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Background The INNODIA consortium has established a pan-European infrastructure using validated centres to prospectively evaluate clinical data from individuals with newly diagnosed type 1 diabetes combined with centralised collection of clinical samples to determine rates of decline in beta-cell function and identify novel biomarkers, which could be used for future stratification of phase 2 clinical trials. Methods In this context, we have developed a Master Protocol, based on the “backbone” of the INNODIA natural history study, which we believe could improve the delivery of phase 2 studies exploring the use of single or combinations of Investigational Medicinal Products (IMPs), designed to prevent or reverse declines in beta-cell function in individuals with newly diagnosed type 1 diabetes. Although many IMPs have demonstrated potential efficacy in phase 2 studies, few subsequent phase 3 studies have confirmed these benefits. Currently, phase 2 drug development for this indication is limited by poor evaluation of drug dosage and lack of mechanistic data to understand variable responses to the IMPs. Identification of biomarkers which might permit more robust stratification of participants at baseline has been slow. Discussion The Master Protocol provides (1) standardised assessment of efficacy and safety, (2) comparable collection of mechanistic data, (3) the opportunity to include adaptive designs and the use of shared control groups in the evaluation of combination therapies, and (4) benefits of greater understanding of endpoint variation to ensure more robust sample size calculations and future baseline stratification using existing and novel biomarkers.

Objectives: The association between human leukocyte antigen (HLA) class II genotypes and susceptibility to type 1 diabetes (T1D) is well established. This study aimed at examining whether there are differences in the presentation of T1D depending on the HLA genotype. Research Design and Methods: We divided the study participants (N = 5798) in the Finnish Pediatric Diabetes Register into two groups based on the T1D risk conferred by their HLA genotype (high and moderate-risk genotypes, Group 1 vs. other genotypes, Group 2). We then examined differences in clinical, metabolic, and immunological characteristics. Children included in the study were 0–14-year-old and diagnosed between January 2003 and December 2019. Results: Participants in Group 1 were younger at the time of diagnosis (P

Objectives: New methods are pivotal in accurately predicting, monitoring, and diagnosing the clinical manifestation of type 1 diabetes (T1D) in high-risk children. Continuous glucose monitoring (CGM) is a valuable tool for patients with T1D, but there is still a knowledge gap regarding its utility in the prediction of diabetes. The current study explored whether 10-day CGM or CGM during an oral glucose tolerance test (OGTT) performed in the laboratory or at home (home-OGTT) could be accurate in detecting stages of T1D. Research Design and Method: Forty-six subjects 4–25 years of age carrying genetic risk for T1D were recruited and classified into the following groups: islet autoantibody (IAb) negative, one IAb, and stages 1–3 of T1D, based on the laboratory OGTT and IAb results at baseline. A 10-day CGM was initiated before the OGTT. Results: In this study, we showed that CGM was sensitive in detecting asymptomatic individuals at stage 3, and dysglycemic individuals in stage 2 of T1D both during OGTT and the 10-day period. CGM also showed significant differences in several variables during the 10-day sensoring among individuals at different stages of T1D. Furthermore, CGM showed different OGTT profiles and detected significantly more abnormal OGTT results when compared with plasma glucose. Conclusions: CGM together with home-OGTT could detect stages of T1D and offer an alternative method to confirm normoglycemia in high-risk individuals.

Objectives: Subtypes in type 1 diabetes pathogenesis have been implicated based on the first-appearing autoantibody (primary autoantibody). We set out to describe the glucose metabolism in preclinical diabetes in relation to the primary autoantibody in children with HLA-conferred disease susceptibility. Design and methods: Dysglycemic markers are defined as a 10% increase in HbA1c in a 3–12 months interval or HbA1c ≥5.9% (41 mmol/mol) in two consecutive samples, impaired fasting glucose or impaired glucose tolerance, or a random plasma glucose value ≥7.8 mmol/L. A primary autoantibody could be detected in 295 children who later developed at least 1 additional biochemical autoantibody. These children were divided into three groups: insulin autoantibody (IAA) multiple (n = 143), GAD antibody (GADA) multiple (n = 126) and islet antigen 2 antibody (IA-2A) multiple (n = 26). Another 229 children seroconverted to positivity only for a single biochemical autoantibody and were grouped as IAA only (n = 87), GADA only (n = 114) and IA-2A only (n = 28). Results: No consistent differences were observed in selected autoantibody groups during the preclinical period. At diagnosis, children with IAA only showed the highest HbA1c (P < 0.001 between groups) and the highest random plasma glucose (P = 0.005 between groups). Children with IA-2A only progressed to type 1 diabetes as frequently as those with IA-2A multiple (46% vs 54%, P = 0.297) whereas those with IAA only or GADA only progressed less often than children with IAA multiple or GADA multiple (22% vs 62% (P < 0.001) and 7% vs 43% (P < 0.001)), respectively. Conclusions: The phenotype of preclinical diabetes defined by the primary autoantibody is not associated with any discernible differences in glucose metabolism before the clinical disease manifestation.

The non-HLA loci conferring susceptibility to type 1 diabetes determine approximately half of the genetic disease risk, and several of them have been shown to affect immune-cell or pancreatic β-cell functions. A number of these loci have shown associations with the appearance of autoantibodies or with progression from seroconversion to clinical type 1 diabetes. In the current study, we have re-analyzed 21 of our loci with prior association evidence using an expanded DIPP follow-up cohort of 976 autoantibody positive cases and 1,910 matched controls. Survival analysis using Cox regression was applied for time periods from birth to seroconversion and from seroconversion to type 1 diabetes. The appearance of autoantibodies was also analyzed in endotypes, which are defined by the first appearing autoantibody, either IAA or GADA. Analyzing the time period from birth to seroconversion, we were able to replicate our previous association findings at PTPN22, INS, and NRP1. Novel findings included associations with ERBB3, UBASH3A, PTPN2, and FUT2. In the time period from seroconversion to clinical type 1 diabetes, prior associations with PTPN2, CD226, and PTPN22 were replicated, and a novel association with STAT4 was observed. Analyzing the appearance of autoantibodies in endotypes, the PTPN22 association was specific for IAA-first. In the progression phase, STAT4 was specific for IAA-first and ERBB3 to GADA-first. In conclusion, our results further the knowledge of the function of non-HLA risk polymorphisms in detailing endotype specificity and timing of disease development.

Objective: To evaluate the role of autoantibodies to N-terminally truncated glutamic acid decarboxylase GAD₆₅(96‐585) (t-GADA) as a marker for type 1 diabetes (T1D) and to assess the potential human leukocyte antigen (HLA) associations with such autoantibodies. Methods: In this cross-sectional study combining data from the Finnish Pediatric Diabetes Register, the Type 1 Diabetes Prediction and Prevention study, the DIABIMMUNE study, and the Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study, venous blood samples from 760 individuals (53.7% males) were analyzed for t-GADA, autoantibodies to full-length GAD65 (f-GADA), and islet cell antibodies. Epitope-specific GAD autoantibodies were analyzed from 189 study participants. Results: T1D had been diagnosed in 174 (23%) participants. Altogether 631 (83%) individuals tested positive for f-GADA and 451 (59%) for t-GADA at a median age of 9.0 (range 0.2‐61.5) years. t-GADA demonstrated higher specificity (46%) and positive predictive value (30%) for T1D than positivity for f-GADA alone (15% and 21%, respectively). Among participants positive for f-GADA, those who tested positive for t-GADA carried more frequently HLA genotypes conferring increased risk for T1D than those who tested negative for t-GADA (77% vs 53%; P < 0.001). Conclusions: Autoantibodies to N-terminally truncated GAD improve the screening for T1D compared to f-GADA and may facilitate the selection of participants for clinical trials. HLA class II-mediated antigen presentation of GAD(96‐585)-derived or structurally similar peptides might comprise an important pathomechanism in T1D.

Objective Growth-based determination of pubertal onset timing would be cheap and practical. We aimed to determine this timing based on pubertal growth markers. Secondary aims were to estimate the differences in growth between cohorts and identify the role of overweight in onset timing. Design This multicohort study includes data from three Finnish cohorts—the Type 1 Diabetes Prediction and Prevention (DIPP, N = 2,825) Study, the Special Turku Coronary Risk Factor Intervention Project (STRIP, N = 711), and the Boy cohort (N = 66). Children were monitored for growth and Tanner staging (except in DIPP). Methods The growth data were analyzed using a Super-Imposition by Translation And Rotation growth curve model, and pubertal onset analyses were run using a time-to-pubertal onset model. Results The time-to-pubertal onset model used age at peak height velocity (aPHV), peak height velocity (PHV), and overweight status as covariates, with interaction between aPHV and overweight status for girls, and succeeded in determining the onset timing. Cross-validation showed a good agreement (71.0% for girls, 77.0% for boys) between the observed and predicted onset timings. Children in STRIP were taller overall (girls: 1.7 [95% CI: 0.9, 2.5] cm, boys: 1.0 [0.3, 2.2] cm) and had higher PHV values (girls: 0.13 [0.02, 0.25] cm/year, boys: 0.35 [0.21, 0.49] cm/year) than those in DIPP. Boys in the Boy cohort were taller (2.3 [0.3, 4.2] cm) compared with DIPP. Overweight girls showed pubertal onset at 1.0 [0.7, 1.4] year earlier compared with other girls. In boys, there was no such difference. Conclusions The novel modeling approach provides an opportunity to evaluate the Tanner breast/genital stage–based pubertal onset timing in cohort studies including longitudinal data on growth but lacking pubertal follow-up.

Dysfunction of FOXP3‐positive regulatory T cells (Tregs) likely plays a major role in the pathogenesis of multiple autoimmune diseases including type 1 diabetes (T1D). Whether genetic polymorphisms associated with the risk of autoimmune diseases affect Treg frequency or function is currently unclear. Here, we analysed the effect of T1D‐associated major HLA class II haplotypes and seven single nucleotide polymorphisms in six non‐HLA genes [INS (rs689), PTPN22 (rs2476601), IL2RA (rs12722495 and rs2104286), PTPN2 (rs45450798), CTLA4 (rs3087243), and ERBB3 (rs2292239)] on peripheral blood Treg frequencies. These were determined by flow cytometry in 65 subjects who had progressed to T1D, 86 islet autoantibody‐positive at‐risk subjects, and 215 islet autoantibody‐negative healthy controls. The PTPN22 rs2476601 risk allele A was associated with an increase in total (p = 6 × 10⁻⁶) and naïve (p = 4 × 10⁻⁵) CD4+CD25+CD127lowFOXP3+ Treg frequencies. These findings were validated in a separate cohort comprising ten trios of healthy islet autoantibody‐negative children carrying each of the three PTPN22 rs2476601 genotypes AA, AG, and GG (p = 0.005 for total and p = 0.03 for naïve Tregs, respectively). In conclusion, our analysis implicates the autoimmune PTPN22 rs2476601 risk allele A in controlling the frequency of Tregs in human peripheral blood.

Objective: We aimed to clarify the association of various HLA risk alleles with different types of autoantibodies initiating islet specific autoimmunity. Methods: Follow-up cohorts from the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study and children diagnosed with type 1 diabetes (T1D) from the Finnish Pediatric Diabetes Register (FPDR) were analyzed for the presence of autoantibodies to insulin (IAA), glutamic acid decarboxylase (GADA), IA-2 antigen (IA-2A), and zinc transporter 8 (ZnT8A); and genotyped for HLA DR/DQ alleles. In the DIPP study, autoantibodies were regularly analyzed from birth up to 15 years of age. Results: In the DIPP cohort, 621 children developed one single persistent autoantibody, GADA in 284, IAA in 268, and IA-2A in 40 cases. Highly significant differences in the specificity of the first autoantibody were observed between HLA genotypes. Homozygotes for the DR3-DQ2 haplotype had almost exclusively GADA as the first autoantibody, whereas a more even distribution between GADA and IAA was found in DR3-DQ2/DR4-DQ8 as well as DR3-DQ/x and DR4-DQ8/x genotypes (x referring to neutral haplotypes). In DR4-DQ8 positive genotypes with the DRB1*04:01 allele IAA was more often the first autoantibody than in DRB1*04:04 positive genotypes. Various neutral haplotypes also significantly affected the relative proportions of different initial autoantibodies. These findings were confirmed and expanded in a series of 1591 T1D children under the age of 10 years from FPDR. Conclusions: These results emphasize the importance of HLA class II polymorphisms in the recognition of autoantigen epitopes in the initiation of various pathways of the autoimmune response.