Costs of Public Health Screening of Children for Presymptomatic Type 1 Diabetes in Bavaria, Germany

[Early Detection Of Type 1 Diabetes By Islet Autoantibody Screening: A Position Paper Of The Fr1daplex Project Leaders And Training Centres, Bvkj Bavaria And Paednetz (Registered) Bavaria]

This position paper is based on the authors' many years of clinical experience and basic science research on the diagnosis and treatment of children and adolescents with a presymptomatic early stage of type 1 diabetes. The benefits as well as potential disadvantages of early detection of type 1 diabetes by islet autoantibody screening are critically discussed. In addition, the perspectives of delaying the onset of the clinical metabolic disease through treatment with teplizumab are addressed. Today, we see the chance for a relevant improvement in therapeutic options and life perspectives of affected children and adolescents. Important next steps for the implementation of islet autoantibody screening in Germany are the training of pediatricians who should inform families about the screening, establishment of a few transregional laboratories that carry out the test, and expansion of regional capacities for the training and care of children with an early stage of type 1 diabetes.

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Prevention of Type 1 Diabetes: Current Perspective

People living with type 1 Diabetes (T1D) and their families have poor perception of health related quality of life. Therapies for T1D are becoming better with time, but they still involve a lot of effort. Prevention of T1D, if successful, has potential to change lives of millions of families across the globe. Type 1 diabetes is an autoimmune disease with underlying genetic predisposition for autoimmunity against beta cell antigens upon exposure to an environmental trigger. Identifying underlying primary antigen responsible for initiating autoimmune cascade, avoiding environmental trigger and modifying immunity has all been used as strategies for preventing or delaying onset of type 1 diabetes. Primary prevention for type 1 diabetes is hindered by difficulty in identifying at-risk population and also due to lack of effective preventive strategy. Secondary prevention, in children with presence of autoimmunity, has recently received a boost with approval of Teplizumab, an immunity modifying drug by its Anti-CD3 action. Application of preventive strategies would also change based on country specific incidence, prevalence and availability of health resources. In current review, an update on preventive strategies for type 1 diabetes is being discussed as well as their applicability in Indian context.

Precision Medicine in Type 1 Diabetes

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.

Risk Modeling to Reduce Monitoring of an Autoantibody-Positive Population to Prevent DKA at Type 1 Diabetes Diagnosis

CONTEXT

The presence of islet autoimmunity identifies individuals likely to progress to clinical type 1 diabetes (T1D). In clinical research studies, autoantibody screening followed by regular metabolic monitoring every 6 months reduces incidence of diabetic ketoacidosis (DKA) at diagnosis.

OBJECTIVE

We hypothesized that DKA reduction can be achieved on a population basis with a reduced frequency of metabolic monitoring visits. We reasoned that prolonged time between the development of T1D and the time of clinical diagnosis ("undiagnosed time") would more commonly result in DKA and thus that limiting undiagnosed time would decrease DKA.

METHODS

An analysis was conducted of data from TrialNet's Pathway to Prevention (PTP), a cross-sectional longitudinal study that identifies and follows at-risk relatives of people with T1D. PTP is a population-based study enrolling across multiple countries. A total of 6193 autoantibody (AAB)-positive individuals participated in PTP from March 2004 to April 2019. We developed models of progression to clinical diagnosis for pediatric and adult populations with single or multiple AAB, and summarized results using estimated hazard rate. An optimal monitoring visit schedule was determined for each model to achieve a minimum average level of undiagnosed time for each population.

RESULTS

Halving the number of monitoring visits usually conducted in research studies is likely to substantially lower the population incidence of DKA at diagnosis of T1D.

CONCLUSION

Our study has clinical implications for the metabolic monitoring of at-risk individuals. Fewer monitoring visits would reduce the clinical burden, suggesting a path toward transitioning monitoring beyond the research setting.

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

AIMS/HYPOTHESIS

The aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.

METHODS

We tested children from Bavaria, Germany, aged 1.75-10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA_1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.

RESULTS

Of 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA_1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.

CONCLUSIONS/INTERPRETATION

Public health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.

Estimating the total incidence of type 1 diabetes in children and adolescents aged 0-19 years from 1990 to 2050: a global simulation-based analysis

BACKGROUND

Previous studies of type 1 diabetes in childhood and adolescence have found large variations in reported incidence around the world. However, it is unclear whether these reported incidence levels are impacted by differences in country health systems and possible underdiagnosis and if so, to what degree. The aim of this study was to estimate both the total and diagnosed incidence of type 1 diabetes globally and to project childhood type 1 diabetes incidence indicators from 1990 to 2050 for each country.

METHODS

We developed the type 1 diabetes global microsimulation model to simulate the natural history and diagnosis of type 1 diabetes for children and adolescents (aged 0-19 years) in 200 countries and territories, accounting for variability in underlying incidence and health system performance. The model follows an open population of children and adolescents in monthly intervals and simulates type 1 diabetes incidence and progression, as well as health system factors which influence diagnosis. We calibrated the model to published data on type 1 diabetes incidence, autoantibody profiles, and proportion of cases diagnosed with diabetic ketoacidosis from 1990 to 2020 and assessed the predictive accuracy using a randomly sampled test set of data withheld from calibration.

FINDINGS

We estimate that in 2021 there were 355 900 (95% UI 334 200-377 300) total new cases of type 1 diabetes globally among children and adolescents, of which 56% (200 400 cases, 95% UI 180 600-219 500) were diagnosed. Estimated underdiagnosis varies substantially by region, with over 95% of new cases diagnosed in Australia and New Zealand, western and northern Europe, and North America, but less than 35% of new cases diagnosed in west Africa, south and southeastern Asia, and Melanesia. The total number of incident childhood cases of type 1 diabetes is projected to increase to 476 700 (95% UI 449 500-504 300) in 2050.

INTERPRETATION

Our research indicates that the total global incidence of childhood and adolescent type 1 diabetes is larger than previously estimated, with nearly one-in-two children currently undiagnosed. Policymakers should plan for adequate diagnostic and medical capacity to improve timely type 1 diabetes detection and treatment, particularly as incidence is projected to increase worldwide, with highest numbers of new cases in Africa.

FUNDING

Novo Nordisk.

Air Pollution, Oxidative Stress, and the Risk of Development of Type 1 Diabetes

Despite multiple studies focusing on environmental factors conducive to the development of type 1 diabetes mellitus (T1DM), knowledge about the involvement of long-term exposure to air pollution seems insufficient. The main focus of epidemiological studies is placed on the relationship between exposure to various concentrations of particulate matter (PM): PM1, PM2.5, PM10, and sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (O3), versus the risk of T1DM development. Although the specific molecular mechanism(s) behind the link between increased air pollution exposure and a higher risk of diabetes and metabolic dysfunction is yet unknown, available data indicate air pollution-induced inflammation and oxidative stress as a significant pathway. The purpose of this paper is to assess recent research examining the association between inhalation exposure to PM and associated metals and the increasing rates of T1DM worldwide. The development of modern and more adequate methods for air quality monitoring is also introduced. A particular emphasis on microsensors, mobile and autonomous measuring platforms, satellites, and innovative approaches of IoT, 5G connections, and Block chain technologies are also presented. Reputable databases, including PubMed, Scopus, and Web of Science, were used to search for relevant literature. Eligibility criteria involved recent publication years, particularly publications within the last five years (except for papers presenting a certain novelty or mechanism for the first time). Population, toxicological and epidemiological studies that focused particularly on fine and ultra-fine PM and associated ambient metals, were preferred, as well as full-text publications.

Beta cell and immune cell interactions in autoimmune type 1 diabetes: How they meet and talk to each other

Background

Scope of review

Major conclusions