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Context: Identification of prognostic biomarkers in pediatric diabetes is important for precision medicine., Objective: We assessed whether C-peptide and islet autoantibodies are useful to predict the natural history of children with new-onset diabetes., Methods: We prospectively studied 72 children with new-onset diabetes (median follow-up: 8 months) by applying the Aβ classification system ("A+": islet autoantibody positive, "β+": random serum C-peptide≥1.3 ng/mL at diagnosis). Beta-cell function was assessed longitudinally with 2h post-prandial/stimulated urinary C-peptide-to-creatinine ratio (UCPCR) 3-12 weeks (V1) and 6-12 months after diagnosis (V2). We obtained a type 1 diabetes genetic risk score (T1D GRS2) for each participant, and compared characteristics at baseline, and clinical outcomes at V2., Results: The cohort was 50% male. Racial distribution was 76.4% White, 20.8% Black and 2.8% Asian or other races. A total of 46.5% participants were Hispanic. Median age (Q1-Q3) was 12.4 (8.3-14.5) years. The Aβ subgroup frequencies were 46 A+β-(63.9%), 1 A-β-(1.4%), 4 A+β+(5.6%), and 21 A-β+(29.2%). Baseline serum C-peptide correlated with UCPCR at both V1 (r=0.36, p=0.002) and V2 (r=0.47, p<0.001). There were significant subgroup differences in age, race, frequency of diabetic ketoacidosis and T1D GRS2 (p<0.01). At V2, the two β- subgroups had lower UCPCR and higher HbA1c compared with the two β+ subgroups (p<0.001 and p=0.02, respectively). Thirty-eight percent of A-β+ but none of the other subgroups were insulin-independent at V2 (p<0.001)., Conclusions: C-peptide and islet autoimmunity at diagnosis define distinct phenotypes and predict beta-cell function and insulin dependence 6-12 months later in racially/ethnically diverse children with new-onset diabetes., (Copyright © 2024. Published by Elsevier Inc.)

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.)

Objectives: We sought to determine if the early months of the coronavirus disease 2019 (COVID-19) pandemic influenced pediatric diabetic ketoacidosis (DKA) hospitalization characteristics., Methods: This is a cross-sectional study of youth with laboratory-confirmed DKA admitted to a large tertiary children's hospital in the USA. Data were collected from admissions in March through July 2019 and March through July 2020, respectively. We evaluated the clinical characteristics of hospitalization, including demographic data and DKA severity. We used univariable ordinal logistic regression followed by multiple ordinal logistic regression to adjust for potential confounders., Results: We included 137 children with diabetes admitted for DKA in the relevant period in 2019 and 173 patients admitted for DKA in the same period in 2020. Hemoglobin A1C (HbA 1c ) upon admission was higher in 2020 (median=12.2 %) than in 2019 (11.5 %, p=0.018). Children who were admitted with DKA in 2020 were less likely to be autoantibody positive than those in 2019 (83 vs. 91 %, p=0.028). In the univariable model, being admitted in 2020 was significantly associated with more severe DKA (p=0.038), as was HbA 1c (p=0.001). After adjusting for HbA 1c upon admission, admission year was no longer significantly associated with more severe DKA., Conclusions: In this study of pediatric diabetes of any type and duration of diabetes, youth admitted for DKA at the start of the COVID-19 pandemic, compared with those admitted during the year before, were more likely to have autoantibody-negative diabetes and had significantly higher HbA 1c . Additionally, higher HbA 1c seemed to mediate more severe DKA during the pandemic., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

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: Mixed-meal tolerance test-stimulated area under the curve (AUC) C-peptide at 12-24 months represents the primary end point for nearly all intervention trials seeking to preserve β-cell function in recent-onset type 1 diabetes. We hypothesized that participant benefit might be detected earlier and predict outcomes at 12 months posttherapy. Such findings would support shorter trials to establish initial efficacy., Research Design and Methods: We examined data from six Type 1 Diabetes TrialNet immunotherapy randomized controlled trials in a post hoc analysis and included additional stimulated metabolic indices beyond C-peptide AUC. We partitioned the analysis into successful and unsuccessful trials and analyzed the data both in the aggregate as well as individually for each trial., Results: Among trials meeting their primary end point, we identified a treatment effect at 3 and 6 months when using C-peptide AUC (P = 0.030 and P < 0.001, respectively) as a dynamic measure (i.e., change from baseline). Importantly, no such difference was seen in the unsuccessful trials. The use of C-peptide AUC as a 6-month dynamic measure not only detected treatment efficacy but also suggested long-term C-peptide preservation (R2 for 12-month C-peptide AUC adjusted for age and baseline value was 0.80, P < 0.001), and this finding supported the concept of smaller trial sizes down to 54 participants., Conclusions: Early dynamic measures can identify a treatment effect among successful immune therapies in type 1 diabetes trials with good long-term prediction and practical sample size over a 6-month period. While external validation of these findings is required, strong rationale and data exist in support of shortening early-phase clinical trials., (© 2024 by the American Diabetes Association.)

Introduction: In the era of next-generation sequencing, clinicians frequently encounter variants of unknown significance (VUS) in genetic testing. VUS may be reclassified over time as genetic knowledge grows. We know little about how best to approach VUS in the maturity-onset diabetes of the young (MODY). Therefore, our study aimed to determine the utility of reanalysis of previous VUS results in genetic confirmation of MODY., Methods: A single-center retrospective chart review identified 85 subjects with a MODY clinical diagnosis. We reanalyzed genetic testing in 10 subjects with 14 unique VUS on MODY genes that was performed &gt;3 years before the study. Demographic, clinical, and biochemical data was collected for those individuals., Results: After reanalysis, 43% (6/14) of the gene variants were reclassified to a different category: 7% (1/14) were "likely pathogenic" and 36% (5/14) were "benign" or "likely benign." The reclassified pathogenic variant was in HNF1A and all reclassified benign variants were in HNF1A, HNF1B and PDX1. The median time between MODY testing and reclassification was 8 years (range: 4-10 years)., Conclusion: In sum, iterative reanalyzing the genetic data from VUS found during MODY testing may provide high-yield diagnostic information. Further studies are warranted to identify the optimal time and frequency for such analyses., (© 2024 S. Karger AG, Basel.)

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.)

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥ 3 years post-diabetes diagnosis (n = 1019), we compared youth with steady versus reclassified diabetes type. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p < 0.05). In the longitudinal subcohort, diabetes type was reclassified in 35 youth (3.4%); this was not statistically associated with any single characteristic. In sum, among racially/ethnically diverse youth with diabetes, 6.4% had inaccurate diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type., (© 2024. The Author(s).)

Background: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies., Methods: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment., Results: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation., Conclusions: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops., (© 2024. The Author(s).)

Aims: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)-supported multicenter, prospective, observational study that enrols children and adolescents with non-secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth., Materials and Methods: The proposed models will evaluate the utility of three existing T1D genetic risk scores in combination with data on islet autoantibodies and other parameters typically available at the time of diabetes onset. Low non-fasting serum C-peptide (<0.6 nmol/L) between 3 and 10 years after diabetes diagnosis will be considered a biomarker for T1D as it reflects the loss of insulin secretion ability. Participating centres are enrolling youth (<19 years old) either with established diabetes (duration 3-10 years) for a cross-sectional evaluation or with recent onset diabetes (duration 3 weeks-15 months) for the longitudinal observation with annual visits for 3 years. Cross-sectional data will be used to develop models. Longitudinal data will be used to externally validate the best-fitting model., Results: The results are expected to improve the ability to classify diabetes type in a large and growing subset of children who have an unclear form of diabetes at diagnosis., Conclusions: Accurate and timely classification of diabetes type will help establish the correct clinical management early in the course of the disease., (© 2023 John Wiley & Sons Ltd.)

Objective: With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D)., Research Design and Methods: We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS)., Results: Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with C-peptide ≥0.75 ng/mL (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under the receiver operating characteristic curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995-0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88-0.96) and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92)., Conclusions: Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D., (© 2024 by the American Diabetes Association.)

Background: A - β + ketosis-prone diabetes (KPD) in adults is characterized by presentation with diabetic ketoacidosis (DKA), negative islet autoantibodies, and preserved β -cell function in persons with a phenotype of obesity-associated type 2 diabetes (T2D). The prevalence of KPD has not been evaluated in children. We investigated children with DKA at "T2D" onset and determined the prevalence and characteristics of pediatric A - β + KPD within this cohort., Methods: We reviewed the records of 716 children with T2D at a large academic hospital and compared clinical characteristics of those with and without DKA at onset. In the latter group, we identified patients with A - β + KPD using criteria of the Rare and Atypical Diabetes Network (RADIANT) and defined its prevalence and characteristics., Results: Mean age at diagnosis was 13.7 ± 2.4 years: 63% female; 59% Hispanic, 29% African American, 9% non-Hispanic White, and 3% other. Fifty-six (7.8%) presented with DKA at diagnosis and lacked islet autoantibodies. Children presenting with DKA were older and had lower C-peptide and higher glucose concentrations than those without DKA. Twenty-five children with DKA (45%) met RADIANT A - β + KPD criteria. They were predominantly male (64%), African American or Hispanic (96%), with substantial C-peptide (1.3 ± 0.7 ng/mL) at presentation with DKA and excellent long-term glycemic control (HbA1c 6.6% ± 1.9% at follow-up (median 1.3 years postdiagnosis))., Conclusions: In children with a clinical phenotype of T2D and DKA at diagnosis, approximately half meet criteria for A - β + KPD. They manifest the key characteristics of obesity, preserved β -cell function, male predominance, and potential to discontinue insulin therapy, similar to adults with A - β + KPD., Competing Interests: Conflicts of Interest The authors have no conflicts of interest to disclose.

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future., Competing Interests: Declaration of interests RDL is on the Advisory Boards of Diamyd and Provention and has received educational grants from Novo Nordisk, Eli Lily, Sanofi, Astra Zeneca, and MSD. RCWM has received research funding from AstraZeneca, Bayer, Novo Nordisk, Pfizer, Roche Diagnostics (HK), and Tricida for carrying out clinical trials, and has received speaker honorarium or consultancy in advisory boards from AstraZeneca, Bayer, Boehringer Ingelheim, Medtronic, Merck, and Kyowa Kirin. All proceeds have been donated to the Chinese University of Hong Kong to support diabetes research. RCWM is a cofounder of GemVCare, a diabetes technology start-up, established through support from the Hong Kong Government Technology Start-up Support Scheme for Universities (TSSSU). PWF has received consulting fees from Zoe, and is an employee of the Novo Nordisk Foundation, a non-profit, philanthropic, enterprise foundation. The views expressed in this Series paper do not necessarily reflect those of the institutions to which the authors are affiliated. ERP has received honoraria for speaking from Lilly, Novo Nordisk, and Illumina. All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care., Methods: Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years)., Results: We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact., Conclusions: Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes., (© 2023. Springer Nature Limited.)

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 & Aims: Metabolic biomarkers with pathophysiological relevance is lacking in pediatric diabetes. We aimed to identify novel metabolic biomarkers in pediatric type 1 (T1D) and type 2 diabetes (T2D). We hypothesized that (1) targeted plasma metabolomics, focused on plasma amino acid concentrations, could identify distinctively altered patterns in children with T1D or T2D, and (2) there are specific changes in concentrations of metabolites related to branch chain amino acids (BCAA) and arginine metabolism in children with T2D., Methods: In a pilot study, we enrolled children with T1D (n = 15) and T2D (n = 13), and healthy controls (n = 15). Fasting plasma amino acid concentrations were measured by ultra-performance liquid chromatography, and compared between the groups after adjustment for confounding factors., Results: The mean age (SD) of participants was 16.4 (0.9) years. There were no group differences in age, gender, race/ethnicity, or 24-h protein intake. Mean BMI percentile was higher in the T2D than the T1D group or controls (p < 0.001). The T2D group had lower arginine, citrulline, glutamine, glycine, phenylalanine, methionine, threonine, asparagine and symmetric dimethylarginine (SDMA) but higher aspartate than controls, after adjusting for BMI percentiles (all p < 0.05). Children with T2D also had lower glycine but higher ornithine, proline, leucine, isoleucine, valine, total BCAA, lysine and tyrosine than those with T1D after adjusting for confounding factors (all p < 0.05). Children with T1D had lower phenylalanine, methionine, threonine, glutamine, tyrosine, asymmetric dimethylarginine (ADMA) and SDMA than controls (all p < 0.05)., Conclusions: Children with T2D and T1D have distinct fasting plasma amino acid signatures that suggest varying pathogenic mechanisms and could serve as biomarkers for these conditions., Competing Interests: Declaration of competing interest The authors have no conflict of interest to disclose., (Copyright © 2023 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)

Despite major advances over the past decade, prevention and treatment of type 1 diabetes mellitus (T1DM) remain suboptimal, with large and unexplained variations in individual responses to interventions. The current classification schema for diabetes mellitus does not capture the complexity of this disease or guide clinical management effectively. One of the approaches to achieve the goal of applying precision medicine in diabetes mellitus is to identify endotypes (that is, well-defined subtypes) of the disease each of which has a distinct aetiopathogenesis that might be amenable to specific interventions. Here, we describe epidemiological, clinical, genetic, immunological, histological and metabolic differences within T1DM that, together, suggest heterogeneity in its aetiology and pathogenesis. We then present the emerging endotypes and their impact on T1DM prediction, prevention and treatment., (© 2023. Springer Nature Limited.)

Objective: Metabolic zones were developed to characterize heterogeneity of individuals with islet autoantibodies., Research Design and Methods: Baseline 2-h oral glucose tolerance test data from 6,620 TrialNet Pathway to Prevention Study (TNPTP) autoantibody-positive participants (relatives of individuals with type 1 diabetes) were used to form 25 zones from five area under the curve glucose (AUCGLU) rows and five area under the curve C-peptide (AUCPEP) columns. Zone phenotypes were developed from demographic, metabolic, autoantibody, HLA, and risk data., Results: As AUCGLU increased, changes of glucose and C-peptide response curves (from mean glucose and mean C-peptide values at 30, 60, 90, and 120 min) were similar within the five AUCPEP columns. Among the zones, 5-year risk for type 1 diabetes was highly correlated with islet antigen 2 antibody prevalence (r = 0.96, P < 0.001). Disease risk decreased markedly in the highest AUCGLU row as AUCPEP increased (0.88-0.41; P < 0.001 from lowest AUCPEP column to highest AUCPEP column). AUCGLU correlated appreciably less with Index60 (an indicator of insulin secretion) in the highest AUCPEP column (r = 0.33) than in other columns (r ≥ 0.78). AUCGLU was positively related to "fasting glucose × fasting insulin" and to "fasting glucose × fasting C-peptide" (indicators of insulin resistance) before and after adjustments for Index60 (P < 0.001)., Conclusions: Phenotypes of 25 zones formed from AUCGLU and AUCPEP were used to gain insights into type 1 diabetes heterogeneity. Zones were used to examine GCRC changes with increasing AUCGLU, associations between risk and autoantibody prevalence, the dependence of glucose as a predictor of risk according to C-peptide, and glucose heterogeneity from contributions of insulin secretion and insulin resistance., (© 2023 by the American Diabetes Association.)

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.)

The incidence of type 2 diabetes in children is rising and carries a worse prognosis than in adults. The influence of sex on pediatric type 2 diabetes outcomes has not been well investigated. We studied 715 youth with type 2 diabetes diagnosed at a median age of 13.7 years and compared sex differences in demographic, clinical, and laboratory characteristics within the first year of diagnosis. Females diagnosed with type 2 diabetes were younger and at a higher stage of pubertal development than males, yet presented with lower A1Cs, a lower prevalence of diabetic ketoacidosis, and higher HDL cholesterol levels., Competing Interests: No potential conflicts of interest relevant to this article were reported., (© 2023 by the American Diabetes Association.)

Introduction: Diabetes mellitus in underrepresented racial and ethnic groups (URG) is rapidly increasing in incidence and has worse outcomes than diabetes in non-Hispanic White individuals. Rare and Atypical Diabetes Network (RADIANT) established recruitment targets based on the racial and ethnic distribution of the USA to enroll a diverse study population. We examined participation of URG across RADIANT study stages and described strategies to enhance recruitment and retention of URG., Materials and Methods: RADIANT is a multicenter NIH-funded study of people with uncharacterized forms of atypical diabetes. RADIANT participants consent online and progress through three sequential study stages, as eligible., Results: We enrolled 601 participants with mean age 44 ± 16.8 years, 64.4% female. At Stage 1, 80.6% were White, 7.2% African American (AA), 12.2% other/more than one race, and 8.4% Hispanic. Enrollment of URG was significantly below preset targets across most stages. Referral sources differed by race ( p < 0.001) but not ethnicity ( p = 0.15). Most AA participants were referred by RADIANT investigators (58.5% vs. 24.5% in Whites), whereas flyers, news, social media, and family or friends were more frequent referral sources for White individuals (26.4% vs. 12.2% in AA). Ongoing initiatives to increase enrollment of URG in RADIANT include engaging with clinics/hospitals serving URG, screening electronic medical records, and providing culturally competent study coordination and targeted advertisement., Conclusions: There is low participation of URG in RADIANT, potentially limiting the generalizability of its discoveries. Investigations into barriers and facilitators for recruitment and retention of URG in RADIANT, with implications for other studies, are ongoing., (© The Author(s) 2023.)

Background: Pediatric Type 2 diabetes (T2D) is highly heterogeneous. Previous reports on adult-onset diabetes demonstrated the existence of diabetes clusters. Therefore, we set out to identify unique diabetes subgroups with distinct characteristics among youth with T2D using commonly available demographic, clinical, and biochemical data., Methods: We performed data-driven cluster analysis (K-prototypes clustering) to characterize diabetes subtypes in pediatrics using a dataset with 722 children and adolescents with autoantibody-negative T2D. The six variables included in our analysis were sex, race/ethnicity, age, BMI Z -score and hemoglobin A1c at the time of diagnosis, and non-HDL cholesterol within first year of diagnosis., Results: We identified five distinct clusters of pediatric T2D, with different features, treatment regimens and risk of diabetes complications: Cluster 1 was characterized by higher A1c; Cluster 2, by higher non-HDL; Cluster 3, by lower age at diagnosis and lower A1c; Cluster 4, by lower BMI and higher A1c; and Cluster 5, by lower A1c and higher age. Youth in Cluster 1 had the highest rate of diabetic ketoacidosis (DKA) ( p = 0.0001) and were most prescribed metformin ( p = 0.06). Those in Cluster 2 were most prone to polycystic ovarian syndrome ( p = 0.001). Younger individuals with lowest family history of diabetes were least frequently diagnosed with diabetic ketoacidosis ( p = 0.001) and microalbuminuria ( p = 0.06). Low-BMI individuals with higher A1c had the lowest prevalence of acanthosis nigricans ( p = 0.0003) and hypertension ( p = 0.03)., Conclusions: Utilizing clinical measures gathered at the time of diabetes diagnosis can be used to identify subgroups of pediatric T2D with prognostic value. Consequently, this advancement contributes to the progression and wider implementation of precision medicine in diabetes management., Competing Interests: Conflicts of Interest The authors declare that they have no conflicts of interest.

Aims/hypothesis: TCF7L2 variants are the strongest genetic risk factor for type 2 diabetes. In individuals with type 1 diabetes, these variants are associated with a higher C-peptide AUC, a lower glucose AUC during an OGTT, single autoantibody positivity near diagnosis, particularly in individuals older than 12 years of age, and a lower frequency of type 1 diabetes-associated HLA genotypes. Based on initial observations from clinical cohorts, we tested the hypothesis that type 2 diabetes-predisposing TCF7L2 genetic variants are associated with a higher percentage of residual insulin-containing cells (ICI%) in pancreases of donors with type 1 diabetes, by examining genomic data and pancreatic tissue samples from the Network for Pancreatic Organ donors with Diabetes (nPOD) programme., Methods: We analysed nPOD donors with type 1 diabetes (n=110; mean±SD age at type 1 diabetes onset 12.2±7.9 years, mean±SD diabetes duration 15.3±13.7 years, 53% male, 80% non-Hispanic White, 12.7% African American, 7.3% Hispanic) using data pertaining to residual beta cell number; quantified islets containing insulin-positive beta cells in pancreatic tissue sections; and expressed these values as a percentage of the total number of islets from each donor (mean ± SD ICI% 9.8±21.5, range 0-92.2)., Results: Donors with a high ICI% (≥5) (n=30; 27%) vs a low ICI% (<5) (n=80; 73%) were older at onset (15.3±6.9 vs 11.1±8 years, p=0.013), had a shorter diabetes duration at donor tissue procurement (7.0±7.4 vs 18.5±14.3 years, p<0.001), a higher African ancestry score (0.2±0.3 vs 0.1±0.2, p=0.043) and a lower European ancestry score (0.7±0.3 vs 0.9±0.3, p=0.023). After adjustment for age of onset (p=0.105), diabetes duration (p<0.001), BMI z score (p=0.145), sex (p=0.351) and African American race (p=0.053), donors with the TCF7L2 rs7903146 T allele (TC or TT, 45.5%) were 2.93 times (95% CI 1.02, 8.47) more likely to have a high ICI% than those without it (CC) (p=0.047)., Conclusions/interpretation: Overall, these data support the presence of a type 1 diabetes endotype associated with a genetic factor that predisposes to type 2 diabetes, with donors in this category exhibiting less severe beta cell loss. It is possible that in these individuals the disease pathogenesis may include mechanisms associated with type 2 diabetes and thus this may provide an explanation for the poor response to immunotherapies to prevent type 1 diabetes or its progression in a subset of individuals. If so, strategies that target both type 1 diabetes and type 2 diabetes-associated factors when they are present may increase the success of prevention and treatment in these individuals., (© 2022. The Author(s).)

Objective: We studied longitudinal differences between progressors and nonprogressors to type 1 diabetes with similar and substantial baseline risk., Research Design and Methods: Changes in 2-h oral glucose tolerance test indices were used to examine variability in diabetes progression in the Diabetes Prevention Trial-Type 1 (DPT-1) study (n = 246) and Type 1 Diabetes TrialNet Pathway to Prevention study (TNPTP) (n = 503) among autoantibody (Ab)+ children (aged <18.0 years) with similar baseline metabolic impairment (DPT-1 Risk Score [DPTRS] of 6.5-7.5), as well as in TNPTP Ab- children (n = 94)., Results: Longitudinal analyses revealed annualized area under the curve (AUC) of C-peptide increases in nonprogressors versus decreases in progressors (P ≤ 0.026 for DPT-1 and TNPTP). Vector indices for AUC glucose and AUC C-peptide changes (on a two-dimensional grid) also differed significantly (P < 0.001). Despite marked baseline metabolic impairment of nonprogressors, changes in AUC C-peptide, AUC glucose, AUC C-peptide-to-AUC glucose ratio (AUC ratio), and Index60 did not differ from Ab- relatives during follow-up. Divergence between nonprogressors and progressors occurred by 6 months from baseline in both cohorts (AUC glucose, P ≤ 0.007; AUC ratio, P ≤ 0.034; Index60, P < 0.001; vector indices of change, P < 0.001). Differences in 6-month change were positively associated with greater diabetes risk (respectively, P < 0.001, P ≤ 0.019, P < 0.001, and P < 0.001) in DPT-1 and TNPTP, except AUC ratio, which was inversely associated with risk (P < 0.001)., Conclusions: Novel findings show that even with similarly abnormal baseline risk, progressors had appreciably more metabolic impairment than nonprogressors within 6 months and that the measures showing impairment were predictive of type 1 diabetes. Longitudinal metabolic patterns did not differ between nonprogressors and Ab- relatives, suggesting persistent β-cell responsiveness in nonprogressors., (© 2022 by the American Diabetes Association.)

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.)