Association of Diabetic Ketoacidosis and HbA1c at Onset with Year-Three HbA1c in Children and Adolescents with Type 1 Diabetes: Data from the International SWEET Registry

Objective: To establish whether diabetic ketoacidosis (DKA) or HbA1c at onset is associated with year-three HbA1c in children with type 1 diabetes (T1D). Methods: Children with T1D from the SWEET registry, diagnosed <18 years, with documented clinical presentation, HbA1c at onset and follow-up were included. Participants were categorized according to T1D onset: (a) DKA (DKA with coma, DKA without coma, no DKA); (b) HbA1c at onset (low [<10%], medium [10 to <12%], high [≥12%]). To adjust for demographics, linear regression was applied with interaction terms for DKA and HbA1c at onset groups (adjusted means with 95% CI). Association between year-three HbA1c and both HbA1c and presentation at onset was analyzed (Vuong test). Results: Among 1420 children (54% males; median age at onset 9.1 years [Q1;Q3: 5.8;12.2]), 6% of children experienced DKA with coma, 37% DKA without coma, and 57% no DKA. Year-three HbA1c was lower in the low compared to high HbA1c at onset group, both in the DKA without coma (7.1% [6.8;7.4] vs 7.6% [7.5;7.8], P = .03) and in the no DKA group (7.4% [7.2;7.5] vs 7.8% [7.6;7.9], P = .01), without differences between low and medium HbA1c at onset groups. Year-three HbA1c did not differ among HbA1c at onset groups in the DKA with coma group. HbA1c at onset as an explanatory variable was more closely associated with year-three HbA1c compared to presentation at onset groups (P = .02). Conclusions: Year-three HbA1c is more closely related to HbA1c than to DKA at onset; earlier hyperglycemia detection might be crucial to improving year-three HbA1c.info:eu-repo/semantics/publishedVersio

Center Size and Glycemic Control: An International Study With 504 Centers From Seven Countries

The variance in glycemic control between different childhood diabetes centers is not fully understood. Although the International Society for Pediatric and Adolescent Diabetes guidelines from 2014 recommended center sizes of more than 150 patients (1), it has not been thoroughly investigated whether glycemic control is associated with center size (2–4). We have data from more than 500 childhood diabetes centers from seven different countries and thereby a unique opportunity to elaborate further on this association. Therefore, this study aims to investigate the relationship between center size and glycemic control in children with type 1 diabetes (T1D). Patient data have been described previously (5). Briefly, the population comprised children with T1D in the age-group 3 months from seven high-income countries during 2013–2014: Austria, Denmark, England, Germany, Norway, Sweden, and Wales. Data were anonymized and obtained from five national registries/audits on children with T1D (Austria and Germany use the same electronic health record and England and Wales have a common National Paediatric Diabetes Audit, while Denmark, Norway, and Sweden have national registries). Mean HbA1c was compared between groups after adjusting for sex, age (<6 years, 6 to <12 years, and 12–18 years), duration of diabetes (<2 years, 2 to <5 years, and ≥5 years), and minority status (yes/no) (HbA1c adj) before and after stratifying for treatment modality (insulin injection/pump). Center size was defined as the number of patients with diabetes reported to be cared for in a center. Center size groupings were 1) <20, 2) 20 to <50, 3) 50 to <100, 4) 100 to <200, and 5) ≥200 patients. In total 54,494 children (48% females) with T1D across 504 centers in seven countries were included in the study. The number of centers per country varied between 14 (Wales) and 219 (Germany). Mean (SD) for age was 12.5 (3.9) years, mean age at T1D onset was 7.5 (4.0) years, and mean T1D duration was 5.0 (3.7) years. A total of 21% of patients had minority status, which varied between 5% (Wales) and 28% (Austria). A total of 38.1% of patients were on pump treatment, and the percentage varied between 25% (England) and 69% (Denmark). National coverage of T1D patients was >95% in all countries, apart from Austria, which had ∼80% data coverage. Included patients had 100% data coverage for all of the following variables: sex, age, diabetes duration, minority status, and HbA1c. Data on treatment modality were not available for 2,428 patients (4.5%); of these, 2,130 were from England and 154 were from Sweden. A total of 23.2% of centers had 200 patients, representing 12.3% of all centers. The distribution of small and large centers in the seven countries varied. England and Sweden had few small centers (34%). HbA1c adj was significantly higher in the centers with 50 patients, in both pen users (P 50 patients managed equally well; therefore, centralizing to very-high-volume diabetes centers may not necessarily be an advantage. Future research should focus on identifying reasons leading to differences in glycemic control in T1D patients cared for in small and large centers, e.g., the lack or presence of an updated multidisciplinary diabetes team

The COVID-19 Pandemic Affects Seasonality, With Increasing Cases of New-Onset Type 1 Diabetes in Children, From the Worldwide SWEET Registry

Objective: To analyze whether the coronavirus disease 2019 (COVID-19) pandemic increased the number of cases or impacted seasonality of new-onset type 1 diabetes (T1D) in large pediatric diabetes centers globally. Research design and methods: We analyzed data on 17,280 cases of T1D diagnosed during 2018-2021 from 92 worldwide centers participating in the SWEET registry using hierarchic linear regression models. Results: The average number of new-onset T1D cases per center adjusted for the total number of patients treated at the center per year and stratified by age-groups increased from 11.2 (95% CI 10.1-12.2) in 2018 to 21.7 (20.6-22.8) in 2021 for the youngest age-group, <6 years; from 13.1 (12.2-14.0) in 2018 to 26.7 (25.7-27.7) in 2021 for children ages 6 to <12 years; and from 12.2 (11.5-12.9) to 24.7 (24.0-25.5) for adolescents ages 12-18 years (all P < 0.001). These increases remained within the expected increase with the 95% CI of the regression line. However, in Europe and North America following the lockdown early in 2020, the typical seasonality of more cases during winter season was delayed, with a peak during the summer and autumn months. While the seasonal pattern in Europe returned to prepandemic times in 2021, this was not the case in North America. Compared with 2018-2019 (HbA1c 7.7%), higher average HbA1c levels (2020, 8.1%; 2021, 8.6%; P < 0.001) were present within the first year of T1D during the pandemic. Conclusions: The slope of the rise in pediatric new-onset T1D in SWEET centers remained unchanged during the COVID-19 pandemic, but a change in the seasonality at onset became apparent.info:eu-repo/semantics/publishedVersio

INNODIA Master Protocol for the evaluation of investigational medicinal products in children, adolescents and adults with newly diagnosed type 1 diabetes

Background The INNODIA consortium has established a pan-European infrastructure using validated centres to prospectively evaluate clinical data from individuals with newly diagnosed type 1 diabetes combined with centralised collection of clinical samples to determine rates of decline in beta-cell function and identify novel biomarkers, which could be used for future stratification of phase 2 clinical trials. Methods In this context, we have developed a Master Protocol, based on the “backbone” of the INNODIA natural history study, which we believe could improve the delivery of phase 2 studies exploring the use of single or combinations of Investigational Medicinal Products (IMPs), designed to prevent or reverse declines in beta-cell function in individuals with newly diagnosed type 1 diabetes. Although many IMPs have demonstrated potential efficacy in phase 2 studies, few subsequent phase 3 studies have confirmed these benefits. Currently, phase 2 drug development for this indication is limited by poor evaluation of drug dosage and lack of mechanistic data to understand variable responses to the IMPs. Identification of biomarkers which might permit more robust stratification of participants at baseline has been slow. Discussion The Master Protocol provides (1) standardised assessment of efficacy and safety, (2) comparable collection of mechanistic data, (3) the opportunity to include adaptive designs and the use of shared control groups in the evaluation of combination therapies, and (4) benefits of greater understanding of endpoint variation to ensure more robust sample size calculations and future baseline stratification using existing and novel biomarkers

Predicting the optimal Basal insulin infusion pattern in children and adolescents on insulin pumps

OBJECTIVE: We aimed at developing and cross-validating a mathematical prediction model for an optimal basal insulin infusion pattern for children with type 1 diabetes on continuous subcutaneous insulin infusion therapy (CSII). RESEARCH DESIGN AND METHODS: We used the German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology and retrieved all CSII patients <20 years of age (November 2009). A total of 1,248 individuals from our previous study were excluded (dataset 1), resulting in 6,063 CSII patients (dataset 2) (mean age 10.6 +/- 4.3 years). Only the most recent basal insulin infusion rates (BRs) were considered. BR patterns were identified and corresponding patients sorted by unsupervised clustering. Logistic regression analysis was applied to calculate the probabilities for each BR pattern. Equations were based on both independent datasets separately, and probabilities for BR patterns were cross-validated using typical test patients. RESULTS: Of the 6,063 children, 5,903 clustered in one of four major circadian BR patterns, confirming our previous study. The oldest age-group (mean age 12.8 years) was represented by 2,490 patients (42.18%) with a biphasic dawn-dusk pattern (BC). A broad single insulin maximum at 9-10 p.m. (F) was unveiled by 853 patients (14.45%) (mean age 6.3 years). Logistic regression analysis revealed that age, to a lesser extent duration of diabetes, and partly sex predicted BR patterns. Cross-validation revealed almost identical probabilities for BR patterns BC and F in the two datasets but some variation in the remaining two BR patterns. CONCLUSIONS: Reconfirmation of four key BR patterns in two very large independent cohorts supports that these patterns are realistic approximations of the circadian distribution of insulin needs in children with type 1 diabetes. Prediction of an optimal pattern a priori can improve initiation and clinical follow-up of CSII in children and adolescents. In addition, these BR patterns represent valuable information for insulin-infusion algorithms in closed-loop CSII

Increased referrals for congenital hyperinsulinism genetic testing in children with trisomy 21 reflects the high burden of non-genetic risk factors in this group

BACKGROUND: Hyperinsulinism results from inappropriate insulin secretion during hypoglycaemia. Down syndrome is causally linked to a number of endocrine disorders including Type 1 diabetes and neonatal diabetes. We noted a high number of individuals with Down syndrome referred for hyperinsulinism genetic testing, and therefore aimed to investigate whether the prevalence of Down syndrome was increased in our hyperinsulinism cohort compared to the population. METHODS: We identified individuals with Down syndrome referred for hyperinsulinism genetic testing to the Exeter Genomics Laboratory between 2008 and 2020. We sequenced the known hyperinsulinism genes in all individuals and investigated their clinical features. RESULTS: We identified 11 individuals with Down syndrome in a cohort of 2011 patients referred for genetic testing for hyperinsulinism. This represents an increased prevalence compared to the population (2.5/2011 expected vs. 11/2011 observed, p = 6.8 × 10(-5) ). A pathogenic ABCC8 mutation was identified in one of the 11 individuals. Of the remaining 10 individuals, five had non-genetic risk factors for hyperinsulinism resulting from the Down syndrome phenotype: intrauterine growth restriction, prematurity, gastric/oesophageal surgery, and asparaginase treatment for leukaemia. For five individuals no risk factors for hypoglycaemia were reported although two of these individuals had transient hyperinsulinism and one was lost to follow-up. CONCLUSIONS: Down syndrome is more common in patients with hyperinsulinism than in the population. This is likely due to an increased burden of non-genetic risk factors resulting from the Down syndrome phenotype. Down syndrome should not preclude genetic testing as coincidental monogenic hyperinsulinism and Down syndrome is possible.The article is available via Open Access. Click on the 'Additional link' above to access the full-text.Published version, accepted version (12 month embargo), submitted versio

Insulin pump therapy in children with type 1 diabetes: analysis of data from the SWEET registry

Background: Intensified insulin delivery using multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII) is recommended in children with type 1 diabetes (T1D) to achieve good metabolic control. Objective: To examine the frequency of pump usage in T1D children treated in SWEET (Better control in Paediatric and Adolescent diabeteS: Working to crEate CEnTers of Reference) centers and to compare metabolic control between patients treated with CSII vs MDI. Methods: This study included 16 570 T1D children participating in the SWEET prospective, multicenter, standardized diabetes patient registry. Datasets were aggregated over the most recent year of treatment for each patient. Data were collected until March 2016. To assess the organization of pump therapy a survey was carried out. Results: Overall, 44.4% of T1D children were treated with CSII. The proportion of patients with pump usage varied between centers and decreased with increasing age compared with children treated with MDI. In a logistic regression analysis adjusting for age, gender and diabetes duration, the use of pump was associated with both: center size [odd ratio 1.51 (1.47-1.55), P &lt;.0001) and the diabetes-related expenditure per capita [odd ratio 1.55 (1.49-1.61), P &lt;.0001]. Linear regression analysis, adjusted for age, gender, and diabetes duration showed that both HbA1c and daily insulin dose (U/kg/d) remained decreased in children treated with CSII compared to MDI (P &lt;.0001). Conclusions: Insulin pump therapy is offered by most Sweet centers. The differences between centers affect the frequency of use of modern technology. Despite the heterogeneity of centers, T1D children achieve relatively good metabolic control, especially those treated with insulin pumps and those of younger age. © 2016 John Wiley &amp; Sons A/S. Published by John Wiley &amp; Sons Lt

Increased referrals for congenital hyperinsulinism genetic testing in children with trisomy 21 reflects the high burden of non-genetic risk factors in this group

BACKGROUND: Hyperinsulinism results from inappropriate insulin secretion during hypoglycaemia. Down syndrome is causally linked to a number of endocrine disorders including Type 1 diabetes and neonatal diabetes. We noted a high number of individuals with Down syndrome referred for hyperinsulinism genetic testing, and therefore aimed to investigate whether the prevalence of Down syndrome was increased in our hyperinsulinism cohort compared to the population. METHODS: We identified individuals with Down syndrome referred for hyperinsulinism genetic testing to the Exeter Genomics Laboratory between 2008 and 2020. We sequenced the known hyperinsulinism genes in all individuals and investigated their clinical features. RESULTS: We identified 11 individuals with Down syndrome in a cohort of 2011 patients referred for genetic testing for hyperinsulinism. This represents an increased prevalence compared to the population (2.5/2011 expected vs. 11/2011 observed, p = 6.8 × 10(−5)). A pathogenic ABCC8 mutation was identified in one of the 11 individuals. Of the remaining 10 individuals, five had non‐genetic risk factors for hyperinsulinism resulting from the Down syndrome phenotype: intrauterine growth restriction, prematurity, gastric/oesophageal surgery, and asparaginase treatment for leukaemia. For five individuals no risk factors for hypoglycaemia were reported although two of these individuals had transient hyperinsulinism and one was lost to follow‐up. CONCLUSIONS: Down syndrome is more common in patients with hyperinsulinism than in the population. This is likely due to an increased burden of non‐genetic risk factors resulting from the Down syndrome phenotype. Down syndrome should not preclude genetic testing as coincidental monogenic hyperinsulinism and Down syndrome is possible