Maternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: the Upstate KIDS Study (2008-2010)

Maternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: the Upstate KIDS Study (2008-2010) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Maternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: the Upstate KIDS Study (2008-2010) Laura E. Jones, Erin Bell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4345399/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background . Numerous studies suggest exposure to the environmentally ubiquitous legacy per/polyfluoroalkyl (PFAS) compounds perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) may be associated with suppressed immune response, including attenuated vaccine-antibody response in children and greater susceptibility to opportunistic infections in general adult populations. We examined associations between neonatal concentrations of legacy PFAS compounds PFOA and PFOS and neonatal cytokine profiles from a large sample of residual newborn dried blood spots (NBDS) in upstate New York. Methods. We measured 30 common cytokines along with PFOA and PFOS in eluted samples of newborn dried blood spots (NDBS) from 3448 neonates participating in the Upstate KIDs Study (2008-2010), following parental consent. We performed adjusted mixed effects regressions for each cytokine against PFAS species, testing for effect modification by infant sex. We then performed exploratory factor analysis (EFA) on PFAS species-specific cytokine subsets selected via the prior regressions, extracting 4 factor axes for the PFOA cytokine subset and 3 for the PFOS cytokine subset based on results from cluster analysis and parallel analysis. Regressions on each PFAS-specific set of factors followed. All models were adjusted for infant birth weight and gestational age at birth, maternal age, race, and use of fertility treatment, and included a random intercept to account for twins. Results. Significant cytokine profiles were dominated by cytokines negatively associated with the given PFAS (9 of 11 cytokines for PFOA; 8 of 11 for PFOS). Regression by PFAS quartile shows evidence of nonlinearity in dose-response for most cytokines. All significant associations between factor groupings defined by EFA are negative for both PFOA and PFOS. Conclusions . There is strong evidence that PFOA and PFOS exposures are associated with disrupted, typically reduced, cytokine levels, both singly and as functional groups defined by EFA and cluster analysis. Biostatistics Statistical Epidemiology Immunology legacy PFAS Exploratory Factor Analysis newborn dried bloodspots cytokines immunity Upstate KIDS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Legacy PFAS compounds perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) were among the most commonly used long-chain PFAS compounds until phased from production, and remain the most commonly detected PFAS compounds in the environment, years after they were banned globally (Wee and Zaharin Aris 2023 ). The 8-carbon PFAS compounds PFOA and PFOS are some of the first PFAS compounds to be synthesized in the US by DuPont in the 1940’s and 1950’s, finding initial use in stain and water resistant products and coatings (Blake and Fenton, 2020). Due to their excellent grease repellant and surfactant properties, they were incorporated into many consumer products such as nonstick cookware, food wrappings, candy and microwave popcorn packaging and take-out food containers; personal products as varied as dental floss, Band-Aids, shampoo, and makeup; waxes and snow sealants, and even class A firefighting foams (until 2000). Though phased out of production by 2006, legacy PFAS compounds PFOA and PFOS are currently detectable in the serum of more than 90% of the US population (Kato, Wong et al. 2011). The environmentally ubiquitous exposure to PFAS in pregnant women, neonates and children yields potential for adverse outcomes for the mother and offspring, extending from infancy into adulthood. A recent relatively small (N = 198) study quantified the effects of multiple legacy and emerging PFAS compounds on 13 cytokines assessed in women of childbearing age, and found evidence of dysregulated cytokine homeostasis in both single pollutant and mixture models (Nian, Zhou et al. 2022), with positive associations between legacy and some alternative PFAS and Th1/Treg cytokines, and negative associations with Th2 and Th17 cytokines. PFAS exposure is associated with increases in multiple pro-inflammatory cytokines in pregnant women, potentially contributing to adverse pregnancy outcomes and immune disruption in the developing fetus (Tan, Taibl et al. 2023). How maternal cytokine and immune dysregulation in pregnancy affects neonates is still not fully quantified, but a recent study of new born dried bloodspot (NBDS) data from a large sample of neonates showed associations between legacy PFAS exposure and disrupted immunoglobulin homeostasis (Jones, Ghassabian et al. 2022). Immune effects in older children are better studied, with serum concentrations of PFOA and PFOS found to be associated with reduced antibody response to vaccines (Grandjean, Andersen et al. 2012, Mogensen, Grandjean et al. 2015, Grandjean, Heilmann et al. 2017, Grandjean, Heilmann et al. 2017, Abraham, Mielke et al. 2020, Timmermann, Jensen et al. 2020) and to attenuated viruses such as measles, mumps and rubella at 18 months (Sigvaldsen, Højsager et al. 2024). Studies of 587 children from the Faroe Islands with moderate PFAS exposures showed associations between PFOA and PFOS concentrations and vaccine antibody response to tetanus and diphtheria at 5, 7 and 13 years of age following vaccination and booster vaccinations respectively (Grandjean, Andersen et al. 2012, Grandjean, Heilmann et al. 2017, Grandjean, Heilmann et al. 2017). Associations were also found between maternal serum PFOA and PFOS at delivery and vaccine responses in their offspring at age 3 (Granum, Haug et al. 2013). A study of 101 one-year-old children in Germany showed consistent results for PFOA, with significant inverse relationships between PFOA and vaccine response to Haemophilus influenza B, tetanus and diphtheria toxoids (Abraham, Mielke et al. 2020), but no association with PFOS levels. PFAS exposure is also associated with altered T-lymphocyte function, showing enhanced Th2 and inhibited Th1 lymphocyte development in asthmatic children, an effect that differs by sex, with males showing greater effects (Zhu, Qin et al. 2016). Asthmatic children were found to have significantly higher serum concentrations of PFAS than non-asthmatic controls. While maternal and childhood effects of legacy PFAS exposure on immune function are well-studied, effects of PFAS on newborn infant immune profiles remain generally unexplored. In this study we will use a large dataset (N = 3448 infants born to 2901 mothers) of cytokine measurements from newborn dried bloodspots (NBDS) to explore effects of legacy PFAS on infant cytokine (antibody) profiles. We will first characterize effects on individual cytokines, then form cytokine functional groupings using PCA-based EFA and cluster analysis, and explore associations between infant serum PFAS, and grouped cytokine expression. Our aim is to explore and quantify evidence of PFAS-associated immune dysregulation and associated impairment of vaccine antibody response, starting from birth. Methods Study Participants. We employ data from the 2008–2010 Upstate Kids Study, described variously in Buck Louis et al (2014), Ghassabian et al ( 2018 ), Jones et al. ( 2022 ) and Adeyeye, Jones et al. (2023). Briefly, the Upstate Kids Study was originally designed to investigate long-term effects of fertility treatment and mode of delivery on child health and development. Of the original 6171 study infants, our data includes blood spot records for 3448 infants for whom we have parental consent, comprising 2280 singleton births, 1168 twins, and with multiple births higher than twins omitted (Table 1). Demographic and other characteristics of parents who consented to the study and those who did not are similar (Yeung, Louis et al. 2016). Infant birth weight, gestational age, sex, parity, plurality and maternal age were obtained from birth certificates, and other maternal data was prepared as described in Jones et al. ( 2022 ). The New York State Department of Health and the University at Albany Institutional Review Board approved this study. Cytokine and PFAS measurements Cytokine Measurements. The New York State Department of Health Newborn Screening program routinely collects infant blood from a heel stick on filter paper before infant discharge, and these samples are made available for public health research projects. For infants for whom we had parental consent, DBS cards were retrieved from cold storage and 3.2 mm punches from the residual bloodspots were taken as previously described (Yeung, Louis et al. 2016). We then assayed 31 immune markers, including basic fibroblast growth factor, IL-8, IL-1 receptor antagonist (IL-1ra), IL-1 alpha (IL-1α), MCP-1, macrophage inflammatory protein-1 alpha (MIP-1α), MIP-1β, vascular endothelial growth factor (VEGF), 6Ckine, cutaneous T-cell-attracting chemokine (CTACK), IL-16, IL-20, MCP-2, MIP-1d, stromal cell derived factor-1 (SDF-1), thymus and activation regulated chemokine (TARC), soluble intercellular adhesion molecule-1, soluble vascular cell adhesion molecule-1 (sVCAM-1), cathepsin D, myeloperoxidase, PDGF-AA, plasminogen activator inhibitor type-1 (PAI-1), neural cell adhesion molecule, and C reactive protein (CRP) as detailed in Ghassabian et al. ( 2018 ). Assays of IL-6, IL- 5, IL-33, TRAIL, SCF and TNF-α were zero-valued for 45% or more of the study population. We consider their distributions and associations with PFAS separately below. See supplemental Table 1 for a list of assayed cytokines by family and function. PFOA and PFOS Measurements. Average infant blood levels of the PFAS compounds PFOS and PFOA were assayed via 1.6 mm punches taken from NDBS sample cards using a solid-liquid extraction method, followed by quantification by HPLC/tandem mass spectrometry. Precision and accuracy of these measurements is assessed as described in Ma et al 2013 (Bell, Yeung et al. 2018, Ghassabian, Bell et al. 2018 , Ghassabian, Sundaram et al. 2018, Yeung, Bell et al. 2019). We assessed level of background contamination by analyzing unspotted areas of NDBS cards as field blanks, one for every 20–30 blood spot punches. Comparison of our samples and these field blanks showed negligible contamination (Ma, Kannan et al. 2013). Limits of detection for PFOA and PFOS were 0.03 and 0.05 ng/ml of whole blood respectively, and a total of 3,175 samples (3,729 after imputation) were available for analysis (Table 2). Statistical Analysis Overview of Analysis. Since the literature on infant immune profiles is limited, our approach to characterizing newborn immune factors is exploratory. Missing cytokine and PFAS values were imputed using a full Markov Chain Monte Carlo approach, with 10 imputed datasets created as described in Ghassabian et al. ( 2018 ). Generally, the analytes were not normally distributed and thus all cytokines were either square root or log-transformed and PFAS covariates log-transformed following imputation; see Table 2 for summary statistics of the unimputed data, including numbers of missing units for each cytokine. The small number of missing units in the demographic covariates were imputed separately via single imputation using a donor-based method that employed the k-means clustering algorithm, and the results merged with the imputed cytokine and PFAS values. The dataset is characterized by zero inflation in a subset of the cytokines, and after bivariate analysis to assess whether any cytokine level is significantly associated with PFAS assay values, we examine bivariate relationships between the zero-inflation in a subset of cytokines, and PFAS concentrations above and below median values. Analysis then proceeds as follows: we examine crude and adjusted bivariate associations between cytokines and each PFAS compound, both as continuous exposure and categorized into quartiles, separately. Those cytokines without significant associations with PFOA and/or PFOS are dropped. After confirming that the cytokine subsets are appropriate for EFA by checking correlations and via the Kaiser–Meyer–Olkin (KMO) test (Kaiser 1970 ; Kaiser and Rice, 1974 ), we perform parallel analysis with these consensus subsets to determine optimal factor number, and follow this with exploratory factor analysis. After extracting factors, we then examine the associations between PFAS compounds and these reduced functional cytokine groups (factors) using mixed effects regression with continuous exposure data. To explore the possibility of nonlinear exposure-outcome effects, we repeat the analysis with exposure data quantized into quartiles. Bivariate analysis. ANOVA. We conducted preliminary assessment of potential associations between the legacy perfluorinated chemicals PFOA and PFOS and newborn cytokine concentrations via ANOVA. We compare cytokine mean values associated with PFAS blood spot concentrations below median value versus cytokine mean values associated with above median value PFAS concentrations. Models included a random intercept to account for correlation due to twin status, and were pooled over all imputed datasets. Analysis was performed on 10 imputed datasets. Post-ANOVA pooling of all estimates and variances followed van Ginkel and Kroonenberg ( 2014 ) and Grund et al. ( 2016 ) and results were corrected for multiple comparisons using the FDR methods of Benjamini and Hochberg ( 1995 ) and Benjamini and Yekutieli ( 2001 ). Zero Inflation. Six out of the 31 cytokines (i.e., IL-5, IL-6, IL-33, TNF-α, SCF, TRAIL) shown in Table 2 are zero-inflated, that is, the data have a large number of zero-values. We conducted ANOVA on the imputed data to determine whether mean values of perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were significantly associated with zero or nonzero cytokine status. For this study, we created a binary categorical variable to describe zero (0) or nonzero (1) cytokine measurements, and models included a random effect to account for twins. Post-ANOVA pooling of all estimates and variances followed van Ginkel and Kroonenberg ( 2014 ) and Grund et al. ( 2016 ). Multivariable analysis. Adjusted Mixed effects regression. We performed mixed effects regressions with PFOA/S as exposure, adjusting for maternal age and race, fertility treatment and infant gestational age, covariates selected based on a directed acyclic graph. Infant sex is omitted as an adjusting covariate as it is a likely effect modifier (Zhu, Qin et al. 2016, Pilkerton, Hobbs et al. 2018, Vuong, Yolton et al. 2018, Vuong, Webster et al. 2021). Models included a random intercept term to account for correlation due to twins. Prior to analysis, the data were standardized, then analysis was performed on 10 imputed datasets and the results pooled via Rubin’s Rules (Rubin 2004) and corrected for multiple comparisons using the FDR methods of Benjamini and Hochberg ( 1995 ) and Benjamini and Yekutieli ( 2001 ). Effect Modification by Sex. We performed mixed effects regressions with continuous PFOA/S as exposure, adjusting for maternal age and race, fertility treatment and infant gestational age, stratified by sex, to examine for possible effect modification. Prior to analysis, the data were standardized, then analysis was performed on 10 imputed datasets and the results pooled via Rubin’s Rules (Rubin 1987 , 2004). Analysis by Exposure Quartile . To explore the possibility of nonlinear relationships between exposures and outcomes, analysis was repeated with the exposures categorized by quartile, on standardized cytokine data, with the first quartile exposure set as reference level. From the results of the adjusted regressions (before correction for multiple comparisons) we selected cytokine subsets that produced significant associations with each PFAS compound. The subset for PFOA included 11 cytokines, including four zero-inflated species, and for PFOS the significant subset included 11 cytokines, including three zero-inflated species. Exploratory Factor Analysis and Regression We next formed functional groups (factors) of cytokines via Exploratory Factor Analysis (EFA ) on the subsets of the imputed cytokine measurements. We first standardized the cytokine data, and since factor analysis is a linear method that does not respond well to highly correlated data, checked for multicollinearity by computing and visualizing a correlation matrix (Friendly 2002 ) then confirmed that selected metrics, all with pairwise correlations less than 0.8, were appropriate for EFA by running the Kaiser–Meyer–Olkin (KMO) test (Kaiser 1970 , Kaiser and Rice 1974 ). Since our measured values vary dramatically by several orders of magnitude across the transformed but unstandardized cytokines, PCA was performed on a pooled correlation matrix comprising all ten imputed datasets, following Rubin’s Rules and outlined by Nassiri et al (Nassiri, Lovik et al. 2018), and pooled weights extracted for each cytokine subset. This method is appropriate for large samples; in the case of smaller samples where normality fails, a bootstrap method must be used (Shao and Sitter 1996 ). Cluster analysis on each subset in conjunction with parallel analysis (Horn’s test of principal components or factors (Horn 1965 )) was used to determine the number of components, thus functional groups, to extract from each subset, yielding 4 functional groups for PFOA and 3 functional groups for PFOS. For each PFAS compound, we then extracted loadings from the PCA on the subset-specific pooled correlation matrix, then computed regression-based factor scores at the imputation level using the ‘Thurstone’ method. Linear Regression with Functional Groups and PFAS compounds. We performed adjusted mixed effects multiple regression using log-transformed PFOA and PFOS as exposure and our extracted factor scores as the outcome variables. To assess nonlinearity in the response, we again repeated the analysis with the exposure data categorized by quartile (reference level quartile 1). Models were adjusted for maternal age and race, fertility treatment and infant gestational age, and included a random effect to account for twins. Donor-based single imputation of demographics via k-means clustering employed the VIM package (Kowarik and Templ 2016 ); parallel analysis utilized the paran package; PCA, factor analysis and score extraction was performed using the psych package, and mixed effects regressions used the lme4 package in R. Results Study Participants . Study participants included 3448 infants (2280 singletons and 1168 twins; 1704 female and 1744 male) born to 2901 primarily white (83.4%), college-educated (85.3%), and older (62.4% over 30) women. 896 (32.4%) of the women conceived via fertility treatment and 1871 (67.6%) conceived naturally. Demographic and birth information for the infants and their mothers is found in Table 1. Cytokine and Exposure Measurements. Summary statistics, including maximum, median and mean values for transformed and untransformed cytokines are shown on Table 2 along with number of missing and zero values, further summarized below. Median untransformed PFOA concentration was 2.075 ng/ml (IQR: 1.565), and median PFOS was slightly higher at 2.69 ng/ml (IQR: 1.61). The exposures and their missing and zero values are summarized in Table 2. Missing Data. Missing data in the cytokine measurements ranges from a minimum of 7% (TARC) to a maximum of 52.8% (MIP-1a), with a median of about 14% missing, and with all measurements having at least some missing units. Cytokines with levels of missingness above 20% included MIP-1a (52.8%), SICAM (34%), MPO (26.5%) and 6-Ckine (20.4%). Missing units in the exposures PFOA and PFOS comprise 14.8%. See Table 2 for details. Bivariate Analysis Zero inflation. The study of cytokine distribution among the zero-inflated covariates TRAIL, TNF-α, IL-6, IL-5, IL-33 and SCF (Table 3) shows a stronger pattern of association of zero-valued cytokines with elevated mean PFAS compound value for PFOA than for PFOS (Table 3). Infants with zero-valued IL-6, IL-33, TRAIL, IL-5, and SCF measurements are all associated with significantly higher mean log PFOA values than infants with nonzero cytokine values. For PFOS, this is only true of SCF, with TNF-α marginal (p = 0.076). ANOVA. There were fewer significant associations between cytokines and above median PFOS (10 cytokines), than for cytokines and above median PFOA (13 cytokines), especially after correction for multiple comparisons. After correction for multiple comparisons, cytokine mean value is significantly reduced for above median levels of PFOA in the cytokines 6-Ckine, Cathepsin, IL-16, IL-5, IL-6, SVCAM, and is marginal for reduced levels of TNF-α, IL-33 and SCF. Above median PFOA has significant and positive/increasing relationships with IL-1α, MIP-1d, MCP-1and CRP (Supplemental Table S2A). After correction for multiple comparisons, 6-Ckine, SCF, Cathepsin, TNF-α, IL-5 and IL-16 remain significantly reduced, and mean IL-1α and NCAM significantly elevated for above-median levels of PFOS (Supplemental Table S2B). Adjusted Models With the intention of selecting significant cytokines for further multivariate analysis, mixed effects regression models were adjusted for maternal age, race, BMI, fertility treatment and infant gestational age, and outcomes standardized to assist with effect size comparisons. Adjusted models for continuous (log-transformed) PFOA as exposure show results largely consistent with crude estimates, with significantly negative associations with IL-16, IL-5, IL-6, 6-Ckine, and TNF-α (Fig. 1A; Supplemental Table S3A). SCF, Cathepsin, and sVCAM are negative, but marginal (0.05 < p < 0.10). There are significantly positive associations for IL-1α, MCP-1 and MIP1-d. Estimates and standard errors from adjusted models for continuous (log-transformed) PFOS are slightly larger, and due to the nature of the FDR correction process and a reduction in the p-value of the most significant covariate following adjustment, associations remain significant after correction for fewer cytokines. Only 6-Ckine remains significant and is negatively associated with increasing PFOS (p < 0.05) after correction for multiplicity. TNF-α and IL-16 are significant and negatively associated with PFOS, while IL-1α and MIP-1d are significantly positive in association before FDR correction (Fig. 1B, Supplemental Table S3B). Stratified models run on log-transformed continuous PFAS exposure data yield evidence both of consistency across the sexes and of differences by sex (Fig. 2); however the latter are not strong enough to constitute effect modification by sex. For both sexes, estimates for IL-1α and MIP-1d were consistent and significantly elevated, while IL-6, IL-16 and 6-Ckine were significantly reduced with increasing PFOA exposure (Fig. 2, panels A and B). For female neonates only, IL-5 was significantly reduced as a function of increasing PFOA, while for males , SCF (stem cell factor) and IL-33 were significantly reduced, though confidence intervals overlap for both. Males only also show significantly elevated inflammatory cytokines CRP and MCP-2 as a function of increasing PFOA. In both sexes, 6-Ckine is again reduced as a function of increasing PFOS. Among female infants only , IL-6, IL-16 and Cathepsin-D are also reduced; while for male infants only SCF is significantly reduced and MIP-1d significantly increased as a function of increasing PFOS (Fig. 2, panels C and D). In all of the above, confidence intervals overlap, so where there are differences, they are not substantial enough to indicate effect modification by sex. Finally, when interaction terms between sex and PFAS are included in models, they are at most just statistically marginal in significance. Regression with PFOA categorized by quartile yields a more nuanced result than regression with continuous data, with evidence of weak to moderate nonlinearity in associations for all significant cytokines except for IL-1α and IL-5 which show significant positive (IL-1α) and negative (IL-5) linear associations with PFOA by quartile, respectively. Notably, Cathepsin-D and sVCAM show U-shaped dose-response curves by quartile, and TNF-α has a weak inverted U-shaped response (Fig. 3A). See Supplemental Table S4A for PFOA quartile regression estimates, all cytokines. Note that by-quartile estimates are not adjusted for multiplicity. Results from adjusted quartile regressions for PFOS are largely consistent with unadjusted bivariate models: 6-Ckine, IL-16, TNF-α, IL-6, SCF, cathepsin-D, SICAM and IL-8 show significant and negative association in one or more quartiles, and MIP-1d, NCAM and IL1-a elevated for one or more PFOS quartiles (Fig. 3B, Supplemental Table S4B). Plots by quartile of these estimates, with confidence intervals, show weak to strong nonlinearity in response for all, with Cathepsin, MIP-1d, SICAM and NCAM displaying a U-shaped dose-response response and IL-8 showing an inverted U-shaped response. We then selected subsets of cytokines for further analysis based on the adjusted continuous and by-quartile regression results. For PFOA, the subset included Th1 family cytokine TNF-α; Th2 family cytokines IL-5, IL-6, and IL-33; IL-1 family cytokine IL-1α, chemokines MIP-1d, 6-Ckine; pleotropic inflammatory cytokine IL-16; SCF, Cathepsin-D, and cell adhesion molecule sVCAM. The subset for PFOS was largely similar, including Th1 family cytokine TNF-α and IL-8; Th2 family cytokine IL-6; IL-1 family cytokine IL-1α, chemokines MIP-1d, 6-Ckine; pleotropic inflammatory cytokine IL-16; and cell adhesion molecules NCAM and SICAM (see Supplemental Table 1 for a listing of cytokine family and function). Exploratory Factor Analysis We performed factor analysis for both subsets with the full N = 3448 sample, using a varimax rotation for the PCA, and extracting loadings for three factors/components for the PFOA cytokine subset and two for the PFOS subset. For the PFOA subset, four factors account for 54% of the variance. See Fig. 3A for the loadings visualized as a heatmap array by factor and cytokine. Factor 1 primary loadings: sVCAM, Cathepsin-D, 6-Ckine, MIP-1d; 15% of variance explained. Factor 2 primary loadings: IL-33, SCF; 14% of variance explained. Factor 3 primary loadings: IL-16, IL-1α, 6-Ckine; 13% of variance. Factor 4 primary loadings: TNF-α, IL-5, IL-6; 12% of variance. For the PFOS subset, three factors were identified by parallel analysis, and accounted for 42% of the variance; see Fig. 3B for a heatmap visualization of the loadings by cytokine and factor. Factor 1 primary loadings: Cathepsin; 6-Ckine, MIP-1d, NCAM; 16% of variance explained. Factor 2 primary loadings: TNF-α, IL-6, IL-8, Cathepsin; 13% of variance explained. Factor 3 primary loadings: IL-16, IL-1α, 6-Ckine; 13% of variance explained. Imputation level regression-based factor scores were then computed for each PFAS-specific subset of cytokines using the loadings above. While not all component cytokines are the same, note the similarity in loadings between the PFAS subsets. Factor Analysis Regressions. Factor analysis regressions are most successful when they reveal significant underlying structure or associations in a dataset that may not be revealed by standard multivariable regressions. In this case, multiple regression models using factor scores computed from pooled loadings do show unexpected structure, in that variables significant by themselves in standard bivariate and adjusted regressions can form groupings that are not statistically significant. We performed mixed effects regressions on both continuous exposure data and exposure data categorized by quartile, with consistent results, though the quartile regressions again reveal more nuance. For continuous (log transformed) PFOA, all axes had negative associations with the exposure, and all except for the third component (primarily loaded by IL-16, IL-1α, and 6-Ckine) were statistically significant (Table 4A). Estimates from quartile regressions were consistent: for the first and second factors, estimates were significant and negative only for the third and fourth quartiles of PFOA (reference level is significant and is quartile 1, Fig. 4A). Estimates were significant and negative across all exposure quartiles for the fourth factor (loaded with Th1 and Th2 family cytokines TNF-α, IL-5, and IL-6). Again estimates for the third component are not significant (Table 4B). Estimates from regressions with continuous (log-transformed) PFOS were significant and negative for the second (Th1 and Th2 family cytokines) and third (primarily IL-16, IL-1α, and 6-Ckine) axes (Table 5A). Note that the first component (Cathepsin; 6-Ckine, MIP-1d + CAM) is significant for PFOA but not PFOS, while the third (primarily IL-16, IL-1α, and 6-Ckine) is significant and negative for PFOS but not for PFOA. There is at least one significant quartile estimate for all factors in quartile regressions with PFOS (reference level is significant and is quartile 1, Fig. 4B). Estimates for the second factor (primarily Th1 and Th2 cytokines plus cathepsin-D) are negative and significant across all PFOS exposure quartiles (Table 5B). Discussion We examined relationships between the PFOA and PFOS with 30 serum cytokines assayed from NDBS samples and observed that with few exceptions, most significant associations were negative both for continuous and quantile categorized exposures. Higher PFOA concentrations were significantly associated with zero values in Th1/Th2 cytokines IL-5, IL-6, and IL-33; and significantly reduced Th1/Th2 family interleukins IL-5, IL-6, IL-33 and Th1 family TNF-α; reduced SCF (stem cell factor) and chemokine 6-Ckine, reduced Cathepsin-D (associated with lysosome activity) and sVCAM (a soluble adhesion molecule); and in significantly elevated inflammatory cytokine IL-1α and chemokine MIP-1d; from adjusted regressions. PFOS concentrations were not associated with significant zero inflation, but were associated with significantly lower levels of Th1/Th2 family interleukins IL-5, IL-6, IL-8 and Th1 family TNF-α; reduced chemokines 6-Ckine and IL-16 (pleotropic/inflammatory); reduced SCF, Cathepsin D, and SICAM (a soluble adhesion molecule); and elevated cytokine IL-1α and chemokine MIP-1d as well as elevated NCAM (a soluble adhesion molecule); from single outcome adjusted models. Models stratified by sex showed both consistencies and some differences in standardized effect sizes by sex with increasing exposure to PFOA and PFOS, with male infants showing more significant standardized effects (both up and down-regulation), especially with increasing exposure to PFOA. However, confidence intervals overlap, and model interaction terms between sex and PFAS exposure are at best marginal in significance. In neonates, the source of PFAS exposure is likely maternal, via accumulated body burden and any new exposures during pregnancy. Indeed, the associations we observe may be ascribed in part to maternal immune disruption associated with the exposure and reflected in the neonate, as cytokines and immunoglobulins readily cross the placenta. In utero exposure to maternal inflammation – due to infection or immune dysregulation – is associated with modulation of infant immune response, including post-natal susceptibility to infections and modified vaccine response (Dauby and Flamand 2022 ). The present study suggests that Th1 and Th2 associated cytokines IL-5, IL-6, and Th1 family TNF-α were significantly and negatively associated with serum PFOA and also for PFOS, with the addition of Th1 cytokine IL-8. In mouse models, direct PFOA exposure resulted in significant reduction of Th2 cytokines, including IL-5; decreased IL-6, and elevated TNF-α, as well as significantly reduced IgM (which binds to antigen and activates complement) at high doses of PFOA. The authors conclude that the modification of Th1/Th2 cytokines may explain PFOA-associated reduction in antibody response, suggesting a role for T helper cells in PFOA immunotoxicity (De Guise and Levin 2021 ). Along with many PFAS-associated down-regulated cytokines, elevated compounds associated with both PFOA and PFOS concentrations included the powerful IL-1 family inflammatory cytokine IL-1α, as well as MIP-1d, or macrophage inflammatory protein. As both a secreted and membrane-bound cytokine, IL-1α functions to activate immune processes and dysregulated signaling can cause severe acute or chronic inflammation (Di Paolo and Shayakhmetov 2016 ). PFAS compounds, including legacy compounds, are immunotoxicants associated with both dysregulated immune-elevation and immunosuppression (DeWitt, Peden-Adams et al. 2012, DeWitt, Blossom et al. 2019). Immuno-enhancement can result in chronic immune activation, leading to autoimmune conditions, chronic inflammation, and immune exhaustion; while immunosuppression results in muted response to immune challenges such as vaccination or exposure to opportunistic infection. Immune dysregulation and exhaustion due to chronic exposure to legacy immunotoxicants - that may also modify or mute Th1/Th2 cytokine response, among myriad other adverse health effects - is a possible explanation for reduced vaccine response in children and adults (DeWitt, Blossom et al. 2019). Strengths and Limitations . Study strengths include large sample size (N = 3448), an imputation strategy to address missingness, and balanced cell counts for potential effect modifiers such as sex. An additional strength is our use of NDBS, an alternative, available, and minimally invasive sampling source for retrospective blood analyses. Limitations include the fact that data were obtained from a fertility study that comprised a 10-fold larger proportion of twins (33%) than the US population proportion (3.26%), thus a larger fraction of infants born prematurely and at low birth weight, all features that may affect immune status including cytokine expression and concentration. We addressed the proportion of twins and associated correlations between twins by using mixed effects models with a random intercept to account for twin status. Note that including twins may also be considered a strength, since they are often excluded from studies due to the issues outlined above. The study also includes a high percentage of older, educated and white participants, potentially limiting its applicability to populations that are vulnerable due to race-ethnicity and SES. However, we note that in a recent study, we found that exactly this cohort (educated and relatively affluent people) may have higher exogenous exposures to legacy PFAS through consumer goods such as clothing, household and personal care products and prepared foods (McAdam et al., in review). We cannot infer causality here because while the data arose from a cohort study, they are cross-sectional for these analyses. However, we argue that while cytokines, immunoglobulin and PFAS concentrations were assayed at the same time, the latter levels reflect much earlier and accumulated exposure in the mother. All immune factors and PFAS compound concentrations were measured from frozen NDBS samples and it is possible that protein compounds (i.e., cytokines) degrade with time. While cytokines recovered from DBS are stable, quantification may not completely reflect the serum values as release of cytokines from the DBS will include lysates from leukocyte cell populations, and values may vary dependent on the expression levels of these populations (Andersen, Mondal et al. 2014). Finally, our storage protocol (cold storage at 4°C) appears to guarantee good preservation of proteins for at least 10 years, with an appreciable decline in quality observed at 30 years when compared to samples stored at -24°C (Carpentieri, Colvard et al. 2021). Finally, while the association of PFAS other than PFOA/PFOS with immune compounds such as cytokines is of critical importance, Upstate KIDS was one of the first prospective studies to measure PFAS in the newborn bloodspot samples using solid-liquid extraction method. We were limited to the legacy compounds PFOA and PFOS for these analyses, as at the time of sample collection, available methods were limited to quantifying PFOA and PFOS, and together with the other analytes we quantified, exhausted our available sample. Conclusions There is strong evidence that PFOA and PFOS exposures are associated with disrupted, typically reduced, cytokine levels, both singly and as functional groups defined by EFA and cluster analysis. We find that PFOA concentrations were significantly associated with zero values in Th1/Th2 cytokines IL-5, IL-6, and IL-33; and significantly reduced Th1/Th2 family interleukins IL-5, IL-6, IL-33 and Th1 family TNF-α, both in individually and as factor groupings. PFOS concentrations were associated with significantly lower levels of Th1/Th2 family interleukins IL-5, IL-6, IL-8 and Th1 family TNF-α; reduced chemokines 6-Ckine and IL-16 (pleotropic/inflammatory); reduced SCF, Cathepsin D, and SICAM (a soluble adhesion molecule); and elevated cytokine IL-1a and chemokine MIP-1d as well as elevated NCAM (a soluble adhesion molecule); from single outcome adjusted models. All significant associations between factor groupings defined by EFA and PFAS compounds were negative, and there is evidence of nonlinearity in dose-response both for single-cytokine regressions and cytokine functional groupings from EFA. While there is evidence of differential effects by sex, with males showing more significant dysregulation for PFOA, and females for PFOS, confidence intervals overlap, possibly due to reduced sample sizes within strata, so effect modification by sex seems unlikely. Declarations Authorship contribution statement: Laura E. 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"Association between a Mixture of Per- and Polyfluoroalkyl Substances (PFAS) and Inflammatory Biomarkers in the Atlanta African American Maternal-Child Cohort." Environ Sci Technol 57 (36): 13419-13428. Timmermann, C. A. G., et al. (2020). "Serum Perfluoroalkyl Substances, Vaccine Responses, and Morbidity in a Cohort of Guinea-Bissau Children." Environ Health Perspect 128 (8): 87002. van Ginkel, J. R. and P. M. Kroonenberg (2014). "Analysis of Variance of Multiply Imputed Data." Multivariate Behav Res 49 (1): 78-91. Vuong, A. M., et al. (2021). "Prenatal exposure to per- and polyfluoroalkyl substances (PFAS) and neurobehavior in US children through 8 years of age: The HOME study." Environ Res 195 : 110825. Vuong, A. M., et al. (2018). "Childhood perfluoroalkyl substance exposure and executive function in children at 8 years." Environ Int 119 : 212-219. Wee, S. Y. and A. Zaharin Aris (2023). "Revisiting the “forever chemicals”, PFOA and PFOS exposure in drinking water." npj Clean Water 6 (57): 35. Yeung, E. H., et al. (2019). "Examining Endocrine Disruptors Measured in Newborn Dried Blood Spots and Early Childhood Growth in a Prospective Cohort." Obesity (Silver Spring) 27 (1): 145-151. Yeung, E. H., et al. (2016). "Eliciting parental support for the use of newborn blood spots for pediatric research." BMC Med Res Methodol 16 : 14. Zhu, Y., et al. (2016). "Associations of serum perfluoroalkyl acid levels with T-helper cell-specific cytokines in children: By gender and asthma status." Sci Total Environ 559 : 166-173. Tables Table 1. Infant and maternal characteristics with archived newborn bloodspot samples in the Upstate KIDS Study, 2008–2010 ( N = 3448 Infants; N=2901 Mothers, (n%) ) Child Characteristics N = 3448 Infant Sex Female 1704 (49) Male 1744 (51) Gestational Age (weeks) < 37 761 (21.8) 37–39 1910 (55.4) ≥ 40 777 (22.5) Birth weight Very Low (< 1500) 94 2.7 Low (1500–2499) 586 (17) Normal (2500–3999) 2502 (72.6) High (≥ 4000) 266 (8) Birth Parity Singleton 2280 (67) Twin 1168 (33) Maternal Characteristics N = 2901 Maternal Age < 20 89 (2.6) 20–29 1209 (35.1) 30–39 1865 (54.1) ≥ 40 285 (8.3) Maternal Education Less than High School diploma 149 (4.3) HS or GED equivalent 359 (10.4) Some College (Reference level) 972 (28.2) College 852 (24.7) Advanced Degree 1116 (32.4) Maternal Race Non-Hispanic White (Reference level) 2876 (83.4) Non-Hispanic Black 118 (3.4) Non-Hispanic Asian 99 (2.9) Hispanic 148 (4.3) Multiracial/other 207 (6.0) Pre-pregnancy BMI Underweight 75 (2.2) Normal weight (Reference level) 1559 (45.2) Overweight 886 (25.7) Obese 874 (25.3) Missing 54 - Birth Parity (Is infant first-born?) Yes 1115 (32.3) No 2309 (66.7) Missing 24 - Smoking Status Never smoke 2227 (64.6) Stopped before birth 836 (24.2) Smoker 383 (11.2) Missing 2 - Infertility Treatment? Yes 1166 (33.8) No 2282 (66.2) Table 2. Unimputed Newborn bloodspot Cytokine data (N=3729) Covariates and cytokines included in NYU dataset; summary of missingness and zero inflation. Analyte Untransformed (ng/ml) Transformed Missing Zeroes Max Mean Median Max Mean Median PFOA 14.1 1.3 1.1 2.71 0.75 0.73 554 - PFOS 71.1 1.94 1.68 4.28 1.00 0.987 554 - BPA 2067 19 7.9 7.64 3.38 3.29 555 - FGF 407.4 52.0 48.7 6.0 3.9 3.9 313 - PDGF 64 20 19 8.03 4.39 4.37 560 - BDNF 17 3.56 3.42 4.07 1.84 1.85 505 32 IL-5 2.23 0.068 0 1.17 0.061 0 429 1722 (46%) IL-6 963.5 0.627 0 6.87 0.16 0 569 1710 (45%) IL-8 1952.3 33.7 23.2 44.2 5.27 4.81 415 - IL-16 2158 469 427 45.45 21.1 20.7 536 - IL-20 2108 281 241 45.9 15.6 15.5 478 49 IL-33 78.4 0.14 0 4.38 0.043 0 465 2986 (80%) IL-1α 171.6 16.2 11.5 5.15 2.47 2.53 412 27 IL-1ra 130780 2228.5 1885.0 11.78 7.52 7.54 578 - MCP-1 806 88.4 81.2 6.69 4.4 4.4 450 - MCP-2 81.9 11.8 10.25 4.42 2.42 2.42 336 17 MIP-1a 3256 83 43.6 8.09 3.04 3.80 1969 429 (12%) MIP-1b 141.7 14.2 13.05 4.96 2.58 2.64 451 12 MIP-1d 2222 415 356 7.7 5.9 5.88 406 - VEG-F 1929.5 38 26 7.57 3.3 3.3 450 - 6-Ckine 2462 505.3 427.2 7.8 6.12 6.06 761 - CTACK 166 35.9 33.7 12.89 5.88 5.80 671 1 SCF 142 0.99 0 4.96 0.19 0 389 2824 (76%) SDF-1ab 6172 1371 1197 78.6 35.4 34.6 552 - TARC 171.4 27.5 23.7 5.15 3.18 3.21 266 1 sVCAM 8592 2879 2770 9.06 7.92 7.93 545 - sICAM 2807 111 20 7.94 2.74 3.06 1268 - NCAM 3437 1117 1096 8.14 7.0 7.0 533 - MPO 1041877 29579 21319 1021 135.3 146.0 989 20 Cathepsin-D 24511 2289 1981 156.5 46.0 44.5 725 29 PAI-1 1045 251.5 238.5 32.3 15.6 15.4 582 - TRAIL 66.8 0.1295 0 4.22 0.045 0 568 2786 (75%) CRP 54137 2662 1145 10.9 7.08 7.04 271 - TNF-α 1.44 0.054 0 0.89 0.042 0 345 2713 (73%) Log transformed: (PFOS, PFOA), FGF, IL-6, IL-33, IL1-ra, IL1-a, MCP-1, MIP1-d, VEGF, 6-Ckine, MIP-1b, TARC, MCP-2, sVCAM, NCAM, MIPa, TRAIL, CRP, TNF-α and SICAM. Square root transformed: IL-8, IL-16, IL-20, CTACK, SDF, Cathepsin-D, MPO, PAI, BDNF, and PDGF. Dashed entry in “zeroes” column indicates that there are no zero entries. Table 3. Associations of cytokine zero inflation with PFAS level above or below population median, imputed data. Shown in boldface: zero entries are associated with significantly higher mean PFAS compounds than nonzero entries. (N=3448) Cytokine % Zeros mean log(PFOA) mean log(PFOS) Zero Nonzero p-value Zero Nonzero p-value TRAIL 75% 0.755 0.74 0.011 1.01 0.99 (0.18) TNF 73% 0.755 0.74 (0.15) 1.01 0.99 ( 0.076 ) IL-6 45% 0.772 0.729 1.2e-06 1.01 1.00 (0.29) IL-5 46% 0.76 0.74 0.034 1.01 1.00 (0.51) SCF 76% 0.76 0.72 0.049 1.01 0.98 0.0011 IL-33 80% 0.76 0.73 0.00062 1.00 1.01 (0.66) Mixed effects ANOVA with a random effect for plurality, 10 imputed datasets, pooling of estimates and variances following Van Ginkel and Kronenburg (2014). Table 4AB. Adjusted Mixed effects regression, responses are cytokine groupings/axes from exploratory factor analysis of PFOA cytokine set. Table part A shows associations with continuous exposure data, and part B shows results by PFOA exposure quartile (reference quartile 1). Models are adjusted for infant gestational age, maternal age and race, and fertility treatment and include a random intercept to account for twins. Pooled over 10 imputed datasets. A. Component 1 Pooled Estimate 95% CI p-value RC1 -0.13 -0.23, -0.030 0.011 RC2 -0.11 -0.21, -0.003 0.043 RC3 -0.028 -0.12, 0.068 0.569 RC4 -0.21 -0.31, -0.11 < 0.0001 B. Component 1 PFOA Quartile Pooled Estimate 95% CI p-value RC1 Q2 0.012 0.009, 0.016 0.96 Q3 -0.104 -0.11, -0.10 < 0.0001 Q4 -0.095 -0.099, -0.091 0.002 RC2 Q2 -0.060 -0.064, -0.056 0.093 Q3 -0.084 -0.088, -0.08 0.014 Q4 -0.108 -0.113, -0.103 0.023 RC3 Q2 0.055 0.051, 0.058 0.10 Q3 0.036 0.032, 0.039 0.47 Q4 -0.013 -0.016, -0.009 0.95 RC4 Q2 -0.203 -0.206, -0.199 < 0.0001 Q3 -0.204 -0.208, -0.201 < 0.0001 Q4 -0.230 -0.234, -0.227 < 0.0001 1 RC1 primary loadings: sVCAM (0.81); Cathepsin (0.70); 6-Ckine (0.37), MIP-1d (0.37). 15% of variance explained. RC2 primary loadings: Il-33 (0.82); SCF (0.79). 14% of variance. RC3 primary loadings: IL-16 (0.78), IL-1α (0.57), 6-Ckine (0.42). 13% of variance. RC4 primary loadings : TNF- a (0.70), IL-5 (0.70), IL-6 (0.59). 12% of variance. Total: 54% of variance explained Table 5AB . Mixed effects regression, responses are cytokine groupings/axes from principal components analysis of PFOS cytokine set by PFOS exposure quartile (reference quartile 1). Models are adjusted for infant gestational age, maternal age and race, and fertility treatment and include a random intercept to account for twins. Pooled over 10 imputed datasets. A. Component 2 Pooled Estimate 95% CI p-value RC1 -0.0296 -0.125, 0.066 0.544 RC2 -0.1491 -0.25, -0.052 0.0026 RC3 -0.0957 -0.19, -0.004 0.041 B. Component 2 PFOS Quartile Pooled Estimate 95% CI p-value RC1 Q2 0.072 0.069, 0.075 0.01 Q3 -0.044 -0.048, -0.041 0.31 Q4 0.013 0.009, 0.017 0.97 RC2 Q2 -0.123 -0.127, -0.12 < 0.0001 Q3 -0.128 -0.132, -0.125 < 0.0001 Q4 -0.137 -0.141, -0.133 < 0.0001 RC3 Q2 0.019 0.015, 0.022 0.86 Q3 -0.041 -0.045, -0.038 0.455 Q4 -0.066 -0.069, -0.063 0.029 2 RC1 primary loadings: Cathepsin (0.66); 6-Ckine (0.35), MIP-1d (0.45), NCAM (0.77). 16% of variance explained. RC2 primary loadings: TNF- a (0.47), IL-6 (0.66), IL-8 (0.57), Cathepsin (0.35). 13% of variance. RC3 primary loadings : IL-16 (0.74), IL-1α (0.53), 6-Ckine (0.49). 13% of variance. Total: 42% of variance explained Additional Declarations The authors declare no competing interests. Supplementary Files JonesetalCytokinesPFASSupplementalTables.docx Jones et al. Supplemental Tables Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4345399","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296960252,"identity":"1dbea81f-b208-42df-a6cd-1a5aa430c9f3","order_by":0,"name":"Laura E. 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Plots show increase (decrease) in each standardized cytokine value per unit increase in each respective PFAS. Cytokines shown in blue show statistically significant association with PFAS. Models are adjusted for maternal age, race, BMI, fertility treatment, and infant gestational age and include a random effect to account for twins born to the same mother. \u0026nbsp;Note that results shown here do not reflect correction for multiple comparison, as this is applied to p-values. Estimates, standard errors, and FDR-adjusted p-values are shown in Supplemental Tables 3AB.\u003c/p\u003e","description":"","filename":"Fig1AB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/5050c01a4733edea16f13254.jpg"},{"id":55767994,"identity":"744346ed-a02e-44b6-8db3-d14141ce505c","added_by":"auto","created_at":"2024-05-02 20:23:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots from regressions stratified by infant sex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanels A, B\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eForest plots from adjusted mixed effects modeling with PFOA as an exposure by sex, female (n = 1704, left) and male (n =1744, right) neonates respectively. \u003cstrong\u003ePanels C, D\u003c/strong\u003e: Forest plots from adjusted mixed effects modeling with PFOS as an exposure, female and male neonates respectively. Models are adjusted for maternal age, race, BMI, fertility treatment, and infant gestational age and include a random effect to account for twins born to the same mother. Cytokines shown in blue show statistically significant association with PFAS. FDR-adjusted p-values are shown in Supplemental Table 3.\u003c/p\u003e","description":"","filename":"Fig2ABCD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/044ad19854ac13d0a3e16d9a.jpg"},{"id":55767993,"identity":"4d27193d-c148-492d-9ab6-da43704c91e4","added_by":"auto","created_at":"2024-05-02 20:23:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":702365,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3A.\u003c/strong\u003e Change in standardized cytokine value by quartile PFOA exposure (reference level is first quartile) from mixed effects regression (n = 3448). Models are adjusted for maternal age and race, fertility treatment and infant gestational age, and include a random intercept to account for twins. Standard errors are extracted from models using a robust sandwich method. Shown are cytokines with the significant associations with PFOA. Results for all cytokines by quartile (estimates, confidence intervals and p-values) are shown in supplemental Table 4A. Estimates pooled over 10 imputed datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3B.\u003c/strong\u003e Change in standardized cytokine value by quartile PFOS exposure (reference level is first quartile) from mixed effects regression (n=3448). Models are adjusted for maternal age and race, fertility treatment and infant gestational age, and include a random intercept to account for twins. Standard errors are extracted from the model using a robust sandwich method. Shown are cytokines with the most significant associations with PFOS when modeled by quartile. Results for all cytokines by quartile (estimates, confidence intervals and p-values) are shown in supplemental Table 4B. Estimates pooled over 10 imputed datasets.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/22c46f836aabebb43c721135.jpg"},{"id":55767995,"identity":"7206adcf-0726-4590-baae-fea169c3aecd","added_by":"auto","created_at":"2024-05-02 20:23:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":286217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e4A. heatmap of loadings from PFOA significant subset of cytokines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDark blue indicates a cytokine has a strongly (\u0026gt; 0.7) positive weighting, while bright yellow indicates the cytokine has a weakly negative (\u0026lt; -0.1) weighting on a given axis. Purple and tan are intermediate colors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4B. heatmap of loadings from PFOS significant subset of cytokines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDark blue indicates a cytokine has a strongly (\u0026gt; 0.6) positive weighting, while bright yellow indicates the cytokine has a weakly negative (\u0026lt; -0.3) weighting on a given axis. Purple and tan are intermediate colors.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/87f7ce6d638f7c31ca021e94.jpg"},{"id":55767996,"identity":"eca67eef-1bf6-45a6-85fc-363405b600ce","added_by":"auto","created_at":"2024-05-02 20:23:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":332102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5A. \u003c/strong\u003eChange in cytokine factor axis by quartile PFOA exposure (reference level is first quartile) from mixed effects regression with factor axes extracted from PFOA subset of cytokines as outcome. Models are adjusted for maternal age and race, fertility treatment and infant gestational age, and include a random intercept to account for twins. Standard errors are extracted from the model using a robust sandwich method. Results for all factor axes by quartile (estimates, confidence intervals and p-values) are shown in Table 4AB. Estimates pooled over 10 imputed datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5B. \u003c/strong\u003eChange in cytokine factor axis by quartile PFOS exposure (reference level is first quartile) from mixed effects regression with factor axes extracted from PFOS subset of cytokines as outcome. Models are adjusted for maternal age and race, fertility treatment and infant gestational age, and include a random intercept to account for twins. Standard errors are extracted from the model using a robust sandwich method. Results for all factor axes by quartile (estimates, confidence intervals and p-values) are shown in Table 5AB. Estimates pooled over 10 imputed datasets.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/83eee04e8687cad0445bcda7.jpg"},{"id":55768538,"identity":"f17b3445-f15c-4771-bbac-212bb7f3b829","added_by":"auto","created_at":"2024-05-02 20:31:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1735913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/1ec47ca4-e86c-40fa-b6a0-6b5fba599d82.pdf"},{"id":55767991,"identity":"11b64067-47ca-42af-8ae9-8bdf3b6e45b3","added_by":"auto","created_at":"2024-05-02 20:23:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49572,"visible":true,"origin":"","legend":"\u003cp\u003eJones et al. Supplemental Tables\u003c/p\u003e","description":"","filename":"JonesetalCytokinesPFASSupplementalTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4345399/v1/b0653eaf9dd21ba5cb51681b.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMaternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: the Upstate KIDS Study (2008-2010)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLegacy PFAS compounds perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) were among the most commonly used long-chain PFAS compounds until phased from production, and remain the most commonly detected PFAS compounds in the environment, years after they were banned globally (Wee and Zaharin Aris \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The 8-carbon PFAS compounds PFOA and PFOS are some of the first PFAS compounds to be synthesized in the US by DuPont in the 1940\u0026rsquo;s and 1950\u0026rsquo;s, finding initial use in stain and water resistant products and coatings (Blake and Fenton, 2020). Due to their excellent grease repellant and surfactant properties, they were incorporated into many consumer products such as nonstick cookware, food wrappings, candy and microwave popcorn packaging and take-out food containers; personal products as varied as dental floss, Band-Aids, shampoo, and makeup; waxes and snow sealants, and even class A firefighting foams (until 2000). Though phased out of production by 2006, legacy PFAS compounds PFOA and PFOS are currently detectable in the serum of more than 90% of the US population (Kato, Wong et al. 2011).\u003c/p\u003e\n\u003cp\u003eThe environmentally ubiquitous exposure to PFAS in pregnant women, neonates and children yields potential for adverse outcomes for the mother and offspring, extending from infancy into adulthood. A recent relatively small (N\u0026thinsp;=\u0026thinsp;198) study quantified the effects of multiple legacy and emerging PFAS compounds on 13 cytokines assessed in women of childbearing age, and found evidence of dysregulated cytokine homeostasis in both single pollutant and mixture models (Nian, Zhou et al. 2022), with positive associations between legacy and some alternative PFAS and Th1/Treg cytokines, and negative associations with Th2 and Th17 cytokines. PFAS exposure is associated with increases in multiple pro-inflammatory cytokines in pregnant women, potentially contributing to adverse pregnancy outcomes and immune disruption in the developing fetus (Tan, Taibl et al. 2023).\u003c/p\u003e\n\u003cp\u003eHow maternal cytokine and immune dysregulation in pregnancy affects neonates is still not fully quantified, but a recent study of new born dried bloodspot (NBDS) data from a large sample of neonates showed associations between legacy PFAS exposure and disrupted immunoglobulin homeostasis (Jones, Ghassabian et al. 2022). Immune effects in older children are better studied, with serum concentrations of PFOA and PFOS found to be associated with reduced antibody response to vaccines (Grandjean, Andersen et al. 2012, Mogensen, Grandjean et al. 2015, Grandjean, Heilmann et al. 2017, Grandjean, Heilmann et al. 2017, Abraham, Mielke et al. 2020, Timmermann, Jensen et al. 2020) and to attenuated viruses such as measles, mumps and rubella at 18 months (Sigvaldsen, H\u0026oslash;jsager et al. 2024). Studies of 587 children from the Faroe Islands with moderate PFAS exposures showed associations between PFOA and PFOS concentrations and vaccine antibody response to tetanus and diphtheria at 5, 7 and 13 years of age following vaccination and booster vaccinations respectively (Grandjean, Andersen et al. 2012, Grandjean, Heilmann et al. 2017, Grandjean, Heilmann et al. 2017). Associations were also found between maternal serum PFOA and PFOS at delivery and vaccine responses in their offspring at age 3 (Granum, Haug et al. 2013). A study of 101 one-year-old children in Germany showed consistent results for PFOA, with significant inverse relationships between PFOA and vaccine response to Haemophilus influenza B, tetanus and diphtheria toxoids (Abraham, Mielke et al. 2020), but no association with PFOS levels.\u003c/p\u003e\n\u003cp\u003ePFAS exposure is also associated with altered T-lymphocyte function, showing enhanced Th2 and inhibited Th1 lymphocyte development in asthmatic children, an effect that differs by sex, with males showing greater effects (Zhu, Qin et al. 2016). Asthmatic children were found to have significantly higher serum concentrations of PFAS than non-asthmatic controls.\u003c/p\u003e\n\u003cp\u003eWhile maternal and childhood effects of legacy PFAS exposure on immune function are well-studied, effects of PFAS on newborn infant immune profiles remain generally unexplored. In this study we will use a large dataset (N\u0026thinsp;=\u0026thinsp;3448 infants born to 2901 mothers) of cytokine measurements from newborn dried bloodspots (NBDS) to explore effects of legacy PFAS on infant cytokine (antibody) profiles. We will first characterize effects on individual cytokines, then form cytokine functional groupings using PCA-based EFA and cluster analysis, and explore associations between infant serum PFAS, and grouped cytokine expression. Our aim is to explore and quantify evidence of PFAS-associated immune dysregulation and associated impairment of vaccine antibody response, starting from birth.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants.\u003c/strong\u003e We employ data from the 2008\u0026ndash;2010 Upstate Kids Study, described variously in Buck Louis et al (2014), Ghassabian et al (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Jones et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Adeyeye, Jones et al. (2023). Briefly, the Upstate Kids Study was originally designed to investigate long-term effects of fertility treatment and mode of delivery on child health and development. Of the original 6171 study infants, our data includes blood spot records for 3448 infants for whom we have parental consent, comprising 2280 singleton births, 1168 twins, and with multiple births higher than twins omitted (Table\u0026nbsp;1). Demographic and other characteristics of parents who consented to the study and those who did not are similar (Yeung, Louis et al. 2016). Infant birth weight, gestational age, sex, parity, plurality and maternal age were obtained from birth certificates, and other maternal data was prepared as described in Jones et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The New York State Department of Health and the University at Albany Institutional Review Board approved this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine and PFAS measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine Measurements.\u003c/strong\u003e The New York State Department of Health Newborn Screening program routinely collects infant blood from a heel stick on filter paper before infant discharge, and these samples are made available for public health research projects. For infants for whom we had parental consent, DBS cards were retrieved from cold storage and 3.2 mm punches from the residual bloodspots were taken as previously described (Yeung, Louis et al. 2016). We then assayed 31 immune markers, including basic fibroblast growth factor, IL-8, IL-1 receptor antagonist (IL-1ra), IL-1 alpha (IL-1\u0026alpha;), MCP-1, macrophage inflammatory protein-1 alpha (MIP-1\u0026alpha;), MIP-1\u0026beta;, vascular endothelial growth factor (VEGF), 6Ckine, cutaneous T-cell-attracting chemokine (CTACK), IL-16, IL-20, MCP-2, MIP-1d, stromal cell derived factor-1 (SDF-1), thymus and activation regulated chemokine (TARC), soluble intercellular adhesion molecule-1, soluble vascular cell adhesion molecule-1 (sVCAM-1), cathepsin D, myeloperoxidase, PDGF-AA, plasminogen activator inhibitor type-1 (PAI-1), neural cell adhesion molecule, and C reactive protein (CRP) as detailed in Ghassabian et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Assays of IL-6, IL- 5, IL-33, TRAIL, SCF and TNF-\u0026alpha; were zero-valued for 45% or more of the study population. We consider their distributions and associations with PFAS separately below. See supplemental Table\u0026nbsp;1 for a list of assayed cytokines by family and function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFOA and PFOS Measurements.\u003c/strong\u003e Average infant blood levels of the PFAS compounds PFOS and PFOA were assayed via 1.6 mm punches taken from NDBS sample cards using a solid-liquid extraction method, followed by quantification by HPLC/tandem mass spectrometry. Precision and accuracy of these measurements is assessed as described in Ma et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e (Bell, Yeung et al. 2018, Ghassabian, Bell et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, Ghassabian, Sundaram et al. 2018, Yeung, Bell et al. 2019). We assessed level of background contamination by analyzing unspotted areas of NDBS cards as field blanks, one for every 20\u0026ndash;30 blood spot punches. Comparison of our samples and these field blanks showed negligible contamination (Ma, Kannan et al. 2013). Limits of detection for PFOA and PFOS were 0.03 and 0.05 ng/ml of whole blood respectively, and a total of 3,175 samples (3,729 after imputation) were available for analysis (Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of Analysis.\u003c/strong\u003e Since the literature on infant immune profiles is limited, our approach to characterizing newborn immune factors is exploratory. Missing cytokine and PFAS values were imputed using a full Markov Chain Monte Carlo approach, with 10 imputed datasets created as described in Ghassabian et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Generally, the analytes were not normally distributed and thus all cytokines were either square root or log-transformed and PFAS covariates log-transformed following imputation; see Table\u0026nbsp;2 for summary statistics of the unimputed data, including numbers of missing units for each cytokine. The small number of missing units in the demographic covariates were imputed separately via single imputation using a donor-based method that employed the k-means clustering algorithm, and the results merged with the imputed cytokine and PFAS values.\u003c/p\u003e\n\u003cp\u003eThe dataset is characterized by zero inflation in a subset of the cytokines, and after bivariate analysis to assess whether any cytokine level is significantly associated with PFAS assay values, we examine bivariate relationships between the zero-inflation in a subset of cytokines, and PFAS concentrations above and below median values. Analysis then proceeds as follows: we examine crude and adjusted bivariate associations between cytokines and each PFAS compound, both as continuous exposure and categorized into quartiles, separately. Those cytokines without significant associations with PFOA and/or PFOS are dropped. After confirming that the cytokine subsets are appropriate for EFA by checking correlations and via the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) test (Kaiser \u003cspan class=\"CitationRef\"\u003e1970\u003c/span\u003e; Kaiser and Rice, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e), we perform parallel analysis with these consensus subsets to determine optimal factor number, and follow this with exploratory factor analysis. After extracting factors, we then examine the associations between PFAS compounds and these reduced functional cytokine groups (factors) using mixed effects regression with continuous exposure data. To explore the possibility of nonlinear exposure-outcome effects, we repeat the analysis with exposure data quantized into quartiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA.\u003c/strong\u003e We conducted preliminary assessment of potential associations between the legacy perfluorinated chemicals PFOA and PFOS and newborn cytokine concentrations via ANOVA. We compare cytokine mean values associated with PFAS blood spot concentrations \u003cem\u003ebelow\u003c/em\u003e median value versus cytokine mean values associated with \u003cem\u003eabove\u003c/em\u003e median value PFAS concentrations. Models included a random intercept to account for correlation due to twin status, and were pooled over all imputed datasets. Analysis was performed on 10 imputed datasets. Post-ANOVA pooling of all estimates and variances followed van Ginkel and Kroonenberg (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Grund et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and results were corrected for multiple comparisons using the FDR methods of Benjamini and Hochberg (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Benjamini and Yekutieli (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZero Inflation.\u003c/strong\u003e Six out of the 31 cytokines (i.e., IL-5, IL-6, IL-33, TNF-\u0026alpha;, SCF, TRAIL) shown in Table\u0026nbsp;2 are zero-inflated, that is, the data have a large number of zero-values. We conducted ANOVA on the imputed data to determine whether mean values of perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were significantly associated with zero or nonzero cytokine status. For this study, we created a binary categorical variable to describe zero (0) or nonzero (1) cytokine measurements, and models included a random effect to account for twins. Post-ANOVA pooling of all estimates and variances followed van Ginkel and Kroonenberg (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Grund et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdjusted Mixed effects regression.\u003c/strong\u003e We performed mixed effects regressions with PFOA/S as exposure, adjusting for maternal age and race, fertility treatment and infant gestational age, covariates selected based on a directed acyclic graph. Infant sex is omitted as an adjusting covariate as it is a likely effect modifier (Zhu, Qin et al. 2016, Pilkerton, Hobbs et al. 2018, Vuong, Yolton et al. 2018, Vuong, Webster et al. 2021). Models included a random intercept term to account for correlation due to twins. Prior to analysis, the data were standardized, then analysis was performed on 10 imputed datasets and the results pooled via Rubin\u0026rsquo;s Rules (Rubin 2004) and corrected for multiple comparisons using the FDR methods of Benjamini and Hochberg (\u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Benjamini and Yekutieli (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect Modification by Sex.\u003c/strong\u003e We performed mixed effects regressions with continuous PFOA/S as exposure, adjusting for maternal age and race, fertility treatment and infant gestational age, stratified by sex, to examine for possible effect modification. Prior to analysis, the data were standardized, then analysis was performed on 10 imputed datasets and the results pooled via Rubin\u0026rsquo;s Rules (Rubin \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e, 2004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis by Exposure Quartile\u003c/strong\u003e. To explore the possibility of nonlinear relationships between exposures and outcomes, analysis was repeated with the exposures categorized by quartile, on standardized cytokine data, with the first quartile exposure set as reference level. From the results of the adjusted regressions (before correction for multiple comparisons) we selected cytokine subsets that produced significant associations with each PFAS compound. The subset for PFOA included 11 cytokines, including four zero-inflated species, and for PFOS the significant subset included 11 cytokines, including three zero-inflated species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploratory Factor Analysis and Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next formed functional groups (factors) of cytokines via Exploratory Factor Analysis (EFA ) on the subsets of the imputed cytokine measurements. We first standardized the cytokine data, and since factor analysis is a linear method that does not respond well to highly correlated data, checked for multicollinearity by computing and visualizing a correlation matrix (Friendly \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) then confirmed that selected metrics, all with pairwise correlations less than 0.8, were appropriate for EFA by running the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) test (Kaiser \u003cspan class=\"CitationRef\"\u003e1970\u003c/span\u003e, Kaiser and Rice \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSince our measured values vary dramatically by several orders of magnitude across the transformed but unstandardized cytokines, PCA was performed on a pooled correlation matrix comprising all ten imputed datasets, following Rubin\u0026rsquo;s Rules and outlined by Nassiri et al (Nassiri, Lovik et al. 2018), and pooled weights extracted for each cytokine subset. This method is appropriate for large samples; in the case of smaller samples where normality fails, a bootstrap method must be used (Shao and Sitter \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). Cluster analysis on each subset in conjunction with parallel analysis (Horn\u0026rsquo;s test of principal components or factors (Horn \u003cspan class=\"CitationRef\"\u003e1965\u003c/span\u003e)) was used to determine the number of components, thus functional groups, to extract from each subset, yielding 4 functional groups for PFOA and 3 functional groups for PFOS. For each PFAS compound, we then extracted loadings from the PCA on the subset-specific pooled correlation matrix, then computed regression-based factor scores at the imputation level using the \u0026lsquo;Thurstone\u0026rsquo; method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear Regression with Functional Groups and PFAS compounds.\u003c/strong\u003e We performed adjusted mixed effects multiple regression using log-transformed PFOA and PFOS as exposure and our extracted factor scores as the outcome variables. To assess nonlinearity in the response, we again repeated the analysis with the exposure data categorized by quartile (reference level quartile 1). Models were adjusted for maternal age and race, fertility treatment and infant gestational age, and included a random effect to account for twins. Donor-based single imputation of demographics via k-means clustering employed the VIM package (Kowarik and Templ \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e); parallel analysis utilized the paran package; PCA, factor analysis and score extraction was performed using the psych package, and mixed effects regressions used the lme4 package in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants\u003c/strong\u003e. Study participants included 3448 infants (2280 singletons and 1168 twins; 1704 female and 1744 male) born to 2901 primarily white (83.4%), college-educated (85.3%), and older (62.4% over 30) women. 896 (32.4%) of the women conceived via fertility treatment and 1871 (67.6%) conceived naturally. Demographic and birth information for the infants and their mothers is found in Table\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine and Exposure Measurements.\u003c/strong\u003e Summary statistics, including maximum, median and mean values for transformed and untransformed cytokines are shown on Table\u0026nbsp;2 along with number of missing and zero values, further summarized below. Median untransformed PFOA concentration was 2.075 ng/ml (IQR: 1.565), and median PFOS was slightly higher at 2.69 ng/ml (IQR: 1.61). The exposures and their missing and zero values are summarized in Table\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMissing Data.\u003c/strong\u003e Missing data in the cytokine measurements ranges from a minimum of 7% (TARC) to a maximum of 52.8% (MIP-1a), with a median of about 14% missing, and with all measurements having at least some missing units. Cytokines with levels of missingness above 20% included MIP-1a (52.8%), SICAM (34%), MPO (26.5%) and 6-Ckine (20.4%). Missing units in the exposures PFOA and PFOS comprise 14.8%. See Table\u0026nbsp;2 for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZero inflation.\u003c/strong\u003e The study of cytokine distribution among the zero-inflated covariates TRAIL, TNF-\u0026alpha;, IL-6, IL-5, IL-33 and SCF (Table\u0026nbsp;3) shows a stronger pattern of association of zero-valued cytokines with elevated mean PFAS compound value for PFOA than for PFOS (Table\u0026nbsp;3). Infants with zero-valued IL-6, IL-33, TRAIL, IL-5, and SCF measurements are all associated with significantly higher mean log PFOA values than infants with nonzero cytokine values. For PFOS, this is only true of SCF, with TNF-\u0026alpha; marginal (p\u0026thinsp;=\u0026thinsp;0.076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOVA.\u003c/strong\u003e There were fewer significant associations between cytokines and above median PFOS (10 cytokines), than for cytokines and above median PFOA (13 cytokines), especially after correction for multiple comparisons. After correction for multiple comparisons, cytokine mean value is significantly reduced for \u003cstrong\u003eabove\u003c/strong\u003e median levels of PFOA in the cytokines 6-Ckine, Cathepsin, IL-16, IL-5, IL-6, SVCAM, and is marginal for reduced levels of TNF-\u0026alpha;, IL-33 and SCF. Above median PFOA has significant and positive/increasing relationships with IL-1\u0026alpha;, MIP-1d, MCP-1and CRP (Supplemental Table S2A). After correction for multiple comparisons, 6-Ckine, SCF, Cathepsin, TNF-\u0026alpha;, IL-5 and IL-16 remain significantly reduced, and mean IL-1\u0026alpha; and NCAM significantly elevated for above-median levels of PFOS (Supplemental Table S2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdjusted Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the intention of selecting significant cytokines for further multivariate analysis, mixed effects regression models were adjusted for maternal age, race, BMI, fertility treatment and infant gestational age, and outcomes standardized to assist with effect size comparisons. Adjusted models for continuous (log-transformed) PFOA as exposure show results largely consistent with crude estimates, with significantly negative associations with IL-16, IL-5, IL-6, 6-Ckine, and TNF-\u0026alpha; (Fig.\u0026nbsp;1A; Supplemental Table S3A). SCF, Cathepsin, and sVCAM are negative, but marginal (0.05\u0026thinsp;\u0026lt;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). There are significantly positive associations for IL-1\u0026alpha;, MCP-1 and MIP1-d. Estimates and standard errors from adjusted models for continuous (log-transformed) PFOS are slightly larger, and due to the nature of the FDR correction process and a reduction in the p-value of the most significant covariate following adjustment, associations remain significant after correction for fewer cytokines. Only 6-Ckine remains significant and is negatively associated with increasing PFOS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after correction for multiplicity. TNF-\u0026alpha; and IL-16 are significant and negatively associated with PFOS, while IL-1\u0026alpha; and MIP-1d are significantly positive in association before FDR correction (Fig.\u0026nbsp;1B, Supplemental Table S3B).\u003c/p\u003e\n\u003cp\u003eStratified models run on log-transformed continuous PFAS exposure data yield evidence both of consistency across the sexes and of differences by sex (Fig.\u0026nbsp;2); however the latter are not strong enough to constitute effect modification by sex. For both sexes, estimates for IL-1\u0026alpha; and MIP-1d were consistent and significantly elevated, while IL-6, IL-16 and 6-Ckine were significantly reduced with increasing PFOA exposure (Fig.\u0026nbsp;2, panels A and B). For \u003cem\u003efemale neonates\u003c/em\u003e only, IL-5 was significantly reduced as a function of increasing PFOA, while for \u003cem\u003emales\u003c/em\u003e, SCF (stem cell factor) and IL-33 were significantly reduced, though confidence intervals overlap for both. \u003cem\u003eMales only\u003c/em\u003e also show significantly elevated inflammatory cytokines CRP and MCP-2 as a function of increasing PFOA. In both sexes, 6-Ckine is again reduced as a function of increasing PFOS. Among \u003cem\u003efemale infants only\u003c/em\u003e, IL-6, IL-16 and Cathepsin-D are also reduced; while for male infants only SCF is significantly reduced and MIP-1d significantly increased as a function of increasing PFOS (Fig.\u0026nbsp;2, panels C and D). In all of the above, confidence intervals overlap, so where there are differences, they are not substantial enough to indicate effect modification by sex. Finally, when interaction terms between sex and PFAS are included in models, they are at most just statistically marginal in significance.\u003c/p\u003e\n\u003cp\u003eRegression with PFOA categorized by quartile yields a more nuanced result than regression with continuous data, with evidence of weak to moderate nonlinearity in associations for all significant cytokines except for IL-1\u0026alpha; and IL-5 which show significant positive (IL-1\u0026alpha;) and negative (IL-5) linear associations with PFOA by quartile, respectively. Notably, Cathepsin-D and sVCAM show U-shaped dose-response curves by quartile, and TNF-\u0026alpha; has a weak inverted U-shaped response (Fig.\u0026nbsp;3A). See Supplemental Table S4A for PFOA quartile regression estimates, all cytokines. Note that by-quartile estimates are not adjusted for multiplicity.\u003c/p\u003e\n\u003cp\u003eResults from adjusted quartile regressions for PFOS are largely consistent with unadjusted bivariate models: 6-Ckine, IL-16, TNF-\u0026alpha;, IL-6, SCF, cathepsin-D, SICAM and IL-8 show significant and negative association in one or more quartiles, and MIP-1d, NCAM and IL1-a elevated for one or more PFOS quartiles (Fig.\u0026nbsp;3B, Supplemental Table S4B). Plots by quartile of these estimates, with confidence intervals, show weak to strong nonlinearity in response for all, with Cathepsin, MIP-1d, SICAM and NCAM displaying a U-shaped dose-response response and IL-8 showing an inverted U-shaped response.\u003c/p\u003e\n\u003cp\u003eWe then selected subsets of cytokines for further analysis based on the adjusted continuous and by-quartile regression results. For PFOA, the subset included Th1 family cytokine TNF-\u0026alpha;; Th2 family cytokines IL-5, IL-6, and IL-33; IL-1 family cytokine IL-1\u0026alpha;, chemokines MIP-1d, 6-Ckine; pleotropic inflammatory cytokine IL-16; SCF, Cathepsin-D, and cell adhesion molecule sVCAM. The subset for PFOS was largely similar, including Th1 family cytokine TNF-\u0026alpha; and IL-8; Th2 family cytokine IL-6; IL-1 family cytokine IL-1\u0026alpha;, chemokines MIP-1d, 6-Ckine; pleotropic inflammatory cytokine IL-16; and cell adhesion molecules NCAM and SICAM (see Supplemental Table\u0026nbsp;1 for a listing of cytokine family and function).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploratory Factor Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed factor analysis for both subsets with the full N\u0026thinsp;=\u0026thinsp;3448 sample, using a varimax rotation for the PCA, and extracting loadings for three factors/components for the PFOA cytokine subset and two for the PFOS subset. For the PFOA subset, four factors account for 54% of the variance. See Fig.\u0026nbsp;3A for the loadings visualized as a heatmap array by factor and cytokine.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 1 primary loadings: sVCAM, Cathepsin-D, 6-Ckine, MIP-1d; 15% of variance explained.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 2 primary loadings: IL-33, SCF; 14% of variance explained.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 3 primary loadings: IL-16, IL-1\u0026alpha;, 6-Ckine; 13% of variance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 4 primary loadings: TNF-\u0026alpha;, IL-5, IL-6; 12% of variance.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor the PFOS subset, three factors were identified by parallel analysis, and accounted for 42% of the variance; see Fig.\u0026nbsp;3B for a heatmap visualization of the loadings by cytokine and factor.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 1 primary loadings: Cathepsin; 6-Ckine, MIP-1d, NCAM; 16% of variance explained.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 2 primary loadings: TNF-\u0026alpha;, IL-6, IL-8, Cathepsin; 13% of variance explained.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFactor 3 primary loadings: IL-16, IL-1\u0026alpha;, 6-Ckine; 13% of variance explained.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eImputation level regression-based factor scores were then computed for each PFAS-specific subset of cytokines using the loadings above. While not all component cytokines are the same, note the similarity in loadings between the PFAS subsets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactor Analysis Regressions.\u003c/strong\u003e Factor analysis regressions are most successful when they reveal significant underlying structure or associations in a dataset that may not be revealed by standard multivariable regressions. In this case, multiple regression models using factor scores computed from pooled loadings do show unexpected structure, in that variables significant by themselves in standard bivariate and adjusted regressions can form groupings that are not statistically significant. We performed mixed effects regressions on both continuous exposure data and exposure data categorized by quartile, with consistent results, though the quartile regressions again reveal more nuance.\u003c/p\u003e\n\u003cp\u003eFor continuous (log transformed) PFOA, all axes had negative associations with the exposure, and all except for the third component (primarily loaded by IL-16, IL-1\u0026alpha;, and 6-Ckine) were statistically significant (Table\u0026nbsp;4A). Estimates from quartile regressions were consistent: for the first and second factors, estimates were significant and negative only for the third and fourth quartiles of PFOA (reference level is significant and is quartile 1, Fig.\u0026nbsp;4A). Estimates were significant and negative across all exposure quartiles for the fourth factor (loaded with Th1 and Th2 family cytokines TNF-\u0026alpha;, IL-5, and IL-6). Again estimates for the third component are not significant (Table\u0026nbsp;4B).\u003c/p\u003e\n\u003cp\u003eEstimates from regressions with continuous (log-transformed) PFOS were significant and negative for the second (Th1 and Th2 family cytokines) and third (primarily IL-16, IL-1\u0026alpha;, and 6-Ckine) axes (Table\u0026nbsp;5A). Note that the first component (Cathepsin; 6-Ckine, MIP-1d\u0026thinsp;+\u0026thinsp;CAM) is significant for PFOA but not PFOS, while the third (primarily IL-16, IL-1\u0026alpha;, and 6-Ckine) is significant and negative for PFOS but not for PFOA. There is at least one significant quartile estimate for all factors in quartile regressions with PFOS (reference level is significant and is quartile 1, Fig.\u0026nbsp;4B). Estimates for the second factor (primarily Th1 and Th2 cytokines plus cathepsin-D) are negative and significant across all PFOS exposure quartiles (Table\u0026nbsp;5B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe examined relationships between the PFOA and PFOS with 30 serum cytokines assayed from NDBS samples and observed that with few exceptions, most significant associations were negative both for continuous and quantile categorized exposures.\u003c/p\u003e\n\u003cp\u003eHigher PFOA concentrations were significantly associated with \u003cstrong\u003ezero values\u003c/strong\u003e in Th1/Th2 cytokines IL-5, IL-6, and IL-33; and significantly reduced Th1/Th2 family interleukins IL-5, IL-6, IL-33 and Th1 family TNF-\u0026alpha;; reduced SCF (stem cell factor) and chemokine 6-Ckine, reduced Cathepsin-D (associated with lysosome activity) and sVCAM (a soluble adhesion molecule); and in significantly elevated inflammatory cytokine IL-1\u0026alpha; and chemokine MIP-1d; from adjusted regressions. PFOS concentrations were not associated with significant zero inflation, but \u003cem\u003ewere\u003c/em\u003e associated with significantly lower levels of Th1/Th2 family interleukins IL-5, IL-6, IL-8 and Th1 family TNF-\u0026alpha;; reduced chemokines 6-Ckine and IL-16 (pleotropic/inflammatory); reduced SCF, Cathepsin D, and SICAM (a soluble adhesion molecule); and elevated cytokine IL-1\u0026alpha; and chemokine MIP-1d as well as elevated NCAM (a soluble adhesion molecule); from single outcome adjusted models.\u003c/p\u003e\n\u003cp\u003eModels stratified by sex showed both consistencies and some differences in standardized effect sizes by sex with increasing exposure to PFOA and PFOS, with male infants showing more significant standardized effects (both up and down-regulation), especially with increasing exposure to PFOA. However, confidence intervals overlap, and model interaction terms between sex and PFAS exposure are at best marginal in significance.\u003c/p\u003e\n\u003cp\u003eIn neonates, the source of PFAS exposure is likely maternal, via accumulated body burden and any new exposures during pregnancy. Indeed, the associations we observe may be ascribed in part to maternal immune disruption associated with the exposure and reflected in the neonate, as cytokines and immunoglobulins readily cross the placenta. \u003cem\u003eIn utero\u003c/em\u003e exposure to maternal inflammation \u0026ndash; due to infection or immune dysregulation \u0026ndash; is associated with modulation of infant immune response, including post-natal susceptibility to infections and modified vaccine response (Dauby and Flamand \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe present study suggests that Th1 and Th2 associated cytokines IL-5, IL-6, and Th1 family TNF-\u0026alpha; were significantly and negatively associated with serum PFOA and also for PFOS, with the addition of Th1 cytokine IL-8. In mouse models, direct PFOA exposure resulted in significant reduction of Th2 cytokines, including IL-5; decreased IL-6, and elevated TNF-\u0026alpha;, as well as significantly reduced IgM (which binds to antigen and activates complement) at high doses of PFOA. The authors conclude that the modification of Th1/Th2 cytokines may explain PFOA-associated reduction in antibody response, suggesting a role for T helper cells in PFOA immunotoxicity (De Guise and Levin \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlong with many PFAS-associated down-regulated cytokines, elevated compounds associated with both PFOA and PFOS concentrations included the powerful IL-1 family inflammatory cytokine IL-1\u0026alpha;, as well as MIP-1d, or macrophage inflammatory protein. As both a secreted and membrane-bound cytokine, IL-1\u0026alpha; functions to activate immune processes and dysregulated signaling can cause severe acute or chronic inflammation (Di Paolo and Shayakhmetov \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). PFAS compounds, including legacy compounds, are immunotoxicants associated with both dysregulated immune-elevation and immunosuppression (DeWitt, Peden-Adams et al. 2012, DeWitt, Blossom et al. 2019). Immuno-enhancement can result in chronic immune activation, leading to autoimmune conditions, chronic inflammation, and immune exhaustion; while immunosuppression results in muted response to immune challenges such as vaccination or exposure to opportunistic infection. Immune dysregulation and exhaustion due to chronic exposure to legacy immunotoxicants - that may also modify or mute Th1/Th2 cytokine response, among myriad other adverse health effects - is a possible explanation for reduced vaccine response in children and adults (DeWitt, Blossom et al. 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eStudy strengths include large sample size (N\u0026thinsp;=\u0026thinsp;3448), an imputation strategy to address missingness, and balanced cell counts for potential effect modifiers such as sex. An additional strength is our use of NDBS, an alternative, available, and minimally invasive sampling source for retrospective blood analyses. Limitations include the fact that data were obtained from a fertility study that comprised a 10-fold larger proportion of twins (33%) than the US population proportion (3.26%), thus a larger fraction of infants born prematurely and at low birth weight, all features that may affect immune status including cytokine expression and concentration. We addressed the proportion of twins and associated correlations between twins by using mixed effects models with a random intercept to account for twin status. Note that including twins may also be considered a strength, since they are often excluded from studies due to the issues outlined above. The study also includes a high percentage of older, educated and white participants, potentially limiting its applicability to populations that are vulnerable due to race-ethnicity and SES. However, we note that in a recent study, we found that exactly this cohort (educated and relatively affluent people) may have higher exogenous exposures to legacy PFAS through consumer goods such as clothing, household and personal care products and prepared foods (McAdam et al., in review). We cannot infer causality here because while the data arose from a cohort study, they are cross-sectional for these analyses. However, we argue that while cytokines, immunoglobulin and PFAS concentrations were assayed at the same time, the latter levels reflect much earlier and accumulated exposure in the mother.\u003c/p\u003e\n\u003cp\u003eAll immune factors and PFAS compound concentrations were measured from frozen NDBS samples and it is possible that protein compounds (i.e., cytokines) degrade with time. While cytokines recovered from DBS are stable, quantification may not completely reflect the serum values as release of cytokines from the DBS will include lysates from leukocyte cell populations, and values may vary dependent on the expression levels of these populations (Andersen, Mondal et al. 2014). Finally, our storage protocol (cold storage at 4\u0026deg;C) appears to guarantee good preservation of proteins for at least 10 years, with an appreciable decline in quality observed at 30 years when compared to samples stored at -24\u0026deg;C (Carpentieri, Colvard et al. 2021).\u003c/p\u003e\n\u003cp\u003eFinally, while the association of PFAS other than PFOA/PFOS with immune compounds such as cytokines is of critical importance, Upstate KIDS was one of the first prospective studies to measure PFAS in the newborn bloodspot samples using solid-liquid extraction method. We were limited to the legacy compounds PFOA and PFOS for these analyses, as at the time of sample collection, available methods were limited to quantifying PFOA and PFOS, and together with the other analytes we quantified, exhausted our available sample.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is strong evidence that PFOA and PFOS exposures are associated with disrupted, typically reduced, cytokine levels, both singly and as functional groups defined by EFA and cluster analysis. We find that PFOA concentrations were significantly associated with \u003cstrong\u003ezero values\u003c/strong\u003e in Th1/Th2 cytokines IL-5, IL-6, and IL-33; and significantly reduced Th1/Th2 family interleukins IL-5, IL-6, IL-33 and Th1 family TNF-α, both in individually and as factor groupings. PFOS concentrations were associated with significantly lower levels of Th1/Th2 family interleukins IL-5, IL-6, IL-8 and Th1 family TNF-α; reduced chemokines 6-Ckine and IL-16 (pleotropic/inflammatory); reduced SCF, Cathepsin D, and SICAM (a soluble adhesion molecule); and elevated cytokine IL-1a and chemokine MIP-1d as well as elevated NCAM (a soluble adhesion molecule); from single outcome adjusted models. All significant associations between factor groupings defined by EFA and PFAS compounds were negative, and there is evidence of nonlinearity in dose-response both for single-cytokine regressions and cytokine functional groupings from EFA. While there is evidence of differential effects by sex, with males showing more significant dysregulation for PFOA, and females for PFOS, confidence intervals overlap, possibly due to reduced sample sizes within strata, so effect modification by sex seems unlikely.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e \u003cstrong\u003econtribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaura E. Jones:\u0026nbsp;\u003c/strong\u003econceptualization, data curation, visualization,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emethodology, formal and statistical analysis, writing, review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkhgar Ghassabian:\u0026nbsp;\u003c/strong\u003edata collection, manuscript review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErin M. Bell\u003c/strong\u003e: data collection, study design, manuscript review and editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbraham, K., et al. (2020). \u0026quot;Internal exposure to perfluoroalkyl substances (PFASs) and biological markers in 101 healthy 1-year-old children: associations between levels of perfluorooctanoic acid (PFOA) and vaccine response.\u0026quot; \u003cu\u003eArch Toxicol\u003c/u\u003e \u003cstrong\u003e94\u003c/strong\u003e(6): 2131-2147.\u003c/li\u003e\n\u003cli\u003eAdeyeye, T. E., et al. (2023). \u0026quot;Effects on neonatal immunoglobulin concentrations by infant mode of delivery in the upstate KIDS study (2008-2010).\u0026quot; \u003cu\u003eAm J Reprod Immunol\u003c/u\u003e \u003cstrong\u003e89\u003c/strong\u003e(4): e13688.\u003c/li\u003e\n\u003cli\u003eAndersen, N. J., et al. 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(2018). \u0026quot;Rubella immunity and serum perfluoroalkyl substances: Sex and analytic strategy.\u0026quot; \u003cu\u003ePLoS One\u003c/u\u003e \u003cstrong\u003e13\u003c/strong\u003e(9): e0203330.\u003c/li\u003e\n\u003cli\u003eRubin, D. B. (1987, 2004). \u003cu\u003eMultiple Imputation for Nonresponse in Surveys\u003c/u\u003e, Wiley-Interscience.\u003c/li\u003e\n\u003cli\u003eShao, J. and R. Sitter (1996). \u0026quot;Bootstrap for imputed survey data.\u0026quot; \u003cem\u003e\u003cu\u003eJournal of the American Statistical Association\u003c/u\u003e\u003c/em\u003e \u003cstrong\u003e91\u003c/strong\u003e(435): 1278-1288.\u003c/li\u003e\n\u003cli\u003eSigvaldsen, A., et al. (2024). \u0026quot;Early-life exposure to perfluoroalkyl substances and serum antibody concentrations towards common childhood vaccines in 18-month-old children in the Odense Child Cohort.\u0026quot; \u003cu\u003eEnviron Res\u003c/u\u003e \u003cstrong\u003e242\u003c/strong\u003e: 117814.\u003c/li\u003e\n\u003cli\u003eTan, Y., et al. (2023). \u0026quot;Association between a Mixture of Per- and Polyfluoroalkyl Substances (PFAS) and Inflammatory Biomarkers in the Atlanta African American Maternal-Child Cohort.\u0026quot; \u003cu\u003eEnviron Sci Technol\u003c/u\u003e \u003cstrong\u003e57\u003c/strong\u003e(36): 13419-13428.\u003c/li\u003e\n\u003cli\u003eTimmermann, C. A. G., et al. (2020). \u0026quot;Serum Perfluoroalkyl Substances, Vaccine Responses, and Morbidity in a Cohort of Guinea-Bissau Children.\u0026quot; \u003cu\u003eEnviron Health Perspect\u003c/u\u003e \u003cstrong\u003e128\u003c/strong\u003e(8): 87002.\u003c/li\u003e\n\u003cli\u003evan Ginkel, J. R. and P. M. Kroonenberg (2014). \u0026quot;Analysis of Variance of Multiply Imputed Data.\u0026quot; \u003cu\u003eMultivariate Behav Res\u003c/u\u003e \u003cstrong\u003e49\u003c/strong\u003e(1): 78-91.\u003c/li\u003e\n\u003cli\u003eVuong, A. M., et al. (2021). \u0026quot;Prenatal exposure to per- and polyfluoroalkyl substances (PFAS) and neurobehavior in US children through 8 years of age: The HOME study.\u0026quot; \u003cu\u003eEnviron Res\u003c/u\u003e \u003cstrong\u003e195\u003c/strong\u003e: 110825.\u003c/li\u003e\n\u003cli\u003eVuong, A. M., et al. (2018). \u0026quot;Childhood perfluoroalkyl substance exposure and executive function in children at 8 years.\u0026quot; \u003cu\u003eEnviron Int\u003c/u\u003e \u003cstrong\u003e119\u003c/strong\u003e: 212-219.\u003c/li\u003e\n\u003cli\u003eWee, S. Y. and A. Zaharin Aris (2023). \u0026quot;Revisiting the \u0026ldquo;forever chemicals\u0026rdquo;, PFOA and PFOS exposure in drinking water.\u0026quot; \u003cem\u003e\u003cu\u003enpj Clean Water\u003c/u\u003e\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e(57): 35.\u003c/li\u003e\n\u003cli\u003eYeung, E. H., et al. (2019). \u0026quot;Examining Endocrine Disruptors Measured in Newborn Dried Blood Spots and Early Childhood Growth in a Prospective Cohort.\u0026quot; \u003cu\u003eObesity (Silver Spring)\u003c/u\u003e \u003cstrong\u003e27\u003c/strong\u003e(1): 145-151.\u003c/li\u003e\n\u003cli\u003eYeung, E. H., et al. (2016). \u0026quot;Eliciting parental support for the use of newborn blood spots for pediatric research.\u0026quot; \u003cu\u003eBMC Med Res Methodol\u003c/u\u003e \u003cstrong\u003e16\u003c/strong\u003e: 14.\u003c/li\u003e\n\u003cli\u003eZhu, Y., et al. (2016). \u0026quot;Associations of serum perfluoroalkyl acid levels with T-helper cell-specific cytokines in children: By gender and asthma status.\u0026quot; \u003cu\u003eSci Total Environ\u003c/u\u003e \u003cstrong\u003e559\u003c/strong\u003e: 166-173.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Infant and maternal characteristics with archived newborn bloodspot samples\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein the Upstate KIDS Study, 2008\u0026ndash;2010\u003c/strong\u003e \u003cstrong\u003e(\u003c/strong\u003eN = 3448 Infants; N=2901 Mothers, (n%)\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" style=\"width: 448px;\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 820.312px;\" colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eChild Characteristics N\u0026thinsp;=\u0026thinsp;3448\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInfant Sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(49)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eGestational Age (weeks)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(21.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e37\u0026ndash;39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1910\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(55.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(22.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eBirth weight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eVery Low (\u0026lt;\u0026thinsp;1500)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eLow (1500\u0026ndash;2499)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNormal (2500\u0026ndash;3999)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(72.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eHigh (\u0026ge;\u0026thinsp;4000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBirth Parity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eSingleton\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eTwin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 638.312px;\" colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eN\u0026thinsp;=\u0026thinsp;2901\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 32px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMaternal Age\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(54.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(8.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eMaternal Education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eLess than High School diploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eHS or GED equivalent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eSome College (Reference level)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(28.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eCollege\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(24.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eAdvanced Degree\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(32.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eMaternal Race\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic White (Reference level)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(83.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(3.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eMultiracial/other\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eUnderweight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNormal weight (Reference level)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(45.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eOverweight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(25.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eObese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBirth Parity\u003c/p\u003e\n\u003cp\u003e(Is infant first-born?)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(32.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(66.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSmoking Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNever smoke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(64.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eStopped before birth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e836\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(24.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(11.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 150px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInfertility Treatment?\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e1166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(33.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 206px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 27px;\" align=\"left\"\u003e\n\u003cp\u003e2282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 437.312px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(66.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Unimputed Newborn bloodspot Cytokine data (N=3729)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates and cytokines included in NYU dataset; summary of missingness and zero inflation.\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAnalyte\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eUntransformed (ng/ml)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTransformed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eZeroes\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePFOA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePFOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBPA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e407.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePDGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBDNF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1722 (46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e963.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.627\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e569\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1710 (45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1952.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2986 (80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-1\u0026alpha;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-1ra\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130780\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2228.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1885.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCP-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCP-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMIP-1a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e429 (12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMIP-1b\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMIP-1d\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVEG-F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1929.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6-Ckine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e505.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e427.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTACK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2824 (76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSDF-1ab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e552\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTARC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esVCAM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2879\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2770\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esICAM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNCAM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1041877\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCathepsin-D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePAI-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e251.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRAIL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2786 (75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNF-\u0026alpha;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2713 (73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLog transformed: (PFOS, PFOA), FGF, IL-6, IL-33, IL1-ra, IL1-a, MCP-1, MIP1-d, VEGF, 6-Ckine, MIP-1b, TARC, MCP-2, sVCAM, NCAM, MIPa, TRAIL, CRP, TNF-\u0026alpha; and SICAM.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSquare root transformed: IL-8, IL-16, IL-20, CTACK, SDF, Cathepsin-D, MPO, PAI, BDNF, and PDGF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDashed entry in \u0026ldquo;zeroes\u0026rdquo; column indicates that there are no zero entries.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociations of cytokine zero inflation with PFAS level above or below population median, imputed data.\u0026nbsp;\u003c/strong\u003eShown in boldface: zero entries are associated with significantly higher mean PFAS compounds than nonzero entries. (N=3448)\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCytokine\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e% Zeros\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003emean log(PFOA)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003emean log(PFOS)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eZero\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonzero\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eZero\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonzero\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRAIL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.755\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.74\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTNF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e1.01\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.99\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003e0.076\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.772\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.729\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.2e-06\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.76\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.74\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.76\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.72\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.01\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.98\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.76\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.00062\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMixed effects ANOVA with a random effect for plurality, 10 imputed datasets, pooling of estimates and variances following Van Ginkel and Kronenburg (2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4AB.\u003c/strong\u003e\u0026nbsp;Adjusted Mixed effects regression, responses are cytokine groupings/axes from exploratory factor analysis of PFOA cytokine set. Table part \u003cstrong\u003eA\u003c/strong\u003e shows associations with continuous exposure data, and part B shows results by PFOA exposure quartile (reference quartile 1). Models are adjusted for infant gestational age, maternal age and race, and fertility treatment and include a random intercept to account for twins. Pooled over 10 imputed datasets.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eComponent\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePooled Estimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.23, -0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21, -0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.12, 0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.569\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.31, -0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFOA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuartile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePooled Estimate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009, 0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.11, -0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.099, -0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eQ2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e-0.060\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e-0.064, -0.056\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.093\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.088, -0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.113, -0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.055\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.051, 0.058\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e0.10\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032, 0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016, -0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.206, -0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.208, -0.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.230\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.234, -0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eRC1 primary loadings: sVCAM (0.81); Cathepsin (0.70); 6-Ckine (0.37), MIP-1d (0.37). 15% of variance explained.\u003c/p\u003e\n\u003cp\u003eRC2 primary loadings: Il-33 (0.82); SCF (0.79). 14% of variance.\u003c/p\u003e\n\u003cp\u003eRC3 primary loadings: IL-16 (0.78), IL-1\u0026alpha; (0.57), 6-Ckine (0.42). 13% of variance.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eRC4 primary loadings\u003c/span\u003e: TNF- a (0.70), IL-5 (0.70), IL-6 (0.59). 12% of variance.\u003c/p\u003e\n\u003cp\u003eTotal: 54% of variance explained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;5AB\u003c/strong\u003e. Mixed effects regression, responses are cytokine groupings/axes from principal components\u0026nbsp;analysis of PFOS cytokine set by PFOS exposure quartile (reference quartile 1). Models are adjusted for infant gestational age, maternal age and race, and fertility treatment and include a random intercept to account for twins. Pooled over 10 imputed datasets.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eComponent\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePooled Estimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.125, 0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.25, -0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0957\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19, -0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFOS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuartile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePooled Estimate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069, 0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.048, -0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009, 0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.127, -0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.132, -0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.141, -0.133\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eRC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015, 0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.045, -0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.455\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.069, -0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eRC1 primary loadings: Cathepsin (0.66); 6-Ckine (0.35), MIP-1d (0.45), NCAM (0.77). 16% of variance explained.\u003c/p\u003e\n\u003cp\u003eRC2 primary loadings: TNF- a (0.47), IL-6 (0.66), IL-8 (0.57), Cathepsin (0.35). 13% of variance.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eRC3 primary loadings\u003c/span\u003e: IL-16 (0.74), IL-1\u0026alpha; (0.53), 6-Ckine (0.49). 13% of variance.\u003c/p\u003e\n\u003cp\u003eTotal: 42% of variance explained\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"School of Public Health, University at Albany","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"legacy PFAS, Exploratory Factor Analysis, newborn dried bloodspots, cytokines, immunity, Upstate KIDS","lastPublishedDoi":"10.21203/rs.3.rs-4345399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4345399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. Numerous studies suggest exposure to the environmentally ubiquitous legacy per/polyfluoroalkyl (PFAS) compounds perfluorooctane sulfonate \u0026nbsp;(PFOS) and perfluorooctanoic acid (PFOA) may be associated with suppressed immune response, including attenuated vaccine-antibody response in children and greater susceptibility to opportunistic infections in general adult populations. We examined associations between neonatal concentrations of legacy PFAS compounds PFOA and PFOS and neonatal cytokine profiles from a large sample of residual newborn dried blood spots (NBDS) in upstate New York.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eWe measured 30 common cytokines along with PFOA and PFOS in eluted samples of newborn dried blood spots (NDBS) from 3448 neonates participating in the Upstate KIDs Study (2008-2010), following parental consent. We performed adjusted mixed effects regressions for each cytokine against PFAS species, testing for effect modification by infant sex. We then performed exploratory factor analysis (EFA) on PFAS species-specific cytokine subsets selected via the prior regressions, extracting 4 factor axes for the PFOA cytokine subset and 3 for the PFOS cytokine subset based on results from cluster analysis and parallel analysis. Regressions on each PFAS-specific set of factors followed. All models were adjusted for infant birth weight and gestational age at birth, maternal age, race, and use of fertility treatment, and included a random intercept to account for twins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003e\u0026nbsp;Significant cytokine profiles were dominated by cytokines negatively associated with the given PFAS (9 of 11 cytokines for PFOA; 8 of 11 for PFOS). Regression by PFAS quartile shows evidence of nonlinearity in dose-response for most cytokines. All significant associations between factor groupings defined by EFA are negative for both PFOA and PFOS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. There is strong evidence that PFOA and PFOS exposures are associated with disrupted, typically reduced, cytokine levels, both singly and as functional groups defined by EFA and cluster analysis.\u003c/p\u003e","manuscriptTitle":"Maternal exposure to legacy PFAS compounds PFOA and PFOS is associated with disrupted cytokine homeostasis in neonates: the Upstate KIDS Study (2008-2010)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:23:51","doi":"10.21203/rs.3.rs-4345399/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c3455a0-68a1-4aa6-b7d2-db2bc45c6f0d","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31323853,"name":"Biostatistics"},{"id":31323854,"name":"Statistical Epidemiology"},{"id":31323855,"name":"Immunology"}],"tags":[],"updatedAt":"2024-05-02T20:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 20:23:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4345399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4345399","identity":"rs-4345399","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}