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Context: The applicability of the MODY risk calculator (Shields et al) to non- White European populations remains unknown., Objective: We aimed to test its real-world application in Hispanic youth., Methods: We conducted a retrospective chart review of Hispanic youth (<23 years) with diabetes (n=2033) in a large pediatric tertiary care center in the U.S. We calculated MODY probability for all subjects, splitting them into two cohorts based on the original model: Individuals who were started on insulin within 6 months of diabetes diagnosis (Cohort 1) and those who were not (Cohort 2)., Results: Cohort 1 consisted of 1566 individuals (median age [25p, 75p]: 16 [13, 19] years, 49% female), while Cohort 2 comprised 467 youth (median age [25p, 75p]: 17 [15, 20] years, 62% female). The mean MODY probability was 5.9% and 61.9% in Cohort 1 and Cohort 2, respectively. The mean probability for both cohorts combined was 18.8% suggesting an expected 382 individuals with MODY, which is much higher than previous estimations (1-5%; i.e. 20-102 individuals in this cohort). A total of 18 individuals tested positive for MODY among the limited number of individuals tested based on clinical suspicion and genetic testing availability (n=44 out of 2033 tested, [2.2% of overall cohort])., Conclusions: The MODY risk calculator likely overestimates the probability of MODY in Hispanic youth, largely driven by an overestimation in those not early-insulin treated (predominantly young-onset type 2 diabetes). The calculator needs updating to improve its applicability in this population. In addition, further research to help better identify MODY in Hispanic youth., (© 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. See the journal About page for additional terms.)

Context: Metabolic measures are frequently used to predict type 1 diabetes (T1D) and to understand effects of disease-modifying therapies., Objective: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D., Methods: Six-month changes in metabolic endpoints were assessed for (1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial, a multicenter clinical trial investigating 14-day intravenous teplizumab infusion and (2) predicting T1D in the TrialNet Pathway to Prevention natural history study. For each metabolic measure, t-Values from t tests for detecting a treatment effect were compared with chi-square values from proportional hazards regression for predicting T1D. Participants in the teplizumab prevention trial and participants in the Pathway to Prevention study selected with the same inclusion criteria used for the teplizumab trial were studied., Results: Six-month changes in glucose-based endpoints predicted diabetes better than C-peptide-based endpoints, yet the latter were better at detecting a teplizumab effect. Combined measures of glucose and C-peptide were more balanced than measures of glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect., Conclusion: The capacity of a metabolic endpoint to detect a treatment effect does not necessarily correspond to its accuracy for predicting T1D. However, combined glucose and C-peptide endpoints appear to be effective for both predicting diabetes and detecting a response to immunotherapy. These findings suggest that combined glucose and C-peptide endpoints should be incorporated into the design of future T1D prevention trials., (© 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.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: Accumulating evidence indicates that type 2 diabetes (T2D) is phenotypically heterogeneous. Defining and classifying variant forms of T2D are priorities to better understand its pathophysiology and usher clinical practice into an era of "precision diabetes.", Evidence Acquisition and Methods: We reviewed literature related to heterogeneity of T2D over the past 5 decades and identified a range of phenotypic variants of T2D. Their descriptions expose inadequacies in current classification systems. We attempt to link phenotypically diverse forms to pathophysiology, explore investigative methods that have characterized "atypical" forms of T2D on an etiological basis, and review conceptual frameworks for an improved taxonomy. Finally, we propose future directions to achieve the goal of an etiological classification of T2D., Evidence Synthesis: Differences among ethnic and racial groups were early observations of phenotypic heterogeneity. Investigations that uncover complex interactions of pathophysiologic pathways leading to T2D are supported by epidemiological and clinical differences between the sexes and between adult and youth-onset T2D. Approaches to an etiological classification are illustrated by investigations of atypical forms of T2D, such as monogenic diabetes and syndromes of ketosis-prone diabetes. Conceptual frameworks that accommodate heterogeneity in T2D include an overlap between known diabetes types, a "palette" model integrated with a "threshold hypothesis," and a spectrum model of atypical diabetes., Conclusion: The heterogeneity of T2D demands an improved, etiological classification scheme. Excellent phenotypic descriptions of emerging syndromes in different populations, continued clinical and molecular investigations of atypical forms of diabetes, and useful conceptual models can be utilized to achieve this important goal., (© 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 set forth to compare ethnicities for metabolic and immunological characteristics at the clinical diagnosis of type 1 diabetes (T1D) and assess the effect of ethnicity on beta-cell functional loss within 3 years after clinical diagnosis., Research Methods and Design: We studied participants in TrialNet New Onset Intervention Trials (n = 624, median age = 14.4 years, 58% male, 8.7% Hispanic) and followed them prospectively for 3 years. Mixed meal tolerance tests (MMTT) were performed within 6 months following clinical diagnosis and repeated semiannually. Unless otherwise indicated, analyses were adjusted for age, sex, BMI Z-score, and diabetes duration., Results: At T1D clinical diagnosis, Hispanics, compared with non-Hispanic whites (NHW), had a higher frequency of diabetic ketoacidosis (DKA) (44.7% vs 25.3%, OR = 2.36, P = 0.01), lower fasting glucose (97 vs 109 mg/dL, P = 0.02) and higher fasting C-peptide (1.23 vs 0.94 ng/mL, P = 0.02) on the first MMTT, and higher frequency of ZnT8 autoantibody positivity (n = 201, 94.1% vs 64%, OR = 7.98, P = 0.05). After exclusion of participants in experimental arms of positive clinical trials, C-peptide area under the curve (AUC) trajectories during the first 3 years after clinical diagnosis were not significantly different between Hispanics and NHW after adjusting for age, sex, BMI-z score, and DKA (n = 413, P = 0.14)., Conclusion: Despite differences in the metabolic and immunological characteristics at clinical diagnosis of T1D between Hispanics and NHW, C-peptide trajectories did not differ significantly in the first 3 years following clinical diagnosis after adjustment for body mass index and other confounders. These findings may inform the design of observational studies and intervention trials in T1D., (© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied., Objective: To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES)., Design: Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES., Setting: Six diabetes centers across the United States., Participants: A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic)., Main Outcome: Racial-ethnic disparity in HbA1c levels., Results: Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black-White glycemic disparity., Conclusion: This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors., (© Endocrine Society 2020.)

Context: Multiple islet autoantibody positivity usually precedes clinical (stage 3) type 1 diabetes (T1D)., Objective: To test the hypothesis that individuals who develop stage 3 T1D with only a single autoantibody have unique metabolic differences., Design: Cross-sectional analysis of participants in the T1D TrialNet study., Setting: Autoantibody-positive relatives of individuals with stage 3 T1D., Participants: Autoantibody-positive relatives who developed stage 3 T1D (at median age 12.4 years, range = 1.4-58.6) and had autoantibody data close to clinical diagnosis (n = 786, 47.4% male, 79.9% non-Hispanic white)., Main Outcome Measures: Logistic regression modeling was used to assess relationships between autoantibody status and demographic, clinical, and metabolic characteristics, adjusting for potential confounders and correcting for multiple comparisons., Results: At diagnosis of stage 3 T1D, single autoantibody positivity, observed in 119 (15.1%) participants (72% GAD65, 13% microinsulin antibody assay, 11% insulinoma-associated antigen 2, 1% islet cell antibody, 3% autoantibodies to zinc transporter 8 [ZnT8]), was significantly associated with older age, higher C-peptide measures (fasting, area under the curve, 2-hour, and early response in oral glucose tolerance test), higher homeostatic model assessment of insulin resistance, and lower T1D Index60 (all P < 0.03). While with adjustment for age, 2-hour C-peptide remained statistically different, controlling for body mass index (BMI) attenuated the differences. Sex, race, ethnicity, human leukocyte antigen DR3-DQ2, and/or DR4-DQ8, BMI category, and glucose measures were not significantly associated with single autoantibody positivity., Conclusions: Compared with multiple autoantibody positivity, single autoantibody at diagnosis of stage 3 T1D was associated with older age and insulin resistance possibly mediated by elevated BMI, suggesting heterogeneous disease pathogenesis. These differences are potentially relevant for T1D prevention and treatment., (© Endocrine Society 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Given the global rise in both type 1 diabetes incidence and obesity, the role of body mass index (BMI) on type 1 diabetes pathophysiology has gained great interest. Sustained excess BMI in pediatric participants of the TrialNet Pathway to Prevention (PTP) cohort increased risk for progression to type 1 diabetes, but the effects of age and obesity in adults remain largely unknown., Objective: To determine the effect of age and sustained obesity on the risk for type 1 diabetes in adult participants in the TrialNet PTP cohort (i.e., nondiabetic autoantibody-positive relatives of patients with type 1 diabetes)., Research Design and Methods: Longitudinally accumulated BMI >25 kg/m2 was calculated to generate a cumulative excess BMI (ceBMI) for each participant, with ceBMI values ≥0 kg/m2 and ≥5 kg/m2 representing sustained overweight or obese status, respectively. Recursive partitioning analysis yielded sex- and age-specific thresholds for ceBMI that confer the greatest risk for type 1 diabetes progression., Results: In this cohort of 665 adults (age 20 to 50 years; median follow-up, 3.9 years), 49 participants developed type 1 diabetes. Age was an independent protective factor for type 1 diabetes progression (hazard ratio, 0.95; P = 0.008), with a threshold of >35 years that reduced risk for type 1 diabetes. In men age >35 years and women age <35 years, sustained obesity (ceBMI ≥5 kg/m2) increased the risk for type 1 diabetes., Conclusions: Age is an important factor for type 1 diabetes progression in adults and influences the impact of elevated BMI, indicating an interplay of excess weight, age, and sex in adult type 1 diabetes pathophysiology., (Copyright © 2017 Endocrine Society)

Context: Genome-wide association studies identified >50 type 1 diabetes (T1D) associated non-human leukocyte antigens (non-HLA) loci., Objective: The purpose of this study was to assess the contribution of non-HLA single nucleotide polymorphisms (SNPs) to risk of disease progression., Design and Setting: The TrialNet Pathway to Prevention Study follows relatives of T1D patients for development of autoantibodies (Abs) and T1D., Participants: Using the Immunochip, we analyzed 53 diabetes-associated, non-HLA SNPs in 1016 Ab-positive, at-risk non-Hispanic white relatives., Main Outcome Measure: Effect of SNPs on the development of multiple Abs and T1D., Results: Cox proportional analyses included all substantial non-HLA SNPs, HLA genotypes, relationship to proband, sex, age at initial screening, initial Ab type, and number. Factors involved in progression from single to multiple Abs included age at screening, relationship to proband, HLA genotypes, and rs3087243 (cytotoxic T lymphocyte antigen-4). Significant factors for diabetes progression included age at screening, Ab number, HLA genotypes, rs6476839 [GLIS family zinc finger 3 (GLIS3)], and rs3184504 [SH2B adaptor protein 3 (SH2B3)]. When glucose area under the curve (AUC) was included, factors involved in disease progression included glucose AUC, age at screening, Ab number, relationship to proband, HLA genotypes, rs6476839 (GLIS3), and rs7221109 (CCR7). In stratified analyses by age, glucose AUC, age at screening, sibling, HLA genotypes, rs6476839 (GLIS3), and rs4900384 (C14orf64) were significantly associated with progression to diabetes in participants <12 years old, whereas glucose AUC, sibling, rs3184504 (SH2B3), and rs4900384 (C14orf64) were significant in those ≥12., Conclusions: In conclusion, we identified five non-HLA SNPs associated with increased risk of progression from Ab positivity to disease that may improve risk stratification for prevention trials., (Copyright © 2017 by the Endocrine Society)

Context: Human leukocyte antigen (HLA) DQ haplotypes have the strongest genetic association with type 1 diabetes (T1DM) risk., Objective: The objective of the study was to analyze whether HLA DQ alleles influence the development of antiislet autoantibodies, the progression to T1DM among autoantibody-positive relatives, or both., Design: The Diabetes Prevention Trial-1 screened more than 90,000 nondiabetic relatives of patients for cytoplasmic islet-cell autoantibody (ICA) expression between 1994 and 2002., Setting: The study was conducted in the general community., Participants: The Diabetes Prevention Trial-1 found 2817 ICA-positive relatives who were tested for biochemical autoantibodies (GAD65, ICA512, and insulin) and HLA-DQ haplotypes, and 2796 of them were followed up for progression to diabetes for up to 8 yr (median, 3.6 yr)., Main Outcome Measure: Progression to T1DM was measured., Results: High-risk DQ haplotypes and genotypes were associated with a higher percentage of relatives expressing multiple biochemical autoantibodies and higher T1DM risk (e.g., respectively, 59 and 36% at 5 yr for carriers of the DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201 genotype). The number of autoantibodies expressed significantly increased T1DM risk and across different DQ genotypes, autoantibody positivity directly correlated with diabetes risk. However, multivariate analyses indicated that the influence of most genotypes on T1DM risk was not independent from autoantibody expression, with the possible exception of DQA1*0102-DQB1*0602. Specific genotypic combinations conferred 5-yr diabetes risks significantly lower (e.g. 7%-DQA1*0201-DQB1*0201/DQA1*0501-DQB1*0201 and 14%-DQA1*0301-DQB1*0301/DQA1*0501-DQB1*0201) than when those haplotypes were found in other combinations., Conclusion: HLA DQ alleles determine autoantibody expression, which is correlated with diabetes progression. Among autoantibody-positive relatives, most HLA DQ genotypes did not further influence T1DM risk.