A cross-sectional examination of immune adaptations during pregnancy in the ECHO Cohort

Sarah M Banker; Rosa Shapiro-Thompson; Sarah Sinsel; Akhgar Ghassabian; Christian Douglas; Morgan E Nelson; Lisa A Peterson; Bharat Thyagarajan; Santiago Morales; Christine W Hockett; Amy J Elliott; Stephanie N Giamberardino; Lauren C Shuffrey

doi:10.3389/fimmu.2026.1803430

. 2026 Mar 23;17:1803430. doi: 10.3389/fimmu.2026.1803430

A cross-sectional examination of immune adaptations during pregnancy in the ECHO Cohort

Sarah M Banker ¹, Rosa Shapiro-Thompson ¹, Sarah Sinsel ², Akhgar Ghassabian ³, Christian Douglas ², Morgan E Nelson ², Lisa A Peterson ⁴, Bharat Thyagarajan ⁵, Santiago Morales ⁶, Christine W Hockett ^7,⁸, Amy J Elliott ^7,⁸, Stephanie N Giamberardino ^9,^†, Lauren C Shuffrey ^1,^*,^†, ECHO Cohort Consortium

¹Department of Child and Adolescent Psychiatry, New York University (NYU) Grossman School of Medicine, New York, NY, United States

²Data Analytics & Digitals Solutions, RTI International, Research Triangle Park, NC, United States

³Departments of Pediatrics and Population Health, New York University (NYU) Grossman School of Medicine, New York, NY, United States

⁴Division of Environmental Health Sciences and Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States

⁵Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States

⁶Department of Psychology, University of Southern California, Los Angeles, CA, United States

⁷Avera Research Institute, Sioux Falls, SD, United States

⁸Department of Pediatrics, University of South Dakota School of Medicine, Sioux Falls, SD, United States

⁹GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, United States

Correspondence: Lauren C. Shuffrey, lauren.shuffrey@nyulangone.org

^†

These authors have contributed equally to this work

Roles

Sarah M Banker: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing, Formal analysis, Visualization

Rosa Shapiro-Thompson: Writing – original draft, Writing – review & editing

Sarah Sinsel: Data curation, Writing – review & editing

Akhgar Ghassabian: Conceptualization, Funding acquisition, Writing – review & editing

Christian Douglas: Data curation, Writing – review & editing

Morgan E Nelson: Data curation, Resources, Writing – review & editing

Lisa A Peterson: Funding acquisition, Investigation, Writing – review & editing

Bharat Thyagarajan: Investigation, Writing – review & editing

Santiago Morales: Writing – review & editing

Christine W Hockett: Funding acquisition, Writing – review & editing

Amy J Elliott: Funding acquisition, Writing – review & editing

Stephanie N Giamberardino: Formal analysis, Supervision, Validation, Writing – original draft, Writing – review & editing

Lauren C Shuffrey: Conceptualization, Data curation, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

PMCID: PMC13050657 PMID: 41948337

Abstract

Background

Pregnancy requires finely tuned immune changes that support implantation, placental development, maternal–fetal tolerance, and preparation for labor, yet the normative trajectories of circulating inflammatory proteins across gestation remain poorly defined. This cross-sectional study investigates how circulating inflammatory proteins vary with gestational age in pregnancy and examines the impacts of fundamental biological characteristics, such as gravidity and fetal sex.

Methods

Data were drawn from 1154 pregnant individuals from six study sites of the National Institutes of Health Environmental influences on Child Health Outcomes (ECHO) Cohort. We used Olink high-throughput proteomic profiling to map cross-sectional associations between protein expression levels and gestational age at blood draw using linear, spline-based, and generalized additive modeling approaches.

Results

Generalized additive models provided the best fit, revealing that immune changes across pregnancy were predominantly nonlinear. Sixty-one proteins showed significant associations with gestational age, with many exhibiting shared inflection points that aligned with major physiological transitions. A small subset of proteins also showed evidence of modification by fetal and maternal characteristics. CD244 displayed different gestational patterns by fetal sex, while CST5 and SIRT2 showed varied gestational associations by maternal gravidity.

Conclusion

The findings highlight pregnancy as a sequence of coordinated immune transitions rather than a simple linear shift and provide one of the most detailed characterizations to date of circulating inflammatory protein dynamics across human gestation. Establishing these normative trajectories offers a crucial reference for detecting early deviations that may signal risk for pregnancy complications and for identifying biomarkers in maternal and fetal health research.

Keywords: gestational age, gravidity, inflammation, pregnancy, proteomics

1. Introduction

Over the course of pregnancy, the maternal immune system undergoes significant changes to support the adapting needs of the developing fetus while safeguarding maternal health. A comprehensive understanding of the normative trajectory of these immunological adaptations is critical, as disruptions to this delicate balance have been implicated in pregnancy complications, including preeclampsia (1, 2), gestational diabetes (3, 4), pregnancy loss (5, 6), and preterm birth (7–9).

Understanding how immune functioning aligns with the physiological stages of pregnancy offers critical context for interpreting changes in the expression of specific proteins. Pregnancy involves both innate immunity, which provides rapid responses to changes in the maternal environment, and adaptive immunity, which develops more slowly and confers long-lasting immune regulation (10–12). Innate immunity is mediated by cells such as uterine natural killer (NK) cells and macrophages, while adaptive immunity is driven by regulatory T cells (Tregs) and T helper (Th) cells (10–12). Both innate and adaptive immune mechanisms rely on a diverse array of signaling molecules, including chemokines, cytokines, growth factors, and immunomodulatory receptors, that orchestrate these processes across pregnancy. Although the precise roles of many specific immune-related proteins remain unclear beyond a well-studied subset (e.g., IL-6, IL-8, TNF-α, and IFN-γ), there is a growing understanding of how broader groups of immune mediators support pregnancy.

During the peri-implantation period and early first trimester, the maternal uterine environment adopts a controlled pro-inflammatory profile to support embryo implantation and early placental development. Innate immune cells accumulate in the decidua and initiate blastocyst attachment, trophoblast invasion, and early vascular remodeling (12–15). Chemokines and classical inflammatory cytokines, including IL-6 and IL-8, recruit leukocytes and promote the localized extracellular matrix breakdown needed for invasion (16, 17). A brief, limited Th1-type signal also contributes during this window, supporting trophoblast invasion before being rapidly downregulated (18). Together, these coordinated innate and adaptive signals establish the conditions necessary for uteroplacental blood flow (19). Shortly after implantation, Th1 immunity is replaced by Th2/Treg-mediated anti-inflammatory responses, which promote maternal–fetal tolerance and create a tolerogenic environment that supports placental and fetal growth (11, 12, 20). Chemokines play an important role in this adaptive immunity by shaping T cell responses, while immunoregulatory receptors such as PD-L1 contribute to maternal–fetal tolerance and maintain an environment that protects the fetus from rejection (18, 21). This anti-inflammatory state extends into the second and third trimesters, accompanied by finely tuned immune modulation and controlled changes in specific mediators that safeguard both maternal and fetal health (22). Near term, the immune environment transitions back to a pro-inflammatory profile, driven primarily by innate immune mechanisms that stimulate uterine contractility and cervical ripening (22, 23).

Pregnancy is often described in terms of these broad immune shifts, such as early pro-inflammatory activation followed by mid-pregnancy immune tolerance. In line with this framework, most empirical studies evaluate gestational differences using linear or trimester-based comparisons. This approach implicitly assumes smooth, monotonic change across pregnancy and may obscure biologically meaningful inflection points that coincide with well-established physiological transitions, such as the onset of placental perfusion near the end of the first trimester or the pre-labor inflammatory surge. Only a handful of studies have examined whether circulating immune markers follow non-linear or stage-specific trajectories (24–28), and comprehensive mapping of these patterns has remained limited by small cohorts, targeted panels, and sparse sampling across gestation. A more flexible, data-driven characterization of immune change is therefore needed to identify when, rather than simply whether, specific immune mediators are most active.

In this study, we aimed to comprehensively describe how circulating inflammatory proteins vary with gestational age (GA) in pregnancy. To do so, we leveraged the breadth of the National Institutes of Health (NIH) Environmental influences on Child Health Outcomes (ECHO) Cohort, which enrolled pregnancies across diverse U.S. populations. Importantly, we did not restrict our sample to exclusively healthy pregnancies; instead, we sought to characterize patterns of immune protein expression across gestation in a way that reflects the range of maternal backgrounds and conditions encountered in the general population. We also sought to examine the impacts of fundamental biological characteristics, such as gravidity, as prior pregnancies are thought to “train” the maternal immune system (29–31), and fetal sex, as pregnancies carrying male fetuses are thought to display more pro-inflammatory maternal immune responses (32, 33). By taking this approach, we aim to establish a baseline characterization of gestational immune trajectories that can serve as a reference point for future investigations into how specific maternal health factors, complications, or exposures may alter these processes.

2. Materials and methods

2.1. Study design

Data for this analysis were collected from participants recruited at six study sites of the ECHO Cohort, which consists of longitudinal birth cohort studies across the U.S. and Puerto Rico. The goal of the ECHO Program is to investigate how early-life environmental exposures affect child health and development across five primary domains: pre-, peri-, and postnatal outcomes; upper and lower airway conditions; obesity; neurodevelopment; and positive health (34–37).

Participants were selected for this assay if they had pregnancy plasma samples banked in the ECHO biorepository, had no limitations on use of biospecimens noted, were part of a site that had at least 50 participants with eligible samples, and were enrolled in ECHO sites that collected information on pregnancy health conditions and administered mental health assessments during the perinatal period (N = 1606). All participants consented to participate in their local ECHO site and share their information with the ECHO consortia. Both a central and site-specific Institutional Review Board monitored human subject activities at each site and the centralized ECHO Data Analysis Center. Written informed consent and the parent’s/guardian’s permission were obtained, along with child assent as appropriate, for ECHO-wide Cohort Data Collection Protocol participation. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies.

2.2. Maternal venous blood collection

Maternal venous blood was collected by trained phlebotomy staff into ethylenediaminetetraacetic acid-treated vacutainer tubes at a single study visit between 5 and 41 weeks’ gestation. Immediately after collection, tubes were gently inverted 8–10 times to ensure anticoagulation and transported on wet ice to the processing laboratory within 2 hours. Blood samples were centrifuged at 1500 × g for 10 minutes at 4 °C to separate plasma. The plasma supernatant was carefully aspirated without disturbing the buffy coat or red blood cell layer and aliquoted into pre-labeled cryovials (120 μL per aliquot) using sterile technique. Aliquots were immediately flash-frozen on dry ice and subsequently transferred to -80 °C long-term storage freezers, then shipped to the ECHO central biorepository, Fisher Bioservices. Once aliquots were selected for analysis, 40 μL were realiquoted into 96-well plates and subsequently shipped on dry ice to the NIH-funded Minnesota Human Health Exposure Analysis Resource (HHEAR) Laboratory for biomarker assays.

2.3. Proteomic profiling and data cleaning

Olink Target 96 Inflammation panel data, which quantified 92 immune-related proteins in plasma using proximity extension assay technology, were generated for a total of 1606 samples. Samples were randomized across assay plates prior to data generation to reduce the risk of systematic confounding between plate effects and biological variables. Each protein is expressed using a Normalized Protein eXpression (NPX) unit, which is a log2-transformed value representing the relative abundance of each protein. Panel quality control and intensity normalization procedures were conducted by the data-generating lab according to manufacturer guidelines. Two samples failed during data generation and were immediately excluded from any analysis. To identify samples that were potential outliers compared to the majority of samples, we followed procedures mirroring those applied by the UK Biobank (38) and excluded samples whose median NPX, interquartile range, and principal component 1 or principal component 2 (the two principal components capturing the largest amount of variance in the dataset) were >5 standard deviations from the dataset mean, resulting in N = 4 samples being flagged for exclusion from association testing. Concurrently, using the N = 1604 samples as input, the proportion of samples with an NPX value ≤ the limit of detection (LOD) (set as the maximum LOD across all assay plates) was calculated for each protein, and proteins for which fewer than 25% of samples were below the LOD were retained for analysis (N = 69). Finally, among N = 57 technical replicate pairs (N = 114 total samples), one was chosen at random to remain in the analysis and the other was excluded from downstream association testing (see Supplementary Table 1 for the coefficients of variation). Samples flagged with a warning for having a control probe >0.3 NPX from a plate’s median remained in the analysis (N = 122 of 1543 samples). In total, 1543 unique samples (1502 individuals, 1543 pregnancies) and 69 proteins (Supplementary Table 2) passed proteomic data cleaning and were eligible for downstream analysis.

2.4. Current analysis

Participants in the current analysis included pregnant participants with valid Olink proteomic data, confirmed singleton pregnancies, and available data on GA at blood draw. Those with GA <4 weeks or >41 weeks were excluded for plausibility, yielding N = 1354 participants. To ensure independence of observations, participants who had proteomic profiling data available from more than one pregnancy (N = 30 participants, N = 62 pregnancies) were restricted to the first available pregnancy; data from subsequent pregnancies from the same individual were excluded from analysis (see Supplementary Figure 1 for an analysis of protein correlations across multiple pregnancies). We additionally excluded participants whose pregnancy was missing information on gravidity (N = 159) and child sex (N = 9), as these are crucial covariates known to impact the prenatal inflammatory environment (29–33). After exclusions, a total of 1154 participants were included in the current analysis (see Supplementary Figure 2 for a flow chart outlining sample exclusions).

GA at blood draw was calculated using the difference between the expected due date and specimen collection date. Specifically, we computed the number of days between the blood draw and the expected due date, converted this value to weeks, and subtracted the result from 40 weeks (full-term). Gravidity was defined as the number of times a participant had been pregnant, including the current pregnancy, regardless of the viability outcomes. Fetal sex was obtained from information on the child’s sex at birth. Information on expected due date, gravidity, and child’s sex at birth was extracted from medical records or self-reported by participants. To account for potential center-level heterogeneity, all downstream association models included a random intercept for ECHO site of enrollment. Olink intensity normalization procedures were applied prior to analysis, and assay reproducibility was assessed via intra- and inter-plate coefficients of variation using control samples and technical replicates (Supplementary Table 1).

2.5. Statistical analysis

2.5.1. Inflammatory associations with gestational age

To identify markers associated continuously with GA, we modeled each protein separately, controlling for maternal age at blood draw, gravidity, and infant sex and accounting for heterogeneity across sites by including a random intercept for ECHO site of enrollment. Gravidity was categorized as first pregnancy, second pregnancy, or third or later pregnancy. To compare linear and non-linear fits for the relationships between GA and protein levels, we initially examined three model families: linear mixed-effects models, spline-based mixed-effects models, and generalized additive models (GAMs). Non-linear models were included given prior evidence that immune adaptation across pregnancy may follow non-linear trajectories, with accelerated or plateauing changes in later gestation. The linear models included GA as a continuous fixed effect. The spline models used natural cubic splines with three degrees of freedom to allow moderate flexibility in the GA–protein association. GAMs were fit using penalized regression splines with a smooth term for GA (estimated by restricted maximum likelihood) and a random-effect smooth for ECHO site of enrollment. For each GAM, we used the Wald F-test from the mgcv package (39) in R to assess whether the smooth for GA differed significantly from a flat (no-effect) line; if mgcv-reported p-values were truncated to zero due to precision limits within the function, they were recomputed manually from the same F-statistic and degrees of freedom. For each model, we extracted the estimated degrees of freedom (EDF) (where applicable), test statistics for the GA term (t-statistic for linear models and F-statistic for spline models and GAMs), and model fit indices including the Akaike information criterion (AIC), marginal R², and conditional R². To evaluate which functional form best described protein–GA relationships, we compared AIC values across model families for each protein and identified the model with the lowest AIC as the best fit. All p-values for GA effects were corrected for multiple testing within each model family using the Benjamini–Hochberg false discovery rate (FDR) procedure at 5%. For easy comparison of our findings with prior work, we also evaluated trimester-level differences in protein expression levels (see Supplementary Figure 3 and Supplementary Table 3).

2.5.2. Effect modification by fetal sex, gravidity, or maternal age

To test whether GA associations differed by key effect modifiers, we compared nested GAMs using maximum likelihood-based joint tests. For categorical modifiers (fetal sex and gravidity), a baseline model including a single smooth for GA (shared across all participants) was compared to a full model that additionally allowed modifier-specific smooths for GA (e.g., separate curves by sex or gravidity group). For the continuous modifiers (maternal age at blood draw), the baseline model included main-effect smooths for GA and maternal age, and the full model additionally included a tensor-product interaction smooth to capture continuous modification of GA trajectories by maternal age. All models adjusted for the remaining covariates (maternal age, gravidity, and fetal sex, as appropriate) and included a random-effect smooth for ECHO site. Smooth terms were represented using thin-plate regression splines with a modest basis dimension (k=5) to balance flexibility and interpretability, and basis adequacy was confirmed via the mgcv diagnostic k-index (40). Model comparison was performed using likelihood-ratio tests, providing a single F-statistic and p-value reflecting the joint evidence that GA–protein trajectories differed across modifier levels (for the categorical modifier) or continuously by maternal age (for the continuous modifier).

For categorical modifiers (fetal sex and gravidity), significant interactions indicate proteins for which the model with separate GA smooths for each group provided a significantly better fit than a model with a single, shared smooth across all participants. For the continuous modifier (maternal age at blood draw), significant interactions indicate proteins for which adding a tensor-product interaction smooth improved model fit relative to a model with only main-effect smooths for GA and maternal age, reflecting continuous modification of GA trajectories by maternal age. P-values were adjusted across proteins for each modifier using the Benjamini–Hochberg FDR procedure. Because interaction tests generally have lower statistical power than main-effect tests, we applied a more lenient FDR threshold (10%).

In a small subset of proteins, the full and baseline models converged to nearly identical fits, yielding zero or negative model deviance differences (i.e., no additional variance explained by the modifier-specific smooths). For these markers, the interaction was considered non-significant, and results were reported as NA to reflect the absence of a valid comparison. For categorical modifiers that showed significant interactions, we conducted additional sensitivity analyses to confirm that apparent group differences were not driven by uneven sampling across GA. Specifically, we compared the overall GA distributions between groups using global density plots and chi-square tests on GA-by-trimester contingency tables.

To evaluate whether overall protein expression levels were related to key maternal and fetal characteristics independent of their associations with GA, we additionally fit linear mixed-effects models testing main effects of fetal sex, gravidity, and maternal age on expression level for each protein. All models adjusted for the other covariates (maternal age, gravidity, and fetal sex, as appropriate) and included a random intercept for ECHO site. The results are provided in the Supplementary Materials (Supplementary Figure 4; Supplementary Tables 4-6).

3. Results

Sample characteristics are presented in Table 1. Maternal age at blood draw was an average of 30 years and ranged from 15 to 44 years. Participants were recruited from six different ECHO sites that spanned recruitment across eight U.S. states. The majority of participants self-identified as White (68.5%), followed by Black (16.4%), and Other Race (3.2%). The majority of participants identified as Non-Hispanic (87.5%) and reported holding a Bachelor’s degree or higher (52.4%). GA at blood draw spanned nearly the full course of pregnancy, ranging from 5 to 40 weeks, though the majority of samples were collected in the third trimester. This distribution highlights the strength of the study in capturing late pregnancy at scale, while still including a sizable number of early- and mid-pregnancy samples. Fetal sex was approximately evenly distributed. Gravidity was also well represented, with roughly 22% of mothers in their first pregnancy, 34% in their second, and 44% in their third or later pregnancy.

Table 1.

Characteristics of the participants (N = 1154).

Characteristic	Mean (sd, range) or count (%)
Maternal age at blood draw (years)	30.71 (4.90, 15.23–44.5)
Maternal race	American Indian or Alaska Native: <25 (<2%) Asian: 35 (3.03%) Black: 189 (16.38%) Native Hawaiian or other Pacific Islander: <15 (<2%) White: 790 (68.46%) Multiple races: 35 (3.03%) Other race: 37 (3.21%)
Maternal ethnicity	Hispanic: 140 (12.13%) Not Hispanic: 1010 (87.52%) Inconsistent or no information: 4 (0.35%)
Maternal education	Less than high school: 43 (3.72%) High school degree, GED or equivalent: 130 (11.27%) Some college, no degree; Associate’s degree; Trade school: 238 (20.62%) Bachelor’s degree or higher: 605 (52.42%) No information: 9 (0.78%)
Gestational age at blood draw (weeks)	30.65 (8.37, 5.71–40.86)
Trimester of blood draw	1^st trimester: 103 (8.93%) 2^nd trimester: 169 (14.65%) 3^rd trimester: 882 (76.43%)
Fetal sex	Male: 564 (48.87%) Female: 590 (51.13%)
Gravidity	1^st pregnancy: 254 (22.01%) 2^nd pregnancy: 390 (33.80%) 3^rd+ pregnancy: 510 (44.19%)
Site (recruitment location; years)	Safe Passage Study (Sioux Falls and Rapid City, South Dakota; 2019-2023): 401 (34.75%) Children’s Respiratory and Environmental Workgroup (CREW 3; Detroit, Michigan; Madison, Wisconsin, Nashville, Tennessee; St. Louis, Missouri; 2020–2023): 243 (21.06%) Pittsburgh Girls Study (Pittsburgh, Pennsylvania; 2019–2022): 85 (7.37%) Michigan Archive for Research on Child Health (MARCH; East Lansing, Michigan; 2019–2023): 176 (15.25%) The NYU Children’s Health and Environment Study (NYU CHES; New York, NY; 2019-2022): 105 (9.10%) PRogramming of Intergenerational Stress Mechanisms (PRISM; Boston, MA; New York, NY; 2019-2022): 144 (12.48%)

Open in a new tab

For continuous variables, the sample mean, standard deviation (sd), and range are presented. For categorical variables, the number and percentage of cases are presented.

3.1. Inflammatory associations with gestational age

Across proteins, GAMs most frequently provided the best model fit, yielding the lowest AIC for 67 of 69 proteins (97.1%). Only two proteins were better fit by spline models, and none were best fit by linear models. Mean and median AIC values were also lowest for GAMs (mean=2106, median=1992) compared to linear models (mean=2138, median=2006) and spline-based mixed-effects models (mean=2122, median 2006). On this basis, GAMs were selected as the primary analytic approach (see Supplementary Tables 7-9 for the full modeling results).

We identified 61 inflammatory proteins out of 69 that showed significant associations with GA after correcting for multiple comparisons with an FDR of 5% (Figure 1).

Scatter plot matrix showing associations between protein expression levels and gestational age for multiple proteins, with each subplot labeled by protein name. Points are colored by trimester, with a non-linear trend line in each panel. X-axes represent gestational age in weeks, y-axes denote protein expression level, and a legend differentiates the three trimesters. — Associations between protein expression levels and gestational age, sorted by beta estimate. Univariate models detected 61 inflammatory proteins that showed significant associations with gestational age at 5% false discovery rate. Generalized additive models controlled for maternal age at blood draw, gravidity, and infant sex and included site as a random effect. Cases are labeled by trimester of blood draw collection.

Notably, a subset of proteins displayed a shared and distinct non-linear pattern with pronounced inflections around approximately 12 and 30 weeks of gestation: SIRT2, AXIN1, STAMBP, EN-RAGE, IL-7, CASP-8, and CXCL5.

3.2. Modification effects of fetal sex, gravidity, or maternal age

One protein, CD244, exhibited a significant difference in GA trajectories by fetal sex (F(4.05, 1138.69)=5.13, p-FDR=0.03; Figure 2). Follow-up smooth-term analyses indicated that this effect was driven by a nonlinear pattern among participants pregnant with female fetuses, who showed a significant dip in early to mid-gestation followed by stabilization later in pregnancy (EDF = 3.05, F = 5.59, p<0.001), whereas trajectories among those pregnant with male fetuses were largely flat across gestation (p=0.99). Two proteins, CST5 and SIRT2, exhibited significant differences in GA trajectories by gravidity group (CST5: F(0.30, 1139.82)=26.50, p-FDR=0.05; SIRT2: F(4.81, 1137.89)=4.03, p-FDR=0.05; Figure 3). Follow-up smooth-term analyses indicated that these effects were driven primarily by nonlinear patterns, with early gestational declines followed by stabilization later in pregnancy among multigravida (3+ pregnancies) participants (CST5: EDF = 2.86, F = 3.72, p=0.02; SIRT2: EDF = 3.18, F = 4.68, p=0.001), whereas trajectories were largely flat in participants experiencing their first and second pregnancies (p values >0.30). No significant GA × maternal age interactions were detected after correction for multiple comparisons (FDR<0.10), indicating that the overall patterns of immune change across gestation were broadly similar across the maternal age range represented in the sample (see Supplementary Tables 10-13 for full interaction modeling results).

Scatter plot showing protein expression level of CD244 on the y-axis versus gestational age in weeks on the x-axis, with separate trend lines for male (blue) and female (pink) fetuses, illustrating slight differences by sex across gestational age. — Interaction between gestational age and fetal sex on CD244 expression level. Smoothed curves illustrate unadjusted relationships between gestational age and CD244 protein expression levels (Normalized Protein eXpression, NPX) by fetal sex. Participants pregnant with female fetuses exhibited an early-to-mid-gestational decline followed by stabilization later in pregnancy, whereas trajectories among male fetuses remained relatively flat across gestation. Shaded bands represent 95% confidence intervals from the fitted generalized additive model curves. Full model results are provided in the **Supplementary Materials**.

Scatterplot graphic showing protein expression (NPX) versus gestational age in weeks for CST5 and SIRT2, with separate trend lines by gravidity (first, second, third or more pregnancies) and shaded confidence intervals. — Interactions between gestational age and maternal gravidity on protein expression level. Smoothed curves illustrate unadjusted relationships between gestational age and CST5 and SIRT2 protein expression levels (Normalized Protein eXpression, NPX) within each gravidity group. Both proteins showed early gestational declines followed by stabilization later in pregnancy among multigravida (3+ pregnancies) participants, whereas trajectories were largely flat among primigravida and secundigravida participants. Shaded bands represent 95% confidence intervals from the fitted generalized additive model curves. Full model results are provided in the **Supplementary Materials**.

To ensure that the observed fetal sex- and gravidity-related effects were not driven by unequal sampling across GA, we compared the GA distributions between groups. For fetal sex, a global Kolmogorov–Smirnov test indicated no overall differences in GA sampling (D<0.05, p>0.30), and a chi-square test of GA-by-trimester frequencies likewise revealed no significant imbalance between male and female fetuses across trimesters (χ² (2)<3.0, p>0.10). These results suggest that the observed divergence in CD244 trajectories by fetal sex reflects genuine biological variation rather than sampling bias. For gravidity, a global Kolmogorov–Smirnov test indicated no overall distributional differences (D = 0.08, p=0.29). However, a chi-square test on GA-by-trimester frequencies revealed modest differences across trimesters (χ2 (4)=10.71, p=0.03), with later-trimester samples slightly overrepresented among multigravida (3+ pregnancies) participants. Importantly, when the comparison was restricted to the first trimester, where modeled group differences were most pronounced, no significant differences in sampling frequency were observed (χ2 (2)=0.37, p=0.83). Such results indicate that the observed early gestational divergence in trajectories was not attributable to uneven sampling.

4. Discussion

In this large multi-site investigation, we found significant gestational changes across a broad network of inflammation-related proteins. GAMs provided the best fit for nearly all proteins examined (97% of 69 proteins), underscoring that immune changes across pregnancy follow mostly non-linear patterns, including phase-like transitions aligned with key physiological milestones. These findings extend prior work that has characterized broad changes by providing a more refined, continuous map of immune signaling from early implantation through term. Importantly, these trajectories represent population-level trends estimated across individuals sampled at different gestational weeks rather than within-person longitudinal change. By leveraging this cross-sectional design, our dataset spans nearly the entire course of human pregnancy, capturing both very early (<6 weeks) and late (>40 weeks) gestational windows that have historically been underrepresented in human studies.

We identified 61 proteins that showed significant associations with GA using flexible non-linear modeling. The observed trajectories align with established prenatal immune frameworks. Proteins involved in early immune activation, including TWEAK, TRANCE, MCP-4, CD8A, and CCL11, were elevated early and declined thereafter, likely reflecting the transient pro-inflammatory signaling necessary for implantation and early placental remodeling (41–44). In contrast, proteins supporting tissue remodeling and vascular maintenance, such as LIF-R, uPA, and OPG, showed progressive increases across gestation, consistent with sustained extracellular matrix remodeling and the preservation of placental and vascular integrity (45–47). Concurrently, mediators of immune tolerance and anti-inflammatory regulation, including PD-L1, LAP–TGFβ1, IL-10RB, and CCL28, also rose over gestation, likely supporting the establishment and maintenance of maternal–fetal immune tolerance (48–52). Within these general trajectories, the models revealed biologically meaningful non-linearities. For instance, PD-L1 exhibited a modest dip toward the end of pregnancy after a steady rise through mid-gestation, potentially reflecting a reduced need for immune suppression as labor approaches. LAP–TGFβ1 and uPA showed delayed increases beginning after approximately 20 weeks, suggesting stage-specific activation of tolerogenic and remodeling pathways. Upon visual inspection, several proteins, including SIRT2, AXIN1, STAMBP, EN-RAGE, IL-7, CASP8, and CXCL5, also displayed pronounced non-linear inflections around approximately 12 and 30 weeks of gestation. These timepoints correspond to major immunological and physiological transitions in pregnancy: namely, the establishment of placental perfusion near the end of the first trimester and the metabolic and immune activation that precedes labor in the third (11, 53).

Gestational immune trajectories were further modulated by fetal sex and maternal reproductive history. The cell-surface receptor CD244, expressed on both NK and T cells, exhibited a striking sex-specific trajectory. Pregnancies with female fetuses showed a decrease in CD244 expression during early-to-mid gestation, followed by an increase during late pregnancy, whereas trajectories among male fetuses remained relatively flat. CD244 serves as a co-regulatory receptor that can transmit inhibitory or activating signals depending on cellular context (54, 55). In decidual NK cells, it predominantly mediates inhibitory signaling (56). The observed U-shaped expression among pregnancies with female fetuses may reflect transient adjustment of activation thresholds during immune adaptation. This interpretation aligns with recent single-cell studies demonstrating that uterine NK cells undergo continuous ligand- and hormone-driven differentiation across gestation, accompanied by dynamic remodeling of their inhibitory and activating receptor repertoire (57). Such coordinated shifts could underlie the observed sex-specific modulation of CD244 expression. Consistent with this framework, female fetuses have been shown to elicit more precisely tuned maternal immune regulation, with inflammatory pathways adjusting in a more targeted and context-dependent manner across gestation (32, 33). In contrast, pregnancies carrying male fetuses display stronger inflammatory responses (32, 33). Given that dysregulation of NK cell signaling and cytotoxic receptor expression has been implicated in recurrent pregnancy loss, implantation failure, and preeclampsia (58–61), this sexually dimorphic pattern in CD244 may represent a mechanism through which fetal sex contributes to differential susceptibility to pregnancy complications (62). Future studies using panels enriched for lineage-defining cell surface markers should examine whether similar gestational patterns are observed for other NK cell receptors. Additionally, longitudinal and cell type-specific studies will be needed to determine whether these circulating patterns reflect localized modulation within the decidua or broader systemic immune tuning.

We also observed that participants with multiple prior pregnancies showed higher early gestational levels of CST5 and SIRT2 expression, followed by stabilization later in pregnancy. CST5 encodes cystatin D, which suppresses enzymes that digest extracellular matrix proteins, such as cathepsins. While cystatin D has not been widely studied in pregnancy, its function as a cysteine-protease inhibitor suggests it could limit the extent of tissue breakdown and remodeling during placental development (46). By limiting excessive protease activity during the period of trophoblast invasion and spiral artery transformation, higher CST5 levels in early pregnancy could help promote controlled placental development, potentially reducing vulnerability to disorders associated with abnormal placentation such as preeclampsia or fetal growth restriction (63). SIRT2 is a deacetylase that modifies metabolic and inflammatory regulators, helping cells balance energy production and immune activation (64, 65). In pregnancy, SIRT2 is expressed in decidual and trophoblast cells, where it may help support placental development by restraining excessive inflammatory and oxidative stress (66, 67). Notably, reduced SIRT2 expression has been reported in preeclampsia, suggesting that adequate SIRT2 activity may be important for maintaining controlled inflammatory and oxidative environments during early placentation (68). The trajectories observed in third and subsequent pregnancies are consistent with the concept of a “trained” uterine immune environment, wherein prior pregnancies condition decidual and vascular tissues to mount a more rapid or contained regulatory response upon subsequent pregnancies (29–31). This priming effect may reduce the need for intense early tissue remodeling signals in multigravida individuals, preventing excessive inflammation and trophoblast invasion in an already permissive uterine environment. Such memory-like adaptations may contribute to the reduced rates of certain placentation-related complications observed with increasing parity, such as preeclampsia (69, 70).

Taken together, these findings support a model of pregnancy as a series of discrete, nonlinear immune transitions that are modulated by fetal and maternal characteristics. Mapping these trajectories at scale provides a framework for distinguishing normative immune adaptation from deviations that may predict pregnancy complications. By identifying which inflammatory mediators exhibit dynamic versus stable patterns, these data help refine candidate biomarker selection for studies of adverse pregnancy outcomes, including hypertensive disorders of pregnancy, gestational diabetes, and spontaneous preterm birth, all of which involve perturbations of the immune–metabolic interface. Furthermore, the observed fetal sex and gravidity effects underscore the importance of incorporating these variables into future analyses rather than treating them as nuisance covariates, as they likely represent biologically meaningful sources of immune variation.

This study has several strengths, including its large and diverse sample drawn from multiple sites within the NIH ECHO Cohort and the breadth of gestational coverage, which spans from 5 to 40 weeks and provides rare insight into early- and late-pregnancy immune states. Nonetheless, some limitations should be acknowledged. The few very early gestational samples (e.g., ~ 5 weeks) were collected from participants recruited through fertility clinics, who may have been undergoing treatment or medication protocols; these samples may therefore not fully represent immune states in unassisted conceptions. The majority of samples were collected in the third trimester, which could reduce sensitivity for detecting transient early-pregnancy peaks despite the flexibility of GAMs. Relatedly, although effect-modification analyses could be influenced by unequal GA sampling, sensitivity analyses showed no imbalance by fetal sex and only modest trimester differences by gravidity. Early-pregnancy effects are therefore unlikely to reflect sampling bias, though later-trimester stabilization among multigravida participants should be interpreted cautiously. Additionally, although gravidity information was available for the full sample, approximately half of participants self-reported this information (n=621, 53.8%), while the remainder had gravidity extracted from medical records (n=533, 46.2%). Medical record-based gravidity may undercount prior pregnancies, particularly early losses that are less consistently documented. We were also unable to analyze the full panel of 96 proteins included in Olink: proteins with more than 25% of values below the LOD were excluded. Future studies employing more sensitive panels may reveal complementary biology in these low-abundance proteins. Additionally, although the multi-site ECHO cohort enhances geographic diversity, the specific recruitment strategies and demographic composition of the sample may limit generalizability to populations with different socioeconomic or racial/ethnic distributions. Finally, the cross-sectional design precludes direct inference of within-individual change, and longitudinal data will be needed to confirm the timing and magnitude of observed nonlinear transitions.

In summary, this study provides one of the most comprehensive characterizations to date of gestational immune trajectories in a cross-sectional sample of human pregnancies. Maternal inflammation and immune regulation evolve through coordinated, often nonlinear shifts that align with key physiological milestones. These patterns suggest that the maternal immune system operates through flexible, phase-dependent reconfiguration to balance tissue remodeling, tolerance, and metabolic demands across pregnancy. Establishing these normative trajectories provides a critical reference point for identifying early immune deviations that may underlie pregnancy complications and for advancing a more precise understanding of maternal–fetal immunology.

Acknowledgments

The authors wish to thank our ECHO Colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohort. We would also like to thank Karin Vevang and Hannah Tollman at the HHEAR laboratory for their contributions to the study’s biomarker assays.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) Program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (Measurement Core), U24OD035523 (Lab Core), ES0266542 (HHEAR), U24ES026539 (HHEAR Barbara O’Brien), U2CES026533 (HHEAR LP), U2CES026542 (HHEAR Patrick Parsons, Kannan Kurunthacalam), U2CES030859 (HHEAR Manish Arora), U2CES030857 (HHEAR Timothy R. Fennell, Susan J. Sumner, Xiuxia Du), U2CES026555 (HHEAR Susan L. Teitelbaum), U2CES026561 (HHEAR Robert O. Wright), U2CES030851 (HHEAR Heather M. Stapleton, P. Lee Ferguson), UG3/UH3OD023251 (Akram Alshawabkeh), UH3OD023320 and UG3OD035546 (Judy Aschner), UH3OD023332 (Clancy Blair, Leonardo Trasande), UG3/UH3OD023253 (Carlos Camargo), UG3/UH3OD023248 and UG3OD035526 (Dana Dabelea), UG3/UH3OD023313 (Daphne Koinis Mitchell), UH3OD023328 (Cristiane Duarte), UH3OD023318 (Anne Dunlop), UG3/UH3OD023279 (AE), UG3/UH3OD023289 (Assiamira Ferrara), UG3/UH3OD023282 (James Gern), UH3OD023287 (Carrie Breton), UG3/UH3OD023365 (Irva Hertz-Picciotto), UG3/UH3OD023244 (Alison Hipwell), UG3/UH3OD023275 (Margaret Karagas), UH3OD023271 and UG3OD035528 (Catherine Karr), UH3OD023347 (Barry Lester), UG3/UH3OD023389 (Leslie Leve), UG3/UH3OD023344 (Debra MacKenzie), UH3OD023268 (Scott Weiss), UG3/UH3OD023288 (Cynthia McEvoy), UG3/UH3OD023342 (Kristen Lyall), UG3/UH3OD023349 (Thomas O’Connor), UH3OD023286 and UG3OD035533 (Emily Oken), UG3/UH3OD023348 (Mike O’Shea), UG3/UH3OD023285 (Jean Kerver), UG3/UH3OD023290 (Julie Herbstman), UG3/UH3OD023272 (Susan Schantz), UG3/UH3OD023249 (Joseph Stanford), UG3/UH3OD023305 (Leonardo Trasande), UG3/UH3OD023337 (Rosalind Wright), UG3OD035508 (Sheela Sathyanarayana), UG3OD035509 (Anne Marie Singh), UG3OD035513 and UG3OD035532 (Annemarie Stroustrup), UG3OD035516 and UG3OD035517 (Tina Hartert), UG3OD035518 (Jennifer Straughen), UG3OD035519 (Qi Zhao), UG3OD035521 (Katherine Rivera-Spoljaric), UG3OD035527 (Emily S Barrett), UG3OD035540 (Monique Marie Hedderson), UG3OD035543 (Kelly J Hunt), UG3OD035537 (Sunni L Mumford), UG3OD035529 (Hong-Ngoc Nguyen), UG3OD035542 (Hudson Santos), UG3OD035550 (Rebecca Schmidt), UG3OD035536 (Jonathan Slaughter), UG3OD035544 (Kristina Whitworth). LS is supported by a K99/R00 award: 5R00HD103910.

Footnotes

Edited by: Maria Laura Zenclussen, National Scientific and Technical Research Council (CONICET), Argentina

Reviewed by: Yohei Sato, University of Fukui, Japan

Guiqin Bai, The First Affiliated Hospital of Xi’an Jiaotong University, China

Data availability statement

Select de-identified data from the ECHO Program are available through NICHD’s Data and Specimen Hub (DASH) (https://dash.nichd.nih.gov/). Information on study data not available on DASH, such as some Indigenous datasets, can be found on the ECHO study DASH webpage (https://dash.nichd.nih.gov/explore/study?q=echo&filters=%5b%5d&page=1&sortBy=relevance&asc=true&size=50).

Ethics statement

All participants consented to participate in their local ECHO site and share their information with the ECHO consortia. Both a central and site-specific Institutional Review Board monitored human subject activities at each site and the centralized ECHO Data Analysis Center. Written informed consent and the parent’s/guardian’s permission were obtained, along with child assent as appropriate, for ECHO-wide Cohort Data Collection Protocol participation. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

SB: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing, Formal analysis, Visualization. RS-T: Writing – original draft, Writing – review & editing. SS: Data curation, Writing – review & editing. AG: Conceptualization, Funding acquisition, Writing – review & editing. CD: Data curation, Writing – review & editing. MN: Data curation, Resources, Writing – review & editing. LP: Funding acquisition, Investigation, Writing – review & editing. BT: Investigation, Writing – review & editing. SM: Writing – review & editing. CH: Funding acquisition, Writing – review & editing. AE: Funding acquisition, Writing – review & editing. SG: Formal analysis, Supervision, Validation, Writing – original draft, Writing – review & editing. LS: Conceptualization, Data curation, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing.

ECHO Cohort Consortium collaborators

P Brian Smith, Division of Neonatology, Department of Pediatrics, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA, L Kristin Newby, Division of Cardiology, Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA, Linda Adair, Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Lisa P. Jacobson, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA, Diane Catellier, N/A, Research Triangle Institute, Research Triangle Park, North Carolina, USA, Monica McGrath, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA, Christian Douglas, N/A, Research Triangle Institute, Research Triangle Park, North Carolina, USA, Priya Duggal, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA, Emily Knapp, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA, Amii Kress, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA, Courtney K. Blackwell, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Maxwell A. Mansolf, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Jin-Shei Lai, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Emily Ho, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, David Cella, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Richard Gershon, Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Michelle L. Macy, Department of Pediatrics, Feinberg School of Medicine, Northwestern University and Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA, Suman R. Das, Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Jane E. Freedman, Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Simon A. Mallal, Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA, John A. McLean, Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA, Ravi V. Shah, Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Meghan H. Shilts, Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Akram N. Alshawabkeh, College of Engineering, Northeastern University, Boston, Massachusetts, USA, Jose F. Cordero, College of Public Health, Department of Epidemiology & Biostatistics, University Georgia, Athens, Georgia; USA, John Meeker, Environmental Health Sciences, School of Public Health, University Michigan, Ann Arbor, Michigan; USA, Leonardo Trasande, Departments of Pediatrics and Population Health, NYU Grossman School of Medicine, New York, New York, USA, Carlos A. Camargo, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA, Kohei Hasegawa, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA, Zhaozhong Zhu, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA, Ashley F. Sullivan, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA, Dana Dabelea, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University Colorado Anschutz Medical Campus, Aurora, Colorado, USA, Wei Perng, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University Colorado Anschutz Medical Campus, Aurora, Colorado, USA, Traci A. Bekelman, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University Colorado Anschutz Medical Campus, Aurora, Colorado, USA, Greta Wilkening, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University Colorado Anschutz Medical Campus, Aurora, Colorado, USA, Sheryl Magzamen, Environmental and Radiological Health Sciences, Colorado School of Public Health, Colorado State University, Fort Collins, Colorado, USA, Brianna F. Moore, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University Colorado Anschutz Medical Campus, Aurora, Colorado, USA, Anne P. Starling, Epidemiology, University North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Deborah J. Rinehart, Center for Health Systems Research, Denver Health and Hospital Authority, Denver, Colorado, USA, Daphne Koinis Mitchell, Department of Pediatrics, Rhode Island Hospital, The Alpert Medical School of Brown University, Providence, Rhode Island, USA, Viren D'Sa, Department of Pediatrics, Rhode Island Hospital, The Alpert Medical School of Brown University, Providence, Rhode Island, USA, Sean C.L. Deoni, Division of Gender Equality, Maternal, Newborn & Child Health Discovery & Tools Team, Bill Melinda Gates Foundation, Seattle, Washington, USA, Hans-Georg Mueller, Department of Statistics, University California, Davis, Davis, California, USA, Cristiane S. Duarte, Division of Child and Adolescent Psychiatry, Columbia University - NYSPI, New York, New York, USA, Catherine Monk, Department of Obstetrics & Gynecology, Columbia University - NYSPI, New York, New York, USA, Glorisa Canino, Behavioral Sciences Research Institute, University Puerto Rico, School of Medicine, Rio Piedras, Puerto Rico, Jonathan Posner, Child & Family Mental Health & Community Psychiatry Division, Duke University School of Medicine, Duke Psychiatry & Behavioral Sciences, Durham, North Carolina, USA, Tenneill Murray, Division of Child and Adolescent Psychiatry, Columbia University - NYSPI, New York, New York, USA, Claudia Lugo-Candelas, Division of Child and Adolescent Psychiatry, Columbia University - NYSPI, New York, New York, USA, Anne L. Dunlop, Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, Georgia, USA, Patricia A. Brennan, Department of Psychology, Emory University, Atlanta, Georgia, USA, Christine Hockett, N/A; Department of Pediatrics, Avera Research Institute; University of South Dakota School of Medicine, Rapid City, South Dakota, USA; Sioux Falls, South Dakota, USA, Amy Elliott, N/A; Department of Pediatrics, Avera Research Institute ; University of South Dakota School of Medicine, Sioux Falls, South Dakota, USA, Assiamira Ferrara, Division of Research, Kaiser Permanente Northern California, Oakland, California, USA, Lisa A. Croen, Division of Research, Kaiser Permanente Northern California, Oakland, California, USA, Monique M. Hedderson, Division of Research, Kaiser Permanente Northern California, Oakland, California, USA, John Ainsworth, Centre for Health Informatics, University Manchester, Manchester, United Kingdom, Leonard B. Bacharier, Department of Pediatrics, Monroe Carell Jr Children’s Hospital at Vanderbilt, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Casper G. Bendixsen, National Farm Medicine Center, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA, James E. Gern, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Diane R. Gold, The Channing Division of Network Medicine; Department of Medicine, Brigham Women’s Hospital; Harvard Medical School, Boston, Massachusetts, USA, Tina V. Hartert, Division of Pediatric Allergy, Immunology, and Pulmonary Medicine, Department of Medicine, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Daniel J. Jackson, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Christine C. Johnson, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Christine L.M. Joseph, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Meyer Kattan, Department of Pediatrics, Columbia University Medical Center, New York, New York, USA, Gurjit K. Khurana Hershey, Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA, Robert F. Lemanske, Jr., Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Susan V. Lynch, Department of Medicine, University California, San Francisco, California, USA, Rachel L. Miller, Department of Medicine; Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York, New York, USA, George T. O’Connor, Department of Pediatrics, Boston University School of Medicine, Boston, Massachusetts, USA, Carole Ober, Department of Human Genetics, University Chicago, Chicago, Illinois, USA, Dennis Ownby, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Katherine Rivera-Spoljaric, Department of Pediatrics, Washington University School of Medicine, St Louis, Missouri, USA, Patrick H. Ryan, Department of Pediatrics and College of Medicine; Division of Biostatistics and Epidemiology, University Cincinnati, Cincinnati, Ohio, USA, Christine M. Seroogy, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Anne Marie Singh, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Robert A. Wood, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA, Edward M. Zoratti, Division of Allergy and Clinical Immunology, Henry Ford Health, Detroit, Michigan, USA, Rima Habre, Department of Population and Public Health Sciences, University Southern California, Los Angeles, California, USA, Shohreh Farzan, Department of Population and Public Health Sciences, University Southern California, Los Angeles, California, USA, Frank D. Gilliland, Department of Population and Public Health Sciences, University Southern California, Los Angeles, California, USA, Irva Hertz-Picciotto, MIND Institute and Department of Public Health Sciences, University California, Davis, Davis, California, USA, Deborah H. Bennett, Department of Public Health Sciences, University California, Davis, Davis, California, USA, Julie B. Schweitzer, Department of Psychiatry and Behavioral Science and the MIND Institute, University California, Davis, Davis, California, USA, Rebecca J. Schmidt, MIND Institute and Department of Public Health Sciences, University California, Davis, Davis, California, USA, Janine M. LaSalle, Medical Microbiology and Immunology; MIND Institute, University California, Davis, Davis, California, USA, Alison E. Hipwell, Psychiatry and Psychology, University Pittsburgh, Pittsburgh, Pennsylvania, USA, Catherine J. Karr, Department of Pediatrics, School of Medicine; Department of Environmental and Occupational Health Sciences; School of Public Health, University Washington, Seattle, Washington, USA, Nicole R. Bush, Department of Psychiatry and Behavioral Sciences and Department of Pediatrics, School of Medicine, University California, San Francisco, San Francisco, California, USA, Kaja Z. LeWinn, Department of Psychiatry and Behavioral Sciences, School of Medicine, University California, San Francisco, San Francisco, California, USA, Sheela Sathyanarayana, Department of Pediatrics, School of Medicine; Department of Environmental and Occupational Health Sciences, School of Public Health, University Washington and Seattle Children's Research Institute, Seattle, Washington, USA, Qi Zhao, Department of Preventive Medicine, University Tennessee Health Science Center, Memphis, Tennessee, USA, Frances Tylavsky, Department of Preventive Medicine, University Tennessee Health Science Center, Memphis, Tennessee, USA, Kecia N. Carroll, Department of Pediatrics, Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA, Christine T. Loftus, Department of Environmental and Occupational Health Sciences; School of Public Health, University Washington, Seattle, Washington, USA, Leslie D. Leve, Department of Counseling Psychology and Human Services & Prevention Science Institute, University Oregon, Eugene, Oregon, USA, Jody M. Ganiban, Department of Psychological and Behavioral Sciences, George Washington University, Washington, DC, USA, Jenae M. Neiderhiser, Department of Psychology, Penn State University, University Park, Pennsylvania, USA, Scott T. Weiss, Channing Division of Network Medicine, Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA, Augusto A. Litonjua, Pediatric Pulmonary Division, Department of Pediatrics, Golisano Children's Hospital, University of Rochester, Rochester, New York, USA, Cindy T. McEvoy, Division of Neonatology, Department of Pediatrics, Oregon Health & Science University, Portland, Oregon, USA, Eliot R. Spindel, Division of Neuroscience, Oregon National Primate Research Center, Beaverton, Oregon, USA, Robert S. Tepper, Division of Pediatric Pulmonology, Department of Pediatrics, Indiana School of Medicine, Indianapolis, Indiana, USA, Craig J. Newschaffer, College of Health and Human Development, Penn State, State College, Pennsylvania, USA, Kristen Lyall, AJ Drexel Autism Institute, Drexel University, Philadelphia, Pennsylvania, USA, Heather E. Volk, Mental Health, Johns Hopkins University, Baltimore, Maryland, USA, Rebecca Landa, Department of Psychiatry and Behavioral Sciences, Center Autism and Related Disorders, Kennedy Krieger Institute, Johns Hopkins University, Baltimore, Maryland, USA, Sally Ozonoff, MIND Institute, Department of Psychiatry, University California Davis, Sacramento, California, USA, Joseph Piven, Department of Psychiatry, University North Carolina, Chapel Hill, North Carolina, USA, Heather Hazlett, Department of Psychiatry, University North Carolina, Chapel Hill, North Carolina, USA, Juhi Pandey, Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA, Robert Schultz, Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA, Steven Dager, Department of Radiology, University Washington, Seattle, Washington, USA, Kelly Botteron, Department of Psychiatry, Washington University, St Louis, Missouri, USA, Daniel Messinger, Department of Psychology, University Miami, Miami, Florida, USA, Wendy Stone, Department of Psychology, University Washington, Seattle, Washington, USA, Jennifer Ames, Kaiser Permanente Division of Research, Kaiser Permanente, Oakland, California, USA, Thomas G. O'Connor, Departments of Psychiatry, Neuroscience, Obstetrics and Gynecology, University Rochester, Rochester, New York, USA, Richard K. Miller, Departments of Obstetrics and Gynecology, University Rochester, Rochester, New York, USA, Emily Oken, Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA, Michele R. Hacker, Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA, Tamarra James-Todd, Department of Environmental Health, Harvard Chan School of Public Health, Boston, Massachusetts, USA, T. Michael O'Shea, Division of Neonatology, Department of Pediatrics, University North Carolina School of Medicine, Chapel Hill, North Carolina, USA, Rebecca C. Fry, Department of Environmental Sciences and Engineering, University North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina, USA, Jean A. Frazier, EK Shriver Center and Psychiatry, UMASS Chan Medical School, Worcster, Massachusetts, USA, Rachana Singh, Department of Pediatrics, Tufts University School of Medicine, Boston, Massachusetts, USA, Caitlin Rollins, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA, Angela Montgomery, Division of Neonatology, Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, USA, Ruben Vaidya, Department of Pediatrics, University Massachusetts Chan Medical School-Baystate, Springfield, Massachusetts, USA, Robert M. Joseph, Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA, Lisa K. Washburn, Pediatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA, Semsa Gogcu, Section of Neonatology, Department of Pediatrics; Department of Pediatrics, Wake Forest School of Medicine; Wake Forest University School of Medicine/Atrium Health Wake Forest, Winston-Salem, North Carolina, USA, Kelly Bear, Section of Neonatology, Department of Pediatrics, ECU Health, Greenville, North Carolina, USA, Julie V. Rollins, Division of Neonatology, Department of Pediatrics, University North Carolina School of Medicine, Chapel Hill, North Carolina, USA, Stephen R. Hooper, Department of Health Sciences, School Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Genevieve Taylor, Pediatrics, School Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Wesley Jackson, Division of Neonatology, Department of Pediatrics, University North Carolina School of Medicine, Chapel Hill, North Carolina, USA, Amanda Thompson, Department of Anthropology, Department of Nutrition, University North Carolina at Chapel Hill; Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Julie Daniels, Epidemiology and Maternal and Child Health, University North Carolina at Chapel Hill; Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Michelle Hernandez, Pediatrics, School Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Kun Lu, Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA, Michael Msall, Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, University Chicago Medicine: Comer Children's Hospital, Chicago Illinois, USA, Madeleine Lenski, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA, Rawad Obeid, Pediatrics, Beaumont Hospital, Royal Oak, Michigan, USA, Steven L. Pastyrnak, Pediatrics, Corewell Health, Helen DeVos Children's Hospital, Grand Rapids, Michigan, USA, Elizabeth Jensen, Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA, Christina Sakai, Pediatrics, Mass General Hospital for Children, Boston, Massachusetts, USA, Hudson Santos, Dean's Office Graduate School, School of Nursing and Health Studies, University Miami, Coral Gables, Florida, USA, Jean M. Kerver, Departments of Epidemiology & Biostatistics, and Pediatrics & Human Development, Michigan State University, College of Human Medicine, East Lansing, Michigan, USA, Nigel Paneth, Departments of Epidemiology & Biostatistics, and Pediatrics & Human Development, Michigan State University, College of Human Medicine, East Lansing, Michigan, USA, Charles J. Barone, Department of Pediatrics, Henry Ford Health, Detroit, Michigan, USA, Michael R. Elliott, Department of Biostatistics, University Michigan, Ann Arbor, Michigan, USA, Douglas M. Ruden, Department of Obstetrics and Gynecology, Institute of Environmental Health Sciences (IEHS), C.S. Mott Center for Human Health and Development, Wayne State University, Detroit, Michigan, USA, Chris Fussman, Lifecourse Epidemiology and Genomics Division, Michigan Department of Health and Human Services (MDHHS), Lansing, Michigan, USA, Julie B. Herbstman, Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA, Amy Margolis, Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA, Susan L. Schantz, Beckman Institute for Advanced Science and Technology; Department of Comparative Biosciences, University Illinois Urbana-Champaign, Urbana, Illinois, USA, Sarah Dee Geiger, Beckman Institute for Advanced Science and Technology; Department of Kinesiology and Community Health, University Illinois Urbana-Champaign, Urbana, Illinois, USA, Andrea Aguiar, Beckman Institute for Advanced Science and Technology; Department of Comparative Biosciences, University Illinois Urbana-Champaign, Urbana, Illinois, USA, Karen Tabb, Beckman Institute for Advanced Science and Technology; Department of Social Work, University Illinois Urbana-Champaign, Urbana, Illinois, USA, Rita Strakovsky, Department of Food Science and Human Nutrition, Michigan State University, East Lansing, Michigan, USA, Tracey Woodruff, Program on Reproductive Health and the Environment, University California, San Francisco, San Francisco, California, USA, Rachel Morello-Frosch, Department of Environmental Science, Policy and Management and School of Public Health, University California, Berkeley, Berkeley, California, USA, Amy Padula, Program on Reproductive Health and the Environment, University California, San Francisco, San Francisco, California, USA, Joseph B. Stanford, Department of Family and Preventive Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA, Christina A. Porucznik, Department of Family and Preventive Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA, Angelo P. Giardino, Department of Pediatrics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA, Rosalind J. Wright, Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA, Robert O. Wright, Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA, Brent Collett, Department of Psychiatry and Behavioral Medicine, University Washington, Seattle Children's Research Institute, Seattle, Washington, USA, Nicole Baumann-Blackmore, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Ronald Gangnon, Department of Population Health Sciences, University Wisconsin, Madison, Wisconsin, USA, Daniel J. Jackson, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Chris G. McKennan, Department of Statistics, University Pittsburgh, Pittsburgh, Pennsylvania, USA, Jo Wilson, Department of Pediatrics, University Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA, Matt Altman, Department of Medicine, University Washington, Seattle, Washington, USA, Judy L. Aschner, Department of Pediatrics, Albert Einstein College of Medicine; Hackensack Meridian School of Medicine; Center for Discovery and Innovation, Bronx, New York, USA; Nutley, New Jersey, USA, Annemarie Stroustrup, Department of Pediatrics, Northwell Health, Cohen Children's Medical Center, and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Stephanie L. Merhar, Department of Pediatrics, Cincinnati Children's, Cincinnati, Ohio, USA, Paul E. Moore, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Gloria S. Pryhuber, Department of Pediatrics, University Rochester Medical Center, Rochester, New York, USA, Mark Hudak, Department of Pediatrics, University Florida College of Medicine, Jacksonville, Florida, USA, Ann Marie Reynolds Lyndaker, Department of Pediatrics, University Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA, Andrea L. Lampland, Department of Pediatrics, Children's Minnesota, Minneapolis, Minnesota, USA, Burton Rochelson, Department of Obstetrics and Gynecology, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Sophia Jan, Department of Pediatrics, Northwell Health, Cohen Children's Medical Center, and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Matthew J. Blitz, Department of Obstetrics and Gynecology, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Michelle W. Katzow, Department of Pediatrics, Northwell Health, Cohen Children's Medical Center, and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Zenobia Brown, Department of Science Education, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Codruta Chiuzan, Institute of Health System Science, Northwell Health, Feinstein Institutes for Medical Research, Manhasset, New York, USA, Timothy Rafael, Department of Obstetrics and Gynecology, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Dawnette Lewis, Department of Obstetrics and Gynecology, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Natalie Meirowitz, Department of Obstetrics and Gynecology, Northwell Health and the Zucker School of Medicine at Hofstra / Northwell, New Hyde Park, New York, USA, Brenda Poindexter, Department of Pediatrics, Children's Healthcare of Atlanta Emory University, Atlanta, Georgia, USA, Tebeb Gebretsadik, Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Sarah Osmundson, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA, Jennifer K. Straughen, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Amy Eapen, Division of Allergy and Clinical Immunology, Henry Ford Health, Detroit, Michigan, USA, Andrea Cassidy-Bushrow, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Ganesa Wegienka, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Alex Sitarik, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Kim Woodcroft, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Audrey Urquhart, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Albert Levin, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Tisa Johnson-Hooper, Department of Pediatrics, Henry Ford Health, Detroit, Michigan, USA, Brent Davidson, Department of Women's Health, Henry Ford Health, Detroit, Michigan, USA, Tengfei Ma, Department of Public Health Sciences, Henry Ford Health, Detroit, Michigan, USA, Emily S. Barrett, Department of Biostatistics and Epidemiology, Environmental Occupational Health Sciences Institute, Rutgers University, Piscataway, New Jersey, USA, Martin J. Blaser, Center for Advanced Biotechnology & Medicine, Rutgers University, Piscataway, New Jersey, USA, Maria Gloria Dominguez-Bello, Departments of Biochemistry and Microbiology & Anthropology, Rutgers University, New Brunswick, New Jersey, USA, Daniel B. Horton, Department of Pediatrics, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA, Manuel Jimenez, Departments of Pediatrics, Family Medicine, and Community Health, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA, Todd Rosen, Department of Obstetrics, Gynecology, and Reproductive Sciences, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA, Kristy Palomares, Department of Obstetrics and Gynecology, Saint Peter's University Hospital, New Brunswick, New Jersey, USA, Lyndsay A. Avalos, Division of Research, Kaiser Permanente Northern California, Oakland, California, USA, Yeyi Zhu, Division of Research, Kaiser Permanente Northern California, Oakland, California, USA, Kelly J . Hunt, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA, Roger B. Newman, Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina, USA, Michael S. Bloom, Department of Global and Community Health, George Mason University, Fairfax, Virginia, USA, Mallory H. Alkis, Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina, USA, James R. Roberts, Department of Pediatrics, Medical University of South Carolina, Charleston, South Carolina, USA, Sunni L. Mumford, Department of Biostatistics, Epidemiology and Informatics; Department of Obstetrics and Gynecology, University Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA, Heather H. Burris, Division of Neonatology, Department of Pediatrics, Children's Hospital of Philadelphia; University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA, Sara B. DeMauro, Division of Neonatology, Department of Pediatrics, Children's Hospital of Philadelphia; University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA, Lynn M. Yee, Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Aaron Hamvas, Division of Neonatology, Department of Pediatrics, Ann Robert H. Lurie Children's Hospital, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Antonia F. Olidipo, Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Hackensack University Medical Center, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA, Andrew S. Haddad, Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Hackensack University Medical Center, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA, Lisa R. Eiland, Division of Neonatology, Department of Pediatrics, Hackensack University Medical Center, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA, Nicole T. Spillane, Division of Neonatology, Department of Pediatrics, Hackensack University Medical Center, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA, Kirin N. Suri, Division of Developmental and Behavioral Pediatrics, Department of Pediatrics, Hackensack University Medical Center, Hackensack Meridian School of Medicine, Nutley, New Jersey, USA, Stephanie A. Fisher, Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Jeffrey A. Goldstein, Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Leena B. Mithal, Division of Infectious Diseases, Department of Pediatrics, Ann Robert H. Lurie Children's Hospital, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Raye-Ann O. DeRegnier, Division of Neonatology, Department of Pediatrics, Ann Robert H. Lurie Children's Hospital, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, Nathalie L. Maitre, Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Cerebral Palsy Foundation, Atlanta, Georgia, USA and New York, New York, USA, Ruby H.N. Nguyen, Division of Epidemiology & Community Health, School Public Health, University of Minnesota, Minneapolis, Minnesota, USA, Meghan M. JaKa, Division of Research & Evaluation, HealthPartners Partners Institute, Minneapolis, Minnesota, USA, Abbey C. Sidebottom, Care Delivery Research, Allina Health, Minneapolis, Minnesota, USA, Michael J. Paidas, Department of Obstetrics and Gynecology, University Miami Miller School of Medicine, Miami, Florida, USA, JoNell E. Potter, Department of Obstetrics, Gynecology and Reproductive Sciences, University Miami Miller School of Medicine, Miami, Florida, USA, Natale Ruby, Mailman Center for Child Development, University Miami Miller School of Medicine, Miami, Florida, USA, Lunthita Duthely, Department of Obstetrics, Gynecology and Reproductive Sciences and Department of Public Health Sciences, University Miami School of Medicine, Miami, Florida, USA, Arumugam Jayakumar, Department of Obstetrics, Gynecology and Reproductive Sciences, University Miami Miller School of Medicine, Miami, Florida, USA, Karen Young, Department of Pediatrics, University Miami Miller School of Medicine, Miami, Florida, USA, Isabel Maldonado, School of Nursing and Health Studies, University Miami, Miami, Florida, USA, Meghan Miller, Psychiatry and Behavioral Sciences; MIND Institute, University California Davis, Sacramento, California, USA, Jonathan L. Slaughter, Center for Perinatal Research, Abigail Wexner Research Institute and Division of Neonatology, Nationwide Children's Hospital and Department of Pediatrics, College of Medicine and Division of Epidemiology, College of Public Health, The Ohio State University, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio, USA, Sarah A. Keim, Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital and Department of Pediatrics, College of Medicine and Division of Epidemiology, College of Public Health, The Ohio State University, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio, USA, Courtney D. Lynch, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, College of Medicine and Division of Epidemiology, College of Public Health, The Ohio State University, The Ohio State University, Columbus, Ohio, USA, Kartik K. Venkatesh, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, College of Medicine and Division of Epidemiology, College of Public Health, The Ohio State University, The Ohio State University, Columbus, Ohio, USA, Kristina W. Whitworth, Center for Precision Environmental Health and Department of Medicine, Baylor College of Medicine, Houston, Texas, USA, Elaine Symanski, Center for Precision Environmental Health and Department of Medicine, Baylor College of Medicine, Houston, Texas, USA, Thomas F. Northrup, Department of Family and Community Medicine, University Texas Health Science Center at Houston (UTHealth Houston) McGovern Medical School, Houston, Texas, USA, Hector Mendez-Figueroa, Department of Obstetrics, Gynecology and Reproductive Sciences, University Texas Health Science Center at Houston (UTHealth Houston) McGovern Medical School, Houston, Texas, USA, Ricardo A. Mosquera, Department of Pediatrics, University Texas Health Science Center at Houston (UTHealth Houston) McGovern Medical School, Houston, Texas, USA, Margaret R. Karagas, Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA, Juliette C. Madan, Departments of Psychiatry, Pediatrics & Epidemiology, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Hanover, New Hampshire, USA, Debra M. MacKenzie, Community Environmental Health Program, Department of Pharmaceutical Sciences, College Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA, Johnnye L. Lewis, Community Environmental Health Program, Department of Pharmaceutical Sciences, College Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA, Brandon J. Rennie, Center for Development and Disability, University New Mexico, Albuquerque, New Mexico, USA, Bennett L. Leventhal, Community Environmental Health Program, Department of Pharmaceutical Sciences UNM, College Pharmacy, University of New Mexico Health Sciences Center; University of Chicago, Albuquerque, New Mexico, USA; Chicago, Illinois, USA, Young Shin Kim, Department of Psychiatry and Behavioral Sciences, University California, San Francisco, San Francisco, California, USA, Somer Bishop, Department of Psychiatry and Behavioral Sciences, University California, San Francisco, San Francisco, California, USA, Sara S. Nozadi, Community Environmental Health Program, Department of Pharmaceutical Sciences, College Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA, Li Luo, Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA, Barry M. Lester, Department of Pediatrics, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA, Carmen J. Marsit, Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA, Todd Everson, Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA, Cynthia M. Loncar, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA, Elisabeth C. McGowan, Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA, Stephen J. Sheinkopf, Department of Pediatrics, Thompson Center for Autism & Neurodevelopment, University of Missouri, Columbia, Missouri, USA, Brian S. Carter, Department of Pediatrics, Children's Mercy-Kansas City, Kansas City, Missouri, USA, Jennifer Check, Department of Pediatrics, Wake Forest School of Medicine, Winston, Salem North Carolina, USA, Jennifer B. Helderman, Department of Pediatrics, Wake Forest School of Medicine, Winston, Salem North Carolina, USA, Charles R. Neal, Department of Pediatrics, University Hawaii John A Burns School of Medicine, Honolulu, Hawaii, USA, Lynne M. Smith, Department of Pediatrics, UCLA Clinical and Translational Science Institute at The Lundquist Institute, Harbor-UCLA Medical Center, Los Angeles, California, USA.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The sponsor, NIH, participated in the overall design and implementation of the ECHO Program, which was funded as a cooperative agreement between NIH and grant awardees. The sponsor approved the Steering Committee-developed ECHO protocol and its amendments including COVID-19 measures. The sponsor had no access to the central database, which was housed at the ECHO Data Analysis Center. Data management and site monitoring were performed by the ECHO Data Analysis Center and Coordinating Center. All analyses for scientific publication were performed by the study statistician, independently of the sponsor. The lead author wrote all drafts of the manuscript and made revisions based on co-authors and the ECHO Publication Committee (a subcommittee of the ECHO Operations Committee) feedback without input from the sponsor. The study sponsor did not review or approve the manuscript for submission to the journal.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2026.1803430/full#supplementary-material

DataSheet1.pdf^{(2.1MB, pdf)}

References

1. Aneman I, Pienaar D, Suvakov S, Simic TP, Garovic VD, McClements L. Mechanisms of key innate immune cells in early- and late-onset preeclampsia. Front Immunol. (2020) 11:1864. doi: 10.3389/fimmu.2020.01864, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Collier AY, Smith LA, Karumanchi SA. Review of the immune mechanisms of preeclampsia and the potential of immune modulating therapy. Hum Immunol. (2021) 82:362–70. doi: 10.1016/j.humimm.2021.01.004, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
3. McElwain CJ, McCarthy FP, McCarthy CM. Gestational diabetes mellitus and maternal immune dysregulation: what we know so far. Int J Mol Sci. (2021) 22:4261. doi: 10.3390/ijms22084261, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Saucedo R, Ortega-Camarillo C, Ferreira-Hermosillo A, Díaz-Velázquez MF, Meixueiro-Calderón C, Valencia-Ortega J. Role of oxidative stress and inflammation in gestational diabetes mellitus. Antioxidants (Basel). (2023) 12:1812. doi: 10.3390/antiox12101812, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Kargar R, Yahoo Z, Rafiemanesh H, Aslani S, Shamosi A, Mohammadi H. Evaluation of Th1-Th2 balance in patients with unexplained recurrent spontaneous abortion: a systematic review and meta-analysis. Am J Reprod Immunol. (2025) 94:e70125. doi: 10.1111/aji.70125, PMID: [DOI] [PubMed] [Google Scholar]
6. Guan D, Sun W, Gao M, Chen Z, Ma X. Immunologic insights in recurrent spontaneous abortion: Molecular mechanisms and therapeutic interventions. BioMed Pharmacother. (2024) 177:117082. doi: 10.1016/j.biopha.2024.117082, PMID: [DOI] [PubMed] [Google Scholar]
7. Areia AL, Mota-Pinto A. Inflammation and preterm birth: a systematic review. Reprod Med. (2022) 3:101–11. doi: 10.3390/reprodmed3020009, PMID: 41725453 [DOI] [Google Scholar]
8. Gomez-Lopez N, StLouis D, Lehr MA, Sanchez-Rodriguez EN, Arenas-Hernandez M. Immune cells in term and preterm labor. Cell Mol Immunol. (2014) 11:571–81. doi: 10.1038/cmi.2014.46, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Romero R, Espinoza J, Gonçalves LF, Kusanovic JP, Friel LA, Nien JK. Inflammation in preterm and term labour and delivery. Semin Fetal Neonatal Med. (2006) 11:317–26. doi: 10.1016/j.siny.2006.05.001, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Burwick RM, Lokki AI, Fleming SD, Regal JF. Editorial: Innate immunity in normal and adverse pregnancy. Front Immunol. (2021) 12:646596. doi: 10.3389/fimmu.2021.646596, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Abu-Raya B, Michalski C, Sadarangani M, Lavoie PM. Maternal immunological adaptation during normal pregnancy. Front Immunol. (2020) 11:575197. doi: 10.3389/fimmu.2020.575197, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Weng J, Couture C, Girard S. Innate and adaptive immune systems in physiological and pathological pregnancy. Biol (Basel). (2023) 12:402. doi: 10.3390/biology12030402, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Lima PD, Zhang J, Dunk C, Lye SJ, Anne Croy B. Leukocyte driven-decidual angiogenesis in early pregnancy. Cell Mol Immunol. (2014) 11:522–37. doi: 10.1038/cmi.2014.63, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Moffett A, Shreeve N. Local immune recognition of trophoblast in early human pregnancy: controversies and questions. Nat Rev Immunol. (2023) 23:222–35. doi: 10.1038/s41577-022-00777-2, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Wang F, Qualls AE, Marques-Fernandez L, Colucci F. Biology and pathology of the uterine microenvironment and its natural killer cells. Cell Mol Immunol. (2021) 18:2101–13. doi: 10.1038/s41423-021-00739-z, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Vilotić A, Nacka-Aleksić M, Pirković A, Bojić-Trbojević Ž, Dekanski D, Jovanović Krivokuća M. IL-6 and IL-8: an overview of their roles in healthy and pathological pregnancies. Int J Mol Sci. (2022) 23:14574. doi: 10.3390/ijms232314574, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Dutta S, Sengupta P, Liew FF. Cytokine landscapes of pregnancy: mapping gestational immune phases. Gynecol Obstet Clin Med. (2024) 4. doi: 10.1136/gocm-2024-000011, PMID: 41802225 [DOI] [Google Scholar]
18. Mor G, Cardenas I, Abrahams V, Guller S. Inflammation and pregnancy: the role of the immune system at the implantation site. Ann N Y Acad Sci. (2011) 1221:80–7. doi: 10.1111/j.1749-6632.2010.05938.x, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Moffett A, Colucci F. Uterine NK cells: active regulators at the maternal-fetal interface. J Clin Invest. (2014) 124:1872–9. doi: 10.1172/JCI68107, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Wang W, Sung N, Gilman-Sachs A, Kwak-Kim J. T helper (Th) cell profiles in pregnancy and recurrent pregnancy losses: Th1/Th2/Th9/Th17/Th22/Tfh cells. Front Immunol. (2020) 11:2025. doi: 10.3389/fimmu.2020.02025, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Zhang YH, Tian M, Tang MX, Liu ZZ, Liao AH. Recent insight into the role of the PD-1/PD-L1 pathway in feto-maternal tolerance and pregnancy. Am J Reprod Immunol. (2015) 74:201–8. doi: 10.1111/aji.12365, PMID: [DOI] [PubMed] [Google Scholar]
22. Garcia de leon R, Hodges TE, Brown HK, Bodnar TS, Galea LAM. Inflammatory signalling during the perinatal period: implications for short- and long-term disease risk. Psychoneuroendocrinology. (2025) 172:107245. doi: 10.1016/j.psyneuen.2024.107245, PMID: [DOI] [PubMed] [Google Scholar]
23. Hedman AM, Lundholm C, Andolf E, Pershagen G, Fall T, Almqvist C. Longitudinal plasma inflammatory proteome profiling during pregnancy in the Born into Life study. Sci Rep. (2020) 10:17819. doi: 10.1038/s41598-020-74722-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Ross KM, Dunkel Schetter C, Carroll JE, Mancuso RA, Breen EC, Okun ML, et al. Inflammatory and immune marker trajectories from pregnancy to one-year post-birth. Cytokine. (2022) 149:155758. doi: 10.1016/j.cyto.2021.155758, PMID: [DOI] [PubMed] [Google Scholar]
25. Jarmund AH, Giskeødegård GF, Ryssdal M, Steinkjer B, Stokkeland LMT, Madssen TS, et al. Cytokine patterns in maternal serum from first trimester to term and beyond. Front Immunol. (2021) 12:752660. doi: 10.3389/fimmu.2021.752660, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Holtan SG, Chen Y, Kaimal R, Creedon DJ, Enninga EAL, Nevala WK, et al. Growth modeling of the maternal cytokine milieu throughout normal pregnancy: macrophage-derived chemokine decreases as inflammation/counterregulation increases. J Immunol Res. (2015) 2015:952571. doi: 10.1155/2015/952571, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Ferguson KK, McElrath TF, Chen YH, Mukherjee B, Meeker JD. Longitudinal profiling of inflammatory cytokines and c-reactive protein during uncomplicated and preterm pregnancy. Am J Reprod Immunol. (2014) 72:326–36. doi: 10.1111/aji.12265, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Stelzer IA, Ghaemi MS, Han X, Ando K, Hédou JJ, Feyaerts D, et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci Transl Med. (2021) 13:eabd9898. doi: 10.1126/scitranslmed.abd9898, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Gamliel M, Goldman-Wohl D, Isaacson B, Gur C, Stein N, Yamin R, et al. Trained memory of human uterine nk cells enhances their function in subsequent pregnancies. Immunity. (2018) 48:951–962.e5. doi: 10.1016/j.immuni.2018.03.030, PMID: [DOI] [PubMed] [Google Scholar]
30. Feyaerts D, Joosten I, van der Molen RG. A pregnancy to remember: trained immunity of the uterine mucosae. . Mucosal Immunol. (2021) 14:539–41. doi: 10.1038/s41385-020-00362-7, PMID: [DOI] [PubMed] [Google Scholar]
31. Laskewitz A, Kieffer TEC, van Benthem KL, Erwich JJHM, Faas MM, Prins JR. Differences in immune phenotype in decidual tissue from multigravid women compared to primigravid women. Am J Reprod Immunol. (2023) 89:e13658. doi: 10.1111/aji.13658, PMID: [DOI] [PubMed] [Google Scholar]
32. Baines KJ, West RC. Sex differences in innate and adaptive immunity impact fetal, placental, and maternal health. Biol Reprod. (2023) 109:256–70. doi: 10.1093/biolre/ioad072, PMID: [DOI] [PubMed] [Google Scholar]
33. Enninga EAL, Nevala WK, Creedon DJ, Markovic SN, Holtan SG. Fetal sex-based differences in maternal hormones, angiogenic factors, and immune mediators during pregnancy and the postpartum period. Am J Reprod Immunol. (2015) 73:251–62. doi: 10.1111/aji.12303, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Knapp EA, Kress AM, Parker CB, Page GP, McArthur K, Gachigi KK, et al. The environmental influences on child health outcomes (ECHO)-wide cohort. Am J Epidemiol. (2023) 192:1249–63. doi: 10.1093/aje/kwad071, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Forrest CB, Blackwell CK, Camargo CA. Advancing the science of children’s positive health in the National Institutes of Health Environmental influences on Child Health Outcomes (ECHO) Research Program. J Pediatr. (2018) 196:298–300. doi: 10.1016/j.jpeds.2018.02.004, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Blaisdell CJ, Park C, Hanspal M, Roary M, Arteaga SS, Laessig S, et al. The NIH ECHO Program: investigating how early environmental influences affect child health. Pediatr Res. (2022) 92:1215–6. doi: 10.1038/s41390-021-01574-8, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Gillman MW, Blaisdell CJ. Environmental influences on child health outcomes, a research program of the national institutes of health. Curr Opin Pediatr. (2018) 30:260–2. doi: 10.1097/MOP.0000000000000600, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. (2023) 622:329–38. doi: 10.1038/s41586-023-06592-6, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol. (2011) 73:3–36. doi: 10.1111/j.1467-9868.2010.00749.x, PMID: 41834780 [DOI] [Google Scholar]
40. Wood SN. Generalized Additive Models: An Introduction with R, Second Edition. 2nd ed. New York: Chapman Hall/CRC. (2017) 496 p. doi: 10.1201/9781315370279 [DOI] [Google Scholar]
41. Robertson SA, Care AS, Moldenhauer LM. Regulatory T cells in embryo implantation and the immune response to pregnancy. J Clin Invest. (2018) 128:4224–35. doi: 10.1172/JCI122182, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Petitbarat M, Rahmati M, Sérazin V, Dubanchet S, Morvan C, Wainer R, et al. TWEAK appears as a modulator of endometrial IL-18 related cytotoxic activity of uterine natural killers. PloS One. (2011) 6:e14497. doi: 10.1371/journal.pone.0014497, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Liao HQ, Han MT, Cheng W, Zhang C, Li H, Li MQ, et al. Decidual-derived RANKL facilitates macrophages accumulation and residence at the maternal-fetal interface in human early pregnancy. Am J Reprod Immunol. (2021) 86:e13406. doi: 10.1111/aji.13406, PMID: [DOI] [PubMed] [Google Scholar]
44. Salamonsen LA, Hannan NJ, Dimitriadis E. Cytokines and chemokines during human embryo implantation: roles in implantation and early placentation. Semin Reprod Med. (2007) 25:437–44. doi: 10.1055/s-2007-991041, PMID: [DOI] [PubMed] [Google Scholar]
45. Rossi F, Luppi S, Fejza A, Giolo E, Ricci G, Andreuzzi E. Extracellular matrix and pregnancy: functions and opportunities caught in the net. Reprod Biol Endocrinol. (2025) 23:24. doi: 10.1186/s12958-025-01348-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Harris LK, Aplin JD. Vascular remodeling and extracellular matrix breakdown in the uterine spiral arteries during pregnancy. Reprod Sci. (2007) 14:28–34. doi: 10.1177/1933719107309588, PMID: [DOI] [PubMed] [Google Scholar]
47. Sharkey AM, King A, Clark DE, Burrows TD, Jokhi PP, Charnock-Jones DS, et al. Localization of leukemia inhibitory factor and its receptor in human placenta throughout pregnancy. Biol Reprod. (1999) 60:355–64. doi: 10.1095/biolreprod60.2.355, PMID: [DOI] [PubMed] [Google Scholar]
48. Zhang YH, Sun HX. Immune checkpoint molecules in pregnancy: focus on regulatory T cells. Eur J Immunol. (2020) 50:160–9. doi: 10.1002/eji.201948382, PMID: [DOI] [PubMed] [Google Scholar]
49. Guleria I, Khosroshahi A, Ansari MJ, Habicht A, Azuma M, Yagita H, et al. A critical role for the programmed death ligand 1 in fetomaternal tolerance. J Exp Med. (2005) 202:231–7. doi: 10.1084/jem.20050019, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Yang D, Dai F, Yuan M, Zheng Y, Liu S, Deng Z, et al. Role of transforming growth factor-β1 in regulating fetal-maternal immune tolerance in normal and pathological pregnancy. Front Immunol. (2021) 12:689181. doi: 10.3389/fimmu.2021.689181, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Chatterjee P, Chiasson VL, Bounds KR, Mitchell BM. Regulation of the anti-inflammatory cytokines interleukin-4 and interleukin-10 during pregnancy. Front Immunol. (2014) 5:253. doi: 10.3389/fimmu.2014.00253, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Berri M, Virlogeux-Payant I, Chevaleyre C, Melo S, Zanello G, Salmon H, et al. CCL28 involvement in mucosal tissues protection as a chemokine and as an antibacterial peptide. Dev Comp Immunol. (2014) 44:286–90. doi: 10.1016/j.dci.2014.01.005, PMID: [DOI] [PubMed] [Google Scholar]
53. Wang Y, Zhao S. “Oxygen tension and placental vascular development”. In: Wang Y, Zhao S, editors. Vascular biology of the placenta. Morgan & Claypool Life Sciences, San Rafael, CA: (2010). [PubMed] [Google Scholar]
54. Waggoner SN, Kumar V. Evolving role of 2B4/CD244 in T and NK cell responses during virus infection. Front Immunol. (2012) 3:377. doi: 10.3389/fimmu.2012.00377, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Yan C, Lu P, Jiang Y, Miao S, Zhao L, Xu X. 2B4/CD244 signaling in immune regulation and its role in infection, cancer, and immune tolerance. Immunotargets Ther. (2025) 14:1111–31. doi: 10.2147/ITT.S538126, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Vacca P, Pietra G, Falco M, Romeo E, Bottino C, Bellora F, et al. Analysis of natural killer cells isolated from human decidua: evidence that 2B4 (CD244) functions as an inhibitory receptor and blocks NK-cell function. Blood. (2006) 108:4078–85. doi: 10.1182/blood-2006-04-017343, PMID: [DOI] [PubMed] [Google Scholar]
57. Strunz B, Bister J, Jönsson H, Filipovic I, Crona-Guterstam Y, Kvedaraite E, et al. Continuous human uterine NK cell differentiation in response to endometrial regeneration and pregnancy. Sci Immunol. (2021) 6:eabb7800. doi: 10.1126/sciimmunol.abb7800, PMID: [DOI] [PubMed] [Google Scholar]
58. Von Woon E, Greer O, Shah N, Nikolaou D, Johnson M, Male V. Number and function of uterine natural killer cells in recurrent miscarriage and implantation failure: a systematic review and meta-analysis. Hum Reprod Update. (2022) 28:548–82. doi: 10.1093/humupd/dmac006, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Fukui A, Yokota M, Funamizu A, Nakamua R, Fukuhara R, Yamada K, et al. Changes of NK cells in preeclampsia. Am J Reprod Immunol. (2012) 67:278–86. doi: 10.1111/j.1600-0897.2012.01120.x, PMID: [DOI] [PubMed] [Google Scholar]
60. Béquet YLBN, Lashley EELO, Goddijn M, van der Hoorn MLP. The role of uterine natural killer cells in recurrent pregnancy loss and possible treatment options. Fertility Sterility. (2023) 120:945–7. doi: 10.1016/j.fertnstert.2023.08.949, PMID: [DOI] [PubMed] [Google Scholar]
61. Xie M, Li Y, Meng YZ, Xu P, Yang YG, Dong S, et al. Uterine natural killer cells: a rising star in human pregnancy regulation. Front Immunol. (2022) 13:918550. doi: 10.3389/fimmu.2022.918550, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Broere-Brown ZA, Adank MC, Benschop L, Tielemans M, Muka T, Gonçalves R, et al. Fetal sex and maternal pregnancy outcomes: a systematic review and meta-analysis. Biol Sex Differ. (2020) 11:26. doi: 10.1186/s13293-020-00299-3, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
63. Screen M, Dean W, Cross JC, Hemberger M. Cathepsin proteases have distinct roles in trophoblast function and vascular remodelling. Development. (2008) 135:3311–20. doi: 10.1242/dev.025627, PMID: [DOI] [PubMed] [Google Scholar]
64. Fortuny L, Sebastián C. Sirtuins as metabolic regulators of immune cells phenotype and function. Genes (Basel). (2021) 12:1698. doi: 10.3390/genes12111698, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Sasso GL, Menzies KJ, Mottis A, Piersigilli A, Perino A, Yamamoto H, et al. SIRT2 deficiency modulates macrophage polarization and susceptibility to experimental colitis. PloS One. (2014) 9:e103573. doi: 10.1371/journal.pone.0103573, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Zgutka K, Tkacz M, Grabowska M, Mikołajek-Bedner W, Tarnowski M. Sirtuins and their implications in the physiopathology of gestational diabetes mellitus. Pharmaceuticals. (2025) 18:41. doi: 10.3390/ph18010041, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Akkaya Fırat A, Alıcı Davutoğlu E, Özel A, Fırtına Tuncer S, Yılmaz N, Madazlı R. Serum FoxO1 and SIRT2 concentrations in healthy pregnant women and complicated by preeclampsia. Ir J Med Sci. (2025) 194:181–8. doi: 10.1007/s11845-024-03865-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
68. Yu Y, An X, Fan D. Histone deacetylase sirtuin 2 enhances viability of trophoblasts through p65-mediated microRNA-146a/ACKR2 axis. Reprod Sci. (2021) 28:1370–81. doi: 10.1007/s43032-020-00398-x, PMID: [DOI] [PubMed] [Google Scholar]
69. Hernández-Díaz S, Toh S, Cnattingius S. Risk of pre-eclampsia in first and subsequent pregnancies: prospective cohort study. BMJ. (2009) 338:b2255. doi: 10.1136/bmj.b2255, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
70. Luo ZC, An N, Xu HR, Larante A, Audibert F, Fraser WD. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr Perinat Epidemiol. (2007) 21:36–45. doi: 10.1111/j.1365-3016.2007.00836.x, PMID: [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

DataSheet1.pdf^{(2.1MB, pdf)}

Data Availability Statement

[B1] 1. Aneman I, Pienaar D, Suvakov S, Simic TP, Garovic VD, McClements L. Mechanisms of key innate immune cells in early- and late-onset preeclampsia. Front Immunol. (2020) 11:1864. doi: 10.3389/fimmu.2020.01864, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B2] 2. Collier AY, Smith LA, Karumanchi SA. Review of the immune mechanisms of preeclampsia and the potential of immune modulating therapy. Hum Immunol. (2021) 82:362–70. doi: 10.1016/j.humimm.2021.01.004, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] 3. McElwain CJ, McCarthy FP, McCarthy CM. Gestational diabetes mellitus and maternal immune dysregulation: what we know so far. Int J Mol Sci. (2021) 22:4261. doi: 10.3390/ijms22084261, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B4] 4. Saucedo R, Ortega-Camarillo C, Ferreira-Hermosillo A, Díaz-Velázquez MF, Meixueiro-Calderón C, Valencia-Ortega J. Role of oxidative stress and inflammation in gestational diabetes mellitus. Antioxidants (Basel). (2023) 12:1812. doi: 10.3390/antiox12101812, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B5] 5. Kargar R, Yahoo Z, Rafiemanesh H, Aslani S, Shamosi A, Mohammadi H. Evaluation of Th1-Th2 balance in patients with unexplained recurrent spontaneous abortion: a systematic review and meta-analysis. Am J Reprod Immunol. (2025) 94:e70125. doi: 10.1111/aji.70125, PMID: [DOI] [PubMed] [Google Scholar]

[B6] 6. Guan D, Sun W, Gao M, Chen Z, Ma X. Immunologic insights in recurrent spontaneous abortion: Molecular mechanisms and therapeutic interventions. BioMed Pharmacother. (2024) 177:117082. doi: 10.1016/j.biopha.2024.117082, PMID: [DOI] [PubMed] [Google Scholar]

[B7] 7. Areia AL, Mota-Pinto A. Inflammation and preterm birth: a systematic review. Reprod Med. (2022) 3:101–11. doi: 10.3390/reprodmed3020009, PMID: 41725453 [DOI] [Google Scholar]

[B8] 8. Gomez-Lopez N, StLouis D, Lehr MA, Sanchez-Rodriguez EN, Arenas-Hernandez M. Immune cells in term and preterm labor. Cell Mol Immunol. (2014) 11:571–81. doi: 10.1038/cmi.2014.46, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B9] 9. Romero R, Espinoza J, Gonçalves LF, Kusanovic JP, Friel LA, Nien JK. Inflammation in preterm and term labour and delivery. Semin Fetal Neonatal Med. (2006) 11:317–26. doi: 10.1016/j.siny.2006.05.001, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B10] 10. Burwick RM, Lokki AI, Fleming SD, Regal JF. Editorial: Innate immunity in normal and adverse pregnancy. Front Immunol. (2021) 12:646596. doi: 10.3389/fimmu.2021.646596, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B11] 11. Abu-Raya B, Michalski C, Sadarangani M, Lavoie PM. Maternal immunological adaptation during normal pregnancy. Front Immunol. (2020) 11:575197. doi: 10.3389/fimmu.2020.575197, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B12] 12. Weng J, Couture C, Girard S. Innate and adaptive immune systems in physiological and pathological pregnancy. Biol (Basel). (2023) 12:402. doi: 10.3390/biology12030402, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B13] 13. Lima PD, Zhang J, Dunk C, Lye SJ, Anne Croy B. Leukocyte driven-decidual angiogenesis in early pregnancy. Cell Mol Immunol. (2014) 11:522–37. doi: 10.1038/cmi.2014.63, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B14] 14. Moffett A, Shreeve N. Local immune recognition of trophoblast in early human pregnancy: controversies and questions. Nat Rev Immunol. (2023) 23:222–35. doi: 10.1038/s41577-022-00777-2, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B15] 15. Wang F, Qualls AE, Marques-Fernandez L, Colucci F. Biology and pathology of the uterine microenvironment and its natural killer cells. Cell Mol Immunol. (2021) 18:2101–13. doi: 10.1038/s41423-021-00739-z, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B16] 16. Vilotić A, Nacka-Aleksić M, Pirković A, Bojić-Trbojević Ž, Dekanski D, Jovanović Krivokuća M. IL-6 and IL-8: an overview of their roles in healthy and pathological pregnancies. Int J Mol Sci. (2022) 23:14574. doi: 10.3390/ijms232314574, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B17] 17. Dutta S, Sengupta P, Liew FF. Cytokine landscapes of pregnancy: mapping gestational immune phases. Gynecol Obstet Clin Med. (2024) 4. doi: 10.1136/gocm-2024-000011, PMID: 41802225 [DOI] [Google Scholar]

[B18] 18. Mor G, Cardenas I, Abrahams V, Guller S. Inflammation and pregnancy: the role of the immune system at the implantation site. Ann N Y Acad Sci. (2011) 1221:80–7. doi: 10.1111/j.1749-6632.2010.05938.x, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B19] 19. Moffett A, Colucci F. Uterine NK cells: active regulators at the maternal-fetal interface. J Clin Invest. (2014) 124:1872–9. doi: 10.1172/JCI68107, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B20] 20. Wang W, Sung N, Gilman-Sachs A, Kwak-Kim J. T helper (Th) cell profiles in pregnancy and recurrent pregnancy losses: Th1/Th2/Th9/Th17/Th22/Tfh cells. Front Immunol. (2020) 11:2025. doi: 10.3389/fimmu.2020.02025, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B21] 21. Zhang YH, Tian M, Tang MX, Liu ZZ, Liao AH. Recent insight into the role of the PD-1/PD-L1 pathway in feto-maternal tolerance and pregnancy. Am J Reprod Immunol. (2015) 74:201–8. doi: 10.1111/aji.12365, PMID: [DOI] [PubMed] [Google Scholar]

[B22] 22. Garcia de leon R, Hodges TE, Brown HK, Bodnar TS, Galea LAM. Inflammatory signalling during the perinatal period: implications for short- and long-term disease risk. Psychoneuroendocrinology. (2025) 172:107245. doi: 10.1016/j.psyneuen.2024.107245, PMID: [DOI] [PubMed] [Google Scholar]

[B23] 23. Hedman AM, Lundholm C, Andolf E, Pershagen G, Fall T, Almqvist C. Longitudinal plasma inflammatory proteome profiling during pregnancy in the Born into Life study. Sci Rep. (2020) 10:17819. doi: 10.1038/s41598-020-74722-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B24] 24. Ross KM, Dunkel Schetter C, Carroll JE, Mancuso RA, Breen EC, Okun ML, et al. Inflammatory and immune marker trajectories from pregnancy to one-year post-birth. Cytokine. (2022) 149:155758. doi: 10.1016/j.cyto.2021.155758, PMID: [DOI] [PubMed] [Google Scholar]

[B25] 25. Jarmund AH, Giskeødegård GF, Ryssdal M, Steinkjer B, Stokkeland LMT, Madssen TS, et al. Cytokine patterns in maternal serum from first trimester to term and beyond. Front Immunol. (2021) 12:752660. doi: 10.3389/fimmu.2021.752660, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B26] 26. Holtan SG, Chen Y, Kaimal R, Creedon DJ, Enninga EAL, Nevala WK, et al. Growth modeling of the maternal cytokine milieu throughout normal pregnancy: macrophage-derived chemokine decreases as inflammation/counterregulation increases. J Immunol Res. (2015) 2015:952571. doi: 10.1155/2015/952571, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B27] 27. Ferguson KK, McElrath TF, Chen YH, Mukherjee B, Meeker JD. Longitudinal profiling of inflammatory cytokines and c-reactive protein during uncomplicated and preterm pregnancy. Am J Reprod Immunol. (2014) 72:326–36. doi: 10.1111/aji.12265, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B28] 28. Stelzer IA, Ghaemi MS, Han X, Ando K, Hédou JJ, Feyaerts D, et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci Transl Med. (2021) 13:eabd9898. doi: 10.1126/scitranslmed.abd9898, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B29] 29. Gamliel M, Goldman-Wohl D, Isaacson B, Gur C, Stein N, Yamin R, et al. Trained memory of human uterine nk cells enhances their function in subsequent pregnancies. Immunity. (2018) 48:951–962.e5. doi: 10.1016/j.immuni.2018.03.030, PMID: [DOI] [PubMed] [Google Scholar]

[B30] 30. Feyaerts D, Joosten I, van der Molen RG. A pregnancy to remember: trained immunity of the uterine mucosae. . Mucosal Immunol. (2021) 14:539–41. doi: 10.1038/s41385-020-00362-7, PMID: [DOI] [PubMed] [Google Scholar]

[B31] 31. Laskewitz A, Kieffer TEC, van Benthem KL, Erwich JJHM, Faas MM, Prins JR. Differences in immune phenotype in decidual tissue from multigravid women compared to primigravid women. Am J Reprod Immunol. (2023) 89:e13658. doi: 10.1111/aji.13658, PMID: [DOI] [PubMed] [Google Scholar]

[B32] 32. Baines KJ, West RC. Sex differences in innate and adaptive immunity impact fetal, placental, and maternal health. Biol Reprod. (2023) 109:256–70. doi: 10.1093/biolre/ioad072, PMID: [DOI] [PubMed] [Google Scholar]

[B33] 33. Enninga EAL, Nevala WK, Creedon DJ, Markovic SN, Holtan SG. Fetal sex-based differences in maternal hormones, angiogenic factors, and immune mediators during pregnancy and the postpartum period. Am J Reprod Immunol. (2015) 73:251–62. doi: 10.1111/aji.12303, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B34] 34. Knapp EA, Kress AM, Parker CB, Page GP, McArthur K, Gachigi KK, et al. The environmental influences on child health outcomes (ECHO)-wide cohort. Am J Epidemiol. (2023) 192:1249–63. doi: 10.1093/aje/kwad071, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B35] 35. Forrest CB, Blackwell CK, Camargo CA. Advancing the science of children’s positive health in the National Institutes of Health Environmental influences on Child Health Outcomes (ECHO) Research Program. J Pediatr. (2018) 196:298–300. doi: 10.1016/j.jpeds.2018.02.004, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] 36. Blaisdell CJ, Park C, Hanspal M, Roary M, Arteaga SS, Laessig S, et al. The NIH ECHO Program: investigating how early environmental influences affect child health. Pediatr Res. (2022) 92:1215–6. doi: 10.1038/s41390-021-01574-8, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B37] 37. Gillman MW, Blaisdell CJ. Environmental influences on child health outcomes, a research program of the national institutes of health. Curr Opin Pediatr. (2018) 30:260–2. doi: 10.1097/MOP.0000000000000600, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] 38. Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. (2023) 622:329–38. doi: 10.1038/s41586-023-06592-6, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] 39. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol. (2011) 73:3–36. doi: 10.1111/j.1467-9868.2010.00749.x, PMID: 41834780 [DOI] [Google Scholar]

[B40] 40. Wood SN. Generalized Additive Models: An Introduction with R, Second Edition. 2nd ed. New York: Chapman Hall/CRC. (2017) 496 p. doi: 10.1201/9781315370279 [DOI] [Google Scholar]

[B41] 41. Robertson SA, Care AS, Moldenhauer LM. Regulatory T cells in embryo implantation and the immune response to pregnancy. J Clin Invest. (2018) 128:4224–35. doi: 10.1172/JCI122182, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B42] 42. Petitbarat M, Rahmati M, Sérazin V, Dubanchet S, Morvan C, Wainer R, et al. TWEAK appears as a modulator of endometrial IL-18 related cytotoxic activity of uterine natural killers. PloS One. (2011) 6:e14497. doi: 10.1371/journal.pone.0014497, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B43] 43. Liao HQ, Han MT, Cheng W, Zhang C, Li H, Li MQ, et al. Decidual-derived RANKL facilitates macrophages accumulation and residence at the maternal-fetal interface in human early pregnancy. Am J Reprod Immunol. (2021) 86:e13406. doi: 10.1111/aji.13406, PMID: [DOI] [PubMed] [Google Scholar]

[B44] 44. Salamonsen LA, Hannan NJ, Dimitriadis E. Cytokines and chemokines during human embryo implantation: roles in implantation and early placentation. Semin Reprod Med. (2007) 25:437–44. doi: 10.1055/s-2007-991041, PMID: [DOI] [PubMed] [Google Scholar]

[B45] 45. Rossi F, Luppi S, Fejza A, Giolo E, Ricci G, Andreuzzi E. Extracellular matrix and pregnancy: functions and opportunities caught in the net. Reprod Biol Endocrinol. (2025) 23:24. doi: 10.1186/s12958-025-01348-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B46] 46. Harris LK, Aplin JD. Vascular remodeling and extracellular matrix breakdown in the uterine spiral arteries during pregnancy. Reprod Sci. (2007) 14:28–34. doi: 10.1177/1933719107309588, PMID: [DOI] [PubMed] [Google Scholar]

[B47] 47. Sharkey AM, King A, Clark DE, Burrows TD, Jokhi PP, Charnock-Jones DS, et al. Localization of leukemia inhibitory factor and its receptor in human placenta throughout pregnancy. Biol Reprod. (1999) 60:355–64. doi: 10.1095/biolreprod60.2.355, PMID: [DOI] [PubMed] [Google Scholar]

[B48] 48. Zhang YH, Sun HX. Immune checkpoint molecules in pregnancy: focus on regulatory T cells. Eur J Immunol. (2020) 50:160–9. doi: 10.1002/eji.201948382, PMID: [DOI] [PubMed] [Google Scholar]

[B49] 49. Guleria I, Khosroshahi A, Ansari MJ, Habicht A, Azuma M, Yagita H, et al. A critical role for the programmed death ligand 1 in fetomaternal tolerance. J Exp Med. (2005) 202:231–7. doi: 10.1084/jem.20050019, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B50] 50. Yang D, Dai F, Yuan M, Zheng Y, Liu S, Deng Z, et al. Role of transforming growth factor-β1 in regulating fetal-maternal immune tolerance in normal and pathological pregnancy. Front Immunol. (2021) 12:689181. doi: 10.3389/fimmu.2021.689181, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B51] 51. Chatterjee P, Chiasson VL, Bounds KR, Mitchell BM. Regulation of the anti-inflammatory cytokines interleukin-4 and interleukin-10 during pregnancy. Front Immunol. (2014) 5:253. doi: 10.3389/fimmu.2014.00253, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B52] 52. Berri M, Virlogeux-Payant I, Chevaleyre C, Melo S, Zanello G, Salmon H, et al. CCL28 involvement in mucosal tissues protection as a chemokine and as an antibacterial peptide. Dev Comp Immunol. (2014) 44:286–90. doi: 10.1016/j.dci.2014.01.005, PMID: [DOI] [PubMed] [Google Scholar]

[B53] 53. Wang Y, Zhao S. “Oxygen tension and placental vascular development”. In: Wang Y, Zhao S, editors. Vascular biology of the placenta. Morgan & Claypool Life Sciences, San Rafael, CA: (2010). [PubMed] [Google Scholar]

[B54] 54. Waggoner SN, Kumar V. Evolving role of 2B4/CD244 in T and NK cell responses during virus infection. Front Immunol. (2012) 3:377. doi: 10.3389/fimmu.2012.00377, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B55] 55. Yan C, Lu P, Jiang Y, Miao S, Zhao L, Xu X. 2B4/CD244 signaling in immune regulation and its role in infection, cancer, and immune tolerance. Immunotargets Ther. (2025) 14:1111–31. doi: 10.2147/ITT.S538126, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B56] 56. Vacca P, Pietra G, Falco M, Romeo E, Bottino C, Bellora F, et al. Analysis of natural killer cells isolated from human decidua: evidence that 2B4 (CD244) functions as an inhibitory receptor and blocks NK-cell function. Blood. (2006) 108:4078–85. doi: 10.1182/blood-2006-04-017343, PMID: [DOI] [PubMed] [Google Scholar]

[B57] 57. Strunz B, Bister J, Jönsson H, Filipovic I, Crona-Guterstam Y, Kvedaraite E, et al. Continuous human uterine NK cell differentiation in response to endometrial regeneration and pregnancy. Sci Immunol. (2021) 6:eabb7800. doi: 10.1126/sciimmunol.abb7800, PMID: [DOI] [PubMed] [Google Scholar]

[B58] 58. Von Woon E, Greer O, Shah N, Nikolaou D, Johnson M, Male V. Number and function of uterine natural killer cells in recurrent miscarriage and implantation failure: a systematic review and meta-analysis. Hum Reprod Update. (2022) 28:548–82. doi: 10.1093/humupd/dmac006, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B59] 59. Fukui A, Yokota M, Funamizu A, Nakamua R, Fukuhara R, Yamada K, et al. Changes of NK cells in preeclampsia. Am J Reprod Immunol. (2012) 67:278–86. doi: 10.1111/j.1600-0897.2012.01120.x, PMID: [DOI] [PubMed] [Google Scholar]

[B60] 60. Béquet YLBN, Lashley EELO, Goddijn M, van der Hoorn MLP. The role of uterine natural killer cells in recurrent pregnancy loss and possible treatment options. Fertility Sterility. (2023) 120:945–7. doi: 10.1016/j.fertnstert.2023.08.949, PMID: [DOI] [PubMed] [Google Scholar]

[B61] 61. Xie M, Li Y, Meng YZ, Xu P, Yang YG, Dong S, et al. Uterine natural killer cells: a rising star in human pregnancy regulation. Front Immunol. (2022) 13:918550. doi: 10.3389/fimmu.2022.918550, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B62] 62. Broere-Brown ZA, Adank MC, Benschop L, Tielemans M, Muka T, Gonçalves R, et al. Fetal sex and maternal pregnancy outcomes: a systematic review and meta-analysis. Biol Sex Differ. (2020) 11:26. doi: 10.1186/s13293-020-00299-3, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B63] 63. Screen M, Dean W, Cross JC, Hemberger M. Cathepsin proteases have distinct roles in trophoblast function and vascular remodelling. Development. (2008) 135:3311–20. doi: 10.1242/dev.025627, PMID: [DOI] [PubMed] [Google Scholar]

[B64] 64. Fortuny L, Sebastián C. Sirtuins as metabolic regulators of immune cells phenotype and function. Genes (Basel). (2021) 12:1698. doi: 10.3390/genes12111698, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B65] 65. Sasso GL, Menzies KJ, Mottis A, Piersigilli A, Perino A, Yamamoto H, et al. SIRT2 deficiency modulates macrophage polarization and susceptibility to experimental colitis. PloS One. (2014) 9:e103573. doi: 10.1371/journal.pone.0103573, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B66] 66. Zgutka K, Tkacz M, Grabowska M, Mikołajek-Bedner W, Tarnowski M. Sirtuins and their implications in the physiopathology of gestational diabetes mellitus. Pharmaceuticals. (2025) 18:41. doi: 10.3390/ph18010041, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B67] 67. Akkaya Fırat A, Alıcı Davutoğlu E, Özel A, Fırtına Tuncer S, Yılmaz N, Madazlı R. Serum FoxO1 and SIRT2 concentrations in healthy pregnant women and complicated by preeclampsia. Ir J Med Sci. (2025) 194:181–8. doi: 10.1007/s11845-024-03865-5, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B68] 68. Yu Y, An X, Fan D. Histone deacetylase sirtuin 2 enhances viability of trophoblasts through p65-mediated microRNA-146a/ACKR2 axis. Reprod Sci. (2021) 28:1370–81. doi: 10.1007/s43032-020-00398-x, PMID: [DOI] [PubMed] [Google Scholar]

[B69] 69. Hernández-Díaz S, Toh S, Cnattingius S. Risk of pre-eclampsia in first and subsequent pregnancies: prospective cohort study. BMJ. (2009) 338:b2255. doi: 10.1136/bmj.b2255, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]

[B70] 70. Luo ZC, An N, Xu HR, Larante A, Audibert F, Fraser WD. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr Perinat Epidemiol. (2007) 21:36–45. doi: 10.1111/j.1365-3016.2007.00836.x, PMID: [DOI] [PubMed] [Google Scholar]

PERMALINK

A cross-sectional examination of immune adaptations during pregnancy in the ECHO Cohort

Sarah M Banker

Rosa Shapiro-Thompson

Sarah Sinsel

Akhgar Ghassabian

Christian Douglas

Morgan E Nelson

Lisa A Peterson

Bharat Thyagarajan

Santiago Morales

Christine W Hockett

Amy J Elliott

Stephanie N Giamberardino

Lauren C Shuffrey

Roles

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. Study design

2.2. Maternal venous blood collection

2.3. Proteomic profiling and data cleaning

2.4. Current analysis

2.5. Statistical analysis

2.5.1. Inflammatory associations with gestational age

2.5.2. Effect modification by fetal sex, gravidity, or maternal age

3. Results

Table 1.

3.1. Inflammatory associations with gestational age

Figure 1.

3.2. Modification effects of fetal sex, gravidity, or maternal age

Figure 2.

Figure 3.

4. Discussion

Acknowledgments

Funding Statement

Footnotes

Data availability statement

Ethics statement

Author contributions

ECHO Cohort Consortium collaborators

Conflict of interest

Generative AI statement

Publisher’s note

Author disclaimer

Supplementary material

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases