Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic

Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic | 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 Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic Ivo Zeller, Andreas Weiss, Stefanie Arnolds, Katharina Schütte-Borkovec, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Infection → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose. To determine the impact of the COVID-19 pandemic on the incidence rates of infection and islet autoimmunity in children at risk for type 1 diabetes. Methods. 1050 children aged 4 to 7 months with an elevated genetic risk for type 1 diabetes were recruited from Germany, Poland, Sweden, Belgium and the UK. Reported infection episodes and islet autoantibody development were monitored until age 40 months from February 2018 to February 2023. Results. The overall infection rate was 311 (95% Confidence Interval [CI], 304–318) per 100 person years. Infection rates differed by age, country, family history of type 1 diabetes, and period relative to the pandemic. Total infection rates were 321 per 100 person-years (95% CI, 304–338) in the pre-pandemic period (until February 2020), 160 (95% CI, 148–173) per 100 person-years in the first pandemic year (March 2020 - February 2021; P < 0.001) and 337 (95% CI, 315–363) per 100 person-years in subsequent years. Similar trends were observed for respiratory and gastrointestinal infections. Islet autoantibody incidence rates were 1.6 (95% CI, 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 (95% CI, 0.8–1.9) per 100 person-years in the first pandemic year ( P = 0.46), and 3.4 (95% CI, 2.3–4.8) per 100 person-years in subsequent years ( P = 0.005 vs. pre-pandemic year; P < 0.001 vs. first pandemic year). Conclusions. The COVID-19 pandemic significantly altered infection patterns. Islet autoantibody incidence rates increased two-fold when infection rates returned to pre-pandemic levels. Infection type 1 diabetes autoimmunity COVID-19 Figures Figure 1 Figure 2 INTRODUCTION Infections are believed to contribute to genetic diversity, thereby influencing susceptibility for immune-mediated diseases [ 1 ]. Type 1 diabetes is a chronic autoimmune disease characterized by the destruction of insulin-producing beta cells in the pancreas, leading to insulin deficiency. Islet autoimmunity precedes clinical type 1 diabetes and often manifests in the first three years of life [ 2 ]. In support of the role of infection in the etiology of type 1 diabetes, various viral response genes have been identified as conferring susceptibility to the disease. Moreover, numerous reports indicate associations between infection and the development of islet autoimmunity in children who have a prior genetic susceptibility, particularly in the first years of life [ 3 – 9 ]. During the COVID-19 pandemic, infections underwent significant changes. A novel virus was introduced to the human population and, within less than three years, had affected the majority of individuals. Additionally, preventive measures implemented during the pandemic have altered healthcare-seeking behavior, patterns of infectious disease transmission, and the incidence of infectious episodes. The pandemic was directly associated with an increase in type 1 diabetes incidence [ 10 – 12 ] and infection with the SARS-CoV-2 virus in early childhood was further associated with the risk of developing islet autoimmunity [ 9 ]. The aim of this study was to assess the exposure rate and potential changes in infection exposures by age within the first 3 years of life. The study also sought to determine whether there was a change in infection rates from the pre-pandemic to post-pandemic periods and whether these were associated with incidence rates of islet autoimmunity. This investigation was performed in the context of a clinical trial in 1050 children with a genetic susceptibility for type 1 diabetes who were monitored with adverse event reporting from infancy. The findings carry implications for understanding the impact of pandemics on the infection landscape and immune mediated diseases. METHODS Participants The study was performed in 1050 children participating in the Primary Oral Insulin Trial (POInT). POInT investigates whether daily intake of oral insulin reduces the incidence of islet autoimmunity and/or type 1 diabetes in children with an increased risk of type 1 diabetes [ 13 ]. Children were eligible if they had an increased risk for developing islet autoimmunity of > 10% by the age of 6.0 years, defined by a genetic risk score. Enrollment commenced in February 2018 and ended in March 2021. Children were enrolled at the age of 4.0–7.0 months, and followed at 2, 4 and 8 months after study enrollment, at 1.5 years of age, and then every 6 months. Daily treatment with oral insulin or placebo continued until age 3 years. The study was conducted in seven clinical research centers including three in Germany (Dresden, Hanover and Munich), one in Sweden (Malmö), one in Poland (Warsaw), one in Belgium (Leuven) and one in the UK (Oxford). Adverse events were recorded at each clinical study visit until 6 months after end of treatment. For the current analysis, reported infections until 02/28/2023 were included. A detailed description of the study protocol has been published previously [ 13 ]. Assessment of exposure to infections At each scheduled clinic visit, adverse events were collected by study personnel and recorded in the clinical trial database. For each event, illness description, date of onset and end date was recorded. Adverse events were categorized using the Medical Dictionary for Regulatory Activities (MedDRA). Classification of infections was performed as previously described [ 14 ] (Suppl Table 1). In brief, the first infection episode category was created from MedDRA Lowest Level Terms (LLT) as respiratory infection. If terms belonging to this category were reported within one week, they were regarded as one respiratory infection episode. Gastrointestinal symptoms are considered to occur frequently in young children with a respiratory infection and therefore gastrointestinal symptoms co-occurring with respiratory infections were considered as part of the respiratory infection episode. The second infection episode category was created from MedDRA LLT as gastrointestinal infections. If terms belonging to this category were reported within the same week, they were regarded as one gastrointestinal infection episode. The third category was defined as “other types of infections”, and the fourth category was unknown febrile episodes. Each LLT within this category is treated as a distinct infection episode. The date of all episodes was the date of the first LLT. A separate category was given to infections associated with Coxsackie virus (Suppl Table 1). In the present study, follow-up data up to 40 months of age were evaluated. Islet autoimmunity outcome Islet autoantibodies were measured centrally at 2 independent GPPAD Core laboratories, located at the Institute of Diabetes Research, Helmholtz Munich, Germany, and at the University of Bristol Medical School, Diabetes and Metabolism, Learning and Research, Southmead Hospital, Bristol, United Kingdom (for confirmation of results). Serum samples from each visit were analyzed for autoantibodies to insulin, GAD65, IA-2 and ZnT8 (ZnT8RA and ZnT8WA) as previously described [ 23 ]. A child was classified as islet autoantibody positive if 2 consecutive samples tested positive at both laboratories. A child was classified as multiple islet autoantibody positive if tested positive for 2 or more autoantibodies in both laboratories. The islet autoimmunity outcome was defined as either development of multiple islet autoantibodies or the development of one or more islet autoantibodies followed by type 1 diabetes. Maternally transferred islet autoantibodies were excluded and identified if the child was positive at the first sample, had declining antibody titers on follow-up, and subsequently became islet autoantibody-negative. For children classified as islet autoantibody-positive, the first positive sample was taken as the age at seroconversion. Study approval Ethical approval for the POInT study was obtained from local ethical committees and regulatory authorities of the Technische Universität München, Medical Faculty (326/17 Af), the Medical University of Warsaw (Institute of Mother and Child) (199/2017), the UK Health Research Authority (18/SC/0019), Onderzoek UZ/KU Leuven (S60711) and the Regionala etikprövningsnämnden i Lund (2017/918). The parents or legal representatives of each participant provided written informed consent, and further agreed to biobank storage of material that was used in this study. Statistical analysis Age- and stage-specific counts of infection episodes were calculated across distinct age intervals: 4-8.99 months, 9-14.99 months, 15-20.99 months, 21-26.99 months, 27-32.99 months, and 33-39.99 months. Infection episode counts were also segmented into pre-pandemic (2018-02-07 to 2020-02-29), pandemic 2020 (2020-03-01 to 2021-02-28), and pandemic 2021–2022 (2021-03-01 to 2023-02-28) periods. Infection episodes for each group combination were expressed as infection rates per 100 person-years. Multivariable Poisson regressions incorporating sex, HLA risk group, GP/FDR status, country, age group and pandemic stage as covariates were applied to model the various infection rates over a maximum 36-month period. The significance of each categorical variable was assessed using Wald tests, comparing each category against its respective reference. Results were expressed as rate ratios (with 95% confidence intervals) or as a percentage change in the rate. To facilitate specific pairwise comparisons among age groups and stages of the pandemic, contrast matrices were developed and employed. Given that these pairwise comparisons entailed conducting multiple tests, the Bonferroni adjustment was applied to account for this multiplicity. In the study of islet autoantibody incidence, rate ratios were calculated as the ratio derived from the calculated incidences across different groups. To assess the statistical significance of differences between these rate ratios a proportion test was performed based on the chi-squared statistic. Throughout the study, statistical significance was determined based on p-values being less than 0.05. Graphs were generated using the ggplot2 package (version 3.4.4), and all statistical analyses were conducted using R software (version 4.3.2, https://www.R-project.org/ ). RESULTS A comprehensive analysis of infection episodes was conducted longitudinally, covering the period from age 4 months to 40 months in the 1050 enrolled children (Table 1 ). The participants included 80 children from Belgium, 504 from Germany, 242 from Poland, 173 from Sweden, and 51 from UK. Enrollment commenced in February 2018 and ended in March 2021. Follow-up for the current analysis ended in February 2023. The cumulative observation time was 2422 person-years (Table 1 .). The total number of infection episodes was 7525, including 5237 (70%) respiratory infection episodes, 767 (10%) gastrointestinal infection episodes, 593 (8%) other classified infection episodes and 928 (12%) unknown febrile episodes (Table 1 ). Table 1 Infection Episodes in Children Aged 4 to 40 months Country N Observation Time (person years) Total Infection Episodes Respiratory Infections n (%) Gastro-intestinal Infections n (%) Other Infections n (%) Unknown Febrile Episodes n (%) Belgium 80 188 636 412 (65%) 56 (9%) 66 (10%) 102 (16%) Germany 504 1148 3448 2315 (67%) 445 (13%) 223 (6%) 465 (13%) Poland 242 570 1506 1066 (71%) 104 (7%) 220 (15%) 116 (8%) Sweden 173 404 1810 1371 (76%) 147 (8%) 55 (3%) 237 (13%) UK 51 112 125 73 (58%) 15 (12%) 29 (23%) 8 (6%) Total 1050 2422 7525 5237 (70%) 767 (10%) 593 (8%) 928 (12%) The overall infection incidence rate was 311 (95% CI, 304–318) episodes per 100 person-years. Incidence rate was influenced by age, increasing from 240 (95% CI, 222–260) per 100 person-years in children aged 4 to 9 months to a peak of 318 (95% CI, 289–349) per 100 person-years in children aged 15 to 21 months ( P < 0.001; Table 2 ). The overall incidence rate of respiratory infections was 216 (95% CI, 210–222) per 100 person-years and the overall rate of gastrointestinal infections was 32 (95% CI, 29–34) per 100 person years with similar age trends. The overall rate of infections attributed to Coxsackie virus, which has been associated with the development of islet autoimmunity, was 9 (95% CI, 8–10) per 100 person-years. In addition to age, the infection incidence rate was affected by site, the family history of type 1 diabetes and the pandemic period, but not sex or HLA genotype (Table 2 ). The adjusted rate of reported infections across the whole age range was highest in Sweden (445 per 100 person-years; 95% CI, 418–472) and lowest in the UK (114 per 100 person-years; 95% CI, 96–135). The adjusted infection rate was higher in children without a first-degree family history of type 1 diabetes (325 per 100 person-years; 95% CI, 301–348) compared to children with a first-degree family history of type 1 diabetes (292 per 100 person-years; 95% CI, 283–302; P = 0.005). Table 2 Influence of age, sex, country, genetics and period on total infection rates Covariate N Adj. Infection Rate (95% CI) Infection Episode Rate Ratio (RR) RR 95% CI P value Age Group 4–9 months 1050 240 (222–260) 1.00 reference 0.11 9–15 months 1035 258 (234–284) 1.08 0.98–1.19 < 0.001 15–21 months 1016 318 (289–349) 1.33 1.21–1.46 < 0.001 21–27 months 1003 304 (275–337) 1.27 1.15–1.41 < 0.001 22–33 months 983 282 (256–315) 1.18 1.07–1.32 0.002 33–40 months 787 292 (261–330) 1.22 1.09–1.38 < 0.001 Sex Female 519 307 (297–317) 1.00 reference Male 531 313 (301–359) 1.02 0.98–1.17 0.33 Country Germany 504 300 (290–311) 1.00 reference Belgium 80 321 (294–359) 1.07 0.98–1.17 0.10 UK 51 114 (96–135) 0.38 0.32–0.45 < 0.001 Poland 242 267 (249–282) 0.89 0.83–0.94 < 0.001 Sweden 173 445 (418–472) 1.48 1.39–1.57 < 0.001 Family History with Type 1 Diabetes Yes 555 292 (283–302) 1.00 reference No 495 325 (30–348) 1.11 1.03–1.19 0.005 HLA genotype DR3/DR4-DQ8 565 278 (258–299) 1.00 reference DR4-DQ8/DR4-DQ8 95 286 (264–311) 1.03 0.95–1.12 0.46 Other genotypes 390 297 (275–322) 1.07 0.99–1.16 0.07 Period Pre-Pandemic 583 321(304–338) 1 reference Pandemic 2020 973 160 (148–173) 0.50 0.46–0.54 < 0.001 Pandemic 2021–2022 941 337 (315–363) 1.05 0.98–1.13 0.15 Infection rates in relation to the COVID-19 pandemic The Poisson model incidence rate of infections decreased by 50% from 321 (95% CI, 304–338) per 100 person-years in the period 2018 to February 2020 to 160 per 100 person-years (95% CI, 148–173) per 100 person-years ( P < 0.001) during the first 12 months of the pandemic (March 2020 to February 2021) (Table 2 , Fig. 1 ). The decrease was observed for each age group (adjusted P < 0.001) and specifically for respiratory infections with a 54% decrease (216 per 100 person-years; 95% CI 210–222 vs. 99 per 100 person years; 95% CI, 91–110; P < 0.001) and gastrointestinal infections with a 76% decrease (32 per 100 person-years; 95% CI 29–34 vs. 8 per 100 person years; 95% CI, 5–10; P < 0.001). The incidence rate of infections attributed to Coxsackie virus infections reduced dramatically by 92% from 9 per 100 person years (95% CI, 8–10) pre-pandemic to 1 (95% CI, 0–2) during the first pandemic year ( P < 0.001). Decreases of any infectious episodes were observed in each country with the least variation observed in Poland (adjusted reduction, 20%) and Sweden (adjusted reduction, 28%) and over 45% reduction in each of Germany, the UK, and Belgium (Suppl Table 2). The association between elevated total and specific infection rates and advancing age was no longer evident during the first pandemic year. Many of the infection preventive measures implemented during the early phase of the pandemic were removed in 2021 and 2022. The incidence rates of infections returned to pre-pandemic levels in the period from March 2021 to March 2023, with an overall infection rate of 337 per 100 person-years (95% CI, 315–363; P < 0.001 vs. first pandemic year). Increases in infection rates as compared to the first pandemic year were also specifically observed for respiratory infections (242 per 100 person-years, 95% CI, 218–264), gastrointestinal infections (34 per 100 person-years; 95% CI, 28–43), and Coxsackie virus infections (9 pers 100 person years; 95% CI, 6–13). In particular, the respiratory infection rate from March 2021 to March 2023 was increased by 12% as compared to the pre-pandemic period ( P = 0.02). The relationship between age and infection rates observed prior to the pandemic also returned for all infections, respiratory infections, gastrointestinal infections and Coxsackie virus infections. Incidence of islet autoantibodies in relation to the pandemic In the pre-pandemic period, the incidence of islet autoantibody seroconversion exhibited a previously reported peak around 12 months of age, with a maximum incidence rate of 2.7 (95% CI; 1.5–4.5) per 100 person-years occurring between 7 to 19 months of age (Fig. 2 ). During the first year of the pandemic, there was no change in the islet autoantibody incidence rate observed at this age (2.7 per 100 person-years; 95% CI, 1.7–4.2) and across all ages. Islet autoantibody incidence rates in the second pandemic year were, however, increased with a peak incidence at 7 to 19 months of 6.0 per 100 person-years (95% CI, 3.5–9.5; P = 0.01 vs. first pandemic year, P = 0.02 vs. pre-pandemic year). The incidence rate of islet autoantibodies before age 36 months was 1.6 (95% CI, 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 per 100 person-years (95% CI, 0.8–1.9) in the first pandemic year ( P = 0.46), and 3.4 per 100 person-years (95% CI, 2.3–4.8) in the subsequent pandemic years ( P = 0.005 vs. pre-pandemic year; P < 0.001 vs. first pandemic year). DISCUSSION Monitoring infection episodes in children from age 4 months to 40 months between 2018 and 2023 demonstrated a marked decline in infection rate across all ages in the first pandemic year with a rebound to pre-pandemic levels thereafter. The fall and return to pre-pandemic levels was observed for both respiratory and gastrointestinal infections and included infections attributed to Coxsackie viruses, which are associated with the development of islet autoimmunity and type 1 diabetes in childhood [ 7 , 15 – 18 ]. Despite the dramatic decline in infections in the first pandemic year, the islet autoantibody incidence did not decline and increased in subsequent years when infection incidence rates returned to pre-pandemic levels. The decline in reported infection rate in children during the initial year of the pandemic aligns with the implementation of measures aimed at curbing infection transmission in 2020 and is evidenced by the reported reduction in specific infections like influenza and RSV during the winter of 2020/2021. Additionally, in line with the less stringent measures adopted by Sweden as compared to other countries in the study, there was a more modest decline in reported infection rate in children enrolled in Sweden than in most other countries. A return to pre-pandemic infection rates was observed in the second and subsequent years of the pandemic. This aligns with the surge in infections observed during the 2021/2022 and 2022/2023 autumn and winter periods [ 19 , 20 ] and the perturbed epidemiology of certain infections in children after the introduction of COVID-19 [ 21 ]. A key finding was the relationship between infection rates and the incidence rate of islet autoantibodies in the children. Despite a substantial reduction in infection rates in the first year of the pandemic, there was no corresponding decrease in islet autoantibodies within the cohort of children. This was unexpected, especially considering the decline in Coxsackie virus-associated infections. We have reported a notable temporal association between COVID-19 infection and the development of islet autoantibodies in these children and have postulated that COVID-19 had substituted for the associations between virus and islet autoimmunity from the pre-pandemic period. In the subsequent years of the pandemic, with infection rates returning to pre-pandemic levels in 2021 to 2023, there was a simultaneous more than doubling of the islet autoantibody incidence rate. One explanation is that COVID-19 as a novel virus has increased the susceptibility of developing islet autoantibodies in early childhood. Its introduction may have led to an overall increase in viral exposures that increase this susceptibility. Other explanations include an infection deficiency that led to less protection against infections that are associated with islet autoimmunity in the later pandemic years. Whether the increased islet autoantibody incidence will result in more cases of childhood type 1 diabetes cases depends on whether the observed increase was caused by an acceleration of islet autoantibody seroconversion during childhood or an actual increase in cases. We also investigated trends in infection age to evaluate whether they align with the incidence rate of islet autoantibodies. The TEDDY study, which analyzed infection rates between 2006 and 2017, has previously reported a peak infection rate at around 1 year of age, followed by a subsequent decline, a pattern consistent with the peak age of islet autoantibody incidence [ 5 ]. However, our observations from 2018 to 2023 did not reveal a parallel age peak between infections and islet autoantibody incidence rates. While the peak islet autoantibody incidence rate in this study occurred at around 12 months of age, as previously reported [ 22 ], the peak infection rate was observed after 12 months, plateauing from around 18 months of age. These findings contradict the existence of a direct relationship between infection rate and the rate of islet autoimmunity. Instead, they suggest that infections are more likely to increase susceptibility to develop islet autoimmunity when they occur very early in life, a pattern demonstrated in the case of COVID-19 [ 9 ]. This interpretation aligns with the notion that the observed peak incidence of islet autoantibodies may be attributed to intrinsic features of the pancreatic islet and/or immune system at this age, rather than solely an abundance of diabetogenic exposures [ 2 , 23 ]. The strength of our evaluation lies in the fact that infections were assessed within the framework of a clinical trial, and their monitoring was regularly conducted by local trial monitors, suggesting high data quality. However, there are limitations to consider. Infections were solely recorded based on medical history and were not corroborated by biomarkers. It is well-established that actual infection episodes, as measured by antibodies, for example, can be significantly higher than those reported by families or detected through virus identification [ 24 ]. Therefore, the reported infection rates are likely to be underestimated. Country differences in reported infection rates were observed, and while some of these variations may be attributed to different practices in the first pandemic year, it is plausible that the disparities in overall rates and rates of specific infection groups also reflect differences in reporting likelihood and nomenclature used by families in various countries. Our study was confined to the first 3 years of life. It has been shown that associations between virus infections and islet autoimmunity observed in early childhood may not persist at older ages [ 25 ]. Therefore, our findings may not be representative of later childhood and adolescence. In conclusion, the analysis reveals a marked perturbation of early childhood infection epidemiology during the pandemic, concurrent to the introduction of COVID-19 to the community. This perturbation was followed by a significant increase in the incidence of islet autoimmunity in young children with an elevated genetic risk for type 1 diabetes. Further studies are warranted to continue the search for viruses that precede the onset of autoimmunity, and that determine the effect of vaccinations on the incidence of islet autoimmunity. Declarations Authors’ relationships and activities The authors A-GZ and EB are inventors of a patent entitled ‘Method for determining the risk to develop type 1 diabetes’. MDS has been an investigator on behalf of the University of Oxford for clinical research funded or otherwise supported by vaccine manufacturers including Pfizer, AstraZeneca, GlaxoSmithKline, Novavax and MCM vaccines. He received no personal payment for this work. Since September 2022 he has been employed by Moderna Biomanufacturing Distributor UK, and holds equity in Moderna Inc. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement IZ and AW performed the data analysis. EB and AGZ supervised the analysis. StA, KS-B, SA, TVDB, KC, AH, OK, HEL, ML, AR, MDS, AS, MV, CW are or have been clinical site investigators and contributed to participant enrolment, adverse event reporting, and study conduct. A-GZ is the principal investigator of POInT and the speaker for GPPAD. IZ, A-GZ, and EB drafted the manuscript. All authors reviewed and approved the final version of the manuscript. IZ, AW, A-GZ, EB are the guarantors of this work, and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Funding The GPPAD studies are supported by The Leona M. and Harry B. Helmsley Charitable Trust (Helmsley) grants 2018PG-T1D022 (GPPAD-02 study and GPPAD coordinating centre), 2003–04286 (GPPAD coordinating center continuation), 2018PG-T1D023 (GPPAD-03 study: POInT – Primary Oral Insulin Trial), and by Helmholtz Munich, German Research Center for Environmental Health, Germany. This project was additionally supported by grants from the Bundesministerium für Bildung und Forschung (FKZ 01KX1818), the EASD-Novo Nordisk Foundation Diabetes Prize for Excellence to AGZ (NNF22SA0081044), and from the German Center for Diabetes Research (DZD e.V.) to Helmholtz Munich. The funding organisations had no role in the design of the study. Author Contribution IZ and AW performed the data analysis. EB and AGZ supervised the analysis. StA, KS-B, SA, TVDB, KC, AH, OK, HEL, ML, AR, MDS, AS, MV, CW are or have been clinical site investigators and contributed to participant enrolment, adverse event reporting, and study conduct. A-GZ is the principal investigator of POInT and the speaker for GPPAD. IZ, A-GZ, and EB drafted the manuscript. All authors reviewed and approved the final version of the manuscript. IZ, AW, A-GZ, EB are the guarantors of this work, and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Acknowledgements We acknowledge the GPPAD study group for their support in collecting data and performing the POInT clinical trial (for details see Supplement). We thank the families for their participation in the type 1 diabetes research and for helping to develop therapies for prevention. Data availability Data will be available on submission of a signed transfer agreement; please email [email protected] and the corresponding author. Conflict of Interest The authors have declared that no conflict of interest exists. References Karlsson Elinor K., Kwiatkowski Dominic P., Sabeti Pardis C. Natural selection and infectious disease in human populations. Nat Rev Genet 2014;15(6):379–93. Doi: https://doi.org/10.1038/nrg3734 . Ziegler Anette-Gabriele. The countdown to type 1 diabetes: when, how and why does the clock start? Diabetologia 2023;66(7):1169–78. Doi: https://doi.org/10.1007/s00125-023-05927-2 . Beyerlein Andreas, Wehweck Fabienne, Ziegler Anette-Gabriele, Pflueger Maren. Respiratory Infections in Early Life and the Development of Islet Autoimmunity in Children at Increased Type 1 Diabetes Risk. JAMA Pediatr 2013;167(9):800. Doi: https://doi.org/10.1001/jamapediatrics.2013.158 . Laitinen Olli H, Honkanen Hanna, Pakkanen Outi, Oikarinen Sami, Hankaniemi Minna M, Huhtala Heini, et al. Coxsackievirus B1 is associated with induction of β-cell autoimmunity that portends type 1 diabetes. Diabetes 2014;63(2):446–55. Doi: https://doi.org/10.2337/db13-0619 . Lönnrot Maria, Lynch Kristian F, Elding Larsson Helena, Lernmark Åke, Rewers Marian J, Törn Carina, et al. Respiratory infections are temporally associated with initiation of type 1 diabetes autoimmunity: the TEDDY study. Diabetologia 2017;60(10):1931–40. Doi: https://doi.org/10.1007/s00125-017-4365-5 . Mustonen N, Siljander H, Peet A, Tillmann V, Härkönen T, Ilonen J, et al. Early childhood infections precede development of beta-cell autoimmunity and type 1 diabetes in children with HLA-conferred disease risk. Pediatr Diabetes 2018;19(2):293–9. Doi: https://doi.org/10.1111/pedi.12547 . Vehik Kendra, Lynch Kristian F., Wong Matthew C., Tian Xiangjun, Ross Matthew C., Gibbs Richard A., et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. Nat Med 2019;25(12):1865–72. Doi: https://doi.org/10.1038/s41591-019-0667-0 . Honeyman M C, Coulson B S, Stone N L, Gellert S A, Goldwater P N, Steele C E, et al. Association between rotavirus infection and pancreatic islet autoimmunity in children at risk of developing type 1 diabetes. Diabetes 2000;49(8):1319–24. Doi: https://doi.org/10.2337/diabetes.49.8.1319 . Lugar Marija, Eugster Anne, Achenbach Peter, von dem Berge Thekla, Berner Reinhard, Besser Rachel E J, et al. SARS-CoV-2 Infection and Development of Islet Autoimmunity in Early Childhood. JAMA 2023;330(12):1151–60. Doi: https://doi.org/10.1001/jama.2023.16348 . Weiss Andreas, Donnachie Ewan, Beyerlein Andreas, Ziegler Anette-G., Bonifacio Ezio. Type 1 Diabetes Incidence and Risk in Children With a Diagnosis of COVID-19. JAMA 2023;329(23):2089. Doi: https://doi.org/10.1001/jama.2023.8674 . Barrett Catherine E., Koyama Alain K., Alvarez Pablo, Chow Wilson, Lundeen Elizabeth A., Perrine Cria G., et al. Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged <18 Years — United States, March 1, 2020–June 28,2021. MMWR Morb Mortal Wkly Rep 2022;71(2):59–65. Doi: https://doi.org/10.15585/mmwr.mm7102e2. Kamrath Clemens, Rosenbauer Joachim, Eckert Alexander J, Siedler Kai, Bartelt Heike, Klose Daniela, et al. Incidence of Type 1 Diabetes in Children and Adolescents During the COVID-19 Pandemic in Germany: Results From the DPV Registry. Diabetes Care 2022;45(8):1762–71. Doi: https://doi.org/10.2337/dc21-0969 . Ziegler Anette-Gabriele, Achenbach Peter, Berner Reinhard, Casteels Kristina, Danne Thomas, Gündert Melanie, et al. Oral insulin therapy for primary prevention of type 1 diabetes in infants with high genetic risk: the GPPAD-POInT (global platform for the prevention of autoimmune diabetes primary oral insulin trial) study protocol. BMJ Open 2019;9(6):e028578. Doi: https://doi.org/10.1136/bmjopen-2018-028578 . Lönnrot Maria, Lynch Kristian, Larsson Helena Elding, Lernmark Åke, Rewers Marian, Hagopian William, et al. A method for reporting and classifying acute infectious diseases in a prospective study of young children: TEDDY. BMC Pediatr 2015;15(1):24. Doi: https://doi.org/10.1186/s12887-015-0333-8 . Sioofy-Khojine Amir-Babak, Lehtonen Jussi, Nurminen Noora, Laitinen Olli H, Oikarinen Sami, Huhtala Heini, et al. Coxsackievirus B1 infections are associated with the initiation of insulin-driven autoimmunity that progresses to type 1 diabetes. Diabetologia 2018;61(5):1193–202. Doi: https://doi.org/10.1007/s00125-018-4561-y . Oikarinen Sami, Tauriainen Sisko, Hober Didier, Lucas Bernadette, Vazeou Andriani, Sioofy-Khojine Amirbabak, et al. Virus Antibody Survey in Different European Populations Indicates Risk Association Between Coxsackievirus B1 and Type 1 Diabetes. Diabetes 2014;63(2):655–62. Doi: https://doi.org/10.2337/db13-0620 . Ifie Eseoghene, Russell Mark A., Dhayal Shalinee, Leete Pia, Sebastiani Guido, Nigi Laura, et al. Unexpected subcellular distribution of a specific isoform of the Coxsackie and adenovirus receptor, CAR-SIV, in human pancreatic beta cells. Diabetologia 2018;61(11):2344–55. Doi: https://doi.org/10.1007/s00125-018-4704-1 . Richardson Sarah J, Willcox Abby, Bone Adrian J, Morgan Noel G, Foulis Alan K. Immunopathology of the human pancreas in type-I diabetes. Semin Immunopathol 2011;33(1):9–21. Doi: https://doi.org/10.1007/s00281-010-0205-0 . Ujiie Mugen, Tsuzuki Shinya, Nakamoto Takato, Iwamoto Noriko. Resurgence of Respiratory Syncytial Virus Infections during COVID-19 Pandemic, Tokyo, Japan. Emerg Infect Dis 2021;27(11):2969–70. Doi: https://doi.org/10.3201/eid2711.211565 . Foley David Anthony, Phuong Linny Kimly, Peplinski Joseph, Lim Selina Mei, Lee Wei Hao, Farhat Asifa, et al. Examining the interseasonal resurgence of respiratory syncytial virus in Western Australia. Arch Dis Child 2022;107(3):e7. Doi: https://doi.org/10.1136/archdischild-2021-322507 . Abu-Raya Bahaa, Viñeta Paramo Marina, Reicherz Frederic, Lavoie Pascal Michel. Why has the epidemiology of RSV changed during the COVID-19 pandemic? EClinicalMedicine 2023;61:102089. Doi: https://doi.org/10.1016/j.eclinm.2023.102089 . Ziegler A-G, Bonifacio E, BABYDIAB-BABYDIET Study Group. Age-related islet autoantibody incidence in offspring of patients with type 1 diabetes. Diabetologia 2012;55(7):1937–43. Doi: https://doi.org/10.1007/s00125-012-2472-x . Warncke Katharina, Weiss Andreas, Achenbach Peter, von dem Berge Thekla, Berner Reinhard, Casteels Kristina, et al. Elevations in blood glucose before and after the appearance of islet autoantibodies in children. Journal of Clinical Investigation 2022;132(20). Doi: https://doi.org/10.1172/JCI162123 . Hippich Markus, Holthaus Lisa, Assfalg Robin, Zapardiel-Gonzalo Jose, Kapfelsperger Heidi, Heigermoser Martin, et al. A Public Health Antibody Screening Indicates a 6-Fold Higher SARS-CoV-2 Exposure Rate than Reported Cases in Children. Med (N Y) 2021;2(2):149–163.e4. Doi: https://doi.org/10.1016/j.medj.2020.10.003 . Krischer Jeffrey P, Lernmark Åke, Hagopian William A, Rewers Marian J, McIndoe Richard, Toppari Jorma, et al. SARS-CoV-2 - No Increased Islet Autoimmunity or Type 1 Diabetes in Teens. N Engl J Med 2023;389(5):474–5. Doi: https://doi.org/10.1056/NEJMc2216477 . Additional Declarations No competing interests reported. Supplementary Files SupplementZelleretall.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Infection → Version 1 posted Editorial decision: Revision requested 09 May, 2024 Reviews received at journal 08 May, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviews received at journal 18 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers invited by journal 17 Mar, 2024 Editor assigned by journal 08 Mar, 2024 Submission checks completed at journal 07 Mar, 2024 First submitted to journal 06 Mar, 2024 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-4022301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277229646,"identity":"33466a15-f2c4-4c66-a60b-2c3be29f432d","order_by":0,"name":"Ivo Zeller","email":"","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Ivo","middleName":"","lastName":"Zeller","suffix":""},{"id":277229647,"identity":"55636745-fddf-4bf5-b36d-ea25e50a2d06","order_by":1,"name":"Andreas Weiss","email":"","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Weiss","suffix":""},{"id":277229648,"identity":"fe4d4428-36f7-4132-88a9-4240cba057f5","order_by":2,"name":"Stefanie Arnolds","email":"","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Stefanie","middleName":"","lastName":"Arnolds","suffix":""},{"id":277229649,"identity":"1f1a31c6-c990-4011-833c-3abdae81e875","order_by":3,"name":"Katharina Schütte-Borkovec","email":"","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Schütte-Borkovec","suffix":""},{"id":277229650,"identity":"c4f4b5d6-4834-47f7-8f75-beb41ce9aecc","order_by":4,"name":"Sari Arabi","email":"","orcid":"","institution":"Technische Universität Dresden","correspondingAuthor":false,"prefix":"","firstName":"Sari","middleName":"","lastName":"Arabi","suffix":""},{"id":277229651,"identity":"acf43b6a-f056-41a2-8a27-06d0983511ac","order_by":5,"name":"Thekla Berge","email":"","orcid":"","institution":"Kinder- und Jugendkrankenhaus AUF DER BULT","correspondingAuthor":false,"prefix":"","firstName":"Thekla","middleName":"","lastName":"Berge","suffix":""},{"id":277229652,"identity":"a8a23f06-b018-4c8d-af22-5614b2aab5e7","order_by":6,"name":"Kristina Casteels","email":"","orcid":"","institution":"University Hospitals Leuven","correspondingAuthor":false,"prefix":"","firstName":"Kristina","middleName":"","lastName":"Casteels","suffix":""},{"id":277229653,"identity":"d229a618-e1dc-4a50-a552-2f39c43f0d09","order_by":7,"name":"Angela Hommel","email":"","orcid":"","institution":"Technische Universität Dresden","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Hommel","suffix":""},{"id":277229654,"identity":"80a1ed88-4e04-4743-b033-8522ceab6eb5","order_by":8,"name":"Olga Kordonouri","email":"","orcid":"","institution":"Kinder- und Jugendkrankenhaus AUF DER BULT","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"","lastName":"Kordonouri","suffix":""},{"id":277229655,"identity":"f1b0b9b3-30e0-44cf-84c7-a14bb814d180","order_by":9,"name":"Helena Elding Larsson","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Helena","middleName":"Elding","lastName":"Larsson","suffix":""},{"id":277229656,"identity":"59b13e63-77ee-4004-907e-1d4c80849e2b","order_by":10,"name":"Markus Lundgren","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Lundgren","suffix":""},{"id":277229657,"identity":"52946b94-1af1-4a07-a3fa-8ceed557a843","order_by":11,"name":"Anne Rochtus","email":"","orcid":"","institution":"University Hospitals Leuven","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Rochtus","suffix":""},{"id":277229658,"identity":"55ddcea6-7767-492a-928d-e14378adbad0","order_by":12,"name":"Matthew D Snape","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"D","lastName":"Snape","suffix":""},{"id":277229659,"identity":"ef09d51f-c13f-4e71-ae88-634b2b274688","order_by":13,"name":"Agnieszka Szypowka","email":"","orcid":"","institution":"Medical University of Warsaw","correspondingAuthor":false,"prefix":"","firstName":"Agnieszka","middleName":"","lastName":"Szypowka","suffix":""},{"id":277229660,"identity":"8e8a72da-9f5f-4ae9-97fa-448e3f078484","order_by":14,"name":"Manu Vatish","email":"","orcid":"","institution":"Nuffield Department of Women’s \u0026 Reproductive Health","correspondingAuthor":false,"prefix":"","firstName":"Manu","middleName":"","lastName":"Vatish","suffix":""},{"id":277229661,"identity":"07c491ba-ce17-4faf-8de5-f37074f6cc00","order_by":15,"name":"Christiane Winkler","email":"","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Christiane","middleName":"","lastName":"Winkler","suffix":""},{"id":277229663,"identity":"db26db22-253a-4820-93ce-bb1c37bf27b4","order_by":16,"name":"Ezio Bonifacio","email":"","orcid":"","institution":"Technische Universität Dresden","correspondingAuthor":false,"prefix":"","firstName":"Ezio","middleName":"","lastName":"Bonifacio","suffix":""},{"id":277229665,"identity":"25708882-493c-41ff-8054-56aa6ec0dfc7","order_by":17,"name":"Anette-Gabriele Ziegler","email":"data:image/png;base64,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","orcid":"","institution":"Helmholtz Munich, German Center for Environmental Health","correspondingAuthor":true,"prefix":"","firstName":"Anette-Gabriele","middleName":"","lastName":"Ziegler","suffix":""}],"badges":[],"createdAt":"2024-03-06 22:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s15010-024-02312-y","type":"published","date":"2024-06-14T15:11:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52450661,"identity":"b7428696-3510-4423-a748-a0bb06dfd62f","added_by":"auto","created_at":"2024-03-11 19:08:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":488071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence of Any Infection Episodes in Children at Increased Risk for Type 1 Diabetes Before and During the COVID-19 Pandemic\u003c/strong\u003e The unadjusted incidence rates of total infection episodes (A), respiratory infections (B), gastrointestinal infections (C), and Coxsackie virus-associated infections (D) are shown for the period 2018-02-07 to 2020-02-29 (pre-pandemic), 2020-03-01 to 2021-02-28 (pandemic 2020), and 2021-03-01 to 2023-02-28 (pandemic 2021-2022). Infection episodes are expressed as incidence rate per 100 person-years, and are presented across distinct age intervals (see color-codes): 4-8.99 months, 9-14.99 months, 15-20.99 months, 21-26.99 months, 27-32.99 months, and 33-39.99 months. The number of children in each age interval is given below the respective bar\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4022301/v1/e5456ff8197756fb2e5de369.png"},{"id":52450657,"identity":"14e71748-b88f-4e1e-81bb-92d08c75a9df","added_by":"auto","created_at":"2024-03-11 19:08:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence of Islet Autoantibodies in Children at Increased Risk for Type 1 Diabetes Before and During the COVID-19 Pandemic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncidence of islet autoimmunity (cases per 100 person years) for the period 2018-02-07 to 2020-02-29 (pre-pandemic, blue line), 2020-03-01 to 2021-02-28 (pandemic 2020, orange line), and 2021-03-01 to 2023-02-28 (pandemic 2021-2022, brown line)\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4022301/v1/99bb71c61da3116d65d6e8b2.png"},{"id":58822862,"identity":"bc585a11-da77-420d-8c6e-c5670da73fdb","added_by":"auto","created_at":"2024-06-21 16:48:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":936820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022301/v1/3a500a6c-0da5-47f4-b35b-b0b053e6a731.pdf"},{"id":52450663,"identity":"68d35d03-9be3-4143-8563-2281a45c2a8b","added_by":"auto","created_at":"2024-03-11 19:08:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57010,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementZelleretall.docx","url":"https://assets-eu.researchsquare.com/files/rs-4022301/v1/db8d94179a902e0a810bbc4f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eInfections are believed to contribute to genetic diversity, thereby influencing susceptibility for immune-mediated diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Type 1 diabetes is a chronic autoimmune disease characterized by the destruction of insulin-producing beta cells in the pancreas, leading to insulin deficiency. Islet autoimmunity precedes clinical type 1 diabetes and often manifests in the first three years of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In support of the role of infection in the etiology of type 1 diabetes, various viral response genes have been identified as conferring susceptibility to the disease. Moreover, numerous reports indicate associations between infection and the development of islet autoimmunity in children who have a prior genetic susceptibility, particularly in the first years of life [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, infections underwent significant changes. A novel virus was introduced to the human population and, within less than three years, had affected the majority of individuals. Additionally, preventive measures implemented during the pandemic have altered healthcare-seeking behavior, patterns of infectious disease transmission, and the incidence of infectious episodes. The pandemic was directly associated with an increase in type 1 diabetes incidence [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and infection with the SARS-CoV-2 virus in early childhood was further associated with the risk of developing islet autoimmunity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to assess the exposure rate and potential changes in infection exposures by age within the first 3 years of life. The study also sought to determine whether there was a change in infection rates from the pre-pandemic to post-pandemic periods and whether these were associated with incidence rates of islet autoimmunity. This investigation was performed in the context of a clinical trial in 1050 children with a genetic susceptibility for type 1 diabetes who were monitored with adverse event reporting from infancy. The findings carry implications for understanding the impact of pandemics on the infection landscape and immune mediated diseases.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study was performed in 1050 children participating in the Primary Oral Insulin Trial (POInT). POInT investigates whether daily intake of oral insulin reduces the incidence of islet autoimmunity and/or type 1 diabetes in children with an increased risk of type 1 diabetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Children were eligible if they had an increased risk for developing islet autoimmunity of \u0026gt;\u0026thinsp;10% by the age of 6.0 years, defined by a genetic risk score. Enrollment commenced in February 2018 and ended in March 2021. Children were enrolled at the age of 4.0\u0026ndash;7.0 months, and followed at 2, 4 and 8 months after study enrollment, at 1.5 years of age, and then every 6 months. Daily treatment with oral insulin or placebo continued until age 3 years. The study was conducted in seven clinical research centers including three in Germany (Dresden, Hanover and Munich), one in Sweden (Malm\u0026ouml;), one in Poland (Warsaw), one in Belgium (Leuven) and one in the UK (Oxford). Adverse events were recorded at each clinical study visit until 6 months after end of treatment. For the current analysis, reported infections until 02/28/2023 were included. A detailed description of the study protocol has been published previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eAssessment of exposure to infections\u003c/h2\u003e \u003cp\u003eAt each scheduled clinic visit, adverse events were collected by study personnel and recorded in the clinical trial database. For each event, illness description, date of onset and end date was recorded. Adverse events were categorized using the Medical Dictionary for Regulatory Activities (MedDRA). Classification of infections was performed as previously described [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] (Suppl Table\u0026nbsp;1). In brief, the first infection episode category was created from MedDRA Lowest Level Terms (LLT) as respiratory infection. If terms belonging to this category were reported within one week, they were regarded as one respiratory infection episode. Gastrointestinal symptoms are considered to occur frequently in young children with a respiratory infection and therefore gastrointestinal symptoms co-occurring with respiratory infections were considered as part of the respiratory infection episode. The second infection episode category was created from MedDRA LLT as gastrointestinal infections. If terms belonging to this category were reported within the same week, they were regarded as one gastrointestinal infection episode. The third category was defined as \u0026ldquo;other types of infections\u0026rdquo;, and the fourth category was unknown febrile episodes. Each LLT within this category is treated as a distinct infection episode. The date of all episodes was the date of the first LLT. A separate category was given to infections associated with Coxsackie virus (Suppl Table\u0026nbsp;1). In the present study, follow-up data up to 40 months of age were evaluated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIslet autoimmunity outcome\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIslet autoantibodies were measured centrally at 2 independent GPPAD Core laboratories, located at the Institute of Diabetes Research, Helmholtz Munich, Germany, and at the University of Bristol Medical School, Diabetes and Metabolism, Learning and Research, Southmead Hospital, Bristol, United Kingdom (for confirmation of results). Serum samples from each visit were analyzed for autoantibodies to insulin, GAD65, IA-2 and ZnT8 (ZnT8RA and ZnT8WA) as previously described [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A child was classified as islet autoantibody positive if 2 consecutive samples tested positive at both laboratories. A child was classified as multiple islet autoantibody positive if tested positive for 2 or more autoantibodies in both laboratories. The islet autoimmunity outcome was defined as either development of multiple islet autoantibodies or the development of one or more islet autoantibodies followed by type 1 diabetes. Maternally transferred islet autoantibodies were excluded and identified if the child was positive at the first sample, had declining antibody titers on follow-up, and subsequently became islet autoantibody-negative. For children classified as islet autoantibody-positive, the first positive sample was taken as the age at seroconversion.\u003c/p\u003e \u003cp\u003e\u003cb\u003eStudy approval\u003c/b\u003e\u0026emsp;Ethical approval for the POInT study was obtained from local ethical committees and regulatory authorities of the Technische Universit\u0026auml;t M\u0026uuml;nchen, Medical Faculty (326/17 Af), the Medical University of Warsaw (Institute of Mother and Child) (199/2017), the UK Health Research Authority (18/SC/0019), Onderzoek UZ/KU Leuven (S60711) and the Regionala etikpr\u0026ouml;vningsn\u0026auml;mnden i Lund (2017/918). The parents or legal representatives of each participant provided written informed consent, and further agreed to biobank storage of material that was used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAge- and stage-specific counts of infection episodes were calculated across distinct age intervals: 4-8.99 months, 9-14.99 months, 15-20.99 months, 21-26.99 months, 27-32.99 months, and 33-39.99 months. Infection episode counts were also segmented into pre-pandemic (2018-02-07 to 2020-02-29), pandemic 2020 (2020-03-01 to 2021-02-28), and pandemic 2021\u0026ndash;2022 (2021-03-01 to 2023-02-28) periods. Infection episodes for each group combination were expressed as infection rates per 100 person-years.\u003c/p\u003e \u003cp\u003eMultivariable Poisson regressions incorporating sex, HLA risk group, GP/FDR status, country, age group and pandemic stage as covariates were applied to model the various infection rates over a maximum 36-month period. The significance of each categorical variable was assessed using Wald tests, comparing each category against its respective reference. Results were expressed as rate ratios (with 95% confidence intervals) or as a percentage change in the rate.\u003c/p\u003e \u003cp\u003eTo facilitate specific pairwise comparisons among age groups and stages of the pandemic, contrast matrices were developed and employed. Given that these pairwise comparisons entailed conducting multiple tests, the Bonferroni adjustment was applied to account for this multiplicity.\u003c/p\u003e \u003cp\u003eIn the study of islet autoantibody incidence, rate ratios were calculated as the ratio derived from the calculated incidences across different groups. To assess the statistical significance of differences between these rate ratios a proportion test was performed based on the chi-squared statistic.\u003c/p\u003e \u003cp\u003eThroughout the study, statistical significance was determined based on p-values being less than 0.05. Graphs were generated using the ggplot2 package (version 3.4.4), and all statistical analyses were conducted using R software (version 4.3.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA comprehensive analysis of infection episodes was conducted longitudinally, covering the period from age 4 months to 40 months in the 1050 enrolled children (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The participants included 80 children from Belgium, 504 from Germany, 242 from Poland, 173 from Sweden, and 51 from UK. Enrollment commenced in February 2018 and ended in March 2021. Follow-up for the current analysis ended in February 2023. The cumulative observation time was 2422 person-years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.). The total number of infection episodes was 7525, including 5237 (70%) respiratory infection episodes, 767 (10%) gastrointestinal infection episodes, 593 (8%) other classified infection episodes and 928 (12%) unknown febrile episodes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfection Episodes in Children Aged 4 to 40 months\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservation Time\u003c/p\u003e \u003cp\u003e(person years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Infection Episodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRespiratory Infections\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGastro-intestinal Infections\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOther Infections n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnknown Febrile Episodes\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e412 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e102 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2315 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e445 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e223 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e465 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1066 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e220 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1371 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e237 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5237 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e767 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e593 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e928 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall infection incidence rate was 311 (95% CI, 304\u0026ndash;318) episodes per 100 person-years. Incidence rate was influenced by age, increasing from 240 (95% CI, 222\u0026ndash;260) per 100 person-years in children aged 4 to 9 months to a peak of 318 (95% CI, 289\u0026ndash;349) per 100 person-years in children aged 15 to 21 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The overall incidence rate of respiratory infections was 216 (95% CI, 210\u0026ndash;222) per 100 person-years and the overall rate of gastrointestinal infections was 32 (95% CI, 29\u0026ndash;34) per 100 person years with similar age trends. The overall rate of infections attributed to Coxsackie virus, which has been associated with the development of islet autoimmunity, was 9 (95% CI, 8\u0026ndash;10) per 100 person-years.\u003c/p\u003e \u003cp\u003eIn addition to age, the infection incidence rate was affected by site, the family history of type 1 diabetes and the pandemic period, but not sex or HLA genotype (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The adjusted rate of reported infections across the whole age range was highest in Sweden (445 per 100 person-years; 95% CI, 418\u0026ndash;472) and lowest in the UK (114 per 100 person-years; 95% CI, 96\u0026ndash;135). The adjusted infection rate was higher in children without a first-degree family history of type 1 diabetes (325 per 100 person-years; 95% CI, 301\u0026ndash;348) compared to children with a first-degree family history of type 1 diabetes (292 per 100 person-years; 95% CI, 283\u0026ndash;302; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluence of age, sex, country, genetics and period on total infection rates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdj. Infection Rate\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eInfection Episode Rate Ratio (RR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;9 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (222\u0026ndash;260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;15 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (234\u0026ndash;284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;21 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318 (289\u0026ndash;349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.21\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;27 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (275\u0026ndash;337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.15\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u0026ndash;33 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (256\u0026ndash;315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.07\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u0026ndash;40 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (261\u0026ndash;330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.09\u0026ndash;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307 (297\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (301\u0026ndash;359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300 (290\u0026ndash;311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321 (294\u0026ndash;359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (96\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.32\u0026ndash;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (249\u0026ndash;282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.83\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (418\u0026ndash;472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.39\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History with Type 1 Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (283\u0026ndash;302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (30\u0026ndash;348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.03\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHLA genotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR3/DR4-DQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (258\u0026ndash;299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR4-DQ8/DR4-DQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (264\u0026ndash;311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.95\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther genotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (275\u0026ndash;322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.99\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeriod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-Pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321(304\u0026ndash;338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePandemic 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (148\u0026ndash;173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.46\u0026ndash;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePandemic 2021\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337 (315\u0026ndash;363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.98\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInfection rates in relation to the COVID-19 pandemic\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Poisson model incidence rate of infections decreased by 50% from 321 (95% CI, 304\u0026ndash;338) per 100 person-years in the period 2018 to February 2020 to 160 per 100 person-years (95% CI, 148\u0026ndash;173) per 100 person-years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during the first 12 months of the pandemic (March 2020 to February 2021) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The decrease was observed for each age group (adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and specifically for respiratory infections with a 54% decrease (216 per 100 person-years; 95% CI 210\u0026ndash;222 vs. 99 per 100 person years; 95% CI, 91\u0026ndash;110; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gastrointestinal infections with a 76% decrease (32 per 100 person-years; 95% CI 29\u0026ndash;34 vs. 8 per 100 person years; 95% CI, 5\u0026ndash;10; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The incidence rate of infections attributed to Coxsackie virus infections reduced dramatically by 92% from 9 per 100 person years (95% CI, 8\u0026ndash;10) pre-pandemic to 1 (95% CI, 0\u0026ndash;2) during the first pandemic year (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Decreases of any infectious episodes were observed in each country with the least variation observed in Poland (adjusted reduction, 20%) and Sweden (adjusted reduction, 28%) and over 45% reduction in each of Germany, the UK, and Belgium (Suppl Table\u0026nbsp;2). The association between elevated total and specific infection rates and advancing age was no longer evident during the first pandemic year.\u003c/p\u003e \u003cp\u003eMany of the infection preventive measures implemented during the early phase of the pandemic were removed in 2021 and 2022. The incidence rates of infections returned to pre-pandemic levels in the period from March 2021 to March 2023, with an overall infection rate of 337 per 100 person-years (95% CI, 315\u0026ndash;363; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. first pandemic year). Increases in infection rates as compared to the first pandemic year were also specifically observed for respiratory infections (242 per 100 person-years, 95% CI, 218\u0026ndash;264), gastrointestinal infections (34 per 100 person-years; 95% CI, 28\u0026ndash;43), and Coxsackie virus infections (9 pers 100 person years; 95% CI, 6\u0026ndash;13). In particular, the respiratory infection rate from March 2021 to March 2023 was increased by 12% as compared to the pre-pandemic period (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). The relationship between age and infection rates observed prior to the pandemic also returned for all infections, respiratory infections, gastrointestinal infections and Coxsackie virus infections.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of islet autoantibodies in relation to the pandemic\u003c/h2\u003e \u003cp\u003eIn the pre-pandemic period, the incidence of islet autoantibody seroconversion exhibited a previously reported peak around 12 months of age, with a maximum incidence rate of 2.7 (95% CI; 1.5\u0026ndash;4.5) per 100 person-years occurring between 7 to 19 months of age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the first year of the pandemic, there was no change in the islet autoantibody incidence rate observed at this age (2.7 per 100 person-years; 95% CI, 1.7\u0026ndash;4.2) and across all ages. Islet autoantibody incidence rates in the second pandemic year were, however, increased with a peak incidence at 7 to 19 months of 6.0 per 100 person-years (95% CI, 3.5\u0026ndash;9.5; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01 vs. first pandemic year, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02 vs. pre-pandemic year). The incidence rate of islet autoantibodies before age 36 months was 1.6 (95% CI, 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 per 100 person-years (95% CI, 0.8\u0026ndash;1.9) in the first pandemic year (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46), and 3.4 per 100 person-years (95% CI, 2.3\u0026ndash;4.8) in the subsequent pandemic years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005 vs. pre-pandemic year; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. first pandemic year).\u003c/p\u003e "},{"header":"DISCUSSION","content":"\u003cp\u003eMonitoring infection episodes in children from age 4 months to 40 months between 2018 and 2023 demonstrated a marked decline in infection rate across all ages in the first pandemic year with a rebound to pre-pandemic levels thereafter. The fall and return to pre-pandemic levels was observed for both respiratory and gastrointestinal infections and included infections attributed to Coxsackie viruses, which are associated with the development of islet autoimmunity and type 1 diabetes in childhood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite the dramatic decline in infections in the first pandemic year, the islet autoantibody incidence did not decline and increased in subsequent years when infection incidence rates returned to pre-pandemic levels.\u003c/p\u003e \u003cp\u003eThe decline in reported infection rate in children during the initial year of the pandemic aligns with the implementation of measures aimed at curbing infection transmission in 2020 and is evidenced by the reported reduction in specific infections like influenza and RSV during the winter of 2020/2021. Additionally, in line with the less stringent measures adopted by Sweden as compared to other countries in the study, there was a more modest decline in reported infection rate in children enrolled in Sweden than in most other countries. A return to pre-pandemic infection rates was observed in the second and subsequent years of the pandemic. This aligns with the surge in infections observed during the 2021/2022 and 2022/2023 autumn and winter periods [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the perturbed epidemiology of certain infections in children after the introduction of COVID-19 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA key finding was the relationship between infection rates and the incidence rate of islet autoantibodies in the children. Despite a substantial reduction in infection rates in the first year of the pandemic, there was no corresponding decrease in islet autoantibodies within the cohort of children. This was unexpected, especially considering the decline in Coxsackie virus-associated infections. We have reported a notable temporal association between COVID-19 infection and the development of islet autoantibodies in these children and have postulated that COVID-19 had substituted for the associations between virus and islet autoimmunity from the pre-pandemic period. In the subsequent years of the pandemic, with infection rates returning to pre-pandemic levels in 2021 to 2023, there was a simultaneous more than doubling of the islet autoantibody incidence rate. One explanation is that COVID-19 as a novel virus has increased the susceptibility of developing islet autoantibodies in early childhood. Its introduction may have led to an overall increase in viral exposures that increase this susceptibility. Other explanations include an infection deficiency that led to less protection against infections that are associated with islet autoimmunity in the later pandemic years. Whether the increased islet autoantibody incidence will result in more cases of childhood type 1 diabetes cases depends on whether the observed increase was caused by an acceleration of islet autoantibody seroconversion during childhood or an actual increase in cases.\u003c/p\u003e \u003cp\u003eWe also investigated trends in infection age to evaluate whether they align with the incidence rate of islet autoantibodies. The TEDDY study, which analyzed infection rates between 2006 and 2017, has previously reported a peak infection rate at around 1 year of age, followed by a subsequent decline, a pattern consistent with the peak age of islet autoantibody incidence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, our observations from 2018 to 2023 did not reveal a parallel age peak between infections and islet autoantibody incidence rates. While the peak islet autoantibody incidence rate in this study occurred at around 12 months of age, as previously reported [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the peak infection rate was observed after 12 months, plateauing from around 18 months of age. These findings contradict the existence of a direct relationship between infection rate and the rate of islet autoimmunity. Instead, they suggest that infections are more likely to increase susceptibility to develop islet autoimmunity when they occur very early in life, a pattern demonstrated in the case of COVID-19 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This interpretation aligns with the notion that the observed peak incidence of islet autoantibodies may be attributed to intrinsic features of the pancreatic islet and/or immune system at this age, rather than solely an abundance of diabetogenic exposures [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of our evaluation lies in the fact that infections were assessed within the framework of a clinical trial, and their monitoring was regularly conducted by local trial monitors, suggesting high data quality. However, there are limitations to consider. Infections were solely recorded based on medical history and were not corroborated by biomarkers. It is well-established that actual infection episodes, as measured by antibodies, for example, can be significantly higher than those reported by families or detected through virus identification [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the reported infection rates are likely to be underestimated. Country differences in reported infection rates were observed, and while some of these variations may be attributed to different practices in the first pandemic year, it is plausible that the disparities in overall rates and rates of specific infection groups also reflect differences in reporting likelihood and nomenclature used by families in various countries. Our study was confined to the first 3 years of life. It has been shown that associations between virus infections and islet autoimmunity observed in early childhood may not persist at older ages [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, our findings may not be representative of later childhood and adolescence.\u003c/p\u003e \u003cp\u003eIn conclusion, the analysis reveals a marked perturbation of early childhood infection epidemiology during the pandemic, concurrent to the introduction of COVID-19 to the community. This perturbation was followed by a significant increase in the incidence of islet autoimmunity in young children with an elevated genetic risk for type 1 diabetes. Further studies are warranted to continue the search for viruses that precede the onset of autoimmunity, and that determine the effect of vaccinations on the incidence of islet autoimmunity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; relationships and activities\u003c/h2\u003e \u003cp\u003eThe authors A-GZ and EB are inventors of a patent entitled \u0026lsquo;Method for determining the risk to develop type 1 diabetes\u0026rsquo;. MDS has been an investigator on behalf of the University of Oxford for clinical research funded or otherwise supported by vaccine manufacturers including Pfizer, AstraZeneca, GlaxoSmithKline, Novavax and MCM vaccines. He received no personal payment for this work. Since September 2022 he has been employed by Moderna Biomanufacturing Distributor UK, and holds equity in Moderna Inc. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eContribution statement\u003c/strong\u003e \u003cp\u003eIZ and AW performed the data analysis. EB and AGZ supervised the analysis. StA, KS-B, SA, TVDB, KC, AH, OK, HEL, ML, AR, MDS, AS, MV, CW are or have been clinical site investigators and contributed to participant enrolment, adverse event reporting, and study conduct. A-GZ is the principal investigator of POInT and the speaker for GPPAD. IZ, A-GZ, and EB drafted the manuscript. All authors reviewed and approved the final version of the manuscript. IZ, AW, A-GZ, EB are the guarantors of this work, and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e The GPPAD studies are supported by The Leona M. and Harry B. Helmsley Charitable Trust (Helmsley) grants 2018PG-T1D022 (GPPAD-02 study and GPPAD coordinating centre), 2003\u0026ndash;04286 (GPPAD coordinating center continuation), 2018PG-T1D023 (GPPAD-03 study: POInT \u0026ndash; Primary Oral Insulin Trial), and by Helmholtz Munich, German Research Center for Environmental Health, Germany. This project was additionally supported by grants from the Bundesministerium f\u0026uuml;r Bildung und Forschung (FKZ 01KX1818), the EASD-Novo Nordisk Foundation Diabetes Prize for Excellence to AGZ (NNF22SA0081044), and from the German Center for Diabetes Research (DZD e.V.) to Helmholtz Munich. The funding organisations had no role in the design of the study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIZ and AW performed the data analysis. EB and AGZ supervised the analysis. StA, KS-B, SA, TVDB, KC, AH, OK, HEL, ML, AR, MDS, AS, MV, CW are or have been clinical site investigators and contributed to participant enrolment, adverse event reporting, and study conduct. A-GZ is the principal investigator of POInT and the speaker for GPPAD. IZ, A-GZ, and EB drafted the manuscript. All authors reviewed and approved the final version of the manuscript. IZ, AW, A-GZ, EB are the guarantors of this work, and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledge the GPPAD study group for their support in collecting data and performing the POInT clinical trial (for details see Supplement). We thank the families for their participation in the type 1 diabetes research and for helping to develop therapies for prevention.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be available on submission of a signed transfer agreement; please email [email protected] and the corresponding author.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no conflict of interest exists.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKarlsson Elinor K., Kwiatkowski Dominic P., Sabeti Pardis C. Natural selection and infectious disease in human populations. \u003cem\u003eNat Rev Genet\u003c/em\u003e 2014;15(6):379\u0026ndash;93. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrg3734\u003c/span\u003e\u003cspan address=\"10.1038/nrg3734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegler Anette-Gabriele. The countdown to type 1 diabetes: when, how and why does the clock start? \u003cem\u003eDiabetologia\u003c/em\u003e 2023;66(7):1169\u0026ndash;78. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-023-05927-2\u003c/span\u003e\u003cspan address=\"10.1007/s00125-023-05927-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeyerlein Andreas, Wehweck Fabienne, Ziegler Anette-Gabriele, Pflueger Maren. Respiratory Infections in Early Life and the Development of Islet Autoimmunity in Children at Increased Type 1 Diabetes Risk. JAMA Pediatr 2013;167(9):800. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamapediatrics.2013.158\u003c/span\u003e\u003cspan address=\"10.1001/jamapediatrics.2013.158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaitinen Olli H, Honkanen Hanna, Pakkanen Outi, Oikarinen Sami, Hankaniemi Minna M, Huhtala Heini, et al. Coxsackievirus B1 is associated with induction of β-cell autoimmunity that portends type 1 diabetes. \u003cem\u003eDiabetes\u003c/em\u003e 2014;63(2):446\u0026ndash;55. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/db13-0619\u003c/span\u003e\u003cspan address=\"10.2337/db13-0619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;nnrot Maria, Lynch Kristian F, Elding Larsson Helena, Lernmark \u0026Aring;ke, Rewers Marian J, T\u0026ouml;rn Carina, et al. Respiratory infections are temporally associated with initiation of type 1 diabetes autoimmunity: the TEDDY study. \u003cem\u003eDiabetologia\u003c/em\u003e 2017;60(10):1931\u0026ndash;40. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-017-4365-5\u003c/span\u003e\u003cspan address=\"10.1007/s00125-017-4365-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustonen N, Siljander H, Peet A, Tillmann V, H\u0026auml;rk\u0026ouml;nen T, Ilonen J, et al. Early childhood infections precede development of beta-cell autoimmunity and type 1 diabetes in children with HLA-conferred disease risk. \u003cem\u003ePediatr Diabetes\u003c/em\u003e 2018;19(2):293\u0026ndash;9. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pedi.12547\u003c/span\u003e\u003cspan address=\"10.1111/pedi.12547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVehik Kendra, Lynch Kristian F., Wong Matthew C., Tian Xiangjun, Ross Matthew C., Gibbs Richard A., et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. \u003cem\u003eNat Med\u003c/em\u003e 2019;25(12):1865\u0026ndash;72. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-019-0667-0\u003c/span\u003e\u003cspan address=\"10.1038/s41591-019-0667-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoneyman M C, Coulson B S, Stone N L, Gellert S A, Goldwater P N, Steele C E, et al. Association between rotavirus infection and pancreatic islet autoimmunity in children at risk of developing type 1 diabetes. \u003cem\u003eDiabetes\u003c/em\u003e 2000;49(8):1319\u0026ndash;24. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/diabetes.49.8.1319\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.49.8.1319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugar Marija, Eugster Anne, Achenbach Peter, von dem Berge Thekla, Berner Reinhard, Besser Rachel E J, et al. SARS-CoV-2 Infection and Development of Islet Autoimmunity in Early Childhood. \u003cem\u003eJAMA\u003c/em\u003e 2023;330(12):1151\u0026ndash;60. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2023.16348\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.16348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss Andreas, Donnachie Ewan, Beyerlein Andreas, Ziegler Anette-G., Bonifacio Ezio. Type 1 Diabetes Incidence and Risk in Children With a Diagnosis of COVID-19. JAMA 2023;329(23):2089. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2023.8674\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.8674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett Catherine E., Koyama Alain K., Alvarez Pablo, Chow Wilson, Lundeen Elizabeth A., Perrine Cria G., et al. Risk for Newly Diagnosed Diabetes \u0026gt;30 Days After SARS-CoV-2 Infection Among Persons Aged \u0026lt;18 Years \u0026mdash; United States, March 1, 2020\u0026ndash;June 28,2021. \u003cem\u003eMMWR Morb Mortal Wkly Rep\u003c/em\u003e 2022;71(2):59\u0026ndash;65. Doi: https://doi.org/10.15585/mmwr.mm7102e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamrath Clemens, Rosenbauer Joachim, Eckert Alexander J, Siedler Kai, Bartelt Heike, Klose Daniela, et al. Incidence of Type 1 Diabetes in Children and Adolescents During the COVID-19 Pandemic in Germany: Results From the DPV Registry. \u003cem\u003eDiabetes Care\u003c/em\u003e 2022;45(8):1762\u0026ndash;71. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/dc21-0969\u003c/span\u003e\u003cspan address=\"10.2337/dc21-0969\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegler Anette-Gabriele, Achenbach Peter, Berner Reinhard, Casteels Kristina, Danne Thomas, G\u0026uuml;ndert Melanie, et al. Oral insulin therapy for primary prevention of type 1 diabetes in infants with high genetic risk: the GPPAD-POInT (global platform for the prevention of autoimmune diabetes primary oral insulin trial) study protocol. \u003cem\u003eBMJ Open\u003c/em\u003e 2019;9(6):e028578. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2018-028578\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-028578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026ouml;nnrot Maria, Lynch Kristian, Larsson Helena Elding, Lernmark \u0026Aring;ke, Rewers Marian, Hagopian William, et al. A method for reporting and classifying acute infectious diseases in a prospective study of young children: TEDDY. BMC Pediatr 2015;15(1):24. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12887-015-0333-8\u003c/span\u003e\u003cspan address=\"10.1186/s12887-015-0333-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSioofy-Khojine Amir-Babak, Lehtonen Jussi, Nurminen Noora, Laitinen Olli H, Oikarinen Sami, Huhtala Heini, et al. Coxsackievirus B1 infections are associated with the initiation of insulin-driven autoimmunity that progresses to type 1 diabetes. \u003cem\u003eDiabetologia\u003c/em\u003e 2018;61(5):1193\u0026ndash;202. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-018-4561-y\u003c/span\u003e\u003cspan address=\"10.1007/s00125-018-4561-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOikarinen Sami, Tauriainen Sisko, Hober Didier, Lucas Bernadette, Vazeou Andriani, Sioofy-Khojine Amirbabak, et al. Virus Antibody Survey in Different European Populations Indicates Risk Association Between Coxsackievirus B1 and Type 1 Diabetes. \u003cem\u003eDiabetes\u003c/em\u003e 2014;63(2):655\u0026ndash;62. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/db13-0620\u003c/span\u003e\u003cspan address=\"10.2337/db13-0620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIfie Eseoghene, Russell Mark A., Dhayal Shalinee, Leete Pia, Sebastiani Guido, Nigi Laura, et al. Unexpected subcellular distribution of a specific isoform of the Coxsackie and adenovirus receptor, CAR-SIV, in human pancreatic beta cells. \u003cem\u003eDiabetologia\u003c/em\u003e 2018;61(11):2344\u0026ndash;55. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-018-4704-1\u003c/span\u003e\u003cspan address=\"10.1007/s00125-018-4704-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson Sarah J, Willcox Abby, Bone Adrian J, Morgan Noel G, Foulis Alan K. Immunopathology of the human pancreas in type-I diabetes. \u003cem\u003eSemin Immunopathol\u003c/em\u003e 2011;33(1):9\u0026ndash;21. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00281-010-0205-0\u003c/span\u003e\u003cspan address=\"10.1007/s00281-010-0205-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUjiie Mugen, Tsuzuki Shinya, Nakamoto Takato, Iwamoto Noriko. Resurgence of Respiratory Syncytial Virus Infections during COVID-19 Pandemic, Tokyo, Japan. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e 2021;27(11):2969\u0026ndash;70. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid2711.211565\u003c/span\u003e\u003cspan address=\"10.3201/eid2711.211565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoley David Anthony, Phuong Linny Kimly, Peplinski Joseph, Lim Selina Mei, Lee Wei Hao, Farhat Asifa, et al. Examining the interseasonal resurgence of respiratory syncytial virus in Western Australia. \u003cem\u003eArch Dis Child\u003c/em\u003e 2022;107(3):e7. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/archdischild-2021-322507\u003c/span\u003e\u003cspan address=\"10.1136/archdischild-2021-322507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu-Raya Bahaa, Vi\u0026ntilde;eta Paramo Marina, Reicherz Frederic, Lavoie Pascal Michel. Why has the epidemiology of RSV changed during the COVID-19 pandemic? \u003cem\u003eEClinicalMedicine\u003c/em\u003e 2023;61:102089. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eclinm.2023.102089\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2023.102089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegler A-G, Bonifacio E, BABYDIAB-BABYDIET Study Group. Age-related islet autoantibody incidence in offspring of patients with type 1 diabetes. \u003cem\u003eDiabetologia\u003c/em\u003e 2012;55(7):1937\u0026ndash;43. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-012-2472-x\u003c/span\u003e\u003cspan address=\"10.1007/s00125-012-2472-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarncke Katharina, Weiss Andreas, Achenbach Peter, von dem Berge Thekla, Berner Reinhard, Casteels Kristina, et al. Elevations in blood glucose before and after the appearance of islet autoantibodies in children. \u003cem\u003eJournal of Clinical Investigation\u003c/em\u003e 2022;132(20). Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/JCI162123\u003c/span\u003e\u003cspan address=\"10.1172/JCI162123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHippich Markus, Holthaus Lisa, Assfalg Robin, Zapardiel-Gonzalo Jose, Kapfelsperger Heidi, Heigermoser Martin, et al. A Public Health Antibody Screening Indicates a 6-Fold Higher SARS-CoV-2 Exposure Rate than Reported Cases in Children. \u003cem\u003eMed (N Y)\u003c/em\u003e 2021;2(2):149\u0026ndash;163.e4. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.medj.2020.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.medj.2020.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrischer Jeffrey P, Lernmark \u0026Aring;ke, Hagopian William A, Rewers Marian J, McIndoe Richard, Toppari Jorma, et al. SARS-CoV-2 - No Increased Islet Autoimmunity or Type 1 Diabetes in Teens. \u003cem\u003eN Engl J Med\u003c/em\u003e 2023;389(5):474\u0026ndash;5. Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMc2216477\u003c/span\u003e\u003cspan address=\"10.1056/NEJMc2216477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Infection, type 1 diabetes, autoimmunity, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-4022301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose. To determine the impact of the COVID-19 pandemic on the incidence rates of infection and islet autoimmunity in children at risk for type 1 diabetes.\u003c/p\u003e \u003cp\u003eMethods. 1050 children aged 4 to 7 months with an elevated genetic risk for type 1 diabetes were recruited from Germany, Poland, Sweden, Belgium and the UK. Reported infection episodes and islet autoantibody development were monitored until age 40 months from February 2018 to February 2023.\u003c/p\u003e \u003cp\u003eResults. The overall infection rate was 311 (95% Confidence Interval [CI], 304\u0026ndash;318) per 100 person years. Infection rates differed by age, country, family history of type 1 diabetes, and period relative to the pandemic. Total infection rates were 321 per 100 person-years (95% CI, 304\u0026ndash;338) in the pre-pandemic period (until February 2020), 160 (95% CI, 148\u0026ndash;173) per 100 person-years in the first pandemic year (March 2020 - February 2021; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 337 (95% CI, 315\u0026ndash;363) per 100 person-years in subsequent years. Similar trends were observed for respiratory and gastrointestinal infections. Islet autoantibody incidence rates were 1.6 (95% CI, 1.0-2.4) per 100 person-years in the pre-pandemic period, 1.2 (95% CI, 0.8\u0026ndash;1.9) per 100 person-years in the first pandemic year (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46), and 3.4 (95% CI, 2.3\u0026ndash;4.8) per 100 person-years in subsequent years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005 vs. pre-pandemic year; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. first pandemic year).\u003c/p\u003e \u003cp\u003eConclusions. The COVID-19 pandemic significantly altered infection patterns. Islet autoantibody incidence rates increased two-fold when infection rates returned to pre-pandemic levels.\u003c/p\u003e","manuscriptTitle":"Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:06:48","doi":"10.21203/rs.3.rs-4022301/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-09T04:13:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T13:05:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9f207498-9f13-4923-bead-9831a00001cd","date":"2024-04-19T05:01:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T19:41:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c235e24f-05f6-4e2d-bd27-2ac64fd7b474","date":"2024-04-16T17:05:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-17T09:49:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T05:12:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-07T06:38:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infection","date":"2024-03-06T22:23:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5f89ff31-5259-4245-94d6-40bd8eac8894","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:11:38+00:00","versionOfRecord":{"articleIdentity":"rs-4022301","link":"https://doi.org/10.1007/s15010-024-02312-y","journal":{"identity":"infection","isVorOnly":false,"title":"Infection"},"publishedOn":"2024-06-14 15:11:38","publishedOnDateReadable":"June 14th, 2024"},"versionCreatedAt":"2024-03-11 19:06:48","video":"","vorDoi":"10.1007/s15010-024-02312-y","vorDoiUrl":"https://doi.org/10.1007/s15010-024-02312-y","workflowStages":[]},"version":"v1","identity":"rs-4022301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022301","identity":"rs-4022301","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}