Search

Objective: Specific alleles of non-HLA genes INS, CTLA-4, and PTPN22 have been associated with type 1 diabetes. We examined whether some of these alleles influence development of islet autoimmunity or progression from persistent islet autoimmunity to type 1 diabetes in children with high-risk HLA-DR,DQ genotypes.Research Design and Methods: Since 1993, the Diabetes Autoimmunity Study in the Young (DAISY) has followed 2,449 young children carrying HLA-DR,DQ genotypes associated with type 1 diabetes. Of those, 112 have developed islet autoimmunity (persistent autoantibodies to insulin, GAD65, and/or IA-2), and 47 of these have progressed to type 1 diabetes. The influence of polymorphisms of INS(-23Hph1), CTLA-4(T17A), and PTPN22(R620W) on development of persistent islet autoimmunity and progression to type 1 diabetes was evaluated by parametric models and by survival analyses.Results: PTPN22(R620W) allele T was associated with development of persistent islet autoimmunity (hazard ratio 1.83 [95% CI 1.27-2.63]) controlling for ethnicity, presence of HLA-DR3/4,DQB1*0302, and having a first-degree relative with type 1 diabetes. Survival analyses showed a significantly (P = 0.002) higher risk of persistent islet autoimmunity by age 10 years for the TT genotype (27.3%) than for the CT or CC genotype (7.9 and 5.3%, respectively). Cumulative risk of persistent islet autoimmunity was slightly higher (P = 0.02) for the INS(-23Hph1) AA genotype (7.8%) than for the AT or TT genotype (4.2 and 6.4% risk by age 10 years, respectively).Conclusions: Whereas the HLA-DR3/4,DQB1*0302 genotype had a dramatic influence on both development of islet autoimmunity and progression to type 1 diabetes, the PTPN22(R620W) T allele significantly influences progression to persistent islet autoimmunity in the DAISY cohort. [ABSTRACT FROM AUTHOR]

Objective: We evaluated predictors of progression to diabetes in children with high-risk HLA genotypes and persistent islet autoantibodies.Research Design and Methods: The Diabetes Autoimmunity Study in the Young (DAISY) followed 2,542 children with autoantibodies measured to GAD, IA-2, and insulin.Results: Persistent islet autoantibodies developed in 169 subjects, and 55 of those progressed to diabetes. Children expressing three autoantibodies showed a linear progression to diabetes with 74% cumulative incidence by the 10-year follow-up compared with 70% with two antibodies and 15% with one antibody (P < 0.0001). Both age of appearance of first autoantibody and insulin autoantibody (IAA) levels, but not GAD or IA-2 autoantibodies, were major determinants of the age of diabetes diagnosis (r = 0.79, P < 0.0001).Conclusions: In the DAISY cohort, 89% of children who progressed to diabetes expressed two or more autoantibodies. Age of diagnosis of diabetes is strongly correlated with age of appearance of first autoantibody and IAA levels. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes., Methods: We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294). Group comparisons included demographics, metabolic markers and the prevalence of autoantibodies against GAD65 (GADA%), IA-2 (IA-2A%) or insulin (IAA%) at enrolment. A p value <0.01 was considered statistically significant., Results: IA-2A% was lower in the DRX/DRX group (20.9%) than in the DR4/non-DR3 (38.5%, p<0.001) and DR3/DR4 (44.8%, p<0.001) groups, but similar to the DR3/non-DR4 group (20.0%). Conversely, IAA% was similar in the DRX/DRX (43.4%), DR4/non-DR3 (41.1%) and DR3/DR4 (41.0%) groups, but lower in the DR3/non-DR4 group (30.1%, p<0.001). Participants in the DRX/DRX group were older, with a lower prevalence of White participants and a higher prevalence of overweight/obesity, and higher preserved C-peptide (as measured by a lower Index60) than those in the DR3/DR4 group (all comparisons, p<0.005), a lower prevalence of White or non-Hispanic participants and a lower Index60 than those in the DR4/non-DR3 group, and younger age, a higher prevalence of Hispanic participants and a lower Index60 than those in the DR3/non-DR4 group (all comparisons, p<0.005). Among the 1292 participants who progressed to clinical type 1 diabetes, those in the DR3/non-DR4 group had higher GADA%, lower IA-2A% and lower IAA% than the other groups (all comparisons, p<0.01), and those in the DR3/DR4 group had the youngest age at diagnosis (all comparisons, p<0.001)., Conclusions/interpretation: Autoantibody-positive individuals who lack both high-risk HLA haplotypes (DRX/DRX) or have HLA-DR3-DQ2 but lack HLA-DR4-DQ8 (DR3/non-DR4) have phenotypic differences compared with DR3/DR4 and DR4/non-DR3 individuals, suggesting that there is aetiological heterogeneity in type 1 diabetes., Competing Interests: Acknowledgements: We are grateful to the TrialNet participants and their families. We thank Amanda L. Posgai, PhD (University of Florida Diabetes Institute, Gainesville, FL, USA) for her assistance in editing the manuscript. Data availability: The datasets generated and analysed during the current study are available from the Type 1 Diabetes TrialNet Coordinating Center on reasonable request. Funding: The Type 1 Diabetes TrialNet Study Group is a clinical trials network that is currently funded by the National Institutes of Health (NIH) through the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Allergy and Infectious Diseases and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (through the cooperative agreements U01 DK061010, U01 DK061034, U01 DK061042, U01 DK061058, U01 DK085461, U01 DK085465, U01 DK085466, U01 DK085476, U01 DK085499, U01 DK085509, U01 DK103180, U01 DK103153, U01 DK103266, U01 DK103282, U01 DK106984, U01 DK106994, U01 DK107013, U01 DK107014, UC4 DK106993 and UC4 DK117009) and by the JDRF. The contents of this article are solely the responsibility of the authors, and do not necessarily represent the official views of the NIH or the JDRF. MJR is supported by NIH/NIDDK grants R01 DK124395 and R01 DK121843. Authors’ relationships and activities: MJR is a member of the Editorial Board of Diabetologia. The authors declare that there are no other relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: MJR contributed to the study design, as well as data analysis and interpretation, and wrote the first draft of the manuscript. DC conducted data analysis and edited the manuscript. HMP contributed to data acquisition and analysis. AKS, KCH, RO, MA, TMB, HMP, JPK, SO-G and SSR contributed to data interpretation. JMS contributed to study design, data analysis, and interpretation and writing. All authors revised and edited the manuscript, and approved the final version to be published. MJR, DC, AKS, KCH, MA, TMB, HMP and JPK were members of the Type 1 Diabetes TrialNet Study Group at the time of the study. MJR and JMS are the guarantors of this article and take full responsibility for the work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript., (© 2024. The Author(s).)

Objective: Identify microbial and microbiota-associated metabolites in monozygotic (MZ) and dizygotic (DZ) twins discordant for type 1 diabetes (T1D) to gain insight into potential environmental factors that may influence T1D., Research Design and Methods: Serum samples from 39 twins discordant for T1D were analyzed using a semi-targeted metabolomics approach via liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Statistical analyses identified significant metabolites (p < 0.1) within three groups: All twins (combined group), MZ twins, and DZ twins., Results: Thirteen metabolites were identified as significant. 3-indoxyl sulfate and 5-hydroxyindole were significantly reduced in T1D individuals across all groups. Carnitine was reduced, and threonine, muramic acid, and 2-oxobutyric acid were significantly elevated in both All and MZ groups. Allantoin was significantly reduced and 3-methylhistidine was significantly elevated in All and DZ groups., Conclusions: Metabolite dysregulation associated with gut dysbiosis was observed. However, further validation of our findings in a larger cohort is needed., Article Highlights: Why did we undertake this study? We believed this cohort of twins discordant for type 1 diabetes (T1D) would allow for control over genetic variability to examine environmental factors.What is the specific question(s) we wanted to answer? We aimed to identify differences in microbial and microbiota-associated metabolites in twins discordant for T1D to examine the effect of the gut microbiome on T1D.What did we find? Thirteen metabolites were identified as significantly different.What are the implications of our findings? Our results show the dysregulation of several microbial metabolites in twin pairs, suggesting that the gut microbiome plays a role in the pathogenesis of T1D.

Aims/hypothesis: We aimed to assess whether continuous glucose monitor (CGM) metrics can accurately predict stage 3 type 1 diabetes diagnosis in those with islet autoantibodies (AAb)., Methods: Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (n=79), BDR (n=22), DAISY (n=18), DIPP (n=8) and TrialNet Pathway to Prevention (n=91). Median follow-up time was 2.6 years (quartiles: 1.5 to 3.6 years). A participant characteristics-only model, a CGM metrics-only model and a full model combining characteristics and CGM metrics were compared., Results: The full model achieved a numerically higher performance predictor estimate (C statistic=0.74; 95% CI 0.66, 0.81) for predicting stage 3 type 1 diabetes diagnosis compared with the characteristics-only model (C statistic=0.69; 95% CI 0.60, 0.77) and the CGM-only model (C statistic=0.68; 95% CI 0.61, 0.75). Greater percentage of time >7.8 mmol/l (p<0.001), HbA 1c (p=0.02), having a first-degree relative with type 1 diabetes (p=0.02) and testing positive for IA-2 AAb (p<0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (p=0.06) and having a negative GAD AAb (p=0.09) were selected but not found to be significant. Participants classified as having low (n=79), medium (n=98) or high (n=41) risk of stage 3 type 1 diabetes diagnosis using the full model had a probability of developing symptomatic disease by 2 years of 5%, 13% and 48%, respectively., Conclusions/interpretation: CGM metrics can help predict disease progression and classify an individual's risk of type 1 diabetes diagnosis in conjunction with other factors. CGM can also be used to better assess the risk of type 1 diabetes progression and define eligibility for potential prevention trials., Competing Interests: Acknowledgements: Selected data were presented at the 84th meeting of the American Diabetes Association, 21–24 June 2024, Orlando, FL, USA. Data availability: The datasets analysed during the current study are not publicly available due to participant confidentiality. Funding: Research reported in this publication was supported by Breakthrough T1D (formerly JDRF; Award number: 2-SRA-2022-1156-S-B). The content is solely the responsibility of the authors and does not necessarily represent the official views of Breakthrough T1D. Authors’ relationships and activities: BK, AD and FG report no personal financial disclosures but their institution has received study supplies from Medtronic and Dexcom. DMW reports funding from the NIH and Breakthrough T1D. RWB reports no personal financial disclosures but reports that his institution has received funding on his behalf as follows: grant funding and study supplies from Tandem Diabetes Care, Beta Bionics, and Dexcom; study supplies from Medtronic, Ascencia and Roche; consulting fees and study supplies from Eli Lilly and Novo Nordisk; and consulting fees from Insulet, Bigfoot Biomedical, vTv Therapeutics and Diasome. PC, CS, AKS, BIF, MAH, RV, JT, MA and SP declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: PC researched and interpreted the data and wrote the manuscript. CS performed statistical analysis and contributed to writing and reviewed the manuscript. AKS, BIF, MAH, BK, RV, JT, AD, FG, MA, DMW, SP and RWB researched data, contributed to the discussion and reviewed/edited the manuscript. All authors reviewed the work critically for important intellectual content and approved the final version submitted for publication. PC is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis., (© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Accurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of severe presentation with diabetic ketoacidosis. We aimed to assess the generalisability of a prediction model developed in children followed from birth. Additionally, we sought to create an application for easy calculation and visualization of individualized risk prediction., Methods: We developed and refined a stratified prediction model combining a genetic risk score, age, islet autoantibodies, and family history using data from children followed since birth by The Environmental Determinants of Diabetes in the Young (TEDDY) study. We tested the validity of the model through external validation in the Type 1 Diabetes TrialNet Pathway to Prevention study, which conducts cross-sectional screening in relatives of people with type 1 diabetes. We recalibrated the model by adjusting for baseline risk and selection criteria in TrialNet using logistic recalibration to improve calibration across all ages., Results: The study included 7,798 TEDDY and 4,068 TrialNet participants, with 305 (4%) and 1,373 (34%) developing type 1 diabetes, respectively. The combined model showed similar discriminative ability in autoantibody-positive individuals across TEDDY and TrialNet (p=0.14), but inferior calibration in TrialNet (Brier score 0.40 [0.38,0.43]). Adjustment for baseline risk and selection criteria in TrialNet using logistic recalibration improved calibration across all ages (Brier score 0.16 [0.14,0.17]; p<0.001). A web calculator was developed to visualise individual risk estimates (https://t1dpredictor.diabetesgenes.org)., Conclusions: A stratified model of type 1 diabetes genetic risk score, family history, age, and autoantibody status accurately predicts type 1 diabetes risk, but may need recalibration according to screening stategy., Competing Interests: Competing interests: The authors have no other relevant conflicts of interest to disclose.

Type 1 Diabetes (T1D) is an autoimmune disease mediated by autoreactive T cells. Our studies indicate that CD4 T cells reactive to Hybrid Insulin Peptides (HIPs) play a critical role in T cell-mediated beta-cell destruction. We have shown that HIPs form in human islets between fragments of the C-peptide and cleavage products of secretory granule proteins. To identify T cell specificities contributing to T1D pathogenesis, we tested T cell reactivity from T1D patients or healthy control using an IFN-γ ELISPOT assay against a library of 240 C-peptide HIPs. We observed elevated T cell responses to peptide pools containing HIPs that form at the amino acid residues G15, A18 and L26 of C-peptide. In a second cohort of healthy controls, at-risk individuals, and T1D patients, T cell reactivity to HIPs forming at these three residues was monitored. Results indicate that, prior to clinical onset of T1D, there were significantly elevated responses to multiple pools of HIPs, and the magnitude of T cell reactivity to HIPs forming at residue A18 of the C-peptide was increased. Overall, our study identifies new T cell specificities in at-risk subjects and indicates that T cell reactivity to HIPs can be observed before T1D onset., (© 2025 by the American Diabetes Association.)

Context: In Colorado children, the prevalence of diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes has been increasing over time., Objective: To evaluate the prevalence of and factors involved in DKA at type 1 diabetes diagnosis among participants followed in monitoring research studies before diagnosis compared to patients from the community., Methods: We studied patients < 18 years diagnosed with type 1 diabetes between 2005 and 2021 at the Barbara Davis Center for Diabetes and compared the prevalence of and factors associated with DKA at diagnosis among participants in preclinical monitoring studies vs those diagnosed in the community., Results: Of 5049 subjects, 164 were active study participants, 42 inactive study participants, and 4843 were community patients. Active study participants, compared to community patients, had lower HbA1c (7.3% vs 11.9%; P < .001) and less frequently experienced DKA (4.9% vs 48.5%; P < .001), including severe DKA (1.2% vs 16.2%; P < .001). Inactive study participants had intermediate levels for both prevalence and severity of DKA. DKA prevalence increased in community patients, from 44.0% to 55%, with less evidence for a temporal trend in study participants. DKA prevalence was highest in children < 2 years (13% in active study participants vs 83% in community patients). In community patients, younger age (P = .0038), public insurance (P < .0001), rural residence (P < .0076), higher HbA1c (P < .0001), and ethnicity minority status (P < .0001) were associated with DKA at diagnosis., Conclusion: While DKA prevalence increases in community patients over time, it stayed < 5% in active research participants, who have a 10 times lower prevalence of DKA at diagnosis, including among minorities., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims/hypothesis: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk., Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation., Results: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics., Conclusions/interpretation: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb + ) children and adults who are at risk of (confirmed single IAb + ) or living with (multiple IAb + ) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb + ; (2) when people who are IAb + are initially identified there is a need for confirmation using a second sample; (3) single IAb + individuals are at lower risk of progression than multiple IAb + individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care., (© 2024. American Diabetes Association and European Association for the Study of Diabetes.)

Background: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers)., Methods: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7)., Results: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions., Conclusion: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Carry, Vanderlinden, Johnson, Buckner, Steck, Kechris, Yang, Fingerlin, Fiehn, Rewers and Norris.)

Context: The 2 peaks of type 1 diabetes incidence occur during early childhood and puberty., Objective: We sought to better understand the relationship between puberty, islet autoimmunity, and type 1 diabetes., Methods: The relationships between puberty, islet autoimmunity, and progression to type 1 diabetes were investigated prospectively in children followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Onset of puberty was determined by subject self-assessment of Tanner stages. Associations between speed of pubertal progression, pubertal growth, weight gain, homeostasis model assessment of insulin resistance (HOMA-IR), islet autoimmunity, and progression to type 1 diabetes were assessed. The influence of individual factors was analyzed using Cox proportional hazard ratios., Results: Out of 5677 children who were still in the study at age 8 years, 95% reported at least 1 Tanner Stage score and were included in the study. Children at puberty (Tanner Stage ≥2) had a lower risk (HR 0.65, 95% CI 0.45-0.93; P = .019) for incident autoimmunity than prepubertal children (Tanner Stage 1). An increase of body mass index Z-score was associated with a higher risk (HR 2.88, 95% CI 1.61-5.15; P < .001) of incident insulin autoantibodies. In children with multiple autoantibodies, neither HOMA-IR nor rate of progression to Tanner Stage 4 were associated with progression to type 1 diabetes., Conclusion: Rapid weight gain during puberty is associated with development of islet autoimmunity. Puberty itself had no significant influence on the appearance of autoantibodies or type 1 diabetes. Further studies are needed to better understand the underlying mechanisms., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

Objective: We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis., Methods: We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis., Results: Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses., Conclusions: The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective: This multicenter prospective cohort study compared pancreas volume as assessed by MRI, metabolic scores derived from oral glucose tolerance testing (OGTT), and a combination of pancreas volume and metabolic scores for predicting progression to stage 3 type 1 diabetes (T1D) in individuals with multiple diabetes-related autoantibodies., Research Design and Methods: Pancreas MRI was performed in 65 multiple autoantibody-positive participants enrolled in the Type 1 Diabetes TrialNet Pathway to Prevention study. Prediction of progression to stage 3 T1D was assessed using pancreas volume index (PVI), OGTT-derived Index60 score and Diabetes Prevention Trial-Type 1 Risk Score (DPTRS), and a combination of PVI and DPTRS., Results: PVI, Index60, and DPTRS were all significantly different at study entry in 11 individuals who subsequently experienced progression to stage 3 T1D compared with 54 participants who did not experience progression (P < 0.005). PVI did not correlate with metabolic testing across individual study participants. PVI declined longitudinally in the 11 individuals diagnosed with stage 3 T1D, whereas Index60 and DPTRS increased. The area under the receiver operating characteristic curve for predicting progression to stage 3 from measurements at study entry was 0.76 for PVI, 0.79 for Index60, 0.79 for DPTRS, and 0.91 for PVI plus DPTRS., Conclusions: These findings suggest that measures of pancreas volume and metabolism reflect distinct components of risk for developing stage 3 type 1 diabetes and that a combination of these measures may provide superior prediction than either alone., (© 2024 by the American Diabetes Association.)

Context: The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown., Objective: Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D., Methods: At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed., Results: Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value)., Conclusion: Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To assess anxiety and risk perception among parents whose children screened positive for islet autoantibodies, indicating elevated risk for type 1 diabetes (T1D)., Research Design and Methods: The Autoimmunity Screening for Kids (ASK) study identified 319 children age 1 to 17 years at risk for T1D via screening for islet autoantibodies; 280 children with confirmed islet autoantibodies and their caregivers enrolled in a follow-up education and monitoring program to prevent diabetic ketoacidosis at diagnosis. Parents completed questionnaires at each monitoring visit, including a 6-item version of the State Anxiety Inventory (SAI), to assess anxiety about their child developing T1D, and a single question to assess risk perception., Results: At the first ASK follow-up monitoring visit, mean parental anxiety was elevated above the clinical cutoff of 40 (SAI 46.1 ± 11.2). At the second follow-up monitoring visit (i.e., visit 2), mean anxiety remained elevated but started to trend down. Approximately half (48.9%) of parents reported their child was at increased risk for T1D at the initial follow-up monitoring visit (visit 1). Parents of children with more than one islet autoantibody and a first-degree relative with T1D were more likely to report their child was at increased risk., Conclusions: Most parents of autoantibody-positive children have high anxiety about their child developing T1D. Information about the risk of developing T1D is difficult to convey, as evidenced by the wide range of risk perception reported in this sample., (© 2023 by the American Diabetes Association.)

Objective: Innate immune responses may be involved in the earliest phases of type 1 diabetes (T1D)., Research Design and Methods: To test whether blocking innate immaune cells modulated progression of the disease, we randomly assigned 273 individuals with stage 1 T1D to treatment with hydroxychloroquine (n = 183; 5 mg/kg per day to a maximum of 400 mg) or placebo (n = 90) and assessed whether hydroxychloroquine treatment delayed or prevented progression to stage 2 T1D (i.e., two or more islet autoantibodies with abnormal glucose tolerance)., Results: After a median follow-up of 23.3 months, the trial was stopped prematurely by the data safety monitoring board because of futility. There were no safety concerns in the hydroxychloroquine arm, including in annual ophthalmologic examinations. Preplanned secondary analyses showed a transient decrease in the glucose average area under the curve to oral glucose in the hydroxychloroquine-treated arm at month 6 and reduced titers of anti-GAD and anti-insulin autoantibodies and acquisition of positive autoantibodies in the hydroxychloroquine arm (P = 0.032)., Conclusions: We conclude that hydroxychloroquine does not delay progression to stage 2 T1D in individuals with stage 1 disease. Drug treatment reduces the acquisition of additional autoantibodies and the titers of autoantibodies to GAD and insulin., (© 2023 by the American Diabetes Association.)

Objective: The article provides practical guidance for (1) interpreting and confirming islet autoantibody screening results for type 1 diabetes (T1D) and (2) follow-up of individuals with early stages of T1D with the goal of ensuring medical safety and providing patients and their families with an assessment of risk for progression to a clinical diagnosis of T1D. Research Design and Methods: We used an explicit a priori methodology to identify areas of agreement and disagreement in how to manage patients with early T1D. We used a modified Delphi method, which is a systematic, iterative approach to identifying consensus. We developed a list of topic questions, ranked them by importance, and developed consensus statements based on available evidence and expert opinion around each of the 30 topic questions consistently ranked as being most important. Results: Consensus statements for screening and monitoring are supported with figures proposing an algorithm for confirmation of T1D diagnosis and management of early T1D until clinical diagnosis. Conclusions: Disseminating and increasing knowledge related to how to interpret T1D screening tests, confirm early T1D diagnosis and monitor for medical safety and clinical disease risk prediction is critically important as there are currently no clinical recommendations. Published guidance will promote better management of T1D screening-detected individuals.

Background: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk., Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation., Findings: The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics., Interpretation: Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Background: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification., Methods: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument., Results: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings., Conclusions: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design., (© 2023. Springer Nature Limited.)

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Efforts to prevent T1D have focused on modulating immune responses and supporting beta cell health; however, heterogeneity in disease progression and responses to therapies have made these efforts difficult to translate to clinical practice, highlighting the need for precision medicine approaches to T1D prevention., Methods: To understand the current state of knowledge regarding precision approaches to T1D prevention, we performed a systematic review of randomized-controlled trials from the past 25 years testing disease-modifying therapies in T1D and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument., Results: We identified 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss in individuals at disease onset. Seventeen agents tested, mostly immunotherapies, showed benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employed precision analyses to assess features linked to treatment response. Age, measures of beta cell function and immune phenotypes were most frequently tested. However, analyses were typically not prespecified, with inconsistent methods reporting, and tended to report positive findings., Conclusions: While the quality of prevention and intervention trials was overall high, low quality of precision analyses made it difficult to draw meaningful conclusions that inform clinical practice. Thus, prespecified precision analyses should be incorporated into the design of future studies and reported in full to facilitate precision medicine approaches to T1D prevention., Plain Language Summary: Type 1 diabetes (T1D) results from the destruction of insulin-producing cells in the pancreas, necessitating lifelong insulin dependence. T1D prevention remains an elusive goal, largely due to immense variability in disease progression. Agents tested to date in clinical trials work in a subset of individuals, highlighting the need for precision medicine approaches to prevention. We systematically reviewed clinical trials of disease-modifying therapy in T1D. While age, measures of beta cell function, and immune phenotypes were most commonly identified as factors that influenced treatment response, the overall quality of these studies was low. This review reveals an important need to proactively design clinical trials with well-defined analyses to ensure that results can be interpreted and applied to clinical practice.

Objective: Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes., Research Design and Methods: One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10)., Results: Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability., Conclusions: CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes., (© 2023 by the American Diabetes Association.)

Context: Participants with stage 1 or 2 type 1 diabetes (T1D) qualify for prevention trials, but factors involved in screening for such trials are largely unknown., Objective: To identify factors associated with screening for T1D prevention trials., Methods: This study included TrialNet Pathway to Prevention participants who were eligible for a prevention trial: oral insulin (TN-07, TN-20), teplizumab (TN-10), abatacept (TN-18), and oral hydroxychloroquine (TN-22). Univariate and multivariate logistic regression models were used to examine participant, site, and study factors at the time of prevention trial accrual., Results: Screening rates for trials were: 50% for TN-07 (584 screened/1172 eligible), 9% for TN-10 (106/1249), 24% for TN-18 (313/1285), 17% for TN-20 (113/667), and 28% for TN-22 (371/1336). Younger age and male sex were associated with higher screening rates for prevention trials overall and for oral therapies. Participants with an offspring with T1D showed lower rates of screening for all trials and oral drug trials compared with participants with other first-degree relatives as probands. Site factors, including larger monitoring volume and US site vs international site, were associated with higher prevention trial screening rates., Conclusions: Clear differences exist between participants who screen for prevention trials and those who do not screen and between the research sites involved in prevention trial screening. Participant age, sex, and relationship to proband are significantly associated with prevention trial screening in addition to key site factors. Identifying these factors can facilitate strategic recruitment planning to support rapid and successful enrollment into prevention trials., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.)

There are few estimates of the seroprevalence of SARS-CoV-2 antibodies among children in the United States. We measured vaccine and infection induced seroprevalence among nearly 5000 healthy 1 to 17-year-old children in Colorado from 2020 to 2021. By December 2021, 89% of older children, ages 12 to 18, had antibodies detected. The increase was largely driven from vaccination rather than infection.

Objective: To define the relationship between glucose and C-peptide during the progression to type 1 diabetes (T1D)., Research Design and Methods: We longitudinally studied glucose and C-peptide response curves (GCRCs), area under curve (AUC) for glucose, and AUC C-peptide from oral glucose tolerance tests (OGTTs), and Index60 (which integrates OGTT glucose and C-peptide values) in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 72) and TrialNet Pathway to Prevention Study (TNPTP) (n = 82) participants who had OGTTs at baseline and follow-up time points before diagnosis., Results: Similar evolutions of GCRC configurations were evident between DPT-1 and TNPTP from baseline to 0.5 years prediagnosis. Whereas AUC glucose increased throughout from baseline to 0.5 years prediagnosis, AUC C-peptide increased from baseline until 1.5 years prediagnosis (DPT-1, P = 0.004; TNPTP, P = 0.012) and then decreased from 1.5 to 0.5 years prediagnosis (DPT-1, P = 0.017; TNPTP, P = 0.093). This change was mostly attributable to change in the late AUC C-peptide response (i.e., 60- to 120-min AUC C-peptide). Median Index60 values of DPT-1 (1.44) and TNPTP (1.05) progressors to T1D 1.5 years prediagnosis (time of transition from increasing to decreasing AUC C-peptide) were used as thresholds to identify individuals at high risk for T1D in the full cohort at baseline (5-year risk of 0.75-0.88 for those above thresholds)., Conclusions: A transition from an increase to a decrease in AUC C-peptide ∼1.5 years prediagnosis was validated in two independent cohorts. The median Index60 value at that time point can be used as a pathophysiologic-based threshold for identifying individuals at high risk for T1D., (© 2022 by the American Diabetes Association.)

T cell receptor (TCR) sequences are exceptionally diverse and can now be comprehensively measured with next-generation sequencing technologies. However, a thorough investigation of longitudinal TCR repertoires throughout childhood in health and during development of a common childhood disease, type 1 diabetes (T1D), has not been undertaken. Here, we deep sequenced the TCR-β chain repertoires from longitudinal peripheral blood DNA samples at 4 time points beginning early in life (median age of 1.4 years) from children who progressed to T1D (n = 29) and age/sex-matched islet autoantibody-negative controls (n = 25). From 53 million TCR-β sequences, we show that the repertoire is extraordinarily diverse early in life and narrows with age independently of disease. We demonstrate the ability to identify specific TCR sequences, including those known to recognize influenza A and, separately, those specific for insulin and its precursor, preproinsulin. Insulin-reactive TCR-β sequences were more common and frequent in number as the disease progressed in those who developed T1D compared with genetically at risk nondiabetic children, and this was not the case for influenza-reactive sequences. As an independent validation, we sequenced and analyzed TCR-β repertoires from a cohort of new-onset T1D patients (n = 143), identifying the same preproinsulin-reactive TCRs. These results demonstrate an enrichment of preproinsulin-reactive TCR sequences during the progression to T1D, highlighting the importance of using disease-relevant TCR sequences as powerful biomarkers in autoimmune disorders.

Longitudinal changes in gene expression during islet autoimmunity (IA) may provide insight into biological processes that explain progression to type 1 diabetes (T1D). We identified individuals from Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, autoantibodies present on two or more visits. Illumina's NovaSeq 6000 was used to quantify gene expression in whole blood. With linear mixed models we tested for changes in expression after IA that differed across individuals who progressed to T1D (progressors) (n = 25), reverted to an autoantibody-negative stage (reverters) (n = 47), or maintained IA positivity but did not develop T1D (maintainers) (n = 66). Weighted gene coexpression network analysis was used to identify coexpression modules. Gene Ontology pathway analysis of the top 150 differentially expressed genes (nominal P < 0.01) identified significantly enriched pathways including leukocyte activation involved in immune response, innate immune response, and regulation of immune response. We identified a module of 14 coexpressed genes with roles in the innate immunity. The hub gene, LTF, is known to have immunomodulatory properties. Another gene within the module, CAMP, is potentially relevant based on its role in promoting β-cell survival in a murine model. Overall, results provide evidence of alterations in expression of innate immune genes prior to onset of T1D., (© 2022 by the American Diabetes Association.)

Objective: Large-scale screening of the general population for islet autoantibodies (IAbs) to detect type 1 diabetes (T1D) has started worldwide. The standard screening method of separate radio-binding assay (RBA) for each IAb is an inefficient bottleneck. Furthermore, most positive results by RBA in screening of general population individuals without a clinical diagnosis of T1D are low-affinity and not predictive of future diabetes. Research Design and Methods: We have developed and validated a novel 6-Plex assay based on electrochemiluminescence (ECL) technology that combines in a single well high-affinity IAbs (to insulin, GAD, IA-2, and ZnT8), transglutaminase autoantibodies for celiac disease, and severe acute respiratory syndrome coronavirus 2 antibodies. The Autoimmunity Screening for Kids (ASK) provided 880 serum samples, from 828 children aged 1-17 years without diabetes who were previously tested for IAbs using single ECL assays and RBA assays. Results: Levels of all six antibodies in the 6-Plex ECL assay correlated well with respective single ECL assay levels. Similar to single ECL assays, the 6-Plex ECL assay positivity was congruent with the RBA in 95% (35/37) of children who later developed T1D and in 88% (105/119) high-risk children with multiple IAbs. In contrast, only 56% (86/154, P  < 0.0001) of children with persistent single IAb by RBA were found to be positive by 6-Plex ECL assay. Of 555 samples negative for all IAbs by RBA, few (0.2%-0.5%) were positive at low levels in the 6-Plex ECL assay. Conclusions: The study demonstrated that the 6-Plex ECL assay compares favorably to the standard RBAs in terms of disease specificity for general population screening in children. The 6-Plex ECL assay was therefore adopted as the primary screening tool in the general population screening ASK program with advantages of high efficiency, low cost, and low serum volume.

Context: Continuous glucose monitoring (CGM) is increasingly being used both for day-to-day management in patients with diabetes and in clinical research. While data on glycemic profiles of healthy, nondiabetic individuals exist, data on nondiabetic very young children are lacking., Objective: This work aimed to establish reference sensor glucose ranges in healthy, nondiabetic young children, using a current-generation CGM sensor., Methods: This prospective observational study took place in an institutional practice with healthy, nondiabetic children aged 1 to 6 years with normal body mass index. A blinded Dexcom G6 Pro CGM was worn for approximately 10 days by each participant. Main outcome measures included CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability., Results: Thirty-nine participants were included in the analyses. Mean average glucose was 103 mg/dL (5.7 mmol/L). Median percentage time between 70 and 140 mg/dL (3.9-7.8 mmol/L) was 96% (interquartile range, 92%-97%), mean within-individual coefficient of variation was 17 ± 3%, median time spent with glucose levels greater than 140 mg/dL was 3.4% (49 min/day), and median time less than 70 mg/dL (3.9 mmol/L) was 0.4% (6 min/day)., Conclusion: Collecting normative sensor glucose data and describing glycemic measures for young children fill an important informational gap and will be useful as a benchmark for future clinical studies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.)

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., (© 2022. Crown.)

Objective: Electrochemiluminescence (ECL) assays are high-affinity autoantibody (Ab) tests that are more specific than Abs detected by traditional radiobinding assays (RBA) for risk screening and prediction of progression to type 1 diabetes. We sought to characterize the association of high-risk human leukocyte antigen (HLA) haplotypes and genotypes with ECL positivity and levels in relatives of individuals with type 1 diabetes., Methods: We analyzed 602 participants from the TrialNet Pathway to Prevention Study who were positive for at least 1 RBA diabetes-related Ab [glutamic acid decarboxylase autoantibodies (GADA) or insulin autoantibodies (IAA)] and for whom ECL and HLA data were available. ECL and RBA Ab levels were converted to SD units away from mean (z-scores) for analyses., Results: Mean age at initial visit was 19.4 ± 13.7 years; 344 (57.1%) were female and 104 (17.3%) carried the high-risk HLA-DR3/4*0302 genotype. At initial visit 424/602 (70.4%) participants were positive for either ECL-GADA or ECL-IAA, and 178/602 (29.6%) were ECL negative. ECL and RBA-GADA positivity were associated with both HLA-DR3 and DR4 haplotypes (all Ps < 0.05), while ECL and RBA-GADA z-score titers were higher in participants with HLA-DR3 haplotypes only (both Ps < 0.001). ECL-IAA (but not RBA-IAA) positivity was associated with the HLA-DR4 haplotype (P < 0.05)., Conclusions: ECL-GADA positivity is associated with the HLA-DR3 and HLA-DR4 haplotypes and levels are associated with the HLA-DR3 haplotype. ECL-IAA positivity is associated with HLA-DR4 haplotype. These studies further contribute to the understanding of genetic risk and islet autoimmunity endotypes in type 1 diabetes., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. Because age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA)., Research Design and Methods: The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0-4, 5-9, and 10-14 years; n = 142, 151, and 86, respectively) with comparisons of autoantibody profiles, HLAs, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis compared those with oral glucose tolerance test data with TEDDY children who did not progress to diabetes., Results: Increasing fasting glucose (hazard ratio [HR] 1.09 [95% CI 1.04-1.14]; P = 0.0003), stimulated glucose (HR 1.50 [1.42-1.59]; P < 0.0001), fasting insulin (HR 0.89 [0.83-0.95]; P = 0.0009), and glucose-to-insulin ratio (HR 1.29 [1.16-1.43]; P < 0.0001) were associated with risk of progression to type 1 diabetes. Younger children had fewer autoantibodies with more symptoms at diagnosis. Twenty-three children (6.1%) had DKA at onset, only 1 (0.97%) of 103 with and 22 (8.0%) of 276 children without a first-degree relative (FDR) with type 1 diabetes (P = 0.008). Children with DKA were more likely to be nonadherent to study protocol (P = 0.047), with longer duration between their last TEDDY evaluation and diagnosis (median 10.2 vs. 2.0 months without DKA; P < 0.001)., Conclusions: DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset, adding to the heterogeneity of type 1 diabetes., (© 2022 by the American Diabetes Association.)

Objective: Children identified with stage 1 type 1 diabetes are at high risk for progressing to stage 3 (clinical) diabetes and require accurate monitoring. Our aim was to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes., Research Design and Methods: In the Autoimmunity Screening for Kids study, 91 children who were persistently islet autoantibody positive (median age 11.5 years; 48% non-Hispanic White; 57% female) with a baseline CGM were followed for development of diabetes for a median of 6 (range 0.2-34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4-29) months., Results: Compared with children who did not progress to clinical diabetes (nonprogressors), those who did (progressors) had significantly higher average sensor glucose levels (119 vs. 105 mg/dL, P < 0.001) and increased glycemic variability (SD 27 vs. 16, coefficient of variation, 21 vs. 15, mean of daily differences 24 vs. 16, and mean amplitude of glycemic excursions 43 vs. 26, all P < 0.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140 >10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed area under the curve of ≥0.89 for both individual and combined CGM metric models., Conclusions: TA140 >10% is associated with a high risk of progression to clinical diabetes within the next year in autoantibody-positive children. CGM should be included in the ongoing monitoring of high-risk children and could be used as potential entry criterion for prevention trials., (© 2022 by the American Diabetes Association.)

Context: Single ZnT8 autoantibody (ZnT8A) positivity by standard radiobinding assay (RBA) is commonly seen in nondiabetes population-based screening and the risk of progression to type 1 diabetes (T1D) in subjects with single ZnT8A is unknown., Objective: Identify the risk of progression to T1D in individuals positive only for ZnT8A., Methods: We developed an electrochemiluminescence (ECL) assay to detect high-affinity ZnT8A and validated it in 3 populations: 302 patients newly diagnosed with T1D, 135 nondiabetic children positive for ZnT8A by RBA among 23 400 children screened by the Autoimmunity Screening for Kids (ASK) study, and 123 nondiabetic children multiple autoantibody positive or single ZnT8A positive by RBA participating in the Diabetes Autoimmunity Study in the Young (DAISY)., Results: In 302 patients with T1D at diagnosis, the positivity for ZnT8A was 62% both in RBA and ECL. Among ASK 135 participants positive for RBA-ZnT8A, 64 were detected ZnT8A as the only islet autoantibody. Of these 64, only 9 were confirmed by ECL-ZnT8A, found to be of high affinity with increased T1D risk. The overall positive predictive value of ECL-ZnT8A for T1D risk was 87.1%, significantly higher than that of RBA-ZnT8A (53.5%, P < .001). In DAISY, 11 of 2547 children who had no positivity previously detected for other islet autoantibodies were identified as single ZnT8A by RBA; of these, 3 were confirmed positive by ECL-ZnT8A and all 3 progressed to clinical T1D., Conclusion: A large proportion of ZnT8A by RBA are single ZnT8A with low T1D risk, whereas ZnT8A by ECL was of high affinity and high prediction for T1D development., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society.)

Objective: To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes., Research Design and Methods: We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed., Results: In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min ( P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ 2 = 25.76 vs. χ 2 = 8.62; PTP: χ 2 = 149.19 vs. χ 2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time., Conclusions: In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression., (© 2021 by the American Diabetes Association.)

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 Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: We aimed to test whether type 2 diabetes (T2D)-associated TCF7L2 genetic variants affect insulin sensitivity or secretion in autoantibody-positive relatives at risk for type 1 diabetes (T1D)., Research Design and Methods: We studied autoantibody-positive TrialNet Pathway to Prevention study participants ( N = 1,061) (mean age 16.3 years) with TCF7L2 single nucleotide polymorphism (SNP) information and baseline oral glucose tolerance test (OGTT) to calculate indices of insulin sensitivity and secretion. With Bonferroni correction for multiple comparisons, P values < 0.0086 were considered statistically significant., Results: None, one, and two T2D-linked TCF7L2 alleles were present in 48.1%, 43.9%, and 8.0% of the participants, respectively. Insulin sensitivity (as reflected by 1/fasting insulin [1/I F ]) decreased with increasing BMI z score and was lower in Hispanics. Insulin secretion (as measured by 30-min C-peptide index) positively correlated with age and BMI z score. Oral disposition index was negatively correlated with age, BMI z score, and Hispanic ethnicity. None of the indices were associated with TCF7L2 SNPs. In multivariable analysis models with age, BMI z score, ethnicity, sex, and TCF7L2 alleles as independent variables, C-peptide index increased with age, while BMI z score was associated with higher insulin secretion (C-peptide index), lower insulin sensitivity (1/I F ), and lower disposition index; there was no significant effect of TCF7L2 SNPs on any of these indices. When restricting the analyses to participants with a normal OGTT ( n = 743; 70%), the results were similar., Conclusions: In nondiabetic autoantibody-positive individuals, TCF7L2 SNPs were not related to insulin sensitivity or secretion indices after accounting for BMI z score, age, sex, and ethnicity., (© 2021 by the American Diabetes Association.)

Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes., Competing Interests: The authors have declared that no competing interests exist.

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10 -8 ) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4 + effector T cells. Using chromatin-accessibility profiling of CD4 + T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D., (© 2021. Crown.)