Introduction
In this year's edition of the Yearbook article focused on the pediatric age group we selected 15 articles from many meritorious publications in the past year. As in previous years, there is a common theme of rapid advancement of diabetes technology in this age group in research as well as translation to clinical implementation. Diabetes technology truly is transforming how pediatric diabetes care is delivered. With the global COVID-19 pandemic, the development and implementation of diabetes technology has never been more important, and pediatric diabetologists are leading the way (1).
In contrast to last year, when studies on continuous glucose monitoring (CGM) were dominant, this year multiple studies were published on the development of closed-loop insulin delivery systems in the pediatric population. These studies ranged from early safety studies performed as a necessary step before larger, pivotal trials for regulatory approval (which were also published this past year) to the addition this year of “real-world” studies on approved closed-loop insulin delivery systems in use in pediatric diabetes clinics.
The ongoing development of these systems and transition from research to clinic will continue to highlight pediatric diabetes care in the years ahead. Common themes continue to be both the challenges and opportunities of diabetes technology in the pediatric population. Usability remains an important goal for translation from research to clinical implementation. Broader access to these life-transforming diabetes technologies will be an on-going mission for all involved in pediatric diabetes care so that all children can benefit.
In addition to closed-loop research, important articles in the pediatric age group were published on novel insulin formulations, national and individual clinical registry data to describe outcomes and best practices, and novel reports on the data generated from diabetes technology and their applications.
To select these 15 articles focused on diabetes technology and therapeutics in the pediatric age group, we conducted a Medline search for articles dealing with the following topics: diabetes technology, insulin pump therapy (CSII), CGM, closed-loop systems, and new therapies in T1D and T2D relating to the pediatric age group (0–18 years). We focused on key articles that offer some insight into these issues and were published between July 1, 2019, and June 30, 2020.
Key Articles Reviewed for the Article
Six months of hybrid closed-loop in the real-world: an evaluation of children and young adults using the 670G system
Berget C, Messer LH, Vigers T, Frohnert BI, Pyle L, Wadwa RP, Driscoll KA, Forlenza GP
Pediatr Diabetes 2020; 21: 310–318
Safety and performance of the Omnipod hybrid closed-loop system in adults, adolescents, and children with type 1 diabetes over 5 days under free-living conditions
Sherr JL, Buckingham BA, Forlenza GP, Galderisi A, Ekhlaspour L, Wadwa RP, Carria L, Hsu L, Berget C, Peyser TA, Lee JB, O'Connor J, Dumais B, Huyett LM, Layn JE, Ly TT
Diabetes Technol Ther 2020; 22: 174–184
Young children have higher variability of insulin requirements: observations during hybrid closed-loop insulin delivery
Dovc K, Boughton C, Tauschmann M, Thabit H, Bally L, Allen JM, Acerini CL, Arnolds S, de Beaufort C, Bergenstal RM, Campbell F, Criego A, Dunger DB, Elleri D, Evans ML, Fröhlich-Reiterer E, Hofer S, Kapellen T, Leelarathna L, Pieber TR, Rami-Merhar B, Shah VN, Sibayan J, Wilinska ME, Hovorka R on behalf of the APCam, AP@Home, and KidsAP Consortia
Diabetes Care 2019; 42: 1344–1347
Real-world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months
Messer LH, Berget C, Vigers T, Pyle L, Geno C, Wadwa RP, Driscoll KA, Forlenza GP
Pediatric Diabetes 2020; 21: 319–327
Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes
Brown SA, Kovatchev BP, Raghinaru D, Lum JW, Buckingham BA, Kudva YC, Laffel LM, Levy CJ, Pinsker JE, Wadwa RP, Dassau E, Doyle 3rd FJ, Anderson SM, Church MM, Dadlani V, Ekhlaspour L, Forlenza GP, Isganaitis E, Lam DW, Kollman C, Beck RW, iDCL Trial Research Group
N Engl J Med 2019; 381: 1707–1717
Reduced burden of diabetes and improved quality of life: experiences from unrestricted day-and-night hybrid closed-loop use in very young children with type 1 diabetes
Musolino G, Dovc K, Boughton CK, Tauschmann M, Allen JM, Nagl K, Fritsch M, Yong J, Metcalfe E, Schaeffer D, Fichelle M, Schierloh U, Thiele AG, Abt D, Kojzar H, Mader JK, Slegtenhorst S, Ashcroft N, Wilinska ME, Sibayan J, Cohen N, Kollman C, Hofer SE, Fröhlich-Reiterer E, Kapellen TM, Acerini CL, de Beaufort C, Campbell F, Rami-Merhar B, Hovorka R on behalf of Kidsap Consortium
Pediatr Diabetes 2019; 20: 794–799
Closed-loop control in adolescents and children during winter sports: use of the Tandem Control-IQ AP system
Ekhlaspour L, Forlenza GP, Chernavvsky D, Maahs DM, Wadwa RP, Deboer MD, Messer LH, Town M, Pinnata J, Kruse G, Kovatchev BP, Buckingham BA, Breton MD
Pediatr Diabetes 2019; 20: 759–768
Efficacy and safety of fast-acting insulin aspart compared with insulin aspart, both in combination with insulin Degludec, in children and adolescents with type 1 diabetes: the ONSET 7 trial
BW Bode, Iotova V, Kovarenko M, Laffel LM, Rao PV, Deenadayalan S, Ekelund M, Larsen SF, Danne T
Diabetes Care 2019; 42: 1255–1262
Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c
Kahkoska AR, Adair LA, Aiello AE, Burger KS, Buse JB, Crandell J, Maahs DM, Nguyen CT, Kosorok MR, Mayer-Davis EJ
Pediatr Diabetes 2019; 20: 556–566
The transatlantic HbA1c gap: differences in glycaemic control across the lifespan between people included in the US T1D Exchange registry and those included in the German/Austrian DPV registry
Hermann JM, Miller KM, Hofer SE, Clements MA, Karges W, Foster NC, Fröhlich-Reiterer E, Rickels MR, Rosenbauer J, DeSalvo DJ, Holl RW, Maahs DM for the T1D Exchange Clinic Network and the DPV initiative
Determinants of glycaemic outcome in the current practice of care for young people up to 21 years old with type 1 diabetes under real-life conditions
Kordonouri O, Lange K, Biester T, Datz N, Kapitzke K, von demBerge T, Weiskorn J, Danne T
Reduction in diabetic ketoacidosis and severe hypoglycemia in pediatric type 1 diabetes during the first year of continuous glucose monitoring: a multicenter analysis of 3,553 subjects from the DPV registry
Tauschmann M, Hermann JM, Freiberg C, Papsch M, Thon A, Heidtmann B, Placzeck K, Agena D, Kapellen TM, Schenk B, Wolf J, Danne T, Rami-Merhar B, Holl RW on behalf of the DPV Initiative
Diabetes Care 2020; 43: e40–e42
Proportion of basal to total insulin dose is associated with metabolic control, body mass index, and treatment modality in children with type 1 diabetes—a cross-sectional study with data from the international SWEET registry
Rasmussen VF, Vestergaard ET, Schwandt A, Beltrand J, Rami-Merhar B, O'Riordan SMP, Jarosz-Chobot P, Castro-Correia C, Gevers EF, Birkebæk NH
Early initiation of diabetes devices relates to improved glycemic control in children with recent-onset type 1 diabetes mellitus
Patton SR, Noser AE, Youngkin EM, Majidi S, Clements MA
Diabetes Technol Ther 2019; 21: 379–384
Glycemic control in adolescents with type 1 diabetes: are computerized simulations effective learning tools?
Dubovi I, Levy ST, Levy M, Zuckerman-Levin N, Dagan E
Six months of hybrid closed-loop in the real-world: an evaluation of children and young adults using the 670G system
Berget C1, Messer LH1, Vigers T2, Frohnert BI1, Pyle L1,2, Wadwa RP1, Driscoll KA1,3, Forlenza GP1
1University of Colorado Anschutz Campus, School of Medicine, Barbara Davis Center for Childhood Diabetes, Aurora, CO; 2Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; 3Department of Clinical and Health Psychology, University of Florida, Gainesville, FL
Pediatr Diabetes 2020; 21: 310–318
Background
The objective of the study was to review glycemic and psychosocial outcomes in youth with T1D using a hybrid closed-loop (HCL) system.
Methods
Youth with T1D (2–25 years) starting the 670G HCL system for their clinical care were enrolled. Observational, prospective data collection took place during routine care. Variables included sensor time in range (70–180 mg/dL), HbA1c, and psychosocial variables. Mixed models were used to analyze change across time.
Results
A total of 92 youth (mean age 15.7±3.6 years, 50% female, HbA1c 8.8%±1.8%) started HCL for their diabetes care. Participants used auto-mode 65.5%±3.0% of the time at month 1, which decreased to 51.2%±3.4% at month 6 (P=0.001). Sensor time in range increased from 50.7%±1.8% at baseline to 56.9%±2.1% at 6 months (P=0.007). HbA1c decreased from 8.7%±0.2% at baseline to 8.4%±0.2% after 6 months of use (P≤0.0001). The greatest HbA1c decline was in participants with high baseline HbA1c. Increased percent time in auto mode was associated with lower HbA1c (P=0.02). Thirty percent of youth discontinued HCL in the first 6 months of use. There were no changes in psychosocial variables across time.
Conclusions
HCL use is correlated with improved time in range and no change in psychosocial outcomes. HCL use declined across time, suggesting that youth encounter barriers in sustaining HCL use. Future research should focus on understanding reasons for HCL discontinuation and developing intervention strategies to improve usability.
Comment
This single-center report from Messer and colleagues describes the clinical experience of youth ages 2–25 years starting the Medtronic 670G HCL system, the first U.S. Food and Drug Administration (FDA)-approved HCL system. These data are important to understand how therapy adapts from a research trial into clinical practice, in this case those published in the open-label research trial that gained FDA approval in youth (2). In the Medtronic-sponsored trial, which included more clinical support than typically available in a U.S. diabetes clinic, time in auto-mode decreased less (87% to 72%) (3) over the course of the study than in this “real-world” clinical experience where 30% of youth stopped using the 670G and decreased to 51% after 6 months of clinical use, similar to previous reports (4). Still, benefits were gained by those who were able to stay in auto-mode, with a strong correlation seen between time in auto-mode and improved glucose metrics. Moreover, those who had the highest HbA1c at entry had the greatest improvement. This highlights the potential benefit of HCL to those patients who have the greatest difficulty achieving glucose targets. It should be noted that the 670G is a first-generation HCL system, and publications such as these are important to drive improved usability and performance.
Safety and performance of the Omnipod hybrid closed-loop system in adults, adolescents, and children with type 1 diabetes over 5 days under free-living conditions
Sherr JL1, Buckingham BA2, Forlenza GP3, Galderisi A1, Ekhlaspour L2, Wadwa RP3, Carria L1, Hsu L2, Berget C3, Peyser TA4, Lee JB5, O'Connor J5, Dumais B5, Huyett LM5, Layn JE5, Ly TT5
1Division of Pediatric Endocrinology & Diabetes, Department of Pediatrics, Yale University, New Haven, CT; 2Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA; 3Barbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, CO; 4ModeAGC LLC, Palo Alto, CA; 5Insulet Corporation, Acton, MA
Diabetes Technol Ther 2020; 22: 174–184
This manuscript is also discussed in the article on Decision Support Systems and Closed-Loop, page S-69.
Background
This study assessed the safety and performance of the Omnipod personalized model predictive control (MPC) algorithm in people with T1D aged ≥6 years under free-living conditions using an investigational system.
Methods
A 96-hour hybrid closed-loop (HCL) study was conducted in a supervised outpatient setting following a 7-day outpatient standard therapy (ST) phase. Participants were aged 6–65 years with HbA1c <10.0% and without restriction on prior insulin delivery. Meals were unrestricted, with boluses administered per usual routine. Daily physical activity was performed. Primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL.
Results
Participants included 11 adults, 10 adolescents, and 15 children. Percentage time ≥250 mg/dL during HCL was 4.5%±4.2%, 3.5%±5.0%, and 8.6%±8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (P=0.1, P=0.02, and P=0.03). Percentage time <70 mg/dL during HCL was 1.9%±1.3%, 2.5%±2.0%, and 2.2%±1.9%, a statistically significant decrease in adults when compared to ST (P=0.005, P=0.3, and P=0.3). Percentage time in range 70–180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7%±7.5% vs ST 68.0%±15.6% for adults (P=0.08), HCL 79.0%±12.6% vs ST 60.6%±13.4% for adolescents (P=0.01), and HCL 69.2%±13.5% vs ST 54.9%±12.9% for children (P=0.003).
Conclusions
The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by participants from 6–65 years of age with T1D under supervised outpatient conditions with challenges, including daily physical activity and unrestricted meals.
Comment
This study includes several important advances for diabetes technology in the pediatric age group, perhaps the most important being the advance of another HCL system closer to being commercially available. Furthermore, this study demonstrates safety in adolescents and children down to 6 years of age with the Omnipod tubeless pump that is a preference of many people with diabetes. This will further expand choice of HCL systems in the near future. While designed to demonstrate safety, which was achieved and needed for ongoing pivotal trials for commercial approval, the data on time in range are encouraging in a wide age range while including exercise and normal bolusing practices.
Young children have higher variability of insulin requirements: observations during hybrid closed-loop insulin delivery
Dovc K1,2, Boughton C1, Tauschmann M1,3, Thabit H1,4, Bally L1,5, Allen JM1,6, Acerini CL1,6, Arnolds S7, de Beaufort C8, Bergenstal RM9, Campbell F10, Criego A9, Dunger DB1,6, Elleri D11, Evans ML1,12, Fröhlich-Reiterer E13, Hofer S14, Kapellen T15, Leelarathna L4, Pieber TR16, Rami-Merhar B3, Shah VN17, Sibayan J18, Wilinska ME1,6, Hovorka R1,6 on behalf of the APCam11, AP@Home, and KidsAP Consortia
1Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK; 2Department of Pediatric Endocrinology, Diabetes, and Metabolic Diseases, University Children's Hospital, University Medical Centre, Ljubljana, Slovenia; 3Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; 4Manchester Diabetes Centre, Manchester University Hospitals National Health Service (NHS) Foundation Trust, Manchester, UK; 5Departments of Diabetes, Endocrinology, Clinical Nutrition, and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; 6Department of Paediatrics, University of Cambridge, Cambridge, UK; 7Profil Institut fuer Stoffwechselforschung GmbH, Neuss, Germany; 8DECCP, Clinique Pediatrique/CH de Luxembourg, Luxembourg, Luxembourg; 9International Diabetes Center at Park Nicollet, St. Louis Park, MN; 10Department of Paediatric Diabetes, Leeds Children's Hospital, Leeds, UK; 11Royal Hospital for Sick Children, Edinburgh, UK; 12Department of Diabetes and Endocrinology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; 13Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria; 14Department of Pediatrics I, Medical University of Innsbruck, Innsbruck, Austria; 15Division for Paediatric Diabetology, University of Leipzig, Leipzig, Germany; 16Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria; 17Barbara Davis Center for Diabetes, University of Colorado, Denver, CO; 18Jaeb Center for Health Research, Tampa, FL
Diabetes Care 2019; 42: 1344–1347
Background
The study aimed to quantify age-related variability of insulin needs during closed-loop insulin delivered both day and night.
Methods
Data were analyzed retrospectively from hybrid closed-loop (HCL) studies of participants with T1D, including young children (1–6 years old, n=20), children (7–12 years, n=21), adolescents (13–17 years, n=15), and adults (>18 years, n=58). Coefficient of variation (CV) quantified variability of insulin needs over 3 weeks of unrestricted living HCL use.
Results
Data were analyzed from 2365 nights and 2367 days in 114 participants. The CV of insulin delivery was higher in young children compared with adults (mean difference at night 10.7 percentage points [95% CI 2.9–18.4], P=0.003; daytime 6.4 percentage points [95% CI 2.0–10.9], P=0.002) and compared with adolescents (mean difference at night 10.2 percentage points [95% CI 0.0–20.4], P=0.049; daytime 7.0 percentage points [95% CI 1.1–12.8], P=0.014).
Conclusions
Young children have higher variability in insulin requirements, which complicates open-loop management. These data support fast-track clinical practice adoption of closed-loop in young children.
Comment
Standard practice in biomedical research is to first test a new therapy in adults before moving to the pediatric population, and then within the pediatric age range to start with adolescents before progressing to the youngest children. These data pooled from previously performed research by the APCam11, AP@Home, and KidsAP Consortia indicate that children less than 6 years of age have greater variability in insulin needs than adolescents or adults. Children with T1D in this youngest age group (<6 years of age) on average have HbA1c that is lower than adolescents (5,6) and are able to achieve HbA1c targets without increased risk of severe hypoglycemia (7). However, these glycemic achievements are attained with a great deal of parental burden and worry (8), especially overnight. The authors conclude that these data should spur investigators, industry, and regulatory agencies to prioritize approval of closed-loop systems in the youngest children, as the benefit to be gained is arguably greater both for the child and their parents. More data are required and future editions of the Yearbook will describe clinical trials currently in process in this youngest age group with T1D.
Real-world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months
Messer LH1, Berget C1, Vigers T1,2, Pyle L1,2, Geno C1, Wadwa RP1, Driscoll KA1,3, Forlenza GP1
1Barbara Davis Center for Childhood Diabetes, School of Medicine, University of Colorado Denver, Denver, CO; 2Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; 3Department of Clinical and Health Psychology, University of Florida, Gainesville, FL
Pediatric Diabetes 2020; 21: 319–327
This manuscript is also discussed in the article on Practical Diabetes Technology: Overcoming Barriers in the Real World, page S-159.
Background
The aim of this study was to evaluate predictors of hybrid closed-loop (HCL) discontinuation and perceived barriers to use in youth with T1D.
Methods
In an observational, prospective study, youth with T1D (eligible age 2–25 years; recruited age 8–25 years) who initiated the MiniMed 670G HCL system were followed for 6 months. Demographic, glycemic, and psychosocial variables were collected for all participants. Participants who discontinued HCL (<10% HCL use at clinical visit) completed a questionnaire on perceived barriers to HCL use.
Results
A total of 92 youth (15.7±3.6 years, HbA1c 8.8±1.3%, 50% female) started HCL, and 28 (30%) discontinued it. The majority (64%) who discontinued did so between 3 and 6 months after HCL start. Baseline HbA1c predicted discontinuation (P=0.026). The odds of discontinuing increased by 2.7 times (95% CI: 1.123, 6.283) for each 1% increase in baseline HbA1c. Among those who discontinued HCL, the most cited problematic aspects were difficulty with calibrations, number of alarms, and too much time needed to make the system work. Qualitatively derived themes included technological difficulties (error alerts, not working correctly), too much work (calibrations, fingersticks), alarms, disappointment in glycemic control, and expense (cited by parents).
Conclusions
The authors of this study concluded that youth with higher HbA1c are at greater risk for discontinuing HCL than youth with lower HbA1c. The youth at greater risk should be the target of new interventions to support HCL use. The primary reasons for discontinuing HCL relate to the workload required.
Comment
In a companion piece to their real-world report of the Medtronic 670G HCL system glucose control (9), Messer and colleagues investigate the reasons for discontinuation among youth with T1D who initiated HCL therapy in clinical care. Such data are important complements to industry-sponsored pivotal trials designed to obtain regulatory approval. The promise of many research discoveries have yet to be translated to clinic, notably lessons on how to achieve tight glucose control from the Diabetes Control and Complications Trial (10), as the recent report of HbA1c across the lifespan in people with T1D from the Type 1 Diabetes Exchange attests (11). In contrast to their companion publication in which the most benefit from HCL was found in those with the highest baseline HbA1c, these data indicate that youth with higher HbA1c were more likely to discontinue the 670G. Reasons for discontinuation included usability and performance challenges, not unexpected with a first-generation HCL system, and cost. Multiple studies are under way from several companies with next-generation systems that will provide choice in the near future for people with T1D.
Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes
Brown SA1, Kovatchev BP1, Raghinaru D2, Lum JW2, Buckingham BA3, Kudva YC5, Laffel LM6, Levy CJ1,8, Pinsker JE4, Wadwa RP9, Dassau E7, Doyle 3rd FJ7, Anderson SM1, Church MM4, Dadlani V5, Ekhlaspour L3, Forlenza GP1,9, Isganaitis E6, Lam DW8, Kollman C2, Beck RW2, iDCL Trial Research Group
1The University of Virginia Center for Diabetes Technology, Charlottesville, VA; 2The Jaeb Center for Health Research, Tampa, FL; 3The Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Stanford, CA; 4The Sansum Diabetes Research Institute, Santa Barbara, CA; 5The Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, MN; 6The Research Division, Joslin Diabetes Center and Department of Pediatrics, Harvard Medical School, Boston, MA; 7The Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA; 8The Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York; 9The Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO
N Engl J Med 2019; 381: 1707–1717
This manuscript is also discussed in the article on Decision Support Systems and Closed-Loop, page S-69.
Background
In patients with T1D, closed-loop systems, which automate insulin delivery, may improve glycemic outcomes.
Methods
Participants with T1D were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group) in this 6-month, randomized, multicenter trial. The percentage of time that the CGM glucose level was within the target range of 70–180 mg/dl (3.9–10.0 mmol/liter) was considered the primary outcome.
Results
In all, 168 patients underwent randomization (112 in the closed-loop group, and 56 in the control group). Age ranged from 14–71 years and HbA1c from 5.4%–10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% at 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001).
The results regarding the main secondary outcomes all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed-loop minus control) in the percentage of time that the blood glucose level was <70 mg/dl was −0.88 percentage points (95% CI, −1.19 to −0.57; P<0.001). The mean adjusted difference in HbA1c after 6 months was −0.33 percentage points (95% CI, −0.53 to −0.13; P=0.001). The median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events were reported in either group; one episode of diabetic ketoacidosis was reported in the closed-loop group.
Conclusions
The use of a closed-loop system was correlated with a greater percentage of time spent in target glycemic range than the use of a sensor-augmented insulin pump during this 6-month trial involving patients with T1D.
Comment
This study by Brown and colleagues included adolescents with T1D and provided the pivotal data for approval of the Control-IQ system by the FDA in the United States. Important take-home points include the increase of 11% (or 2.6 hours/day) time spent in target range of 70–180 mg/dl while also significantly reducing time spent <70 mg/dl by 0.88% (or 21 minutes/day). Previous studies have demonstrated short-term improvement in glucose metrics with use of closed-loop insulin delivery systems; however, these data demonstrate persistent high rates of usability (>90% time in closed-loop mode after 6 months of study). As seen in this article with “real-world” reports of the 670G HCL system, next steps will include data on “real-world” translation of the Control-IQ system and outcomes on glucose metrics, usability, and patient satisfaction. The growing number of diabetes technology options available to the pediatric age group is encouraging and holds promise for reduced burden of care, improved glucose control, and reduction of short- and long-term complications of T1D.
Reduced burden of diabetes and improved quality of life: experiences from unrestricted day-and-night hybrid closed-loop use in very young children with type 1 diabetes
Musolino G1, Dovc K2, Boughton CK1, Tauschmann M1,3,4, Allen JM1,3, Nagl K4, Fritsch M4, Yong J5, Metcalfe E5, Schaeffer D6, Fichelle M6, Schierloh U6, Thiele AG7, Abt D8, Kojzar H9, Mader JK9, Slegtenhorst S10, Ashcroft N1, Wilinska ME1,3, Sibayan J11, Cohen N11, Kollman C11, Hofer SE8, Fröhlich-Reiterer E12, Kapellen TM7, Acerini CL3, de Beaufort C6,13, Campbell F5, Rami-Merhar B4, Hovorka R 1,3 on behalf of Kidsap Consortium
1Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK; 2Department of Paediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, University Medical Centre, Ljubljana, Slovenia; 3Department of Paediatrics, University of Cambridge, Cambridge, UK; 4Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; 5Department of Paediatric Diabetes, Leeds Children's Hospital, Leeds, UK; 6Department of Pediatric Diabetes and Endocrinology, Clinique Pédiatrique, Centre Hospitalier, Luxembourg City, Luxembourg; 7Division for Paediatric Diabetology, University of Leipzig, Leipzig, Germany; 8Department of Pediatrics I, Medical University of Innsbruck, Innsbruck, Austria; 9Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria; 10Department of Nutrition & Dietetics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; 11Jaeb Center for Health Research, Tampa, FL; 12Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria; 13Department of Pediatrics, Free University VUB, Brussels, Belgium
Pediatr Diabetes 2019; 20: 794–799
Background
This study assessed the experiences of families with very young children (aged 1 to 7 years) with T1D using a day-and-night hybrid closed-loop system during free-living conditions.
Methods
Caregivers of 20 children with T1D completed a closed-loop experience survey following two 3-week periods of unrestricted day-and-night hybrid closed-loop insulin therapy comparing standard strength insulin aspart with diluted insulin aspart using the Cambridge FlorenceM system (a modified 640G insulin pump, Medtronic continuous glucose sensor, and a model predictive control algorithm hosted on an Android phone at home). Authors explored the benefits, limitations, and improvements of closed-loop technology.
Results
Most of the caregivers reported reduced burden of diabetes care, less time spent managing diabetes, and improved quality of sleep while using the system. In all, 90% of the responders were less worried about their child's glucose control using closed-loop. Areas for improvement were identified as the size of study devices, battery performance, and connectivity issues. Caregivers would have liked more options to input information into the system, such as temporary glucose targets and microboluses before food intake.
Conclusions
The results of this study indicated overall satisfaction of the parents/caregivers of young children with using the closed-loop system and the importance of the quality-of-life benefits associated with using the system.
Comment
The artificial pancreas (closed-loop system) appears to improve glycemic control, particularly the overnight time in range in pediatric studies; at the same time, it is safe and reduces the risk of hypoglycemia. However, diabetes management in very young children presents additional challenges given their increased rapid rate of change in glucose values, small insulin requirements, need for frequent meals as well as activity routines, inability to communicate symptoms of hypoglycemia (hypoglycemia unawareness), and parental fear of hypoglycemia. Indeed, data from the Type 1 Diabetes Exchange Clinic Network (11) show that only a minority of young children with T1D achieve the recommended targets by the American Diabetes Association.
Tauschmann and colleagues' study is one of the few and largest studies to date that tested a closed-loop system in children aged 1–7 years old using 3 weeks of standard strength insulin vs 3 weeks of diluted insulin. Glycemic control was similar in the two arms of the study, with no adverse events reported (12).
The psychosocial impact of closed-loop systems in T1D patients has been studied, mostly in older individuals, including adolescents and adults. One of the strengths of Musolino's current study is the assessment of the experience in families of young children from different countries using the FlorenceM closed-loop system in a free-living environment without remote monitoring. This system was found to increase the quality of life and decrease diabetes burden across these demographics. The human factor is an essential segment of closed-loop clinical trials. However, one of the limitations of this study, similar to most of the other closed-loop trials, is the short length of the study as well the potential of volunteer bias. These families are motivated to be compliant with all the study requirements, complicating the generalization of the results to a broad T1D population. Since closed-loop systems will soon be the standard of care, it is essential to consider, in addition to glucose management, all the varying psychosocial aspects (mental burden, diabetes distress, mistrust, and fear of hypoglycemia) that can accompany the use of different devices (12,13).
Closed-loop control in adolescents and children during winter sports: use of the Tandem Control-IQ AP system
Ekhlaspour L1, Forlenza GP2, Chernavvsky D3, Maahs DM1,4, Wadwa RP2, Deboer MD3, Messer LH2, Town M1, Pinnata J3, Kruse G5, Kovatchev BP3, Buckingham BA1,4, Breton MD3
1Department of Pediatrics, Stanford University, Palo Alto, CA; 2Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO; 3Center for Diabetes Technology, University of Virginia, Charlottesville, VA; 4Stanford Diabetes Research Center, Stanford, CA; 5Tandem Diabetes Care, San Diego, CA
Pediatr Diabetes 2019; 20: 759–768
Background
Studies show that artificial pancreas (AP) systems improve glycemic control in adults, adolescents, and children. However, glucose management during periods of intense and prolonged exercise remains challenging, particularly in adolescents and children. This study aimed to assess the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp environment, where high altitude; low temperatures; intense, prolonged activity; and stress challenged different components of the system (pump, continuous glucose monitor, and control algorithm).
Methods
In a randomized controlled trial, children with T1D (24 aged 13–18 years and 24 aged 6-12 years) participated in a 48-hour ski camp at three sites: Wintergreen, Virginia, and Kirkwood and Breckenridge, Colorado. Participants were randomized 1:1 into two groups at each site, with half using a sensor-augmented pump (SAP) and half wearing the t:slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours/day by the study physicians.
Results
The Control-IQ system improved the percentage of time within range (70–180 mg/dL) during the camp study: 66.4±16.4 vs 53.9±24.8%; P=0.01 in both children and adolescents. The Control-IQ system was correlated with significantly lower average glucose based on continuous glucose monitor values: 161±29.9 vs 176.8±36.5 mg/dL; P=0.023. The Control IQ performed similarly to the SAP for hypoglycemia exposure and carbohydrate treatment. There were no adverse events.
Conclusions
Control-IQ AP use in children with T1D improved glycemic control and reduced exposure to hyperglycemia without an increase in hypoglycemia during prolonged intensive winter sports activities despite the challenging environment.
Comment
Several clinical trials have proven the efficacy and safety of closed-loop systems by increasing time in range and decreasing hypoglycemia (14–16). However, glucose management during exercise is still a challenge, particularly during high-intensity exercise. This study is unique in terms of testing an artificial pancreas system (Control IQ) during winter activities in children and adolescents with T1D that can challenge the system in many ways. Both arms of the study (SAP and Control IQ) were supervised remotely through CGM by study physicians. However, this close supervision cannot truly assess the performance of closed-loop systems in the real world.
Results of this study showed that Control IQ increased both overall and overnight time in range without an increase in hypoglycemia in both adolescents and younger children. The only other study testing a closed-loop system during skiing was conducted by the same group. In that study, using a closed-loop system by adolescents (the DiAs system connected to t-slim or Roche insulin pumps) improved time in range while exposure to hypoglycemia was reduced. Interestingly, overnight exposure to hypoglycemia decreased more in beginner skiers compared to advanced skiers (17).
As seen in previous issues of the Yearbook, the performance of automated insulin delivery systems and studies in the pediatric population have become a theme. In December 2019, the Control IQ system was approved by the FDA for T1D patients older than 14 years following the iDCL trial (18), and in June 2020, the pediatric indication was expanded to children older than 6 years (19). Therefore, additional and larger studies in a free-living environment are required to test the system and its exercise activity feature on glycemic control during exercise in both adult and pediatric T1D populations.
Efficacy and safety of fast-acting insulin aspart compared with insulin aspart, both in combination with insulin Degludec, in children and adolescents with type 1 diabetes: the ONSET 7 trial
BW Bode1, Iotova V2, Kovarenko M3, Laffel LM4, Rao PV5, Deenadayalan S6, Ekelund M6, Larsen SF6, Danne T7
1Atlanta Diabetes Associates, Atlanta, GA; 2University Hospital St. Marina, Medical University Varna, Varna, Bulgaria; 3Pediatric Department, Novosibirsk State Medical University of the Ministry of Health of the Russian Federation, Novosibirsk, Russia; 4Joslin Diabetes Center, Harvard Medical School, Boston, MA; 5Diabetes Research Society, Hyderabad, India; 6Novo Nordisk A/S, Søborg, Denmark; 7Children's Hospital Auf der Bult, Hannover, Germany
Diabetes Care 2019; 42: 1255–1262
Background
To achieve optimal postprandial glycemic control, faster-acting mealtime insulins, compared to those currently available, are required. The aim of the current study was to assess the efficacy and safety of fast-acting insulin aspart (faster aspart) versus insulin aspart (IAsp), in combination with insulin degludec, in pediatric patients (aged 1–18 years old) with T1D.
Methods
This study was designed as follows: 26-week, phase 3, treat to target, multicenter, randomized, double-blind, multinational (n=17 countries), parallel group (n=150 sites). The main inclusion criteria comprised T1D children receiving basal-bolus treatment for ≥90 days before screening. Participants were also required to have an HbA1c ≤9.5% (80 mmol/mol) and be willing to not use real-time CGM during the trial. The patients were randomized (age stratified ≥1 to <3 years, ≥3 to <6 years, ≥6 to <12 years, and ≥12 to <18 years) to double-blind mealtime faster aspart (n=260), mealtime IAsp (n=258), or open-label postmeal faster aspart (n=259). The primary endpoint was change from baseline in glycated hemoglobin (HbA1c) after 26 weeks of treatment. Other efficacy secondary endpoints included change from baseline to week 26 in the following: 8-point, self-measured, plasma glucose profiles; fasting plasma glucose; and 1,5-anhydroglucitol (1,5-AG). Safety endpoints included treatment-emergent adverse events, treatment-emergent hypoglycemic episodes, change in body weight, and insulin dose.
Results
A total of 756 participants (97.3%) completed the 26-week treatment period. At week 26, both mealtime and postmeal faster aspart exhibited changes similar to IAsp regarding change from baseline in HbA1c (P<0.001 for noninferiority [0.4% margin]), with a statistically significant difference in favor of mealtime faster aspart (estimated treatment difference 20.17% [95% CI 20.30; 20.03], 21.82 mmol/mol [23.28; 20.36]; P=0.014). No statistically significant difference in mean self-monitoring blood glucose (SMBG) was observed with mealtime or postmeal faster aspart compared with mealtime IAsp. Among the participants with a blinded CGM, change from baseline in 1-hour postprandial glucose increments significantly favored mealtime faster aspart versus IAsp at breakfast, the main evening meal, and overall meals (P<0.01 for all). There were no statistically significant differences in the overall rate of severe hypoglycemia. The adverse event profiles were comparable among all treatment groups. Change from baseline in 1,5-AG was statistically significant in favor of mealtime faster aspart compared with mealtime IAsp (estimated treatment difference 0.52 mg/mL [95% CI 0.09; 0.95], P=0.018). The average total daily insulin dose was 0.92 units/kg for mealtime faster aspart, 0.92 units/kg for postmeal faster aspart, and 0.88 units/kg for mealtime IAsp.
Conclusions
This study demonstrated that mealtime and postmeal faster aspart with insulin degludec provides effective glycemic control with aspart, and revealed no additional significant safety risk in children and adolescents with T1D. Compared with IAsp, mealtime faster aspart provided superior HbA1c control and 1,5-AG.
Comment
Postprandial hyperglycemia contributes to diabetes micro- and macrovascular complications (20). Therefore, limiting postprandial glucose excursions is one of the key challenges in glycemic control for T1D patients. Based on American Diabetes Association guidelines, postprandial glucose should be less than 180 mg/dl (21); however, few pediatric patients meet this recommended guideline (22). To improve postprandial hyperglycemia, rapid-acting insulin analogues need to be administered 15–20 minutes before meal start time, which is complicated and inconvenient, in particular, for children. New formulations of faster-acting insulin have been tested in order to mimic the physiological profile of normal mealtime insulin secretion and action. This strategy improves glucose control in both adult and pediatric patients (23,24). Fast-acting insulin aspart (faster aspart) is a new formulation of IAsp containing the added excipients niacinamide and L-arginine. Niacinamide is considered responsible for faster initial absorption after subcutaneous administration, while L-arginine serves as a stabilizing agent.
The Onset 7 trial demonstrated that, after randomization in children and adolescents with T1D independent of their age, mealtime faster aspart with insulin degludec provides effective glucose management since it was superior to IAsp in terms of change in HbA1C as well as 1,5-AG from baseline to 26 weeks. A similar finding was reported in the adult study (25). The administration of faster aspart 20 minutes following the meal was noninferior in HbA1c control compared with mealtime IAsp. Compared with IAsp, an improvement was observed in 1-hour glucose level both based on SMBG and CGM readings over the mean of all meals with mealtime faster aspart. There was no significant difference in overall hypoglycemia or the timing of hypoglycemia among treatment groups. No significant difference in insulin dosage was observed among groups.
As the first trial testing an ultra-fast-acting insulin analogue in pediatric population, one of the strengths of this study is that it is a large international multicenter trial with double-blind design except for the postmeal insulin administration group. Although the researchers used several endpoints including SMBG and CGM, HbA1C, liquid meal test, and fasting plasma glucose level to assess the efficacy, only a subgroup of the participants wore CGMs that could be taken as evidence for improvement in postprandial glucose values as well as being indicative of real-life experience.
We concluded that mealtime faster aspart analogue can be used in T1D pediatric patients who use multiple daily injections to help with postprandial glucose excursions without increased risk of hypoglycemia.
Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c
Kahkoska AR1, Adair LA1, Aiello AE2, Burger KS1, Buse JB3, Crandell J4, Maahs DM5,6, Nguyen CT7, Kosorok MR7,8, Mayer-Davis EJ1,3
1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC; 2Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; 3Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC; 4School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC; 5Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA; 6Stanford Diabetes Research Center, Stanford University, Stanford, CA; 7Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC; 8Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC
Pediatr Diabetes 2019; 20: 556–566
Background
The objective of this study was to use CGM data from adolescents with T1D and elevated hemoglobin A1C to identify and characterize clinically relevant subgroups sharing patterns of dysglycemia.
Methods
Baseline CGM data from the Flexible Lifestyles Empowering Change randomized trial were used. T1D adolescents diagnosed longer than >1 year with HbA1c of 8% to 13% (64–119 mmol/mol) were eligible. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18-month changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]).
Results
The study included 234 adolescents with a mean age of 14.8±1.1 years, while the mean diabetes duration was 6.4±3.7 years. Mean HbA1c was 81±13 mmol/mol (9.6%±1.2%). Three dysglycemia clusters were identified with significant differences across all CGM metrics (P<0.001). Cluster 1: 141 individuals (60.3%) with severe daytime hyperglycemia with low exposure to hypoglycemia. Cluster 2: 53 individuals (22.7%) with severe hyperglycemia, particularly overnight, with moderate hypoglycemia. Cluster 3: 40 individuals (17.1%) with moderate hyperglycemia with the highest measures of hypoglycemia exposure and incidence. This cluster showed increases in HbA1c over 18 months (p-for-interaction=0.006). No other baseline characteristics were associated with dysglycemia clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P<0.05).
Conclusions
In adolescents with T1D and elevated HbA1C, CGM data analysis may uncover distinct dysglycemia phenotypes, for which glycemic control is challenged by different patterns in hypoglycemia, hyperglycemia, and glycemic variability. Further studies are needed to understand the characteristics that contribute to these phenotypes and develop interventions that improve glycemic control.
Comment
Following the landmark of the Diabetes Control and Complications Trial (DCCT) in T1D patients, hemoglobin A1C was established as the gold standard to determine glycemic management. However, recent advances in CGM systems and mounting evidence have provided a better understanding of the effects of increased hypoglycemia and glycemic variability in addition to hyperglycemia on diabetes complications. CGM data in particular have been used to predict the risk of complications and assess patients' glycemic management to provide better clinical care and reduce the diabetes burden. To develop future clinical interventions, Kahkoska and colleagues identified “dysglycemia phenotypes” using a neural network approach to clustering and grouped individuals according to their placement on a self-organizing map (SOM) constructed from eight CGM metrics. This study is unique since the investigators used CGM data from adolescents and young adults with T1D, a population often characterized by poor glycemic control. Despite the differences in CGM metrics, baseline, and longitudinal HbA1Cs, no differences were found in sociodemographic, clinical, or psychosocial characteristics. The other interesting finding is that factors affecting poor glycemic control, including lower insulin pump use, greater insulin doses, and lower motivation and adherence to diabetes self-management, were associated with high baseline HbA1C in the FLEX dataset, although this association was not observed in the clusters. Despite this study's limitations of small sample size and data validation, further efforts to build a larger platform are essential to fully understand other mediators, including psychosocial and behavioral factors. This approach can address additional treatment options and interventions to improve the clinical care of diabetes.
The transatlantic HbA1c gap: differences in glycaemic control across the lifespan between people included in the US T1D Exchange registry and those included in the German/Austrian DPV registry
Hermann JM1,2, Miller KM3, Hofer SE4, Clements MA5, Karges W6, Foster NC3, Fröhlich-Reiterer E7, Rickels MR8, Rosenbauer J2,9, DeSalvo DJ10, Holl RW1,2, Maahs DM11,12 for the T1D Exchange Clinic Network and the DPV initiative
1University of Ulm, Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm, Germany; 2German Centre for Diabetes Research, Munich-Neuherberg, Germany; 3Jaeb Centre for Health Research, Tampa, FL; 4Department of Paediatrics, Medical University of Innsbruck, Innsbruck, Austria; 5Children's Mercy Hospital, Kansas City, MO; 6Division of Endocrinology and Diabetes, RWTH Aachen University, Aachen, Germany; 7Department of Pediatrics, Medical University of Graz, Graz, Austria; 8Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 9German Diabetes Centre, Institute for Biometrics and Epidemiology, Leibniz Centre for Diabetes Research at Düsseldorf University, Düsseldorf, Germany; 10Baylor College of Medicine, Houston, TX; 11Stanford University, Palo Alto, CA; 12Stanford Diabetes Research Centre, Stanford, CA
Background
Studies from various regions of the world have demonstrated that individuals with type 1 diabetes (T1D), especially adolescents and young adults, often do not meet HbA1c targets. The present study aimed to compare HbA1c across the lifespan of people included in the T1D Exchange (T1DX) Registry in the United States and those included in the Prospective Diabetes Follow-up Registry, the DPV, in Germany and Austria, and to examine differences in the HbA1c patterns between sexes, between individuals with and without ethnic minority status/migration background, and between insulin delivery methods (insulin pump or injection).
Methods
Data were extracted from the US T1DX Registry (73 sites with 18,381 participants) and from the DPV (362 sites with 32,643 participants).
Mean HbA1c was calculated for each year of age for individuals aged ≤25 years, and at 2-year age intervals for individuals aged >25 years. HbA1c differences between registries, sexes, insulin delivery methods, and minority status were assessed by age group using multiple linear regression analysis.
Results
The median age was 15 (lower, upper quartile: 12, 20) years in the DPV and 17 (lower, upper quartile: 13, 33) years in the T1DX. The median duration of diabetes was significantly longer in the T1DX than in the DPV, 9 (6, 17) and 7 (4, 11) years, respectively (P<0.001).
In the T1DX, use of insulin pump therapy ranged between 59% (18 to <30 years) and 66% (6 to <12 years), whereas in the DPV pump use was most frequent in the youngest age group (89%). CGM use was higher in the T1DX compared with the DPV in all age groups (P<0.001).
In both registries, mean HbA1c increased by ∼11 mmol/mol (1.0%) between the ages of 9–18 years, with different absolute levels: from 66 mmol/mol (8.2%) to 77 mmol/mol (9.2%) in the T1DX Registry, and from 56 mmol/mol (7.3%) to 66 mmol/mol (8.2%) in the DPV. Sex differences were observed in the DPV only. In both registries, minority status was significantly associated with higher HbA1c in most age groups.
In the T1DX, injection users had higher mean HbA1c than pump users across the lifespan, whereas in the DPV higher HbA1c levels in injection users were observed in the age groups 6–12 years, 12–18 years (P<0.001).
Among individuals in the DPV aged <6 years, 6–12 years, and 12–18 years, 59%, 41%, and 39%, respectively, achieved the International Society for Pediatric and Adolescent Diabetes (ISPAD) HbA1c target of <58 mmol/mol (7.5%); the respective percentages in the T1DX were lower (26%, 22%, and 16%).
Conclusions
In this cross-sectional lifespan comparison, significant differences in HbA1c were noted between two diabetes registries, with disparities more pronounced in early childhood through young adulthood.
Comment
Studies from various regions of the world have demonstrated that individuals with T1D, especially adolescents and young adults, often do not meet HbA1c targets (6,26,27).
Both the DPV and T1DX registries were established with an objective to improve the care of patients with T1D by collecting clinical outcome data on a large population and sharing best practices.
Interestingly, both registries showed that those treated with pump therapy achieved better HbA1c levels than those treated with injection in patients 6–18 years old, without a significant difference in HbA1c between both modes of therapy in the group of patients younger than 6 years. Although, previously it was stated that in the younger ages the treatment with pump therapy was associated with better glycemic control than in those treated with injections (28,29).
Previous results based on these registries already indicated that in children with T1D, those included in the T1DX had higher HbA1c than those in the DPV (7,30), despite whether they were CGM users or nonusers; however, the difference was less pronounced in CGM users (31). The results of the current study again demonstrate that although a similar increase in HbA1c on reaching adolescence was observed, young people in the DPV registry had HbA1c levels that were 1.0% lower compared with young people included in the T1DX. These differences may be related to systemic differences in access to medical care, diabetes education, provider prescribing practices, regulatory approval, and insurance coverage of advanced technologies, as well as differences in adherence to treatment regimen, eating patterns, level of physical activity, obesity rate, and ethnic and cultural variances.
Yet, the results of both registries demonstrate that despite the increased use of the diabetes technologies, a large percentage of young patients and especially adolescents with T1D do not meet HbA1c targets. Previous data has shown the correlation between early achievement of target HbA1c levels and glycemic control later in life (“metabolic memory”) and the risk for cardiovascular complications (32). This highlights the need for increased efforts to improve care and particularly to lower HbA1c in this young group. Barriers to more effective use of current treatments need to be addressed and new therapies are needed to achieve optimal metabolic control in patients with T1D.
Limitations of the present study include the differences in the methods of data collection between the two registries, the lack of a central laboratory for measurements of HbA1c, the cross-sectional nature of the data, and the lack of data about the period of pump or injection use, which could diminish the ability to detect differences in HbA1c between the insulin delivery methods.
However, these data serve as an additional reminder of the power of large registries, including transnational comparison, to identify shortcoming in diabetes care and identify targets for intervention studies.
Determinants of glycaemic outcome in the current practice of care for young people up to 21 years old with type 1 diabetes under real-life conditions
Kordonouri O1, Lange K2, Biester T1, Datz N1, Kapitzke K1, von demBerge T1, Weiskorn J1, Danne T1
1Diabetes Centre for Children and Adolescents, Children's Hospital AUF DER BULT, Hannover, Germany; 2Medical Psychology, Hannover Medical School, Hannover, Germany
Background
There are huge differences in diabetes care throughout the “developed” countries. National and international guidelines clearly state that the treatment of T1D in childhood and adolescence is a task for a multidisciplinary diabetes team (MDT). The aim of the study was to determine current metabolic outcomes in a large pediatric cohort with T1D treated under real-life conditions using an individualized MDT approach, taking into consideration psychosocial factors, treatment modalities, and comorbidities.
Methods
A single-center observational study of treatment and outcome covered 1 year of continuous outpatient care of young people with T1D aged up to 21 years. Participants were recruited from the outpatient clinic at the Children's Hospital Auf Der Bult, Hannover, Germany, between July 2017 and June 30, 2018. Determinants of glycemic outcome (sex, age, comorbidities, sociodemographic factors, diabetes technology) were analyzed in an entire cohort. Glycemic outcome was defined as an individual's median HbA1c and the prevalence of acute complications (severe hypoglycemia and diabetic ketoacidosis [DKA]) over this period.
Results
Of 700 young people with T1D (age 13.6 years [range: 1.4–20.9 years]; diabetes duration 5.8 years [range: 0.1–18.3 years]), 63% were using an insulin pump and 32% were using any type of CGM. In the 12-month period, the mean number of visits was 5.4 (95% CI 5.3–5.5; range 3–12).
Mean HbA1c was 61 mmol/mol (95% confidence interval [CI] 60–62; 7.7%, 95% CI 7.5–7.8). In total, 63% of children aged <12 years reached HbA1c (58 mmol/mol) (<7.5%) compared with 43% of older participants. Using a multiple regression model with individual median HbA1c as the dependent variable, only age and duration of diabetes were significantly predictive of glycemic outcome, but not sex, type of insulin substitution, use of any CGM system, or comorbidity with celiac disease or thyroiditis. Having a father not born in Germany, psychiatric comorbidities, and family structure were associated with glycemic control. The prevalence of severe hypoglycemia was 2.41 events and that of DKA 1.4 events per 100 person-years.
Conclusions
Implementation of modern diabetes treatment and technology in all age groups is associated with a low level of acute complications, such as severe hypoglycemia and DKA during childhood and adolescence. Current technologies and an MDT approach allow high numbers of children and adolescents to realize tight glycemic control with a low prevalence of acute complications. Yet, age-related challenges, sociodemographic factors, and psychological comorbidities are barriers to achieve the target glycemic control.
Comment
The results of the present study emphasize the significance of the MDT, especially in the treatment of young patients with T1D. In addition, the philosophy of the MDT and communication between members seem to have a crucial role in treatment results.
In the MDT of the current study, in order to reach a high level of agreement among MDT members, internal written guidelines for the care of children and adolescents with T1D were updated regularly and there were weekly meetings, with protocols, for all MDT members. This demonstrates that when MDT members show high agreement regarding glycemic targets, the patients achieve better glycemic control. Also, a high level of continuing education is essential for a successful MDT approach to T1D.
Early introduction of modern diabetes technology with clear therapeutic targets and realistic expectations may have a long-lasting effect on a person's understanding of the disease and behavior, and help to achieve the glycemic targets.
The strengths of the study are the inclusion of a large and complete real-world sample of young patients with T1D. All individual data were assessed using the same methods and in the same place.
However, the data are based on a large pediatric diabetes center that has national funding and reimbursement conditions for diabetes technology and treatment. Also, the center actively participates in national (DPV [33] and international SWEET [34]) benchmarking initiatives twice a year to control quality of care, as well as an educational course and exchange with other national MDTs in pediatric diabetes. Therefore, their results may not be applicable in other healthcare systems with less resources and fewer team members.
Nevertheless, we can conclude that use of modern technology alone is not sufficient to optimize glycemic outcomes, and diabetes management plans coordinated by an MDT need to be individualized and focused on barriers and psychosocial limitations and to be tailored to the family's situation and coping capacity.
Reduction in diabetic ketoacidosis and severe hypoglycemia in pediatric type 1 diabetes during the first year of continuous glucose monitoring: a multicenter analysis of 3,553 subjects from the DPV registry
Tauschmann M1, Hermann JM2,3, Freiberg C4, Papsch M5, Thon A6, Heidtmann B7, Placzeck K8, Agena D9, Kapellen TM10, Schenk B11, Wolf J12, Danne T13, Rami-Merhar B1, Holl RW2,3 on behalf of the DPV Initiative
1Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; 2ZIBMT, Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4Department of Pediatrics and Adolescent Medicine, University Medical Center Göttingen, Göttingen, Germany; 5Department of Pediatrics and Adolescent Medicine, Marienhospital GmbH, Gelsenkirchen, Germany; 6Department of Pediatrics, Hannover Medical School, Hannover, Germany; 7Catholic Children's Hospital Wilhelmstift, Hamburg, Germany; 8Pediatric and Adolescent Medicine, University Hospital, Martin-Luther University, Halle, Germany; 9Hildesheim Kinderarztpraxis, Hildesheim, Germany; 10Division for Paediatric Diabetology, University of Leipzig, Leipzig, Germany; 11Department of Pediatrics, Helios Kliniken Schwerin, Schwerin, Germany; 12Department of Pediatric and Adolescent Medicine, St. Vincenz Hospital, Paderborn, Germany; 13Diabetes Center for Children and Adolescents, Kinder- und Jugendkrankenhaus Auf der Bult, Hannover, Germany
Diabetes Care 2020; 43: e40–e42
Background
Previous clinical trials indicated that use of CGM leads to improved metabolic control and reduction in nonsevere hypoglycemia compared with self-monitoring of capillary blood glucose. The aim was to assess longitudinally HbA1c levels, severe hypoglycemia (SH), and diabetic ketoacidosis (DKA) during the first year after initiation of CGM, including real-time CGM and intermittently scanned/viewed CGM.
Methods
The results were based on real-world data from the German-Austrian-Swiss-Luxembourgian Diabetes Prospective Follow-up (DPV) registry. Anonymized patient registry records were analyzed and included patients with type 1 diabetes (T1D) younger than 18 years with at least 1 year of diabetes duration, with available registry data 6 months prior to CGM start (baseline period), and with at least 1 year of follow-up after CGM initiation. All HbA1c values were Diabetes Control and Complications Trial (DCCT) standardized. SH was defined as events requiring external assistance by another person and events resulting in coma/convulsion. DKA was defined by pH level.
Results
Inclusion criteria were met by 3,553 pediatric patients (53% males) of median age of 12.1 years and median diabetes duration of 4.2 years, with 62% of subjects on insulin pumps. A total of 14% were using real-time CGM and 46% were on intermittently scanned/viewed CGM, and for 39% of subjects no definitive sensor type was recorded.
HbA1c levels were statistically lower during the first 6 months (P<0.0001) and months 6–12 (P<0.0001) after CGM start compared with baseline. The percentage of patients achieving HbA1c levels <7.5% (58 mmol/mol) was higher after 6 and 12 months of CGM use (for both baseline vs 6 months and baseline vs 12 months, P<0.0001).
The proportion of patients experiencing at least one DKA episode was significantly lower after 6 (P=0.0055) and 12 (P=0.0143) months on CGM compared with baseline, as were DKA event rates (events/100 patient-years) during months 6–12 on CGM (P=0.0254).
Six months and 12 months after CGM initiation, significantly fewer patients experienced at least one SH event requiring external help (baseline vs 6 months, P<0.0001; baseline vs 12 months, P<0.0366) and there were significantly fewer patients with one or more episodes of SH coma (baseline vs 6 months, P<0.0001; baseline vs 12 months, P<0.0153). Although not statistically significant, SH event rates requiring external assistance, and event rates for SH with coma/convulsion, were lower with CGM use compared with self-monitoring of capillary blood glucose.
Conclusions
Longitudinal analysis of real-world data confirms results from randomized clinical trials showing that initiation and regular use of CGM in children and adolescents with T1D were associated with a reduction in DKA and SH and a modest improvement in metabolic control.
Comment
Earlier data based also on the DPV registry showed that CGM use increased from 2011 to 2016 across all age groups, regardless of gender, ethnic minority status, or insulin delivery method, with the most pronounced increase in the youngest patients. CGM users were more likely to achieve glycemic target of HbA1c <7.5% (30).
Previous RCT studies usually evaluated the impact of CGM use on glycemic control (35,36) and were not powered to detect differences in DKA or SH. This study provides us another important benefit of CGM use, which is the significant reduction of acute diabetes complications (SH and DKA).
Severe hypoglycemia is frequently reported as an obstacle in obtaining optimal metabolic control. Both severe hypoglycemia and DKA especially in the younger ages may also be associated with cognitive dysfunction (37,38). Therefore, the advantage of decreasing the rate of these complications by the use of CGM is remarkable.
The strengths of the study include that it is based on a large sample size population-based multicenter with longitudinal analysis of real-world data, whereas previous studies were usually based on participants that are often biased toward higher education level, greater therapy adherence, and better self-management. The limitations include the lack of subgroup analysis on baseline metabolic control, diabetes treatment type, type of CGM, use of alarms, and predictive low-glucose suspend, all of which may impact the results.
Proportion of basal to total insulin dose is associated with metabolic control, body mass index, and treatment modality in children with type 1 diabetes—a cross-sectional study with data from the international SWEET registry
Rasmussen VF1, Vestergaard ET2,3, Schwandt A4,5, Beltrand J6, Rami-Merhar B7, O'Riordan SMP8, Jarosz-Chobot P9, Castro-Correia C10, Gevers EF11,12, Birkebæk NH3
1Department of Pediatrics, Aalborg University Hospital, Aalborg, Denmark; 2Department of Pediatrics, Randers Regional Hospital, Randers, Denmark; 3Department of Pediatrics, Aarhus University Hospital, Aarhus, Denmark; 4Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany; 5German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 6Department of Pediatrics, Hospital Necker Enfants Malades, Paris, France; 7Department of Pediatrics, Medical University Vienna, Vienna, Austria; 8Department of Pediatrics and Endocrinology, Cork University Hospital, Cork, Ireland; 9Department of Children's Diabetology, Medical University of Silesia, Katowice, Poland; 10Department of Pediatrics, Hospital S João, Porto, Portugal; 11Department of Pediatric Endocrinology and Diabetes, Barts Heath NHS Trust - Royal London Children's Hospital, London, UK; 12Center for Endocrinology, William Harvey Research Institute, John Vane Science Centre, Queen Mary University London, UK
Background
The International Society for Pediatric and Adolescent Diabetes and the American Diabetes Association recommend a basal insulin dose (BD) to daily total insulin dose (TD)—BD/TD—of 30%–50%. However, the optimal BD/TD has not yet been determined. The aim of the study was to investigate the proportion of daily BD/TD and its association with HbA1c, body mass index (BMI)-standard deviation score (SDS), and treatment modality in children with type 1 diabetes (T1D).
Methods
This was a cross-sectional study of children with T1D, age ≤18 years and diabetes duration of ≥2 years. The data were collected from the SWEET database in March 2018. Variables included region, sex, age, diabetes duration, treatment modality (multiple daily injections [MDI] or continuous subcutaneous insulin infusion [CSII]), self-monitoring of blood glucose, HbA1c, BD/TD, and BMI-SDS. Hierarchical linear regression models were applied with adjustment for age, sex, and diabetes duration.
Results
A total of 19,687 children with T1D (49% female, 49% CSII users) of median age 14.8 (11.5; 17.2) years and diabetes duration 6.0 (3.9; 9.0) years were included. HbA1c was 63 (55; 74) mmol/mol (7.9% [7.2; 8.9]), and BMI-SDS 0.55 (−0.13; 1.21).
In all analyses, a significant association with BD/TD was observed: male sex, younger age-group, shorter diabetes duration, lower BMI-SDS group, lower HbA1c, CSII therapy, and higher number of SMBG per day were associated with lower BD/TD (all P<0.001).
Logistic hierarchical regression adjusting for age, sex, and diabetes duration was also analyzed regarding associations between BD/TD and percentage of events of SH and events of DKA. The risk for SH events was 1.32 (1.08; 1.62) higher in the group with BD/TD ≥0.5 compared with the group with BD/TD <0.5 (P<0.01), and the odds ratio for occurrence of DKA vs no events was 1.17 (0.93; 1.46) (P=0.18).
Conclusions
Lower BD/TD proportion is associated with lower HbA1c and lower BMI-SDS in children on CSII, after adjustment for sex, age, and diabetes duration.
Comment
The basal insulin requirement is influenced by age, pubertal stage, BMI, glycemic control, and residual β-cell function, whereas the bolus insulin dosage is usually determined according to the amount of carbohydrate in the daily diet, as assessed by carbohydrate counting and the correction factor in order to achieve the glucose targets.
Previous studies that included a relatively small number of patients reported that with lower basal insulin levels, lower HbA1C was achieved (39,40). The current study that was based on a multinational large population confirms this observation.
The lower BD/TD insulin that was associated with younger ages may express the unpredicted daily eating and physical habits that may require lower basal doses and more bolus adjustments, and the lower BD/TD insulin that was associated with higher numbers of daily SMBG may point to a higher frequency of performing SMBG that may facilitate bolus corrections.
Frequently, physicians face the challenge to adjust insulin dosages in those that skip boluses (especially adolescents) and may increase the basal dose in order to replace insulin requirements for meals in order to improve glycemic control. However, the results of the current study indicate not only that HbA1c levels are associated with higher BD/TD, but these patients also have increased rate of increased BMI and severe hypoglycemia.
The limitations of the study include data based on center information that may be inaccurate to some degree and lead to information bias, and also a lack of information about the number of insulin boluses, type of insulin pump (inclusive bolus calculator and/or continuous glucose monitoring), pubertal status, and range of the activity level of the children, which may influence the basal insulin requirement.
Early initiation of diabetes devices relates to improved glycemic control in children with recent-onset type 1 diabetes mellitus
Patton SR1,2, Noser AE2,3, Youngkin EM4, Majidi S4, Clements MA2,5
1Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS; 2Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Kansas City, Kansas City, MO; 3Clinical Child Psychology Program, University of Kansas, Lawrence, KS; 4Division of Endocrinology, Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO; 5Division of Endocrinology and Diabetes, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
Diabetes Technol Ther 2019; 21: 379–384
Background
While previous research supports the potential efficacy of adding an insulin pump and/or CGM early in the management of youth with T1D to help with glycemic management, and in lowering HbA1c levels, there is limited research exploring the effect of these devices on HbA1c in youth with recent-onset T1D. The aim of the study was to test whether the addition of an insulin pump or CGM related to reduced HbA1c in a large cohort of children within 1 year of their diabetes diagnosis.
Methods
The study uses data from families of children with recent-onset T1D and who were 5–9 years old with a mean diabetes duration of 4.7–3.28 months at baseline. Study analyses used children's HbA1c values at baseline and at the 6-month follow-up. Parents reported on family demographics and children's diabetes device use in their daily management (e.g., insulin pump or CGM).
Results
A total of 111 families participated. At baseline, 17% of children used an insulin pump and 17.1% of children used CGM. Mean HbA1c was 7.65%±1.40%. Six months later, 35.1% of children had started an insulin pump and 25.2% had started CGM.
In independent sample comparisons, children who started a pump between baseline and 6-month follow-up had a significantly lower HbA1c at 6-month follow-up (t=−2.84, P=0.01; mean difference=0.53%) and a shorter duration of diabetes diagnosis at baseline (t=−2.22, P=0.03; mean difference=1.36 months) compared to children who did not start a pump during this time.
For CGM initiation between baseline and 6 months, independent sample comparisons showed that children who started a CGM between baseline and 6-month follow-up had a significantly lower HbA1c at 6-month follow-up compared to children who did not start a CGM during this time (t=−2.89, P=0.01; mean difference=0.66%).
Conclusions
The current data suggest that initiating insulin pump or CGM use close to the time of diagnosis may have beneficial effects on children's HbA1c through 12 months of diabetes.
Comment
In earlier studies, Pinhas-Hamiel and colleagues (41) reported that among patients with T1D on pump therapy, those diagnosed up to 1 year before had significantly lower mean HbA1c levels during the entire follow-up period than those who had the disease for a longer time. Both Ramchandani et al. (42) and Thrailkill et al. (43) noted that CSII instituted within 1 month of diagnosis of T1D in young patients led to improved metabolic outcome and apparently better preserved beta-cell function than multiple daily injections (MDI).
However, a 63-center analysis of German and Austrian toddlers (<5 years old) with T1D (44) who started CSII therapy within 4 weeks of presentation of diabetes found no significant difference in HbA1c values compared to treatment with MDI. Also, in our group (45) no significant difference was found in HbA1c levels in those that initiated CSII less than 1 year from diabetes diagnosis and those that initiated it later.
It is noteworthy that although insulin replacement by CSII more closely resembles the normal physiology than MDI treatment, starting CSII shortly after diabetes onset can be more time-consuming and stressful for both patient and family. In addition to teaching patients and their families about diabetes, the diabetes team also has to teach them about the pump mechanics and troubleshooting, which requires extra training hours. The patients and families are burdened not only with the need to cope with the new diagnosis but also with the need to learn new skills and techniques. Nevertheless, it can be argued that starting with MDI may be just as complicated due to the required learning about the differences between insulin duration of action and how to adjust the insulin dosage to food and exercise requirements.
Early initiation of CGM use can decrease the requirement of frequent SBGM and lower the burden of the patient and caregiver regarding the fear of hypoglycemia, especially in the younger age group and during the night, and also can facilitate insulin dose adjustments and improve glycemic control.
The strengths of the present study include its longitudinal design, its use of a relatively large sample size, and its focus on young children diagnosed with T1D.
However, the study results are limited since they may not be generalized to children from a racial or ethnic background that is considerably different from the present sample. Furthermore, the study used parent report to measure use of devices (CGM and CSII) at baseline and at the 6-month follow-up, which may be biased by under-reported device use if their child was not using a device on the day of the study visit. Also, it is not possible to conduct sensitivity analyses to determine if time using a device may have influenced children's HbA1c levels at the 6-month assessment point.
Another limitation is the lack of data regarding the association between HbA1c and simultaneous use of a pump and CGM (which may even better improve glycemic control) due to the small number using both devices. The study also lacks data about the impact of CGM or pump starts on child and caregiver quality of life and details such as socioeconomic status, medical insurance, etc., that may also impact glycemic control.
Nevertheless, the clinical implication of these findings further support early introduction of diabetes devices in children's daily management even within the first few months of diabetes. Perhaps the sooner these skills are learned, the better their implementation. However, future research is needed to explore the longer time duration—whether or not early introduction of the devices leads to better assimilation of these devices in children's daily management, better maintenance of these devices in daily care, and better quality of life and glycemic control.
Glycemic control in adolescents with type 1 diabetes: are computerized simulations effective learning tools?
Dubovi I1, Levy ST2, Levy M3, Zuckerman-Levin N3,4, Dagan E5
1Nursing Department, The Stanley Steyer School of Health Professions, Sackler Faculty of Medicine, Tel Aviv University, Israel; 2Department of Learning, Instruction and Teaching, University of Haifa, Haifa, Israel; 3Pediatric Diabetes Clinic, Institute of Diabetes, Endocrinology and Metabolism, Rambam Health Care Campus, Haifa, Israel; 4Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel; 5The Cheryl Spencer Department of Nursing, University of Haifa, Haifa, Israel
Pediatr Diabetes 2020; 21: 328–338
Background
Glycemic control often worsens during adolescence in patients with T1D, and adherence to treatment has been suggested to improve its management in order to achieve target hemoglobin A1cs. Learning about glucose regulation may improve decision making, leading to better self–diabetes management. Therefore, the aim of this study was to investigate the role of computerized simulations to support: (1) mechanistic understanding of the diabetes pathophysiology; (2) diabetes self-management knowledge; and (3) glucose control.
Methods
This was a prospective case-control study among 85 adolescents (12–18 years old) in an outpatient diabetes clinic over 3 months. The intervention group (n=45) received educational activities with computerized simulations in addition to the routine diabetes care that the control group received (n=40). Clinical data and glycated hemoglobin (HbA1c) levels were collected from medical records. All the participants completed a set of questionnaires regarding sociodemographic characteristics, diabetes mechanistic reasoning, and diabetes self-management.
Results
The computerized simulations favored the intervention group and improved the HbA1c levels after 3 months (intervention 8.7%±1.7% vs the controls 9.6%±1.6% [P<0.05]). Multiple regression analysis demonstrated that sociodemographic parameters, levels of diabetes mechanistic understanding, as well as diabetes self-management knowledge accounted for 31% of the HbA1c variance (P<0.001).
Conclusions
The present data suggest that learning with computerized simulations of biochemical processes can improve adolescents' adherence to the treatment regimen, leading to improvement in glycemic control.
Comment
Adolescence is known as a challenging time for patients with T1D due to several factors, including hormonal and psychological changes related to puberty, as well as transitioning to independent diabetes care (46). Despite recent advances in technology and treatment methods, the data from the Type 1 Diabetes Exchange Clinic Network show that patients between the ages of 15 to 18 years old have the highest HbA1c (9.3% or 78 mmol/mol) (11). Since treatment adherence worsens during adolescence, teens with T1D have higher rates of acute complications than adults (20). Since educational interventions help improve glucose control and promote health, Dubovi et al. tested a novel technology-based educational approach customized for diverse and low literacy adolescent groups. Their findings suggest that the incorporation of computerized simulations that provide both mechanistic information about biochemical processes and self-management knowledge can improve treatment adherence.
This study is particularly important since systematic educational programs are rare. Initial evidence suggests that these educational simulations and computerized learning technology interventions can play a role in adolescents' diabetes self-management skills. However, improving adherence to therapy requires a complex approach, including a behavior component as well as attention to the role of providers. A systematic review of interventions to enhance treatment adherence in patients with chronic illness shows that education interventions alone are not adequate to promote treatment adherence in children and adolescents; rather, to have the most significant impact, it is essential to add a behavioral approach (47). The patient–provider relationship will also become increasingly important as technology advances.
Additional clinical randomized trials with a larger sample size in populations of a wider range of demographic variables are needed to assess the role of educational interventions and extend the findings to provide best practice recommendations for adolescents with T1D.
Author Disclosure Statement
DMM has had research support from the NIH, JDRF, NSF, and the Helmsley Charitable Trust, and his institution has had research support from Medtronic, Dexcom, Insulet, Bigfoot Biomedical, Tandem, and Roche. DMM has consulted for Abbott, Aditxt, the Helmsley Charitable Trust, Sanofi, Novo Nordisk, Eli Lilly, Medtronic, Insulet, and Dompe. LE has consulted for Tandem Diabetes Care and Ypsomed. SS has no competing financial interests.
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