Primary prevention of beta-cell autoimmunity and type 1 diabetes - The Global Platform for the Prevention of Autoimmune Diabetes (GPPAD) perspectives

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

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.

Clinical care advice for monitoring of islet autoantibody positive individuals with presymptomatic type 1 diabetes

BACKGROUND/AIM

Type 1 diabetes is an autoimmune disease that involves the development of autoantibodies against pancreatic islet beta-cell antigens, preceding clinical diagnosis by a period of preclinical disease activity. As screening activity to identify autoantibody-positive individuals increases, a rise in presymptomatic type 1 diabetes individuals seeking medical attention is expected. Current guidance on how to monitor these individuals in a safe but minimally invasive way is limited. This article aims to provide clinical guidance for monitoring individuals with presymptomatic type 1 diabetes to reduce the risk of diabetic ketoacidosis (DKA) at diagnosis.

METHODS

Expert consensus was obtained from members of the Fr1da, GPPAD, and INNODIA consortia, three European diabetes research groups. The guidance covers both specialist and primary care follow-up strategies.

RESULTS

The guidance outlines recommended monitoring approaches based on age, disease stage and clinical setting. Individuals with presymptomatic type 1 diabetes are best followed up in specialist care. For stage 1, biannual assessments of random plasma glucose and HbA1c are suggested for children, while annual assessments are recommended for adolescents and adults. For stage 2, 3-monthly clinic visits with additional home monitoring are advised. The value of repeat OGTT in stage 1 and the use of continuous glucose monitoring in stage 2 are discussed. Primary care is encouraged to monitor individuals who decline specialist care, following the guidance presented.

CONCLUSIONS

As type 1 diabetes screening programs become more prevalent, effective monitoring strategies are essential to mitigate the risk of complications such as DKA. This guidance serves as a valuable resource for clinicians, providing practical recommendations tailored to an individual's age and disease stage, both within specialist and primary care settings.

Screening and Prevention of Type 1 Diabetes: Where Are We?

A diagnosis of type 1 diabetes (T1D) and the subsequent requirement for exogenous insulin treatment is associated with considerable acute and chronic morbidity and a substantial effect on patient quality of life. Importantly, a large body of work suggests that early identification of presymptomatic T1D can accurately predict clinical disease, and when paired with education and monitoring, can yield improved health outcomes. Furthermore, a growing cadre of effective disease-modifying therapies provides the potential to alter the natural history of early stages of T1D. In this mini review, we highlight prior work that has led to the current landscape of T1D screening and prevention, as well as challenges and next steps moving into the future of these rapidly evolving areas of patient care.

The countdown to type 1 diabetes: when, how and why does the clock start?

'The clock to type 1 diabetes has started when islet antibodies are first detected', commented George Eisenbarth with regard to the pathogenesis of type 1 diabetes. This review focuses on 'starting the clock', i.e. the initiation of pre-symptomatic islet autoimmunity/the first appearance of islet autoantibodies. In particular, this review addresses why susceptibility to developing islet autoimmunity is greatest in the first 2 years of life and why beta cells are a frequent target of the immune system during this fertile period. A concept for the development of beta cell autoimmunity in childhood is discussed and three factors are highlighted that contribute to this early predisposition: (1) high beta cell activity and potential vulnerability to stress; (2) high rates of and first exposures to infection; and (3) a heightened immune response, with a propensity for T helper type 1 (Th1) immunity. Arguments are presented that beta cell injury, accompanied by activation of an inflammatory immune response, precedes the initiation of autoimmunity. Finally, the implications for strategies aimed at primary prevention for a world without type 1 diabetes are discussed.

The importance of biomarker development for monitoring type 1 diabetes progression rate and therapeutic responsiveness

Type 1 diabetes (T1D) is an autoimmune condition of children and adults in which immune cells target insulin-producing pancreatic β-cells for destruction. This results in a chronic inability to regulate blood glucose levels. The natural history of T1D is well-characterized in childhood. Evidence of two or more autoantibodies to the islet antigens insulin, GAD, IA-2 or ZnT8 in early childhood is associated with high risk of developing T1D in the future. Prediction of risk is less clear in adults and, overall, the factors controlling the progression rate from multiple islet autoantibody positivity to onset of symptoms are not fully understood. An anti-CD3 antibody, teplizumab, was recently shown to delay clinical progression to T1D in high-risk individuals including adults and older children. This represents an important proof of concept for those at risk of future T1D. Given their role in risk assessment, islet autoantibodies might appear to be the most obvious biomarkers to monitor efficacy. However, monitoring islet autoantibodies in clinical trials has shown only limited effects, although antibodies to the most recently identified autoantigen, tetraspanin-7, have not yet been studied in this context. Measurements of beta cell function remain fundamental to assessing efficacy and different models have been proposed, but improved biomarkers are required for both progression studies before onset of diabetes and in therapeutic monitoring. In this mini-review, we consider some established and emerging predictive and prognostic biomarkers, including markers of pancreatic function that could be integrated with metabolic markers to generate improved strategies to measure outcomes of therapeutic intervention.

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.

Successful integration of newborn genetic testing into UK routine screening using prospective consent to determine eligibility for clinical trials

OBJECTIVE

INGR1D (INvestigating Genetic Risk for type 1 Diabetes) was a type 1 diabetes (T1D) genetic screening study established to identify participants for a primary prevention trial (POInT, Primary Oral Insulin Trial).

METHODS

The majority of participants were recruited by research midwives in antenatal clinics from 18 weeks' gestation. Using the NHS Newborn Bloodspot Screening Programme (NBSP) infrastructure, participants enrolled in INGR1D had an extra sample taken from their day 5 bloodspot card sent for T1D genetic screening. Those at an increased risk of T1D were informed of the result, given education about T1D and the opportunity to take part in POInT.

RESULTS

Between April 2018 and November 2020, 66% of women approached about INGR1D chose to participate. 15 660 babies were enrolled into INGR1D and 14 731 blood samples were processed. Of the processed samples, 157 (1%) had confirmed positive results, indicating an increased risk of T1D, of whom a third (n=49) enrolled into POInT (20 families were unable to participate in POInT due to COVID-19 lockdown restrictions).

CONCLUSION

The use of prospective consent to perform personalised genetic testing on samples obtained through the routine NBSP represents a novel mechanism for clinical genetic research in the UK and provides a model for further population-based genetic studies in the newborn.

Elevations in blood glucose before and after the appearance of islet autoantibodies in children

The etiology of type 1 diabetes has polygenic and environmental determinants that lead to autoimmune responses against pancreatic β cells and promote β cell death. The autoimmunity is considered silent without metabolic consequences until late preclinical stages,and it remains unknown how early in the disease process the pancreatic β cell is compromised. To address this, we investigated preprandial nonfasting and postprandial blood glucose concentrations and islet autoantibody development in 1,050 children with high genetic risk of type 1 diabetes. Pre- and postprandial blood glucose decreased between 4 and 18 months of age and gradually increased until the final measurements at 3.6 years of age. Determinants of blood glucose trajectories in the first year of life included sex, body mass index, glucose-related genetic risk scores, and the type 1 diabetes-susceptible INS gene. Children who developed islet autoantibodies had early elevations in blood glucose concentrations. A sharp and sustained rise in postprandial blood glucose was observed at around 2 months prior to autoantibody seroconversion, with further increases in postprandial and, subsequently, preprandial values after seroconversion. These findings show heterogeneity in blood glucose control in infancy and early childhood and suggest that islet autoimmunity is concurrent or subsequent to insults on the pancreatic islets.

Innovative Designs and Logistical Considerations for Expedited Clinical Development of Combination Disease-Modifying Treatments for Type 1 Diabetes

It has been 100 years since the life-saving discovery of insulin, yet daily management of type 1 diabetes (T1D) remains challenging. Even with closed-loop systems, the prevailing need for persons with T1D to attempt to match the kinetics of insulin activity with the kinetics of carbohydrate metabolism, alongside dynamic life factors affecting insulin requirements, results in the need for frequent interventions to adjust insulin dosages or consume carbohydrates to correct mismatches. Moreover, peripheral insulin dosing leaves the liver underinsulinized and hyperglucagonemic and peripheral tissues overinsulinized relative to their normal physiologic roles in glucose homeostasis. Disease-modifying therapies (DMT) to preserve and/or restore functional β-cell mass with controlled or corrected autoimmunity would simplify exogenous insulin need, thereby reducing disease mortality, morbidity, and management burdens. However, identifying effective DMTs for T1D has proven complex. There is some consensus that combination DMTs are needed for more meaningful clinical benefit. Other complexities are addressable with more innovative trial designs and logistics. While no DMT has yet been approved for marketing, existing regulatory guidance provides opportunities to further "de-risk" development. The T1D development ecosystem can accelerate progress by using more innovative ways for testing DMTs for T1D. This perspective outlines suggestions for accelerating evaluation of candidate T1D DMTs, including combination therapies, by use of innovative trial designs, enhanced logistical coordination of efforts, and regulatory guidance for expedited development, combination therapies, and adaptive designs.

Heterogeneity of DKA Incidence and Age-Specific Clinical Characteristics in Children Diagnosed With Type 1 Diabetes in the TEDDY Study

OBJECTIVE

The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. Because age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA).

RESEARCH DESIGN AND METHODS

The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0-4, 5-9, and 10-14 years; n = 142, 151, and 86, respectively) with comparisons of autoantibody profiles, HLAs, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis compared those with oral glucose tolerance test data with TEDDY children who did not progress to diabetes.

RESULTS

Increasing fasting glucose (hazard ratio [HR] 1.09 [95% CI 1.04-1.14]; P = 0.0003), stimulated glucose (HR 1.50 [1.42-1.59]; P < 0.0001), fasting insulin (HR 0.89 [0.83-0.95]; P = 0.0009), and glucose-to-insulin ratio (HR 1.29 [1.16-1.43]; P < 0.0001) were associated with risk of progression to type 1 diabetes. Younger children had fewer autoantibodies with more symptoms at diagnosis. Twenty-three children (6.1%) had DKA at onset, only 1 (0.97%) of 103 with and 22 (8.0%) of 276 children without a first-degree relative (FDR) with type 1 diabetes (P = 0.008). Children with DKA were more likely to be nonadherent to study protocol (P = 0.047), with longer duration between their last TEDDY evaluation and diagnosis (median 10.2 vs. 2.0 months without DKA; P < 0.001).

CONCLUSIONS

DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset, adding to the heterogeneity of type 1 diabetes.

Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia

The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delayed diagnosis of T1D. This paper has utilised Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Random Forest (RF) to model and predict the age at onset of T1D in children in Saudi Arabia (S.A.) which is ranked as the 7th for the highest number of T1D and 5th in the world for the incidence rate of T1D. De-identified data between (2010-2020) from three cities in S.A. were used to model and predict the age at onset of T1D. The best subset model selection criteria, coefficient of determination, and diagnostic tests were deployed to select the most significant variables. The efficacy of models for predicting the age at onset was assessed using multi-prediction accuracy measures. The average age at onset of T1D is 6.2 years and the most common age group for onset is (5-9) years. Most of the children in the sample (68%) are from urban areas of S.A., 75% were delivered after a full term pregnancy length and 31% were delivered through a cesarean section. The models of best fit were the MLR and RF models with R2 = (0.85 and 0.95), the root mean square error = (0.25 and 0.15) and mean absolute error = (0.19 and 0.11) respectively for logarithm of age at onset. This study for the first time has utilised MLR, ANN and RF models to predict the age at onset of T1D in children in S.A. These models can effectively aid health care providers to monitor and create intervention strategies to reduce the impact of T1D in children in S.A.

Supplementation with Bifidobacterium longum subspecies infantis EVC001 for mitigation of type 1 diabetes autoimmunity: the GPPAD-SINT1A randomised controlled trial protocol

INTRODUCTION

The Global Platform for the Prevention of Autoimmune Diabetes-SINT1A Study is designed as a randomised, placebo-controlled, double-blind, multicentre, multinational, primary prevention study aiming to assess whether daily administration of Bifidobacterium infantis from age 7 days to 6 weeks until age 12 months to children with elevated genetic risk for type 1 diabetes reduces the cumulative incidence of beta-cell autoantibodies in childhood.

METHODS AND ANALYSIS

Infants aged 7 days to 6 weeks from Germany, Poland, Belgium, UK and Sweden are eligible for study participation if they have a >10.0% expected risk for developing multiple beta-cell autoantibodies by age 6 years as determined by genetic risk score or family history and HLA genotype. Infants are randomised 1:1 to daily administration of B. infantis EVC001 or placebo until age 12 months and followed for a maximum of 5.5 years thereafter. The primary outcome is the development of persistent confirmed multiple beta-cell autoantibodies. Secondary outcomes are (1) Any persistent confirmed beta-cell autoantibody, defined as at least one confirmed autoantibody in two consecutive samples, including insulin autoantibodies, glutamic acid decarboxylase, islet tyrosine phosphatase 2 or zinc transporter 8, (2) Diabetes, (3) Transglutaminase autoantibodies associated with coeliac disease, (4) Respiratory infection rate in first year of life during supplementation and (5) Safety. Exploratory outcomes include allergy, antibody response to vaccines, alterations of the gut microbiome or blood metabolome, stool pH and calprotectin.

ETHICS AND DISSEMINATION

The study was approved by the local ethical committees of the Technical University Munich, Medical Faculty, the Technische Universität Dresden, the Medizinische Hochschule Hannover, the Medical University of Warsaw, EC Research UZ Leuven and the Swedish ethical review authority. The results will be disseminated through peer-reviewed journals and conference presentations and will be openly shared after completion of the study.

TRIAL REGISTRATION NUMBER

NCT04769037.

100 Years of Insulin: Lifesaver, immune target, and potential remedy for prevention

In this review, we bring our personal experiences to showcase insulin from its breakthrough discovery as a life-saving drug 100 years ago to its uncovering as the autoantigen and potential cause of type 1 diabetes and eventually as an opportunity to prevent autoimmune diabetes. The work covers the birth of insulin to treat patients, which is now 100 years ago, the development of human insulin, insulin analogues, devices, and the way into automated insulin delivery, the realization that insulin is the primary autoimmune target of type 1 diabetes in children, novel approaches of immunotherapy using insulin for immune tolerance induction, the possible limitations of insulin immunotherapy, and an outlook how modern vaccines could remove the need for another 100 years of insulin therapy.

A Key to T1D Prevention: Screening and Monitoring Relatives as Part of Clinical Care

The 2019 report of a randomized, placebo-controlled clinical trial demonstrating that immune therapy can delay the onset of clinical type 1 diabetes (T1D) in antibody-positive relatives by a median of 2 years stands out as a landmark in the decades-long effort to prevent T1D. With this important step achieved, it is now time to consider what is needed to bring disease-modifying therapy for prevention or delay of T1D to clinical use from this point. Long considered a chicken and egg problem (why screen for T1D risk when we have no therapy, and how can we develop therapies without more screening), we now have the opportunity to break this impasse. The purpose of this article is to place this clinical trial result in context, highlighting key foundational studies leading to this accomplishment, addressing the current gaps, and suggesting that a key next step for prevention of T1D is to screen and monitor relatives for T1D risk in the context of clinical care.

A Public Health Antibody Screening Indicates a 6-Fold Higher SARS-CoV-2 Exposure Rate than Reported Cases in Children

BACKGROUND

Antibody responses to virus reflect exposure and potential protection.

METHODS

We developed a highly specific and sensitive approach to measuring antibodies against SARS-CoV-2 for population-scale immune surveillance. Antibody positivity was defined as a dual-positive response against both the receptor-binding domain and nucleocapsid proteins of SARS-CoV-2. Antibodies were measured by immunoprecipitation assays in capillary blood from 15,771 children aged 1 to 18 years living in Bavaria, Germany, and participating in a public health type 1 diabetes screening program (ClinicalTrials.gov: NCT04039945), in 1,916 dried blood spots from neonates in a Bavarian screening study (ClinicalTrials.gov: NCT03316261), and in 75 SARS-CoV-2-positive individuals. Virus positive incidence was obtained from the Bavarian health authority data.

FINDINGS

Dual-antibody positivity was detected in none of the 3,887 children in 2019 (100% specificity) and 73 of 75 SARS-CoV-2-positive individuals (97.3% sensitivity). Antibody surveillance in children during 2020 resulted in frequencies of 0.08% in January to March, 0.61% in April, 0.74% in May, 1.13% in June, and 0.91% in July. Antibody prevalence from April 2020 was 6-fold higher than the incidence of authority-reported cases (156 per 100,000 children), showed marked variation between the seven Bavarian regions (p < 0.0001), and was not associated with age or sex. Transmission in children with virus-positive family members was 35%. 47% of positive children were asymptomatic. No association with type 1 diabetes autoimmunity was observed. Antibody frequency in newborns was 0.47%.

CONCLUSIONS

We demonstrate the value of population-based screening programs for pandemic monitoring.

FUNDING

The work was supported by funding from the BMBF (FKZ01KX1818).

A combined risk score enhances prediction of type 1 diabetes among susceptible children

Type 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis10-12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.

Insulin is necessary but not sufficient: changing the therapeutic paradigm in type 1 diabetes

Despite the clear evidence that type 1 diabetes (T1D) begins well before hyperglycemia is evident, there are no clinically available disease-modifying therapies for early-stage disease. However, following the exciting results of the Teplizumab Prevention Study, the first study to demonstrate that overt T1D can be delayed with immunotherapy, there is renewed optimism that in the future, T1D will be treated before hyperglycemia develops. A different treatment paradigm is needed, as a majority of people with T1D do not meet the glycemic targets that are associated with a lower risk of T1D complications and therefore remain vulnerable to complications and shortened life expectancy. The following review will outline the history and current status of immunotherapy for T1D and highlight some challenges and ideas for the future. Although such efforts have been worldwide, we will focus particularly on the activities of Diabetes TrialNet, a National Institutes of Health consortium launched in 2004.

Autoantigen Treatment in Type 1 Diabetes: Unsolved Questions on How to Select Autoantigen and Administration Route

Autoantigen treatment has been tried for the prevention of type 1 diabetes (T1D) and to preserve residual beta-cell function in patients with a recent onset of the disease. In experimental animal models, efficacy was good, but was insufficient in human subjects. Besides the possible minor efficacy of peroral insulin in high-risk individuals to prevent T1D, autoantigen prevention trials have failed. Other studies on autoantigen prevention and intervention at diagnosis are ongoing. One problem is to select autoantigen/s; others are dose and route. Oral administration may be improved by using different vehicles. Proinsulin peptide therapy in patients with T1D has shown possible minor efficacy. In patients with newly diagnosed T1D, subcutaneous injection of glutamic acid decarboxylase (GAD) bound to alum hydroxide (GAD-alum) can likely preserve beta-cell function, but the therapeutic effect needs to be improved. Intra-lymphatic administration may be a better alternative than subcutaneous administration, and combination therapy might improve efficacy. This review elucidates some actual problems of autoantigen therapy in the prevention and/or early intervention of type 1 diabetes.

Changing the landscape for type 1 diabetes: the first step to prevention

Over several decades, studies have described the progression of autoimmune diabetes, from the first appearance of autoantibodies until, and after, the diagnosis of clinical disease with hyperglycaemia and insulin dependence. Despite the improved management of type 1 diabetes with exogenous insulin, most patients do not meet clinical glycaemic goals, and diabetes remains an important medical problem that affects children and adults. Clinical and preclinical studies have suggested strategies to prevent the diagnosis of type 1 diabetes in people at risk, but the outcomes of previous clinical trials have not met their primary endpoints of disease prevention or delay. The results from the TN-10 teplizumab prevention trial show that the diagnosis of type 1 diabetes can be delayed by treatment with a FcR non-binding monoclonal antibody to CD3 in people at high risk for disease. This Series paper discusses how this clinical achievement raises new questions about for whom, and when, immunological strategies might be developed to prevent type 1 diabetes, and how to achieve this goal.

New insights on the genetics of type 1 diabetes

PURPOSE OF REVIEW

The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.

RECENT FINDINGS

We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.

SUMMARY

The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.

Oral insulin therapy for primary prevention of type 1 diabetes in infants with high genetic risk: the GPPAD-POInT (global platform for the prevention of autoimmune diabetes primary oral insulin trial) study protocol

INTRODUCTION

The POInT study, an investigator initiated, randomised, placebo-controlled, double-blind, multicentre primary prevention trial is conducted to determine whether daily administration of oral insulin, from age 4.0 months to 7.0 months until age 36.0 months to children with elevated genetic risk for type 1 diabetes, reduces the incidence of beta-cell autoantibodies and diabetes.

METHODS AND ANALYSIS

Infants aged 4.0 to 7.0 months from Germany, Poland, Belgium, UK and Sweden are eligible if they have a >10.0% expected risk for developing multiple beta-cell autoantibodies as determined by genetic risk score or family history and human leucocyte antigen genotype. Infants are randomised 1:1 to daily oral insulin (7.5 mg for 2 months, 22.5 mg for 2 months, 67.5 mg until age 36.0 months) or placebo, and followed for a maximum of 7 years. Treatment and follow-up is stopped if a child develops diabetes. The primary outcome is the development of persistent confirmed multiple beta-cell autoantibodies or diabetes. Other outcomes are: (1) Any persistent confirmed beta-cell autoantibody (glutamic acid decarboxylase (GADA), IA-2A, autoantibodies to insulin (IAA) and zinc transporter 8 or tetraspanin 7), or diabetes, (2) Persistent confirmed IAA, (3) Persistent confirmed GADA and (4) Abnormal glucose tolerance or diabetes.

ETHICS AND DISSEMINATION

The study is approved by the ethical committees of all participating clinical sites. The results will be disseminated through peer-reviewed journals and conference presentations and will be openly shared after completion of the trial.

TRIAL REGISTRATION NUMBER

NCT03364868.

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

OBJECTIVE

Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

RESEARCH DESIGN AND METHODS

In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

RESULTS

The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

CONCLUSIONS

An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

Association of Dendritic Cell Signatures With Autoimmune Inflammation Revealed by Single-Cell Profiling

OBJECTIVE

To identify single-cell transcriptional signatures of dendritic cells (DCs) that are associated with autoimmunity, and determine whether those DC signatures are correlated with the clinical heterogeneity of autoimmune disease.

METHODS

Blood-derived DCs were single-cell sorted from the peripheral blood of patients with rheumatoid arthritis, systemic lupus erythematosus, or type 1 diabetes as well as healthy individuals. DCs were analyzed using single-cell gene expression assays, performed immediately after isolation or after in vitro stimulation of the cells. In addition, protein expression was measured using fluorescence-activated cell sorting.

RESULTS

CD1c+ conventional DCs and plasmacytoid DCs from healthy individuals exhibited diverse transcriptional signatures, while the DC transcriptional signatures in patients with autoimmune disease were altered. In particular, distinct DC clusters, characterized by up-regulation of TAP1, IRF7, and IFNAR1, were abundant in patients with systemic autoimmune disease, whereas DCs from patients with type 1 diabetes had decreased expression of the regulatory genes PTPN6, TGFB, and TYROBP. The frequency of CD1c+ conventional DCs that expressed a systemic autoimmune profile directly correlated with the extent of disease activity in patients with rheumatoid arthritis (Spearman's r = 0.60, P = 0.03).

CONCLUSION

DC transcriptional signatures are altered in patients with autoimmune disease and are associated with the level of disease activity, suggesting that immune cell transcriptional profiling could improve our ability to detect and understand the heterogeneity of these diseases, and could guide treatment choices in patients with a complex autoimmune disease.

Reduction in White Blood Cell, Neutrophil, and Red Blood Cell Counts Related to Sex, HLA, and Islet Autoantibodies in Swedish TEDDY Children at Increased Risk for Type 1 Diabetes

Islet autoantibodies (IAs) precede the clinical onset of type 1 diabetes (T1D); however, the knowledge is limited about whether the prodrome affects complete blood counts (CBCs) in 4- to 12-year-old children with increased genetic risk for T1D. This study tested whether CBCs were altered in 4- to 12-year-old children without (n = 376) or with one or several IAs against insulin, GAD65, or IA-2 (n = 72). CBC was analyzed during longitudinal follow-up in 448 Swedish children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study. A linear mixed-effects model was used to assess potential association between IA and CBC measurements over time. The white blood cell and neutrophil counts were reduced in children with IAs, primarily in boys. In contrast, girls had lower levels of hemoglobin and hematocrit. Positivity for multiple IAs showed the lowest counts in white blood cells and neutrophils in boys and red blood cells, hemoglobin, and hematocrit in girls. These associations were primarily observed in children with the HLA-DR3-DQ2/DR4-DQ8 genotype. We conclude that the reduction in neutrophils and red blood cells in children with multiple IAs and HLA-DR3-DQ2/DR4-DQ8 genotype may signal a sex-dependent islet autoimmunity detected in longitudinal CBCs.

The Environmental Determinants of Diabetes in the Young (TEDDY) Study: 2018 Update

PURPOSE OF REVIEW

The environmental triggers of islet autoimmunity leading to type 1 diabetes (T1D) need to be elucidated to inform primary prevention. The Environmental Determinants of Diabetes in the Young (TEDDY) Study follows from birth 8676 children with T1D risk HLA-DR-DQ genotypes in the USA, Finland, Germany, and Sweden. Most study participants (89%) have no first-degree relative with T1D. The primary outcomes include the appearance of one or more persistent islet autoantibodies (islet autoimmunity, IA) and clinical T1D.

RECENT FINDINGS

As of February 28, 2018, 769 children had developed IA and 310 have progressed to T1D. Secondary outcomes include celiac disease and autoimmune thyroid disease. While the follow-up continues, TEDDY has already evaluated a number of candidate environmental triggers, including infections, probiotics, micronutrient, and microbiome. TEDDY results suggest that there are multiple pathways leading to the destruction of pancreatic beta-cells. Ongoing measurements of further specific exposures, gene variants, and gene-environment interactions and detailed "omics" studies will provide novel information on the pathogenesis of T1D.

Approaches and advances in the genetic causes of autoimmune disease and their implications

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.

Type 1 diabetes

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.

Clinical and research uses of genetic risk scores in type 1 diabetes

Type 1 diabetes (T1D) is a chronic disease of high blood glucose caused by autoimmune destruction of pancreatic beta cells eventually resulting in severe insulin deficiency. T1D has a significant heritable risk. Genetic associations found are particularly strong in the HLA class II region but T1D is a polygenic disease associated with over 60 loci across the genome. Polygenic risk scores are one method of summing these genetic risk elements as a single continuous variable. This review discusses the clinical and research utility of genetic risk scores in T1D particularly in disease prediction and progression. We also explore creative uses of genetic risk scores in big data and the limitations of using a genetic risk score. The increase in publically available genetic data and rapid fall in costs of genotyping mean that a T1D genetic risk score (T1D GRS) is likely to prove useful for disease prediction, discrimination, investigation of unusual cohorts, and investigation of biology in large datasets where genetic data are available.

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

BACKGROUND

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.

METHODS AND FINDINGS

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.

CONCLUSIONS

A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.

Understanding Pre-Type 1 Diabetes: The Key to Prevention

While the incidence of type 1 diabetes continues to rise by 3% each year, the ability to prevent this disease remains elusive. Hybrid closed loop devices, artificial pancreas systems, and continuous glucose monitoring technology have helped to ease the daily burden for many people living with type 1 diabetes. However, the artificial pancreas is not a cure; more research is needed to achieve our ultimate goal of preventing type 1 diabetes. The preceding decades have generated a wealth of information regarding the natural history of pre-type 1 diabetes. Islet autoimmunity in the form of multiple autoantibodies is known to be highly predictive of progression to disease. Staging systems have been devised to better characterize pre-type 1, direct mechanistic understanding of disease, and guide the design of prevention studies. However, there are no evidence-based recommendations for practitioners caring for autoantibody patients other than to encourage enrollment in research studies. Close monitoring of high-risk patients in natural history studies markedly reduces diabetic ketoacidosis rates at diagnosis and research participation is critical to finding a means of preventing type 1 diabetes. The discovery of an effective preventative strategy for type 1 diabetes will justify universal risk screening for all children.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis

PURPOSE OF REVIEW

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment.

RECENT FINDINGS

T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.