Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody–positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

The prevalence of type 1 diabetes is increasing, with significant implications for public health systems worldwide. This review provides a global overview of the current epidemiology of type 1 diabetes, examining the trends, risk factors, and regional variations in incidence. We explore the influence of genetic, environmental, and socio‐economic factors on the rising incidence of type 1 diabetes. The review also highlights temporal trends in the management of type 1 diabetes and the risk of mortality and morbidity from acute and long‐term complications, including hypoglycaemia, diabetic ketoacidosis, and retinopathy. By synthesizing global and regional data, we aim to provide valuable insights for local health service planning, disease prediction, and tailored interventions. This article underscores the importance of continued research into the epidemiology of type 1 diabetes to better inform prevention, treatment, and management strategies of this growing global health challenge. Plain Language Summary: Type 1 diabetes (T1D) is an autoimmune condition that begins silently, with the body attacking insulin‐producing cells in the pancreas. This process occurs in two early, silent stages, detectable through blood tests for antibodies, before symptoms appear. It eventually progresses to stage 3, when symptoms develop and insulin treatment becomes necessary. Prevalence: T1D affects about 9 million people globally, including over 1.5 million children. In addition to those with symptoms, many children and adults may unknowingly have early stage T1D. These can be detected through screening and are estimated to affect around 0.3% of the population. Incidence: In 2024, there were over 500,000 new diagnoses of T1D worldwide, with cases increasing each year. Rates vary by region, age, and sex. Most diagnoses occur in childhood or early adulthood, with a peak around puberty, though it can develop later in life. Rates are highest in high‐income countries such as Finland and Australia. Lower rates in other regions may reflect limited surveillance. Some countries have reported fluctuating trends, possibly linked to infections. Improved awareness and diagnostics explain and changing risk factors may also play a role. Risk Factors: The risk of T1D is impacted by genetic, demographic, and environmental factors. Family history increases risk, though the majority of people diagnosed don’t have a family history. Genetic risk scores can also help identify children at higher risk. Risk changes with age, early signs often appear before age 3, and younger children tend to progress more quickly. Two childhood diagnosis peaks occur at ages 4–7 years and 10–14 years. Researchers are exploring possible subtypes of T1D based on age and disease behaviour. White European populations have the highest rates, but increases are also seen in other groups. Environmental factors like viral infections, caesarean birth, and early diet are being studied. Complications: Managing T1D is complex, however poor control can lead to short‐term emergencies like hypoglycaemia or diabetic ketoacidosis (DKA), and long‐term complications affecting the eyes, kidneys, and nerves. In recent decades, treatment has improved with better insulin regimens and technologies such as insulin pumps, continuous glucose monitors, and hybrid closed‐loop systems. These reduce complications and improve quality of life. Yet challenges remain, including high DKA rates at diagnosis—especially in young children and ethnic minorities, and high diabetic retinopathy rates that can impact vision. Early, tight glucose control reduces long‐term risks, but people with T1D still face a higher risk of early death. Future Research Directions: Major research gaps remain, especially in low‐ and middle‐income countries. Data on early‐stage and adult‐onset T1D is limited but improving. As new therapies emerge, like teplizumab which is used to delay progression to stage 3, disease patterns may shift. Long‐term data from modern technologies is still developing, with linked administrative data offering a promising solution. Conclusion: T1D is rising globally and still carries serious health risks. Understanding who is affected, when, and how the disease progresses is essential to improving prevention, care, and outcomes.

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb⁺) children and adults who are at risk of (confirmed single IAb⁺) or living with (multiple IAb⁺) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb⁺; (2) when people who are IAb⁺ are initially identified there is a need for confirmation using a second sample; (3) single IAb⁺ individuals are at lower risk of progression than multiple IAb⁺ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care. Graphical Abstract

OBJECTIVE The American Diabetes Association recommends individuals with type 1 diabetes (T1D) adjust insulin for dietary fat; however, optimal adjustments are not known. This study aimed to determine 1) the relationship between the amount and type of dietary fat and glycemia and 2) the optimal insulin adjustments for dietary fat. RESEARCH DESIGN AND METHODS Adults with T1D using insulin pump therapy attended the research clinic on 9–12 occasions. On the first six visits, participants consumed meals containing 45 g carbohydrate with 0 g, 20 g, 40 g, or 60 g fat and either saturated, monounsaturated, or polyunsaturated fat. Insulin was dosed using individual insulin/carbohydrate ratio as a dual-wave 50/50% over 2 h. On subsequent visits, participants repeated the 20–60-g fat meals with the insulin dose estimated using a model predictive bolus, up to twice per meal, until glycemic control was achieved. RESULTS With the same insulin dose, increasing the amount of fat resulted in a significant dose-dependent reduction in incremental area under the curve for glucose (iAUC_glucose) in the early postprandial period (0–2 h; P = 0.008) and increase in iAUC_glucose in the late postprandial period (2–5 h; P = 0.004). The type of fat made no significant difference to the 5-h iAUC_glucose. To achieve glycemic control, on average participants required dual-wave insulin bolus: for 20 g fat, +6% insulin, 74/26% over 73 min; 40 g fat, +6% insulin, 63/37% over 75 min; and 60 g fat, +21% insulin, 49/51% over 105 min. CONCLUSIONS This study provides clinical guidance for mealtime insulin dosing recommendations for dietary fat in T1D.

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.

Aim: One third of Australian children diagnosed with type 1 diabetes present with life‐threatening diabetic ketoacidosis (DKA) at diagnosis. Screening for early‐stage, presymptomatic type 1 diabetes, with ongoing follow‐up, can substantially reduce this risk (<5% risk). Several screening models are being trialled internationally, without consensus on the optimal approach. This pilot study aims to assess three models for a routine, population‐wide screening programme in Australia.Methods: An implementation science‐guided pilot study to evaluate the feasibility, acceptability and costs of three screening models in children will be conducted between July 2022 and June 2024. These models are as follows: (1) Genetic risk‐stratified screening using newborn heel prick dried bloodspots, followed by autoantibody testing from 11 months of age; (2) genetic risk‐stratified screening of infant (6–12 months) saliva followed by autoantibody testing from 10 months of age; and (3) autoantibody screening using capillary dried bloodspots collected from children aged 2, 6 or 10 years. Cohorts for each model will be recruited from targeted geographic areas across Australia involving ≥2 states per cohort, with a recruitment target of up to 3000 children per cohort (total up to 9000 children). The primary outcome is screening uptake for each cohort. Secondary outcomes include programme feasibility, costs, parental anxiety, risk perception, satisfaction, well‐being and quality of life, and health professional attitudes and satisfaction.Conclusions: This pilot is the first direct comparison of three screening implementation models for general population screening. Findings will provide evidence to inform a potential national screening programme for Australian children.Trial Registration: ACTRN12622000381785.