BACKGROUND. Residual C-peptide is detected in many people for years following the diagnosis of type 1 diabetes; however, the physiologic significance of low levels of detectable C-peptide is not known. METHODS. We studied 63 adults with type 1 diabetes classified by peak mixed-meal tolerance test (MMTT) C-peptide as negative (<0.007 pmol/mL; n = 15), low (0.017-0.200; n = 16), intermediate (>0.200-0.400; n = 15), or high (>0.400; n = 17). We compared the groups' glycemia from continuous glucose monitoring (CGM), β cell secretory responses from a glucose-potentiated arginine (GPA) test, insulin sensitivity from a hyperinsulinemic-euglycemic (EU) clamp, and glucose counterregulatory responses from a subsequent hypoglycemic (HYPO) clamp. RESULTS. Low and intermediate MMTT C-peptide groups did not exhibit β cell secretory responses to hyperglycemia, whereas the high C-peptide group showed increases in both C-peptide and proinsulin (P ≤ 0.01). All groups with detectable MMTT C-peptide demonstrated acute C-peptide and proinsulin responses to arginine that were positively correlated with peak MMTT C-peptide (P < 0.0001 for both analytes). During the EU-HYPO clamp, C-peptide levels were proportionately suppressed in the low, intermediate, and high C-peptide compared with the negative group (P ≤ 0.0001), whereas glucagon increased from EU to HYPO only in the high C-peptide group compared with negative (P = 0.01). CGM demonstrated lower mean glucose and more time in range for the high C-peptide group. CONCLUSION. These results indicate that in adults with type 1 diabetes, β cell responsiveness to hyperglycemia and α cell responsiveness to hypoglycemia are observed only at high levels of residual C-peptide that likely contribute to glycemic control.

BackgroundPerioperative anaphylaxis (PA) in children is an uncommon but potentially life‐threatening complication associated with anesthesia. Early identification and management of PA is essential to optimize clinical outcomes.MethodsWe performed a retrospective study of anesthesia records from pediatric patients with PA from centers in the United Kingdom, France, and the United States over a period of 10 years. Time sequence of clinical signs and physiological variables during PA were collected, along with results of allergy testing.ResultsTwenty‐nine children with PA were included. Median age was 11 years. Based on the modified Ring and Messmer Grading Scale, severe reactions were seen in 25 (86%) members of this cohort, with 4 (14%) experiencing cardiac arrest. Life‐threatening hypotension was the first clinical sign of PA in 59% of cases, followed by tachycardia and bronchospasm. In 16 (55%) cases, the initial signs of PA involved multiple organ systems. When the initial signs of PA were cardiovascular and/or respiratory, more epinephrine doses were administered. Average time from initial sign of PA to treatment with epinephrine was 6 minutes (SD: 6, range: 1‐25). The causative allergen was identified in 15 patients.ConclusionSevere hypotension is the most common presenting sign of PA in children. Initial cardiovascular and/or respiratory signs are associated with the need for increased epinephrine doses. Further studies should optimize the prediction, identification, and early management of PA in children.

BackgroundThe LEAP study has shown the effectiveness of early peanut introduction in prevention of peanut allergy (PA). In the EAT study, a statistically significant reduction in PA was present only in per-protocol (PP) analyses, which can be subject to bias. ObjectiveTo combine individual-level data from the LEAP and EAT trials and provide robust evidence on the bias-corrected, causal effect of early peanut introduction. MethodAs part of the European Union-funded iFAAM project, this pooled analysis of individual paediatric patient data combines and compares effectiveness and efficacy estimates of oral tolerance induction among different risk strata and analysis methods.

Context There is an unmet need for biomarkers of pancreatic beta-cell death to improve early diagnosis of type 1 diabetes, enroll subjects into clinical trials, and assess treatment response. To address this need, several groups developed assays measuring insulin deoxyribonucleic acid (DNA) with unmethylated CpG sites in cell-free DNA. Unmethylated insulin DNA should be derived predominantly from beta-cells and indicate ongoing beta-cell death. Objective To assess the performance of three unmethylated insulin DNA assays. Design and Participants Plasma or serum samples from 13 subjects undergoing total pancreatectomy and islet autotransplantation were coded and provided to investigators to measure unmethylated insulin DNA. Samples included a negative control taken post-pancreatectomy but pretransplant, and a positive control taken immediately following islet infusion. We assessed technical reproducibility, linearity, and persistence of detection of unmethylated insulin DNA for each assay. Results All assays discriminated between the negative sample and samples taken directly from the islet transplant bag; 2 of 3 discriminated negative samples from those taken immediately after islet infusion. When high levels of unmethylated insulin DNA were present, technical reproducibility was generally good for all assays. Conclusions The measurement of beta cell cell-free DNA, including insulin, is a promising approach, warranting further testing and development in those with or at-risk for type 1 diabetes, as well as in other settings where understanding the frequency or kinetics of beta cell death could be useful.

Aims/hypothesis Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age. Methods Using longitudinal data from single-and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes. Results Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibodypositive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single-and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single-and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate. Conclusions/interpretation Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.Michelle So and Colin O'Rourke contributed equally to this work.