Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children

Looking back at the TEDDY study: lessons and future directions

The goal of the TEDDY (The Environmental Determinants of Diabetes in the Young) study is to elucidate factors leading to the initiation of islet autoimmunity (first primary outcome) and those related to progression to type 1 diabetes mellitus (T1DM; second primary outcome). This Review outlines the key findings so far, particularly related to the first primary outcome. The background, history and organization of the study are discussed. Recruitment and follow-up (from age 4 months to 15 years) of 8,667 children showed high retention and compliance. End points of the presence of autoantibodies against insulin, GAD65, IA-2 and ZnT8 revealed the HLA-associated early appearance of insulin autoantibodies (1-3 years of age) and the later appearance of GAD65 autoantibodies. Competing autoantibodies against tissue transglutaminase (marking coeliac disease autoimmunity) also appeared early (2-4 years). Genetic and environmental factors, including enterovirus infection and gastroenteritis, support mechanistic differences underlying one phenotype of autoimmunity against insulin and another against GAD65. Infant growth and both probiotics and high protein intake affect the two phenotypes differently, as do serious life events during pregnancy. As the end of the TEDDY sampling phase is approaching, major omics approaches are in progress to further dissect the mechanisms that might explain the two possible endotypes of T1DM.

Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles

OBJECTIVE

To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes.

RESEARCH DESIGN AND METHODS

The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual's temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis.

RESULTS

We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0-79.2) was observed in children who first developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9-95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a single, often reverting, autoantibody.

CONCLUSIONS

The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes.

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications

Aims/hypothesis

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity.

Methods

Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects.

Results

Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications.

Conclusions

Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

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.

Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity

OBJECTIVE

To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA).

RESEARCH DESIGN AND METHODS

Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis.

RESULTS

Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85-92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5-40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes.

CONCLUSIONS

The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA.

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.

Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering

AIMS/HYPOTHESIS

Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset.

METHODS

Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis.

RESULTS

We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk.

CONCLUSIONS/INTERPRETATION

This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.

Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes

In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not.

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

Background

Scope of review

Major conclusions

Characterising the age-dependent effects of risk factors on type 1 diabetes progression

AIMS/HYPOTHESIS

Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age.

METHODS

Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes.

RESULTS

Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibody-positive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single- and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single- and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate.

CONCLUSIONS/INTERPRETATION

Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.

Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories

Development of islet autoimmunity precedes the onset of type 1 diabetes in children, however, the presence of autoantibodies does not necessarily lead to manifest disease and the onset of clinical symptoms is hard to predict. Here we show, by longitudinal sampling of islet autoantibodies (IAb) to insulin, glutamic acid decarboxylase and islet antigen-2 that disease progression follows distinct trajectories. Of the combined Type 1 Data Intelligence cohort of 24662 participants, 2172 individuals fulfill the criteria of two or more follow-up visits and IAb positivity at least once, with 652 progressing to type 1 diabetes during the 15 years course of the study. Our Continuous-Time Hidden Markov Models, that are developed to discover and visualize latent states based on the collected data and clinical characteristics of the patients, show that the health state of participants progresses from 11 distinct latent states as per three trajectories (TR1, TR2 and TR3), with associated 5-year cumulative diabetes-free survival of 40% (95% confidence interval [CI], 35% to 47%), 62% (95% CI, 57% to 67%), and 88% (95% CI, 85% to 91%), respectively (p < 0.0001). Age, sex, and HLA-DR status further refine the progression rates within trajectories, enabling clinically useful prediction of disease onset.

Presence of islet autoantibodies precedes the onset of type 1 diabetes but it does not predict whether and how fast symptomatic disease appears. Here authors present a model to predict and visualize progression to diabetes by using a large longitudinal data set on autoantibodies and clinical parameters as input.

Enteroviruses and Type 1 Diabetes: Multiple Mechanisms and Factors?

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by insulin deficiency and resultant hyperglycemia. Complex interactions of genetic and environmental factors trigger the onset of autoimmune mechanisms responsible for development of autoimmunity to β cell antigens and subsequent development of T1D. A potential role of virus infections has long been hypothesized, and growing evidence continues to implicate enteroviruses as the most probable triggering viruses. Recent studies have strengthened the association between enteroviruses and development of autoimmunity in T1D patients, potentially through persistent infections. Enterovirus infections may contribute to different stages of disease development. We review data from both human cohort studies and experimental research exploring the potential roles and molecular mechanisms by which enterovirus infections can impact disease outcome.

Enteroviruses and Type 1 Diabetes: Multiple Mechanisms and Factors?

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by insulin deficiency and resultant hyperglycemia. Complex interactions of genetic and environmental factors trigger the onset of autoimmune mechanisms responsible for development of autoimmunity to β cell antigens and subsequent development of T1D. A potential role of virus infections has long been hypothesized, and growing evidence continues to implicate enteroviruses as the most probable triggering viruses. Recent studies have strengthened the association between enteroviruses and development of autoimmunity in T1D patients, potentially through persistent infections. Enterovirus infections may contribute to different stages of disease development. We review data from both human cohort studies and experimental research exploring the potential roles and molecular mechanisms by which enterovirus infections can impact disease outcome.

Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies: Beyond a Simple Count

Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject's age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

The virome in early life and childhood and development of islet autoimmunity and type 1 diabetes: A systematic review and meta-analysis of observational studies

Viruses are postulated as primary candidate triggers of islet autoimmunity (IA) and type 1 diabetes (T1D), based on considerable epidemiological and experimental evidence. Recent studies have investigated the association between all viruses (the 'virome') and IA/T1D using metagenomic next-generation sequencing (mNGS). Current associations between the early life virome and the development of IA/T1D were analysed in a systematic review and meta-analysis of human observational studies from Medline and EMBASE (published 2000-June 2020), without language restriction. Inclusion criteria were as follows: cohort and case-control studies examining the virome using mNGS in clinical specimens of children ≤18 years who developed IA/T1D. The National Health and Medical Research Council level of evidence scale and Newcastle-Ottawa scale were used for study appraisal. Meta-analysis for exposure to specific viruses was performed using random-effects models, and the strength of association was measured using odds ratios (ORs) and 95% confidence intervals (CIs). Eligible studies (one case-control, nine nested case-control) included 1,425 participants (695 cases, 730 controls) and examined IA (n = 1,023) or T1D (n = 402). Meta-analysis identified small but significant associations between IA and number of stool samples positive for all enteroviruses (OR 1.14, 95% CI 1.00-1.29, p = 0.05; heterogeneity χ2  = 1.51, p = 0.68, I2  = 0%), consecutive positivity for enteroviruses (1.55, 1.09-2.20, p = 0.01; χ2  = 0.19, p = 0.91, I2  = 0%) and number of stool samples positive specifically for enterovirus B (1.20, 1.01-1.42, p = 0.04; χ2  = 0.03, p = 0.86, I2  = 0%). Virome analyses to date have demonstrated associations between enteroviruses and IA that may be clinically significant. However, larger prospective mNGS studies with more frequent sampling and follow-up from pregnancy are required to further elucidate associations between early virus exposure and IA/T1D.

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

Proinsulin-Reactive CD4 T Cells in the Islets of Type 1 Diabetes Organ Donors

Proinsulin is an abundant protein that is selectively expressed by pancreatic beta cells and has been a focus for development of antigen-specific immunotherapies for type 1 diabetes (T1D). In this study, we sought to comprehensively evaluate reactivity to preproinsulin by CD4 T cells originally isolated from pancreatic islets of organ donors having T1D. We analyzed 187 T cell receptor (TCR) clonotypes expressed by CD4 T cells obtained from six T1D donors and determined their response to 99 truncated preproinsulin peptide pools, in the presence of autologous B cells. We identified 14 TCR clonotypes from four out of the six donors that responded to preproinsulin peptides. Epitopes were found across all of proinsulin (insulin B-chain, C-peptide, and A-chain) including four hot spot regions containing peptides commonly targeted by TCR clonotypes derived from multiple T1D donors. Of importance, these hot spots overlap with peptide regions to which CD4 T cell responses have previously been detected in the peripheral blood of T1D patients. The 14 TCR clonotypes recognized proinsulin peptides presented by various HLA class II molecules, but there was a trend for dominant restriction with HLA-DQ, especially T1D risk alleles DQ8, DQ2, and DQ8-trans. The characteristics of the tri-molecular complex including proinsulin peptide, HLA-DQ molecule, and TCR derived from CD4 T cells in islets, provides an essential basis for developing antigen-specific biomarkers as well as immunotherapies.

Modeling Disease Progression Trajectories from Longitudinal Observational Data

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.

Multiple inducers and novel roles of autoantibodies against the obligatory NMDAR subunit NR1: a translational study from chronic life stress to brain injury

Circulating autoantibodies (AB) of different immunoglobulin classes (IgM, IgA, and IgG), directed against the obligatory N-methyl-D-aspartate-receptor subunit NR1 (NMDAR1-AB), belong to the mammalian autoimmune repertoire, and appear with age-dependently high seroprevalence across health and disease. Upon access to the brain, they can exert NMDAR-antagonistic/ketamine-like actions. Still unanswered key questions, addressed here, are conditions of NMDAR1-AB formation/boosting, intraindividual persistence/course in serum over time, and (patho)physiological significance of NMDAR1-AB in modulating neuropsychiatric phenotypes. We demonstrate in a translational fashion from mouse to human that (1) serum NMDAR1-AB fluctuate upon long-term observation, independent of blood-brain barrier (BBB) perturbation; (2) a standardized small brain lesion in juvenile mice leads to increased NMDAR1-AB seroprevalence (IgM + IgG), together with enhanced Ig-class diversity; (3) CTLA4 (immune-checkpoint) genotypes, previously found associated with autoimmune disease, predispose to serum NMDAR1-AB in humans; (4) finally, pursuing our prior findings of an early increase in NMDAR1-AB seroprevalence in human migrants, which implicated chronic life stress as inducer, we independently replicate these results with prospectively recruited refugee minors. Most importantly, we here provide the first experimental evidence in mice of chronic life stress promoting serum NMDAR1-AB (IgA). Strikingly, stress-induced depressive-like behavior in mice and depression/anxiety in humans are reduced in NMDAR1-AB carriers with compromised BBB where NMDAR1-AB can readily reach the brain. To conclude, NMDAR1-AB may have a role as endogenous NMDAR antagonists, formed or boosted under various circumstances, ranging from genetic predisposition to, e.g., tumors, infection, brain injury, and stress, altogether increasing over lifetime, and exerting a spectrum of possible effects, also including beneficial functions.

Dynamics of Islet Autoantibodies During Prospective Follow-Up From Birth to Age 15 Years

CONTEXT

We set out to characterize the dynamics of islet autoantibodies over the first 15 years of life in children carrying genetic susceptibility to type 1 diabetes (T1D). We also assessed systematically the role of zinc transporter 8 autoantibodies (ZnT8A) in this context.

DESIGN

HLA-predisposed children (N = 1006, 53.0% boys) recruited from the general population during 1994 to 1997 were observed from birth over a median time of 14.9 years (range, 1.9-15.5 years) for ZnT8A, islet cell (ICA), insulin (IAA), glutamate decarboxylase (GADA), and islet antigen-2 (IA-2A) antibodies, and for T1D.

RESULTS

By age 15.5 years, 35 (3.5%) children had progressed to T1D. Islet autoimmunity developed in 275 (27.3%) children at a median age of 7.4 years (range, 0.3-15.1 years). The ICA seroconversion rate increased toward puberty, but the biochemically defined autoantibodies peaked at a young age. Before age 2 years, ZnT8A and IAA appeared commonly as the first autoantibody, but in the preschool years IA-2A- and especially GADA-initiated autoimmunity increased. Thereafter, GADA-positive seroconversions continued to appear steadily until ages 10 to 15 years. Inverse IAA seroconversions occurred frequently (49.3% turned negative) and marked a prolonged delay from seroconversion to diagnosis compared to persistent IAA (8.2 vs 3.4 years; P = .01).

CONCLUSIONS

In HLA-predisposed children, the primary autoantibody is characteristic of age and might reflect the events driving the disease process toward clinical T1D. Autoantibody persistence affects the risk of T1D. These findings provide a framework for identifying disease subpopulations and for personalizing the efforts to predict and prevent T1D.

Hierarchical Order of Distinct Autoantibody Spreading and Progression to Type 1 Diabetes in the TEDDY Study

OBJECTIVE

The first-appearing β-cell autoantibody has been shown to influence risk of type 1 diabetes (T1D). Here, we assessed the risk of autoantibody spreading to the second-appearing autoantibody and further progression to clinical disease in The Environmental Determinants of Diabetes in the Young (TEDDY) study.

RESEARCH DESIGN AND METHODS

Eligible children with increased HLA-DR-DQ genetic risk for T1D were followed quarterly from age 3 months up to 15 years for development of a single first-appearing autoantibody (GAD antibody [GADA], insulin autoantibody [IAA], or insulinoma antigen-2 autoantibody [IA-2A]) and subsequent development of a single second-appearing autoantibody and progression to T1D. Autoantibody positivity was defined as positivity for a specific autoantibody at two consecutive visits confirmed in two laboratories. Zinc transporter 8 autoantibody (ZnT8A) was measured in children who developed another autoantibody.

RESULTS

There were 608 children who developed a single first-appearing autoantibody (IAA, n = 282, or GADA, n = 326) with a median follow-up of 12.5 years from birth. The risk of a second-appearing autoantibody was independent of GADA versus IAA as a first-appearing autoantibody (adjusted hazard ratio [HR] 1.12; 95% CI 0.88-1.42; P = 0.36). Second-appearing GADA, IAA, IA-2A, or ZnT8A conferred an increased risk of T1D compared with children who remained positive for a single autoantibody, e.g., IAA or GADA second (adjusted HR 6.44; 95% CI 3.78-10.98), IA-2A second (adjusted HR 16.33; 95% CI 9.10-29.29; P < 0.0001), or ZnT8A second (adjusted HR 5.35; 95% CI 2.61-10.95; P < 0.0001). In children who developed a distinct second autoantibody, IA-2A (adjusted HR 3.08; 95% CI 2.04-4.65; P < 0.0001) conferred a greater risk of progression to T1D as compared with GADA or IAA. Additionally, both a younger initial age at seroconversion and shorter time to the development of the second-appearing autoantibody increased the risk for T1D.

CONCLUSIONS

The hierarchical order of distinct autoantibody spreading was independent of the first-appearing autoantibody type and was age-dependent and augmented the risk of progression to T1D.

Relevance of Essential Trace Elements in Nutrition and Drinking Water for Human Health and Autoimmune Disease Risk

Trace elements produce double-edged effects on the lives of animals and particularly of humans. On one hand, these elements represent potentially toxic agents; on the other hand, they are essentially needed to support growth and development and confer protection against disease. Certain trace elements and metals are particularly involved in humoral and cellular immune responses, playing the roles of cofactors for essential enzymes and antioxidant molecules. The amount taken up and the accumulation in human tissues decisively control whether the exerted effects are toxic or beneficial. For these reasons, there is an urgent need to re-consider, harmonize and update current legislative regulations regarding the concentrations of trace elements in food and in drinking water. This review aims to provide information on the interrelation of certain trace elements with risk of autoimmune disease, with a particular focus on type 1 diabetes and multiple sclerosis. In addition, an overview of the current regulations and regulatory gaps is provided in order to highlight the importance of this issue for everyday nutrition and human health.

T-Cell Epitopes and Neo-epitopes in Type 1 Diabetes: A Comprehensive Update and Reappraisal

The autoimmune disease type 1 diabetes is characterized by effector T-cell responses to pancreatic β-cell-derived peptides presented by HLA class I and class II molecules, leading ultimately to β-cell demise and insulin insufficiency. Although a given HLA molecule presents a vast array of peptides, only those recognized by T cells are designated as epitopes. Given their intimate link to etiology, the discovery and characterization of T-cell epitopes is a critical aspect of type 1 diabetes research. Understanding epitope recognition is also crucial for the pursuit of antigen-specific immunotherapies and implementation of strategies for T-cell monitoring. For these reasons, a cataloging and appraisal of the T-cell epitopes targeted in type 1 diabetes was completed over a decade ago, providing an important resource for both the research and the clinical communities. Here we present a much needed update and reappraisal of this earlier work and include online supplementary material where we cross-index each epitope with its primary references and Immune Epitope Database (IEDB) identifier. Our analysis includes a grading scale to score the degree of evidence available for each epitope, which conveys our perspective on several useful criteria for epitope evaluation. While providing an efficient summary of the arguably impressive current state of knowledge, this work also brings to light several deficiencies. These include the need for improved epitope validation, as few epitopes score highly by the criteria employed, and the dearth of investigations of the epitopes recognized in the context of several understudied type 1 diabetes-associated HLA molecules.

From immunohistological to anatomical alterations of human pancreas in type 1 diabetes: New concepts on the stage

The histological analysis of human pancreatic samples in type 1 diabetes (T1D) has been proven essential to move forward in the evaluation of in situ events characterizing T1D. Increasing availability of pancreatic tissues collected from diabetic multiorgan donors by centralized biorepositories, which have shared tissues among researchers in the field, has allowed a deeper understanding of T1D pathophysiology, using novel immunohistological and high-throughput methods. In this review, we provide a comprehensive update of the main recent advancements in the characterization of cellular and molecular events involving endocrine and exocrine pancreas as well as the immune system in the onset and progression of T1D. Additionally, we underline novel elements, which provide evidence that T1D pathological changes affect not only islet β-cells but also the entire pancreas.

The risk of progression to type 1 diabetes is highly variable in individuals with multiple autoantibodies following screening

AIMS/HYPOTHESIS

Young children who develop multiple autoantibodies (mAbs) are at very high risk for type 1 diabetes. We assessed whether a population with mAbs detected by screening is also at very high risk, and how risk varies according to age, type of autoantibodies and metabolic status.

METHODS

Type 1 Diabetes TrialNet Pathway to Prevention participants with mAbs (n = 1815; age, 12.35 ± 9.39 years; range, 1-49 years) were analysed. Type 1 diabetes risk was assessed according to age, autoantibody type/number (insulin autoantibodies [IAA], glutamic acid decarboxylase autoantibodies [GADA], insulinoma-associated antigen-2 autoantibodies [IA-2A] or zinc transporter 8 autoantibodies [ZnT8A]) and Index60 (composite measure of fasting C-peptide, 60 min glucose and 60 min C-peptide). Cox regression and cumulative incidence curves were utilised in this cohort study.

RESULTS

Age was inversely related to type 1 diabetes risk in those with mAbs (HR 0.97 [95% CI 0.96, 0.99]). Among participants with 2 autoantibodies, those with GADA had less risk (HR 0.35 [95% CI 0.22, 0.57]) and those with IA-2A had higher risk (HR 2.82 [95% CI 1.76, 4.51]) of type 1 diabetes. Those with IAA and GADA had only a 17% 5 year risk of type 1 diabetes. The risk was significantly lower for those with Index60 <1.0 (HR 0.23 [95% CI 0.19, 0.30]) vs those with Index60 values ≥1.0. Among the 12% (225/1815) ≥12.0 years of age with GADA positivity, IA-2A negativity and Index60 <1.0, the 5 year risk of type 1 diabetes was 8%.

CONCLUSIONS/INTERPRETATION

Type 1 diabetes risk varies substantially according to age, autoantibody type and metabolic status in individuals screened for mAbs. An appreciable proportion of older children and adults with mAbs appear to have a low risk of progressing to type 1 diabetes at 5 years. With this knowledge, clinical trials of type 1 diabetes prevention can better target those most likely to progress.

Uncommon Presentations of Diabetes: Zebras in the Herd

The majority of patients with diabetes are diagnosed as having either type 1 or type 2 diabetes. However, when encountered in clinical practice, some patients may not match the classic diagnostic criteria or expected clinical presentation for either type of the disease. Latent autoimmune, ketosis-prone, and monogenic diabetes are nonclassical forms of diabetes that are often misdiagnosed as either type 1 or type 2 diabetes. Recognizing the distinguishing clinical characteristics and understanding the diagnostic criteria for each will lead to appropriate treatment, facilitate personalized medicine, and improve patient outcomes.

Birth and coming of age of islet autoantibodies

This review takes the reader through 45 years of islet autoantibody research, from the discovery of islet‐cell antibodies in 1974 to today’s population‐based screening for presymptomatic early‐stage type 1 diabetes. The review emphasizes the current practical value of, and factors to be considered in, the measurement of islet autoantibodies.

What Have Slow Progressors Taught Us About T1D-Mind the Gap!

PURPOSE OF REVIEW

Progression rate from islet autoimmunity to clinical diabetes is unpredictable. In this review, we focus on an intriguing group of slow progressors who have high-risk islet autoantibody profiles but some remain diabetes free for decades.

RECENT FINDINGS

Birth cohort studies show that islet autoimmunity presents early in life and approximately 70% of individuals with multiple islet autoantibodies develop clinical symptoms of diabetes within 10 years. Some "at risk" individuals however progress very slowly. Recent genetic studies confirm that approximately half of type 1 diabetes (T1D) is diagnosed in adulthood. This creates a conundrum; slow progressors cannot account for the number of cases diagnosed in the adult population. There is a large "gap" in our understanding of the pathogenesis of adult onset T1D and a need for longitudinal studies to determine whether there are "at risk" adults in the general population; some of whom are rapid and some slow adult progressors.

The Evolving Landscape of Autoantigen Discovery and Characterization in Type 1 Diabetes

Type 1 diabetes (T1D) is an autoimmune disease that is caused, in part, by T cell-mediated destruction of insulin-producing β-cells. High risk for disease, in those with genetic susceptibility, is predicted by the presence of two or more autoantibodies against insulin, the 65-kDa form of glutamic acid decarboxylase (GAD65), insulinoma-associated protein 2 (IA-2), and zinc transporter 8 (ZnT8). Despite this knowledge, we still do not know what leads to the breakdown of tolerance to these autoantigens, and we have an incomplete understanding of T1D etiology and pathophysiology. Several new autoantibodies have recently been discovered using innovative technologies, but neither their potential utility in monitoring disease development and treatment nor their role in the pathophysiology and etiology of T1D has been explored. Moreover, neoantigen generation (through posttranslational modification, the formation of hybrid peptides containing two distinct regions of an antigen or antigens, alternative open reading frame usage, and translation of RNA splicing variants) has been reported, and autoreactive T cells that target these neoantigens have been identified. Collectively, these new studies provide a conceptual framework to understand the breakdown of self-tolerance, if such modifications occur in a tissue- or disease-specific context. A recent workshop sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases brought together investigators who are using new methods and technologies to identify autoantigens and characterize immune responses toward these proteins. Researchers with diverse expertise shared ideas and identified resources to accelerate antigen discovery and the detection of autoimmune responses in T1D. The application of this knowledge will direct strategies for the identification of improved biomarkers for disease progression and treatment response monitoring and, ultimately, will form the foundation for novel antigen-specific therapeutics. This Perspective highlights the key issues that were addressed at the workshop and identifies areas for future investigation.

Antibodies to oxidized insulin improve prediction of type 1 diabetes in children with positive standard islet autoantibodies

BACKGROUND

Antibodies to posttranslationally modified insulin (oxPTM-INS-Ab) are a novel biomarker of type 1 diabetes (T1D). Here, we evaluated whether oxPTM-INS-Ab can improve T1D prediction in children with positive standard islet autoantibodies (AAB).

METHODS

We evaluated sensitivity, specificity, accuracy, and risk for progression to T1D associated with oxPTM-INS-Ab and the standard islet AAB that include insulin (IAA), GAD (GADA), and tyrosine phosphatase 2 (IA-2A) in a cohort of islet AAB-positive (AAB⁺ ) children from the general population (median follow-up 8.8 years).

RESULTS

oxPTM-INS-Ab was the most sensitive and specific autoantibody biomarker (74% sensitivity, 91% specificity), followed by IA-2A (71% sensitivity, 91% specificity). GADA and IAA showed lower sensitivity (65% and 50%, respectively) and specificity (66% and 68%, respectively). Accuracy (AUC of ROC) of oxPTM-INS-Ab was higher than GADA and IAA (P = 0.003 and P = 0.017, respectively), and similar to IA-2A (P = 0.896). oxPTM-INS-Ab and IA-2A were more effective than IAA for detecting progr-T1D when used as second-line biomarker in GADA⁺ children. Risk for diabetes was higher (P = 0.03) among multiple AAB⁺ who were also oxPTM-INS-Ab⁺ compared with those who were oxPTM-INS-Ab^- . Importantly, when replacing IAA with oxPTM-INS-Ab, diabetes risk increased to 100% in children with oxPTM-INS-Ab⁺ in combination with GADA⁺ and IA-2A⁺ , compared with 84.37% in those with IAA⁺ , GADA⁺ , and IA-2A⁺ (P = 0.04).

CONCLUSIONS

Antibodies to oxidized insulin (oxPTM-INS-Ab), compared with IAA which measure autoantibodies to native insulin, improve T1D risk assessment and prediction accuracy in AAB⁺ children.