Maternal Sleep and the Risk of Childhood Infections: Findings from the Rhea Birth Cohort in Greece

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We aim to examine the effects of maternal sleep characteristics during late pregnancy on the risk of infection across early childhood (from birth to 6 years of age). Methods This study used data of 545 mother-child pairs from the Rhea mother-child cohort in Greece. Maternal sleep characteristics during the third trimester including excessive daytime sleepiness, sleep disturbances, short sleep duration, and snoring, were assessed through validated questionnaires. A maternal sleep score was created encompassing all the sleep characteristics mentioned above, ranging from 0 (indicating optimal sleep) to 4 (indicating poor sleep). IgG levels to five polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyV), two herpesviruses (EBV, CMV), Helicobacter pylori , Adenovirus-36, and Toxoplasma gondii were measured at ages 4 and 6. Cox proportional hazards regression models were used to estimate associations between sleep characteristics and risk infection during early childhood. Results Maternal sleep disturbances were associated with increased risk of childhood infection to BKPyV (HR:1.36; 95%CI: 1.05–1.77), to KIPyV (HR:1.43; 95% CI: 1.08–1.89), to WUPyV (HR:1.71; 95% CI: 1.28–2.29), to MCVPyV (HR:1.51; 95% CI: 1.15-2.00), to H. pylori (HR: 2.17; 95% CI: 1.14–4.15) and Adv-36 (HR:1.57; 95% CI: 1.08–2.27). Moreover, a maternal sleep score of 3, indicating three poor sleep indicators, was associated with nearly double the risk of infection with WUPyV (HR: 1.92; 95% CI: 1.08–3.42). Conclusion Our findings suggest that poor maternal sleep during late pregnancy, specifically sleep disturbances, may increase the risk of infection through childhood. This is the first study to link prenatal sleep to childhood infection susceptibility, providing novel evidence of the potential long-term immunological impact of prenatal sleep quality. Maternal sleep childhood sleep infection risk immune system prenatal exposure Figures Figure 1 Figure 2 Figure 3 BACKGROUND Adequate sleep quality and sufficient sleep duration are fundamental for a healthy immune system. Sleep disruption can cause an overall dysregulation of immune cell population and cytokine levels. These ultimately lead to a higher susceptibility and worse recovery from infections, and a greater risk of systemic chronic inflammation ( 1 – 4 ). During pregnancy, many women report sleep disturbances, mostly in the first and third trimester, due to hormonal and physical changes ( 4 – 6 ). Frequency and severity of sleep problems increase as gestation progresses, particularly during the third trimester ( 7 – 9 ). Sleep disruption during pregnancy induces changes in inflammatory and pro-inflammatory markers that modulate the development of the offspring's immune system ( 10 , 11 ). These alterations during critical developmental windows have been linked to increased risks of autoimmune diseases and allergies later in life ( 12 , 13 ). Importantly, physiological and hormonal changes across gestation contribute to a progressive worsening of maternal sleep, with women in late pregnancy experiencing reduced sleep duration, greater sleep fragmentation, and poorer overall sleep quality compared to earlier stages ( 9 ). Such progressive sleep disruption may therefore represent a significant and evolving exposure influencing fetal immune development. Using data from the Rhea mother-child cohort in Crete, Greece, we aimed to examine the effects of maternal sleep during pregnancy on the risk of infections across early childhood (from 0 to 6 years of age). We hypothesized that short sleep duration and poorer sleep quality during pregnancy are associated with a higher risk of childhood infections. METHODS Study design and population The Rhea study is a birth cohort of 1081 pregnant women recruited from February 2007 to January 2008. Recruitment occurred mostly during the first 15 weeks of gestation. To be eligible for inclusion, women had to have a good understanding of the Greek language and be older than 16 years of age. Detailed information on the cohort is available elsewhere ( 14 ). Mothers were contacted again at 24 weeks gestation, at birth, and for the children’s follow up at 9 months, 18 months and at 4, 6, 11 and 15 years of age. The study was approved by the Ethics Committee of the University Hospital of Heraklion (reference no. 96 − 6/2/2007), and all participating mothers provided written, informed consent. The research was conducted in accordance with national and international ethical guidelines, including the Declaration of Helsinki and the relevant ethical codes. We required participants for the present analysis to have a) complete maternal sleep data (for maternal-child analyses), b) at least serology information at 4 years of age, and c) complete covariate information to be included in our analytic sample. An overview of exclusions and the final sample size is included in Fig. 1 . Exposure: maternal sleep Maternal sleep duration during pregnancy (3rd trimester questionnaire) was obtained from the following closed-ended question: “How many hours do you usually sleep during your pregnancy”. The mother reported sleep duration as ≤ 5 hours, 6–7 h, 8–9h, and ≥ 10 hours. Sleep duration was then classified as ≤ 7 or > 7 hours of sleep, according to the WHO sleep duration during pregnancy recommendations, and as similarly done in previous papers ( 15 ). The Epworth Sleepiness Scale was used to determine the level of daytime sleepiness. This scale rates the likelihood of falling asleep in 8 situations on a scale of 0 to 3 (0 = never, 1 = slight, 2 = moderate, 3 = high) and can range from 0 to 24. Excessive daytime sleepiness was defined as a total score of 10 or more ( 16 , 17 ). Snoring frequency was measured from the Apnea Score within the Berlin Questionnaire ( 18 ), and was recorded into two categories: never or rarely (≤ 2 times or less per month) and frequently or always (≥ 1 times or more per week). Sleep disturbances during the 3rd trimester were obtained by the following closed-ended question: “In the last 3 months, did you wake up during the night due to breathing difficulties?”. The complete questionnaire can be found in the supplementary file ( Appendix S1). We created an adverse maternal sleep score encompassing all the sleep characteristics evaluated in this study (ranging 0 to 4), which included: excessive daytime sleepiness (≥ 10 = 1); presence of sleep disturbances (Yes = 1); inadequate sleep duration (≤ 7 hours = 1); snoring frequency (frequently or always = 1). Higher sleep scores indicate worse sleep quality. Outcome: infection during childhood Outcome: infection during childhood The exposure to five polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyV), two herpesviruses (EBV, CMV), Adenovirus-36 (Adv-36), Helicobacter pylori ( H.pylori ) and Toxoplasma gondii ( T.gondii ) were determined by serological testing of immunoglobulin G (IgG) to the pathogen specific antigens in plasma samples collected at 4 and 6 years of age. IgG antibodies are typically produced a short time after initial infection and persist months or years, thus their presence indicates that an individual was infected at some point in their life, but not when the initial infection occurred. Details regarding the methods used to detect IgG specific to each pathogen are provided in Appendix S2. The serological assays were performed at the Immune Response and Biomarkers Core Facility at the Barcelona Institute for Global Health (ISGlobal). Covariates Potential confounders were identified a priori based on the literature ( 19 – 21 ) and included: age (continuous, years), education (low, middle and high), marital status (married/engaged, other), pre-pregnancy body mass index (BMI) status (underweight, normal, overweight, obese) and smoking at 30 weeks gestation. Statistical analysis Descriptive statistics were used to examine the population’s sleep and demographic characteristics, and we also described the prevalence of infections at 4 and 6 years of age. We also examined correlations between each of the different indicators of poor sleep. Cox regression models were used to study the associations between prenatal maternal indicators of poor sleep and seroconversion (indicative of infection) through 6 years of age. Hazard ratios (HR) and 95% confidence intervals (CIs) from Cox proportional hazards regression models were estimated using time since birth as the time scale. Participants were followed until the 1st indication of a given infection at one of the follow-up visits or the occurrence of a censoring event. Participants were censored at the last available time point if they did not attend the next follow-up. In total, 402 participants had serological data available at the 6-year follow-up visit. Models were adjusted for the aforementioned confounders. The average time at risk is provided in supplementary Tables S1. As a sensitivity analysis, we additionally adjusted the models for child sleep duration at age 4 (i.e., whether the child met the recommended 10 hours of total daily sleep ( 22 )) to assess whether child sleep could account for part of the observed associations. All analyses were conducted using RStudio Software 1.4.1106. RESULTS Population characteristics Of the 1,081 mother–child pairs in the Rhea study, 565 mothers had complete data on sleep during pregnancy. Among these, 545 had complete data on relevant covariates and child infection data at the 4-year follow-up. (Fig. 1 ). Characteristics of the study population are presented in Table 1 . The mean age among the mothers was 30 (SD: 5) years old. During pregnancy, 8% had excessive daytime sleepiness, 14% experienced sleep disturbances, 36% slept ≤ 7 hours at night, 20% snored and 16% presented multiple indicators of poor sleep (sleep score above 1). No mother presented all indicators of poor sleep (sleep score of 4) (Table 1 ). There were no associations among the different maternal indicators of poor sleep when comparing the prevalence in a one to one fashion (Supplementary Table S2 ). Table 1 Maternal demographic and sleep characteristics (n = 545). Demographic characteristics Overall % / Mean (SD) Sleep characteristics Overall % / Mean (SD) Age 29.9 (4.9) Excessive daytime sleepiness 8.1 Pre-pregnancy BMI status a Sleep disturbances 13.9 Underweight 2.9 ≤ 7 hours of sleep b 35.9 Normal 62.4 Snoring 20.0 Overweight 21.3 Adverse sleep score Obese 13.4 0 (healthier sleep score) 41.5 Education 1 41.8 Low 15.1 2 13.9 Middle 51.7 3 2.8 High 33.2 4 (poorer sleep score) 0 Marital status Married/engaged 89.5 Other 10.5 Smoking (30 weeks) 16.7 a Based on the International Obesity Task Force cut offs. b ≤ 7 hours of sleep is considered short sleep. Based on the International Obesity Task Force cut offs. The most prevalent infections both at age 4 and 6 were BKPyV (88% and 94%) and MCVPyV (77% and 85%) whereas the least prevalent was H.pylori (10% and 13.2%) (Table 2 ). Table 2 Prevalence of infections among children at 4 (n = 545) and 6 (n = 402) years of age / follow up. Infection Prevalence (%) Age 4 Prevalence (%) Age 6 BKPyV 88.3 94.8 JCPyV 28.6 39.3 KIPyV 71.4 73.9 WUPyV 60.2 64.9 MCVPyV 76.5 85.6 EVB 70.3 78.6 CMV 33.0 31.6 Helicobacter pylori 9.4 13.2 Adv-36 36.4 46.0 Toxoplasma gondii 40.6 40.1 Maternal sleep characteristics and childhood infections Figure 2 depicts the adjusted hazard ratios of the associations between maternal sleep during pregnancy and the risk of infection in offspring through 6 years of age. Children of mothers who reported sleep disturbances during the third trimester of pregnancy had a higher risk of infection compared to those without sleep disturbances, specifically to BKPyV (HR:1.36, 95%CI (1.05–1.77), p-value: 0.02), to KIPyV (HR:1.43, 95%CI (1.08–1.89), p-value: 0.01), to WUPyV (HR:1.71, 95%CI (1.28–2.29), p-value: 0.001), to MCVPyV (HR:1.51, 95%CI (1.15-2.00), p-value: 0.01), to H. pylori (HR: 2.17, 95%CI (1.14–4.15), p-value: 0.02) and Adv-36 (HR:1.57, 95%CI (1.08–2.27), p-value: 0.02). No statistically significant associations were revealed for the remaining individual maternal sleep characteristics. Children from women with several indicators of poor sleep (sleep score of 3) had a higher risk of infection to WUPyV (HR:1.92, 95%CI (1.08–3.42), p-value: 0.03) compared to children from mothers with no indicators or poor sleep (score of 0) (Fig. 3 ). In sensitivity analyses, further adjustment for child total sleep duration at age 4 (81.3% slept ≥ 10h) did not materially alter the associations between prenatal maternal indicators of poor sleep and sleep score and infection outcomes (supplementary Figures S1 and S2). DISCUSSION This study explored the association between poor sleep characteristics during the third trimester of pregnancy, with the risk of infection through childhood. Maternal indicators of poor sleep were linked with a higher risk of acquisition of several common infections in childhood (polyomaviruses, H.pylori and Adv-36). Our study contributes to a growing body of literature on the relationship between the quality of sleep and immunity in humans, focusing on the importance of optimal sleep during critical developmental periods, such as pregnancy. Disruptions in maternal sleep can lead to adverse outcomes for both mother and child, as they may increase susceptibility to intrauterine exposures that influence offspring development ( 23 , 24 ). Existing research highlights a strong link between sleep disturbances during pregnancy with a wide range of developmental challenges in the offspring including immune system development ( 25 , 26 ). In our dataset, sleep disturbances emerged as a risk factor for susceptibility to common infections (polyomaviruses, Adv-36 and H.pylori ). In a review, Irwin et. al. concluded that sleep disturbances were more strongly associated with inflammatory dysregulation than short sleep duration ( 27 ). To our knowledge, there are no studies evaluating how maternal sleep affects the risk of infections in offspring. However animal studies have shown that maternal sleep patterns influence the immune development of the offspring ( 28 , 29 ). It is very common for children to experience infections during early childhood. We evaluated using serology previous exposure to common pathogens including viruses, bacteria and parasites. Gestational sleep disturbances were linked to higher risk to several viral infections as well as to H.pylori , a bacterium that colonises the gastric stomach and is linked to several gastric and extragastric conditions already in childhood ( 30 ).Moreover, offspring of mothers who experienced several sleep disorders during late pregnancy had a higher risk of infections with WUPyV, a respiratory pathogen that might play a role in asthma development ( 31 ). Strengths of our study include the cohort design with multiple follow-ups visits at different life-stages from pregnancy to childhood, the use of biomarkers to evaluate exposure to common infections and the availability of information on important confounders. There are some limitations as well. Firstly, sleep characteristics were self-reported, which may introduce reporting bias. Moreover, the indicators of poor sleep were measured only at the end of pregnancy (30 weeks). However, maternal sleep disturbances progressively worsen across gestation, particularly during the third trimester ( 7 – 9 ) and although early and mid- pregnancy are critical windows for fetal immune development, sleep disturbances later in pregnancy have been linked to adverse fetal immune development and adverse neonatal outcomes ( 32 ). Additionally, even though we only have data on maternal sleep at one time point (third trimester), we had data on several sleep metrics, so we were able to not only evaluate sleep duration but also sleep quality. In addition, sleep in early childhood is known to influence immune function and infection risk ( 33 ). To account for this, we conducted a sensitivity analysis, further adjusting for total sleep duration at 4 years of age, and the results remained consistent, suggesting that the associations we observed are not meaningfully influenced by children’s sleep. Further, there was some loss of participants to follow-up. However, we do not believe that it introduced differential bias, since random loss does not impact our results as systematic censoring in a specific group would. In addition, we could not determine the exact moment of infection, as data on presence of infections was only available at the follow-up visits at 4 and 6 years of age. This limits our ability to draw precise conclusions about the causal relationships between sleep characteristics and subsequent infections. However, our prospective design allows us to avoid this concern, since childhood infection does not impact maternal sleep. Moreover, though the possibility of false-negative IgG results cannot be entirely excluded, the assays used in our study incorporated rigorous quality control measures (blanks, positive and negative controls), and for some pathogens, multiple antigen responses were measured to define seropositivity, minimizing the risk of misclassification. Even though outbreaks of relevant pathogens could potentially influence risk, no major changes in the epidemiology of the infections were reported during the study period. Lastly, our data come from a Greek population, which can limit the generalizability of our findings to other populations, specifically to populations where cultural norms around sleep differ. CONCLUSION Our study provides evidence that sleep disturbances in utero could impact the immune system function in the offspring, increasing the risk of acquisition of common pathogens during childhood. These findings highlight the importance of sleep as a critical factor in childhood health and suggest that interventions to improve sleep could play a role in infection prevention. To our knowledge, this is the first research on sleep and infection that examines the effect of maternal sleep during pregnancy on the risk of infection through childhood. Therefore additional studies need to replicate these findings. If consistency of these results is found, sleep interventions should be considered to reduce infection rates in children. Abbreviations Rhea Mother Child Cohort Rhea Immunoglobulin G IgG Polyomavirus BK BKPyV Polyomavirus JC JCPyV Polyomavirus KI KIPyV Polyomavirus WU WUPyV Polyomavirus MCV MCVPyV Herpesvirus EBV EBV Herpesvirus CMV CMV Adenovirus 36–Adv–36 Helicobacter pylori H.pylori Toxoplasma gondii T.gondii Hazard Ratio HR Declarations Ethics approval and consent to participate All studies received approval from the ethics committees of the centers involved and written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The data underlying this article cannot be shared publicly due to ethical and privacy considerations. The data will be shared on reasonable request to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding RHEA: The "Rhea" project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 NV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX) and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; “Rhea Plus”: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). La Marato de TV3 2017, Project number 201819, Reg. 78 / 437, Title “Infection acquisition in early life and health outcomes in childhood”. Authors' contributions Conceptualization:MKa,BH ; Methodology: MKa, AE, KP, MKo, BH; Formal analysis: RG, AE ; Investigation: RG ; Data curation:RA, MV, CD, TR, KM; Writing – original draft preparation: RG,BH ; Writing – review and editing: ALL; Supervision: BH ; Project administration: MKa, MKo; Funding acquisition: MKo. Acknowledgements We are grateful to all the participants, health professionals and researchers who have made Rhea happen. Clinical trial number Not applicable AUTHORS’ CONTACT INFORMATION Marianna Karachaliou [email protected] , Ana Espinosa [email protected] , Kyriaki Papantoniou [email protected] , Ruth Aguilar [email protected] , Marta Vidal [email protected] , Theano Roumeliotaki [email protected] ,Despo Ierodiakonou [email protected] , Katerina Margetaki [email protected] , Katerina Koutra [email protected] , Emmanouil Symvoulakis [email protected] , Carlota Dobaño [email protected] , Manolis Kogevinas [email protected] , Barbara N. 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Supplementary Files FigureS1.Maternalsleepandchildhoodinfectionadjustedforsleepat4years.pdf.png FigureS2.Maternalsleepscoreandchildhoodinfectionadjustedforsleepat4years.png SupplementaryfileMaternalandChildhoodinfections.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":31748,"visible":true,"origin":"","legend":"\u003cp\u003eSample size flow chart.\u003c/p\u003e","description":"","filename":"Figure1.Flowchart.pdf1.png","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/c7d771858fa1fd2e906571ce.png"},{"id":93543427,"identity":"829b4115-fb9c-4e22-93ed-e30d03890715","added_by":"auto","created_at":"2025-10-15 02:42:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315498,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted estimates of the association between maternal indicators of poor sleep during pregnancy and the risk of infection in offspring (n=545).\u003c/p\u003e\n\u003cp\u003eCaption: Forest plots, hazard ratios (HR), 95% confidence interval and p-value for maternal (n= 545) sleep characteristics and infections [Polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyv), herpesviruses (EBV, CMV), Adenovirus-36 (Adv-36), \u003cem\u003eHelicobacter pylori \u003c/em\u003e(\u003cem\u003eH.pylori)\u003c/em\u003e and \u003cem\u003eToxoplasma gondii\u003c/em\u003e (\u003cem\u003eT.gondii\u003c/em\u003e)] in offspring. In red, statistically significant associations. Models are adjusted by maternal age, maternal education, marital status, pre-pregnancy BMI and smoking at 30 weeks of gestation.\u003c/p\u003e","description":"","filename":"Figure2.Maternalsleepandchildhoodinfection.pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/79125b21f9ec0c05b5d356d3.png"},{"id":93543439,"identity":"fe3ca0bd-4984-43ab-bff7-88f7532649d0","added_by":"auto","created_at":"2025-10-15 02:42:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":261335,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted estimates of the association between maternal sleep score and the risk of infection in offspring (n= 545).\u003c/p\u003e\n\u003cp\u003eCaption: Forest plots, hazard ratios (HR), 95% confidence interval and p-value for maternal adverse sleep score and infections [Polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyv), herpesviruses (EBV, CMV), Adenovirus-36 (Adv-36), \u003cem\u003eHelicobacter pylori \u003c/em\u003e(\u003cem\u003eH.pylori)\u003c/em\u003e and \u003cem\u003eToxoplasma gondii\u003c/em\u003e (\u003cem\u003eT.gondii\u003c/em\u003e)] in offspring. In red, statistically significant associations. The numbers below each antigen indicated the sleep score assessed. Sleep scores (0 to 4) encompasses all the sleep characteristics evaluated in this study. Higher scores reflect poorer sleep. Models are adjusted by maternal age, maternal education, marital status, pre-pregnancy BMI and smoking at 30 weeks of gestation.\u003c/p\u003e","description":"","filename":"Figure3.Maternalsleepscoreandchildhoodinfections.png","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/b3a756b44c6ead2f2c52e409.png"},{"id":93669783,"identity":"2d24287a-31eb-4bb6-b02d-cc1cf0ab892d","added_by":"auto","created_at":"2025-10-16 09:47:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1294397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/236de42d-2466-430f-8488-4cffa1075091.pdf"},{"id":93543424,"identity":"36391092-b1f0-437b-96c9-ef9dea7fc6d8","added_by":"auto","created_at":"2025-10-15 02:42:51","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":317054,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.Maternalsleepandchildhoodinfectionadjustedforsleepat4years.pdf.png","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/f28f47d5abdb6f5a3c553958.png"},{"id":93543429,"identity":"adf39d89-c0c7-4829-a953-640c5a17ce53","added_by":"auto","created_at":"2025-10-15 02:42:51","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":259754,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.Maternalsleepscoreandchildhoodinfectionadjustedforsleepat4years.png","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/3e8533cd95c43b0ab3c50407.png"},{"id":93543436,"identity":"069df175-1847-448e-ac35-c3183ea76fd4","added_by":"auto","created_at":"2025-10-15 02:42:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14008,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileMaternalandChildhoodinfections.docx","url":"https://assets-eu.researchsquare.com/files/rs-7633169/v1/eff5d47de565419342d2df2d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Maternal Sleep and the Risk of Childhood Infections: Findings from the Rhea Birth Cohort in Greece","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAdequate sleep quality and sufficient sleep duration are fundamental for a healthy immune system. Sleep disruption can cause an overall dysregulation of immune cell population and cytokine levels. These ultimately lead to a higher susceptibility and worse recovery from infections, and a greater risk of systemic chronic inflammation (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). During pregnancy, many women report sleep disturbances, mostly in the first and third trimester, due to hormonal and physical changes (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Frequency and severity of sleep problems increase as gestation progresses, particularly during the third trimester (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Sleep disruption during pregnancy induces changes in inflammatory and pro-inflammatory markers that modulate the development of the offspring's immune system (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These alterations during critical developmental windows have been linked to increased risks of autoimmune diseases and allergies later in life (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Importantly, physiological and hormonal changes across gestation contribute to a progressive worsening of maternal sleep, with women in late pregnancy experiencing reduced sleep duration, greater sleep fragmentation, and poorer overall sleep quality compared to earlier stages (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Such progressive sleep disruption may therefore represent a significant and evolving exposure influencing fetal immune development.\u003c/p\u003e\u003cp\u003eUsing data from the Rhea mother-child cohort in Crete, Greece, we aimed to examine the effects of maternal sleep during pregnancy on the risk of infections across early childhood (from 0 to 6 years of age). We hypothesized that short sleep duration and poorer sleep quality during pregnancy are associated with a higher risk of childhood infections.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThe Rhea study is a birth cohort of 1081 pregnant women recruited from February 2007 to January 2008. Recruitment occurred mostly during the first 15 weeks of gestation. To be eligible for inclusion, women had to have a good understanding of the Greek language and be older than 16 years of age. Detailed information on the cohort is available elsewhere (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Mothers were contacted again at 24 weeks gestation, at birth, and for the children\u0026rsquo;s follow up at 9 months, 18 months and at 4, 6, 11 and 15 years of age. The study was approved by the Ethics Committee of the University Hospital of Heraklion (reference no. 96\u0026thinsp;\u0026minus;\u0026thinsp;6/2/2007), and all participating mothers provided written, informed consent. The research was conducted in accordance with national and international ethical guidelines, including the Declaration of Helsinki and the relevant ethical codes. We required participants for the present analysis to have a) complete maternal sleep data (for maternal-child analyses), b) at least serology information at 4 years of age, and c) complete covariate information to be included in our analytic sample. An overview of exclusions and the final sample size is included in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExposure: maternal sleep\u003c/h3\u003e\n\u003cp\u003eMaternal sleep duration during pregnancy (3rd trimester questionnaire) was obtained from the following closed-ended question: \u0026ldquo;How many hours do you usually sleep during your pregnancy\u0026rdquo;. The mother reported sleep duration as \u0026le;\u0026thinsp;5 hours, 6\u0026ndash;7 h, 8\u0026ndash;9h, and \u0026ge;\u0026thinsp;10 hours. Sleep duration was then classified as \u0026le;\u0026thinsp;7 or \u0026gt;\u0026thinsp;7 hours of sleep, according to the WHO sleep duration during pregnancy recommendations, and as similarly done in previous papers (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The Epworth Sleepiness Scale was used to determine the level of daytime sleepiness. This scale rates the likelihood of falling asleep in 8 situations on a scale of 0 to 3 (0\u0026thinsp;=\u0026thinsp;never, 1\u0026thinsp;=\u0026thinsp;slight, 2\u0026thinsp;=\u0026thinsp;moderate, 3\u0026thinsp;=\u0026thinsp;high) and can range from 0 to 24. Excessive daytime sleepiness was defined as a total score of 10 or more (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Snoring frequency was measured from the Apnea Score within the Berlin Questionnaire (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and was recorded into two categories: never or rarely (\u0026le;\u0026thinsp;2 times or less per month) and frequently or always (\u0026ge;\u0026thinsp;1 times or more per week). Sleep disturbances during the 3rd trimester were obtained by the following closed-ended question: \u0026ldquo;In the last 3 months, did you wake up during the night due to breathing difficulties?\u0026rdquo;. The complete questionnaire can be found in the supplementary file ( Appendix S1).\u003c/p\u003e\u003cp\u003eWe created an adverse maternal sleep score encompassing all the sleep characteristics evaluated in this study (ranging 0 to 4), which included: excessive daytime sleepiness (\u0026ge;\u0026thinsp;10\u0026thinsp;=\u0026thinsp;1); presence of sleep disturbances (Yes\u0026thinsp;=\u0026thinsp;1); inadequate sleep duration (\u0026le;\u0026thinsp;7 hours\u0026thinsp;=\u0026thinsp;1); snoring frequency (frequently or always\u0026thinsp;=\u0026thinsp;1). Higher sleep scores indicate worse sleep quality.\u003c/p\u003e\n\u003ch3\u003eOutcome: infection during childhood\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome: infection during childhood\u003c/div\u003e\u003cp\u003eThe exposure to five polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyV), two herpesviruses (EBV, CMV), Adenovirus-36 (Adv-36), \u003cem\u003eHelicobacter pylori\u003c/em\u003e (\u003cem\u003eH.pylori\u003c/em\u003e) and \u003cem\u003eToxoplasma gondii\u003c/em\u003e (\u003cem\u003eT.gondii\u003c/em\u003e) were determined by serological testing of immunoglobulin G (IgG) to the pathogen specific antigens in plasma samples collected at 4 and 6 years of age. IgG antibodies are typically produced a short time after initial infection and persist months or years, thus their presence indicates that an individual was infected at some point in their life, but not when the initial infection occurred. Details regarding the methods used to detect IgG specific to each pathogen are provided in Appendix S2. The serological assays were performed at the Immune Response and Biomarkers Core Facility at the Barcelona Institute for Global Health (ISGlobal).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003ePotential confounders were identified a priori based on the literature (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and included: age (continuous, years), education (low, middle and high), marital status (married/engaged, other), pre-pregnancy body mass index (BMI) status (underweight, normal, overweight, obese) and smoking at 30 weeks gestation.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to examine the population\u0026rsquo;s sleep and demographic characteristics, and we also described the prevalence of infections at 4 and 6 years of age.\u003c/p\u003e\u003cp\u003eWe also examined correlations between each of the different indicators of poor sleep. Cox regression models were used to study the associations between prenatal maternal indicators of poor sleep and seroconversion (indicative of infection) through 6 years of age. Hazard ratios (HR) and 95% confidence intervals (CIs) from Cox proportional hazards regression models were estimated using time since birth as the time scale. Participants were followed until the 1st indication of a given infection at one of the follow-up visits or the occurrence of a censoring event. Participants were censored at the last available time point if they did not attend the next follow-up. In total, 402 participants had serological data available at the 6-year follow-up visit. Models were adjusted for the aforementioned confounders. The average time at risk is provided in supplementary Tables S1.\u003c/p\u003e\u003cp\u003eAs a sensitivity analysis, we additionally adjusted the models for child sleep duration at age 4 (i.e., whether the child met the recommended 10 hours of total daily sleep (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)) to assess whether child sleep could account for part of the observed associations. All analyses were conducted using RStudio Software 1.4.1106.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePopulation characteristics\u003c/h2\u003e\u003cp\u003eOf the 1,081 mother\u0026ndash;child pairs in the Rhea study, 565 mothers had complete data on sleep during pregnancy. Among these, 545 had complete data on relevant covariates and child infection data at the 4-year follow-up. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCharacteristics of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age among the mothers was 30 (SD: 5) years old. During pregnancy, 8% had excessive daytime sleepiness, 14% experienced sleep disturbances, 36% slept\u0026thinsp;\u0026le;\u0026thinsp;7 hours at night, 20% snored and 16% presented multiple indicators of poor sleep (sleep score above 1). No mother presented all indicators of poor sleep (sleep score of 4) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were no associations among the different maternal indicators of poor sleep when comparing the prevalence in a one to one fashion (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaternal demographic and sleep characteristics (n\u0026thinsp;=\u0026thinsp;545).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDemographic characteristics\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e% / Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSleep characteristics\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e% / Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.9 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExcessive daytime sleepiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI status \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSleep disturbances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnderweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;7 hours of sleep \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNormal\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSnoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdverse sleep score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObese\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0 (healthier sleep score)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMiddle\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e4 (poorer sleep score)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMarried/engaged\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking (30 weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eBased on the International Obesity Task Force cut offs.\u003c/em\u003e \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026le; 7 hours of sleep is considered short sleep. Based on the International Obesity Task Force cut offs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe most prevalent infections both at age 4 and 6 were BKPyV (88% and 94%) and MCVPyV (77% and 85%) whereas the least prevalent was \u003cem\u003eH.pylori\u003c/em\u003e (10% and 13.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrevalence of infections among children at 4 (n\u0026thinsp;=\u0026thinsp;545) and 6 (n\u0026thinsp;=\u0026thinsp;402) years of age / follow up.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003cp\u003eAge 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003cp\u003eAge 6\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBKPyV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJCPyV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIPyV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWUPyV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCVPyV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEVB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHelicobacter pylori\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdv-36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eToxoplasma gondii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMaternal sleep characteristics and childhood infections\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the adjusted hazard ratios of the associations between maternal sleep during pregnancy and the risk of infection in offspring through 6 years of age. Children of mothers who reported sleep disturbances during the third trimester of pregnancy had a higher risk of infection compared to those without sleep disturbances, specifically to BKPyV (HR:1.36, 95%CI (1.05\u0026ndash;1.77), p-value: 0.02), to KIPyV (HR:1.43, 95%CI (1.08\u0026ndash;1.89), p-value: 0.01), to WUPyV (HR:1.71, 95%CI (1.28\u0026ndash;2.29), p-value: 0.001), to MCVPyV (HR:1.51, 95%CI (1.15-2.00), p-value: 0.01), to \u003cem\u003eH. pylori\u003c/em\u003e (HR: 2.17, 95%CI (1.14\u0026ndash;4.15), p-value: 0.02) and Adv-36 (HR:1.57, 95%CI (1.08\u0026ndash;2.27), p-value: 0.02). No statistically significant associations were revealed for the remaining individual maternal sleep characteristics. Children from women with several indicators of poor sleep (sleep score of 3) had a higher risk of infection to WUPyV (HR:1.92, 95%CI (1.08\u0026ndash;3.42), p-value: 0.03) compared to children from mothers with no indicators or poor sleep (score of 0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In sensitivity analyses, further adjustment for child total sleep duration at age 4 (81.3% slept\u0026thinsp;\u0026ge;\u0026thinsp;10h) did not materially alter the associations between prenatal maternal indicators of poor sleep and sleep score and infection outcomes (supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study explored the association between poor sleep characteristics during the third trimester of pregnancy, with the risk of infection through childhood. Maternal indicators of poor sleep were linked with a higher risk of acquisition of several common infections in childhood (polyomaviruses, \u003cem\u003eH.pylori\u003c/em\u003e and Adv-36).\u003c/p\u003e\u003cp\u003eOur study contributes to a growing body of literature on the relationship between the quality of sleep and immunity in humans, focusing on the importance of optimal sleep during critical developmental periods, such as pregnancy.\u003c/p\u003e\u003cp\u003eDisruptions in maternal sleep can lead to adverse outcomes for both mother and child, as they may increase susceptibility to intrauterine exposures that influence offspring development (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Existing research highlights a strong link between sleep disturbances during pregnancy with a wide range of developmental challenges in the offspring including immune system development (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In our dataset, sleep disturbances emerged as a risk factor for susceptibility to common infections (polyomaviruses, Adv-36 and \u003cem\u003eH.pylori\u003c/em\u003e). In a review, Irwin \u003cem\u003eet. al.\u003c/em\u003e concluded that sleep disturbances were more strongly associated with inflammatory dysregulation than short sleep duration (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). To our knowledge, there are no studies evaluating how maternal sleep affects the risk of infections in offspring. However animal studies have shown that maternal sleep patterns influence the immune development of the offspring (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is very common for children to experience infections during early childhood. We evaluated using serology previous exposure to common pathogens including viruses, bacteria and parasites. Gestational sleep disturbances were linked to higher risk to several viral infections as well as to \u003cem\u003eH.pylori\u003c/em\u003e, a bacterium that colonises the gastric stomach and is linked to several gastric and extragastric conditions already in childhood (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).Moreover, offspring of mothers who experienced several sleep disorders during late pregnancy had a higher risk of infections with WUPyV, a respiratory pathogen that might play a role in asthma development (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStrengths of our study include the cohort design with multiple follow-ups visits at different life-stages from pregnancy to childhood, the use of biomarkers to evaluate exposure to common infections and the availability of information on important confounders. There are some limitations as well. Firstly, sleep characteristics were self-reported, which may introduce reporting bias. Moreover, the indicators of poor sleep were measured only at the end of pregnancy (30 weeks). However, maternal sleep disturbances progressively worsen across gestation, particularly during the third trimester (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and although early and mid- pregnancy are critical windows for fetal immune development, sleep disturbances later in pregnancy have been linked to adverse fetal immune development and adverse neonatal outcomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Additionally, even though we only have data on maternal sleep at one time point (third trimester), we had data on several sleep metrics, so we were able to not only evaluate sleep duration but also sleep quality. In addition, sleep in early childhood is known to influence immune function and infection risk (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). To account for this, we conducted a sensitivity analysis, further adjusting for total sleep duration at 4 years of age, and the results remained consistent, suggesting that the associations we observed are not meaningfully influenced by children\u0026rsquo;s sleep. Further, there was some loss of participants to follow-up. However, we do not believe that it introduced differential bias, since random loss does not impact our results as systematic censoring in a specific group would. In addition, we could not determine the exact moment of infection, as data on presence of infections was only available at the follow-up visits at 4 and 6 years of age. This limits our ability to draw precise conclusions about the causal relationships between sleep characteristics and subsequent infections. However, our prospective design allows us to avoid this concern, since childhood infection does not impact maternal sleep. Moreover, though the possibility of false-negative IgG results cannot be entirely excluded, the assays used in our study incorporated rigorous quality control measures (blanks, positive and negative controls), and for some pathogens, multiple antigen responses were measured to define seropositivity, minimizing the risk of misclassification. Even though outbreaks of relevant pathogens could potentially influence risk, no major changes in the epidemiology of the infections were reported during the study period. Lastly, our data come from a Greek population, which can limit the generalizability of our findings to other populations, specifically to populations where cultural norms around sleep differ.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study provides evidence that sleep disturbances in utero could impact the immune system function in the offspring, increasing the risk of acquisition of common pathogens during childhood. These findings highlight the importance of sleep as a critical factor in childhood health and suggest that interventions to improve sleep could play a role in infection prevention. To our knowledge, this is the first research on sleep and infection that examines the effect of maternal sleep during pregnancy on the risk of infection through childhood. Therefore additional studies need to replicate these findings. If consistency of these results is found, sleep interventions should be considered to reduce infection rates in children.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRhea Mother Child Cohort\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRhea\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eImmunoglobulin G\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIgG\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePolyomavirus BK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBKPyV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePolyomavirus JC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJCPyV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePolyomavirus KI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKIPyV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePolyomavirus WU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWUPyV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePolyomavirus MCV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMCVPyV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHerpesvirus EBV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEBV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHerpesvirus CMV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCMV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAdenovirus\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e36\u0026ndash;Adv\u0026ndash;36\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHelicobacter pylori\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eH.pylori\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eToxoplasma gondii\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eT.gondii\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHazard Ratio\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll studies received approval from the ethics committees of the centers involved and written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article cannot be shared publicly due to ethical and privacy considerations. The data will be shared on reasonable request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRHEA: The \u0026quot;Rhea\u0026quot; project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 NV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX) and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; \u0026ldquo;Rhea Plus\u0026rdquo;: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). La Marato de TV3 2017, Project number 201819, Reg. 78 / 437, Title \u0026ldquo;Infection acquisition in early life and health outcomes in childhood\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:MKa,BH ; Methodology: MKa, AE, KP, MKo, BH; Formal analysis: RG, AE ; Investigation: RG ; Data curation:RA, MV, CD, TR, KM; Writing \u0026ndash; original draft preparation: RG,BH ; Writing \u0026ndash; review and editing: ALL; Supervision: BH ; Project administration: MKa, MKo; Funding acquisition: MKo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants, health professionals and researchers who have made Rhea happen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTACT INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarianna Karachaliou [email protected], Ana Espinosa [email protected] , Kyriaki Papantoniou [email protected] , Ruth Aguilar [email protected], Marta Vidal [email protected] , Theano Roumeliotaki [email protected] ,Despo Ierodiakonou [email protected], Katerina Margetaki [email protected], Katerina Koutra [email protected], Emmanouil Symvoulakis [email protected], Carlota Doba\u0026ntilde;o [email protected], Manolis Kogevinas [email protected], Barbara N. Harding [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBesedovsky L, Lange T, Haack M. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99(3):1325\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/physrev.00010.2018\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00010.2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoluektov MG. Sleep and immunity. 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PMID: 33895421; PMCID: PMC8801162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFelder JN, Baer RJ, Rand L, Jelliffe-Pawlowski LL, Prather AA. Sleep disturbance during pregnancy and neonatal outcomes: A systematic review and meta-analysis. Sleep Health. 2020;6(4):393\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sleh.2020.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.sleh.2020.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatricciani L, Paquet C, Galland B, Short M, Olds T. Children\u0026rsquo;s sleep and health: A meta-review. Sleep Med Rev. 2019;46:136\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.smrv.2019.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.smrv.2019.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Maternal sleep, childhood sleep, infection risk, immune system, prenatal exposure","lastPublishedDoi":"10.21203/rs.3.rs-7633169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7633169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSleep is essential for immune function, but little is known about how maternal sleep during pregnancy may influence infection risk later in life. We aim to examine the effects of maternal sleep characteristics during late pregnancy on the risk of infection across early childhood (from birth to 6 years of age).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study used data of 545 mother-child pairs from the Rhea mother-child cohort in Greece. Maternal sleep characteristics during the third trimester including excessive daytime sleepiness, sleep disturbances, short sleep duration, and snoring, were assessed through validated questionnaires. A maternal sleep score was created encompassing all the sleep characteristics mentioned above, ranging from 0 (indicating optimal sleep) to 4 (indicating poor sleep). IgG levels to five polyomaviruses (BKPyV, JCPyV, KIPyV, WUPyV, MCVPyV), two herpesviruses (EBV, CMV), \u003cem\u003eHelicobacter pylori\u003c/em\u003e, Adenovirus-36, and \u003cem\u003eToxoplasma gondii\u003c/em\u003e were measured at ages 4 and 6. Cox proportional hazards regression models were used to estimate associations between sleep characteristics and risk infection during early childhood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMaternal sleep disturbances were associated with increased risk of childhood infection to BKPyV (HR:1.36; 95%CI: 1.05\u0026ndash;1.77), to KIPyV (HR:1.43; 95% CI: 1.08\u0026ndash;1.89), to WUPyV (HR:1.71; 95% CI: 1.28\u0026ndash;2.29), to MCVPyV (HR:1.51; 95% CI: 1.15-2.00), to \u003cem\u003eH. pylori\u003c/em\u003e (HR: 2.17; 95% CI: 1.14\u0026ndash;4.15) and Adv-36 (HR:1.57; 95% CI: 1.08\u0026ndash;2.27). Moreover, a maternal sleep score of 3, indicating three poor sleep indicators, was associated with nearly double the risk of infection with WUPyV (HR: 1.92; 95% CI: 1.08\u0026ndash;3.42).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings suggest that poor maternal sleep during late pregnancy, specifically sleep disturbances, may increase the risk of infection through childhood. This is the first study to link prenatal sleep to childhood infection susceptibility, providing novel evidence of the potential long-term immunological impact of prenatal sleep quality.\u003c/p\u003e","manuscriptTitle":"Maternal Sleep and the Risk of Childhood Infections: Findings from the Rhea Birth Cohort in Greece","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:42:46","doi":"10.21203/rs.3.rs-7633169/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":"5e7a679b-b306-4a55-a694-a9eed2940801","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T09:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:42:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7633169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7633169","identity":"rs-7633169","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}