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Lever CS, Williman JA, Boucsein A, Watson A, Sampson RS, Sergel-Stringer OT, Keesing C, Chepulis L, Wheeler BJ, de Bock MI, Paul RG. Real time continuous glucose monitoring in high-risk people with insulin-requiring type 2 diabetes: A randomised controlled trial. Diabet Med 2024:e15348. [PMID: 38758653 DOI: 10.1111/dme.15348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/19/2024]
Abstract
AIMS To investigate the impact of real-time continuous glucose monitoring (rtCGM) on glycaemia in a predominantly indigenous (Māori) population of adults with insulin-requiring type 2 diabetes (T2D) in New Zealand. METHODS Twelve-week, multicentre randomised controlled trial (RCT) of adults with T2D using ≥0.2 units/kg/day of insulin and elevated glycated haemoglobin (HbA1c) ≥64 mmol/mol (8.0%). Following a 2-week blinded CGM run-in phase, participants were randomised to rtCGM or control (self-monitoring blood glucose [SMBG]). The primary outcome was time in the target glucose range (3.9-10 mmol/L; TIR) during weeks 10-12, with data collected by blinded rtCGM in the control group. RESULTS Sixty-seven participants entered the RCT phase (54% Māori, 57% female), median age 53 (range 16-70 years), HbA1c 85 (IQR 74, 94) mmol/mol (9.9 [IQR 8.9, 10.8]%), body mass index (36.7 ± 7.7 kg/m2). Mean (±SD) TIR increased from 37 (24)% to 53 (24)% [Δ 13%; 95% CI 4.2 to 22; P = 0.007] in the rtCGM group but did not change in the SMBG group [45 (21)% to 45 (25)%, Δ 2.5%, 95% CI -6.1 to 11, P = 0.84]. Baseline-adjusted between-group difference in TIR was 10.4% [95% CI -0.9 to 21.7; P = 0.070]. Mean HbA1c (±SD) decreased in both groups from 85 (18) mmol/mol (10.0 [1.7]%) to 64 (16) mmol/mol (8.0 [1.4]%) in the rtCGM arm and from 81 (12) mmol/mol (9.6 [1.1]%) to 65 (13) mmol/mol (8.1 [1.2]%) in the SMBG arm (P < 0.001 for both). There were no severe hypoglycaemic or ketoacidosis events in either group. CONCLUSIONS Real-time CGM use in a supportive treat-to-target model of care likely improves glycaemia in a population with insulin-treated T2D and elevated HbA1c.
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Affiliation(s)
- Claire S Lever
- Te Huataki Waiora, School of Health, University of Waikato, Hamilton, New Zealand
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
| | - Jonathan A Williman
- Biostatistics and Computation Biology Unit, University of Otago, Christchurch, New Zealand
| | - Alisa Boucsein
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Antony Watson
- Department of Paediatrics, University of Otago, Christchurch, New Zealand
| | - Rachael S Sampson
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
| | - Oscar T Sergel-Stringer
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Celeste Keesing
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
- Pinnacle Midlands Health Network, New Zealand
| | - Lynne Chepulis
- Te Huataki Waiora, School of Health, University of Waikato, Hamilton, New Zealand
| | - Benjamin J Wheeler
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
- Department of Paediatrics, Te Whatu Ora Southern, Dunedin, New Zealand
| | - Martin I de Bock
- Department of Paediatrics, University of Otago, Christchurch, New Zealand
- Department of Paediatrics, Te Whatu Ora Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Ryan G Paul
- Te Huataki Waiora, School of Health, University of Waikato, Hamilton, New Zealand
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand Waikato, Hamilton, New Zealand
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Prahalad P, Scheinker D, Desai M, Ding VY, Bishop FK, Lee MY, Ferstad J, Zaharieva DP, Addala A, Johari R, Hood K, Maahs DM. Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes. Nat Med 2024:10.1038/s41591-024-02975-y. [PMID: 38702523 DOI: 10.1038/s41591-024-02975-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/03/2024] [Indexed: 05/06/2024]
Abstract
Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969 .
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Affiliation(s)
- Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
| | - David Scheinker
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
| | - Manisha Desai
- Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Victoria Y Ding
- Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Franziska K Bishop
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Ming Yeh Lee
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - Johannes Ferstad
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Dessi P Zaharieva
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - Ananta Addala
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Ramesh Johari
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Korey Hood
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - David M Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA, USA
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Dovc K, Bode BW, Battelino T. Continuous and Intermittent Glucose Monitoring in 2023. Diabetes Technol Ther 2024; 26:S14-S31. [PMID: 38441451 DOI: 10.1089/dia.2024.2502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Affiliation(s)
- Klemen Dovc
- University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Bruce W Bode
- Atlanta Diabetes Associates and Emory University School of Medicine, Atlanta, GA, USA
| | - Tadej Battelino
- University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Addala A, Weinzimer SA. Diabetes Technology in the "Real World": Expanding Access and Addressing Disparities. Diabetes Technol Ther 2024; 26:S187-S200. [PMID: 38441450 DOI: 10.1089/dia.2024.2512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Affiliation(s)
- Ananta Addala
- Division of Pediatric Endocrinology & Diabetes, Department of Pediatrics, Stanford University, Palo Alto, CA
| | - Stuart A Weinzimer
- Department of Pediatrics, School of Medicine, Yale University, New Haven, CT
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Zhong T, He B, Li X, Lei K, Tang R, Zhao B, Li X. Glycaemia risk index uncovers distinct glycaemic variability patterns associated with remission status in type 1 diabetes. Diabetologia 2024; 67:42-51. [PMID: 37889319 DOI: 10.1007/s00125-023-06042-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023]
Abstract
AIMS/HYPOTHESIS The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping. METHODS A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics. RESULTS The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia. CONCLUSIONS/INTERPRETATION GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.
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Affiliation(s)
- Ting Zhong
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Binbin He
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xinyu Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Kang Lei
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Rong Tang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Bin Zhao
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
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Addala A. Making a Good Thing Even Better: Expanding Access and Applicability of Automated Insulin Delivery Systems to Benefit All Youth With Type 1 Diabetes. Diabetes Care 2023; 46:2126-2128. [PMID: 38011525 DOI: 10.2337/dci23-0057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/10/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Ananta Addala
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA
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Hughes MS, Addala A, Buckingham B. Digital Technology for Diabetes. N Engl J Med 2023; 389:2076-2086. [PMID: 38048189 DOI: 10.1056/nejmra2215899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Affiliation(s)
- Michael S Hughes
- From the Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine (M.S.H.), and the Division of Pediatric Endocrinology, Department of Pediatrics (A.A., B.B), Stanford University, Stanford, CA
| | - Ananta Addala
- From the Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine (M.S.H.), and the Division of Pediatric Endocrinology, Department of Pediatrics (A.A., B.B), Stanford University, Stanford, CA
| | - Bruce Buckingham
- From the Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine (M.S.H.), and the Division of Pediatric Endocrinology, Department of Pediatrics (A.A., B.B), Stanford University, Stanford, CA
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Prahalad P, Maahs DM. Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program. Diabetes Spectr 2023; 36:299-305. [PMID: 37982062 PMCID: PMC10654131 DOI: 10.2337/dsi23-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.
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Affiliation(s)
- Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA
| | - David M. Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA
- Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA
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Ebekozien O, Mungmode A, Sanchez J, Rompicherla S, Demeterco-Berggren C, Weinstock RS, Jacobsen LM, Davis G, McKee A, Akturk HK, Maahs DM, Kamboj MK. Longitudinal Trends in Glycemic Outcomes and Technology Use for Over 48,000 People with Type 1 Diabetes (2016-2022) from the T1D Exchange Quality Improvement Collaborative. Diabetes Technol Ther 2023; 25:765-773. [PMID: 37768677 DOI: 10.1089/dia.2023.0320] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Objective: Previous studies revealed that hemoglobin A1c (HbA1c) increased overall in the United States in the past decade. In addition, health inequities in type 1 diabetes (T1D) outcomes by race/ethnicity and insurance type persist. This study examines the trends in HbA1c from 2016 to 2022 stratified by race/ethnicity and insurance in a large multicenter national database. Research Design and Methods: We analyzed glycemic outcomes and diabetes device use trends for >48,000 people living with type 1 diabetes (PwT1D) from 3 adult and 12 pediatric centers in the T1D Exchange Quality Improvement Collaborative (T1DX-QI), comparing data from 2016 to 2017 with data from 2021 to 2022. Results: The mean HbA1c in 2021-2022 was lower at 8.4% compared with the mean HbA1c in 2016-2017 of 8.7% (0.3% improvement; P < 0.01). Over the same period, the percentage of PwT1D using a continuous glucose monitor (CGM), insulin pump, or hybrid closed-loop system increased (45%, 12%, and 33%, respectively). However, these improvements were not equitably demonstrated across racial/ethnic groups or insurance types. Racial/ethnic and insurance-based inequities persisted over all 7 years across all outcomes; comparing non-Hispanic White and non-Hispanic Black PwT1D, disparate gaps in HbA1c (1.2%-1.6%), CGM (30%), pump (25%-35%), and hybrid-closed loop system (up to 20%) are illuminated. Conclusion: Population-level data on outcomes, including HbA1c, can provide trends and insights into strategies to improve health for PwT1D. The T1DX-QI cohort showed a significant improvement in HbA1c from 2016 to 2022. Improvements in diabetes device use are also demonstrated. However, these increases were inconsistent across all racial/ethnic groups or insurance types, an important focus for future T1D population health improvement work.
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Affiliation(s)
- Osagie Ebekozien
- Office of Chief Medical Officer, T1D Exchange, Boston, Massachusetts, USA
- School of Population Health, University of Mississippi, Jackson, Mississippi, USA
| | - Ann Mungmode
- Office of Chief Medical Officer, T1D Exchange, Boston, Massachusetts, USA
| | - Janine Sanchez
- Department of Endocrinology, Miller School of Medicine, University of Miami, Maimi, Florida, USA
| | - Saketh Rompicherla
- Office of Chief Medical Officer, T1D Exchange, Boston, Massachusetts, USA
| | - Carla Demeterco-Berggren
- Department of Endocrinology, Rady Children's Hospital, San Diego, California, USA
- Department of Endocrinology, University of California, San Francisco, California, USA
| | - Ruth S Weinstock
- Department of Endocrinology, SUNY Upstate Medical University, Syracuse, New York, USA
| | - Laura M Jacobsen
- Department of Endocrinology, University of Florida, Gainsville, Florida, USA
| | | | - Alexis McKee
- Department of Endocrinology, Washington University at St Louis, St Louis, USA
| | - Halis K Akturk
- Department of Endocrinology, Barbara Davis Center, Aurora, Colorado, USA
| | - David M Maahs
- Department of Pediatric Endocrinology, Lucile Packard Children's Hospital at Stanford University, Palo Alto, California, USA
| | - Manmohan K Kamboj
- Department of Endocrinology, Nationwide Children's Hospital, Columbus, Ohio, USA
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