Abstract
Introduction
In this year's edition of the ATTD Yearbook, for the article focused on the pediatric age group we selected 20 articles from the numerous impactful publications in the past year. These articles have a common theme of advancement of diabetes technology in the pediatric age group in research with increasing translation to clinical practice. Diabetes technology has transformed pediatric diabetes care in a very positive way. However, challenges remain to further refine diabetes technology and very importantly to address barriers to increase access to the best possible care for all children, adolescents, and young adults with diabetes.
Multiple studies were published on the development of automated 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) with increasing “real-world” studies in which data describe approved closed-loop insulin delivery systems in use in pediatric diabetes clinics.
The continued development of these systems and the transition from research to the clinic will continue to be highlighted in 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 as does research in the youngest age groups. Broader access to diabetes technology will be an ongoing 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 20 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 (continuous subcutaneous insulin infusion, CSII), continuous glucose monitoring (CGM), closed-loop systems, and new therapies in type 1 diabetes (T1D) relating to the pediatric age group (0–18 years). We focused on key articles that offer some insight into these issues that were published between July 1, 2022, and June 30, 2023.
Key Articles Reviewed
Wilson DM, Pietropaolo SL, Acevedo-Calado M, Huang S, Anyaiwe D, Scheinker D, Steck AK, Vasudevan MM, McKay SV, Sherr JL, Herold KC, Dunne JL, Greenbaum CJ, Lord SM, Haller MJ, Schatz DA, Atkinson MA, Nelson PW, Pietropaolo M and the Type 1 Diabetes TrialNet Study Group
Pollé OG, Delfosse A, Martin M, Louis J, Gies I, den Brinker M, Seret N, Lebrethon MC, Mouraux T, Gatto L, Lysy PA on behalf of the DIATAG Working Group
Addala A, Ding V, Zaharieva DP, Bishop FK, Adams AS, King AC, Johari R, Scheinker D, Hood KK, Desai M, Maahs DM, Prahalad P for the Teamwork, Targets, Technology, and Tight Control (4T) Study Group
Karges B, Tittel SR, Bey A, Freiberg C, Klinkert C, Kordonouri O, Thiele-Schmitz S, Schröder C, Steigleder-Schweiger C, Holl RW
Everett EM, Wright D, Williams A, Divers J, Pihoker C, Liese AD, Bellatorre A, Kahkoska AR, Bell R, Mendoza J, Mayer-Davis E, Wisk LE
Wadwa RP, Reed ZW, Buckingham BA, DeBoer MD, Ekhlaspour L, Forlenza GP, Schoelwer M, Lum J, Kollman C, Beck RW, Breton MD for the PEDAP Trial Study Group
Lombardo F, Passanisi S, Alibrandi A, Bombaci B, Bonfanti R, Delvecchio M, Di Candia F, Mozzillo E, Piccinno E, Piona CA, Rigamonti A, Scialabba F, Maffeis C, Salzano G
Petrovski G, Campbell J, Pasha M, Day E, Hussain K, Khalifa A, van den Heuvel T
Alonso GT, Triolo TM, Akturk HK, Pauley ME, Sobczak M, Forlenza GP, Sakamoto C, Pyle L, Frohnert BI
Beck RW, Kanapka LG, Breton MD, Brown SA, Wadwa RP, Buckingham BA, Kollman C, Kovatchev B
Burnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MI
Petruzelkova L, Neuman V, Plachy L, Kozak M, Obermannova B, Kolouskova S, Pruhova S, Sumnik Z
Burnside MJ, Lewis DM, Crocket HR, Meier RA, Williman JA, Sanders OJ, Jefferies CA, Faherty AM, Paul RG, Lever CS, Price SKJ, Frewen CM, Jones SD, Gunn TC, Lampey C, Wheeler BJ, de Bock MI
Bionic Pancreas Research Group; Russell SJ, Beck RW, Damiano ER, El-Khatib FH, Ruedy KJ, Balliro CA, Li Z, Calhoun P, Wadwa RP, Buckingham B, Zhou K, Daniels M, Raskin P, White PC, Lynch J, Pettus J, Hirsch IB, Goland R, Buse JB, Kruger D, Mauras N, Muir A, McGill JB, Cogen F, Weissberg-Benchell J, Sherwood JS, Castellanos LE, Hillard MA, Tuffaha M, Putman MS, Sands MY, Forlenza G, Slover R, Messer LH, Cobry E, Shah VN, Polsky S, Lal R, Ekhlaspour L, Hughes MS, Basina M, Hatipoglu B, Olansky L, Bhangoo A, Forghani N, Kashmiri H, Sutton F, Choudhary A, Penn J, Jafri R, Rayas M, Escaname E, Kerr C, Favela-Prezas R, Boeder S, Trikudanathan S, Williams KM, Leibel N, Kirkman MS, Bergamo K, Klein KR, Dostou JM, Machineni S, Young LA, Diner JC, Bhan A, Jones JK, Benson M, Bird K, Englert K, Permuy J, Cossen K, Felner E, Salam M, Silverstein JM, Adamson S, Cedeno A, Meighan S, Dauber A
Castellanos LE, Russell SJ, Damiano ER, Beck RW, Shah VN, Bailey R, Calhoun P, Bird K, Mauras N; Bionic Pancreas Research Group
Dovc K, Lanzinger S, Cardona-Hernandez R, Tauschmann M, Marigliano M, Cherubini V, Preikša R, Schierloh U, Clapin H, AlJaser F, Pelicand J, Shukla R, Biester T
Lal RA, Robinson H, Lanzinger S, Miller KM, Pons Perez S, Kovacic R, Calhoun P, Campbell F, Naeke A, Maahs DM, Holl RW, Warner J
DeSalvo DJ, Lanzinger S, Noor N, Steigleder-Schweiger C, Ebekozien O, Sengbusch SV, Yayah Jones NH, Laubner K, Maahs DM, Holl RW
McVean J, Forlenza GP, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A for the CLVer Study Group
Forlenza GP, McVean J, Beck RW, Bauza C, Bailey R, Buckingham B, DiMeglio LA, Sherr JL, Clements M, Neyman A, Evans-Molina C, Sims EK, Messer LH, Ekhlaspour L, McDonough R, Van Name M, Rojas D, Beasley S, DuBose S, Kollman C, Moran A; for the CLVer Study Group
Continuous Glucose Monitoring
CGM Metrics Identify Dysglycemic States in Participants from the TrialNet Pathway to Prevention Study
Wilson DM1, Pietropaolo SL2, Acevedo-Calado M2, Huang S3, Anyaiwe D4, Scheinker D1, Steck AK5, Vasudevan MM2, McKay SV2,6, Sherr JL7, Herold KC8, Dunne JL9, Greenbaum CJ10, Lord SM10, Haller MJ11, Schatz DA11, Atkinson MA11, Nelson PW4, Pietropaolo M2, and the Type 1 Diabetes TrialNet Study Group
1Division of Pediatric Endocrinology, Stanford University School of Medicine, Palo Alto, CA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 2Division of Endocrinology, Diabetes, and Metabolism, Diabetes Research Center, Department of Medicine, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 3Department of Industrial & Systems Engineering, University of Washington, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 4Department of Mathematics & Computer Science, Lawrence Technological University, Southfield, MI; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 5Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 6Department of Pediatrics, Baylor College of Medicine, Houston, TX; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 7Division of Pediatric Endocrinology, Yale University School of Medicine, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 8Departments of Immunobiology and Internal Medicine, Yale University, New Haven, CT; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 9JDRF, New York, NY; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 10Center for Interventional Immunology and Diabetes Program, Benaroya Research Institute, Seattle, WA; University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL; 11Department of Pediatrics, University of Florida Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL
This study is also discussed in DIA-2024-2502, page S-14.
Because continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes (T1D), this study examined whether CGM metrics could provide additional insight into the progression to clinical stage 3 T1D.
Methods
From the TrialNet Pathway to Prevention study, 105 relatives of individuals in T1D probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The three groups evaluated were individuals with (1) stage 2 T1D (n = 42) with two or more diabetes-related autoantibodies and abnormal OGTT; (2) stage 1 T1D (n = 53) with two or more diabetes-related autoantibodies and normal OGTT; and (3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).
Results
After the baseline data assessment, multiple CGM metrics were found to be associated with progression to stage 3 T1D: spending ≥ 5% time with glucose levels ≥ 140 mg/dL (P = 0.01), ≥ 8% time with glucose levels ≥ 140 mg/dL (P = 0.02), ≥ 5% time with glucose levels ≥ 160 mg/dL (P = 0.0001), and ≥ 8% time with glucose levels ≥ 160 mg/dL (P = 0.02). The stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; they spent more time with glucose values over 120, 140, and 160 mg/dL, and they had greater variability.
Conclusions
CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 T1D.
Comments
Recent approval by the U.S. Food and Drug Association (FDA) of teplizumab for stage 2 T1D finally provides diabetes clinicians with an immunomodulatory treatment option to delay the development of stage 3 T1D. Now that such a treatment exists—and other immunotherapies are certain to follow—the case for increasing efforts for screening for T1D goes beyond prevention of diabetic ketoacidosis (DKA) to include therapies to delay initiation of insulin therapy. With the likelihood of increased screening efforts in the future, clinicians will be faced with questions of how to monitor progression of T1D and when to recommend immunotherapy. The data from CGM wearers provided by Wilson and TrialNet colleagues indicate clear thresholds at which rapid progression to stage 3 T1D occurs.
As CGM has become more user friendly and more accepted by clinicians, it may well replace burdensome oral glucose tolerance tests as a more effective method to monitor for progression of T1D. This study and others will strengthen the evidence for the use of CGM in early stages of T1D. As diabetes technology becomes more effective in supporting care for T1D—and, we hope, more accessible to all people with T1D—we will see the role of CGM in ascertaining preclinical stages of T1D and timing of immunotherapies.
Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 Diabetes
Pollé OG1,2, Delfosse A1,2, Martin M3, Louis J4, Gies I5,6, den Brinker M7,8, Seret N9, Lebrethon MC10, Mouraux T11, Gatto L3, Lysy PA1,2, on behalf of the DIATAG Working Group
1Pôle de PEDI, Institut de Recherche Experimentale et Clinique, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 2Specialized Pediatrics Service, Cliniques Universitaires Saint-Luc, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 3Computational Biology and Bioinformatics Unit, de Duve Institute, UCLouvain, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 4Division of Pediatric Endocrinology, Department of Pediatrics, Grand Hôpital de Charleroi, Charleroi, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 5Division of Pediatric Endocrinology, Department of Pediatrics, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 6Research Group GRON, Vrije Universiteit Brussel, Brussels, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 7Laboratory of Experimental Medicine and Pediatrics and member of the Infla-Med Centre of Excellence, University of Antwerp, Faculty of Medicine and Health Sciences, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 8Division of Pediatric Endocrinology, Department of Pediatrics, Antwerp University Hospital, Antwerp, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 9Division of Pediatric Endocrinology, Department of Pediatrics, Centre Hospitalier Chrétien MontLégia, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 10Division of Pediatric Endocrinology, Department of Pediatrics, CHU Liège, Liège, Belgium; Department of Pediatrics, CHU Namur, Namur, Belgium; 11Division of Pediatric Endocrinology, Department of Pediatrics, CHU Namur, Namur, Belgium
Glucose variability parameters (also called CGM metrics) measured by continuous glucose monitoring (CGM) systems may strongly correlate with features of diabetes control related to β-cell function. CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with type 1 diabetes (T1D). This study evaluated whether indexes of glycemic variability may overcome residual β-cell secretion estimates in the longitudinal evaluation of partial remission (PR) in pediatric patients with new-onset T1D.
Methods
In this multicenter, prospective trial researchers tried to identify biomarkers of PR in children and adolescents (n = 78) with new-onset T1D. Values of residual β-cell secretion estimates, clinical parameters (e.g., HbA1c or insulin daily dose), and CGM data were longitudinally collected during 1 year and underwent cross-sectional comparison. Circadian patterns of CGM metrics were characterized and correlated to PR status using an adjusted mixed-effects model. Patients were clustered based on 46 CGM metrics and clinical parameters and were compared using nonparametric analysis of variance.
Results
The mean age of the participants was 10.4 ± 3.6 years at diabetes onset; 65% of them underwent PR at 3 months. β-Cell residual secretion estimates demonstrated weak-to-moderate correlations with clinical parameters and CGM metrics. CGM metrics strongly correlated with clinical parameters (P < 0.05) and were satisfactory to distinguish those with PR from those without PR. Also, CGM metrics from those with PR showed specific early morning circadian patterns characterized by increased glycemic stability across days (within 63–140 mg/dL range) and decreased rate of grade II hypoglycemia (P < 0.0001) compared with those without PR. CGM analysis allowed the identification of four novel glucotypes (P < 0.001) that segregate patients into subgroups and reflect the evolution of PR after diabetes onset.
Conclusions
CGM metrics (e.g., hyperglycemia and time in range) demonstrated a strong correlation with routine clinical parameters and demonstrated, for most of them, a specific circadian pattern that distinguished both remission groups.
Comments
Partial remission (PR) is a state of low glycemic variability, low daily insulin needs, and lower HbA1c levels. Studies in young adults also have suggested that patients entering PR after diabetes onset were less at risk of vascular complications (1). Currently, little is known about the influence of PR and its duration on short-term glucose homeostasis outcomes, especially in children.
PR can be evaluated by C-peptide levels and is commonly defined as the persistence of C-peptide secretion above a certain threshold (peak C-peptide > 200 pmol/L) (2). However, assays lack the power to discriminate residual β-cell mass from β-cell function. Therefore, new tools are needed that may both reflect the presence and predict the evolution of significant residual β-cell function, which qualifies PR.
With the use of CGM systems, it seems that glucose variability parameters (CGM metrics) may strongly correlate with features of diabetes control related to β-cell function. Previous studies (3,4) showed that CGM metrics improve the estimation of glucose control provided by HbA1c measurement and may help to better stratify existing phenotypes among patients with T1D.
In the study by Pollé and colleagues, patients without PR had a significantly higher prevalence of DKA at the onset of diabetes which may reflect a lower β-cell function or mass. Residual C-peptide secretion estimates, evaluated using either a single blood test or stimulation testing, were only weakly correlated with glucose homeostasis evaluated by CGM metrics and with clinical parameters (HbA1c, total daily dose of insulin, and insulin dose-adjusted A1c [IDAA1c]) without significant difference between the patients with and without PR. However, the CGM metrics showed strong correlations with the clinical parameters and allowed deeper characterization of glucose homeostasis (i.e., hypoglycemia episodes and glucose variability). As expected, the patients with PR spent more time in the target range and less time in hyperglycemia during the whole day.
By using CGM data, the investigators identified specific circadian patterns among remission groups for most CGM metrics, which peaked in their discriminative features in the early morning period. By integrating CGM metrics and clinical parameters, they identified four clinically meaningful clusters that exhibit specific glucotypes and reflect the progressive loss of glucose homeostasis during the first year after T1D onset. Therefore, CGM metrics provided additional information to segregate patients.
The study's limitations included its relatively small number of patients and the cross-sectional analysis of parameters (i.e., clinic, secretion, and CGM data) that were only available for a subset of patients. The study's strength is the novelty that integrates CGM, clinical parameters, and residual β-cell secretion data to find the characteristics of PR and identify new glucotypes during the first year of T1D.
The implementation of various CGM metrics as end points in trials of residual β-cell function preservation may provide applicable and more noninvasive precise clues to select the subgroup of patients who are better candidates for intervention and to evaluate the patient response to treatment. Another implication of more comprehensive sampling as obtained with CGM metrics may improve diagnostic accuracy of the transition from health to prediabetes or stage 2 T1D. New insights may offer earlier therapeutic options for reversing dysglycemia more successfully.
Disparities in Hemoglobin A1c Levels in the First Year after Diagnosis among Youths with Type 1 Diabetes Offered Continuous Glucose Monitoring
Addala A1, Ding V2, Zaharieva DP1, Bishop FK1, Adams AS1,3,4,5, King AC3,6, Johari R7, Scheinker D1,5,7,8, Hood KK1,5, Desai M2, Maahs DM1,3,5, Prahalad P1,5 for the Teamwork, Targets, Technology, and Tight Control (4T) Study Group
1Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 2Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 3Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 4Department of Health Policy, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 5Stanford Diabetes Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 6Stanford Prevention Research Center Division, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Stanford University, Stanford, CA; 7Clinical Excellence Research Center, Stanford University, Stanford, CA; Stanford University, Stanford, CA; 8Department of Management Science and Engineering, Stanford University, Stanford, CA
This study is also discussed in DIA-2024-2502, page S-14, and DIA-2024-2512, page S-187.
Although continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D), youths from minoritized racial and ethnic groups and those with public insurance face greater challenges accessing this treatment. This study examined whether HbA1c decreases differed by ethnicity and insurance status among the youths in the Teamwork, Targets, Technology, and Tight Control (4T) study with newly diagnosed T1D on CGM.
Methods
The 4T study was a clinical research program that aimed to initiate CGM within 1 month of T1D diagnosis for all youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020. For the Pilot-4T study cohort, youths at Stanford Children's Hospital were followed for 12 months and compared with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. HbA1c change over the study period was assessed, and the analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private).
Results
The Pilot-4T cohort comprised 135 youths, 71 male (52.6%), with a median age of 9.7 years (IQR, 6.8–12.7 years) at diagnosis. Participants' race was based on self-report and was categorized as 19 Asian or Pacific Islander (14.1%), 62 White (45.9%), and 39 other race (28.9%); 15 participants (11.1%) did not supply race information. The participants also self-reported their ethnicity: 29 Hispanic (21.5%) and 92 non-Hispanic (68.1%). A total of 104 participants (77.0%) had private insurance, and 31 (23.0%) had public insurance. Compared with the historical cohort, the Pilot-4T cohort had similar reductions in HbA1c at 6, 9, and 12 months after diagnosis observed for both the Hispanic individuals: estimated difference, −0.26% (95% CI, −1.05% to 0.43%), −0.60% (95% CI, −1.46% to 0.21%), and −0.15% (95% CI, −1.48% to 0.80%), respectively; and the non-Hispanic individuals: estimated difference, −0.27% (95% CI, −0.62% to 0.10%), −0.50% (95% CI, −0.81% to −0.11%), and −0.47% (95% CI, −0.91% to 0.06%), respectively. Similar reductions in HbA1c at 6, 9, and 12 months after diagnosis were also observed in the Pilot-4T cohort for both the publicly insured individuals: estimated difference, −0.52% (95% CI, −1.22% to 0.15%), −0.38% (95% CI, −1.26% to 0.33%), and −0.57% (95% CI, −2.08% to 0.74%, respectively; and the privately insured individuals: estimated difference, −0.34% (95% CI, −0.67% to 0.03%), −0.57% (95% CI, −0.85% to −0.26%), and −0.43% (−0.85% to 0.01%), respectively. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months after diagnosis than the non-Hispanic youths (estimated difference, 0.28% [95% CI, −0.46% to 0.86%], 0.63% [95% CI, 0.02%–1.20%], and 1.39% [95% CI, 0.37%–1.96%]), as did the publicly insured youths compared with the privately insured youths (estimated difference, 0.39% [95% CI, −0.23% to 0.99%], 0.95%[95% CI, 0.28%–1.45%], and 1.16% [95% CI, −0.09% to 2.13%]).
Conclusions
For Hispanic and non-Hispanic youths as well as for publicly and privately insured youths, CGM initiation soon after diagnosis is associated with similar improvements in HbA1c. Equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely.
Comments
CGM initiation early in the course of T1D is associated with improved clinical outcomes (5,6). The Pilot 4T study showed that a team-based approach to CGM initiation within the first month of diagnosis, supported by remote patient monitoring, can improve HbA1c at 1 year after diabetes diagnosis in a general diabetes clinic population (5).
Addala and colleagues performed further analysis on the Pilot 4T population to determine whether disparities in clinical outcomes persisted among those from minoritized communities when receiving a standardized new onset protocol that removed provider-level bias. The results from their analysis demonstrated that the 4T intervention improved outcomes in Hispanic and non-Hispanic youth as well as those on public insurance and those on private insurance equally. Unfortunately, this intervention was not able to close the outcomes gap.
The results of this study are important in showing that clinic-wide technology-based interventions are beneficial for all populations equally. It is important that advocacy efforts are in place to ensure CGM coverage for all youths. In addition, protocols should be developed at the clinic level to prevent provider-level bias. However, the 4T intervention was not designed to address all variables contributing to the pre-existing outcomes gap, and the results suggest that future interventions should address social determinants of health (7 –9).
Continuous Glucose Monitoring versus Blood Glucose Monitoring for Risk of Severe Hypoglycaemia and Diabetic Ketoacidosis in Children, Adolescents, and Young Adults with Type 1 Diabetes: A Population-based Study
Karges B1, Tittel SR2,3, Bey A4, Freiberg C5, Klinkert C6, Kordonouri O7, Thiele-Schmitz S8, Schröder C9, Steigleder-Schweiger C10, Holl RW2,3
1Division of Endocrinology and Diabetes, Medical Faculty, RWTH Aachen University, Aachen, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 2Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 3German Center for Diabetes Research, Neuherberg, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 4Department of Pediatrics, St Marien Hospital Düren, Düren, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 5Department of Pediatrics and Adolescent Medicine, University of Göttingen, Göttingen, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 6Practice of Pediatrics and Pediatric Diabetes, Herford, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 7Diabetes Center for Children and Adolescents, Children's Hospital Auf der Bult, Hannover, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 8Department of Pediatric and Adolescent Medicine, St Vincenz Hospital, Paderborn, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 9Department of Pediatrics, Division of Endocrinology and Diabetes, University of Greifswald, Greifswald, Germany; Paracelsus Medical University Salzburg, Salzburg, Austria; 10Department of Pediatrics, Paracelsus Medical University Salzburg, Salzburg, Austria
This study is also discussed in DIA-2024-2502, page S-14.
Blood glucose monitoring or sensor-based continuous glucose monitoring (CGM) is mandatory to optimize insulin therapy and inform other management decisions in type 1 diabetes (T1D) to achieve glycemic targets. However, the effect of CGM use on the events of severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) remains unclear. This study determined whether the rates of SH and DKA are lower with use of CGM compared with blood glucose monitoring in young patients with T1D and investigated which CGM metrics are informative for these acute diabetes complications.
Methods
A population-based cohort study including patients with T1D younger than 25 years with disease duration of > 1 year who were identified from diabetes centers (n = 511) between January 1, 2014, and June 30, 2021, across Austria, Germany, Luxembourg, and Switzerland participated in the Diabetes Prospective Follow-up initiative. SH and DKA rates during the most recent treatment year were examined in people using CGM and in those using blood glucose monitoring. Adjustments of the statistical models included age, sex, diabetes duration, migration background, mode of insulin therapy (pump or injections), and treatment period. The rates of SH and DKA were evaluated by CGM metrics, including percentage of time below target glucose range (< 3.9 mmol/L), glycemic variability (measured as the coefficient of variation), and mean sensor glucose.
Results
The cohort included 32,117 patients (53.1% males) with T1D (median age 16.8 years [IQR, 13.3–18.1]). Of these participants, 10,883 used CGM (median 289 days/year), and 21,234 used blood glucose monitoring. Compared with those using blood glucose monitoring, patients using CGM had lower rates of SH (6.74 [95% CI, 5.90–7.69] per 100 patient-years vs 8.84 [8.09–9.66] per 100 patient-years; incidence rate ratio [IRR] 0.76 [95% CI, 0.64–0.91], P = 0.0017) and DKA (3.72 [95% CI, 3.32–4.18] per 100 patient-years vs 7.29 [95% CI, 6.83–7.78] per 100 patient-years; IRR, 0.51 [95% CI, 0.44–0.59]; P < 0.0001). The SH rates increased with percentage of time below target glucose range (IRR, 1.69 [95% CI, 1.18–2.43], P = 0.0024, for 4.0%–7.9% vs < 4.0%; and IRR, 2.38 [95% CI, 1.51–3.76], P < 0.0001, for ≥ 8 · 0% vs < 4.0%) and glycemic variability (coefficient of variation ≥ 36% vs < 36%, IRR, 1.52 [95% CI, 1.06–2.17], P = 0.022). The DKA rates increased with mean sensor glucose values (IRR, 1.77 [95% CI, 0.89–3.51], P = 0.13, for 8.3–9.9 mmol/L vs < 8.3 mmol/L; IRR, 3.56 [95% CI, 1.83–6.93], P < 0.0001, for 10.0–11.6 mmol/L vs < 8.3 mmol/L; and IRR, 8.66 [95% CI, 4.48–16.75], P < 0.0001, for ≥ 11.7 mmol/L vs < 8.3 mmol/L).
Conclusions
Young patients with T1D using CGM might have lower risks of SH and DKA than those using blood glucose monitoring. The CGM metrics associated with lower rates of SH and of DKA might serve as additional tools to advance personalized treatment in young patients with T1D.
Comments
Previous studies based on large registries have reported improved glycemic control and fewer DKA episodes observed in participants using CGM (10,11). Few studies also reported the impact of CGM use on decreased rates of SH episodes (12 –14). The study by Karges and colleagues is the first prospective, multicenter study to analyze the effect of CGM on the risk of SH and DKA in a large cohort of young patients with T1D.
The observation of decreased rates of SH in those using CGM compared with those using blood glucose monitoring might be explained by earlier recognition of impending SH with real-time glucose monitoring and alert function, allowing time for counteractive actions. Also, the higher awareness of hyperglycemia with CGM than with blood glucose monitoring might allow timely correction of hyperglycemia to prevent DKA.
The strengths of the study are the provision of real-world data based on a very large number of participants and a large age range from centers from different countries, which allows generalization of the results. Moreover, the study was powered to provide a valid statement regarding the question of whether the rates of SH and DKA are lower in patients using CGM compared with patients using blood glucose monitoring, and which CGM metrics are associated with these acute diabetes complications. Using the CGM metrics associated with lower rates of SH and DKA might help to identify individual risk to predict and prevent these acute diabetes complications in young people with T1D.
Insulin Pump Therapy
A Longitudinal View of Disparities in Insulin Pump Use among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study
Everett EM1,2,3, Wright D4, Williams A5, Divers6 J, Pihoker C7, Liese AD8, Bellatorre A9, Kahkoska AR10, Bell R11, Mendoza J7,12, Mayer-Davis E10, Wisk LE2,13
1Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA; Fielding School of Public Health, University of California, Los Angeles, CA; 2Division of General Internal Medicine & Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA; Fielding School of Public Health, University of California, Los Angeles, CA; 3V.A. Greater Los Angeles Healthcare System, Los Angeles, CA; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA; Fielding School of Public Health, University of California, Los Angeles, CA; 5DNA Data Solutions, LLC, Petersburg, FL; Fielding School of Public Health, University of California, Los Angeles, CA; 6Division of Health Services Research, Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, NY; Fielding School of Public Health, University of California, Los Angeles, CA; 7Department of Pediatrics, University of Washington, Seattle, WA; Fielding School of Public Health, University of California, Los Angeles, CA; 8Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC; Fielding School of Public Health, University of California, Los Angeles, CA; 9University of Colorado Denver Lifecourse Epidemiology of Adiposity and Diabetes Center, Aurora, CO; Fielding School of Public Health, University of California, Los Angeles, CA; 10Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC; Fielding School of Public Health, University of California, Los Angeles, CA; 11Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC; Fielding School of Public Health, University of California, Los Angeles, CA; 12Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA; Fielding School of Public Health, University of California, Los Angeles, CA; 13Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, CA
Insulin pump use has revolutionized diabetes management, improving glycemic control, quality of life, and lower diabetes distress. Although the prevalence of insulin pump use has increased, understanding potential barriers to optimized uptake of these devices remains of interest (15,16). Therefore, researchers from the SEARCH for Diabetes in Youth study evaluated changes in insulin pump use and whether inequities in insulin pump use among youth with type 1 diabetes (T1D) may have increased or diminished over time. The study assessed temporal trends in the use of insulin pumps for T1D management in youth and young adults in the United States over nearly two decades.
Methods
Changes over time in insulin pump use for T1D were evaluated for participants less than 20 years old by racial and ethnic group, health insurance, household income, and level of parental education across four time periods: 2001–2005, 2006–2010, 2011–2015, and 2016–2019. For analysis, multivariable generalized estimating equations with a binomial distribution were used to assess the probability of insulin pump use, and models were further adjusted for the other predictors, including age at each visit, diabetes duration, sex, and clinic site in the estimation of the probability of insulin pump use.
Results
The overall prevalence of insulin pump use increased from 30% in 2001–2005 to 58.3% in 2016–2019. When compared with non-Hispanic White youths, Hispanic youths saw some improvement: the adjusted odds ratio (OR) for pump use in this group was 0.08 (95% CI, 0.01–0.63) in 2001–2006 and 0.65 (95% CI, 0.48–0.87) in 2016–2019, which was a statistically significant improvement (P = 0.004). However, the OR for Black and other races was 0.28 ([95% CI, 0.21–0.37], P = 0.864) and 0.43 ([95% CI, 0.26–0.71], P = 0.439), respectively, and did not change through the course of the study. Those whose parents had some high school education (OR, 0.38 [95% CI, 0.30–0.48], P = 0.160) or a high school degree (OR, 0.69 [95% CI 0.57–0.82], P = 0.894) were less likely to be using an insulin pump compared with those whose parents had a bachelor's degree or more, and this did not change over time. Compared with their counterparts who had private insurance, those with public health insurance were less likely to use an insulin pump (OR, 0.84 [95% CI, 0.68–1.03], P = 0.815), which did not change over time. Compared with those who had an annual household income of $75,000 or more, those with an income of <$25,000, $25,000–$49,000, and $50,000–$74,000 had an OR for pump use of 0.43 ([95% CI, 0.34–0.54], P = 0.937), 0.57 ([95% CI, 0.46–0.71] P = 0.870), and 0.80 ([95% CI, 0.65–0.97], P = 0.821), respectively. And this also did not change throughout the study period.
Conclusions
The study showed that over the past 20 years, despite the overall increase in the use of insulin pumps, racial and ethnic minority groups and those of lower socioeconomic status still had unequal access to this very beneficial management tool. Therefore, interventions are needed to address these disparities. Additional studies are required to test interventions to improve technology access and promote its effective use over time.
Comments
The data analysis from the SEARCH for Diabetes in Youth study provides valuable insights into the disparities in insulin pump use for managing T1D among youth and young adults in the United States. Over the past two decades, insulin pump use increased from 31.7% to 58.8%. However, despite the overall increase in the use of insulin pumps across all racial-ethnic and socioeconomic groups over time, there has been limited progress in reducing the inequities in insulin pump use based on race and ethnicity and socioeconomic status. At all points in time, the higher rates of pump use occurred in White non-Hispanic individuals compared with Hispanic and non-Hispanic Black. Furthermore, those with lower parental education levels and household incomes were also found to have lower insulin pump use than those with higher education and income levels.
These concerning results emphasize the importance of reducing disparities in insulin pump use to improve diabetes management outcomes for all individuals with T1D, irrespective of their race and ethnicity and socioeconomic background. The study also highlighted other potential barriers to insulin pump use, such as provider bias, poor physician communication, and financial limitations related to insurance coverage and out-of-pocket costs. Everett and colleagues emphasized the importance of ongoing efforts to address disparities in insulin pump use and promote equitable access to diabetes technology for all individuals with T1D. Therefore, changes in health policies and interventions in diabetes care should be implemented, such as improving provider awareness of unconscious biases, developing standardized pathways for diabetes technology use, providing culturally sensitive communication, and offering decision support tools. The authors conclude that further studies to evaluate barriers or tests of effective interventions are needed to ensure that advancements in diabetes management benefit all populations equally.
Closed Loop Systems
Trial of Hybrid Closed-Loop Control in Young Children with Type 1 Diabetes
Wadwa RP1, Reed ZW2, Buckingham BA3, DeBoer MD5, Ekhlaspour L4, Forlenza GP1, Schoelwer M5, Lum J2, Kollman C2, Beck RW2, Breton MD5 for the PEDAP Trial Study Group
1Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO; Charlottesville, VA; 2Jaeb Center for Health Research, Tampa, FL; Charlottesville, VA; 3Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Stanford, CA; Charlottesville, VA; 4Division of Pediatric Endocrinology, University of California, San Francisco, San Francisco, CA; Charlottesville, VA; 5University of Virginia Center for Diabetes Technology, Charlottesville, VA
This study is also discussed in DIA-2024-2505, page S-68.
Closed-loop control systems of insulin delivery may improve glycemic outcomes in young children with type 1 diabetes (T1D), but the efficacy and safety of initiating a closed-loop system virtually have not been determined.
Methods
In this 13-week, multicenter trial, children who were at least 2 years of age but younger than 6 years of age who had T1D were randomly assigned in a 2:1 ratio to receive treatment with a closed-loop system of insulin delivery or standard care, which included either an insulin pump or multiple daily injections of insulin plus a continuous glucose monitor (CGM). The primary outcome was the percentage of time that the glucose level was in the target range of 70–180 mg/dL as measured by CGM. The secondary outcomes included the percentage of time that the glucose level was > 250 mg/dL or < 70 mg/dL, the mean glucose level, the glycated hemoglobin (HbA1c) level, and the safety outcomes.
Results
In randomization, a total of 102 children were assigned to two groups: 68 to the closed-loop group, and 34 to the standard-care group. The HbA1c levels at baseline ranged from 5.2% to 11.5%. Initiation of the closed-loop system was virtual in 55 patients (81%). The mean percentage of time that the glucose level was within the target range increased from 56.7% ± 18.0% SD at baseline to 69.3% ± 11.1% SD during the 13-week follow-up period in the closed-loop group and from 54.9% ± 14.7% SD to 55.9% ± 12.6% SD in the standard-care group: mean adjusted difference, 12.4 percentage points (equivalent to approximately 3 hours per day [95% CI, 9.5–15.3], P < 0.001). Similar treatment effects were observed (although favoring the closed-loop system) for the percentage of time that the glucose level was > 250 mg/dL, on the mean glucose level, and on the HbAc1 level with no significant between-group difference in the percentage of time that the glucose level was < 70 mg/dL. The closed-loop group had two cases of severe hypoglycemia; the standard care group had one case. One case of diabetic ketoacidosis occurred in the closed-loop group.
Conclusions
For young children with T1D the glucose level was in the target range for a greater percentage of time with a closed-loop system than with standard care.
Comments
A main emphasis for this ATTD Yearbook article has been the importance of conducting diabetes technology research in the youngest populations of people with T1D to ensure safety and efficacy and so that governmental agencies will approve and fund the use of these technologies. A strong argument can be made that children younger than 6 years are the most vulnerable to the challenges of glucose management in T1D. The data presented by Wadwa and colleagues from the PEDAP Trial Study Group translate their previous findings in older age groups to 2- to 6-year-olds with T1D. They report an additional 3 hours daily of time in range with no increased time spent less than 70 mg/dL compared with standard care, which included a CGM and insulin delivered either by a pump or multiple daily injections. The mean time in range approached the consensus guidelines target of 70%, indicating that this goal is achievable with currently available diabetes technologies even in this youngest age group. Further data are anticipated on the usability and quality of life benefits for children as well as for their families who bear the burden of care for this age group.
MiniMed 780G Six-Month Use in Children and Adolescents with Type 1 Diabetes: Clinical Targets and Predictors of Optimal Glucose Control
Lombardo F1, Passanisi S1, Alibrandi A2, Bombaci B1, Bonfanti R3, Delvecchio M4, Di Candia F5, Mozzillo E5, Piccinno E4, Piona CA6, Rigamonti A3, Scialabba F3, Maffeis C6, Salzano G1
1Department of Human Pathology in Adult and Developmental Age ‘‘Gaetano Barresi,’’ University of Messina, Messina, Italy; Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy; 2Department of Economics, Unit of Statistical and Mathematical Sciences, University of Messina, Messina, Italy; Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy; 3Diabetes Research Institute, San Raffaele Hospital, Milano, Italy; Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy; 4Metabolic Disease and Genetics Unit, Giovanni XXIII Children's Hospital, Bari, Italy; Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy; 5Department of Translational Medical Science, Section of Pediatrics, Regional Center of Pediatric Diabetes, Federico II University of Naples, Naples, Italy; Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy; 6Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, University City Hospital, Verona, Italy
The MiniMed 780G system represents a second-generation automated insulin delivery (AID) system characterized by the capability to integrate automatic correction boluses and to set up an adjustable target glucose value between 100 and 120 mg/dL. This study investigated glycemic outcomes in children and adolescents with type 1 diabetes (T1D) over the first 6-month use of MiniMed 780G, and evaluated demographic and clinical factors that may be associated with achievement of the therapeutic goals.
Methods
In this multicenter, observational, real-world study the demographic and clinical data of study participants were collected at the time of enrollment. Data on ambulatory glucose profile were acquired at 3 and 6 months after activating the automatic mode. Aggregated glucose metrics and device settings of the entire study period were analyzed to identify predictors of optimal glycemic control, assessed by the concomitant achievement of time in range (TIR) > 70%, coefficient of variation (CV) < 36%, glucose management indicator (GMI) < 7%, and the time below range (TBR) < 4%.
Results
The study included children and adolescents (n = 111, 54.1% female) aged 7–18 years. Most of the relevant clinical targets were achieved according to recommendations from the International Consensus both at 3 and 6 months. Primary goals in terms of TIR, CV, GMI, and TBR were achieved, respectively, by 72.1%, 74.8%, 68.5%, and 74.8% of participants. In addition, 39.6% of the patients concomitantly addressed all these clinical targets. Regression analysis revealed that older age, shorter duration of disease, and shorter active insulin time were significant predictors of optimal glucose control. When two groups of patients stratified according to the mean glycated hemoglobin (HbA1c) value in the year preceding MiniMed 780G use were compered, the subgroup with lower HbA1c achieved better glycemic targets.
Conclusions
The study highlights the effectiveness and safety of MiniMed 780G in children and adolescents with T1D with significant improvement of their glycemic goals since the first months of its use.
Comments
Hybrid closed-loop (HCL) systems are characterized by integrating CGM with insulin pumps that automate insulin delivery via specific algorithms and user-initiated insulin delivery. The advanced-HCL MiniMed 780G system changes basal insulin delivery every 5 minutes and autobolus in response to sensor glucose values. The system is approved for patients aged > 7 years.
Recent studies have assessed the performance of the MiniMed 780G system among young individuals with T1D and demonstrated rapid and sustained improvement of glycemic control, reaching the recommended time in range (17,18), and minimizing hypoglycemia (19). However, there are still few data on the performance of this device in pediatric patients with T1D in the real-world settings.
The strengths of the current study are its multicenter, prospective design with data based on real-world experience. Also, the study included both patients that were treated with continuous subcutaneous insulin infusion therapy and MDI before starting the MiniMed 780G, indicating that both patients on MDI or pump therapy are eligible to be switched to treatment with MiniMed 780G.
Lombardo and colleagues showed that their participants spent most of their time in the target glucose values along with low glycemic variability and with a low rate of hypoglycemia after activating the automatic mode during a relatively long period of time of 6 months. These findings are encouraging because they confirm the ability of advanced HCL systems to prevent hypoglycemic events and to reduce short-term glucose variability. The ability to decrease glycemic variability is also a crucial aspect considering the impact of glycemic fluctuations on the risk of early appearance of microvascular and macrovascular complications (20).
An important highlight is the fact that in this study, before starting a child on the automatic mode, all children and their caregivers received extensive training provided by the medical and technical staff on carbohydrate counting, bolus wizard function use, interpretation, and sharing of CGM data. Therefore, it emphasizes that continuous education remains essential to support even the most advanced technology.
It will be of interest to compare real-life clinical data on achievement of international recommended glycemic targets between users of different advanced HCL systems in pediatric patients with T1D for a longer period of time.
Simplified Meal Announcement versus Precise Carbohydrate Counting in Adolescents with Type 1 Diabetes Using the MiniMed 780G Advanced Hybrid Closed Loop System: A Randomized Controlled Trial Comparing Glucose Control
Petrovski G1, Campbell J1, Pasha M1, Day E1, Hussain K1, Khalifa A1, van den Heuvel T2
1Division of Endocrinology and Diabetes, Sidra Medicine, Doha, Qatar; Tolochenaz, Switzerland; 2Medtronic International Trading Sàrl, Tolochenaz, Switzerland
Carbohydrate counting is essential to diabetes management, but it can be burdensome and challenging for some individuals with type 1 diabetes (T1D). The MiniMed 780G system is an advanced hybrid closed-loop (HCL) system with autocorrection boluses and automated insulin adjustments based on glucose levels that have been shown to improve glycemic outcomes in individuals with T1D. The study compared glucose control in adolescents with T1D using the MiniMed 780G hybrid closed-loop (HCL) system with simplified meal announcements versus those using precise carbohydrate counting.
Methods
A total of 34 adolescents aged 12–18 years with T1D, who completed a 7-day run-in period to assess carbohydrate counting skills and carbohydrate intake using a food diary, were randomized 1:1 to the MiniMed 780G system with two different meal announcement methods: simplified meal announcement (Fix group) and precise carbohydrate counting (Flex group). The simplified plan involved users choosing one of three personalized, fixed carbohydrate amounts according to meal size (regular, larger than normal, or snack) for each meal announcement. After randomization, both groups followed a 10-day initiation protocol with the MiniMed 780G system and continued with a 12-week study phase.
Results
At 12 weeks, both groups significantly improved their time in range (TIR) during the study phase compared with baseline, with the Flex group demonstrating a significantly higher TIR (80.3%) than the Fix group (73.5%), and the time above range was superior (2.7% lower) in the carbohydrate-counting group. No serious adverse events occurred. The participants in the Fix group announced fewer meals per day but had similar total daily reported carbohydrates compared with the Flex group. The proportion of insulin delivered by autocorrection was higher in the Fix group, indicating increased automated insulin delivery.
Conclusions
Although precise carbohydrate counting led to better TIR in adolescents using the MiniMed 780G system compared with simplified meal announcement, both methods reached the international target criteria for glucose control. The study highlights the importance of meal management skills in diabetes management and shows that automated insulin delivery can compensate for lower precision in carbohydrate counting, leading to improved outcomes. Therefore, simplified meal announcements may be a valuable alternative for adolescents struggling with conventional carbohydrate counting.
Comments
This study provides valuable insights into using the MiniMed 780G advanced HCL system with a simplified meal announcement leveraging fixed carbohydrate amounts instead of exact carbohydrate calculations in adolescents with T1D. It is encouraging to see that adopting simplified meal announcements is an alternative option for individuals who find carbohydrate counting burdensome or challenging (21). The results suggest that reduced accuracy in carbohydrate counting can be overcome by the next generation automated insulin delivery provided by the MiniMed 780G system because this approach reached the glycemic goals (22) and reduced hyperglycemia. Therefore, a simplified meal management approach with the MiniMed 780G system can benefit users who struggle with strict carbohydrate-counting practices.
Overall, this study underscores the need to consider individual preferences and capabilities when selecting the most appropriate meal announcement method for optimal glucose control and adherence to the treatment plan for adolescents with T1D. The relatively small sample size may limit the generalizability of the findings. However, building on these findings through further research with a larger and more diverse cohort could provide more comprehensive guidance for health-care providers in tailoring diabetes management approaches.
Increased Technology Use Associated with Lower A1C in a Large Pediatric Clinical Population
Alonso GT1, Triolo TM1, Akturk HK1, Pauley ME1, Sobczak M2, Forlenza GP1, Sakamoto C1, Pyle L1,3, Frohnert BI1
1Barbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Colorado School of Public Health, Aurora, CO; 2School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO; Colorado School of Public Health, Aurora, CO; 3Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO
This real-world observational study assessed the impact of commercially available newer-generation continuous glucose monitors (CGMs) and hybrid closed-loop (HCL) systems on glycemic control in children and young adults with type 1 diabetes (T1D).
Methods
The researchers evaluated glycated hemoglobin (HbA1c) trends and technology use between two 6-month periods (2016–2017 and 2020–2021) at a large U.S. pediatric diabetes center to analyze the association between diabetes technology use and HbA1c levels.
Results
The study included 4103 children and young adults aged < 22 years with T1D duration > 3 months. Of these, 1455 patients had data in both periods. The results showed that over the study period, there was a significant decrease in mean HbA1c across the entire hospital population (from 8.9% to 8.6%, P < 0.0001), which was associated with a substantial increase in CGM and HCL uptake. Insulin pump use also increased slightly. Individuals using CGM and HCL had similar HbA1c levels across the two periods, thus indicating that the broader uptake of these technologies contributed to improved glycemic control. However, the study found that a quarter of the patients did not use CGM, and more than two-thirds did not use HCL. Some demographic subgroups or people with Medicaid insurance had even lower rates of technology use although they had similar increases in pump and CGM use.
Conclusions
This real-world study confirmed that increased use of CGM and HCL systems was strongly associated with better glycemic control in children and young adults with T1D, ultimately reducing the risk of diabetes-related complications. Addressing barriers to adopting diabetes technologies may further improve HbA1c trends and narrow the disparities in glycemic outcomes.
Comments
This study provides valuable real-world data regarding the impact of CGMs and HCL systems on glycemic control in children and young adults with T1D. The study's strengths include a large sample size and temporal comparison of trends before and after introducing commercially available diabetes management technology. However, the study's limitations may be that it used retrospective observational data analysis from a single center. The findings demonstrated a significant decrease in mean HbA1c levels across a pediatric clinic population, associated with increased CGM and HCL technologies uptake. These encouraging results further support the notion that the rise in technology use is strongly associated with better glycemic control (23).
However, the study also highlights the need to address barriers to technology utilization because a considerable proportion of the people with T1D were still not using CGM or HCL. Exploring and addressing the factors influencing this underutilization is essential, especially in specific communities (24). The study highlights the significant benefits of technological advancements in diabetes care, particularly regarding glycemic control and potentially improved quality of life for people with T1D, so attempts should be made to encourage broader uptake and sustained use of these technologies while addressing disparities in diabetes treatment. Moreover, future research should aim to demonstrate successful implementation across diverse populations. Conducting cost-effectiveness studies will also be pivotal in influencing policy-making and facilitating better reimbursement for diabetes technology.
A Meta-analysis of Randomized Trial Outcomes for the t:slim X2 Insulin Pump with Control-IQ Technology in Youth and Adults from Age 2 to 72
Beck RW1, Kanapka LG1, Breton MD2, Brown SA2, Wadwa RP3, Buckingham BA4, Kollman C1, Kovatchev B2
1Jaeb Center for Health Research, Tampa, FL; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; 2University of Virginia Center for Diabetes Technology, Charlottesville, VA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; 3Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, CO; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; 4Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
A meta-analysis of three randomized clinical trials examined the effectiveness of the Control-IQ hybrid closed-loop (HCL) system in improving glycemic control across various subgroups of individuals with type 1 diabetes (T1D), including different age groups, racial and ethnic backgrounds, socioeconomic status, and insulin delivery methods (pump or multiple daily injections). Automatic correction blousing is an automated insulin dosing feature in the t:slim X2 insulin pump with Control-IQ technology.
Methods
A pooled analysis combined data from three randomized controlled trials of Control-IQ involving 369 participants with T1D ranging in age from 2 to 72 years and various baseline characteristics. Of the overall cohort, 54% were younger than 14 years. Of the 369 individuals, 256 were randomized to the t:slim X2 insulin pump with Dexcom G6 continuous glucose monitor (CGM) (the Control-IQ group), and 113 were randomized to the control group. Most participants in the control group used an insulin pump (91%), some with predictive low glucose suspend technology. The primary outcome of interest for the study was the difference in time in range (TIR) of 70–180 mg/dL after the 13 weeks of follow-up. Secondary outcomes of interest included mean glucose, hyperglycemia metrics, hypoglycemic metrics, and glycated hemoglobin (HbA1c).
Results
The mean time in range (TIR) improved from 57% at baseline to 70% at the end of the trial in the Control-IQ group compared with 56% to 57% in the control group, for a mean adjusted difference of 11.5%. The Control-IQ group showed an average increase in the time individuals spent in the target glucose range (70–180 mg/dL) of 2.8 hours per day compared with the control group. Further analysis demonstrated that using Control-IQ significantly reduced mean glucose levels and daytime and nighttime hyperglycemia (> 250 mg/dL) compared with the control group. The most significant difference in mean glucose was between 4:00
Conclusions
The findings highlight the potential benefits of HCL technology in diabetes management and provide strong evidence supporting the Control-IQ system because it consistently improved glycemic outcomes across various demographic and clinical characteristics. The automatic correction blousing feature of the Control-IQ algorithm may have a substantial impact on improving glycemic control in individuals with poorly controlled diabetes.
Comments
The current study provides clinicians and researchers with an overview of the effects of the advanced HCL system across a wide range of age groups (2–72 years old) and various baseline characteristics, including race-ethnicity, parental education, family income, baseline glycated hemoglobin (HbA1c) level, virtual versus in-person training format, and prestudy insulin delivery method. Beck and colleagues conducted a pooled analysis of three randomized controlled trials of the Control-IQ system using data from the Pivotal Study of t:Slim X2 With Control-IQ Technology (DLCP3) (25), the International Diabetes Closed Loop (iDCL) Trial (DCLP5) (26), and the Pediatric Artificial Pancreas (PEDAP) Trial (27), which included patients aged 14–72 years, 6–13 years, and 2–5 years, respectively.
The results showed consistent benefits of the HCL technology on glycemic control across a wide range of ages and demographics of people with T1D. Significant reductions in TIR, as well as mean glucose, hyperglycemia metrics, hypoglycemic metrics, and HbA1c, were observed regardless of age, ethnicity, education, or previous pump experience. The findings suggest that HCL technology like Control-IQ offers a promising solution for individuals of all ages with T1D seeking to improve their diabetes management, quality of life, and overall outcomes.
Additionally, the study emphasizes the importance of the automated features of the Control-IQ technology in improving glycemic outcomes, especially for individuals with the worst baseline glycemic control who experienced the greatest benefit from the automatic correction bolusing of the Control-IQ algorithm. The system's high number of automated boluses in this group may likely compensate for previously missed meal boluses or lack of manual correction boluses when on conventional pump therapy or MDI.
With metanalysis of data from three randomized, controlled trials completed, Control-IQ technology has the dataset supporting its benefits on glycemic control across diverse populations, with a greater age range, ethnicity subgroups, and different levels of socioeconomic status. Moreover, Control-IQ HCL systems have shown good performance in people with baseline HbA1c, typically underrepresented in the randomized control trials of AID systems. The results are consistent with data from real-world studies of Control-IQ technology users. Further data from longer-duration real-world studies are essential to provide more generalizable and realistic estimates of the treatment effect on glycemic control, cost-effectiveness, and psychosocial impact (28).
Open-Source Automated Insulin Delivery in Type 1 Diabetes
Burnside MJ1,3, Lewis DM11, Crocket HR4, Meier RA1, Williman JA2, Sanders OJ1,3, Jefferies CA6,7, Faherty AM6, Paul RG5, Lever CS5, Price SKJ5, Frewen CM8, Jones SD8, Gunn TC10, Lampey C6, Wheeler BJ8,9, de Bock MI1,3
1Departments of Pediatrics, University of Otago, Dunedin, New Zealand; Seattle, WA; 2Department of Population Health, University of Otago, Dunedin, New Zealand; Seattle, WA; 3Department of Pediatrics, Canterbury District Health Board Christchurch, New Zealand; Seattle, WA; 4Te Huataki Waiora School of Health, Sport and Human Performance, University of Waikato, Hamilton, New Zealand; Seattle, WA; 5Waikato Regional Diabetes Service, Waikato District Health Board, Hamilton, New Zealand; Seattle, WA; 6Department of Pediatric Endocrinology, Starship Children's Health, Auckland District Health Board, Auckland, New Zealand; Seattle, WA; 7Liggins Institute, University of Auckland, Auckland, New Zealand; Seattle, WA; 8Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; Seattle, WA; 9Pediatric Department, Southern District Health Board, Dunedin, New Zealand; Seattle, WA; 10Nightscout New Zealand, Hamilton, New Zealand; Seattle, WA; 11OpenAPS, Seattle, WA
This study is also discussed in DIA-2024-2505, page S-68.
People with type 1 diabetes (T1D) use open-source automated insulin delivery (AID) systems, and more data are needed on the efficacy and safety of these systems.
Methods
In this multicenter, open-label, randomized, controlled trial, participants with T1D were assigned in a 1:1 ratio to use an open-source AID system or a sensor-augmented insulin pump (control). The participants included both children (defined as 7–15 years of age) and adults (defined as 16–70 years of age). A modified version of AndroidAPS 2.8 (with a standard OpenAPS 0.7.0 algorithm) paired with a preproduction DANA-i insulin pump and Dexcom G6 CGM, which has an Android smartphone application as the user interface, was used as the AID system. The primary outcome was the percentage of time in the target glucose range of 70–180 mg/dL (3.9–10.0 mmol/L) between days 155 and 168, the final 2 weeks of the trial.
Results
A total of 97 participants (48 children and 49 adults) underwent randomization, 44 to open-source AID and 53 to the control group. At 24 weeks, the mean time in the target range increased from 61.2% ± 12.3% SD to 71.2% ± 12.1% SD in the AID group and decreased from 57.7% ± 14.3% SD to 54.5% ± 16.0% SD in the control group (adjusted difference, 14 percentage points [95% CI, 9.2–18.8], P < 0.001), with no treatment effect according to age (P = 0.56). Participants in the AID group spent 3 hours and 21 minutes more in the target range per day compared with those in the control group. There were no episodes of severe hypoglycemia or diabetic ketoacidosis in either group. Two participants in the AID group withdrew from the trial due to connectivity issues.
Conclusions
The use of an open-source AID system in children and adults with T1D resulted in a significantly higher percentage of time in the target glucose range compared with the use of a sensor-augmented insulin pump.
Comments
AID systems are recommended as standard of care for people with T1D (29 –31). Although commercially available AID systems are available in some regions, many people with diabetes choose to use open-source systems due to limited commercial AID availability or the features in open-source systems. Despite the popularity of these systems, there is only one that has received regulatory approval (32).
Burnside and colleagues present data from a multicenter randomized, controlled trial in which participants are randomized to either open-source AID (Android APS 2.8) or sensor-augmented pump. Compared with the sensor-augmented pump group, the participants using the open-source AID system had a higher TIR. The strength of this study is that it involved both children and adults in free living conditions. Although this system was not compared with other AID systems, the TIR outcomes were similar to those achieved by individuals on commercial systems.
Studies like this show that open-source AID systems can be safely used by people with diabetes. It is important to continue studying these systems and pursue regulatory approval to make these systems more accessible to people with diabetes and their providers.
First Use of Open-Source Automated Insulin Delivery AndroidAPS in Full Closed-Loop Scenario: Pancreas4ALL Randomized Pilot Study
Petruzelkova L1, Neuman V1, Plachy L1, Kozak M2, Obermannova B1, Kolouskova S1, Pruhova S1, Sumnik Z1
1Department of Pediatrics, Motol University Hospital, 2nd Faculty of Medicine, Charles University, Prague, Czech Republic; CLOSED LOOP Systems and Sysop, Prague, Czech Republic; 2IT Department, CLOSED LOOP Systems and Sysop, Prague, Czech Republic
We evaluated the safety and feasibility of open-source automated insulin delivery AndroidAPS in adolescents and young adults with type 1 diabetes (T1D) and compared its efficacy in three different scenarios: hybrid closed-loop (HCL) with meal boluses, meal announcement (MA) only, and full closed-loop (FCL).
Methods
In an open-label, prospective, randomized crossover trial, 16 adolescents with T1D (10 females) with mean age of 17 years (range, 15–20), glycated hemoglobin (HbA1c) 56 mmol/mol (range, 43–75), and mean duration of diabetes 5.9 years (9 –15) underwent three distinct 3-day periods of camp living using AndroidAPS and comparing the HCL, MA only, and FCL. A modified and locked version of AndroidAPS 3.1.03, Pancreas4ALL, was used. The order of MA only and FCL for participants was randomly assigned. The primary end points were feasibility and safety of the system represented by percentage of time of glucose control by the system and time in hypoglycemia below 3 mmol/L.
Results
Glycemia was controlled by the system 95% time of the study, and the proportion of time below 3 mmol/L did not exceed 1% over the whole study period (0.72%). The HCL scenario had a significantly higher percentage of time below 3 mmol/L (HCL 1.05% vs MA only 0.0% vs FCL 0.0%; P = 0.05) compared with other scenarios. There was no difference observed between the scenarios in the percentage of time between 3.9 and 10 mmol/L (HCL 83.3% vs MA only 79.85% vs FCL 81.03%, P = 0.58) corresponding to mean glycemia (HCL 6.65 mmol/L vs MA only 7.34 mmol/L vs FCL 7.05 mmol/L, P = 0.28). No difference was observed in the mean daily dose of insulin or in the daily carbohydrate intake. No serious adverse events occurred during the study period.
Conclusions
Our pilot study showed that FCL might be a realistic mode of treatment for people with T1D.
Comments
HCL therapy is associated with improved clinical outcomes including lower HbA1c, improved TIR, a low incidence of severe hypoglycemia, low rates of DKA (25,26,33,34), and improved quality of life measures (35). These devices are now recommended as the standard of care for all people with T1D (29,31).
Previous work has shown that people with diabetes want closed-loop systems that require less human interaction and decrease the burden of diabetes (36). Moving toward MA only and FCL systems can help achieve these goals. Petruzelkova and colleagues describe a camp-based study of an open-source system, AndroidAPS, used in HCL, MA only, and FCL modes. The TIR (3.9–10 mmol/L) was similar among the three modes, but HCL mode had the highest time below 3 mmol/L.
Although this study was small (n = 16), the data are promising that AID systems can run safely and effectively in less burdensome modes (MA only and FCL). This study used an open-source HCL system, and it would be beneficial to conduct studies to achieve regulatory approval for this system. In addition, it is important that similar studies be conducted with systems that are commercially available to make less burdensome systems more accessible.
Extended Use of an Open-Source Automated Insulin Delivery System in Children and Adults with Type 1 Diabetes: The 24-Week Continuation Phase Following the CREATE Randomized Controlled Trial
Burnside MJ1,2, Lewis DM3, Crocket HR4, Meier RA1, Williman JA5, Sanders OJ1,2, Jefferies CA6,7, Faherty AM6, Paul RG4,8, Lever CS8, Price SKJ8, Frewen CM9, Jones SD9, Gunn TC10, Lampey C6, Wheeler BJ9,11, de Bock MI1,2
1Department of Pediatrics, University of Otago, Christchurch, Christchurch, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 2Pediatric Department, Te Whatu Ora Health New Zealand Waitaha Canterbury, Christchurch, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 3OpenAPS, Seattle, WA; Te Whatu Ora Southern, Dunedin, New Zealand; 4Te Huataki Waiora School of Health, Sport & Human Performance, University of Waikato, Hamilton, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 5Department of Population Health, University of Otago, Christchurch, Christchurch, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 6Department of Pediatric Endocrinology, Starship Children's Health, Te Whatu Ora Te Toka Tumai, Auckland, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 7Liggins Institute and Department of Pediatrics, University of Auckland, Auckland, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 8Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 9Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 10Nightscout New Zealand, Hamilton, New Zealand; Te Whatu Ora Southern, Dunedin, New Zealand; 11Pediatric Department, Te Whatu Ora Southern, Dunedin, New Zealand
This study assessed the long-term efficacy and safety of an open-source automated insulin delivery (AID) in children and adults (7–70 years) with type 1 diabetes (T1D).
Methods
Both arms of a 24-week, randomized, controlled trial comparing open-source AID (OpenAPS algorithm within a modified version of AndroidAPS, preproduction DANA-insulin pump, Dexcom G6 continuous glucose monitor) with sensor-augmented pump therapy (SAPT), entered a 24-week continuation phase where the SAPT arm (termed SAPT-AID) crossed over to join the open-source AID arm. Most participants (69 of 94) used a preproduction YpsoPump insulin pump during the continuation phase. Analyses incorporated all 52 weeks of data and combined between-group and within-subject differences to calculate an overall ‘‘treatment effect’’ of AID versus SAPT.
Results
Mean time in range (TIR, 3.9–10 mmol/L [70–180 mg/dL]) was 12.2% higher with AID than SAPT ([95% CI, 10.4–14.1] P < 0.001). TIR was 56.9% (95% CI, 54.2–59.6) with SAPT and 69.1% (95% CI, 67.1–71.1) with AID. The treatment effect did not differ by age (P = 0.39) or insulin pump type (P = 0.37). HbA1c was 5.1 mmol/mol lower [0.5%] with AID ([95% CI, −6.6 to −3.6], P < 0.001). There were no episodes of diabetic ketoacidosis or severe hypoglycemia with either treatment over the 48 weeks. Six participants (all in SAPT-AID) withdrew: three with hardware issues, two preferred SAPT, and one with infusion-site skin irritation.
Conclusions
Further evaluation of the Community-derived Automated Insulin Delivery (CREATE) trial to 48 weeks confirms that open-source AID is efficacious and safe with different insulin pumps and demonstrates sustained glycemic improvements without additional safety concerns.
Comments
The study by Burnside and colleagues previously discussed lasted for 24 weeks; this report extends that period to 48 weeks to show durability of effectiveness and safety of open-source AID for people with T1D. Academia and industry have advanced diabetes technology over the past 2 decades, but the important role played by people with T1D and their advocates in the development of do-it-yourself (DIY) systems for T1D care must be acknowledged. Many would claim that “DIY” is not an appropriate term; rather, “open source” is more accurate, given the extensive network of people on these systems and those supporting their use.
This report adds to the now extensive literature and user experience of open-source AID, showing its effectiveness and safety. In parallel, health-care providers have generally become more accepting of these systems, as evidenced by recent consensus statements in support of open-source AID systems. We are sure to see continued use of these systems, so continued documentation of their safety and effectiveness is greatly appreciated.
Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 Diabetes
Bionic Pancreas Research Group; Russell SJ1, Beck RW4, Damiano ER2,3, El-Khatib FH3, Ruedy KJ4, Balliro CA1, Li Z4, Calhoun P4, Wadwa RP6, Buckingham B7, Zhou K10, Daniels M8, Raskin P11, White PC11, Lynch J12, Pettus J9, Hirsch IB13, Goland R14, Buse JB15, Kruger D16, Mauras N5, Muir A17, McGill JB18, Cogen F19, Weissberg-Benchell J20, Sherwood JS1, Castellanos LE1, Hillard MA1, Tuffaha M1, Putman MS1, Sands MY1, Forlenza G6, Slover R6, Messer LH6, Cobry E6, Shah VN6, Polsky S6, Lal R7, Ekhlaspour L7, Hughes MS7, Basina M7, Hatipoglu B10, Olansky L10, Bhangoo A8, Forghani N8, Kashmiri H8, Sutton F8, Choudhary A11, Penn J11, Jafri R12, Rayas M12, Escaname E12, Kerr C12, Favela-Prezas R12, Boeder S9, Trikudanathan S13, Williams KM14, Leibel N14, Kirkman MS15, Bergamo K15, Klein KR15, Dostou JM15, Machineni S15, Young LA15, Diner JC15, Bhan A16, Jones JK16, Benson M5, Bird K5, Englert K5, Permuy J5, Cossen K17, Felner E17, Salam M18, Silverstein JM18, Adamson S18, Cedeno A18, Meighan S19, Dauber A19
1Diabetes Research Center, Massachusetts General Hospital, Boston, MA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 2Boston University, Boston, MA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 3Beta Bionics, Concord, MA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 4Jaeb Center for Health Research, Tampa, FL; Ann and Robert Lurie Children's Hospital, Chicago, IL; 5Nemours Children's Health Jacksonville, Jacksonville, FL; Ann and Robert Lurie Children's Hospital, Chicago, IL; 6Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO; Ann and Robert Lurie Children's Hospital, Chicago, IL; 7Stanford University School of Medicine, Palo Alto, CA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 8Children's Hospital of Orange County, Orange, CA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 9University of California, San Diego, La Jolla, CA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 10Cleveland Clinic, Cleveland, OH; Ann and Robert Lurie Children's Hospital, Chicago, IL; 11University of Texas Southwestern Medical Center, Dallas, TX; Ann and Robert Lurie Children's Hospital, Chicago, IL; 12University of Texas Health Science Center, San Antonio, TX; Ann and Robert Lurie Children's Hospital, Chicago, IL; 13University of Washington, Seattle, WA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 14Naomi Berrie Diabetes Center, Columbia University, New York, NY; Ann and Robert Lurie Children's Hospital, Chicago, IL; 15University of North Carolina, Chapel Hill, NC; Ann and Robert Lurie Children's Hospital, Chicago, IL; 16Henry Ford Health System, Detroit, MI; Ann and Robert Lurie Children's Hospital, Chicago, IL; 17Emory University, Atlanta, GA; Ann and Robert Lurie Children's Hospital, Chicago, IL; 18Washington University in St. Louis, St. Louis, MO; Ann and Robert Lurie Children's Hospital, Chicago, IL; 19Children's National Hospital, Washington, DC; Ann and Robert Lurie Children's Hospital, Chicago, IL; 20Pritzker Department of Psychiatry and Behavioral Health, Ann and Robert Lurie Children's Hospital, Chicago, IL
This study is also discussed in DIA-2024-2505, page S-68.
For routine operation, semiautomated insulin-delivery systems require individualized insulin regimens for the initialization of therapy and meal doses based on carbohydrate counting. By contrast, the bionic pancreas is initialized with only body weight; it makes all dose decisions, delivers insulin autonomously, and uses meal announcements without carbohydrate counting.
Methods
This 13-week, multicenter, randomized trial with persons 6 years of age and older with type 1 diabetes (T1D) randomly assigned the participants in a 2:1 ratio either to receive bionic pancreas treatment with either insulin aspart or insulin lispro, or to receive standard care (any insulin-delivery method with unmasked, real-time continuous glucose monitoring [CGM]). The primary outcome was the glycated hemoglobin (HbA1c) level at 13 weeks. The key secondary outcome was the percentage of time that the CGM-assessed glucose level was below 54 mg/dL (the prespecified noninferiority limit for this outcome was 1 percentage point), and safety was also assessed.
Results
A total of 219 participants aged 6–79 years were assigned to the bionic pancreas group, and 107 to the standard care group. In the bionic pancreas group the HbA1c level decreased from 7.9% to 7.3%; in the standard care group it showed no change (7.7% at both time points) (mean adjusted difference at 13 weeks: −0.5 percentage points [95% CI, −0.6 to −0.3], P < 0.001). The percentage of time that the CGM-assessed glucose level was below 54 mg/dL did not differ significantly between the two groups (13-week adjusted difference: 0.0 percentage points [95% CI, −0.1 to 0.04], P < 0.001 for noninferiority). The rate of severe hypoglycemia was 17.7 events per 100 participant-years in the bionic pancreas group and 10.8 events per 100 participant-years in the standard care group (P = 0.39). No episodes of diabetic ketoacidosis occurred in either group.
Conclusions
Use of a bionic pancreas was associated with a greater reduction in HbA1c compared with standard care in this 13-week, randomized trial involving adults and children with T1D.
Comments
Research on AID has made remarkable progress in the past two decades from early in-hospital studies to the current situation of clinical translation. An important issue for people with diabetes and for diabetes clinicians is the availability of choice in AID systems because one size does not fit all. Having options will lead to increased satisfaction for users and drive competition for future innovation. These data from the Bionic Pancreas Research Group demonstrate the benefit of their insulin-only system to improve HbA1c over 13 weeks. Further data are expected from this group on the system's usability as well as future iterations of its system with the addition of glucagon for a bihormonal approach. With FDA approval in the summer of 2023, one next step will be data on the clinical translation and performance of the bionic pancreas.
The Insulin-Only Bionic Pancreas Improves Glycemic Control in non-Hispanic White and Minority Adults and Children with Type 1 Diabetes
Castellanos LE1, Russell SJ2, Damiano ER2,3, Beck RW4, Shah VN5, Bailey R4, Calhoun P4, Bird K6, Mauras N6; Bionic Pancreas Research Group
1Massachusetts General Hospital, Boston, MA; Jacksonville, FL; 2Beta Bionics, Concord, MA; Jacksonville, FL; 3Boston University, Boston, MA; Jacksonville, FL; 4Jaeb Center for Health Research, Tampa, FL; Jacksonville, FL; 5Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO; Jacksonville, FL; 6Nemours Children's Health, Jacksonville, FL
This subanalysis of the randomized Insulin-only Bionic Pancreas Pivotal clinical trial investigated the impact of the investigational iLet bionic pancreas use on glycemic control in a diverse population of individuals with type 1 diabetes (T1D) with a specific focus on racial and ethnic minority groups.
Methods
From January to July 2021, the study randomized 326 participants, aged 6 to 79 years old. The trial included a more diverse cohort of people with T1D, representing various racial and ethnic backgrounds, including minority groups. The participants were 74% non-Hispanic White, 10% non-Hispanic Black, 10% Hispanic, and 6% other or mixed race. The study compared the use of the iLet bionic pancreas to standard care, which included one-third on currently available automated insulin delivery systems, one-third on insulin pump therapy with continuous glucose monitoring (CGM), and one-third on multiple daily injections with CGM.
Results
The results demonstrated that both non-Hispanic White and minority participants experienced improvements in glycemic control when using the iLet bionic pancreas. A similar treatment effect of the bionic pancreas on change in glycated hemoglobin (HbA1c) levels from baseline to 13 weeks, adjusting for baseline HbA1c, was observed in non-Hispanic Whites and minorities (P for interaction = 0.57). In non-Hispanic Whites, the mean baseline-adjusted difference in 13-week HbA1c between the bionic pancreas and standard care groups was 0.45%, and this difference among minorities was 0.53%. In non-Hispanic Whites, the mean baseline adjusted difference in time in the range between those using iLet and standard care groups was 10% (95% CI, 7–12; P < 0.001) and in minorities was 14% (95% CI, 10–18; P < 0.001). The groups that started with higher baseline HbA1c levels experienced more significant reductions in HbA1c levels, and the results were similar among the 165 pediatric participants.
Conclusions
The study's findings suggest that the iLet bionic pancreas can positively impact glycemic outcomes in a racially and ethnically diverse population with T1D. The disparities in mean HbA1c between non-Hispanic White and minority participants diminished with the use of the iLet system. The autonomous dosing capabilities of the bionic pancreas may have contributed to these improvements, as the system autonomously determines the size of each insulin dose, potentially relying less on user or health-care provider skills.
Comments
The randomized Insulin-only Bionic Pancreas Pivotal clinical trial investigated the impact of the investigational iLet bionic pancreas use on glycemic control in a diverse population of individuals with T1D. Previous research had shown significant disparities in diabetes treatment modalities and outcomes among different racial and ethnic groups, even after adjustment for socioeconomic factors (37): people from minority backgrounds often face worse metabolic control, higher rates of complications, and limited access to technology for managing their diabetes. This randomized trial addressed those disparities in diabetes care and outcomes by including a more diverse T1D population with regard to minority representation than previous studies of HCL systems. The iLet bionic pancreas improved glycemic control across racial groups and socioeconomic/educational levels, indicating that using iLet could help reduce disparities in diabetes treatment.
Overall, this study highlights the importance of including a representative and diverse population in clinical trials (38) and opens new possibilities for improving diabetes care and health outcomes for everyone. On the other hand, the study's limitations are the relatively small number of participants from specific minority backgrounds and the short duration of the trial. Therefore, more research is needed to explore further the relationship between race and ethnicity, socioeconomic and educational levels, and glycemic control with AID systems. In addition, by addressing barriers like limited access to care and implicit bias, we can improve diabetes management more equitably (39).
Implementation of Diabetes Technology in Registries
Association of Achieving Time in Range Clinical Targets with Treatment Modality among Youths with Type 1 Diabetes
Dovc K1,2, Lanzinger S3,4, Cardona-Hernandez R5, Tauschmann M6, Marigliano M7,8, Cherubini V9, Preikša R10, Schierloh U11, Clapin H12, AlJaser F13, Pelicand J14,15, Shukla R16, Biester T17
1Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, Ljubljana, Slovenia; Auf der Bult, Hannover, Germany; 2Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia; Auf der Bult, Hannover, Germany; 3Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany; Auf der Bult, Hannover, Germany; 4German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; Auf der Bult, Hannover, Germany; 5Division of Pediatric Endocrinology, Hospital Sant Joan de Déu, Barcelona, Spain; Auf der Bult, Hannover, Germany; 6Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; Auf der Bult, Hannover, Germany; 7Pediatric Diabetes and Metabolic Disorders Unit, Regional Center for Pediatric Diabetes, University City Hospital of Verona, Verona, Italy; Auf der Bult, Hannover, Germany; 8Department of Surgery, Dentistry, Paediatrics and Gynaecology, University of Verona, Verona, Italy; Auf der Bult, Hannover, Germany; 9Division of Pediatric Diabetology, Department of Women's and Children's Health, Salesi Hospital, Ancona, Italy; Auf der Bult, Hannover, Germany; 10Institute and Clinic of Endocrinology, Lithuanian University of Health Sciences, Kaunas, Lithuania; Auf der Bult, Hannover, Germany; 11Department of Pediatric Diabetes and Endocrinology, Centre Hospitalier Luxembourg, Luxembourg, Luxembourg; Auf der Bult, Hannover, Germany; 12Department of Diabetes and Endocrinology, Perth Children's Hospital, Perth, Australia; Auf der Bult, Hannover, Germany; 13Department of Pediatrics, Amiri Hospital, Ministry of Health, Dasman, Kuwait; Auf der Bult, Hannover, Germany; 14Pediatric and Adolescent Diabetes Program, Department of Pediatrics, San Camilo Hospital, San Felipe, Chile; Auf der Bult, Hannover, Germany; 15Medicine School, Universidad de Valparaiso, San Felipe, Chile; Auf der Bult, Hannover, Germany; 16Department of Diabetes and Endocrinology, Center for Diabetes & Endocrine Diseases, Kanpur, India; Auf der Bult, Hannover, Germany; 17Children's Hospital, Auf der Bult, Hannover, Germany
This study is also discussed in DIA-2024-2502, page S-14, and DIA-2024-2512, page S-187.
Continuous glucose monitoring (CGM) devices have demonstrated efficacy in people with type 1 diabetes (T1D). In adults with T1D, the use of real-time CGM (rtCGM) has been associated with improved glycemic control compared with intermittently scanned CGM (isCGM), but there are limited data available for youths. This study assessed real-world data on achievement of time in range clinical targets associated with different treatment modalities in youths with T1D.
Methods
This multinational cohort study included individuals younger than 21 years (youths) with T1D for a duration of at least 6 months who provided CGM data between January 1, 2016, and December 31, 2021. The participants were enrolled from the international Better Control in Pediatric and Adolescent Diabetes: Working to Create Centers of Reference (SWEET) registry. Data from 21 countries were included. Participants were divided into four treatment modalities: isCGM with or without insulin pump use and rtCGM with or without insulin pump use. The primary outcome was the proportion of individuals in each treatment modality group achieving recommended CGM clinical targets.
Results
Among the 5219 participants (2714 [52.0%] male; median age, 14.4 [IQR, 11.2–17.1] years), the median duration of diabetes was 5.2 (IQR, 2.7–8.7) years and median hemoglobin A1c (HbA1c) level was 7.4% (IQR, 6.8%–8.0%). The treatment modality was associated with the proportion of individuals achieving the recommended clinical targets. Adjusted for sex, age, diabetes duration, and body mass index standard deviation score, the proportion achieving the recommended greater than 70% time in range target (70–180 mg/dL) was highest with rtCGM plus insulin pump use (36.2% [95% CI, 33.9%–38.4%]), followed by rtCGM plus injection use (20.9% [95% CI, 18.0%–24.1%]), isCGM plus injection use (12.5% [95% CI, 10.7%–14.4%]), and isCGM plus insulin pump use (11.3% [95% CI, 9.2%–13.8%]) (P < 0.001). Similar trends were observed for less than 25% time above range (> 180 mg/dL) (rtCGM plus insulin pump, 32.5% [95% CI, 30.4%–34.7%]; vs isCGM plus insulin pump, 12.8% [95% CI, 10.6%–15.4%]; P < 0.001) and less than 4% time below range (< 70 mg/dL) target (rtCGM plus insulin pump, 73.1% [95% CI, 71.1%–75.0%]; vs isCGM plus insulin pump, 47.6% [95% CI, 44.1%–51.1%], P < 0.001). The adjusted time in range was highest among rtCGM plus insulin pump users (64.7% [95% CI, 62.6%–66.7%]). Treatment modality was associated with the proportion of participants experiencing severe hypoglycemia and diabetic ketoacidosis events.
Conclusions
Use of rtCGM and insulin pump was associated with increased time in range and a decrease in time below range.
Comments
CGM is recommended as the standard of care for youth with T1D (30,40). CGM can be either real-time or intermittently scanned, and access to each technology may be limited by availability, individual preferences, provider preferences, and cost. Data from adults suggest that real-time CGM can be more effective than intermittently scanned CGM in achieving glycemic targets (41). Unfortunately, there had been no large-scale studies in youth to assess the efficacy of each modality.
Clinical trials have strict protocols and often draw participants from a subset of the population (38), but diabetes registries can provide real-world data on diabetes outcomes. Dovc and colleagues use CGM data from the SWEET international diabetes registry to assess how different treatment modalities affect the proportion of youths meeting CGM targets. They found that youths treated with real-time CGM were more likely to achieve glycemic targets than those treated with intermittently scanned CGM. The youths who used real-time CGM combined with insulin pump therapy were most likely to meet targets, followed by those with real-time CGM and MDI.
The strengths of this study are that it includes a large number of youths (n = 5219) from around the world. The authors were able to differentiate the pump users from the MDI users but could not differentiate those using AID delivery systems, which rely on real-time CGM, from those using open loop. This is important because AID systems are independently associated with improved glycemic outcomes. In addition, the study did not include demographic factors such as race and ethnicity or socioeconomic status in their analysis. Despite these limitations, this study's results suggest that real-time CGM may help youths reach glycemic targets and should be recommended to youths with T1D.
Temporal Changes in Hemoglobin A1c and Diabetes Technology Use in DPV, NPDA, and T1DX Pediatric Cohorts from 2010 to 2018
Lal RA1,2,3, Robinson H4, Lanzinger S5,6, Miller KM7, Pons Perez S4, Kovacic R8, Calhoun P7, Campbell F9, Naeke A10, Maahs DM1,3, Holl RW5,6, Warner J11
1Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; Cardiff, UK; 2Division of Endocrinology, Department of Medicine, Stanford University School of Medicine, Stanford, CA; Cardiff, UK; 3Stanford Diabetes Research Center, Stanford, CA; Cardiff, UK; 4Royal College of Pediatrics and Child Health, London, UK; Cardiff, UK; 5Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, Ulm, Germany; Cardiff, UK; 6German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; Cardiff, UK; 7Jaeb Center for Health Research, Tampa, FL; Cardiff, UK; 8Bezirkskrankenhaus Lienz, Lienz, Austria; Cardiff, UK; 9Leeds Children's Hospital, Leeds, UK; Cardiff, UK; 10Universitätsklinikum Dresden, Dresden, Germany; Cardiff, UK; 11Children's Hospital for Wales, Cardiff, UK
The German/Austrian Diabetes Patient Follow-up Registry (Diabetes-Patienten-Verlaufsdokumentation or DPV), England/Wales National Pediatric Diabetes Audit (NPDA), and Type 1 Diabetes Exchange (T1DX) in the United States investigated changes in hemoglobin A1c (HbA1c) and diabetes technology use from 2010 to 2018.
Methods
Registry/audit data from 2010 to 2018 were analyzed in annual cohorts using linear regression for those < 18 years of age with T1D diagnosed at age > 6 months. Time trends in HbA1c, pump, and continuous glucose monitoring (CGM) use were studied using repeated measurements linear and logistic regression models with an autoregressive covariance structure and with year and data source as independent variables.
Results
A total of 1,172,980 visits among 114,264 (54,119 DPV, 43,550 NPDA, 16,595 T1DX) patients were identified. From 2010 to 2018, HbA1c levels remained clinically stable in DPV (7.7% [61 mmol/mol] to 7.6% [60 mmol/mol]), decreased in the NPDA (8.7% [72 mmol/mol] to 7.9% [63 mmol/mol]), and increased in T1DX (8.0% [64 mmol/mol] to 8.5% [69 mmol/mol]). In all registries/audits, insulin pump, and CGM use increased over time, with greatest pump use in T1DX and lowest uptake reported in NPDA.
Conclusions
These data reveal three different longitudinal patterns of change in registry/audit HbA1c levels from 2010 to 2018. Diabetes technology use increased throughout at different rates. Quality improvement (QI) programs have been ongoing in DPV for 25 years, began in NPDA in 2009, and began in T1DX in 2016. It may be speculated that in England and Wales the development of networks, peer review, and implementation of QI measures contributed to the reductions in population HbA1c. Many of these interventions had been implemented in DPV before 2010. Further efforts to understand this improvement, including the role of QI, and continued success within standardized documentation and benchmarking could inform T1DX programs to reduce HbA1c.
Comments
Diabetes registries and audits play an important role in benchmarking outcomes in care and documenting the real-world benefits (or lack thereof) of how advances in diabetes care (and diabetes technology) translate to clinical populations. This collaborative study from registries/audits in Germany/Austria, England/Wales, and the United States tells a story of three systems with different experiences in quality improvement (QI).
Germany and Austria, who have had a long-standing program of benchmarking and QI, had the best HbA1c outcomes. England and Wales at the beginning of this study period in 2010 had similarly elevated HbA1c levels compared with the United States until they implemented a comprehensive QI initiative to improve care and outcomes. These data clearly show the success of the England/Wales QI program with their 0.8% reduction in HbA1c. By contrast, QI efforts in the United States in the T1DX have been more recent; as of the publication of this study, they had not yet translated into improved clinical outcomes.
The experiences of DPV and NPDA strongly suggest that national or registry-wide QI programs are successful in achieving better outcomes, but it takes sustained and coordinated effort. This study teaches us the importance of QI work for T1D outcomes, and documents for the United States the potential benefit and need for continued work to improve outcomes. Furthermore, funders should consider the population benefit gained in the England/Wales cohort after the 0.8% reduction in HbA1c and should support efforts to fully realize the translation of new discoveries in diabetes technology and therapeutics to the full population who can benefit.
Transatlantic Comparison of Pediatric Continuous Glucose Monitoring Use in the Diabetes-Patienten-Verlaufsdokumentation Initiative and Type 1 Diabetes Exchange Quality Improvement Collaborative
DeSalvo DJ1, Lanzinger S2, Noor N3,4, Steigleder-Schweiger C5, Ebekozien O3,6, Sengbusch SV7, Yayah Jones NH8, Laubner K9, Maahs DM10, Holl RW2
1Division of Pediatric Diabetes & Endocrinology. Baylor College of Medicine, Texas Children's Hospital, Houston, TX; Department of Pediatrics, Stanford University, Stanford, CA; 2Department of Epidemiology and Biometry ZIBMT, Ulm University, Ulm, Germany; Department of Pediatrics, Stanford University, Stanford, CA; 3T1D Exchange, Boston, MA; Department of Pediatrics, Stanford University, Stanford, CA; 4Aga Khan University, Department of Emergency Medicine, Sindh, Pakistan; Department of Pediatrics, Stanford University, Stanford, CA; 5Department of Paediatrics, Paracelsus Medical University, Salzburg, Austria; Department of Pediatrics, Stanford University, Stanford, CA; 6University of Mississippi Medical Center, Jackson, MS; Department of Pediatrics, Stanford University, Stanford, CA; 7Department of Paediatrics and Adolescent Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Germany; Department of Pediatrics, Stanford University, Stanford, CA; 8UC Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, Stanford University, Stanford, CA; 9Division of Endocrinology and Diabetology, Department of Medicine II, Medical Center–University of Freiburg, Faculty of Medicine, Freiburg im Breisgau, Germany; Department of Pediatrics, Stanford University, Stanford, CA; 10Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA
Youths and young adults with type 1 diabetes (T1D) struggle to meet glycemic targets. Diabetes devices, including continuous glucose monitors (CGM), may impact glycemic control.
Methods
CGM use was determined in youth and young adults with T1D at nine U.S. T1D Exchange Quality Improvement (T1DX-QI) Collaborative centers and 402 European diabetes prospective follow-up registry sites (Diabetes-Patienten-Verlaufsdokumentation [DPV]) from 2017 to 2020. The association of CGM use to glycemic control as measured by hemoglobin A1c (HbA1c) was examined.
Results
Use of CGM increased each year from 2017 to 2020 across all age ranges (< 6, 6–<12, 12–<18, 18–<25 years) in both registries, and the CGM users had lower mean HbA1c levels compared with the nonusers, regardless of insulin delivery method, for all years analyzed.
Conclusions
CGM use appeared to increase more in the European DPV than in the U.S. T1DX-QI, which may be due to transatlantic differences in health-care systems, insurance coverage, and prescriber habits.
Comments
Multiple studies have shown that use of CGM is associated with improved glycemic outcomes in people with T1D (6,10,37,42). Both the American Diabetes Association (30) and the International Society for Pediatric and Adolescent Diabetes (42) recommend CGM for all people with T1D. Unfortunately, CGM access is not universal. Previous work comparing CGM use in the DPV registry compared with the T1D Exchange clinical registry, which only included research participants, showed that CGM use increased in both registries, but the youths in the DPV registry were more likely to achieve HbA1c targets (43). In addition, disparities in CGM use and HbA1c grew between the highest and lowest socioeconomic quintiles in the T1D Exchange clinical registry but not in the DPV registry (44).
Registry studies allow for the tracking of real-world diabetes metrics. The DPV registry collects data from all clinics in Germany, Austria, Luxembourg, and Switzerland whereas the T1DX-QI collects clinic-level data from over 50 diabetes centers in the United States. Analyses of registry data can help identify trends in diabetes technology use and associations with clinical outcomes.
This study compares adoption of CGM in the DPV registry compared with the T1DX-QI registry from 2017–2020. The data show that although CGM adoption increased in both registries, the increase was more pronounced in the DPV registry. This study does not identify the cause of these differences, but DeSalvo and colleagues hypothesize that differences in health-care systems, CGM coverage, and prescriber habits may play a role. Performing further analyses of registry data may help elucidate contributors to the differences between the outcomes in both registries. Future work should focus on the implementation of interventions to close gaps.
Other Therapies for Preservation of Pancreatic β-Cell Function
Effect of Tight Glycemic Control on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical Trial
McVean J1,2, Forlenza GP3, Beck RW4, Bauza C4, Bailey R4, Buckingham B5, DiMeglio LA6, Sherr JL7, Clements M8, Neyman A6, Evans-Molina C6, Sims EK6, Messer LH3,9, Ekhlaspour L5,10, McDonough R8, Van Name7 M, Rojas D4, Beasley S1, DuBose S4,11, Kollman C4, Moran A1 for the CLVer Study Group
1University of Minnesota, MN; Atlanta, GA; 2Now with Medtronic, Northridge, CA; Atlanta, GA; 3Barbara Davis Center, University of Colorado Anschutz Medical Campus, Denver, CO; Atlanta, GA; 4Jaeb Center for Health Research, Tampa, FL; Atlanta, GA; 5Stanford University, Stanford, CA; Atlanta, GA; 6Indiana University School of Medicine, Indianapolis, IN; Atlanta, GA; 7Yale School of Medicine, New Haven, CT; Atlanta, GA; 8Children's Mercy Hospital, Kansas City, MO; Atlanta, GA; 9Now with Tandem Diabetes Care, San Diego, CA; Atlanta, GA; 10Now with University of California, San Francisco, CA; Atlanta, GA; 11Now with Emory University, Atlanta, GA
Residual pancreatic β-cell function is associated with a reduced risk of long-term complications. Methods to preserve β-cell function in newly diagnosed type 1 diabetes (T1D) have been sought due to their potential short- and long-term clinical benefit. This study determined the effectiveness of intensive diabetes management to achieve near normalization of glucose levels on preservation of pancreatic β-cell function in youths with newly diagnosed T1D.
Methods
This multicenter (n = 6) randomized, double-blind, clinical trial was conducted in the United States and included youths with newly diagnosed T1D aged 7–17 years. Patients were randomly assigned to intensive diabetes management, which included use of an automated insulin delivery system (n = 61), or standard care, which included use of a continuous glucose monitor (CGM) (n = 52). Participants weighing ≥ 30 kg were also assigned to receive either oral verapamil or placebo. The primary outcome was mixed-meal tolerance test–stimulated C-peptide area under the curve (a measure of pancreatic β-cell function) at 52 weeks from diagnosis.
Results
Among 113 participants (43% females) of a mean age of 11.8 ± 2.8 years and a mean time from diagnosis to randomization of 24 ± 5 days, 108 participants (96%) completed the trial. The mean C-peptide area under the curve decreased from 0.57 pmol/mL at baseline to 0.45 pmol/mL at 52 weeks in the intensive management group, and from 0.60 to 0.50 pmol/mL in the standard care group. No significant difference in C-peptide levels were measured during a mixed-meal tolerance test 52 weeks after diagnosis between the intensive management and standard care groups (treatment group difference, −0.01 [95%CI, −0.11 to 0.10], P = 0.89). The mean time in the target range of 70–180 mg/dL, measured with CGM, at 52 weeks was 78% in the intensive management group versus 64% in the standard care group (adjusted difference, 16% [95% CI, 10%–22%]). One severe hypoglycemia event and one diabetic ketoacidosis event occurred in each group.
Conclusions
Intensive diabetes management with automated insulin delivery achieved good glucose control but did not affect the decline in pancreatic C-peptide secretion at 52 weeks in youths with newly diagnosed T1D.
Effect of Verapamil on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical Trial
Forlenza GP1, McVean J2,3, Beck RW4, Bauza C4, Bailey R4, Buckingham B5, DiMeglio LA6, Sherr JL7, Clements M8, Neyman A6, Evans-Molina C6, Sims EK6, Messer LH1,9, Ekhlaspour L5,10, McDonough R8, Van Name M7, Rojas D4, Beasley S2, DuBose S4,11, Kollman C4, Moran A2; for the CLVer Study Group
1Barbara Davis Center, Anschutz Medical Campus, University of Colorado, Aurora, CO; Atlanta, GA; 2University of Minnesota, Minneapolis, MN; Atlanta, GA; 3now with Medtronic, Northridge, CA; Atlanta, GA; 4Jaeb Center for Health Research, Tampa, FL; Atlanta, GA; 5Stanford University, Stanford, CA; Atlanta, GA; 6Indiana University School of Medicine, Indianapolis, IN; Atlanta, GA; 7Yale School of Medicine, Yale University, New Haven, CT; Atlanta, GA; 8Children's Mercy Hospital, Kansas City, MO; Atlanta, GA; 9Tandem Diabetes Care, San Diego, CA; Atlanta, GA; 10University of California, San Francisco, CA; Atlanta, GA; 11Emory University, Atlanta, GA
This study is also discussed in DIA-2024-2514, page S-212.
Thioredoxin-interacting protein (TXNIP) overexpression has been shown to induce pancreatic β-cell apoptosis and can be involved in glucotoxicity-induced β-cell death in culture and mouse models. Calcium channel blockers, such as verapamil, reduce TXNIP expression and β-cell apoptosis and may be beneficial in β-cell preservation after type 1 diabetes (T1D) diagnosis. This study evaluated whether once-daily oral verapamil preserves pancreatic β-cell function in children and adolescents with newly diagnosed T1D.
Methods
This multicenter (n = 6), double-blind, randomized clinical trial conducted in the United States included children and adolescents aged 7–17 years with newly diagnosed T1D who weighed ≥ 30 kg. The participants were randomly assigned 1:1 to once-daily oral verapamil (n = 47) or placebo (n = 41). The participants also were assigned to receive either intensive diabetes management or standard diabetes care.
The primary outcome was area under the curve values for C-peptide level stimulated by a mixed-meal tolerance test at 52 weeks from diagnosis of T1D.
Results
The study included 88 participants (41% females) of a mean age of 12.7 ± 2.4 years; and the mean time from diagnosis to randomization was 24 ± 4 days. Of them 83 (94%) completed the trial. In the verapamil group, the mean C-peptide area under the curve was 0.66 pmol/mL at baseline and 0.65 pmol/mL at 52 weeks compared with 0.60 pmol/mL at baseline and 0.44 pmol/mL at 52 weeks in the placebo group (adjusted between-group difference, 0.14 pmol/mL [95%CI, 0.01 to 0.27 pmol/mL]; P = 0.04). C-peptide levels measured during a mixed-meal tolerance test 52 weeks after diagnosis were 30% higher with verapamil compared with placebo. The percentage of participants with a 52-week peak C-peptide level of ≥ 0.2 pmol/mL was 95% in the verapamil group versus 71% in the placebo group. At 52 weeks, the glycated hemoglobin (HbA1c) level was 6.6% in the verapamil group versus 6.9% in the placebo group (adjusted between-group difference, 0.3% [95% CI, −1.0% to 0.4%]). Eight participants (17%) in the verapamil group and 8 participants (20%) in the placebo group had a nonserious adverse event considered to be related to treatment.
Conclusions
Verapamil partially preserved stimulated C-peptide secretion at 52 weeks compared with placebo and was well tolerated in pediatric patients with newly diagnosed T1D.
Comments
Maintenance of even modest residual β-cell function, which is assessed by stimulated C-peptide secretion, is a desirable goal and is associated with improved glucose control and a lower risk of diabetes-related vascular complications and hypoglycemia (45,46). Therefore, a variety of approaches to preserve β-cell function in newly diagnosed T1D have been tested. Until now, the majority of disease modification strategies for T1D have focused on targeting the immune responses (47).
A recent systematic review and meta-analysis of all randomized controlled trials of interventions to preserve β-cell function in people with newly diagnosed T1D reported the results of interventions for improving glucose control to assess whether they successfully preserve β-cell function (48). This meta-analysis included 28 studies with 1662 participants who were grouped by intervention into six subgroups: alternative insulins, subcutaneous or intravenous insulin delivery, intensive therapy, glucose sensing, and adjuncts. The data demonstrated a lack of robust evidence that interventions to improve glucose control preserve β-cell function in new onset T1D, although the analysis was hampered by low-quality evidence and inconsistent reporting of studies.
The last two reviewed studies by McVean and colleagues and by Forlenza and colleagues tried to evaluate through a randomized control trial different approaches in newly diagnosed pediatric patients with T1D for preservation of β-cell function. It has been postulated that near normalization of glucose levels beginning shortly after diagnosis of T1D could help preserve β-cell function by reducing glucotoxicity.
Therefore, in the first study, McVean and colleagues attempted an intensive approach to management to achieve as close to normal glycemia as is currently possible in order to test the hypothesis that normalization of glucose levels beginning shortly after T1D diagnosis can preserve β-cell function. However, even with the use of the advanced technologies they did not find a significant difference in C-peptide levels measured during a mixed-meal tolerance test (a measure of pancreatic β-cell function) 52 weeks after diagnosis of T1D between the intensive management and the standard care groups. This finding was despite a higher rate of improved glycemic control, measured with CGM at 52 weeks, among the intensive management group compared with the standard care group.
Their study was limited by the occurrence of clinically significant hyperglycemia, which measured a relatively high percentage of the time (∼about 5 hours/day) even in the intensive treatment group. That is, even patients undergoing intensive treatment did not achieve the desired near-normal glycemia, which may have affected their results.
The second study by Forlenza and colleagues evaluated the impact of verapamil administration in children and adolescents with newly diagnosed T1D on preservation of β-cell function. In recent years, a growing number of pathways intrinsic to the β-cell have been linked with T1D pathophysiology. Thioredoxin-interacting protein (TXNIP) overexpression has emerged as a major factor regulating pancreatic β-cell dysfunction and apoptosis, key processes in the pathogenesis of T1D and T2D. TXNIP overexpression has been reported to be involved in glucotoxicity-induced β-cell death in culture and mouse models (49). Accumulating evidence based on basic, preclinical, and retrospective epidemiological research has suggested that TXNIP represents a promising therapeutic target for diabetes.
Calcium channel blockers such as verapamil reduce TXNIP expression and β-cell apoptosis (50), so they may be beneficial in β-cell preservation after diagnosis of T1D. Forlenza and colleagues found that administration of verapamil partially preserved stimulated C-peptide secretion at 52 weeks compared with placebo, and it was well tolerated. These results support the observation of a previous randomized placebo-controlled study in adult patients with newly diagnosed T1D (51) treated with verapamil compared with placebo, which found a 35% relative increase in stimulated C-peptide levels after 1 year.
The limitation of the latter study is its relatively small sample size. Additional studies with a larger number of patients are needed to determine the longitudinal durability of C-peptide improvement and the optimal length of therapy of verapamil. Moreover, TXNIP seems to be an exciting and promising candidate, and its inhibition may be a viable therapeutic target for promoting functional β-cell mass in humans. This needs to be further evaluated in clinical trials.
The strengths of both these studies, which were based on the same cohort of patients, are the very early intervention (within 31 days from diagnosis) and the multicenter, randomized design.
Footnotes
Author Disclosure Statement
No competing financial interests.