Abstract
Introduction
I
Multiple studies were published on the development of closed-loop insulin delivery systems in the pediatric population. These studies ranged from early safety studies that were necessary before larger, pivotal trials for regulatory approval (which were also published this past year) could be conducted. This year, building on this prior work, real-world studies provided further evaluations of approved closed-loop insulin delivery systems in use in pediatric diabetes clinics.
The continued development of these systems and their transition from research settings to clinical practice will continue to advance 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 one important goal for translation from research to clinical implementation; another is research in the youngest age groups. Broader access to diabetes technology will be an on-going mission for all involved in pediatric diabetes care so that all children can benefit.
In addition to research on closed-loop systems, other important papers published this year and related to pediatric diabetes care include those 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 22 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 (T1D) and type 2 diabetes (T2D) relating to the pediatric age group (0–18 years). We focused on key articles that offer some insight into these issues and were published between July 1, 2021, and June 30, 2022.
Key Articles Reviewed
Sanderson EE, Abraham MB, Smith GJ, Mountain JA, Jones TW, Davis EA
Rose S, Styles SE, Wiltshire EJ, Stanley J, Galland BC, de Bock MI, Tomlinson PA, Rayns JA, MacKenzie KE, Wheeler BJ
Prahalad P, Ding VY, Zaharieva DP, Addala A, Johari R, Scheinker D, Desai M, Hood K, Maahs DM
Elbalshy MM, Styles S, Haszard JJ, Galland BC, Crocket H, Jefferies C, Wiltshire E, Tomlinson P, de Bock MI, Wheeler BJ
Johnson SR, Holmes-Walker DJ, Chee M, Earnest A, Jones TW on behalf of the CGM Advisory Committee and Working Party and the ADDN Study
Everett EM, Copeland TP, Moin T, Wisk LE
Brown SA, Forlenza GP, Bode BW, Pinsker JE, Levy CJ, Criego AB, Hansen DW, Hirsch IB, Carlson AL, Bergenstal RM, Sherr JL, Mehta SN, Laffel LM, Shah VN, Bhargava A, Weinstock RS, MacLeish SA, DeSalvo DJ, Jones TC, Aleppo G, Buckingham BA, Ly TT for the Omnipod 5 Research Group
Tauschmann M, Schwandt A, Prinz N, Becker M, Biester T, Hess M, Holder M, Karges B, Näke A, Kuss O, von Sengbusch S, Holl RW, DPV Initiative
Shilo S, Godneva A, Rachmiel M, Korem T, Kolobkov D, Karady T, Bar N, Wolf BC, Glantz-Gashai Y, Cohen M, Zuckerman Levin N, Shehadeh N, Gruber N, Levran N, Koren S, Weinberger A, Pinhas-Hamiel O, Segal E
Carlson AL, Sherr JL, Shulman DI, Garg SK, Pop-Busui R, Bode BW, Lilenquist DR, Brazg RL, Kaiserman KB, Kipnes MS, Thrasher JR, Reed JHC, Slover RH, Philis-Tsimikas A, Christiansen M, Grosman B, Roy A, Vella M, Jonkers RAM, Chen X, Shin J, Cordero TL, Lee SW, Rhinehart AS, Vigersky RA, and MiniMed AHCL Study Group
Abraham MB, de Bock M, Smith GJ, Dart J, Fairchild JM, King BR, Ambler GR, Cameron FJ, McAuley SA, Keech AC, Jenkins A, Davis EA, O'Neal DN, Jones TW, Australian Juvenile Diabetes Research Fund Closed-Loop Research Group
Ware J, Allen JM, Boughton CK, Wilinska ME, Hartnell S, Thankamony A, de Beaufort C, Schierloh U, Fröhlich-Reiterer E, Mader JK, Kapellen TM, Rami-Merhar B, Tauschmann M, Nagl K, Hofer SE, Campbell FM, Yong J, Hood KK, Lawton J, Roze S, Sibayan J, Bocchino LE, Kollman C, Hovorka R for the KidsAP Consortium
Cobry EC, Bisio A, Wadwa RP, Breton MD
Renard E, Tubiana-Rufi N, Bonnemaison E, Coutant R, Dalla-Vale F, Bismuth E, Faure N, Bouhours-Nouet N, Farret A, Storey C, Donzeau A, Poidvin A, Amsellem-Jager J, Place J, Breton MD, Free-life Kid AP Study Group
Forlenza GP, Ekhlaspour L, DiMeglio LA, Fox LA, Rodriguez H, Shulman DI, Kaiserman KB, Liljenquist DR, Shin J, Lee SW, Buckingham BA
Noor N, Ebekozien O, Levin L, Stone S, Sparling DP, Rapaport R, Maahs DM
Gerhardsson P, Schwandt A, Witsch M, Kordonouri O, Svensson J, Forsander G, Battelino T, Veeze H, Danne T on Behalf of the SWEET Study Group
Marigliano M, Eckert AJ, Guness PK, Herbst A, Smart CE, Witsch M, Maffeis C, SWEET Study Group
Malik FS, Sauder KA, Isom S, Reboussin BA, Dabelea D, Lawrence JM, Roberts A, Mayer-Davis EJ, Marcovina S, Dolan L, Igudesman D, Pihoker C for the SEARCH for Diabetes in Youth Study
Battelino T, Tehranchi R, Bailey T, Dovc K, Melgaard A, Yager Stone J, Woerner S, von dem Berge T, DiMeglio L, Danne T
Shankar RR, Zeitler P, Deeb A, Jalaludin MY, Garcia R, Newfield RS, Samoilova Y, Rosario CA, Shehadeh N, Saha CK, Zhang Y, Zilli M, Scherer LW, Lam RLH, Golm GT, Engel SS, Kaufman KD
Jalaludin MY, Deeb A, Zeitler P, Garcia R, Newfield RS, Samoilova Y, Rosario CA, Shehadeh N, Saha CK, Zhang Y, Zilli M, Scherer LW, Lam RLH, Golm GT, Engel SS, Kaufman KD, Shankar RR
CONTINUOUS GLUCOSE MONITORING
Continuous Glucose Monitoring Improves Glycemic Outcomes in Children with Type 1 Diabetes: Real-World Data from a Population-Based Clinic
Sanderson EE1, Abraham MB1,2,3, Smith GJ2,3, Mountain JA1, Jones TW1,2,3, Davis EA1,2,3
1Department of Diabetes and Endocrinology, Perth Children's Hospital, Nedlands, Western Australia, Australia; 2Telethon Kids Institute, Nedlands, Western Australia, Australia; 3The University of Western Australia, Perth, Western Australia, Australia
Background
Recent research has shown that continuous glucose monitoring (CGM) improves glycemic control, but there are not enough data on CGM's effects outside of research settings. Hence, the purpose of this study was to determine glycemic outcomes in a cohort of pediatric participants in real-world setting before and after starting subsidized CGM.
Methods
In a longitudinal observational study, 348 children with a mean (SD) age of 13.4 (2.9) years at CGM onset, mean duration of type 1 diabetes (T1D) of 7.2 (3.0) years, and mean HbA1c at CGM start of 8.5% (1.5%) (69 mmol/mol) were followed up on average for 3.6 (0.66) years with HbA1c measured at clinic visits every 3 months. The mean duration of follow-up was 2.4 (0.5) years before CGM and 1.2 (0.5) years after CGM initiation. Participants were using Dexcom or Medtronic CGM through subsidy. Model-estimated HbA1c was based on pre-CGM and post-CGM growth parameters. Changes in the trend of HbA1c over time and the proportion of patients meeting the International Society for Pediatric and Adolescent Diabetes (ISPAD) HbA1c target after commencing CGM were presented.
Results
The HbA1c time trend before CGM was on average a 0.29% increase per year (95% CI, 0.24% to 0.34%) before uptake of CGM and 0.15% increase per year (95% CI, 0.03% to 0.27%) after uptake of CGM, with a significant difference in slope between before and after CGM initiation (difference −0.14% [95% CI −0.26 to −0.02], P=.02). CGM use was associated with a significant level change of −0.39% (95% CI, −0.50 to −0.28; P<.001). There were no significant differences in level or slope change as a function of socioeconomic status, insulin regimen, or duration of diabetes. Likelihood of achievement of HbA1c <7% (53 mmol/mol) showed significant difference in level (P<.001) and slope (P<.001) following CGM use. The pre-CGM proportion of patients achieving the target over time was decreasing. On the other hand, the post-CGM proportion of patients with HbA1c in the target range was significantly higher and maintained.
Conclusion
In this real-world observational study of children and adolescents with T1D, HbA1c levels immediately reduced in participants with subsidized CGM, and that reduction was sustained over time. Furthermore, CGM resulted in better diabetes management in a pediatric age group, regardless of socioeconomic status.
Comments
Despite the mounting evidence for the benefits of CGM usage from randomized controlled trials (1,2), data from longitudinal studies in real-world settings and younger age groups are lacking. The study by Sanderson et al. has shown a small but statistically significant and sustained improvement of HbA1c after commencement of CGM. Furthermore, improvement of glycemic control was independent of socioeconomic status, thus emphasizing the importance of the general reimbursement of this technology. In addition, the majority of the study cohort commenced CGM at the age of early adolescence. Since adolescents exhibit a trend of worsening glycemic control, this study argues that the use of CGM should be encouraged in this age group. The limitation of this study might be a low number of participants, but longitudinal data documenting the benefits of CGM usage under daily life conditions is of particular importance. This study provides a good argument for health-care systems to reimburse CGM for people living with T1D, especially in the pediatric age group. An important area for future research would be outcomes over a more extended period and the potential for the cost-effectiveness of CGM.
Use of Intermittently Scanned Continuous Glucose Monitoring in Young People with High-Risk Type 1 Diabetes—Extension Phase Outcomes Following a 6-Month Randomized Control Trial
Rose S1,2, Styles SE3, Wiltshire EJ2,4, Stanley J5, Galland BC1, de Bock MI6,7, Tomlinson PA8, Rayns JA9, MacKenzie KE6,7, Wheeler BJ1,10
1Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; 2Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand; 3Department of Human Nutrition, University of Otago, Dunedin, New Zealand; 4Paediatric Department, Capital and Coast District Health Board, Wellington, New Zealand; 5Biostatistical Group, Dean's Department, University of Otago Wellington, Wellington, New Zealand; 6Department of Paediatrics, University of Otago, Christchurch, New Zealand; 7Paediatric Department, Canterbury District Health Board, Christchurch, New Zealand; 8Paediatric Department, Southern District Health Board, Invercargill, New Zealand; 9Endocrinology Department, Southern District Health Board, Dunedin, New Zealand; 10Paediatric Department, Southern District Health Board, Dunedin, New Zealand
Background
In a previous 6-month randomized controlled trial (RCT) comparing flash glucose monitoring, also known as intermittently scanned continuous glucose monitoring (isCGM), to self-monitoring blood glucose (SMBG) in adolescents and young adults with type 1 diabetes (T1D) and high-risk glycemic control (HbA1c ≥75 mmol/mol [≥9%]), no significant differences were found in HbA1c between the groups at the end of the intervention phase. However, glucose monitoring frequency was 2.8 times higher among those using isCGM, and increased diabetes satisfaction was observed. Therefore, the current study aimed to describe the ongoing impact of isCGM on glycemic outcomes and glucose test frequency during a 6-month free-living extension phase following the original 6-month RCT.
Methods
The previous 6-month randomized parallel-group study included 64 young people with T1D, aged 13–20 years, with a mean diabetes duration of 7.5±3.8 years and high-risk glycemic control (HbA1c ≥75 mmol/mol [≥9%]). Thirty-three commenced isCGM intervention, and the rest continued SMBG. In the 6-month extension phase, both groups received isCGM. HbA1c, glucose time-in-range (TIR; target range 3.9–10.0 mmol/L), and combined glucose test frequency were assessed at 9 and 12 months of isCGM use.
Results
In the isCGM intervention group, the mean difference in HbA1c from baseline to 9 months was −8 mmol/mol (95% CI, −13 to −3 mmol/mol), equivalent to −0.7% (95% CI, −1.2% to −0.3%), and was significant (P<.05); however, difference did not remain statistically significant at 12 months (−4 mmol/mol [−0.4%]; 95% CI, −8 to 1 mmol/mol [−0.8% to 0.1%]; P=.14). In the control SMBG group, there was no significant mean difference in HbA1c after 6 months of isCGM use (−2 mmol/mol [−0.2%]; 95% CI, −6 to 3 mmol/mol [−0.6% to 0.3%]; P=.42) from the 6-month RCT endpoint or from the baseline RCT endpoint (−7 mmol/mol [−0.7%]; 95% CI, −16 to 1 mmol/mol [−1.5% to 0.1%], P=.08). No participants achieved ≥70% glucose TIR. In the isCGM intervention group, the mean rate of daily glucose testing was highest at 9 months (2.4 times baseline rates [P<.001]) and then returned to baseline by 12 months. In addition, only 48% of participants were still wearing isCGM devices at the 12-month visit.
Conclusion
This was an extended 6-month observational study of young people with high-risk glycemic levels. The participants who used isCGM did show improved HbA1c levels and higher frequency of glucose monitoring during the first 9 months. However, the improvements did not last for the full 12 months. Furthermore, although isCGM was free, the time that participants wore the device decreased over the 12 months. These results are similar to those of earlier CGM studies that found CGM use and HbA1c improvements could not be sustained in this age group.
Comments
To achieve recommended glycemic target of HbA1c <53 mmol/mol (<7.0%), children, adolescents, and young adults living with T1D are advised to check glucose levels 6–10 times each day (3). Finger pricking may represent an additional burden of care, especially for adolescents and young adults living with T1D. Therefore, using isCGM may help increase the frequency of glucose checking, because it decreases the need for fingerstick glucose monitoring (4). This extended observational study aimed to assess realistic isCGM use in the population of young people with T1D (age range 13–25 years) and preexisting unhealthy glycemic control (baseline HbA1c levels HbA1c ≥75 mmol/mol [≥9%]) who had availability of wearing isCGM during the cumulative 12-month period. At 9 months, there was an improvement in glycemic control measured as HbA1c compared to the baseline value, as well as increased frequency of glucose monitoring. However, improvements in HbA1c and frequency of glucose measurements were not sustained at 12 months. Likewise, despite its availability, the wear time of isCGM was reduced at 12 months. This study showed that using isCGM can improve glycemic control and increase adherence to the much-needed recommended frequency of glucose measurements. However, despite the ease of its use, isCGM failed to sustain sufficient wear time and frequency of glucose measurements over a longer period. Results of this study suggest that transitioning from CGM only to integrative devices with automated delivery systems coupled with educational and behavioral interventions is vital for the broader use of glucose management technologies in higher-risk populations.
Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: The Pilot 4T Study
Prahalad P1,2, Ding VY3, Zaharieva DP1, Addala A1, Johari R2,4, Scheinker D1,2,4,5, Desai M3, Hood K1,2, Maahs DM1,2,6
1Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA; 2Stanford Diabetes Research Center, Stanford University, Stanford, CA; 3Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA; 4Department of Management Science and Engineering, Stanford University, Stanford, CA; 5Clinical Excellence Research Center, Stanford University, Stanford, CA; 6Department of Health Research and Policy (Epidemiology) Stanford University, Stanford, CA
This manuscript is also discussed in DIA-2023-2506, page S-90.
Background
Glycated hemoglobin A1C (HbA1c) targets are not being met by youth with type 1 diabetes (T1D). The purpose of this study is to evaluate the effectiveness of the Teamwork, Targets, Technology, and Tight Control (4T) Study on the HbA1c levels of children with new-onset T1D.
Methods
Youth with new onset T1D diagnosed between July 2018 and June 2020 who enrolled in the study initiated CGM in the first month of diagnosis. Patients enrolled after March 2019 were offered remote patient monitoring (RPM). The primary outcome was HbA1c. HbA1c levels were compared between the pilot 4T cohort and historical cohort (diagnosed 2014–2016) using locally estimated scattered plot smoothing (LOESS). Change from nadir (month 4) to 12 months after diagnosis was estimated by cohort by using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type.
Results
At 6, 9, and 12 months after diagnosis, youth in the pilot 4T cohort (n=135) had a lower HbA1c than those in the historical cohort (n=272) (–0.54%, –0.52%, and –0.58%, respectively). Those receiving RPM (n=89) had even lower HbA1c at 6, 9, and 12 months after diagnosis than those who did not receive RPM (n=46) (–0.14%, –0.18%, and –0.14%, respectively). Based on results of the multivariate regression, those in the pilot 4T cohort experienced a lower increase in HbA1c between months 4 and 12 (P<.001).
Conclusions
An intensified new onset education program involving a team-based approach, CGM technology, RPM, and target setting can lead to significant decrease in HbA1c in youth 12 months after diagnosis.
Comments
The Diabetes Control and Complications Trial (DCCT) demonstrated that intensive glucose management is associated with decreased microvascular complications in people with type 1 diabetes (T1D) (5,6). Despite the findings from the DCCT, many young people with T1D fail to meet glucose targets (7 –9). Unfortunately, the decline in glucose control occurs soon after the diagnosis of T1D (10,11). Standard clinical care typically involves four visits per year with a diabetes care provider during which insulin dose adjustments occur. However, this may not be sufficient in an individual with changing insulin needs. In the DCCT, participants received frequent insulin dose adjustments, which contributed to maintenance of glycemia. Unfortunately, limitations in technology and provider bandwidth have made this learning difficult to implement in practice.
Prahalad et al. describe the year 1 results of the Teamwork, Targets, Technology, and Tight Control (4T) pilot study, which aimed to improve glucose control by using an intensified education program in the first year of diabetes diagnosis. This program started all youth with new onset T1D on continuous glucose monitoring (CGM) in the first month of diabetes diagnosis (2,9,12 –15) and combined this with a remote patient monitoring (RPM) program to allow for more frequent dose adjustments. In addition, the program implemented previous learnings demonstrating the benefits of consistent and tight target setting by the diabetes care team (16,17). Compared to historical controls, youth enrolled in the 4T program had a 0.5% reduction in HbA1c at 1 year after diabetes diagnosis, with 58% achieving a HbA1c within the target range (<7.5% at time of study initiation).
This study demonstrates that CGM technology combined with RPM and an intensified education program has the potential to improve outcomes in youth with new onset T1D. One of the strengths of this study is that it was available to all patients with newly diagnosed T1D and therefore it enrolled a diverse study population. This was aided by grant and philanthropic funding to provide CGM and WiFi-enabled devices to those who could not afford the technology or those who did not have insurance coverage. To adapt this program more broadly, it is important to advocate for universal CGM coverage. An additional challenge for more broad expansion is allowing youth who do not have WiFi-enabled devices or access to WiFi to participate in RPM. Although the overall cohort had improvements in HbA1c, it is important to identify those who did not benefit from the program and develop additional interventions to support these individuals. Although this study was performed at a single site, many components of this program are widely generalizable, and further work is needed to identify how to scale the program more broadly.
The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Psychological and Glycemic Variables in Children with Type 1 Diabetes: A Randomized Crossover Trial
Elbalshy MM1, Styles S2, Haszard JJ2, Galland BC1, Crocket H3, Jefferies C4,5, Wiltshire E6,7, Tomlinson P, de Bock MI9,10, Wheeler BJ1,8
1Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, Otago, New Zealand; 2Department of Human Nutrition, University of Otago, Dunedin, New Zealand; 3Te Huataki Waiora School of Health, University of Waikato, Hamilton, New Zealand; 4Paediatric Endocrinology, Starship Children's Health, Auckland, New Zealand; 5Liggins Institute, University of Auckland, Auckland, New Zealand; 6Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand; 7Paediatrics and Child Health, Capital and Coast District Health Board, Wellington, New Zealand; 8Paediatric Endocrinology, Southern District Health Board, Dunedin, New Zealand; 9Department of Paediatrics, University of Otago Christchurch, Christchurch, New Zealand; 10Department of Paediatrics, Canterbury District Health Board, Christchurch, New Zealand
This manuscript is also discussed in DIA-2023-2512, page S-191.
Background
Continuous glucose monitoring (CGM) can help people with type 1 diabetes (T1D) reduce fear of hypoglycemia (FOH) and have better glycemic control. Currently, there is no research on the use of do-it-yourself real-time continuous glucose monitoring (DIY RT-CGM) on psychological and glycemic outcomes.
Methods
Dyads of children (aged 2–13 years) who used intermittently scanned CGM (isCGM) and their parents were recruited for a multicenter randomized crossover trial. Dyads received either 6 weeks of DIY RT-CGM with parental remote monitoring (intervention) or 6 weeks of isCGM plus usual care (control). This was followed by a 4-week washout period, and then the groups crossed over. The primary outcome was parental FOH. Secondary outcomes were glycemic control and other psychosocial outcomes.
Results
A total of 55 child-parent dyads were included, and the children's mean age was 9.1±2.8 years. There was no effect of DIY RT-CGM on parental FOH (−0.1 [95% CI, −0.3 to −0.1]; P=.4), but the time in range (TIR, 70–180 mg/dL) was significantly higher with DIY RT-CGM than with isCGM (54.3%±13.7% vs 48.1%±13.6%), with a mean difference of 5.7% (95% CI, 1.8 to 9.6; P<.004). There was no change in the time below range (<70 mg/dL). Parental diabetes treatment satisfaction was higher with DIY RT-CGM than with CGM (mean difference 5.4% [95% CI, 2.3 to 8.2]; P<.001).
Conclusions
Compared to isCGM, DIY RT-CGM did not have a stronger positive impact on FOH levels; however, TIR and parental satisfaction with diabetes treatment were significantly better. This indicates that in the short term, DIY RT-CGM seems safe and may offer families some clinically important advantages over isCGM.
Comments
The use of continuous glucose monitoring (CGM) is associated with improved clinical and psychosocial outcomes in children with T1D (18 –20). CGM technology can either be real-time (RT-CGM), in which data are displayed continuously, or intermittently scanned (isCGM), in which the device needs to be scanned to display glucose data. Several studies have shown RT-CGM to be more effective at improving clinical outcomes compared to isCGM devices (18,19). However, RT-CGM devices can be expensive and may not be funded in many countries (21). The cheaper alternative is isCGM; furthermore, first-generation devices, which are often more readily available, do not offer safety alerts or continuous remote monitoring. To make RT-CGM more accessible, the DIY community has developed a third-party device that is placed over the isCGM and uses near field communication (NFC) technology to read raw data from the isCGM and send the data to a user's cellphone using Bluetooth technology (DIY RT-CGM).
Elbalshy et al. conducted a multicenter randomized crossover trial to assess whether DIY RT-CGM is associated with decreased FOH in parents on children with T1D. Secondary analyses examined glycemic outcomes and other psychosocial measures. This group found that although time in range (TIR, 70–180 mg/dL) is longer in those using DIY RT-CGM than in those on isCGM alone, there is no difference in parental fear of hypoglycemia or time spent in hypoglycemia. DIY RT-CGM use was associated with parental satisfaction in diabetes treatment.
Although this group did not reach their primary endpoint, decreased caregiver FOH, this study highlights the importance of having real-time access to glucose data for patients and caregivers to help improve TIR and parental satisfaction with diabetes treatment. Although DIY provides options for people with diabetes and their caregivers, use of DIY systems requires extra out-of-pocket costs and comfort with technology. To reduce barriers to adoption and increase health equity, it is important for device manufacturers to develop low-cost RT-CGM devices and for payers to provide coverage.
Universal Subsidized Continuous Glucose Monitoring Funding for Young People with Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based Study
Johnson SR1,2, Holmes-Walker DJ3,4, Chee M5, Earnest A6, Jones TW7,8 on behalf of the CGM Advisory Committee and Working Party and the ADDN Study
1Department of Endocrinology and Diabetes, Queensland Children's Hospital, Brisbane, Queensland, Australia; 2Faculty of Medicine, University of Queensland, Herston, Queensland, Australia; 3Department of Diabetes and Endocrinology, Westmead Hospital, Sydney, New South Wales, Australia; 4Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; 5JDRF Australia, St Leonard's, New South Wales, Australia; 6Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; 7Perth Children's Hospital, Nedlands, Western Australia, Australia; 8Telethon Kids Institute, Nedlands, Western Australia, Australia
This manuscript is also discussed in DIA-2023-2502, page S-15 and DIA-2023-2511, page S-176.
Objective
Different funding models exist for continuous glucose monitoring (CGM), a method used to monitor type 1 diabetes. The new national health policy subsidizes CGM for people younger than 21 years who have type 1 diabetes. This study aimed to assess changes in CGM use and glycemic outcomes after the policy took effect.
Methods
Longitudinal data from 12 months before the subsidy until 24 months after were analyzed. Measures and outcomes included age, diabetes duration, HbA1c, episodes of diabetic ketoacidosis and severe hypoglycemia, insulin regimen, CGM uptake, and percentage CGM use. Two data sources were used: the Australasian Diabetes Database Network (ADDN) registry (a prospective diabetes database) and the National Diabetes Service Scheme (NDSS) registry, which includes almost all individuals with type 1 diabetes nationally.
Results
CGM uptake increased from 5% before the subsidy to 79% after 2 years. The odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P<.001) and was maintained at 24 months (OR 2.3, P<.001) after the CGM subsidy. The OR for HbA1c ≥9.0% decreased to 0.34 (P<.001) at 24 months. For CGM users, 65% used CGM >75% of time and had a lower HbA1c at 24 months compared with those whose usage was <25% (7.8%±1.3% vs 8.6%±1.8%; P<.001). Diabetic ketoacidosis was also reduced in the group of patients that used CGM >75% of time (incidence rate ratio 0.49; 95% CI, 0.33–0.74; P<.001).
Conclusions
After the national subsidy took effect, CGM use greatly increased and was associated with sustained improvement in glycemic control. These results can be used in economic analyses and to support similar government initiatives in the future. In addition, the methods in this study can be used to evaluate other diabetes technologies.
Comments
Once diabetes technology is developed and demonstrates clinical benefit to tighten glucose control and improve quality of life, the major challenge becomes to disseminate these diabetes technologies to all people with diabetes. The article by Johnson and colleagues provides a shining example of how an equity-based health-care system can lower the barriers of access to diabetes technology. The data in this study show how a national health policy change to introduce universal subsidized CGM funding for people younger than 21 years of age with T1D led to a variety of benefits. For example, improvements in glucose control that were sustained for 2 years include a 3-fold reduction in the odds of an HbA1c over 9% and a 2-fold increase in the odds of achieving the HbA1c target of less than 7%. Moreover, a 2-fold reduction in diabetic ketoacidosis (DKA) was found. Additional analyses are underway to investigate the economic impact. As the authors note, this model can be employed for future studies of emerging diabetes technology, such as automated insulin delivery systems. These data also serve as an example for other countries with disjointed health-care systems or those who do not provide support for diabetes technology. One would hope that the future economic analyses would include the time saved by the diabetes care team, the person with diabetes, and their families when access to CGM is automatically granted. In the United States, many certified diabetes caregivers and education specialists spend a significant amount of their work day helping families obtain CGM because of health-care system and insurance barriers. Families of youth with diabetes share this frustration and often encounter barriers that prevent CGM use. Just as access to insulin should be an unquestioned necessity provided by a health-care system, this study demonstrates the benefits of universal access to CGM. Perhaps the next study will include the population outcomes for universal access to automated insulin delivery and the benefit on glucose control, quality of life, and economic benefit. With this paper, the investigators and their health-care system set an example of what is possible, and we hope to see more of this expanded access to diabetes technology followed by improved patient population outcomes.
INSULIN PUMP THERAPY
Insulin Pump-Related Inpatient Admissions in a National Sample of Youth with Type 1 Diabetes
Everett EM1,2,3, Copeland TP4, Moin T1,2,3,5, Wisk LE2,4
1Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, David Geffen School of Medicine, 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; 3Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA; 4Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, CA; 5HSR&D Center for the Study of Healthcare Innovation, Implementation & Policy, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA
Background
Throughout the world, the use of insulin pumps by children and adolescents with type 1 diabetes has rapidly increased. Nonetheless, there are not enough data to determine whether those using insulin pumps are more likely to be admitted as inpatients for acute diabetes complications.
Methods
Data were sourced from the Kids' Inpatient Database to identify all-cause T1D hospital admissions in 2006, 2009, 2012, and 2016 in those with and without documented insulin pump use and insulin pump failure. Investigators assessed data of youth aged 20 years and younger at 42,000 hospitals in 46 states to describe the prevalence of acute diabetes complications, the severity of illness during hospitalization, the disposition after discharge, length of stay (LOS), and inpatient costs.
Results
A total of 228,474 hospitalizations were identified. Insulin pump use was documented in 7% of admissions, of which 20% had documented pump failure. Insulin pump users and nonusers differed significantly for all demographic data. Insulin pump users were more likely female (57%) and White (67%) and more likely had private insurance (61%). Pump nonusers were more likely to be Black (17% vs 8%), be Hispanic (12% vs 7%), be from the lowest income quartile (31% vs 23%), and have public insurance (43% vs 31%). Between 2006 and 2016, the number of all-cause admissions and admissions among pump users in T1D youth increased by 10%. Nearly half of all patients (∼48%) had diabetic ketoacidosis (DKA) during hospitalization, which was more common in pump nonusers (47%) than pump users (39%) but reached 60% in those with pump failure. DKA admissions with documented pump failure increased over time from 423 in 2006 to 1032 in 2016. Most (∼85%) of youth with T1D had minor or moderate severity of illness. Admissions with pump failure had a slightly higher proportion of admissions classified as major severity of illness (∼15%) but had the lowest LOS and health-care costs. The hospital stay was the longest for pump nonusers, followed by pump users. Similarly, hospital charges were highest for nonusers, followed by pump users. Admissions for hyperglycemia without DKA, hypoglycemia, sepsis, and soft tissue infections were rare and similar across all groups.
Conclusion
In this study, insulin pump users had lower DKA admission rates, shorter hospital stays, and lower hospital charges than did nonusers. Although the prevalence of insulin pump use has increased in the United States, insulin pump use was documented in only a minority of the pediatric inpatient admission records. However, this low rate may have been the result of undercoding.
Comments
Everett et al. assessed inpatient admissions in 2006, 2009, 2012, and 2016 to evaluate trends in acute diabetic complications in youth with T1D, given the rising technology use. The Kids' Inpatient Database served as a nationally representative sample. In accordance with other studies, the number of all-cause admissions among youth with T1D has increased over time, and DKA was a common diagnosis (22). The prevalence of hypoglycemia and soft tissue infection admissions was low across all groups. Investigators found that those who used an insulin pump had a lower rate of DKA, shorter hospital stays, and lower hospital charges than nonusers. Among pump users, pump failure was associated with higher rates of DKA (60%) and had a slightly higher proportion of admissions classified as major severity of illness. On the other hand, LOS and admission costs were the lowest in this group. Despite the dramatic increase in insulin pump use in T1D management (23), pump use was documented only in a minority of inpatient admitted youth with T1D (7%). Although insulin pump users may be much less likely to be admitted than nonusers (24), this may have resulted from undercoding. Therefore, undercoding of insulin pump use in inpatient administrative data may be a limitation of this study. Improved accuracy in coding practices or other approaches are needed to determine whether insulin pump use is associated with fewer inpatient admissions and lower rates of DKA.
Multicenter Trial of a Tubeless, On-Body Automated Insulin Delivery System with Customizable Glycemic Targets in Pediatric and Adult Participants with Type 1 Diabetes
Brown SA1, Forlenza GP2, Bode BW3, Pinsker JE4, Levy CJ5, Criego AB6, Hansen DW7, Hirsch IB8, Carlson AL9, Bergenstal RM9, Sherr JL10, Mehta SN11, Laffel LM11, Shah VN2, Bhargava A12, Weinstock RS13, MacLeish SA14, DeSalvo DJ15, Jones TC16, Aleppo G17, Buckingham BA18, Ly TT19 for the Omnipod 5 Research Group
1Division of Endocrinology, Center for Diabetes Technology, University of Virginia, Charlottesville, VA; 2Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; 3Atlanta Diabetes Associates, Atlanta, GA; 4Sansum Diabetes Research Institute, Santa Barbara, CA; 5Icahn School of Medicine at Mount Sinai, New York, NY; 6International Diabetes Center, Park Nicollet Pediatric Endocrinology, Minneapolis, MN; 7Department of Pediatrics, SUNY Upstate Medical University, Syracuse, NY; 8Department of Medicine, University of Washington, Seattle, WA; 9International Diabetes Center, Park Nicollet, HealthPartners, Minneapolis, MN; 10Department of Pediatrics, Yale School of Medicine, New Haven, CT; 11Joslin Diabetes Center, Harvard Medical School, Boston, MA; 12Department of Research, Iowa Diabetes Research, West Des Moines, IA; 13Department of Medicine, SUNY Upstate Medical University, Syracuse, NY; 14Department of Pediatrics, University Hospitals Cleveland Medical Center, Rainbow Babies and Children's Hospital, Cleveland, OH; 15Department of Pediatrics, Baylor College of Medicine, Houston, TX; 16Department of Research, East Coast Institute for Research at The Jones Center, Macon, GA; 17Feinberg School of Medicine, Northwestern University, Chicago, IL; 18Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA; 19Insulet Corporation, Acton, MA
Background
Although diabetes technology has advanced, people with type 1 diabetes still have a burdensome care routine. The aim of this study is to gather safety data for the tubeless on-body automated insulin delivery system with customizable glucose targets.
Methods
A total of 112 children (age 6–13.9 years) and 129 adults (age 14–70 years) participated in this single-arm multicenter prospective study. During the 2-week standard therapy phase, participants used their usual insulin regimen. This was followed by 3 months of automated insulin delivery. The primary safety outcomes were incidence of severe hypoglycemia and diabetic ketoacidosis. Primary effectiveness outcomes were change in HbA1c and percent time in sensor glucose range 70–180 mg/dL (“time in range”).
Results
Of the 252 total participants, 235 (98%; 111 children and 124 adults) completed the study. The HbA1cc was reduced by 0.71% in children (7.8 mmol/mol) (mean±SD: 7.67±0.95% to 6.99±0.63% [60±10.4 mmol/mol to 53±6.9 mmol/mol]; P<.0001) and by 0.38% in adults (4.2 mmol/mol) (7.16%±0.86% to 6.78%±0.68% [55±9.4 mmol/mol to 51±7.4 mmol/mol]; P<.0001). In children, the time in range improved by 15.6±11.5% or 3.7 h/d, and in adults, the improvement was 9.3±11.8% or 2.2 h/d (both P<.0001). In adults, the time in hypoglycemia <70 mg/dL decreased (median [IQR]: 2.00% [0.63–4.06] to 1.09% [0.46–1.75], P<.0001). There was no change in children.
Conclusions
The results of this study indicate that tubeless automated insulin delivery is safe. Furthermore, those using the system had significant improvements in HbA1c levels and time in target range. The incidence of hypoglycemia was low.
Comments
In many people with type 1 diabetes, glucose control can be suboptimal but may be improved by using diabetes technology including continuous glucose monitoring (CGM) and insulin pumps (8,9,13,25). Pivotal studies of other automated insulin delivery systems, which use set glucose targets and tubing to connect the insulin pump with an infusion set on the user's body, have shown improvements in clinical outcomes, such as lower HbA1c, improved time in range, low incidence of severe hypoglycemia, and low rates of diabetic ketoacidosis (26 –29).
Brown et al. describe a novel automated insulin delivery system that uses a tubeless insulin pump controlled by a smartphone app that allows users to customize glucose targets. Continued use of this system in automated insulin delivery mode was high during both the study and extension phases in both children and adults. Participants in this study had an improvement in their HbA1c levels and time in range. Although the time spent in hypoglycemia decreased in adult participants, this reduction was not seen in children. There were low incidences of severe hypoglycemia and diabetic ketoacidosis.
However, these results need to be interpreted in light of the fact that a majority of participants had prior experience with diabetes technology and were predominantly non-Hispanic white. In addition, the mean HbA1c levels in children and adults (7.67±0.95 and 7.16±0.86, respectively) may not be reflective of the general population. Therefore, it is uncertain how this might translate from a clinical trial to a real-world setting.
The strengths of this study lie in the large age range of participants from across the United States using this system. In addition, participants in this study did not need to have prior history of CGM or insulin pump use, although most were previously on these devices. This system offers people with diabetes a tubeless method of insulin delivery and mobile device control, which can help the user experience (30).
Three-Variate Trajectories of Metabolic Control, Body Mass Index, and Insulin Dose: Heterogeneous Response to Initiation of Pump Therapy in Youth with Type 1 Diabetes
Tauschmann M1, Schwandt A2,3, Prinz N2,3, Becker M4, Biester T5, Hess M6, Holder M7, Karges B8, Näke A9, Kuss O3,10, von Sengbusch S11, Holl RW2,3; DPV Initiative
1Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; 2Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4DECCP, Clinique Pédiatrique Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; 5Diabetes-Center for Children and Adolescents, Children's Hospital “Auf der Bult”, Hannover, Germany; 6Pediatric Endocrinology and Diabetology, University Children's Hospital Basel, Basel, Switzerland; 7Klinikum Stuttgart, Olgahospital, Department of Pediatric Endocrinology and Diabetology, Stuttgart, Germany; 8Division of Endocrinology and Diabetes, Medical Faculty, RWTH Aachen University, Aachen, Germany; 9Children's Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany; 10Institute of Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; 11Department of Paediatrics and Adolescent Medicine, University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany
Background
Compared with multiple daily injections (MDIs), continuous subcutaneous insulin infusion (CSII) has been associated with lower HbA1c, lower total daily dose of insulin (TDD), and lower body mass index (BMI) in youth who have type 1 diabetes (T1D). In this study, three-variate patterns of changes in HbA1c, TDD, and BMI standard deviation score (BMI-SDS) are seen in young T1D patients who switch from MDIs to CSII therapy.
Methods
Youth (aged ≤20 years) in the multicenter DPV registry with T1D duration ≥3 years when switching to CSII therapy were included in this analysis (N=5133; 48% male; median age at pump, 12.5 years). Group-based multitrajectory modeling was used to identify groups of individuals with similar trajectories. Over a 3-year follow-up period, measurements were aggregated quarterly. The trajectory variables were changes of HbA1c, BMI-SDS, and TDD from baseline.
Results
There were four groups of diverging patterns identified based on changes in HbA1c, TDD, and BMI-SDS. All groups had improvements in HbA1c during the first 3 months. Group 1 (12%) had a modest increase in HbA1c, reduction in TDD, and stable BMI-SDS. Group 2 (39%) had increasing HbA1c, stable TDD, and decreasing BMI-SDS. Group 3 (32%) had sustainably improved HbA1c, stable TDD, and increasing BMI-SDS. Group 4 (17%) had increases in HbA1c, TDD, and BMI-SDS. There were between-group differences in HbA1c, BMI-SDS, TDD, sex ratio, age at diabetes onset, and age at CSII start.
Conclusions
Identification of definite trajectories of glycemic control, BMI, and TDD over 3 years following CSII initiation can allow for personalized treatment recommendations for youth with T1D.
Comments
Continuous subcutaneous insulin infusion (CSII) is associated with improved HbA1c, decreased incidences of severe hypoglycemia and diabetic ketoacidosis, and improved quality of life in people with T1D (31 –34). In Western countries, 40%–60% of people with T1D use CSII (23,35).
Tauschmann et al. explored the relationship between HbA1c, TDD, and BMI-SDS in youth 20 years or younger in the DPV registry (January 2005 through December 2019) who initiated CSII ≥3 years after T1D diagnosis and had at least seven quarterly visits over a 3-year period. Using group-based multitrajectory (GBMT) modeling, they were able to identify four distinct trajectories among the 5133 participants. All four groups experienced a decrease in the HbA1c in the first 3 months of CSII initiation. Following those 3 months, Group 1 had a modest increase in HbA1c, a decrease in TDD and stable BMI-SDS. Members of Group 2 had increasing HbA1c, stable TDD, and decreasing BMI-SDS. Group 3 had a sustained improvement in HbA1c, stable TDD, and increased BMI-SDS. Group 4 had increased HbA1c, TDD, and BMI-SDS. The worsening of HbA1c was greater in males than in females, whereas the increase in BMI-SDS levels was greater in females. Group 2 was the youngest at diabetes onset and CSII initiation. Group 1 was the oldest for diabetes onset and CSII initiation. Groups 1 and 3 had a higher percentage of female members. Individuals with a migration background were equally present across groups. Continuous glucose monitoring (CGM) use was highest in Groups 1 and 4.
This analysis from a large registry identified four distinct trajectories of HbA1c, TDD, and BMI-SDS among youth with established diabetes who started on CSII therapy. This analysis can help providers tailor education based on baseline characteristics, but it is important to continue to personalize care. The data from this analysis were from the DPV registry, which includes youth from Germany, Austria, and Luxembourg. Therefore, this analysis may not be generalizable worldwide. In addition, this analysis took into account only migration background and did not include other social determinants of health that could affect trajectories. As more youth with diabetes are starting on automated insulin delivery systems and starting on technology earlier than 3 years after diagnosis, it is important to continue to analyze these variables to improve the care of youth with T1D.
Prediction of Personal Glycemic Responses to Food for Individuals with Type 1 Diabetes Through Integration of Clinical and Microbial Data
Shilo S1,2,3, Godneva A1,2, Rachmiel M4,5, Korem T1,2,6, Kolobkov D1,2, Karady T1,2, Bar N1,2, Wolf BC1,2, Glantz-Gashai Y3, Cohen M3,7, Zuckerman Levin N,3,7 Shehadeh N3,7, Gruber N5,8, Levran N8,9, Koren S5,10, Weinberger A1,2, Pinhas-Hamiel O5,8, Segal E1,2
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; 2Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; 3Pediatric Diabetes Clinic, Institute of Diabetes, Endocrinology and Metabolism, Rambam Health Care Campus, Haifa, Israel; 4Pediatric Endocrinology Unit, Shamir Medical Center, Zerifin, Israel; 5Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 6Department of Systems Biology, Columbia University, New York, NY; 7Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel; 8Pediatric Endocrine and Diabetes Unit, Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat-Gan, Israel; 9Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel; 10Diabetes Unit, Shamir Medical Center, Zerifin, Israel
This manuscript is also discussed in DIA-2023-2509, page S-146.
Background
Although diabetes-management technology has advanced over the last several years, many people with type 1 diabetes (T1D) still have difficulty meeting glycemic targets. One of the main issues is determining the correct amount of insulin to match the postprandial glycemic response (PPGR) for each meal. The aim of this study was to develop a prediction model for PPGR in individuals with T1D.
Methods
Patients recruited for the study were aged 3–70 years, had T1D for at least 1 year, used continuous glucose monitoring (CGM) and pump devices simultaneously, and had the capability to work with a mobile phone app on a daily basis for the real-time recording of the dietary intake by participants or their parents (in the case of young children) for 2 weeks. Their PPGRs were measured, and machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota, were used. Data related to PPGRs of healthy individuals (n=900) to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation.
Results
A total of 121 individuals with T1D (46 children [<18 years of age] and 75 adults) with median disease duration of 8 years (IQR, 4–15 years), and mean HbA1c level of 7.5%±1.1% (58.5±12.1 mmol/mol) were included in the analysis. PPGRs to 6377 meals were measured. The PPGR prediction model substantially outperformed a baseline model with emulation of standard of care (R=0.59 compared with R=0.40 for predicted and observed PPGR, respectively; P<.001). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal carbohydrate-to-fat ratio were the most influential features for the model.
Conclusions
The developed model can predict glycemic responses to meals in individuals with T1D, substantially outperforming standard of care. This may allow a better adjustment of the required insulin dosage for meals.
Comments
The standards of medical care in diabetes guidelines, published by the American Diabetes Association, state that patients should match prandial insulin to carbohydrate intake, premeal blood glucose, and anticipated physical activity (36). However, it was previously shown that conventional therapy resulted in suboptimal insulin counteraction of postprandial glycemic responses (PPGRs) (37). More sophisticated models exist, such as those that also include adjustment for the meal's fat and protein content, but have so far failed to provide a significant improvement in glycemic control, and their implementation in real practice is limited (38,39).
PPGR is an important contributor to the overall glycemic control (40), and decreasing it may improve the time spent in the target glycemic range, which has a strong inverse association with the risk of future microvascular complications in individuals with T1D (41).
This study was based on previous observations in healthy participants that carbohydrate content alone is not a good predictor of glycemic responses to meals, that a significant interindividual variability in PPGR exists (42), and that environmental factors, as gut microbiota, are associated with the glycemic response to meals. The advantage of the prediction model described in the current study for the glycemic response to real-life meals is that it takes into account a comprehensive clinical and microbiome profile as input.
The subgroup analysis has shown that the current model is overall robust and performs similarly across individuals in different age groups and with different levels of glycemic control. The research demonstrated that additional to meal composition, other factors, which are not considered in the standards of care for insulin administration today, are influencing PPGR, such as the glucose trend 30 min prior to the beginning of the meal start and the ratio of carbohydrate to fat in the meal.
The utility of this model in real life is limited by the requirement of accurate input of meals. However, the current model enables a more accurate prediction of the glycemic response to meals and therefore may allow a better adjustment of the required insulin dosage for meals, possibly leading to improved glycemic control.
It can be further implemented in closed-loop systems, personalized decision systems, and alarm systems for expected high and low blood glucose events for individuals with T1D. The use of such models may also lead to personalized nutritional interventions for individuals with T1D.
CLOSED LOOP SYSTEMS
Safety and Glycemic Outcomes During the MiniMed Advanced Hybrid Closed-Loop System Pivotal Trial in Adolescents and Adults with Type 1 Diabetes
Carlson AL1, Sherr JL2, Shulman DI3, Garg SK4, Pop-Busui R5, Bode BW6, Lilenquist DR7, Brazg RL8, Kaiserman KB9, Kipnes MS10, Thrasher JR11, Reed JHC12, Slover RH4, Philis-Tsimikas A13, Christiansen M14, Grosman B15, Roy A15, Vella M15, Jonkers RAM15, Chen X15, Shin J15, Cordero TL15, Lee SW15, Rhinehart AS15, Vigersky RA15 and MiniMed AHCL Study Group
1International Diabetes Center, HealthPartners Institute, Minneapolis, MN; 2Yale University School of Medicine Pediatric Endocrinology, New Haven, CT; 3University of South Florida Diabetes and Endocrinology, Tampa, FL; 4Barbara Davis Center of Childhood Diabetes, Aurora, CO; 5Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI; 6Atlanta Diabetes Associates, Atlanta, GA; 7Rocky Mountain Diabetes and Osteoporosis Center, Idaho Falls, ID; 8Rainier Clinical Research Center, Renton, WA; 9SoCal Diabetes, Torrance, CA; 10Diabetes and Glandular Disease Clinic, San Antonio, TX; 11Arkansas Diabetes and Endocrinology Center, Little Rock, AR; 12Endocrine Research Solutions, Inc., Roswell, GA; 13Scripps Whittier Diabetes Institute, La Jolla, CA; 14Diablo Clinical Research Center, Walnut Creek, CA; 15Medtronic, Northridge, CA
Introduction
Safety and effectiveness of an advanced hybrid closed-loop (AHCL) system with automated basal (Auto Basal) and automated bolus correction (Auto Correction) in adolescents and adults with type 1 diabetes (T1D) were assessed.
Materials and Methods
A population of 157 individuals (39 adolescents aged 14–21 years and 118 adults aged 22–75 years) with T1D participated in a multicenter single-arm intent-to-treat study using the MiniMed AHCL system. A baseline run-in period included sensor-augmented pump with or without predictive low glucose management or Auto Basal for 14 days. Thereafter, Auto Basal and Auto Correction were enabled for a study phase (90 days), with the glucose target set to 100 or 120 mg/dL for 45 days, followed by the other target for 45 days. Study endpoints included safety events and change in mean A1c, time in range (TIR, 70–180 mg/dL) and time below range (TBR, <70 mg/dL). Run-in and study phase values were compared using the Wilcoxon signed-rank test or a paired t test.
Results
Overall group time spent in closed loop averaged 94.9%±5.4% with only 1.2±0.8 exits weekly. Compared with run-in, AHCL reduced A1C from 7.5%±0.8% to 7.0%±0.5% (Wilcoxon signed-rank test, <0.001; n=155), TIR increased from 68.8%±10.5% to 74.5%±6.9% (Wilcoxon signed-rank test, <0.001), and TBR reduced from 3.3%±2.9% to 2.3%±1.7% (Wilcoxon signed-rank test, <0.001). Similar benefits to glycemia were more pronounced for the nighttime (12:00 am to 6:00 am) and were observed for each age group. The 100 mg/dL target increased TIR to 75.4% (n=155), which was further optimized at a lower active insulin time (AIT) setting (i.e., 2 h), without increasing TBR. No severe hypoglycemic or diabetic ketoacidosis events were reported.
Conclusions
The MiniMed AHCL system is safe and allows adolescents and adults with T1D to achieve recommended glycemic targets. Adjustments in target and AIT settings may further optimize glycemia and improve user experience.
Comments
Significant progress has been made in automated insulin delivery systems in the past decade. In this study, Carlson and colleagues report that the Medtronic AHCL system increased TIR, reduced TBR, and addressed usability concerns with previous systems, including the need for blood glucose calibration and unwanted exits from Auto Mode (closed-loop control) to open loop. Time in Auto Mode was ∼95% with only ∼1 Auto Mode exit per week on average. This system also included a meal autodetect system developed by DreaMed Diabetes to allow for more aggressive autocorrection boluses. Targets of both 100 and 120 mg/dL were tested. Among the 157 participants were 39 adolescents 14–21 years of age, and outcomes were similar in adolescents and in adults. As first-generation automated insulin delivery systems are increasing in clinical use, the development of more refined systems promises to bring tighter glucose control and increased usability, as demonstrated in this clinical trial. Customizable settings, such as desired glucose target and meal detection, are among the advances that people with diabetes and clinicians will see more of as diabetes technology progresses. Transition of this AHCL system to clinical use and the collection of its real-world data can be anticipated in future volumes of the ATTD Yearbook.
Effect of a Hybrid Closed-Loop System on Glycemic and Psychosocial Outcomes in Children and Adolescents with Type 1 Diabetes: A Randomized Clinical Trial
Abraham MB1,2,3, de Bock M1,2,3, Smith GJ2, Dart J1,2, Fairchild JM4, King BR5, Ambler GR6, Cameron FJ7, McAuley SA8,9, Keech AC10, Jenkins A8,9,10, Davis EA1,2,3, O'Neal DN8,9, Jones TW1,2,3, Australian Juvenile Diabetes Research Fund Closed-Loop Research Group
1Children's Diabetes Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; 2Department of Endocrinology and Diabetes, Perth Children's Hospital, Perth, Australia; 3Division of Paediatrics, University of Western Australia Medical School, Perth, Australia; 4Department of Endocrinology and Diabetes, Women's and Children's Hospital, Adelaide, Australia; 5Department of Endocrinology and Diabetes, John Hunter Children's Hospital, Newcastle, Australia; 6Institute of Endocrinology and Diabetes, Children's Hospital at Westmead, University of Sydney, Sydney, Australia; 7Department of Endocrinology and Diabetes, Royal Children's Hospital, Melbourne, Australia; 8Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia; 9Department of Endocrinology and Diabetes, St Vincent's Hospital, Melbourne, Victoria, Australia; 10National Health and Medical Research Council Clinical Trials Centre, Faculty of Medicine and Health, University of Sydney, Australia
This manuscript is also discussed in DIA-2023-2508, page S-118.
Background
Hybrid closed-loop (HCL) therapy has improved glycemic control in children and adolescents with type 1 diabetes (T1D); however, the efficacy of HCL on glycemic and psychosocial outcomes have not been established in a long-term randomized clinical trial. The objective was to determine the percentage of time spent in the target glucose range using HCL vs current conventional therapies of continuous subcutaneous insulin infusion (CSII) or multiple daily insulin injections (MDIs) with or without continuous glucose monitoring (CGM).
Methods
This 6-month, multicenter, randomized clinical trial included children and adolescents with T1D in Australia. Participants were randomized to either the control group (CSII or MDIs with or without CGM) or the intervention group for HCL therapy. The primary outcome was the percentage of time in range (TIR, 70–180 mg/dL) using 3-week masked CGM data collected at the end of the study in both groups. Secondary outcomes included CGM metrics for hypoglycemia, hyperglycemia, and glycemic variability; psychosocial measures were also included.
Results
A total of 135 patients (mean [SD] age, 15.3 [3.1] years; 76 girls [56%]) were included, with 68 randomized to the control group and 67 to the HCL group. Patients had a mean (SD) T1D duration of 7.7 (4.3) years and mean HbA1c of 64 (11) mmol/mol, with 110 participants (81%) receiving CSII and 72 (53%) receiving CGM. In the intention-to-treat analyses, TIR increased from a mean of 53.1±13.0% at baseline to 62.5±12.0% at the end of the study in the HCL group and from 54.6±12.5% to 56.1±12.2% in the control group. The mean adjusted difference between the two groups was 6.7% (95% CI, 2.7% to 10.8%; P=.002). HCL therapy reduced the time that patients spent in hypoglycemia (<70 mg/dL) range (difference, −1.9%; 95%CI, −2.5% to −1.3%) and improved glycemic variability (coefficient of variation difference, −5.7%; 95% CI, −10.2% to −0.9%). HCL therapy was associated with improved diabetes-specific quality of life (difference, 4.4 points; 95% CI, 0.4 to 8.4 points), with no change in diabetes distress. No episodes of severe hypoglycemia or diabetic ketoacidosis were reported in either group.
Conclusions
In this 6-month randomized clinical trial, HCL therapy improved glycemic control and quality of life significantly more than conventional therapy in children and adolescents with T1D.
Comments
This study from Australia and New Zealand reports on the first RCT in children and adolescents with T1D to provide conclusive evidence that HCL, in this case the Medtronic 670G system, improves glycemic outcomes and quality of life in children and adolescents with T1D. This study also demonstrates the challenges of performing randomized controlled trials in the rapidly evolving field of diabetes technology. The study team designed the randomized controlled trial in 2016, began recruitment in 2017, but did not complete the trial until 2020. Newer systems to address usability concerns have been developed during the course of this study, and data from Medtronic AHCL trials and real-world data are being gathered. However, data from this RCT with the 670G system are important, high-quality evidence that strengthen the case for coverage and use of automated insulin delivery systems. Designers of future studies will face the challenges of finding appropriate control groups and outcome measures as diabetes technology becomes the standard of care.
Randomized Trial of Closed-Loop Control in Very Young Children with Type 1 Diabetes
Ware J, Allen JM, Boughton CK, Wilinska ME, Hartnell S, Thankamony A, de Beaufort C, Schierloh U, Fröhlich-Reiterer E, Mader JK, Kapellen TM, Rami-Merhar B, Tauschmann M, Nagl K, Hofer SE, Campbell FM, Yong J, Hood KK, Lawton J, Roze S, Sibayan J, Bocchino LE, Kollman C, Hovorka R for the KidsAP Consortium
Wellcome Trust-Medical Research Council (MRC) Institute of Metabolic Science (JW, JMA, CKB, MEW, RH) and the Department of Paediatrics (JW, MEW, AT, RH), University of Cambridge, and the Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust (SH), Cambridge, the Department of Paediatric Diabetes, Leeds Children's Hospital, Leeds (FMC, JY), and Usher Institute, University of Edinburgh, Edinburgh (JL) - all in the United Kingdom; Diabetes and Endocrine Care Clinique Pédiatrique, Clinique Pédiatrique, Centre Hospitalier de Luxembourg, Luxembourg (CB, US); the Department of Pediatric Endocrinology, Universitair Ziekenhuis Brussel-Vrije Universiteit Brussel, Brussels (CB); the Department of Pediatric and Adolescent Medicine (EF-R), and the Division of Endocrinology and Diabetology, Department of Internal Medicine (JKM), Medical University of Graz, Graz, the Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna (BR-M, MT, KN), and the Department of Pediatrics I, Medical University of Innsbruck, Innsbruck (SEH) - all in Austria; the Hospital for Children and Adolescents, University of Leipzig, Leipzig, and the Hospital for Children and Adolescents “am Nicolausholz,” Bad Kösen - both in Germany (TMK); the Division of Pediatric Endocrinology, Stanford University, Stanford, CA (KKH); Vyoo Agency, Lyon, France (SR); and the Jaeb Center for Health Research, Tampa, FL (JS, LEB, CK)
This manuscript is also discussed in DIA-2023-2505, page S-70.
Background
It is unclear whether hybrid closed-loop therapy (i.e., artificial pancreas) is more efficacious than sensor-augmented pump therapy in young children with type 1 diabetes.
Methods
This was a multicenter randomized crossover trial. The participants were children with type 1 diabetes who were between 1 and 7 years of age and were receiving insulin-pump therapy at atleast one of seven centers in Austria, Germany, Luxembourg, or the United Kingdom. Participants received treatment in two 16-week periods, in random order, to compare the closed-loop system with sensor-augmented pump therapy (control). The primary endpoint was the between-treatment difference in the percentage of time that the sensor glucose measurement was in the target range (70–180 mg/dL) during each 16-week period. The analysis was conducted according to the intention-to-treat principle. Key secondary endpoints included the percentage of time spent in a hyperglycemic state (glucose level >180 mg/dL), the glycated hemoglobin level, the mean sensor glucose level, and the percentage of time spent in a hypoglycemic state (glucose level <70 mg/dL). Safety was assessed.
Results
A total of 74 participants underwent randomization. The mean±SD age of the participants was 5.6±1.6 years, and the baseline glycated hemoglobin level was 7.3±0.7%. The percentage of time with the glucose level in the target range was 8.7 percentage points (95% CI, 7.4–9.9) higher during the closed-loop period than during the control period (P<.001). The mean adjusted difference (closed-loop minus control) in the percentage of time spent in a hyperglycemic state was −8.5 percentage points (95% CI, −9.9 to −7.1), the difference in the glycated hemoglobin level was −0.4 percentage points (95% CI, −0.5 to −0.3), and the difference in the mean sensor glucose level was −12.3 mg/dL (95% CI, −14.8 to −9.8) (P<.001 for all comparisons). The time spent in a hypoglycemic state was similar with the two treatments (P=0.74). The median time spent in the closed-loop mode was 95% (IQR, 92 to 97) over the 16-week closed-loop period. One serious adverse event of severe hypoglycemia occurred during the closed-loop period. There was also one serious adverse event that was deemed to be unrelated to treatment.
Conclusions
Glycemic control was definitely better in children with type 1 diabetes who used a hybrid closed-loop system; for children who used this system, the length of time spent in hypoglycemia did not increase.
Comments
A common theme of this ATTD Yearbook article over the past decade has been the importance of studying diabetes technology in the pediatric age group. This is even more important in the youngest children with diabetes, as this age group is the most reliant on their parents and caretakers to monitor their glucose values and dose insulin. (As children go through each developmental stage, they become increasingly capable of providing their own diabetes care.) In this New England Journal of Medicine paper by the KidsAP Consortium, 74 children aged 1 to 7 years were randomized to a crossover design comparing SAP to HCL. TIR, mean glucose, and HbA1c were all significantly better during the HCL phase of the study than during the SAP phase. The time spent in a hypoglycemic state was similar with the two treatments. Usability was high, with close to 95% of time spent in closed loop during the 16-week study. Development of diabetes technology understandably starts in older populations to ensure safety and efficacy prior to testing in the most vulnerable and youngest population. However, there is a risk that emerging diabetes technology will not be adequately tested prior to off-label adoption or that some insurers may not cover diabetes technology, citing a lack of data in this age group. Furthermore, one can persuasively argue that the youngest age group have the most to gain from tighter glucose control, as their brain development can be adversely affected by hyperglycemia and hypoglycemia and cause frequent sleep disturbances for the child and their parent. This study serves as an important model for testing diabetes technology in the youngest age group.
Improvements in Parental Sleep, Fear of Hypoglycemia, and Diabetes Distress with Use of an Advanced Hybrid Closed-Loop System
Cobry EC1, Bisio A2, Wadwa RP1, Breton MD2
1Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; 2Center for Diabetes Technology, University of Virginia, Charlottesville, VA
Background
Parents of children with type 1 diabetes (T1D) are prone to sleep disruptions that may contribute to their child's glycemic control problems. This study describes the association between subjective sleep in parents of youth using the Tandem Control-IQ (CIQ) hybrid closed-loop (HCL) system and nocturnal glycemic and psychosocial outcomes, focusing on parents with poor baseline sleep quality.
Methods
The study was a multicenter, randomized trial of children ages 6–13 years with T1D of at least 1 year duration. Patient-reported outcomes (PROs) were assessed at three time points (baseline, 16 weeks, and 28 weeks) with the Pittsburgh Sleep Quality Index (PSQI) (for parents) and the Hypoglycemia Fear Survey (HFS)-II and Problem Areas in Diabetes (PAID) survey (for parents and children). The PSQI is formulated to assess sleep quality and disturbance over the previous month. Higher scores indicate worse sleep quality, and total score >5 indicates poor sleep. The HFS for Parents (HFS-P), which is validated for parents, and the HFS for Children (HFS-C), which is validated for children aged ≥6 years, have a 0 to 4 scale, with higher scores indicating more fear. A total score and two subscales (behavior and worry) are generated. The PAID survey (validated for parent and children age ≥8 years) has a 0 to 6 scale, with higher scores reflecting more diabetes distress. Glycemic and psycho-behavioral outcomes before and after CIQ use were analyzed in poor sleepers (n=49) and their children.
Results
Child nocturnal glycemic data showed significant improvements in mean sensor glucose, sensor glucose standard deviation, time in range (TIR), and time with glucose level >180 mg/dL (P<.05). Time <54 mg/dL increased (from 0.0 to 0.1%; P<.04). HFS-P score (P<.001), PAID scale score (P<0.001), PSQI score (P<.001), and HFS-C score (P=.025) significantly improved. In analyses of parent poor sleeper PSQI score, changes showed improvement after CIQ use (P<.001). All parent PROs had significant improvements. Twenty-seven of the 49 poor sleepers became good sleepers. Significant reduction in total HFS-C score occurred.
Conclusions
Use of HCL systems significantly improves PROs in parents of children with T1D classified as poor sleepers while simultaneously improving child's nocturnal glycemic control.
Comments
Type 1 diabetes (T1D) causes nocturnal disruptors. Parents of children with T1D are especially prone to sleep disruptions and sleep deprivation, and they may have chronic anxiety that may endanger their child. Sleep disruptions may potentially influence the diabetes daily-care tasks that parents are required to provide. Parents of young children with T1D may expect that the use of diabetes technologies, and especially use of CGM, may improve their sleep quality following initiation of the system. However, previous studies have shown that CGM use actually had a negative effect on parental sleep continuity (43).
Hybrid closed-loop (HCL) systems improve time in range (TIR; 70–180 mg/dL), and TIR improvement is most notable during the nighttime. Use of HCL systems reduces time spent in hypoglycemia (<70 mg/dL) and fear of hypoglycemia, contributors of insufficient sleep for youth with T1D and their parents (27,44,45). Therefore, it is important to evaluate the impact of HCL on sleep in youth with T1D and their parents. The findings of this study demonstrate that use of the CIQ system was associated with improved sleep after only 12–16 weeks of use in parents of children with T1D who met criteria for inadequate sleep. Fear of hypoglycemia and diabetes distress, common sleep disruptors for parents, was reduced. Also, children of poor sleepers had significant improvement in nocturnal TIR and time in hyperglycemia after using CIQ.
The main limitations of this study include the absence of child sleep assessment and that the sleep measures were based on subjective self-report. Researchers on a previous study found moderate improvements in parental subjective report of sleep quality despite no change in objective measures of sleep duration (46). Therefore, further research is needed to determine whether the same findings exist with objective sleep measures, such as actigraphy, and with long-term HCL use in a larger sample size.
Nevertheless, pediatric diabetes care teams should be aware of diabetes-related factors potentially affecting parental sleep and the impacts of diabetes technologies; teams should also consider tailored parental support and education to reduce the burden of nocturnal care.
Outcomes of Hybrid Closed-Loop Insulin Delivery Activated 24/7 Versus Evening and Night in Free-Living Prepubertal Children with Type 1 Diabetes: A Multicentre, Randomized Clinical Trial
Renard E1,2,3, Tubiana-Rufi N4, Bonnemaison E5, Coutant R6, Dalla-Vale F7, Bismuth E4, Faure N5, Bouhours-Nouet N6, Farret A1,3, Storey C4, Donzeau A6, Poidvin A4, Amsellem-Jager J6, Place J3, Breton MD8, Free-life Kid AP Study Group
1Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France; 2INSERM Clinical Investigation Centre 1411, Montpellier, France; 3Institute of Functional Genomics, University of Montpellier, CNRS, INSERM, Montpellier, France; 4Department of Pediatric Endocrinology and Diabetology, Robert Debré University Hospital, University of Paris, Paris, France; 5Department of Pediatrics, Tours University Hospital, Tours, France; 6Department of Pediatric Endocrinology and Diabetology, Angers University Hospital, Angers, France; 7Center for Diabetes Technology, University of Virginia, Charlottesville, VA; 8Department of Pediatrics, Montpellier University Hospital, Montpellier, France
Background
The aim of this study is to examine a hybrid closed-loop insulin delivery system in 24/7 mode and evening-and-night (E/N) mode in prepubertal free-living children with type 1 diabetes (T1D) who previously used insulin pump therapy. The system's safety and efficacy were evaluated.
Methods
Prepubertal children (N=122; 49 females; age, 8.6±1.6 years; diabetes duration, 5.2±2.3 years; insulin pump use, 4.6±2.5 years; HbA1c, 7.7%±0.7% [61±5 mmol/mol]) from four centers were randomized for 24/7 versus E/N activation of the Tandem Control-IQ system for 18 weeks. Subsequently, participants use the activated system 24/7 for 18 additional weeks. The primary outcome was the percentage of time spent in range (TIR, 70–180 mg/dL).
Results
HCL was active for 94.1% of the time in 24/7 mode and 51.1% of the time in E/N mode. The increase in TIR from baseline was greater in the 24/7 group than in the E/N group (52.9±9.5% to 67.3±5.6% [+14.4%; 95% CI, 12.4%–16.7%] vs 55.1%±10.8% to 64.7%±7.0% [+9.6%; 95% CI, 7.4%–11.6%]; P=.001). The mean time below range decreased to 2.7% in both groups. There was a greater decrease in the time above range in the 24/7 mode group than in the E/N group (41.9% to 30.0% [−11.9%, 95% CI −9.7% to −14.6%] vs 39.8% to 32.6% [−7.2%; 95% CI, −5% to −9.9%]; P=.007). The results were consistent in the extension phase for those initially in the 24/7 group and improved in those in the E/N group. There were no episodes of diabetic ketoacidosis or severe hypoglycemia.
Conclusions
The Tandem Control-IQ system is safe and effective for free-living prepubertal children with T1D. Outcomes were better with 24/7 use, and improvements were sustained for 36 weeks without serious adverse events.
Comments
Hybrid closed-loop systems are associated with improved clinical outcomes including lower HbA1c, increased time in range, and decreased time below range (26 –29,47). Individuals who participated in the pivotal trial for the Control-IQ hybrid closed-loop system saw similar improvements in glycemia (27).
Study populations are not always representative of the full spectrum of people with diabetes. Therefore, Renard et al. studied the 24/7 versus E/N use of hybrid closed loop in prepubertal children (ages 6–12) across four centers in France. Participants had to have had experience with an insulin pump for at least 6 months prior to enrollment, but the study did not require prior continuous glucose monitoring (CGM) use. There was an increase in TIR in both groups, but the increase was greater in the 24/7 group. The decrease in the incidence of time below range was similar in both groups. At 18 weeks, all participants were enrolled in an extension phase with 24/7 hybrid closed-loop use. The greatest improvements in the TIR were seen in those with the lowest TIR at baseline. At the end of this period, all participants had similar glycemic metrics. There was a low incidence of severe adverse events.
This study highlights the benefits of hybrid closed-loop systems, even in younger children. More importantly, this study enrolled participants with a spectrum of HbA1c levels at baseline and demonstrated that hybrid closed-loop therapy can safely and effectively be administered in those with suboptimal care. However, participants were required to have at least 6 months of prior insulin pump use, and the outcomes may not directly be generalizable to individuals who transition to a hybrid closed-loop system from multiple daily injections. The strength of this study is that it highlights the benefits and safety of using hybrid closed-loop therapy 24/7 across the real-world population.
Glycemic Outcomes of Children 2–6 Years of Age with Type 1 Diabetes During the Pediatric MiniMed 670G System Trial
Forlenza GP1, Ekhlaspour L2, DiMeglio LA3, Fox LA4, Rodriguez H5, Shulman DI5, Kaiserman KB6, Liljenquist DR7, Shin J8, Lee SW8, Buckingham BA2
1Division of Pediatric Endocrinology, Barbara Davis Center, Aurora, CO; 2Division of Pediatric Endocrinology, Stanford University, Stanford, CA; 3Division of Pediatric Endocrinology and Diabetology, Wells Center for Pediatric Research, Indiana University, Indianapolis, IN; 4Division of Endocrinology, Diabetes and Metabolism, Nemours Children's Health System, Jacksonville, FL; 5Division of Pediatric Endocrinology, University of South Florida, Tampa, FL; 6SoCal Diabetes, Torrance, CA; 7Rocky Mountain Diabetes Center, Idaho Falls, ID; 8Medtronic Diabetes, Northridge, CA
Background
Management of diabetes in young children (2–6 years) is challenging because of highly variable insulin sensitivity, susceptibility to hypoglycemia, and inability to communicate symptoms of hypoglycemia.
Methods
Participants (N=46, ages 4.6±1.4 years) were enrolled from seven centers. There was a 2-week run-in period of using the MiniMed 670G in Manual Mode followed by a 3-month study period in which participants used Auto Mode. Safety events, mean Hb1Ac, sensor glucose (SG), and percentage of time spent in range (TIR, 70–180 mg/dL), below range (TBR, <70 mg/dL), and above range (TAR, >180 mg/dL) were assessed for the run-in and study phases and compared using a paired t test or the Wilcoxon signed-rank test.
Results
Participants spent a median of 87.1% in Auto Mode during the study period. The mean HbA1c and SG changed from 8.0±0.9% to 7.5±0.6% (P<.001) and from 173±24 to 161±16 mg/dL (P<.001), respectively. The TIR increased from 55.7±13.4% to 63.8±9.4% (P<.001). The TBR and TAR decreased from 3.3±2.5% to 3.2±1.6% (P=0.996) and 41.0±14.7% to 33.0±9.9% (P<0.001), respectively. There were improvements in TAR from 12:00 AM to 6:00 AM. There were no episodes of severe hypoglycemia or diabetic ketoacidosis (DKA) or other severe adverse device-related events.
Conclusions
Young children could safely use the MiniMed 670G system in Auto Mode at home, and they had better glycemic values when using Auto Mode than when using Manual Mode (open loop). These results were similar to those seen in older children, adolescents, and young adults with T1D on this same system over the same duration of time.
Comments
Numerous studies have demonstrated the benefits of hybrid closed-loop systems in children, adolescents, and adults (26 –29). Automated insulin delivery systems have the potential to improve diabetes care in young children, who are often difficult to manage because of their increased risk of hypoglycemia and their inability to express symptoms of hypoglycemia. As these systems continue to advance, they are being examined in an increasing number of studies in young children (ages 2 to 6 years) (48,49).
Forlenza et al. describe the safety and glycemic outcomes of the MinMed 670G system with Auto Mode use in children aged 2–6 years old. Following 2 weeks in Manual Mode (run-in period), children were transitioned to Auto Mode. There were no episodes of severe hypoglycemia, diabetic ketoacidosis, or serious adverse device-related events. Youth had an increase in TIR and a decrease in TAR and TBR from the levels during the run-in period.
These results demonstrate the safety and efficacy of Auto Mode use in young children. The youth enrolled in this study had prior experience with insulin pumps, and study duration was relatively short (3 months). Therefore, the results may not directly translate to a broad clinic population. However, it is important to continue the evaluation of automated insulin delivery systems in young children and more diverse populations.
IMPLEMENTATION OF DIABETES TECHNOLOGY IN REGISTRIES
Diabetes Technology Use for Management of Type 1 Diabetes Is Associated with Fewer Adverse COVID-19 Outcomes: Findings from the T1D Exchange COVID-19 Surveillance Registry
Noor N1, Ebekozien O1,2, Levin L3 Stone S4, Sparling DP5, Rapaport R6, Maahs DM7
1Quality Improvement and Population Health, T1D Exchange, Boston, MA; 2University of Mississippi Medical Center, Jackson, MS; 3Lurie Children Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL; 4State University of New York Upstate Medical University, Syracuse, NY; 5Harold Hamm Diabetes Center, University of Oklahoma College of Medicine, Oklahoma City, OK; 6Icahn School of Medicine at Mount Sinai, New York, NY; 7Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA
Introduction
Although an increasing number of studies demonstrate that diabetes technology improves glycemic control, these advanced mechanisms are not being widely used. The low uptake is partly due to systemic racism and social inequities, which are seen in diabetes and device education, peer support, and patient motivation. The purpose of this study was to evaluate the association between technology use and clinical outcomes during the COVID-19 pandemic.
Methods
Data from the type 1 diabetes exchange (T1DX) COVID-19 Surveillance Registry, a US-based multicenter study for people with type 1 diabetes (T1D), were analyzed to examine the frequency of adverse outcomes across categories of technology use. This analysis included 447 people with T1D and laboratory-confirmed COVID-19 infection from March 2020 through December 2020. Categories of diabetes technology use were “no device use,” patients who did not report using a continuous glucose monitor (CGM) or insulin pump device; “CGM use,” all patients currently using a CGM device, regardless of insulin pump use; “insulin pump use,” all patients currently using an insulin pump, regardless of CGM device; and “CGM and insulin pump use,” patients who reported using a CGM in combination with an insulin pump. Adverse outcomes, including hospitalization, diabetic ketoacidosis (DKA), severe hypoglycemia, and death, were reported through medical chart review. Patients who were hospitalized or admitted to an intensive care unit were classified as hospitalized patients.
Results
Diabetes technology use differed across race and ethnicity; non-Hispanic White individuals used CGM devices significantly more than non-Hispanic Black and Hispanic individuals (67% vs 10% and 16%, respectively; P<.01). Rates of hospitalization and DKA were lower among all device users compared with rates among nonusers (61% and 36%, P<.01). In the subgroup that used CGM only (n=85), the hospitalization rate was 18% and the DKA rate was 13%; in the insulin-pump-only subgroup (n=25), both the hospitalization and the DKA rates were 12%. The odds of hospitalization among nonusers of technology were higher than the odds for those using any device after adjustment for age, insurance status, and race/ethnicity (OR 6.1 [95% CI, 3.7–10.1]). Additionally, the odds of DKA among nonusers of technology were also higher than the odds for those using a device (OR 4.3 [2.4–7.8]).
Conclusions
The findings of this study further demonstrate that diabetes technology improves glycemic outcomes and illustrate the need to consider social inequities, such as socioeconomic status and education level, when advocating for measures to increase the number of diabetes patients using this technology.
Comments
In this brief report, the T1DX Quality Improvement Collaborative used data collected from their COVID surveillance registry to investigate the potential benefits of diabetes technology on glycemic outcomes and potential disparities due to socioeconomic status and education level. Rates of diabetes technology use differed by race/ethnicity, and hospitalization and DKA rates were lower among diabetes technology users. These data add to those from previous reports on inequities in use of diabetes technology and subsequent outcomes, in this case with COVID-related outcomes for people with T1D. Significant data continue to be gathered on the effects of COVID on those in the pediatric age group with T1D and, to a lesser extent, those with T2D. Disparities in health care are a risk factor for poorer outcomes, although age and comorbid conditions remain strong risk factors for COVID-related outcomes.
The SWEET Project 10-Year Benchmarking in 19 Countries Worldwide Is Associated with Improved HbA1c and Increased Use of Diabetes Technology in Youth with Type 1 Diabetes
Gerhardsson P1, Schwandt A2,3, Witsch M4, Kordonouri O5, Svensson J6,7, Forsander G8, Battelino T9, Veeze H10, Danne T5,11 on Behalf of the SWEET Study Group
1Department of Epidemiology, Institute of Applied Economics and Health Research, Copenhagen, Denmark; 2Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4Department of Pediatrics DCCP, Center Hospitalier de Luxembourg, Luxembourg; 5Children's Hospital AUF DER BULT, Hannover Medical School, Hannover, Germany; 6Department of Pediatrics and Adolescents, Copenhagen University Hospital, Herlev and Gentofte, Herlev, Denmark; 7Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 8Department of Pediatrics, Institute for Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Region Västra Götaland, Sahlgrenska University Hospital, Queen Silvia Children's Hospital, Gothenburg, Sweden; 9UMC-University Children's Hospital and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia; 10Diabeter, Diabetes Center for Pediatric and Adolescent Diabetes Care and Research, Rotterdam, Netherlands; 11SWEET e.V., Hannoversche Kinderheilanstalt, Hannover, Germany.
This manuscript is also discussed in DIA-2023-2511, page S-176.
Background
During the past 10 years, centers worldwide have implemented treatment with modern technology including insulin pumps and continuous glucose monitoring (CGM) in children with type 1 diabetes (T1D). The international SWEET registry was initiated in 2008 to improve outcomes in pediatric diabetes. The aim of this study was to analyze the key quality indicators from the initial SWEET database over the past decade and to study the impact of treatment changes in youth with T1D.
Methods
Data from eligible children and youth <25 years of age with T1D from participating centers were aggregated for each participant during the observation periods. Data were compared between the 2008–2010 and the 2016–2018 periods. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age, and diabetes duration groups.
Results
In this study, 16,082 participants with T1D from 22 centers were included. The first and second time periods included 4930 vs 13,654 persons, 51% vs 52% male, median age 11.3 years (IQR, 7.9–14.5 years) vs 13.3 years (IQR, 9.7–16.4 years), and diabetes duration 2.9 years (IQR, 0.8–6.4 years) vs 4.2 years (IQR, 1.4–7.7 years). The adjusted HbA1c improved from 68 mmol/mol (95% CI, 66–70 mmol/mol) to 63 mmol/mol (95% CI, 60–65 mmol/mol), or from 8.4% (95% CI, 8.2%–8.6%) to 7.9% (95% CI, 7.6%–8.1%) (P<.0001). This difference remained significant when adjusted for gender, age, and diabetes duration.
Across all age groups, HbA1c was significantly lower in pump and sensor users.
The improvement of HbA1c over the 10-year period was accompanied with significant improvements in body mass index-standard deviation score (BMI-SDS) (0.55 [IQR, 0.46–0.64] vs 0.42 [IQR, 0.33–0.51]; P<.0001). Over time, the increase in pump use from 34% to 44% preceded the increase in HbA1c target achievement (<53 mmol/mol or <7%) from 21% to 34%. Frequency of severe hypoglycemia was significantly reduced between the two time periods from 3.8% (IQR, 2.9%–4.9%) to 2.4% (IQR, 1.9%–3.1%) (P<.0001). A doubling in the diabetic ketoacidosis (DKA) rate was seen in those using injection therapy, whereas no significant difference between time points was observed in pump users.
Conclusions
The SWEET database, based on twice yearly benchmarking, demonstrates sustained improvements in HbA1c over the 10-year period throughout all pediatric age groups; these improvements are possibly associated with the use of diabetes technology, the use of a unified electronic data reporting system, benchmarking, peer review, and the SWEET quality improvement strategies.
Comments
Improved technologies to manage diabetes are increasingly being used in pediatric patients with T1D (14,50,51). Insulin delivery method and CGM use vary widely, depending on the geographic area and the characteristics of the different health-care systems. Therefore, the evaluation of clinical outcomes and treatment modalities in international multicenter registries is essential to determine the impact of these technologies on the achievement of glycemic targets and the occurrence of acute diabetes complications (severe hypoglycemia and DKA) worldwide.
In this study, 16,082 children and youth with T1D from 22 centers from 19 countries in Asia, Australia, Europe, and North America (Canada) were included. The data show that the use of diabetes technologies increased significantly from the 2008–2010 period to the 2016–2018 period. The results of this study are encouraging because they show that nearly all SWEET centers showed an improvement in the average HbA1c over the 10-year period across all age groups, with an increased percentage of children and adolescents achieving the current International Society for Pediatric and Adolescent Diabetes (ISPAD) glycemic target.
We have to consider that HbA1c values for both time periods were available for only 15.5% of the entire cohort (n=2502 persons). Nevertheless, after adjustment for age, gender, and diabetes duration, a decline in HbA1c was seen between the two time periods in this subgroup as well.
The sustained improvement in HbA1c may be attributed to the use of diabetes technology. Of note, the improvement was also accompanied with significant improvement in BMI-SDS and decreased rate of severe hypoglycemia.
However, these results are different from those from the T1D Exchange registry: although increasing use of CGM was also associated with improved glycemic outcomes, the total T1D Exchange registry cohort showed deteriorating glycemic control (8). This may be explained by the fact that T1D Exchange registry included individuals who agreed to participate and were recruited within a specified period of time. By contrast, most pediatric patients who receive care at SWEET participating centers are supposed to be included in the SWEET data collection. Also, unlike SWEET, the T1D Exchange did not perform benchmarking or peer audit, further limiting the comparability. Yet most of the young patients with T1D still remain above the recommended HbA1c target (3).
Another significant observation is the decreased rate of severe hypoglycemia both in pump and multiple daily injection (MDI) users despite the improvement in HbA1C levels; this decrease may be attributed to increased use of CGM and higher frequency of glucose monitoring.
One of the concerns raised when initiating pump therapy is problems that can occur with insulin infusion due to pump failure, insulin infusion set blockage with interruption of insulin infusion, stability issues of insulin, user error, and lack of a long-acting insulin. Therefore, pump users may be exposed to potential hazards that can result in DKA. However, the current study showed that the rate of DKA episodes was significantly lower in participants using a pump than in those using MDIs, as was also reported previously (8,24).
Finally, although the results are based on real-world data, the limitation of the study is that the data represent the frequency of pump and CGM therapy only in the larger SWEET centers with multidisciplinary care and that fulfill the criteria of being “centers of reference”; thus, the participating centers cannot necessarily reflect the management of diabetes and the glycemic control by country or region.
Association of the Use of Diabetes Technology with HbA1c and BMI-SDS in an International Cohort of Children and Adolescents with Type 1 Diabetes: The SWEET Project Experience
Marigliano M1, Eckert AJ2,3, Guness PK4, Herbst A5, Smart CE6, Witsch M7, Maffeis C1, SWEET Study Group
1Regional Center for Pediatric Diabetes, University of Verona, University City Hospital, Verona, Italy; 2Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4Nongovernment Organization, Quatres Bornes, Mauritius 5Department of Pediatric and Adolescent Medicine, Hospital Leverkusen gGmbH, Leverkusen, Germany; 6Department of Paediatric Endocrinology, John Hunter Children's Hospital, New Lambton Heights, Australia; 7Pediatric Diabetology, Centre Hospitalier de Luxembourg, Luxembourg
Background
In recent years, the use of technology related to diabetes treatment (continuous subcutaneous insulin infusion [CSII], continuous glucose monitoring [CGM] systems, and the combination of both) has grown remarkably and has modified the therapeutic habits of patients with type 1 diabetes (T1D). The aim of this study was to evaluate the association between the use of diabetes technology with glycemic control (HbA1c) and body adiposity (body mass index standard deviation score [BDM-SDS]) over time in a large international cohort of young patients with T1D that have never used these technologies before.
Methods
The analysis was based on data from the SWEET registry. Data were collected at two time points (2016 and 2019). For the present analysis, patients aged 2–18 years who had T1D for ≥1 year, who did not have celiac disease, who did not use CSII or CGM before 2016, and who had documented data from both 2016 and 2019 were included (N=4634). Datasets were aggregated for 2 years of treatment (2016 and 2019) separately for each patient. The current analysis involved 21 European countries (32 centers) and 14 countries (23 centers) outside Europe. Metabolic control was assessed by HbA1c and body adiposity by BMI-SDS. Patients were categorized by treatment modality (multiple daily injections [MDIs] or CSII) and by the use or nonuse of CGM. Linear regression models, adjusted for age, gender, diabetes duration, and region, were applied to assess differences in HbA1c and BMI-SDS among patient groups.
Results
The proportion of patients who were using MDIs or CSII significantly increased in 2019 (P<.001). Across the whole group, the percentage of patients who increased their technology use between 2016 and 2019 was 53.0%. Linear regression models showed a significantly lower HbA1c in groups that switched from MDIs to CSII with or without CGM (P<.001), but a higher BMI-SDS from MDIs without CGM to CSII with CGM (P<.05) and from MDIs without CGM to CSII without CGM (P<.01).
Conclusions
Children and adolescents with T1D who switched from MDI therapy to CSII with or without the use of CGM had a significant improvement in glucose control but a simultaneous increased BMI-SDS at the end of the observation period. These results emphasize the role of diabetes technology in improving glucose control in pediatric population with T1D and the need for developing further strategies to prevent excess weight.
Comments
This study is also based on the SWEET registry data from more recent years (2016 and 2019). Its main findings are that in the analyzed population of young patients with T1D, patients who modified their insulin delivery method in the observed time span from MDI to insulin pump therapy, with or without CGM, had a clinically relevant improvement in HbA1c but, at the same time, worsened body adiposity (BMI-SDS). Unexpectedly, the entire analyzed population shows a worsening of HbA1c over time, regardless of the treatment used. This observation contrasts the findings of the previous study of SWEET (reviewed above) that demonstrated a sustained improvement in HbA1c over the 10-year period. The reason for this may be that in the time frame of the study, a high percentage of the enrolled children had entered adolescence, which is usually associated with a worsening of metabolic control.
The other main finding of this study was that the use of intensified insulin treatment via CSII was associated with an increased BMI-SDS. The higher BMI-SDS could be due to more intensive insulin treatment requiring extra carbohydrates because of hypoglycemic events and use of more insulin because of frequent meals. Again, this contrasts with the data from the previous manuscript about the SWEET registry, which reported a significant improvement in BMI-SDS. The reasons explaining these different findings is not clear, but may reflect the increasing prevalence of overweight and obesity among the young general population worldwide across all age groups and ethnicities that may affect also children and adolescents with T1D.
The strengths of this study were the worldwide dataset with the SWEET data quality control and 3-year follow-up (2016–2019) of the same population whose members were from many countries. However, the limitation is that the data of the current study represent only the percentage of patients who started a new technology (insulin pump therapy or CGM) in the SWEET centers' enrolled population, so the data do not necessarily reflect the management of T1D by center or country.
Trends in Glycemic Control Among Youth and Young Adults with Diabetes: The SEARCH for Diabetes in Youth Study
Malik FS1, Sauder KA2, Isom S3, Reboussin BA3, Dabelea D2, Lawrence JM4, Roberts A1, Mayer-Davis EJ5, Marcovina S6, Dolan L7, Igudesman D5, Pihoker C1 for the SEARCH for Diabetes in Youth Study
1Department of Pediatrics, University of Washington, Seattle, WA; 2Department of Epidemiology, Colorado School of Public Health, Aurora, CO; 3Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC; 4Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA; 5Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill, NC; 6Medpace Reference Laboratories, Cincinnati, OH; 7Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Background
Using data from the SEARCH for Diabetes in Youth study, the investigators aimed to find temporal trends in glucose control in youth and young adults who have youth-onset type 1 diabetes (T1D) or type 2 diabetes (T2D).
Methods
The study included data from 6369 participants with T1D (5482) and T2D (887) from the SEARCH for Diabetes in Youth study beginning in 2002. Participant visit data used in the current study were categorized into three groups: time periods of 2002–2007, 2008–2013, and 2014–2019; diabetes durations of 1–4, 5–9, and ≥10 years; age groups of 1–9, 10–14, 15–19, 20–24, and ≥25 years. Multivariable regression models were used to determine differences in HbA1c over time by diabetes type. Models were adjusted for clinical site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and disease duration and stratified by diabetes duration and age group.
Results
The adjusted mean HbA1c for the 2014–2019 cohort (n=1742; 50.8% female) with T1D was 8.8±0.04% (72 mmol/mol). The average adjusted HbA1c for the 2014–2019 cohort with T1D in the 10–14-year-old, 15–19-year-old, and 20–24-year-old age groups was 0.3% higher than the mean HbA1c for the 2002–2007 cohort (8.5±0.03% [70 mmol/mol]; n=3398; 49.4% female). Glycemic control in the 2014–2019 cohort was significantly associated with race/ethnicity, household income, and treatment regimen. The adjusted mean HbA1c was 8.6±0.12% for 2014–2019 cohort with T2D, which was similar to that of the 2002–2007 cohort but was higher than the 2008–2013 cohort. Those receiving metformin had a lower HbA1c than those on insulin.
Conclusion
The study authors concluded that later cohorts of youth and young adults with diabetes were less likely to reach desired glycemic targets than earlier cohorts despite the greater availability of advanced diabetes technologies, newer medications, and the increased aggressiveness of more recently recommended glycemic targets.
Comments
The SEARCH for Diabetes in Youth (SEARCH) represents the largest multiethnic, population-based registry and cohort study of youth diagnosed with diabetes before 20 years of age in the United States (52). In the current analysis, researchers examined trends in glycemic control in 6492 SEARCH participants (n=5482 T1D and n=887 T2D) who had diabetes for at least 1 year. Unadjusted linear regression models stratified by duration group were used to evaluate differences in HbA1c across time periods. In addition, the clinical site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen, and disease duration were adjusted. Results of the study indicated that a substantial percentage of current youth and young adults with diabetes are not meeting current expert-recommended HbA1c goals. For example, the estimated HbA1c for the most recent cohort of youth and young adults with T1D was 8.8±0.04% (72 mmol/mol), while the most recent group with T2D had an estimated average HbA1c of 8.6±0.12% (70 mmol/mol). Overall, youth and young adults with T1D exhibited a temporal trend of worsening glycemic control. On the other hand, the adjusted mean HbA1c in participants with T2D was relatively unchanged when comparing the 2002–2007 cohort to the most recent cohort, 2014–2019.
The limitation of the interpretation of the results was the availability of data only from the participants who attended study visits at the research sites, which could affect the generalizability of the findings. In addition, the impact of the increased use of continuous glucose monitoring (CGM) on glycemic control could not be meaningfully estimated in the absence of data on the frequency of manual blood glucose monitoring.
The evidence that lower HbA1c levels in childhood and young adulthood are associated with lower risk and rate of microvascular and macrovascular complications highlights the importance of tight glycemic control even at an early age (53). The results of this study show that many youth and young adults with diabetes are not meeting desired glycemic targets despite the increased availability of diabetes technology, newer therapies, and more aggressive glycemic target recommendations. This study underscores the urgent need to implement effective diabetes management strategies that address the complexity of diabetes care during adolescence and young adulthood to improve metabolic status. Further longitudinal studies are needed to identify gaps in diabetes care and track the effectiveness of approaches for better glucose control and prevention of diabetes complications.
OTHER THERAPIES FOR DIABETES
Dasiglucagon, a Next-Generation Ready-to-Use Glucagon Analog, for Treatment of Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: Results of a Phase 3, Randomized Controlled Trial
Battelino T1, Tehranchi R2, Bailey T3, Dovc K1, Melgaard A2, Yager Stone J3, Woerner S4, von dem Berge T5, DiMeglio L4, Danne T5
1Department of Pediatrics, University Medical Centre Ljubljana, Ljubljana, Slovenia; 2Zealand Pharma A/S, Søborg, Denmark; 3AMCR Institute, Escondido, CA; 4Department of Pediatrics, Wells Center for Pediatric Research, Indiana University, Indianapolis, IN; 5Department of General Pediatrics, Children's Hospital AUF DER BULT, Hannover Medical School, Hannover, Germany
Background
Glucagon is the first-line treatment for severe hypoglycemia in individuals with diabetes. Dasiglucagon is a next-generation, ready-to-use aqueous glucagon analogue formulation that does not require reconstitution. This trial aimed to evaluate the safety and efficacy of dasiglucagon in children and adolescents with type 1 diabetes (T1D).
Methods
In this multicenter, randomized, placebo-controlled, double-blind phase 3 trial, 42 children and adolescents (6–17 years) with T1D for at least 1 year and who were receiving daily insulin were randomly allocated (2:1:1) to a single subcutaneous (SC) injection of dasiglucagon (0.6 mg), placebo, or reconstituted glucagon (GlucaGen; 1.0 mg) during insulin-induced hypoglycemia. Hypoglycemia was induced by intravenous (IV) infusion of insulin glulisine (Apidra, 100 U/mL) to facilitate a steady decline in plasma glucose concentration to a target of 80 mg/dL (4.4 mmol/L). The primary endpoint was time to plasma glucose recovery, defined as time to first plasma glucose increase of ≥20 mg/dL (1.1 mmol/L) from time of administration without IV glucose rescue treatment. The primary comparison was dasiglucagon vs placebo; glucagon acted as a reference.
Results
Dasiglucagon was superior to placebo for the primary endpoint of observed time from subcutaneous injection to plasma glucose recovery, with a median time of 10 minutes for dasiglucagon versus 30 minutes for placebo (P<.001). The median time for glucagon was similar to the results obtained for dasiglucagon (10 minutes) but did not include the time taken to reconstitute the lyophilized powder. All participants in the dasiglucagon group had plasma glucose recovery by 20 min but only 18% did so in the placebo group. None of the dasiglucagon and glucagon group participants required intravenous glucose rescue. No serious or severe adverse events were reported. As expected with glucagon treatment, nausea and vomiting were the most frequently reported adverse events.
Conclusion
Dasiglucagon effectively and reliably restored plasma glucose levels following insulin-induced hypoglycemia in children and adolescents with T1D. In addition, the overall safety profile of dasiglucagon was similar to that of reconstituted lyophilized glucagon.
Comments
Severe hypoglycemia is an event with severe cognitive impairment requiring external assistance by another person. Severe hypoglycemia may lead to a coma or even death without prompt treatment. Multiple hypoglycemic episodes could have adverse cognitive effects, particularly in children during early development (54). Incidence rates of severe hypoglycemia have reduced in recent years because of increased education and monitoring tools. Severe hypoglycemia requires emergency care, and glucagon is the first line of treatment in individuals with diabetes (55).
Glucagon emergency kits require time-consuming reconstitution of the drug product, which creates a barrier to the timely and accurate administration of the treatment and may lead to underutilization of glucagon for the treatment of hypoglycemia (56). The need for timely and proper treatment of severe hypoglycemia in individuals with diabetes led to the development of products that do not require reconstruction, such as subcutaneous injection (57) or lyophilized nasal powder (58). Dasiglucagon is a chemically stable, ready-to-use aqueous glucagon analogue formulation (59).
Previously reported phase 3 study data for adults with T1D suggested that dasiglucagon is a promising candidate for treating severe hypoglycemia (60). In the current randomized, placebo-controlled, double-blind phase 3 trial, investigators evaluated the safety and efficacy of dasiglucagon in a pediatric group with T1D aged 6 to 17 years. Dasiglucagon had rapidly and effectively restored plasma glucose levels after insulin-induced hypoglycemia, consistent with adult phase 3 trial results. The median time to plasma glucose recovery was the same among individuals given dasiglucagon and the participants given glucagon as a reference control. However, it must be noted that the time taken to reconstitute the lyophilized glucagon was not included. Therefore, the real-world time to respond to lyophilized glucagon would be expected to be longer. Dasiglucagon was well tolerated and had a safety profile consistent with the known side effects of glucagon treatment.
Dasiglucagon was an effective, reliable treatment in restoring plasma glucose levels following insulin-induced hypoglycemia in children and adolescents with T1D. These data suggest that dasiglucagon is a promising candidate for treating severe hypoglycemia in the pediatric population.
TREATMENT OF TYPE 2 DIABETES
A Randomized Clinical Trial of the Efficacy and Safety of Sitagliptin as Initial Oral Therapy in Youth with Type 2 Diabetes
Shankar RR1, Zeitler P2, Deeb A3, Jalaludin MY4, Garcia R5, Newfield RS6, Samoilova Y7, Rosario CA8, Shehadeh N9, Saha CK10, Zhang Y1, Zilli M1, Scherer LW1, Lam RLH1, Golm GT1, Engel SS1, Kaufman KD1
1Merck Research Laboratories, Merck & Co., Inc., Kenilworth, NJ; 2Department of Endocrinology, Children's Hospital Colorado Clinical, University of Colorado Anschutz Medical Campus, Aurora, CO; 3Department of Pediatric Endocrinology, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates; 4Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; 5Department of Internal Medicine and Clinical Endocrinology, Centro de Estudios Clínicos y Especialidades Medicas (CECEM), Nuevo Leon, Mexico; 6Department of Pediatric Endocrinology, Rady Children's Hospital, University of California San Diego, San Diego, CA; 7Department of Pediatric Endocrinology and Diabetology, Siberian State Medical University, Tomsk, Russia; 8Department of Pediatric Endocrinology, Hospital General Plaza de la Salud, Santo Domingo, Dominican Republic; 9Department of Pediatrics A and the Pediatric Diabetes Unit, Institute of Diabetes, Endocrinology, and Metabolism, Rambam Medical Center, Haifa, Israel; 10Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN
Background
Inhibition of dipeptidyl peptidase-4 (DPP-4) by sitagliptin results in enhanced glucose-stimulated insulin release. The primary aim was to assess the efficacy (change in HbA1c from baseline at week 20), safety, and tolerability of sitagliptin 100 mg once daily (the dose approved for adults) in 10- to 17-year-olds with type 2 diabetes (T2D). Secondary objectives included assessment of the effect of treatment with sitagliptin on fasting plasma glucose (FPG), the proportion of participants requiring rescue therapy, and the proportion of participants with HbA1c <7.0% after 20 weeks.
Methods
This was a 54-week, multicenter, two-part, double-blind, randomized, parallel group study. To be eligible, participants were required to have T2D and HbA1c ≥6.5% and ≤10.0% (if not on antihyperglycemic therapy) or ≥7.0% and ≤10.0% (if on insulin therapy); other eligibility criteria included body mass index (BMI) ≥85th percentile (or history of being overweight or obese at T2D diagnosis), fasting C-peptide >0.6 ng/mL, and fasting finger stick glucose <240 mg/ dL (13.3 mmol/L) at randomization. The trial was placebo controlled for the first 20 weeks, after which metformin replaced placebo.
Results
Of the 191 participants randomized to sitagliptin or placebo, 152 (79.6%) completed the study and 140 (73.3%) completed on study medication. At week 20, there was no notable difference in the estimated percentage of participants with HbA1c <7% between the sitagliptin (49.5% [47/95]) and placebo groups (36.8% [35/95]). At week 20, small and similar increases from baseline were observed in fasting plasma glucose (FPG) in both treatment groups. No notable difference was observed between the sitagliptin and placebo groups in the proportion of participants who initiated glycemic rescue therapy through week 20.
The percentages of participants with HbA1c <7.0% at week 54 were not notably different between treatment groups, with 28.4% [27/95] in the sitagliptin group and 40% [36/90] in the placebo/metformin group. There were no notable between-group differences in the adverse event profiles through week 54.
Conclusions
The data obtained in this study indicate that DPP-4 inhibition with sitagliptin did not provide significant improvement in glycemic control in youth with T2D. Sitagliptin was generally well tolerated and had a safety profile similar to that reported for adults.
Efficacy and Safety of the Addition of Sitagliptin to Treatment of Youth with Type 2 Diabetes and Inadequate Glycemic Control on Metformin Without or with Insulin
Jalaludin MY1, Deeb A2, Zeitler P3, Garcia R4, Newfield RS5, Samoilova Y6, Rosario CA7, Shehadeh N8, Saha CK9, Zhang Y10, Zilli M10, Scherer LW10, Lam RLH10, Golm GT10, Engel SS10, Kaufman KD10, Shankar RR10
1Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; 2Department of Pediatric Endocrinology, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates; 3Department of Endocrinology, Children's Hospital Colorado Clinical, University of Colorado Anschutz Medical Campus, Aurora, CO; 4Department of Internal Medicine and Clinical Endocrinology, Centro de Estudios Clínicos y Especialidades Medicas (CECEM), Nuevo Leon, Mexico; 5Department of Pediatric Endocrinology, Rady Children's Hospital, University of California San Diego, San Diego, CA; 6Department of Pediatric Endocrinology and Diabetology, Siberian State Medical University, Tomsk, Russia; 7Department of Pediatric Endocrinology, Hospital General Plaza de la Salud, Santo Domingo, Dominican Republic; 8Department of Pediatrics A and the Pediatric Diabetes Unit, Institute of Diabetes, Endocrinology, and Metabolism, Rambam Medical Center, Haifa, Israel; 9Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN; 10Merck Research Laboratories, Merck & Co., Inc., Kenilworth, NJ
Background
While metformin can improve glycemic control in youth with type 2 diabetes (T2D), up to 50% of youth require a supplemental antihyperglycemic agent. Both glucagon-like peptide-1 receptor agonists (GLP-1RAs) and insulin may serve this purpose, but each requires injections. Therefore, additional oral therapies that provide durable improvements in glycemic control are needed. The aim of the study was to evaluate the efficacy and safety of the oral antihyperglycemic agent sitagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor, in youth 10 to 17 years old with T2D inadequately controlled with metformin with or without insulin.
Methods
Data were pooled from two 54-week, double-blind, randomized, placebo-controlled studies of sitagliptin 100 mg daily or placebo added on to treatment of youth with T2D and inadequate glycemic control on metformin with or without insulin. Participants (N=220) had HbA1c 6.5%–10% (7.0%–10% if on insulin), were overweight or obese at screening or diagnosis, and were negative for pancreatic autoantibodies. The primary objectives of these placebo-controlled studies were to assess the effects of adding sitagliptin to treatment with metformin with or without insulin on HbA1c after 20 weeks and to assess the safety and tolerability of the addition of sitagliptin over 54 weeks. Secondary objectives were to assess the effect of the addition of sitagliptin on fasting plasma glucose (FPG), the proportion of participants with HbA1c <7.0%, the proportion of participants initiating glycemic rescue therapy after 20 weeks of treatment, and the proportion of participants with HbA1c <7% after 54 weeks.
Results
The dose of background metformin was >1500 mg/day for 71.8% of participants; 15.0% of participants were on insulin therapy. At week 20, least squares (LS) mean changes from baseline in HbA1c for sitagliptin/metformin and placebo/metformin were −0.58% (95% CI, −0.94 to −0.22) and −0.09% (95% CI, −0.43 to 0.26), respectively; the difference was −0.49% (95% CI, −0.90 to −0.09; P=.018); at week 54, the LS mean changes were 0.35% (95% CI, −0.48 to 1.19) and 0.73% (95% CI, −0.08 to 1.54), respectively. No meaningful differences between the adverse event profiles of the treatment groups emerged through week 54.
Conclusions
These data do not suggest that addition of sitagliptin to treatment with metformin with or without insulin provides durable improvement in glycemic control in youth with T2D. Sitagliptin was well tolerated and had a safety profile similar to the one seen in adults.
Comments
The incidence of type 2 diabetes (T2D) in children and adolescents is increasing, with the increase driven by childhood obesity (61). Despite this, approved treatments for pediatric patients with T2D remain limited; metformin and insulin are approved for use, and, more recently, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have also been approved. However, GLP-1RAs and insulin require injections, and insulin increases risk of hypoglycemia and weight gain. Moreover, 25%– 50% of pediatric patients with T2D need an additional agent within a year of initiating treatment of metformin to control blood glucose levels (62). Therefore, alternative and effective oral therapies are needed to provide durable improvements in glycemic control in this population.
The dipeptidyl peptidase-4 (DPP-4) inhibitor sitagliptin, which is an oral antihyperglycemic agent approved for the treatment of T2D in adults, seems to be a valuable additional agent for treatment of T2D in youth. Inhibition of DPP-4 by sitagliptin results in stabilization of the incretin peptides GLP-1 and glucose-dependent insulinotropic polypeptide (GIP), both of which enhance glucose-stimulated insulin release. By this mechanism, sitagliptin decreases blood glucose with minimal risk of hypoglycemia (63).
In adults with T2D, sitagliptin was reported to be efficacious as monotherapy (64,65) and when used in combination with metformin and/or insulin (66 –69). Previous studies of short duration with a small sample of pediatric patients with T2D demonstrated the clinical efficacy and safety profile of DPP inhibitors (70,71).
Both studies reviewed above are well designed with multicenter, double-blind, randomized, parallel group participation of relatively long-time duration. However, both studies demonstrated that in youth with T2D, sitagliptin was not efficacious either as monotherapy or when combined with metformin, regardless of insulin use. These results may differ from the results for adult T2D patients because β-cell failure is more aggressive in youth with T2D than in adults with T2D (72,73), Therefore, sitagliptin seems to be unable to overcome this more progressive process.
Although the GLP-1RAs are efficacious in young patients with T2D, the difference in efficacy between the GLP-1RAs in young patients with T2D (74,75) and treatment with DPP-4 inhibitors may be related to their respective pharmacologic characteristics. Sitagliptin increases active GLP-1 levels through inhibition of DPP-4 in the postprandial state by about 2- to 3-fold whereas there is a significantly higher, sustained exposure to GLP-1 activity with GLP-1RA treatment (71). The same observation is demonstrated in the comparison of the effect on body weight reduction between DPP-4 inhibitors and GLP-1RAs. Also, the patients of both these studies had a more significant degree of obesity than did the adults, a factor that may also contribute to the differences in response reported here.
The results of these studies also demonstrate that not every medication that seems to be efficacious in adults with T2D is suitable for the young patients with T2D. Moreover, long-term evaluation is required to determine if a medication is efficacious.
Footnotes
Author Disclosure Statement
Dr. Maahs has had research support from the NIH, JDRF, NSF, and the Helmsley Charitable Trust, and his institution has had research support from Medtronic, Dexcom, Insulet, Bigfoot Biomedical, Tandem, and Roche. Dr. Maahs has consulted for Abbott, Aditxt, the Helmsley Charitable Trust, Lifescan, Mannkind, Sanofi, Novo Nordisk, Eli Lilly, Medtronic, Insulet, Dompe, and Biospex.
Shlomit Shalitin has no competing financial interests.
DSS has no competing financial interests.
PP has no conflicts of interest to disclose.