A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

Type 1 Diabetes Genetics Consortium

Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing β cells. Genetic factors account for approximately 50% of the risk for T1D but, by the late 1990s, the genetic basis was limited. The Type 1 Diabetes Genetics Consortium (T1DGC) was formed in 2002 to accelerate discovery of genes contributing to T1D risk through a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to assemble existing data and samples from affected sib-pair families and to establish new collections. In recognition of the 75th anniversary of the NIDDK, this manuscript highlights the contributions made by the T1DGC to understanding the genetic basis of T1D using both family (for linkage) and case-control (for genome-wide association) designs. The T1DGC conducted large-scale genetic research and used fine mapping to define risk regions. The T1DGC data, results, and samples have been made available to the scientific community, leading to the discovery of more than 100 loci associated with T1D risk, many with small effects and relevant to autoimmune pathways. The T1DGC not only expanded the list of genes contributing to disease risk but also identified noncoding genetic variation in disease-relevant cell types that contribute to the etiology of T1D. The success of the T1DGC and the NIDDK investment in the global consortium is highlighted in its continuing effect on mapping genetic variants to their function and identifying pathways that provide new targets for the prediction, prevention, and treatment of T1D.

Type 1 Diabetes: A Guide to Autoimmune Mechanisms for Clinicians

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.

Type 1 diabetes prediction in autoantibody-positive individuals: performance, time and money matter

AIMS/HYPOTHESIS

Efficient prediction of clinical type 1 diabetes is important for risk stratification and monitoring of autoantibody-positive individuals. In this study, we compared type 1 diabetes predictive models for predictive performance, cost and participant time needed for testing.

METHODS

We developed 1943 predictive models using a Cox model based on a type 1 diabetes genetic risk score (GRS2), autoantibody count and types, BMI, age, self-reported gender and OGTT-derived glucose and C-peptide measures. We trained and validated the models using halves of a dataset comprising autoantibody-positive first-degree relatives of individuals with type 1 diabetes (n=3967, 49% female, 14.9 ± 12.1 years of age) from the TrialNet Pathway to Prevention study. The median duration of follow-up was 4.7 years (IQR 2.0-8.1), and 1311 participants developed clinical type 1 diabetes. Models were compared for predictive performances, estimated cost and participant time.

RESULTS

Models that included metabolic measures had best performance, with most exhibiting small performance differences (less than 3% and p>0.05). However, the cost and participant time associated with measuring metabolic variables ranged between US$56 and US$293 and 10-165 min, respectively. The predictive model performance had temporal variability, with the highest GRS2 influence and discriminative power being exhibited in the earliest preclinical stages. OGTT-derived metabolic measures had a similar performance to HbA1c- or Index60-derived models, with an important difference in cost and participant time.

CONCLUSIONS/INTERPRETATION

Cost-performance model analyses identified trade-offs between cost and performance models, and identified cost-minimising options to tailor risk-screening strategies.

Genetic protection from type 1 diabetes resulting from accelerated insulin mRNA decay

Insulin gene (INS) variation and beta-cell stress are associated with the risk of development of type 1 diabetes (T1D) and autoimmunity against insulin. The unfolded protein response alleviating endoplasmic reticulum (ER) stress involves activation of inositol-requiring enzyme 1α (IRE1α) that impedes translation by mRNA decay. We discover that the IRE1α digestion motif is present in insulin mRNA carrying SNP rs3842752 (G>A). This SNP in the 3' untranslated region of INS associates with protection from T1D (INSP). ER stress in beta cells with INSP led to accelerated insulin mRNA decay compared with the susceptible INS variant (INSS). Human islets with INSP showed improved vitality and function and reversed diabetes more rapidly when transplanted into diabetic mice than islets carrying INSS only. Surrogate beta cells with INSP expressed less ER stress and INS-DRiP neoantigen. This explanation for genetic protection from T1D may act instead of or in concert with the previously proposed mechanism attributed to INS promoter polymorphism.

Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling

Background: Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. Methods: Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m2) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). Results: Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (P = 0.020 and P = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. Conclusions: A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.

Regulatory T cell-based therapies for type 1 diabetes: a narrative review

Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of pancreatic insulin-secreting beta cells, resulting in hyperglycemia and a lifelong need for exogenous insulin therapy. Regulatory T cells (Tregs) are essential for maintaining immune tolerance and preventing autoimmune reactions. It has been shown that dysfunctional Tregs participate in the pathophysiology of T1D. Therapeutic approaches designed to enhance Treg stability, survival, and function have progressively emerged as a promising treatment strategy for T1D. This narrative review explores the potential of Treg cell-based therapy as a therapeutic tool to alter the natural history of T1D. It discusses different pharmacological strategies to enhance Treg stability and function, as well as the latest advances in Treg cell-based therapies, including adoptive Treg cell therapy and genetic engineering of Tregs. It also outlines current challenges and future research directions for integrating Treg cell-based therapy into clinical practice, aiming to provide a comprehensive overview of its potential benefits and limitations as an innovative therapeutic intervention for T1D.

Genetic Associations With C-peptide Levels Before Type 1 Diabetes Diagnosis in At-risk Relatives

OBJECTIVE

We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms are associated with C-peptide preservation before type 1 diabetes diagnosis.

METHODS

We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known single nucleotide polymorphisms and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.

RESULTS

Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β = -.06, P < .0001) and multivariate (β = -.03, P = .005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β = -.11, P = .002) and multivariate (β = -.06, P = .018) analyses.

CONCLUSION

The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC before diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Metabolic, genetic and immunological features of relatives of type 1 diabetes patients with elevated insulin resistance

PURPOSE

Insulin resistance plays a pivotal role in the preclinical stages of type 1 diabetes (T1D).

OBJECTIVE

This study aims at exploring the genetic, metabolic, and immunological features associated with insulin resistance among individuals at risk of developing T1D.

METHODS

We retrospectively selected relatives of individuals with T1D from participants in the TrialNet Pathway to Prevention study. They were categorized into two groups: high-H (n = 27) and low-H (n = 30), based on the upper and lower quartiles of insulin resistance assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Genetic predisposition was determined using the T1D Genetic Risk Score 1 (GRS1). Additionally, glucose control was evaluated through an oral glucose tolerance test and levels of metabolic hormones and inflammatory cytokines were measured in the serum. Flow cytometry analysis was employed to assess frequency and phenotype of islet-specific CD8 T cells.

RESULTS

While GRS1 were similar between the low-H and high-H groups, high-H individuals displayed a distinct metabolic profile, characterized by compensatory hyperinsulinemia, even while maintaining normoglycemia. Circulating cytokine levels were similar between the two groups. However, immune profiling revealed a central memory and activated profile of GAD65-specific CD8 T cells, along with an increased frequency of insulin-specific CD8 T cells in high-H individuals. The enrichment in insulin-specific CD8 T cells was independent of body mass.

CONCLUSION

These findings highlight the intricate interplay between insulin resistance, genetic factors, and immune activation in the context of T1D susceptibility, indicating potential connections between insulin resistance and immune responses specific to islet cells.

Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes

AIMS/HYPOTHESIS

Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

METHODS

We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294). Group comparisons included demographics, metabolic markers and the prevalence of autoantibodies against GAD65 (GADA%), IA-2 (IA-2A%) or insulin (IAA%) at enrolment. A p value <0.01 was considered statistically significant.

RESULTS

IA-2A% was lower in the DRX/DRX group (20.9%) than in the DR4/non-DR3 (38.5%, p<0.001) and DR3/DR4 (44.8%, p<0.001) groups, but similar to the DR3/non-DR4 group (20.0%). Conversely, IAA% was similar in the DRX/DRX (43.4%), DR4/non-DR3 (41.1%) and DR3/DR4 (41.0%) groups, but lower in the DR3/non-DR4 group (30.1%, p<0.001). Participants in the DRX/DRX group were older, with a lower prevalence of White participants and a higher prevalence of overweight/obesity, and higher preserved C-peptide (as measured by a lower Index60) than those in the DR3/DR4 group (all comparisons, p<0.005), a lower prevalence of White or non-Hispanic participants and a lower Index60 than those in the DR4/non-DR3 group, and younger age, a higher prevalence of Hispanic participants and a lower Index60 than those in the DR3/non-DR4 group (all comparisons, p<0.005). Among the 1292 participants who progressed to clinical type 1 diabetes, those in the DR3/non-DR4 group had higher GADA%, lower IA-2A% and lower IAA% than the other groups (all comparisons, p<0.01), and those in the DR3/DR4 group had the youngest age at diagnosis (all comparisons, p<0.001).

CONCLUSIONS/INTERPRETATION

Autoantibody-positive individuals who lack both high-risk HLA haplotypes (DRX/DRX) or have HLA-DR3-DQ2 but lack HLA-DR4-DQ8 (DR3/non-DR4) have phenotypic differences compared with DR3/DR4 and DR4/non-DR3 individuals, suggesting that there is aetiological heterogeneity in type 1 diabetes.

Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions

PURPOSE OF REVIEW

To review the genetics of type 1 diabetes (T1D) and T1D genetic risk scores, focusing on their development, research and clinical applications, and future directions.

RECENT FINDINGS

More than 90 genetic loci have been linked to T1D risk, with approximately half of the genetic risk attributable to the human leukocyte antigen (HLA) locus, along with non-HLA loci that have smaller effects to disease risk. The practical use of T1D genetic risk scores simplifies the complex genetic information, within the HLA and non-HLA regions, by combining the additive effect and interactions of single nucleotide polymorphisms (SNPs) associated with risk. Genetic risk scores have proven to be useful in various aspects, including classifying diabetes (e.g., distinguishing between T1D vs. neonatal, type 2 or other diabetes types), predicting the risk of developing T1D, assessing the prognosis of the clinical course (e.g., determining the risk of developing insulin dependence and glycemic control), and research into the heterogeneity of diabetes (e.g., atypical diabetes). However, there are gaps in our current knowledge including the specific sets of genes that regulate transition between preclinical stages of T1D, response to disease modifying therapies, and other outcomes of interest such as persistence of beta cell function. Several T1D genetic risk scores have been developed and shown to be valuable in various contexts, from classification of diabetes to providing insights into its etiology and predicting T1D risk across different stages of T1D. Further research is needed to develop and validate T1D genetic risk scores that are effective across all populations and ancestries. Finally, barriers such as cost, and training of medical professionals have to be addressed before the use of genetic risk scores can be incorporated into routine clinical practice.

A type 1 diabetes prediction model has utility across multiple screening settings with recalibration

Background

Accurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of severe presentation with diabetic ketoacidosis. We aimed to assess the generalisability of a prediction model developed in children followed from birth. Additionally, we sought to create an application for easy calculation and visualization of individualized risk prediction.

Methods

We developed and refined a stratified prediction model combining a genetic risk score, age, islet autoantibodies, and family history using data from children followed since birth by The Environmental Determinants of Diabetes in the Young (TEDDY) study. We tested the validity of the model through external validation in the Type 1 Diabetes TrialNet Pathway to Prevention study, which conducts cross-sectional screening in relatives of people with type 1 diabetes. We recalibrated the model by adjusting for baseline risk and selection criteria in TrialNet using logistic recalibration to improve calibration across all ages.

Results

The study included 7,798 TEDDY and 4,068 TrialNet participants, with 305 (4%) and 1,373 (34%) developing type 1 diabetes, respectively. The combined model showed similar discriminative ability in autoantibody-positive individuals across TEDDY and TrialNet (p=0.14), but inferior calibration in TrialNet (Brier score 0.40 [0.38,0.43]). Adjustment for baseline risk and selection criteria in TrialNet using logistic recalibration improved calibration across all ages (Brier score 0.16 [0.14,0.17]; p<0.001). A web calculator was developed to visualise individual risk estimates (https://t1dpredictor.diabetesgenes.org).

Conclusions

A stratified model of type 1 diabetes genetic risk score, family history, age, and autoantibody status accurately predicts type 1 diabetes risk, but may need recalibration according to screening stategy.

ISPAD Clinical Practice Consensus Guidelines 2024: Screening, Staging, and Strategies to Preserve Beta-Cell Function in Children and Adolescents with 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.

Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes

BACKGROUND

Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple autoantibodies (AABs) and normal glucose tolerance define stage 1 diabetes; abnormal glucose tolerance defines stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation.

METHODS

We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The small model included AAB status, fasting glucose, hemoglobin A1c, and age, while the medium and large models added predictors of disease progression measured via oral glucose tolerance testing.

FINDINGS

All models markedly improved granularity regarding personalized risk missing from current categories of stages of type 1 diabetes. Model-derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; a lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of the rate of disease progression within stage 2.

CONCLUSION

While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.

Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes: Report of Diabetes Mellitus Interagency Coordinating Committee Workshop

The approval of teplizumab to delay the onset of type 1 diabetes is an important inflection point in the decades-long pursuit to treat the cause of the disease rather than its symptoms. The National Institute of Diabetes and Digestive and Kidney Diseases convened a workshop of the Diabetes Mellitus Interagency Coordinating Committee titled "Evolving Concepts in Pathophysiology, Screening, and Prevention of Type 1 Diabetes" to review this accomplishment and identify future goals. Speakers representing Type 1 Diabetes TrialNet (TrialNet) and the Immune Tolerance Network emphasized that the ability to robustly identify individuals destined to develop type 1 diabetes was essential for clinical trials. The presenter from the U.S. Food and Drug Administration described how regulatory approval relied on data from the single clinical trial of TrialNet with testing of teplizumab for delay of clinical diagnosis, along with confirmatory evidence from studies in patients after diagnosis. The workshop reviewed the etiology of type 1 diabetes as a disease involving multiple immune pathways, highlighting the current understanding of prognostic markers and proposing potential strategies to improve the therapeutic response of disease-modifying therapies based on the mechanism of action. While celebrating these achievements funded by the congressionally appropriated Special Diabetes Program, panelists from professional organizations, nonprofit advocacy/funding groups, and industry also identified significant hurdles in translating this research into clinical care.

Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis

AIMS/HYPOTHESIS

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS/INTERPRETATION

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.

Association of polymorphism of NLRP3, ICAM-1, PTPN22, INS genes in childhood onset type 1 diabetes in a Pakistani population

BACKGROUND

Type 1 diabetes (T1D) is an organ-specific autoimmune disorder characterized by the destruction of pancreatic β cells, leading to absolute insulin deficiency. The genes NLRP3, ICAM-1, PTPN22, and INS are reportedly associated with T1D in other populations. However, the genetic pattern of T1D in the Pakistani population is not clear. This study aimed to find the association of polymorphisms in the PTPN22, INS, NLRP3, and ICAM-1 genes with T1D susceptibility in the Pakistani population.

METHODOLOGY

This case-control study includes 100 T1D patients (3-14 years), recruited randomly from the pediatric endocrinology department of Fatima Memorial Hospital, Lahore, Pakistan and 100 age-matched healthy controls were selected from different localities of the same population. The polymorphisms in PTPN22 (rs601, rs33996649, rs2488457), INS (rs80356664), NLRP3 (rs10754558, rs35829419), and ICAM-1 (rs1799969, rs5498) genes were genotyped by Sanger sequencing. The genotypic and allelic frequencies, haplotypes, and linkage disequilibrium were computed using the genetic toolset PLINK to investigate their relationship to T1D.

RESULTS

The results indicate that the occurrence of the GT genotype of the rs33996649 variant is significantly higher in children with T1D compared to a control group of healthy individuals (P = 0.001, OR: 2.0, 95% CI = 0.15-0.45). Furthermore, the CT genotype of rs2488457 was notably associated with T1D patients (P = 0.007, OR: 2.8, 95% CI = 0.56-0.67). The CG genotype of rs80356664 showed a slight association with T1D (P = 0.03, OR: 1.9, 95% CI = 0.35-0.59). The prevalence of the AT genotype of rs10754558 showed a strong association with T1D (P = 0.005, OR: 3.4, 95% CI = 0.45-0.69). The TG genotype of rs5498 was also strongly associated with T1D (P = 0.009, OR: 2.8, 95% CI = 0.75-0.89).

CONCLUSION

The present study provides evidence that SNPs in the PTPN22, INS, NLRP3, and ICAM-1 genes are associated with the development of T1D. Further research is needed to explore their potential use in genetic screening and personalized medication.

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.

A Review of Stage 0 Biomarkers in Type 1 Diabetes: The Holy Grail of Early Detection and Prevention?

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.

Risk for progression to type 1 diabetes in first-degree relatives under 50 years of age

Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

The immunology of type 1 diabetes

Following the seminal discovery of insulin a century ago, treatment of individuals with type 1 diabetes (T1D) has been largely restricted to efforts to monitor and treat metabolic glucose dysregulation. The recent regulatory approval of the first immunotherapy that targets T cells as a means to delay the autoimmune destruction of pancreatic β-cells highlights the critical role of the immune system in disease pathogenesis and tends to pave the way for other immune-targeted interventions for T1D. Improving the efficacy of such interventions across the natural history of the disease will probably require a more detailed understanding of the immunobiology of T1D, as well as technologies to monitor residual β-cell mass and function. Here we provide an overview of the immune mechanisms that underpin the pathogenesis of T1D, with a particular emphasis on T cells.

From islet transplantation to beta-cell regeneration: an update on beta-cell-based therapeutic approaches in type 1 diabetes

INTRODUCTION

Type 1 diabetes (T1D) mellitus is an autoimmune disease in which immune cells, predominantly effector T cells, destroy insulin-secreting beta-cells. Beta-cell destruction led to various consequences ranging from retinopathy and nephropathy to neuropathy. Different strategies have been developed to achieve normoglycemia, including exogenous glucose compensation, whole pancreas transplantation, islet transplantation, and beta-cell replacement.

AREAS COVERED

The last two decades of experience have shown that indigenous glucose compensation through beta-cell regeneration and protection is a peerless method for T1D therapy. Tremendous studies have tried to find an unlimited source for beta-cell regeneration, on the one hand, and beta-cell protection against immune attack, on the other hand. Recent advances in stem cell technology, gene editing methods, and immune modulation approaches provide a unique opportunity for both beta-cell regeneration and protection.

EXPERT OPINION

Pluripotent stem cell differentiation into the beta-cell is considered an unlimited source for beta-cell regeneration. Devising engineered pancreas-specific regulatory T cells using Chimeric Antigen Receptor (CAR) technology potentiates an effective immune tolerance induction for beta-cell protection. Beta-cell regeneration using pluripotent stem cells and beta-cell protection using pancreas-specific engineered regulatory T cells promises to develop a curative protocol in T1D.

Delayed menarche in children and adolescents with type 1 diabetes mellitus: a systematic review and meta-analysis

Several studies have reported an association between age at menarche and the onset of type-1 diabetes mellitus (T1DM). This review compared the age at menarche in patients who had menarche after T1DM diagnosis with that of patients who were healthy and/or had menarche before T1DM diagnosis. Searches were conducted using four databases. The outcome was the age at menarche of patients who had menarche after T1DM diagnosis and patients who were healthy and/or had menarche before T1DM diagnosis. A qualitative analysis was performed using the JBI (Joanna Briggs Institute) Critical Appraisal. Quantitative analysis of the mean differences was performed using Revman 5.4 tool. A total of 1952 studies were obtained from the initial search. The final results were 13 articles that met the inclusion criteria for the qualitative assessment and eight for the quantitative assessment. Eight studies included 1030 patients who had menarche after being diagnosed with T1DM and 1282 patients who were healthy and/or had menarche before T1DM diagnosis. The meta-analysis showed a cumulative effect on a mean difference of 0.87 (95% CI: 0.75; 0.99, p-value < 0.00001), indicating a later age at menarche in patients who had menarche after T1DM diagnosis. The age at menarche was later in patients who had menarche after T1DM diagnosis compared to healthy subjects and those who had menarche beforehand.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BACKGROUND

Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.

METHODS

We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.

RESULTS

Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.

CONCLUSIONS

Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease, type 1 diabetes mellitus (T1DM) is a complicated disorder that requires a lifelong insulin supply due to the immune-mediated destruction of pancreatic β cells. Although it is an organ-specific autoimmune disorder, T1DM is often associated with multiple other autoimmune disorders. The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease (AITD), which mainly exhibits two extremes of phenotypes: hyperthyroidism [Graves' disease (GD)] and hypo-thyroidism [Hashimoto's thyroiditis, (HT)]. However, the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications. Thus, routine screening of thyroid function has been recommended when T1DM is diagnosed. Here, first, we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases. Subsequently, an updated review of the association between T1DM and AITD is offered. Finally, we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD, suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study

BACKGROUND

Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data.

METHODS

Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

RESULTS

The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88.

CONCLUSIONS

A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project

BACKGROUND

Population screening for risk of type 1 diabetes (T1D) has been proposed to identify those with islet autoimmunity (presence of islet autoantibodies). As islet autoantibodies can be transient, screening with a genetic risk score has been proposed as an entry into autoantibody testing.

METHODS

Children were recruited from eight general pediatric and specialty clinics across Virginia with diverse community settings. Recruiters in each clinic obtained informed consent/assent, a medical history, and a saliva sample for DNA extraction in children with and without a history of T1D. A custom genotyping panel was used to define T1D genetic risk based upon associated SNPs in European- and African-genetic ancestry. Subjects at "high genetic risk" were offered a separate blood collection for screening four islet autoantibodies. A follow-up contact (email, mail, and telephone) in one half of the participants determined interest and occurrence of subsequent T1D.

RESULTS

A total of 3818 children aged 2-16 years were recruited, with 14.2% (n = 542) having a "high genetic risk." Of children with "high genetic risk" and without pre-existing T1D (n = 494), 7.0% (34/494) consented for autoantibody screening; 82.4% (28/34) who consented also completed the blood collection, and 7.1% (2/28) of them tested positive for multiple autoantibodies. Among children with pre-existing T1D (n = 91), 52% (n = 48) had a "high genetic risk." In the sample of children with existing T1D, there was no relationship between genetic risk and age at T1D onset. A major factor in obtaining islet autoantibody testing was concern over SARS-CoV-2 exposure.

CONCLUSIONS

Minimally invasive saliva sampling implemented using a genetic risk score can identify children at genetic risk of T1D. Consent for autoantibody screening, however, was limited largely due to the SARS-CoV-2 pandemic and need for blood collection.

The Role of Selenium Nanoparticles in Addressing Diabetic Complications: A Comprehensive Study

Diabetes, as an emerging epidemic, has put forward a significant spotlight on the evolving population worldwide grounded upon the remarkable affliction of healthcare along with economical conflict. Various studies suggested that, in modern society, lack of maintenance of a healthy life style leads to the occurrence of diabetes as insulin resistant, later having a damaging effect on the pancreatic β-cells, suggesting various complications. Furthermore, diabetes management is controversial owing to different opinions based on the prevention of complications. For this purpose, nanostructured materials (NSM) like selenium nanoparticles (SeNPs) have proved their efficiency in the therapeutic management of such serious diseases. This review offers an in- -depth idea regarding the pathophysiology, diagnosis and various conventional therapeutics of type 1 and type 2 diabetes, shedding light on Diabetic Nephropathy (DN), a case study of type 1 diabetes. Moreover, this review provides an exhaustive study by highlighting the economic and healthcare burdens associated with diabetes along with the controversies associated with conventional therapeutic management and the promising role of NSM like selenium nanoparticles (SeNPs), as a novel weapon for encountering such fatal diseases.

Challenges of CRISPR/Cas-Based Cell Therapy for Type 1 Diabetes: How Not to Engineer a "Trojan Horse"

Type 1 diabetes mellitus (T1D) is an autoimmune disease caused by the destruction of insulin-producing β-cells in the pancreas by cytotoxic T-cells. To date, there are no drugs that can prevent the development of T1D. Insulin replacement therapy is the standard care for patients with T1D. This treatment is life-saving, but is expensive, can lead to acute and long-term complications, and results in reduced overall life expectancy. This has stimulated the research and development of alternative treatments for T1D. In this review, we consider potential therapies for T1D using cellular regenerative medicine approaches with a focus on CRISPR/Cas-engineered cellular products. However, CRISPR/Cas as a genome editing tool has several drawbacks that should be considered for safe and efficient cell engineering. In addition, cellular engineering approaches themselves pose a hidden threat. The purpose of this review is to critically discuss novel strategies for the treatment of T1D using genome editing technology. A well-designed approach to β-cell derivation using CRISPR/Cas-based genome editing technology will significantly reduce the risk of incorrectly engineered cell products that could behave as a "Trojan horse".

Exploring lncRNAs associated with human pancreatic islet cell death induced by transfer of adoptive lymphocytes in a humanized mouse model

Background

Long noncoding RNA (lncRNA)-mediated posttranscriptional and epigenetic landscapes of gene regulation are associated with numerous human diseases. However, the regulatory mechanisms governing human β-cell function and survival remain unknown. Owing to technical and ethical constraints, studying the direct role of lncRNAs in β-cell function and survival in humans in vivo is difficult. Therefore, we utilized humanized mice with human islets to investigate lncRNA expression using whole transcriptome shotgun sequencing. Our study aimed to characterize lncRNAs that may be crucial for human islet cell function and survival.

Methods

Human β-cell death was induced in humanized mice engrafted with functional human islets. Using these humanized mice harboring human islets with induced β-cell death, we investigated lncRNA expression through whole transcriptome shotgun sequencing. Additionally, we systematically identified, characterized, and explored the regulatory functions of lncRNAs that are potentially important for human pancreatic islet cell function and survival.

Results

Human islet cell death was induced in humanized mice engrafted with functional human islets. RNA sequencing analysis of isolated human islets, islet grafts from humanized mice with and without induced cell death, revealed aberrant expression of a distinct set of lncRNAs that are associated with the deregulated mRNAs important for cellular processes and molecular pathways related to β-cell function and survival. A total of 10 lncRNA isoforms (SCYL1-1:22, POLG2-1:1, CTRB1-1:1, SRPK1-1:1, GTF3C5-1:1, PPY-1:1, CTRB1-1:5, CPA5-1:1, BCAR1-2:1, and CTRB1-1:4) were identified as highly enriched and specific to human islets. These lncRNAs were deregulated in human islets from donors with different BMIs and with type 2 diabetes (T2D), as well as in cultured human islets with glucose stimulation and induced cell death induced by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the medium used to culture islets with cytokines.

Conclusion

Islet-enriched and specific human lncRNAs are deregulated in human islet grafts and cultured human islets with induced cell death. These lncRNAs may be crucial for human β-cell function and survival and could have an impact on identifying biomarkers for β-cell loss and discovering novel therapeutic targets to enhance β-cell function and survival.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

Background

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods

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 (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings

The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation

Demographic, metabolic, immunological, and genetic markers can 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.

Maternal type 1 diabetes and relative protection against offspring transmission

Type 1 diabetes is around twice as common in the offspring of men with type 1 diabetes than in the offspring of women with type 1 diabetes, but the reasons for this difference are unclear. This Review summarises the evidence on the rate of transmission of type 1 diabetes to the offspring of affected fathers compared with affected mothers. The findings of nine major studies are presented, describing the magnitude of the effect observed and the relative strengths and weaknesses of these studies. This Review also explores possible underlying mechanisms for this effect, such as genetic mechanisms (eg, the selective loss of fetuses with high-risk genes in mothers with type 1 diabetes, preferential transmission of susceptibility genes from fathers, and parent-of-origin effects influencing gene expression), environmental exposures (eg, exposure to maternal hyperglycaemia, exogenous insulin exposure, and transplacental antibody transfer), and maternal microchimerism. Understanding why type 1 diabetes is more common in the offspring of men versus women with type 1 diabetes will help in the identification of individuals at high risk of the disease and can pave the way in the development of interventions that mimic the protective elements of maternal type 1 diabetes to reduce the risk of disease in individuals at high risk.

Utility of genetic risk scores in type 1 diabetes

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

Heterogeneity and endotypes in type 1 diabetes mellitus

Despite major advances over the past decade, prevention and treatment of type 1 diabetes mellitus (T1DM) remain suboptimal, with large and unexplained variations in individual responses to interventions. The current classification schema for diabetes mellitus does not capture the complexity of this disease or guide clinical management effectively. One of the approaches to achieve the goal of applying precision medicine in diabetes mellitus is to identify endotypes (that is, well-defined subtypes) of the disease each of which has a distinct aetiopathogenesis that might be amenable to specific interventions. Here, we describe epidemiological, clinical, genetic, immunological, histological and metabolic differences within T1DM that, together, suggest heterogeneity in its aetiology and pathogenesis. We then present the emerging endotypes and their impact on T1DM prediction, prevention and treatment.

Prevention of Type 1 Diabetes in Children: A Worthy Challenge?

Nowadays, the development of new immuno-therapeutic drugs has made it possible to alter the course of many autoimmune diseases. Type 1 diabetes is a chronic disease with a progressive dependence on exogenous insulin administration. The ability to intercept individuals at high risk of developing type 1 diabetes is the first step toward the development of therapies that can delay the process of β-cell destruction, thus permitting a better glycemic control and reducing the incidence of ketoacidosis. The knowledge of the main pathogenetic mechanisms underlying the three stages of the disease may be helpful to identify the best immune therapeutic approach. In this review, we aim to give an overview of the most important clinical trials conducted during the primary, secondary and tertiary phases of prevention.

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

A Concerted Vision to Advance the Knowledge of Diabetes Mellitus Related to Immune Checkpoint Inhibitors

The rubric of immune-related (ir) diabetes mellitus (DM) (irDM) encompasses various hyperglycemic disorders related to immune checkpoint inhibitors (ICPis). Beyond sharing similarities with conventional DM, irDM is a distinct, yet important, entity. The present narrative review provides a comprehensive overview of the literature regarding irDM published in major databases from January 2018 until January 2023. Initially considered rare, irDM is increasingly being reported. To advance the knowledge of irDM, the present review suggests a concerted vision comprising two intertwined aspects: a scientific-centered and a patient-centered view. The scientific-centered aspect addresses the pathophysiology of irDM, integrating: (i) ICPi-induced pancreatic islet autoimmunity in genetically predisposed patients; (ii) altered gut microbiome; (iii) involvement of exocrine pancreas; (iv) immune-related acquired generalized lipodystrophy. The patient-centered aspect is both nurtured by and nurturing the four pillars of the scientific-centered aspect: awareness, diagnosis, treatment, and monitoring of irDM. The path forward is a multidisciplinary initiative towards: (i) improved characterization of the epidemiological, clinical, and immunological profile of irDM; (ii) standardization of reporting, management, and surveillance protocols for irDM leveraging global registries; (iii) patient stratification according to personalized risk for irDM; (iv) new treatments for irDM; and (v) uncoupling ICPi efficacy from immunotoxicity.

Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia

The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow's milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.

The need and benefit of immune monitoring to define patient and disease heterogeneity, mechanisms of therapeutic action and efficacy of intervention therapy for precision medicine in type 1 diabetes

The current standard of care for type 1 diabetes patients is limited to treatment of the symptoms of the disease, insulin insufficiency and its complications, not its cause. Given the autoimmune nature of type 1 diabetes, immunology is critical to understand the mechanism of disease progression, patient and disease heterogeneity and therapeutic action. Immune monitoring offers the key to all this essential knowledge and is therefore indispensable, despite the challenges and costs associated. In this perspective, I attempt to make this case by providing evidence from the past to create a perspective for future trials and patient selection.

Type 1 Diabetes Mellitus: A Review on Advances and Challenges in Creating Insulin Producing Devices

Type 1 diabetes mellitus (T1DM) is the most common autoimmune chronic disease in young patients. It is caused by the destruction of pancreatic endocrine β-cells that produce insulin in specific areas of the pancreas, known as islets of Langerhans. As a result, the body becomes insulin deficient and hyperglycemic. Complications associated with diabetes are life-threatening and the current standard of care for T1DM consists still of insulin injections. Lifesaving, exogenous insulin replacement is a chronic and costly burden of care for diabetic patients. Alternative therapeutic options have been the focus in these fields. Advances in molecular biology technologies and in microfabrication have enabled promising new therapeutic options. For example, islet transplantation has emerged as an effective treatment to restore the normal regulation of blood glucose in patients with T1DM. However, this technique has been hampered by obstacles, such as limited islet availability, extensive islet apoptosis, and poor islet vascular engraftment. Many of these unsolved issues need to be addressed before a potential cure for T1DM can be a possibility. New technologies like organ-on-a-chip platforms (OoC), multiplexed assessment tools and emergent stem cell approaches promise to enhance therapeutic outcomes. This review will introduce the disorder of type 1 diabetes mellitus, an overview of advances and challenges in the areas of microfluidic devices, monitoring tools, and prominent use of stem cells, and how they can be linked together to create a viable model for the T1DM treatment. Microfluidic devices like OoC platforms can establish a crucial platform for pathophysiological and pharmacological studies as they recreate the pancreatic environment. Stem cell use opens the possibility to hypothetically generate a limitless number of functional pancreatic cells. Additionally, the integration of stem cells into OoC models may allow personalized or patient-specific therapies.

Matching for HLA-DR excluding diabetogenic HLA-DR3 and HLA-DR4 predicts insulin independence after pancreatic islet transplantation

Introduction

In pancreatic islet transplantation, the exact contribution of human leukocyte antigen (HLA) matching to graft survival remains unclear. Islets may be exposed to allogenic rejection but also the recurrence of type 1 diabetes (T1D). We evaluated the HLA-DR matching, including the impact of diabetogenic HLA-DR3 or HLA-DR4 matches.

Methods

We retrospectively examined the HLA profile in 965 transplant recipients and 2327 islet donors. The study population was obtained from patients enrolled in the Collaborative Islet Transplant Registry. We then identified 87 recipients who received a single-islet infusion. Islet-kidney recipients, 2nd islet infusion, and patients with missing data were excluded from the analysis (n=878).

Results

HLA-DR3 and HLA-DR4 were present in 29.7% and 32.6% of T1D recipients and 11.6% and 15.8% of the donors, respectively. We identified 52 T1D islet recipients mismatched for HLA-DR (group A), 11 with 1 or 2 HLA-DR-matches but excluding HLA-DR3 and HLA- DR4 (group B), and 24 matched for HLA-DR3 or HLA-DR4 (group C). Insulin-independence was maintained in a significantly higher percentage of group B recipients from year one through five post-transplantation (p<0.01). At five-year post-transplantation, 78% of group B was insulin-independent compared to 24% (group A) and 35% (group C). Insulin-independence correlated with significantly better glycemic control (HbA1c <7%), fasting blood glucose, and reduced severe hypoglycemic events. Matching HLA-A-B-DR (≥3) independently of HLA- DR3 or HLA-DR4 matching did not improve graft survival.

Conclusion

This study suggests that matching HLA-DR but excluding the diabetogenic HLA-DR3 and/or 4 is a significant predictor for long-term islet survival.

Low risk for diabetic complications in type 1 diabetes patients carrying a protective insulin gene variant

Type 1 diabetes patients carrying a 'protective' insulin gene (INS) variant present a disease endotype with reduced insulin antibody titers, preserved beta cell function and improved glycemic control. We tested whether this protective INS variant associated with lowered risk for development of proliferative diabetic retinopathy (PDR) and diabetic kidney disease (DKD) as long-term diabetic complications. Insulin gene polymorphisms were evaluated in 1,363 type 1 diabetes patients participating in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study that compared intensive versus conventional insulin therapy in relation with development of PDR and DKD with a follow-up of over two decades. PDR and DKD were absent in type 1 diabetes patients carrying the protective INS variant and receiving intensive insulin therapy (the current standard of clinical care) 1-5 years from their diagnosis (n = 67; mean post-diagnosis follow up of 20.4 ± 1.6 years), versus 11 of 258 patients (4.3%) lacking this variant (20.4 ± 1.8 years follow up). In the secondary intervention group of the intensive therapy arm (1-15 years of disease), PDR was significantly less frequent in carriers of the protective INS variant than those without it (4 of 83 [4.8%] vs. 31 of 260 [11.9%]; p = 0.032; 26.1 ± 3.9 and 26.3 ± 4.1 years follow-up, respectively), whereas DKD frequencies were no different between those with or without this variant (5 of 83 [6.0%] vs. 11 of 260 [4.2%]). Carrying a copy of this protective INS variant further reduces the risk of diabetic complications achieved by intensive insulin therapy and marks a disease endotype with superior glycemic control, increased and extended beta cell function, and prevention of DKD and PDR.

Clinical and experimental treatment of type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease resulting in the destruction of the insulin-producing pancreatic beta cells. Disease progression occurs along a trajectory from genetic risk, the development of islet autoantibodies, and autoreactive T cells ultimately progressing to clinical disease. Natural history studies and mechanistic studies linked to clinical trials have provided insight into the role of the immune system in disease pathogenesis. Here, we review our current understanding of the underlying etiology of T1D, focusing on the immune cell types that have been implicated in progression from pre-symptomatic T1D to clinical diagnosis and established disease. This knowledge has been foundational for the development of immunotherapies aimed at the prevention and treatment of T1D.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

Combined detection of islet autoantibodies for clinical diagnosis of type 1 diabetes in the low-prevalence population

CONTEXT

Single positive islet autoantibodies (IAbs), sometimes detected in healthy individuals and low risk type 1 diabetes (T1D) patients, are considered to be irrelevant to the development of diabetes, making it difficult to diagnose and classify adult-onset diabetics.

OBJECTIVE

To determine the significance and clinical value of IAbs in T1D diagnosis in the low-prevalence population; and to explore whether electrochemiluminescence (ECL)-IAb detection assay can improve the clinical utility of IAbs in the immunodiagnosis of T1D in the low-prevalence population.

PARTICIPANTS AND METHODS

A total of 633 newly-diagnosed adult-onset diabetic patients (≥18 years old) were divided into two groups according to their clinical phenotypes: 575 patients with age at diagnosis ≥35 years and body mass index (BMI) ≥ 24 kg/m2 were considered a low-prevalence population (population with a low prevalence of T1D) and the other 58 patients were considered a high-prevalence population. All the samples from 633 participants were tested with IAbs using standard radiobinding assays (RBA) and electrochemiluminescence (ECL) assay, in parallel.

RESULTS

Compared with the high-prevalence population, fewer positive IAbs (94/575, 16.3% vs. 28/58, 48.3%) were detected in the low-prevalence population, and more of which (69/94, 73.4% vs. 9/28, 32.2%) were positive for a single IAb, with GADA being the most prevalent single-IAb. Single-IAb detection in the low-prevalence population did not always suggest T1D phenotype. Combined detection of IAbs by RBA and ECL assays had a significant clinical utility to distinguish autoimmune diabetes in the low-prevalence population with low BMI, poor β-cell function at the diagnosis, and an accelerated decline in β-cell function during the follow-up.

CONCLUSIONS

Combined autoantibody detection by RBA and ECL assays improved differentiating autoimmune from non-autoimmune diabetes in the low-prevalence population.

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.

Persistence of β-Cell Responsiveness for Over Two Years in Autoantibody-Positive Children With Marked Metabolic Impairment at Screening

OBJECTIVE

We studied longitudinal differences between progressors and nonprogressors to type 1 diabetes with similar and substantial baseline risk.

RESEARCH DESIGN AND METHODS

Changes in 2-h oral glucose tolerance test indices were used to examine variability in diabetes progression in the Diabetes Prevention Trial-Type 1 (DPT-1) study (n = 246) and Type 1 Diabetes TrialNet Pathway to Prevention study (TNPTP) (n = 503) among autoantibody (Ab)+ children (aged <18.0 years) with similar baseline metabolic impairment (DPT-1 Risk Score [DPTRS] of 6.5-7.5), as well as in TNPTP Ab- children (n = 94).

RESULTS

Longitudinal analyses revealed annualized area under the curve (AUC) of C-peptide increases in nonprogressors versus decreases in progressors (P ≤ 0.026 for DPT-1 and TNPTP). Vector indices for AUC glucose and AUC C-peptide changes (on a two-dimensional grid) also differed significantly (P < 0.001). Despite marked baseline metabolic impairment of nonprogressors, changes in AUC C-peptide, AUC glucose, AUC C-peptide-to-AUC glucose ratio (AUC ratio), and Index60 did not differ from Ab- relatives during follow-up. Divergence between nonprogressors and progressors occurred by 6 months from baseline in both cohorts (AUC glucose, P ≤ 0.007; AUC ratio, P ≤ 0.034; Index60, P < 0.001; vector indices of change, P < 0.001). Differences in 6-month change were positively associated with greater diabetes risk (respectively, P < 0.001, P ≤ 0.019, P < 0.001, and P < 0.001) in DPT-1 and TNPTP, except AUC ratio, which was inversely associated with risk (P < 0.001).

CONCLUSIONS

Novel findings show that even with similarly abnormal baseline risk, progressors had appreciably more metabolic impairment than nonprogressors within 6 months and that the measures showing impairment were predictive of type 1 diabetes. Longitudinal metabolic patterns did not differ between nonprogressors and Ab- relatives, suggesting persistent β-cell responsiveness in nonprogressors.

Efficacy of a new enzyme-linked immunosorbent assay system for three islet cell autoantibodies in Japanese patients with acute-onset type 1 diabetes

To evaluate the clinical efficacy of a new enzyme-linked immunosorbent assay (ELISA) system for simultaneously detecting three islet cell autoantibodies against glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) (3 Screen ICA ELISA) in Japanese patients with acute-onset type 1 diabetes (T1D). In addition, clinical factors affecting the 3 Screen ICA ELISA index were investigated. We compared the positivity values of 3 Screen ICA ELISA with that of each autoantibody alone in 97 patients with acute-onset T1D (mean age 48.7 years, 49% male) and 100 non-diabetic subjects (mean age 47.0 years, 50% male). Serum thyroid stimulating hormone receptor antibody, thyroid peroxidase antibody (TPOAb) and thyroglobulin autoantibody levels were also evaluated. The cut-off value of the 3 Screen ICA ELISA was determined based on the 97^th percentile of 100 non-diabetic controls (threshold for positivity, ≥14 index). The mean age of disease onset and duration of diabetes were 34.2 years and 14.5 years, respectively. Among all T1D patients, the positivity of 3 Screen ICA ELISA was 71.1%, while that of GADA, IA-2A, and ZnT8A were 59.8%, 25.8%, and 25.8%, respectively. The median 3 Screen ICA index was 121.9 (8.7-468.2) and was associated with titers of each autoantibody, most so with GADA, and was significantly higher in TPOAb-positive patients than in TPOAb-negative patients. Our findings suggests that the 3 Screen ICA ELISA may be a time-saving diagnostic tool for evaluating islet autoantibodies in acute-onset T1D patients.