Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers

Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers Bushra Amreen, Floriana Milazzo, Frederieke Gigase, Darwin D’souza, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8105673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Since the beginning of the pandemic, millions of pregnant women have been exposed to SARS-CoV-2, raising concerns about maternal and fetal sequelae. Yet, the impact of SARS-CoV-2 on the child’s immune response remains largely unexplored. Herein, we leverage 833 mother-infant dyads from a New York City-based pregnancy cohort, to explore prospective associations between maternal gestational SARS-CoV-2 infection and inflammatory biomarkers in newborns. Of the mothers, 100 were infected with SARS-CoV-2 during pregnancy, as confirmed through self-report, antibody and/or PCR test results. We obtained 92 inflammatory biomarker levels in neonatal dried blood spots (DBS) using the Olink® Target 96 Inflammation panel. Empirical Bayes method was used to fit linear regression models to assess the effects of maternal infection during pregnancy on neonatal inflammatory markers at birth. We also conducted stratified analyses by timing of infection in early (<20 weeks) versus late (≥20 weeks) gestation. Results: Higher levels of 22 inflammatory biomarkers ( p adj <0.05), including CD5, TNFSF14, CD8a, TGF-α, and CD244, were observed in neonates prenatally exposed to SARS-CoV-2 compared to unexposed neonates ( p adj <0.05 ). Early-gestation infection was associated with increased levels of eight inflammatory biomarker, including TNSF14, TGF-α, EN-RAGE, and decreased IL-18 levels, while late-gestation infection was linked to elevations in 12 biomarkers, including CD5, CD6, PD-L1. Conclusion: Our results indicate that maternal SARS-CoV-2 infection during pregnancy impacts inflammatory biomarkers in newborns, with the timing of infection playing a critical role in shaping these immune profiles. Thus, this study underscores the need for further research and long-term follow-up to assess any potential future health consequences for the child. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research SARS-CoV-2 neonatal inflammation prenatal infection COVID-19 Olink® Cytokines Figures Figure 1 Figure 2 Figure 3 Background Since the beginning of the COVID-19 pandemic, millions of pregnant individuals worldwide have been infected with the severe acute respiratory syndrome coronavirus-2 (SARS‑CoV‑2). 1 Evidence from historical pandemics suggests that prenatal exposure to certain infectious agents, such as rubella and influenza, is associated with an elevated risk of adverse health outcomes in children born during those periods. 2 Consistent with this pattern, gestational SARS-CoV-2 infection has been associated with adverse pregnancy and neonatal outcomes 3 , 4 , although findings are heterogeneous. 5 – 7 The overall risk of adverse maternal and neonatal outcomes appears greatest among individuals with symptomatic and severe infections 8 – 10 , with emerging evidence suggesting associations with neonatal complications, including respiratory distress. 11 A recent meta-analysis further reported a substantially higher risk of respiratory distress syndrome (RDS) among neonates born to SARS-CoV-2 positive mothers 12 ; and some studies suggested neurodevelopmental delays in children prenatally exposed to SARS-CoV-2. However, the evidence is inconsistent. 13 – 15 Unlike other infectious agents, vertical transmission of SARS-CoV-2 occurs in only 1–3% of cases and viral placental infection is rarely reportedly. 16 – 19 Nevertheless, even in the absence of neonatal infection, maternal SARS-CoV-2 infection seems to impact both the placenta and the fetus through placental immune activation and maternal vascular malperfusions. 20 – 24 This is concerning because the development of the fetal immune system is orchestrated through carefully timed and sensitive stages starting from conception. 25 , 26 Any perturbation in this finely tuned developmental process may disrupt neonatal immune regulation and influence the child’s health outcomes down the line. Supporting this idea, animal studies have demonstrated that maternal inflammation alone, without direct viral transmission, can dysregulate cytokine levels in offspring. 27 Additionally, studies also show that maternal immune activation or the trans-placental transfer of inflammatory mediators can shape the fetal immune system. 28 , 29 Research exploring the association between prenatal exposures to SARS-CoV-2 and neonatal immune response are sparse. Due to ethical and practical considerations 29 , cohorts that bank neonatal blood samples are rare and typically small. One approach to obtaining neonatal blood samples involves cord blood collection. As such, one study of 30 SARS-CoV-2 exposed and 15 unexposed mother-infant dyads measured multiple immune cell types and 13 cytokines in neonatal cord blood plasma, reporting elevated natural killer cells and regulatory T-cells in exposed neonates 30 . A more scalable and cost-effective method of obtaining and storing neonatal blood involves the use of dried blood spots (DBS). In one study, 42 cytokines/chemokines were measured in DBS from 460 neonates born to SARS-CoV-2 positive mothers and 85 neonates born to SARS-CoV-2 negative mothers, with IL-22 and GM-CSF showing significantly higher levels in exposed neonates before multiple testing correction. 31 While these studies provide important early insights into the impact of prenatal SARS-CoV-2 exposure on the neonatal immune response, it remains challenging to form a comprehensive picture of the mechanisms at play, due to the variability in study design and findings. These discrepancies arise from the use of different biological matrices for measuring inflammatory markers such as DBS versus cord blood, as well as differences in maternal infection severity and definition of infection timing. Comparability is further limited by the minimal overlap in the specific inflammatory markers assessed in each study. Hence, additional studies are essential to strengthen the current body of evidence. In this study, we measured 92 inflammatory markers in neonatal DBS collected at birth in 100 children prenatally exposed to SARS-CoV-2 and 726 unexposed children. We explored associations between gestational SARS-CoV-2 exposure and neonatal inflammatory profiles, and further investigated whether these associations varied by timing of infection (early vs. late pregnancy). Methods Study population Between April 2020 and February 2022, the prospective cohort study Generation C recruited pregnant individuals (≥ 18 years) receiving obstetric care within the Mount Sinai Health System (MSHS). This cohort is described in detail elsewhere. 6 For the present analysis, we examined a sub-cohort of 833 mother-infant dyads from the Generation C study. This sub-cohort comprised all mother-child pairs with known maternal SARS-CoV-2 infection status during pregnancy and consent for neonatal dried blood spot (DBS) retrieval. Five sibling pairs were included in this sub-cohort. For the analysis, if both siblings were unexposed to SARS-CoV-2 during gestation, one sibling per pair was excluded at random ( Fig. 1 ) . In cases where one of the siblings was exposed to SARS-CoV-2 during pregnancy, the unexposed sibling was excluded. All participants in this study provided informed consent. The study was approved by the Institutional Review Board (IRB-20-03352 and IRB-22-00566) at the Icahn School of Medicine at Mount Sinai, reviewed by the US Centers for Disease Control and Prevention (CDC), and conducted in compliance with relevant federal laws, CDC policies, and the Declaration of Helsinki. SARS-CoV-2 infection status and timing In this study, maternal blood specimens were collected as part of routine clinical blood draws. A participant was considered to have evidence of SARS-CoV-2 if (1) there was a positive RT-PCR report or (2) there was a diagnosis by a medical health official reported in either the electronic medical record (EMR) or self-report questionnaire and (3) there was anti-S IgG antibody presence AND one of the following: a) anti-S IgG antibody before an individual’s first COVID-19 vaccination, b) anti-S IgG antibody before the COVID-19 vaccination rollout in NYC (Dec 14, 2020), or c) anti-spike IgG antibody presence and anti-N IgG antibody. 6 For the first two scenarios, the date of diagnoses or report was considered the date of evidence of SARS-CoV-2 positivity while for the third, the date of sample collection was considered the date of positivity. If any of these dates were during pregnancy, a participant was considered to have evidence of SARS-CoV-2 exposure during pregnancy. To maximize power, we defined timing of infection as being infected early in gestation (< 20 weeks) and late in gestation (≥ 20 weeks), referred to below as early infection and late infection, respectively. Participants were considered unexposed if there was no evidence of SARS-CoV-2 positivity using any of the criteria above. Dried Blood Spot (DBS) collection and processing Under the Newborn screening program (NBS), United States mandates collection of neonatal DBS at birth with the aim to screen for serious but treatable congenital diseases. 32 Neonatal DBS were obtained from New York State Department of Health’s Newborn Screening Program (NYSDOH NBS). NYSDOH NBS collects five small blood spots by pricking the newborn’s heel, using a sterile lancet, within ~ 24 to 36 hours of delivery. The blood spots are collected on standard Whatman 903 contaminant free specimen cards. The spots are dried for at least three hours on a flat, clean, non-absorbent surface, away from direct heat and sunlight. Then residual DBS not used for clinical purposes are stored at room temperature for up to 27 years. 33 We received six 3mm punches from each specimen card, one of which was eluted and incubated at room temperature for an hour, in 20ul of buffer, consisting of 1X PBS, 0.05% TWEEN 20, and 1X protease inhibitors. The eluted blood was used for further analysis. Olink® Target 96 Inflammation panel Neonatal inflammatory cytokines were quantified using the Olink® Target 96 Inflammation Proteomics platform (Olink® Bioscience, Uppsala, Sweden), with analyses conducted by the Human Immune Monitoring Center (HIMC) at Mount Sinai. Olink® has become a widely adopted platform for large-scale proteomic analysis 34 and has been leveraged in multiple DBS-based studies to date. 34 – 37 This Olink® Target 96 Inflammation panel employs a highly sensitive and specific proximity extension assay to quantitatively evaluate relative changes in the expression of 92 inflammation-related proteins. 38 Briefly, pairs of oligonucleotide-conjugated antibodies, each recognizing a distinct target protein, were incubated with the samples. Upon concurrent binding to proximal epitopes, the antibody-bound oligonucleotides hybridized to form a double-stranded DNA template, which is subsequently amplified by PCR. The amplified DNA is then transferred to a microfluidic chip (Fluidigm BioMark HD instrument) and quantified using real-time quantitative PCR (qPCR). The raw data from the qPCR readout produces Cycle threshold (Ct) values, representing the number of amplification cycles required for the signal to reach a predetermined threshold. A lower Ct value indicates a higher initial concentration of the target protein. These Ct values are converted into Normalized Protein Expression (NPX) values, an arbitrary, relative unit on a log2 scale through a multistep process: 1) Normalization using Extension Controls: For each protein assay, the sample’s Ct value is subtracted from its corresponding Extension Control Ct value to yield ΔCt; 2) Inter-plate Normalization: The median ΔCt value for the Plate Control wells is subtracted from each sample’s ΔCt, resulting in a ΔΔCt value; 3) Final NPX Calculation: A pre-determined constant value (referred to as a correction factor) is subtracted from the ΔΔCt value to invert the scale so that a higher NPX value corresponds to a higher protein concentration, making the data more intuitive for biological interpretation. NPX values are on a log2 scale, meaning that a difference of one NPX represents a doubling of protein concentration. Because NPX values represent relative, sample-specific quantification, they are intended for group comparisons. 39 Covariates Clinical and sociodemographic characteristics of interest were collected through EMR review and questionnaires, including maternal age (in years), maternal race/ethnicity (Asian, Black, Hispanic, White, Other), COVID-19 vaccination status (vaccinated, unvaccinated), pre-pregnancy BMI (kg/m 2 ), cardiometabolic pregnancy complications (yes, no), preterm birth (yes, no), delivery mode (C-section, vaginal birth), child sex (male, female), age at DBS collection (in hours). COVID-19 vaccination status was defined as vaccinated if the first vaccine dose was administered before or during pregnancy. All mothers who were never vaccinated or those who were vaccinated after pregnancy were considered unvaccinated, as postnatal vaccination is unlikely to impact neonatal immune activation. Cardiometabolic pregnancy complications were defined as diagnoses of pre-eclampsia, gestational hypertension and/or gestational diabetes mellitus (GDM). COVID-19 symptom severity was determined using data from self-reported questionnaires and electronic medical records documenting concurrent symptom patterns, including fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, loss of taste or smell, sore throat, congestion, nausea or vomiting, and diarrhea. Participants were categorized as having asymptomatic, mild, moderate, severe, or critical illness according to WHO COVID-19 clinical management guidelines 40 , based on symptom presentation, respiratory status, and clinical findings. Severity classification was applied only when symptoms were reported in the same trimester as the documented infection. Statistical analysis To explore summary statistics of continuous and categorical variables, we used means, frequencies and ranges. We conducted bivariate analyses on sociodemographic and pregnancy outcome variables, using Chi-square test and Wilcoxon signed-rank, to compare how these variables differ between the SARS-CoV-2 exposed and unexposed groups. Correlations between NPX levels of the inflammatory markers were explored using Spearman’s rank-order correlation. To evaluate any possible effects of technical (e.g. assay batch) or biological (e.g. child sex) covariates on the inflammatory markers (NPX), we used principal component analysis. Any samples that were more than four standard deviations away from the mean on at least one principal component were excluded from the analysis. We excluded two mother-child pairs because their neonatal inflammatory cytokine measurements were identified as severe technical outliers (Supplementary Fig. 1). Differential analyses on inflammatory marker levels between SARS-CoV-2 exposed and unexposed groups were conducted using the Limma R package. 41 This package uses an empirical Bayes method to fit linear regression models with moderated standard errors for each inflammatory marker as a continuous outcome and SARS-CoV-2 exposure status as dichotomous predictor variable. It also uses the Benjamini-Hochberg (BH) method to control the false discovery rate (FDR). Additionally, to explore the impact of infection timing during pregnancy on neonatal inflammatory markers, we conducted a stratified sensitivity analysis by time of SARS-CoV-2 exposure during gestation. We stratified the overall SARS-CoV-2 exposed group into two groups, those exposed in early gestation (< 20 weeks) and those exposed in late gestation (≥ 20 weeks). We compared each group’s neonatal inflammatory marker levels independently to those of the unexposed group using Limma. Volcano plots were used to visualize log2-fold changes (Log2FC) in inflammatory marker levels for both overall and stratified analyses. All final linear regression models were adjusted for maternal age, race/ethnicity, COVID-19 vaccination status, pre-pregnancy BMI, cardiometabolic pregnancy complications, preterm birth, delivery mode, child sex and age at DBS collection. All adjustment variables were categorical except maternal age, pre-pregnancy BMI and age at DBS collection. Statistical significance was set at ≤ 0.05 for nominal p-values and at 5% for false discovery rate (FDR). All analyses were conducted using R statistical computing software version 4.3.1. Results Cohort Characteristics: After quality control and pre-processing, 826 mother-infant dyads were included in the study ( Fig. 1 ). Table 1 describes the characteristics of the 826 mother-infant dyads, stratified by prenatal SARS-CoV-2 exposure. In this sample, 100 dyads had evidence of SARS-CoV-2 infection during pregnancy. Of these, 38 were exposed during early gestation (< 20 weeks) and 62 were exposed during late gestation (≥ 20 weeks). SARS-CoV-2 severity was classified as asymptomatic, mild, or moderate in 16%, 81%, and 3% of the exposed participants, respectively. In both the exposed and unexposed groups, mean maternal age was 33 years. Mean pre-pregnancy BMI was 27.5 kg/m 2 (Standard Deviation (SD) = 6.6) and 26.7 kg/m 2 (SD = 7.0) in the exposed and unexposed groups, respectively. Frequency of C-sections was similar between the exposed and unexposed group with vaginal birth being more prevalent in both. In the exposed group, 58.0% of the infants were female, compared to 50.0% of infants in the unexposed group. Mean gestational age at delivery in both groups was 39 weeks, with 12.0% (n = 12) and 10.1% (n = 73) preterm births in the exposed and unexposed groups, respectively. In the exposed group, 26.0% of the mothers had cardiometabolic pregnancy complications compared to 29.1% in the unexposed group. None of the aforementioned demographic and clinical variables were significantly different between the two study groups. However, we observed that exposed mothers were more likely to be Hispanic or Black compared to the unexposed group ( p = 0.04) , on par with the larger cohort. 6 . Additionally, 33% of the exposed group was vaccinated before or during pregnancy, while 22.2% of the unexposed group was vaccinated before or during pregnancy ( p = 0.01). Table 1 Population Characteristics mother child dyads (n = 826). Exposed to SARS-CoV-2 during pregnancy (N = 100) Unexposed to SARS-CoV2 during pregnancy (N = 726) p -value* Mother's age at delivery Mean (SD) 33.3 (4.69) 33.2 (5.07) 0.971 Median [Min, Max] 33.0 [21.0, 42.0] 34.0 [18.0, 50.0] Child’s Gestational age at delivery Mean (SD) 38.6 (1.77) 38.8 (1.89) 0.122 Median [Min, Max] 39.0 [32.9, 42.3] 39.1 [24.9, 42.0] Preterm birth Yes 12 (12.0%) 73 (10.1%) 0.671 No 88 (88.0%) 653 (89.9%) Child sex Female 58 (58.0%) 363 (50.0%) 0.163 Male 42 (42.0%) 363 (50.0%) Delivery mode C-Section 41 (41.0%) 285 (39.3%) 0.822 Vaginal 59 (59.0%) 441 (60.7%) Cardio-metabolic pregnancy complications Yes 26 (26.0%) 211 (29.1%) 0.605 No 74 (74.0%) 515 (70.9%) Child Age at DBS collection (hours) Mean (SD) 25.3 (12.1) 26.2 (10.5) 0.162 Median [Min, Max] 24.0 [1.00, 124] 25.0 [1.00, 110] Pre-Pregnancy BMI (kg/m 2 ) Mean (SD) 27.7 (6.64) 26.7 (7.03) 0.114 Median [Min, Max] 26.1 [17.4, 45.4] 25.1 [14.2, 61.0] Race/Ethnicity Asian 6 (6.0%) 84 (11.6%) 0.044* Black or African American 18 (18.0%) 94 (12.9%) Hispanic 35 (35.0%) 178 (24.5%) White 5 (5.0%) 37 (5.1%) Other 36 (36.0%) 333 (45.9%) COVID-19 vaccination status Unvaccinated 66 (66.0%) 565 (77.8%) 0.013* Vaccinated 34 (34.0%) 161 (22.2%) SARS-CoV-2 exposure timing Early in gestation(< 20 weeks) 38 (38.0%) 0 (0%) NA Late in gestation (≥ 20 weeks) 62 (62.0%) 0 (0%) No SARS-CoV-2 infection 0 (0%) 726 (100%) SARS-CoV-2 symptom severity Asymptomatic 16 (16.0%) 0 (0%) NA Mild Illness 81 (81.0%) 0 (0%) Moderate Illness 3 (3.0%) 0 (0%) No SARS-CoV-2 infection 0 (0%) 726 (100%) *The p-values represented here are calculated through Chi-square test for categorical variables and Wilcoxon signed-rank test for continuous variables with nominal significance level set to p-value ≤ 0.05. *SD = Standard Deviation; Min = Minimum; Max = Maximum; Differential Inflammatory marker expression analysis We conducted differential protein expression analyses by SARS-CoV-2 status using linear regression models comparing inflammatory marker levels of 100 exposed infants to 726 unexposed infants. In the exposed group, 22 neonatal inflammatory marker levels were increased ( p adj <0.05) compared to the unexposed group. These include T-cell surface glycoproteins (CD5 and CD8A), T-cell differentiation antigen (CD6), Tumor necrosis factor receptor and ligand superfamily members (TNFSF14, TNFSF9 and CD40), TNF-related apoptosis-inducing ligand (TRAIL), C-C motif chemokines (CCL20, CCL25, MCP-4), C-X-C motif chemokines (CXCL6, CXCL5 and CXCL25), growth factors (TGF-α, HGF, VGFA) and several other cytokines, chemokines and growth factors (see Table 2 & Fig. 2 A for details). Table 2 Differentially Regulated Inflammatory Markers in Overall Analysis, significant at 5% FDR. Inflammatory Markers Inflammatory Marker Description Log2FC p -value Adjusted p -value CD5 T-cell surface glycoprotein CD5 0.377 0.000 0.001 CD8A T-cell surface glycoprotein CD8 alpha chain 0.274 0.000 0.004 CD6 T-cell differentiation antigen CD6 0.293 0.000 0.005 TNFSF14 Tumor necrosis factor ligand superfamily member 14 0.262 0.000 0.005 PD-L1 Programmed cell death ligand 1 0.179 0.000 0.007 CD244 Natural killer cell receptor 2B4 0.212 0.000 0.007 CD40 CD40L receptor 0.218 0.001 0.010 uPA Urokinase-type plasminogen activator 0.190 0.001 0.010 TNFRSF9 Tumor necrosis factor receptor superfamily member 9 0.181 0.001 0.011 IL-18R1 Interleukin-18 receptor 1 0.240 0.002 0.015 TGF-α Protransforming growth factor alpha 0.176 0.002 0.018 TRAIL TNF-related apoptosis-inducing ligand 0.162 0.003 0.022 CCL20 C-C motif chemokine 20 0.252 0.004 0.025 CXCL6 C-X-C motif chemokine 6 0.196 0.005 0.032 OPG Osteoprotegerin 0.128 0.006 0.037 HGF Hepatocyte growth factor 0.196 0.007 0.037 SIRT2 NAD-dependent protein deacetylase sirtuin-2 0.069 0.008 0.037 CCL25 C-C motif chemokine 25 0.149 0.008 0.037 CXCL5 C-X-C motif chemokine 5 0.245 0.008 0.037 MMP-1 Matrix metalloproteinase-1 0.263 0.010 0.045 VEGFA Vascular endothelial growth factor A 0.167 0.010 0.045 MCP-4 Monocyte chemotactic protein 4 0.207 0.012 0.050 We used linear regression models to compare inflammatory marker levels of neonates exposed in early gestation (n = 38) and unexposed neonates (n = 726). Early infection analysis showed eight markers that were elevated ( p adj <0.05 ) in the early infection group compared to the unexposed group, namely TGF-α, HGF, FGF-19, TNFSF14, TRAIL, RAGE-binding protein EN-RAGE, uPA and IL18R1. In contrast, interleukin-18 (IL-18) levels were lower ( p adj <0.05 ) in the early infection group compared to the unexposed group ( Table 3 & Fig. 2 B ) . Table 3 Differentially Regulated Inflammatory Markers in Early Infection model, significant at 5% FDR. Inflammatory Markers Inflammatory Marker Description Log2FC p -value Adjusted p -value TNFSF14 Tumor necrosis factor ligand superfamily member 14 0.463 0.000 0.001 TGF-α Protransforming growth factor alpha 0.373 0.000 0.001 HGF Hepatocyte growth factor 0.403 0.000 0.008 EN-RAGE RAGE-binding protein (EN-RAGE) 0.318 0.000 0.008 FGF-19 Fibroblast growth factor 19 0.402 0.001 0.027 IL-18R1 Interleukin-18 receptor 1 0.355 0.002 0.030 TRAIL TNF-related apoptosis-inducing ligand 0.255 0.003 0.030 uPA Urokinase-type plasminogen activator 0.265 0.003 0.030 IL18 Interleukin-18 -0.309 0.004 0.044 Comparing inflammatory marker levels of neonates exposed to SARS-CoV-2 during late gestation (n = 62) and unexposed neonates (n = 726), twelve inflammatory markers showed differential expression ( p adj <0.05). Namely, CD5, CD8A, CD6 and CD244, C-C and C-X-C motif chemokines (CXCL5 CXCL6, MCP-4, and CCL20), as well as CD40, PDL-1, CUB domain-containing protein 1 (CDCP1) and interleukin-12B (IL-12B) were elevated in the late infection group compared to the unexposed group ( Table 4 & Fig. 2 C ) . Table 4 Differentially Regulated Inflammatory Markers in Late Infection model, significant at 5%FDR. Inflammatory Markers Inflammatory Marker Description Log2FC p -value Adjusted p -value CD8A T-cell surface glycoprotein CD8 alpha chain 0.387 0.000 0.001 CD5 T-cell surface glycoprotein CD5 0.450 0.000 0.001 CD6 T-cell differentiation antigen CD6 0.388 0.000 0.001 CD40 CD40L receptor 0.327 0.000 0.001 MCP-4 Monocyte chemotactic protein 4 0.393 0.000 0.002 CCL20 C-C motif chemokine 20 0.409 0.000 0.002 CD244 Natural killer cell receptor 2B4 0.267 0.000 0.005 PD-L1 Programmed cell death ligand 1 0.198 0.002 0.019 CXCL5 C-X-C motif chemokine 5 0.352 0.002 0.023 CDCP1 CUB domain-containing protein 1 0.153 0.003 0.030 IL-12B Interleukin-12 subunit beta 0.238 0.004 0.035 CXCL6 C-X-C motif chemokine 6 0.240 0.006 0.043 Figure 3 presents a Venn diagram illustrating the overlap in significant inflammation markers identified in the overall analysis and the stratified analyses. In general, the top significant inflammatory markers reaching 5% FDR in the overall analysis were also present in the early and late gestation analyses. Some markers in the overall analysis that do not overlap in Fig. 3 were within the 10% FDR significance threshold of the stratified analyses (Supplementary Tables: 1, 2 & 3). Discussion In the largest study of the association between prenatal exposure to SARS-CoV-2 and neonatal inflammatory profiles to date, we found that SARS-CoV-2 exposure during pregnancy influences inflammatory marker levels in the neonate. Several interesting markers and marker groups were differentially regulated in exposed compared to unexposed infants. Our data further revealed a striking divergence in immune responses based on timing of prenatal exposure to SARS-CoV-2. Whereas early gestational exposure was associated with post-inflammatory repair signatures and markers indicative of lung injury and recovery, late gestational exposure was associated with ongoing inflammatory signaling, as detailed below. In neonates exposed to maternal SARS-CoV-2 infection during early gestation, we observed upregulation of pro-inflammatory proteins (EN-RAGE, TNFSF14, TRAIL, uPA), growth factors (HGF, FGF-19, TGF-α), and IL-18R, alongside reduced IL-18, suggesting enhanced IL-18/IL-18R1 binding. The discordant IL-18/IL-18R pattern points to NF-κB pathway activation, 42 a key driver of inflammation in adult COVID-19 43 and pediatric lung disease. 44 TNFSF14 (LIGHT) has also been implicated in acute respiratory distress (ARDS) in hospitalized adult COVID-19 cases 45 and virus-induced asthma exacerbation in children. 46 In adults, growth factors, typically induced following lung injury, are associated with COVID-19 severity and tissue repair processes. 47 – 50 Elevation of growth factors in neonates may indicate similar roles. Further, EN-RAGE, secreted by activated granulocytes, has also been linked to severe COVID-19 and impaired T-cell responses in adults. 51 – 54 Elevated TRAIL in children, despite often being reduced in severe adult COVID-19 55 , may be reflective of distinct prenatal immune activation. Collectively, these patterns indicate that prenatal exposure to SARS-CoV-2, particularly during early gestation, may prime neonates toward inflammatory and tissue-repair responses, potentially reflecting a post-infection recovery phase rather than ongoing immune activation. In contrast, neonates exposed to SARS-CoV-2 later in gestation exhibited a distinct immune profile characterized by upregulation of T-cell surface glycoproteins and Natural killer cell surface proteins (CD8A, CD5, CD44 and CD6), chemokines (CCL20, MCP-4, CXCL5 and CXCL6), interleukin IL-12B, and immune-checkpoint related proteins (CD40, CDCP1 and PD-L1). These markers are primarily linked to adaptive immunity, particularly CD8 + T-cells and TH1 responses, typically observed in asymptomatic or mild adult infections. 56 , 57 The chemokines elevated in this group mediate immune cell recruitment bridging innate and adaptive immune response. They also have been implicated in COVID-19 pathogenesis 57 and the cytokine storm. 57 – 61 Upregulation of PD-L1 and CD40, both associated with immune dysregulation and long COVID in children, further suggests altered immune signaling. 62 , 63 CDCP1, similarly elevated, has been linked to persistent post-infectious inflammation. 64 It should be noted that increased T-cell surface markers maybe representative of higher of T-cell subpopulations in our exposed compared to unexposed neonates. Taken together, this constellation of markers indicates ongoing immune activation in late-exposed neonates, resembling patterns of active infection or post-infection hyperinflammation observed in adults and in multisystem inflammatory syndrome in children. Expectedly, the observed patterns in the overall group align with the results described above for early and late gestation groups. For example, elevation of CCL25 in the overall group is in line with patterns in late gestation infection group, where we observe indication of ongoing immune activation. This is further supported by increased levels of immune markers like OPG, which has been associated with COVID-19 severity. 65 Similarly, upregulation of VEGFA and MMP-1 is consistent with the upregulation of growth factors and lung injury-related proteinases in the early infection group 66 , 67 While, most markers increased in the overall analysis showed elevation in either the early or the late gestation exposure group, some markers, including CCL25, MMP-1 OPG, SIRT2, and VEGFA, were not clearly elevated in the smaller, time-stratified analyses. This discrepancy may be due to the limited statistical power in the time-stratified analyses. Supporting this possibility is that several of these markers, namely VEGFA, TNFRSF9, MMP-1, and SIRT2 in the early group, and OPG and CCL25 in the late group, met the 10% FDR threshold within their respective stratum. These differences in neonatal immune profiles after early compared with late infection exposure may reflect the developmental stage of the fetal immune system at the time of exposure. The presence of adaptive immune markers (e.g., CD8A, IL-12B, PD-L1) in our late gestation-exposure group suggests prenatal immune priming. Given that vertical transmission of SARS-CoV-2 is rare, these immune signatures likely arise from maternal immune consequences in response to the SARS-CoV-2 infection or trans-placental transfer of inflammatory mediators rather than direct fetal infection. 28 , 29 Animal studies have shown that maternal inflammation can elevate offspring cytokine levels in the absence of viral transmission 27 , supporting this mechanism. Moreover, several upregulated proteins in our cohort, such as uPA and CD40, have been identified as biomarkers of long COVID in children, raising important questions about potential long-term immune and developmental consequences of prenatal exposure. When compared to previously published studies, our results provide new insights into the neonatal inflammatory signatures associated with prenatal SAR-CoV-2 exposure. One previous study reported elevated IL-10 levels in cord blood plasma from neonates born to mothers with recent or ongoing infection compared to those who had recently recovered, with no significant differences in IL-12, GM-CSF, IFN and TNF levels. 30 Beyond their smaller sample size and differences in exposure group classifications, the narrower and distinct cytokine profile reported in this study may reflect their exclusive focus on cord blood plasma, which captures soluble cytokines only. In contrast, our analysis of neonatal DBS, which contain both cellular and plasma components, enabled detection of cell-associated cytokines absent from plasma-only measurements. This distinction aligns with their cord blood mononuclear cell (CBMC) analyses, which identified alterations in particular T-cell subsets, patterns that resemble the elevated T-cell surface-related biomarkers we observed in the late gestation exposure group. Similar to the present analysis, another study investigated neonatal DBS and reported elevated cytokine levels (IL-22 and GM-CSF) prior to multiple comparison adjustment. Although both studies used DBS, methodological differences likely contributed to divergent findings. Specifically, Kim et al. quantified 42 cytokines/chemokines using the Luminex xMAP platform, which differs from Olink® in sensitivity and target overlap. 30 Additionally, differences in group balance between exposed and unexposed neonates (ours: 100 vs. 726; Kim et al. : 460 vs. 85) may have further contributed to the variability in study findings. Thus, our study extends existing evidence by integrating a larger sample, a broader proteomic panel, and cell-inclusive biospecimens to reveal timing-specific inflammatory signatures not captured in previous investigations. Several limitations should be acknowledged. The number of participants differed between exposure groups, with a smaller sample size in the early gestation group (n = 38). Moreover, while the majority of dried blood spot (DBS) samples were collected within 24 hours of birth, a small subset was obtained later (> 24hr to 124hrs). To address these potential sources of variation, all models were adjusted for maternal vaccination status and the child’s age at DBS collection. Conclusions Our study is among the first to examine a comprehensive panel of 92 neonatal inflammatory markers in a large cohort of children born to mothers infected with SARS-CoV-2 during pregnancy, and the first to assess how the timing of prenatal exposure influences inflammatory profiles at birth. Our results indicate that maternal SARS-CoV-2 infection during pregnancy impacts inflammatory biomarkers in the neonate, and that timing of infection plays a critical role in shaping these immune profiles. Thus, this study underscores the need for further research and long-term follow-up, to assess any potential future health consequences on the child. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review Board (IRB-20-03352 and IRB-22-00566) at the Icahn School of Medicine at Mount Sinai, reviewed by the US Centers for Disease Control and Prevention (CDC), and conducted in compliance with relevant federal laws, CDC policies, and the Declaration of Helsinki. Consent for publication All participants Availability of data and materials Data will be made available upon request. Competing interests The authors declare no competing interests. Funding This work was supported by the Simons Foundation (866027) and the National Institute of Child Health and Human Development (NICHD) (R01HD109613). Additional support was provided through the computational and data resources and staff expertise of Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai, made possible by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences and US National Institutes of Health [NIH/ NICHD R00HD097286]. The findings and conclusions in this report are those of the authors and do not necessarily represent the position of the funding agencies. Authors' contributions BA performed formal data analysis, applying statistical methods to interpret study data. She also led the writing, including main draft preparation, and was responsible for producing and editing the manuscript. She generated the figures, tables, and graphical representations of data and results. She participated in coordinating the research activity planning and execution. FM participated in writing, reviewing, editing, and providing critical feedback on manuscript drafts. FG participated in writing, reviewing, editing, and providing critical feedback on manuscript drafts. DD participated in writing, reviewing, editing, and contributed to quality control and normalization of experimental data generated by the Olink® assay. NS participated in writing, reviewing, editing, and contributed to performing investigations related to Olink®. JTM participated in writing, reviewing, editing, and contributed to performing investigations related to Olink®. SKS led the Olink® investigation, supervised Olink® experimental procedures and data acquisition, and participated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor. WL participated in writing, reviewing, editing, and contributed to study coordination, participant recruitment, and providing critical feedback on manuscript drafts. LDDW participated in writing, reviewing, editing, revising the manuscript, and ensuring rigor in scientific content and accuracy. VB participated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor, content, and accuracy. JC participated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor, content, and accuracy. CL contributed to formulating the overarching research goals, statistical analysis plans, and aims of this study. She supervised the statistical analysis and design/approach for the study. She also provided supervision, oversaw the research activity, and contributed to reviewing, editing, and critically revising the manuscript, ensuring conceptual integrity . ASR* is the corresponding author of this study and the PI of the Generation C cohort. ASR led the conceptualization, design, and framing of the study, contributing to formulating the overarching research goals and aims. She also provided supervision, guided the research, and oversaw progress. ASR contributed resources, facilitated data access, materials, or instrumentation. She also led funding acquisition, securing financial support for the project, and participated in writing, reviewing, editing, and heavily contributed to manuscript revisions. Acknowledgements We sincerely thank all Generation C study participants for their valuable time and contributions. Their involvement was essential to the success of this research. References WHO. Coronavirus (COVID-19) Dashboard: Cases World Health Organization; 2025 [cited 2025]. Available from: https://data.who.int/dashboards/covid19/cases?n=o Lorea, C. F., Pressman, K. & Schuler-Faccini, L. Infections during pregnancy: An ongoing threat. Semin Perinatol. 49 (4), 152075. 10.1016/j.semperi.2025.152075 (2025). Epub 20250406. Matsuo, K., Green, J. M., Herrman, S. A., Mandelbaum, R. S. & Ouzounian, J. G. 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Epub 20230427. Salomao, R. et al. Involvement of Matrix Metalloproteinases in COVID-19: Molecular Targets, Mechanisms, and Insights for Therapeutic Interventions. Biology (Basel). ;12(6). Epub 20230610. (2023). 10.3390/biology12060843 . PubMed PMID: 37372128; PMCID: PMC10295079. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx SUPPLEMENTALTABLES.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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09:49:37","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183458,"visible":true,"origin":"","legend":"","description":"","filename":"ff4aca0c8de440f08445aa47df1b70a61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/fdc8588c15c5b99d0d55ef36.xml"},{"id":98778647,"identity":"8478bcf5-3c04-4221-ad68-32cc6d327856","added_by":"auto","created_at":"2025-12-22 12:29:29","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204582,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/fc106642a39c74721906775f.html"},{"id":98779925,"identity":"18ea0950-83fe-4cc1-ae89-e1189166e4f4","added_by":"auto","created_at":"2025-12-22 12:30:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":386540,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the final sub-cohort selection.\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/ca01631ded37d4a399b8e13b.png"},{"id":98780766,"identity":"b89c3604-e07f-4647-a6c3-6b732c7507fb","added_by":"auto","created_at":"2025-12-22 12:31:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1107493,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots of differential inflammatory marker levels, in neonatal dried blood spots, using linear regression models adjusted for maternal age, race/ethnicity, vaccination status, pre-pregnancy BMI, preterm birth, delivery mode, infant sex, age at DBS collection and cardiometabolic pregnancy complications. \u003cstrong\u003eA.\u003c/strong\u003eOverall model of the exposed group (n=100) compared to the unexposed group (n=726). \u003cstrong\u003eB.\u003c/strong\u003e Model of the late infection group (n=62) compared to the unexposed group (n=726). \u003cstrong\u003eC. \u003c/strong\u003eModel of the early gestational exposure group (n=38) compared to the unexposed group (n=726). The x-axis is the log2-fold change (Log2FC) and y-axis is the –log10 P-value. The points highlighted in blue and maroon denote markers significant at FDR\u0026lt;0.05. Markers to the right of the vertical dashed line represent upregulation while markers to the left represent down regulation.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/dd694fd360c804c3006d5423.png"},{"id":98778638,"identity":"181d5207-e95b-40b6-8998-dd8ad66e5cd5","added_by":"auto","created_at":"2025-12-22 12:29:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372959,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of overlapping inflammatory markers significant at FDR 5% early gestation, late gestation and overall analysis.\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/899231a296e888ca0baab512.png"},{"id":99311142,"identity":"77ad8001-a689-4bd1-8885-6f46cd7b3d7e","added_by":"auto","created_at":"2025-12-31 16:13:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3144345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/7cb79e0c-59bb-44dd-ab65-467085bba355.pdf"},{"id":98759613,"identity":"98fc3184-df1d-45f9-b238-f99f1d67901b","added_by":"auto","created_at":"2025-12-22 09:49:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49742,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/d37fc85ec416556b62592115.docx"},{"id":98779744,"identity":"fe47d456-15c6-45a1-b062-0562d7b6c4e4","added_by":"auto","created_at":"2025-12-22 12:30:41","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54646,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALTABLES.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8105673/v1/dd82f2ff279bc210349233e2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers","fulltext":[{"header":"Background","content":"\u003cp\u003eSince the beginning of the COVID-19 pandemic, millions of pregnant individuals worldwide have been infected with the severe acute respiratory syndrome coronavirus-2 (SARS‑CoV‑2).\u003csup\u003e1\u003c/sup\u003e Evidence from historical pandemics suggests that prenatal exposure to certain infectious agents, such as rubella and influenza, is associated with an elevated risk of adverse health outcomes in children born during those periods.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Consistent with this pattern, gestational SARS-CoV-2 infection has been associated with adverse pregnancy and neonatal outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, although findings are heterogeneous.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The overall risk of adverse maternal and neonatal outcomes appears greatest among individuals with symptomatic and severe infections\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, with emerging evidence suggesting associations with neonatal complications, including respiratory distress.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e A recent meta-analysis further reported a substantially higher risk of respiratory distress syndrome (RDS) among neonates born to SARS-CoV-2 positive mothers\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; and some studies suggested neurodevelopmental delays in children prenatally exposed to SARS-CoV-2. However, the evidence is inconsistent.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUnlike other infectious agents, vertical transmission of SARS-CoV-2 occurs in only 1\u0026ndash;3% of cases and viral placental infection is rarely reportedly.\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Nevertheless, even in the absence of neonatal infection, maternal SARS-CoV-2 infection seems to impact both the placenta and the fetus through placental immune activation and maternal vascular malperfusions.\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e This is concerning because the development of the fetal immune system is orchestrated through carefully timed and sensitive stages starting from conception.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Any perturbation in this finely tuned developmental process may disrupt neonatal immune regulation and influence the child\u0026rsquo;s health outcomes down the line. Supporting this idea, animal studies have demonstrated that maternal inflammation alone, without direct viral transmission, can dysregulate cytokine levels in offspring.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Additionally, studies also show that maternal immune activation or the trans-placental transfer of inflammatory mediators can shape the fetal immune system.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eResearch exploring the association between prenatal exposures to SARS-CoV-2 and neonatal immune response are sparse. Due to ethical and practical considerations\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, cohorts that bank neonatal blood samples are rare and typically small. One approach to obtaining neonatal blood samples involves cord blood collection. As such, one study of 30 SARS-CoV-2 exposed and 15 unexposed mother-infant dyads measured multiple immune cell types and 13 cytokines in neonatal cord blood plasma, reporting elevated natural killer cells and regulatory T-cells in exposed neonates\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A more scalable and cost-effective method of obtaining and storing neonatal blood involves the use of dried blood spots (DBS). In one study, 42 cytokines/chemokines were measured in DBS from 460 neonates born to SARS-CoV-2 positive mothers and 85 neonates born to SARS-CoV-2 negative mothers, with IL-22 and GM-CSF showing significantly higher levels in exposed neonates before multiple testing correction.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e While these studies provide important early insights into the impact of prenatal SARS-CoV-2 exposure on the neonatal immune response, it remains challenging to form a comprehensive picture of the mechanisms at play, due to the variability in study design and findings. These discrepancies arise from the use of different biological matrices for measuring inflammatory markers such as DBS versus cord blood, as well as differences in maternal infection severity and definition of infection timing. Comparability is further limited by the minimal overlap in the specific inflammatory markers assessed in each study. Hence, additional studies are essential to strengthen the current body of evidence. In this study, we measured 92 inflammatory markers in neonatal DBS collected at birth in 100 children prenatally exposed to SARS-CoV-2 and 726 unexposed children. We explored associations between gestational SARS-CoV-2 exposure and neonatal inflammatory profiles, and further investigated whether these associations varied by timing of infection (early vs. late pregnancy).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eBetween April 2020 and February 2022, the prospective cohort study Generation C recruited pregnant individuals (\u0026ge;\u0026thinsp;18 years) receiving obstetric care within the Mount Sinai Health System (MSHS). This cohort is described in detail elsewhere.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e For the present analysis, we examined a sub-cohort of 833 mother-infant dyads from the Generation C study. This sub-cohort comprised all mother-child pairs with known maternal SARS-CoV-2 infection status during pregnancy and consent for neonatal dried blood spot (DBS) retrieval. Five sibling pairs were included in this sub-cohort. For the analysis, if both siblings were unexposed to SARS-CoV-2 during gestation, one sibling per pair was excluded at random \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In cases where one of the siblings was exposed to SARS-CoV-2 during pregnancy, the unexposed sibling was excluded. All participants in this study provided informed consent. The study was approved by the Institutional Review Board (IRB-20-03352 and IRB-22-00566) at the Icahn School of Medicine at Mount Sinai, reviewed by the US Centers for Disease Control and Prevention (CDC), and conducted in compliance with relevant federal laws, CDC policies, and the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSARS-CoV-2 infection status and timing\u003c/h3\u003e\n\u003cp\u003eIn this study, maternal blood specimens were collected as part of routine clinical blood draws. A participant was considered to have evidence of SARS-CoV-2 if (1) there was a positive RT-PCR report or (2) there was a diagnosis by a medical health official reported in either the electronic medical record (EMR) or self-report questionnaire and (3) there was anti-S IgG antibody presence AND one of the following: a) anti-S IgG antibody before an individual\u0026rsquo;s first COVID-19 vaccination, b) anti-S IgG antibody before the COVID-19 vaccination rollout in NYC (Dec 14, 2020), or c) anti-spike IgG antibody presence and anti-N IgG antibody.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e For the first two scenarios, the date of diagnoses or report was considered the date of evidence of SARS-CoV-2 positivity while for the third, the date of sample collection was considered the date of positivity. If any of these dates were during pregnancy, a participant was considered to have evidence of SARS-CoV-2 exposure during pregnancy. To maximize power, we defined timing of infection as being infected early in gestation (\u0026lt;\u0026thinsp;20 weeks) and late in gestation (\u0026ge;\u0026thinsp;20 weeks), referred to below as early infection and late infection, respectively. Participants were considered unexposed if there was no evidence of SARS-CoV-2 positivity using any of the criteria above.\u003c/p\u003e\n\u003ch3\u003eDried Blood Spot (DBS) collection and processing\u003c/h3\u003e\n\u003cp\u003eUnder the Newborn screening program (NBS), United States mandates collection of neonatal DBS at birth with the aim to screen for serious but treatable congenital diseases.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Neonatal DBS were obtained from New York State Department of Health\u0026rsquo;s Newborn Screening Program (NYSDOH NBS). NYSDOH NBS collects five small blood spots by pricking the newborn\u0026rsquo;s heel, using a sterile lancet, within ~\u0026thinsp;24 to 36 hours of delivery. The blood spots are collected on standard Whatman 903 contaminant free specimen cards. The spots are dried for at least three hours on a flat, clean, non-absorbent surface, away from direct heat and sunlight. Then residual DBS not used for clinical purposes are stored at room temperature for up to 27 years.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e We received six 3mm punches from each specimen card, one of which was eluted and incubated at room temperature for an hour, in 20ul of buffer, consisting of 1X PBS, 0.05% TWEEN 20, and 1X protease inhibitors. The eluted blood was used for further analysis.\u003c/p\u003e\n\u003ch3\u003eOlink® Target 96 Inflammation panel\u003c/h3\u003e\n\u003cp\u003eNeonatal inflammatory cytokines were quantified using the Olink\u0026reg; Target 96 Inflammation Proteomics platform (Olink\u0026reg; Bioscience, Uppsala, Sweden), with analyses conducted by the Human Immune Monitoring Center (HIMC) at Mount Sinai. Olink\u0026reg; has become a widely adopted platform for large-scale proteomic analysis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and has been leveraged in multiple DBS-based studies to date.\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e This Olink\u0026reg; Target 96 Inflammation panel employs a highly sensitive and specific proximity extension assay to quantitatively evaluate relative changes in the expression of 92 inflammation-related proteins.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Briefly, pairs of oligonucleotide-conjugated antibodies, each recognizing a distinct target protein, were incubated with the samples. Upon concurrent binding to proximal epitopes, the antibody-bound oligonucleotides hybridized to form a double-stranded DNA template, which is subsequently amplified by PCR. The amplified DNA is then transferred to a microfluidic chip (Fluidigm BioMark HD instrument) and quantified using real-time quantitative PCR (qPCR). The raw data from the qPCR readout produces Cycle threshold (Ct) values, representing the number of amplification cycles required for the signal to reach a predetermined threshold. A lower Ct value indicates a higher initial concentration of the target protein. These Ct values are converted into Normalized Protein Expression (NPX) values, an arbitrary, relative unit on a log2 scale through a multistep process: 1) Normalization using Extension Controls: For each protein assay, the sample\u0026rsquo;s Ct value is subtracted from its corresponding Extension Control Ct value to yield ΔCt; 2) Inter-plate Normalization: The median ΔCt value for the Plate Control wells is subtracted from each sample\u0026rsquo;s ΔCt, resulting in a ΔΔCt value; 3) Final NPX Calculation: A pre-determined constant value (referred to as a correction factor) is subtracted from the ΔΔCt value to invert the scale so that a higher NPX value corresponds to a higher protein concentration, making the data more intuitive for biological interpretation. NPX values are on a log2 scale, meaning that a difference of one NPX represents a doubling of protein concentration. Because NPX values represent relative, sample-specific quantification, they are intended for group comparisons.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eClinical and sociodemographic characteristics of interest were collected through EMR review and questionnaires, including maternal age (in years), maternal race/ethnicity (Asian, Black, Hispanic, White, Other), COVID-19 vaccination status (vaccinated, unvaccinated), pre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), cardiometabolic pregnancy complications (yes, no), preterm birth (yes, no), delivery mode (C-section, vaginal birth), child sex (male, female), age at DBS collection (in hours). COVID-19 vaccination status was defined as vaccinated if the first vaccine dose was administered before or during pregnancy. All mothers who were never vaccinated or those who were vaccinated after pregnancy were considered unvaccinated, as postnatal vaccination is unlikely to impact neonatal immune activation. Cardiometabolic pregnancy complications were defined as diagnoses of pre-eclampsia, gestational hypertension and/or gestational diabetes mellitus (GDM). COVID-19 symptom severity was determined using data from self-reported questionnaires and electronic medical records documenting concurrent symptom patterns, including fever or chills, cough, shortness of breath, fatigue, muscle or body aches, headache, loss of taste or smell, sore throat, congestion, nausea or vomiting, and diarrhea. Participants were categorized as having asymptomatic, mild, moderate, severe, or critical illness according to WHO COVID-19 clinical management guidelines\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, based on symptom presentation, respiratory status, and clinical findings. Severity classification was applied only when symptoms were reported in the same trimester as the documented infection.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo explore summary statistics of continuous and categorical variables, we used means, frequencies and ranges. We conducted bivariate analyses on sociodemographic and pregnancy outcome variables, using Chi-square test and Wilcoxon signed-rank, to compare how these variables differ between the SARS-CoV-2 exposed and unexposed groups. Correlations between NPX levels of the inflammatory markers were explored using Spearman\u0026rsquo;s rank-order correlation. To evaluate any possible effects of technical (e.g. assay batch) or biological (e.g. child sex) covariates on the inflammatory markers (NPX), we used principal component analysis. Any samples that were more than four standard deviations away from the mean on at least one principal component were excluded from the analysis. We excluded two mother-child pairs because their neonatal inflammatory cytokine measurements were identified as severe technical outliers \u003cb\u003e(Supplementary Fig.\u0026nbsp;1).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDifferential analyses on inflammatory marker levels between SARS-CoV-2 exposed and unexposed groups were conducted using the Limma R package.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e This package uses an empirical Bayes method to fit linear regression models with moderated standard errors for each inflammatory marker as a continuous outcome and SARS-CoV-2 exposure status as dichotomous predictor variable. It also uses the Benjamini-Hochberg (BH) method to control the false discovery rate (FDR).\u003c/p\u003e \u003cp\u003eAdditionally, to explore the impact of infection timing during pregnancy on neonatal inflammatory markers, we conducted a stratified sensitivity analysis by time of SARS-CoV-2 exposure during gestation. We stratified the overall SARS-CoV-2 exposed group into two groups, those exposed in early gestation (\u0026lt;\u0026thinsp;20 weeks) and those exposed in late gestation (\u0026ge;\u0026thinsp;20 weeks). We compared each group\u0026rsquo;s neonatal inflammatory marker levels independently to those of the unexposed group using Limma. Volcano plots were used to visualize log2-fold changes (Log2FC) in inflammatory marker levels for both overall and stratified analyses.\u003c/p\u003e \u003cp\u003eAll final linear regression models were adjusted for maternal age, race/ethnicity, COVID-19 vaccination status, pre-pregnancy BMI, cardiometabolic pregnancy complications, preterm birth, delivery mode, child sex and age at DBS collection. All adjustment variables were categorical except maternal age, pre-pregnancy BMI and age at DBS collection. Statistical significance was set at \u0026le;\u0026thinsp;0.05 for nominal p-values and at 5% for false discovery rate (FDR). All analyses were conducted using R statistical computing software version 4.3.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCohort Characteristics:\u003c/h2\u003e \u003cp\u003eAfter quality control and pre-processing, 826 mother-infant dyads were included in the study \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the characteristics of the 826 mother-infant dyads, stratified by prenatal SARS-CoV-2 exposure. In this sample, 100 dyads had evidence of SARS-CoV-2 infection during pregnancy. Of these, 38 were exposed during early gestation (\u0026lt;\u0026thinsp;20 weeks) and 62 were exposed during late gestation (\u0026ge;\u0026thinsp;20 weeks). SARS-CoV-2 severity was classified as asymptomatic, mild, or moderate in 16%, 81%, and 3% of the exposed participants, respectively. In both the exposed and unexposed groups, mean maternal age was 33 years. Mean pre-pregnancy BMI was 27.5 kg/m\u003csup\u003e2\u003c/sup\u003e (Standard Deviation (SD)\u0026thinsp;=\u0026thinsp;6.6) and 26.7 kg/m\u003csup\u003e2\u003c/sup\u003e (SD\u0026thinsp;=\u0026thinsp;7.0) in the exposed and unexposed groups, respectively. Frequency of C-sections was similar between the exposed and unexposed group with vaginal birth being more prevalent in both. In the exposed group, 58.0% of the infants were female, compared to 50.0% of infants in the unexposed group. Mean gestational age at delivery in both groups was 39 weeks, with 12.0% (n\u0026thinsp;=\u0026thinsp;12) and 10.1% (n\u0026thinsp;=\u0026thinsp;73) preterm births in the exposed and unexposed groups, respectively. In the exposed group, 26.0% of the mothers had cardiometabolic pregnancy complications compared to 29.1% in the unexposed group. None of the aforementioned demographic and clinical variables were significantly different between the two study groups. However, we observed that exposed mothers were more likely to be Hispanic or Black compared to the unexposed group (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.04)\u003c/em\u003e, on par with the larger cohort.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, 33% of the exposed group was vaccinated before or during pregnancy, while 22.2% of the unexposed group was vaccinated before or during pregnancy (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.01).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation Characteristics mother child dyads (n\u0026thinsp;=\u0026thinsp;826).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed to SARS-CoV-2\u003c/p\u003e \u003cp\u003eduring pregnancy\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnexposed to SARS-CoV2\u003c/p\u003e \u003cp\u003eduring pregnancy\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;726)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age at delivery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3 (4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.2 (5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.0 [21.0, 42.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.0 [18.0, 50.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild\u0026rsquo;s Gestational age at delivery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.6 (1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.0 [32.9, 42.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.1 [24.9, 42.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreterm birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDelivery mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardio-metabolic pregnancy complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (74.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild Age at DBS collection (hours)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0 [1.00, 124]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0 [1.00, 110]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-Pregnancy BMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.7 (6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7 (7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1 [17.4, 45.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.1 [14.2, 61.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOVID-19 vaccination status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnvaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSARS-CoV-2 exposure timing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly in gestation(\u0026lt;\u0026thinsp;20 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate in gestation (\u0026ge;\u0026thinsp;20 weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SARS-CoV-2 infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e726 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSARS-CoV-2 symptom severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymptomatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild Illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SARS-CoV-2 infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e726 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*The p-values represented here are calculated through Chi-square test for categorical variables and Wilcoxon signed-rank test for continuous variables with nominal significance level set to p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e*SD\u0026thinsp;=\u0026thinsp;Standard Deviation; Min\u0026thinsp;=\u0026thinsp;Minimum; Max\u0026thinsp;=\u0026thinsp;Maximum;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Inflammatory marker expression analysis\u003c/h2\u003e \u003cp\u003eWe conducted differential protein expression analyses by SARS-CoV-2 status using linear regression models comparing inflammatory marker levels of 100 exposed infants to 726 unexposed infants. In the exposed group, 22 neonatal inflammatory marker levels were increased (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05) compared to the unexposed group. These include T-cell surface glycoproteins (CD5 and CD8A), T-cell differentiation antigen (CD6), Tumor necrosis factor receptor and ligand superfamily members (TNFSF14, TNFSF9 and CD40), TNF-related apoptosis-inducing ligand (TRAIL), C-C motif chemokines (CCL20, CCL25, MCP-4), C-X-C motif chemokines (CXCL6, CXCL5 and CXCL25), growth factors (TGF-α, HGF, VGFA) and several other cytokines, chemokines and growth factors (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA for details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially Regulated Inflammatory Markers in Overall Analysis, significant at 5% FDR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory Markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory Marker Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell surface glycoprotein CD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell surface glycoprotein CD8 alpha chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell differentiation antigen CD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor necrosis factor ligand superfamily member 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgrammed cell death ligand 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural killer cell receptor 2B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD40L receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrokinase-type plasminogen activator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFRSF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor necrosis factor receptor superfamily member 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-18R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-18 receptor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtransforming growth factor alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF-related apoptosis-inducing ligand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-C motif chemokine 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-X-C motif chemokine 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteoprotegerin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatocyte growth factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAD-dependent protein deacetylase sirtuin-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-C motif chemokine 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-X-C motif chemokine 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix metalloproteinase-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVascular endothelial growth factor A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCP-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonocyte chemotactic protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used linear regression models to compare inflammatory marker levels of neonates exposed in early gestation (n\u0026thinsp;=\u0026thinsp;38) and unexposed neonates (n\u0026thinsp;=\u0026thinsp;726). Early infection analysis showed eight markers that were elevated (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026lt;0.05\u003c/em\u003e) in the early infection group compared to the unexposed group, namely TGF-α, HGF, FGF-19, TNFSF14, TRAIL, RAGE-binding protein EN-RAGE, uPA and IL18R1. In contrast, interleukin-18 (IL-18) levels were lower (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05\u003cem\u003e)\u003c/em\u003e in the early infection group compared to the unexposed group \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially Regulated Inflammatory Markers in Early Infection model, significant at 5% FDR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory Markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory Marker Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor necrosis factor ligand superfamily member 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGF-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtransforming growth factor alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatocyte growth factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEN-RAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAGE-binding protein (EN-RAGE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblast growth factor 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-18R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-18 receptor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNF-related apoptosis-inducing ligand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrokinase-type plasminogen activator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparing inflammatory marker levels of neonates exposed to SARS-CoV-2 during late gestation (n\u0026thinsp;=\u0026thinsp;62) and unexposed neonates (n\u0026thinsp;=\u0026thinsp;726), twelve inflammatory markers showed differential expression (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.05). Namely, CD5, CD8A, CD6 and CD244, C-C and C-X-C motif chemokines (CXCL5 CXCL6, MCP-4, and CCL20), as well as CD40, PDL-1, CUB domain-containing protein 1 (CDCP1) and interleukin-12B (IL-12B) were elevated in the late infection group compared to the unexposed group \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially Regulated Inflammatory Markers in Late Infection model, significant at 5%FDR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory Markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory Marker Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell surface glycoprotein CD8 alpha chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell surface glycoprotein CD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT-cell differentiation antigen CD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD40L receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCP-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonocyte chemotactic protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-C motif chemokine 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural killer cell receptor 2B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgrammed cell death ligand 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-X-C motif chemokine 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDCP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUB domain-containing protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-12B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-12 subunit beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-X-C motif chemokine 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a Venn diagram illustrating the overlap in significant inflammation markers identified in the overall analysis and the stratified analyses. In general, the top significant inflammatory markers reaching 5% FDR in the overall analysis were also present in the early and late gestation analyses. Some markers in the overall analysis that do not overlap in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were within the 10% FDR significance threshold of the stratified analyses \u003cb\u003e(Supplementary Tables: 1, 2 \u0026amp; 3).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the largest study of the association between prenatal exposure to SARS-CoV-2 and neonatal inflammatory profiles to date, we found that SARS-CoV-2 exposure during pregnancy influences inflammatory marker levels in the neonate. Several interesting markers and marker groups were differentially regulated in exposed compared to unexposed infants. Our data further revealed a striking divergence in immune responses based on timing of prenatal exposure to SARS-CoV-2. Whereas early gestational exposure was associated with post-inflammatory repair signatures and markers indicative of lung injury and recovery, late gestational exposure was associated with ongoing inflammatory signaling, as detailed below.\u003c/p\u003e \u003cp\u003eIn neonates exposed to maternal SARS-CoV-2 infection during early gestation, we observed upregulation of pro-inflammatory proteins (EN-RAGE, TNFSF14, TRAIL, uPA), growth factors (HGF, FGF-19, TGF-α), and IL-18R, alongside reduced IL-18, suggesting enhanced IL-18/IL-18R1 binding. The discordant IL-18/IL-18R pattern points to NF-κB pathway activation,\u003csup\u003e42\u003c/sup\u003e a key driver of inflammation in adult COVID-19\u003csup\u003e43\u003c/sup\u003e and pediatric lung disease.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e TNFSF14 (LIGHT) has also been implicated in acute respiratory distress (ARDS) in hospitalized adult COVID-19 cases\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and virus-induced asthma exacerbation in children.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e In adults, growth factors, typically induced following lung injury, are associated with COVID-19 severity and tissue repair processes.\u003csup\u003e\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Elevation of growth factors in neonates may indicate similar roles. Further, EN-RAGE, secreted by activated granulocytes, has also been linked to severe COVID-19 and impaired T-cell responses in adults.\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e Elevated TRAIL in children, despite often being reduced in severe adult COVID-19\u003csup\u003e55\u003c/sup\u003e, may be reflective of distinct prenatal immune activation. Collectively, these patterns indicate that prenatal exposure to SARS-CoV-2, particularly during early gestation, may prime neonates toward inflammatory and tissue-repair responses, potentially reflecting a post-infection recovery phase rather than ongoing immune activation.\u003c/p\u003e \u003cp\u003eIn contrast, neonates exposed to SARS-CoV-2 later in gestation exhibited a distinct immune profile characterized by upregulation of T-cell surface glycoproteins and Natural killer cell surface proteins (CD8A, CD5, CD44 and CD6), chemokines (CCL20, MCP-4, CXCL5 and CXCL6), interleukin IL-12B, and immune-checkpoint related proteins (CD40, CDCP1 and PD-L1). These markers are primarily linked to adaptive immunity, particularly CD8\u0026thinsp;+\u0026thinsp;T-cells and TH1 responses, typically observed in asymptomatic or mild adult infections.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e The chemokines elevated in this group mediate immune cell recruitment bridging innate and adaptive immune response. They also have been implicated in COVID-19 pathogenesis\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and the cytokine storm.\u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59 CR60\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e Upregulation of PD-L1 and CD40, both associated with immune dysregulation and long COVID in children, further suggests altered immune signaling.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e CDCP1, similarly elevated, has been linked to persistent post-infectious inflammation.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e It should be noted that increased T-cell surface markers maybe representative of higher of T-cell subpopulations in our exposed compared to unexposed neonates. Taken together, this constellation of markers indicates ongoing immune activation in late-exposed neonates, resembling patterns of active infection or post-infection hyperinflammation observed in adults and in multisystem inflammatory syndrome in children.\u003c/p\u003e \u003cp\u003eExpectedly, the observed patterns in the overall group align with the results described above for early and late gestation groups. For example, elevation of CCL25 in the overall group is in line with patterns in late gestation infection group, where we observe indication of ongoing immune activation. This is further supported by increased levels of immune markers like OPG, which has been associated with COVID-19 severity.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e Similarly, upregulation of VEGFA and MMP-1 is consistent with the upregulation of growth factors and lung injury-related proteinases in the early infection group\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e While, most markers increased in the overall analysis showed elevation in either the early or the late gestation exposure group, some markers, including CCL25, MMP-1 OPG, SIRT2, and VEGFA, were not clearly elevated in the smaller, time-stratified analyses. This discrepancy may be due to the limited statistical power in the time-stratified analyses. Supporting this possibility is that several of these markers, namely VEGFA, TNFRSF9, MMP-1, and SIRT2 in the early group, and OPG and CCL25 in the late group, met the 10% FDR threshold within their respective stratum.\u003c/p\u003e \u003cp\u003eThese differences in neonatal immune profiles after early compared with late infection exposure may reflect the developmental stage of the fetal immune system at the time of exposure. The presence of adaptive immune markers (e.g., CD8A, IL-12B, PD-L1) in our late gestation-exposure group suggests prenatal immune priming. Given that vertical transmission of SARS-CoV-2 is rare, these immune signatures likely arise from maternal immune consequences in response to the SARS-CoV-2 infection or trans-placental transfer of inflammatory mediators rather than direct fetal infection.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Animal studies have shown that maternal inflammation can elevate offspring cytokine levels in the absence of viral transmission \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, supporting this mechanism. Moreover, several upregulated proteins in our cohort, such as uPA and CD40, have been identified as biomarkers of long COVID in children, raising important questions about potential long-term immune and developmental consequences of prenatal exposure.\u003c/p\u003e \u003cp\u003eWhen compared to previously published studies, our results provide new insights into the neonatal inflammatory signatures associated with prenatal SAR-CoV-2 exposure. One previous study reported elevated IL-10 levels in cord blood plasma from neonates born to mothers with recent or ongoing infection compared to those who had recently recovered, with no significant differences in IL-12, GM-CSF, IFN and TNF levels.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Beyond their smaller sample size and differences in exposure group classifications, the narrower and distinct cytokine profile reported in this study may reflect their exclusive focus on cord blood plasma, which captures soluble cytokines only. In contrast, our analysis of neonatal DBS, which contain both cellular and plasma components, enabled detection of cell-associated cytokines absent from plasma-only measurements. This distinction aligns with their cord blood mononuclear cell (CBMC) analyses, which identified alterations in particular T-cell subsets, patterns that resemble the elevated T-cell surface-related biomarkers we observed in the late gestation exposure group. Similar to the present analysis, another study investigated neonatal DBS and reported elevated cytokine levels (IL-22 and GM-CSF) prior to multiple comparison adjustment. Although both studies used DBS, methodological differences likely contributed to divergent findings. Specifically, Kim et al. quantified 42 cytokines/chemokines using the Luminex xMAP platform, which differs from Olink\u0026reg; in sensitivity and target overlap.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Additionally, differences in group balance between exposed and unexposed neonates (ours: 100 vs. 726; Kim \u003cem\u003eet al.\u003c/em\u003e: 460 vs. 85) may have further contributed to the variability in study findings. Thus, our study extends existing evidence by integrating a larger sample, a broader proteomic panel, and cell-inclusive biospecimens to reveal timing-specific inflammatory signatures not captured in previous investigations.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The number of participants differed between exposure groups, with a smaller sample size in the early gestation group (n\u0026thinsp;=\u0026thinsp;38). Moreover, while the majority of dried blood spot (DBS) samples were collected within 24 hours of birth, a small subset was obtained later (\u0026gt;\u0026thinsp;24hr to 124hrs). To address these potential sources of variation, all models were adjusted for maternal vaccination status and the child\u0026rsquo;s age at DBS collection.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study is among the first to examine a comprehensive panel of 92 neonatal inflammatory markers in a large cohort of children born to mothers infected with SARS-CoV-2 during pregnancy, and the first to assess how the timing of prenatal exposure influences inflammatory profiles at birth. Our results indicate that maternal SARS-CoV-2 infection during pregnancy impacts inflammatory biomarkers in the neonate, and that timing of infection plays a critical role in shaping these immune profiles. Thus, this study underscores the need for further research and long-term follow-up, to assess any potential future health consequences on the child.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board (IRB-20-03352 and IRB-22-00566) at the Icahn School of Medicine at Mount Sinai, reviewed by the US Centers for Disease Control and Prevention (CDC), and conducted in compliance with relevant federal laws, CDC policies, and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Simons Foundation (866027) and the National Institute of Child Health and Human Development (NICHD) (R01HD109613). Additional support was provided through the computational and data resources and staff expertise of Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai, made possible by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences and US National Institutes of Health [NIH/ NICHD R00HD097286]. The findings and conclusions in this report are those of the authors and do not necessarily represent the position of the funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBA\u0026nbsp;\u003c/strong\u003eperformed formal data analysis, applying statistical methods to interpret study data. She also led the writing, including main draft preparation, and was responsible for producing and editing the manuscript. She generated the figures, tables, and graphical representations of data and results. She participated in coordinating the research activity planning and execution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFM\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and providing critical feedback on manuscript drafts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFG\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and providing critical feedback on manuscript drafts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDD\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and contributed to quality control and normalization of experimental data generated by the Olink\u0026reg; assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNS\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and contributed to performing investigations related to Olink\u0026reg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJTM\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and contributed to performing investigations related to Olink\u0026reg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSKS\u0026nbsp;\u003c/strong\u003eled the Olink\u0026reg; investigation, supervised Olink\u0026reg; experimental procedures and data acquisition, and participated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWL\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, and contributed to study coordination, participant recruitment, and providing critical feedback on manuscript drafts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDDW\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, revising the manuscript, and ensuring rigor in scientific content and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVB\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor, content, and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJC\u0026nbsp;\u003c/strong\u003eparticipated in writing, reviewing, editing, revising the manuscript, and ensuring scientific rigor, content, and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCL\u0026nbsp;\u003c/strong\u003econtributed to formulating the overarching research goals, statistical analysis plans, and aims of this study. She supervised the statistical analysis and design/approach for the study. She also provided supervision, oversaw the research activity, and contributed to reviewing, editing, and critically revising the manuscript, ensuring conceptual integrity\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASR*\u0026nbsp;\u003c/strong\u003eis the corresponding author of this study and the PI of the Generation C cohort. ASR led the conceptualization, design, and framing of the study, contributing to formulating the overarching research goals and aims. She also provided supervision, guided the research, and oversaw progress. ASR contributed resources, facilitated data access, materials, or instrumentation. She also led funding acquisition, securing financial support for the project, and participated in writing, reviewing, editing, and heavily contributed to manuscript revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all Generation C study participants for their valuable time and contributions. Their involvement was essential to the success of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Coronavirus (COVID-19) Dashboard: Cases World Health Organization; 2025 [cited 2025]. 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PubMed PMID: 37372128; PMCID: PMC10295079.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, neonatal inflammation, prenatal infection, COVID-19, Olink®, Cytokines","lastPublishedDoi":"10.21203/rs.3.rs-8105673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8105673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Since the beginning of the pandemic, millions of pregnant women have been exposed to SARS-CoV-2, raising concerns about maternal and fetal sequelae. Yet, the impact of SARS-CoV-2 on the child’s immune response remains largely unexplored. Herein, we leverage 833 mother-infant dyads from a New York City-based pregnancy cohort, to explore prospective associations between maternal gestational SARS-CoV-2 infection and inflammatory biomarkers in newborns. Of the mothers, 100 were infected with SARS-CoV-2 during pregnancy, as confirmed through self-report, antibody and/or PCR test results. We obtained 92 inflammatory biomarker levels in neonatal dried blood spots (DBS) using the Olink® Target 96 Inflammation panel. Empirical Bayes method was used to fit linear regression models to assess the effects of maternal infection during pregnancy on neonatal inflammatory markers at birth. We also conducted stratified analyses by timing of infection in early (\u0026lt;20 weeks) versus late (≥20 weeks) gestation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Higher levels of 22 inflammatory biomarkers (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), including CD5, TNFSF14, CD8a, TGF-α, and CD244, were observed in neonates prenatally exposed to SARS-CoV-2 compared to unexposed neonates (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026lt;0.05\u003c/em\u003e). Early-gestation infection was associated with increased levels of eight inflammatory biomarker, including TNSF14, TGF-α, EN-RAGE, and decreased IL-18 levels, while late-gestation infection was linked to elevations in 12 biomarkers, including CD5, CD6, PD-L1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur results indicate that maternal SARS-CoV-2 infection during pregnancy impacts inflammatory biomarkers in newborns, with the timing of infection playing a critical role in shaping these immune profiles. Thus, this study underscores the need for further research and long-term follow-up to assess any potential future health consequences for the child.\u003c/p\u003e","manuscriptTitle":"Impact of Gestational Maternal SARS-CoV-2 Infection on Neonatal Inflammatory Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:49:31","doi":"10.21203/rs.3.rs-8105673/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c62a082-36fd-4f7a-8b17-563d99aa2885","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59782446,"name":"Health sciences/Biomarkers"},{"id":59782447,"name":"Health sciences/Diseases"},{"id":59782448,"name":"Biological sciences/Immunology"},{"id":59782449,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-12-24T12:40:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:49:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8105673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8105673","identity":"rs-8105673","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}