Simranjeet Kaur

Background: First‐degree relatives (FDRs) of individuals with type 1 diabetes (T1D) carry a significantly elevated risk of developing autoimmune diseases, including celiac disease (CD) and autoimmune thyroid disease (AITD). The DiaUnion 1.0 study aimed to characterize one‐time autoantibodies, genetic risk profiles and long‐term progression to T1D, CD or AITD in siblings of children newly diagnosed with T1D. Methods: Between 1997 and 2010, 1427 Danish siblings provided plasma and DNA samples to the DanDiabKids biobank. Autoantibodies associated with T1D (GADA, IAA, IA2A, ZnT8A), CD (tTGA) and AITD (TPOA) were measured using ADAP technology and confirmed by radiobinding assays. Genotyping was performed to compute a T1D Genetic Risk Score (GRS). National registry data were used to track disease incidence up to year 2024. Results: Out of 1417 screened samples, 7.8% were positive for islet autoantibodies (IAab), and 4.6% had multiple IAab. Disease incidence rate increased substantially with IAab positivity: 0.11 per 100 person‐years in IAab‐negative siblings, 2.46 in single‐IAab positive and 8.10 in those with multiple IAab. Similarly, 1.97 per 100 person‐years of tTGA‐positive siblings developed CD, and 0.69 per 100 person‐years of TPOA‐positive individuals developed AITD. Higher GRS and HLA DR3/DR4 haplotypes were associated with autoantibody positivity ( p < 0.0001). Conclusions: A single screening for T1D, CD and AITD autoantibodies combined with genetic risk scoring stratified long‐term risk of clinically diagnosed autoimmune disease. These findings support further evaluation of targeted screening and follow‐up strategies in FDRs to enable surveillance and potential early intervention before clinical onset.

Background: Mesenchymal stem cells (MSCs) represent a promising regenerative therapy associated with anti-inflammatory, anti-apoptotic, and pro-proliferative properties. Despite their significant potential for treating diabetes, the clinical application of MSCs has been hindered mainly due to the low number of intravenously (IV) injected cells that successfully reach the pancreas. Methods: In this study, we assessed the protective potential of adipose tissue-derived MSCs (AD-MSCs) in preventing the development of diabetes using a streptozotocin (STZ)-induced diabetic rat model. AD-MSCs were administered 4 h after STZ either via conventional IV injection or through direct intra-arterial (IA) administration into the pancreas. Healthy (no STZ, no MSCs) and diabetic (STZ, no MSCs) control groups were also included. Results: Our proof-of-concept data indicate that IA administration of AD-MSCs allowed rats to maintain glycemic control and respond appropriately to glucose challenges following STZ treatment, comparable to healthy controls. In contrast, rats receiving IV AD-MSCs developed hyperglycemia and failed to respond adequately to glucose challenges. In vitro studies demonstrated that AD-MSCs can enhance the viability and function (i.e. insulin secretion) of injured islets. Conclusion: Local IA delivery of AD-MSCs into the pancreatic circulation effectively prevents the onset of diabetes in a preclinical rat model, highlighting the need for considering delivery techniques to ensure MSCs can reach their targets in vivo.

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. These miRNAs measured across n = 2,204 individuals from four contexts (4C: Australia, Denmark, Hong Kong SAR People’s Republic of China, India) led to a four-context, miRNA-based dynamic risk score (DRS) that effectively stratified individuals with and without T1D. Generative artificial intelligence was used to create an enhanced four-context, miRNA-based DRS, which offered good predictive power (area under the curve = 0.84) for T1D stratification in a separate multicontext validation dataset (n = 662), and accurately predicted future exogenous insulin requirement at 1 hour of islet transplantation. In a clinical trial assessing the imatinib drug therapy, baseline miRNA signature, rather than clinical characteristics, distinguished drug responders from nonresponders at 1 year. This study harnessed machine learning/generative artificial intelligence approaches, identifying and validating a miRNA-based DRS for T1D discrimination and treatment efficacy prediction.

Identifying biomarkers of functional β-cell loss is critical in risk stratification for Type 1 Diabetes (T1D). We report a microRNA-based dynamic (responsive to environment) risk score developed using multi-center, multi-ethnic/country (“multi-context”) cohorts. Discovery (wet-lab and dry-lab) analysis identified 50 microRNAs that were measured across n=2,204 individuals from four contexts (4C=AUS/Australia, DNK/Denmark, HKG/Hong Kong SAR China, IND/India). A microRNA-based dynamic risk score (DRS) was generated (DRS4C), which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C that showed AUC >0.84 on an independent Validation set (n=313) from AUS, IND and NZL and predicted future exogenous-insulin requirement in islet transplantation recipients from Canada (CAN). In another T1D therapy, this microRNA signature stratified 1-year response to imatinib based on their profile at the study baseline. Utilizing machine learning and GAI, this study identified and validated a microRNA-based DRS for T1D stratification and treatment efficacy prediction.

It is internationally recognized to use clinical decision limits (CDL) when interpreting the lipid levels in both adults and children, even though the evidence for children is scarce. The purpose of this study is to describe how lipid levels progress in healthy Danish children ages 5 to 17 years. This study is based on the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-study DK) consisting of 1456 observations of schoolchildren aged 5 to 17 years. Participants have been tested for blood levels of total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and remnant cholesterol levels are calculated. Finally, sex-specific percentile reference curves are presented. Percentile reference curves stratified by sex were generated for all cholesterols and showed that the total cholesterol level peaks at 4.32 mmol/l in 10-year-old boys and 4.46 mmol/l in nine-year-old girls. HDL levels in boys peak at 1.72 mmol/l in nine-year-old boys. HDL levels in girls and LDL levels in both sexes are nearly constant. Triglycerides kept rising to the age of 17 years in both sexes and remnant cholesterol decreased from age 5 to 17 years in both sexes. BMI z-score adjustment revealed no significant association with total cholesterol in both sexes but a significant association between HDL, LDL, triglycerides, and remnant cholesterol. This study is the first to generate percentile reference curves for blood levels of total cholesterol, LDL, HDL, triglycerides, and remnant cholesterol in a cohort of healthy Danish children aged 5 to 17 years.