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OBJECTIVE: To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA). RESEARCH DESIGN AND METHODS: Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis. RESULTS: Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85–92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5–40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes. CONCLUSIONS: The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA. [ABSTRACT FROM AUTHOR]

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes. [ABSTRACT FROM AUTHOR]

Background: Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10‐factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first‐degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. Methods: The 10‐factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3‐factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). Results: Stratification by risk score significantly predicted progression to diabetes by Kaplan‐Meier analysis (GP: P = .00006; FDR: P = .0022). The 10‐factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3‐factor model was superior to HLA (P = .03), but not different from the 10‐factor model (P = .22). In contrast, for FDR the 3‐factor model did not show improvement over HLA (P = .12) and performed worse than the 10‐factor model (P = .02) Conclusions: We have shown a 10‐factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups. [ABSTRACT FROM AUTHOR]

Recent increases in the incidence of both type 1 ( T1D) and type 2 diabetes ( T2D) in children and adolescents point to the importance of environmental factors in the development of these diseases. Metabolomic analysis explores the integrated response of the organism to environmental changes. Metabolic profiling can identify biomarkers that are predictive of disease incidence and development, potentially providing insight into disease pathogenesis. This review provides an overview of the role of metabolomic analysis in diabetes research and summarizes recent research relating to the development of T1D and T2D in children. [ABSTRACT FROM AUTHOR]

Obesity-associated insulin resistance has long been linked to both increased adipocyte oxidative stress as well as the presence of inflammatory changes in adipose tissue, including the infiltration and activation of tissue-resident macrophages. In order to investigate the connections between obesity-associated oxidative stress in adipocytes and increased inflammation in adipose tissue associated with the development of insulin resistance, our laboratory recently demonstrated that adipocytes form glutathionylated products of oxidative stress including glutathionyl-4-hydroxy-2-nonenal (GS-HNE) and glutathionyl-1,4-dihydroxynonene (GS-DHN). The abundance of both GS-HNE and GS-DHN were increased in the visceral adipose tissue of ob/ob mice and diet-induced obese, insulin-resistant mice. Further, these products of lipid peroxidation were shown to induce inflammatory changes in macrophages. Finally, in a mouse model, overproduction of GS-HNE was associated with increased fasting glucose levels and moderately impaired glucose tolerance. Together, these findings suggest a novel mechanism by which obesity-induced oxidative stress in adipocytes may lead to activation of tissue-resident macrophages. As adipose tissue inflammation has been shown to play an important role in the development of insulin resistance, further study of this pathway may lead to potential interventions to attenuate the metabolic consequences of obesity. [ABSTRACT FROM AUTHOR]

Obesity-induced insulin resistance has been linked to adipose tissue lipid aldehyde production and protein carbonylation. Trans-4-hydroxy-2-nonenal (4-HNE) is the most abundant lipid aldehyde in murine adipose tissue and is metabolized by glutathione S-transferase A4 (GSTA4), producing glutathionyl-HNE (GS-HNE) and its metabolite glutathionyl-1,4-dihydroxynonene (GS-DHN). The objective of this study was to evaluate adipocyte production of GS-HNE and GS-DHN and their effect on macrophage inflammation. Compared with lean controls, GS-HNE and GS-DHN were more abundant in visceral adipose tissue of ob/ob mice and diet-induced obese, insulin-resistant mice. High glucose and oxidative stress induced production of GS-HNE and GS-DHN by 3T3-L1 adipocytes in a GSTA4-dependent manner, and both glutathionylated metabolites induced secretion of tumor necrosis factor-a from RAW 264.7 and primary peritoneal macrophages. Targeted microarray analysis revealed GS-HNE and GS-DHN induced expression of inflammatory genes, including C3, C4b, c-Fos, igtb2, Nf kb1, and Nos2. Transgenic overexpression of GSTA4 in mouse adipose tissue led to increased production of GS-HNE associated with higher fasting glucose levels and moderately impaired glucose tolerance. These results indicated adipocyte oxidative stress results in GSTA4-dependent production of proinflammatory glutathione metabolites, GS-HNE and GS-DHN, which may represent a novel mechanism by which adipocyte dysfunction results in tissue inflammation and insulin resistance. [ABSTRACT FROM AUTHOR]

The objective of this study was to describe longitudinal relations of serum total free fatty acids (FFAs) to insulin resistance (IR) and cardiovascular (CV) risk factors from adolescence into adulthood. The cohort included participants in a longitudinal study of obesity and IR with complete data, including anthropometric measures, FFAs, IR measured by euglycemic clamp, blood pressure, fasting serum lipids, and insulin at mean 15 and 22 years of age (n = 207) and their parents (n = 272). FFAs and IR were not significantly related at mean 15 years of age but were significantly related at mean age 22 years. FFA did not relate to BMI at either age. FFA at 15 years of age estimated IR at 22 years of age. In parents (mean age 51 years), FFA was significantly correlated with BMI, percent body fat, systolic blood pressure, LDL, and IR. Associations with all risk factors except IR in parents were attenuated by adjustment for BMI. Most 22 years of age correlations with parents were higher than corresponding 15 years of age correlations. This study finds that FFA is associated with IR starting in young adulthood. The relation between FFA and CV risk factors does not become significant until later adulthood. The results support a significant impact of early metabolic dysfunction on later CV risk. [ABSTRACT FROM AUTHOR]

OBJECTIVE-- While glucose tolerance abnormalities are common in cystic fibrosis (CF), impaired fasting glucose (IFG) has scarcely been explored. No studies have examined the relation between IFG and clinical status. RESEARCH DESIGN AND METHODS-- Data were retrieved from the University of Minnesota CF database on oral glucose tolerance tests (OGTTs) performed in 1996-2005. Subjects were identified as normal glucose tolerance (NGT), impaired glucose tolerance (IGT), or CF-related diabetes without fasting hyperglycemia (CFRD FH -). Patients with fasting hyperglycemia were excluded. The presence of IFG was assessed within each category. In a separate case-control cohort study, subjects with IFG were matched to CF control subjects by age, sex, and OGTT class to explore outcomes. RESULTS-- For the total population (n = 310), the prevalence of IFG was 22%, and by OGTT class was NGT 14%, IGT 31%, CFRD FH - 53%. Within the cohort study, mortality was significantly reduced in IFG (two vs. nine deaths, odds ratio [OR] = 0.2 [95% CI 0.04-0.9]). IFG did not confer increased risk of progression to diabetes (OR 0.66 [0.29 -1.48]). Lung function was better in pediatric IFG subjects with IGT and not significantly worse in adults with IGT or adults and children with NGT and CFRD FH -. BMI was not significantly different in IFG subjects versus control subjects. CONCLUSIONS-- Contrary to expectations in patients with CF, IFG appeared to be associated with improved survival and was not associated with worse nutritional or pulmonary status or increased progression to fasting hyperglycemia. [ABSTRACT FROM AUTHOR]

The article discusses issues related to challenges in delivering doses of insulin. Topics discussed inlcudes issues raised in the report "Effect of Insulin Dilution on Lowering Glycemic Variability in Pump-Treated Young Children with Inadequately Controlled Type 1 Diabetes," need to characterize the technical problems and accuracy of insulin pump infusion at small doses and safety of insulin delivering practice with current tools.

The objective of this study was to determine if islet autoantibodies (IA) can develop de novo in youth ≥10 y. We also explored the risk of progression to T1D in youth seroconverting to IA after 10 y. From the prospective DAISY birth cohort (n=2547), we excluded those who were ever previously IA positive, disenrolled prior to age 10, or had >24 month gap in annual IA testing, leaving 917 for analysis. Seroconversion was defined as IA+ by radiobinding assays for insulin, GAD, IA-2 or ZnT8 autoantibodies, confirmed within 3-6 months. The same autoantibodies were also measured by electrochemiluminescence (ECL) assays, to identify high-affinity IA. IA developed after age 10 at a higher rate (p=0.049) in first-degree relatives than in general population youth with increased-risk HLA (Figure 1); there was no difference by sex or race/ethnicity. Of the 35 IA+ seroconverters, 6 progressed to T1D, during a median follow-up of 5.04 years (IQR: 3.56-8.45). This included 2 of 5 individuals who were multiple IA+ at seroconversion, with at least one IA confirmed as high-affinity by ECL. Among those with a single persistent IA+ at seroconversion, 4/9 youth with high-affinity IA (ECL+) progressed to T1D, compared to 0/21 of the youth with low-affinity IA (ECL-). In conclusion, seroconversion continues after age 10. In our genetically at-risk cohort, all who progress to T1D had high-affinity IA confirmed by ECL. Disclosure: B.I. Frohnert: None. F. Dong: None. K. Waugh: None. A. Steck: None. J.M. Norris: None. L. Yu: None. M. Rewers: None. Funding: JDRF (5-ECR-2017-388-A-N); National Institutes of Health (R01DK32493) [ABSTRACT FROM AUTHOR]

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody–positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes RESEARCH DESIGN AND METHODS: The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual's temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis. RESULTS: We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0–79.2) was observed in children who first developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9–95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a single, often reverting, autoantibody. CONCLUSIONS: The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes. [ABSTRACT FROM AUTHOR]

Despite increasing incidence of T1D worldwide, most newly-diagnosed individuals have no family history. Many experience delay in diagnosis, resulting in >30% of children with new-onset T1D presenting in diabetic ketoacidosis (DKA). Screening for T1D is currently restricted to the research setting. We sought to describe differences in health care utilization at T1D diagnosis in known at-risk patients identified through research studies compared to matched controls. Cases (n=39) were known islet autoantibody positive children (0-19 y) diagnosed with T1D between 2010-2017 at the Barbara Davis Center. Controls were matched 3:1 by dates of birth and diagnosis from community-diagnosed patients (n=117). The Colorado All Payer's Claims Database was used to count diagnoses, procedures, and healthcare claims in the year before and following diagnosis. Continuous variables were compared using t-test or Wilcoxon rank sum test, categorical by chi-square. There was no difference between cases and controls for age of onset (9.6 +/- 3.6 vs. 9.6 +/- 3.5 y p=1.0) or gender (54 vs. 46% male, p=1.0); however, cases had higher rate of private insurance (72 vs. 49%, p=.01) and higher percentage of non-Hispanic whites (87 vs. 65%, p=.008). At diagnosis with T1D, cases had lower HbA1c (6.7 [6.1, 8.1]% vs. 12.1 [10.5, 14.1]%, p<.0001) and rate of DKA (5 vs. 57%, p<.0001). In the year prior to diagnosis, there were no differences in the number of claims, procedures, diagnoses, or medications. In contrast, in the year of diagnosis, cases had fewer emergency room claims (0 [0, 2] vs. 2 [0, 4], p<.0001), total claims (20 [9, 30] vs. 33 [19, 48], p<.0001), procedures (31 [16, 43] vs. 52 [28, 77] p<.0001) and unique diagnoses (7 [4, 10] vs. 12 [7, 19] p=.0003), while there was no difference in number of medications (8 [4, 10] vs10 [7, 13] p=.07). In conclusion, antibody-screened patients had lower healthcare utilization in year of diagnosis. Further study, including cost estimation, may lend insight into benefits of T1D screening programs. Disclosure: B.I. Frohnert: None. K. Manseau: None. G.T. Alonso: None. Funding: JDRF (5-ECR-2017-388-A-N) [ABSTRACT FROM AUTHOR]

Objective: Detection of pre-symptomatic T1D by islet autoantibody (IA) screening prevents hospitalization and diabetic ketoacidosis at clinical onset. It is unknown if presence of diabetic symptoms and random blood glucose predict islet autoantibodies. We report the efficacy of random blood glucose (RBG) measurement, among those who reported multiple symptoms for T1D, for detecting T1D autoimmunity. Methods: In 2017-18, the Autoimmunity Screening for Kids (ASK) study screened 14,418 Denver children 1-17 y old for autoantibodies to insulin, GAD, IA-2, and ZnT8. At the time of screening, the parent and/or child were asked whether or not the child had increased urination, thirst, weight loss or vomiting in the last 3 months. If two or more symptoms were reported, RBG was tested using a glucometer and IA testing was fast-tracked with results available in <7 days. Results: Of the children screened, 0.9% were positive for multiple IA or a single high-affinity IA conferring, respectively, a 44% and 29% 5-year risk of clinical T1D. A fast-track protocol with RBG testing was applied to 501/12,726 children screened between 6/2017-12/2018, flagged for priority testing. Ten (2%) of the 501 fast-track participants had multiple or single high-affinity IA, compared to 91/12,050 (0.8%) IA in those with one or no symptoms (p=0.002). At screening, children found later to have multiple or high-affinity IA more often reported ≥2 symptoms: 9.6% (10/104) compared with low-affinity IA, 4.3% (11/254, p=0.05), or negative, 4.9% (602/12,368, p=0.03). Among those with increased thirst and polyuria, 16/358 (4.5%) had IA. For IA positive, none reported all symptoms (12 IA neg reported all symptoms). RBG>140 mg/dl was detected in 1/14 (7.1%) children with multiple symptoms and IA vs. 15/440 (3.4%) children with symptoms and no IA (p=0.5). None had RBG>200. Conclusions:Symptoms history is helpful but RBG is not, in finding children with pre-symptomatic T1D. Disclosure: K. Waugh: None. B.I. Frohnert: None. M. Rewers: None. J. Baxter: None. C.R. Geno Rasmussen: None. Funding: JDRF [ABSTRACT FROM AUTHOR]

Objectives: Low‐carbohydrate and ketogenic diets are becoming increasingly popular choices for people with type 1 diabetes (T1D) aiming to achieve optimal glycemic control. A carbohydrate‐restricted diet in children has been associated with negative health effects including poor linear growth and inadequate bone mineralization. Guidelines for monitoring children and adolescents choosing to follow a carbohydrate‐restricted diet do not exist. We aimed to create a clinical protocol outlining how to clinically and biochemically follow patients choosing a carbohydrate‐restricted diet with the goal of medical safety. Methods: An interdisciplinary committee was formed and reviewed current consensus guidelines for pediatric patients on carbohydrate‐restricted diets for epilepsy and metabolic disorders. A literature search was done to determine management strategies for children with T1D on a low‐carbohydrate or ketogenic diet. Key health parameters that require monitoring were identified: growth, glycemic control, bone health, cardiometabolic health, and nutritional status. These health outcomes were used to develop a protocol for monitoring children on carbohydrate‐restricted diets. Results: A one‐page protocol for medical providers and educational materials for families interested in following a low‐carbohydrate or ketogenic diet were developed and successfully implemented into clinical care. Conclusion: Implementing a protocol for children on carbohydrate‐restricted diets in clinic allows medical providers to ensure medical safety while being open to discussing a family's dietary preferences. Following children in the protocol over time will lead to informed clinical guidelines for patients with T1D who choose to follow a carbohydrate‐restricted diet. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To assess anxiety and risk perception among parents whose children screened positive for islet autoantibodies, indicating elevated risk for type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: The Autoimmunity Screening for Kids (ASK) study identified 319 children age 1 to 17 years at risk for T1D via screening for islet autoantibodies; 280 children with confirmed islet autoantibodies and their caregivers enrolled in a follow-up education and monitoring program to prevent diabetic ketoacidosis at diagnosis. Parents completed questionnaires at each monitoring visit, including a 6-item version of the State Anxiety Inventory (SAI), to assess anxiety about their child developing T1D, and a single question to assess risk perception. RESULTS: At the first ASK follow-up monitoring visit, mean parental anxiety was elevated above the clinical cutoff of 40 (SAI 46.1 ± 11.2). At the second follow-up monitoring visit (i.e., visit 2), mean anxiety remained elevated but started to trend down. Approximately half (48.9%) of parents reported their child was at increased risk for T1D at the initial follow-up monitoring visit (visit 1). Parents of children with more than one islet autoantibody and a first-degree relative with T1D were more likely to report their child was at increased risk. CONCLUSIONS: Most parents of autoantibody-positive children have high anxiety about their child developing T1D. Information about the risk of developing T1D is difficult to convey, as evidenced by the wide range of risk perception reported in this sample. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate gastrointestinal infection episodes (GIEs) in relation to the appearance of islet autoantibodies in The Environmental Determinants of Diabetes in the Young (TEDDY) cohort. RESEARCH DESIGN AND METHODS: GIEs on risk of autoantibodies against either insulin (IAA) or GAD (GADA) as the first-appearing autoantibody were assessed in a 10-year follow-up of 7,867 children. Stool virome was characterized in a nested case-control study. RESULTS: GIE reports (odds ratio [OR] 2.17 [95% CI 1.39–3.39]) as well as Norwalk viruses found in stool (OR 5.69 [1.36–23.7]) at <1 year of age were associated with an increased IAA risk at 2–4 years of age. GIEs reported at age 1 to <2 years correlated with a lower risk of IAA up to 10 years of age (OR 0.48 [0.35–0.68]). GIE reports at any other age were associated with an increase in IAA risk (OR 2.04 for IAA when GIE was observed 12–23 months prior [1.41–2.96]). Impacts on GADA risk were limited to GIEs <6 months prior to autoantibody development in children <4 years of age (OR 2.16 [1.54–3.02]). CONCLUSIONS: Bidirectional associations were observed. GIEs were associated with increased IAA risk when reported before 1 year of age or 12–23 months prior to IAA. Norwalk virus was identified as one possible candidate factor. GIEs reported during the 2nd year of life were associated with a decreased IAA risk. [ABSTRACT FROM AUTHOR]

While full oral glucose tolerance test (OGTT) helps improve prediction, it requires intravenous access with 6 sample collections for glucose and C-peptide. The objective of this study was to explore less costly and less time-consuming options. All children being prospectively followed by the Diabetes Autoimmunity Study in the Young (DAISY) who had a complete baseline OGTT and at least one confirmed islet autoantibody (Ab+) were included in this study (n = 68). Of 68 Ab+ subjects with a baseline OGTT, 25 developed diabetes after a mean follow-up 5.7 yrs, at a mean age of 12.4 yrs. Univariate proportional hazards (PH) models suggested that age at seroconversion, number of Ab+, IA-2A levels, HbA1c and metabolic variables from the OGTT predicted progression to diabetes, while HLA DR3/4, BMI, levels of IAA or GADA did not. Five multivariate PH predictive models were similar (p = 0.32). All five models included age at seroconversion, number of Ab+, IA-2A levels and HbA1c, and in addition included: model 1 - 1 h glucose and 1 h C-peptide; model 2 - 2 h glucose and 2 h C-peptide; model 3 - glucose sum and C-peptide sum; model 4 - glucose AUC and C-peptide AUC; and model 5: index 60. A model containing age at seroconversion, number of Ab+, IA-2A levels, HbA1c, 1 h glucose and 1 h C-peptide was as predictive for type 1 diabetes progression as models including all sum or AUC values for glucose and C-peptide from full OGTT. The performance of this model should be confirmed in an independent population of Ab+ children. [ABSTRACT FROM AUTHOR]

OBJECTIVE: While continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop (HCL) systems each improve glycemic control in type 1 diabetes, it is unclear how the use of these technologies impacts real-world pediatric care. RESEARCH DESIGN AND METHODS: We found 1,455 patients aged <22 years, with type 1 diabetes duration >3 months, and who had data from a single center in between both 2016–2017 (n = 2,827) and 2020–2021 (n = 2,731). Patients were grouped by multiple daily injections or insulin pump, with or without an HCL system, and using a blood glucose monitor or CGM. Glycemic control was compared using linear mixed-effects models adjusting for age, diabetes duration, and race/ethnicity. RESULTS: CGM use increased from 32.9 to 75.3%, and HCL use increased from 0.3 to 27.9%. Overall A1C decreased from 8.9 to 8.6% (P < 0.0001). CONCLUSIONS: Adoption of CGM and HCL was associated with decreased A1C, suggesting promotion of these technologies may yield glycemic benefits. [ABSTRACT FROM AUTHOR]

In our previous data-driven analysis of evolving patterns of islet autoantibodies (IAb) against insulin (IAA), GAD (GADA), and islet antigen 2 (IA-2A), we discovered three trajectories, characterized according to multiple IAb (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n = 643) with those remaining undiagnosed (n = 1,502). With use of thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlaid onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (P < 0.001), but for IAA dwell times differed only within TR2 (P < 0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has long been appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb-positive children who progressed to type 1 diabetes from those who did not. [ABSTRACT FROM AUTHOR]

The correction notice in Diabetes Care addresses an error in the affiliation information for author Linda A. DiMeglio, clarifying that she is affiliated with the Department of Pediatrics at Indiana University School of Medicine in Indianapolis, IN. The online version of the article has been updated to reflect this correction. The article, "Consensus Guidance for Monitoring Individuals With Islet Autoantibody–Positive Pre-Stage 3 Type 1 Diabetes," includes contributions from a wide range of authors in the field. [Extracted from the article]

Objective: Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies.Research Design and Methods: Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190).Results: A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7-18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3-7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82).Conclusions: An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies. [ABSTRACT FROM AUTHOR]

Objective: To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children.Research Design and Methods: Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models.Results: Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes.Conclusions: Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes. [ABSTRACT FROM AUTHOR]

Objective: Children identified with stage 1 type 1 diabetes are at high risk for progressing to stage 3 (clinical) diabetes and require accurate monitoring. Our aim was to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes.Research Design and Methods: In the Autoimmunity Screening for Kids study, 91 children who were persistently islet autoantibody positive (median age 11.5 years; 48% non-Hispanic White; 57% female) with a baseline CGM were followed for development of diabetes for a median of 6 (range 0.2-34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4-29) months.Results: Compared with children who did not progress to clinical diabetes (nonprogressors), those who did (progressors) had significantly higher average sensor glucose levels (119 vs. 105 mg/dL, P < 0.001) and increased glycemic variability (SD 27 vs. 16, coefficient of variation, 21 vs. 15, mean of daily differences 24 vs. 16, and mean amplitude of glycemic excursions 43 vs. 26, all P < 0.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140 >10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed area under the curve of ≥0.89 for both individual and combined CGM metric models.Conclusions: TA140 >10% is associated with a high risk of progression to clinical diabetes within the next year in autoantibody-positive children. CGM should be included in the ongoing monitoring of high-risk children and could be used as potential entry criterion for prevention trials. [ABSTRACT FROM AUTHOR]

Background: Participants' study satisfaction is important for both compliance with study protocols and retention, but research on parent study satisfaction is rare. This study sought to identify factors associated with parent study satisfaction in The Environmental Determinants of Diabetes in the Young (TEDDY) study, a longitudinal, multinational (US, Finland, Germany, Sweden) study of children at risk for type 1 diabetes. The role of staff consistency to parent study satisfaction was a particular focus. Methods: Parent study satisfaction was measured by questionnaire at child-age 15 months (5579 mothers, 4942 fathers) and child-age four years (4010 mothers, 3411 fathers). Multiple linear regression analyses were used to identify sociodemographic factors, parental characteristics, and study variables associated with parent study satisfaction at both time points. Results: Parent study satisfaction was highest in Sweden and the US, compared to Finland. Parents who had an accurate perception of their child's type 1 diabetes risk and those who believed they can do something to prevent type 1 diabetes were more satisfied. More educated parents and those with higher depression scores had lower study satisfaction scores. After adjusting for these factors, greater study staff change frequency was associated with lower study satisfaction in European parents (mothers at child-age 15 months: − 0.30,95% Cl − 0.36, − 0.24, p < 0.001; mothers at child-age four years: -0.41, 95% Cl − 0.53, − 0.29, p < 0.001; fathers at child-age 15 months: -0.28, 95% Cl − 0.34, − 0.21, p < 0.001; fathers at child-age four years: -0.35, 95% Cl − 0.48, − 0.21, p < 0.001). Staff consistency was not associated with parent study satisfaction in the US. However, the number of staff changes was markedly higher in the US compared to Europe. Conclusions: Sociodemographic factors, parental characteristics, and study-related variables were all related to parent study satisfaction. Those that are potentially modifiable are of particular interest as possible targets of future efforts to improve parent study satisfaction. Three such factors were identified: parent accuracy about the child's type 1 diabetes risk, parent beliefs that something can be done to reduce the child's risk, and study staff consistency. However, staff consistency was important only for European parents. Trial registration: NCT00279318. [ABSTRACT FROM AUTHOR]

Objective: To use islet autoantibody titers to improve the estimation of future type 1 diabetes risk in children.Research Design and Methods: Prospective cohort studies in Finland, Germany, Sweden, and the U.S. followed 24,662 children at increased genetic or familial risk to develop islet autoimmunity and diabetes. For 1,604 children with confirmed positivity, titers of autoantibodies against insulin (IAA), GAD antibodies (GADA), and insulinoma-associated antigen 2 (IA-2A) were harmonized for diabetes risk analyses.Results: Survival analysis from time of confirmed positivity revealed markedly different 5-year diabetes risks associated with IAA (n = 909), GADA (n = 1,076), and IA-2A (n = 714), when stratified by quartiles of titer, ranging from 19% (GADA 1st quartile) to 60% (IA-2A 4th quartile). The minimum titer associated with a maximum difference in 5-year risk differed for each autoantibody, corresponding to the 58.6th, 52.4th, and 10.2nd percentile of children specifically positive for each of IAA, GADA, and IA-2A, respectively. Using these autoantibody type-specific titer thresholds in the 1,481 children with all autoantibodies tested, the 5-year risk conferred by single (n = 954) and multiple (n = 527) autoantibodies could be stratified from 6 to 75% (P < 0.0001). The thresholds effectively identified children with a ≥50% 5-year risk when considering age-specific autoantibody screening (57-65% positive predictive value and 56-74% sensitivity for ages 1-5 years). Multivariable analysis confirmed the significance of associations between the three autoantibody titers and diabetes risk, informing a childhood risk surveillance strategy.Conclusions: This study defined islet autoantibody type-specific titer thresholds that significantly improved type 1 diabetes risk stratification in children. [ABSTRACT FROM AUTHOR]

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease. [ABSTRACT FROM AUTHOR]

Objective: To combine prospective cohort studies, by including HLA harmonization, and estimate risk of islet autoimmunity and progression to clinical diabetes.Research Design and Methods: For prospective cohorts in Finland, Germany, Sweden, and the U.S., 24,662 children at increased genetic risk for development of islet autoantibodies and type 1 diabetes have been followed. Following harmonization, the outcomes were analyzed in 16,709 infants-toddlers enrolled by age 2.5 years.Results: In the infant-toddler cohort, 1,413 (8.5%) developed at least one autoantibody confirmed at two or more consecutive visits (seroconversion), 865 (5%) developed multiple autoantibodies, and 655 (4%) progressed to diabetes. The 15-year cumulative incidence of diabetes varied in children with one, two, or three autoantibodies at seroconversion: 45% (95% CI 40-52), 85% (78-90), and 92% (85-97), respectively. Among those with a single autoantibody, status 2 years after seroconversion predicted diabetes risk: 12% (10-25) if reverting to autoantibody negative, 30% (20-40) if retaining a single autoantibody, and 82% (80-95) if developing multiple autoantibodies. HLA-DR-DQ affected the risk of confirmed seroconversion and progression to diabetes in children with stable single-autoantibody status. Their 15-year diabetes incidence for higher- versus lower-risk genotypes was 40% (28-50) vs. 12% (5-38). The rate of progression to diabetes was inversely related to age at development of multiple autoantibodies, ranging from 20% per year to 6% per year in children developing multipositivity in ≤2 years or >7.4 years, respectively.Conclusions: The number of islet autoantibodies at seroconversion reliably predicts 15-year type 1 diabetes risk. In children retaining a single autoantibody, HLA-DR-DQ genotypes can further refine risk of progression. [ABSTRACT FROM AUTHOR]

Background: The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies. Methods: We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time‐varying metabolomics data integrated with time‐invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble‐based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity. Results: The final integrative machine learning model included 42 disparate features, returning a cross‐validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of ~0.65 on an independent validation dataset. The model identified a principal set of 20 time‐invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA‐DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies. Conclusions: The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The Autoimmunity Screening for Kids (ASK) study is a large scale pediatric screening study in Colorado for celiac disease (CD) and type 1 diabetes. This is a report of the CD outcomes for the first 9,973 children screened through ASK. METHODS: ASK screens children aged1-17 years for CDusing 2 highly sensitive assays for tissue transglutaminase autoantibodies (TGA): a radiobinding (RBA) assay for IgA TGA and an electrochemiluminescence (ECL) assay that detects all TGA isotypes. Children who test positive on either assay are asked to return for confirmatory testing. Those with a confirmed RBA TGA level ≥ 0.1 (twice the upper limit of normal) are referred to the Colorado Center for Celiac Disease for further evaluation; all others are referred to primary care. RESULTS: Of the initial 9,973 children screened, 242 children were TGA1by any assay. Of those initially positive, 185 children (76.4%) have completed a confirmation blood draw with 149 children (80.5%) confirming positive byRBA TGA. ConfirmedRBA TGA1was associated with a family history of CD(odds ratio [OR] 5 1.83; 95% confidence interval 1.06-3.16), non-Hispanic white ethnicity (OR 5 3.34; 2.32-4.79), and female sex (OR 5 1.43; 1.03-1.98). Gastrointestinal symptoms of CD, assessed at the initial screening, were reported equally often among the RBA TGA1 vs TGA2 children (32.1% vs 30.5%, P 5 0.65). DISCUSSION: The initial results of this ongoing mass-screening program confirm a high prevalence of undiagnosedCD autoimmunity in a screened USpopulation. Symptoms at initial screening were not associated with TGA status (see Visual abstract, Supplementary Digital Content 5, http://links.lww.com/AJG/B587). [ABSTRACT FROM AUTHOR]

Objective: The first-appearing β-cell autoantibody has been shown to influence risk of type 1 diabetes (T1D). Here, we assessed the risk of autoantibody spreading to the second-appearing autoantibody and further progression to clinical disease in The Environmental Determinants of Diabetes in the Young (TEDDY) study.Research Design and Methods: Eligible children with increased HLA-DR-DQ genetic risk for T1D were followed quarterly from age 3 months up to 15 years for development of a single first-appearing autoantibody (GAD antibody [GADA], insulin autoantibody [IAA], or insulinoma antigen-2 autoantibody [IA-2A]) and subsequent development of a single second-appearing autoantibody and progression to T1D. Autoantibody positivity was defined as positivity for a specific autoantibody at two consecutive visits confirmed in two laboratories. Zinc transporter 8 autoantibody (ZnT8A) was measured in children who developed another autoantibody.Results: There were 608 children who developed a single first-appearing autoantibody (IAA, n = 282, or GADA, n = 326) with a median follow-up of 12.5 years from birth. The risk of a second-appearing autoantibody was independent of GADA versus IAA as a first-appearing autoantibody (adjusted hazard ratio [HR] 1.12; 95% CI 0.88-1.42; P = 0.36). Second-appearing GADA, IAA, IA-2A, or ZnT8A conferred an increased risk of T1D compared with children who remained positive for a single autoantibody, e.g., IAA or GADA second (adjusted HR 6.44; 95% CI 3.78-10.98), IA-2A second (adjusted HR 16.33; 95% CI 9.10-29.29; P < 0.0001), or ZnT8A second (adjusted HR 5.35; 95% CI 2.61-10.95; P < 0.0001). In children who developed a distinct second autoantibody, IA-2A (adjusted HR 3.08; 95% CI 2.04-4.65; P < 0.0001) conferred a greater risk of progression to T1D as compared with GADA or IAA. Additionally, both a younger initial age at seroconversion and shorter time to the development of the second-appearing autoantibody increased the risk for T1D.Conclusions: The hierarchical order of distinct autoantibody spreading was independent of the first-appearing autoantibody type and was age-dependent and augmented the risk of progression to T1D. [ABSTRACT FROM AUTHOR]

Objective: To assess the costs and project the potential lifetime cost-effectiveness of the ongoing Autoimmunity Screening for Kids (ASK) program, a large-scale, presymptomatic type 1 diabetes screening program for children and adolescents in the metropolitan Denver region.Research Design and Methods: We report the resource utilization, costs, and effectiveness measures from the ongoing ASK program compared with usual care (i.e., no screening). Additionally, we report a practical screening scenario by including utilization and costs relevant to routine screening in clinical practice. Finally, we project the potential cost-effectiveness of ASK and routine screening by identifying clinical benchmarks (i.e., diabetic ketoacidosis [DKA] events avoided, HbA1c improvements vs. no screening) needed to meet value thresholds of $50,000-$150,000 per quality-adjusted life-year (QALY) gained over a lifetime horizon.Results: Cost per case detected was $4,700 for ASK screening and $14,000 for routine screening. To achieve value thresholds of $50,000-$150,000 per QALY gained, screening costs would need to be offset by cost savings through 20% reductions in DKA events at diagnosis in addition to 0.1% (1.1 mmol/mol) improvements in HbA1c over a lifetime compared with no screening for patients who develop type 1 diabetes. Value thresholds were not met from avoiding DKA events alone in either scenario.Conclusions: Presymptomatic type 1 diabetes screening may be cost-effective in areas with a high prevalence of DKA and an infrastructure facilitating screening and monitoring if the benefits of avoiding DKA events and improved HbA1c persist over long-run time horizons. As more data are collected from ASK, the model will be updated with direct evidence on screening effects. [ABSTRACT FROM AUTHOR]

Objective: Mechanisms underlying the role of non‐human leukocyte antigen (HLA) genetic risk variants in type 1 diabetes (T1D) are poorly understood. We aimed to test the association between methylation and non‐HLA genetic risk. Methods: We conducted a methylation quantitative trait loci (mQTL) analysis in a nested case‐control study from the Dietary Autoimmunity Study in the Young. Controls (n = 83) were frequency‐matched to T1D cases (n = 83) based on age, race/ethnicity, and sample availability. We evaluated 13 non‐HLA genetic markers known be associated with T1D. Genome‐wide methylation profiling was performed on peripheral blood samples collected prior to T1D using the Illumina 450 K (discovery set) and infinium methylation EPIC beadchip (EPIC validation) platforms. Linear regression models, adjusting for age and sex, were used to test to each single nucleotide polymorphism (SNP) ‐probe combination. Logistic regression models were used to test the association between T1D and methylation levels among probes with a significant mQTL. A meta‐analysis was used to combine odds ratios from the two platforms. Results: We identified 10 SNP‐methylation probe pairs (false discovery rate (FDR) adjusted P <.05 and validation P <.05). Probes were associated with the GSDMB, C1QTNF6, IL27, and INS genes. The cg03366382 (OR: 1.9, meta‐P =.0495), cg21574853 (OR: 2.5, meta‐P =.0232), and cg25336198 (odds ratio: 6.6, meta‐P =.0081) probes were significantly associated with T1D. The three probes were located upstream from the INS transcription start site. Conclusions: We confirmed an association between DNA methylation and rs689 that has been identified in related studies. Measurements in our study preceded the onset of T1D suggesting methylation may have a role in the relationship between INS variation and T1D development. [ABSTRACT FROM AUTHOR]

Objective: To describe glycemic and psychosocial outcomes in youth with type 1 diabetes using a hybrid closed loop (HCL) system. Subjects: Youth with type 1 diabetes (2‐25 years) starting the 670G HCL system for their diabetes care were enrolled in an observational study. Methods: Prospective data collection occurred during routine clinical care and included glycemic variables (sensor time in range [70‐180 mg/dL], HbA1c), and psychosocial variables (Hypoglycemia Fear Survey [HFS]; Problem Areas in Diabetes [PAID]). Mixed models were used to analyze change across time. Results: Ninety‐two youth (mean age 15.7 ± 3.6 years, 50% female, HbA1c 8.8% ± 1.8%) started HCL for their diabetes care. Youth used Auto Mode 65.5% ± 3.0% of the time at month 1, which decreased to 51.2% ± 3.4% at month 6 (P =.001). Sensor time in range increased from 50.7% ± 1.8% at baseline to 56.9% ± 2.1% at 6 months (P =.007). HbA1c decreased from 8.7% ± 0.2% at baseline to 8.4% ± 0.2% after 6 months of use (P ≤.0001), with the greatest HbA1c decline in participants with high baseline HbA1c. Increased percent time in auto mode was associated with lower HbA1c (P =.02). Thirty percent of youth discontinued HCL in the first 6 months of use. There were no changes in the HFS or PAID scores across time. Conclusions: HCL use is associated with improved glycemic control and no change in psychosocial outcomes in this clinical sample. The decline in HCL use across time suggests that youth experience barriers in sustaining use of HCL. Further research is needed to understand reasons for HCL discontinuation and determine intervention strategies. [ABSTRACT FROM AUTHOR]

DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450 K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. Change in methylation with age differed between cases and controls in 10 regions. Average longitudinal methylation differed between cases and controls at two genomic positions and 28 regions. Some methylation differences were detectable and consistent as early as birth, including before and after the onset of preclinical islet autoimmunity. Results map to transcription factors, other protein coding genes, and non-coding regions of the genome with regulatory potential. The identification of methylation differences that predate islet autoimmunity and clinical diagnosis may suggest a role for epigenetics in T1D pathogenesis; however, functional validation is warranted. [ABSTRACT FROM AUTHOR]

Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10 −7 ) with Bonferroni-corrected significance and another known region near PTPN22 ( HR = 1.46, p = 2.17 × 10 −6 ) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10 −7 ) with suggestive evidence (p < 10 −5 ). Two known regions ( PTPN22 : p = 2.25 × 10 −6 , INS ; p = 1.32 × 10 −7 ) and one novel region ( PXK/PDHB: p = 8.99 × 10 −6 ) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions ( INS : p = 5.67 × 10 −6 and TTC34/PRDM16 : 6.45 × 10 −6 ) were associated if the fist appearing autoantibody was IAA and one region ( RBFOX1 : p = 8.02 × 10 −6 ) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D ( INS : p = 3.13 × 10 −7 ) and three novel regions ( RNASET2, PLEKHA1, and PPIL2 ; 5.42 × 10 −6  > p > 2.31 × 10 −6 ). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach. [ABSTRACT FROM AUTHOR]

Background: Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.Methods and Findings: The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.Conclusions: A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials. [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (TID) is characterized by a loss of pancreatic islet beta cell function resulting in loss of insulin production. Genetic and environmental factors may trigger immune responses targeting beta cells thus generating islet antibodies (IA). Immune response pathways involve a cascade of events, initiated by cytokines and chemokines, producing inflammation which can result in tissue damage. Methods: A nested case-control study was performed to identify temporal changes in cytokine levels in 75 DAISY subjects: 25 diagnosed T1D, 25 persistent IA, and 25 controls. Serum samples were selected at four time points: (T1) earliest, (T2) just prior to IA, (T3) just after IA, and (T4) prior to T1D diagnosis or most recent. Cytokines (IFN-α2a, IL-6, IL-17, IL-1β, IP-10, MCP-1, IFN-γ, IL-1α, and IL-1ra) were measured using the Meso Scale Discovery system Human Custom Cytokine 9-Plex assay. Results: Multivariate mixed models adjusting for HLA risk, first-degree relative status, age, and gender, showed MCP-1 and IFN-үto be significantly higher at T3 in T1D compared to IA subjects. At T4, IP-10 was significantly higher in IA subjects than controls. Conclusions: This repeated measures nested case-control study identified increased inflammatory markers in IA children who developed T1D compared to IA children who had not progressed to clinical disease. It also showed increased inflammation in both T1D and IA children when compared to controls. Results suggest inflammation may be related to both the development of IA and progression to T1D. [ABSTRACT FROM AUTHOR]

Although most children with multiple islet autoantibodies develop type 1 diabetes, rate of progression is highly variable. The goal of this study was to explore potential factors involved in rate of progression to diabetes in children with multiple islet autoantibodies. The Diabetes Autoimmunity Study in the Young (DAISY) has followed 118 children with multiple islet autoantibodies for progression to diabetes. After excluding 27 children currently diabetes-free but followed for <10 years, the study population was grouped into: rapid progressors (N = 39) who developed diabetes in <5 years; moderate progressors (N = 25), diagnosed with diabetes within 5–10 years; and slow progressors (N = 27), diabetes-free for >10 years. Islet autoimmunity appeared at 4.0 ± 3.5, 3.2 ± 1.8 and 5.8 ± 3.1 years of age in rapid, moderate and slow progressors, respectively (p = 0.006). Insulin autoantibody levels were lower in slow progressors compared to moderate and rapid progressors. The groups did not differ by gender, ethnicity, family history, susceptibility HLA and non-HLA genes. The rate of development of individual islet autoantibodies including mIAA, GADA, IA-2A and ZnT8A were all slower in the slow versus moderate/rapid progressors. In multivariate analyses, older age at seroconversion and lower initial mIAA levels independently predicted slower progression to diabetes. Later onset of islet autoimmunity and lower autoantibody levels predicted slower progression to diabetes among children with multiple islet autoantibodies. These factors may need to be considered in the design of trials to prevent type 1 diabetes. [ABSTRACT FROM AUTHOR]

Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10–8) and three regions, 1q21.3 (MRPS21–PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10–8) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes. [ABSTRACT FROM AUTHOR]

Islet autoantibody (IAb) assays for a general population screening should ideally be high-troughput, low-cost, and offer high-sensitivity and high positive predictive value in a low-risk population. The Autoimmunity Screening for Kids (ASK) study has compared the performance of the 'gold standard' radiobinding assays (RBAs) and a multiplex electrochemiluminesence assay (ECL) in a large unselected pediatric population. Single IAb by RBA but negative by ECL have been previously shown to be low-affinity and of low predictive value for progression to clinical diabetes. From 2017-2019, ASK screened for IAbs 23,400 Colorado children ages 1-17 y. A high-throughput multiplex ECL assay combining IAbs to insulin, GAD and IA-2 in one single well was used in parallel with standard non-multiplex RBAs. At the initial screening with RBA, 3.0% (697/23400) of children were positive for at least one IAb, including 0.4% (98/23400) for multiple IAbs and 2.6% (599/23400) for a single IAb. Among children with ≥2 IAbs, ECL assay results were 89.8% congruent with RBA. In contrast, only 21.5% (129/599 p<0.0001) of children with single IAb by RBA were ECL positive (i.e., high-affinity). During a median follow-up of 0.5 y (range 0.1-2.5 y), 90% (98/109) of children with a single IAb by both RBA and ECL remained persistently positive and 9.2% of those progressed to ≥2 IAbs. In contrast, 74% (242/328, p=0.0003) of children with a single IAb by RBA but ECL negative remained persistently positive; only 1.8% (p=0.001) of those converted to ≥2 IAbs (half became ECL positive). ASK results suggest that applying the ECL assay as the confirmation test may help to remove nearly 80% of subjects with single IAb from further follow-up and monitoring, with little loss of sensitivity. Alternatively, the multiplex ECL could be used as the primary screening tool to leverage its high throughput and low cost, compared to RBAs. Disclosure: X. Jia: None. L. He: None. D. Miao: None. K. Waugh: None. B.I. Frohnert: None. A. Steck: None. M. Rewers: None. L. Yu: None. C.R. Geno Rasmussen: None. [ABSTRACT FROM AUTHOR]

Our goal was to explore feasibility of screening for pre-symptomatic type 1 diabetes (T1D) and celiac disease (CD) in the general population. Efficiency of and barriers to sustainable screening in various health care settings are described. From 2017-2019, the ASK study screened 23,578 children in Colorado. Of those, 0.94% were positive for islet (95% CI 0.82-1.08) and 2.10% for transglutaminase (95% CI 1.92-2.29) autoantibodies. The screening aimed to engage providers in hospital specialty clinics (HSC), emergency departments (ED), private primary care (PPC), government primary care (GPC), diabetes center (BDC), community events (CE), and retail clinics (RC). ASK provided, as needed, on-site screening staff, provider reimbursement, and electronic consenting. Efficiency was defined as #screened/research staff full-time effort per week. The majority (72%) of screenings were completed at an ED or HSC, with 91% of screenings conducted by ASK staff. Highest screening efficiency was in the ED (Figure 1). While efficient, RC yielded small numbers. Challenges to screening program implementation included inconsistent provider time and commitment, limited clinic space, competing priorities, and research constraints. Translation to a sustainable screening in primary care will need to address these challenges in a practice-specific fashion. Disclosure: C.R. Geno Rasmussen: None. K. Waugh: None. J. Baxter: None. A. Steck: None. B.I. Frohnert: None. M. Rewers: None. Funding: JDRF (3-SRA-2018-564-M-N) [ABSTRACT FROM AUTHOR]

We explored the association between growth features and T1D development using landmark analysis at different ages. Analysis included individuals from 2 birth cohort studies: DAISY and BABYDIAB (n=2,664; 129 progressed to T1D). Using height and weight measured over time, percentiles for age were calculated. Missing values were imputed using LMS parameters of CDC growth charts. Rates of change of percentiles were computed over the prior year. Twelve ages (1-12 years) were used for landmark analysis with Random Survival Forest to predict probability of T1D onset in the 1- to 19-year follow up windows. The baseline model used HLA risk group, sex, T1D family history and breastfeeding history. The full model added growth features: height and weight percentiles at age and change in percentiles. Performance was measured using C-index and feature importance was ranked. Incorporating growth features significantly improved prediction accuracy of T1D onset for 95% of combinations of landmark ages and prediction window sizes (Table 1, not all ages and windows shown). The order of features from most to least predictive is: HLA group, rates of height and weight changes, height and weight percentiles, family history, breastfeeding and sex. This analysis demonstrates that using growth features can significantly improve prediction of T1D. Table 1: C-Index with and without inclusion of growth features for landmark age of 3 years. Disclosure: Z. Li: None. V. Anand: None. J.L. Dunne: None. B.I. Frohnert: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. H. Hyoty: None. M. Maziarz: None. A. Ziegler: None. J. Toppari: None. Funding: JDRF (1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019-720-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-723-I-X) [ABSTRACT FROM AUTHOR]

We investigated evolution of islet autoantibodies (IAs) prior to onset of T1D from 5 large-scale birth cohort studies. Our analysis revealed three distinct IA trajectories leading up to diagnosis of T1D. Of 24673 children from five prospective studies (DAISY, DiPiS, DIPP, DEW-IT, and BABYDIAB), 688 who were diagnosed with T1D and had 3 or more visits were included in this analysis. Hidden Markov Models were developed to label visit-level observation of each subject based on three IAs: GADA, IAA, and IA-2A. Interactive visualizations were then applied to explore model outcomes, identify IA evolution trajectories, and examine their clinical characteristics. Three trajectories were identified (Figure 1) with a majority of children having multiple IA (Tr1: n=265) or IAA first (Tr2: n=282) at seroconversion; the minority seroconverted with GADA first (Tr3: n=131). The Tr3 group had seroconversion and T1D onset at an older age in months (58, 132) than Tr1 (42, 96) and Tr2 (31, 88), P <.01. Distribution of HLA DR/DQ differed between groups: higher DRX/X and lower DR3/4 in Tr3 (18%, 24%) than Tr1 (11%, 26%) and Tr2 (10%, 31%). The three IA trajectories show distinctive antibody patterns, ages of seroconversion and T1D onset and HLA DR/DQ group distributions among them. Furthermore, heterogeneity is also shown within each trajectory in terms of progression time and needs further investigation. Disclosure: B. Kwon: None. P. Achenbach: None. V. Anand: None. J.L. Dunne: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. M. Lundgren: None. R. Veijola: None. B.I. Frohnert: None. Funding: JDRF (1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019-723-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-720-I-X) [ABSTRACT FROM AUTHOR]

Background: Type 1 diabetes (T1D) is determined by unknown environmental factors in genetically susceptible individuals. The design of most genome wide association studies of T1D involves comparison of cases (with varying duration of disease) with controls; hence, the genetic variants associated with T1D reflect current disease, ignoring variants that play a key role in the initiation of islet autoimmunity and the progression of subclinical disease. In this study, we focus on the genetic contribution to progression of islet autoimmunity using whole-genome sequencing in a collection of participants of the Diabetes AutoImmunity Study in the Young (DAISY). Methods: A total of 160 persistent islet autoantibody positive DAISY subjects were sequenced, including 87 subjects who progressed to T1D and 73 "non-progressors" over a mean of 11 years since seroconversion. Following whole genome sequence alignment, single nucleotide polymorphisms (SNPs) and small insertion/deletions (indels) were identified (∼6.8 million variants), and statistical analyses performed. Results: Genes with multiple SNPs associated with progression to T1D in subjects with islet autoimmunity (P < 8.5x10-8) did not overlap with known T1D risk loci; instead, four novel regions were identified - 1q21.3 (MRPS21-PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), and 20p12.1 (TASP1). Functional mapping of the associated SNPs within these risk loci indicates pathways critical for response to viral infections and response to interferon signaling contribute to progression to T1D once islet autoimmunity is initiated. Discussion: This study presents evidence that different genetic pathways may contribute to progression of islet autoimmunity from those identified once disease is established and support the need for follow-up studies to understand genetic risk factors that modulate progression of subclinical disease. Disclosure: S. Onengut-Gumuscu: None. U. Paila: None. W. Chen: None. A. Ratan: None. Z. Zhu: None. A. Steck: None. B.I. Frohnert: None. K. Waugh: None. B. Webb-Robertson: None. J.M. Norris: None. L. Lange: None. M. Rewers: None. S.S. Rich: None. [ABSTRACT FROM AUTHOR]

The development of multiple islet autoantibodies (IA) predicts progression to type 1 diabetes (T1D), and forms the basis of current staging of T1D. However, age at seroconversion, persistence of IA, and frequency of sampling influence the determination of multiple IA status. Individual IA may appear at different timepoints and fluctuate. This analysis aims to understand how differences in the stringency of multiple IA status definition impact risk estimates of progression to stage 3 T1D. We combined data from three birth-cohort studies (N=18,537): DAISY (U.S.), DiPiS (Sweden) and DIPP (Finland), defining four groups by IA positivity pattern and accounting for variability in persistence and timing in concurrent and sequential patterns of IAA, IA2A, and GADA. Subjects were placed exclusively in their most stringent IA group. Cumulative incidence of progression to T1D was estimated by survival analysis and log-rank test, which showed group differences (p<0.0001). T1D risk 15 years after seroconversion [95% CI] increased according to increasing stringency of the definition: Single IA/Persistent 10% [6%, 14%], multiple IA/Any 17% [3%, 29%], multiple IA/Same Visit 40% [17%, 57%], and multiple IA/Persistent 74% [69%, 77%]. Enhanced understanding of the characteristics of IA patterns can better inform recruitment for intervention studies and improve communication of risk during pre-symptomatic T1D screening. Disclosure: B. Liu: None. M. Ghalwash: None. V. Anand: None. E. Koski: None. K. Ng: None. J.L. Dunne: None. M. Lundgren: None. H. Elding Larsson: None. W. Hagopian: Research Support; Self; Novo Nordisk A/S. R. Veijola: None. B.I. Frohnert: None. Funding: JDRF [ABSTRACT FROM AUTHOR]

The article examines the prevalence of antibodies and HLA haplotypes known to be associated with type 1 diabetes in cystic fibrosis-related diabetes (CFRD). It discusses the etiology of CFRD. It also suggests that patients with CFRD represent the severe end of the glucose intolerance spectrum and might be the most likely to exhibit associations with type 1 diabetes.