Search
Phthalate metabolites are detectable within the majority of the population. Evidence suggests that a prenatal exposure to phthalates may be associated with the subsequent risks of obesity and elevated blood pressure. We hypothesised that a prenatal exposure to phthalates would lead to an increase in adverse cardiometabolic parameters through childhood and adulthood.
The COVID-19 pandemic has highlighted personal protective equipment (PPE) supply, distribution, and disposal issues worldwide. Calls to conserve PPE stocks and increase supply resulted in the rapid development of potential disinfection methods, with the possibility of improvements in medical waste reduction. However, how receptive health-care workers are to PPE reuse remains unknown. We aimed to examine the views of health-care workers who used PPE during the first COVID-19 wave in Aotearoa New Zealand, in relation to acceptability of PPE disinfection and reuse.
To analyze whether the coronavirus disease 2019 (COVID-19) pandemic increased the number of cases or impacted seasonality of new-onset type 1 diabetes (T1D) in large pediatric diabetes centers globally.
Adolescents with Type 1 diabetes (T1D) often need to undertake self-management tasks in public or disclose their diagnosis to others. Therefore, they may be subjected to negative reactions from the public, known as enacted stigma.
Identifying individuals at high risk of type 1 diabetes (T1D) is crucial as disease-delaying medications are available. Here we report a microRNA (miRNA)-based dynamic (responsive to the environment) risk score developed using multicenter, multiethnic and multicountry ('multicontext') cohorts for T1D risk stratification. Discovery (wet and dry lab) analysis identified 50 miRNAs associated with functional β cell loss, which is a hallmark of T1D.
To explore trends in the receipt of commonly prescribed medications (beyond insulin) in people with type 1 diabetes in Australia, including polypharmacy, and to investigate socioeconomic disparities across these trends.
Temporal eating patterns and ultra-processed food (UPF) consumption have independently been associated with obesity and non-communicable diseases. Little is known about the temporal patterns of UPF consumption, as data is challenging to collect. Temporal data can be extracted from mobile food records (mFRs). The aim of this study was to identify the temporal eating patterns of those consuming UPFs using an mFR.
A lifelong auto-immune condition that can affect anyone, but is most commonly diagnosed in childhood.
Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited.