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Impact of Missing Data on the Accuracy of Glucose Metrics from Continuous Glucose Monitoring Assessed Over a 2-Week Period

To explore the impact of missing data on the accuracy of continuous glucose monitoring (CGM) metrics collected for a 2-week period in a clinical trial.

Childhood-onset type 1 diabetes in Western Australia: An update on incidence and temporal trends from 2001 to 2022

To determine the incidence and incidence trends over 2001-2022 of childhood-onset type 1 diabetes (T1D) in Western Australia and assess the impact of the COVID-19 pandemic.

Perspectives of culturally and linguistically diverse families in the management of children with type 1 diabetes in Western Australia

Children with Type 1 diabetes (T1D) from different ethnic backgrounds are growing in proportion in clinical practice and tend to have a higher risk of poor health outcomes. The study aimed to investigate the perspectives of culturally and linguistically diverse families in the management of children with T1D in Western Australia.

Differences in birth weight between immigrants' and natives' children in Europe and Australia: a LifeCycle comparative observational cohort study

Research on adults has identified an immigrant health advantage, known as the 'immigrant health paradox', by which migrants exhibit better health outcomes than natives. Is this health advantage transferred from parents to children in the form of higher birth weight relative to children of natives?

Coexisiting type 1 diabetes and celiac disease is associated with lower Hba1c when compared to type 1 diabetes alone: data from the Australasian Diabetes Data Network (ADDN) registry

To compare HbA1c and clinical outcomes in adolescents and young adults with type 1 diabetes, with or without celiac disease. 

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort

The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D.

Developing type 1 diabetes resources: a qualitative study to identify resources needed to upskill and support community sport coaches

Community sport coaches in Western Australia lack an understanding, the confidence, and knowledge in supporting young people with Type 1 diabetes (T1D). This study aims to identify what T1D educational resources are required to upskill coaches in Western Australia. 

Efficacy of the MiniMed™ 670G hybrid closed loop system in managing postprandial glucose excursion with high protein high fat foods in children and adolescents under free-living conditions

High protein high fat meals are considered “difficult” foods because they can cause prolonged hyperglycemia after ingestion. The potential of hybrid closed loop therapy in managing postprandial glucose excursions with these difficult foods remains unknown. This pilot study aimed to explore the impact of manual mode in standard insulin pump therapy and auto mode with hybrid closed loop pump therapy in managing glucose excursions caused by HPHF foods and to obtain feedback from families about each mode.

Developmental origins of psycho-cardiometabolic multimorbidity in adolescence and their underlying pathways through methylation markers: a two-cohort study

Understanding the biological mechanisms behind multimorbidity patterns in adolescence is important as they may act as intermediary risk factor for long-term health. We aimed to explore relationship between prenatal exposures and adolescent's psycho-cardiometabolic intermediary traits mediated through epigenetic biomarkers, using structural equation modeling (SEM). 

A systematic review of the use of artificial intelligence in mental health–based diabetes care: Current applications and future directions

To map and systematise existing research on the use of artificial intelligence (AI) in mental health-based diabetes care contexts, identify trends and potential gaps in the literature, examine methodological limitations and highlight future research directions.