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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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability., Study Design and Setting: The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores., Results: In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing., Conclusion: We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) study is an Australia-wide pregnancy-birth cohort study following children who have a first-degree relative with type 1 diabetes (ACTRN1261300794707). A dedicated ENDIA Facebook page was established in 2013 with the aim of enhancing recruitment and supporting participant retention through dissemination of study information. To measure the impact of Facebook, we evaluated the sources of referral to the study, cohort demographics, and withdrawal rates. We also investigated whether engagement with Facebook content was associated with specific post themes., Methods: Characteristics of Facebook versus conventional recruits were compared using linear, logistic, and multinomial logistic regression models. Logistic regression was used to determine the risk of study withdrawal. Data pertaining to 794 Facebook posts over 7.5 years were included in the analysis., Results: Facebook was the third largest source of referral (300/1511; 19.9%). Facebook recruits were more frequently Australian-born ( P < .001) enrolling postnatally ( P = .01) and withdrew from the study at a significantly lower rate compared with conventional recruits (4.7% vs 12.3%; P < .001) after a median of follow-up of 3.3 years. Facebook content featuring stories and images of participants received the highest engagement even though <20% of the 2337 Facebook followers were enrolled in the study., Conclusions: Facebook was a valuable recruitment tool for ENDIA. Compared with conventional recruits, Facebook recruits were three times less likely to withdraw during long-term follow-up and had different sociodemographic characteristics. Facebook content featuring participants was the most engaging. These findings inform social media strategies for future cohort and type 1 diabetes studies., Trial Registration: Australia New Zealand Clinical Trials Registry: ACTRN1261300794707.

Introduction: Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM., Methods and Analysis: We undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators., Ethics and Dissemination: This study was approved by the Human Research Ethics Committee of Monash Health (RES-19-0000713 L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals., Trial Registration Details: Systematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results., Competing Interests: Competing interests: SDC reports grants from the National Health and Medical Research Council (NHMRC), Diabetes Australia, the Australian Academy of Science and the Australian Government Department of Education and Training during the conduct of the study; JB reports grants from the NHMRC during the conduct of the study; BFF reports grants from CIBER (Biomedical Research Network in Epidemiology and Public Health, Madrid, Spain) during the conduct of the study and HJT reports grants from the NHMRC and the Medical Research Future Fund during the conduct of the study; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Hybrid closed-loop systems may offer improved HbA1c levels, more time-in-range, and less hypoglycemia than alternative treatment strategies. However, it is unclear if glycemic improvements offset this technology's higher acquisition costs. Among adults with type 1 diabetes in Australia, we sought to evaluate the cost-effectiveness of a hybrid closed-loop system in comparison with the current standard of care, comprising insulin injections and capillary glucose testing. Methods: Cost-effectiveness analysis was performed using decision analysis in combination with a Markov model to simulate disease progression in a cohort of adults with type 1 diabetes and compare the downstream health and economic consequences of hybrid closed-loop therapy versus current standard of care. Transition probabilities and utilities were sourced from published studies. Costs were considered from the perspective of the Australian health care system. A lifetime horizon was considered, with annual discount rates of 5% applied to future costs and outcomes. Uncertainty was assessed with probabilistic and deterministic sensitivity analyses. Results: Use of a hybrid closed-loop system resulted in an incremental cost-effectiveness ratio of Australian dollars (AUD) 37,767 per quality-adjusted life year (QALY) gained. This is below the traditionally cited willingness to pay a threshold of $50,000 per QALY gained in the Australian setting. Sensitivity analyses that varied baseline glycemic control, treatment effects, technology costs, age, discount rates, and time horizon indicated the results to be robust. Conclusions: For adults with type 1 diabetes, hybrid closed-loop therapy is likely to be cost-effective compared with multiple daily injections and capillary glucose testing in Australia.

Background: Tobacco smoking and diabetes mellitus contribute significantly to the overall health burden and mortality of Australians. We aimed to assess the relationship of smoking with glycemic control, metabolic profile and complications in Australian patients living with diabetes., Methods: We analysed the 2011-2017 biennial Australian National Diabetes Audit cross-sectional data. Patients were classified as current, past or never smokers. Linear (or quantile) and logistic regression models were used to assess for associations., Results: Data from 15,352 patients were analysed, including 72.2% with type 2 diabetes. Current smokers comprised 13.5% of the study population. Current and past smokers had a median HbA 1c that was 0.49% and 0.14% higher than never smokers, respectively, as well as higher triglyceride and lower HDL levels (all p values < .0001). Compared to never smokers, current smokers had higher odds of severe hypoglycemia and current and past smokers had higher odds of myocardial infarction, stroke, peripheral vascular disease, lower limb amputation, erectile dysfunction and peripheral neuropathy (all p values ≤.001), with no significant change over time., Conclusion: When compared to never smokers, current and past smokers had poorer glycemic and lipid control and higher odds of macrovascular and microvascular complications. Despite this, current smoking remains prevalent among Australians with diabetes., Competing Interests: Declaration of competing interest All authors have declared no conflicts of interest regarding this work., (Copyright © 2020 Elsevier Inc. All rights reserved.)