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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.

ABSTRACT Aims/Introduction Autoantibodies to pancreatic islet antigens identify young children at high risk of type 1 diabetes. On a background of genetic susceptibility, islet autoimmunity is thought to be driven by environmental factors, of which enteric viruses are prime candidates. We sought evidence for enteric pathology in children genetically at‐risk for type 1 diabetes followed from birth who had developed islet autoantibodies (“seroconverted”), by measuring mucosa‐associated cytokines in their sera. Materials and Methods Sera were collected 3 monthly from birth from children with a first‐degree type 1 diabetes relative, in the Environmental Determinants of Islet Autoimmunity (ENDIA) study. Children who seroconverted were matched for sex, age, and sample availability with seronegative children. Luminex xMap technology was used to measure serum cytokines. Results Of eight children who seroconverted, for whom serum samples were available at least 6 months before and after seroconversion, the serum concentrations of mucosa‐associated cytokines IL‐21, IL‐22, IL‐25, and IL‐10, the Th17‐related cytokines IL‐17F and IL‐23, as well as IL‐33, IFN‐γ, and IL‐4, peaked from a low baseline in seven around the time of seroconversion and in one preceding seroconversion. These changes were not detected in eight sex‐ and age‐matched seronegative controls, or in a separate cohort of 11 unmatched seronegative children. Conclusions In a cohort of children at risk for type 1 diabetes followed from birth, a transient, systemic increase in mucosa‐associated cytokines around the time of seroconversion lends support to the view that mucosal infection, e.g., by an enteric virus, may drive the development of islet autoimmunity.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Background: Pathway-based analysis primarily focuses on sub-pathway-based analysis, which aids in understanding biological reactions. Several studies have found abnormalities in pathways caused by certain regions based on the etiology of diseases. The Differential Expression Analysis for Pathways (DEAP) method is one such sub-pathway-based analysis method, that identifies a local region perturbed by complex diseases based on larger pathway data. However, the method has low performance in identifying informative pathways and sub-pathways. Methods: In this paper we propose an improved DEAP (iDEAP) method for enhanced identification of perturbed sub-pathways that achieves higher performance in the detection of differentially expressed pathways. Firstly, a search algorithm adapted from the Detect Module from Seed Protein (DMSP) algorithm was implemented as part of the DEAP method to search for informative sub-pathways. Secondly, the relation among sub-pathways was taken into account by averaging the maximum absolute value for the DEAP score for the reaction among sub-pathways to support the efficient identification of informative pathways. Three gene expression data sets were applied to this research. Results: The proposed improved method shows a better performance over the previous methods. In fact, when the identified genes from the results were assessed using 10-fold cross validation to classify cancer based on accuracy, the improved method shows higher accuracy for colorectal cancer (90%) and breast cancer (94%). Conclusions: This shows that the proposed method effectively identifies informative genes related to the targeted phenotype. A biological validation was also conducted on the top five significant pathways and selected genes based on biological literature. The results from this analysis will be useful especially in the medical field for disease detection. In 10 years and beyond, computational biology will become ever more entwined with biomedical research and medicine.