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OBJECTIVE: To explore if oral insulin could delay onset of stage 3 type 1 diabetes (T1D) among patients with stage 1/2 who carry HLA DR4-DQ8 and/or have elevated levels of IA-2 autoantibodies (IA-2As). RESEARCH AND METHODS: Next-generation targeted sequencing technology was used to genotype eight HLA class II genes (DQA1 , DQB1 , DRB1 , DRB3 , DRB4 , DRB5 , DPA1 , and DPB1) in 546 participants in the TrialNet oral insulin preventative trial (TN07). Baseline levels of autoantibodies against insulin (IAA), GAD65 (GADA), and IA-2A were determined prior to treatment assignment. Available clinical and demographic covariables from TN07 were used in this post hoc analysis with the Cox regression model to quantify the preventive efficacy of oral insulin. RESULTS: Oral insulin reduced the frequency of T1D onset among participants with elevated IA-2A levels (HR 0.62; P = 0.012) but had no preventive effect among those with low IA-2A levels (HR 1.03; P = 0.91). High IA-2A levels were positively associated with the HLA DR4-DQ8 haplotype (OR 1.63; P = 6.37 × 10−6) and negatively associated with the HLA DR7– containing DRB1*07:01-DRB4*01:01-DQA1*02:01-DQB1*02:02 extended haplotype (OR 0.49; P = 0.037). Among DR4-DQ8 carriers, oral insulin delayed the progression toward stage 3 T1D onset (HR 0.59; P = 0.027), especially if participants also had high IA-2A level (HR 0.50; P = 0.028). CONCLUSIONS: These results suggest the presence of a T1D endotype characterized by HLA DR4-DQ8 and/or elevated IA-2A levels; for those patients with stage 1/2 disease with such an endotype, oral insulin delays the clinical T1D onset. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1 , DQB1 , DRB1 , DRB3 , DRB4 , DRB5 , DPA1 , DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression. RESULTS: First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10−3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10−3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ , DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10−3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02 , sharing the DRB1*03:01 , had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression. CONCLUSIONS: These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To examine whether iron intake and genetically determined iron overload interact in predisposing to the development of childhood islet autoimmunity (IA) and type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: In The Environmental Determinants of Diabetes in the Young (TEDDY) study, 7,770 genetically high-risk children were followed from birth until the development of IA and progression to T1D. Exposures included energy-adjusted iron intake in the first 3 years of life and a genetic risk score (GRS) for increased circulating iron. RESULTS: We found a U-shaped association between iron intake and risk of GAD antibody as the first autoantibody. In children with GRS ≥2 iron risk alleles, high iron intake was associated with an increased risk of IA, with insulin as first autoantibody (adjusted hazard ratio 1.71 [95% CI 1.14; 2.58]) compared with moderate iron intake. CONCLUSIONS: Iron intake may alter the risk of IA in children with high-risk HLA haplogenotypes. [ABSTRACT FROM AUTHOR]

Context: Some individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes. Objective: To develop an analytical approach to identify and cluster phenotypes of AD. Methods: We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B). Results: We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)--prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA. Conclusion: DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables. [ABSTRACT FROM AUTHOR]

Our aim was to investigate the associations between erythrocyte fatty acids and the risk of islet autoimmunity in children. The Environmental Determinants of Diabetes in the Young Study (TEDDY) is a longitudinal cohort study of children at high genetic risk for type 1 diabetes (n = 8676) born between 2004 and 2010 in the U.S., Finland, Sweden, and Germany. A nested case–control design comprised 398 cases with islet autoimmunity and 1178 sero-negative controls matched for clinical site, family history, and gender. Fatty acids composition was measured in erythrocytes collected at the age of 3, 6, and 12 months and then annually up to 6 years of age. Conditional logistic regression models were adjusted for HLA risk genotype, ancestry, and weight z-score. Higher eicosapentaenoic and docosapentaenoic acid (n − 3 polyunsaturated fatty acids) levels during infancy and conjugated linoleic acid after infancy were associated with a lower risk of islet autoimmunity. Furthermore, higher levels of some even-chain saturated (SFA) and monounsaturated fatty acids (MUFA) were associated with increased risk. Fatty acid status in early life may signal the risk for islet autoimmunity, especially n − 3 fatty acids may be protective, while increased levels of some SFAs and MUFAs may precede islet autoimmunity. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) genetic risk score (GRS) and islet autoantibody (AAb) specificity and titers influence T1D risk; however, these factors collected at baseline in cross sectional studies have not been effectively incorporated in the current models for prediction of progression along pre-clinical stages of T1D. Our aim was to develop an updated model of T1D prediction for research and clinical practice. Methods: AAb-positive relatives of individuals with T1D (n=1,233, 52% female, 88% European ancestry, age mean 15 yrs [SD 12.2]) from the TrialNet Pathway to Prevention were followed for a median of 3.1 years (IQR 1.1 -7.1). T1D developed in 498/1,233 (40.4%). T1D GRS, AAb number, AAb combination, AAb titer, age and sex were evaluated on the combined T1D prediction model, estimating its performance using 3-fold cross-validation (10x repeated). Results: Machine learning (survival random forest) and traditional methods (Cox models, extended Cox model) performed equivalently as measured by 5 yr horizon time-dependent AUC ROC (respectively 0.74, 0.74, 0.72), on calibration by integrated Brier score (respectively 0.15, 0.15, 0.16), and by calibration plots. AAb combinations with specific variables predicted T1D. In multivariate analysis, T1D GRS was a significant predictor of T1D in individuals positive for GADA, IAA or both. For any autoantibody combination including IA2A, the IA2A titer was predictive while the T1D GRS was not. Variable importance analyses showed that T1D was predicted by IA2A titer level, followed by T1D GRS and combinations of specific AAbs. Conclusion: We modeled individual estimates of T1D risk with T1D GRS, AAb characteristics, age, and sex. T1D prediction improved by adding T1D GRS and AAb characteristics to other variables, and was similar using machine learning or traditional models. T1D GRS was a significant predictor only in the absence of IA2A. In the presence of IA2A, IA2A titer was the most influential factor. Disclosure: L. A. Ferrat: None. M. J. Redondo: Advisory Panel; Self; Provention Bio, Inc. A. Steck: None. H. M. Parikh: None. L. You: None. S. Onengut-gumuscu: None. P. Gottlieb: Advisory Panel; Self; Janssen Research & Development, LLC, Tolerion, Inc., Viacyte, Inc., Other Relationship; Self; ImmunoMolecular Therapeutics, Inc., Research Support; Self; Caladrius Biosciences, Inc., Immune Tolerance Network, Mercia Pharma Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Tolerion, Inc. S. S. Rich: None. J. Krischer: None. R. A. Oram: Consultant; Self; Janssen Research & Development, LLC. Funding: National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK121843-01); JDRF (3-SRA-2019-827-S-B) [ABSTRACT FROM AUTHOR]

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.)