Literature DB >> 29923262

Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: International comparison from the T1D Exchange and DPV Initiative.

Daniel J DeSalvo1, Kellee M Miller2, Julia M Hermann3,4, David M Maahs5, Sabine E Hofer6, Mark A Clements7, Eggert Lilienthal8, Jennifer L Sherr9, Martin Tauschmann10, Reinhard W Holl3,4.   

Abstract

BACKGROUND: To assess the change in rates of pediatric real-time or intermittent scanning continuous glucose monitoring (CGM) use over the past 5 years, and how it impacts glycemic control, data from two registries were compared: the US-based type 1 diabetes Exchange Registry (T1DX) and the German/Austrian DPV (Prospective Diabetes Follow-Up Registry).
METHODS: Registry participants aged <18 years with T1D duration ≥1 year encompassed 29 007 individuals in 2011 and 29 150 participants in 2016. Demographic data, CGM use and hemoglobin A1c (HbA1c) were obtained from medical records.
RESULTS: CGM use increased from 2011 to 2016 in both registries across all age groups, regardless of gender, ethnic minority status or insulin delivery method. The increase in CGM use was most pronounced in the youngest patients, and usage rates remain lowest for adolescent patients in 2016. For both registries in 2016, mean HbA1c was lower among CGM users regardless of insulin delivery method compared to pump only (P < 0.001) and injection only (P < 0.001), and CGM users were more likely to achieve glycemic target of HbA1c <7.5% (56% vs 43% for DPV and 30% vs 15% for T1DX, P < 0.001). T1DX participants had a higher mean HbA1c compared with DPV despite whether they were CGM users or non-users; however, the difference was less pronounced in CGM users (P < 0.001).
CONCLUSIONS: Pediatric CGM use increased in both registries and was associated with lower mean HbA1c regardless of insulin delivery modality.
© 2018 The Authors. Pediatric Diabetes published by John Wiley & Sons Ltd.

Entities:  

Keywords:  continuous glucose monitoring; longitudinal analysis; type 1 diabetes

Mesh:

Substances:

Year:  2018        PMID: 29923262      PMCID: PMC6175652          DOI: 10.1111/pedi.12711

Source DB:  PubMed          Journal:  Pediatr Diabetes        ISSN: 1399-543X            Impact factor:   4.866


continuous glucose monitoring Prospective Diabetes Follow‐Up Registry hemoglobin A1c T1D Exchange Registry

INTRODUCTION

The goal of diabetes management in children and adolescents with type 1 diabetes (T1D) is to achieve tight glycemic control to prevent acute and chronic complications; however, youth often fail to meet hemoglobin A1c (HbA1c) targets.1, 2 Growing evidence shows the benefit of continuous glucose monitoring (CGM) as a basis for improving glycemic control in those who wear the CGM device almost daily.3, 4, 5 Although the potential benefits of CGM are well known to clinicians, the actual rates of CGM device use in T1D youth in prior studies have been low. In an earlier study of the T1D Exchange Registry (T1DX), only 6% of children <13 years and 4% of adolescents 13 to <18 years were using CGM.6 Previously, DPV (Prospective Diabetes Follow‐Up Registry) data were used to compare pump and injection users.7 In the DPV registry, rates of CGM use were even lower; however, reimbursement by health insurance for CGM in Germany and Austria recently started in summer of 2016. The JDRF (Juvenile Diabetes Research Foundation International) CGM randomized controlled trial published in 20083 highlighted the challenge of CGM use in adolescents and young adults. After 6 months, only 30% of the adolescents and young adults (15‐24 years) were using CGM 6 or more days per week compared to 83% of adults aged 25 years or older. However, among those who were using CGM on a daily or near‐daily basis, improvement in glycemic control was similar, independent of age group. Over the past 10 years, the accuracy8 and usability of CGM devices has improved considerably, which may be accompanied by an increase in CGM use, and associated improvement in glycemic control. To assess this, we analyzed data from the US T1DX and German/Austrian DPV registries to assess the increase in CGM use over the past 5 years, compare current CGM use between registries, and compare glycemic control (HbA1c %) between current CGM users and non‐users.

METHODS

We analyzed T1DX Registry and DPV Initiative participants aged <18 years with T1D duration ≥1 year in 2011 and again in 2016. This encompassed 29 007 individuals in 2011 (N = 11 608 from T1DX and N = 17 399 from DPV) and 29 150 participants in 2016 (N = 8186 from 72 T1DX sites and N = 20 964 from 309 DPV sites). Demographic data, CGM use (either real‐time or intermittent “flash” CGM), insulin modality (injections or insulin pump) and the most recent HbA1c value were obtained from clinic medical records. Definitions of migration background or ethnic minority status for each registry were as described previously9 (DPV: patient or at least one parent born outside of Austria/Germany, T1DX: any race/ethnicity other than non‐Hispanic white). Sites for the DPV and T1DX are listed in S1 (Supporting Information). Logistic and linear regression modeling were used to compare demographic and clinical characteristics in 2011 vs 2016 within each registry. Differences in CGM use between registries was assessed separately for 2011 and 2016 time points in a logistic regression model stratified by age group and adjusted for gender, minority status and diabetes duration. The interaction between CGM use and registry on mean HbA1c in 2016 was assessed in a linear regression model adjusted for age, gender, and minority status. Separate statistical tests were performed comparing T1DX vs DPV in CGM users and non‐users for each age group. The interaction between insulin delivery method and CGM use on mean HbA1c within each registry was assessed in linear regression models adjusted for age, gender and minority status. All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, North Carolina). All P‐values are two‐sided. A priori, in view of the large sample size and multiple comparisons, only P‐values <0.01 were considered statistically significant.

RESULTS

Demographic and clinical characteristics for both T1DX and DPV registries in 2011 vs 2016 are displayed in Table 1. In the last 5 years, CGM use increased from 4% to 19% for the DPV Initiative (P < 0.001) and from 3% to 22% in the T1DX Registry (P < 0.001). CGM use increased for all age groups in both registries, and was most pronounced in the youngest patients (1 to <6 years) with an increase from 6% to 28% for DPV and 4% to 45% for T1DX. Although CGM use increased significantly from 2011, adolescent patients (13 to <18 years) had the lowest rates of CGM use in 2016 (DPV: 3% in 2011, 16% in 2016; T1DX: 3% in 2011, 17% in 2016). For both registries, CGM use increased from 2011 to 2016 regardless of gender or ethnic minority status, as well as for both pump and injection users. Notably, insulin pump use increased for both T1DX and DPV registries between 2011 and 2016, with usage rates exceeding 50% in DPV and 60% in T1DX in 2016.
Table 1

Participant characteristics and diabetes management data in 2011 vs 2016 for each registry

DPV P‐valueT1DX P‐value
2011 (N = 17 399)2016 (N = 20 964)2011 (N = 11 608)2016 (N = 8186)
Gender (male)52%52%0.651%52%0.4
Age (y), mean ± SD12 ± 412 ± 4<0.00112 ± 413 ± 4<0.001
<66%5%6%5%
6 to <1334%32%46%37%
13 to <1860%64%48%58%
Duration of diabetes (y), mean ± SD5 ± 35 ± 3<0.0015 ± 47 ± 4<0.001
HbA1c, % (mmol/mol), mean ± SD7.9 ± 1.4 (63 ± 15)7.8 ± 1.3 (62 ± 14)<0.0018.5 ± 1.5 (70 ± 16)8.8 ± 1.6 (72 ± 18)<0.001
Percentage of subjects with HbA1c <7.5% (<58 mmol/mol)43%46%<0.00122%19%<0.001
Ethnic minority status20%23%<0.00122%22%0.8
Pump use43%56%<0.00156%64%<0.001
Overall CGM use4%19%<0.0013%22%<0.001
CGM use by age (y)
<66%28%<0.0014%45%<0.001
6 to <124%23%<0.0014%27%<0.001
12 to <183%16%<0.0013%17%<0.001
CGM use by gender
Male4%18%<0.0013%21%<0.001
Female4%19%<0.0013%22%<0.001
CGM use by ethnicity
Minority status: yes3%14%<0.0012%12%<0.001
Minority status: no4%20%<0.0014%25%<0.001
CGM use by insulin delivery method
Injections3%14%<0.0011%9%<0.001
Pump5%22%<0.0015%29%<0.001
Participant characteristics and diabetes management data in 2011 vs 2016 for each registry After adjustment for gender, ethnic/minority status and diabetes duration, CGM use was similar between registries in 2011 for patients aged 1 to <6 years (P = 0.20) and 6 to <12 years (P = 0.94) with higher CGM use in T1DX compared to DPV for 12 to <18 years (P = 0.005). In 2016, higher CGM use was observed in T1DX compared with DPV among 1 to <6 and 6 to <13 years (both adjusted P < 0.001). There were no significant differences in CGM use between registries for 13 to <18‐year‐old age groups (adjusted P = 0.15). In 2011, CGM use was not associated with a difference in HbA1c among DPV participants (7.9% vs 7.9% [63 vs 63 mmol/mol], P = 0.55); however, in 2016 mean HbA1c was lower in CGM users (7.6% vs 7.9% [60 vs 63 mmol/mol], P < 0.001). For T1DX CGM users, lower mean HbA1c compared to non‐users was seen in both 2011 (7.9% vs 8.6% [63 vs 70 mmol/mol], P < 0.001) and 2016 (8.1% vs 9.0% [65 vs 75 mmol/mol], P < 0.001). Mean HbA1c in DPV vs T1DX according to CGM use and age group for the more recent 2016 data is shown in Figure 1. T1DX participants had a higher mean HbA1c compared with DPV despite whether they were CGM users or non‐users. However, the difference between mean HbA1c in DPV vs T1DX was less pronounced in CGM users (P < 0.001) (Figure 1). In 2016, mean HbA1c was lower among CGM users regardless of insulin delivery method compared to pump only (P < 0.001) and injection only (P < 0.001) in both T1DX and DPV registries (Figure 1B). Additionally, CGM users were more likely to achieve glycemic targets (HbA1c < 7.5% [<58 mmol/mol]) for both DPV (56% vs 43%, P < 0.001) and T1DX (30% vs 15%, P < 0.001) registries in 2016.
Figure 1

(A) Mean HbA1c in 2016 for each registry stratified by CGM use and age group. Solid black bar represents CGM users. Solid white bar represents non‐CGM users. (B) Mean HbA1c by insulin delivery method and CGM use within each registry in 2016. BGM, blood glucose monitoring; CGM, continuous glucose monitoring or intermittent flash glucose monitoring. *P‐values compared with the reference group of pump + CGM

(A) Mean HbA1c in 2016 for each registry stratified by CGM use and age group. Solid black bar represents CGM users. Solid white bar represents non‐CGM users. (B) Mean HbA1c by insulin delivery method and CGM use within each registry in 2016. BGM, blood glucose monitoring; CGM, continuous glucose monitoring or intermittent flash glucose monitoring. *P‐values compared with the reference group of pump + CGM

DISCUSSION

Utilization of CGM in pediatric T1D management provides opportunity to improve glycemic control if consistent CGM use can be achieved in this population. In this study, CGM use was associated with lower mean HbA1c across all ages and regardless of insulin delivery modality for both registries. Additionally, a higher percentage of CGM users compared to non‐users achieved a glycemic target of <7.5% (<58 mmol/mol) in both registries. The cross‐sectional nature of our study does not preclude the possibility that those with lower HbA1c were more likely to initiate CGM; however, our findings align with results from controlled trials showing improved glycemic control with CGM use compared to SMBG only.3, 4, 5 Other limitations to our study include: data on CGM brand and consistency of use by patients were not available, and we did not differentiate between real‐time CGM vs flash glucose monitoring in this analysis. Additionally, the difference in ethnic minority definitions between the DPV and T1DX Registries limits the ability to compare across continents. Improvement in CGM technology over the last 10 years, as well as growing evidence for clinical efficacy of CGM, has been associated with increased CGM use in both the DPV Initiative and T1DX Registry. Increased rates of CGM use were evident across all age ranges, but especially in the youngest children where CGM usage rates increased dramatically. As penetrance of this technology is lowest in adolescents, a group noted to have the highest mean HbA1c; strategies to engage this cohort of youth in adoption and long‐term use of CGM are needed. Additionally, reimbursement practices for CGM remains quite complex and varies across countries, states, regions and insurance companies, so advocacy efforts for insurance coverage are needed especially in the United States where coverage by Medicaid programs varies greatly across states. A majority of pediatric patients are now utilizing insulin pumps for diabetes management in both DPV and T1DX registries in 2016. CGM use has increased over the last 5 years, still less than half of pediatric patients are utilizing these devices. This is especially concerning in light of the challenges in achieving glycemic targets (HbA1c < 7.5% [<58 mmol/mol]) in pediatrics. Further analysis is needed to determine the reasons for low frequency of CGM use including whether reimbursement or cost of CGM may be a barrier to use. Robust clinical protocols, quality improvement and research efforts to optimize real‐time CGM use are essential to ensuring durability of CGM use, facilitating automation of insulin delivery and improving glycemic outcomes.9 AppendixS1. A list of the T1D Exchange Clinic Network sites with participating principal investigators (PI), co‐investigators (I) and coordinators (C) ordered by the number of participants recruited per site as of June 1, 2017. Click here for additional data file.
  10 in total

1.  Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry.

Authors:  Kellee M Miller; Nicole C Foster; Roy W Beck; Richard M Bergenstal; Stephanie N DuBose; Linda A DiMeglio; David M Maahs; William V Tamborlane
Journal:  Diabetes Care       Date:  2015-06       Impact factor: 19.112

2.  Longitudinal Trajectories of Metabolic Control From Childhood to Young Adulthood in Type 1 Diabetes From a Large German/Austrian Registry: A Group-Based Modeling Approach.

Authors:  Anke Schwandt; Julia M Hermann; Joachim Rosenbauer; Claudia Boettcher; Désirée Dunstheimer; Jürgen Grulich-Henn; Oliver Kuss; Birgit Rami-Merhar; Christian Vogel; Reinhard W Holl
Journal:  Diabetes Care       Date:  2016-12-22       Impact factor: 19.112

3.  Design and Methods of a Randomized Trial of Continuous Glucose Monitoring in Persons With Type 1 Diabetes With Impaired Glycemic Control Treated With Multiple Daily Insulin Injections (GOLD Study).

Authors:  Marcus Lind; William Polonsky; Irl B Hirsch; Tim Heise; Jan Bolinder; Sofia Dahlqvist; Nils-Gunnar Pehrsson; Peter Moström
Journal:  J Diabetes Sci Technol       Date:  2016-05-03

4.  Association of Insulin Pump Therapy vs Insulin Injection Therapy With Severe Hypoglycemia, Ketoacidosis, and Glycemic Control Among Children, Adolescents, and Young Adults With Type 1 Diabetes.

Authors:  Beate Karges; Anke Schwandt; Bettina Heidtmann; Olga Kordonouri; Elisabeth Binder; Ulrike Schierloh; Claudia Boettcher; Thomas Kapellen; Joachim Rosenbauer; Reinhard W Holl
Journal:  JAMA       Date:  2017-10-10       Impact factor: 56.272

Review 5.  Future of Automated Insulin Delivery Systems.

Authors:  Jessica R Castle; J Hans DeVries; Boris Kovatchev
Journal:  Diabetes Technol Ther       Date:  2017-06       Impact factor: 6.118

6.  Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial.

Authors:  Roy W Beck; Tonya Riddlesworth; Katrina Ruedy; Andrew Ahmann; Richard Bergenstal; Stacie Haller; Craig Kollman; Davida Kruger; Janet B McGill; William Polonsky; Elena Toschi; Howard Wolpert; David Price
Journal:  JAMA       Date:  2017-01-24       Impact factor: 56.272

7.  Real-time continuous glucose monitoring among participants in the T1D Exchange clinic registry.

Authors:  Jenise C Wong; Nicole C Foster; David M Maahs; Dan Raghinaru; Richard M Bergenstal; Andrew J Ahmann; Anne L Peters; Bruce W Bode; Grazia Aleppo; Irl B Hirsch; Lora Kleis; H Peter Chase; Stephanie N DuBose; Kellee M Miller; Roy W Beck; Saleh Adi
Journal:  Diabetes Care       Date:  2014-07-10       Impact factor: 19.112

8.  Continuous glucose monitoring and intensive treatment of type 1 diabetes.

Authors:  William V Tamborlane; Roy W Beck; Bruce W Bode; Bruce Buckingham; H Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Irl B Hirsch; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Lori Laffel; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing
Journal:  N Engl J Med       Date:  2008-09-08       Impact factor: 91.245

9.  Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: International comparison from the T1D Exchange and DPV Initiative.

Authors:  Daniel J DeSalvo; Kellee M Miller; Julia M Hermann; David M Maahs; Sabine E Hofer; Mark A Clements; Eggert Lilienthal; Jennifer L Sherr; Martin Tauschmann; Reinhard W Holl
Journal:  Pediatr Diabetes       Date:  2018-07-01       Impact factor: 4.866

Review 10.  Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges.

Authors:  Andrea Facchinetti
Journal:  Sensors (Basel)       Date:  2016-12-09       Impact factor: 3.576

  10 in total
  65 in total

1.  Communication matters: The role of autonomy-supportive communication by health care providers and parents in adolescents with type 1 diabetes.

Authors:  Eveline R Goethals; Sarah S Jaser; Chris Verhaak; Sofie Prikken; Kristina Casteels; Koen Luyckx; Alan M Delamater
Journal:  Diabetes Res Clin Pract       Date:  2020-04-20       Impact factor: 5.602

2.  The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial.

Authors:  Stamatina Zavitsanou; Jennifer Massa; Sunil Deshpande; Jordan E Pinsker; Mei Mei Church; Camille Andre; Francis J Doyle Iii; Alicia Michelson; Jamie Creason; Eyal Dassau; David M Eisenberg
Journal:  Diabetes Technol Ther       Date:  2019-06-21       Impact factor: 6.118

Review 3.  Use of Diabetes Technology in Children: Role of Structured Education for Young People with Diabetes and Families.

Authors:  Hannah R Desrochers; Alan T Schultz; Lori M Laffel
Journal:  Endocrinol Metab Clin North Am       Date:  2020-03       Impact factor: 4.741

4.  Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

Authors:  Gregory P Forlenza
Journal:  Diabetes Technol Ther       Date:  2019-06       Impact factor: 6.118

5.  Home Visits for Children and Adolescents with Uncontrolled Type 1 Diabetes.

Authors:  Stephanie S Crossen; James P Marcin; Lihong Qi; Hadley S Sauers-Ford; Allison M Reggiardo; Shelby T Chen; Victoria A Tran; Nicole S Glaser
Journal:  Diabetes Technol Ther       Date:  2019-09-18       Impact factor: 6.118

6.  Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

Authors:  Anna R Kahkoska; Linda A Adair; Allison E Aiello; Kyle S Burger; John B Buse; Jamie Crandell; David M Maahs; Crystal T Nguyen; Michael R Kosorok; Elizabeth J Mayer-Davis
Journal:  Pediatr Diabetes       Date:  2019-04-29       Impact factor: 4.866

7.  Greater parental comfort with lower glucose targets in young children with Type 1 diabetes using continuous glucose monitoring.

Authors:  M A Van Name; K M Miller; P V Commissariat; A L Whitehouse; K R Harrington; B J Anderson; M G Mantravadi; W Levy; D J DeSalvo; W V Tamborlane; M E Hilliard; L M Laffel; L A DiMeglio
Journal:  Diabet Med       Date:  2019-07-25       Impact factor: 4.359

8.  Continuous Glucose Monitoring Associated With Less Diabetes-Specific Emotional Distress and Lower A1c Among Adolescents With Type 1 Diabetes.

Authors:  Anthony T Vesco; Aneta M Jedraszko; Kimberly P Garza; Jill Weissberg-Benchell
Journal:  J Diabetes Sci Technol       Date:  2018-03-29

Review 9.  Practical Implementation of Diabetes Technology: Real-World Use.

Authors:  Laurel H Messer; Stuart A Weinzimer
Journal:  Diabetes Technol Ther       Date:  2020-02       Impact factor: 6.118

10.  Longitudinal Changes in Continuous Glucose Monitoring Use Among Individuals With Type 1 Diabetes: International Comparison in the German and Austrian DPV and U.S. T1D Exchange Registries.

Authors:  Kellee M Miller; Julia Hermann; Nicole Foster; Sabine E Hofer; Michael R Rickels; Thomas Danne; Mark A Clements; Eggert Lilienthal; David M Maahs; Reinhard W Holl
Journal:  Diabetes Care       Date:  2019-10-31       Impact factor: 19.112

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