Utility of genetic risk scores in type 1 diabetes

2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Screening and care for preclinical stage 1-2 type 1 diabetes in first-degree relatives: French expert position statement

The natural history of type 1 diabetes (T1D) evolves from stage 1 (islet autoimmunity with normoglycemia; ICD-10 diagnostic code E10.A1) to stage 2 (autoimmunity with dysglycemia; E10.A2) and subsequent clinical stage 3 (overt hyperglycemia), which is commonly the first time of referral. Autoantibody testing can diagnose T1D at its preclinical stages 1-2 and lead to earlier initiation of care, particularly for first-degree relatives of people living with T1D, who are at higher genetic risk. Preclinical T1D screening and monitoring aims to avoid inaugural ketoacidosis and prolong preservation of endogenous insulin secretion, thereby improving glycemic control and reducing long-term morbidity. Moreover, early management can help coping with T1D and correct modifiable risk factors (obesity, sedentary lifestyle). New treatments currently under clinical deployment or trials also offer the possibility of delaying clinical progression. All these arguments lead to the proposition of a national screening and care pathway open to interested first-degree relatives. This pathway represents a new expertise to acquire for healthcare professionals. By adapting international consensus guidance to the French specificities, the proposed screening strategy involves testing for ≥ 2 autoantibodies (among IAA, anti-GAD, anti-IA-2) in relatives aged 2-45 years. Negative screening (∼95% of cases) should be repeated every 4 years until the age of 12. A management workflow is proposed for relatives screening positive (∼5% of cases), with immuno-metabolic monitoring by autoantibody testing, OGTT, glycemia and/or HbA1c of variable frequency, depending on T1D stage, age, patient preference and available resources, as well as the definition of expert centers for preclinical T1D.

Protocol for the Australian Type 1 Diabetes National Screening Pilot: Assessing the feasibility and acceptability of three general population screening models in children

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.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Fifty years of HLA-associated type 1 diabetes risk: history, current knowledge, and future directions

More than 50 years have elapsed since the association of human leukocyte antigens (HLA) with type 1 diabetes (T1D) was first reported. Since then, methods for identification of HLA have progressed from cell based to DNA based, and the number of recognized HLA variants has grown from a few to tens of thousands. Current genotyping methodology allows for exact identification of all HLA-encoding genes in an individual's genome, with statistical analysis methods evolving to digest the enormous amount of data that can be produced at an astonishing rate. The HLA region of the genome has been repeatedly shown to be the most important genetic risk factor for T1D, and the original reported associations have been replicated, refined, and expanded. Even with the remarkable progress through 50 years and over 5,000 reports, a comprehensive understanding of all effects of HLA on T1D remains elusive. This report represents a summary of the field as it evolved and as it stands now, enumerating many past and present challenges, and suggests possible paradigm shifts for moving forward with future studies in hopes of finally understanding all the ways in which HLA influences the pathophysiology of T1D.

Exploring the HLA complex in autoimmunity: From the risk haplotypes to the modulation of expression

The genes mapping at the HLA region show high density, strong linkage disequilibrium and high polymorphism, which affect the association of HLA class I and class II genes with autoimmunity. We focused on the HLA haplotypes, genomic structures consisting of an array of specific alleles showing some degrees of genetic association with different autoimmune disorders. GWASs in many pathologies have identified variants in either the coding loci or the flanking regulatory regions, both in linkage disequilibrium in haplotypes, that are frequently associated with increased risk and may influence gene expression. We discuss the relevance of the HLA gene expression because the level of surface heterodimers determines the number of complexes presenting self-antigen and, thus, the strength of pathogenic autoreactive T cells immune response.

Integrating Omics into Functional Biomarkers of Type 1 Diabetes

Biomarkers are critical to the staging and diagnosis of type 1 diabetes (T1D). Functional biomarkers offer insights into T1D immunopathogenesis and are often revealed using "omics" approaches that integrate multiple measures to identify involved pathways and functions. Application of the omics biomarker discovery may enable personalized medicine approaches to circumvent the more recently appreciated heterogeneity of T1D progression and treatment. Use of omics to define functional biomarkers is still in its early years, yet findings to date emphasize the role of cytokine signaling and adaptive immunity in biomarkers of progression and response to therapy. Here, we share examples of the use of omics to define functional biomarkers focusing on two signatures, T-cell exhaustion and T-cell help, which have been associated with outcomes in both the natural history and treatment contexts.

Comparison of blended learning and traditional lecture method on learning outcomes in the evidence-based medicine course: a comparative study

BACKGROUND

Blended learning comprised with flipped classroom (FC) and "internet plus" is a new learning strategy that reverses the position of teacher and students in class, and provides abundant learning resources before and after class. This study aimed to assess the impact of blended learning on learning outcomes in evidence-based medicine course, and compare with traditional learning method.

METHODS

The participants of the two groups were from two difference cohorts in Air force medical university in China. The two groups toke the same pre-test before class and then were given the teaching of same chapters of evidence-based medicine with two different learning strategy. In the blended learning group, the participants were required to create a debriefing slide about their learning outcomes and the answers of questions given in advance after study the learning material sent by teacher a week before class, and the teacher gave a detailed summary based on the common problems, and distributed multimedia resources for review. After the experiment was carried out, learning outcomes including mastering knowledge, learning satisfaction, and self-evaluation were compared.

RESULTS

37 and 39 participants were enrolled to blended learning and traditional learning groups, respectively, and no statistically significant difference were found in baseline information and pre-test grades. Statistically significant differences were found in learning outcomes including post-test score (t = 2.90, p = 0.005), changes of scores between pre-test and post-test (t = 2.49, p = 0.022), learning satisfaction (t = 12.41, p = 0.001), and self-evaluation of the two groups (t = 7.82, p = 0.001). Especially, the changes of scores between pre-test and post-test of blended learning and traditional learning groups were 4.05 (4.26), and 2.00 (2.85), respectively.

CONCLUSIONS

This study showed that compared with traditional learning strategy, blended learning can effectively enhanced participants' acquisition of knowledge, learning satisfaction, and self-evaluation in evidence-based medicine. Using blended learning method including "internet plus" and flipped classroom is recommended in the teaching of evidence-based medicine course.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Immunological and virological triggers of type 1 diabetes: insights and implications

Type 1 diabetes (T1D) is caused by an autoimmune process which culminates in the destruction of insulin-producing beta cells in the pancreas. It is widely believed that a complex and multifactorial interplay between genetic and environmental factors, such as viruses, play a crucial role in the development of the disease. Research over the past few decades has shown that there is not one single viral culprit, nor one single genetic pathway, causing the disease. Rather, viral infections, most notably enteroviruses (EV), appear to accelerate the autoimmune process leading to T1D and are often seen as a precipitator of clinical diagnosis. In support of this hypothesis, the use of anti-viral drugs has recently shown efficacy in preserving beta cell function after onset of diabetes. In this review, we will discuss the various pathways that viral infections utilize to accelerate the development of T1D. There are three key mechanisms linking viral infections to beta-cell death: One is modulated by the direct infection of islets by viruses, resulting in their impaired function, another occurs in a more indirect fashion, by modulating the immune system, and the third is caused by heightened stress on the beta-cell by interferon-mediated increase of insulin resistance. The first two aspects are surprisingly difficult to study, in the case of the former, because there are still many questions about how viruses might persist for longer time periods. In the latter, indirect/immune case, viruses might impact immunity as a hit-and-run scenario, meaning that many or all direct viral footprints quickly vanish, while changes imprinted upon the immune system and the anti-islet autoimmune response persist. Given the fact that viruses are often associated with the precipitation of clinical autoimmunity, there are concerns regarding the impact of the recent global coronavirus-2019 (COVID-19) pandemic on the development of autoimmune disease. The long-term effects of COVID-19 infection on T1D will therefore be discussed, including the increased development of new cases of T1D. Understanding the interplay between viral infections and autoimmunity is crucial for advancing our knowledge in this field and developing targeted therapeutic interventions. In this review we will examine the intricate relationship between viral infections and autoimmunity and discuss potential considerations for prevention and treatment strategies.

Imaging in Type 1 Diabetes, Current Perspectives and Directions

Type 1 diabetes (T1D) is characterized by the autoimmune-mediated attack of insulin-producing beta cells in the pancreas, leading to reliance on exogenous insulin to control a patient's blood glucose levels. As progress is being made in understanding the pathophysiology of the disease and how to better develop therapies to treat it, there is an increasing need for monitoring technologies to quantify beta cell mass and function throughout T1D progression and beta cell replacement therapy. Molecular imaging techniques offer a possible solution through both radiologic and non-radiologic means including positron emission tomography, magnetic resonance imaging, electron paramagnetic resonance imaging, and spatial omics. This commentary piece outlines the role of molecular imaging in T1D research and highlights the need for further applications of such methodologies in T1D.