mothers.

When we examined various PFAS for association with 3^rd trimester gut alpha diversity metrics, only 1^st trimester NMFOSAA was significantly positively associated with 3^rd trimester richness of gut microbiome in minimally adjusted models (β [95% CI] = 15.23 [0.22, 30.23]; p=0.047; Table 2). However, this association was no longer statistically significant in fully adjusted models (β [95% CI] = 17.23 [−1.49, 35.95]; p=0.070; Table 2), and no other associations reached significance in fully adjusted models. When compositional differences in gut (i.e., beta-diversity) by PFAS levels were assessed using minimally adjusted models, PFAS levels explained roughly 1.1% to 1.7% of the variance in microbiota composition (based on estimated R² values), which were non-significant (Table 3; all p≥0.12). Results were similar after full covariate adjustment (Table 3; roughly 1.6% to 2.1% of variance explained by various PFAS, all p≥0.18).

Table 2.

Association between 1^st or 3^rd trimester PFAS and 3^rd trimester gut alpha diversity in ATL AA.

Alpha Diversity Metric	PFAS Measurement	Minimally Adjusted^a			Fully Adjusted^b
Alpha Diversity Metric	PFAS Measurement	N	β [95% CI]^c	p-value	N	β [95% CI]^c	p-value
1^st Trimester PFAS
Richness	NMFOSAA	66	15.23 [0.22, 30.23]	0.047	47	17.23 [−1.49, 35.95]	0.070
	PFDA	66	−1.41 [−19.84, 17.02]	0.88	47	5.47 [−17.17, 28.10]	0.63
	PFHxS	76	−4.65 [−31.74, 22.45]	0.73	53	3.87 [−34.98, 42.72]	0.84
	PFNA	76	−4.44 [−22.47, 13.59]	0.63	53	−5.40 [−28.01, 17.20]	0.63
	PFOA	76	−0.19 [−18.15, 17.76]	0.98	53	4.48 [−22.98, 31.94]	0.74
	PFOS	76	−0.53 [−23.59, 22.52]	0.96	53	3.16 [−34.21, 40.53]	0.87
	PFuNDA	66	−6.99 [−27.22, 13.23]	0.49	47	−4.69 [−29.93, 20.56]	0.71
Evenness	NMFOSAA	66	0.015 [−0.007, 0.037]	0.18	47	0.017 [−0.009, 0.043]	0.20
	PFDA	66	0.002 [−0.025, 0.029]	0.88	47	0.007 [−0.024, 0.038]	0.67
	PFHxS	76	−0.008 [−0.047, 0.031]	0.67	53	0.002 [−0.051, 0.055]	0.94
	PFNA	76	−0.008 [−0.034, 0.018]	0.55	53	−0.016 [−0.046, 0.015]	0.30
	PFOA	76	−0.010 [−0.036, 0.015]	0.42	53	−0.009 [−0.047, 0.028]	0.62
	PFOS	76	−0.010 [−0.043, 0.024]	0.57	53	−0.003 [−0.054, 0.048]	0.91
	PFuNDA	66	−0.010 [−0.040, 0.019]	0.49	47	−0.023 [−0.056, 0.011]	0.18
Diversity	NMFOSAA	66	0.14 [−0.01, 0.29]	0.068	47	0.15 [−0.03, 0.34]	0.097
	PFDA	66	−0.01 [−0.19, 0.18]	0.93	47	0.04 [−0.18, 0.26]	0.70
	PFHxS	76	−0.05 [−0.32, 0.22]	0.70	53	0.01 [−0.36, 0.39]	0.94
	PFNA	76	−0.05 [−0.23, 0.13]	0.61	53	−0.09 [−0.31, 0.13]	0.41
	PFOA	76	−0.04 [−0.22, 0.13]	0.62	53	−0.04 [−0.30, 0.23]	0.78
	PFOS	76	−0.04 [−0.27, 0.19]	0.75	53	−0.01 [−0.37, 0.35]	0.96
	PFuNDA	66	−0.08 [−0.29, 0.12]	0.42	47	−0.12 [−0.37, 0.12]	0.31
3^rd Trimester PFAS
Richness	NMFOSAA	77	1.66 [−9.96, 13.28]	0.78	50	2.10 [−13.02, 17.23]	0.78
	PFDA	77	−4.72 [−15.19, 5.75]	0.37	50	−9.04 [−22.67, 4.60]	0.19
	PFHxS	77	−4.98 [−45.04, 35.09]	0.81	50	3.51 [−50.20, 57.22]	0.90
	PFNA	77	−10.71 [−36.78, 15.36]	0.42	50	−24.60 [−59.65, 10.45]	0.16
	PFOA	77	−4.37 [−17.49, 8.75]	0.51	50	−6.72 [−25.73, 12.29]	0.48
	PFOS	77	−14.60 [−31.27, 2.07]	0.085	50	−16.43 [−49.66, 16.81]	0.32
	PFuNDA	77	−2.85 [−13.89, 8.19]	0.61	50	−5.76 [−19.37, 7.84]	0.40
Evenness	NMFOSAA	77	0.005 [−0.012, 0.022]	0.57	50	0.013 [−0.008, 0.033]	0.21
	PFDA	77	0.002 [−0.013, 0.018]	0.75	50	−0.003 [−0.022, 0.016]	0.77
	PFHxS	77	−0.006 [−0.064, 0.052]	0.84	50	−0.024 [−0.098, 0.049]	0.51
	PFNA	77	−0.005 [−0.042, 0.033]	0.81	50	−0.031 [−0.079, 0.017]	0.20
	PFOA	77	−0.005 [−0.024, 0.014]	0.57	50	−0.003 [−0.029, 0.024]	0.83
	PFOS	77	−0.010 [−0.035, 0.014]	0.40	50	0.001 [−0.045, 0.048]	0.95
	PFuNDA	77	0.006 [−0.010, 0.022]	0.43	50	0.005 [−0.014, 0.024]	0.58
Diversity	NMFOSAA	77	0.03 [−0.08, 0.15]	0.57	50	0.07 [−0.08, 0.22]	0.35
	PFDA	77	0.00 [−0.10, 0.11]	0.98	50	−0.04 [−0.18, 0.09]	0.52
	PFHxS	77	−0.01 [−0.42, 0.39]	0.96	50	−0.07 [−0.60, 0.46]	0.79
	PFNA	77	−0.03 [−0.29, 0.24]	0.85	50	−0.23 [−0.58, 0.12]	0.19
	PFOA	77	−0.03 [−0.16, 0.10]	0.64	50	−0.04 [−0.23, 0.15]	0.65
	PFOS	77	−0.09 [−0.26, 0.08]	0.28	50	−0.04 [−0.37, 0.29]	0.81
	PFuNDA	77	0.03 [−0.08, 0.14]	0.63	50	0.01 [−0.13, 0.14]	0.92

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Adjusted for sequencing depth.

Adjusted for sequencing depth, maternal age, maternal BMI at first prenatal visit, tobacco use since month prior to pregnancy, and maternal diet.

Change in specified alpha diversity metric, per log(ng/mL) increase in PFAS measurement with 95% confidence intervals. PFAS values <LOD were imputed as $LOD/ \sqrt{2}$ . Pielou’s evenness, Shannon’s diversity

PFAS, per- and polyfluoroalkyl substances; ATL AA, Atlanta African American cohort; NMFOSAA, N-methyl Perfluorooctane sulfonamido acetic acid; PFDA, perfluorodecanoic acid; PFHxS, perfluorohexane sulfonic acid; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctanesulfonic acid; PFUnDA, Perfluoroundecanoic acid.

Table 3.

Association between 1st or 3rd trimester PFAS and 3rd trimester gut beta diversity in ATL AA

Beta Diversity Metric	PFAS Measurement	Minimally Adjusted^a			Fully Adjusted^b
Beta Diversity Metric	PFAS Measurement	N	R²	p-value^c	N	R²	p-value^c
1^st Trimester PFAS
Canberra	NMFOSAA	66	0.016	0.22	47	0.021	0.61
	PFDA	66	0.015	0.65	47	0.021	0.67
	PFHxS	76	0.013	0.35	53	0.019	0.68
	PFNA	76	0.013	0.57	53	0.018	0.88
	PFOA	76	0.013	0.43	53	0.019	0.59
	PFOS	76	0.013	0.60	53	0.019	0.49
	PFUNDA	66	0.015	0.70	47	0.020	0.89
Bray-Curtis	NMFOSAA	66	0.017	0.24	47	0.023	0.25
	PFDA	66	0.015	0.46	47	0.018	0.78
	PFHxS	76	0.012	0.54	53	0.017	0.66
	PFNA	76	0.013	0.48	53	0.018	0.62
	PFOA	76	0.012	0.64	53	0.018	0.64
	PFOS	76	0.012	0.62	53	0.020	0.34
	PFUNDA	66	0.015	0.52	47	0.020	0.66
3^rd Trimester PFAS
Canberra	NMFOSAA	77	0.012	0.78	50	0.019	0.92
	PFDA	77	0.013	0.58	50	0.021	0.35
	PFHxS	77	0.012	0.81	50	0.019	0.83
	PFNA	77	0.013	0.44	50	0.020	0.45
	PFOA	77	0.013	0.38	50	0.021	0.34
	PFOS	77	0.013	0.40	50	0.020	0.74
	PFUNDA	77	0.014	0.12	50	0.021	0.18
Bray-Curtis	NMFOSAA	77	0.012	0.66	50	0.016	0.89
	PFDA	77	0.011	0.86	50	0.019	0.63
	PFHxS	77	0.011	0.86	50	0.019	0.54
	PFNA	77	0.012	0.58	50	0.020	0.44
	PFOA	77	0.012	0.54	50	0.019	0.53
	PFOS	77	0.011	0.74	50	0.018	0.66
	PFUNDA	77	0.013	0.36	50	0.021	0.35

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Adjusted for sequencing depth.

Adjusted for sequencing depth, maternal age, maternal BMI at first prenatal visit, tobacco use since month prior to pregnancy, and maternal diet.

PERMANOVA proportion of variance explained by PFAS measurement (R²) and corresponding p-value.

PFAS values <LOD were imputed as $LOD/ \sqrt{2}$ .

Difference in stool family and ASV- level abundances by PFAS levels in ATL AA.

When ANCOM-BC2 was used to test for family-level differences in 3^rd trimester gut microbiome by specific PFAS levels, a total of 60 tests representing 26 of 82 unique families were found to be significant at the p_FDR<0.05 level in unadjusted models (45 families were positively associated with PFAS measures and 15 were negatively associated with PFAS measures; Table 4). After full covariate adjustment, one family remained significant. Specifically, 3^rd trimester PFOA was positively associated with the relative abundance of Clostridiaceae (p_FDR=0.024). Clostridiales_vadinBB60_group was nearly negatively associated with 1^st trimester PFHxS (p_FDR=0.073). When ANCOM-BC2 was used to test for differences at the highest resolution available (i.e., individual ASV abundances) in 3^rd trimester gut by PFAS levels, 2026 associations were significant prior to covariate adjustment (p_FDR<0.05, supplemental data download). After covariate adjustment, a total of 13 significant associations were identified (across all ASV/PFAS compound combinations; Supplemental data download). Interestingly, all 13 significant associations were with PFOA and 12 of 13 were with 3^rd trimester PFOA. Specifically, 3^rd trimester PFOA was significantly inversely associated with ASVs of the genera Streptococcus, Bacteroides, Anaerostipes, Intestinibacter, Christensenellaceae_R-7_group, Bifidobacterium, and Coprococcus_1 and was significantly positively associated with ASVs of the taxonomy Prevotella buccalis, Porphyromonas, Lachnospiraceae_NK4A136_group, Ruminiclostridium_6, and Lachnospiraceae_UCG-010. Of these, Bifidobacterium exhibited the largest negative effect size (natural log fold change (LFC)= −8.41), while Lachnospiraceae_UCG-010 exhibited the largest positive effect size (LFC= 3.77). Finally, first trimester PFOA was significantly positively associated with a Sutterella ASV (LFC=1.52; p_FDR=0.012).

Table 4.

Association between 1st and 3rd trimester PFAS compounds and family level abundance among ATL AA women.

Family	PFAS	Trimester	Unadjusted				Adjusted

			N	LFC^a	p_FDR	N	LFC^b	p_FDR
f_Aerococcaceae	PFHxS	1st Trimester	76	0.57	1.20E-02	53	1.1	6.46E-01
f_Aerococcaceae	PFOA	1st Trimester	76	0.4	2.51E-02	53	1.01	1.00E+00
f_Aerococcaceae	PFOA	3rd Trimester	77	0.45	4.11E-04	50	0.21	1.00E+00
f_Aerococcaceae	PFOS	1st Trimester	76	0.81	9.11E-05	53	1.14	1.00E+00
f_Akkermansiaceae	PFHxS	1st Trimester	76	−0.64	4.35E-02	53	−0.59	1.00E+00
f_Akkermansiaceae	PFNA	1st Trimester	76	−0.73	4.32E-02	53	−0.61	1.00E+00
f_Akkermansiaceae	PFOA	1st Trimester	76	−0.52	3.95E-02	53	−0.63	1.00E+00
f_Alcaligenaceae	PFHxS	1st Trimester	76	5.8	5.11E-03	53	−16.34	1.00E+00
f_Alcaligenaceae	PFHxS	3rd Trimester	77	−2.65	1.28E-02	50	−0.04	1.00E+00
f_Alcaligenaceae	PFOA	1st Trimester	76	4.08	9.11E-03	53	2.96	1.00E+00
f_Alcaligenaceae	PFOS	1st Trimester	76	3.02	2.02E-03	53	3.5	1.00E+00
f_Anaerolineaceae	PFOS	1st Trimester	76	0.54	2.55E-06	53	−0.07	1.00E+00
f_Anaerovoracaceae	PFHxS	1st Trimester	76	−0.57	8.70E-03	53	−0.53	1.00E+00
f_Bacillaceae	PFOS	1st Trimester	76	−0.13	2.80E-02	53	4.55	1.00E+00
f_Brevibacteriaceae	PFOA	1st Trimester	76	1.82	5.45E-06	53	11.67	5.07E-01
f_Brevibacteriaceae	PFOA	3rd Trimester	77	1.13	5.19E-04	50	7.09	1.00E+00
f_Burkholderiaceae	PFOS	1st Trimester	76	1.67	3.19E-02	53	2.41	1.00E+00
f_Butyricicoccaceae	PFHxS	1st Trimester	76	0.68	1.09E-03	53	0.85	6.46E-01
f_Butyricicoccaceae	PFOS	1st Trimester	76	0.37	1.61E-04	53	−0.13	1.00E+00
f_Clostridiaceae	PFOA	3rd Trimester	77	0.42	7.40E-03	50	0.94	2.42E-02
f_Clostridiales_vadinBB60_group	PFHxS	1st Trimester	76	−1.18	3.31E-05	53	5.6	7.27E-02
f_Clostridiales_vadinBB60_group	PFOA	1st Trimester	76	−0.41	2.51E-02	53	−0.71	7.22E-01
f_Dermabacteraceae	PFHxS	1st Trimester	76	5.59	6.65E-04	53	0.56	1.00E+00
f_Dermabacteraceae	PFOA	1st Trimester	76	1.06	2.51E-02	53	0.36	1.00E+00
f_Dermabacteraceae	PFOS	1st Trimester	76	0.75	3.20E-03	53	0.16	1.00E+00
f_Enterococcaceae	PFHxS	1st Trimester	76	1.18	5.22E-04	53	1.43	6.46E-01
f_Enterococcaceae	PFHxS	3rd Trimester	77	1.45	4.97E-04	50	−0.28	1.00E+00
f_Enterococcaceae	PFOS0	1st Trimester	76	0.7	1.19E-05	53	1.08	1.00E+00
f_Flavobacteriaceae	PFOA	1st Trimester	76	1.27	4.37E-02	53	9.76	1.00E+00
f_Flavobacteriaceae	PFOS	1st Trimester	76	−1.6	2.02E-03	53	−6.54	1.00E+00
f_Listeriaceae	PFOS	1st Trimester	76	1.75	3.19E-02	53	NA	NA
f_Microbacteriaceae	PFOS	1st Trimester	76	2.35	2.75E-02	53	4.16	1.00E+00
f_Moraxellaceae	PFHxS	1st Trimester	76	2.4	2.18E-02	53	NA	NA
f_Moraxellaceae	PFOS	1st Trimester	76	1.5	5.35E-03	53	NA	NA
f_Peptostreptococcaceae	PFOS	1st Trimester	76	0.58	3.54E-02	53	0.33	1.00E+00
f_Propionibacteriaceae	PFHxS	1st Trimester	76	0.91	5.13E-03	53	−3.18	1.00E+00
f_Propionibacteriaceae	PFOA	1st Trimester	76	0.65	8.72E-03	53	−11.91	1.00E+00
f_Propionibacteriaceae	PFOS	1st Trimester	76	1.08	1.30E-06	53	10.32	1.00E+00
f_Pseudomonadaceae	PFHxS	1st Trimester	76	12.38	1.27E-03	53	14.13	1.00E+00
f_Pseudomonadaceae	PFNA	1st Trimester	76	3.68	4.32E-02	53	3.5	1.00E+00
f_Pseudomonadaceae	PFOA	1st Trimester	76	4.69	8.72E-03	53	4.38	1.00E+00
f_Pseudomonadaceae	PFOS	1st Trimester	76	3.84	4.53E-04	53	4.73	1.00E+00
f_Rhodospirillaceae	PFHxS	1st Trimester	76	−2.9	3.58E-05	53	NA	NA
f_Rhodospirillaceae	PFHxS	3rd Trimester	77	13.55	2.38E-04	50	NA	NA
f_Rhodospirillaceae	PFNA	1st Trimester	76	−0.89	4.32E-02	53	NA	NA
f_Rhodospirillaceae	PFOA	3rd Trimester	77	0.52	1.99E-02	50	NA	NA
f_Rhodospirillaceae	PFOS	1st Trimester	76	0.42	2.02E-03	53	NA	NA
f_Selenomonadaceae	PFDA	3rd Trimester	77	0.54	3.13E-02	50	1.78	1.00E+00
f_Selenomonadaceae	PFHxS	1st Trimester	76	−1.9	3.31E-05	53	0.42	1.00E+00
f_Selenomonadaceae	PFNA	1st Trimester	76	1.12	1.31E-02	53	−1.53	1.00E+00
f_Selenomonadaceae	PFOA	1st Trimester	76	0.74	1.67E-03	53	−1.06	6.40E-01
f_Selenomonadaceae	PFOA	3rd Trimester	77	1.4	1.48E-05	50	10.95	1.00E+00
f_Spirochaetaceae	PFHxS	1st Trimester	76	−1.01	6.65E-04	53	−1.31	6.75E-01
f_Staphylococcaceae	PFHxS	1st Trimester	76	0.74	7.64E-04	53	1.51	4.42E-01
f_Staphylococcaceae	PFOA	3rd Trimester	77	0.32	2.02E-02	50	0.38	1.00E+00
f_Staphylococcaceae	PFOS	1st Trimester	76	0.93	2.12E-06	53	1.28	1.00E+00
f_Victivallaceae	PFOA	3rd Trimester	77	0.67	2.02E-02	50	NA	NA
f_Victivallaceae	PFOS	1st Trimester	76	0.35	4.68E-02	53	NA	NA
f_Weeksellaceae	PFOA	1st Trimester	76	−1.33	4.21E-02	53	−12.93	1.00E+00
f_Weeksellaceae	PFOS	1st Trimester	76	−1.34	2.30E-03	53	−2.01	1.00E+00

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Natural log fold change (LFC) in family abundance, per log(ng/mL) increase in PFAS measurement, prior to covariate adjustment.

Natural log fold change (LFC) in family abundance, per log(ng/mL) increase in PFAS measurement, after adjusting for maternal age, maternal BMI at first prenatal visit, tobacco use since month prior to pregnancy, and maternal diet. All tests where either the unadjusted or adjusted model was significant is shown.

Differences in stool diversity metrics by PFAS levels in Michigan (MARCH) mothers.

In minimally adjusted models no significant associations were found between PFAS levels and stool bacterial richness, evenness, or diversity (Table 5; all p≥0.19). Results were similar in fully adjusted models (Table 5; all p≥0.16). When compositional differences (i.e., beta-diversity) in stool by PFAS levels were assessed in minimally adjusted models, both PFOA and PFNA were significantly associated with stool community composition when the Bray-Curtis metric was used. While there is no directionality to beta diversity associations, 2.9% to 4.3% of the variability in composition was explained (Table 6). PFOS explained 2.8% variability, but this was not significant (p=0.058). However, in fully adjusted models, associations were no longer significant for either PFOA (Bray-Curtis R²=0.018, p=0.37) or PFNA (Bray-Curtis R²=0.022, p=0.23), or PFOS (Bray-Curtis R²=0.031, p=0.06) with the percentage of variability described by the PFAS concentration being reduced compared to the minimally adjusted model

Table 5.

Association between PFAS and stool alpha diversity in later pregnancy in MARCH women.

Alpha Diversity Metric	PFAS Measurement	Minimally Adjusted^a		Fully Adjusted^b

		β [95% CI]^c	p-value	β [95% CI]^c	p-value
Richness	PFHxS	−7.31 [−19.61, 5.00]	0.24	−5.91 [−18.37, 6.54]	0.34
	PFOS	−0.66 [−10.77, 9.45]	0.90	0.46 [−9.77, 10.69]	0.93
	PFOA	0.96 [−5.02, 6.95]	0.75	0.88 [−6.03, 7.79]	0.80
	PFNA	−0.68 [−10.34, 8.97]	0.89	−1.62 [−11.75, 8.50]	0.75
	PFDA	−3.90 [−9.75, 1.95]	0.19	−1.37 [−7.90, 5.17]	0.68

Evenness	PFHxS	−0.008 [−0.046, 0.030]	0.69	−0.021 [−0.058, 0.016]	0.25
	PFOS	0.007 [−0.024, 0.038]	0.67	−0.001 [−0.031, 0.029]	0.94
	PFOA	0.008 [−0.010, 0.026]	0.38	−0.003 [−0.024, 0.017]	0.76
	PFNA	0.014 [−0.015, 0.044]	0.33	−0.004 [−0.034, 0.026]	0.81
	PFDA	0.009 [−0.009, 0.027]	0.33	−0.006 [−0.025, 0.013]	0.53

Diversity	PFHxS	−0.07 [−0.27, 0.12]	0.45	−0.13 [−0.32, 0.06]	0.16
	PFOS	0.03 [−0.12, 0.19]	0.67	0.001 [−0.16, 0.16]	0.99
	PFOA	0.04 [−0.05, 0.13]	0.37	−0.01 [−0.12, 0.09]	0.83
	PFNA	0.06 [−0.09, 0.21]	0.39	−0.03 [−0.18, 0.13]	0.75
	PFDA	0.03 [−0.06, 0.12]	0.55	−0.03 [−0.13, 0.07]	0.50

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Adjusted for sequencing depth (N=61).

Adjusted for sequencing depth, maternal age at birth, maternal race, maternal pre-pregnancy BMI, maternal diet, and smoking prior to pregnancy (N=53).

Change in specified alpha diversity metric, per log(ng/mL) increase in PFAS measurement with 95% confidence intervals. Pielou’s evenness, Shannon’s diversity

Michigan Archive for Research on Child Health Cohort; MARCH, PFAS, per- and polyfluoroalkyl substances; PFHxS, perfluorohexane sulfonic acid; PFOS, perfluorooctanesulfonic acid; PFOA, perfluorooctanoic acid; PFNA, perfluorononanoic acid; PFDA, perfluorodecanoic acid

Table 6.

Association between PFAS and stool beta diversity in later pregnancy in MARCH women.

Beta Diversity Metric	PFAS Measurement	Minimally Adjusted^a		Fully Adjusted^b

		R²	p-value^c	R²	p-value^c
Canberra	PFHxS	0.017	0.29	0.021	0.20
	PFOS	0.017	0.32	0.020	0.30
	PFOA	0.019	0.15	0.018	0.48
	PFNA	0.016	0.56	0.017	0.74
	PFDA	0.015	0.75	0.018	0.50
Bray-Curtis	PFHxS	0.015	0.43	0.020	0.25
	PFOS	0.028	0.058	0.031	0.063
	PFOA	0.043	0.009	0.018	0.37
	PFNA	0.029	0.041	0.022	0.23
	PFDA	0.011	0.69	0.024	0.16

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Adjusted for sequencing depth (N=61).

Adjusted for sequencing depth, maternal age at birth, maternal race, maternal pre-pregnancy BMI, maternal diet, and smoking prior to pregnancy (N=53).

PERMANOVA proportion of variance explained by PFAS measurement (R²) and corresponding p-value.

Difference in stool family and OTU-level abundances by PFAS levels in MARCH mothers and comparisons with ATL AA.

In unadjusted models, the relative abundance of 6 (of 171 tested) bacterial families were significantly associated (p_FDR<0.05) with at least one PFAS measurement (Supplemental data download): Carnobacteriaceae, Desulfovibrionales, Fulvivirgaceae, Porphyromonadaceae, Selenomonadales, and Xanthomonadaceae. Carnobacteriaceae was the most replicable finding, being positively associated with 4 of the 5 PFAS measurements (all but PFHxS). Selenomonadales was positively associated with PFHxS only (LFC=1.56, p_FDR=0.035), while Desulfovibrionales was negatively associated with PFOA only (LFC=−0.06, p_FDR<0.001), Porphyromonadaceae was negatively associated with PFHxS only (LFC=−1.39, p_FDR=0.035), and Xanthomonadaceae (LFC=−0.04, p_FDR<0.001) and Fulvivirgaceae (LFC=−0.06, p_FDR=0.034) were negatively associated with PFDA only. In fully adjusted models, a total of 8 significant tests across 6 bacterial families (of 171 tested, Table 7) reached statistical significance. PFHxS was inversely associated with 5 families including Bacteroidales_unclassified (LFC=−2.48, p_FDR=0.0091), Bifidobacteriaceae (LFC=−1.27, p_FDR=0.0096), Campylobacteraceae (LFC=−1.63, p_FDR=0.0024), Desulfovibrionaceae (LFC=−1.84, p_FDR=0.0024), and Enterobacterales_unclassified (LFC=−1.93, p_FDR=0.0011). PFOS was also significantly inversely associated with Desulfovibrionaceae (LFC=−1.53, p_FDR=0.012). Finally, Selenomonadales (unclassified) was positively associated with both PFHxS (LFC=2.1, p_FDR=0.016) and PFOS (LFC=2.46, p_FDR=0.012).

Table 7.

Gut microbiota families significantly associated with PFAS concentrations in fully adjusted models in MARCH women.

Family	PFAS	LFC^a	p_FDR
Bacteroidales_unclassified	PFHxS	−2.48	9.09E-03
Bifidobacteriaceae	PFHxS	−1.27	9.65E-03
Campylobacteraceae	PFHxS	−1.63	2.38E-03
Desulfovibrionaceae	PFHxS	−1.84	2.38E-03
Desulfovibrionaceae	PFOS	−1.53	1.23E-02
Enterobacterales_unclassified	PFHxS	−1.93	1.15E-03
Selenomonadales_unclassified	PFHxS	2.1	1.63E-02
Selenomonadales unclassified	PFOS	2.46	1.23E-02

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Natural log fold change (LFC) in family abundance, per log(ng/mL) increase in PFAS measurement, after adjusting for maternal age at birth, maternal race, maternal pre-pregnancy BMI, maternal diet, and smoking prior to pregnancy. Calculated using ANCOM-BC2.(N=53).

Michigan Archive for Research on Child Health Cohort; MARCH, PFAS, per- and polyfluoroalkyl substances; PFHxS, perfluorohexane sulfonic acid; PFOS, perfluorooctanesulfonic acid.

At higher taxonomic resolution, in unadjusted models, a total of 13 individual OTUs (of 393 tested) reached statistical significance (p_FDR<0.05) for at least one PFAS measurement (Table S2). Consistent with family-level testing, OTU 197 of the family Carnobacteriaceae and the genus Granulicatella was positively associated with PFDA, PFNA, and PFOA. Additionally, OTU 284 of the family Flavobacteriaceae was negatively associated with PFDA and PFOS. The remaining 11 OTUs were significantly associated with one PFAS measurement. After full covariate adjustment, a total of 40 significant associations were identified (across all OTU/PFAS compound combinations; Table 8). Significant associations were evenly split between positive and inverse relationships and significant PFOS and PFHxS associations were most observed (21 and 12 respectively). Of these, Otu128 (Succiniclasticum) had the largest negative effect sizes with PFNA (LFC= −5.43) and PFOS (LFC= −3.3), followed by Otu133 (Senegalimassilia) and its association with PFHxS (LFC= −2.83). Conversely, Anaerococcus (LFC= 3.99) and Burkholderiales (LFC= 3.59) OTUs had the largest positive effect sizes with PFOS and PFNA, respectively. Also of note, certain taxa were significantly associated with more than one PFAS; Otu044 (Bilophila) was significantly negatively associated with PFHxS and PFOS, Otu124 (Burkholderiales) was significantly positively associated with PFDA, PFNA, PFOA, and PFOS, Otu128 (Succiniclasticum) was significantly negatively associated with PFNA, PFOA, and PFOS, and Otu145 (Selenomonadales) was significantly positively associated with PFHxS and PFOS.

Table 8.

Gut microbiota OTUs significantly associated with PFAS concentrations in fully adjusted models in MARCH women.

OTU	PFAS	Phylum	Class	Order	Family	Genus	LFC^a	p_FDR
Otu006	PFOS	Firmicutes	Negativicutes	Veillonellales	Veillonellaceae	Megasphaera	−1.56	0.0076
Otu009	PFHxS	Actinobacteria	Actinobacteria	Bifidobacteriales	Bifidobacteriaceae	Bifidobacterium	−1.27	0.0132
Otu023	PFHxS	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidales_unclassified	Bacteroidales_unclassified	−2.48	0.0123
Otu024	PFOS	Firmicutes	Negativicutes	Acidaminococcales	Acidaminococcaceae	Acidaminococcus	−2.1	0.0004
Otu038	PFOS	Actinobacteria	Actinobacteria	Bifidobacteriales	Bifidobacteriaceae	Bifidobacteriaceae_unclassified	2.36	0.0085
Otu044	PFHxS	Proteobacteria	Deltaproteobacteria	Desulfovibrionales	Desulfovibrionaceae	Bilophila	−1.82	0.0042
Otu044	PFOS	Proteobacteria	Deltaproteobacteria	Desulfovibrionales	Desulfovibrionaceae	Bilophila	−1.4	0.0120
Otu069	PFDA	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotellamassilia	−2.14	0.0022
Otu071	PFOS	Actinobacteria	Coriobacteriia	Coriobacteriia_unclassified	Coriobacteriia_unclassified	Coriobacteriia_unclassified	2.46	0.0017
Otu080	PFHxS	Proteobacteria	Gammaproteobacteria	Enterobacterales	Enterobacterales_unclassified	Enterobacterales_unclassified	−1.93	0.0027
Otu081	PFOS	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotellaceae_unclassified	2.2	0.0002
Otu104	PFOS	Firmicutes	Negativicutes	Acidaminococcales	Acidaminococcaceae	Acidaminococcaceae_unclassified	3.34	0.0002
Otu112	PFHxS	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	1.12	0.0132
Otu114	PFHxS	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Massiliimalia	−1.4	0.0046
Otu116	PFOS	Firmicutes	Erysipelotrichia	Erysipelotrichales	Erysipelotrichaceae	Holdemanella	1.22	0.0011
Otu117	PFHxS	Actinobacteria	Coriobacteriia	Eggerthellales	Eggerthellaceae	Gordonibacter	−1.4	0.0027
Otu120	PFOS	Actinobacteria	Actinobacteria	Actinomycetales	Actinomycetaceae	Actinomyces	1.16	0.0020
Otu122	PFHxS	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lacticaseibacillus	1.14	0.0146
Otu123	PFHxS	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Pseudoflavonifractor	−1.21	0.0341
Otu124	PFDA	Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiales_unclassified	Burkholderiales_unclassified	2.5	0.0022
Otu124	PFNA	Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiales_unclassified	Burkholderiales_unclassified	3.59	0.0367
Otu124	PFOA	Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiales_unclassified	Burkholderiales_unclassified	2.2	0.0093
Otu124	PFOS	Proteobacteria	Betaproteobacteria	Burkholderiales	Burkholderiales_unclassified	Burkholderiales_unclassified	3.22	0.0002
Otu128	PFNA	Firmicutes	Negativicutes	Acidaminococcales	Acidaminococcaceae	Succiniclasticum	−5.43	0.0030
Otu128	PFOA	Firmicutes	Negativicutes	Acidaminococcales	Acidaminococcaceae	Succiniclasticum	−2.38	0.0058
Otu128	PFOS	Firmicutes	Negativicutes	Acidaminococcales	Acidaminococcaceae	Succiniclasticum	−3.3	0.0002
Otu133	PFHXS	Actinobacteria	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Senegalimassilia	−2.83	0.0042
Otu134	PFOS	Firmicutes	Bacilli	Lactobacillales	Streptococcaceae	Lactococcus	0.78	0.0071
Otu137	PFOS	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Harryflintia	−0.62	0.0120
Otu145	PFHXS	Firmicutes	Negativicutes	Selenomonadales	Selenomonadales_unclassified	Selenomonadales_unclassified	2.1	0.0213
Otu145	PFOS	Firmicutes	Negativicutes	Selenomonadales	Selenomonadales_unclassified	Selenomonadales_unclassified	1.95	0.0023
Otu147	PFHXS	Campilobacterota	Campylobacteria	Campylobacterales	Campylobacteraceae	Campylobacter	−1.63	0.0042
Otu150	PFOS	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Faecalicatena	0.57	0.0120
Otu155	PFOA	Actinobacteria	Coriobacteriia	Eggerthellales	Eggerthellaceae	Slackia	−1.88	0.0172
Otu165	PFOS	Firmicutes	Clostridia	Clostridiales	Peptoniphilaceae	Peptoniphilus	1.24	0.0076
Otu175	PFOS	Actinobacteria	Coriobacteriia	Eggerthellales	Eggerthellaceae	Raoultibacter	−0.93	0.0120
Otu179	PFOS	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Moraxellaceae	Acinetobacter	−0.79	0.0085
Otu209	PFOS	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Oribacterium	1.08	0.0026
Otu215	PFOS	Firmicutes	Clostridia	Clostridiales	Peptoniphilaceae	Anaerococcus	3.99	0.0026
Otu234	PFOS	Firmicutes	Clostridia	Clostridiales	Peptostreptococcaceae	Peptostreptococcus	−0.86	0.0085

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Natural log fold change (LFC) in OTU abundance, per log(ng/mL) increase in PFAS measurement, after adjusting for maternal age at birth, maternal race, maternal pre-pregnancy BMI, maternal diet, and smoking prior to pregnancy. Calculated using ANCOM-BC2. (N=53).

Finally, the association between PFAS compounds and family-level abundances were next visualized between cohorts (ATL AA vs. MARCH) incorporating results from an additional, less conservative testing method (DESeq), as shown in Figure 3 (numeric data available in Supplemental data download). A total of 18 families were significantly associated with at least one PFAS compound across cohorts and testing methods, but none of these were shared across cohorts. LFC effect sizes were generally consistent across testing methods, (spearman correlation = 0.46, p<0.001), but which specific families were declared statistically significant were discrepant, with more significant results being identified by DESeq than ANCOM-BC2.

Figure 3: — Association between PFAS compounds and family level abundances in both cohorts, using two different testing methods (ANCOM-BC2 vs. DESeq). 3^rd trimester PFAS was used in ATL AA, while 2^nd and 3^rd trimester PFAS was used in the Michigan cohort. Models are adjusted for maternal age, maternal race (MARCH only), maternal BMI, smoking prior to pregnancy, and maternal diet. N=50 for adjusted ATL AA, while N=53 for adjusted MARCH cohort models. A 1% prevalence threshold is used for all tests. Grey areas indicate either 1.) family not found in cohort, 2.) family did not reach prevalence threshold in cohort, or 3.) model did not converge. PFUNDA and NMFOSAA are only included in the ATL AA analysis. Natural log fold change (LFC) values are plotted, with an asterisk provided if p_FDR<0.05. PFAS, per- and polyfluoroalkyl substances; ATL AA, Atlanta African American cohort; NMFOSAA, N-methyl Perfluorooctane sulfonamido acetic acid; PFDA, perfluorodecanoic acid; PFHxS, perfluorohexane sulfonic acid; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctanesulfonic acid; PFUnDA, Perfluoroundecanoic acid; ANCOM-BC2, Analysis of Compositions of Microbiomes with Bias Correction; DESeq, Differential gene expression analysis based on the negative binomial distribution

Examining associations with PFAS and microbiome diversity using mixture methods in ATL AA and MARCH mothers.

With our statistically significant observations linking individual PFAS to specific microbial taxa, we next utilized two distinct mixture methods, BKMR, which can determine overall effect estimation as well as interactions between toxicants as well as ENR, which has been shown useful under multicollinearity conditions⁸⁷ to identify associations between PFAS mixtures and overall microbial composition and diversity. The association between PFAS mixtures and stool richness, evenness, and diversity (i.e., alpha-diversity measures) was first examined using BKMR in both ATL AA and MARCH. In both ATL AA and MARCH, overall mixture effects were non-significant (i.e., 95% credible intervals include zero) for all alpha-diversity measures (Table 9A). Similarly, posterior inclusion probabilities (PIPs) were generally low (Table 9B) with only PFOA in MARCH nearing what can be interpreted as a weak effect (Evenness; PIP=0.33, Diversity; PIP=0.38); (for reference, between 0.5 and 0.75-weak, 0.75 and 0.95-positive, 0.95 and 0.99-strong, and values > 0.99 as decisive evidence.⁸⁸)

Table 9A.

BKMR overall mixture effects for ATL AA and MARCH mothers.

Outcome	β [95% CI]^a
ATL AA
Richness	−5.42 [−29.84, 19.00]
Evenness	0.00 [−0.02, 0.02]
Diversity	0.01 [−0.16, 0.17]
Canberra PCO₁	−0.02 [−0.10, 0.05]
Canberra PCO₂	−0.02 [−0.07, 0.04]
Bray-Curtis PCO₁	−0.05 [−0.17, 0.06]
Bray-Curtis PCO₂	−0.01 [−0.08, 0.07]

MARCH
Richness	−0.51 [−6.96, 5.93]
Evenness	0.00 [−0.02, 0.02]
Diversity	0.01 [−0.08, 0.11]
Canberra PCO₁	−0.00 [−0.06, 0.05]
Canberra PCO₂	0.00 [−0.04, 0.04]
Bray-Curtis PCO₁	0.05 [−0.03, 0.13]
Bray-Curtis PCO₂	0.00 [−0.04, 0.05]

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The overall mixture effect, defined as the mean value of the outcome when all PFAS concentrations are at the 75th percentile minus the mean value of the outcome when all PFAS concentrations are at the 25th percentile. N=77 for ATL AA (N with 3rd trimester PFAS); N=61 for MARCH. Adjusted for sequencing depth.

PFAS, per- and polyfluoroalkyl substances; ATL AA, Atlanta African American cohort; MARCH, Michigan Archive for Research on Child Health Cohort; NMFOSAA, N-methyl Perfluorooctane sulfonamido acetic acid; PFDA, perfluorodecanoic acid; PFHxS, perfluorohexane sulfonic acid; PFNA, perfluorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctanesulfonic acid; PFUnDA, Perfluoroundecanoic acid. BKMR, Bayesian kernel machine regression; PCO, Principle Component.

Table 9B.

BKMR Posterior Inclusion Probabilities (PIPs).

PFAS	Richness	Evenness	Diversity	Canberra PCO₁	Canberra PCO₂	Bray-Curtis PCO₁	Bray-Curtis PCO₂
ATL AA
NMFOSAA	0.08	0.02	0.02	0.44	0.29	0.46	0.36
PFUNDA	0.14	0.08	0.09	0.62	0.40	0.62	0.39
PFDA	0.15	0.02	0.03	0.43	0.33	0.42	0.38
PFOA	0.11	0.02	0.04	0.50	0.32	0.53	0.37
PFNA	0.12	0.01	0.02	0.46	0.28	0.37	0.49
PFHxS	0.05	0.02	0.02	0.36	0.31	0.33	0.29
PFOS	0.16	0.00	0.03	0.47	0.31	0.44	0.39

MARCH
PFHxS	0.11	0.08	0.08	0.32	0.21	0.38	0.34
PFOS	0.12	0.10	0.11	0.32	0.28	0.39	0.38
PFOA	0.17	0.33	0.38	0.47	0.25	0.69	0.37
PFNA	0.13	0.08	0.05	0.29	0.28	0.40	0.34
PFDA	0.21	0.08	0.03	0.27	0.31	0.38	0.35

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Model output is summarized and visualized in 3 different ways, as shown in Figure 4, with Fig 4C–D examining mixing effects (i.e. interactions). Similarly, no significant associations were found, and effect sizes were consistent regardless of the quantile used for fixed PFAS measurements. For beta diversity, similar null results for the overall mixture effect were found for the first and second principal coordinates (PCO₁ and PCO₂) of both Canberra and Bray-Curtis dissimilarity in both ATL AA and MARCH. However, compared to alpha-diversity measures, PIPs were generally higher. For example, in ATL AA, PFUNDA received a PIP of 0.62 for PCO₁ of Canberra and Bray Curtis dissimilarity as well as PFOA receiving PIP values of 0.50 and 0.53 for the two diversity measures respectively. In MARCH, PFOA received a PIP of 0.69 for PCO₁ of Bray Curtis dissimilarity, meaning a 69% probability of inclusion of PFOA. When Elastic Net Regressions were run, results were consistent with linear regression results, with no associations identified between PFAS compounds and alpha diversity (i.e., all coefficients were shrunk to zero and not retained in models; Table 10). For beta-diversity measures, in ATL AA, R² values were generally low (ranging from 0.016 to 0.329), indicating PFAS compounds did not explain a large amount of variability in microbiota measurements (Table 10). In fact, the largest observed R² value was for Bray-Curtis PCO₂ (32.9% of variability explained), which was solely due to sequencing depth rather than PFAS compounds, as shrinkage was applied to all models, and PFAS were not retained in the model. Multiple PFAS compounds were only retained for Canberra PCO₁, where PFUNDA exhibited a negative association and PFNA exhibited a positive one. In MARCH however, PFOS and PFOA were retained in the Canberra PCO₁ and Bray-Curtis PCO₂ models, while PFHxS and PFOA were retained in the Bray-Curtis PCO₁ model. PFOA was retained in 3 of the 4 beta diversity models, consistent with previous PERMANOVA testing. However, the percentage of variability explained by sequencing depth and retained PFAS compounds was again low, with 0.1% of the variability explained for Canberra PCO₁, 3.6% of the variability explained for Bray-Curtis PCO₂, and 5.9% of the variability explained for Bray-Curtis PCO₁.

Table 10:

Elastic Net model results for associating PFAS measurements with late pregnancy microbiota outcomes in ATL AA and MARCH women.

Outcome	R²^*	β^†
Outcome	R²^*	NMFOSAA	PFUNDA	PFDA	PFOA	PFNA	PFHxS	PFOS
ATL AA
Richness	0.085	.	.	.	.	.	.	.
Evenness	0.210	.	.	.	.	.	.	.
Diversity	0.050	.	.	.	.	.	.	.
Canberra PCO₁	0.032	.	−0.008	.	.	0.003	.	.
Canberra PCO₂	0.093	−0.002	.	.	.	.	.	.
Bray-Curtis PCO₁	0.016	.	−0.001	.	.	.	.	.
Bray-Curtis PCO₂	0.329	.	.	.	.	.	.	.

MARCH
Richness	0.398	.	.	.	.	.	.	.
Evenness	0.003	.	.	.	.	.	.	.
Diversity	0.053	.	.	.	.	.	.	.
Canberra PCO₁	0.001	.	.	.	−0.042	.	.	0.016
Canberra PCO₂	0.485	.	.	.	.	.	.	.
Bray-Curtis PCO₁	0.059	.	.	.	0.055	.	0.004	.
Bray-Curtis PCO₂	0.036	.	.	.	−0.038	.	.	0.067

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Amount of variability in outcome explained by retained PFAS measurements and sequencing depth

^†

Change in outcome, per log(ng/mL) increase in PFAS measurement. 3^rd trimester PFAS only used for ATL AA, while 2^nd and 3^rd trimester PFAS used for Michigan cohort. N=77 for ATL AA (N with 3rd trimester PFAS); N=61 for MARCH. Adjusted for sequencing depth.

Discussion

This study aimed to describe associations of prenatal PFAS exposure with maternal gut microbiome diversity and composition using data from two distinct ECHO sites collected during early and later pregnancy. In both the Michigan and Atlanta cohorts, individual PFAS were not associated with statistically significant differences in stool bacterial richness, evenness, or diversity, and associations with overall compositional differences were diminished after covariate adjustment. Mixtures analysis focusing on beta-diversity measures did not result in strong evidence of interaction or mixture effects, although multiple PFAS were retained in ENR and BKMR models for both cohorts (although the amount of variability explained was low). However, in both cohorts, there were significant associations between prenatal PFAS and the relative abundance of several bacterial families and individual taxa, indicating that PFAS may impact the abundance of specific taxa of the gut microbiome. In this study, we observed significant associations between bacterial families and specific taxa in the gut microbiome of pregnant women primarily with PFOA and PFHxS, but these associations were not shared between cohorts (associations with PFOS, PFNA, PFDA, PFUNDA, and NMFOSAA were also observed when DESeq analysis was completed).

In our study we observed multiple family and ASV/OTU level associations with at least one of 7 PFAS measured in unadjusted or adjusted models, and these associations were predominately cohort-specific. In the ATL AA cohort, where both early and late pregnancy PFAS levels were available, using our most conservative and specialized analysis method, ANCOM-BC2, even after full adjustment we observed a significant positive association between the family Clostridiaceae and 3^rd trimester PFOA. At the higher taxonomic resolution, a total of 13 significant associations were identified with all 13 significant associations being with PFOA (12 of 13 were with 3^rd trimester PFOA). These included ASVs of the genera Streptococcus, Bacteroides, Anaerostipes, Intestinibacter, Christensenellaceae_R-7_group, Bifidobacterium, and Coprococcus_1, Prevotella buccalis, Porphyromonas, Lachnospiraceae_NK4A136_group, Ruminiclostridium_6, and Lachnospiraceae_UCG-010. Of these, Bifidobacterium exhibited the largest negative effect size (LFC= −8.41), while Lachnospiraceae_UCG-010 exhibited the largest positive effect size (LFC= 3.77). Bifidobacterium normally increases throughout pregnancy and has been implicated as a positive mediator of feto-placental development.⁸⁹ Our observation linking PFOA exposure to strong decreases in this beneficial bacterium is intriguing and implicate the importance of possible future mechanistic intervention studies focused on this genera. Similarly, the observation that PFOA was significantly negatively associated with Anaerostipes, is potentially clinically relevant as this genus is known to be the main butyrate producer and multiple studies have linked decreased SCFA production to pregnancy outcomes.⁹⁰ In the Michigan cohort, 6 families reached statistical significance for at least one PFAS measurement after full adjustment using ANCOM-BC2 analyses (i.e., Bacteroidales_unclassified, Bifidobacteriaceae, Campylobacteraceae, Selenomonadales, Enterobacterales_unclassified, Desulfovibrionales, and Selenomonadales_unclassified), but none of these significant associations were seen in ATL AA. In MARCH, most of these family-level associations were with PFHxS and in the negative direction. Interestingly, Bifidobacterium (within the family Bifidobacteriaceae), has been linked to pregnancy complications such as preeclampsia and is known as the primary microbial member in the gut of healthy infants.⁹¹ Bifidobacteriaceae species are important for the primary colonization and immune system education during early life, thus any environmental maternal stressors that may modulate levels of these microbes may impact the vertical transmission to offspring.⁹¹ At the higher resolution, most bacteria were significantly associated with a single PFAS, however Burkholderiales_unclassified was significantly positively associated with PFDA, PFNA, PFOA, and PFOS while Succiniclasticum was negatively associated with PFNA, PFOA, and PFOS. Information related to these genera and relationships to pregnancy are limited. Similarities between the two cohorts at the genus level were also null. The lack of overlap in observed associations between the two cohorts may be due to differences in sample collection (rectal swab versus whole stool), due to the distinct 16S rRNA primers used to amplify distinct regions of the 16S gene (V3-V4 versus V4), inherent differences in cohort demographics, or the approach to not harmonize datasets prior to analysis. As 7 of 8 major phyla were different in ATL AA compared to MARCH, there was a high likelihood that any significant associations with PFAS would be cohort specific. Although information linking the above-mentioned bacterial families/genera to adverse pregnancy outcomes or offspring health are limited, some evidence points to possible links to metabolism and growth. For example, in unadjusted models, we observed a robust positive association between multiple PFAS and the bacterial family, Carnobacteriaceae, in the Michigan cohort. Carnobacteriaceae are lactic acid-producing bacilli involved in carbohydrate metabolism, and increased levels have been associated with relapsing cholelithiasis.^{92, 93} Additionally, PFAS were associated with multiple members of the Firmicutes phyla in ATL AA and in MARCH; members of this phylum have been linked to metabolic disorders including obesity during pregnancy.⁹⁴ Finally, multiple PFAS:Proteobacteria phyla associations were observed in ATL AA (e.g., positive associations between Sutterella and PFOA) and MARCH (e.g., Burkholderiales_unclassified positively associated with PFDA, PFNA, PFOA, and PFOS). Proteobacteria are gram negative bacteria with lipopolysaccharide (LPS) on the microbial membrane which can lead to low-grade inflammation implicated in maternal and child health.^{95, 96}

Our study is one of a few epidemiological studies that have examined associations between PFAS and the maternal gut microbiome. Other human studies have identified associations between PFAS exposures and offspring microbiome composition but have primarily focused on breast milk PFAS concentrations. For example, breastmilk PFOS concentrations were associated with increased diversity at 6 weeks and the sum of breastmilk PFAS associated with Bacteroides vulgatus at 1 year of age.⁴⁶ In a Norwegian cohort, 1-month postpartum breast milk PFOS levels were significantly associated with 5% less microbiome alpha-diversity while children exposed to high breastmilk PFOA levels lacked a sub-OTU of Lactobacillus zeae and displayed a 1.1-fold increase of a sub-OTU within the genus Enterococcus.⁴² To our knowledge only one other study has investigated maternal PFAS concentrations and maternal gut microbiome during pregnancy. In a study of 30 pregnant mothers from Finland, six species differed during pregnancy by PFAS quartiles. Methanobrevibacter smithii, unclassified Methanobrevibacter sequences, and Subdoligranulum variabile abundances were increased in highly exposed mothers, while Erysipelotrichaceae bacterium 6145, Bacteroides nordii and Ruminococcus lactaris were decreased with higher exposures. This important work used metagenomic sequencing techniques which limits direct comparisons with our observations but provides additional insights into how PFAS may impact on maternal and offspring microbiome form and function.⁴⁵

Since PFAS exposures do not occur in isolation, we used ENR and BKMR to correlate a mixture of PFAS with alpha and beta diversity metrics and found that PFAS mixtures also had a minor effect on variability of gut bacteria. Over the past several years, mixture modeling, such as weight quantile sum (WQS) regression, Lasso, ENR, and BKMR, has been adapted for use in the environmental health sciences to estimate the health effects of multi-pollutant mixtures. ^{78, 82, 97–99} We utilized ENR and BKMR for their flexibility and robustness. Other pregnancy studies have utilized BKMR to investigate how mixtures of PFAS are associated with a variety of outcomes including gestational diabetes, thyroid hormones and preterm birth. PIPs for specific PFAS reported in these studies were similar to what we report here for beta diversity measures.^100–102 Here, no strong mixing effects of PFAS (i.e., statistical interactions) were demonstrated between PFAS and diversity metrics. The null results observed may be influenced by our relatively small number of detectable PFAS used in these models and relatively small number of subjects. As exposomics and untargeted-based approaches become more common in large pregnancy cohort studies such as ECHO, these types of mixture methodologies may become more useful in elucidating links between body burden of exposure and health outcomes.

The mechanism for how PFAS alter the microbiome is still unclear, but multiple direct and indirect effects have been hypothesized. Previous studies in mice suggest PFAS accumulate in the liver and greatly impact hepatic metabolic pathways, such as lipid and glucose metabolism, and increase oxidative stress and inflammation.¹⁰³ Alterations in lipid and glucose metabolism can impact what is absorbed from the digestive system and therefore could alter the substrates available for the microbiome to utilize, leading to a change in relative microbiota populations. Additionally, we and others have shown that PFAS can impact on primary and secondary bile acid levels through multiple mechanisms including effects at the site of liver and intestine.^{104, 105} Bile acids have been shown to have inherent antimicrobial properties, bile acid levels shift throughout pregnancy, and extremely high levels can lead to intrahepatic cholestasis of pregnancy and subsequently adverse pregnancy outcomes.^106–108 Also, PFAS have been shown to increase oxidative stress and impact inflammation which could contribute to a leaky-gut membrane and decreased production of SCFAs, and perhaps lead to alterations in gut microbiome substrates and populations.^{109, 110} In addition to these indirect effects on the host, there is some evidence of direct cytotoxicity of PFAS on bacteria, although most of this work has focused on environmentally-relevant bacteria.¹¹¹ Researchers have shown that PFAS, especially long-chain PFAS, can cause oxidative stress or DNA damage, growth inhibition, and impacts on bacterial cell walls.¹¹² More work needs to be done using mammalian relevant bacteria to elucidate if these surfactant chemicals can directly modulate microbiome form and function.

One strength of this study is the utilization of two distinct ECHO sites of pregnant women, with each cohort representing distinct populations of mothers. Although sample sizes were relatively similar for each cohort, we did observe more significant taxa associations between later pregnancy PFAS and later maternal microbiome in MARCH than in ATL AA when ANCOM-BC2 analysis was utilized. The majority of the MARCH cohort participants were from Ann Arbor, Michigan, and predominately white, older than ATL AA and with lower BMI. Studying associations between PFAS and microbiome composition in diverse cohorts is important as exposures to environmental chemicals as well as toxicological effects can vary due to geographical, structural racism, or lifestyle differences. Increased age has been associated with higher body burdens of PFAS.^{113, 114} In our study however, some concentrations of PFAS were higher in ATL AA mothers compared to MARCH mothers, particularly PFHxS, PFNA, and PFDA during later pregnancy, but we hesitate to make strong comparisons due to serum versus plasma differences (although concentrations within the two blood matrices appear similar for many PFAS).¹¹⁵ Additionally, there were differences in when later pregnancy plasma or serum was collected which could impact on PFAS concentrations.¹¹⁶ Multiple large studies including the National Health and Nutrition Examination Survey have shown that race is a significant predictor of PFAS concentrations, but the direction of association is not always reproducible. In comparison to other pregnancy cohorts of similar timeframe and national averages, concentrations of the abundant PFAS here were for the most part similar except for PFHxS which seems to be higher in both ATL AA and MARCH and PFOA which is lower.^{40, 117} An interesting finding in our study is PFAS levels increased over the progression of pregnancy. This finding was recently reported for the full Atlanta African American Maternal-Child Cohort and authors implicate continued exposures via drinking water and increased exposures via dietary changes, personal care, or cosmetics perhaps unique to the African American cohort.⁶⁴ This interesting observation is different compared to other cohorts which report decreased circulating PFAS throughout pregnancy.¹¹⁶ Hypotheses of this decrease include physiological changes during pregnancy in renal function, hepatic metabolism, and blood volume, as well as placental transfer to the fetus.^{116, 118, 119} More work to elucidate underlying biobehavioral sources of PFAS variability, especially during pregnancy, is critical, as concentrations of these pollutants are likely socially patterned and impacted by dietary and other behavior-related choices.^{114, 120, 121} Fish and meat associated with increased PFAS levels in some studies^{113, 122} as well as processed or packaged foods³² in other reports. Here, we did not observe strong associations between maternal diet variables and circulating PFAS levels except for an inverse relationship with fiber intake. Other studies have shown inverse fiber associations with PFOA, PFNA, and PFOS, but it is not well established if these observations are due to increased excretion of PFAS or by possible cholesterol confounding.¹²³ Lastly, geographical location can impact PFAS levels through ground water sources, with residents living close to wastewater treatment facilities, manufacturing plants, and military bases seen to have higher levels of PFAS.^{124, 125}

Our study has limitations that may have impacted observed associations between PFAS exposures and the pregnancy gut microbiome. The primary limitation is an analysis strategy that did not attempt to harmonize the 16S results between the two cohorts. Due to the multiple reasons described above, especially differences in microbiome sampling strategies, we instead focused on separate analyses and a “posthoc” examination of similar trends between cohorts. With the initiation of the second phase of the ECHO program, harmonization of microbiome datasets will be more feasible and will better allow for comparisons between cohorts. The authors here had no control of cohort-specific sampling conditions, timing, sequencing strategies, and PFAS quantitation. A limitation of the ECHO cycle 1 research platform is that it represents a collection of independent cohorts and investigators with unique study protocols, data collection, and biological sampling across numerous sites. For example, although both cohorts collected biological specimens during later pregnancy, analyses for MARCH included data from 2^nd and 3^rd trimester whereas ATL AA was solely from 3^rd trimester, which also limits direct comparisons between the cohorts. Additionally, PFAS as a class represents over 4000 individual chemicals found in the environment, but the analytical approaches used for this study focused on a small subset of primarily well characterized long-chain legacy compounds. Although emerging short-chain and fluorotelomer replacements (e.g., 6:2 FTS/GenX) likely have considerably shorter biological half-lives than legacy long-chain (e.g., PFOS/PFOA), these newer compounds have been recently found at high concentrations throughout the world and are detectable in the blood of humans. While focusing on a subset of abundantly identified PFAS, as done here (i.e. 50% detectable here), is common in environmental epidemiology. Mixture studies incorporating the lower abundant PFAS may increase the chance of observing significant interactions. A third limitation is a lack of an additional more complex regression model that includes additional covariates known to relate to the variability of the microbiome or exposure. Although we have added covariates including maternal diet, additional unexplored variables including maternal exercise, sleep patterns, and socioeconomic status^126–128 may have also impacted on the significance of observed associations and may be useful to investigate in future studies. Similarly, a brief dietary screener (in the MARCH cohort) and a FFQ (in the Atlanta cohort) were chosen as the diet assessment tools for their brevity and their previous validation against gold-standard diet assessment methods (i.e., multiple 24-hour dietary recalls). Comprehensive diet assessment, including probiotics/prebiotics was not feasible at the time due to the many competing data elements that were collected. An additional limitation is a lack of examining the impact of first trimester PFAS on how the maternal microbiome shifts from early to late pregnancy. As PFAS have been shown to be modulators of the immune system, act as endocrine disrupting chemicals, and perturb metabolism, it is plausible to hypothesize that PFAS could impact on microbiome flux during pregnancy. Finally, this study is focusing solely on 16S rRNA results to identify microbiome composition/form, and each site included here used distinct 16S primers and unique sequencing processing pipelines. Future research should include metagenomics-based approaches as well as metabolomics to allow for the measurement of microbiota-derived metabolites and identification of specific biological pathways associated with perturbations in the function of the microbiome.

The clinical significance of the observed microbiome alterations is difficult to estimate and likely premature, however, several species/strains within bacterial families significantly associated with PFAS have been associated with human pathophysiology. For example, Bacteroidales, negatively associated with PFAS here, function to produce SCFAs to maintain gut barrier stability and metabolize bile acids.¹²⁹ Decreased levels could lead to leaky-gut barrier, inability to re-use bile acid products, and could predispose colonization of the gut by pathogenic bacteria. Additionally, the family Clostridiaceae, seen to be positively associated with PFOA in third trimester women in ATL AA, contains the pathogen Clostridium difficile, which causes pseudomembranous colitis and toxic megacolon as well as Clostridium botulinum and tetani, responsible for botulism and tetanus illnesses, respectively.^{130, 131}. With these observations, it would be interesting to examine associations between circulating PFAS and toxic metabolites created by pathogens as well as disease severity and recalcitrance to therapeutics. As the gut microbiome has a systemic impact on overall human health including critical periods of development such as pregnancy, it is extremely relevant to further investigate this mechanism and human health outcomes. Future studies that leverage metagenomics-based approaches in well characterized pregnancy cohorts are critical to identify microbiota-focused biomarkers of environmental perturbation or possible therapeutic targets to decrease disease risk associated with environmental chemicals.

In summary, maternal PFAS were significantly associated with changes in cohort-specific taxa of the gut microbiome during pregnancy, but not overall diversity measures. Future studies will examine if these microbiome associations mediate PFAS-induced maternal and offspring health outcomes.

Supplementary Material

NIHMS2080631-supplement-1.xlsx^{(194KB, xlsx)}

NIHMS2080631-supplement-2.pdf^{(2.7MB, pdf)}

Highlights.

ATL AA mothers had different microbiome composition than MARCH as well as higher circulating PFHxS, PFNA and PFDA.
16 significant family-level associations were identified for ATL AA and 13 significant family-level associations identified for MARCH.
In both cohorts, alpha and beta diversity measures were not significantly associated with any PFAS.
Chemical mixture analyses provided modest evidence of inclusion of individual PFAS in beta diversity models in both cohorts.

Acknowledgements:

We acknowledge Wadsworth HHEAR collaborators for measuring maternal PFAS concentrations for this study. The authors wish to thank our ECHO Colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohorts. We also acknowledge the contribution of the following ECHO Program collaborators: ECHO Components—Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith PB, Newby LK; Data Analysis Center: Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland: Jacobson LP; Research Triangle Institute, Durham, North Carolina: Catellier DJ; Person-Reported Outcomes Core: Northwestern University, Evanston, Illinois: Gershon R, Cella D; • Human Health Exposure Analysis Resource: Wadsworth Center, Menands, New York: Parsons P, Kurunthachalam K; RTI International, Research Triangle Park, North Carolina: Fennell TR, Sumner SJ, Du X. ECHO Awardees and Cohorts— Michigan State University, East Lansing, MI: Kerver JM; Henry Ford Health, Detroit, MI: Barone C; Michigan Department of Health and Human Services, Lansing, MI: Fussman C; Michigan State University, East Lansing, MI: Paneth N; University of Michigan, Ann Arbor, MI: Elliott M; Wayne State University, Detroit, MI: Ruden D.

Funding Source:

Research reported in this publication was supported by the Office of the Director at the National Institutes of Health under award numbers U2COD023375 (Coordinating Center and Petriello’s Opportunities and Infrastructure Fund Award), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (PRO Core), U2CES026542 (HHEAR Parsons, Kannan), U2CES030857 (HHEAR Fennell, Sumner, Du), UG3/UH3OD023318 (Dunlop), UG3/OD023285 and UH3OD023285 (Kerver), the Michigan Health Endowment Fund under award numbers G-1608-140432 and R-1605-140007 and from Michigan State University Center for Research in Autism, Intellectual and Neurodevelopmental Disabilities (C-RAIND). This research could not have been done without the collaboration of Hurley Medical Center and Hurley Residency Clinic in Flint, MI, Hutzel Medical Center, DMC Center for Obstetrics and Gynecology, and University Health Center in Detroit, MI Munson Hospital and Grand Traverse Women’s Clinic in Traverse City, MI, University of Michigan Hospital, Von Voigtlander Women’s Center, West Ann Arbor Health Center, and Obstetrics and Gynecology at Briarwood Center in Ann Arbor, MI, St. John’s Providence Park, Metro Partners in Women’s Health, and Women’s Health Consultants in Novi, MI, St. Joseph Mercy Hospital, IHA Canton Obstetrics and Gynecology, and IHA Domino Farms in Ann Arbor, MI, Sinai Grace Hospital, Sinai Grace OB-GYN Women’s Health Centers, Northwest Women’s Care, and DMC Northwest Women’s Care in Detroit, MI, Beaumont Dearborn Hospital and Oakwood OB-GYN Associates in Dearborn, MI, Covenant Hospital, Central Michigan University Health in Saginaw, MI, SHMG OB/GYN, Grand Rapids Women’s Health, and Spectrum Health in Grand Rapids, MI, Blue Water OB/GYN, NorthPointe OB/GYN, McLaren Port Huron Hospital in Port Huron, MI. This work was supported by the Environmental Influences on Child Health Outcomes (ECHO) program, Office of the Director, National Institutes of Health, under Award Numbers 5U2COD023375-05/A03-3824, the National Institute of Health (NIH) research grants [R21ES032117, R01NR014800, R01MD009064, R24ES029490, R01MD009746], NIH Center Grants [P50ES02607, P30ES019776, UH3OD023318, U2CES026560, U2CES026542], and Environmental Protection Agency (USEPA) center grant [83615301]. Associated faculty were supported in part by the National Institute of Environmental Health Sciences [P30ES036084, P42ES030991, R01ES034407-01A1, R01ES035692-01A1] at the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

Conflicts of Interest: No conflicts financial or otherwise to report.

Declaration of Interest Statement

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

References

1.Edwards SM, Cunningham SA, Dunlop AL, Corwin EJ. The Maternal Gut Microbiome During Pregnancy. MCN Am J Matern Child Nurs. 2017;42(6):310–7. Epub 2017/08/09. doi: 10.1097/NMC.0000000000000372. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Mohajeri MH, Brummer RJM, Rastall RA, Weersma RK, Harmsen HJM, Faas M, Eggersdorfer M. The role of the microbiome for human health: from basic science to clinical applications. Eur J Nutr. 2018;57(Suppl 1):1–14. Epub 2018/05/12. doi: 10.1007/s00394-018-1703-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Gorczyca K, Obuchowska A, Kimber-Trojnar Z, Wierzchowska-Opoka M, Leszczynska-Gorzelak B. Changes in the Gut Microbiome and Pathologies in Pregnancy. Int J Environ Res Public Health. 2022;19(16). Epub 2022/08/27. doi: 10.3390/ijerph19169961. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Flint HJ, Duncan SH, Scott KP, Louis P. Interactions and competition within the microbial community of the human colon: links between diet and health. Environ Microbiol. 2007;9(5):1101–11. Epub 2007/05/03. doi: 10.1111/j.1462-2920.2007.01281.x. [DOI] [PubMed] [Google Scholar]
5.Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474(11):1823–36. Epub 2017/05/18. doi: 10.1042/BCJ20160510. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Suganya K, Koo BS. Gut-Brain Axis: Role of Gut Microbiota on Neurological Disorders and How Probiotics/Prebiotics Beneficially Modulate Microbial and Immune Pathways to Improve Brain Functions. Int J Mol Sci. 2020;21(20). Epub 2020/10/18. doi: 10.3390/ijms21207551. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Mayer EA. Gut feelings: the emerging biology of gut-brain communication. Nat Rev Neurosci. 2011;12(8):453–66. Epub 2011/07/14. doi: 10.1038/nrn3071. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Heiss CN, Olofsson LE. The role of the gut microbiota in development, function and disorders of the central nervous system and the enteric nervous system. J Neuroendocrinol. 2019;31(5):e12684. Epub 2019/01/08. doi: 10.1111/jne.12684. [DOI] [PubMed] [Google Scholar]
9.Rhee SH, Pothoulakis C, Mayer EA. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol. 2009;6(5):306–14. Epub 2009/05/01. doi: 10.1038/nrgastro.2009.35. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Stanislawski MA, Dabelea D, Wagner BD, Sontag MK, Lozupone CA, Eggesbo M. Pre-pregnancy weight, gestational weight gain, and the gut microbiota of mothers and their infants. Microbiome. 2017;5(1):113. Epub 2017/09/06. doi: 10.1186/s40168-017-0332-0. [DOI] [PMC free article] [PubMed] [Google Scholar]; Regional Ethics Committee for Medical Research in Norway (approval ref. 2002, S-02216) and the Norwegian Data Inspectorate (ref 2002/1934-2). The approvals, as well as informed consent from the mothers, were obtained prior to collection of data and samples. CONSENT FOR PUBLICATION: N/A. COMPETING INTERESTS: The authors declare that they have no competing interests. PUBLISHER’S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
11.Mulak A, Tache Y, Larauche M. Sex hormones in the modulation of irritable bowel syndrome. World J Gastroenterol. 2014;20(10):2433–48. Epub 2014/03/15. doi: 10.3748/wjg.v20.i10.2433. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Neuman H, Koren O. The Pregnancy Microbiome. Nestle Nutr Inst Workshop Ser. 2017;88:1–9. Epub 2017/03/28. doi: 10.1159/000455207. [DOI] [PubMed] [Google Scholar]
13.Sinha T, Brushett S, Prins J, Zhernakova A. The maternal gut microbiome during pregnancy and its role in maternal and infant health. Curr Opin Microbiol. 2023;74:102309. Epub 2023/04/18. doi: 10.1016/j.mib.2023.102309. [DOI] [PubMed] [Google Scholar]
14.Ionescu RF, Enache RM, Cretoiu SM, Gaspar BS. Gut Microbiome Changes in Gestational Diabetes. Int J Mol Sci. 2022;23(21). Epub 2022/11/12. doi: 10.3390/ijms232112839. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Crusell MKW, Hansen TH, Nielsen T, Allin KH, Ruhlemann MC, Damm P, Vestergaard H, Rorbye C, Jorgensen NR, Christiansen OB, Heinsen FA, Franke A, Hansen T, Lauenborg J, Pedersen O. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome. 2018;6(1):89. Epub 2018/05/17. doi: 10.1186/s40168-018-0472-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Kers JG, Saccenti E. The Power of Microbiome Studies: Some Considerations on Which Alpha and Beta Metrics to Use and How to Report Results. Front Microbiol. 2021;12:796025. Epub 2022/03/22. doi: 10.3389/fmicb.2021.796025. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Teffera M, Veith AC, Ronnekleiv-Kelly S, Bradfield CA, Nikodemova M, Tussing-Humphreys L, Malecki K. Diverse mechanisms by which chemical pollutant exposure alters gut microbiota metabolism and inflammation. Environ Int. 2024;190:108805. Epub 2024/06/21. doi: 10.1016/j.envint.2024.108805. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Lawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138(1):1–11. Epub 2012/12/18. doi: 10.1111/j.1365-2567.2012.03616.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Kho ZY, Lal SK. The Human Gut Microbiome - A Potential Controller of Wellness and Disease. Front Microbiol. 2018;9:1835. Epub 2018/08/30. doi: 10.3389/fmicb.2018.01835. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Stolfi C, Maresca C, Monteleone G, Laudisi F. Implication of Intestinal Barrier Dysfunction in Gut Dysbiosis and Diseases. Biomedicines. 2022;10(2). Epub 2022/02/26. doi: 10.3390/biomedicines10020289. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Jin Y, Wu S, Zeng Z, Fu Z. Effects of environmental pollutants on gut microbiota. Environ Pollut. 2017;222:1–9. Epub 2017/01/14. doi: 10.1016/j.envpol.2016.11.045. [DOI] [PubMed] [Google Scholar]
22.Bailey MJ, Holzhausen EA, Morgan ZEM, Naik N, Shaffer JP, Liang D, Chang HH, Sarnat J, Sun S, Berger PK, Schmidt KA, Lurmann F, Goran MI, Alderete TL. Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age. Gut Microbes. 2022;14(1):2105096. Epub 2022/08/16. doi: 10.1080/19490976.2022.2105096. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Tian Y, Gui W, Rimal B, Koo I, Smith PB, Nichols RG, Cai J, Liu Q, Patterson AD. Metabolic impact of persistent organic pollutants on gut microbiota. Gut Microbes. 2020;12(1):1–16. Epub 2020/12/10. doi: 10.1080/19490976.2020.1848209. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Defois C, Ratel J, Garrait G, Denis S, Le Goff O, Talvas J, Mosoni P, Engel E, Peyret P. Food Chemicals Disrupt Human Gut Microbiota Activity And Impact Intestinal Homeostasis As Revealed By In Vitro Systems. Sci Rep. 2018;8(1):11006. Epub 2018/07/22. doi: 10.1038/s41598-018-29376-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Popli S, Badgujar PC, Agarwal T, Bhushan B, Mishra V. Persistent organic pollutants in foods, their interplay with gut microbiota and resultant toxicity. Sci Total Environ. 2022;832:155084. Epub 2022/04/09. doi: 10.1016/j.scitotenv.2022.155084. [DOI] [PubMed] [Google Scholar]
26.Agarwal M, Hoffman J, Ngo Tenlep SY, Santarossa S, Pearson KJ, Sitarik AR, Cassidy-Bushrow AE, Petriello MC. Maternal polychlorinated biphenyl 126 (PCB 126) exposure modulates offspring gut microbiota irrespective of diet and exercise. Reprod Toxicol. 2023;118:108384. Epub 2023/04/16. doi: 10.1016/j.reprotox.2023.108384. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Petriello MC, Hoffman JB, Vsevolozhskaya O, Morris AJ, Hennig B. Dioxin-like PCB 126 increases intestinal inflammation and disrupts gut microbiota and metabolic homeostasis. Environ Pollut. 2018;242(Pt A):1022–32. Epub 2018/10/31. doi: 10.1016/j.envpol.2018.07.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Trudel D, Horowitz L, Wormuth M, Scheringer M, Cousins IT, Hungerbuhler K. Estimating consumer exposure to PFOS and PFOA. Risk Anal. 2008;28(2):251–69. Epub 2008/04/19. doi: 10.1111/j.1539-6924.2008.01017.x. [DOI] [PubMed] [Google Scholar]
29.Bradley EL, Read WA, Castle L. Investigation into the migration potential of coating materials from cookware products. Food Addit Contam. 2007;24(3):326–35. Epub 2007/03/17. doi: 10.1080/02652030601013711. [DOI] [PubMed] [Google Scholar]
30.Trier X, Granby K, Christensen JH. Polyfluorinated surfactants (PFS) in paper and board coatings for food packaging. Environ Sci Pollut Res Int. 2011;18(7):1108–20. Epub 2011/02/18. doi: 10.1007/s11356-010-0439-3. [DOI] [PubMed] [Google Scholar]
31.Butenhoff JL, Olsen GW, Pfahles-Hutchens A. The applicability of biomonitoring data for perfluorooctanesulfonate to the environmental public health continuum. Environ Health Perspect. 2006;114(11):1776–82. Epub 2006/11/17. doi: 10.1289/ehp.9060. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Schaider LA, Balan SA, Blum A, Andrews DQ, Strynar MJ, Dickinson ME, Lunderberg DM, Lang JR, Peaslee GF. Fluorinated Compounds in U.S. Fast Food Packaging. Environ Sci Technol Lett. 2017;4(3):105–11. Epub 2017/01/01. doi: 10.1021/acs.estlett.6b00435. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Li J, Wang L, Zhang X, Liu P, Deji Z, Xing Y, Zhou Y, Lin X, Huang Z. Per- and polyfluoroalkyl substances exposure and its influence on the intestinal barrier: An overview on the advances. Sci Total Environ. 2022;852:158362. Epub 2022/09/03. doi: 10.1016/j.scitotenv.2022.158362. [DOI] [PubMed] [Google Scholar]
34.Lykkebo CA, Mortensen MS, Davidsen N, Bahl MI, Ramhoj L, Granby K, Svingen T, Licht TR. Antibiotic induced restructuring of the gut microbiota does not affect oral uptake and accumulation of perfluorooctane sulfonic acid (PFOS) in rats. Environ Pollut. 2023;334:122179. Epub 2023/07/17. doi: 10.1016/j.envpol.2023.122179. [DOI] [PubMed] [Google Scholar]
35.Domingo JL, Nadal M. Human exposure to per- and polyfluoroalkyl substances (PFAS) through drinking water: A review of the recent scientific literature. Environ Res. 2019;177:108648. Epub 2019/08/20. doi: 10.1016/j.envres.2019.108648. [DOI] [PubMed] [Google Scholar]
36.Sunderland EM, Hu XC, Dassuncao C, Tokranov AK, Wagner CC, Allen JG. A review of the pathways of human exposure to poly- and perfluoroalkyl substances (PFASs) and present understanding of health effects. J Expo Sci Environ Epidemiol. 2019;29(2):131–47. Epub 2018/11/25. doi: 10.1038/s41370-018-0094-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Beale DJ, Nguyen TV, Shah RM, Bissett A, Nahar A, Smith M, Gonzalez-Astudillo V, Braun C, Baddiley B, Vardy S. Host-Gut Microbiome Metabolic Interactions in PFAS-Impacted Freshwater Turtles (Emydura macquarii macquarii). Metabolites. 2022;12(8). Epub 2022/08/26. doi: 10.3390/metabo12080747. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Beale DJ, Bissett A, Nilsson S, Bose U, Nelis JLD, Nahar A, Smith M, Gonzalez-Astudillo V, Braun C, Baddiley B, Vardy S. Perturbation of the gut microbiome in wild-caught freshwater turtles (Emydura macquarii macquarii) exposed to elevated PFAS levels. Sci Total Environ. 2022;838(Pt 3):156324. Epub 2022/06/03. doi: 10.1016/j.scitotenv.2022.156324. [DOI] [PubMed] [Google Scholar]
39.Thompson KN, Oulhote Y, Weihe P, Wilkinson JE, Ma S, Zhong H, Li J, Kristiansen K, Huttenhower C, Grandjean P. Effects of Lifetime Exposures to Environmental Contaminants on the Adult Gut Microbiome. Environ Sci Technol. 2022;56(23):16985–95. Epub 2022/11/18. doi: 10.1021/acs.est.2c03185. [DOI] [PubMed] [Google Scholar]
40.Padula AM, Ning X, Bakre S, Barrett ES, Bastain T, Bennett DH, Bloom MS, Breton CV, Dunlop AL, Eick SM, Ferrara A, Fleisch A, Geiger S, Goin DE, Kannan K, Karagas MR, Korrick S, Meeker JD, Morello-Frosch R, O’Connor TG, Oken E, Robinson M, Romano ME, Schantz SL, Schmidt RJ, Starling AP, Zhu Y, Hamra GB, Woodruff TJ, program collaborators for Environmental influences on Child Health O. Birth Outcomes in Relation to Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Stress in the Environmental Influences on Child Health Outcomes (ECHO) Program. Environ Health Perspect. 2023;131(3):37006. Epub 2023/03/16. doi: 10.1289/EHP10723. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Balaguer-Trias J, Deepika D, Schuhmacher M, Kumar V. Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity. Int J Environ Res Public Health. 2022;19(3). Epub 2022/02/16. doi: 10.3390/ijerph19031368. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Iszatt N, Janssen S, Lenters V, Dahl C, Stigum H, Knight R, Mandal S, Peddada S, Gonzalez A, Midtvedt T, Eggesbo M. Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month. Microbiome. 2019;7(1):34. Epub 2019/03/01. doi: 10.1186/s40168-019-0645-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Shi F, Almerick TBD, Wan HT, Chan TF, Zhang EL, Lai KP, Wong CK. Hepatic metabolism gene expression and gut microbes in offspring, subjected to in-utero PFOS exposure and postnatal diet challenges. Chemosphere. 2022;308(Pt 1):136196. Epub 2022/08/31. doi: 10.1016/j.chemosphere.2022.136196. [DOI] [PubMed] [Google Scholar]
44.Naspolini NF, Meyer A, Moreira JC, Sun H, Froes-Asmus CIR, Dominguez-Bello MG. Environmental pollutant exposure associated with altered early-life gut microbiome: Results from a birth cohort study. Environ Res. 2022;205:112545. Epub 2021/12/14. doi: 10.1016/j.envres.2021.112545. [DOI] [PubMed] [Google Scholar]
45.Lamichhane S, Harkonen T, Vatanen T, Hyotylainen T, Knip M, Oresic M. Impact of exposure to per- and polyfluoroalkyl substances on fecal microbiota composition in mother-infant dyads. Environ Int. 2023;176:107965. Epub 2023/05/22. doi: 10.1016/j.envint.2023.107965. [DOI] [PubMed] [Google Scholar]
46.Laue HE, Moroishi Y, Palys TJ, Christensen BC, Criswell RL, Peterson LA, Huset CA, Baker ER, Karagas MR, Madan JC, Romano ME. Early-life exposure to per- and polyfluoroalkyl substances and infant gut microbial composition. Environ Epidemiol. 2023;7(1):e238. Epub 2023/02/14. doi: 10.1097/EE9.0000000000000238. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Chang CJ, Barr DB, Ryan PB, Panuwet P, Smarr MM, Liu K, Kannan K, Yakimavets V, Tan Y, Ly V, Marsit CJ, Jones DP, Corwin EJ, Dunlop AL, Liang D. Per- and polyfluoroalkyl substance (PFAS) exposure, maternal metabolomic perturbation, and fetal growth in African American women: A meet-in-the-middle approach. Environ Int. 2022;158:106964. Epub 2021/11/05. doi: 10.1016/j.envint.2021.106964. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Taibl KR, Dunlop AL, Barr DB, Li YY, Eick SM, Kannan K, Ryan PB, Schroder M, Rushing B, Fennell T, Chang CJ, Tan Y, Marsit CJ, Jones DP, Liang D. Newborn metabolomic signatures of maternal per- and polyfluoroalkyl substance exposure and reduced length of gestation. Nat Commun. 2023;14(1):3120. Epub 2023/05/31. doi: 10.1038/s41467-023-38710-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Blaisdell CJ, Park C, Hanspal M, Roary M, Arteaga SS, Laessig S, Luetkemeier E, Gillman MW, program collaborators for Environmental influences on Child Health O. The NIH ECHO Program: investigating how early environmental influences affect child health. Pediatr Res. 2022;92(5):1215–6. Epub 2021/06/17. doi: 10.1038/s41390-021-01574-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Paneth N, Monk C. The importance of cohort research starting early in life to understanding child health. Curr Opin Pediatr. 2018;30(2):292–6. Epub 2018/01/27. doi: 10.1097/MOP.0000000000000596. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Corwin EJ, Hogue CJ, Pearce B, Hill CC, Read TD, Mulle J, Dunlop AL. Protocol for the Emory University African American Vaginal, Oral, and Gut Microbiome in Pregnancy Cohort Study. BMC Pregnancy Childbirth. 2017;17(1):161. Epub 2017/06/03. doi: 10.1186/s12884-017-1357-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Brennan PA, Dunlop AL, Smith AK, Kramer M, Mulle J, Corwin EJ. Protocol for the Emory University African American maternal stress and infant gut microbiome cohort study. BMC Pediatr. 2019;19(1):246. Epub 2019/07/25. doi: 10.1186/s12887-019-1630-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Tan Y, Taibl KR, Dunlop AL, Barr DB, Panuwet P, Yakimavets V, Kannan K, Corwin EJ, Ryan PB, Eatman JA, Liang D, Eick SM. Association between a Mixture of Per- and Polyfluoroalkyl Substances (PFAS) and Inflammatory Biomarkers in the Atlanta African American Maternal-Child Cohort. Environ Sci Technol. 2023;57(36):13419–28. Epub 2023/08/31. doi: 10.1021/acs.est.3c04688. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Eatman JA, Dunlop AL, Barr DB, Corwin EJ, Hill CC, Brennan PA, Ryan PB, Panuwet P, Taibl KR, Tan Y, Liang D, Eick SM. Exposure to phthalate metabolites, bisphenol A, and psychosocial stress mixtures and pregnancy outcomes in the Atlanta African American maternal-child cohort. Environ Res. 2023;233:116464. Epub 2023/06/22. doi: 10.1016/j.envres.2023.116464. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Taibl KR, Liang D, Dunlop AL, Barr DB, Smith MR, Steenland K, Tan Y, Ryan PB, Panuwet P, Everson T, Marsit CJ, Kannan K, Jones DP, Eick SM. Pregnancy-related hemodynamic biomarkers in relation to trimester-specific maternal per - and polyfluoroalkyl substances exposures and adverse birth outcomes. Environ Pollut. 2023;323:121331. Epub 2023/02/23. doi: 10.1016/j.envpol.2023.121331. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Ma T, Bu S, Paneth N, Kerver JM, Comstock SS. Vitamin D Supplementation in Exclusively Breastfed Infants Is Associated with Alterations in the Fecal Microbiome. Nutrients. 2022;14(1). Epub 2022/01/12. doi: 10.3390/nu14010202. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Elovitz MA, Gajer P, Riis V, Brown AG, Humphrys MS, Holm JB, Ravel J. Cervicovaginal microbiota and local immune response modulate the risk of spontaneous preterm delivery. Nat Commun. 2019;10(1):1305. Epub 2019/03/23. doi: 10.1038/s41467-019-09285-9. [DOI] [PMC free article] [PubMed] [Google Scholar]; submitted by the Trustees of the University of Pennsylvania that covers compositions and methods for predicting risk of preterm birth. The authors declare no other competing interests.
58.Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, Yang H, Gajer P, He X, McComb E, Gravitt PE, Ghanem KG, Brotman RM, Ravel J. Ultrahigh-Throughput Multiplexing and Sequencing of >500-Base-Pair Amplicon Regions on the Illumina HiSeq 2500 Platform. mSystems. 2019;4(1). Epub 2019/02/26. doi: 10.1128/mSystems.00029-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Dunlop AL, Satten GA, Hu YJ, Knight AK, Hill CC, Wright ML, Smith AK, Read TD, Pearce BD, Corwin EJ. Vaginal Microbiome Composition in Early Pregnancy and Risk of Spontaneous Preterm and Early Term Birth Among African American Women. Front Cell Infect Microbiol. 2021;11:641005. Epub 2021/05/18. doi: 10.3389/fcimb.2021.641005. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Haddad EN, Comstock SS. Archive for Research in Child Health (ARCH) and Baby Gut: Study Protocol for a Remote, Prospective, Longitudinal Pregnancy and Birth Cohort to Address Microbiota Development and Child Health. Methods Protoc. 2021;4(3). Epub 2021/08/28. doi: 10.3390/mps4030052. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Sugino KY, Paneth N, Comstock SS. Michigan cohorts to determine associations of maternal pre-pregnancy body mass index with pregnancy and infant gastrointestinal microbial communities: Late pregnancy and early infancy. PLoS One. 2019;14(3):e0213733. Epub 2019/03/19. doi: 10.1371/journal.pone.0213733. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Jeske JT, Gallert C. Microbiome Analysis via OTU and ASV-Based Pipelines-A Comparative Interpretation of Ecological Data in WWTP Systems. Bioengineering (Basel). 2022;9(4). Epub 2022/04/22. doi: 10.3390/bioengineering9040146. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Kannan K, Stathis A, Mazzella MJ, Andra SS, Barr DB, Hecht SS, Merrill LS, Galusha AL, Parsons PJ. Quality assurance and harmonization for targeted biomonitoring measurements of environmental organic chemicals across the Children’s Health Exposure Analysis Resource laboratory network. Int J Hyg Environ Health. 2021;234:113741. Epub 2021/03/28. doi: 10.1016/j.ijheh.2021.113741. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Tan Y, Eick SM, Dunlop AL, Barr DB, Taibl KR, Steenland K, Kannan K, Robinson M, Chang CJ, Panuwet P, Yakimavets V, Marsit CJ, Ryan PB, Liang D. A Prospective Analysis of Per- and Polyfluoroalkyl Substances from Early Pregnancy to Delivery in the Atlanta African American Maternal-Child Cohort. Environ Health Perspect. 2024;132(11):117001. Epub 2024/11/06 22:19. doi: 10.1289/EHP14334. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Liang D, Taibl KR, Dunlop AL, Barr DB, Ryan PB, Everson T, Huels A, Tan Y, Panuwet P, Kannan K, Marsit C, Jones DP, Eick SM. Metabolic Perturbations Associated with an Exposure Mixture of Per- and Polyfluoroalkyl Substances in the Atlanta African American Maternal-Child Cohort. Environ Sci Technol. 2023;57(43):16206–18. Epub 2023/10/20. doi: 10.1021/acs.est.3c04561. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Liu Y, Wosu AC, Fleisch AF, Dunlop AL, Starling AP, Ferrara A, Dabelea D, Oken E, Buckley JP, Chatzi L, Karagas MR, Romano ME, Schantz S, O’Connor TG, Woodruff TJ, Zhu Y, Hamra GB, Braun JM, the program collaborators for Environmental influences on Child Health O. Associations of Gestational Perfluoroalkyl Substances Exposure with Early Childhood BMI z-Scores and Risk of Overweight/Obesity: Results from the ECHO Cohorts. Environ Health Perspect. 2023;131(6):67001. Epub 2023/06/07. doi: 10.1289/EHP11545. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Honda M, Robinson M, Kannan K. A rapid method for the analysis of perfluorinated alkyl substances in serum by hybrid solid-phase extraction. Environ Chem. 2018;15(1-2):92–9. doi: 10.1071/En17192. [DOI] [Google Scholar]
68.Wei R, Wang J, Su M, Jia E, Chen S, Chen T, Ni Y. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Sci Rep. 2018;8(1):663. Epub 2018/01/14. doi: 10.1038/s41598-017-19120-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Colwell RK. III.1 Biodiversity: Concepts, Patterns, and Measurement. In: Simon AL, Stephen RC, Godfray HCJ, Ann PK, Michel L, Jonathan BL, Brian W, David SW, editors. The Princeton Guide to Ecology. Princeton: Princeton University Press; 2009. p. 257–63. [Google Scholar]
70.Herrmann B, Cerbule K, Brčić J, Grimaldo E, Geoffroy M, Daase M, Berge J. Accounting for Uncertainties in Biodiversity Estimations: A New Methodology and Its Application to the Mesopelagic Sound Scattering Layer of the High Arctic2022;10. doi: 10.3389/fevo.2022.775759. [DOI] [Google Scholar]
71.Lemos LN, Fulthorpe RR, Triplett EW, Roesch LF. Rethinking microbial diversity analysis in the high throughput sequencing era. J Microbiol Methods. 2011;86(1):42–51. Epub 2011/04/05. doi: 10.1016/j.mimet.2011.03.014. [DOI] [PubMed] [Google Scholar]
72.Oksanen Jari [aut c, Simpson Gavin L. [aut], Guillaume Blanchet F [aut], Kindt Roeland [aut], Legendre Pierre [aut], Minchin Peter R. [aut], O’Hara RB [aut], Peter Solymos M Stevens Henry H. [aut], Szoecs Eduard [aut], Wagner Helene [aut], Barbour Matt [aut], Bedward Michael [aut], Bolker Ben [aut], Borcard Daniel [aut], Carvalho Gustavo [aut], Chirico Michael [aut], De Caceres Miquel [aut], Durand Sebastien [aut], Beatriz Antoniazi Evangelista Heloisa [aut], FitzJohn Rich [aut], Friendly Michael [aut], Furneaux Brendan [aut], Hannigan Geoffrey [aut], Hill Mark O. [aut], Lahti Leo [aut], McGlinn Dan [aut], Ouellette Marie-Helene [aut], Ribeiro Cunha Eduardo [aut], Smith Tyler [aut], Stier Adrian [aut], Ter Braak Cajo J.F. [aut], Weedon James [aut]. vegan: Community Ecology Package 2022; 2.6–4:[Available from: https://CRAN.R-project.org/package=vegan. [Google Scholar]
73.Drost H-G. Philentropy: Information Theory and Distance Quantification with R. Journal of Open Source Software. 2018. doi: 10.21105/joss.00765. [DOI] [Google Scholar]
74.Bray JR, Curtis JT. An Ordination of the Upland Forest Communities of Southern Wisconsin1957;27(4):325–49. doi: 10.2307/1942268. [DOI] [Google Scholar]
75.Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11(1):3514. Epub 2020/07/16. doi: 10.1038/s41467-020-17041-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Epub 2014/12/18. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, Jones CMA, Wright RJ, Dhanani AS, Comeau AM, Langille MGI. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun. 2022;13(1):342. Epub 2022/01/19. doi: 10.1038/s41467-022-28034-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 2015;16(3):493–508. Epub 2014/12/24. doi: 10.1093/biostatistics/kxu058. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33(1):1–22. Epub 2010/09/03. [PMC free article] [PubMed] [Google Scholar]
80.Kuhn M. Building Predictive Models in R Using the caret Package. Journal of Statistical Software. 2008;28(5):1 - 26. doi: 10.18637/jss.v028.i05.27774042 [DOI] [Google Scholar]
81.Bashir T, Obeng-Gyasi E. Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression. Int J Environ Res Public Health. 2023;20(10). Epub 2023/05/27. doi: 10.3390/ijerph20105808. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Zou H, Zhang HH. On the Adaptive Elastic-Net with a Diverging Number of Parameters. Ann Stat. 2009;37(4):1733–51. Epub 2009/01/01. doi: 10.1214/08-AOS625. [DOI] [PMC free article] [PubMed] [Google Scholar]
83.Maher SE, O’Brien EC, Moore RL, Byrne DF, Geraghty AA, Saldova R, Murphy EF, Van Sinderen D, Cotter PD, McAuliffe FM. The association between the maternal diet and the maternal and infant gut microbiome: a systematic review. Br J Nutr. 2020:1–29. Epub 2020/03/05. doi: 10.1017/S0007114520000847. [DOI] [PubMed] [Google Scholar]
84.Wang Y, Gui J, Howe CG, Emond JA, Criswell RL, Gallagher LG, Huset CA, Peterson LA, Botelho JC, Calafat AM, Christensen B, Karagas MR, Romano ME. Association of diet with per- and polyfluoroalkyl substances in plasma and human milk in the New Hampshire Birth Cohort Study. Sci Total Environ. 2024;933:173157. Epub 2024/05/14. doi: 10.1016/j.scitotenv.2024.173157. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Siega-Riz AM, Bodnar LM, Savitz DA. What are pregnant women eating? Nutrient and food group differences by race. Am J Obstet Gynecol. 2002;186(3):480–6. Epub 2002/03/21. doi: 10.1067/mob.2002.121078. [DOI] [PubMed] [Google Scholar]
86.Toolkit P. Fiber Intake, PhenX Toolkit. Available from: https://www.phenxtoolkit.org/protocols/view/50601.
87.Ao J, Zhang R, Huo X, Zhu W, Zhang J. Environmental exposure to legacy and emerging per- and polyfluoroalkyl substances and endometriosis in women of childbearing age. Sci Total Environ. 2024;907:167838. Epub 2023/10/16. doi: 10.1016/j.scitotenv.2023.167838. [DOI] [PubMed] [Google Scholar]
88.Jeffreys H. The theory of probability. 3 ed. Oxford Classic Texts in the Physical Sciences: OuP Oxford; 1961. [Google Scholar]
89.Lopez-Tello J, Schofield Z, Kiu R, Dalby MJ, van Sinderen D, Le Gall G, Sferruzzi-Perri AN, Hall LJ. Maternal gut microbiota Bifidobacterium promotes placental morphogenesis, nutrient transport and fetal growth in mice. Cell Mol Life Sci. 2022;79(7):386. Epub 2022/06/28. doi: 10.1007/s00018-022-04379-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Liu ZZ, Sun JH, Wang WJ. Gut microbiota in gastrointestinal diseases during pregnancy. World J Clin Cases. 2022;10(10):2976–89. Epub 2022/06/02. doi: 10.12998/wjcc.v10.i10.2976. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Miao T, Yu Y, Sun J, Ma A, Yu J, Cui M, Yang L, Wang H. Decrease in abundance of bacteria of the genus Bifidobacterium in gut microbiota may be related to pre-eclampsia progression in women from East China. Food Nutr Res. 2021;65. Epub 2021/07/16. doi: 10.29219/fnr.v65.5781. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Rossi F. Special Issue “Functional Characterization of Lactic Acid Bacteria”: Editorial. Microorganisms. 2023;11(5). Epub 2023/06/15. doi: 10.3390/microorganisms11051190. [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Liu Q, Zheng L, Wang Y, Huang Z, Zhu J, Fang M, Xie L, Ding C, Gu Y, Xu D, Jin H, Yang J, Zhang X, Shen H. Primary choledocholithiasis occurrence and recurrence is synergetcally modulated by the bile microbiome and metabolome alternations. Life Sci. 2023;331:122073. Epub 2023/09/08. doi: 10.1016/j.lfs.2023.122073. [DOI] [PubMed] [Google Scholar]
94.Dunlop AL, Mulle JG, Ferranti EP, Edwards S, Dunn AB, Corwin EJ. Maternal Microbiome and Pregnancy Outcomes That Impact Infant Health: A Review. Adv Neonatal Care. 2015;15(6):377–85. Epub 2015/09/01. doi: 10.1097/ANC.0000000000000218. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Wang H, Yang LL, Hu YF, Wang BW, Huang YY, Zhang C, Chen YH, Xu DX. Maternal LPS exposure during pregnancy impairs testicular development, steroidogenesis and spermatogenesis in male offspring. PLoS One. 2014;9(9):e106786. Epub 2014/09/26. doi: 10.1371/journal.pone.0106786. [DOI] [PMC free article] [PubMed] [Google Scholar]
96.Wang J, Gu X, Yang J, Wei Y, Zhao Y. Gut Microbiota Dysbiosis and Increased Plasma LPS and TMAO Levels in Patients With Preeclampsia. Front Cell Infect Microbiol. 2019;9:409. Epub 2019/12/19. doi: 10.3389/fcimb.2019.00409. [DOI] [PMC free article] [PubMed] [Google Scholar]
97.Wheeler DC, Rustom S, Carli M, Whitehead TP, Ward MH, Metayer C. Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk. Int J Environ Res Public Health. 2021;18(2). Epub 2021/01/14. doi: 10.3390/ijerph18020504. [DOI] [PMC free article] [PubMed] [Google Scholar]
98.Gibson EA, Nunez Y, Abuawad A, Zota AR, Renzetti S, Devick KL, Gennings C, Goldsmith J, Coull BA, Kioumourtzoglou MA. An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length. Environ Health. 2019;18(1):76. Epub 2019/08/30. doi: 10.1186/s12940-019-0515-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Taylor KW, Joubert BR, Braun JM, Dilworth C, Gennings C, Hauser R, Heindel JJ, Rider CV, Webster TF, Carlin DJ. Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop. Environ Health Perspect. 2016;124(12):A227–A9. Epub 2016/12/03. doi: 10.1289/EHP547. [DOI] [PMC free article] [PubMed] [Google Scholar]; child lead exposure for the plaintiffs in a public nuisance case related to childhood lead poisoning. None of these activities were directly related to the present study. The other authors declare they have no actual or potential competing financial interests.
100.Lebeaux RM, Doherty BT, Gallagher LG, Zoeller RT, Hoofnagle AN, Calafat AM, Karagas MR, Yolton K, Chen A, Lanphear BP, Braun JM, Romano ME. Maternal serum perfluoroalkyl substance mixtures and thyroid hormone concentrations in maternal and cord sera: The HOME Study. Environ Res. 2020;185:109395. Epub 2020/03/31. doi: 10.1016/j.envres.2020.109395. [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Preston EV, Rifas-Shiman SL, Hivert MF, Zota AR, Sagiv SK, Calafat AM, Oken E, James-Todd T. Associations of Per- and Polyfluoroalkyl Substances (PFAS) With Glucose Tolerance During Pregnancy in Project Viva. J Clin Endocrinol Metab. 2020;105(8):e2864–76. Epub 2020/06/02. doi: 10.1210/clinem/dgaa328. [DOI] [PMC free article] [PubMed] [Google Scholar]
102.Siwakoti RC, Cathey A, Ferguson KK, Hao W, Cantonwine DE, Mukherjee B, McElrath TF, Meeker JD. Prenatal per- and polyfluoroalkyl substances (PFAS) exposure in relation to preterm birth subtypes and size-for-gestational age in the LIFECODES cohort 2006-2008. Environ Res. 2023;237(Pt 2):116967. Epub 2023/08/28. doi: 10.1016/j.envres.2023.116967. [DOI] [PMC free article] [PubMed] [Google Scholar]
103.Wang G, Pan R, Liang X, Wu X, Wu Y, Zhang H, Zhao J, Chen W. Perfluorooctanoic acid-induced liver injury is potentially associated with gut microbiota dysbiosis. Chemosphere. 2021;266:129004. Epub 2020/12/07. doi: 10.1016/j.chemosphere.2020.129004. [DOI] [PubMed] [Google Scholar]
104.Sen P, Qadri S, Luukkonen PK, Ragnarsdottir O, McGlinchey A, Jantti S, Juuti A, Arola J, Schlezinger JJ, Webster TF, Oresic M, Yki-Jarvinen H, Hyotylainen T. Exposure to environmental contaminants is associated with altered hepatic lipid metabolism in non-alcoholic fatty liver disease. J Hepatol. 2022;76(2):283–93. Epub 2021/10/11. doi: 10.1016/j.jhep.2021.09.039. [DOI] [PubMed] [Google Scholar]
105.Roth K, Yang Z, Agarwal M, Birbeck J, Westrick J, Lydic T, Gurdziel K, Petriello MC. Exposure of Ldlr−/− Mice to a PFAS Mixture and Outcomes Related to Circulating Lipids, Bile Acid Excretion, and the Intestinal Transporter ASBT. Environ Health Perspect. 2024;132(8):87007. Epub 2024/08/23. doi: 10.1289/EHP14339. [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Bustos AY, Font de Valdez G, Fadda S, Taranto MP. New insights into bacterial bile resistance mechanisms: the role of bile salt hydrolase and its impact on human health. Food Res Int. 2018;112:250–62. Epub 2018/08/23. doi: 10.1016/j.foodres.2018.06.035. [DOI] [PubMed] [Google Scholar]
107.Ovadia C, Perdones-Montero A, Spagou K, Smith A, Sarafian MH, Gomez-Romero M, Bellafante E, Clarke LCD, Sadiq F, Nikolova V, Mitchell A, Dixon PH, Santa-Pinter N, Wahlstrom A, Abu-Hayyeh S, Walters JRF, Marschall HU, Holmes E, Marchesi JR, Williamson C. Enhanced Microbial Bile Acid Deconjugation and Impaired Ileal Uptake in Pregnancy Repress Intestinal Regulation of Bile Acid Synthesis. Hepatology. 2019;70(1):276–93. Epub 2019/04/16. doi: 10.1002/hep.30661. [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Abdul Waheed MI, Jaiswal A, Yelne S, Nandanwar V. Navigating Perinatal Challenges: A Comprehensive Review of Cholestasis of Pregnancy and Its Impact on Maternal and Fetal Health. Cureus. 2024;16(4):e58699. Epub 2024/05/23. doi: 10.7759/cureus.58699. [DOI] [PMC free article] [PubMed] [Google Scholar]
109.Sun Y, Wang X, Li L, Zhong C, Zhang Y, Yang X, Li M, Yang C. The role of gut microbiota in intestinal disease: from an oxidative stress perspective. Front Microbiol. 2024;15:1328324. Epub 2024/02/29. doi: 10.3389/fmicb.2024.1328324. [DOI] [PMC free article] [PubMed] [Google Scholar]
110.Roth K, Yang Z, Agarwal M, Liu W, Peng Z, Long Z, Birbeck J, Westrick J, Liu W, Petriello MC. Exposure to a mixture of legacy, alternative, and replacement per- and polyfluoroalkyl substances (PFAS) results in sex-dependent modulation of cholesterol metabolism and liver injury. Environ Int. 2021;157:106843. Epub 2021/09/04. doi: 10.1016/j.envint.2021.106843. [DOI] [PMC free article] [PubMed] [Google Scholar]
111.Rosal R, Rodea-Palomares I, Boltes K, Fernandez-Pinas F, Leganes F, Petre A. Ecotoxicological assessment of surfactants in the aquatic environment: combined toxicity of docusate sodium with chlorinated pollutants. Chemosphere. 2010;81(2):288–93. Epub 2010/06/29. doi: 10.1016/j.chemosphere.2010.05.050. [DOI] [PubMed] [Google Scholar]
112.Liu G, Zhang S, Yang K, Zhu L, Lin D. Toxicity of perfluorooctane sulfonate and perfluorooctanoic acid to Escherichia coli: Membrane disruption, oxidative stress, and DNA damage induced cell inactivation and/or death. Environ Pollut. 2016;214:806–15. Epub 2016/05/08. doi: 10.1016/j.envpol.2016.04.089. [DOI] [PubMed] [Google Scholar]
113.Park SK, Peng Q, Ding N, Mukherjee B, Harlow SD. Determinants of per- and polyfluoroalkyl substances (PFAS) in midlife women: Evidence of racial/ethnic and geographic differences in PFAS exposure. Environ Res. 2019;175:186–99. Epub 2019/05/28. doi: 10.1016/j.envres.2019.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
114.Calafat AM, Kuklenyik Z, Reidy JA, Caudill SP, Tully JS, Needham LL. Serum concentrations of 11 polyfluoroalkyl compounds in the u.s. population: data from the national health and nutrition examination survey (NHANES). Environ Sci Technol. 2007;41(7):2237–42. Epub 2007/04/19. doi: 10.1021/es062686m. [DOI] [PubMed] [Google Scholar]
115.Poothong S, Thomsen C, Padilla-Sanchez JA, Papadopoulou E, Haug LS. Distribution of Novel and Well-Known Poly- and Perfluoroalkyl Substances (PFASs) in Human Serum, Plasma, and Whole Blood. Environ Sci Technol. 2017;51(22):13388–96. Epub 2017/10/24. doi: 10.1021/acs.est.7b03299. [DOI] [PubMed] [Google Scholar]
116.Nielsen C, Andersson Hall U, Lindh C, Ekstrom U, Xu Y, Li Y, Holmang A, Jakobsson K. Pregnancy-induced changes in serum concentrations of perfluoroalkyl substances and the influence of kidney function. Environ Health. 2020;19(1):80. Epub 2020/07/10. doi: 10.1186/s12940-020-00626-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
117.Luo Y, Li X, Li J, Gong X, Wu T, Li X, Li Z, Zhai Y, Wei Y, Wang Y, Jiang G. Prenatal Exposure of PFAS in Cohorts of Pregnant Women: Identifying the Critical Windows of Vulnerability and Health Implications. Environ Sci Technol. 2024;58(31):13624–35. Epub 2024/07/26. doi: 10.1021/acs.est.4c00453. [DOI] [PubMed] [Google Scholar]
118.Appel M, Forsthuber M, Ramos R, Widhalm R, Granitzer S, Uhl M, Hengstschlager M, Stamm T, Gundacker C. The transplacental transfer efficiency of per- and polyfluoroalkyl substances (PFAS): a first meta-analysis. J Toxicol Environ Health B Crit Rev. 2022;25(1):23–42. Epub 2021/12/22. doi: 10.1080/10937404.2021.2009946. [DOI] [PubMed] [Google Scholar]
119.Verner MA, Loccisano AE, Morken NH, Yoon M, Wu H, McDougall R, Maisonet M, Marcus M, Kishi R, Miyashita C, Chen MH, Hsieh WS, Andersen ME, Clewell HJ 3rd, Longnecker MP. Associations of Perfluoroalkyl Substances (PFAS) with Lower Birth Weight: An Evaluation of Potential Confounding by Glomerular Filtration Rate Using a Physiologically Based Pharmacokinetic Model (PBPK). Environ Health Perspect. 2015;123(12):1317–24. Epub 2015/05/27. doi: 10.1289/ehp.1408837. [DOI] [PMC free article] [PubMed] [Google Scholar]; Sciences, an independent nonprofit organization. The following authors received no compensation from DuPont or 3M: N.H.M., R.M., M.M., M.M., R.K., C.M., M.H.C., W.S.H., and M.P.L. R.M. was employed by Aegis Technologies, Huntsville, Alabama. All authors certify that their freedom to design, conduct, interpret, and publish research was not compromised by any sponsor. The authors declare they have no actual or potential competing financial interests.
120.Boronow KE, Brody JG, Schaider LA, Peaslee GF, Havas L, Cohn BA. Serum concentrations of PFASs and exposure-related behaviors in African American and non-Hispanic white women. J Expo Sci Environ Epidemiol. 2019;29(2):206–17. Epub 2019/01/10. doi: 10.1038/s41370-018-0109-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
121.Kingsley SL, Eliot MN, Kelsey KT, Calafat AM, Ehrlich S, Lanphear BP, Chen A, Braun JM. Variability and predictors of serum perfluoroalkyl substance concentrations during pregnancy and early childhood. Environ Res. 2018;165:247–57. Epub 2018/05/08. doi: 10.1016/j.envres.2018.04.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
122.Jain RB. Contribution of diet and other factors to the levels of selected polyfluorinated compounds: data from NHANES 2003-2008. Int J Hyg Environ Health. 2014;217(1):52–61. Epub 2013/04/23. doi: 10.1016/j.ijheh.2013.03.008. [DOI] [PubMed] [Google Scholar]
123.Dzierlenga MW, Keast DR, Longnecker MP. The concentration of several perfluoroalkyl acids in serum appears to be reduced by dietary fiber. Environ Int. 2021;146:106292. Epub 2021/01/06. doi: 10.1016/j.envint.2020.106292. [DOI] [PubMed] [Google Scholar]
124.DeLuca NM, Thomas K, Mullikin A, Slover R, Stanek LW, Pilant AN, Cohen Hubal EA. Geographic and demographic variability in serum PFAS concentrations for pregnant women in the United States. J Expo Sci Environ Epidemiol. 2023;33(5):710–24. Epub 2023/01/26. doi: 10.1038/s41370-023-00520-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
125.Hu XC, Andrews DQ, Lindstrom AB, Bruton TA, Schaider LA, Grandjean P, Lohmann R, Carignan CC, Blum A, Balan SA, Higgins CP, Sunderland EM. Detection of Poly- and Perfluoroalkyl Substances (PFASs) in U.S. Drinking Water Linked to Industrial Sites, Military Fire Training Areas, and Wastewater Treatment Plants. Environ Sci Technol Lett. 2016;3(10):344–50. Epub 2016/10/19. doi: 10.1021/acs.estlett.6b00260. [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Kwak S, Usyk M, Beggs D, Choi H, Ahdoot D, Wu F, Maceda L, Li H, Im EO, Han HR, Lee E, Wu AH, Hayes RB, Ahn J. Sociobiome - Individual and neighborhood socioeconomic status influence the gut microbiome in a multi-ethnic population in the US. NPJ Biofilms Microbiomes. 2024;10(1):19. Epub 2024/03/12. doi: 10.1038/s41522-024-00491-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Yao ZY, Li XH, Zuo L, Xiong Q, He WT, Li DX, Dong ZF. Maternal sleep deprivation induces gut microbial dysbiosis and neuroinflammation in offspring rats. Zool Res. 2022;43(3):380–90. Epub 2022/04/02. doi: 10.24272/j.issn.2095-8137.2022.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
128.Santarossa S, Sitarik AR, Cassidy-Bushrow AE, Comstock SS. Prenatal physical activity and the gut microbiota of pregnant women: results from a preliminary investigation. Phys Act Nutr. 2023;27(2):1–7. Epub 2023/08/16. doi: 10.20463/pan.2023.0011. [DOI] [PMC free article] [PubMed] [Google Scholar]
129.Shin JH, Tillotson G, MacKenzie TN, Warren CA, Wexler HM, Goldstein EJC. Bacteroides and related species: The keystone taxa of the human gut microbiota. Anaerobe. 2024;85:102819. Epub 2024/01/13. doi: 10.1016/j.anaerobe.2024.102819. [DOI] [PubMed] [Google Scholar]
130.Alam MZ, Madan R. Clostridioides difficile Toxins: Host Cell Interactions and Their Role in Disease Pathogenesis. Toxins (Basel). 2024;16(6). Epub 2024/06/26. doi: 10.3390/toxins16060241. [DOI] [PMC free article] [PubMed] [Google Scholar]
131.Uzal FA, Navarro MA, Li J, Freedman JC, Shrestha A, McClane BA. Comparative pathogenesis of enteric clostridial infections in humans and animals. Anaerobe. 2018;53:11–20. Epub 2018/06/09. doi: 10.1016/j.anaerobe.2018.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[R1] 1.Edwards SM, Cunningham SA, Dunlop AL, Corwin EJ. The Maternal Gut Microbiome During Pregnancy. MCN Am J Matern Child Nurs. 2017;42(6):310–7. Epub 2017/08/09. doi: 10.1097/NMC.0000000000000372. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Mohajeri MH, Brummer RJM, Rastall RA, Weersma RK, Harmsen HJM, Faas M, Eggersdorfer M. The role of the microbiome for human health: from basic science to clinical applications. Eur J Nutr. 2018;57(Suppl 1):1–14. Epub 2018/05/12. doi: 10.1007/s00394-018-1703-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Gorczyca K, Obuchowska A, Kimber-Trojnar Z, Wierzchowska-Opoka M, Leszczynska-Gorzelak B. Changes in the Gut Microbiome and Pathologies in Pregnancy. Int J Environ Res Public Health. 2022;19(16). Epub 2022/08/27. doi: 10.3390/ijerph19169961. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Flint HJ, Duncan SH, Scott KP, Louis P. Interactions and competition within the microbial community of the human colon: links between diet and health. Environ Microbiol. 2007;9(5):1101–11. Epub 2007/05/03. doi: 10.1111/j.1462-2920.2007.01281.x. [DOI] [PubMed] [Google Scholar]

[R5] 5.Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474(11):1823–36. Epub 2017/05/18. doi: 10.1042/BCJ20160510. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Suganya K, Koo BS. Gut-Brain Axis: Role of Gut Microbiota on Neurological Disorders and How Probiotics/Prebiotics Beneficially Modulate Microbial and Immune Pathways to Improve Brain Functions. Int J Mol Sci. 2020;21(20). Epub 2020/10/18. doi: 10.3390/ijms21207551. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Mayer EA. Gut feelings: the emerging biology of gut-brain communication. Nat Rev Neurosci. 2011;12(8):453–66. Epub 2011/07/14. doi: 10.1038/nrn3071. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Heiss CN, Olofsson LE. The role of the gut microbiota in development, function and disorders of the central nervous system and the enteric nervous system. J Neuroendocrinol. 2019;31(5):e12684. Epub 2019/01/08. doi: 10.1111/jne.12684. [DOI] [PubMed] [Google Scholar]

[R9] 9.Rhee SH, Pothoulakis C, Mayer EA. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol. 2009;6(5):306–14. Epub 2009/05/01. doi: 10.1038/nrgastro.2009.35. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Stanislawski MA, Dabelea D, Wagner BD, Sontag MK, Lozupone CA, Eggesbo M. Pre-pregnancy weight, gestational weight gain, and the gut microbiota of mothers and their infants. Microbiome. 2017;5(1):113. Epub 2017/09/06. doi: 10.1186/s40168-017-0332-0. [DOI] [PMC free article] [PubMed] [Google Scholar]; Regional Ethics Committee for Medical Research in Norway (approval ref. 2002, S-02216) and the Norwegian Data Inspectorate (ref 2002/1934-2). The approvals, as well as informed consent from the mothers, were obtained prior to collection of data and samples. CONSENT FOR PUBLICATION: N/A. COMPETING INTERESTS: The authors declare that they have no competing interests. PUBLISHER’S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[R11] 11.Mulak A, Tache Y, Larauche M. Sex hormones in the modulation of irritable bowel syndrome. World J Gastroenterol. 2014;20(10):2433–48. Epub 2014/03/15. doi: 10.3748/wjg.v20.i10.2433. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Neuman H, Koren O. The Pregnancy Microbiome. Nestle Nutr Inst Workshop Ser. 2017;88:1–9. Epub 2017/03/28. doi: 10.1159/000455207. [DOI] [PubMed] [Google Scholar]

[R13] 13.Sinha T, Brushett S, Prins J, Zhernakova A. The maternal gut microbiome during pregnancy and its role in maternal and infant health. Curr Opin Microbiol. 2023;74:102309. Epub 2023/04/18. doi: 10.1016/j.mib.2023.102309. [DOI] [PubMed] [Google Scholar]

[R14] 14.Ionescu RF, Enache RM, Cretoiu SM, Gaspar BS. Gut Microbiome Changes in Gestational Diabetes. Int J Mol Sci. 2022;23(21). Epub 2022/11/12. doi: 10.3390/ijms232112839. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Crusell MKW, Hansen TH, Nielsen T, Allin KH, Ruhlemann MC, Damm P, Vestergaard H, Rorbye C, Jorgensen NR, Christiansen OB, Heinsen FA, Franke A, Hansen T, Lauenborg J, Pedersen O. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome. 2018;6(1):89. Epub 2018/05/17. doi: 10.1186/s40168-018-0472-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Kers JG, Saccenti E. The Power of Microbiome Studies: Some Considerations on Which Alpha and Beta Metrics to Use and How to Report Results. Front Microbiol. 2021;12:796025. Epub 2022/03/22. doi: 10.3389/fmicb.2021.796025. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Teffera M, Veith AC, Ronnekleiv-Kelly S, Bradfield CA, Nikodemova M, Tussing-Humphreys L, Malecki K. Diverse mechanisms by which chemical pollutant exposure alters gut microbiota metabolism and inflammation. Environ Int. 2024;190:108805. Epub 2024/06/21. doi: 10.1016/j.envint.2024.108805. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Lawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138(1):1–11. Epub 2012/12/18. doi: 10.1111/j.1365-2567.2012.03616.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Kho ZY, Lal SK. The Human Gut Microbiome - A Potential Controller of Wellness and Disease. Front Microbiol. 2018;9:1835. Epub 2018/08/30. doi: 10.3389/fmicb.2018.01835. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Stolfi C, Maresca C, Monteleone G, Laudisi F. Implication of Intestinal Barrier Dysfunction in Gut Dysbiosis and Diseases. Biomedicines. 2022;10(2). Epub 2022/02/26. doi: 10.3390/biomedicines10020289. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Jin Y, Wu S, Zeng Z, Fu Z. Effects of environmental pollutants on gut microbiota. Environ Pollut. 2017;222:1–9. Epub 2017/01/14. doi: 10.1016/j.envpol.2016.11.045. [DOI] [PubMed] [Google Scholar]

[R22] 22.Bailey MJ, Holzhausen EA, Morgan ZEM, Naik N, Shaffer JP, Liang D, Chang HH, Sarnat J, Sun S, Berger PK, Schmidt KA, Lurmann F, Goran MI, Alderete TL. Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age. Gut Microbes. 2022;14(1):2105096. Epub 2022/08/16. doi: 10.1080/19490976.2022.2105096. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Tian Y, Gui W, Rimal B, Koo I, Smith PB, Nichols RG, Cai J, Liu Q, Patterson AD. Metabolic impact of persistent organic pollutants on gut microbiota. Gut Microbes. 2020;12(1):1–16. Epub 2020/12/10. doi: 10.1080/19490976.2020.1848209. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Defois C, Ratel J, Garrait G, Denis S, Le Goff O, Talvas J, Mosoni P, Engel E, Peyret P. Food Chemicals Disrupt Human Gut Microbiota Activity And Impact Intestinal Homeostasis As Revealed By In Vitro Systems. Sci Rep. 2018;8(1):11006. Epub 2018/07/22. doi: 10.1038/s41598-018-29376-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Popli S, Badgujar PC, Agarwal T, Bhushan B, Mishra V. Persistent organic pollutants in foods, their interplay with gut microbiota and resultant toxicity. Sci Total Environ. 2022;832:155084. Epub 2022/04/09. doi: 10.1016/j.scitotenv.2022.155084. [DOI] [PubMed] [Google Scholar]

[R26] 26.Agarwal M, Hoffman J, Ngo Tenlep SY, Santarossa S, Pearson KJ, Sitarik AR, Cassidy-Bushrow AE, Petriello MC. Maternal polychlorinated biphenyl 126 (PCB 126) exposure modulates offspring gut microbiota irrespective of diet and exercise. Reprod Toxicol. 2023;118:108384. Epub 2023/04/16. doi: 10.1016/j.reprotox.2023.108384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Petriello MC, Hoffman JB, Vsevolozhskaya O, Morris AJ, Hennig B. Dioxin-like PCB 126 increases intestinal inflammation and disrupts gut microbiota and metabolic homeostasis. Environ Pollut. 2018;242(Pt A):1022–32. Epub 2018/10/31. doi: 10.1016/j.envpol.2018.07.039. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Trudel D, Horowitz L, Wormuth M, Scheringer M, Cousins IT, Hungerbuhler K. Estimating consumer exposure to PFOS and PFOA. Risk Anal. 2008;28(2):251–69. Epub 2008/04/19. doi: 10.1111/j.1539-6924.2008.01017.x. [DOI] [PubMed] [Google Scholar]

[R29] 29.Bradley EL, Read WA, Castle L. Investigation into the migration potential of coating materials from cookware products. Food Addit Contam. 2007;24(3):326–35. Epub 2007/03/17. doi: 10.1080/02652030601013711. [DOI] [PubMed] [Google Scholar]

[R30] 30.Trier X, Granby K, Christensen JH. Polyfluorinated surfactants (PFS) in paper and board coatings for food packaging. Environ Sci Pollut Res Int. 2011;18(7):1108–20. Epub 2011/02/18. doi: 10.1007/s11356-010-0439-3. [DOI] [PubMed] [Google Scholar]

[R31] 31.Butenhoff JL, Olsen GW, Pfahles-Hutchens A. The applicability of biomonitoring data for perfluorooctanesulfonate to the environmental public health continuum. Environ Health Perspect. 2006;114(11):1776–82. Epub 2006/11/17. doi: 10.1289/ehp.9060. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Schaider LA, Balan SA, Blum A, Andrews DQ, Strynar MJ, Dickinson ME, Lunderberg DM, Lang JR, Peaslee GF. Fluorinated Compounds in U.S. Fast Food Packaging. Environ Sci Technol Lett. 2017;4(3):105–11. Epub 2017/01/01. doi: 10.1021/acs.estlett.6b00435. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Li J, Wang L, Zhang X, Liu P, Deji Z, Xing Y, Zhou Y, Lin X, Huang Z. Per- and polyfluoroalkyl substances exposure and its influence on the intestinal barrier: An overview on the advances. Sci Total Environ. 2022;852:158362. Epub 2022/09/03. doi: 10.1016/j.scitotenv.2022.158362. [DOI] [PubMed] [Google Scholar]

[R34] 34.Lykkebo CA, Mortensen MS, Davidsen N, Bahl MI, Ramhoj L, Granby K, Svingen T, Licht TR. Antibiotic induced restructuring of the gut microbiota does not affect oral uptake and accumulation of perfluorooctane sulfonic acid (PFOS) in rats. Environ Pollut. 2023;334:122179. Epub 2023/07/17. doi: 10.1016/j.envpol.2023.122179. [DOI] [PubMed] [Google Scholar]

[R35] 35.Domingo JL, Nadal M. Human exposure to per- and polyfluoroalkyl substances (PFAS) through drinking water: A review of the recent scientific literature. Environ Res. 2019;177:108648. Epub 2019/08/20. doi: 10.1016/j.envres.2019.108648. [DOI] [PubMed] [Google Scholar]

[R36] 36.Sunderland EM, Hu XC, Dassuncao C, Tokranov AK, Wagner CC, Allen JG. A review of the pathways of human exposure to poly- and perfluoroalkyl substances (PFASs) and present understanding of health effects. J Expo Sci Environ Epidemiol. 2019;29(2):131–47. Epub 2018/11/25. doi: 10.1038/s41370-018-0094-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Beale DJ, Nguyen TV, Shah RM, Bissett A, Nahar A, Smith M, Gonzalez-Astudillo V, Braun C, Baddiley B, Vardy S. Host-Gut Microbiome Metabolic Interactions in PFAS-Impacted Freshwater Turtles (Emydura macquarii macquarii). Metabolites. 2022;12(8). Epub 2022/08/26. doi: 10.3390/metabo12080747. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Beale DJ, Bissett A, Nilsson S, Bose U, Nelis JLD, Nahar A, Smith M, Gonzalez-Astudillo V, Braun C, Baddiley B, Vardy S. Perturbation of the gut microbiome in wild-caught freshwater turtles (Emydura macquarii macquarii) exposed to elevated PFAS levels. Sci Total Environ. 2022;838(Pt 3):156324. Epub 2022/06/03. doi: 10.1016/j.scitotenv.2022.156324. [DOI] [PubMed] [Google Scholar]

[R39] 39.Thompson KN, Oulhote Y, Weihe P, Wilkinson JE, Ma S, Zhong H, Li J, Kristiansen K, Huttenhower C, Grandjean P. Effects of Lifetime Exposures to Environmental Contaminants on the Adult Gut Microbiome. Environ Sci Technol. 2022;56(23):16985–95. Epub 2022/11/18. doi: 10.1021/acs.est.2c03185. [DOI] [PubMed] [Google Scholar]

[R40] 40.Padula AM, Ning X, Bakre S, Barrett ES, Bastain T, Bennett DH, Bloom MS, Breton CV, Dunlop AL, Eick SM, Ferrara A, Fleisch A, Geiger S, Goin DE, Kannan K, Karagas MR, Korrick S, Meeker JD, Morello-Frosch R, O’Connor TG, Oken E, Robinson M, Romano ME, Schantz SL, Schmidt RJ, Starling AP, Zhu Y, Hamra GB, Woodruff TJ, program collaborators for Environmental influences on Child Health O. Birth Outcomes in Relation to Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Stress in the Environmental Influences on Child Health Outcomes (ECHO) Program. Environ Health Perspect. 2023;131(3):37006. Epub 2023/03/16. doi: 10.1289/EHP10723. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Balaguer-Trias J, Deepika D, Schuhmacher M, Kumar V. Impact of Contaminants on Microbiota: Linking the Gut-Brain Axis with Neurotoxicity. Int J Environ Res Public Health. 2022;19(3). Epub 2022/02/16. doi: 10.3390/ijerph19031368. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Iszatt N, Janssen S, Lenters V, Dahl C, Stigum H, Knight R, Mandal S, Peddada S, Gonzalez A, Midtvedt T, Eggesbo M. Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month. Microbiome. 2019;7(1):34. Epub 2019/03/01. doi: 10.1186/s40168-019-0645-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Shi F, Almerick TBD, Wan HT, Chan TF, Zhang EL, Lai KP, Wong CK. Hepatic metabolism gene expression and gut microbes in offspring, subjected to in-utero PFOS exposure and postnatal diet challenges. Chemosphere. 2022;308(Pt 1):136196. Epub 2022/08/31. doi: 10.1016/j.chemosphere.2022.136196. [DOI] [PubMed] [Google Scholar]

[R44] 44.Naspolini NF, Meyer A, Moreira JC, Sun H, Froes-Asmus CIR, Dominguez-Bello MG. Environmental pollutant exposure associated with altered early-life gut microbiome: Results from a birth cohort study. Environ Res. 2022;205:112545. Epub 2021/12/14. doi: 10.1016/j.envres.2021.112545. [DOI] [PubMed] [Google Scholar]

[R45] 45.Lamichhane S, Harkonen T, Vatanen T, Hyotylainen T, Knip M, Oresic M. Impact of exposure to per- and polyfluoroalkyl substances on fecal microbiota composition in mother-infant dyads. Environ Int. 2023;176:107965. Epub 2023/05/22. doi: 10.1016/j.envint.2023.107965. [DOI] [PubMed] [Google Scholar]

[R46] 46.Laue HE, Moroishi Y, Palys TJ, Christensen BC, Criswell RL, Peterson LA, Huset CA, Baker ER, Karagas MR, Madan JC, Romano ME. Early-life exposure to per- and polyfluoroalkyl substances and infant gut microbial composition. Environ Epidemiol. 2023;7(1):e238. Epub 2023/02/14. doi: 10.1097/EE9.0000000000000238. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Chang CJ, Barr DB, Ryan PB, Panuwet P, Smarr MM, Liu K, Kannan K, Yakimavets V, Tan Y, Ly V, Marsit CJ, Jones DP, Corwin EJ, Dunlop AL, Liang D. Per- and polyfluoroalkyl substance (PFAS) exposure, maternal metabolomic perturbation, and fetal growth in African American women: A meet-in-the-middle approach. Environ Int. 2022;158:106964. Epub 2021/11/05. doi: 10.1016/j.envint.2021.106964. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Taibl KR, Dunlop AL, Barr DB, Li YY, Eick SM, Kannan K, Ryan PB, Schroder M, Rushing B, Fennell T, Chang CJ, Tan Y, Marsit CJ, Jones DP, Liang D. Newborn metabolomic signatures of maternal per- and polyfluoroalkyl substance exposure and reduced length of gestation. Nat Commun. 2023;14(1):3120. Epub 2023/05/31. doi: 10.1038/s41467-023-38710-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Blaisdell CJ, Park C, Hanspal M, Roary M, Arteaga SS, Laessig S, Luetkemeier E, Gillman MW, program collaborators for Environmental influences on Child Health O. The NIH ECHO Program: investigating how early environmental influences affect child health. Pediatr Res. 2022;92(5):1215–6. Epub 2021/06/17. doi: 10.1038/s41390-021-01574-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Paneth N, Monk C. The importance of cohort research starting early in life to understanding child health. Curr Opin Pediatr. 2018;30(2):292–6. Epub 2018/01/27. doi: 10.1097/MOP.0000000000000596. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Corwin EJ, Hogue CJ, Pearce B, Hill CC, Read TD, Mulle J, Dunlop AL. Protocol for the Emory University African American Vaginal, Oral, and Gut Microbiome in Pregnancy Cohort Study. BMC Pregnancy Childbirth. 2017;17(1):161. Epub 2017/06/03. doi: 10.1186/s12884-017-1357-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Brennan PA, Dunlop AL, Smith AK, Kramer M, Mulle J, Corwin EJ. Protocol for the Emory University African American maternal stress and infant gut microbiome cohort study. BMC Pediatr. 2019;19(1):246. Epub 2019/07/25. doi: 10.1186/s12887-019-1630-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Tan Y, Taibl KR, Dunlop AL, Barr DB, Panuwet P, Yakimavets V, Kannan K, Corwin EJ, Ryan PB, Eatman JA, Liang D, Eick SM. Association between a Mixture of Per- and Polyfluoroalkyl Substances (PFAS) and Inflammatory Biomarkers in the Atlanta African American Maternal-Child Cohort. Environ Sci Technol. 2023;57(36):13419–28. Epub 2023/08/31. doi: 10.1021/acs.est.3c04688. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Eatman JA, Dunlop AL, Barr DB, Corwin EJ, Hill CC, Brennan PA, Ryan PB, Panuwet P, Taibl KR, Tan Y, Liang D, Eick SM. Exposure to phthalate metabolites, bisphenol A, and psychosocial stress mixtures and pregnancy outcomes in the Atlanta African American maternal-child cohort. Environ Res. 2023;233:116464. Epub 2023/06/22. doi: 10.1016/j.envres.2023.116464. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Taibl KR, Liang D, Dunlop AL, Barr DB, Smith MR, Steenland K, Tan Y, Ryan PB, Panuwet P, Everson T, Marsit CJ, Kannan K, Jones DP, Eick SM. Pregnancy-related hemodynamic biomarkers in relation to trimester-specific maternal per - and polyfluoroalkyl substances exposures and adverse birth outcomes. Environ Pollut. 2023;323:121331. Epub 2023/02/23. doi: 10.1016/j.envpol.2023.121331. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Ma T, Bu S, Paneth N, Kerver JM, Comstock SS. Vitamin D Supplementation in Exclusively Breastfed Infants Is Associated with Alterations in the Fecal Microbiome. Nutrients. 2022;14(1). Epub 2022/01/12. doi: 10.3390/nu14010202. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Elovitz MA, Gajer P, Riis V, Brown AG, Humphrys MS, Holm JB, Ravel J. Cervicovaginal microbiota and local immune response modulate the risk of spontaneous preterm delivery. Nat Commun. 2019;10(1):1305. Epub 2019/03/23. doi: 10.1038/s41467-019-09285-9. [DOI] [PMC free article] [PubMed] [Google Scholar]; submitted by the Trustees of the University of Pennsylvania that covers compositions and methods for predicting risk of preterm birth. The authors declare no other competing interests.

[R58] 58.Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, Yang H, Gajer P, He X, McComb E, Gravitt PE, Ghanem KG, Brotman RM, Ravel J. Ultrahigh-Throughput Multiplexing and Sequencing of >500-Base-Pair Amplicon Regions on the Illumina HiSeq 2500 Platform. mSystems. 2019;4(1). Epub 2019/02/26. doi: 10.1128/mSystems.00029-19. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Dunlop AL, Satten GA, Hu YJ, Knight AK, Hill CC, Wright ML, Smith AK, Read TD, Pearce BD, Corwin EJ. Vaginal Microbiome Composition in Early Pregnancy and Risk of Spontaneous Preterm and Early Term Birth Among African American Women. Front Cell Infect Microbiol. 2021;11:641005. Epub 2021/05/18. doi: 10.3389/fcimb.2021.641005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Haddad EN, Comstock SS. Archive for Research in Child Health (ARCH) and Baby Gut: Study Protocol for a Remote, Prospective, Longitudinal Pregnancy and Birth Cohort to Address Microbiota Development and Child Health. Methods Protoc. 2021;4(3). Epub 2021/08/28. doi: 10.3390/mps4030052. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Sugino KY, Paneth N, Comstock SS. Michigan cohorts to determine associations of maternal pre-pregnancy body mass index with pregnancy and infant gastrointestinal microbial communities: Late pregnancy and early infancy. PLoS One. 2019;14(3):e0213733. Epub 2019/03/19. doi: 10.1371/journal.pone.0213733. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Jeske JT, Gallert C. Microbiome Analysis via OTU and ASV-Based Pipelines-A Comparative Interpretation of Ecological Data in WWTP Systems. Bioengineering (Basel). 2022;9(4). Epub 2022/04/22. doi: 10.3390/bioengineering9040146. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Kannan K, Stathis A, Mazzella MJ, Andra SS, Barr DB, Hecht SS, Merrill LS, Galusha AL, Parsons PJ. Quality assurance and harmonization for targeted biomonitoring measurements of environmental organic chemicals across the Children’s Health Exposure Analysis Resource laboratory network. Int J Hyg Environ Health. 2021;234:113741. Epub 2021/03/28. doi: 10.1016/j.ijheh.2021.113741. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Tan Y, Eick SM, Dunlop AL, Barr DB, Taibl KR, Steenland K, Kannan K, Robinson M, Chang CJ, Panuwet P, Yakimavets V, Marsit CJ, Ryan PB, Liang D. A Prospective Analysis of Per- and Polyfluoroalkyl Substances from Early Pregnancy to Delivery in the Atlanta African American Maternal-Child Cohort. Environ Health Perspect. 2024;132(11):117001. Epub 2024/11/06 22:19. doi: 10.1289/EHP14334. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Liang D, Taibl KR, Dunlop AL, Barr DB, Ryan PB, Everson T, Huels A, Tan Y, Panuwet P, Kannan K, Marsit C, Jones DP, Eick SM. Metabolic Perturbations Associated with an Exposure Mixture of Per- and Polyfluoroalkyl Substances in the Atlanta African American Maternal-Child Cohort. Environ Sci Technol. 2023;57(43):16206–18. Epub 2023/10/20. doi: 10.1021/acs.est.3c04561. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R66] 66.Liu Y, Wosu AC, Fleisch AF, Dunlop AL, Starling AP, Ferrara A, Dabelea D, Oken E, Buckley JP, Chatzi L, Karagas MR, Romano ME, Schantz S, O’Connor TG, Woodruff TJ, Zhu Y, Hamra GB, Braun JM, the program collaborators for Environmental influences on Child Health O. Associations of Gestational Perfluoroalkyl Substances Exposure with Early Childhood BMI z-Scores and Risk of Overweight/Obesity: Results from the ECHO Cohorts. Environ Health Perspect. 2023;131(6):67001. Epub 2023/06/07. doi: 10.1289/EHP11545. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R67] 67.Honda M, Robinson M, Kannan K. A rapid method for the analysis of perfluorinated alkyl substances in serum by hybrid solid-phase extraction. Environ Chem. 2018;15(1-2):92–9. doi: 10.1071/En17192. [DOI] [Google Scholar]

[R68] 68.Wei R, Wang J, Su M, Jia E, Chen S, Chen T, Ni Y. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Sci Rep. 2018;8(1):663. Epub 2018/01/14. doi: 10.1038/s41598-017-19120-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R69] 69.Colwell RK. III.1 Biodiversity: Concepts, Patterns, and Measurement. In: Simon AL, Stephen RC, Godfray HCJ, Ann PK, Michel L, Jonathan BL, Brian W, David SW, editors. The Princeton Guide to Ecology. Princeton: Princeton University Press; 2009. p. 257–63. [Google Scholar]

[R70] 70.Herrmann B, Cerbule K, Brčić J, Grimaldo E, Geoffroy M, Daase M, Berge J. Accounting for Uncertainties in Biodiversity Estimations: A New Methodology and Its Application to the Mesopelagic Sound Scattering Layer of the High Arctic2022;10. doi: 10.3389/fevo.2022.775759. [DOI] [Google Scholar]

[R71] 71.Lemos LN, Fulthorpe RR, Triplett EW, Roesch LF. Rethinking microbial diversity analysis in the high throughput sequencing era. J Microbiol Methods. 2011;86(1):42–51. Epub 2011/04/05. doi: 10.1016/j.mimet.2011.03.014. [DOI] [PubMed] [Google Scholar]

[R72] 72.Oksanen Jari [aut c, Simpson Gavin L. [aut], Guillaume Blanchet F [aut], Kindt Roeland [aut], Legendre Pierre [aut], Minchin Peter R. [aut], O’Hara RB [aut], Peter Solymos M Stevens Henry H. [aut], Szoecs Eduard [aut], Wagner Helene [aut], Barbour Matt [aut], Bedward Michael [aut], Bolker Ben [aut], Borcard Daniel [aut], Carvalho Gustavo [aut], Chirico Michael [aut], De Caceres Miquel [aut], Durand Sebastien [aut], Beatriz Antoniazi Evangelista Heloisa [aut], FitzJohn Rich [aut], Friendly Michael [aut], Furneaux Brendan [aut], Hannigan Geoffrey [aut], Hill Mark O. [aut], Lahti Leo [aut], McGlinn Dan [aut], Ouellette Marie-Helene [aut], Ribeiro Cunha Eduardo [aut], Smith Tyler [aut], Stier Adrian [aut], Ter Braak Cajo J.F. [aut], Weedon James [aut]. vegan: Community Ecology Package 2022; 2.6–4:[Available from: https://CRAN.R-project.org/package=vegan. [Google Scholar]

[R73] 73.Drost H-G. Philentropy: Information Theory and Distance Quantification with R. Journal of Open Source Software. 2018. doi: 10.21105/joss.00765. [DOI] [Google Scholar]

[R74] 74.Bray JR, Curtis JT. An Ordination of the Upland Forest Communities of Southern Wisconsin1957;27(4):325–49. doi: 10.2307/1942268. [DOI] [Google Scholar]

[R75] 75.Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11(1):3514. Epub 2020/07/16. doi: 10.1038/s41467-020-17041-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R76] 76.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Epub 2014/12/18. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R77] 77.Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, Jones CMA, Wright RJ, Dhanani AS, Comeau AM, Langille MGI. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun. 2022;13(1):342. Epub 2022/01/19. doi: 10.1038/s41467-022-28034-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R78] 78.Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 2015;16(3):493–508. Epub 2014/12/24. doi: 10.1093/biostatistics/kxu058. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R79] 79.Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33(1):1–22. Epub 2010/09/03. [PMC free article] [PubMed] [Google Scholar]

[R80] 80.Kuhn M. Building Predictive Models in R Using the caret Package. Journal of Statistical Software. 2008;28(5):1 - 26. doi: 10.18637/jss.v028.i05.27774042 [DOI] [Google Scholar]

[R81] 81.Bashir T, Obeng-Gyasi E. Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression. Int J Environ Res Public Health. 2023;20(10). Epub 2023/05/27. doi: 10.3390/ijerph20105808. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R82] 82.Zou H, Zhang HH. On the Adaptive Elastic-Net with a Diverging Number of Parameters. Ann Stat. 2009;37(4):1733–51. Epub 2009/01/01. doi: 10.1214/08-AOS625. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R83] 83.Maher SE, O’Brien EC, Moore RL, Byrne DF, Geraghty AA, Saldova R, Murphy EF, Van Sinderen D, Cotter PD, McAuliffe FM. The association between the maternal diet and the maternal and infant gut microbiome: a systematic review. Br J Nutr. 2020:1–29. Epub 2020/03/05. doi: 10.1017/S0007114520000847. [DOI] [PubMed] [Google Scholar]

[R84] 84.Wang Y, Gui J, Howe CG, Emond JA, Criswell RL, Gallagher LG, Huset CA, Peterson LA, Botelho JC, Calafat AM, Christensen B, Karagas MR, Romano ME. Association of diet with per- and polyfluoroalkyl substances in plasma and human milk in the New Hampshire Birth Cohort Study. Sci Total Environ. 2024;933:173157. Epub 2024/05/14. doi: 10.1016/j.scitotenv.2024.173157. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R85] 85.Siega-Riz AM, Bodnar LM, Savitz DA. What are pregnant women eating? Nutrient and food group differences by race. Am J Obstet Gynecol. 2002;186(3):480–6. Epub 2002/03/21. doi: 10.1067/mob.2002.121078. [DOI] [PubMed] [Google Scholar]

[R86] 86.Toolkit P. Fiber Intake, PhenX Toolkit. Available from: https://www.phenxtoolkit.org/protocols/view/50601.

[R87] 87.Ao J, Zhang R, Huo X, Zhu W, Zhang J. Environmental exposure to legacy and emerging per- and polyfluoroalkyl substances and endometriosis in women of childbearing age. Sci Total Environ. 2024;907:167838. Epub 2023/10/16. doi: 10.1016/j.scitotenv.2023.167838. [DOI] [PubMed] [Google Scholar]

[R88] 88.Jeffreys H. The theory of probability. 3 ed. Oxford Classic Texts in the Physical Sciences: OuP Oxford; 1961. [Google Scholar]

[R89] 89.Lopez-Tello J, Schofield Z, Kiu R, Dalby MJ, van Sinderen D, Le Gall G, Sferruzzi-Perri AN, Hall LJ. Maternal gut microbiota Bifidobacterium promotes placental morphogenesis, nutrient transport and fetal growth in mice. Cell Mol Life Sci. 2022;79(7):386. Epub 2022/06/28. doi: 10.1007/s00018-022-04379-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R90] 90.Liu ZZ, Sun JH, Wang WJ. Gut microbiota in gastrointestinal diseases during pregnancy. World J Clin Cases. 2022;10(10):2976–89. Epub 2022/06/02. doi: 10.12998/wjcc.v10.i10.2976. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R91] 91.Miao T, Yu Y, Sun J, Ma A, Yu J, Cui M, Yang L, Wang H. Decrease in abundance of bacteria of the genus Bifidobacterium in gut microbiota may be related to pre-eclampsia progression in women from East China. Food Nutr Res. 2021;65. Epub 2021/07/16. doi: 10.29219/fnr.v65.5781. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R92] 92.Rossi F. Special Issue “Functional Characterization of Lactic Acid Bacteria”: Editorial. Microorganisms. 2023;11(5). Epub 2023/06/15. doi: 10.3390/microorganisms11051190. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R93] 93.Liu Q, Zheng L, Wang Y, Huang Z, Zhu J, Fang M, Xie L, Ding C, Gu Y, Xu D, Jin H, Yang J, Zhang X, Shen H. Primary choledocholithiasis occurrence and recurrence is synergetcally modulated by the bile microbiome and metabolome alternations. Life Sci. 2023;331:122073. Epub 2023/09/08. doi: 10.1016/j.lfs.2023.122073. [DOI] [PubMed] [Google Scholar]

[R94] 94.Dunlop AL, Mulle JG, Ferranti EP, Edwards S, Dunn AB, Corwin EJ. Maternal Microbiome and Pregnancy Outcomes That Impact Infant Health: A Review. Adv Neonatal Care. 2015;15(6):377–85. Epub 2015/09/01. doi: 10.1097/ANC.0000000000000218. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R95] 95.Wang H, Yang LL, Hu YF, Wang BW, Huang YY, Zhang C, Chen YH, Xu DX. Maternal LPS exposure during pregnancy impairs testicular development, steroidogenesis and spermatogenesis in male offspring. PLoS One. 2014;9(9):e106786. Epub 2014/09/26. doi: 10.1371/journal.pone.0106786. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R96] 96.Wang J, Gu X, Yang J, Wei Y, Zhao Y. Gut Microbiota Dysbiosis and Increased Plasma LPS and TMAO Levels in Patients With Preeclampsia. Front Cell Infect Microbiol. 2019;9:409. Epub 2019/12/19. doi: 10.3389/fcimb.2019.00409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R97] 97.Wheeler DC, Rustom S, Carli M, Whitehead TP, Ward MH, Metayer C. Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk. Int J Environ Res Public Health. 2021;18(2). Epub 2021/01/14. doi: 10.3390/ijerph18020504. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R98] 98.Gibson EA, Nunez Y, Abuawad A, Zota AR, Renzetti S, Devick KL, Gennings C, Goldsmith J, Coull BA, Kioumourtzoglou MA. An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length. Environ Health. 2019;18(1):76. Epub 2019/08/30. doi: 10.1186/s12940-019-0515-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R99] 99.Taylor KW, Joubert BR, Braun JM, Dilworth C, Gennings C, Hauser R, Heindel JJ, Rider CV, Webster TF, Carlin DJ. Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop. Environ Health Perspect. 2016;124(12):A227–A9. Epub 2016/12/03. doi: 10.1289/EHP547. [DOI] [PMC free article] [PubMed] [Google Scholar]; child lead exposure for the plaintiffs in a public nuisance case related to childhood lead poisoning. None of these activities were directly related to the present study. The other authors declare they have no actual or potential competing financial interests.

[R100] 100.Lebeaux RM, Doherty BT, Gallagher LG, Zoeller RT, Hoofnagle AN, Calafat AM, Karagas MR, Yolton K, Chen A, Lanphear BP, Braun JM, Romano ME. Maternal serum perfluoroalkyl substance mixtures and thyroid hormone concentrations in maternal and cord sera: The HOME Study. Environ Res. 2020;185:109395. Epub 2020/03/31. doi: 10.1016/j.envres.2020.109395. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R101] 101.Preston EV, Rifas-Shiman SL, Hivert MF, Zota AR, Sagiv SK, Calafat AM, Oken E, James-Todd T. Associations of Per- and Polyfluoroalkyl Substances (PFAS) With Glucose Tolerance During Pregnancy in Project Viva. J Clin Endocrinol Metab. 2020;105(8):e2864–76. Epub 2020/06/02. doi: 10.1210/clinem/dgaa328. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R102] 102.Siwakoti RC, Cathey A, Ferguson KK, Hao W, Cantonwine DE, Mukherjee B, McElrath TF, Meeker JD. Prenatal per- and polyfluoroalkyl substances (PFAS) exposure in relation to preterm birth subtypes and size-for-gestational age in the LIFECODES cohort 2006-2008. Environ Res. 2023;237(Pt 2):116967. Epub 2023/08/28. doi: 10.1016/j.envres.2023.116967. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R103] 103.Wang G, Pan R, Liang X, Wu X, Wu Y, Zhang H, Zhao J, Chen W. Perfluorooctanoic acid-induced liver injury is potentially associated with gut microbiota dysbiosis. Chemosphere. 2021;266:129004. Epub 2020/12/07. doi: 10.1016/j.chemosphere.2020.129004. [DOI] [PubMed] [Google Scholar]

[R104] 104.Sen P, Qadri S, Luukkonen PK, Ragnarsdottir O, McGlinchey A, Jantti S, Juuti A, Arola J, Schlezinger JJ, Webster TF, Oresic M, Yki-Jarvinen H, Hyotylainen T. Exposure to environmental contaminants is associated with altered hepatic lipid metabolism in non-alcoholic fatty liver disease. J Hepatol. 2022;76(2):283–93. Epub 2021/10/11. doi: 10.1016/j.jhep.2021.09.039. [DOI] [PubMed] [Google Scholar]

[R105] 105.Roth K, Yang Z, Agarwal M, Birbeck J, Westrick J, Lydic T, Gurdziel K, Petriello MC. Exposure of Ldlr−/− Mice to a PFAS Mixture and Outcomes Related to Circulating Lipids, Bile Acid Excretion, and the Intestinal Transporter ASBT. Environ Health Perspect. 2024;132(8):87007. Epub 2024/08/23. doi: 10.1289/EHP14339. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R106] 106.Bustos AY, Font de Valdez G, Fadda S, Taranto MP. New insights into bacterial bile resistance mechanisms: the role of bile salt hydrolase and its impact on human health. Food Res Int. 2018;112:250–62. Epub 2018/08/23. doi: 10.1016/j.foodres.2018.06.035. [DOI] [PubMed] [Google Scholar]

[R107] 107.Ovadia C, Perdones-Montero A, Spagou K, Smith A, Sarafian MH, Gomez-Romero M, Bellafante E, Clarke LCD, Sadiq F, Nikolova V, Mitchell A, Dixon PH, Santa-Pinter N, Wahlstrom A, Abu-Hayyeh S, Walters JRF, Marschall HU, Holmes E, Marchesi JR, Williamson C. Enhanced Microbial Bile Acid Deconjugation and Impaired Ileal Uptake in Pregnancy Repress Intestinal Regulation of Bile Acid Synthesis. Hepatology. 2019;70(1):276–93. Epub 2019/04/16. doi: 10.1002/hep.30661. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R108] 108.Abdul Waheed MI, Jaiswal A, Yelne S, Nandanwar V. Navigating Perinatal Challenges: A Comprehensive Review of Cholestasis of Pregnancy and Its Impact on Maternal and Fetal Health. Cureus. 2024;16(4):e58699. Epub 2024/05/23. doi: 10.7759/cureus.58699. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R109] 109.Sun Y, Wang X, Li L, Zhong C, Zhang Y, Yang X, Li M, Yang C. The role of gut microbiota in intestinal disease: from an oxidative stress perspective. Front Microbiol. 2024;15:1328324. Epub 2024/02/29. doi: 10.3389/fmicb.2024.1328324. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R110] 110.Roth K, Yang Z, Agarwal M, Liu W, Peng Z, Long Z, Birbeck J, Westrick J, Liu W, Petriello MC. Exposure to a mixture of legacy, alternative, and replacement per- and polyfluoroalkyl substances (PFAS) results in sex-dependent modulation of cholesterol metabolism and liver injury. Environ Int. 2021;157:106843. Epub 2021/09/04. doi: 10.1016/j.envint.2021.106843. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R111] 111.Rosal R, Rodea-Palomares I, Boltes K, Fernandez-Pinas F, Leganes F, Petre A. Ecotoxicological assessment of surfactants in the aquatic environment: combined toxicity of docusate sodium with chlorinated pollutants. Chemosphere. 2010;81(2):288–93. Epub 2010/06/29. doi: 10.1016/j.chemosphere.2010.05.050. [DOI] [PubMed] [Google Scholar]

[R112] 112.Liu G, Zhang S, Yang K, Zhu L, Lin D. Toxicity of perfluorooctane sulfonate and perfluorooctanoic acid to Escherichia coli: Membrane disruption, oxidative stress, and DNA damage induced cell inactivation and/or death. Environ Pollut. 2016;214:806–15. Epub 2016/05/08. doi: 10.1016/j.envpol.2016.04.089. [DOI] [PubMed] [Google Scholar]

[R113] 113.Park SK, Peng Q, Ding N, Mukherjee B, Harlow SD. Determinants of per- and polyfluoroalkyl substances (PFAS) in midlife women: Evidence of racial/ethnic and geographic differences in PFAS exposure. Environ Res. 2019;175:186–99. Epub 2019/05/28. doi: 10.1016/j.envres.2019.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R114] 114.Calafat AM, Kuklenyik Z, Reidy JA, Caudill SP, Tully JS, Needham LL. Serum concentrations of 11 polyfluoroalkyl compounds in the u.s. population: data from the national health and nutrition examination survey (NHANES). Environ Sci Technol. 2007;41(7):2237–42. Epub 2007/04/19. doi: 10.1021/es062686m. [DOI] [PubMed] [Google Scholar]

[R115] 115.Poothong S, Thomsen C, Padilla-Sanchez JA, Papadopoulou E, Haug LS. Distribution of Novel and Well-Known Poly- and Perfluoroalkyl Substances (PFASs) in Human Serum, Plasma, and Whole Blood. Environ Sci Technol. 2017;51(22):13388–96. Epub 2017/10/24. doi: 10.1021/acs.est.7b03299. [DOI] [PubMed] [Google Scholar]

[R116] 116.Nielsen C, Andersson Hall U, Lindh C, Ekstrom U, Xu Y, Li Y, Holmang A, Jakobsson K. Pregnancy-induced changes in serum concentrations of perfluoroalkyl substances and the influence of kidney function. Environ Health. 2020;19(1):80. Epub 2020/07/10. doi: 10.1186/s12940-020-00626-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R117] 117.Luo Y, Li X, Li J, Gong X, Wu T, Li X, Li Z, Zhai Y, Wei Y, Wang Y, Jiang G. Prenatal Exposure of PFAS in Cohorts of Pregnant Women: Identifying the Critical Windows of Vulnerability and Health Implications. Environ Sci Technol. 2024;58(31):13624–35. Epub 2024/07/26. doi: 10.1021/acs.est.4c00453. [DOI] [PubMed] [Google Scholar]

[R118] 118.Appel M, Forsthuber M, Ramos R, Widhalm R, Granitzer S, Uhl M, Hengstschlager M, Stamm T, Gundacker C. The transplacental transfer efficiency of per- and polyfluoroalkyl substances (PFAS): a first meta-analysis. J Toxicol Environ Health B Crit Rev. 2022;25(1):23–42. Epub 2021/12/22. doi: 10.1080/10937404.2021.2009946. [DOI] [PubMed] [Google Scholar]

[R119] 119.Verner MA, Loccisano AE, Morken NH, Yoon M, Wu H, McDougall R, Maisonet M, Marcus M, Kishi R, Miyashita C, Chen MH, Hsieh WS, Andersen ME, Clewell HJ 3rd, Longnecker MP. Associations of Perfluoroalkyl Substances (PFAS) with Lower Birth Weight: An Evaluation of Potential Confounding by Glomerular Filtration Rate Using a Physiologically Based Pharmacokinetic Model (PBPK). Environ Health Perspect. 2015;123(12):1317–24. Epub 2015/05/27. doi: 10.1289/ehp.1408837. [DOI] [PMC free article] [PubMed] [Google Scholar]; Sciences, an independent nonprofit organization. The following authors received no compensation from DuPont or 3M: N.H.M., R.M., M.M., M.M., R.K., C.M., M.H.C., W.S.H., and M.P.L. R.M. was employed by Aegis Technologies, Huntsville, Alabama. All authors certify that their freedom to design, conduct, interpret, and publish research was not compromised by any sponsor. The authors declare they have no actual or potential competing financial interests.

[R120] 120.Boronow KE, Brody JG, Schaider LA, Peaslee GF, Havas L, Cohn BA. Serum concentrations of PFASs and exposure-related behaviors in African American and non-Hispanic white women. J Expo Sci Environ Epidemiol. 2019;29(2):206–17. Epub 2019/01/10. doi: 10.1038/s41370-018-0109-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R121] 121.Kingsley SL, Eliot MN, Kelsey KT, Calafat AM, Ehrlich S, Lanphear BP, Chen A, Braun JM. Variability and predictors of serum perfluoroalkyl substance concentrations during pregnancy and early childhood. Environ Res. 2018;165:247–57. Epub 2018/05/08. doi: 10.1016/j.envres.2018.04.033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R122] 122.Jain RB. Contribution of diet and other factors to the levels of selected polyfluorinated compounds: data from NHANES 2003-2008. Int J Hyg Environ Health. 2014;217(1):52–61. Epub 2013/04/23. doi: 10.1016/j.ijheh.2013.03.008. [DOI] [PubMed] [Google Scholar]

[R123] 123.Dzierlenga MW, Keast DR, Longnecker MP. The concentration of several perfluoroalkyl acids in serum appears to be reduced by dietary fiber. Environ Int. 2021;146:106292. Epub 2021/01/06. doi: 10.1016/j.envint.2020.106292. [DOI] [PubMed] [Google Scholar]

[R124] 124.DeLuca NM, Thomas K, Mullikin A, Slover R, Stanek LW, Pilant AN, Cohen Hubal EA. Geographic and demographic variability in serum PFAS concentrations for pregnant women in the United States. J Expo Sci Environ Epidemiol. 2023;33(5):710–24. Epub 2023/01/26. doi: 10.1038/s41370-023-00520-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R125] 125.Hu XC, Andrews DQ, Lindstrom AB, Bruton TA, Schaider LA, Grandjean P, Lohmann R, Carignan CC, Blum A, Balan SA, Higgins CP, Sunderland EM. Detection of Poly- and Perfluoroalkyl Substances (PFASs) in U.S. Drinking Water Linked to Industrial Sites, Military Fire Training Areas, and Wastewater Treatment Plants. Environ Sci Technol Lett. 2016;3(10):344–50. Epub 2016/10/19. doi: 10.1021/acs.estlett.6b00260. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R126] 126.Kwak S, Usyk M, Beggs D, Choi H, Ahdoot D, Wu F, Maceda L, Li H, Im EO, Han HR, Lee E, Wu AH, Hayes RB, Ahn J. Sociobiome - Individual and neighborhood socioeconomic status influence the gut microbiome in a multi-ethnic population in the US. NPJ Biofilms Microbiomes. 2024;10(1):19. Epub 2024/03/12. doi: 10.1038/s41522-024-00491-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R127] 127.Yao ZY, Li XH, Zuo L, Xiong Q, He WT, Li DX, Dong ZF. Maternal sleep deprivation induces gut microbial dysbiosis and neuroinflammation in offspring rats. Zool Res. 2022;43(3):380–90. Epub 2022/04/02. doi: 10.24272/j.issn.2095-8137.2022.023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R128] 128.Santarossa S, Sitarik AR, Cassidy-Bushrow AE, Comstock SS. Prenatal physical activity and the gut microbiota of pregnant women: results from a preliminary investigation. Phys Act Nutr. 2023;27(2):1–7. Epub 2023/08/16. doi: 10.20463/pan.2023.0011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R129] 129.Shin JH, Tillotson G, MacKenzie TN, Warren CA, Wexler HM, Goldstein EJC. Bacteroides and related species: The keystone taxa of the human gut microbiota. Anaerobe. 2024;85:102819. Epub 2024/01/13. doi: 10.1016/j.anaerobe.2024.102819. [DOI] [PubMed] [Google Scholar]

[R130] 130.Alam MZ, Madan R. Clostridioides difficile Toxins: Host Cell Interactions and Their Role in Disease Pathogenesis. Toxins (Basel). 2024;16(6). Epub 2024/06/26. doi: 10.3390/toxins16060241. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R131] 131.Uzal FA, Navarro MA, Li J, Freedman JC, Shrestha A, McClane BA. Comparative pathogenesis of enteric clostridial infections in humans and animals. Anaerobe. 2018;53:11–20. Epub 2018/06/09. doi: 10.1016/j.anaerobe.2018.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Prenatal PFAS exposure and outcomes related to maternal gut microbiome composition in later pregnancy

Stephanie Morgan

Sayed Hassan Raza Shah

Sarah S Comstock

Jaclyn M Goodrich

Donghai Liang

Youran Tan

Kimberly McKee

Douglas Ruden

Alexandra R Sitarik

Andrea E Cassidy-Bushrow

Anne L Dunlop

Michael C Petriello

Abstract

Introduction

Methods:

Cohort Descriptions:

Table 1.

Atlanta African American Maternal Child Cohort.

The Michigan Archive for Research on Child Health Cohort.

Microbiome Datasets:

ATL AA cohort.

MARCH cohort.

PFAS Datasets:

ATL AA cohort.

MARCH cohort.

Figure 1:

Statistical Analyses

Maternal Diet;

Results:

Cohort demographics, PFAS concentrations, and microbiome composition.

Figure 2:

Differences in stool diversity metrics by PFAS levels in Atlanta (ATL AA) mothers.

Table 2.

Table 3.

Difference in stool family and ASV- level abundances by PFAS levels in ATL AA.

Table 4.

Differences in stool diversity metrics by PFAS levels in Michigan (MARCH) mothers.

Table 5.

Table 6.

Difference in stool family and OTU-level abundances by PFAS levels in MARCH mothers and comparisons with ATL AA.

Table 7.

Table 8.

Figure 3:

Examining associations with PFAS and microbiome diversity using mixture methods in ATL AA and MARCH mothers.

Table 9A.

Table 9B.

Figure 4:

Table 10:

Discussion

Supplementary Material

Highlights.

Acknowledgements:

Funding Source:

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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