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Abstract
Introduction The Environmental Determinants of Islet Autoimmunity (ENDIA) Study is an ongoing Australian prospective cohort study investigating how modifiable prenatal and early-life exposures drive the development of islet autoimmunity and type 1 diabetes (T1D) in children. In this profile, we describe the cohort’s parental demographics, maternal and neonatal outcomes and human leukocyte antigen (HLA) genotypes.
Research design and methods Inclusion criteria were an unborn child, or infant aged less than 6 months, with a first-degree relative (FDR) with T1D. The primary outcome was persistent islet autoimmunity, with children followed until a T1D diagnosis or 10 years of age. Demographic data were collected at enrollment. Lifestyle, clinical and anthropometric data were collected at each visit during pregnancy and clinical pregnancy and birth data were verified against medical case notes. Data were compared between mothers with and without T1D. HLA genotyping was performed on the ENDIA child and all available FDRs.
Results The final cohort comprised 1473 infants born to 1214 gestational mothers across 1453 pregnancies, with 80% enrolled during pregnancy. The distribution of familial T1D probands was 62% maternal, 28% paternal and 11% sibling. The frequency of high-risk HLA genotypes was highest in T1D probands, followed by ENDIA infants, and lowest among unaffected family members. Mothers with T1D had higher rates of pregnancy complications and perinatal intervention, and larger babies of shorter gestation. Parent demographics were comparable to the Australian population for age, parity and obesity. A greater percentage of ENDIA parents were Australian born, lived in a major city and had higher socioeconomic advantage and education.
Conclusions This comprehensive profile provides the context for understanding ENDIA’s scope, methodology, unique strengths and limitations. Now fully recruited, ENDIA will provide unique insights into the roles of early-life factors in the development of islet autoimmunity and T1D in the Australian environment.
Trial registration number ACTRN12613000794707.
- Pregnancy
- Islet Autoimmunity
- Diabetes Mellitus, Type 1
- Cohort Studies
Data availability statement
Data are available on reasonable request.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
The incidence of type 1 diabetes (T1D) in children is increasing worldwide.
Seroconversion to islet autoimmunity peaks early in life, indicating initiators and protectors of the autoimmune process are encountered during pregnancy and early infancy.
The Environmental Determinants of Islet Autoimmunity (ENDIA) Study is investigating the modifiable exposures in a prospective cohort of at-risk children recruited from pregnancy in Australia.
WHAT THIS STUDY ADDS
This paper characterizes the parental demographics, maternal and neonatal health and human leukocyte antigen genotypes of participants recruited in ENDIA.
The findings position the ENDIA cohort within the broader Australian population and facilitate comparisons with other early-life T1D risk cohorts globally.
ENDIA’s study design also provides a large prospective cohort of mothers with T1D comprehensively followed during pregnancy.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The ENDIA cohort is a globally significant asset for understanding the complex interplay of genetics, environmental influences and their impact on biological systems in the development of T1D to inform new primary prevention strategies from early life.
Introduction
The incidence of type 1 diabetes (T1D) in children is increasing worldwide1–3 and doubled in Australia between 1980s and 2000s.4 In 2022, 8.75 million people were living with T1D globally5 and this is predicted to increase to 13.5–17.4 million by 2040.6 While genetics is known to play a role in T1D risk, increasing incidence cannot be solely explained by genetic predisposition, but rather by environmental factors and gene–environment interactions. Relative frequency of the highest-risk genotypes in newly diagnosed children has been steadily decreasing since 1950s.7–9 The increase in incidence represents a growing number of children with lower-risk human leukocyte antigen (HLA) genes, giving rise to an increased penetrance of moderate-risk T1D genotypes in the modern environment.7
The majority of children who develop T1D by 18 years have detectable islet autoantibodies targeting beta cell antigens by 3 years of age.10 Islet autoimmunity peaks between 9 and 24 months in at-risk populations.11 In children with persistent islet autoantibodies, 70% with two or more and 15% with one, progress to clinical T1D within 10 years of seroconversion.10 The young age at seroconversion to islet autoimmunity indicates initiators and protectors of the autoimmune process leading to beta cell destruction are encountered very early in life, during infancy, pregnancy or possibly preconception. Therefore, prospective follow-up during pregnancy, and early postnatally, is vital to inform modifiable causes of the development of childhood T1D. Large, well-established US and European cohorts have followed children at-risk of T1D (on the basis of high-moderate risk HLA types or family history) from 3 to 9 months of age.12–15
The significance of early-life factors in disease pathogenesis is reflected within the Developmental Origins of Health and Disease hypothesis. This proposes exposure to environmental influences during critical periods of development and growth may significantly impact an individual’s short-term and long-term health.16 Several in utero exposures have been linked to T1D risk, including maternal overweight and obesity,17 18 diet,19 20 infections21 and mode of delivery.22 23
The Environmental Determinants of Islet Autoimmunity (ENDIA) is the first study worldwide to investigate gene–environment–omic interactions from pregnancy and early life that may drive the development of islet autoimmunity and T1D.24 25 ENDIA is an Australian, prospective, observational cohort study following children who have a first-degree relative (FDR) with T1D, from pregnancy through childhood. The goal is to inform primary prevention strategies in early life, such as immune therapies, viral vaccines and targeted prebiotics or probiotics.26–28 Here, we provide a comprehensive profile of the ENDIA cohort focusing on parental demographics, maternal and neonatal outcomes and the distribution of HLA types across all available FDRs.
Methods
Study design and protocol amendments
A detailed study protocol was published previously.24 In brief, participants were longitudinally followed each trimester of pregnancy, at birth, every 3 months to 2 years and every 6 months thereafter until 10 years (online supplemental figure 1). Data on demographic, lifestyle, clinical and anthropometric measurements, maternal and child dietary intake and biological samples are collected at multiple time points, throughout follow-up. Several amendments to the original study protocol published in 2013 have been made (summarized in online supplemental table 1).
Supplemental material
The primary outcome was persistent islet autoimmunity defined as antibodies to one or more islet autoantigens (insulin, glutamic acid decarboxylase 65, tyrosine phosphatase-like insulinoma antigen and zinc transporter 8) detected on two consecutive tests, taken at least 3 months apart. The secondary outcome was detection of persistent coeliac autoimmunity defined as antibodies to tissue transglutaminase and/or deamidated gliadin peptide, for which children with genetic susceptibility to T1D are at elevated risk.29
Study population and sites
The inclusion criteria for ENDIA were an unborn child, or infant aged<6 months, with an FDR diagnosed with T1D. The exclusion criterion was the incapacity of the pregnant person or child’s primary caregiver to understand the requirements of their own and/or child’s participation in the study.
Participants were recruited from February 2013 to December 2019. Families could enroll multiple children if conceived during the recruitment period. Postnatal participants were recruited from February 2013 to May 2019. Participants were recruited nationwide in Australia using a variety of methods.30 Lead recruitment sites were: The Women’s and Children’s Hospital, Adelaide, South Australia (190 participants); The Royal Melbourne Hospital (286 participants) and Monash Medical Centre (44 participants), Melbourne, Victoria; Barwon Health, Geelong, Victoria (66 participants); The Children’s Hospital at Westmead (172 participants), Randwick Hospital for Women (112 participants) and St George Hospital (44 participants), Sydney, New South Wales; Mater Hospital, Brisbane, Queensland (246 participants); and Princess Margaret Hospital/Perth Children’s Hospital, Perth, Western Australia (209 participants). Participants outside of these cities enrolled through a regional participant program from 2016 that was administered through Adelaide’s Women’s and Children’s Hospital (142 participants).31
Clinical data collection and definitions
Detailed demographic information was collected at enrollment. Pregnancy data pertaining to health, nutrition, exercise and lifestyle were collected at each study visit during pregnancy and birth data were collected shortly after birth, or at the time of enrollment for postnatal recruits. Clinical pregnancy and birth data were verified against medical case notes. Parental preconception data were collected retrospectively. Validated data collection tools and custom questionnaires used during pregnancy and infancy have been described previously24 or outlined in online supplemental table 1.
Body mass index was calculated using self-report pre pregnancy or measured early pregnancy weight. Gestational weight gain was calculated from the pre pregnancy or early first trimester weight and the latest weight recorded during pregnancy. Mothers who underwent cesarean sections without experiencing prior labor are categorized as ‘no labor’. Large for gestational age (LGA) was defined as ≥90th centile and small for gestational age was defined as ≤10th centile from population-based Australian birth weight centile charts.32 The Australian Statistical Geography Standard Remoteness Structure was used to categorize participants into classes of remoteness based on a measure of relative distance to access services.33 Participants were classified as living in a major city, regional (inner and outer regional) or remote (remote or very remote) area based on the postcode of their residential address at the time of study enrollment. This postcode was also used to assign an area-based measure of socioeconomic status (SES) using the Index of Relative Socioeconomic Disadvantage which is constructed from variables such as the proportion of individuals in an area who are on a low income, are of Indigenous descent and are unemployed or in relatively low skill occupations.34
Biological data collections and analysis
Blood, urine, stool, saliva, breastmilk, swabs from various body sites and a deciduous tooth were collected throughout follow-up as described24 or outlined in online supplemental table 1. Saliva samples for genetic studies were collected from the ENDIA child and all available FDRs. The OG-250 Oragene DNA disc kit was used for infants and OG-500 Oragene DNA tubes for adults and older children (DNA Genotek, Ontario, Canada). For 8% of participants (n=344) where a saliva sample was not available, DNA was extracted from other biological specimens. QIAamp Fast DNA Stool Mini kits (Qiagen, Cat No: 51604, Germany) were used to extract DNA from stool samples while ISOLATE II Genomic DNA kits (Biolone Meridian Bioscience, Cat No: BIO-52066, USA) were used for other sample types (ie, blood, swabs and milk). HLA DR typing was performed on DNA by TaqMan-based PCR typing and imputation from three single-nucleotide polymorphisms (rs3104413, rs2187668 and rs9275495), as described previously.35
Statistical analysis
Data are provided as mean (SD) or median (quartiles 1–3) for continuous variables and number (proportion of that category) for categorical values. For descriptive comparison to the Australian population, data were obtained for all babies born in Australia in 2017 as this was the median year of birth in ENDIA.36–38 Data for the comparison of education and male smoking levels were obtained for reproductive aged adults39 40 and data for breastfeeding initiation were from 2010.41
For each continuous response, to identify differences in mothers with and without T1D, an adjusted linear-mixed model was fitted. Where underlying model assumptions were not met, the response variable was transformed. For binary, categorical or discrete (count) responses, a generalized linear-mixed model (logistic, multinomial or Poisson regression, respectively) was fitted. In these approaches, a fixed factor for T1D status and a random effect for mother was included in the model. The random mother effect induced a correlation between observations from different pregnancies of the same mother. For maternal and infant outcomes, adjustment was made for maternal age at birth and for parity. For analyses of infant birth weight, admission to neonatal intensive care unit (NICU), total days in NICU and admission to special care nursery (SCN), adjustments were made for gestational age at birth. A p value of 0.05 was used for determining statistical significance for all models. Data analysis was performed in R V.4.3.0.42
Results
Description of study population
The ENDIA Study completed recruitment of 1511 participants in December 2019 and the last baby was born in July 2020. 55% of participants who expressed interest provided informed consent and were enrolled in the study (figure 1). 38 participants were excluded due to adverse pregnancy and birth outcomes (n=26), loss to follow-up (n=5), withdrawal prior to study commencement (n=4) or ineligibility (n=3). The 26 adverse outcomes included 17 miscarriages (13 from pregnancies with T1D), 7 stillbirths (6 from pregnancies with T1D) and 2 neonatal deaths (twins from a pregnancy without T1D).
Diagram of study flow for the ENDIA study, including specific number of infants, gestational mothers and pregnancies. ENDIA, Environmental Determinants of Islet Autoimmunity; T1D, type 1 diabetes.
The final cohort comprised 1473 infants born to 1214 gestational mothers across 1453 pregnancies. Of the 1214 gestational mothers, 79% enrolled 1 pregnancy, 19% enrolled 2 pregnancies and 2% enrolled 3 pregnancies. 80% of participants (n=1178) commenced the study during pregnancy (median gestational age was 22.1 (14.0–30.3) weeks); 22% during first trimester (<13 weeks), 48% during second trimester (13–27.9 weeks), 31% during third trimester (≥28 weeks) and 20% were enrolled at or after birth (median age when commenced study was 3.6 (2.8–6.3) months). The majority were singleton pregnancies with 18 sets of twins (12 in mothers with T1D and 6 in mothers without T1D) and 1 set of triplets (mother without T1D). The median date of birth was 21 September 2017 (ranged from 29 November 2012 to 10 July 2020).
Parentage
The term ‘mothers’ in this paper is defined as the gestational carriers of the ENDIA pregnancies (unless specified as genetic mother). 1209 individuals gave birth to their own biological children, and 5 birthed a child conceived using a donor oocyte (all women without T1D). Additionally, one oocyte donor also participated with their own pregnancy. This resulted in 1214 gestational mothers but only 1213 unique contributors of maternal genes. There were 1217 unique individuals who contributed paternal genes and are referred to as ‘fathers’. Of 1217 fathers, 178 (15%) declined participation, their identity was unknown or not disclosed to the study. These data are considered missing.
In summary, the 1473 live born ENDIA children had 2435 unique ‘parents’, comprising genetic gestational mothers (n=1209), non-genetic gestational mothers (n=5), oocyte donors (n=4) and genetic fathers (n=1217). The 1473 children had 900 unique siblings who were not undergoing longitudinal follow-up. In total, there were 4808 people across 1214 families in ENDIA.
T1D proband relationships
At the time of birth, 912 children (62%) had a maternal proband, 413 (28%) had a paternal proband and 167 (11%) had a sibling proband, including 21 half siblings on the maternal side and 2 half siblings on the paternal side. More than one FDR with T1D was noted in 2% of families (eight maternal and paternal, nine maternal and sibling, seven paternal and sibling and four with two siblings). The median age of T1D diagnosis was 13.7 (9.0–21.4) years for maternal probands, 17.6 (11.0–25.9) years for paternal probands and 3.8 (2.0–6.5) years for sibling probands. The median T1D duration was 17.3 (10.4–23.0) years for mothers, 16.4 (8.2–22.9) years for fathers and 2.2 (0.8–4.2) years for siblings. 12 family members were diagnosed with T1D after the ENDIA child’s birth.
Demographic and clinical characteristics of ENDIA parents
Most ENDIA parents were born in Australia (80%) and 1% identified as Indigenous (Aboriginal or Torres Strait Islander). ENDIA parent demographics were generally comparable to the Australian population (table 1); exceptions included a greater proportion of ENDIA parents born in Australia, lived in a major city, achieved tertiary education and had higher socioeconomic status.
Demographic and clinical characteristics of parents enrolled in the Environmental Determinants of Islet Autoimmunity (ENDIA) Study at the time of child’s birth compared with published data available for all Australian births in 2017
Comorbidities of ENDIA parents
The prevalence of overweight (30%) and obesity (20%) among ENDIA parents was consistent with the Australian population. 1 mother and 8 fathers had type 2 diabetes. 6% of mothers had known retinopathy (84 mothers with T1D), 2% had nephropathy (25 mothers with T1D and 1 mother without T1D), 5% had coeliac disease (65 mothers with T1D and 7 mothers without T1D) and 24% reported another autoimmune disease (269 mothers with T1D and 75 mothers without T1D).
Pregnancy and birth outcomes
Several significant differences in pregnancy characteristics were identified between ENDIA mothers with and without T1D (table 2). ENDIA mothers with T1D were 1.6 times more likely to be nulliparous compared with mothers without T1D (95% CI 1.3, 2.0; p<0.001). Similarly, mothers with T1D were 1.3 times more likely to be experiencing their first pregnancy (primigravida) (95% CI 1.0, 1.7; p=0.02). There was no difference in assisted conception or in gestational weight gain for mothers with and without T1D. The odds of a mother with T1D having pre-eclampsia were 6.0 times that of a mother without T1D (95% CI 3.7, 10.4; p<0.001). For those mothers who were tested for group B streptococcus, the odds of a mother with T1D having a positive test (vs a negative test) were 1.6 times the odds of a mother without T1D (95% CI 1.1, 2.2; p=0.01). Gestational diabetes rates were similar in the mothers without T1D compared with the Australian population (12.7%).36
Pregnancy characteristics and outcomes in the Environmental Determinants of Islet Autoimmunity (ENDIA) Study for mothers overall and by type 1 diabetes (T1D) status
Characteristics of birth outcomes are presented in table 3. Mothers with T1D were less likely to have spontaneous onset of labor and more likely to deliver via cesarean section than mothers without T1D. 52% of infants were assigned male sex at birth. Gestation at birth revealed a 2-week shorter duration for infants of mothers with T1D compared with those without T1D. Premature birth was significantly more common among mothers with T1D compared with either mothers without T1D or the Australian average. Infants born to mothers with T1D were 648 g heavier than infants born to mothers without T1D, and more likely to be classified as LGA. Admission to NICU was higher among infants born to mothers with T1D compared with those without T1D, although duration of stay in NICU was 40% shorter in mothers with T1D. Congenital abnormalities, neonatal hypoglycemia and neonatal jaundice were more common among infants born to mothers with T1D, when compared with those born to mothers without T1D. Infants born to mothers with T1D were also less likely to score 7–10 on the Apgar scale at 5 min compared with those born to mothers without T1D. The majority of mothers with and without T1D initiated breastfeeding.
Labor, delivery and neonatal characteristics and outcomes for infants followed in the Environmental Determinants of Islet Autoimmunity (ENDIA) Study overall, by maternal type 1 diabetes (T1D) status and compared with published data available for all Australian births in 2017
Genetics
HLA genotyping was conducted on 4088 individuals in ENDIA families (85% of the total cohort; table 4). There were 115 individuals (2%) who provided DNA, but their HLA type could not be imputed due to poor quality material, rare allelic combination or sequencing errors. HLA types and T1D status were available for 2612 FDRs, including mothers, fathers and siblings. The prevalence of the highest-risk HLA type DR3,4 was 31% in the FDR with T1D, 6% in the FDRs without T1D and 12% in the ENDIA infants. Likewise, the proportion of the lowest-risk HLA type, DRXX, was intermediate in the ENDIA infants relative to the FDRs with and without T1D at 22%, 9% and 37%, respectively. The occurrence of moderate-risk HLA types was similar across the groups, with 60% for FDRs with T1D, 57% for FDRs without T1D and 66% for ENDIA infants.
Human leukocyte antigen (HLA) type for infants and family members followed in the Environmental Determinants of Islet Autoimmunity (ENDIA) Study by type 1 diabetes (T1D) status
Discussion
With the ENDIA cohort fully recruited, we report on the cohort’s characteristics in terms of parental demographics, maternal and neonatal outcomes and the distribution of HLA genotypes with respect to T1D proband status. We can now contextualize the ENDIA Study in the broader Australian population and highlight distinctions with other T1D cohorts from early life. Moreover, by virtue of the study design, which recruited 901 pregnancies from mothers with T1D, we envisage that ENDIA outcomes will provide insights into improving the care of mothers with T1D and their babies.
Observed cohort biases
Several observed patterns in the ENDIA cohort do not reflect natural distributions but are influenced by methodological choices made during study recruitment. There is an imbalance among the T1D proband distribution in the cohort. Although men and women are equally affected by T1D, we report a much higher proportion of mothers with T1D compared with fathers with T1D. This is a result of the study eligibility. ENDIA required women that were pregnant or had recently given birth. As such, face to face recruitment through high risk and specialist diabetes antenatal clinics provided the most cost-effective and successful recruitment strategy. Strategies to target fathers with T1D and parents of children with T1D having another baby included social media posts and liaison with adult and pediatric endocrinologists. However, these proved less fruitful. Details of recruitment strategies used in ENDIA have been described previously.30 There are also more nulliparous women with T1D compared with those without T1D. This arose due to the study’s eligibility criteria. For a family to be eligible to participate in ENDIA, the child had to have an FDR with T1D. For mothers without T1D, this means that it was either the father or a sibling. So, for mothers without T1D, around a third had to already have a child that had developed T1D to be eligible for the study.
ENDIA as a T1D pregnancy cohort
With over 900 mothers with T1D enrolled in ENDIA, we believe this is one of the largest prospective studies of T1D in pregnancy globally. As expected, mothers with T1D had substantially higher rates of adverse pregnancy outcomes and pregnancy complications themselves, with six times the odds of having pre-eclampsia. Similar rates of pre-eclampsia are reported in Australia (17.9%)43 and Europe (18.1% in T1D).44
Mothers with T1D had a fourfold higher likelihood of induced labor, six times higher rates of cesarean section and a 12-fold higher risk of premature birth compared with mothers without T1D. Similar rates of premature birth in T1D pregnancies are reported in Australia45 and Europe.44 46 Birth outcomes for the mothers without T1D were comparable to the general Australian population birth outcomes.36 ENDIA babies born to mothers with T1D were, on average, 150 g heavier at 2 weeks shorter gestation, amounting to a 10-fold higher odds of being LGA in association with more neonatal complications and admission to SCN and NICUs. Again, these complications are comparable to other Australian43 and Danish44 data, but higher than those reported in the UK (52.5%).46 The high rates of preconception maternal and paternal obesity, maternal obesity in pregnancy, higher birth weight (>3.5 kg) and cesarean section are important as potential prenatal initiators of islet autoimmunity.47
Comparison with Australian population
ENDIA is a national study with participants located in all Australian states and territories. The location of the majority of participants is consistent with the distribution of the Australian population.36 The regional participant program and outreach clinics allowed for participation anywhere in Australia, which enabled representativeness of the Australian population. However, a higher proportion of the ENDIA population lived in major cities where lead recruitment sites were located (81% ENDIA vs 73% Australian population). The nine recruitment sites across Australia covered 92% of the Australian population.
Although this is a cohort at risk of developing T1D, parental demographics were similar to that of the Australian general population. For mothers without T1D, this was less likely to be their first pregnancy (37%) compared with the Australian population (42.4%).36 This was an artefact of the FDR study design, which was mentioned previously. The 20% prevalence of obesity observed in ENDIA parents was similar to that reported in the Australian population (21%).36 A higher proportion of ENDIA parents were born in Australia, achieved tertiary education and had less socioeconomic disadvantage compared with the general population. An increase in incidence of childhood T1D has been shown with higher socioeconomic status and residence in urban areas of Western Australia,48 potentially increasing the risk of islet autoimmunity in our ENDIA children.
Distinctions between ENDIA and other T1D risk cohorts globally
The major points of difference between ENDIA and most T1D risk cohorts with follow-up in early life are (1) ENDIA commenced recruitment in pregnancy, (2) eligibility was based on FDR status alone without consideration of HLA genotype and (3) the ENDIA population is based in the Southern Hemisphere. Details of the 14 major birth cohorts are provided in online supplemental figure 2 and online supplemental table 2, which summarizes the commencement date, cohort size, countries of recruitment and eligibility criteria. Only one established cohort, the EDIA intervention trial based in Finland,49 commenced recruitment in pregnancy, but followed only a small number of mothers and offspring (n=87). 80% of live born children in ENDIA were followed from pregnancy (n=1178) with two-thirds recruited before the third trimester. The inclusion of data and samples in pregnancy will allow, for the first time, a prospective assessment of prenatal exposures that have already been associated with islet autoimmunity and T1D risk in epidemiological studies. This includes maternal viral infections and diet,47 as well as identifying new exposures and risk factors such as the function of the gestational microbiome, and other omic systems (metabolome, lipidome, proteome, glycome, etc).
The inclusion of children based exclusively on FDR status, without exclusion of specific ‘low-risk’ HLA types, is a feature of ENDIA, BabyDiab and Australian BabyDiab. All other birth cohorts have excluded children with DRXX HLA types. The prevalence of DRXX HLA types was 22% among the ENDIA children and 16.4% among the BabyDiab children50 and may provide insight into the action of protective genotypes.
Future plans
Follow-up of the 1473 children at-risk for the development of islet autoimmunity and T1D continues until they reach 10 years old, are diagnosed with T1D, or withdraw. The current median age of the cohort is 6.4 years old. A nested case–control study is currently being undertaken to investigate associations between persistent islet autoimmunity and longitudinal omics exposures.25 A definitive case–cohort analysis is planned to define associations from the pregnancy onwards with the development of persistent islet autoimmunity and coeliac autoimmunity.
Opportunities and limitations
ENDIA’s investigation of prenatal and postnatal exposures affecting offspring’s risk is limited by current duration of follow-up and the need for sufficient numbers of children to develop persistent islet autoimmunity to adequately power biomarker studies.25 The heterogeneity of T1D pathogenesis that is increasingly unraveled makes this particularly relevant. For this reason, opportunities to combine later analysis with larger postnatal cohorts will be valuable, whereby signatures in larger cohorts can be measured in ENDIA in relation to the unique exposures in prenatal and postnatal life that ENDIA provides. The COVID-19 pandemic occurred during the final stages of pregnancy and birth. Each state and hospital had differing levels of restrictions during 2020–2022 which impacted some sample collection. Modifications were made to allow samples and data to still be collected,51 and the opportunity arose to study the impact of COVID-19 exposure on the development of islet autoimmunity.
Collaboration
The ENDIA Study is involved in many national and international collaborations and we welcome collaboration with other researchers. Researchers who are interested in potential collaboration should contact ENDIA (endia@adelaide.edu.au) to complete a sample and data access request evaluation by the ENDIA Study Steering Committee. For more information, refer to the ENDIA website (https://www.endia.org.au/for-researchers/).
Conclusions
In conclusion, the ENDIA cohort stands as a globally valuable resource for unraveling the intricate connections between genetics, omic systems and environmental exposures in the development of T1D, during pregnancy and early life. Now fully recruited and characterized, the study’s strengths include its unique focus on early-life recruitment, consideration of familial risk without HLA genotype exclusions and representation of the Southern Hemisphere, which may experience different exposures (eg, viral exposures, vitamin D levels, dietary patterns and models of food production). The inclusion of diverse data, especially during pregnancy, allows for prospective assessment of various exposures linked to islet autoimmunity and T1D risk. ENDIA parent demographics were mostly comparable to the Australian population, except a greater percentage were Australian born, lived in a major city and had higher socioeconomic advantage and education. Mothers with T1D had larger babies at a shorter gestation and higher rates of pregnancy and neonatal complications and perinatal intervention. The observed differences in birth outcomes underscore the challenges faced by mothers with T1D, emphasizing the need for improved care. Another opportunity provided by ENDIA’s design is to provide a large prospective cohort of mothers with T1D comprehensively followed during pregnancy. Looking ahead, ongoing follow-up with the current nested case–control study25 and planned case–cohort analyses, with wide collaborations, will drive the overarching goals to inform new primary prevention strategies in early life.
Data availability statement
Data are available on reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants. ENDIA was reviewed and approved nationally by the study’s lead human research ethics committee (HREC) at the Women’s and Children’s Health Network (WCHN) under the Australian National Mutual Acceptance Scheme (current approval no: 2020/HRE01400). Study conduct in Western Australia was approved by the Women and Newborn Health Service (RGS0000002639) and Child and Adolescent Health Service (RGS0000002402) HRECs. Local governance approval was obtained at all participating study sites. Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The authors thank current and past co-ordinators, laboratory staff, project management team and especially the ENDIA families for their dedication to establishing and continuing this important research. Co-ordinators: Heather Anderson, Tracey Baskerville, Sarah Beresford, Samantha Bertram, Debra Bezuidenhout, Jessica Bolitho, Susan Brandrick, Emma Brownrigg, Carlie Butterworth, Jacki Catteau, Nakita Clements, Sheryl Curran, Danielle Edwards, Jasmin Foster, Jane French, Kyana Gartrell, Helen Griffiths, Alison Gwiazdzinski, Candice Hall, Gail Harper, Amanda Hulley, Lee Henneken, Mikayla Hoffman, Azita Keytash, Julie Kirby, Renee Kludas, Meredith Krieg, Catherine Lloyd-Johnson, Ying Mateevici, Christine Monagle, Belinda Moore, Magenta Musgrove, Solveig Ober, Bina Patel, Megan Poth, Michelle Power, Benjamin Ramoso, Bernadette Rice, Alison Roberts, Angela Schmidt, Wayne Soon, Kelly Spooner, Stephanie Tan, Alexandra Tully, Isabelle Vicary, Julianne Wilson, Rosemary Wood. Research officers: Sabrina Binkowski, Minh Bui, Ace Choo, Dylan Foskett, Ryan Galea, Emily Gibson, Abbey Gilbert, Christopher Hope, Dexing Huang, Beth Kamitakahara, Stuti Kapadia, Ana Karceva, Danielle Kennedy, Soi Law, Murali Maradana, Brydie-Rose Mellor, Asma Minhaj, Partha Mitra, Trung Nguyen, Vanessa Prajitno, Alana Smith, Nathan Stone, Carling Southall, Thao Tran, Sapphire Vaega, Marcus Vallejo, Emily Ward, Pascale Wehr, Emily Wood, Luke Wood, Yan Xu, Cynthia Yau. Clinical scientists: Kelly Watson, Cecilia Hsieh, Yeon Park. Project management: Leanne Cavenett, Su-San Lee. Past investigators: Andrew Cotterill, Lynne Giles, Mark Harris, Tim Jones, Claire Morbey, Anthony Papenfuss. Associate investigators: Fergus Cameron, Andrew Day, Prudence Lopez. Software developers: William Hu, Chris Duran. Statisticians: Peter Baghurst, Emma Knight, Jennie Louise. The ENDIA Study Group would like to thank the following institutions and individuals for their contribution to ENDIA recruitment and follow-up: Lead clinical recruitment/follow-up sites: The Women’s and Children’s Hospital, South Australia (Jennifer Couper). Royal Melbourne Hospital, Victoria (Peter Colman, John Wentworth, Leonard Harrison). Barwon Health, Victoria (Peter Vuillermin). Monash Health, Victoria (Georgia Soldatos). Children’s Hospital at Westmead, New South Wales (Maria Craig). Royal Hospital for Women, New South Wales (Maria Craig). St George Hospital, New South Wales (Maria Craig). John Hunter Children’s Hospital, New South Wales (Prudence Lopez). Princess Margaret Hospital/Perth Children’s Hospital, Western Australia (Elizabeth Davis, Aveni Haynes). Mater Mother’s Hospital/Queensland Children’s Hospital, Queensland (Tony Huynh, Mark Harris, Andrew Cotterill). Lead academic sites: The University of Adelaide/Robinson Research Institute, South Australia (Jennifer Couper, Megan Penno, Rebecca Thomson, Kelly McGorm, Helena Oakey, Simon Barry). WEHI, Victoria (Leonard Harrison, John Wentworth). University of New South Wales, New South Wales (Maria Craig, William Rawlinson). University of Sydney, New South Wales (Maria Craig). University of Western Australia/Telethon Kids Institute/Harry Perkins Institute, Western Australia (Elizabeth Davis, Aveni Haynes, Grant Morahan), University of Melbourne, Victoria (Richard Sinnott). University of Queensland, Queensland (Mark Harris). Satellite recruitment/birthing sites (South Australia): Country Health South Australia (Jennifer Couper), Flinders Medical Centre (Brian Coppin), Lyell McEwin Hospital (Jennifer Couper), Ashford Hospital, Flinders Private Hospital, North Eastern Community Hospital. Satellite recruitment/birthing sites (Victoria): Royal Women’s Hospital (Alison Nankervis), Ballarat Base Hospital (David Song), Bendigo Health (Mark Savage), Epworth Geelong Hospital (Peter Vuillermin), Mercy Hospital for Women (Christine Houlihan, Peter Colman), St. John of God Geelong (Peter Vuillermin), Sunshine Hospital/Joan Kirner Women's and Children's Hospital (Balasubramanian Krishnamurthy), Werribee Mercy Hospital (Sheetal Tipnis). Satellite recruitment/birthing sites (New South Wales): Hunter Diabetes Centre (Claire Morbey), John Hunter Hospital (Maria Craig), John Hunter Children’s Hospital (Maria Craig, Prudence Lopez), Sydney Children’s Hospital (Maria Craig), The Sutherland Hospital (Maria Craig), Westmead Hospital (Maria Craig), North Shore Private Hospital (Maria Craig). Satellite Recruitment/Birthing Sites (Western Australia, all under Elizabeth Davis): Armidale Hospital, Bentley Hospital, Joondalup Health Campus, King Edward Memorial Hospital, Rockingham General Hospital, St John of God Mt. Lawley, St John of God Murdoch, St John of God Subiaco. Satellite recruitment/birthing sites (Queensland): Royal Brisbane and Women’s Hospital (Michael d’Emden), Wesley Hospital (Stephen Cook, Andrew Cotterill). Satellite recruitment/birthing sites (Northern Territory): Royal Darwin Hospital (Louise Maple-Brown). Referring physicians in private: Natalie Harrison/Geelong Diabetes & Endocrinology (Victoria), Dorothy Graham (Western Australia), Linda McKendrick (South Australia), Amanda Terry (South Australia). Other birthing hospitals: Albury-Wodonga Hospital, Angliss Hospital, Auburn Public Hospital, Bankstown Hospital, Bathurst Base Hospital, Beaudesert Hospital, Beijing United Family Hospital (China), Belmont Hospital, Berri Hospital, Blacktown Hospital, Box Hill Hospital, Broome Hospital, Buderim Private Hospital, Bunbury Hospital, Burnside War Memorial Hospital, Caboolture Hospital, Cabrini Hospital Malvern, Cairns Hospital, Cairns Private Hospital, Calvary Hospital, Calvary Hospital Bruce, Calvary Hospital Lenah Valley, Calvary Hospital Wagga, Calvary John James Hospital, Campbelltown Hospital, Canberra Hospital, Casey Hospital, Centenary Hospital for Women and Children, Coffs Harbour Base Hospital, Dandenong Hospital, Darwin Birth Centre, Darwin Private Hospital, Dubbo Base Hospital, Echuca Regional Health, Emerald Hospital, Epworth Freemasons Hospital, Fiona Stanley Hospital, Frances Perry House, Frankston Hospital, Gawler Hospital, Gippsland Health, Glengarry Private Hospital, Gold Coast Private Hospital, Gold Coast University Hospital, Gosford Hospital, Gosford Private Hospital, Goulburn Valley Health, Grafton Base Hospital, Greenslopes Private Hospital, Griffith Base Hospital, Gunnedah Hospital, Hawkes Bay Hospital (New Zealand), Hervey Bay Hospital, Hobart Private Hospital, Hornsby Hospital, Hurstville Private Hospital, Ipswich Hospital, Jessie McPherson Private Hospital, John Flynn Private Hospital, Kalgoorlie Hospital, Kapunda Hospital, Kareena Private Hospital, Katoomba Hospital, Launceston General Hospital, Lismore Base Hospital, Liverpool Hospital, Logan Hospital, Mackay Base Hospital, Maitland Hospital, Manly Hospital, Mater Hospital Sydney, Mater Mothers' Hospital, Mater Mothers' Private Hospital, Mater Private Hospital Mackay, Mater Private Hospital Redland, Mater Women's and Children's Hospital Townsville, Mitcham Private Hospital, Moruya District Hospital, Mount Barker Hospital, Narrabri Hospital, Nepean Hospital, Nepean Private Hospital, Newcastle Private Hospital, North Gosford Private Hospital, North West Hospital, North West Private Hospital, Northeast Health Wangaratta, Northern Beaches Hospital, Northern Hospital, Northpark Private Hospital, Norwest Private Hospital, Orange Base Hospital, Osborne Park Hospital, Peel Health Campus, Peninsula Private Hospital, Pindara Private Hospital, Port Augusta Hospital, Port Macquarie Hospital, Prince of Wales Hospital, Prince of Wales Private Hospital, Queanbeyan District Hospital, Queen Victoria Hospital, Redcliffe Hospital, Redland Hospital, Riverland General Hospital, Rockhampton Base Hospital, Royal Hobart Hospital, Royal Hospital for Women, Royal North Shore Hospital, Royal Prince Alfred Hospital, Sandringham Hospital, St George Private Hospital, St John of God Ballarat Hospital, St John of God Bendigo Hospital, St John of God Berwick Hospital, St John of God Bunbury Hospital, St John of God Midland Hospital, St Vincent's Hospital, St Vincent's Private Hospital, St Vincent’s Private Hospital Toowoomba, Stanthorpe Hospital, Sunnybank Private Hospital, Sunshine Coast University Hospital, Swan District Hospital, Sydney Adventist Hospital, Tamworth Hospital, Tanunda Hospital, The Canberra Hospital, The Tweed Hospital, Toowoomba Base Hospital, Toowoomba Private Hospital, Townsville University Hospital, Wallaroo Hospital, Wangaratta Hospital, Waverley Private Hospital, Westmead Private Hospital, Wodonga Hospital, Wollongong Hospital, Wollongong Private Hospital.
References
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Footnotes
Presented at This work was presented at the Australasian Paediatric Endocrinology Group Annual Scientific Meeting (September 2019), 55th Annual Meeting of the European Association for the Study of Diabetes (September 2019), 11th DoHaD World Congress (October 2019) and the 45th Annual Conference for the International Society for Pediatric and Adolescent Diabetes (October–November 2019).
Contributors RLT wrote the original draft of the manuscript with input from MASP, AH, HO and JJC. All authors provided critical revision and approved the final version of the manuscript. The ENDIA Study was conceptualized by JJC and LCH and was designed by JJC, LCH, MEC, PGC and WR. JJC, PA, MASP, DH, PGC, MEC, EAD, TH, LCH, AH, GS, PJV and JMW oversaw implementation of ethical practice and the ENDIA protocol at the clinical sites. HO performed the statistical analysis. ROS supervised the storage and maintenance of all data for the study. BB and GM performed genetics analysis. RLT and HO 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 This research was supported by JDRF Australia (grant key 3-SRA-2023-1374-M-N, 3-SRA-2020-966-M-N, 1-SRA-2019-871-M-B, 4-SRA-2015-127-M-B), the recipient of the Commonwealth of Australia grant for Accelerated Research under the Medical Research Future Fund, and with funding from the Leona M. and Harry B. Helmsley Charitable Trust. In addition, support was provided by The National Health and Medical Research Council of Australia (Centre of Research Excellence for the Protection of Pancreatic Beta Cell and project grant APP1025083). Funders did not have a role in the conduct of the research and preparation of the article. MEC was supported by an NHMRC Practitioner Fellowship (APP1136735).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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