A novel approach for the analysis of longitudinal profiles reveals delayed progression to type 1 diabetes in a subgroup of multiple-islet-autoantibody-positive children

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.

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.

Preclinical Autoimmune Disease: a Comparison of Rheumatoid Arthritis, Systemic Lupus Erythematosus, Multiple Sclerosis and Type 1 Diabetes

The preclinical phase of autoimmune disorders is characterized by an initial asymptomatic phase of varying length followed by nonspecific signs and symptoms. A variety of autoimmune and inflammatory manifestations can be present and tend to increase in the last months to years before a clinical diagnosis can be made. The phenotype of an autoimmune disease depends on the involved organs, the underlying genetic susceptibility and pathophysiological processes. There are different as well as shared genetic or environmental risk factors and pathophysiological mechanisms between separate diseases. To shed more light on this, in this narrative review we compare the preclinical disease course of four important autoimmune diseases with distinct phenotypes: rheumatoid arthritis (RA), Systemic Lupus Erythematosus (SLE), multiple sclerosis (MS) and type 1 diabetes (T1D). In general, we observed some notable similarities such as a North-South gradient of decreasing prevalence, a female preponderance (except for T1D), major genetic risk factors at the HLA level, partly overlapping cytokine profiles and lifestyle risk factors such as obesity, smoking and stress. The latter risk factors are known to produce a state of chronic systemic low grade inflammation. A central characteristic of all four diseases is an on average lengthy prodromal phase with no or minor symptoms which can last many years, suggesting a gradually evolving interaction between the genetic profile and the environment. Part of the abnormalities may be present in unaffected family members, and autoimmune diseases can also cluster in families. In conclusion, a promising strategy for prevention of autoimmune diseases might be to address adverse life style factors by public health measures at the population level.

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.

Detecting Autoantibodies by Multiparametric Assays: Impact on Prevention, Diagnosis, Monitoring, and Personalized Therapy in Autoimmune Diseases

BACKGROUND

The introduction of multiparametric autoantibody tests has been proposed to improve the accuracy of the immunological diagnosis of autoimmune diseases (AID) and to accelerate time for completing the diagnostic process. Multiplex tests are capable of detecting many autoantibodies in a single run whereas a traditional immunoassay uses a single antigen to detect only a single specificity of autoantibodies. The reasons why multiplex tests could replace conventional immunoassays lie in the evidence that they allow for more efficient handling of large numbers of samples by the laboratory, while ensuring greater diagnostic sensitivity in AID screening.

CONTENT

This review aims to highlight the important role that multiparametric tests could assume when designed for defined profiles they are used not only for diagnostic purposes but also to predict the onset of AID to identify clinical phenotypes and to define prognosis. Furthermore, differences in the antibody profile could identify which subjects will be responsive or not to a specific pharmacological treatment.

SUMMARY

The use of autoantibody profiles, when specifically requested and performed with clinically validated technologies, can represent a significant step toward personalized medicine in autoimmunology.

Diagnostic Capabilities of Islet Autoantibodies in Children with New-Onset Type 1 Diabetes Mellitus and Healthy Siblings

The aim of the study is to determine the diagnostic utility of several islet autoantibodies and their combinations in order to identify individuals susceptible to type 1 diabetes mellitus (T1DM) among healthy siblings in the pediatric population within the scope of the development of a screening program.

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.

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.

Insulin-Like Growth Factor Dysregulation Both Preceding and Following Type 1 Diabetes Diagnosis

Insulin-like growth factors (IGFs), specifically IGF1 and IGF2, promote glucose metabolism, with their availability regulated by IGF-binding proteins (IGFBPs). We hypothesized that IGF1 and IGF2 levels, or their bioavailability, are reduced during type 1 diabetes development. Total serum IGF1, IGF2, and IGFBP1-7 levels were measured in an age-matched, cross-sectional cohort at varying stages of progression to type 1 diabetes. IGF1 and IGF2 levels were significantly lower in autoantibody (AAb)⁺ compared with AAb^- relatives of subjects with type 1 diabetes. Most high-affinity IGFBPs were unchanged in individuals with pre-type 1 diabetes, suggesting that total IGF levels may reflect bioactivity. We also measured serum IGFs from a cohort of fasted subjects with type 1 diabetes. IGF1 levels significantly decreased with disease duration, in parallel with declining β-cell function. Additionally, plasma IGF levels were assessed in an AAb⁺ cohort monthly for a year. IGF1 and IGF2 showed longitudinal stability in single AAb⁺ subjects, but IGF1 levels decreased over time in subjects with multiple AAb and those who progressed to type 1 diabetes, particularly postdiagnosis. In sum, IGFs are dysregulated both before and after the clinical diagnosis of type 1 diabetes and may serve as novel biomarkers to improve disease prediction.

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.

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.

Type 1 Diabetes-related Autoantibodies in Different Forms of Diabetes

Autoantibodies against Glutamic Acid Decarboxylase (GADA), insulinoma antigen-2 (IA- 2A), insulin (IAA) and the most recently Zinc Transporter 8 (ZnT8A) are one of the most reliable biomarkers for autoimmune diabetes in both children and adults. They are today the only biomarkers that can distinguish Latent Autoimmune Diabetes in Adults (LADA) from phenotypically type 2 diabetes. As the frequency of autoantibodies at diagnosis in childhood type 1 diabetes depends on age, GADA is by far the most common in adult onset autoimmune diabetes, especially LADA. Being multiple autoantibody positive have also shown to be more common in childhood diabetes compared to adult onset diabetes, and multiple autoantibody positivity have a high predictive value of childhood type 1 diabetes. Autoantibodies have shown inconsistent results to predict diabetes in adults. Levels of autoantibodies are reported to cause heterogeneity in LADA. Reports indicate that individuals with high levels of autoantibodies have a more type 1 diabetes like phenotype and individuals with low levels of autoantibody positivity have a more type 2 diabetes like phenotype. It is also well known that autoantibody levels can fluctuate and transient autoantibody positivity in adult onset autoimmune diabetes have been reported to affect the phenotype.

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

Progression to clinical type 1 diabetes varies among children who develop β-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (n = 370) were clustered, and progression from seroconversion to clinical diabetes within 5 years ranged between clusters from 6% (95% CI 0, 17.4) to 84% (59.2, 93.6). Children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up had the highest risk of diabetes, and this risk was unaffected by GADA status. Clusters of children who lacked stable-positive GADA responses contained more boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of β-cell autoantibody-positive children who distinctly progressed to clinical type 1 diabetes, and it provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.

Serum biomarkers for diagnosis and prediction of type 1 diabetes

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic βcells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including a combination of glucose, glycated molecules, C-peptide, and autoantibodies have been well established for the diagnosis of T1D. However, these molecules often mark a late stage of the disease when ∼90% of the pancreatic insulin-producing β-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive "-omics" technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D but mainly focus on the novel biomarkers that are identified as markers of β-cell destruction and screened with the use of state-of-the-art "-omics" technologies.

Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts: the SNAIL study

AIMS/HYPOTHESIS

Multiple islet autoimmunity increases risk of diabetes, but not all individuals positive for two or more islet autoantibodies progress to disease within a decade. Major islet autoantibodies recognise insulin (IAA), GAD (GADA), islet antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A). Here we describe the baseline characteristics of a unique cohort of 'slow progressors' (n = 132) who were positive for multiple islet autoantibodies (IAA, GADA, IA-2A or ZnT8A) but did not progress to diabetes within 10 years.

METHODS

Individuals were identified from five studies (BABYDIAB, Germany; Diabetes Autoimmunity Study in the Young [DAISY], USA; All Babies in Southeast Sweden [ABIS], Sweden; Bart's Oxford Family Study [BOX], UK and the Pittsburgh Family Study, USA). Multiple islet autoantibody characteristics were determined using harmonised assays where possible. HLA class II risk was compared between slow progressors and rapid progressors (n = 348 diagnosed <5 years old from BOX) using the χ² test.

RESULTS

In the first available samples with detectable multiple antibodies, the most frequent autoantibodies were GADA (92%), followed by ZnT8A (62%), IAA (59%) and IA-2A (41%). High risk HLA class II genotypes were less frequent in slow (28%) than rapid progressors (42%, p = 0.011), but only two slow progressors carried the protective HLA DQ6 allele.

CONCLUSION

No distinguishing characteristics of slow progressors at first detection of multiple antibodies have yet been identified. Continued investigation of these individuals may provide insights into slow progression that will inform future efforts to slow or prevent progression to clinical diabetes.

Accelerated Progression to Type 1 Diabetes in the Presence of HLA-A*24 and -B*18 Is Restricted to Multiple Islet Autoantibody-Positive Individuals With Distinct HLA-DQ and Autoantibody Risk Profiles

OBJECTIVE

We investigated the effect of HLA class I risk alleles on disease progression in various phases of subclinical islet autoimmunity in first-degree relatives of patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

A registry-based group of siblings/offspring (aged 0-39 years) was monitored from single- to multiple-autoantibody positivity (n = 267) and from multiple-autoantibody positivity to clinical onset (n = 252) according to HLA-DQ, -A*24, -B*18, and -B*39 status. Genetic markers were determined by PCR sequence-specific oligotyping.

RESULTS

Unlike HLA-B*18 or -B*39, HLA-A*24 was associated with delayed progression from single- to multiple-autoantibody positivity (P = 0.009) but not to type 1 diabetes. This occurred independently from older age (P < 0.001) and absence of HLA-DQ2/DQ8 or -DQ8 (P < 0.001 and P = 0.003, respectively), and only in the presence of GAD autoantibodies. In contrast, HLA-A*24 was associated with accelerated progression from multiple-autoantibody positivity to clinical onset (P = 0.006), but its effects were restricted to HLA-DQ8⁺ relatives with IA-2 or zinc transporter 8 autoantibodies (P = 0.002). HLA-B*18, but not -B*39, was also associated with more rapid progression, but only in HLA-DQ2 carriers with double positivity for GAD and insulin autoantibodies (P = 0.004).

CONCLUSIONS

HLA-A*24 predisposes to a delayed antigen spreading of humoral autoimmunity, whereas HLA-A*24 and -B*18 are associated with accelerated progression of advanced subclinical autoimmunity in distinct risk groups. The relation of these alleles to the underlying disease process requires further investigation. Their typing should be relevant for the preparation and interpretation of observational and interventional studies in asymptomatic type 1 diabetes.

Primary islet autoantibody at initial seroconversion and autoantibodies at diagnosis of type 1 diabetes as markers of disease heterogeneity

OBJECTIVE

The relationship between patterns of islet autoantibodies at diagnosis and specificity of the first islet autoantibody at the initiation of autoimmunity was analyzed with the aim of identifying patterns informative of the primary autoantibodies.

METHODS

Information about a single first autoantibody at seroconversion and autoantibody data at diagnosis were available for 128 children participating in the follow-up cohort of the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study. Autoantibody data at diagnosis and genotyping results were also obtained from children in the Finnish Pediatric Diabetes Register (FPDR).

RESULTS

Insulin autoantibodies (IAA) were the most common primary antibodies (N = 68), followed by those for glutamic acid decarboxylase (GADA; N = 38), IA-2 antigen (IA-2A; N = 13), and zinc transporter 8 (ZnT8A; N = 9), whereas at diagnosis, IA-2A were most frequent (N = 103), followed by IAA (N = 78), ZnT8A (N = 73), and GADA (N = 71). Accordingly, the presence of many specific autoantibodies at diagnosis was due to the secondary antibodies appearing after primary antibodies, and in some cases, the primary autoantibody, most often IAA, had already disappeared at the time of diagnosis. Many of the autoantibody combinations present at diagnosis could be assembled into groups associated with either IAA or GADA as first autoantibodies. These combinations, in children diagnosed below the age of 10 years in the FPDR, were found to be strongly associated with risk genotypes in either INS (IAA first) or IKZF4-ERBB3 (GADA first) genes.

CONCLUSIONS

Autoantibody patterns at diagnosis may be informative on primary autoantibodies initiating autoimmunity in young children developing type 1 diabetes.

Type 1 Diabetes: Disease Stratification

Type 1 diabetes, a disorder characterized by immune-mediated loss of functional pancreatic beta cells, is a disease continuum with specific presymptomatic stages with defined risk of progression to symptomatic disease. Prognostic biomarkers have been developed for disease staging and for stratification of subjects that address the heterogeneity in rate of disease progression. Using biomarkers for stratification of subjects at different stages of type 1 diabetes will enable smaller and shorter intervention clinical trials with greater effect size. Addressing the heterogeneity of the disease will allow precision medicine-based approaches to prevention and interception of presymptomatic stages of disease and treatment and cure of symptomatic disease.

Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

AIMS

The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models.

METHODS

We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D.

RESULTS

For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion.

CONCLUSIONS

These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.

Early prediction of autoimmune (type 1) diabetes

Underlying type 1 diabetes is a genetic aetiology dominated by the influence of specific HLA haplotypes involving primarily the class II DR-DQ region. In genetically predisposed children with the DR4-DQ8 haplotype, exogenous factors, yet to be identified, are thought to trigger an autoimmune reaction against insulin, signalled by insulin autoantibodies as the first autoantibody to appear. In children with the DR3-DQ2 haplotype, the triggering reaction is primarily against GAD signalled by GAD autoantibodies (GADA) as the first-appearing autoantibody. The incidence rate of insulin autoantibodies as the first-appearing autoantibody peaks during the first years of life and declines thereafter. The incidence rate of GADA as the first-appearing autoantibody peaks later but does not decline. The first autoantibody may variably be followed, in an apparently non-HLA-associated pathogenesis, by a second, third or fourth autoantibody. Although not all persons with a single type of autoantibody progress to diabetes, the presence of multiple autoantibodies seems invariably to be followed by loss of functional beta cell mass and eventually by dysglycaemia and symptoms. Infiltration of mononuclear cells in and around the islets appears to be a late phenomenon appearing in the multiple-autoantibody-positive with dysglycaemia. As our understanding of the aetiology and pathogenesis of type 1 diabetes advances, the improved capability for early prediction should guide new strategies for the prevention of type 1 diabetes.

Twenty-Year Progression Rate to Clinical Onset According to Autoantibody Profile, Age, and HLA-DQ Genotype in a Registry-Based Group of Children and Adults With a First-Degree Relative With Type 1 Diabetes

OBJECTIVE

We investigated whether islet autoantibody profile, HLA-DQ genotype, and age influenced a 20-year progression to diabetes from first autoantibody positivity (autoAb⁺) in first-degree relatives of patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Persistently islet autoAb⁺ siblings and offspring (n = 462) under 40 years of age were followed by the Belgian Diabetes Registry. AutoAbs against insulin (IAA), GAD (GADA), IA-2 antigen (IA-2A), and zinc transporter 8 (ZnT8A) were determined by radiobinding assay.

RESULTS

The 20-year progression rate of multiple-autoAb⁺ relatives (n = 194) was higher than that for single-autoAb⁺ participants (n = 268) (88% vs. 54%; P < 0.001). Relatives positive for IAA and GADA (n = 54) progressed more slowly than double-autoAb⁺ individuals carrying IA-2A and/or ZnT8A (n = 38; P = 0.001). In multiple-autoAb⁺ relatives, Cox regression analysis identified the presence of IA-2A or ZnT8A as the only independent predictors of more rapid progression to diabetes (P < 0.001); in single-autoAb⁺ relatives, it identified younger age (P < 0.001), HLA-DQ2/DQ8 genotype (P < 0.001), and IAA (P = 0.028) as independent predictors of seroconversion to multiple positivity for autoAbs. In time-dependent Cox regression, younger age (P = 0.042), HLA-DQ2/DQ8 genotype (P = 0.009), and the development of additional autoAbs (P = 0.012) were associated with more rapid progression to diabetes.

CONCLUSIONS

In single-autoAb⁺ relatives, the time to multiple-autoAb positivity increases with age and the absence of IAA and HLA-DQ2/DQ8 genotype. The majority of multiple-autoAb⁺ individuals progress to diabetes within 20 years; this occurs more rapidly in the presence of IA-2A or ZnT8A, regardless of age, HLA-DQ genotype, and number of autoAbs. These data may help to refine the risk stratification of presymptomatic type 1 diabetes.