Pro-inflammatory IL-6/IL-10 imbalance in SARS-CoV-2-exposed pregnancies and early language development

Pro-inflammatory IL-6/IL-10 imbalance in SARS-CoV-2-exposed pregnancies and early language development | 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 Research Article Pro-inflammatory IL-6/IL-10 imbalance in SARS-CoV-2-exposed pregnancies and early language development Alexandre Díaz-Pons, Sergio Castaño Castaño, Carlos Martínez Asensi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9651346/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 : Maternal immune activation (MIA) triggered by prenatal infections, including SARS-CoV-2, is a potential risk factor for neurodevelopmental alterations, with cytokines mediating the associated inflammatory responses. However, the specific impact of SARS-CoV-2–related MIA on early language acquisition remains unclear. Methods : At 18 months, expressive language was assessed using an online version of the MacArthur–Bates Communicative Development Inventories (CDI) in 87 mother–infant dyads from the COGESTCOV-19 cohort (51 SARS-CoV-2–exposed, 36 controls). Maternal IL-6/IL-10 ratios were contrasted with CDI percentiles to probe immune–language associations. Analyses were adjusted for key sociodemographic and perinatal covariates, and sex-stratified models were additionally explored. Results : Across MacArthur CDI vocabulary outcomes did not differ between SARS-CoV-2–exposed and control infants, and sex-stratified differences were small and not sustained after adjustment. Higher maternal IL-6/IL-10 ratios were associated with lower Difficult Verbs percentiles among exposed infants (p ≈ 0.03). Difficult Verbs showed modest correlations with other CDI domains (r ≈ 0.04–0.39). Among exposed females, higher maternal IL-6/IL-10 ratios were also associated with lower Vocalizations percentiles (p ≈ 0.02). Conclusions : Mild-to-moderate SARS-CoV-2–related MIA was not associated with global early-language impairment at 18 months. Instead, immune–language links were limited to a pro-inflammatory IL-6/IL-10 imbalance and confined to specific language domains. Female-specific patterns further suggested sex-dependent viral pathways in SARS-CoV-2–related MIA effects. These findings support the need for larger longitudinal multimodal studies to clarify whether these associations are transient, compensatory, or early markers of later cognitive trajectories in pandemic-exposed children. Immunology Developmental Neuroscience Audiology & Speech-Language Pathology Maternal Immune Activation (MIA) SARS-CoV-2 Neurodevelopment Language Acquisition MacArthur-Bates Communicative Development Inventory (CDI) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights At 18 months, language percentiles on MacArthur CDI subdomains were similar—and often low—in SARS-CoV-2–exposed and control infants, consistent with broad pandemic-related influences beyond maternal immune activation. IL-6, IL-10 and IL-6/IL-10 ratio concentrations did not reach statistical significance between cases and controls. In exposed pregnancies, pro- but not anti-inflammatory imbalance (higher IL-6/IL-10) predicted lower Difficult-Verbs percentiles: subscale’s relative independence from other CDI domains supports a selective, viral–linked inflammatory effect, rather than non-specific immune tone or a global language deficit. Among exposed females, higher IL-6/IL-10 predicted poorer Vocalization percentiles, though limited sample size constrains inference. Longer follow-up, larger samples, and broader immune and genetic profiling are required to determine the durability and mechanistic scope of these immune–language associations. 1. Introduction Prenatal exposure to infection has long been implicated in the etiology of neurodevelopmental disorders (NDD). Ongoing epidemiological, clinical, and preclinical evidence has increasingly supported the hypothesis that these associations are mediated not solely by the pathogen itself, but by the maternal immune response it elicits. This framework—formally articulated by Gilmore and Jarskog (1997)—proposed maternal immune activation (MIA) as a cytokine-mediated pathway through which prenatal inflammatory signals may disrupt fetal brain development and confer vulnerability to later neuropsychiatric outcomes, including autism spectrum disorder (ASD) and schizophrenia spectrum disorders (SSD)(1–7). Several viral infections, including influenza, cytomegalovirus, and Zika virus, have demonstrated clear and, in some cases, severe neurodevelopmental consequences when occurring during pregnancy. In contrast, the neurodevelopmental impact of maternal infection with SARS-CoV-2 remains incompletely understood(8–10). This uncertainty reflects both the biological heterogeneity of immune responses to SARS-CoV-2 and the relatively recent onset of the COVID-19 pandemic, which has limited the availability of long-term developmental follow-up data. The plausibility of SARS-CoV-2–related MIA influencing fetal neurodevelopment is supported by extensive evidence implicating cytokine signaling in neuronal migration, synaptogenesis, and circuit formation(11). Although elevated interleukin-6 (IL-6) has been linked to altered connectivity and atypical developmental trajectories, neurodevelopmental risk is unlikely to be driven by IL-6—or any single cytokine—in isolation, particularly in the context of predominantly mild-to-moderate infection(12,13). Rather, risk appears to emerge from the balance between pro- and anti-inflammatory signaling(14,15), with interleukin-10 (IL-10) playing a key regulatory role in constraining inflammation and maintaining immune homeostasis(14,15). Accordingly, even modest inflammatory exposures may become biologically consequential when anti-inflammatory capacity is insufficient. Reflecting this perspective, recent work has emphasized the limitations of single-biomarker approaches and the value of balance- or composite-based indices that better capture the integrated gestational immune milieu(16,17). Within this framework, the IL-6/IL-10 ratio provides a parsimonious, mechanistically interpretable index of cytokine balance, that has been used to characterize SARS-CoV-2 severity, and may index immune states relevant to fetal neurodevelopment(12–14). However, biomarker-based definitions may preferentially reflect chronic or low-grade inflammation rather than acute pathogen-specific responses, a limitation heightened in viral, time-dependent immune processes where cytokine kinetics make inference highly contingent on biospecimen timing, a requirement difficult to meet in most pregnancy cohorts(18). The developmental impact of SARS-CoV-2–related MIA therefore remains uncertain and likely moderated by inter-individual susceptibility, including sex: males appear more vulnerable to prenatal inflammatory perturbations, whereas effects in females may be subtler or atypical and less readily detected(19–24). Consistent with this complexity, evidence from later developmental stages has been heterogeneous, with large longitudinal studies reporting null associations between prenatal inflammation and brain outcomes in childhood and adolescence, raising the possibility that early MIA-related alterations may partially normalize over time through developmental “catch-up” processes(16,18). Rather than refuting the MIA hypothesis, these findings highlight the critical importance of developmental timing, outcome sensitivity, and phenotypic selection. If MIA-related effects are most pronounced during early sensitive periods, they may be detectable only transiently or in domains that are particularly vulnerable during infancy and toddlerhood. Language acquisition represents one such domain. As a core neurodevelopmental milestone with few viable preclinical analogues, early language development provides a uniquely informative window into emerging cognitive function(25). Early linguistic abilities are robust predictors of later cognitive, social, and academic outcomes(26,27). The vocabulary explosion, typically occurring between 18 and 24 months of age, marks a period of rapid representational growth and neural reorganization. Disruptions prior to or during this phase may signal underlying neurodevelopmental vulnerability(28). Across theoretical frameworks—including innatist, cognitivist, social-interactionist, behaviorist, statistical learning, and functionalist perspectives—language acquisition depends on the integrity of distributed neural systems supporting auditory processing, symbolic representation, social communication, and the extraction of statistical regularities from input. MIA-related perturbations could plausibly bias language development by subtly altering the maturation or coordination of these systems, even in the absence of gross structural abnormalities(27). At the same time, language emergence is fundamentally experience-dependent: biologically prepared neural circuits are tuned by the quantity and quality of linguistic input during critical and sensitive periods. As a result, postnatal environmental factors may either buffer prenatal vulnerabilities or exacerbate them, constraining the degree to which typical language trajectories can be recovered when early development is disrupted(29–31). Given the relatively short interval since the onset of the COVID-19 pandemic, empirical evidence on early language outcomes following prenatal SARS-CoV-2 exposure remains limited and mixed. While some studies report no significant developmental differences, others suggest subtle cognitive effects or mild delays that may not meet clinical thresholds but nonetheless reflect meaningful variation in early development(32–36). These inconsistencies further motivate approaches that move beyond binary exposure models and instead examine developmental outcomes along continuous dimensions of immune variation. In this context, human cohort studies focusing on early-emerging, sensitive neurodevelopmental phenotypes are essential for clarifying the potential impact of SARS-CoV-2–related MIA. Considering the unprecedented scale of prenatal exposure during the COVID-19 pandemic, continued monitoring is warranted even following mild-to-moderate maternal infection(37). The present study extends our previous work in this cohort(38–40) by examining whether prenatal exposure to maternal SARS-CoV-2 infection is associated with early language outcomes at 18 months of age. Adopting a dimensional framework, we investigate whether early language development varies as a function of fetal sex and the gestational inflammatory milieu. We hypothesize that (i) children exposed to SARS-CoV-2 will exhibit a delayed language explosion relative to normative developmental controls with specific sex effects, and (ii) language-score distributions will vary with the gestational inflammatory milieu along a continuum from predominantly anti-inflammatory to predominantly pro-inflammatory, with peak performance occurring a near-equilibrium inflammatory profile. 2. Methods 2.1. Study design and participants The COGESTCOV-19 study(INNVAL20/03) recruited a total of 107 mother-infant dyads at baseline between December 1, 2020, and February 28, 2022, in Cantabria, Spain, with cases and controls matched for maternal age, parity, and estimated delivery date(40). Participant recruitment was facilitated through the voluntary collaboration of midwives and obstetricians from the Marqués de Valdecilla University Hospital (HUMV) and various primary care canters across Cantabria, who informed eligible pregnant women about the study. Additional outreach was conducted via social media platforms. Inclusion criteria required that all pregnancies were singleton and resulted in live births, with no history of severe perinatal complications. Women in the exposed group had confirmed SARS-CoV-2 infection during pregnancy, verified via polymerase chain reaction (PCR) testing. The study was approved by the HUMV Review Board (approval code 2020.190) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. At 18 months of age, mothers were invited to participate in the follow-up. This study analyzed data from 87 dyads who continued participation between June 2022 and March 2024, comprising 51 infants born to SARS-CoV-2-exposed mothers (cases) and 36 to unexposed mothers (controls) ( Figure 1) . 2.2. Interviews and data collection Maternal sociodemographic, psychological, and medical information was collected through semi-structured interviews conducted by trained clinical psychologists. Psychological assessments included: Anxiety: State-Trait Anxiety Inventory (STAI)(41), Prenatal Stress: Prenatal Distress Questionnaire (PDQ)(42), Life Stressors: Social Readjustment Rating Scale (SRSS)(43), Relationship Quality: Couple Relationship Scale (CRP)(44), Sleep Quality: Oviedo Sleep Questionnaire (COS)(45), Pandemic-Related Anxiety: Fear of COVID-19 Scale. Postnatally(46), Postpartum depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS)(47). Clinical data evaluated cases for symptom presence (yes/no)—including fever, cough, fatigue, myalgia, diarrhea, headache, and others—and Infection severity was classified using standard clinical criteria, distinguishing between mild cases managed at home and moderate-severe cases requiring hospitalization(48). Data on delivery outcomes, neonatal health, and birth parameters, including gestational age, Apgar scores, and birth weight and length, were retrieved from medical records. 2.3. Cytokines Maternal venous blood was drawn at enrollment during the first prenatal interview, following confirmation of no recent non-steroidal anti-inflammatory or systemic corticosteroid use. Plasma was obtained by double centrifugation (1,500 g for 10 min at 4 °C, followed by 2,500 g for 15 min at 4 °C). Plasma aliquots were then stored at −80 °C in the Valdecilla Biobank until analysis. Interleukin-6 (IL-6) and Interleukin-10 (IL-10) concentrations were quantified using high-sensitivity ELISA kits from Enzo Life Sciences (IL-6: ENZ-KIT178-0001; IL-10: ADI-900-036), following the manufacturer’s instructions. Concentrations were log-transformed prior to statistical analysis(39,40). 2.4. Language assessment At 18 months, children’s language development was assessed using an online adaptation of the MacArthur-Bates Communicative Development Inventories (CDI)(49,50), a parent-report instrument widely used in neurodevelopmental research. Mothers reported their child’s language abilities using a structured checklist, classifying words as “understood” or “understood and spoken.” CDI percentile scores were calculated based on age- and sex-normative data. Six core language measures are analyzed: Vocabulary (lexicon size), MLU (utterance length as a proxy for syntax), Complexity (diversity and structure of word combinations), Vocalizations (preverbal phonological and speech motor output), Inflections (morphological endings and articulation), and Difficult Verbs (use of higher order action verbs). 2.5. Statistical analysis Analyses were conducted in R 4.3.2. Categorical variables were compared using Pearson’s χ² or Fisher’s exact tests and continuous variables using t tests (Welch’s when appropriate), Mann–Whitney U tests, ANOVA, or ANCOVA, testing maternal–infant covariate sets (education, income; gestational age, birth weight) and, given consistent results, reporting the least-missing models to maximize sample size. Group equivalence was assessed with two one-sided tests (TOST; bounds 0.5–1.5 SD). Associations between log₁₀(IL-6/IL-10) and language percentiles were modeled using GAMs and spline-based linear models. Model support was compared using AIC, BIC, RMSE, adjusted R², and deviance explained (ΔAIC < 2). Exploratory regression models, we report unadjusted p and increasingly stringent multiple-comparison control: false discovery rate (FDR)—the expected false-positive proportion among significant results—and family-wise error rate—the chance of ≥1 false positive across tests (Bonferroni); all tests were two-sided with p < 0.05 considered statistically significant. 3. Results 3.1. Sociodemographic, clinical and physiological characteristics No significant differences were found between the case and control groups in maternal age, parity, or marital status. However, a significantly higher proportion of mothers in the case group had an annual income below €20,000 (64.6% vs. 25.7%, χ²=12.25, p<0.001), and a trend toward lower education levels was observed (p<0.1). Cytokine balance concentrations showed no significant differences ( Figure 2 ). Newborns exhibited optimal and comparable neonatal health parameters, with no statistical differences observed (5th-minute APGAR scores: 9.8 vs. 9.5). A trend toward higher gestational age was noted in the case group (40.18 vs. 39.59 weeks, p<0.1). All case–control comparisons are presented in Table 1 , with sex-stratified results provided in Supplementary Table 1 . Table 1. Sociodemographic, clinical and phisiological baseline data comparisons from COGESTCOV19 case-control maternal-newborn dyads Case Control N = 51 N = 36 n Mean SD n Mean SD Statistic( df ) Value p-value Sociodemographic Mother Age (years) 51 33,69 3,72 36 34,47 3,73 t(85) -0,969 0,335 Years of education 51 15,71 4,80 36 17,50 4,42 t(85) -1,774 0,080 N n (%) N n (%) Married or with parther 51 49 96,1 36 34 94,4 Fisher 0,000 1,000 Anual salary (<€20k) 48 31 64,6 35 9 25,7 χ2 (1) 12,248 <0.001*** Primiparous (Yes) 51 29 56,9 36 20 55,6 χ2 (1) 0,015 0,904 Alcohol (Yes) 51 3 5,9 35 1 2,9 Fisher 0,018 0,643 Tabaco (Yes) 51 3 5,9 36 1 2,8 Fisher 0,026 0,639 Trimester of infection 51 1 st - 11 21,6 - - - - - - 2 nd - 25 49,0 - - - - - - 3 rd - 15 29,4 - - - - - - Newborn n Mean SD n Mean SD Gestational age (weeks) 51 40,18 0,94 36 39,59 1,75 W 1116,5 0,088 Age (days) 51 566,1 36,3 36 562,3 24,7 W 1080,5 0,162 N n (%) N n (%) Sex (male) 51 30 58.8 36 19 52.8 χ2 (1) 0.314 0.662 Full term (Full term) 48 48 100,0 35 35 100,0 - - - Natural birth (Yes) 48 32 66,7 35 18 51,4 χ2 (1) 1,962 0,161 Clinical n Mean SD n Mean SD PDQ 51 22,3 8,1 36 24,7 8,5 t(85) -1,328 0,188 SRRS 51 156,7 85,3 36 144,6 71,0 t(85) 0,698 0,487 CRP Anxiety 51 51,0 16,0 36 54,1 14,3 t(85) -0,945 0,348 STAI-E 51 11,3 6,6 36 10,7 6,8 t(85) 0,366 0,715 COS 51 32,6 8,0 36 33,7 8,9 t(85) -0,578 0,565 PSS 51 21,3 6,5 36 19,1 7,0 t(85) 1,503 0,136 Fear to COVID-19 scale 51 13,4 4,1 36 13,9 5,5 W 897,0 0,859 EPDS 51 5,5 3,4 36 5,2 3,1 t(85) 0,412 0,681 N n (%) N n (%) Hospitalization (yes) 51 1 1,97 - - - - - - Symptoms during COVID-19 (yes) 50 48 96,0 - - - - - - Fever (yes) 48 27 56,3 - - - - - - Cough (yes) 48 30 62,5 - - - - - - Fatigue (yes) 48 23 47,9 - - - - - - Muscle pain (yes) 48 23 47,9 - - - - - - Diarrhea (yes) 48 8 16,7 - - - - - - Headache (yes) 48 25 52,1 - - - - - - Others (yes) 48 44 91,7 - - - - - - Physiological n Mean SD N Mean SD Mother Weight (kg) 51 72,55 11,88 36 73,03 12,95 t(85) -0,179 0,859 Lenght (cm) 51 164,00 5,92 36 164,78 6,87 t(85) -0,565 0,574 BMI 51 27,01 4,51 36 26,89 4,39 t(85) 0,121 0,904 Pospartum systolic blood pressure 44 111,61 12,30 29 112,31 10,58 t(71) -0,250 0,803 Postpartum diastolic blood pressure 44 68,43 8,21 29 67,14 7,57 t(71) 0,679 0,499 IL-6 (log10(pg/mL)) 39 0.71 0.87 25 0.89 0.65 t W(60) -0.904 0.370 IL-10 (log10(pg/mL)) 39 1.44 1.07 25 1.74 0.3 t W(60) -1.240 0.220 IL-6/IL-10 ratio (log10(pg/mL)) 39 -0.73 0.84 25 -0.85 0.56 t W(62) 0.707 0.483 Newborn Weight (g) 48 3388.29 466.7 35 3411.57 479.33 t(81) -0.222 0.825 Length (cm) 47 50.2447 1.757 35 50.4 2.0715 t(80) -0.367 0.715 1st minute APGAR score 50 8,4 1,4 35 8,4 1,1 W 920,0 0,605 5th minute APGAR score 50 9,4 1,1 35 9,5 0,8 W 903,0 0,771 10th minute APGAR score 50 10 0 35 10 0 - - - 3.2. Early language development outcomes CDI percentile scores were examined for six core language measures ( Vocabulary , LME , Complexity , Vocalizations , Inflections , and Difficult Verbs ) in case–control comparisons ( Table 2 ), with additional analyses stratified by sex ( Supplementary Table 1 ). Unadjusted analyses revealed no significant case–control differences in early language development, except for Difficult Verbs in sex-stratified comparisons. For this measure, both case and control females outperformed case males (all p<0.05). In addition, case females also outperformed control males (M=67.3, SD=12.5, p<0.05). However, after adjustment for covariates, none of these differences remained significant. Supplementary TOST analyses reinforced statistical equivalence ( Supplementary Table 1 and Supplementary Figure 1) . Table 2. MacArthur CDI percentile scores: unadjusted and adjusted case–control comparisons at 18 months in the COGESTCOV19 cohort. Case Control Unadjusted Adjusted (years of education & gestational age) N = 51 N = 36 n Mean SD n Mean SD Statistic (df) Value p-value Statistic (df) p-value Vocabulary 51 35,3 29,5 36 28,3 24,8 t(85) 1,155 0,251 F(1) 0,282 MLU 51 32,3 37,5 36 23,8 33,3 t(85) 1,078 0,284 F(1) 0,238 Complexity 51 32,7 26,8 36 26,5 25,0 t(85) 1,098 0,275 F(1) 0,239 Vocalizations 51 75,6 23,7 36 80,6 18,7 W 820,0 0,388 F(1) 0,501 Inflections 51 38,7 13,5 36 36,3 12,1 t(85) 0,880 0,382 F(1) 0,395 Difficult Verbs 51 70,4 13,8 36 67,6 10,3 W 1027,5 0,323 F(1) 0,486 3.3. Maternal immune–language associations We modelled 18-month CDI percentiles as a function of maternal log₁₀(IL-6/IL-10) using GAMs and spline-based linear models adjusted for exposure, maternal education, vaccination, and age, with comparable fit across approaches (ΔAIC < 2). Most immune effects were non-significant after correction, with signal limited to two patterns: an exposure-specific non-linear association between higher IL-6/IL-10 and lower Difficult Verbs, and a female-specific association with lower Vocalizations among exposed girls, alongside a maternal-age effect. No robust IL-6/IL-10 associations were observed for Vocabulary, MLU, or Inflections, and IL-6/IL-10–independent case–control differences were small ( Supplementary Table 1). For Difficult Verbs , the main immune-related signal was an exposure-specific association with maternal IL-6/IL-10. In pooled case control models, the exposed group showed a nonlinear effect in the GAM (F≈3.54, p≤0.05) mirrored by the spline-based LM (t≈−2.52, p≤0.05), such that higher IL-6/IL-10 in exposed pregnancies was associated with lower Difficult Verbs percentiles, with no comparable pattern in controls. However, these effects did not survive FDR correction (all p>0.05). In sex stratified analyses, the effect was weaker but directionally consistent in girls, with an exposed specific GAM smooth (F≈3.05, p≤0.05) and only a trend in the corresponding LM (t≈−2.05, p>0.05), again not surviving FDR correction. For Vocalizations , the main immune-related pattern suggested a female-specific vulnerability among exposed girls. In females, the GAM showed an exposed-specific IL-6/IL-10 smooth (F≈4.33, p≤0.05) indicating lower Vocalization percentiles at higher maternal IL-6/IL-10, although this did not survive FDR correction (p>0.05). In contrast, the spline-based LM showed an IL-6/IL-10 spline term that remained FDR significant (t≈−3.01, p≤ 0.01; p(FDR)≤ 0.05), and a maternal age spline term that was also FDR significant (t≈2.91, p≤0.01; p(FDR)≤0.05). To describe the internal structure of language skills in the regression sample, we estimated case- and control-specific partial correlations among MacArthur CDI percentiles adjusting for maternal age and education ( Figure 5 ). Vocabulary , MLU , and Complexity formed a moderately to strongly intercorrelated cluster in both groups (r≈0.38–0.74; mostly p≤0.05), with Vocalizations showing modest-to-moderate correlations with this cluster (r≈0.35–0.60; often p≤0.05). Inflections correlated moderately with Vocabulary / MLU / Complexity in cases (r=0.44–0.62; p≤0.01) but were weak and non-significant in controls (|r|≤0.19). Difficult Verbs showed the weakest connectivity, with only small-to-moderate associations (cases: r=0.34–0.39 with Vocabulary / Complexity / Vocalizations ; controls: r=0.40–0.48 with MLU / Complexity ; other pairs generally non-significant). 4. Discussion We did not observe case–control differences in language scores, with most comparisons satisfying equivalence criteria. By contrast, within the exposed group, more pro-inflammatory IL-6/IL-10 ratios were associated with lower Difficult Verbs percentiles and, among exposed girls, with lower Vocalizations . This pattern points to a domain- and sex-modulated sensitivity to immune imbalance against a background of broadly similar language performance in a shared pandemic context. Early Language Development at 18 Months Our results do not support a robust global delay in early language among infants prenatally exposed to maternal SARS-CoV-2 infection. Case–control comparisons indicate that mild-to-moderate maternal infection during pregnancy is not systematically associated with clinically meaningful decrements in early vocabulary or related language domains at 18 months. This pattern aligns with studies reporting no substantial impact of prenatal SARS-CoV-2 exposure on early language development(32,35) and diverges from reports suggesting more generalized delays(51,52). Importantly, this lack of group-level difference holds when analyses are stratified by sex: we did not identify robust case–control differences in global CDI language scores among either boys or girls. These findings suggest that, at least by 18 months and in the context of predominantly mild-to-moderate infection, prenatal SARS-CoV-2 exposure does not produce a uniform language delay detectable with parent-report measures in one sex over the other(53,54). At the same time, both exposed and non-exposed children in our sample scored in comparatively low percentiles for several CDI subscales. This convergence at the lower end of the normative distribution is consistent with broader pandemic-era sociocultural factors—such as reduced opportunities for social interaction, altered caregiving routines, and changes in healthcare access—exerting diffuse pressure on early communicative environments(53–56). Such pressures are likely to affect boys and girls alike, potentially overshadowing small infection-specific effects in global scores. Overall, the findings suggest that mild-to-moderate SARS-CoV-2 infection per se is unlikely to produce a uniform, early language delay at 18 months, although more severe infections or later follow-ups may yield different patterns and should be examined in future work. IL-6/IL-10 balance, domain-specific vulnerability, and sex modulation Although we initially expected an inverted U-shaped association, the observed effects were concentrated in the pro-inflammatory direction, consistent with the idea that risk may track regulatory balance rather than isolated cytokine concentrations. The absence of group differences in absolute cytokine concentrations, combined with these ratio effects, is consistent with the idea that risk is more closely tied to regulatory balance than to isolated levels(57). The combination of largely null case–control cytokine differences with ratio-linked outcomes supports a balance-based interpretation and aligns with preclinical work suggesting that insufficiently counter-regulated pro-inflammatory signaling can bias circuit maturation under moderate inflammatory load(58–60), even when gross milestones remain intact(61,62). Given our single time-point immune measurement and modest sample size, mechanistic inference remains tentative. Immune–language associations were restricted to Difficult Verbs and, in exposed girls, Vocalizations , arguing against a generalized language slowdown and instead supporting, if any, selective vulnerability of specific components. Difficult Verbs may reflect higher-order lexical–conceptual operations (e.g. action representation, argument structure, flexible verb morphology) that place demands on frontotemporal and frontoparietal integration(63), whereas Vocalizations index speech-motor and phonological output(64,65). An hypothesis is that pro-inflammatory imbalance subtly influences refinement within long-range association pathways supporting these skills, yielding small domain-specific shifts without broad impairment(66,67). A testable prediction for future work is that higher prenatal IL-6/IL-10 ratios will be more strongly related to microstructural or connectivity variation in language- and motor-related association tracts than to global brain measures(61,62) An additional nuance is that IL-6/IL-10–language associations were detectable only in the SARS-CoV-2–exposed group, despite comparable distributions of the ratio across cases and controls. This suggests that IL-6/IL-10 may act as a marker of risk only within a broader, virus-specific immune context. Viral infections such as SARS-CoV-2 are characterized by distinct interferon and chemokine profiles, placental transcriptional responses, and endothelial changes that are not fully mirrored by non-viral inflammatory states(57,68,69). Thus, a given degree of pro-inflammatory IL-6/IL-10 imbalance may be embedded in a qualitatively different immune and placental milieu in exposed pregnancies, making the same nominal ratio more neurobiologically consequential than in controls. Although our data lack a broader immune panel and detailed non-viral histories, this yields a clear empirical test: multivariate immune profiles combining IL-6/IL-10 with viral-response markers should explain more variance in language outcomes than the ratio alone, and similar IL-6/IL-10 values arising from non-viral inflammation should show weaker or qualitatively different associations with language. The female-specific association between higher IL-6/IL-10 ratios and lower Vocalizations in exposed infants further invites a sex-sensitive pathway. Experimental and human studies indicate that females differ from males in placental responsiveness, X-linked immune regulation, microglial activation profiles, and the timing of synaptic pruning and myelination in socio-communicative and motor networks(68,70,71). A plausible interpretation is that pro-inflammatory gestational imbalance interacts with female developmental trajectories, yielding subtle alterations in vocal-motor and frontotemporal circuitry that reduce early vocal output without producing a global language delay. A testable prediction is that, in larger, deeply phenotyped samples, immune–language associations will emerge earlier or show steeper gradients in girls than in boys, particularly for vocal and socio-communicative measures. Implications for understanding MIA and pandemic-era influences Taken together, these findings support SARS-CoV-2–related maternal immune activation as a graded and context-dependent influence rather than a deterministic cause of early language delay(7,57,72). Mild-to-moderate infection does not appear to uniformly shift early language milestones; instead, individual differences in immune balance—embedded within virus-specific immune milieus and modulated by sex—may leave subtle, domain-specific imprints on early expressive skills. Conceptually, this pattern aligns with models in which prenatal immune imbalance biases developmental trajectories while postnatal experience shapes their expression(73,74). Given that language emergence is strongly experience-dependent and that pandemic-era disruptions likely altered the communicative environment of both exposed and non-exposed children(73,75–77), these results argue against simple exposed–unexposed contrasts and instead support integrative frameworks that jointly consider immune phenotypes, sex, and key features of the postnatal ecology when interpreting MIA-related developmental variation(74,78–80). Strengths and limitations Key strengths include detailed perinatal clinical characterization, maternal cytokine data, and domain-level language phenotyping using the MacArthur–Bates CDI. Analytically, equivalence testing strengthened interpretation of null case–control contrasts, and flexible models of IL-6/IL-10 balance were well suited to detect non-linear and sex-specific patterns. Recruiting cases and controls within the same pandemic period also reduced confounding by secular changes in the broader sociocultural context. Limitations include modest sample size, especially for sex-stratified and non-linear effects, increasing uncertainty around interaction patterns. Most infections were mild-to-moderate, limiting generalization to severe COVID-19 or other pathogens. Cytokines were measured at a single time point with a restricted panel centered on IL-6 and IL-10, providing only a partial snapshot of a dynamic immune trajectory. Language was assessed at one age via parent report, which may not capture all facets of communicative competence. Finally, multiple domains and models raise the possibility of chance findings, so the immune-linked, domain- and sex-specific patterns should be treated as hypothesis-generating. Future directions and conclusion Future work should test whether the domain- and sex-specific associations observed at 18 months persist and predict later language, cognitive, or socio-emotional outcomes while jointly modelling immune measures and environmental moderators. Deeper immune profiling of existing samples (broader cytokine panels and molecular signatures) could clarify trigger-specific vulnerability profiles without additional pregnancy sampling. Follow-up neurobiological measures in children, such as diffusion MRI, EEG, or fNIRS, would enable direct tests of whether prenatal immune balance maps onto variation in language- and motor-relevant circuits. More broadly, new pregnancy cohorts with repeated biospecimen sampling across gestation will be needed to capture immune dynamics and differentiate viral from non-viral inflammatory states, refining MIA as a trigger-specific set of immune signatures with distinct neurodevelopmental consequences. Conclusions In this cohort of pandemic-born infants, mild-to-moderate maternal SARS-CoV-2 infection during pregnancy was not associated with a global delay in parent-reported language at 18 months. By contrast, a higher pro-inflammatory IL-6/IL-10 balance was linked to subtle, domain-specific reductions in expressive skills—particularly Difficult Verbs , and Vocalizations in exposed girls—suggesting graded, sex-modulated effects rather than a uniform impact of MIA. These findings support a view of prenatal immune activation as a context-dependent influence embedded within broader pandemic-related environments, and highlight the need for longitudinal, mechanistically oriented follow-up before drawing firm conclusions about long-term neurodevelopmental risk. Declarations Funding This study was funded by Fundación Instituto de Investigación Marqués de Valdecilla (grants INNVAL20/02 and INNVAL23/21). Dra. Rosa Ayesa-Arriola was financed by a Miguel Servet contract from the Carlos III Health Institute (CP18/00003) and a Consolidator Grant from the Ministerio de Ciencia e Innovación (CNS2022-136110). Ángel Yorca Ruiz was financed by a predoctoral contract (PFIS_FI/00162) from the Carlos III Health Institute. Authorship contribution statement ADP: Conceptualization, Data Curation, Software, Formal Analysis, Visualization, Writing – Original Draft, Writing – Review & Editing. SCC: Software, Formal Analysis, Validation, Visualization, Writing – Original Draft, Writing – Review & Editing. CMA: Data Curation, Software, Formal Analysis, Visualization. VOG: Data Curation, Software, Formal Analysis, Visualization. CP: Writing – Original Draft, Writing – Review & Editing, AYR: Writing – Original Draft, Writing – Review & Editing. SI: Writing – Original Draft, Writing – Review & Editing. RAA: Conceptualization, Data Curation, Funding Acquisition, Investigation, Project Administration, Resources, Supervision, Writing – Original Draft, Writing – Review & Editing. Ethical Standards Written informed consent was obtained from all participants prior to their inclusion in the study. Declaration of Competing Interest None. Acknowledgement We thank the women who participated in this study and the IDIVAL BIOBANK workers. Data availability The data supporting the findings of this article is available upon request from the corresponding author, RAA. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the author(s) used ChatGPT-5 in order to improve the readability and language of the manuscript. After using these tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. References Gilmore JH, Fredrik Jarskog L. Exposure to infection and brain development: cytokines in the pathogenesis of schizophrenia. Schizophr Res. 1997 Apr 11;24(3):365–7. Hall MB, Willis DE, Rodriguez EL, Schwarz JM. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9651346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636730338,"identity":"4e112ef9-f481-425b-a32a-8569035d90f2","order_by":0,"name":"Alexandre Díaz-Pons","email":"","orcid":"https://orcid.org/0000-0002-5844-3141","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Díaz-Pons","suffix":""},{"id":636730686,"identity":"4b9e8807-7d7e-48d2-8fc9-0eb86fbcbf6d","order_by":1,"name":"Sergio Castaño Castaño","email":"","orcid":"https://orcid.org/0000-0003-4571-769X","institution":"Universidad de Burgos","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"Castaño","lastName":"Castaño","suffix":""},{"id":636731151,"identity":"dfe4cbd8-b2bb-41a6-b8c6-95e2347158c7","order_by":2,"name":"Carlos Martínez Asensi","email":"","orcid":"https://orcid.org/0000-0003-3880-6301","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Martínez","lastName":"Asensi","suffix":""},{"id":636731507,"identity":"255133a8-02d5-4b76-8a91-0021bb437abc","order_by":3,"name":"Víctor Ortiz García de la Foz","email":"","orcid":"https://orcid.org/0000-0002-0627-1827","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":false,"prefix":"","firstName":"Víctor","middleName":"Ortiz García de la","lastName":"Foz","suffix":""},{"id":636731781,"identity":"7dfe5d7f-97c2-40b2-8513-d23e4959e8c6","order_by":4,"name":"Claudia Parás","email":"","orcid":"https://orcid.org/0009-0009-5455-2605","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Parás","suffix":""},{"id":636732144,"identity":"2a2fcb92-c038-4ec7-9ac5-98ee4913ad34","order_by":5,"name":"Ángel Yorca Ruiz","email":"","orcid":"https://orcid.org/0000-0002-2701-3755","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":false,"prefix":"","firstName":"Ángel","middleName":"Yorca","lastName":"Ruiz","suffix":""},{"id":636732534,"identity":"bcdfaef5-77a1-4320-8a46-10236b029782","order_by":6,"name":"Sara Incera","email":"","orcid":"https://orcid.org/0000-0001-9124-9204","institution":"Eastern Kentucky University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Incera","suffix":""},{"id":636734011,"identity":"fd6f6ba2-cf06-4cc3-9bc1-638eaa6e7acb","order_by":7,"name":"Rosa Ayesa Arriola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3PQWrCQBTG8S8MjJvB9SzEXGEkYAu1eJWEgG4UPECxi0Cyaw7QQq+QI6Q8cDUHyFIJuMoiQikuSjHpSjej7gTnv3vwfjweYLPddizPhQK6VxDu/xN+BWn2BS4gD0myqfFCfTfRu7xakMvBVrtvA+lp7UmsyFN6nn19KBrE4OF7z0CknEGCU5BhnpFQEyeG8Jg0kmm5xx+9fqbVuiXjC4g/lE5MPooZGjIKWuLUJiL08DF4mw6yYquaX0ZhzHjIDAKyk5RF/fPkumlY1tWvfE47ETl7k2nzER1NzQkmzhFgeTqev2Kz2Wz31AGSq0jtG9fFawAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0570-5352","institution":"Instituto de Investigación Marqués de Valdecilla","correspondingAuthor":true,"prefix":"","firstName":"Rosa","middleName":"Ayesa","lastName":"Arriola","suffix":""}],"badges":[],"createdAt":"2026-05-08 08:44:17","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9651346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9651346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108979933,"identity":"25beb728-ea2e-4955-a11a-fcf1fbfc1e7e","added_by":"auto","created_at":"2026-05-11 12:02:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34849,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the COGESTCOV19 cohort. At baseline, 117 pregnant women were enrolled (66 cases, 51 controls), with follow-up at 6 weeks postpartum (n = 107) and at child age 18 months (n = 87). Two analytic samples were defined at the current analytic time point: (i) a case–control comparison (51 cases, 36 controls); and (ii) a regression subset including participants with maternal blood drawn ≤9 weeks from the index event (SARS-CoV-2 infection for cases, n = 39; COVID-19 vaccination for controls, n = 25), with unvaccinated controls additionally included as not anchored to a specific index event.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/5adb8350dac2983f436a70bc.png"},{"id":108979979,"identity":"ddaeb6a8-93cd-44b1-8c1d-19e098170124","added_by":"auto","created_at":"2026-05-11 12:02:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21348,"visible":true,"origin":"","legend":"\u003cp\u003eViolin/box plots of log₁₀ IL-6/IL-10 ratio in cases (n=39) and controls (n=25), showing no significant group difference (Welch t-test p = 0.48). Because the ratio is plotted on a log₁₀ scale, cytokine balance (raw IL-6/IL-10=1, i.e., IL-6=IL-10) corresponds to 0 on the y-axis (dashed line); values\u0026gt;0 indicate relative IL-6 predominance (pro-inflammatory), and values \u0026lt; 0 indicate relative IL-10 predominance (anti-inflammatory).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/a92d7ee43bc02cf83a9237d5.png"},{"id":108979996,"identity":"964078de-b63c-4476-bbe7-72abe4cac9d0","added_by":"auto","created_at":"2026-05-11 12:03:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340762,"visible":true,"origin":"","legend":"\u003cp\u003eGAMs of \u003cem\u003eDifficult Verbs\u003c/em\u003e percentile scores by log₁₀ IL-6/IL-10 for cases vs controls (top) and by sex (bottom). Lines and shaded areas show fitted means and 95% CIs. Header model performance metrics: \u003cem\u003en\u003c/em\u003e=sample size; AIC/BIC=model fit (lower=better); RMSE=average prediction error (percentile points); adj. R² and deviance explained=variance accounted for; method=REML=estimation method. For each group, the smooth-term \u003cem\u003ep\u003c/em\u003e tests the association between IL-6/IL-10 and \u003cem\u003eDifficult Verbs\u003c/em\u003e; Bonferroni \u003cem\u003ep\u003c/em\u003eis the multiple-comparison–adjusted value.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/7f96c195c1859f7cd0e2b778.png"},{"id":108979995,"identity":"a72ef208-e1dc-4ffc-8de1-f394dae4135b","added_by":"auto","created_at":"2026-05-11 12:03:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33295,"visible":true,"origin":"","legend":"\u003cp\u003eGAM of\u003cem\u003eVocalizations\u003c/em\u003e percentile scores by log₁₀ IL-6/IL-10 ratio in females, for case and control groups. Lines and shaded areas show predicted means and 95% CIs. Model performance metrics in the header (n, AIC, BIC, RMSE, adj. R², deviance explained, estimation method, and smooth-term p and Bonferroni-p values) are interpreted as in \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/c036c04641d74ce7a370c455.png"},{"id":108980130,"identity":"cae7fe04-8b21-41b0-963c-504d0b8d2fd2","added_by":"auto","created_at":"2026-05-11 12:03:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67335,"visible":true,"origin":"","legend":"\u003cp\u003ePartial correlations among language subscales in cases (left) and controls (right), adjusted for maternal age and education. Color indicates partial r; asterisks denote significance (p \u0026lt; .05, .01, .001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/f000934d6ce2a96aba88d719.png"},{"id":108982200,"identity":"b40bffdf-f9fb-4b27-8025-b7fba56b5d9b","added_by":"auto","created_at":"2026-05-11 12:23:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9651346/v1/86cb889f-a36e-4148-9925-3b02e59498fc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePro-inflammatory IL-6/IL-10 imbalance in SARS-CoV-2-exposed pregnancies and early language development\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eAt 18 months, language percentiles on MacArthur CDI subdomains were similar\u0026mdash;and often low\u0026mdash;in SARS-CoV-2\u0026ndash;exposed and control infants, consistent with broad pandemic-related influences beyond maternal immune activation.\u003c/li\u003e\n \u003cli\u003eIL-6, IL-10 and IL-6/IL-10 ratio concentrations did not reach statistical significance between cases and controls.\u003c/li\u003e\n \u003cli\u003eIn exposed pregnancies, pro- but not anti-inflammatory imbalance (higher IL-6/IL-10) predicted lower Difficult-Verbs percentiles: subscale\u0026rsquo;s relative independence from other CDI domains supports a selective, viral\u0026ndash;linked inflammatory effect, rather than non-specific immune tone or a global language deficit.\u003c/li\u003e\n \u003cli\u003eAmong exposed females, higher IL-6/IL-10 predicted poorer Vocalization percentiles, though limited sample size constrains inference.\u003c/li\u003e\n \u003cli\u003eLonger follow-up, larger samples, and broader immune and genetic profiling are required to determine the durability and mechanistic scope of these immune\u0026ndash;language associations.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003ePrenatal exposure to infection has long been implicated in the etiology of neurodevelopmental disorders (NDD). Ongoing epidemiological, clinical, and preclinical evidence has increasingly supported the hypothesis that these associations are mediated not solely by the pathogen itself, but by the maternal immune response it elicits. This framework\u0026mdash;formally articulated by Gilmore and Jarskog (1997)\u0026mdash;proposed maternal immune activation (MIA) as a cytokine-mediated pathway through which prenatal inflammatory signals may disrupt fetal brain development and confer vulnerability to later neuropsychiatric outcomes, including autism spectrum disorder (ASD) and schizophrenia spectrum disorders (SSD)(1\u0026ndash;7). Several viral infections, including influenza, cytomegalovirus, and Zika virus, have demonstrated clear and, in some cases, severe neurodevelopmental consequences when occurring during pregnancy. In contrast, the neurodevelopmental impact of maternal infection with SARS-CoV-2 remains incompletely understood(8\u0026ndash;10). This uncertainty reflects both the biological heterogeneity of immune responses to SARS-CoV-2 and the relatively recent onset of the COVID-19 pandemic, which has limited the availability of long-term developmental follow-up data.\u003c/p\u003e\n\u003cp\u003eThe plausibility of SARS-CoV-2\u0026ndash;related MIA influencing fetal neurodevelopment is supported by extensive evidence implicating cytokine signaling in neuronal migration, synaptogenesis, and circuit formation(11). Although elevated interleukin-6 (IL-6) has been linked to altered connectivity and atypical developmental trajectories, neurodevelopmental risk is unlikely to be driven by IL-6\u0026mdash;or any single cytokine\u0026mdash;in isolation, particularly in the context of predominantly mild-to-moderate infection(12,13). Rather, risk appears to emerge from the balance between pro- and anti-inflammatory signaling(14,15), with interleukin-10 (IL-10) playing a key regulatory role in constraining inflammation and maintaining immune homeostasis(14,15). Accordingly, even modest inflammatory exposures may become biologically consequential when anti-inflammatory capacity is insufficient. Reflecting this perspective, recent work has emphasized the limitations of single-biomarker approaches and the value of balance- or composite-based indices that better capture the integrated gestational immune milieu(16,17). Within this framework, the IL-6/IL-10 ratio provides a parsimonious, mechanistically interpretable index of cytokine balance, that has been used to characterize SARS-CoV-2 severity, and may index immune states relevant to fetal neurodevelopment(12\u0026ndash;14). However, biomarker-based definitions may preferentially reflect chronic or low-grade inflammation rather than acute pathogen-specific responses, a limitation heightened in viral, time-dependent immune processes where cytokine kinetics make inference highly contingent on biospecimen timing, a requirement difficult to meet in most pregnancy cohorts(18). The developmental impact of SARS-CoV-2\u0026ndash;related MIA therefore remains uncertain and likely moderated by inter-individual susceptibility, including sex: males appear more vulnerable to prenatal inflammatory perturbations, whereas effects in females may be subtler or atypical and less readily detected(19\u0026ndash;24). Consistent with this complexity, evidence from later developmental stages has been heterogeneous, with large longitudinal studies reporting null associations between prenatal inflammation and brain outcomes in childhood and adolescence, raising the possibility that early MIA-related alterations may partially normalize over time through developmental \u0026ldquo;catch-up\u0026rdquo; processes(16,18). Rather than refuting the MIA hypothesis, these findings highlight the critical importance of developmental timing, outcome sensitivity, and phenotypic selection. If MIA-related effects are most pronounced during early sensitive periods, they may be detectable only transiently or in domains that are particularly vulnerable during infancy and toddlerhood.\u003c/p\u003e\n\u003cp\u003eLanguage acquisition represents one such domain. As a core neurodevelopmental milestone with few viable preclinical analogues, early language development provides a uniquely informative window into emerging cognitive function(25). Early linguistic abilities are robust predictors of later cognitive, social, and academic outcomes(26,27). The vocabulary explosion, typically occurring between 18 and 24 months of age, marks a period of rapid representational growth and neural reorganization. Disruptions prior to or during this phase may signal underlying neurodevelopmental vulnerability(28). Across theoretical frameworks\u0026mdash;including innatist, cognitivist, social-interactionist, behaviorist, statistical learning, and functionalist perspectives\u0026mdash;language acquisition depends on the integrity of distributed neural systems supporting auditory processing, symbolic representation, social communication, and the extraction of statistical regularities from input. MIA-related perturbations could plausibly bias language development by subtly altering the maturation or coordination of these systems, even in the absence of gross structural abnormalities(27). At the same time, language emergence is fundamentally experience-dependent: biologically prepared neural circuits are tuned by the quantity and quality of linguistic input during critical and sensitive periods. As a result, postnatal environmental factors may either buffer prenatal vulnerabilities or exacerbate them, constraining the degree to which typical language trajectories can be recovered when early development is disrupted(29\u0026ndash;31).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the relatively short interval since the onset of the COVID-19 pandemic, empirical evidence on early language outcomes following prenatal SARS-CoV-2 exposure remains limited and mixed. While some studies report no significant developmental differences, others suggest subtle cognitive effects or mild delays that may not meet clinical thresholds but nonetheless reflect meaningful variation in early development(32\u0026ndash;36). These inconsistencies further motivate approaches that move beyond binary exposure models and instead examine developmental outcomes along continuous dimensions of immune variation. In this context, human cohort studies focusing on early-emerging, sensitive neurodevelopmental phenotypes are essential for clarifying the potential impact of SARS-CoV-2\u0026ndash;related MIA. Considering the unprecedented scale of prenatal exposure during the COVID-19 pandemic, continued monitoring is warranted even following mild-to-moderate maternal infection(37). The present study extends our previous work in this cohort(38\u0026ndash;40) by examining whether prenatal exposure to maternal SARS-CoV-2 infection is associated with early language outcomes at 18 months of age. Adopting a dimensional framework, we investigate whether early language development varies as a function of fetal sex and the gestational inflammatory milieu. We hypothesize that (i) children exposed to SARS-CoV-2 will exhibit a delayed language explosion relative to normative developmental controls with specific sex effects, and (ii) language-score distributions will vary with the gestational inflammatory milieu along a continuum from predominantly anti-inflammatory to predominantly pro-inflammatory, with peak performance occurring a near-equilibrium inflammatory profile.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Study design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COGESTCOV-19 study(INNVAL20/03) recruited a total of 107 mother-infant dyads at baseline between December 1, 2020, and February 28, 2022, in Cantabria, Spain, with cases and controls matched for maternal age, parity, and estimated delivery date(40). Participant recruitment was facilitated through the voluntary collaboration of midwives and obstetricians from the Marqu\u0026eacute;s de Valdecilla University Hospital (HUMV) and various primary care canters across Cantabria, who informed eligible pregnant women about the study. Additional outreach was conducted via social media platforms. Inclusion criteria required that all pregnancies were singleton and resulted in live births, with no history of severe perinatal complications. Women in the exposed group had confirmed SARS-CoV-2 infection during pregnancy, verified via polymerase chain reaction (PCR) testing. The study was approved by the HUMV Review Board (approval code 2020.190) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eAt 18 months of age, mothers were invited to participate in the follow-up. This study analyzed data from 87 dyads who continued participation between June 2022 and March 2024, comprising 51 infants born to SARS-CoV-2-exposed mothers (cases) and 36 to unexposed mothers (controls) (\u003cstrong\u003eFigure 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interviews and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaternal sociodemographic, psychological, and medical information was collected through semi-structured interviews conducted by trained clinical psychologists. Psychological assessments included: Anxiety: State-Trait Anxiety Inventory (STAI)(41), Prenatal Stress: Prenatal Distress Questionnaire (PDQ)(42), Life Stressors: Social Readjustment Rating Scale (SRSS)(43), Relationship Quality: Couple Relationship Scale (CRP)(44), Sleep Quality: Oviedo Sleep Questionnaire (COS)(45), Pandemic-Related Anxiety: Fear of COVID-19 Scale. Postnatally(46), Postpartum depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS)(47). Clinical data evaluated cases for symptom presence (yes/no)\u0026mdash;including fever, cough, fatigue, myalgia, diarrhea, headache, and others\u0026mdash;and Infection severity was classified using standard clinical criteria, distinguishing between mild cases managed at home and moderate-severe cases requiring hospitalization(48). Data on delivery outcomes, neonatal health, and birth parameters, including gestational age, Apgar scores, and birth weight and length, were retrieved from medical records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cytokines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaternal venous blood was drawn at enrollment during the first prenatal interview, following confirmation of no recent non-steroidal anti-inflammatory or systemic corticosteroid use. Plasma was obtained by double centrifugation (1,500 g for 10 min at 4 \u0026deg;C, followed by 2,500 g for 15 min at 4 \u0026deg;C). Plasma aliquots were then stored at \u0026minus;80 \u0026deg;C in the Valdecilla Biobank until analysis. Interleukin-6 (IL-6) and Interleukin-10 (IL-10) concentrations were quantified using high-sensitivity ELISA kits from Enzo Life Sciences (IL-6: ENZ-KIT178-0001; IL-10: ADI-900-036), following the manufacturer\u0026rsquo;s instructions. Concentrations were log-transformed prior to statistical analysis(39,40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Language assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt 18 months, children\u0026rsquo;s language development was assessed using an online adaptation of the MacArthur-Bates Communicative Development Inventories (CDI)(49,50), a parent-report instrument widely used in neurodevelopmental research. Mothers reported their child\u0026rsquo;s language abilities using a structured checklist, classifying words as \u0026ldquo;understood\u0026rdquo; or \u0026ldquo;understood and spoken.\u0026rdquo; CDI percentile scores were calculated based on age- and sex-normative data. Six core language measures are analyzed: \u003cem\u003eVocabulary\u003c/em\u003e (lexicon size), \u003cem\u003eMLU\u003c/em\u003e (utterance length as a proxy for syntax), \u003cem\u003eComplexity\u003c/em\u003e (diversity and structure of word combinations), \u003cem\u003eVocalizations\u003c/em\u003e (preverbal phonological and speech motor output), \u003cem\u003eInflections\u003c/em\u003e (morphological endings and articulation), and \u003cem\u003eDifficult Verbs\u003c/em\u003e (use of higher order action verbs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted in R 4.3.2. Categorical variables were compared using Pearson\u0026rsquo;s \u0026chi;\u0026sup2; or Fisher\u0026rsquo;s exact tests and continuous variables using t tests (Welch\u0026rsquo;s when appropriate), Mann\u0026ndash;Whitney U tests, ANOVA, or ANCOVA, testing maternal\u0026ndash;infant covariate sets (education, income; gestational age, birth weight) and, given consistent results, reporting the least-missing models to maximize sample size. Group equivalence was assessed with two one-sided tests (TOST; bounds 0.5\u0026ndash;1.5 SD). Associations between log₁₀(IL-6/IL-10) and language percentiles were modeled using GAMs and spline-based linear models. Model support was compared using AIC, BIC, RMSE, adjusted R\u0026sup2;, and deviance explained (\u0026Delta;AIC \u0026lt; 2). Exploratory regression models, we report unadjusted p and increasingly stringent multiple-comparison control: false discovery rate (FDR)\u0026mdash;the expected false-positive proportion among significant results\u0026mdash;and family-wise error rate\u0026mdash;the chance of \u0026ge;1 false positive across tests (Bonferroni); all tests were two-sided with p \u0026lt; 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sociodemographic, clinical and physiological characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant differences were found between the case and control groups in maternal age, parity, or marital status. However, a significantly higher proportion of mothers in the case group had an annual income below \u0026euro;20,000 (64.6% vs. 25.7%, \u0026chi;\u0026sup2;=12.25, p\u0026lt;0.001), and a trend toward lower education levels was observed (p\u0026lt;0.1). Cytokine balance concentrations showed no significant differences (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Newborns exhibited optimal and comparable neonatal health parameters, with no statistical differences observed (5th-minute APGAR scores: 9.8 vs. 9.5). A trend toward higher gestational age was noted in the case group (40.18 vs. 39.59 weeks, p\u0026lt;0.1). All case\u0026ndash;control comparisons are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e, with sex-stratified results provided in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sociodemographic, clinical and phisiological baseline data comparisons from COGESTCOV19 case-control maternal-newborn dyads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic(\u003cem\u003edf\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMother\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1,774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried or with parther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnual salary (\u0026lt;\u0026euro;20k)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;2\u003c/em\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12,248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimiparous (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;2\u003c/em\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTabaco (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrimester of infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNewborn\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGestational age (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1116,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e566,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e562,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1080,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;2\u003c/em\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFull term (Full term)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNatural birth (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;2\u003c/em\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClinical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1,328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSRRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP Anxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTAI-E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFear to COVID-19 scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e897,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEPDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHospitalization (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptoms during COVID-19 (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFever (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e56,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCough (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFatigue (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle pain (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiarrhea (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeadache (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e52,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e91,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePhysiological\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMother\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72,55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e73,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0,179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLenght (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e164,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e164,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0,565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e27,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePospartum systolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e111,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e112,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePostpartum diastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e68,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e67,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6 (log10(pg/mL))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et W(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-10 (log10(pg/mL))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et W(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6/IL-10 ratio (log10(pg/mL))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003et W(62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNewborn\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3388.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e466.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3411.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e479.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLength (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.2447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1st minute APGAR score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e920,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5th minute APGAR score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e903,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10th minute APGAR score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Early language development outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCDI percentile scores were examined for six core language measures (\u003cem\u003eVocabulary\u003c/em\u003e, \u003cem\u003eLME\u003c/em\u003e, \u003cem\u003eComplexity\u003c/em\u003e, \u003cem\u003eVocalizations\u003c/em\u003e, \u003cem\u003eInflections\u003c/em\u003e, and \u003cem\u003eDifficult\u003c/em\u003e \u003cem\u003eVerbs\u003c/em\u003e) in case\u0026ndash;control comparisons (\u003cstrong\u003eTable 2\u003c/strong\u003e), with additional analyses stratified by sex (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eUnadjusted analyses revealed no significant case\u0026ndash;control differences in early language development, except for Difficult Verbs in sex-stratified comparisons. For this measure, both case and control females outperformed case males (all p\u0026lt;0.05). In addition, case females also outperformed control males (M=67.3, SD=12.5, p\u0026lt;0.05). However, after adjustment for covariates, none of these differences remained significant. Supplementary TOST analyses reinforced statistical equivalence (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figure 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e MacArthur CDI percentile scores: unadjusted and adjusted case\u0026ndash;control comparisons at 18 months in the COGESTCOV19 cohort.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted (years of education \u0026amp; gestational age)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eN = 36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eVocabulary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMLU\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eComplexity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eVocalizations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e820,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eInflections\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eDifficult Verbs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1027,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Maternal immune\u0026ndash;language associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe modelled 18-month CDI percentiles as a function of maternal log₁₀(IL-6/IL-10) using GAMs and spline-based linear models adjusted for exposure, maternal education, vaccination, and age, with comparable fit across approaches (\u0026Delta;AIC \u0026lt; 2). Most immune effects were non-significant after correction, with signal limited to two patterns: an exposure-specific non-linear association between higher IL-6/IL-10 and lower Difficult Verbs, and a female-specific association with lower Vocalizations among exposed girls, alongside a maternal-age effect. No robust IL-6/IL-10 associations were observed for Vocabulary, MLU, or Inflections, and IL-6/IL-10\u0026ndash;independent case\u0026ndash;control differences were small (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e 1).\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eDifficult\u003c/em\u003e \u003cem\u003eVerbs\u003c/em\u003e, the main immune-related signal was an exposure-specific association with maternal IL-6/IL-10. In pooled case control models, the exposed group showed a nonlinear effect in the GAM (F\u0026asymp;3.54, p\u0026le;0.05) mirrored by the spline-based LM (t\u0026asymp;\u0026minus;2.52, p\u0026le;0.05), such that higher IL-6/IL-10 in exposed pregnancies was associated with lower \u003cem\u003eDifficult\u003c/em\u003e \u003cem\u003eVerbs\u003c/em\u003e percentiles, with no comparable pattern in controls. However, these effects did not survive FDR correction (all p\u0026gt;0.05). In sex stratified analyses, the effect was weaker but directionally consistent in girls, with an exposed specific GAM smooth (F\u0026asymp;3.05, p\u0026le;0.05) and only a trend in the corresponding LM (t\u0026asymp;\u0026minus;2.05, p\u0026gt;0.05), again not surviving FDR correction.\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eVocalizations\u003c/em\u003e, the main immune-related pattern suggested a female-specific vulnerability among exposed girls. In females, the GAM showed an exposed-specific IL-6/IL-10 smooth (F\u0026asymp;4.33, p\u0026le;0.05) indicating lower \u003cem\u003eVocalization\u003c/em\u003e percentiles at higher maternal IL-6/IL-10, although this did not survive FDR correction (p\u0026gt;0.05). In contrast, the spline-based LM showed an IL-6/IL-10 spline term that remained FDR significant (t\u0026asymp;\u0026minus;3.01, p\u0026le; 0.01; p(FDR)\u0026le; 0.05), and a maternal age spline term that was also FDR significant (t\u0026asymp;2.91, p\u0026le;0.01; p(FDR)\u0026le;0.05).\u003c/p\u003e\n\u003cp\u003eTo describe the internal structure of language skills in the regression sample, we estimated case- and control-specific partial correlations among MacArthur CDI percentiles adjusting for maternal age and education (\u003cstrong\u003eFigure 5\u003c/strong\u003e). \u003cem\u003eVocabulary\u003c/em\u003e, \u003cem\u003eMLU\u003c/em\u003e, and \u003cem\u003eComplexity\u003c/em\u003e formed a moderately to strongly intercorrelated cluster in both groups (r\u0026asymp;0.38\u0026ndash;0.74; mostly p\u0026le;0.05), with \u003cem\u003eVocalizations\u003c/em\u003e showing modest-to-moderate correlations with this cluster (r\u0026asymp;0.35\u0026ndash;0.60; often p\u0026le;0.05). Inflections correlated moderately with \u003cem\u003eVocabulary\u003c/em\u003e/\u003cem\u003eMLU\u003c/em\u003e/\u003cem\u003eComplexity\u003c/em\u003e in cases (r=0.44\u0026ndash;0.62; p\u0026le;0.01) but were weak and non-significant in controls (|r|\u0026le;0.19). \u003cem\u003eDifficult Verbs\u003c/em\u003e showed the weakest connectivity, with only small-to-moderate associations (cases: r=0.34\u0026ndash;0.39 with \u003cem\u003eVocabulary\u003c/em\u003e/\u003cem\u003eComplexity\u003c/em\u003e/\u003cem\u003eVocalizations\u003c/em\u003e; controls: r=0.40\u0026ndash;0.48 with \u003cem\u003eMLU\u003c/em\u003e/\u003cem\u003eComplexity\u003c/em\u003e; other pairs generally non-significant).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe did not observe case\u0026ndash;control differences in language scores, with most comparisons satisfying equivalence criteria. By contrast, within the exposed group, more pro-inflammatory IL-6/IL-10 ratios were associated with lower \u003cem\u003eDifficult Verbs\u003c/em\u003e percentiles and, among exposed girls, with lower \u003cem\u003eVocalizations\u003c/em\u003e. This pattern points to a domain- and sex-modulated sensitivity to immune imbalance against a background of broadly similar language performance in a shared pandemic context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarly Language Development at 18 Months\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results do not support a robust global delay in early language among infants prenatally exposed to maternal SARS-CoV-2 infection. Case\u0026ndash;control comparisons indicate that mild-to-moderate maternal infection during pregnancy is not systematically associated with clinically meaningful decrements in early vocabulary or related language domains at 18 months. This pattern aligns with studies reporting no substantial impact of prenatal SARS-CoV-2 exposure on early language development(32,35)\u0026nbsp;and diverges from reports suggesting more generalized delays(51,52). Importantly, this lack of group-level difference holds when analyses are stratified by sex: we did not identify robust case\u0026ndash;control differences in global CDI language scores among either boys or girls. These findings suggest that, at least by 18 months and in the context of predominantly mild-to-moderate infection, prenatal SARS-CoV-2 exposure does not produce a uniform language delay detectable with parent-report measures in one sex over the other(53,54).\u003c/p\u003e\n\u003cp\u003eAt the same time, both exposed and non-exposed children in our sample scored in comparatively low percentiles for several CDI subscales. This convergence at the lower end of the normative distribution is consistent with broader pandemic-era sociocultural factors\u0026mdash;such as reduced opportunities for social interaction, altered caregiving routines, and changes in healthcare access\u0026mdash;exerting diffuse pressure on early communicative environments(53\u0026ndash;56). Such pressures are likely to affect boys and girls alike, potentially overshadowing small infection-specific effects in global scores. Overall, the findings suggest that mild-to-moderate SARS-CoV-2 infection per se is unlikely to produce a uniform, early language delay at 18 months, although more severe infections or later follow-ups may yield different patterns and should be examined in future work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-6/IL-10 balance, domain-specific vulnerability, and sex modulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough we initially expected an inverted U-shaped association, the observed effects were concentrated in the pro-inflammatory direction, consistent with the idea that risk may track regulatory balance rather than isolated cytokine concentrations. The absence of group differences in absolute cytokine concentrations, combined with these ratio effects, is consistent with the idea that risk is more closely tied to regulatory balance than to isolated levels(57). The combination of largely null case\u0026ndash;control cytokine differences with ratio-linked outcomes supports a balance-based interpretation and aligns with preclinical work suggesting that insufficiently counter-regulated pro-inflammatory signaling can bias circuit maturation under moderate inflammatory load(58\u0026ndash;60), even when gross milestones remain intact(61,62). Given our single time-point immune measurement and modest sample size, mechanistic inference remains tentative.\u003c/p\u003e\n\u003cp\u003eImmune\u0026ndash;language associations were restricted to \u003cem\u003eDifficult Verbs\u003c/em\u003e and, in exposed girls, \u003cem\u003eVocalizations\u003c/em\u003e, arguing against a generalized language slowdown and instead supporting, if any, selective vulnerability of specific components. \u003cem\u003eDifficult Verbs\u003c/em\u003e may reflect higher-order lexical\u0026ndash;conceptual operations (e.g. action representation, argument structure, flexible verb morphology) that place demands on frontotemporal and frontoparietal integration(63), whereas \u003cem\u003eVocalizations\u003c/em\u003e index speech-motor and phonological output(64,65). An hypothesis is that pro-inflammatory imbalance subtly influences refinement within long-range association pathways supporting these skills, yielding small domain-specific shifts without broad impairment(66,67). A testable prediction for future work is that higher prenatal IL-6/IL-10 ratios will be more strongly related to microstructural or connectivity variation in language- and motor-related association tracts than to global brain measures(61,62)\u003c/p\u003e\n\u003cp\u003eAn additional nuance is that IL-6/IL-10\u0026ndash;language associations were detectable only in the SARS-CoV-2\u0026ndash;exposed group, despite comparable distributions of the ratio across cases and controls. This suggests that IL-6/IL-10 may act as a marker of risk only within a broader, virus-specific immune context. Viral infections such as SARS-CoV-2 are characterized by distinct interferon and chemokine profiles, placental transcriptional responses, and endothelial changes that are not fully mirrored by non-viral inflammatory states(57,68,69). Thus, a given degree of pro-inflammatory IL-6/IL-10 imbalance may be embedded in a qualitatively different immune and placental milieu in exposed pregnancies, making the same nominal ratio more neurobiologically consequential than in controls. Although our data lack a broader immune panel and detailed non-viral histories, this yields a clear empirical test: multivariate immune profiles combining IL-6/IL-10 with viral-response markers should explain more variance in language outcomes than the ratio alone, and similar IL-6/IL-10 values arising from non-viral inflammation should show weaker or qualitatively different associations with language.\u003c/p\u003e\n\u003cp\u003eThe female-specific association between higher IL-6/IL-10 ratios and lower Vocalizations in exposed infants further invites a sex-sensitive pathway. Experimental and human studies indicate that females differ from males in placental responsiveness, X-linked immune regulation, microglial activation profiles, and the timing of synaptic pruning and myelination in socio-communicative and motor networks(68,70,71). A plausible interpretation is that pro-inflammatory gestational imbalance interacts with female developmental trajectories, yielding subtle alterations in vocal-motor and frontotemporal circuitry that reduce early vocal output without producing a global language delay. A testable prediction is that, in larger, deeply phenotyped samples, immune\u0026ndash;language associations will emerge earlier or show steeper gradients in girls than in boys, particularly for vocal and socio-communicative measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for understanding MIA and pandemic-era influences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaken together, these findings support SARS-CoV-2\u0026ndash;related maternal immune activation as a graded and context-dependent influence rather than a deterministic cause of early language delay(7,57,72). Mild-to-moderate infection does not appear to uniformly shift early language milestones; instead, individual differences in immune balance\u0026mdash;embedded within virus-specific immune milieus and modulated by sex\u0026mdash;may leave subtle, domain-specific imprints on early expressive skills. Conceptually, this pattern aligns with models in which prenatal immune imbalance biases developmental trajectories while postnatal experience shapes their expression(73,74). \u0026nbsp;Given that language emergence is strongly experience-dependent and that pandemic-era disruptions likely altered the communicative environment of both exposed and non-exposed children(73,75\u0026ndash;77), these results argue against simple exposed\u0026ndash;unexposed contrasts and instead support integrative frameworks that jointly consider immune phenotypes, sex, and key features of the postnatal ecology when interpreting MIA-related developmental variation(74,78\u0026ndash;80).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey strengths include detailed perinatal clinical characterization, maternal cytokine data, and domain-level language phenotyping using the MacArthur\u0026ndash;Bates CDI. Analytically, equivalence testing strengthened interpretation of null case\u0026ndash;control contrasts, and flexible models of IL-6/IL-10 balance were well suited to detect non-linear and sex-specific patterns. Recruiting cases and controls within the same pandemic period also reduced confounding by secular changes in the broader sociocultural context.\u003c/p\u003e\n\u003cp\u003eLimitations include modest sample size, especially for sex-stratified and non-linear effects, increasing uncertainty around interaction patterns. Most infections were mild-to-moderate, limiting generalization to severe COVID-19 or other pathogens. Cytokines were measured at a single time point with a restricted panel centered on IL-6 and IL-10, providing only a partial snapshot of a dynamic immune trajectory. Language was assessed at one age via parent report, which may not capture all facets of communicative competence. Finally, multiple domains and models raise the possibility of chance findings, so the immune-linked, domain- and sex-specific patterns should be treated as hypothesis-generating.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture directions and conclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture work should test whether the domain- and sex-specific associations observed at 18 months persist and predict later language, cognitive, or socio-emotional outcomes while jointly modelling immune measures and environmental moderators. Deeper immune profiling of existing samples (broader cytokine panels and molecular signatures) could clarify trigger-specific vulnerability profiles without additional pregnancy sampling. Follow-up neurobiological measures in children, such as diffusion MRI, EEG, or fNIRS, would enable direct tests of whether prenatal immune balance maps onto variation in language- and motor-relevant circuits. More broadly, new pregnancy cohorts with repeated biospecimen sampling across gestation will be needed to capture immune dynamics and differentiate viral from non-viral inflammatory states, refining MIA as a trigger-specific set of immune signatures with distinct neurodevelopmental consequences.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this cohort of pandemic-born infants, mild-to-moderate maternal SARS-CoV-2 infection during pregnancy was not associated with a global delay in parent-reported language at 18 months. By contrast, a higher pro-inflammatory IL-6/IL-10 balance was linked to subtle, domain-specific reductions in expressive skills\u0026mdash;particularly \u003cem\u003eDifficult Verbs\u003c/em\u003e, and \u003cem\u003eVocalizations\u003c/em\u003e in exposed girls\u0026mdash;suggesting graded, sex-modulated effects rather than a uniform impact of MIA. These findings support a view of prenatal immune activation as a context-dependent influence embedded within broader pandemic-related environments, and highlight the need for longitudinal, mechanistically oriented follow-up before drawing firm conclusions about long-term neurodevelopmental risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Fundaci\u0026oacute;n Instituto de Investigaci\u0026oacute;n Marqu\u0026eacute;s de Valdecilla (grants INNVAL20/02 and INNVAL23/21). Dra. Rosa Ayesa-Arriola was financed by a Miguel Servet contract from the Carlos III Health Institute (CP18/00003) and a Consolidator Grant from the Ministerio de Ciencia e Innovaci\u0026oacute;n (CNS2022-136110). \u0026Aacute;ngel Yorca Ruiz was financed by a predoctoral contract (PFIS_FI/00162) from the Carlos III Health Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADP: Conceptualization, Data Curation, Software, Formal Analysis, Visualization, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. SCC: Software, Formal Analysis, Validation, Visualization, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. CMA: Data Curation, Software, Formal Analysis, Visualization. VOG: Data Curation, Software, Formal Analysis, Visualization. CP: Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, AYR: Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. SI: \u0026nbsp;Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. RAA: Conceptualization, Data Curation, Funding Acquisition, Investigation, Project Administration, Resources, Supervision, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the women who participated in this study and the IDIVAL BIOBANK workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this article is available upon request from the corresponding author, RAA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the author(s) used ChatGPT-5 in order to improve the readability and language of the manuscript. After using these tools, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGilmore JH, Fredrik Jarskog L. Exposure to infection and brain development: cytokines in the pathogenesis of schizophrenia. Schizophr Res. 1997 Apr 11;24(3):365\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eHall MB, Willis DE, Rodriguez EL, Schwarz JM. Maternal immune activation as an epidemiological risk factor for neurodevelopmental disorders: Considerations of timing, severity, individual differences, and sex in human and rodent studies. Front Neurosci. 2023 Apr 13;17:1135559. \u003c/li\u003e\n\u003cli\u003eEstes ML, McAllister AK. Maternal immune activation: Implications for neuropsychiatric disorders. Science. 2016 Aug 19;353(6301):772\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eBrown AS, Derkits EJ. 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A comprehensive approach to modeling maternal immune activation in rodents. Front Neurosci [Internet]. 2022 Dec 16 [cited 2025 Dec 10];16. Available from: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1071976/full\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Souza D, D\u0026rsquo;Souza H, Karmiloff-Smith A. Precursors to language development in typically and atypically developing infants and toddlers: the importance of embracing complexity. J Child Lang. 2017 May;44(3):591\u0026ndash;627. \u003c/li\u003e\n\u003cli\u003eKnudsen EI. Sensitive periods in the development of the brain and behavior. J Cogn Neurosci. 2004 Oct;16(8):1412\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eWhite EJ, Hutka SA, Williams LJ, Moreno S. Learning, neural plasticity and sensitive periods: implications for language acquisition, music training and transfer across the lifespan. Front Syst Neurosci. 2013 Nov 20;7:90. \u003c/li\u003e\n\u003cli\u003eFerrari E, Palandri L, Lucaccioni L, Talucci G, Passini E, Trevisani V, et al. The Kids Are Alright (?). Infants\u0026rsquo; Development and COVID-19 Pandemic: A Cross-Sectional Study. Int J Public Health. 2022;67:1604804. \u003c/li\u003e\n\u003cli\u003ePejovic J, Severino C, Vig\u0026aacute;rio M, Frota S. Prolonged COVID-19 related effects on early language development: A longitudinal study. Early Hum Dev. 2024 Aug 1;195:106081. \u003c/li\u003e\n\u003cli\u003eOtani T, Kato M, Haraguchi H, Goma H. Effect of the COVID-19 pandemic on infants\u0026rsquo; development: analyzing the results of developmental assessments at ages 10-11 and 18-24 months. Front Psychol. 2024;15:1430135. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"3b8b3bf6-d2da-4737-9414-c39ceec0a37d","identifier":"10.13039/501100018812","name":"Instituto de Investigación Marqués de Valdecilla","awardNumber":"INNVAL20/02","order_by":0},{"identity":"77c2ebca-23e6-46e7-a418-ad007201e1b3","identifier":"10.13039/501100018812","name":"Instituto de Investigación Marqués de Valdecilla","awardNumber":"INNVAL23/21","order_by":1},{"identity":"03ba83e6-d598-445d-af81-c3346f1b190b","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"CP18/00003","order_by":2},{"identity":"a8e5c0e1-be09-4681-819d-d955595317cf","identifier":"10.13039/501100004837","name":"Ministerio de Ciencia e Innovación","awardNumber":"CNS2022-136110","order_by":3},{"identity":"86955133-f6a2-4fdd-8688-643aa1b4ead7","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PFIS_FI/00162","order_by":4}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Instituto de Investigación Marqués de Valdecilla","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":"Maternal Immune Activation (MIA), SARS-CoV-2, Neurodevelopment, Language Acquisition, MacArthur-Bates Communicative Development Inventory (CDI)","lastPublishedDoi":"10.21203/rs.3.rs-9651346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9651346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Maternal immune activation (MIA) triggered by prenatal infections, including SARS-CoV-2, is a potential risk factor for neurodevelopmental alterations, with cytokines mediating the associated inflammatory responses. However, the specific impact of SARS-CoV-2–related MIA on early language acquisition remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: At 18 months, expressive language was assessed using an online version of the MacArthur–Bates Communicative Development Inventories (CDI) in 87 mother–infant dyads from the COGESTCOV-19 cohort (51 SARS-CoV-2–exposed, 36 controls). Maternal IL-6/IL-10 ratios were contrasted with CDI percentiles to probe immune–language associations. Analyses were adjusted for key sociodemographic and perinatal covariates, and sex-stratified models were additionally explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Across MacArthur CDI vocabulary outcomes did not differ between SARS-CoV-2–exposed and control infants, and sex-stratified differences were small and not sustained after adjustment. Higher maternal IL-6/IL-10 ratios were associated with lower \u003cem\u003eDifficult Verbs\u003c/em\u003e percentiles among exposed infants (p ≈ 0.03). \u003cem\u003eDifficult Verbs\u003c/em\u003eshowed modest correlations with other CDI domains (r ≈ 0.04–0.39). Among exposed females, higher maternal IL-6/IL-10 ratios were also associated with lower \u003cem\u003eVocalizations \u003c/em\u003epercentiles (p ≈ 0.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Mild-to-moderate SARS-CoV-2–related MIA was not associated with global early-language impairment at 18 months. Instead, immune–language links were limited to a pro-inflammatory IL-6/IL-10 imbalance and confined to specific language domains. Female-specific patterns further suggested sex-dependent viral pathways in SARS-CoV-2–related MIA effects. These findings support the need for larger longitudinal multimodal studies to clarify whether these associations are transient, compensatory, or early markers of later cognitive trajectories in pandemic-exposed children.\u003c/p\u003e","manuscriptTitle":"Pro-inflammatory IL-6/IL-10 imbalance in SARS-CoV-2-exposed pregnancies and early language development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:58:54","doi":"10.21203/rs.3.rs-9651346/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":"f91456db-7a40-4d38-8e39-2eb3ce91b84c","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67771677,"name":"Immunology"},{"id":67771678,"name":"Developmental Neuroscience"},{"id":67771679,"name":"Audiology \u0026 Speech-Language Pathology"}],"tags":[],"updatedAt":"2026-05-11T10:58:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:58:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9651346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9651346","identity":"rs-9651346","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}