Early life infections and life-course health and disease in the EU Child Cohort Network; study design

Early life infections and life-course health and disease in the EU Child Cohort Network; study design | 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 Early life infections and life-course health and disease in the EU Child Cohort Network; study design Marianna Karachaliou, Manolis Kogevinas, George Davey Smith, Anne-Marie Nybo Andersen, and 26 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8280798/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Research into the developmental origins of, and lifecourse influences on, health and disease has a significant gap: infections. Clinical and subclinical infections are nearly universal in early life and can induce structural and functional changes in developing organ systems with life-long consequences. Emerging evidence links common childhood infections—such as Epstein–Barr virus, Cytomegalovirus and Helicobacter pylori—to so-called non-communicable diseases ranging from respiratory to neurodevelopmental and cardiometabolic diseases. Identifying infectious determinants of non-communicable diseases offers substantial opportunities for prevention and early intervention. We describe a newly developed infectious data resource within the EU Child Cohort Network, designed to enable triangulation of evidence on the impact of early life infections on children's health and long-term trajectories of disease. The network established in 2017, currently brings together 37 pregnancy and childhood cohorts from 16 predominantly European countries. It spans the first 20 years of life beginning in the prenatal period, with many cohorts continuing follow-up into adulthood. Cohorts include a comprehensive set of harmonized variables covering health and disease phenotypes (cardiometabolic, respiratory, and mental) that are also well-established early indicators of later-life non-communicable disease risk. Building upon this, we present here the extensive infections data available in the cohorts ranging from questionnaires and registries to serology. Harmonizing and integrating infection data within the EU Child Cohort Network will allow to comprehensively explore how infections interact with genetic and environmental factors to shape health and disease across the lifecourse, advancing the exposome framework and transforming our understanding of non-communicable disease aetiology. DOHaD Birth cohort Cross-cohort collaboration Lifecourse epidemiology Data harmonization meta-research Figures Figure 1 Introduction Infections remain among the leading causes of morbidity and occasionally mortality in early childhood, even in high income countries [1]. It is estimated that children in Europe experience a median of 16 infectious episodes during their first 3 years of life [2]. In addition to clinically apparent infections, children also acquire subclinical infections, which likely represent the large majority of infectious exposures. These infections are attributable both to pathogens that are typically acquired silently e.g. polyomaviruses [3] and those that present with a variable disease severity including asymptomatically e.g. Epstein–Barr virus [4]. Beyond acute effects (acute infectious syndromes), these clinical and subclinical infections in early life can set long-term trajectories of health and disease, sometimes irreversibly, contributing to development and progression of perhaps misleadingly knowns as non-communicable diseases (NCDs) [5] ranging from respiratory and allergic to neurodevelopmental and cardiometabolic disorders [6]. Beyond certain congenital infections (e.g. rubella) and infections with epidemic/pandemic impact (e.g. human immunodeficiency virus, malaria, Zika virus, SARS-CoV-2), the long-term effects of the vast majority of early life infections on NCDs risk has received little systematic attention within the framework of the Developmental Origins of Health and Disease (DOHaD) or broader life-course epidemiology [7]. At the same time, infections are largely neglected from current exposome research, despite the exposome framework originally encompassing infectious exposures as part of the totality of environmental influences on health[8]. Critical questions can often be answered efficiently by leveraging existing data. Significant investments have already been made in birth cohort studies across Europe [9], several of which have collected (e.g. through questionnaires), produced (e.g. serology) or have access (e.g. electronic health records) to data on infections. Moreover, these cohorts contain longitudinally collected detailed phenotypic information spanning the childhood, adolescent and young adulthood years and a wealth of data on other core, sociodemographic, environmental and health characteristics that are already harmonized through other initiatives [10,11]. Complementing these existing harmonized data resources with harmonized infectious data is feasible and necessary as it will allow for large multi-cohort analyses on the impact of early life infections on children's health across Europe. This cross-cohort collaboration allows use of a variety of study designs and triangulation of their findings to strengthen causal inference [12]. Aiming to facilitate discoveries on the role of early life infections on NCDs from their earliest stages, we mapped the infection-related data available within the EU Child Cohort Network. In this paper we present: (i) the methods used to define exposure to infections and examples of their application across cohorts; (ii) the availability of data for each infection, age group (including the pregnancy period) and cohort with a particular focus on serology as the most direct and informative method for assessing lifetime exposure to specific infections; and (iii) the utility of these infection-related data within the EU Child Cohort Network. The word “infection” rather than infectious disease or pathogen is intentionally used because infectious disease usually refers to a specific clinical syndrome produced due to an infection and pathogen does not necessarily imply that the human host has been exposed to it or that it has triggered any response. EU Child Cohort Network-overview The EU Child Cohort Network was established in 2017 by the Horizon 2020-funded LifeCycle Project (https://euchildcohortnetwork.eu)[10]. It is an open network, currently including 37 pregnancy and child cohorts established to study exposure-to-outcome associations and trajectories across the life course. Together, they include more than 250,000 children and their parents or caregivers from 15 European countries and Australia (Figure 1). Recruitment to the cohorts began prior to or during pregnancy, or in childhood and the follow-up of the children extends into young adulthood in the oldest cohorts. This collaboration provides an opportunity to answer questions no single cohort could answer alone benefiting from increased statistical power as well as greater ethnic, sociodemographic, cultural and geographic diversity critical for identifying susceptible individuals. The Network has demonstrated excellent collaboration and substantial scientific impact, as reflected in the more than 100 publications produced to date even beyond the original duration of the LifeCycle project. Collectively these studies have pioneered work in life-course epidemiology, illustrating the unique value of combining multiple cohorts to investigate developmental processes, early life stressors, and their interactions. Existing Harmonized Data Resources in the EU Child Cohort Network The EU Child Cohort Network has established an extensive, harmonized set of variables covering multiple domains and with information on the harmonization accessible via the Molgenis data catalogue (https://molgeniscatalogue.org/EUChildNetwork). These domains include socioeconomic factors, migration status, urban environment characteristics (e.g. green space)[13], child-rearing practices (e.g. daycare)[14], lifestyle (e.g. diet, physical activity, sleep)[15], maternal and pregnancy characteristics[16], epigenetic markers, child cardiometabolic outcomes [body mass index (BMI), body composition, blood pressure, cardiac structure and function, lipid profiles, insulin, and glucose levels][17]; respiratory outcomes (allergy, wheezing, respiratory infections, lung function, and asthma)[18], and mental health and neurodevelopmental outcomes (behavioral problems, cognitive development, educational attainment, autism spectrum disorder, attention deficit hyperactivity disorder, anxiety, and depression)[19]. These harmonized data are available and highly relevant for our research on infectious origins of NCDs either as confounders (e.g. daycare attendance), effect modifiers (e.g. ethnicity), mediators (e.g. epigenetics, immunoproteomics) and health phenotypes of interest (e.g cognition). Additional genotyped data are available as well harmonized data covering multiple layers of the exposome (external, chemical, physical, lifestyle, and social) through connected projects (ATHLETE and LONGITOOLS)[11,20]. Existing Harmonized Data on Infections The EU Child Cohort Network variable catalogue currently includes two harmonized variables related to infections. The first is "LRTI_" labelled as "lower respiratory tract infections (bronchiolitis, bronchitis, pneumonia, chest infection, or equivalent)" and the second is "URTI_" labelled as "upper respiratory tract infections (ear infection, throat infections, laryngitis, croup, whooping cough or equivalent)". Both variables are binary and available as repeated measures for ages 0-17 years and reflect any upper or lower respiratory tract infection in the last 6 or 12 months (https://molgeniscatalogue.org/EUChildNetwork). In most cohorts, the information was based on parental reports to questionnaires and infections were preferably doctor-diagnosed [18]. Extension of harmonization to other infectious syndromes including gastrointestinal, skin and soft tissues, genitourinary and neurological infections can easily be conducted following similar procedures used for harmonizing respiratory infections. New Efforts to Harmonize Data on Infections Building on the existing harmonized variables, we extend the scope of infection data in the EU Child Cohort Network to include information on infections caused by specific viruses, bacteria, and parasites. These data will enable investigation of whether associations between early-life infections and health are pathogen-specific or pathogen-agnostic. Cohorts have used different methods to assess infection exposure including questionnaires, diaries, registries, microbiological tests, and serology (Table 1). We briefly describe each method and provide cohort examples. Serological data, capturing both symptomatic and asymptomatic infections, provide the most direct and pathogen-specific measure of exposure. Accordingly, our current harmonization efforts focus on serology, which we describe in greater detail below. Other methods to assess exposure to infection data also provide valuable complementary information and will be considered in future phases of harmonization. Methods to assess exposure to infections 1. Questionnaires Questionnaires are widely used in cohort research because they are cost-effective, relatively simple to administer, and often integrated within broader data collection instruments. They can gather parental or self-reported information on doctor-diagnosed infections, infection-related symptoms (e.g. fever, cough), treatments (e.g. antibiotics), and related outcomes such as hospitalization. For instance, the MeDALL questionnaire (Figure S1), applied across 11 European birth cohorts, ask parents to report on specific childhood infections including chickenpox, measles, mumps, rubella, pertussis, scarlet fever, mononucleosis, morbilliform, parotid diseases, and any other exanthematous disease, along with age at onset and whether a doctor was consulted [21]. In another example, the MoBa cohort previously defined influenza-like illness based on repeated questionnaire data collected during pregnancy [22]. 2. Symptom Diaries Symptom diaries are a less commonly used method across pregnancy and child cohorts. Participants record daily symptoms over a specified period, reducing recall bias and enabling detailed assessment of symptom duration and progression. However, this approach is resource-intensive, burdensome for participants, and prone to missing data and loss to follow-up. Digital applications may help overcome some of these challenges by simplifying data entry, sending reminders, and improving real-time data capture. For example, within the Danish population-based Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) 2010 cohort, daily diary cards during the first 3 years have been used to record infection episodes [2,23]. Α scoping review of birth cohort studies using diaries for the collection of respiratory symptoms has been previously published [24]. 3. Electronic health records Electronic health records and registry-based data sources constitute valuable tools for capturing infection exposures in early life. These include registries of notifiable infectious diseases, registries of laboratory test results potentially including antenatal screening testing, hospital and primary care records coded using ICD-10 and prescription registries. Registries for notifiable infectious diseases can be valuable sources of information, particularly for severe or epidemiologically important pathogens. When available, they often cover large populations and provide systematically collected data. A good example is SmiNet in Sweden, a national electronic surveillance system for infectious diseases established in 1997. It collects mandatory reports from both clinicians and diagnostic laboratories for a list of notifiable diseases (Table S1) and voluntary reports for others, such as Respiratory syncytial virus [25]. The clinical notifications contain detailed epidemiological information and the laboratory notifications, the relevant microbiological information. To ensure data quality, SmiNet requires that each notification is reviewed by two epidemiologists, one at the county level and one at the Swedish Public Health Agency, before being entered into the national surveillance archive. An important consideration when using such registries is the year a disease became notifiable, as reporting practices and diagnostic criteria might vary before and after notification status was assigned. The BAMSE mother-child cohort in Sweden has used SmiNet to define SARS-CoV-2 exposure [26], although this cohort is currently not yet part of the EU Child Cohort Network. Registries of laboratory test results, where available, can provide valuable data for assessing exposure to infections during early life. For example, in many countries national antenatal screening programs routinely test pregnant women for serostatus against cytomegalovirus, toxoplasmosis, and rubella. The results from these programs are often archived within national or regional laboratory information systems, creating repositories that can be accessed, under appropriate governance, for secondary research purposes. For example, such access seems to be feasible for BiB cohort through Connected Bradford Infrastructure [27] and DNBC cohort through linkage with clinical laboratory information system (LABKA) research database in Northern and Central Denmark [28]. A similar source of information has been recently used in a study to explore infection-NCD associations in adults [29]. ICD-10 codes (International Statistical Classification of Diseases, Tenth Revision) are widely used in healthcare systems for coding diagnoses and hospital admissions. While these codes can be extracted from medical registries to capture infectious disease episodes, they typically reflect severe cases requiring hospital care or specialized follow-up (e.g. congenital infections) and are expected to miss mild or community-managed infections. Similarly, registries on prescription medications can complement infection data by identifying antimicrobial treatments classified with Anatomical Therapeutic Chemical codes. In the Born in Bradford (BiB) cohort (UK), an infection was defined as a primary care record with either an infection-related diagnosis code or a prescription for an antibiotic [30]. We should note that harmonizing infection data from different healthcare systems, registries, and potential coding systems across countries presents significant challenges as datasets may vary in format, completeness and diagnostic or reporting criteria. Efforts to harmonize and integrate such data across European cohorts are closely aligned with the objectives of the European Health Data Space . 4. Microbiological tests Microbiological test results including polymerase chain reaction, culture and antigen detection enable the direct identification of infectious agents in specific biological samples. They provide data on the pathogen/s involved in an acute infection as well as asymptomatic carriage at a particular body site. However, because detection typically reflects a single time point, frequent and systematic sampling is necessary to reliably capture acute infectious episodes and transient colonization. To address this, several studies in the EU Child Cohort Network have incorporated parent-collected specimens or home visits by healthcare workers for regular sampling, including during asymptomatic periods. In the Generation R cohort (the Netherlands), swabs of the nose and nasopharynx area were taken by trained research nurses at ages 6 weeks, 6 and 14 months, and 2, 3 and 6 years [31]. Swabs were classified as either negative or positive for Staphylococcus aureus, Haemophilus influenzae, Moxarella catarrhalis, or Streptococcus pneumoniae. 5. Serology Serological assessment is potentially the most direct and informative technique available to define previous infection. This is particularly relevant when considering common infections, which cause subclinical or non-specific disease symptoms. Upon exposure to a pathogen, the immune system generates antibodies (e.g., IgM, IgG, IgA) targeting antigens of that infectious agent. Serology refers to the detection and quantification of these antibodies mainly in a blood sample although it can also be applied in mucosal secretions (e.g. nasal)[32]. Serology is not just a biomarker of exposure but also provides a functional readout of the host immune response, reflecting both the magnitude and breadth of immune memory to a given infection. The isotype-antigen combination along with the antibody levels can give insights into whether an infection is acute, recent, latent, persistent or reactivated [33]. Serology in repeated blood samples allows identification of age of first exposure. There are several serological assays mainly Enzyme-Linked Immunosorbent Assay (ELISA), chemiluminescent Immunoassay, Multiplex Serology[34] and more recent technologies such as Phage immunoprecipitation sequencing (PhIP-Seq) spanning multiple bacterial, viral and parasitic epitopes[35]. Serology-Based Infectious Data across EU Child Cohort Network Table 2 summarizes serological data on infections available in different cohorts and across ages. We focused on data from pregnancy to 20 years of age to align with the age range of other harmonized variables across cohorts. Cohort-specific information is available in Appendix (Excel1 and Text S1). Pathogens include viruses (e.g., CMV, EBV), bacteria (e.g., H. pylori), and parasites (e.g., T. gondii). Apart from HSV-2 there is no information on sexually transmitted infections. The most studied pathogens are EBV, CMV, VZV, and H. pylori. There are pathogens (e.g. HAV, TSPyV) assessed in single cohorts only. Information spans the period from pregnancy, birth, to postnatal life until 15 years of age. No cohort to date has available serological data from the preconception period and the age period 16-20 years old. Serological data collected during pregnancy or at birth provides information about the mother's serological status and as such her lifetime exposure to infections. Maternal IgG antibodies are transferred through the placenta to the fetus during gestation [36]. These antibodies gradually decline after birth, typically disappearing by 9-12 months of age. Therefore, maternal serology during pregnancy/birth provides information on both maternal infection history and the infant’s early immunological environment. Child’s serostatus (seropositive or seronegative) at a given age captures previous exposure to a given pathogen. Repeated serological data are available for some pathogens and participants (e.g. CMV, EBV, H.pylori) allowing estimation of time of first infection across the study period (e.g. early years, mid childhood, adolescence or never) [37]. In addition, serological data from the fathers at the time of pregnancy are available only in Generation XXI cohort for H.pylori infection [38]. Although serological data during pregnancy are useful, only MoBA cohort assessed repeated samples spanning different stages of pregnancy[39]. Rhea and ALSPAC cohorts hold the most comprehensive serological profiling in terms of pathogens examined and time points. Across cohorts, there are different methodologies in respect to targeted antigens, isotype and exact methodology (Appendix, excel 1). In BiB, INMA and Rhea cohorts, a common serological protocol has been recently applied[37]. Sometimes, within the same cohort, different assays have been applied. For example, in ALSPAC cohort, EBV serology was performed with indirect immunofluorescence test and with ELISA as part of different protocols [40,41]. Most cohorts assessed uniformly all study participants with adequate blood samples. Case-control or case-cohort design has also been opted in some cohorts (Appendix, Excel1). Some studies combined serological data with information from other sources to define exposure to specific infections. For example, in BiB cohort they integrated serology and electronic heath records to describe the respiratory syncytial virus epidemiology in early life [42]. Data management and privacy protection The EU Child Cohort Network has set up a federated analysis IT infrastructure (DataSHIELD), removing the need to physically transfer data. Participating cohorts have their harmonised data stored on secure servers that are connected to the infrastructure and are in full control of which researchers are getting access to their data and to what exact data [43]. Via an RStudio Open Source central analysis server, that is also part of the infrastructure, the cohorts’ individual-level data stored on the servers are accessed via DataSHIELD [44], which sends blocks of code to each server and then combines the summary statistics that are sent back from the servers. In this setting, no individual participant data is transferred to the researcher and analyses are non-disclosive as ensured by a number of disclosure control filters thus many ethical, legal and societal implications of transferring data from one site to another are avoided. Data access Proposals for research based on EU Child Cohort Network data can be put forward by both members of the network and external researchers by contacting the coordinating center ( [email protected] ) and submitting a paper proposal according to a prespecified template. Proposals for research may be based on all EU Child Cohort Network cohorts or a subset of cohorts with available data (with cohorts themselves having the final say in whether or not they wish to contribute with data in each study); they may also include requests for further data harmonization, which can likewise be restricted to a subset of cohorts with data. Harmonized serological data on infections release is expected to open to research applications in late 2026, while expressions of interest to collaborate are open and encouraged now. The harmonized data will be presented in the FAIR (findable, accessible, interoperable, reusable) data infrastructure which has been developed in the LifeCycle project, and which includes an open access web-based catalogue (https://molgeniscatalogue.org/EUChildNetwork). Join in the action If your cohort has data on infections from any of the aforementioned sources and you want to join this initiative, please contact us. Cohorts can join the network by contacting the coordinating center ( [email protected] ) provided they meet the following criteria: (1) commenced before or during pregnancy or in infancy; (2) plan to follow-up or already have followed-up the cohort throughout childhood; (3) are willing to harmonize data and make them available to researchers using the network. Utility and Analytical Potential of Harmonized Infection Data The new harmonized serological data on infections will allow addressing several existing research gaps. Nonetheless, some gaps remain and this knowledge can be used to inform planning of future research in the field (table 3). This data resource will primarily enable robust answers to the fundamental epidemiology questions of who (which individuals), when (timing of infection in lifecourse, with minimal variability in calendar time), and where (geographic distribution). Furthermore, it will be possible to define key determinants of acute infection severity that may also contribute to NCD risk. These include i) the presence or absence of maternal origin antibodies, assessed through serological data in pregnancy/birth [45]; ii) the age at first exposure, determined from repeated serological measures during childhood [46]; and iii) the likely route of infection [47], inferred from individual-level characteristics—for example, in cytomegalovirus infection, potential transmission routes include contact with secretions during vaginal delivery, breastfeeding, or contact in daycare settings. This new harmonized serological data on infections will be a springboard for new discoveries on the role of early life infections on NCDs (figure 1). Currently, the data span the first 16 years of life enabling examinations of windows of susceptibility, including the critical period of pregnancy as well as interactive and cumulative effects throughout the life course. Recognizing that one infection might determine multiple outcomes and multiple infections one outcome[6], a broader “exposure-wide, outcome-wide” approach can assess multiple infections across a spectrum of health and disease phenotypes, including neurodevelopmental, cardiometabolic, and respiratory outcomes, thereby maximizing the potential to uncover novel associations. Both protective and detrimental effects are expected. We encourage such a comprehensive and systematic approach as it is both conceptually and ethically crucial to inform interventions. Eradicating or preventing certain infections at specific ages might have unintended consequences if those infections also confer protective effects[48]. Data on infections obtained from mothers during pregnancy could be also used to investigate associations with maternal health outcomes later in life. For example, building on existing evidence on the association between maternal H. pylori seropositivity and preeclampsia [49,50], researchers could also explore potential links between H. pylori and the subsequent development of hypertension or other cardiometabolic conditions in women [51]. Such cross-cohort collaboration allows use of large sample sizes (e.g. CMV serological data available for 10,000 children) enhancing statistical power for more precise estimates and robust findings. Beyond the gain in sample size, the cohorts span different geographic, cultural and social settings enabling triangulation of evidence from cross-context comparisons [52]. Additional advanced triangulation methodologies are feasible within the EU Child Cohort Network including i) the use of negative exposure controls (e.g. father serostatus when hypothesizing in utero exposure to be important), ii) mendelian randomization [53] and other iii) non-genetic instrumental variables particularly vaccination against a specific pathogen [54]. In addition, several of the cohorts include genotypic data that can support genome-wide association studies on infections. This data resource will allow examining the interplay between infections and environment in determining health and disease. Emerging data highlight the deep inter-connections between environment and infections. The environment, including the social environment, is likely to determine vulnerability, susceptibility, exposure and transmission of the infection [55]. Infections are also likely to modify the effects of environmental exposures on health; for example, HCV-related hepatitis is associated with altered regulation of metal metabolism [56]. Co-exposure and interactions between several environmental pollutants (e.g. air pollutants, microplastics) and pathogens is also possible [57,58]. Without a comprehensive understanding of the infectious alongside non-infectious causes that contribute to NCD risk, healthcare systems are left addressing only fragments of the problem, and prevention strategies remain incomplete. An exposome initiative aiming to integrate infectious and non-infectious exposures could help bridge this gap and promote a more holistic understanding of health and disease trajectories. The network also facilitates in-depth mechanistic studies to uncover the downstream effects of infections, which remain poorly understood and have primarily been studied through the lens of the immune system. A critical missing layer of information for our research concerns the totality of antibody responses to immune triggers beyond infections, including those from the microbiota and allergens. Understanding this collective modulation of immunity by infections, microbiota, and allergens is essential for developing holistic models of immune development and disease risk. Beyond the immune system, integrating existing mechanistic data spanning structural (e.g., brain MRI) and systems-level functional mechanisms (e.g., proteomics, metabolomics, metagenomics, and epigenomics) will help identify key biological pathways and responses to target for early intervention [59,60]. Current research gaps can inform optimal planning of future studies (table 3). There is a lack of serological data on infections during the first 2 years of life as well as during adolescence. The limited availability of data for young children can be attributed to the ethical and practical challenges associated with blood sample collection in this age group. Dried blood spots can serve as an alternative to traditional blood draws, enabling serological assessment with minimal invasiveness, low blood volume, and reduced cost. New pediatric sampling devices have also been developed, offering a convenient and virtually painless collection experience; however, these devices typically increase the overall sampling cost. Adolescence represents another critical window for assessing infection exposure, as it is a period defined by a behavioral transition. As individuals develop greater independence and influence their own environments, they are exposed to new sources and routes of infection, such as those related to Herpes simplex virus-2 and Human papillomaviruses. Many cohorts have existing samples stored in biobanks which could be used for future common investigations. Such samples could be used to design new studies and validate previous notable findings as between adenovirus 36 and obesity [37]. It is also crucial to consider the potential long-term impacts of infection treatments, including antibiotics and vaccines at the individual level and policy level. Conclusion We benefit from the unprecedented availability of data on infections in the mother-child cohorts that have so far remained underused. By integrating the new harmonized serology based data on early life infections to existing harmonized datasets on environmental factors, mediators and health phenotypes we are filling important gaps in current research frameworks of exposome, DOHaD and lifecourse epidemiology and bridging fragmented research agendas on communicable and traditionally considered non-communicable diseases[5] under the developmental origins of health and disease and lifecourse hypothesis. Within the EU Child Cohort Network we are developing the methodologies and synergies to exploit these resources. The new knowledge created on early life infectious determinants of NCDs will help to tailor personalized preventive and treatment actions from the earliest stages of life. Answering these critical questions will require deep collaboration across disciplines. Statements & Declarations Acknowledgments The authors are grateful to researchers and participants who have supported and contributed to the cohorts included in this study. Cohort specific acknowledgements can be found in the Appendix (Text S2). Funding This project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206, 2016; EUCAN-Connect grant agreement No 824989; ATHLETE, grant agreement No 874583, LongITools grant No 874739). Cohort specific funding details can be found in Appendix. Marianna Karachaliou (grant no. 101152509) and Demetris Avraam (grant no. 101106261) has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowships. Role of the funding source The funding sources did not play any role in the study design, the data collection, the data analysis, the interpretation of results, the writing of the report and in the decision to submit the study for publication. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Marianna Karachaliou and confirmed by all authors. The first draft of the manuscript was written by Marianna Karachaliou and Vincent Jaddoe and subsequently all authors commented on previous versions of the manuscript and the accuracy of cohort specific data. All authors read and approved the final manuscript. Ethics approval Each study was approved by each center‘s ethics committee. Cohort-specific ethical approvals can be found in Appendix. Ethical approval was granted by local research ethics committees for each individual study, and participants or child’s legal guardians provided informed consent, where applicable Consent to participate Before enrolment, all participants signed an informed consent in accordance with each center‘s ethics committee. Data sharing statement The data presented in this study is not freely available as access is managed by each individual cohort. Data access would need to be requested from each individual cohort. References Kyu HH, Stein CE, Boschi Pinto C, Rakovac I, Weber MW, Dannemann Purnat T, et al. Causes of death among children aged 5–14 years in the WHO European Region: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Child & Adolescent Health. 2018;2:321–37. https://doi.org/10.1016/S2352-4642(18)30095-6 Kyvsgaard JN, Hesselberg LM, Sunde RB, Brustad N, Vahman N, Schoos A-MM, et al. Burden and Subtypes of Early Life Infections Increase the Risk of Asthma. The Journal of Allergy and Clinical Immunology: In Practice. 2024;12:2056-2065.e10. https://doi.org/10.1016/j.jaip.2024.04.006 Cook L. Polyomaviruses. Hayden RT, Wolk DM, Carroll KC, Tang Y-W, editors. Microbiol Spectr. 2016;4:4.4.24. https://doi.org/10.1128/microbiolspec.DMIH2-0010-2015 Silins SL, Sherritt MA, Silleri JM, Cross SM, Elliott SL, Bharadwaj M, et al. Asymptomatic primary Epstein-Barr virus infection occurs in the absence of blood T-cell repertoire perturbations despite high levels of systemic viral load. Blood. 2001;98:3739–44. https://doi.org/10.1182/blood.V98.13.3739 Davey Smith G. Post–Modern Epidemiology: When Methods Meet Matter. American Journal of Epidemiology. 2019;188:1410–9. https://doi.org/10.1093/aje/kwz064 O’Connor SM, Taylor CE, Hughes JM. Emerging Infectious Determinants of Chronic Diseases. Emerg Infect Dis. 2006;12:1051–7. https://doi.org/10.3201/eid1207.060037 Paixao ES, Lawlor DA, Barreto ML, Rodrigues LC. Integrating infectious diseases into life course epidemiology. International Journal of Epidemiology. 2025;54:dyaf059. https://doi.org/10.1093/ije/dyaf059 Wild CP. The exposome: from concept to utility. International Journal of Epidemiology. 2012;41:24–32. https://doi.org/10.1093/ije/dyr236 Larsen PS, Kamper‐Jørgensen M, Adamson A, Barros H, Bonde JP, Brescianini S, et al. Pregnancy and Birth Cohort Resources in Europe: a Large Opportunity for Aetiological Child Health Research. Paediatric Perinatal Epid. 2013;27:393–414. https://doi.org/10.1111/ppe.12060 Jaddoe VWV, Felix JF, Andersen A-MN, Charles M-A, Chatzi L, Corpeleijn E, et al. The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents. Eur J Epidemiol. 2020;35:709–24. https://doi.org/10.1007/s10654-020-00662-z Vrijheid M, Basagaña X, Gonzalez JR, Jaddoe VWV, Jensen G, Keun HC, et al. Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview. Environmental Epidemiology. 2021;5:e166. https://doi.org/10.1097/EE9.0000000000000166 Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2017;dyw314. https://doi.org/10.1093/ije/dyw314 Fernandes A, Avraam D, Cadman T, Dadvand P, Guxens M, Binter A-C, et al. Green spaces and respiratory, cardiometabolic, and neurodevelopmental outcomes: An individual-participant data meta-analysis of >35.000 European children. Environment International. 2024;190:108853. https://doi.org/10.1016/j.envint.2024.108853 Barry KM, Avraam D, Cadman T, Elhakeem A, El Marroun H, Jansen PW, et al. Early childcare arrangements and children’s internalizing and externalizing symptoms: an individual participant data meta-analysis of six prospective birth cohorts in Europe. The Lancet Regional Health - Europe. 2024;45:101036. https://doi.org/10.1016/j.lanepe.2024.101036 Descarpentrie A, Calas L, Cornet M, Heude B, Charles M, Avraam D, et al. Lifestyle patterns in European preschoolers: Associations with socio‐demographic factors and body mass index. Pediatric Obesity. 2023;18:e13079. https://doi.org/10.1111/ijpo.13079 Elhakeem A, Ronkainen J, Mansell T, Lange K, Mikkola TM, Mishra BH, et al. Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study. BMC Med. 2023;21:23. https://doi.org/10.1186/s12916-022-02711-8 Warkentin S, Fossati S, Marquez S, Andersen A-MN, Andrusaityte S, Avraam D, et al. Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts. Environment International. 2025;200:109527. https://doi.org/10.1016/j.envint.2025.109527 Van Meel ER, Mensink-Bout SM, Den Dekker HT, Ahluwalia TS, Annesi-Maesano I, Arshad SH, et al. Early-life respiratory tract infections and the risk of school-age lower lung function and asthma: a meta-analysis of 150 000 European children. Eur Respir J. 2022;60:2102395. https://doi.org/10.1183/13993003.02395-2021 Nader JL, López-Vicente M, Julvez J, Guxens M, Cadman T, Elhakeem A, et al. Measures of Early-life Behavior and Later Psychopathology in the LifeCycle Project - EU Child Cohort Network: A Cohort Description. J Epidemiol. 2023;33:321–31. https://doi.org/10.2188/jea.JE20210241 Ronkainen J, Nedelec R, Atehortua A, Balkhiyarova Z, Cascarano A, Ngoc Dang V, et al. LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases. Environmental Epidemiology. 2022;6:e184. https://doi.org/10.1097/EE9.0000000000000184 Hohmann C, Pinart M, Tischer C, Gehring U, Heinrich J, Kull I, et al. The development of the MeDALL Core Questionnaires for a harmonized follow-up assessment of eleven European birth cohorts on asthma and allergies. Int Arch Allergy Immunol. 2014;163:215–24. https://doi.org/10.1159/000357732 Mahic M, Che X, Susser E, Levin B, Reichborn-Kjennerud T, Magnus P, et al. Epidemiological and Serological Investigation into the Role of Gestational Maternal Influenza Virus Infection and Autism Spectrum Disorders. mSphere. 2017;2:e00159-17. https://doi.org/10.1128/mSphere.00159-17 Brustad N, Buchvald F, Jensen SK, Kyvsgaard JN, Vahman N, Thorsen J, et al. Burden of Infections in Early Life and Risk of Infections and Systemic Antibiotics Use in Childhood. JAMA Netw Open. 2025;8:e2453284. https://doi.org/10.1001/jamanetworkopen.2024.53284 Langer S, Klee B, Gottschick C, Mikolajczyk R. Birth cohort studies using symptom diaries for assessing respiratory diseases-a scoping review. PLoS One. 2022;17:e0263559. https://doi.org/10.1371/journal.pone.0263559 Rolfhamre P, Jansson A, Arneborn M, Ekdahl K. SmiNet-2: Description of an internet-based surveillance system for communicable diseases in Sweden. Euro Surveill. 2006;11:103–7. Yu Z, Bellander T, Bergström A, Dillner J, Eneroth K, Engardt M, et al. Association of Short-term Air Pollution Exposure With SARS-CoV-2 Infection Among Young Adults in Sweden. JAMA Netw Open. 2022;5:e228109. https://doi.org/10.1001/jamanetworkopen.2022.8109 Sohal K, Mason D, Birkinshaw J, West J, McEachan RRC, Elshehaly M, et al. Connected Bradford: a Whole System Data Linkage Accelerator. Wellcome Open Res. 2022;7:26. https://doi.org/10.12688/wellcomeopenres.17526.2 Grann AF, Erichsen R, Nielsen AG, Frøslev T, Thomsen RW. Existing data sources for clinical epidemiology: The clinical laboratory information system (LABKA) research database at Aarhus University, Denmark. Clin Epidemiol. 2011;3:133–8. https://doi.org/10.2147/CLEP.S17901 Lape M, Schnell D, Parameswaran S, Ernst K, O’Connor S, Salomonis N, et al. A survey of pathogenic involvement in non-communicable human diseases. Commun Med. 2025;5:242. https://doi.org/10.1038/s43856-025-00956-x Coathup V, Ashdown HF, Carson C, Santorelli G, Quigley MA. Associations between maternal body mass index and childhood infections in UK primary care: findings from the Born in Bradford birth cohort study. Arch Dis Child. 2024;110:59–66. https://doi.org/10.1136/archdischild-2024-326951 Lebon A, Moll HA, Tavakol M, Van Wamel WJ, Jaddoe VWV, Hofman A, et al. Correlation of Bacterial Colonization Status between Mother and Child: the Generation R Study. J Clin Microbiol. 2010;48:960–2. https://doi.org/10.1128/JCM.01799-09 Roubidoux EK, Brigleb PH, Vegesana K, Souquette A, Whitt K, Freiden P, et al. Utility of nasal swabs for assessing mucosal immune responses towards SARS-CoV-2. Sci Rep. 2023;13:17820. https://doi.org/10.1038/s41598-023-44989-5 De Paschale M, Clerici P. Serological diagnosis of Epstein-Barr virus infection: Problems and solutions. World J Virol. 2012;1:31–43. https://doi.org/10.5501/wjv.v1.i1.31 Mentzer AJ, Brenner N, Allen N, Littlejohns TJ, Chong AY, Cortes A, et al. Identification of host–pathogen-disease relationships using a scalable multiplex serology platform in UK Biobank. Nat Commun. 2022;13:1818. https://doi.org/10.1038/s41467-022-29307-3 Shrock E, Shrock C, Elledge SJ. VirScan: High-throughput Profiling of Antiviral Antibody Epitopes. BIO-PROTOCOL [Internet]. 2022 [cited 2025 Oct 17];12. https://doi.org/10.21769/BioProtoc.4464 Dolatshahi S, Butler AL, Pou C, Henckel E, Bernhardsson AK, Gustafsson A, et al. Selective transfer of maternal antibodies in preterm and fullterm children. Sci Rep. 2022;12:14937. https://doi.org/10.1038/s41598-022-18973-4 Karachaliou M, Vidal M, Pembrey L, Bustamante M, Espinosa A, Mason D, et al. Seroepidemiology of maternal and childhood pathogen exposure in three European mother-child cohorts. International Journal of Infectious Diseases. 2025;159:107994. https://doi.org/10.1016/j.ijid.2025.107994 Lunet N, Peleteiro B, Bastos J, Correia S, Marinho A, Guimarães JT, et al. Child day-care attendance and Helicobacter pylori infection in the Portuguese birth cohort Geração XXI. Eur J Cancer Prev. 2014;23:193–8. https://doi.org/10.1097/CEJ.0b013e328364742a Barlinn R, Dudman SG, Trogstad L, Gibory M, Muller F, Magnus P, et al. Maternal and congenital cytomegalovirus infections in a population‐based pregnancy cohort study. APMIS. 2018;126:899–906. https://doi.org/10.1111/apm.12899 Khandaker GM, Stochl J, Zammit S, Lewis G, Jones PB. Childhood Epstein-Barr Virus infection and subsequent risk of psychotic experiences in adolescence: a population-based prospective serological study. Schizophr Res. 2014;158:19–24. https://doi.org/10.1016/j.schres.2014.05.019 Mitchell RE, Jones HJ, Yolken RH, Ford G, Jones-Brando L, Ring SM, et al. Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort. Wellcome Open Res. 2018;3:49. https://doi.org/10.12688/wellcomeopenres.14565.2 Zylbersztejn A, Pembrey L, Goldstein H, Berbers G, Schepp R, Van Der Klis F, et al. Respiratory syncytial virus in young children: community cohort study integrating serological surveys, questionnaire and electronic health records, Born in Bradford cohort, England, 2008 to 2013. Eurosurveillance [Internet]. 2021 [cited 2025 Nov 12];26. https://doi.org/10.2807/1560-7917.ES.2021.26.6.2000023 Pinot de Moira A, Haakma S, Strandberg-Larsen K, van Enckevort E, Kooijman M, Cadman T, et al. The EU Child Cohort Network’s core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables. Eur J Epidemiol. 2021;36:565–80. https://doi.org/10.1007/s10654-021-00733-9 Avraam D, Wilson RC, Aguirre Chan N, Banerjee S, Bishop TRP, Butters O, et al. DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform. Bioinform Adv. 2025;5:vbaf046. https://doi.org/10.1093/bioadv/vbaf046 Zinkernagel RM. Maternal antibodies, childhood infections, and autoimmune diseases. N Engl J Med. 2001;345:1331–5. https://doi.org/10.1056/NEJMra012493 Glynn JR, Moss PAH. Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children. Sci Data. 2020;7:329. https://doi.org/10.1038/s41597-020-00668-y Demars A, Lison A, Machelart A, Van Vyve M, Potemberg G, Vanderwinden J-M, et al. Route of Infection Strongly Impacts the Host-Pathogen Relationship. Front Immunol. 2019;10:1589. https://doi.org/10.3389/fimmu.2019.01589 Blaser MJ. Who are we?: Indigenous microbes and the ecology of human diseases. EMBO Reports. 2006;7:956–60. https://doi.org/10.1038/sj.embor.7400812 Galan R, Pembrey L, Bustamante M, Aguilar R, Mason D, Vidal M, et al. The association of Helicobacter pylori with adverse pregnancy outcomes in three European birth cohorts. BMC Pregnancy Childbirth. 2024;24:745. https://doi.org/10.1186/s12884-024-06901-5 den Hollander WJ, Schalekamp-Timmermans S, Holster IL, Jaddoe VW, Hofman A, Moll HA, et al. Helicobacter pylori colonization and pregnancies complicated by preeclampsia, spontaneous prematurity, and small for gestational age birth. Helicobacter. 2017;22. https://doi.org/10.1111/hel.12364 Rich-Edwards JW, Fraser A, Lawlor DA, Catov JM. Pregnancy characteristics and women’s future cardiovascular health: an underused opportunity to improve women’s health? Epidemiol Rev. 2014;36:57–70. https://doi.org/10.1093/epirev/mxt006 Gutierrez S, Glymour MM, Smith GD. Evidence triangulation in health research. Eur J Epidemiol. 2025;40:743–57. https://doi.org/10.1007/s10654-024-01194-6 Hamilton F, Butler-Laporte G, Davey Smith G. Mendelian Randomization and Infection: Pitfalls and Promises. J Infect Dis. 2025;232:525–33. https://doi.org/10.1093/infdis/jiaf251 Eyting M, Xie M, Michalik F, Heß S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature. 2025;641:438–46. https://doi.org/10.1038/s41586-025-08800-x Division on Earth and Life Studies, National Academies of Sciences, Engineering, and Medicine. Pivotal Interfaces of Environmental Health and Infectious Disease Research to Inform Responses to Outbreaks, Epidemics, and Pandemics: Proceedings of a Workshop-in Brief [Internet]. Johnson AF, Bremer A, Liao J, Thévenon A, editors. Washington, D.C.: National Academies Press; 2021 [cited 2025 Nov 12]. p. 26270. https://doi.org/10.17226/26270 Barouki R, Samson M, Blanc EB, Colombo M, Zucman-Rossi J, Lazaridis KN, et al. The exposome and liver disease - how environmental factors affect liver health. J Hepatol. 2023;79:492–505. https://doi.org/10.1016/j.jhep.2023.02.034 Qi Y, Chen Y, Yan X, Liu W, Ma L, Liu Y, et al. Co-Exposure of Ambient Particulate Matter and Airborne Transmission Pathogens: The Impairment of the Upper Respiratory Systems. Environ Sci Technol. 2022;56:15892–901. https://doi.org/10.1021/acs.est.2c03856 Kutralam-Muniasamy G, Shruti VC, Pérez-Guevara F. Plastisphere-hosted viruses: A review of interactions, behavior, and effects. Journal of Hazardous Materials. 2024;472:134533. https://doi.org/10.1016/j.jhazmat.2024.134533 Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics. 2023;22:100561. https://doi.org/10.1016/j.mcpro.2023.100561 Yoon JH, Lee H, Kwon D, Lee D, Lee S, Cho E, et al. Integrative approach of omics and imaging data to discover new insights for understanding brain diseases. Brain Commun. 2024;6:fcae265. https://doi.org/10.1093/braincomms/fcae265 Mitchell RE, Jones HJ, Yolken RH, Ford G, Jones-Brando L, Ring SM, et al. Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort. Wellcome Open Res. 2018;3:49. https://doi.org/10.12688/wellcomeopenres.14565.2 Chong AHW, Mitchell RE, Hemani G, Davey Smith G, Yolken RH, Richmond RC, et al. Genetic Analyses of Common Infections in the Avon Longitudinal Study of Parents and Children Cohort. Front Immunol. 2021;12:727457. https://doi.org/10.3389/fimmu.2021.727457 Pembrey L, Raynor P, Griffiths P, Chaytor S, Wright J, Hall AJ. Seroprevalence of Cytomegalovirus, Epstein Barr Virus and Varicella Zoster Virus among Pregnant Women in Bradford: A Cohort Study. Nevels M, editor. PLoS ONE. 2013;8:e81881. https://doi.org/10.1371/journal.pone.0081881 Pembrey L, Waiblinger D, Griffiths P, Patel M, Azad R, Wright J. Cytomegalovirus, Epstein-Barr virus and varicella zoster virus infection in the first two years of life: a cohort study in Bradford, UK. BMC Infect Dis. 2017;17:220. https://doi.org/10.1186/s12879-017-2319-7 Pembrey L, Waiblinger D, Griffiths P, Wright J. Age at cytomegalovirus, Epstein Barr virus and varicella zoster virus infection and risk of atopy: The Born in Bradford cohort, UK. Pediatric Allergy Immunology. 2019;30:604–13. https://doi.org/10.1111/pai.13093 Van Den Heuvel D, Jansen MAE, Dik WA, Bouallouch-Charif H, Zhao D, Van Kester KAM, et al. Cytomegalovirus- and Epstein-Barr Virus–Induced T-Cell Expansions in Young Children Do Not Impair Naive T-cell Populations or Vaccination Responses: The Generation R Study. J Infect Dis. 2016;213:233–42. https://doi.org/10.1093/infdis/jiv369 Jansen MAE, van den Heuvel D, Bouthoorn SH, Jaddoe VWV, Hooijkaas H, Raat H, et al. Determinants of Ethnic Differences in Cytomegalovirus, Epstein-Barr Virus, and Herpes Simplex Virus Type 1 Seroprevalence in Childhood. J Pediatr. 2016;170:126-134.e1-6. https://doi.org/10.1016/j.jpeds.2015.11.014 Karachaliou M, Waterboer T, Casabonne D, Chalkiadaki G, Roumeliotaki T, Michel A, et al. The Natural History of Human Polyomaviruses and Herpesviruses in Early Life--The Rhea Birth Cohort in Greece. Am J Epidemiol. 2016;183:671–9. https://doi.org/10.1093/aje/kwv281 Karachaliou M, de Sanjose S, Roumeliotaki T, Margetaki K, Vafeiadi M, Waterboer T, et al. Heterogeneous associations of polyomaviruses and herpesviruses with allergy-related phenotypes in childhood. Ann Allergy Asthma Immunol. 2021;127:191-199.e3. https://doi.org/10.1016/j.anai.2021.04.019 Karachaliou M, de Sanjose S, Waterboer T, Roumeliotaki T, Vassilaki M, Sarri K, et al. Is early life exposure to polyomaviruses and herpesviruses associated with obesity indices and metabolic traits in childhood? Int J Obes (Lond). 2018;42:1590–601. https://doi.org/10.1038/s41366-018-0017-1 Karachaliou M, Chatzi L, Michel A, Kyriklaki A, Kampouri M, Koutra K, et al. Helicobacter pylori Seropositivity and Childhood Neurodevelopment, the Rhea Birth Cohort in Crete, Greece. Paediatr Perinat Epidemiol. 2017;31:374–84. https://doi.org/10.1111/ppe.12374 Karachaliou M, Chatzi L, Roumeliotaki T, Kampouri M, Kyriklaki A, Koutra K, et al. Common infections with polyomaviruses and herpesviruses and neuropsychological development at 4 years of age, the Rhea birth cohort in Crete, Greece. J Child Psychol Psychiatry. 2016;57:1268–76. https://doi.org/10.1111/jcpp.12582 Sun Y, Christensen J, Olsen J. Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res. 2013;107:61–74. https://doi.org/10.1016/j.eplepsyres.2013.08.019 Strand KM, Odland ML, Iversen A-C, Nordbø SA, Vik T, Austgulen R. Cytomegalovirus antibody status at 17-18 weeks of gestation and pre-eclampsia: a case-control study of pregnant women in Norway. BJOG. 2012;119:1316–23. https://doi.org/10.1111/j.1471-0528.2012.03420.x Mirinaviciute G, Barlinn R, Gjeruldsen Dudman S, Flem E. Immunity to varicella zoster virus among pregnant women in the Norwegian Mother and Child Cohort Study. PLoS One. 2019;14:e0221084. https://doi.org/10.1371/journal.pone.0221084 Sun Y, Christensen J, Olsen J. Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res. 2013;107:61–74. https://doi.org/10.1016/j.eplepsyres.2013.08.019 Mahic M, Mjaaland S, Bøvelstad HM, Gunnes N, Susser E, Bresnahan M, et al. Maternal Immunoreactivity to Herpes Simplex Virus 2 and Risk of Autism Spectrum Disorder in Male Offspring. mSphere. 2017;2:e00016-17. https://doi.org/10.1128/mSphere.00016-17 Karachaliou M, Moncunill G, Espinosa A, Castaño-Vinyals G, Jiménez A, Vidal M, et al. Infection induced SARS-CoV-2 seroprevalence and heterogeneity of antibody responses in a general population cohort study in Catalonia Spain. Sci Rep. 2021;11:21571. https://doi.org/10.1038/s41598-021-00807-4 Charles MA, Ancel P-Y, Simeon T, Marchand-Martin L, Zaros C, Dufourg M-N, et al. SARS-CoV-2 seroprevalence in French 9-year-old children and their parents after the first lockdown in 2020. Front Pediatr. 2023;11:1274113. https://doi.org/10.3389/fped.2023.1274113 Barlinn R, Rollag H, Trogstad L, Vainio K, Basset C, Magnus P, et al. High incidence of maternal parvovirus B19 infection in a large unselected population-based pregnancy cohort in Norway. J Clin Virol. 2017;94:57–62. https://doi.org/10.1016/j.jcv.2017.07.010 Mahic M, Mjaaland S, Bøvelstad HM, Gunnes N, Susser E, Bresnahan M, et al. Maternal Immunoreactivity to Herpes Simplex Virus 2 and Risk of Autism Spectrum Disorder in Male Offspring. mSphere. 2017;2:e00016-17. https://doi.org/10.1128/mSphere.00016-17 den Hollander WJ, Sonnenschein-van der Voort AMM, Holster IL, de Jongste JC, Jaddoe VW, Hofman A, et al. Helicobacter pylori in children with asthmatic conditions at school age, and their mothers. Aliment Pharmacol Ther. 2016;43:933–43. https://doi.org/10.1111/apt.13572 den Hollander WJ, Holster IL, van Gilst B, van Vuuren AJ, Jaddoe VWV, Hofman A, et al. Intergenerational reduction in Helicobacter pylori prevalence is similar between different ethnic groups living in a Western city. Gut. 2015;64:1200–8. https://doi.org/10.1136/gutjnl-2014-307689 den Hollander WJ, Holster IL, den Hoed CM, van Deurzen F, van Vuuren AJ, Jaddoe VW, et al. Ethnicity is a strong predictor for Helicobacter pylori infection in young women in a multi-ethnic European city. J Gastroenterol Hepatol. 2013;28:1705–11. https://doi.org/10.1111/jgh.12315 Tables Table 1. Methods to assess exposure to specific infections in the EU Child Cohort Network and their characteristics. method information strengths limitations Questionnaires Self-report of medical diagnoses (e.g. influenza), related medical tests (e.g. prenatal screening for TORCH) or specific clinical symptoms and their severity Scalable, low cost, captures clinical symptoms, harmonization feasible across cohorts Non-specific, recall bias, does not capture asymptomatic infections Symptom diaries Recording of specific symptoms, severity, duration and dates Real-time recording, no recall bias Time consuming for participants, missing data, loss to follow-up Electronic Health Records Registry for notifiable infectious diseases Confirmed infections as required by national reporting systems Real-world data from multiple sources; standardized case definitions; often longitudinal Limited to legally notifiable diseases; country specific; dependent on registry availability and accessibility Registries of laboratory test results Results of laboratory tests performed in healthcare settings. Laboratory-confirmed data; often near-complete population coverage where centralized laboratory reporting systems exist. Dependent on registry availability and access, usually excludes private care, delays to gain access, country specific, limited clinical context (e.g., reason for testing) unless recorded; ICD10 from primary, secondary, tertiary health care services Clinically confirmed diagnoses with dates of infection, number of visits, clinical manifestation (e.g. hospitalization) Entire population, standardized diagnostic data, comparable across countries Dependent on registry availability and access, usually excludes private care, delays to gain access Anatomical Therapeutic Chemical codes J01.X Prescriptions of drugs typically used for infections Clinical suspicion of infection Does not confirm actual treatment or infection; dependent on registry availability and access, usually excludes private care, delays to gain access Microbiological tests (e.g. PCR, culture, rapid test) Direct detection of a pathogen in a specific tissue, captures both acute infection and carriage High specificity; can identify pathogen and sometimes antimicrobial resistance; tissue specific Requires biological sample collection; costly; risk of contamination; Serology Presence and levels of antibodies (IgG, IgM, IgA isotypes); indicates prior exposure and the magnitude of humoral immune response Captures asymptomatic and symptomatic cases; Pathogen-specific; scalable with modern platforms (e.g., multiplex assays, PhIP-Seq); allows assessment of immunity over time Requires blood or other biospecimens; cannot always distinguish natural from vaccine-induced immunity; maternal antibodies detectable in infants up to 9–12 months; no information on clinical presentation; limited use for detecting acute infection because antibodies are typically produced 1–2 weeks after exposure Table 2. Overview of serologically based information on infections across cohorts and ages in the EU Child Cohort Network. infection cohorts pregnancy cord blood 1 year 2-3 years 4-6 years 7-9 years 10-12 years 13-15 years disease associations examined EBV ALSPAC [40,61,62], BiB [37,63–65], COPSAC2010, Gen R [66,67], INMA [37], Rhea [37,68–72] 2027 626 1000 1544 7640 6100 5296 3073 birth defects, allergy, respiratory health, cognition, behavior, psychiatric outcomes, multiple sclerosis, obesity CMV ALSPAC [40,61,62], BiB [37,63–65], COPSAC2010, DNBC [73], Gen R [66,67], INMA [37],, MoBA [39,74], Rhea [37,68–72] 5113 2299 1000 1544 7640 11107 5296 3073 pre-eclampsia, birth defects, allergy, respiratory health, neurodevelopment, autism, epilepsy, obesity VZV BiB [37,63–65], Gen R [66], INMA[37], MoBA [75], Rhea[37] 3183 324 1000 1238 3010 1090 393 0 immune effects, allergy HSV-1 DNBC[76], Gen R [67], Rhea [68–70,72], MoBA[77], COPSAC2010 1049 626 0 81 5220 683 592 419 birth defects, allergy neurodevelopment, epilepsy, autism, obesity HSV-2 MoBA[77], DNBC [73], Rhea [68] 767 626 0 81 690 0 0 0 autism, epilepsy Measles ALSPAC [61,62], Gen R [66] 0 0 0 0 291 683 592 419 none BKPyV , JCPyV , KIPyV , WUPyV , MCPyV BiB [37], INMA [37], Rhea [37,68–70,72] 1078 626 0 319 1931 1090 393 0 allergy, obesity, cognition, behavior HHV-8, HPyV6, HPyV7, HPyV9, TSPyV, HPyV10 Rhea [68–70,72] 21 626 0 81 690 0 0 0 allergy, obesity, cognition, behavior Influenza ALSPAC [61,62], MoBA [77] 348 0 0 0 66 683 592 419 autism RSV BiB [42] 0 700 490 490 0 0 0 0 none SARS-CoV-2 BiSC [78], ELFE[79], INMA [78] 395 0 0 0 0 2642 0 260 none HAV ALSPAC [61,62] 0 0 0 0 530 0 0 0 none Adenovirus 36 BiB [37], INMA [37], Rhea [37] 1057 324 0 238 1931 1090 393 0 obesity Parvovirus B19 MoBA [80] 1349 517 0 0 0 0 0 0 none T.gondii ALSPAC [61,62], BiB [37], COPSAC2010, INMA [37], MoBA [81], Rhea [37] 1521 324 0 238 2624 7328 5753 3818 autism H.pylori ALSPAC [61,62], BiB [37,49], Gen R [50,82–84], Geração XXI [38], INMA[37,49], Rhea [37,49,68,71] 7873 626 0 315 6204 5655 4432 2897 pregnancy outcomes, allergy, cognition Abbreviations: EBV, Epstein Barr Virus; CMV cytomegalovirus; VZV, varicella zoster virus; HSV-1, herpes simplex virus 1; PyV, polyomavirus; RSV, respiratory syncytial virus; HAV, hepatitis A virus; T. gondii, Toxoplasma gondii; H.pylori, Helicobacter pylori Table 3. Existing research gaps in the field of early life infections and lifelong health and disease, and approaches to fill these gaps. Existing gaps Approach to fill the gaps 1.DOHaD and life course research largely lacks investigations on infections Achievable though EU Child Cohort Network 2.Exposome research largely neglects infections Achievable though EU Child Cohort Network and linked exposome projects 3.Lack of causal inference in infections-NCDs research Achievable though EU Child Cohort Network and application of triangulation framework 4.Focused exposure-outcome associations Achievable though EU Child Cohort Network 5.Studies on biological pathways are limited Partly achievable through EU Child Cohort Network and linked omics and imaging related projects 6.Narrow set of infections examined so far Application of new technologies in stored samples 7.Paternal status largely missing New analyses when available stored samples, Informing design of future studies 8.Repeated data in pregnancy are largely missing New analyses when available stored samples, Informing design of future studies 9.Lack of data from the preconception period New analyses when available stored samples, Informing design of future studies 10.Lack of data at early ages potentially due to lack of samples Informing design of future studies (e.g. use of dried blood spots, new pediatric sampling devices) 11.Lack of data in adolescence, samples are likely available New analyses when available stored samples including sexually transmitted infections, Informing design of future studies Supplementary Files 3.SupplementarymaterialEUCCNinfections.docx Copyof2.SIexcelpathogencohortEUCCNinfections.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor invited by journal 08 Dec, 2025 Editor assigned by journal 06 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-8280798","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579238537,"identity":"ff181bcc-79ef-4943-af5b-0e9b81c7bcf0","order_by":0,"name":"Marianna Karachaliou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYFACNjCZwMDO2MDAUIEsw05ICzNIyxlkGWaCWoAkYxsRWnQb2BI/V9TY5fE3Mzc+5p1nk8/ffzpN8meOHYM5Di1mB9gOS545llwscZix2Zh3W5rljBu526R5tyUzWDbj0sLeINnAdiCx4TBjG1DlYQOGG7zbpBm3HWAwOIxTS/PPhn8HEueDtcz5byB//uw2yZ94tbAdk2xsO5C4Aayl4YCBwYHcbRK8+LQcZkuzbOxLTtwI9IvhnGPJBoY3cjdbA/3Cg1PL8Tbjmw3f7BLnHW9/+OBNjZ2B3PmzG2/+3GYnZ3C8AbsenPHFg0tiFIyCUTAKRgFhAAA/GV27ZHKszQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3014-0747","institution":"Erasmus Medical Centre: Erasmus MC","correspondingAuthor":true,"prefix":"","firstName":"Marianna","middleName":"","lastName":"Karachaliou","suffix":""},{"id":579238538,"identity":"8436bd30-eceb-44ff-87f1-b2484984175d","order_by":1,"name":"Manolis Kogevinas","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Manolis","middleName":"","lastName":"Kogevinas","suffix":""},{"id":579238539,"identity":"37c9b488-63db-4bde-8b36-771b019f3b95","order_by":2,"name":"George Davey Smith","email":"","orcid":"","institution":"Bristol Medical School: University of Bristol Medical School","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"Davey","lastName":"Smith","suffix":""},{"id":579238540,"identity":"190e366a-d451-4aef-989e-3408d167d067","order_by":3,"name":"Anne-Marie Nybo Andersen","email":"","orcid":"","institution":"University of Copenhagen Department of Public Health: Kobenhavns Universitet Institut for Folkesundhedsvidenskab","correspondingAuthor":false,"prefix":"","firstName":"Anne-Marie","middleName":"Nybo","lastName":"Andersen","suffix":""},{"id":579238541,"identity":"520e0c31-b408-4fc3-a8d0-b9320361c341","order_by":4,"name":"Demetris Avraam","email":"","orcid":"","institution":"University of Copenhagen Department of Public Health: Kobenhavns Universitet Institut for Folkesundhedsvidenskab","correspondingAuthor":false,"prefix":"","firstName":"Demetris","middleName":"","lastName":"Avraam","suffix":""},{"id":579238542,"identity":"15578948-56d3-4b54-8644-8bf0091574c4","order_by":5,"name":"Henrique Barros","email":"","orcid":"","institution":"ISPUP: Universidade do Porto Instituto de Saude Publica","correspondingAuthor":false,"prefix":"","firstName":"Henrique","middleName":"","lastName":"Barros","suffix":""},{"id":579238543,"identity":"6ef81c31-00a0-47b7-89b3-0c5450cb8b75","order_by":6,"name":"Sunil Bhopal","email":"","orcid":"","institution":"Bradford Institute for Health Research","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Bhopal","suffix":""},{"id":579238544,"identity":"0d707835-333c-4e9d-91cd-738fb09e83f0","order_by":7,"name":"Nicklas Brustad","email":"","orcid":"","institution":"University of Copenhagen Herlev and Gentofte Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nicklas","middleName":"","lastName":"Brustad","suffix":""},{"id":579238545,"identity":"490e0026-d40c-4fd1-bb02-5fc0996c1e64","order_by":8,"name":"Mariona Bustamante","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Mariona","middleName":"","lastName":"Bustamante","suffix":""},{"id":579238546,"identity":"b544a6bb-1431-46a8-960b-393f0de40cdd","order_by":9,"name":"Klaus Bønnelykke","email":"","orcid":"","institution":"University of Copenhagen, Herlev and Gentofte Hospital","correspondingAuthor":false,"prefix":"","firstName":"Klaus","middleName":"","lastName":"Bønnelykke","suffix":""},{"id":579238547,"identity":"6ae81afc-7900-429e-87b1-4e18de5e2185","order_by":10,"name":"Marie-Aline Charles","email":"","orcid":"","institution":"INSERM, CRESS","correspondingAuthor":false,"prefix":"","firstName":"Marie-Aline","middleName":"","lastName":"Charles","suffix":""},{"id":579238548,"identity":"e2bb316c-1f0c-469e-a67f-35d57f7bcabe","order_by":11,"name":"Payam Dadvand","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Payam","middleName":"","lastName":"Dadvand","suffix":""},{"id":579238549,"identity":"37a81ccb-c2b8-416d-9d66-bd89dc9040fc","order_by":12,"name":"Carlota Dobaño","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Carlota","middleName":"","lastName":"Dobaño","suffix":""},{"id":579238550,"identity":"842e330c-c636-4acd-ae9d-df3f78efbc1e","order_by":13,"name":"Marie-Noëlle Dufourg","email":"","orcid":"","institution":"INSERM, INED","correspondingAuthor":false,"prefix":"","firstName":"Marie-Noëlle","middleName":"","lastName":"Dufourg","suffix":""},{"id":579238551,"identity":"2c1139c0-4738-4f76-9969-1d7c9b1eab4b","order_by":14,"name":"Mireia Gascon","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Mireia","middleName":"","lastName":"Gascon","suffix":""},{"id":579238552,"identity":"52a4a653-0cc5-4ba9-bb7e-d8b63deffc0e","order_by":15,"name":"Fergus Hamilton","email":"","orcid":"","institution":"Bristol Medical School: University of Bristol Medical School","correspondingAuthor":false,"prefix":"","firstName":"Fergus","middleName":"","lastName":"Hamilton","suffix":""},{"id":579238553,"identity":"1ac140cd-3eb8-455d-a914-a42da7310d8b","order_by":16,"name":"Tarik Karramass","email":"","orcid":"","institution":"Erasmus Medical Centre: Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Tarik","middleName":"","lastName":"Karramass","suffix":""},{"id":579238554,"identity":"700373ce-50c3-4e76-8c60-fdd28c687142","order_by":17,"name":"Aitana Lertxundi","email":"","orcid":"","institution":"Biogipuzkoa Health Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Aitana","middleName":"","lastName":"Lertxundi","suffix":""},{"id":579238555,"identity":"1727ec3e-924d-42a7-9b24-13e509550591","order_by":18,"name":"Sabrina Llop","email":"","orcid":"","institution":"FISABIO: Fundacio per al Foment de la Investigacio Sanitaria i Biomedica","correspondingAuthor":false,"prefix":"","firstName":"Sabrina","middleName":"","lastName":"Llop","suffix":""},{"id":579238556,"identity":"d7eb3507-3047-4472-858b-5c59de6c5210","order_by":19,"name":"Per Magnus","email":"","orcid":"","institution":"nORWEGIAN iNSTITUTE oF PUBLIC hEALTH","correspondingAuthor":false,"prefix":"","firstName":"Per","middleName":"","lastName":"Magnus","suffix":""},{"id":579238557,"identity":"de06ba1f-6964-4d5a-b11e-911024dad456","order_by":20,"name":"Susana Santos","email":"","orcid":"","institution":"ISPUP: Universidade do Porto Instituto de Saude Publica","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Santos","suffix":""},{"id":579238558,"identity":"9d41cac8-3377-4b59-aa8a-2d92c793e23a","order_by":21,"name":"Lill-Iren Schou Trogstad","email":"","orcid":"","institution":"Norwegian Institute of public health","correspondingAuthor":false,"prefix":"","firstName":"Lill-Iren","middleName":"Schou","lastName":"Trogstad","suffix":""},{"id":579238559,"identity":"6cb5b1c2-e05c-4031-a0dc-41872dfb1433","order_by":22,"name":"Nic J Timpson","email":"","orcid":"","institution":"Bristol Medical School: University of Bristol Medical School","correspondingAuthor":false,"prefix":"","firstName":"Nic","middleName":"J","lastName":"Timpson","suffix":""},{"id":579238560,"identity":"5645b970-2c6d-440a-b309-3fad47b107ab","order_by":23,"name":"Martine Vrijheid","email":"","orcid":"","institution":"ISGLOBAL: Instituto de Salud Global de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Martine","middleName":"","lastName":"Vrijheid","suffix":""},{"id":579238561,"identity":"0a274d5b-1e11-47c1-ba2a-740805112218","order_by":24,"name":"Tim Waterboer","email":"","orcid":"","institution":"DKFZ: Deutsches Krebsforschungszentrum","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Waterboer","suffix":""},{"id":579238562,"identity":"b8ab41dc-9f43-49b0-bed6-b431ae213af4","order_by":25,"name":"Marieke Welten","email":"","orcid":"","institution":"Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Marieke","middleName":"","lastName":"Welten","suffix":""},{"id":579238563,"identity":"3573ac5c-0042-46c8-89d2-71cd1554acd3","order_by":26,"name":"John Wright","email":"","orcid":"","institution":"Bradford Institute for Health Research","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Wright","suffix":""},{"id":579238564,"identity":"d80cb618-1cf4-408c-b337-046af028780c","order_by":27,"name":"Lucy Pembrey","email":"","orcid":"","institution":"LSHTM: London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Pembrey","suffix":""},{"id":579238565,"identity":"e99c2bb1-7112-44f1-b691-a83fa8e1d649","order_by":28,"name":"Liesbeth Duijts","email":"","orcid":"","institution":"Erasmus Medical Centre: Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Liesbeth","middleName":"","lastName":"Duijts","suffix":""},{"id":579238566,"identity":"fc28ff64-11a2-4ce9-85c1-960d76a3b23b","order_by":29,"name":"Vincent Jaddoe","email":"","orcid":"","institution":"Erasmus MC Sophia Children Hospital: Erasmus MC Sophia Kinderziekenhuis","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Jaddoe","suffix":""}],"badges":[],"createdAt":"2025-12-04 15:17:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8280798/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8280798/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101363782,"identity":"cbf794cd-c4a1-40f6-8e23-57a8eea0be96","added_by":"auto","created_at":"2026-01-29 00:39:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178555,"visible":true,"origin":"","legend":"\u003cp\u003eData resource on infections in the EU Child cohort network. The new harmonized data on infections based on serology will be available in 11 cohorts and will be an additional layer of information to existing harmonized data in the cohorts to understand the early life origins of co-called non-communicable diseases.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8280798/v1/a92c1f0c63fc9106581fc6d6.png"},{"id":101751543,"identity":"ecdcc2a8-bae0-42bf-a38d-9d8c114c9315","added_by":"auto","created_at":"2026-02-03 10:21:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8280798/v1/964bdfd9-d697-4a9b-9753-9f3146b4e9ca.pdf"},{"id":101363783,"identity":"c98be063-9004-4e9c-941e-99b45ff512cf","added_by":"auto","created_at":"2026-01-29 00:39:09","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":129775,"visible":true,"origin":"","legend":"","description":"","filename":"3.SupplementarymaterialEUCCNinfections.docx","url":"https://assets-eu.researchsquare.com/files/rs-8280798/v1/6e95b4483d3a525fe9be3c35.docx"},{"id":101363785,"identity":"f8db1ac2-6783-4e6f-80e6-baac02773678","added_by":"auto","created_at":"2026-01-29 00:39:09","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":42203,"visible":true,"origin":"","legend":"","description":"","filename":"Copyof2.SIexcelpathogencohortEUCCNinfections.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8280798/v1/ee487d48ff74e9e39ca65ee8.xlsx"}],"financialInterests":"","formattedTitle":"Early life infections and life-course health and disease in the EU Child Cohort Network; study design","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfections remain among the leading causes of morbidity and occasionally mortality in early childhood, even in high income countries\u0026nbsp;[1]. It is estimated that children in Europe experience a median of 16 infectious episodes during their first 3 years of life\u0026nbsp;[2]. In addition to clinically apparent infections, children also acquire subclinical infections, which likely represent the large majority of infectious exposures. These infections are attributable both to pathogens that are typically acquired silently e.g. polyomaviruses\u0026nbsp;[3]\u0026nbsp;and those that present with a variable disease severity including asymptomatically e.g. \u003cem\u003eEpstein\u0026ndash;Barr virus\u003c/em\u003e[4].\u003c/p\u003e\n\u003cp\u003eBeyond acute effects (acute infectious syndromes), these clinical and subclinical infections in early life can set long-term trajectories of health and disease, sometimes irreversibly, contributing to development and progression of perhaps misleadingly knowns as non-communicable diseases (NCDs)\u0026nbsp;[5]\u0026nbsp;ranging from respiratory and allergic to neurodevelopmental and cardiometabolic disorders\u0026nbsp;[6]. Beyond certain congenital infections (e.g. rubella) and infections with epidemic/pandemic impact (e.g. human immunodeficiency virus, malaria, Zika virus, SARS-CoV-2), the long-term effects of the vast majority of early life infections on NCDs risk has received little systematic attention within the framework of the Developmental Origins of Health and Disease (DOHaD) or broader life-course epidemiology\u0026nbsp;[7]. At the same time, infections are largely neglected from current exposome research, despite the exposome framework originally encompassing infectious exposures as part of the totality of environmental influences on health[8].\u003c/p\u003e\n\u003cp\u003eCritical questions can often be answered efficiently by leveraging existing data. Significant investments have already been made in birth cohort studies across Europe\u0026nbsp;[9], several of which have collected (e.g. through questionnaires), produced (e.g. serology) or have access (e.g. electronic health records) to data on infections. Moreover, these cohorts contain longitudinally collected detailed phenotypic information spanning the childhood, adolescent and young adulthood years and a wealth of data on other core, sociodemographic, environmental and health characteristics that are already harmonized through other initiatives\u0026nbsp;[10,11]. Complementing these existing harmonized data resources with harmonized infectious data is feasible and necessary as it will allow for large multi-cohort analyses on the impact of early life infections on children\u0026apos;s health across Europe. This cross-cohort collaboration allows use of a variety of study designs and triangulation of their findings to strengthen causal inference\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eAiming to facilitate discoveries on the role of early life infections on NCDs from their earliest stages, we mapped the infection-related data available within the EU Child Cohort Network. In this paper we present: (i) the methods used to define exposure to infections and examples of their application across cohorts; (ii) the availability of data for each infection, age group (including the pregnancy period) and cohort with a particular focus on serology as the most direct and informative method for assessing lifetime exposure to specific infections; and (iii) the utility of these infection-related data within the EU Child Cohort Network. The word \u0026ldquo;infection\u0026rdquo; rather than infectious disease or pathogen is intentionally used because infectious disease usually refers to a specific clinical syndrome produced due to an infection and pathogen does not necessarily imply that the human host has been exposed to it or that it has triggered any response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEU Child Cohort Network-overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EU Child Cohort Network was established in 2017 by the Horizon 2020-funded LifeCycle Project (https://euchildcohortnetwork.eu)[10]. It is an open network, currently including 37 pregnancy and child cohorts established to study exposure-to-outcome associations and trajectories across the life course. Together, they include more than 250,000 children and their parents or caregivers from 15 European countries and Australia (Figure 1). Recruitment to the cohorts began prior to or during pregnancy, or in childhood and the follow-up of the children extends into young adulthood in the oldest cohorts. This collaboration provides an opportunity to answer questions no single cohort could answer alone benefiting from increased statistical power as well as greater ethnic, sociodemographic, cultural and geographic diversity critical for identifying susceptible individuals. The Network has demonstrated excellent collaboration and substantial scientific impact, as reflected in the more than 100 publications produced to date even beyond the original duration of the LifeCycle project. Collectively these studies have pioneered work in life-course epidemiology, illustrating the unique value of combining multiple cohorts to investigate developmental processes, early life stressors, and their interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExisting Harmonized Data Resources in the EU Child Cohort Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EU Child Cohort Network has established an extensive, harmonized set of variables covering multiple domains and with information on the harmonization accessible via the Molgenis data catalogue (https://molgeniscatalogue.org/EUChildNetwork). These domains include socioeconomic factors, migration status, urban environment characteristics (e.g. green space)[13], child-rearing practices (e.g. daycare)[14], lifestyle (e.g. diet, physical activity, sleep)[15], maternal and pregnancy characteristics[16], epigenetic markers, child cardiometabolic outcomes [body mass index (BMI), body composition, blood pressure, cardiac structure and function, lipid profiles, insulin, and glucose levels][17]; respiratory outcomes (allergy, wheezing, respiratory infections, lung function, and asthma)[18], and mental health and neurodevelopmental outcomes (behavioral problems, cognitive development, educational attainment, autism spectrum disorder, attention deficit hyperactivity disorder, anxiety, and depression)[19]. These harmonized data are available and highly relevant for our research on infectious origins of NCDs either as confounders (e.g. daycare attendance), effect modifiers (e.g. ethnicity), mediators (e.g. epigenetics, immunoproteomics) and health phenotypes of interest (e.g cognition). Additional genotyped data are available as well harmonized data covering multiple layers of the exposome (external, chemical, physical, lifestyle, and social) through connected projects (ATHLETE and LONGITOOLS)[11,20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExisting Harmonized Data on Infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EU Child Cohort Network variable catalogue currently includes two harmonized variables related to infections. The first is \u0026quot;LRTI_\u0026quot; labelled as \u0026quot;lower respiratory tract infections (bronchiolitis, bronchitis, pneumonia, chest infection, or equivalent)\u0026quot; and the second is \u0026quot;URTI_\u0026quot; labelled as \u0026quot;upper respiratory tract infections (ear infection, throat infections, laryngitis, croup, whooping cough or equivalent)\u0026quot;. Both variables are binary and available as repeated measures for ages 0-17 years and reflect any upper or lower respiratory tract infection in the last 6 or 12 months (https://molgeniscatalogue.org/EUChildNetwork). In most cohorts, the information was based on parental reports to questionnaires and infections were preferably doctor-diagnosed [18]. Extension of harmonization to other infectious syndromes including gastrointestinal, skin and soft tissues, genitourinary and neurological infections can easily be conducted following similar procedures used for harmonizing respiratory infections.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNew Efforts to Harmonize Data on Infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on the existing harmonized variables, we extend the scope of infection data in the EU Child Cohort Network to include information on infections caused by specific viruses, bacteria, and parasites. These data will enable investigation of whether associations between early-life infections and health are pathogen-specific or pathogen-agnostic. Cohorts have used different methods to assess infection exposure including questionnaires, diaries, registries, microbiological tests, and serology (Table 1). We briefly describe each method and provide cohort examples. Serological data, capturing both symptomatic and asymptomatic infections, provide the most direct and pathogen-specific measure of exposure. Accordingly, our current harmonization efforts focus on serology, which we describe in greater detail below. Other methods to assess exposure to infection data also provide valuable complementary information and will be considered in future phases of harmonization.\u003c/p\u003e"},{"header":"Methods to assess exposure to infections","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Questionnaires \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuestionnaires are widely used in cohort research because they are cost-effective, relatively simple to administer, and often integrated within broader data collection instruments. They can gather parental or self-reported information on doctor-diagnosed infections, infection-related symptoms (e.g. fever, cough), treatments (e.g. antibiotics), and related outcomes such as hospitalization. For instance, the MeDALL questionnaire (Figure S1), applied across 11 European birth cohorts, ask parents to report on specific childhood infections including chickenpox, measles, mumps, rubella, pertussis, scarlet fever, mononucleosis, morbilliform, parotid diseases, and any other exanthematous disease, along with age at onset and whether a doctor was consulted\u0026nbsp;[21]. In another example, the MoBa cohort previously defined influenza-like illness based on repeated questionnaire data collected during pregnancy\u0026nbsp;[22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Symptom Diaries\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSymptom diaries are a less commonly used method across pregnancy and child cohorts. Participants record daily symptoms over a specified period, reducing recall bias and enabling detailed assessment of symptom duration and progression. However, this approach is resource-intensive, burdensome for participants, and prone to missing data and loss to follow-up. Digital applications may help overcome some of these challenges by simplifying data entry, sending reminders, and improving real-time data capture. For example, within the Danish population-based Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) 2010 cohort, daily diary cards during the first 3 years have been used to record infection episodes\u0026nbsp;[2,23]. Α scoping review of birth cohort studies using diaries for the collection of respiratory symptoms has been previously published\u0026nbsp;[24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Electronic health records\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectronic health records and registry-based data sources constitute valuable tools for capturing infection exposures in early life. These include registries of notifiable infectious diseases, registries of laboratory test results potentially including antenatal screening testing, hospital and primary care records coded using ICD-10 and prescription registries.\u003c/p\u003e\n\u003cp\u003eRegistries for notifiable infectious diseases can be valuable sources of information, particularly for severe or epidemiologically important pathogens. When available, they often cover large populations and provide systematically collected data. A good example is SmiNet in Sweden, a national electronic surveillance system for infectious diseases established in 1997. It collects mandatory reports from both clinicians and diagnostic laboratories for a list of notifiable diseases (Table S1) and voluntary reports for others, such as Respiratory syncytial virus\u0026nbsp;[25]. The clinical notifications contain detailed epidemiological information and the laboratory notifications, the relevant microbiological information. To ensure data quality, SmiNet requires that each notification is reviewed by two epidemiologists, one at the county level and one at the Swedish Public Health Agency, before being entered into the national surveillance archive. An important consideration when using such registries is the year a disease became notifiable, as reporting practices and diagnostic criteria might vary before and after notification status was assigned. The BAMSE mother-child cohort in Sweden has used SmiNet to define SARS-CoV-2 exposure\u0026nbsp;[26], although this cohort is currently not yet part of the EU Child Cohort Network.\u003c/p\u003e\n\u003cp\u003eRegistries of laboratory test results, where available, can provide valuable data for assessing exposure to infections during early life. For example, in many countries national antenatal screening programs routinely test pregnant women for serostatus against cytomegalovirus, toxoplasmosis, and rubella. The results from these programs are often archived within national or regional laboratory information systems, creating repositories that can be accessed, under appropriate governance, for secondary research purposes. For example, such access seems to be feasible for BiB cohort through Connected Bradford Infrastructure\u0026nbsp;[27]\u0026nbsp;and DNBC cohort through linkage with clinical laboratory information system (LABKA) research database in Northern and Central Denmark\u0026nbsp;[28]. A similar source of information has been recently used in a study to explore infection-NCD associations in adults\u0026nbsp;[29].\u003c/p\u003e\n\u003cp\u003eICD-10 codes (International Statistical Classification of Diseases, Tenth Revision) are widely used in healthcare systems for coding diagnoses and hospital admissions. While these codes can be extracted from medical registries to capture infectious disease episodes, they typically reflect severe cases requiring hospital care or specialized follow-up (e.g. congenital infections) and are expected to miss mild or community-managed infections. Similarly, registries on prescription medications can complement infection data by identifying antimicrobial treatments classified with Anatomical Therapeutic Chemical codes. In the Born in Bradford (BiB) cohort (UK), an infection was defined as a primary care record with either an infection-related diagnosis code or a prescription for an antibiotic\u0026nbsp;[30]. We should note that harmonizing infection data from different healthcare systems, registries, and potential coding systems across countries presents significant challenges as datasets may vary in format, completeness and diagnostic or reporting criteria. Efforts to harmonize and integrate such data across European cohorts are closely aligned with the objectives of the European Health Data Space .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Microbiological tests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrobiological test results including polymerase chain reaction, culture and antigen detection enable the direct identification of infectious agents in specific biological samples. They provide data on the pathogen/s involved in an acute infection as well as asymptomatic carriage at a particular body site. However, because detection typically reflects a single time point, frequent and systematic sampling is necessary to reliably capture acute infectious episodes and transient colonization. To address this, several studies in the EU Child Cohort Network have incorporated parent-collected specimens or home visits by healthcare workers for regular sampling, including during asymptomatic periods. In the Generation R cohort (the Netherlands), swabs of the nose and nasopharynx area were taken by trained research nurses at ages 6 weeks, 6 and 14 months, and 2, 3 and 6 years\u0026nbsp;[31]. Swabs were classified as either negative or positive for Staphylococcus aureus, Haemophilus influenzae, Moxarella catarrhalis, or Streptococcus pneumoniae.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Serology\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerological assessment is potentially the most direct and informative technique available to define previous infection. This is particularly relevant when considering common infections, which cause subclinical or non-specific disease symptoms. Upon exposure to a pathogen, the immune system generates antibodies (e.g., IgM, IgG, IgA) targeting antigens of that infectious agent. Serology refers to the detection and quantification of these antibodies mainly in a blood sample although it can also be applied in mucosal secretions (e.g. nasal)[32]. Serology is not just a biomarker of exposure but also provides a functional readout of the host immune response, reflecting both the magnitude and breadth of immune memory to a given infection. The isotype-antigen combination along with the antibody levels can give insights into whether an infection is acute, recent, latent, persistent or reactivated\u0026nbsp;[33]. Serology in repeated blood samples allows identification of age of first exposure. There are several serological assays mainly Enzyme-Linked Immunosorbent Assay (ELISA), chemiluminescent Immunoassay, Multiplex Serology[34]\u0026nbsp;and more recent technologies such as Phage immunoprecipitation sequencing (PhIP-Seq) spanning multiple bacterial, viral and parasitic epitopes[35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerology-Based Infectious Data across EU Child Cohort Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes serological data on infections available in different cohorts and across ages. We focused on data from pregnancy to 20 years of age to align with the age range of other harmonized variables across cohorts. Cohort-specific information is available in Appendix (Excel1 and Text S1). Pathogens include viruses (e.g., CMV, EBV), bacteria (e.g., H. pylori), and parasites (e.g., T. gondii). Apart from HSV-2 there is no information on sexually transmitted infections. The most studied pathogens are EBV, CMV, VZV, and H. pylori. There are pathogens (e.g. HAV, TSPyV) assessed in single cohorts only.\u003c/p\u003e\n\u003cp\u003eInformation spans the period from pregnancy, birth, to postnatal life until 15 years of age. No cohort to date has available serological data from the preconception period and the age period 16-20 years old. Serological data collected during pregnancy or at birth provides information about the mother's serological status and as such her lifetime exposure to infections. Maternal IgG antibodies are transferred through the placenta to the fetus during gestation\u0026nbsp;[36].\u0026nbsp;\u0026nbsp;These antibodies gradually decline after birth, typically disappearing by 9-12 months of age.\u0026nbsp;Therefore, maternal serology during pregnancy/birth provides information on both maternal infection history and the infant’s early immunological environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChild’s serostatus (seropositive or seronegative) at a given age captures previous exposure to a given pathogen.\u0026nbsp;Repeated serological data are available for some pathogens and participants (e.g. CMV, EBV, H.pylori) allowing estimation of time of first infection across the study period (e.g. early years, mid childhood, adolescence or never)\u0026nbsp;[37]. In addition, serological data from the fathers at the time of pregnancy are available only in\u0026nbsp;Generation XXI\u0026nbsp;cohort for H.pylori infection\u0026nbsp;[38]. Although serological data during pregnancy are useful, only MoBA cohort assessed repeated samples spanning different stages of pregnancy[39].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRhea and ALSPAC cohorts hold the most comprehensive serological profiling in terms of pathogens examined and time points. Across cohorts, there are different methodologies in respect to targeted antigens, isotype and exact methodology (Appendix, excel 1). In BiB, INMA and Rhea cohorts, a common serological protocol has been recently applied[37]. Sometimes, within the same cohort, different assays have been applied. For example, in ALSPAC cohort, EBV serology was performed with indirect immunofluorescence test and with ELISA as part of different protocols\u0026nbsp;[40,41]. Most cohorts assessed uniformly all study participants with adequate blood samples. Case-control or case-cohort design has also been opted in some cohorts (Appendix, Excel1). Some studies combined serological data with information from other sources to define exposure to specific infections. For example, in BiB cohort they integrated serology and electronic heath records to describe the respiratory syncytial virus epidemiology in early life\u0026nbsp;[42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management and privacy protection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EU Child Cohort Network has set up a federated analysis IT infrastructure (DataSHIELD), removing the need to physically transfer data. Participating cohorts have their harmonised data stored on secure servers that are connected to the infrastructure and are in full control of which researchers are getting access to their data and to what exact data\u0026nbsp;[43]. Via an RStudio Open Source central analysis server, that is also part of the infrastructure, the cohorts’ individual-level data stored on the servers are accessed via DataSHIELD\u0026nbsp;[44], which sends blocks of code to each server and then combines the summary statistics that are sent back from the servers. In this setting, no individual participant data is transferred to the researcher and analyses are non-disclosive as ensured by a number of disclosure control filters thus many ethical, legal and societal implications of transferring data from one site to another are avoided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProposals for research based on EU Child Cohort Network data can be put forward by both members of the network and external researchers by contacting the coordinating center ([email protected]) and submitting a paper proposal according to a prespecified template. Proposals for research may be based on all EU Child Cohort Network cohorts or a subset of cohorts with available data (with cohorts themselves having the final say in whether or not they wish to contribute with data in each study); they may also include requests for further data harmonization, which can likewise be restricted to a subset of cohorts with data. Harmonized serological data on infections release is expected to open to research applications in late 2026, while expressions of interest to collaborate are open and encouraged now. The harmonized data will be presented in the FAIR (findable, accessible, interoperable, reusable) data infrastructure which has been developed in the LifeCycle project, and which includes an open access web-based catalogue (https://molgeniscatalogue.org/EUChildNetwork).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoin in the action\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf your cohort has data on infections from any of the aforementioned sources and you want to join this initiative, please contact us. Cohorts can join the network by contacting the coordinating center ([email protected]) provided they meet the following criteria: (1) commenced before or during pregnancy or in infancy; (2) plan to follow-up or already have followed-up the cohort throughout childhood; (3) are willing to harmonize data and make them available to researchers using the network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUtility and Analytical Potential of Harmonized Infection Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe new harmonized serological data on infections will allow addressing several existing research gaps. Nonetheless, some gaps remain and this knowledge can be used to inform planning of future research in the field (table 3).\u003c/p\u003e\n\u003cp\u003eThis data resource will primarily enable robust answers to the fundamental epidemiology questions of \u003cem\u003ewho\u0026nbsp;\u003c/em\u003e(which individuals), \u003cem\u003ewhen\u0026nbsp;\u003c/em\u003e(timing of infection in lifecourse, with minimal variability in calendar time), and \u003cem\u003ewhere\u0026nbsp;\u003c/em\u003e(geographic distribution). Furthermore, it will be possible to define key determinants of acute infection severity that may also contribute to NCD risk. These include i) the presence or absence of maternal origin antibodies, assessed through serological data in pregnancy/birth\u0026nbsp;[45]; ii) the age at first exposure, determined from repeated serological measures during childhood\u0026nbsp;[46]; and iii) the likely route of infection\u0026nbsp;[47], inferred from individual-level characteristics—for example, in cytomegalovirus infection, potential transmission routes include contact with secretions during vaginal delivery, breastfeeding, or contact in daycare settings.\u003c/p\u003e\n\u003cp\u003eThis new harmonized serological data on infections will be a springboard for new discoveries on the role of early life infections on NCDs (figure 1). Currently, the data span the first 16 years of life enabling examinations of windows of susceptibility, including the critical period of pregnancy as well as interactive and cumulative effects throughout the life course. Recognizing that one infection might determine multiple outcomes and multiple infections one outcome[6], a broader “exposure-wide, outcome-wide” approach can assess multiple infections across a spectrum of health and disease phenotypes, including neurodevelopmental, cardiometabolic, and respiratory outcomes, thereby maximizing the potential to uncover novel associations. Both protective and detrimental effects are expected. We encourage such a comprehensive and systematic approach as it is both conceptually and ethically crucial to inform interventions. Eradicating or preventing certain infections at specific ages might have unintended consequences if those infections also confer protective effects[48].\u003c/p\u003e\n\u003cp\u003eData on infections obtained from mothers during pregnancy could be also used to investigate associations with maternal health outcomes later in life. For example, building on existing evidence on the association between maternal H. pylori seropositivity and preeclampsia\u0026nbsp;[49,50], researchers could also explore potential links between H. pylori and the subsequent development of hypertension or other cardiometabolic conditions in women\u0026nbsp;[51].\u003c/p\u003e\n\u003cp\u003eSuch cross-cohort collaboration allows use of large sample sizes (e.g. CMV serological data available for 10,000 children) enhancing statistical power for more precise estimates and robust findings. Beyond the gain in sample size, the cohorts span different geographic, cultural and social settings enabling triangulation of evidence from cross-context comparisons\u0026nbsp;[52]. Additional advanced triangulation methodologies are feasible within the EU Child Cohort Network including i) the use of negative exposure controls (e.g. father serostatus when hypothesizing in utero exposure to be important), ii) mendelian randomization\u0026nbsp;[53]\u0026nbsp;and other iii) non-genetic instrumental variables particularly vaccination against a specific pathogen\u0026nbsp;[54]. In addition, several of the cohorts include genotypic data that can support genome-wide association studies on infections.\u003c/p\u003e\n\u003cp\u003eThis data resource will allow examining the interplay between infections and environment in determining health and disease. Emerging data highlight the deep inter-connections between environment and infections. The environment, including the social environment, is likely to determine vulnerability, susceptibility, exposure and transmission of the infection\u0026nbsp;[55]. Infections are also likely to modify the effects of environmental exposures on health; for example, HCV-related hepatitis is associated with altered regulation of metal metabolism\u0026nbsp;[56]. Co-exposure and interactions between several environmental pollutants (e.g. air pollutants, microplastics) and pathogens is also possible\u0026nbsp;[57,58]. Without a comprehensive understanding of the infectious alongside non-infectious causes that contribute to NCD risk, healthcare systems are left addressing only fragments of the problem, and prevention strategies remain incomplete. An exposome initiative aiming to integrate infectious and non-infectious exposures could help bridge this gap and promote a more holistic understanding of health and disease trajectories.\u003c/p\u003e\n\u003cp\u003eThe network also facilitates in-depth mechanistic studies to uncover the downstream effects of infections, which remain poorly understood and have primarily been studied through the lens of the immune system. \u0026nbsp;A critical missing layer of information for our research concerns the totality of antibody responses to immune triggers beyond infections, including those from the microbiota and allergens. Understanding this collective modulation of immunity by infections, microbiota, and allergens is essential for developing holistic models of immune development and disease risk. Beyond the immune system, integrating existing mechanistic data spanning structural (e.g., brain MRI) and systems-level functional mechanisms (e.g., proteomics, metabolomics, metagenomics, and epigenomics) will help identify key biological pathways and responses to target for early intervention\u0026nbsp;[59,60].\u003c/p\u003e\n\u003cp\u003eCurrent research gaps can inform optimal planning of future studies (table 3). There is a lack of serological data on infections during the first 2 years of life as well as during adolescence. The limited availability of data for young children can be attributed to the ethical and practical challenges associated with blood sample collection in this age group. Dried blood spots can serve as an alternative to traditional blood draws, enabling serological assessment with minimal invasiveness, low blood volume, and reduced cost. New pediatric sampling devices have also been developed, offering a convenient and virtually painless collection experience; however, these devices typically increase the overall sampling cost. Adolescence represents another critical window for assessing infection exposure, as it is a period defined by a behavioral transition. As individuals develop greater independence and influence their own environments, they are exposed to new sources and routes of infection, such as those related to Herpes simplex virus-2 and Human papillomaviruses. \u0026nbsp;Many cohorts have existing samples stored in biobanks which could be used for future common investigations. Such samples could be used to design new studies and validate previous notable findings as between adenovirus 36 and obesity [37]. It is also crucial to consider the potential long-term impacts of infection treatments, including antibiotics and vaccines at the individual level and policy level.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe benefit from the unprecedented availability of data on infections in the mother-child cohorts that have so far remained underused. By integrating the new harmonized serology based data on early life infections to existing harmonized datasets on environmental factors, mediators and health phenotypes we are filling important gaps in current research frameworks of exposome, DOHaD and lifecourse epidemiology and bridging fragmented research agendas on communicable and traditionally considered non-communicable diseases[5] under the developmental origins of health and disease and lifecourse hypothesis. Within the EU Child Cohort Network we are developing the methodologies and synergies to exploit these resources. The new knowledge created on early life infectious determinants of NCDs will help to tailor personalized preventive and treatment actions from the earliest stages of life. Answering these critical questions will require deep collaboration across disciplines.\u003c/p\u003e"},{"header":"Statements \u0026 Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to researchers and participants who have supported and contributed to the cohorts included in this study. Cohort specific acknowledgements can be found in the Appendix (Text S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206, 2016; EUCAN-Connect grant agreement No 824989; ATHLETE, grant agreement No 874583, LongITools grant No 874739). Cohort specific funding details can be found in Appendix.\u003c/p\u003e\n\u003cp\u003eMarianna Karachaliou (grant no. 101152509) and Demetris Avraam (grant no. 101106261) has received funding from the European Union’s Horizon Europe research and innovation\u0026nbsp;programme\u0026nbsp;under the Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the funding source\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding sources did not play any role in the study design, the data collection, the data analysis, the interpretation of results, the writing of the report and in the decision to submit the study for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by Marianna Karachaliou and confirmed by all authors. The first draft of the manuscript was written by Marianna Karachaliou and Vincent Jaddoe and subsequently all authors commented on previous versions of the manuscript and the accuracy of cohort specific data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach study was approved by each center‘s ethics committee. Cohort-specific ethical approvals can be found in Appendix.\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by local research ethics committees for each individual study, and participants or child’s legal guardians provided informed consent, where applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore enrolment, all participants signed an informed consent in accordance with each center‘s ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study is not freely available as access is managed by each individual cohort. Data access would need to be requested from each individual cohort.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKyu HH, Stein CE, Boschi Pinto C, Rakovac I, Weber MW, Dannemann Purnat T, et al. Causes of death among children aged 5\u0026ndash;14 years in the WHO European Region: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Child \u0026amp; Adolescent Health. 2018;2:321\u0026ndash;37. https://doi.org/10.1016/S2352-4642(18)30095-6\u003c/li\u003e\n\u003cli\u003eKyvsgaard JN, Hesselberg LM, Sunde RB, Brustad N, Vahman N, Schoos A-MM, et al. Burden and Subtypes of Early Life Infections Increase the Risk of Asthma. The Journal of Allergy and Clinical Immunology: In Practice. 2024;12:2056-2065.e10. https://doi.org/10.1016/j.jaip.2024.04.006\u003c/li\u003e\n\u003cli\u003eCook L. Polyomaviruses. Hayden RT, Wolk DM, Carroll KC, Tang Y-W, editors. Microbiol Spectr. 2016;4:4.4.24. https://doi.org/10.1128/microbiolspec.DMIH2-0010-2015\u003c/li\u003e\n\u003cli\u003eSilins SL, Sherritt MA, Silleri JM, Cross SM, Elliott SL, Bharadwaj M, et al. Asymptomatic primary Epstein-Barr virus infection occurs in the absence of blood T-cell repertoire perturbations despite high levels of systemic viral load. Blood. 2001;98:3739\u0026ndash;44. https://doi.org/10.1182/blood.V98.13.3739\u003c/li\u003e\n\u003cli\u003eDavey Smith G. Post\u0026ndash;Modern Epidemiology: When Methods Meet Matter. American Journal of Epidemiology. 2019;188:1410\u0026ndash;9. https://doi.org/10.1093/aje/kwz064\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Connor SM, Taylor CE, Hughes JM. Emerging Infectious Determinants of Chronic Diseases. Emerg Infect Dis. 2006;12:1051\u0026ndash;7. https://doi.org/10.3201/eid1207.060037\u003c/li\u003e\n\u003cli\u003ePaixao ES, Lawlor DA, Barreto ML, Rodrigues LC. Integrating infectious diseases into life course epidemiology. International Journal of Epidemiology. 2025;54:dyaf059. https://doi.org/10.1093/ije/dyaf059\u003c/li\u003e\n\u003cli\u003eWild CP. The exposome: from concept to utility. International Journal of Epidemiology. 2012;41:24\u0026ndash;32. https://doi.org/10.1093/ije/dyr236\u003c/li\u003e\n\u003cli\u003eLarsen PS, Kamper‐J\u0026oslash;rgensen M, Adamson A, Barros H, Bonde JP, Brescianini S, et al. Pregnancy and Birth Cohort Resources in Europe: a Large Opportunity for Aetiological Child Health Research. Paediatric Perinatal Epid. 2013;27:393\u0026ndash;414. https://doi.org/10.1111/ppe.12060\u003c/li\u003e\n\u003cli\u003eJaddoe VWV, Felix JF, Andersen A-MN, Charles M-A, Chatzi L, Corpeleijn E, et al. The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents. Eur J Epidemiol. 2020;35:709\u0026ndash;24. https://doi.org/10.1007/s10654-020-00662-z\u003c/li\u003e\n\u003cli\u003eVrijheid M, Basaga\u0026ntilde;a X, Gonzalez JR, Jaddoe VWV, Jensen G, Keun HC, et al. Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview. Environmental Epidemiology. 2021;5:e166. https://doi.org/10.1097/EE9.0000000000000166\u003c/li\u003e\n\u003cli\u003eLawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2017;dyw314. https://doi.org/10.1093/ije/dyw314\u003c/li\u003e\n\u003cli\u003eFernandes A, Avraam D, Cadman T, Dadvand P, Guxens M, Binter A-C, et al. Green spaces and respiratory, cardiometabolic, and neurodevelopmental outcomes: An individual-participant data meta-analysis of \u0026gt;35.000 European children. Environment International. 2024;190:108853. https://doi.org/10.1016/j.envint.2024.108853\u003c/li\u003e\n\u003cli\u003eBarry KM, Avraam D, Cadman T, Elhakeem A, El Marroun H, Jansen PW, et al. Early childcare arrangements and children\u0026rsquo;s internalizing and externalizing symptoms: an individual participant data meta-analysis of six prospective birth cohorts in Europe. The Lancet Regional Health - Europe. 2024;45:101036. https://doi.org/10.1016/j.lanepe.2024.101036\u003c/li\u003e\n\u003cli\u003eDescarpentrie A, Calas L, Cornet M, Heude B, Charles M, Avraam D, et al. Lifestyle patterns in European preschoolers: Associations with socio‐demographic factors and body mass index. Pediatric Obesity. 2023;18:e13079. https://doi.org/10.1111/ijpo.13079\u003c/li\u003e\n\u003cli\u003eElhakeem A, Ronkainen J, Mansell T, Lange K, Mikkola TM, Mishra BH, et al. Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study. BMC Med. 2023;21:23. https://doi.org/10.1186/s12916-022-02711-8\u003c/li\u003e\n\u003cli\u003eWarkentin S, Fossati S, Marquez S, Andersen A-MN, Andrusaityte S, Avraam D, et al. Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts. Environment International. 2025;200:109527. https://doi.org/10.1016/j.envint.2025.109527\u003c/li\u003e\n\u003cli\u003eVan Meel ER, Mensink-Bout SM, Den Dekker HT, Ahluwalia TS, Annesi-Maesano I, Arshad SH, et al. Early-life respiratory tract infections and the risk of school-age lower lung function and asthma: a meta-analysis of 150 000 European children. Eur Respir J. 2022;60:2102395. https://doi.org/10.1183/13993003.02395-2021\u003c/li\u003e\n\u003cli\u003eNader JL, L\u0026oacute;pez-Vicente M, Julvez J, Guxens M, Cadman T, Elhakeem A, et al. Measures of Early-life Behavior and Later Psychopathology in the LifeCycle Project - EU Child Cohort Network: A Cohort Description. J Epidemiol. 2023;33:321\u0026ndash;31. https://doi.org/10.2188/jea.JE20210241\u003c/li\u003e\n\u003cli\u003eRonkainen J, Nedelec R, Atehortua A, Balkhiyarova Z, Cascarano A, Ngoc Dang V, et al. LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases. Environmental Epidemiology. 2022;6:e184. https://doi.org/10.1097/EE9.0000000000000184\u003c/li\u003e\n\u003cli\u003eHohmann C, Pinart M, Tischer C, Gehring U, Heinrich J, Kull I, et al. The development of the MeDALL Core Questionnaires for a harmonized follow-up assessment of eleven European birth cohorts on asthma and allergies. Int Arch Allergy Immunol. 2014;163:215\u0026ndash;24. https://doi.org/10.1159/000357732\u003c/li\u003e\n\u003cli\u003eMahic M, Che X, Susser E, Levin B, Reichborn-Kjennerud T, Magnus P, et al. Epidemiological and Serological Investigation into the Role of Gestational Maternal Influenza Virus Infection and Autism Spectrum Disorders. mSphere. 2017;2:e00159-17. https://doi.org/10.1128/mSphere.00159-17\u003c/li\u003e\n\u003cli\u003eBrustad N, Buchvald F, Jensen SK, Kyvsgaard JN, Vahman N, Thorsen J, et al. Burden of Infections in Early Life and Risk of Infections and Systemic Antibiotics Use in Childhood. JAMA Netw Open. 2025;8:e2453284. https://doi.org/10.1001/jamanetworkopen.2024.53284\u003c/li\u003e\n\u003cli\u003eLanger S, Klee B, Gottschick C, Mikolajczyk R. Birth cohort studies using symptom diaries for assessing respiratory diseases-a scoping review. PLoS One. 2022;17:e0263559. https://doi.org/10.1371/journal.pone.0263559\u003c/li\u003e\n\u003cli\u003eRolfhamre P, Jansson A, Arneborn M, Ekdahl K. SmiNet-2: Description of an internet-based surveillance system for communicable diseases in Sweden. Euro Surveill. 2006;11:103\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eYu Z, Bellander T, Bergstr\u0026ouml;m A, Dillner J, Eneroth K, Engardt M, et al. Association of Short-term Air Pollution Exposure With SARS-CoV-2 Infection Among Young Adults in Sweden. JAMA Netw Open. 2022;5:e228109. https://doi.org/10.1001/jamanetworkopen.2022.8109\u003c/li\u003e\n\u003cli\u003eSohal K, Mason D, Birkinshaw J, West J, McEachan RRC, Elshehaly M, et al. Connected Bradford: a Whole System Data Linkage Accelerator. Wellcome Open Res. 2022;7:26. https://doi.org/10.12688/wellcomeopenres.17526.2\u003c/li\u003e\n\u003cli\u003eGrann AF, Erichsen R, Nielsen AG, Fr\u0026oslash;slev T, Thomsen RW. Existing data sources for clinical epidemiology: The clinical laboratory information system (LABKA) research database at Aarhus University, Denmark. Clin Epidemiol. 2011;3:133\u0026ndash;8. https://doi.org/10.2147/CLEP.S17901\u003c/li\u003e\n\u003cli\u003eLape M, Schnell D, Parameswaran S, Ernst K, O\u0026rsquo;Connor S, Salomonis N, et al. A survey of pathogenic involvement in non-communicable human diseases. Commun Med. 2025;5:242. https://doi.org/10.1038/s43856-025-00956-x\u003c/li\u003e\n\u003cli\u003eCoathup V, Ashdown HF, Carson C, Santorelli G, Quigley MA. Associations between maternal body mass index and childhood infections in UK primary care: findings from the Born in Bradford birth cohort study. Arch Dis Child. 2024;110:59\u0026ndash;66. https://doi.org/10.1136/archdischild-2024-326951\u003c/li\u003e\n\u003cli\u003eLebon A, Moll HA, Tavakol M, Van Wamel WJ, Jaddoe VWV, Hofman A, et al. Correlation of Bacterial Colonization Status between Mother and Child: the Generation R Study. J Clin Microbiol. 2010;48:960\u0026ndash;2. https://doi.org/10.1128/JCM.01799-09\u003c/li\u003e\n\u003cli\u003eRoubidoux EK, Brigleb PH, Vegesana K, Souquette A, Whitt K, Freiden P, et al. Utility of nasal swabs for assessing mucosal immune responses towards SARS-CoV-2. Sci Rep. 2023;13:17820. https://doi.org/10.1038/s41598-023-44989-5\u003c/li\u003e\n\u003cli\u003eDe Paschale M, Clerici P. Serological diagnosis of Epstein-Barr virus infection: Problems and solutions. World J Virol. 2012;1:31\u0026ndash;43. https://doi.org/10.5501/wjv.v1.i1.31\u003c/li\u003e\n\u003cli\u003eMentzer AJ, Brenner N, Allen N, Littlejohns TJ, Chong AY, Cortes A, et al. Identification of host\u0026ndash;pathogen-disease relationships using a scalable multiplex serology platform in UK Biobank. Nat Commun. 2022;13:1818. https://doi.org/10.1038/s41467-022-29307-3\u003c/li\u003e\n\u003cli\u003eShrock E, Shrock C, Elledge SJ. VirScan: High-throughput Profiling of Antiviral Antibody Epitopes. BIO-PROTOCOL [Internet]. 2022 [cited 2025 Oct 17];12. https://doi.org/10.21769/BioProtoc.4464\u003c/li\u003e\n\u003cli\u003eDolatshahi S, Butler AL, Pou C, Henckel E, Bernhardsson AK, Gustafsson A, et al. Selective transfer of maternal antibodies in preterm and fullterm children. Sci Rep. 2022;12:14937. https://doi.org/10.1038/s41598-022-18973-4\u003c/li\u003e\n\u003cli\u003eKarachaliou M, Vidal M, Pembrey L, Bustamante M, Espinosa A, Mason D, et al. Seroepidemiology of maternal and childhood pathogen exposure in three European mother-child cohorts. International Journal of Infectious Diseases. 2025;159:107994. https://doi.org/10.1016/j.ijid.2025.107994\u003c/li\u003e\n\u003cli\u003eLunet N, Peleteiro B, Bastos J, Correia S, Marinho A, Guimar\u0026atilde;es JT, et al. Child day-care attendance and Helicobacter pylori infection in the Portuguese birth cohort Gera\u0026ccedil;\u0026atilde;o XXI. Eur J Cancer Prev. 2014;23:193\u0026ndash;8. https://doi.org/10.1097/CEJ.0b013e328364742a\u003c/li\u003e\n\u003cli\u003eBarlinn R, Dudman SG, Trogstad L, Gibory M, Muller F, Magnus P, et al. Maternal and congenital cytomegalovirus infections in a population‐based pregnancy cohort study. APMIS. 2018;126:899\u0026ndash;906. https://doi.org/10.1111/apm.12899\u003c/li\u003e\n\u003cli\u003eKhandaker GM, Stochl J, Zammit S, Lewis G, Jones PB. Childhood Epstein-Barr Virus infection and subsequent risk of psychotic experiences in adolescence: a population-based prospective serological study. Schizophr Res. 2014;158:19\u0026ndash;24. https://doi.org/10.1016/j.schres.2014.05.019\u003c/li\u003e\n\u003cli\u003eMitchell RE, Jones HJ, Yolken RH, Ford G, Jones-Brando L, Ring SM, et al. Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort. Wellcome Open Res. 2018;3:49. https://doi.org/10.12688/wellcomeopenres.14565.2\u003c/li\u003e\n\u003cli\u003eZylbersztejn A, Pembrey L, Goldstein H, Berbers G, Schepp R, Van Der Klis F, et al. Respiratory syncytial virus in young children: community cohort study integrating serological surveys, questionnaire and electronic health records, Born in Bradford cohort, England, 2008 to 2013. Eurosurveillance [Internet]. 2021 [cited 2025 Nov 12];26. https://doi.org/10.2807/1560-7917.ES.2021.26.6.2000023\u003c/li\u003e\n\u003cli\u003ePinot de Moira A, Haakma S, Strandberg-Larsen K, van Enckevort E, Kooijman M, Cadman T, et al. The EU Child Cohort Network\u0026rsquo;s core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables. Eur J Epidemiol. 2021;36:565\u0026ndash;80. https://doi.org/10.1007/s10654-021-00733-9\u003c/li\u003e\n\u003cli\u003eAvraam D, Wilson RC, Aguirre Chan N, Banerjee S, Bishop TRP, Butters O, et al. DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform. Bioinform Adv. 2025;5:vbaf046. https://doi.org/10.1093/bioadv/vbaf046\u003c/li\u003e\n\u003cli\u003eZinkernagel RM. Maternal antibodies, childhood infections, and autoimmune diseases. N Engl J Med. 2001;345:1331\u0026ndash;5. https://doi.org/10.1056/NEJMra012493\u003c/li\u003e\n\u003cli\u003eGlynn JR, Moss PAH. Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children. Sci Data. 2020;7:329. https://doi.org/10.1038/s41597-020-00668-y\u003c/li\u003e\n\u003cli\u003eDemars A, Lison A, Machelart A, Van Vyve M, Potemberg G, Vanderwinden J-M, et al. Route of Infection Strongly Impacts the Host-Pathogen Relationship. Front Immunol. 2019;10:1589. https://doi.org/10.3389/fimmu.2019.01589\u003c/li\u003e\n\u003cli\u003eBlaser MJ. Who are we?: Indigenous microbes and the ecology of human diseases. EMBO Reports. 2006;7:956\u0026ndash;60. https://doi.org/10.1038/sj.embor.7400812\u003c/li\u003e\n\u003cli\u003eGalan R, Pembrey L, Bustamante M, Aguilar R, Mason D, Vidal M, et al. The association of Helicobacter pylori with adverse pregnancy outcomes in three European birth cohorts. BMC Pregnancy Childbirth. 2024;24:745. https://doi.org/10.1186/s12884-024-06901-5\u003c/li\u003e\n\u003cli\u003eden Hollander WJ, Schalekamp-Timmermans S, Holster IL, Jaddoe VW, Hofman A, Moll HA, et al. Helicobacter pylori colonization and pregnancies complicated by preeclampsia, spontaneous prematurity, and small for gestational age birth. Helicobacter. 2017;22. https://doi.org/10.1111/hel.12364\u003c/li\u003e\n\u003cli\u003eRich-Edwards JW, Fraser A, Lawlor DA, Catov JM. Pregnancy characteristics and women\u0026rsquo;s future cardiovascular health: an underused opportunity to improve women\u0026rsquo;s health? Epidemiol Rev. 2014;36:57\u0026ndash;70. https://doi.org/10.1093/epirev/mxt006\u003c/li\u003e\n\u003cli\u003eGutierrez S, Glymour MM, Smith GD. Evidence triangulation in health research. Eur J Epidemiol. 2025;40:743\u0026ndash;57. https://doi.org/10.1007/s10654-024-01194-6\u003c/li\u003e\n\u003cli\u003eHamilton F, Butler-Laporte G, Davey Smith G. Mendelian Randomization and Infection: Pitfalls and Promises. J Infect Dis. 2025;232:525\u0026ndash;33. https://doi.org/10.1093/infdis/jiaf251\u003c/li\u003e\n\u003cli\u003eEyting M, Xie M, Michalik F, He\u0026szlig; S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature. 2025;641:438\u0026ndash;46. https://doi.org/10.1038/s41586-025-08800-x\u003c/li\u003e\n\u003cli\u003eDivision on Earth and Life Studies, National Academies of Sciences, Engineering, and Medicine. Pivotal Interfaces of Environmental Health and Infectious Disease Research to Inform Responses to Outbreaks, Epidemics, and Pandemics: Proceedings of a Workshop-in Brief [Internet]. Johnson AF, Bremer A, Liao J, Th\u0026eacute;venon A, editors. Washington, D.C.: National Academies Press; 2021 [cited 2025 Nov 12]. p. 26270. https://doi.org/10.17226/26270\u003c/li\u003e\n\u003cli\u003eBarouki R, Samson M, Blanc EB, Colombo M, Zucman-Rossi J, Lazaridis KN, et al. The exposome and liver disease - how environmental factors affect liver health. J Hepatol. 2023;79:492\u0026ndash;505. https://doi.org/10.1016/j.jhep.2023.02.034\u003c/li\u003e\n\u003cli\u003eQi Y, Chen Y, Yan X, Liu W, Ma L, Liu Y, et al. Co-Exposure of Ambient Particulate Matter and Airborne Transmission Pathogens: The Impairment of the Upper Respiratory Systems. Environ Sci Technol. 2022;56:15892\u0026ndash;901. https://doi.org/10.1021/acs.est.2c03856\u003c/li\u003e\n\u003cli\u003eKutralam-Muniasamy G, Shruti VC, P\u0026eacute;rez-Guevara F. Plastisphere-hosted viruses: A review of interactions, behavior, and effects. Journal of Hazardous Materials. 2024;472:134533. https://doi.org/10.1016/j.jhazmat.2024.134533\u003c/li\u003e\n\u003cli\u003eBabu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics. 2023;22:100561. https://doi.org/10.1016/j.mcpro.2023.100561\u003c/li\u003e\n\u003cli\u003eYoon JH, Lee H, Kwon D, Lee D, Lee S, Cho E, et al. Integrative approach of omics and imaging data to discover new insights for understanding brain diseases. Brain Commun. 2024;6:fcae265. https://doi.org/10.1093/braincomms/fcae265\u003c/li\u003e\n\u003cli\u003eMitchell RE, Jones HJ, Yolken RH, Ford G, Jones-Brando L, Ring SM, et al. Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort. Wellcome Open Res. 2018;3:49. https://doi.org/10.12688/wellcomeopenres.14565.2\u003c/li\u003e\n\u003cli\u003eChong AHW, Mitchell RE, Hemani G, Davey Smith G, Yolken RH, Richmond RC, et al. Genetic Analyses of Common Infections in the Avon Longitudinal Study of Parents and Children Cohort. Front Immunol. 2021;12:727457. https://doi.org/10.3389/fimmu.2021.727457\u003c/li\u003e\n\u003cli\u003ePembrey L, Raynor P, Griffiths P, Chaytor S, Wright J, Hall AJ. Seroprevalence of Cytomegalovirus, Epstein Barr Virus and Varicella Zoster Virus among Pregnant Women in Bradford: A Cohort Study. Nevels M, editor. PLoS ONE. 2013;8:e81881. https://doi.org/10.1371/journal.pone.0081881\u003c/li\u003e\n\u003cli\u003ePembrey L, Waiblinger D, Griffiths P, Patel M, Azad R, Wright J. Cytomegalovirus, Epstein-Barr virus and varicella zoster virus infection in the first two years of life: a cohort study in Bradford, UK. BMC Infect Dis. 2017;17:220. https://doi.org/10.1186/s12879-017-2319-7\u003c/li\u003e\n\u003cli\u003ePembrey L, Waiblinger D, Griffiths P, Wright J. Age at cytomegalovirus, Epstein Barr virus and varicella zoster virus infection and risk of atopy: The Born in Bradford cohort, UK. Pediatric Allergy Immunology. 2019;30:604\u0026ndash;13. https://doi.org/10.1111/pai.13093\u003c/li\u003e\n\u003cli\u003eVan Den Heuvel D, Jansen MAE, Dik WA, Bouallouch-Charif H, Zhao D, Van Kester KAM, et al. Cytomegalovirus- and Epstein-Barr Virus\u0026ndash;Induced T-Cell Expansions in Young Children Do Not Impair Naive T-cell Populations or Vaccination Responses: The Generation R Study. J Infect Dis. 2016;213:233\u0026ndash;42. https://doi.org/10.1093/infdis/jiv369\u003c/li\u003e\n\u003cli\u003eJansen MAE, van den Heuvel D, Bouthoorn SH, Jaddoe VWV, Hooijkaas H, Raat H, et al. Determinants of Ethnic Differences in Cytomegalovirus, Epstein-Barr Virus, and Herpes Simplex Virus Type 1 Seroprevalence in Childhood. J Pediatr. 2016;170:126-134.e1-6. https://doi.org/10.1016/j.jpeds.2015.11.014\u003c/li\u003e\n\u003cli\u003eKarachaliou M, Waterboer T, Casabonne D, Chalkiadaki G, Roumeliotaki T, Michel A, et al. The Natural History of Human Polyomaviruses and Herpesviruses in Early Life--The Rhea Birth Cohort in Greece. Am J Epidemiol. 2016;183:671\u0026ndash;9. https://doi.org/10.1093/aje/kwv281\u003c/li\u003e\n\u003cli\u003eKarachaliou M, de Sanjose S, Roumeliotaki T, Margetaki K, Vafeiadi M, Waterboer T, et al. Heterogeneous associations of polyomaviruses and herpesviruses with allergy-related phenotypes in childhood. Ann Allergy Asthma Immunol. 2021;127:191-199.e3. https://doi.org/10.1016/j.anai.2021.04.019\u003c/li\u003e\n\u003cli\u003eKarachaliou M, de Sanjose S, Waterboer T, Roumeliotaki T, Vassilaki M, Sarri K, et al. Is early life exposure to polyomaviruses and herpesviruses associated with obesity indices and metabolic traits in childhood? Int J Obes (Lond). 2018;42:1590\u0026ndash;601. https://doi.org/10.1038/s41366-018-0017-1\u003c/li\u003e\n\u003cli\u003eKarachaliou M, Chatzi L, Michel A, Kyriklaki A, Kampouri M, Koutra K, et al. Helicobacter pylori Seropositivity and Childhood Neurodevelopment, the Rhea Birth Cohort in Crete, Greece. Paediatr Perinat Epidemiol. 2017;31:374\u0026ndash;84. https://doi.org/10.1111/ppe.12374\u003c/li\u003e\n\u003cli\u003eKarachaliou M, Chatzi L, Roumeliotaki T, Kampouri M, Kyriklaki A, Koutra K, et al. Common infections with polyomaviruses and herpesviruses and neuropsychological development at 4 years of age, the Rhea birth cohort in Crete, Greece. J Child Psychol Psychiatry. 2016;57:1268\u0026ndash;76. https://doi.org/10.1111/jcpp.12582\u003c/li\u003e\n\u003cli\u003eSun Y, Christensen J, Olsen J. Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res. 2013;107:61\u0026ndash;74. https://doi.org/10.1016/j.eplepsyres.2013.08.019\u003c/li\u003e\n\u003cli\u003eStrand KM, Odland ML, Iversen A-C, Nordb\u0026oslash; SA, Vik T, Austgulen R. Cytomegalovirus antibody status at 17-18 weeks of gestation and pre-eclampsia: a case-control study of pregnant women in Norway. BJOG. 2012;119:1316\u0026ndash;23. https://doi.org/10.1111/j.1471-0528.2012.03420.x\u003c/li\u003e\n\u003cli\u003eMirinaviciute G, Barlinn R, Gjeruldsen Dudman S, Flem E. Immunity to varicella zoster virus among pregnant women in the Norwegian Mother and Child Cohort Study. PLoS One. 2019;14:e0221084. https://doi.org/10.1371/journal.pone.0221084\u003c/li\u003e\n\u003cli\u003eSun Y, Christensen J, Olsen J. Childhood epilepsy and maternal antibodies to microbial and tissue antigens during pregnancy. Epilepsy Res. 2013;107:61\u0026ndash;74. https://doi.org/10.1016/j.eplepsyres.2013.08.019\u003c/li\u003e\n\u003cli\u003eMahic M, Mjaaland S, B\u0026oslash;velstad HM, Gunnes N, Susser E, Bresnahan M, et al. Maternal Immunoreactivity to Herpes Simplex Virus 2 and Risk of Autism Spectrum Disorder in Male Offspring. mSphere. 2017;2:e00016-17. https://doi.org/10.1128/mSphere.00016-17\u003c/li\u003e\n\u003cli\u003eKarachaliou M, Moncunill G, Espinosa A, Casta\u0026ntilde;o-Vinyals G, Jim\u0026eacute;nez A, Vidal M, et al. Infection induced SARS-CoV-2 seroprevalence and heterogeneity of antibody responses in a general population cohort study in Catalonia Spain. Sci Rep. 2021;11:21571. https://doi.org/10.1038/s41598-021-00807-4\u003c/li\u003e\n\u003cli\u003eCharles MA, Ancel P-Y, Simeon T, Marchand-Martin L, Zaros C, Dufourg M-N, et al. SARS-CoV-2 seroprevalence in French 9-year-old children and their parents after the first lockdown in 2020. Front Pediatr. 2023;11:1274113. https://doi.org/10.3389/fped.2023.1274113\u003c/li\u003e\n\u003cli\u003eBarlinn R, Rollag H, Trogstad L, Vainio K, Basset C, Magnus P, et al. High incidence of maternal parvovirus B19 infection in a large unselected population-based pregnancy cohort in Norway. J Clin Virol. 2017;94:57\u0026ndash;62. https://doi.org/10.1016/j.jcv.2017.07.010\u003c/li\u003e\n\u003cli\u003eMahic M, Mjaaland S, B\u0026oslash;velstad HM, Gunnes N, Susser E, Bresnahan M, et al. Maternal Immunoreactivity to Herpes Simplex Virus 2 and Risk of Autism Spectrum Disorder in Male Offspring. mSphere. 2017;2:e00016-17. https://doi.org/10.1128/mSphere.00016-17\u003c/li\u003e\n\u003cli\u003eden Hollander WJ, Sonnenschein-van der Voort AMM, Holster IL, de Jongste JC, Jaddoe VW, Hofman A, et al. Helicobacter pylori in children with asthmatic conditions at school age, and their mothers. Aliment Pharmacol Ther. 2016;43:933\u0026ndash;43. https://doi.org/10.1111/apt.13572\u003c/li\u003e\n\u003cli\u003eden Hollander WJ, Holster IL, van Gilst B, van Vuuren AJ, Jaddoe VWV, Hofman A, et al. Intergenerational reduction in Helicobacter pylori prevalence is similar between different ethnic groups living in a Western city. Gut. 2015;64:1200\u0026ndash;8. https://doi.org/10.1136/gutjnl-2014-307689\u003c/li\u003e\n\u003cli\u003eden Hollander WJ, Holster IL, den Hoed CM, van Deurzen F, van Vuuren AJ, Jaddoe VW, et al. Ethnicity is a strong predictor for Helicobacter pylori infection in young women in a multi-ethnic European city. J Gastroenterol Hepatol. 2013;28:1705\u0026ndash;11. https://doi.org/10.1111/jgh.12315\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"869\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 869px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMethods to assess exposure to specific infections in the EU Child Cohort Network and their characteristics.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003einformation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003estrengths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elimitations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestionnaires\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eSelf-report of medical diagnoses (e.g. influenza), related medical tests (e.g. prenatal screening for TORCH) or specific clinical symptoms and their severity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eScalable, low cost, captures clinical symptoms, harmonization feasible across cohorts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNon-specific, recall bias, does not capture asymptomatic infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymptom diaries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eRecording of specific symptoms, severity, duration and dates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eReal-time recording, no recall bias\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eTime consuming for participants, missing data, loss to follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 869px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectronic Health Records\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Registry for notifiable\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;infectious diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eConfirmed infections as required by national reporting systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eReal-world data from multiple sources; standardized case definitions; often longitudinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eLimited to legally notifiable diseases; country specific; dependent on registry availability and accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Registries of laboratory\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;test results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eResults of laboratory tests performed in healthcare settings.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLaboratory-confirmed data; often near-complete population coverage where centralized laboratory reporting systems exist.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDependent on registry availability and access, usually excludes private care, delays to gain access, country specific, limited clinical context (e.g., reason for testing) unless recorded;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; ICD10 from primary,\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;secondary, tertiary\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;health care services\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eClinically confirmed diagnoses with dates of infection, number of visits, clinical manifestation (e.g. hospitalization)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eEntire population, standardized diagnostic data, comparable across countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDependent on registry availability and access, usually excludes private care, delays to gain access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Anatomical\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;Therapeutic Chemical\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;codes J01.X\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003ePrescriptions of drugs typically used for infections\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eClinical suspicion of infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDoes not confirm actual treatment or infection; dependent on registry availability and access, usually excludes private care, delays to gain access\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobiological tests (e.g. PCR, culture, rapid test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eDirect detection of a pathogen in a specific tissue, captures both acute infection and carriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHigh specificity; can identify pathogen and sometimes antimicrobial resistance; tissue specific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRequires biological sample collection; costly; risk of contamination;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003ePresence and levels of antibodies (IgG, IgM, IgA isotypes); indicates prior exposure and the magnitude of humoral immune response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCaptures asymptomatic and symptomatic cases; Pathogen-specific; scalable with modern platforms (e.g., multiplex assays,\u0026nbsp;PhIP-Seq); allows assessment of immunity over time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRequires blood or other biospecimens; cannot always distinguish natural from vaccine-induced immunity; maternal antibodies detectable in infants up to 9\u0026ndash;12 months; no information on clinical presentation; limited use for detecting acute infection because antibodies are typically produced 1\u0026ndash;2 weeks after exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1025\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 1025px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Overview of serologically based information on infections across cohorts and ages in the EU Child Cohort Network.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003einfection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecohorts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecord\u003cbr\u003e\u0026nbsp;blood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026nbsp;\u003cbr\u003e\u0026nbsp;year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-3 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-6 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7-9 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-12 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13-15 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edisease associations examined\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEBV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[40,61,62], BiB\u0026nbsp;[37,63\u0026ndash;65], COPSAC2010, Gen R\u0026nbsp;[66,67], INMA\u0026nbsp;[37], Rhea\u0026nbsp;[37,68\u0026ndash;72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ebirth defects, allergy, respiratory health, cognition, behavior, psychiatric outcomes, multiple sclerosis, obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[40,61,62],\u0026nbsp;BiB [37,63\u0026ndash;65], COPSAC2010, DNBC [73], Gen R [66,67], INMA\u0026nbsp;[37],, MoBA\u0026nbsp;[39,74], Rhea\u0026nbsp;[37,68\u0026ndash;72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e5113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003epre-eclampsia, birth defects, allergy, respiratory health, neurodevelopment, autism, epilepsy, obesity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVZV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eBiB [37,63\u0026ndash;65], Gen R [66], INMA[37], MoBA [75], Rhea[37]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e3183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eimmune effects, allergy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSV-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eDNBC[76], Gen R\u0026nbsp;[67], Rhea\u0026nbsp;[68\u0026ndash;70,72], MoBA[77], COPSAC2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ebirth defects, allergy neurodevelopment, epilepsy, autism, obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSV-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eMoBA[77], DNBC\u0026nbsp;[73], Rhea\u0026nbsp;[68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eautism, epilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC [61,62], Gen R\u0026nbsp;[66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBKPyV\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJCPyV\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKIPyV\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWUPyV\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMCPyV\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eBiB\u0026nbsp;[37], INMA\u0026nbsp;[37], Rhea\u0026nbsp;[37,68\u0026ndash;70,72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eallergy, obesity, cognition, behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHHV-8, HPyV6, HPyV7, HPyV9, TSPyV, HPyV10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eRhea\u0026nbsp;[68\u0026ndash;70,72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eallergy, obesity, cognition, behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[61,62], MoBA\u0026nbsp;[77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eautism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRSV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eBiB\u0026nbsp;[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSARS-CoV-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eBiSC\u0026nbsp;[78], ELFE[79],\u0026nbsp;INMA\u0026nbsp;[78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[61,62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdenovirus 36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eBiB\u0026nbsp;[37], INMA\u0026nbsp;[37], Rhea\u0026nbsp;[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eobesity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParvovirus B19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eMoBA\u0026nbsp;[80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT.gondii\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[61,62], BiB\u0026nbsp;[37], COPSAC2010, INMA\u0026nbsp;[37], MoBA\u0026nbsp;[81], Rhea\u0026nbsp;[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eautism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH.pylori\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eALSPAC\u0026nbsp;[61,62], BiB\u0026nbsp;[37,49],\u0026nbsp;Gen R [50,82\u0026ndash;84], Gera\u0026ccedil;\u0026atilde;o XXI\u0026nbsp;[38], INMA[37,49], Rhea\u0026nbsp;[37,49,68,71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e7873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003epregnancy outcomes, allergy, cognition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 1025px;\"\u003e\n \u003cp\u003e\u003cem\u003eAbbreviations: EBV, Epstein Barr Virus; CMV cytomegalovirus; VZV, varicella zoster virus; HSV-1, herpes simplex virus 1; PyV, polyomavirus; RSV, respiratory syncytial virus; HAV, hepatitis A virus; T. gondii, Toxoplasma gondii; H.pylori, Helicobacter pylori\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Existing research gaps in the field of early life infections and lifelong health and disease, and approaches to fill these gaps.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExisting gaps\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApproach to fill the gaps\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.DOHaD and life course research\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elargely lacks investigations on infections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eAchievable though EU Child Cohort Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.Exposome research largely neglects infections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eAchievable though EU Child Cohort Network and linked exposome projects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.Lack of causal inference in infections-NCDs research\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eAchievable though EU Child Cohort Network and application of triangulation framework\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.Focused exposure-outcome associations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eAchievable though EU Child Cohort Network\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.Studies on biological pathways are limited\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003ePartly achievable through EU Child Cohort Network and linked omics and imaging related projects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.Narrow set of infections examined so far\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eApplication of new technologies in stored samples\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.Paternal status largely missing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eNew analyses when available stored samples, Informing design of future studies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.Repeated data in pregnancy are largely missing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eNew analyses when available stored samples, Informing design of future studies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.Lack of data from the preconception period\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eNew analyses when available stored samples, Informing design of future studies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.Lack of data at early ages potentially due to lack of samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eInforming design of future studies (e.g. use of dried blood spots, new pediatric sampling devices)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 447px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.Lack of data in adolescence, samples are likely available\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 554px;\"\u003e\n \u003cp\u003eNew analyses when available stored samples including sexually transmitted infections, \u0026nbsp;\u003cbr\u003e\u0026nbsp;Informing design of future studies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"DOHaD, Birth cohort, Cross-cohort collaboration, Lifecourse epidemiology, Data harmonization, meta-research","lastPublishedDoi":"10.21203/rs.3.rs-8280798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8280798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Research into the developmental origins of, and lifecourse influences on, health and disease has a significant gap: infections. Clinical and subclinical infections are nearly universal in early life and can induce structural and functional changes in developing organ systems with life-long consequences. Emerging evidence links common childhood infections—such as Epstein–Barr virus, Cytomegalovirus and Helicobacter pylori—to so-called non-communicable diseases ranging from respiratory to neurodevelopmental and cardiometabolic diseases. Identifying infectious determinants of non-communicable diseases offers substantial opportunities for prevention and early intervention. We describe a newly developed infectious data resource within the EU Child Cohort Network, designed to enable triangulation of evidence on the impact of early life infections on children's health and long-term trajectories of disease. The network established in 2017, currently brings together 37 pregnancy and childhood cohorts from 16 predominantly European countries. It spans the first 20 years of life beginning in the prenatal period, with many cohorts continuing follow-up into adulthood. Cohorts include a comprehensive set of harmonized variables covering health and disease phenotypes (cardiometabolic, respiratory, and mental) that are also well-established early indicators of later-life non-communicable disease risk. Building upon this, we present here the extensive infections data available in the cohorts ranging from questionnaires and registries to serology. Harmonizing and integrating infection data within the EU Child Cohort Network will allow to comprehensively explore how infections interact with genetic and environmental factors to shape health and disease across the lifecourse, advancing the exposome framework and transforming our understanding of non-communicable disease aetiology.","manuscriptTitle":"Early life infections and life-course health and disease in the EU Child Cohort Network; study design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:39:02","doi":"10.21203/rs.3.rs-8280798/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-23T09:33:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T09:21:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Epidemiology","date":"2025-12-08T12:58:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-06T14:47:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Epidemiology","date":"2025-12-04T10:16:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b98242cc-5f72-4a87-9fdb-551c2a01ea24","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T00:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:39:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8280798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8280798","identity":"rs-8280798","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}