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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.

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.

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 unchanged versus changed diabetes classification. 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

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. Through screening of 835 peer-reviewed articles followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years), we conclude 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. 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.Preface (Lay Abstract)We reviewed literature of the last decade to evaluate the impact of technology on the treatment of people living with type 1 diabetes. Screening of 835 articles and in-depth review of 70 showed 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 and time in range, while reducing hypoglycemia risk. Of importance, several studies showed a positive impact on person-reported outcomes, like quality of life or, at a minimum, no increase in self-care burden compared with standard care.

BackgroundHeterogeneity exists in type 1 diabetes (T1D) development and presentation. Islet autoantibodies form the foundation for T1D diagnostic and staging efforts. We hypothesized that autoantibodies can be used to identify heterogeneity in T1D before, at, and after diagnosis, and in response to disease modifying therapies. at clinically relevant timepoints throughout T1D progression.MethodsWe performed a systematic review assessing 10 years of original research studies examining relationships between autoantibodies and heterogeneity during disease progression, at the time of diagnosis, after diagnosis, and in response to disease modifying therapies in individuals at risk for T1D or within 1 year of T1D diagnosis.Results10,067 papers were screened. Out of 151 that met data extraction criteria, 90 studies characterized heterogeneity before clinical diagnosis. Autoantibody type/target was most commonly examined, followed by autoantibody number, titer, order of seroconversion, affinity, and novel islet autoantibodies/epitopes. Recurring themes included positive relationships of autoantibody number and specific types and titers with disease progression, differing clinical phenotypes based on the order of autoantibody seroconversion, and interactions with age and genetics. Overall, reporting of autoantibody assay performance was commonly included; however, only 43% (65/151) included information about autoantibody assay standardization efforts. Populations studied were almost exclusively of European ancestry.ConclusionsCurrent evidence most strongly supports the application of autoantibody features to more precisely define T1D before clinical diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly when considered in relation to age and genetic risk, could offer more precise stratification. Increased participation in autoantibody standardization efforts is a critical step to improving future applicability of autoantibody-based precision medicine in T1D.Plain Language SummaryWe performed a systematic review to ascertain whether islet autoantibodies, biomarkers of autoimmunity against insulin-producing cells, could aid in stratifying individuals with different clinical presentations of type 1 diabetes. We found existing evidence most strongly supporting the application of these biomarkers to the period before clinical diagnosis, when certain autoantibody features (number, type) and the age when they develop, can provide important information for patients and care providers on what to expect for future type 1 diabetes progression.

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

Objective Metabolic zones were developed to characterize heterogeneity of individuals with islet autoantibodies. Research Design and Methods Baseline 2-hour oral glucose tolerance test (OGTT) 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 5 AUC glucose (AUCGLU) rows and 5 AUC 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 (GCRCs; from mean glucose and mean C-peptide values at 30,60,90,120 minutes) were similar within the 5 AUCPEP columns. Among the zones, 5-year risk for type 1 diabetes was highly correlated with IA-2A prevalence (r=0.96, p Conclusions Phenotypes of 25 zones formed from AUCGLU and AUCPEP were used to gain insights into type 1 diabetes heterogeneity. Zones were utilized 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.

Introduction The evolution of patients with obstructive sleep apnea (OSA) non-eligible for PAP-therapy at diagnosis is unknown. Currently, the severity of OSA is based on the apnea-hypopnea index (AHI), but its prognostic relevance has raised concerns. The Baveno classification may allow a better stratification of severity and therapeutic guidance in OSA. Methods Patients with AHI≥5/h in 2015, classified into Baveno groups A and B and non-eligible for PAP therapy at diagnosis and over 5 years, were analyzed. Patients were reclassified into Baveno groups (A-D) and changes in groups over 5 years were explored. Patients in Baveno groups C and D, who developed major cardiovascular comorbidities (CVC) or end-organ damage (EOD group), were compared with patients in Baveno groups A and B (non-EOD group). To identify predictors of the development of major CVC or EOD, a logistic regression analysis was performed. Results There were 76 patients, 58% male, mean age 51.9 ± 10.1 years, mean body mass index (BMI) of 30.3 ± 5.0 kg/m2 and median AHI of 8.9 (5.9–12.0) events/h. At diagnosis, 46% and 54% of patients were classified into Baveno group A and group B, respectively. In total, 21% of patients developed major CVC or EOD (Baveno group C or D); higher age (p = 0.011) and BMI (p = 0.004) and a higher percentage of central apneas (p = 0.012) at diagnosis significantly predicted it, while sex, sleepiness, insomnia, AHI, ODI and T90 were not. Conclusions A significant percentage of patients non-eligible for PAP-therapy at diagnosis of OSA developed CVC or EOD; higher age and BMI and a higher percentage of central apneas were significant predictors.

OBJECTIVE We assessed whether Index60, a composite measure of fasting C-peptide, 60-min C-peptide, and 60-min glucose, could improve the metabolic staging of type 1 diabetes for progression to clinical disease (stage 3) among autoantibody-positive (Ab+) individuals with normal 2-h glucose values ( RESEARCH DESIGN AND METHODS We analyzed 3,058 Type 1 Diabetes TrialNet Pathway to Prevention participants with 2-h glucose RESULTS HI-IND60 and HI-BOTH were younger, with greater frequency of more than two Ab+, and lower C-peptide levels, than either HI-2HGLU or LO-BOTH (all P < 0.001). The cumulative incidence for stage 3 was greater for HI-IND60 and HI-BOTH than for either HI-2HGLU or LO-BOTH (all P < 0.001). Those with Index60 values above the median were younger and had higher frequency of two or more Ab+ (P < 0.001) and DR3-DQ2/DR4-DQ8 prevalence (P < 0.001) and lower area under the curve (AUC) C-peptide levels (P < 0.001) than those below. Those above the 2-h glucose median had higher AUC C-peptide levels (P < 0.001), but otherwise did not differ from those below. CONCLUSIONS Index60 identifies individuals with characteristics of type 1 diabetes at appreciable risk for progression who would otherwise be missed by 2-h glucose staging criteria.

Objective Puberty-induced insulin resistance is considered critical in the pathogenesis of type 2 diabetes (T2D) in youth. The development of T2D before puberty suggests distinct risk factors and pathophysiology but, because of its rarity, this has not been well studied. We aimed to describe the clinical characteristics of children with T2D diagnosed before the onset of puberty. Research design and methods We retrospectively studied all children with autoantibody-negative T2D and available pubertal development assessment seen at our center between July 2016 and July 2019, and compared characteristics of those at Tanner stage I (prepubertal, n = 35) versus those at Tanner II-V of pubertal development (n = 341). Results At T2D diagnosis, prepubertal children compared with those at Tanner II-V had higher body mass index z-score (p = 0.003) and higher C-peptide (p = 0.003) (while glucose levels were not significantly different), with differences retaining significance after adjustment for glucose, race/ethnicity and sex. Dyslipidemia occurred in 100% of prepubertal children versus 89.7% of those diagnosed later (p = 0.036). Of the prepubertal children diagnosed under age 10 (n = 13), 69.2% were female, 100% racial/ethnic minority, 100% had obesity with history of dyslipidemia and none with diabetic ketoacidosis. Conclusions T2D, although rarely, can develop before puberty. Children with T2D diagnosed in the prepubertal period have more severe obesity, greater insulin resistance, and more frequent dyslipidemia than older youth. These findings suggest that children with prepubertal T2D are at increased risk for associated morbidity compared with older youth and underscore the significance of interventions to prevent and treat obesity in early childhood.

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.

We aimed to study whether islet autoantibody type marks differential characteristics at the time of type 1 diabetes (T1D) diagnosis.We studied 711 children with newly diagnosed autoimmune T1D. We compared demographic (sex, age, race/ethnicity), clinical (pubertal development, BMI percentile, diabetic ketoacidosis [DKA]) and laboratory (glucose, hemoglobin A1c [HbA1c], C-peptide, tissue transglutaminase antibodies [tTGA], thyroglobulin antibodies, and thyroid peroxidase antibodies [TPOA]) characteristics by presence/absence of autoantibodies to insulin (IAA), GAD65 (GADA), or IA-2/ICA512 (IA-2A). Islet autoantibody titers were evaluated among the children positive for the relevant autoantibody type. We used multivariable analysis to adjust for potential confounders.IAA+ was statistically associated with younger age (p 0.0001) and lower HbA1c (p = 0.049) while Tanner stage, GADA status and number of positive islet autoantibodies were not significant in the multivariable model. GADA+ was associated with female sex (OR = 4.0, p = 0.002) and negatively with elevated tTGA titers (50 U/mL) (OR = 0.21, p = 0.026) but not with age, IAA status, IA-2A status, islet autoantibody number, or thyroid autoimmunity. None of the associations with IA-2A positivity was statistically significant in the multivariable analysis. In multivariable models, IAA titer was significantly associated with younger age (p = 0.006), DKA (p = 0.017) and higher tTGA levels (p = 0.002); GADA titer with female sex (p = 0.028), racial minority (p = 0.046) and TPOA positivity (p = 0.021); and IA-2A titer with older age (p = 0.001) and not being African American (p = 0.024).Islet autoantibody type is associated with differential characteristics at diagnosis of pediatric T1D. Longitudinal and mechanistic studies are needed to evaluate T1D endotypes by autoantibody type.

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 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.

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.

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% (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, pp=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. Graphical abstract

A comprehensive classification accounting for the heterogeneity of pediatric diabetes is lacking. The Aβ classification system, based on islet autoantibody status (A+ or A-) and preserved beta-cell functional reserve (β+ or β-) , was used successfully to define variable phenotypes in adults with Ketosis-prone Diabetes. We set out to investigate the utility of the Aβ classification system in children with new-onset diabetes. “A+” was defined as the presence of ≥1 islet autoantibodies (GAD65, ICA512, insulin or ZnT8 autoantibodies) and "β+" as random C-peptide level ≥0.6 ng/mL at diagnosis. We calculated T1D genetic risk score 2 (T1D GRS2) for each participant, and compared clinical characteristics at diabetes onset and T1D GRS2 between the groups. The study cohort (n=76) was 50% female, 29% non-Hispanic white, 46% Hispanic, 22% non-Hispanic black, and 3% Other. The mean age was 11.7 years and the mean BMI was 58.3%ile. Aβ classification at diagnosis defined the following groups: A+β- (34%) , A+β+ (30%) , A-β+ (33%) and A-β+ (3%) . Results are summarized in Table 1. The Aβ classification system defines phenotypically and genetically distinct diabetes groups in children with new-onset diabetes. This classification system may be useful in clinical settings as well as the design of clinical trials. Disclosure M.Tosur: Advisory Panel; Provention Bio, Inc. S.Deen: None. S.Uysal: n/a. M.Astudillo: None. F.Jahoor: None. W.Hagopian: Research Support; Janssen Research & Development, LLC. R.A.Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. M.J.Redondo: Advisory Panel; Provention Bio, Inc. A.Balasubramanyam: None. Funding Texas Children's Pilot Award

Even after stratification based on established risk factors, the progression to T1D is variable. Thus, we aimed to examine differences in metabolic patterns of change between NP and P to T1D, and identify non-metabolic factors contributing to the differences. Specifically, we analyzed OGTT and autoantibody (Aab) data from children In conclusion, when NP and P have a similar degree of metabolic abnormality, IA-2A titers are greater in P. Moreover, as AUC ratio declines in P and improves in NP, IA-2A titers increase more in P than in NP. These findings suggest that IA-2A is a sensitive indicator of active β-cell pathology. Disclosure E.K.Sims: Other Relationship; Medscape, Patent. D.D.Cuthbertson: None. L.M.Jacobsen: None. H.M.Ismail: n/a. B.M.Nathan: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. J.Sosenko: None.

To investigate arginine metabolism in pediatric T2D, we measured fasting amino acid levels in 28 children (T2D: n=13, healthy controls [H/C]: n=15) . In the T2D group, the mean HbA1c was 8.5 ± 3.4%, with mean diabetes duration of 2.7 ± 2.4 years, and mean BMI percentile higher (98.5 ± 1) than in H/C (71.5 ± 32.6, p=0.006) . There were no statistical differences between T2D and H/C in age (16.4 ± 0.8 years) , sex (46% female) , race/ethnicity (61% Hispanic, 21% Black, 18% non-Hispanic White) , or protein intake in the preceding day (64 ± 30.5 gram) . We found lower fasting arginine (77 ± 21.9 vs. 97.2 ± 15.9 μmol/L, p=0.01) , citrulline (a precursor of arginine) (21.1 ± 7.3 vs. 27.8 ± 5.5 μmol/L, p=0.01) , and glutamine (a precursor of citrulline) (424.6 ± 64.7 vs. 539.5 ± 76.3, p= Disclosure M. Tosur: Advisory Panel; Provention Bio, Inc. S. Deen: None. M.J. Redondo: Advisory Panel; Provention Bio, Inc. F. Jahoor: None. A. Balasubramanyam: None. Funding Texas Children's Hospital Pilot Grant

With the high prevalence of pediatric obesity, it is challenging to accurately classify diabetes (DM) type, especially in a diverse population of youth. Maintained fasting C-peptide ≥ 0.75ng/ml (250pmol/L) , a level associated with type 2 diabetes and successful insulin withdrawal in adult cohorts, may have implications for treatment and risks of acute complications. We developed clinical prediction models to identify individuals with youth onset DM who will retain endogenous insulin secretion. We studied 2991 youth in the SEARCH study (DM diagnosed ≤ age years) to develop logistic regression based prediction models using clinical measures (race/ethnicity, BMI Z-score, age at diagnosis, triglycerides and HDL-C) with or without islet autoantibodies (Aab) (GADA, IA-2A, ZnT8A) , to identify those with fasting C-peptide ≥ 0.75ng/ml at 3-yr (median 75 months) DM duration. Model covariates (measured median 9 months duration) were chosen based on previous literature and availability. A prediction model using clinical measures was highly accurate in identifying participants with C-peptide ≥75 ng/ml (17% of participants, 3% and 59% of those with and without positive Aab) : area under receiver operator curve (AUC ROC) 0.950 (95%CI 0.938-0.962) , model pseudo-R2 0.69. Adding Aab improved discrimination to AUC ROC 0.976 (0.968,0.983) , pseudo-R2 0.81. In internal validation, optimism was very low, with excellent calibration (slope 0.995-0.999) . Performance remained high in difficult to classify groups: AUC ROC was 0.947 (0.927, 0.966) in those with negative Aab and 0.923 (0.960, 0.984) (model with Aab) in youth with high BMI (Z-score >2) . Discrimination was lower in non-Hispanic white participants (AUC ROC 0.94) compared to participants of other race and ethnic groups (AUC ROC 0.97-0.99) (model with Aab) . In conclusion, prediction models combining routine clinical measures can accurately identify youth with DM who maintain endogenous insulin secretion. Disclosure A.Jones: None. A.H.Williams: None. S.M.Marcovina: None. C.Pihoker: None. J.Divers: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. B.Shields: None. R.A.Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. D.Dabelea: None. W.Hagopian: Research Support; Janssen Research & Development, LLC. E.Lustigova: None. A.S.Shah: None. A.K.Mottl: Advisory Panel; Bayer AG, Board Member; Bayer AG, Research Support; Alexion Pharmaceuticals, Inc., Aurinia, Bayer AG, Boehringer Ingelheim International GmbH, Pfizer Inc. R.Dagostino: Consultant; Aetion, Inc., AstraZeneca, Biogen, Bristol-Myers Squibb Company, Daiichi Sankyo, Merck & Co., Inc. Funding Centers for Disease Control and Prevention (PA numbers 00097, DP-05-069, and DP-10-001) and the National Institute of Diabetes and Digestive and Kidney Diseases. This work is also supported by NIH RO1 awards DK124395 and DK121843-01.

Metformin used as adjuvant therapy in youth with T1D and elevated BMI decreased total daily insulin dose (TDID) and insulin resistance, although hemoglobin A1c (A1c) did not change. Yet, the factors that are associated with greater benefit from metformin are not well known. Here, we tested whether baseline measures predict response to metformin in youth with T1D and increased BMI-z score. We analyzed publicly available data from the metformin treatment arm of the T1D Exchange Metformin Study (NCT01881828) , with 61 adolescents (median age 15.3 yrs, range 12-18.9; 36% males; 72% non-Hispanic Whites, 98% overweight or obese; median diabetes duration 7.4 yrs, range 1.7-15) . We assessed baseline age, sex, diabetes duration, race/ethnicity, BMI z-score, waist circumference, body fat percentage (BF%) measured by Dual-energy X-ray Absorptiometry (DXA) scan and serum adipokines. Outcome measures included changes in A1c, TDID and BF% at 13 and 26 weeks (wks) . After adjusting for baseline values, multiple linear regression models were used to identify predictors for the outcomes. Decreases in TDID at 13 and 26 wks showed a significant negative association with baseline leptin (p=0.0008, p=0.004, respectively) . Decreases in BF% at 26 wks were positively associated with baseline BMI z-score (p=0.03) and negatively associated with baseline BF% (p=0.0002) . Increase in A1c at 26 wks trended towards a positive association with baseline BF% (p=0.07) and a negative association with baseline adiponectin (p=0.06) . Compared to females, males showed a significant reduction in BF% at 26 wks (p Disclosure J.Wang: None. S.Barua: None. M.Tosur: Advisory Panel; Provention Bio, Inc. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Ismail: n/a.

Modeling the course of C-peptide decline in autoantibody (Ab) positive individuals is important for type 1 diabetes (T1D) prediction and implementation of T1D prevention trials. Single nucleotide polymorphisms in TCF7L2, JAZF1, SLC30A8, INS, PTPRK, G6CP2, CLEC16, PTPN22 and HLA genes are associated with persistent C-peptide at or after T1D diagnosis. We sought to determine whether 12 SNPs in these genes and the T1D genetic risk score-2 (GRS2) can predict C-peptide trajectory before diagnosis of T1D. We studied Ab positive at-risk participants in the TrialNet Pathway to Prevention Study who had ImmunoChip data (N=1217, age at initial screen (mean±SD) 16.1±12.7 years, 51.5% female, 81.0% non-Hispanic white) . Over a mean follow up of 3.4±2. years, 255 (21.0%) developed multiple Ab and 336 (27.6%) developed clinical T1D. We analyzed the influence of these 12 SNPs and the T1D GRS2 on C-peptide AUC during progression from single to multiple Abs, from single Ab to clinical T1D, and from multiple Abs to clinical T1D. Analyses were adjusted for baseline C-peptide AUC, age, glucose AUC, BMI Z-score and HbA1c; the presence of high-risk HLA haplotypes (DR3 and DR4-DQ8) ; and the first 3 principal components. The type 2 diabetes (T2D) -associated TCF7L2 rs7901695 and rs4506565 SNPs were significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p In conclusion, T2D-associated SNPs in the TCF7L2 gene and lower T1D GRS2 predict higher C-peptide particularly in progression from multiple Abs to clinical T1D. Disclosure T.M.Triolo: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Parikh: None. M.Tosur: Advisory Panel; Provention Bio, Inc. P.Gottlieb: Advisory Panel; Janssen Research & Development, LLC, ViaCyte, Inc., Other Relationship; IM Therapeutics, Research Support; Caladrius Biosciences, Inc., Immune Tolerance Network, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Precigen, Inc., Tolerion, Inc. R.A.Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. S.Onengut-gumuscu: None. J.Krischer: None. S.S.Rich: None. A.Steck: None. Funding NIH K12 DK094712NIH RDK121843NIH RDK124395

Investigating atypical forms of diabetes (other than types 1 or 2, monogenic or secondary) is the focus of research in the Rare and Atypical Diabetes Network (RADIANT) . It is difficult to identify persons with atypical diabetes using standard electronic health record (EHR) queries because there are no specific ICD codes for these phenotypes and wide range of clinical features. We aimed to develop an algorithm to identify individuals with searchable characteristics of atypical diabetes within the Baylor Medicine EHR using Informatics for Integrating Biology at the Bedside (i2b2) . I2b2 enables integration, standardization, and analysis of heterogenous quantitative and qualitative data from healthcare and research databases. Our i2b2 algorithm intended to look for patients coded as type 2 diabetes (T2D) but were very lean and lacked clinical features suggestive of systemic insulin resistance. We included new patient visits with ICD codes for T2D in the past year but excluded those with BMI >20 kg/m2 and triglycerides >100 mg/dL, or ICD codes for hypertension and hyperlipidemia. Next, we excluded patients with secondary causes of diabetes (cystic fibrosis, acromegaly, Cushing's syndrome, chronic/acute pancreatitis or presence of islet autoantibodies (GAD65Ab, IA2Ab) . The algorithm identified 133 persons from a total EHR population of 3,685,797. Manual chart review confirmed that 18 patients (13.5%) met RADIANT criteria for atypical diabetes, while information was missing in the EHR to fully classify 8 patients (6%) and 1 patient was deceased (0.8%) . Those who fully met criteria for atypical diabetes had the following characteristics: 72% female, median BMI at diagnosis 19.8 kg/m2, median age at diagnosis 29 y, 75% with history of diabetic ketoacidosis and 19.5% currently on insulin. Algorithms built on data platforms such as i2b2 may be helpful in identifying patients who should be evaluated for atypical diabetes within standard EHR databases. They could optimize recruitment for research, identify disease trends and permit improved classification of diabetes subgroups. Disclosure N. Kikani: None. G. Montes: None. R. Gaba: None. G. Liao: None. M. Tosur: Advisory Panel; Provention Bio, Inc. M. J. Redondo: Advisory Panel; Provention Bio, Inc. A. Balasubramanyam: None. Funding The RADIANT Study is funded by U54 DK118638 and U54 DK118612 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

The American Diabetes Association 2020 Standards of Medical Care in Diabetes (Standards of Care) recommends a hemoglobin A1c (A1C) of

Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The ‘palette model’ and the ‘threshold hypothesis’ can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes. Since the first remedies for ‘honey urine’ were described over 25 centuries ago, untangling the pathways that lead to what we call today diabetes has enabled better treatments. The current classification of diabetes recognises the major forms of type 1 diabetes, type 2 diabetes, gestational diabetes and other less common types, such as monogenic diabetes (including neonatal diabetes and MODY), diseases of the exocrine pancreas (e.g. cystic fibrosis-related diabetes and pancreatogenic [or type 3c] diabetes) or drug-induced diabetes (Table 1) [1]. Advances in molecular biology and genetic methods during the past decades have facilitated the discovery of a myriad of genetic associations that suggest diverse pathophysiology within each of those types, as well as some commonalities between types. The recent acceleration in the ability to share large population-based datasets has further increased the evidence for heterogeneity within and between diabetes types [2]. Integrating our understanding of genetics, immunology and metabolic abnormalities in each type of diabetes will boost the ability to use precision medicine to tailor treatments and improve health outcomes.

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.

ContextMinority 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.ObjectiveTo describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES).DesignCross-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.SettingSix diabetes centers across the United States.ParticipantsA total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic).Main OutcomeRacial-ethnic disparity in HbA1c levels.ResultsNon-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P ConclusionThis 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.

OBJECTIVE Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S. RESEARCH DESIGN AND METHODS We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/−) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other). RESULTS T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA−/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA−/IS individuals had a greater probability of T1D than T2D. In DAA− individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency. CONCLUSIONS Diabetes type–specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA− children.

Several factors, including genetics, family history, diet, physical activity, obesity, and insulin resistance in puberty, appear to increase the risk of type 2 diabetes in youth. Youth-onset type 2 diabetes is often thought of as a single entity but rather exists as a spectrum of disease with differences in presentation, metabolic characteristics, clinical progression, and complication rates. We review what is currently known regarding the risks associated with developing type 2 diabetes in youth. Additionally, we focus on the spectrum of phenotypes of pediatric type 2 diabetes, discuss the pathogenic underpinnings and potential therapeutic relevance of this heterogeneity, and compare youth-onset type 2 diabetes with type 1 diabetes and adult-onset type 2 diabetes. Finally, we highlight knowledge gaps in prediction and prevention of youth-onset type 2 diabetes.

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.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 diagnosis25 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.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.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.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the “single disease” approach appears untenable, as does the notion of individualizing each single patient’s care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

One hundred years have passed since the discovery of insulin—an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research’s Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health’s National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

We examined whether the timing of the C-peptide response during an oral glucose tolerance test (OGTT) in relatives of patients with type 1 diabetes (T1D) is predictive of disease onset. We examined baseline 2-h OGTTs from 670 relatives participating in the Diabetes Prevention Trial-Type 1 (age: 13.8 ± 9.6 years; body mass index z score: 0.3 ± 1.1; 56% male) using univariate regression models. T1D risk increased with lower early C-peptide responses (30–0 min) (χ(2) = 28.8, P < 0.001), and higher late C-peptide responses (120–60 min) (χ(2) = 23.3, P < 0.001). When both responses were included in a proportional hazards model, they remained independently and oppositely associated with T1D, with a stronger overall association for the combined model than either response alone (χ(2) = 41.1; P < 0.001). Using receiver operating characteristic curve analysis, the combined early and late C-peptide response was more accurately predictive of T1D than area under the curve C-peptide (P = 0.005). Our findings demonstrate that lower early and higher late C-peptide responses serve as indicators of increased T1D risk.

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 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.

The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.

Objective Commercial gene panels identify pathogenic variants in as low as 27% of patients suspected to have MODY, suggesting the role of yet unidentified pathogenic variants. We sought to identify novel gene variants associated with MODY. Research design and methods We recruited 10 children with a clinical suspicion of MODY but non-diagnostic commercial MODY gene panels. We performed exome sequencing (ES) in them and their parents. Results Mean age at diabetes diagnosis was 10 (± 3.8) years. Six were females; 4 were non-Hispanic white, 5 Hispanic, and 1 Asian. Our variant prioritization analysis identified a pathogenic, de novo variant in INS (c.94G > A, p.Gly32Ser), confirmed by Sanger sequencing, in a proband who was previously diagnosed with "autoantibody-negative type 1 diabetes (T1D)" at 3 y/o. This rare variant, absent in the general population (gnomAD database), has been reported previously in neonatal diabetes. We also identified a frameshift deletion (c.2650delC, p.Gln884AsnfsTer57) in RFX6 in a child with a previous diagnosis of "autoantibody-negative T1D" at 12 y/o. The variant was inherited from the mother, who was diagnosed with "thin type 2 diabetes" at 25 y/o. Heterozygous protein-truncating variants in RFX6 gene have been recently reported in individuals with MODY. Conclusions We diagnosed two patients with MODY using ES in children initially classified as "T1D". One has a likely pathogenic novel gene variant not previously associated with MODY. We demonstrate the clinical utility of ES in patients with clinical suspicion of MODY.

PurposeType 1 diabetes (T1D) is the most common type of diabetes in children, but the frequency of type 2 diabetes (T2D) is increasing rapidly. Classification of diabetes is based on a constellation of features that are typical of each type. We aimed to compare demographic, clinical and laboratory characteristics at diabetes diagnosis in pediatric T1D and T2D.MethodsWe studied children who attended a large academic hospital in Houston, Texas (USA) with a new diagnosis of T2D (n=753) or T1D (n=758). We compared age, sex, race/ethnicity, presence of obesity, glucose, hemoglobin A1c, islet autoantibody positivity, C-peptide, and presence of diabetic ketoacidosis (DKA) at diabetes diagnosis.ResultsAt diagnosis of diabetes, children with T2D, compared with those with T1D, were older (13.6 vs 9.7% years old), more likely females (63.2% vs 47.8%), of racial/ethnic minority (91.1%% versus 42.3%) and obese (90.9% vs 19.4%), and were less likely to have DKA (7.8% vs 35.0%) and diabetes autoantibodies (5.5% vs 95.4%). Children with T2D also had significantly less marked elevation of glucose and hemoglobin A1c, and lower C-peptide levels (all comparisons, pConclusionsThere are important demographic, clinical and laboratory differences between T1D and T2D in children. However, none of the characteristics was unique to either diabetes type, which poses challenges to diabetes classification at diagnosis.

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

OBJECTIVE To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes. RESEARCH DESIGN AND METHODS We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed. RESULTS In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ2 = 25.76 vs. χ2 = 8.62; PTP: χ2 = 149.19 vs. χ2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time. CONCLUSIONS In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression.