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BackgroundFamily conflict can reduce adolescent adherence to type 1 diabetes management tasks. The Family Teamwork in-person intervention was shown to be efficacious in reducing conflict and low adherence to diabetes-related tasks. Its reach and potential impact, however, were limited by the need to deliver the intervention sessions in person. Relational agents (ie, computerized versions of humans) have been shown to appeal to diverse audiences and may be an acceptable replacement for a human in technology-based behavior change interventions. ObjectiveThe purpose of this paper is to present the results of a pilot study assessing feasibility and acceptability of Diabetes Family Teamwork Online, an adapted version of the Family Teamwork intervention, delivered over the internet and guided by a relational agent. MethodsParent-adolescent dyads were recruited through a diabetes care clinic at a large tertiary care hospital in the southwestern United States. A one-group design, with assessments at baseline, immediate postintervention, and 3 months later, was used to assess feasibility. A priori feasibility criteria included an assessment of recruitment, completion, attrition, program satisfaction, therapeutic alliance, attitudes toward the relational agent, and data collection. The institutional review board at Baylor College of Medicine approved the protocol (H-37245). ResultsTwenty-seven adolescents aged 10 to 15 years with type 1 diabetes and their parents were enrolled. Criteria used to assess feasibility were (1) recruitment goals were met (n=20), (2) families completed ≥75% of the modules, (3) attrition rate was ≤10%, (4) program satisfaction was high (≥80% of families), (5) therapeutic alliance was high (average score of ≥60/84), (6) families expressed positive attitudes toward the relational agent (average item score of ≥5 on ≥4 items), (7) ≥80% of data were collected at post 1 and post 2, and (8) few technical issues (≤10%) occurred during intervention delivery. All feasibility criteria were met. Qualitative data confirmed that adolescents and parents had positive reactions to both the content and approach. ConclusionsThe Diabetes Family Teamwork Online intervention proved to be a feasible and acceptable method for enhancing communication around diabetes management tasks in families with an adolescent who has type 1 diabetes. International Registered Report Identifier (IRRID)RR2-10.2196/resprot.5817

Type 1 diabetes mellitus (T1D) is a heterogeneous condition. We aimed to study the associations between age and sex with clinical characteristics at the onset of pediatric T1D. A secondary analysis was conducted on data collected retrospectively from 706 children newly diagnosed with T1D at a large tertiary hospital in southeastern USA. Age (stratified across three cohorts from 0.84 to 18.08 years), sex, and their interaction were compared for associations with clinical characteristics of T1D at presentation by multivariable regression analyses and pairwise comparisons. Within the participants (mean age 9.71 (SD 4.10), 48.3 % female, 21.0 % Hispanic, 15.3 % non-Hispanic black and 58.7 % non-Hispanic white), children under 6 years had higher glucose (p<0.001), lower hemoglobin A1c (HbA1c) (p<0.001), and lower C-peptide (p<0.001) than the older age groups. Diabetic ketoacidosis (DKA) was more prevalent in the youngest (p=0.005) and the intermediate-aged cohorts (p=0.005), compared to the oldest group. Among the children with DKA, bicarbonate was lower in the youngest (p<0.001) and middle cohorts (p=0.013), compared to the oldest group. Younger age was associated with higher prevalence of insulin autoantibodies (IAA; p<0.001) and IA-2 autoantibodies (IA-2A; p=0.006). Males had higher glucose (p=0.001), but lower HbA1c (p=0.003), lower C-peptide (p<0.001), and lower GAD autoantibody (GADA) prevalence (p=0.001) than females. There was no significant interaction between age and sex. In children with new onset T1D, younger age and male sex were associated with findings suggestive of more rapid and aggressive T1D preclinical course, including poorer beta-cell function, and distinct islet autoantibody profiles. [ABSTRACT FROM AUTHOR]

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 and 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. [ABSTRACT FROM AUTHOR]

Background: The TrialNet Oral Insulin (OI) prevention trial showed no overall treatment effect, using the diagnosis of type 1 diabetes as an endpoint. A significant delay in onset was only found in a high‐risk stratum (termed secondary stratum 1) of participants with low first‐phase insulin release (FPIR). Methods: Since trials with an endpoint of type 1 diabetes take years to complete, in this post hoc analysis, we assessed whether a novel combination of glucose and C‐peptide markers could identify a therapeutic benefit after 1 year of follow‐up (trial participants followed for a median 2.7 years). Results: Participants were relatives with multiple islet autoantibodies and low FPIR (n = 40). Glucose rose, and C‐peptide declined in the placebo group, whereas glucose rose minimally, and C‐peptide increased in the OI group. When glucose and C‐peptide were plotted on two‐dimensional grids using 30–120‐min oral glucose tolerance test (OGTT) time points, changes in ratios of their central points (centroid ratio) differed between groups (p = 0.037 adjusted for age, BMI, and baseline C‐peptide and glucose). Conclusions: These findings support a favorable early effect of OI on combined glucose and C‐peptide endpoints in high‐risk individuals, indicating metabolic benefit. With further study, these measures may allow for shorter trials compared to the standard endpoint of type 1 diabetes diagnosis. [ABSTRACT FROM AUTHOR]

We sought to determine if the early months of the coronavirus disease 2019 (COVID-19) pandemic influenced pediatric diabetic ketoacidosis (DKA) hospitalization characteristics. 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. 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 (HbA1c) 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 HbA1c (p=0.001). After adjusting for HbA1c upon admission, admission year was no longer significantly associated with more severe DKA. 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 HbA1c. Additionally, higher HbA1c seemed to mediate more severe DKA during the pandemic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (R 2 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. [ABSTRACT FROM AUTHOR]

Background. Elevated serum IgA levels have been observed in various autoimmune conditions, including type 1 diabetes (T1D). However, whether children with T1D and elevated serum IgA have unique features has not been studied. We aimed to evaluate the prevalence and characteristics associated with elevated serum IgA at the onset of pediatric T1D. Materials and Methods. We analyzed demographic, clinical, and laboratory data retrospectively collected from 631 racially diverse children (6 months–18 years of age) with T1D who had serum IgA levels measured within 90 days of T1D diagnosis. Univariable and multivariable logistic regression models were used to identify characteristics that were significantly associated with elevated versus normal IgA. Results. Elevated serum IgA was present in 20.3% (128/631) of the children with newly diagnosed T1D. After adjusting for other variables, A1c level (p = 0.029), positive insulin autoantibodies (IAA) (p = 0.041), negative glutamic acid decarboxylase autoantibodies (GADA) (p = 0.005) and Hispanic ethnicity (p < 0.001) were significantly associated with elevated serum IgA. After adjustment for confounders, the odds of elevated serum IgA were significantly increased with positive IAA (OR 1.653, 95% CI 1.019–2.679), higher HbA1c (OR 1.132, 95% CI 1.014–1.268) and Hispanic ethnicity (OR 3.279, 95% CI 2.003–5.359) but decreased with GADA positivity (OR 0.474, 95% CI 0.281–0.805). Conclusions. Elevated serum IgA is present in 20.3% of the children at T1D onset and is associated with specific demographic and clinical characteristics, suggesting a unique pathogenesis in a subset of individuals. Further studies are warranted to investigate the IgA response, its role in T1D pathogenesis, and whether these associations persist over time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It has been shown to be an indicator of insulin resistance, independent of body fat mass.[[10]] Therefore, it is possible that higher baseline leptin levels reflect higher insulin resistance and thus, predict more pronounced reduction in TDID in response to metformin. Keywords: insulin resistance; metformin; obesity therapy; type 1 diabetes EN insulin resistance metformin obesity therapy type 1 diabetes 3420 3423 4 10/05/23 20231101 NES 231101 BACKGROUND There is an increased prevalence of overweight and obesity among children and adults with type 1 diabetes (T1D).[[1], [3]] Further, the rate of overweight and obesity continues to be on the rise among people with T1D compared with general population trends.[5] This is problematic because obesity-induced insulin resistance increases exogenous insulin needs, with a higher risk of hypoglycaemia, weight gain, chronic inflammation, dyslipidaemia and long-term cardiovascular complications.[[4], [6]] Metformin is the first-line diabetes therapy in overweight and obese children and adults with type 2 diabetes. Insulin resistance, metformin, obesity therapy, type 1 diabetes. [Extracted from the article]

Background. Youth with Type 1 diabetes (T1D) who are Black, Hispanic, or lower socioeconomic status (SES) have lower rates of diabetes device use, higher hemoglobin A1c (HbA1c), and higher rates of diabetic ketoacidosis (DKA). However, the associations of individual-level social determinants of health (SDoH) and neighborhood-level factors with device use and clinical outcomes are unknown. Area deprivation index (ADI) is a neighborhood level measure of SES reported in deciles (range 1–10 with 10 representing most deprived neighborhood). Methods. We evaluated the association of ADI and other SDoH factors with pump/continuous glucose monitor (CGM) use, HbA1c, and DKA in 1,461 youth with T1D (50% female, age 12.8 ± 3.6 years, HbA1c 8.7 ± 2.1%, 52% pump, 70% CGM) seen between October 1, 2020 and September 30, 2021 at a large pediatric diabetes center. Multiple logistic regression and multiple linear regression analyses were used to determine statistically significant associations adjusting for potential confounders. Results. Youth were less likely to use an insulin pump if they lived in a higher ADI neighborhood, were Black or Hispanic, had Medicaid or were uninsured, or received government assistance (e.g., Supplemental Security Income, Supplemental Nutritional Assistance Program). Youth were less likely to use a CGM if they lived in a higher ADI neighborhood, were Black or Hispanic, had Medicaid or were uninsured. Youth had higher risk of DKA event in the past year if they used government assistance, whereas pump and CGM use were associated with lower DKA risk. HbA1c (%) increased by 0.09 (95% CI: 0.05, 0.13) per unit increase in ADI. HbA1c was 0.62 lower (95% CI: −0.82, −0.42) in pump users vs. nonusers and 0.78 lower (95% CI: −0.99, −0.56) in CGM users vs. nonusers. Conclusions. Interventions that tailor care plans to address SDoH in families living in deprived neighborhoods may be needed to increase successful technology uptake, optimize HbA1c, and prevent DKA. [ABSTRACT FROM AUTHOR]

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. There is a growing awareness that type 1 diabetes mellitus (T1DM) is a heterogeneous disease that can be characterized into distinct endotypes. This Review discusses the evidence for endotypes in T1DM and explores the implications for clinical practice. Key points: Type 1 diabetes mellitus (T1DM) is heterogeneous; defining endotypes, or disease subtypes each of which has a unique aetiopathogenesis that is amenable to a particular intervention, will help apply precision medicine to T1DM. T1DM endotype 1 (T1DE1) includes T1DM diagnosed in early childhood and is characterized by extensive, early, β-cell destruction, aggressive insulitis with abundant CD8+ T and CD20+ B cells, aberrant proinsulin processing and an elevated circulating proinsulin to C-peptide ratio. T1DM endotype 2 (T1DE2) includes T1DM diagnosed in adolescence or adulthood and is characterized by retention of many residual insulin-containing islets and without insulitis, fewer infiltrating CD8+ T cells, few CD20+ B cells, normal proinsulin processing and lower proinsulin to C-peptide ratio than T1DE2. Evidence is emerging that T1DE1 might respond better than T1DE2 to interventional immunotherapy with agents targeted to specific immune cell subsets, such as rituximab or teplizumab, while GAD–alum therapy might be effective for treating T1DE2. The T1DE2 endotype could underlie a spectrum of phenotypes with different degrees of severity of the autoimmune attack and thus, different rates of progression to insulin dependence, ranging from classic T1DM to latent autoimmune diabetes mellitus in adults or slowly progressive insulin-dependent diabetes mellitus. Whether T1DM endotypes exist is still a matter of debate, but data are accumulating that support this framework, which will benefit from further research, including testing interventions directed to their underlying aetiopathogenesis. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes. RESEARCH DESIGN AND METHODS: The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments. RESULTS: Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes. CONCLUSIONS: The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Maturity‐onset diabetes of the young (MODY) is often misdiagnosed as type 1/type 2 diabetes. We aimed to define patient characteristics to guide the decision to test for MODY in youth with diabetes. Research Design and Methods: Of 4750 patients enrolled in the Diabetes Registry at Texas Children's Hospital between July 2016 and July 2019, we selected ("Study Cohort", n = 350) those with: (1) diabetes diagnosis <25 years, (2) family history of diabetes in three consecutive generations, and (3) absent islet autoantibodies except for GAD65. We retrospectively studied their clinical and biochemical characteristics and available MODY testing results. Cluster analysis was then performed to identify the cluster with highest rate of MODY diagnosis. Results: Patients in the Study Cohort were 3.5 times more likely to have been diagnosed with MODY than in the overall Diabetes Registry (4.6% vs. 1.3%, p < 0.001). The cluster (n = 16) with the highest rate of clinician‐diagnosed MODY (25%, n = 4/16) had the lowest age (10.9 ± 2.5 year), BMI‐z score (0.5 ± 0.9), C‐peptide level (1.5 ± 1.2 ng/ml) and acanthosis nigricans frequency (12.5%) at diabetes diagnosis (all p < 0.05). In this cluster, three out of five patients who underwent MODY genetic testing had a pathogenic variant. Conclusions: Using a stepwise approach, we identified that younger age, lower BMI, lower C‐peptide, and absence of acanthosis nigricans increase likelihood of MODY in racially/ethnically diverse children with diabetes who have a multigenerational family history of diabetes and negative islet autoantibodies, and can be used by clinicians to select patients for MODY testing. [ABSTRACT FROM AUTHOR]