Type 1 diabetes (T1D) results from the destruction of pancreatic beta cells by autoreactive T lymphocytes, leading to insulin deficiency and lifelong insulin dependence. It develops in genetically predisposed individuals, triggered by environmental or immunological factors. Although the exact causes of T1D remain unknown, the autoimmune pathogenesis of the disease is clearly indicated by the genetic risk conferred by allelic human leukocyte antigens (HLA), the almost obligatory presence of islet cell autoantibodies (AAbs) and immune cell infiltration of pancreatic islets from patients. At the same time, epidemiological data point to a role of environmental factors, notably enteroviral infections, in the disease, although precise causative links between specific pathogens and T1D have been difficult to establish. Studies of human pancreas organs from patients made available through repositories and the advent of high-dimensional high-throughput technologies for genomic and proteomic studies have significantly elucidated our understanding of the disease in recent years and provided mechanistic insights that can be exploited for innovative targeted therapeutic approaches. This short overview will summarise current salient knowledge on immune cell and beta cell dysfunction in T1D pathogenesis. PLAIN LANGUAGE SUMMARY: Type 1 diabetes (T1D) is a chronic disease where the body's own immune system attacks and destroys the insulin-producing beta cells in the pancreas. This leads to a lack of insulin, a hormone essential for regulating blood sugar, which means people with T1D need insulin for life. The disease can develop at any age but is most diagnosed in children and young adults. Despite advances in treatment, T1D still significantly reduces life expectancy, especially in countries with fewer healthcare resources. T1D develops in people with a genetic predisposition, often triggered by environmental factors such as viral infections or changes in the gut microbiome. The disease progresses silently through three stages: Stage 1: Autoantibodies to beta cell components appear, signalling the immune system is reacting against the pancreas, but there are no symptoms; Stage 2: Beta cell function starts to decline, but fasting blood sugar is still normal; Stage 3: Enough beta cells are destroyed that fasting blood sugar rises, and symptoms of diabetes appear. The risk of progressing from stage 1 to full-blown diabetes is about 35-50% within five years, and even higher from stage 2. Over 60 genes are linked to T1D risk, most of which affect how the immune system works. The strongest genetic risk comes from specific versions of histocompatibility genes, which help the immune system distinguish between the body's own cells and invaders. Some types of these genes make it easier for the immune system to mistakenly attack beta cells. However, 90% of people diagnosed with T1D have no family member with T1D, showing that genetics is only part of the story. Environmental factors also play a big role. For example, certain viral infections, especially with viruses infecting the intestine, are associated with a higher risk of developing T1D. The gut microbiome - the community of bacteria living in our intestines - also influences risk, with healthier, more diverse microbiomes appearing to offer some protection. In T1D, immune cells - especially so-called T lymphocytes - mistake beta cells in the pancreas for threats and destroy them. This process is called autoimmunity. The attack is often reflected by the presence of autoantibodies against proteins found in beta cells. Over time, as more beta cells are lost, the body can no longer produce enough insulin, leading to the symptoms of diabetes. Interestingly, not all people with T1D have the same pattern of disease. For example, children diagnosed before age 7 often have more aggressive disease, more autoantibodies, and stronger genetic risk factors than those diagnosed later. Much of our understanding of T1D has come from studying animal models, but new technologies now allow researchers to study human pancreas tissue and blood immune cells in greater detail. Scientists are also exploring how the gut microbiome, diet, and environmental exposures contribute to T1D risk and progression. Treatment currently focuses on replacing insulin, but researchers are working on therapies that target the immune system or aim to protect or replace beta cells. Strategies include immunotherapy, gene therapy, and even modifying the gut microbiome. The goal is to prevent or reverse the disease, not just manage its symptoms. In summary, T1D is a complex autoimmune disease influenced by both genes and the environment. It progresses silently before symptoms appear, and while insulin therapy is life-saving, new research is paving the way for treatments that could one day halt or even prevent the disease.

Immune-related adverse events (irAEs), including endocrine irAEs, can occur in response to cancer immunotherapy using immune checkpoint inhibitors (ICIs). Of the endocrine irAEs, pituitary and thyroid irAEs are most frequently observed, followed by primary adrenal insufficiency, type 1 diabetes mellitus and hypoparathyroidism. Notably, pituitary irAEs and type 1 diabetes mellitus can be lethal if overlooked, potentially leading to adrenal crisis and diabetic ketoacidosis, respectively. On the other hand, pituitary and thyroid irAEs are reported to be associated with more favourable prognoses in some cancers if treated appropriately with hormone-replacement therapies. It would be useful to identify those people who are likely to develop endocrine irAEs before initiating therapy with ICIs. Anti-pituitary antibodies and thyroid autoantibodies have been identified as potential biomarkers for the development of pituitary and thyroid irAEs, respectively. This Review elaborates on the clinical characteristics and management strategies of several endocrine irAEs, using the latest research findings and guidelines published by several academic societies.

Genome-wide association study identified C1QTNF6 as a candidate gene for type 1 diabetes (T1D) in Caucasians. We aimed to investigate if rs229541 in C1QTNF6 conferred susceptibility to T1D in Chinese, independent of DR-DQ genotypes and if this gene polymorphism affected the clinical profiles of T1D.

In this case-control study, genotypes of C1QTNF6 rs229541 were obtained from 1278 patients with T1D and 1282 nondiabetic controls using MassARRAY.

Genotypic (P = 0.0210) and allelic (P = 0.0084) frequencies were significantly different between the T1D group and the control group. When the model was adjusted for DR-DQ genotypes, G allele carriers were observed less often in the T1D group (P = 0.0423, OR 0.82, 95% CI 0.68-0.99) than in the control group, and the G allele was associated with reduced T1D risk(P = 0.0167, OR 0.83, 95% CI 0.71-0.97). T1D patients who were homozygous for the G allele showed a higher positive rate of ZnT8A than carriers of the A allele (P = 0.0171, OR 1.88, 95% CI 1.12-3.16). By detection of fasting C-peptide, G allele carriers exhibited a lower frequency of beta-cell failure compared to those with A/A genotype (P = 0.0058, OR 0.70, 95% CI 0.54-0.90). C1QTNF6 was not found to be correlated with GADA, IA-2A or age at T1D diagnosis.

The polymorphism in C1QTNF6 was independently associated with T1D risk in Chinese and broadly modified clinical features of the disease. This loci might be utilized to construct genetic risk model in combination with the well-known DR-DQ region for future screening of genetically T1D prone individuals among Chinese.

HLA-DQ mismatch has been identified as a predictor of de novo donor-specific HLA antibody formation and antibody-mediated rejection. There are insufficient data to guide the incorporation of DQ mismatch into organ allocation decisions.

We used a retrospective longitudinal cohort of adult living donor kidney transplant recipients from 11 centers across the United States for whom high-resolution class II typing was available. HLA-DQαβ heterodimer allele mismatch was quantified for all donor-recipient pairs, and outcome data were obtained through linkage with the Scientific Registry of Transplant Recipients.

We studied 3916 donor-recipient pairs. Recipient characteristics were notable for a median age of 51 (38-61) y, primarily unsensitized, with 74.5% of the cohort having 0% calculated panel-reactive antibody, and 60.4% with private insurance, for a median follow-up time of 5.86 y. We found that the HLA-DQαβ allele and HLA-DR antigen mismatch were each individually associated with an increased hazard of all-cause graft failure (adjusted hazard ratio [aHR] DQ = 1.03 1.14 1.28 ; aHR DR = 1.03 1.15 1.328 ), death-censored graft failure (aHR DQ = 1.01 1.19 1.40 ; aHR DR = 0.099 1.18 1.39 ), and rejection. Having 2 HLA-DQαβ allele mismatches further increased the hazard of rejection even when controlling for HLA-DR mismatch (aHR 1.03 1.68 2.74 ).

HLA-DQαβ allele mismatch predicted allograft rejection even when controlling for HLA-DR antigen mismatch and were both independently associated with increased risk of graft failure or rejection in adult living kidney transplant recipients. Given the strong burden of disease arising from the HLA-DQ antibody formation, we suggest that HLA-DQαβ should be prioritized over HLA-DR in donor selection.

Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.

This study aims to identify drugs associated with an increased risk of FT1DM using the FDA Adverse Event Reporting System (FAERS) database, evaluate reporting patterns, and provide actionable insights to reduce FT1DM occurrence and improve medication safety.

A retrospective analysis of FAERS data from 2013 to 2023 was conducted. Drug names were standardized using text mining tools, and safety signals were evaluated using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS).

A total of 706 FT1DM cases were identified, predominantly in older individuals and males. Nineteen drugs were implicated, including immune checkpoint inhibitors (nivolumab, ipilimumab, pembrolizumab, avelumab, durvalumab, atezolizumab), lenvatinib, eribulin, psychiatric drugs (atomoxetine, carbamazepine, lamotrigine), anti-infectives (sulfamethoxazole, trimethoprim, amoxicillin), and metabolic modulators (dapagliflozin, sitagliptin, hydrochlorothiazide, allopurinol).

This study highlights drugs potentially triggering FT1DM and emphasizes the need for pharmacovigilance and glucose monitoring in patients treated with these medications.

Type 1 diabetes mellitus (T1D) is a chronic autoimmune disease caused by the selective destruction of pancreatic beta cells, leading to insulin deficiency. Both genetic and environmental factors contribute to disease susceptibility. Among genetic factors, human leukocyte antigen (HLA) class II molecules, particulary DQA1 and DQB1 haplotypes, have been associated with T1D risk. This study aimed to identify haplotypes that increase susceptibility to or provide protection against T1D in Jordanian population.

A total of 200 healthy individuals and 200 T1D patients were included in the study. Genomic DNA was extracted from blood samples and HLA-DQA1, HLA-DQB1 and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) gene regions were amplified by PCR. The PCR products were then subjected to restriction enzyme digestion and analyzed through agarose gel electrophoresis to determine different haplotypes.

Among the analyzed haplotypes, HLA-DQA1*01:01 was found to be significantly associated with increased susceptibility to T1D. In contrast, HLA-DQA1*02:01 and HLA-DQB1*05:01 appeared to provide protective effects against T1D. No significant differences were observed for other haplotypes between the control and patient groups. Additionally, no significant difference has been observed in terms of CTLA-4 polymorphisms.

These findings suggest that HLA-DQA1*01:01 may serve as a genetic marker for T1D susceptibility, while HLA-DQA1*02:01 and HLA-DQB1*05:01 may confer protectionin the Jordanian population. Identifying these genetic risk factors could contribute to early disease prevention strategies and advanced research into additional genetic markers associated with T1D.

Type 1 diabetes incidence continues to increase in children, especially among Hispanic White (HW) children.

We investigated the clinical, immunologic, and genetic characteristics of HW and non-Hispanic White (NHW) children who presented at type 1 diabetes diagnosis.

In this single-center, observational study, children who were diagnosed with type 1 diabetes (≤20 years old) and tested for islet autoantibodies within 1 year of diagnosis were included in the study and divided into 2 groups by Hispanic ethnicity.

Of 1297 children, 398 HW children presented with a younger age at diabetes onset (10.2 ± 3.9 vs 11.1 ± 4.1 years, P < .001) and more diabetic ketoacidosis (62.4% vs 51.9%, P < .001) than NHW children (n = 899). There was no difference in sex, A1c levels, or the number and prevalence of islet autoantibodies between the 2 cohorts. A subset of our cohort was human leukocyte antigen (HLA) typed as specific alleles confer strong genetic risk for type 1 diabetes (eg, HLA-DR4 and DQ8). Among 637 HLA-typed children, HW children had a significantly higher prevalence of the DR4-DQ8 haplotype than NHW children (79.1% vs 60.1%, P < .001), and this frequency was much higher than a reference Hispanic population (OR 6.5, 95% CI 4.6-9.3).

Hispanic White children developing type 1 diabetes have a high prevalence of HLA DR4-DQ8, which can be utilized to select individuals for immune monitoring with islet autoantibodies to lessen diabetic ketoacidosis and potentially prevent diabetes onset.

Dysregulation and loss of immune tolerance toward pancreatic β-cell autoantigens are features of type 1 diabetes (T1D). Until recently, life-long insulin injection was the only approved treatment for T1D, but it does not address the underlying disease pathology. The aim for antigen-specific immunotherapy (ASI) is to restore tolerance. ASI holds potential as a new therapeutic strategy for treating autoimmune diseases with well-characterized antigens. Peptide ASI using processing-independent CD4+ T-cell epitopes (PIPs) shows promising results in several autoimmune diseases. Here, we successfully applied the principles of PIP design to the T1D autoantigen glutamate decarboxylase 65 (GAD65). Peptides spanning GAD65 predicted to be pan-HLA-DR binding were selected. Peptide 10 (P10) displayed enriched responses in peripheral blood mononuclear cells from people with T1D. The minimal epitope of the P10 peptide was fine mapped using T-cell hybridomas generated from HLA-DRB1*04:01 transgenic mice. This minimal epitope, P10Sol, was demonstrated, using a novel activation-induced marker assay, to induce tolerance to the parent peptide in the transgenic mice. Finally, we show that GAD65 P10Sol PIP is recognized by CD4+ T cells from people with T1D who possess a range of HLA-DR alleles and, therefore, can be defined as a pan-DR-binding peptide with therapeutic potential.

There are currently no approved antigen-specific immunotherapies (ASIs) for people with type 1 diabetes (T1D). We aimed to develop a peptide for ASI for T1D based on the T1D-associated auto-antigen glutamate decarboxylase 65 (GAD65). A minimal and soluble peptide derived from GAD65 was demonstrated to induce tolerance in an HLA transgenic mouse. Our data suggest this peptide derived from the GAD65 islet protein should be tested for therapeutic potential in people with T1D who have residual β-cell function.

After a century of extensive scientific investigations, there is still no curative or disease-modifying treatment available that can provide long-lasting remission for patients diagnosed with type 1 diabetes (T1D). Although T1D has historically been regarded as a classic autoimmune disorder targeting and destroying pancreatic islet β-cells, significant research has recently demonstrated that β-cells themselves also play a substantial role in the disease's progression, which could explain some of the unfavorable clinical outcomes. We offer a thorough review of scientific and clinical insights pertaining to molecular mechanisms behind pathogenesis and the different therapeutic interventions in T1D covering over 20 possible pharmaceutical intervention treatments. The interventions are categorized as immune therapies, treatments targeting islet endocrine dysfunctions, medications with dual modes of action in immune and islet endocrine cells, and combination treatments with a broader spectrum of activity. We suggest that these collective findings can provide a valuable platform to discover new combinatorial synergies in search of the curative disease-modifying intervention for T1D. SIGNIFICANCE STATEMENT: This research delves into the underlying causes of T1D and identifies critical mechanisms governing β-cell function in both healthy and diseased states. Thus, we identify specific pathways that could be manipulated by existing or new pharmacological interventions. These interventions fall into several categories: (1) immunomodifying therapies individually targeting immune cell processes, (2) interventions targeting β-cells, (3) compounds that act simultaneously on both immune cell and β-cell pathways, and (4) combinations of compounds simultaneously targeting immune and β-cell pathways.

Although type 1 diabetes mellitus (T1DM) is traditionally viewed as a youth-onset disorder, the number of older adults being diagnosed with this disease is growing. Improvements in the average life expectancy of people with T1DM have also contributed to the growing number of older people living with this disease. We summarize the evidence regarding the epidemiology (incidence, prevalence and excess mortality) of T1DM in older adults (ages ≥60 years) as well as the genetics, immunology and diagnostic challenges. Several studies report an incidence peak of T1DM in older adults of a similar size to or exceeding that in children, and population prevalence generally increases with increasing age. Glutamic acid decarboxylase antibody positivity is frequently observed in adult-onset T1DM. Guidelines for differentiating T1DM from type 2 diabetes mellitus in older adults recommend measuring levels of C-peptide and autoantibodies, including glutamic acid decarboxylase antibodies. However, there is no gold standard for differentiating T1DM from type 2 diabetes mellitus in people aged 60 years and over. As such, the global variation observed in T1DM epidemiology might be in part explained by misclassification, which increases with increasing age of diabetes mellitus onset. With a growing global population of older adults with T1DM, improved genetic and immunological evidence is needed to differentiate diabetes mellitus type at older ages so that a clear epidemiological picture can emerge.

Type 1 Diabetes (T1D) is a progressive autoimmune disease often identified in childhood or adolescence, with early stages detectable through pre-diabetic markers such as autoantibodies and subclinical beta-cell dysfunction. The identification of the pre-T1D stage is critical for preventing complications, such as diabetic ketoacidosis, and for enabling timely interventions that may alter disease progression. This review examines the multifaceted approach to managing T1D risk in adolescents and teens, emphasizing early detection, nutritional interventions, beta-cell preservation strategies, and psychosocial support. Screening for T1D-associated autoantibodies offers predictive insight into disease risk, particularly when combined with education and family resources that promote lifestyle adjustments. Although nutritional interventions alone are not capable of preventing T1D, certain lifestyle interventions, such as weight management and specific nutritional choices, have shown the potential to preserve insulin sensitivity, reduce inflammation, and mitigate metabolic strain. Pharmacological strategies, including immune-modulating drugs like teplizumab, alongside emerging regenerative and cell-based therapies, offer the potential to delay disease onset by protecting beta-cell function. The social and psychological impacts of a T1D risk diagnosis are also significant, affecting adolescents' quality of life, family dynamics, and mental health. Supportive interventions, including counseling, cognitive-behavioral therapy (CBT), and group support, are recommended for managing the emotional burden of pre-diabetes. Future directions call for integrating universal or targeted screening programs within schools or primary care, advancing research into nutrition and psychosocial support, and promoting policies that enhance access to preventive resources. Advocacy for the insurance coverage of screening, nutritional counseling, and mental health services is also crucial to support families in managing T1D risk. By addressing these areas, healthcare systems can promote early intervention, improve beta-cell preservation, and support the overall well-being of adolescents at risk of T1D.

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.

Previous observational studies have established the high prevalence of bacterial pneumonia in diabetic patients, which in turn leads to increased mortality. However, the presence of a causal connection between bacterial pneumonia and diabetes remains unobserved.

We chose genome-wide significant (Ρ < 1 × 10-5 and Ρ < 1 × 10-6) and independent (r2 < 0.001) single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to proceed a bidirectional two-sample MR study. The extracted SNPs explored the relationship between bacterial pneumonia and diabetes by Inverse variance weighted (IVW), MR-Egger, and weighted median methods. In addition, we conducted the Heterogeneity test, the Pleiotropy test, MR-presso and the Leave-one-out (LOO) sensitivity test to validate the reliability of results.

In an MR study with bacterial pneumonia as an exposure factor, four different types of diabetes as outcome. It was observed that bacterial pneumonia increases the incidence of GDM (OR = 1.150 (1.027-1.274, P = 0.011) and T1DM (OR = 1.277 (1.024-1.531), P = 0.016). In the reverse MR analysis, it was observed that GDM (OR = 1.112 (1.023-1.201, P = 0.009) is associated with an elevated risk of bacterial pneumonia. However, no significant association was observed bacterial pneumonia with T1DM and other types of diabetes (P > 0.05).

This study utilizing MR methodology yields robust evidence supporting a bidirectional causal association between bacterial pneumonia and GDM. Furthermore, our findings suggest a plausible causal link between bacterial pneumonia and T1DM.

To survey the epidemiology of type 1 diabetes in all age groups living in the Gansu Province, China during 2018-2022.

Using the data from the Gansu Province Health Commission Information Center and medical records, the crude incidence and 95%CI were calculated by region, age group, and sex assuming a Poisson distribution. The incidence differences were evaluated using the χ2 test. Spearman correlation was used to analyze the relation between latitude and incidence. The seasonality was analyzed using concentration, seasonal index and circular distribution method.

1393 cases of newly diagnosed type 1 diabetes were ascertained. The crude incidence of type 1 diabetes per 100,000 person-years in all individuals in Gansu Province was 1.09(95% confidence interval 1.03 to 1.15). The estimated incidence per 100,000 person-years by age group was 1.39 (95%CI:1.24-1.54) for 0-14 years, 3.58 (95%CI:3.33-3.83) for 15-29 years, 0.33(95%CI:0.29-0.37) for ≥ 30 years, with a peak in age group 15-19 years. There was a difference between males and females. Incidence of type 1 diabetes in Gansu Province was strongly correlated with latitude among children aged 0-29 years, and all age groups, but such correlation was not observed in adults aged ≥ 30 years. The seasonality of the type 1 diabetes is not obvious.

The incidence of type 1 diabetes was relatively lower from 2018 to 2022 in Gansu Province, with variations across different regions and a positive correlation with latitude observed in all age groups. The incidence peak was noted in the 15-19 years group, and the incidence among males was higher than in females in all age groups. There was no significant seasonal variation in the incidence of type 1 diabetes.

The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed. The International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This guideline serves as an update to the 2022 ISPAD consensus guideline on staging for type 1 diabetes (T1D). Key additions include an evidence-based summary of recommendations for screening for risk of T1D and monitoring those with early-stage T1D. In addition, a review of clinical trials designed to delay progression to Stage 3 T1D and efforts seeking to preserve beta-cell function in those with Stage 3 T1D are included. Lastly, opportunities and challenges associated with the recent US Food and Drug Administration (FDA) approval of teplizumab as an immunotherapy to delay progression are discussed.

Inhibitory killer cell immunoglobulin-like receptors (iKIRs) are a family of inhibitory receptors that are expressed by natural killer (NK) cells and late-stage differentiated T cells. There is accumulating evidence that iKIRs regulate T cell-mediated immunity. Recently, we reported that T cell-mediated control was enhanced by iKIRs in chronic viral infections. We hypothesized that in the context of autoimmunity, where an enhanced T cell response might be considered detrimental, iKIRs would have an opposite effect. We studied Type 1 diabetes (T1D) as a paradigmatic example of autoimmunity. In T1D, variation in the Human Leucocyte Antigen (HLA) genes explains up to 50% of the genetic risk, indicating that T cells have a major role in T1D etiopathogenesis. To investigate if iKIRs affect this T cell response, we asked whether HLA associations were modified by iKIR genes. We conducted an immunogenetic analysis of a case-control T1D dataset (N = 11,961) and found that iKIR genes, in the presence of genes encoding their ligands, have a consistent and significant effect on protective HLA class II genetic associations. Our results were validated in an independent data set. We conclude that iKIRs significantly decrease HLA class II protective associations and suggest that iKIRs regulate CD4+ T cell responses in T1D.

Immune checkpoint inhibitors (ICIs) have become extensively utilized in the early-stage treatment of various cancers, offering additional therapeutic possibilities for patients with advanced cancer. However, certain patient populations are susceptible to experiencing toxic adverse effects from ICIs, such as thyrotoxicosis, rashes, among others. Specifically, ICIDM, induced by immune checkpoint inhibitors, exhibits characteristics similar to insulin-dependent diabetes mellitus (Type 1 Diabetes Mellitus, T1DM). ICIDM is characterized by a rapid onset and may coincide with severe ketoacidosis. Despite a favorable response to insulin therapy, patients typically require lifelong insulin dependence. After discussing the autoimmune adverse effects and the specifics of ICIs-induced diabetes mellitus (ICIDM), it is important to note that certain patient populations are particularly susceptible to experiencing toxic adverse effects from ICIs. Specifically, ICIDM, which is triggered by immune checkpoint inhibitors, mirrors the characteristics of insulin-dependent diabetes mellitus (Type 1 Diabetes Mellitus, T1DM). This article conducts an in-depth analysis of the literature to explore the pathogenesis, disease progression, and treatment strategies applicable to diabetes induced by immune checkpoint inhibitors (ICIDM).

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.

The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.

Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

When optimizing transplants, clinical decision-makers consider HLA-A, -B, -C, -DRB1 (8 matched alleles out of 8), and sometimes HLA-DQB1 (10 out of 10) matching between the patient and donor. HLA-DQ is a heterodimer formed by the β chain product of HLA-DQB1 and an α chain product of HLA-DQA1. In addition to molecules defined by the parentally inherited cis haplotypes, α-β trans-dimerization is possible between certain alleles, leading to unique molecules and a potential source of mismatched molecules. Recently, researchers uncovered that clinical outcome after HLA-DQB1-mismatched unrelated donor HCT depends on the total number of HLA-DQ molecule mismatches and the specific α-β heterodimer mismatch. Our objective in this study is to develop an automated tool for analyzing HLA-DQ heterodimer data and validating it through numerous datasets and analyses. By doing so, we provide an HLA-DQ heterodimer tool for DQα-DQβ trans-heterodimer evaluation, HLA-DQ imputation, and HLA-DQ-featured source selection to the transplant field. In our study, we leverage 352,148 high-confidence, statistically phased (via a modified expectation-maximization algorithm) HLA-DRB1∼DQA1∼DQB1 haplotypes, 1,052 pedigree-phased HLA-DQA1∼DQB1 haplotypes, and 13,663 historical transplants to characterize HLA-DQ heterodimers data. Using our developed QLASSy (HLA-DQA1 and HLA-DQB1 Heterodimers Assessment) tool, we first assessed the data quality of HLA-DQ heterodimers in our data for trans-dimers, missing HLA-DQA1 typing, and unexpected HLA-DQA1 and HLA-DQB1 combinations. Since trans-dimers enable up to four unique HLA-DQ molecules in individuals, we provide in-silico validations for 99.7% of 275 unique trans-dimers generated by 176,074 U.S. donors with HLA-DQA1 and HLA-DQB1 data. Many individuals lack HLA-DQA1 typing, so we developed and validated high-confidence HLA-DQ annotation imputation via HLA-DRB1 with >99% correct predictions in 23,698 individuals. A select few individuals displayed unexpected HLA-DQ combinations. We revisited the typing of 61 donors with unexpected HLA-DQ combinations based on their HLA-DQA1 and HLA-DQB1 typing and corrected 22 out of 61 (36%) cases of donors through data review or retyping and used imputation to resolve unexpected combinations. After verifying the data quality of our datasets, we analyzed our datasets further: we explored the frequencies of observed HLA-DQ combinations to compare HLA-DQ across populations (for instance, we found more high-risk molecules in Asian/Pacific Islander and Black/African American populations), demonstrated the effect of HLA-DQA1 and HLA-DQB1 mismatching on HLA-DQ molecular mismatches, and highlighted where donor selections could be improved at the time of search for historical transplants with this new HLA-DQ information (where 51.9% of G2-mismatched transplants had lower-risk, G2-matched alternatives). We encapsulated our findings into a tool that imputes missing HLA-DQA1 as needed, annotates HLA-DQ (mis)matches, and highlights other important HLA-DQ data to consider for the present and future. Altogether, these valuable datasets, analyses, and a culminating tool serve as actionable resources to enhance donor selection and improve patient outcomes.

Diabetic kidney disease (DKD) is a severe diabetic complication that affects one third of individuals with type 1 diabetes. Although several genes and common variants have been shown to be associated with DKD, much of the predicted inheritance remains unexplained. Here, we performed next-generation sequencing to assess whether low-frequency variants, extending to a minor allele frequency (MAF) ≤10% (single or aggregated) contribute to the missing heritability in DKD.

We performed whole-exome sequencing (WES) of 498 individuals and whole-genome sequencing (WGS) of 599 individuals with type 1 diabetes. After quality control, next-generation sequencing data were available for a total of 1064 individuals, of whom 541 had developed either severe albuminuria or end-stage kidney disease, and 523 had retained normal albumin excretion despite a long duration of type 1 diabetes. Single-variant and gene-aggregate tests for protein-altering variants (PAV) and protein-truncating variants (PTV) were performed separately for WES and WGS data and combined in a meta-analysis. We also performed genome-wide aggregate analyses on genomic windows (sliding window), promoters and enhancers using the WGS dataset.

In the single-variant meta-analysis, no variant reached genome-wide significance, but a suggestively associated common THAP7 rs369250 variant (p=1.50 × 10-5, MAF=49%) was replicated in the FinnGen general population genome-wide association study (GWAS) data for chronic kidney disease and DKD phenotypes. The gene-aggregate meta-analysis provided suggestive evidence (p<4.0 × 10-4) at four genes for DKD, of which NAT16 (MAFPAV≤10%) and LTA (also known as TNFβ, MAFPAV≤5%) are replicated in the FinnGen general population GWAS data. The LTA rs2229092 C allele was associated with significantly lower TNFR1, TNFR2 and TNFR3 serum levels in a subset of FinnDiane participants. Of the intergenic regions suggestively associated with DKD, the enhancer on chromosome 18q12.3 (p=3.94 × 10-5, MAFvariants≤5%) showed interaction with the METTL4 gene; the lead variant was replicated, and predicted to alter binding of the MafB transcription factor.

Our sequencing-based meta-analysis revealed multiple genes, variants and regulatory regions that were suggestively associated with DKD. However, as no variant or gene reached genome-wide significance, further studies are needed to validate the findings.

Microorganisms have long been suspected to influence the outcome of immune-related syndromes, particularly autoimmune diseases. Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing beta cells of pancreatic islets, causing high glycemia levels. Genetics is part of its aetiology, but environmental factors, particularly infectious microorganisms, also play a role. Bacteria, viruses, and parasites influence the outcome of T1D in mice and humans. We used nonobese diabetic (NOD) mice, which spontaneously develop T1D, to investigate the influence of a parasitic infection, leishmaniasis. Leishmania amazonensis is an intracellular eukaryotic parasite that replicates predominantly in macrophages and is responsible for cutaneous leishmaniasis. The implication of Th1 immune responses in T1D and leishmaniasis led us to study this parasite in the NOD mouse model. We previously constructed osteopontin knockout mice with a NOD genetic background and demonstrated that this protein plays a role in the T1D phenotype. In addition, osteopontin (OPN) has been found to play a role in the immune response to various infectious microorganisms and to be implicated in other autoimmune conditions, such as multiple sclerosis in humans and experimental autoimmune encephalomyelitis (EAE) in mice. We present herein data demonstrating the role of OPN in the response to Leishmania in NOD mice and the influence of this parasitic infection on T1D. This exploratory study aimed to investigate the environmental infectious component of the autoimmune response, including Th1 immunity, which is common to both T1D and leishmaniasis.

Autoimmune disorders are characterized by aberrant T cell and B cell reactivity to the body's own components, resulting in tissue destruction and organ dysfunction. Autoimmune diseases affect a wide range of people in many parts of the world and have become one of the major concerns in public health. In recent years, there have been substantial progress in our understanding of the epidemiology, risk factors, pathogenesis and mechanisms of autoimmune diseases. Current approved therapeutic interventions for autoimmune diseases are mainly non-specific immunomodulators and may cause broad immunosuppression that leads to serious adverse effects. To overcome the limitations of immunosuppressive drugs in treating autoimmune diseases, precise and target-specific strategies are urgently needed. To date, significant advances have been made in our understanding of the mechanisms of immune tolerance, offering a new avenue for developing antigen-specific immunotherapies for autoimmune diseases. These antigen-specific approaches have shown great potential in various preclinical animal models and recently been evaluated in clinical trials. This review describes the common epidemiology, clinical manifestation and mechanisms of autoimmune diseases, with a focus on typical autoimmune diseases including multiple sclerosis, type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus, and sjögren's syndrome. We discuss the current therapeutics developed in this field, highlight the recent advances in the use of nanomaterials and mRNA vaccine techniques to induce antigen-specific immune tolerance.

Type 1 diabetes (T1D) is an autoimmune disease characterized by the specific destruction of insulin-producing beta cells in the pancreas by the immune system, including CD4 cells which orchestrate the attack and CD8 cells which directly destroy the beta cells, resulting in the loss of glucose homeostasis.

This comprehensive document delves into the complex interplay between the immune system and beta cells, aiming to shed light on the mechanisms driving their destruction in T1D. Insights into the genetic predisposition, environmental triggers, and autoimmune responses provide a foundation for understanding the autoimmune attack on beta cells. From the role of viral infections as potential triggers to the inflammatory response of beta cells, an intricate puzzle starts to unfold. This exploration highlights the importance of beta cells in breaking immune tolerance and the factors contributing to their targeted destruction. Furthermore, it examines the potential role of autophagy and the impact of cytokine signaling on beta cell function and survival.

This review collectively represents current research findings on T1D which offers valuable perspectives on novel therapeutic approaches for preserving beta cell mass, restoring immune tolerance, and ultimately preventing or halting the progression of T1D. By unraveling the complex dynamics between the immune system and beta cells, we inch closer to a comprehensive understanding of T1D pathogenesis, paving the way for more effective treatments and ultimately a cure.

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.

The Canary Islands inhabitants, a recently admixed population with significant North African genetic influence, has the highest incidence of childhood-onset type 1 diabetes (T1D) in Spain and one of the highest in Europe. HLA accounts for half of the genetic risk of T1D.

To characterize the classical HLA-DRB1 and HLA-DQB1 alleles in children from Gran Canaria with and without T1D.

We analyzed classic HLA-DRB1 and HLA-DQB1 alleles in childhood-onset T1D patients (n = 309) and control children without T1D (n = 222) from the island of Gran Canaria. We also analyzed the presence or absence of aspartic acid at position 57 in the HLA-DQB1 gene and arginine at position 52 in the HLA-DQA1 gene. Genotyping of classical HLA-DQB1 and HLA-DRB1 alleles was performed at two-digit resolution using Luminex technology. The chi-square test (or Fisher's exact test) and odds ratio (OR) were computed to assess differences in allele and genotype frequencies between patients and controls. Logistic regression analysis was also used.

Mean age at diagnosis of T1D was 7.4 ± 3.6 years (46% female). Mean age of the controls was 7.6 ± 1.1 years (55% female). DRB1*03 (OR = 4.2; p = 2.13-13), DRB1*04 (OR = 6.6; p ≤ 2.00-16), DRB1* 07 (OR = 0.37; p = 9.73-06), DRB1*11 (OR = 0.17; p = 6.72-09), DRB1*12, DRB1*13 (OR = 0.38; p = 1.21-05), DRB1*14 (OR = 0.0; p = 0.0024), DRB1*15 (OR = 0.13; p = 7.78-07) and DRB1*16 (OR = 0.21; p = 0.003) exhibited significant differences in frequency between groups. Among the DQB1* alleles, DQB1*02 (OR: 2.3; p = 5.13-06), DQB1*03 (OR = 1.7; p = 1.89-03), DQB1*05 (OR = 0.64; p = 0.027) and DQB1*06 (OR = 0.19; p = 6.25-14) exhibited significant differences. A total of 58% of the studied HLA-DQB1 genes in our control population lacked aspartic acid at position 57.

In this population, the overall distributions of the HLA-DRB1 and HLA-DQB1 alleles are similar to those in other European populations. However, the frequency of the non-Asp-57 HLA-DQB1 molecules is greater than that in other populations with a lower incidence of T1D. Based on genetic, historical and epidemiological data, we propose that a common genetic background might help explain the elevated pediatric T1D incidence in the Canary Islands, North-Africa and middle eastern countries.

Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease that has a strong HLA association, where a number of self-epitopes have been implicated in disease pathogenesis. Human pancreatic islet-infiltrating CD4+ T cell clones not only respond to proinsulin C-peptide (PI40-54; GQVELGGGPGAGSLQ) but also cross-react with a hybrid insulin peptide (HIP; PI40-47-IAPP74-80; GQVELGGG-NAVEVLK) presented by HLA-DQ8. How T cell receptors recognize self-peptide and cross-react to HIPs is unclear. We investigated the cross-reactivity of the CD4+ T cell clones reactive to native PI40-54 epitope and multiple HIPs fused at the same N-terminus (PI40-54) to the degradation products of two highly expressed pancreatic islet proteins, neuropeptide Y (NPY68-74) and amyloid polypeptide (IAPP23-29 and IAPP74-80). We observed that five out of the seven selected SKW3 T cell lines expressing TCRs isolated from CD4+ T cells of people with T1D responded to multiple HIPs. Despite shared TRAV26-1-TRBV5-1 gene usage in some T cells, these clones cross-reacted to varying degrees with the PI40-54 and HIP epitopes. Crystal structures of two TRAV26-1+-TRBV5-1+ T cell receptors (TCRs) in complex with PI40-54 and HIPs bound to HLA-DQ8 revealed that the two TCRs had distinct mechanisms responsible for their differential recognition of the PI40-54 and HIP epitopes. Alanine scanning mutagenesis of the PI40-54 and HIPs determined that the P2, P7, and P8 residues in these epitopes were key determinants of TCR specificity. Accordingly, we provide a molecular basis for cross-reactivity towards native insulin and HIP epitopes presented by HLA-DQ8.

Celiac disease (CD) is an autoimmune enteropathy triggered by gluten ingestion in genetically susceptible individuals. The haplotypes HLA-DQ2 and DQ8, transglutaminase (TGA) antibodies, and biopsy findings are the main tests performed in the evaluation and CD diagnosis. The objective was to establish possible correlations between transglutaminase levels, genetic markers tests, and qualitative intestinal biopsy findings (modified Marsh classification) at the diagnosis.

A retrospective cohort study. The selection criteria were confirmed CD cases with genetic tests performed. Statistical analysis was done mainly through One-way ANOVA, Kendall's correlation coefficient (T), and linear regression.

The study included 112 patients, with a mean age of 6 ± 4 years. All cases were tested to HLA-DQ2, and it was positive in 93%. HLA-DQ8 was tested in 73% of cases and it was positive in 61%. The percentage of negative genetic markers (DQ2/DQ8) was 4.5% for patients tested to both haplotypes. A comparison of DQ2/DQ8 (positive and negative) with clinical findings and tests performed did not identify any differences for most of the parameters analyzed. Cases of type I diabetes presented significant negative expression for DQ2(-); p = 0.05 and positive expression for DQ8(+); p = 0.023. The TGA antibody levels ranged from 18 to 36,745 U/ml. An inverse correlation was found between age and TGA-L level (p = 0.043). In 23% of the cases, the TGA levels were greater than 1,000 U/ml and presented a moderate positive correlation with the atrophy biopsy profile (T = 0.245). Patients with an atrophic biopsy profile (Marsh III) had a moderate positive correlation with growth failure (T = 0.218) but a negative correlation with constipation (T = -0.277).

In terms of diagnosis tests for CD, transglutaminase levels and age presented an inverse correlation, with the level decreasing as age increased. A moderately positive correlation was found between mean transglutaminase with intestinal atrophy and growth retardation. The genetic test DQ2 was positive for 93% and negative genetic markers (DQ2/DQ8) represented 4.5% of cases studied.

Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a "stage 0" of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such "stage 0" biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children.

The detection of pancreatic autoantibodies in first-degree relatives of patients with type 1 diabetes (T1D) is considered a risk factor for disease. Novel available immunotherapies to delay T1D progression highlight the importance of identifying individuals at risk who might benefit from emerging treatments. The objective was to assess the autoimmunity in first-degree relatives of patients with T1D, estimate the time from autoimmunity detection to the onset of clinical diabetes, and identify the associated risk factors.

Retrospective multicenter study of 3,015 first-degree relatives of patients with T1D recruited between 1992 and 2018. Pancreatic autoantibodies (IAA, GADA, IA2A, and ZnT8A) were determined by radioimmunoassay, starting the analyses at diagnosis of the proband. All those with positive autoimmunity and normal fasting blood glucose without clinical symptoms of diabetes were followed up in the study. The progression rate to T1D was assessed according to sex, relationship with the proband, age at autoimmunity detection, type/number of autoantibodies, and HLA-DRB1 genotype. Cox proportional-hazard models and Kaplan-Meier survival plots were used for statistical analyses.

Among the relatives, 21 progenitors [43.7 years (IQR: 38.1-47.7)] and 27 siblings [7.6 years (IQR: 5.8-16.1)] had positive autoantibodies. Of these, 54.2% (95% CI: 39.2%-68.6%) developed T1D (age at autoimmunity detection 11 months to 39 years) in a median of 5 years (IQR: 3.6-8.7; ranged from 0.9 to 22.6 years). Risk factors associated with faster progression to T1D were multiple autoimmunity and <20 years at autoimmunity detection. Younger relatives (<20 years) with multiple autoantibodies had a 5-year cumulative risk of developing diabetes of 52.9% (95% CI: 22.1%-71.6%) and a 20-year risk of 91.2% (95% CI: 50.5%-98.4%). The 20-year risk decreased to 59.9% (95% CI: 21.9%-79.5%) if only one risk factor was met and to 35.7% (95% CI: 0.0%-66.2%) if the relative was older than 20 years with one autoantibody.

In first-degree relatives with autoimmunity, the time to progression to T1D is faster in children and adolescents with multiple autoantibodies. Young adults are also at risk, which supports their consideration in screening strategies for people at risk of developing T1D.

Type 1 diabetes is an autoimmune disease with an significant genetic component, played mainly by the HLA class II genes. Although evidence on the role of HLA class I genes in developing type 1 diabetes and its onset have emerged, current HLA screening is limited to determining DR3 and DR4 haplotypes. This study aimed to investigate the role of HLA genes on type 1 diabetes risk and age of onset by extensive typing.

This study included 115 children and young adults with type 1 diabetes for whom typing of HLA-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1 and -DPB1 genes was conducted using Next Generation Sequencing.

We observed that 13% of type 1 diabetes subjects had non-classical HLA haplotypes that predispose to diabetes. We also found that compared to type 1 diabetes subjects with classical HLA haplotypes, non-classical HLA subjects had a significantly higher frequency of HLA-B*39:06:02 (p-value=0.01) and HLA-C*07:02:01 (p-value=0.03) alleles, known to be involved in activating the immune response. Non-classical HLA subjects also presented peculiar clinical features compared to classical HLA subjects, such as multiple diabetic antibodies and the absence of other autoimmune diseases (i.e., coeliac disease and thyroiditis). We also observed that subjects with early onset had a higher frequency of DQ2/DQ8 genotype than late-onset individuals. Moreover, subjects with late-onset had a higher frequency of alleles HLA-B*27 (p-value=0.003), HLA-C*01:02:01 (p-value=0.027) and C*02:02:02 (p-value=0.01), known to be associated with increased protection against viral infections.

This study reveals a broader involvement of the HLA locus in the development and onset of type 1 diabetes, providing insights into new possible disease prevention and management strategies.

Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.

Type 1 diabetes (T1D) results from a complex interplay of genetic predisposition, immunological dysregulation, and environmental triggers, that culminate in the destruction of insulin-secreting pancreatic β cells. This review provides a comprehensive examination of the multiple factors underpinning T1D pathogenesis, to elucidate key mechanisms and potential therapeutic targets. Beginning with an exploration of genetic risk factors, we dissect the roles of human leukocyte antigen (HLA) haplotypes and non-HLA gene variants associated with T1D susceptibility. Mechanistic insights gleaned from the NOD mouse model provide valuable parallels to the human disease, particularly immunological intricacies underlying β cell-directed autoimmunity. Immunological drivers of T1D pathogenesis are examined, highlighting the pivotal contributions of both effector and regulatory T cells and the multiple functions of B cells and autoantibodies in β-cell destruction. Furthermore, the impact of environmental risk factors, notably modulation of host immune development by the intestinal microbiome, is examined. Lastly, the review probes human longitudinal studies, unveiling the dynamic interplay between mucosal immunity, systemic antimicrobial antibody responses, and the trajectories of T1D development. Insights garnered from these interconnected factors pave the way for targeted interventions and the identification of biomarkers to enhance T1D management and prevention strategies.

Primary biliary cholangitis (PBC) is an autoimmune disease often accompanied by multisystem damage. This study aimed to explore the causal association between genetically predicted PBC and diabetes, as well as multiple cardiovascular diseases (CVDs).

Genome-wide association studies (GWAS) summary data of PBC in 24,510 individuals of European ancestry from the European Association for the Study of the Liver was used to identify genetically predicted PBC. We conducted 2-sample single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR) to estimate the impacts of PBC on diabetes (N = 17,685 to 318,014) and 20 CVDs from the genetic consortium (N = 171,875 to 1,030,836).

SVMR provided evidence that genetically predicted PBC is associated with an increased risk of type 1 diabetes (T1D), type 2 diabetes (T2D), myocardial infarction (MI), heart failure (HF), hypertension, atrial fibrillation (AF), stroke, ischemic stroke, and small-vessel ischemic stroke. Additionally, there was no evidence of a causal association between PBC and coronary atherosclerosis. In the MVMR analysis, PBC maintained independent effects on T1D, HF, MI, and small-vessel ischemic stroke in most models.

Our findings revealed the causal effects of PBC on diabetes and 7 CVDs, and no causal relationship was detected between PBC and coronary atherosclerosis.

Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise.

Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D.

We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.

Widespread use of immune checkpoint inhibitors (ICIs) in cancer treatment has led to an increase in the number of reported cases of immunotherapy-related endocrinopathies. This study aimed to analyze and compare human leukocyte antigen (HLA) signatures associated with ICI-induced type 1 diabetes (ICI-T1D) and isolated adrenocorticotropic hormone deficiency (ICI-IAD) in patients with both conditions.

HLA signatures were examined for their frequencies of occurrence in 22 patients with ICI-T1D without concurrent IAD, including 16 patients from nationwide reports (ICI-T1D group) and 14 patients with ICI-IAD without concurrent T1D (ICI-IAD group). The HLA signatures were also compared for their respective frequencies in 11 patients with ICI-T1D and ICI-IAD, including eight from nationwide reports (ICI-T1D/IAD group).

In the ICI-T1D group, HLA-DRB1*09:01-DQB1*03:03 and DQA1*03:02, which are in linkage disequilibrium with DRB1*09:01-DQB1*03:03 and DRB1*13:02-DQB1*06:04, were susceptible to ICI-T1D, whereas DRB1*15:02-DQB1*06:01 was protective against ICI-T1D. In the ICI-IAD group, DPB1*09:01, C*12:02-B*52:01, and DRB1*15:02-DRB1*06:01, which are in strong linkage disequilibrium, were associated with susceptibility to ICI-IAD. Moreover, DRB1*15:02-DRB1*06:01 was not detected in the ICI-T1D/IAD group.

This study revealed specific HLA signatures associated with ICI-T1D and ICI-IAD. Moreover, HLA-DRB1*15:02-DRB1*06:01, an ICI-IAD-susceptible HLA haplotype, coincides with the ICI-T1D-protective HLA haplotype, suggesting that the presence of DRB1*15:02-DRB1*06:01 may protect against the co-occurrence of T1D in patients with ICI-IAD.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection, which led to the coronavirus 2019 (COVID-19) pandemic, has promoted the development of novel therapeutic agents and vaccines to combat the global spread of the virus. While the COVID-19 vaccines approved thus far have proven to be effective in clinical settings, there have been reports of autoimmune diseases occurring following vaccination, including autoimmune/inflammatory syndrome induced by adjuvant syndrome. We herein report two cases of type 1 diabetes mellitus that occurred following COVID-19 vaccination and provide a literature review. Both cases received multiple vaccinations as recommended to ensure optimal antibody titers. Moreover, the HLA associated with susceptibility to type 1 diabetes was prototypic in both cases. This indirect evidence suggests that the COVID-19 vaccines may be implicated in the pathogenesis of type 1 diabetes. Further case reports to establish a clearer understanding of a potential association are warranted.

The online version contains supplementary material available at 10.1007/s13340-024-00695-9.

We sought to identify genetic/immunologic contributors of type 2 diabetes (T2D) in an indigenous American community by genotyping all study participants for both high-resolution HLA-DRB1 alleles and SLC16A11 to test their risk and/or protection for T2D. These genes were selected based on independent reports that HLA-DRB1*16:02:01 is protective for T2D and that SLC16A11 associates with T2D in individuals with BMI <35 kg/m2. Here, we test the interaction of the two loci with a more complete data set and perform a BMI sensitivity test. We defined the risk protection haplotype of SLC16A11, T-C-G-T-T, as allele 2 of a diallelic genetic model with three genotypes, SLC16A11*11, *12, and *22, where allele 1 is the wild type. Both earlier findings were confirmed. Together in the same logistic model with BMI ≥35 kg/m2, DRB1*16:02:01 remains protective (odds ratio [OR] 0.73), while SLC16A11 switches from risk to protection (OR 0.57 [*22] and 0.78 [*12]); an added interaction term was statistically significant (OR 0.49 [*12]). Bootstrapped (b = 10,000) statistical power of interaction, 0.4801, yielded a mean OR of 0.43. Sensitivity analysis demonstrated that the interaction is significant in the BMI range of 30-41 kg/m2. To investigate the epistasis, we used the primary function of the HLA-DRB1 molecule, peptide binding and presentation, to search the entire array of 15-mer peptides for both the wild-type and ancient human SLC16A11 molecules for a pattern of strong binding that was associated with risk and protection for T2D. Applying computer binding algorithms suggested that the core peptide at SLC16A11 D127G, FSAFASGLL, might be key for moderating risk for T2D with potential implications for type 1 diabetes.