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Schoelwer MJ, DeBoer MD, Breton MD. Use of diabetes technology in children. Diabetologia 2024:10.1007/s00125-024-06218-0. [PMID: 38995398 DOI: 10.1007/s00125-024-06218-0] [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: 03/19/2024] [Accepted: 05/23/2024] [Indexed: 07/13/2024]
Abstract
Children with type 1 diabetes and their caregivers face numerous challenges navigating the unpredictability of this complex disease. Although the burden of managing diabetes remains significant, new technology has eased some of the load and allowed children with type 1 diabetes to achieve tighter glycaemic management without fear of excess hypoglycaemia. Continuous glucose monitor use alone improves outcomes and is considered standard of care for paediatric type 1 diabetes management. Similarly, automated insulin delivery (AID) systems have proven to be safe and effective for children as young as 2 years of age. AID use improves not only blood glucose levels but also quality of life for children with type 1 diabetes and their caregivers and should be strongly considered for all youth with type 1 diabetes if available and affordable. Here, we review key data on the use of diabetes technology in the paediatric population and discuss management issues unique to children and adolescents.
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Affiliation(s)
| | - Mark D DeBoer
- Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
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2
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Fonseca LM, Schmidt JJ, Snoek FJ, Weinstock RS, Chaytor N, Stuckey H, Ryan CM, van Duinkerken E. Barriers and Facilitators of Self-Management in Older People with Type 1 Diabetes: A Narrative Review Focusing on Cognitive Impairment. Diabetes Metab Syndr Obes 2024; 17:2403-2417. [PMID: 38872713 PMCID: PMC11175657 DOI: 10.2147/dmso.s410363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/30/2024] [Indexed: 06/15/2024] Open
Abstract
Over the past decades, life expectancy of people with type 1 diabetes has increased considerably, which brings potential challenges due to the process of aging. Cognitive aging and dementia, as well as reductions in visual acuity, hearing and dexterity, can influence the frequency and quality of daily self-management activities, including medication taking and insulin dosing, glucose self-monitoring, and healthy eating. This can increase the risk for hypo- and hyperglycemic events, which, in turn, may contribute to cognitive decline. Because there is a gap in understanding the barriers and facilitators of self-management in older adults with type 1 diabetes and the relationship to cognitive functioning, the authors 1) review the available literature on cognitive aging and type 1 diabetes, 2) describe what self-management in later adulthood entails and the cognitive functions required for effective self-management behaviors, 3) analyze the interaction between type 1 diabetes, cognition, aging, and self-management behaviors, and 4) describe the barriers and facilitators for self-management throughout the life span and how they may differ for older people. Potential evidence-based practices that could be developed for older adults with type 1 diabetes are discussed. There is need for further studies that clarify the impact of aging on T1D self-management, ultimately to improve diabetes care and quality of life.
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Affiliation(s)
- Luciana Mascarenhas Fonseca
- Department of Community and Behavioral Health, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
- Programa Terceira Idade (PROTER, Old Age Research Group), Department and Institute of Psychiatry, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Juliana Janeiro Schmidt
- Post-Graduate Program in Neurology, Universidade Federal Do Estado Do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Frank J Snoek
- Department of Medical Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Ruth S Weinstock
- Department of Medicine, Upstate Medical University, Syracuse, NY, USA
| | - Naomi Chaytor
- Department of Community and Behavioral Health, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Heather Stuckey
- Department of Medicine, Penn State University College of Medicine, Hershey, PA, USA
| | - Christopher M Ryan
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eelco van Duinkerken
- Post-Graduate Program in Neurology, Universidade Federal Do Estado Do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Medical Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
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3
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Boughton CK, Hovorka R. The role of automated insulin delivery technology in diabetes. Diabetologia 2024:10.1007/s00125-024-06165-w. [PMID: 38740602 DOI: 10.1007/s00125-024-06165-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 03/21/2024] [Indexed: 05/16/2024]
Abstract
The role of automated insulin delivery systems in diabetes is expanding. Hybrid closed-loop systems are being used in routine clinical practice for treating people with type 1 diabetes. Encouragingly, real-world data reflects the performance and usability observed in clinical trials. We review the commercially available hybrid closed-loop systems, their distinctive features and the associated real-world data. We also consider emerging indications for closed-loop systems, including the treatment of type 2 diabetes where variability of day-to-day insulin requirements is high, and other challenging applications for this technology. We discuss issues around access and implementation of closed-loop technology, and consider the limitations of present closed-loop systems, as well as innovative approaches that are being evaluated to improve their performance.
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Affiliation(s)
- Charlotte K Boughton
- Wellcome-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
| | - Roman Hovorka
- Wellcome-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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Phillip M, Kowalski A, Battelino T. Type 1 diabetes: from the dream of automated insulin delivery to a fully artificial pancreas. Nat Med 2024; 30:1232-1234. [PMID: 38448742 DOI: 10.1038/d41591-024-00013-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
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van Bon AC, Blauw H, Jansen TJP, Laverman GD, Urgert T, Geessink-Mennink J, Mulder AH, Out M, Groote Veldman R, Onvlee AJ, Schouwenberg BJJW, Vermeulen MAR, Diekman MJM, Gerding MN, van Wijk JPH, Klaassen M, Witkop M, DeVries JH. Bihormonal fully closed-loop system for the treatment of type 1 diabetes: a real-world multicentre, prospective, single-arm trial in the Netherlands. Lancet Digit Health 2024; 6:e272-e280. [PMID: 38443309 DOI: 10.1016/s2589-7500(24)00002-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/15/2023] [Accepted: 01/05/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Management of insulin administration for intake of carbohydrates and physical activity can be burdensome for people with type 1 diabetes on hybrid closed-loop systems. Bihormonal fully closed-loop (FCL) systems could help reduce this burden. In this trial, we assessed the long-term performance and safety of a bihormonal FCL system. METHODS The FCL system (Inreda AP; Inreda Diabetic, Goor, Netherlands) that uses two hormones (insulin and glucagon) was assessed in a 1 year, multicentre, prospective, single-arm intervention trial in adults with type 1 diabetes. Participants were recruited in eight outpatient clinics in the Netherlands. We included adults with type 1 diabetes aged 18-75 years who had been using flash glucose monitoring or continuous glucose monitors for at least 3 months. Study visits were integrated into standard care, usually every three months, to evaluate glycaemic control, adverse events, and person-reported outcomes. The primary endpoint was time in range (TIR; glucose concentration 3·9-10·0 mmol/L) after 1 year. The study is registered in the Dutch Trial Register, NL9578. FINDINGS Between June 1, 2021, and March 2, 2022, we screened 90 individuals and enrolled 82 participants; 78 were included in the analyses. 79 started the intervention and 71 were included in the 12 month analysis. Mean age was 47.7 (SD 12·4) years and 38 (49%) were female participants. The mean preintervention TIR of participants was 55·5% (SD 17·2). After 1 year of FCL treatment, mean TIR was 80·3% (SD 5·4) and median time below range was 1·36% (IQR 0·80-2·11). Questionnaire scores improved on Problem Areas in Diabetes (PAID) from 30·0 (IQR 18·8-41·3) preintervention to 10·0 (IQR 3·8-21·3; p<0·0001) at 12 months and on World Health Organization-Five Well-Being Index (WHO-5) from 60·0 (IQR 44·0-72·0) preintervention to 76·0 (IQR 60·0-80·0; p<0·0001) at 12 months. Five serious adverse events were reported (one cerebellar stroke, two severe hypoglycaemic, and two hyperglycaemic events). INTERPRETATION Real-world data obtained in this trial demonstrate that use of the bihormonal FCL system was associated with good glycaemic control in patients who completed 1 year of treatment, and could help relieve these individuals with type 1 diabetes from making treatment decisions and the burden of carbohydrate counting. FUNDING Inreda Diabetic.
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Affiliation(s)
- A C van Bon
- Department of Internal Medicine, Rijnstate Hospital, Arnhem, Netherlands.
| | - H Blauw
- Inreda Diabetic, Goor, Netherlands
| | | | - G D Laverman
- Department of Internal Medicine, ZGT Hospital, Hengelo, Netherlands
| | - T Urgert
- Department of Internal Medicine, ZGT Hospital, Hengelo, Netherlands
| | - J Geessink-Mennink
- Department of Internal Medicine, Slingeland Hospital, Doetinchem, Netherlands
| | - A H Mulder
- Department of Internal Medicine, Slingeland Hospital, Doetinchem, Netherlands
| | - M Out
- Department of Internal Medicine, MST, Enschede, Netherlands
| | | | - A J Onvlee
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - B J J W Schouwenberg
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - M J M Diekman
- Department of Internal Medicine, Deventer Hospital, Deventer, Netherlands
| | - M N Gerding
- Department of Internal Medicine, Deventer Hospital, Deventer, Netherlands
| | - J P H van Wijk
- Department of Internal Medicine, Hospital Gelderse Vallei, Ede, Netherlands
| | | | - M Witkop
- Inreda Diabetic, Goor, Netherlands
| | - J H DeVries
- Department of Internal Medicine, Amsterdam UMC, Amsterdam, Netherlands
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Hadid S, Zhang E, Frishman WH, Brutsaert E. Insulin's Legacy: A Century of Breakthroughs and Innovation. Cardiol Rev 2024:00045415-990000000-00229. [PMID: 38477588 DOI: 10.1097/crd.0000000000000680] [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: 03/14/2024]
Abstract
The clinical use of insulin to treat diabetes started just over 100 years ago. The past century has witnessed remarkable innovations in insulin therapy, evolving from animal organ extracts to bioengineered human insulins with ultra-rapid onset or prolonged action. Insulin delivery systems have also progressed to current automated insulin delivery systems. In this review, we discuss the history of insulin and the pharmacology and therapeutic indications for a variety of available insulins, especially newer analog insulins. We highlight recent advances in insulin pump therapy and review evidence on the therapeutic benefits of automated insulin delivery. As with any form of progress, there have been setbacks, and insulin has recently faced an affordability crisis. We address the challenges of insulin accessibility, along with recent progress to improve insulin affordability. Finally, we mention research on glucose-responsive insulins and hepato-preferential insulins that are likely to shape the future of insulin therapy.
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Affiliation(s)
- Somar Hadid
- From the School of Medicine, New York Medical College, Valhalla NY
| | - Emily Zhang
- From the School of Medicine, New York Medical College, Valhalla NY
| | - William H Frishman
- From the School of Medicine, New York Medical College, Valhalla NY
- Department of Cardiology, Westchester Medical Center, Valhalla NY
| | - Erika Brutsaert
- From the School of Medicine, New York Medical College, Valhalla NY
- Department of Endocrinology, Westchester Medical Center, Hawthorne NY
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Åm MK, Teigen IA, Riaz M, Fougner AL, Christiansen SC, Carlsen SM. The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control. J Endocrinol Invest 2024; 47:513-521. [PMID: 37715091 PMCID: PMC10904408 DOI: 10.1007/s40618-023-02193-2] [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: 04/28/2023] [Accepted: 09/01/2023] [Indexed: 09/17/2023]
Abstract
INTRODUCTION Diabetes mellitus type 1 is a chronic disease that implies mandatory external insulin delivery. The patients must monitor their blood glucose levels and administer appropriate insulin boluses to keep their blood glucose within the desired range. It requires a lot of time and endeavour, and many patients struggle with suboptimal glucose control despite all their efforts. MATERIALS AND METHODS This narrative review combines existing knowledge with new discoveries from animal experiments. DISCUSSION In the last decade, artificial pancreas (AP) devices have been developed to improve glucose control and relieve patients of the constant burden of managing their disease. However, a feasible and fully automated AP is yet to be developed. The main challenges preventing the development of a true, subcutaneous (SC) AP system are the slow dynamics of SC glucose sensing and particularly the delay in effect on glucose levels after SC insulin infusions. We have previously published studies on using the intraperitoneal space for an AP; however, we further propose a novel and potentially disruptive way to utilize the vasodilative properties of glucagon in SC AP systems. CONCLUSION This narrative review presents two lesser-explored viable solutions for AP systems and discusses the potential for improvement toward a fully automated system: A) using the intraperitoneal approach for more rapid insulin absorption, and B) besides using glucagon to treat and prevent hypoglycemia, also administering micro-boluses of glucagon to increase the local SC blood flow, thereby accelerating SC insulin absorption and SC glucose sensor site dynamics.
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Affiliation(s)
- M K Åm
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8900, 7491, Trondheim, Norway.
| | - I A Teigen
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8900, 7491, Trondheim, Norway
- Cancer Clinic, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - M Riaz
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8900, 7491, Trondheim, Norway
- Department of Endocrinology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - A L Fougner
- Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - S C Christiansen
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8900, 7491, Trondheim, Norway
- Department of Endocrinology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - S M Carlsen
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8900, 7491, Trondheim, Norway
- Department of Endocrinology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
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Zimmer RT, Auth A, Schierbauer J, Haupt S, Wachsmuth N, Zimmermann P, Voit T, Battelino T, Sourij H, Moser O. (Hybrid) Closed-Loop Systems: From Announced to Unannounced Exercise. Diabetes Technol Ther 2023. [PMID: 38133645 DOI: 10.1089/dia.2023.0293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Physical activity and exercise have many beneficial effects on general and type 1 diabetes (T1D) specific health and are recommended for individuals with T1D. Despite these health benefits, many people with T1D still avoid exercise since glycemic management during physical activity poses substantial glycemic and psychological challenges - which hold particularly true for unannounced exercise when using an AID system. Automated insulin delivery (AID) systems have demonstrated their efficacy in improving overall glycemia and in managing announced exercise in numerous studies. They are proven to increase time in range (70-180 mg/dL) and can especially counteract nocturnal hypoglycemia, even when evening exercise was performed. AID-systems consist of a pump administering insulin as well as a CGM sensor (plus transmitter), both communicating with a control algorithm integrated into a device (insulin pump, mobile phone/smart watch). Nevertheless, without manual pre-exercise adaptions, these systems still face a significant challenge around physical activity. Automatically adapting to the rapidly changing insulin requirements during unannounced exercise and physical activity is still the Achilles' heel of current AID systems. There is an urgent need for improving current AID-systems to safely and automatically maintain glucose management without causing derailments - so that going forward, exercise announcements will not be necessary in the future. Therefore, this narrative literature review aimed to discuss technological strategies to how current AID-systems can be improved in the future and become more proficient in overcoming the hurdle of unannounced exercise. For this purpose, the current state-of-the-art therapy recommendations for AID and exercise as well as novel research approaches are presented along with potential future solutions - in order to rectify their deficiencies in the endeavor to achieve fully automated AID-systems even around unannounced exercise.
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Affiliation(s)
- Rebecca Tanja Zimmer
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Alexander Auth
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Janis Schierbauer
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Sandra Haupt
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Nadine Wachsmuth
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Paul Zimmermann
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Thomas Voit
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Bayreuth, Bavaria, Germany;
| | - Tadej Battelino
- University Children's Hospital, Ljubljana, Slovenia, Department of Endocrinology, Diabetes and Metabolism, Bohoriceva 20, Ljubljana, Slovenia, 1000
- Slovenia;
| | - Harald Sourij
- Medical University of Graz, 31475, Auenbruggerplatz 15, 8036 Graz, Graz, Austria, 8036;
| | - Othmar Moser
- University of Bayreuth, 26523, Division Exercise Physiology and Metabolism Institute of Sport Science, Universitätsstraße 30, Bayreuth, Bayern, Germany, 95440;
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Boughton CK, Hartnell S, Lakshman R, Nwokolo M, Wilinska ME, Ware J, Allen JM, Evans ML, Hovorka R. Fully Closed-Loop Glucose Control Compared With Insulin Pump Therapy With Continuous Glucose Monitoring in Adults With Type 1 Diabetes and Suboptimal Glycemic Control: A Single-Center, Randomized, Crossover Study. Diabetes Care 2023; 46:1916-1922. [PMID: 37616583 DOI: 10.2337/dc23-0728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 08/01/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVE We evaluated the safety and efficacy of fully closed-loop with ultrarapid insulin lispro in adults with type 1 diabetes and suboptimal glycemic control compared with insulin pump therapy with continuous glucose monitoring (CGM). RESEARCH DESIGN AND METHODS This single-center, randomized, crossover study enrolled 26 adults with type 1 diabetes using insulin pump therapy with suboptimal glycemic control (mean ± SD, age 41 ± 12 years, HbA1c 9.2 ± 1.1% [77 ± 12 mmol/mol]). Participants underwent two 8-week periods of unrestricted living to compare fully closed-loop with ultrarapid insulin lispro (CamAPS HX system) with insulin pump therapy with CGM in random order. RESULTS In an intention-to-treat analysis, the proportion of time glucose was in range (primary end point 3.9-10.0 mmol/L) was higher during closed-loop than during pump with CGM (mean ± SD 50.0 ± 9.6% vs. 36.2 ± 12.2%, mean difference 13.2 percentage points [95% CI 9.5, 16.9], P < 0.001). Time with glucose >10.0 mmol/L and mean glucose were lower during closed-loop than during pump with CGM (mean ± SD time >10.0 mmol/L: 49.0 ± 9.9 vs. 62.9 ± 12.6%, mean difference -13.3 percentage points [95% CI -17.2, -9.5], P < 0.001; mean ± SD glucose 10.7 ± 1.1 vs. 12.0 ± 1.6 mmol/L, mean difference -1.2 mmol/L [95% CI -1.8, -0.7], P < 0.001). The proportion of time with glucose <3.9 mmol/L was similar between periods (median [interquartile range (IQR)] closed-loop 0.88% [0.51-1.55] vs. pump with CGM 0.64% [0.28-1.10], P = 0.102). Total daily insulin requirements did not differ (median [IQR] closed-loop 51.9 units/day [35.7-91.2] vs. pump with CGM 50.7 units/day [34.0-70.0], P = 0.704). No severe hypoglycemia or ketoacidosis occurred. CONCLUSIONS Fully closed-loop insulin delivery with CamAPS HX improved glucose control compared with insulin pump therapy with CGM in adults with type 1 diabetes and suboptimal glycemic control.
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Affiliation(s)
- Charlotte K Boughton
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
- Cambridge University Hospitals NHS Foundation Trust, Wolfson Diabetes and Endocrine Clinic, Cambridge, U.K
| | - Sara Hartnell
- Cambridge University Hospitals NHS Foundation Trust, Wolfson Diabetes and Endocrine Clinic, Cambridge, U.K
| | - Rama Lakshman
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | - Munachiso Nwokolo
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | | | - Julia Ware
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
- Department of Paediatrics, University of Cambridge, Cambridge, U.K
| | - Janet M Allen
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
| | - Mark L Evans
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
- Cambridge University Hospitals NHS Foundation Trust, Wolfson Diabetes and Endocrine Clinic, Cambridge, U.K
| | - Roman Hovorka
- Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K
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10
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Russell-Jones D, Bawlchhim Z. Discovery of insulin 100 years on. Postgrad Med J 2023; 99:661-668. [PMID: 37389580 DOI: 10.1136/postgradmedj-2022-141651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/25/2022] [Indexed: 11/04/2022]
Abstract
The discovery of insulin 100 years ago ranks among the greatest medical achievements ever. This sparked a revolution of scientific discovery and therapeutic intervention to treat people suffering with diabetes. A light was shone for other areas of medicine to illuminate what was possible with detailed scientific endeavour. There followed a range of firsts leading to the current time in which we now know more about this peptide hormone than almost any other protein in existence. This has allowed therapeutic advancement from a positon of knowledge leading to stunning innovation. This innovation is likely to lead to more physiological insulin replacement reducing the disease burden to individuals and society as whole.
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Affiliation(s)
- David Russell-Jones
- CEDAR, Royal Surrey County Hospital, Guildford, UK
- Diabetes & Endocrinology, University of Surrey, Guildford, UK
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11
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Nwokolo M, Hovorka R. The Artificial Pancreas and Type 1 Diabetes. J Clin Endocrinol Metab 2023; 108:1614-1623. [PMID: 36734145 PMCID: PMC10271231 DOI: 10.1210/clinem/dgad068] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/23/2023] [Accepted: 02/01/2023] [Indexed: 02/04/2023]
Abstract
Diabetes technologies represent a paradigm shift in type 1 diabetes care. Continuous subcutaneous insulin infusion (CSII) pumps and continuous glucose monitors (CGM) improve glycated hemoglobin (HbA1c) levels, enhance time in optimal glycemic range, limit severe hypoglycemia, and reduce diabetes distress. The artificial pancreas or closed-loop system connects these devices via a control algorithm programmed to maintain target glucose, partially relieving the person living with diabetes of this constant responsibility. Automating insulin delivery reduces the input required from those wearing the device, leading to better physiological and psychosocial outcomes. Hybrid closed-loop therapy systems, requiring user-initiated prandial insulin doses, are the most advanced closed-loop systems commercially available. Fully closed-loop systems, requiring no user-initiated insulin boluses, and dual hormone systems have been shown to be safe and efficacious in the research setting. Clinical adoption of closed-loop therapy remains in early stages despite recent technological advances. People living with diabetes, health care professionals, and regulatory agencies continue to navigate the complex path to equitable access. We review the available devices, evidence, clinical implications, and barriers regarding these innovatory technologies.
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Affiliation(s)
- Munachiso Nwokolo
- Wellcome Trust-MRC Institute of Metabolic Science, Box 289, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, Box 289, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
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12
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Lu JC, Lee P, Ierino F, MacIsaac RJ, Ekinci E, O'Neal D. Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery. J Diabetes Sci Technol 2023:19322968231174040. [PMID: 37162092 DOI: 10.1177/19322968231174040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.
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Affiliation(s)
- Jean C Lu
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, VIC, Australia
| | - Petrova Lee
- Department of Nephrology, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Francesco Ierino
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Nephrology, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
- St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia
| | - Richard J MacIsaac
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, VIC, Australia
| | - Elif Ekinci
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, VIC, Australia
- Department of Endocrinology and Diabetes, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, Austin Hospital, The University of Melbourne, Heidelberg, VIC, Australia
| | - David O'Neal
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, VIC, Australia
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13
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Phillip M, Nimri R, Bergenstal RM, Barnard-Kelly K, Danne T, Hovorka R, Kovatchev BP, Messer LH, Parkin CG, Ambler-Osborn L, Amiel SA, Bally L, Beck RW, Biester S, Biester T, Blanchette JE, Bosi E, Boughton CK, Breton MD, Brown SA, Buckingham BA, Cai A, Carlson AL, Castle JR, Choudhary P, Close KL, Cobelli C, Criego AB, Davis E, de Beaufort C, de Bock MI, DeSalvo DJ, DeVries JH, Dovc K, Doyle FJ, Ekhlaspour L, Shvalb NF, Forlenza GP, Gallen G, Garg SK, Gershenoff DC, Gonder-Frederick LA, Haidar A, Hartnell S, Heinemann L, Heller S, Hirsch IB, Hood KK, Isaacs D, Klonoff DC, Kordonouri O, Kowalski A, Laffel L, Lawton J, Lal RA, Leelarathna L, Maahs DM, Murphy HR, Nørgaard K, O’Neal D, Oser S, Oser T, Renard E, Riddell MC, Rodbard D, Russell SJ, Schatz DA, Shah VN, Sherr JL, Simonson GD, Wadwa RP, Ward C, Weinzimer SA, Wilmot EG, Battelino T. Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice. Endocr Rev 2023; 44:254-280. [PMID: 36066457 PMCID: PMC9985411 DOI: 10.1210/endrev/bnac022] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/22/2022] [Indexed: 02/06/2023]
Abstract
The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.
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Affiliation(s)
- Moshe Phillip
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
- Sacker Faculty of Medicine, Tel-Aviv University, 39040 Tel-Aviv, Israel
| | - Revital Nimri
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
- Sacker Faculty of Medicine, Tel-Aviv University, 39040 Tel-Aviv, Israel
| | - Richard M Bergenstal
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | | | - Thomas Danne
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Boris P Kovatchev
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Laurel H Messer
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | | | | | - Lia Bally
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Roy W Beck
- Jaeb Center for Health Research Foundation, Inc., Tampa, FL 33647, USA
| | - Sarah Biester
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Torben Biester
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Julia E Blanchette
- College of Nursing, University of Utah, Salt Lake City, UT 84112, USA
- Center for Diabetes and Obesity, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Emanuele Bosi
- Diabetes Research Institute, IRCCS San Raffaele Hospital and San Raffaele Vita Salute University, Milan, Italy
| | - Charlotte K Boughton
- Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, UK
| | - Marc D Breton
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Sue A Brown
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Division of Endocrinology, University of Virginia, Charlottesville, VA 22903, USA
| | - Bruce A Buckingham
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA 94304, USA
| | - Albert Cai
- The diaTribe Foundation/Close Concerns, San Diego, CA 94117, USA
| | - Anders L Carlson
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Pratik Choudhary
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kelly L Close
- The diaTribe Foundation/Close Concerns, San Diego, CA 94117, USA
| | - Claudio Cobelli
- Department of Woman and Child’s Health, University of Padova, Padova, Italy
| | - Amy B Criego
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Elizabeth Davis
- Telethon Kids Institute, University of Western Australia, Perth Children’s Hospital, Perth, Australia
| | - Carine de Beaufort
- Diabetes & Endocrine Care Clinique Pédiatrique DECCP/Centre Hospitalier Luxembourg, and Faculty of Sciences, Technology and Medicine, University of Luxembourg, Esch sur Alzette, GD Luxembourg/Department of Paediatrics, UZ-VUB, Brussels, Belgium
| | - Martin I de Bock
- Department of Paediatrics, University of Otago, Christchurch, New Zealand
| | - Daniel J DeSalvo
- Division of Pediatric Diabetes and Endocrinology, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX 77598, USA
| | - J Hans DeVries
- Amsterdam UMC, University of Amsterdam, Internal Medicine, Amsterdam, The Netherlands
| | - Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children’s Hospital, Ljubljana, Slovenia, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Laya Ekhlaspour
- Lucile Packard Children’s Hospital—Pediatric Endocrinology, Stanford University School of Medicine, Palo Alto, CA 94304, USA
| | - Naama Fisch Shvalb
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
| | - Gregory P Forlenza
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Satish K Garg
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Dana C Gershenoff
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Linda A Gonder-Frederick
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Ahmad Haidar
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Sara Hartnell
- Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - Simon Heller
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Irl B Hirsch
- Department of Medicine, University of Washington Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Korey K Hood
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Diana Isaacs
- Cleveland Clinic, Endocrinology and Metabolism Institute, Cleveland, OH 44106, USA
| | - David C Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA 94010, USA
| | - Olga Kordonouri
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | | | - Lori Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Julia Lawton
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rayhan A Lal
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lalantha Leelarathna
- Manchester University Hospitals NHS Foundation Trust/University of Manchester, Manchester, UK
| | - David M Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA 94304, USA
| | - Helen R Murphy
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Kirsten Nørgaard
- Steno Diabetes Center Copenhagen and Department of Clinical Medicine, University of Copenhagen, Gentofte, Denmark
| | - David O’Neal
- Department of Medicine and Department of Endocrinology, St Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Sean Oser
- Department of Family Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Tamara Oser
- Department of Family Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, and Institute of Functional Genomics, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Michael C Riddell
- School of Kinesiology & Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, MD, USA
| | - Steven J Russell
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Desmond A Schatz
- Department of Pediatrics, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL 02114, USA
| | - Viral N Shah
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jennifer L Sherr
- Department of Pediatrics, Yale University School of Medicine, Pediatric Endocrinology, New Haven, CT 06511, USA
| | - Gregg D Simonson
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - R Paul Wadwa
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Candice Ward
- Institute of Metabolic Science, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Stuart A Weinzimer
- Department of Pediatrics, Yale University School of Medicine, Pediatric Endocrinology, New Haven, CT 06511, USA
| | - Emma G Wilmot
- Department of Diabetes & Endocrinology, University Hospitals of Derby and Burton NHS Trust, Derby, UK
- Division of Medical Sciences and Graduate Entry Medicine, University of Nottingham, Nottingham, England, UK
| | - Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children’s Hospital, Ljubljana, Slovenia, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Sherr JL, Heinemann L, Fleming GA, Bergenstal RM, Bruttomesso D, Hanaire H, Holl RW, Petrie JR, Peters AL, Evans M. Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association. Diabetologia 2023; 66:3-22. [PMID: 36198829 PMCID: PMC9534591 DOI: 10.1007/s00125-022-05744-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/07/2022] [Indexed: 01/15/2023]
Abstract
A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.
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Affiliation(s)
| | | | | | - Richard M Bergenstal
- International Diabetes Center and HealthPartners Institute, Minneapolis, MN, USA
| | - Daniela Bruttomesso
- Unit of Metabolic Diseases, Department of Medicine, University of Padova, Padova, Italy
| | - Hélène Hanaire
- Department of Diabetology, University Hospital of Toulouse, University of Toulouse, Toulouse, France
| | - Reinhard W Holl
- Institute of Epidemiology and Medical Biometry, Central Institute of Biomedical Engineering (ZIBMT), University of Ulm, Ulm, Germany
- German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - John R Petrie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Anne L Peters
- Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Mark Evans
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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15
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Hormonpumpen. JOURNAL FÜR KLINISCHE ENDOKRINOLOGIE UND STOFFWECHSEL 2022. [DOI: 10.1007/s41969-022-00184-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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16
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Sherr JL, Heinemann L, Fleming GA, Bergenstal RM, Bruttomesso D, Hanaire H, Holl RW, Petrie JR, Peters AL, Evans M. Automated Insulin Delivery: Benefits, Challenges, and Recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association. Diabetes Care 2022; 45:3058-3074. [PMID: 36202061 DOI: 10.2337/dci22-0018] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/07/2022] [Indexed: 02/03/2023]
Abstract
A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialized for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organizations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organization has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonization of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organizations for confirmation.
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Affiliation(s)
| | | | | | | | - Daniela Bruttomesso
- Unit of Metabolic Diseases, Department of Medicine, University of Padova, Padova, Italy
| | - Hélène Hanaire
- Department of Diabetology, University Hospital of Toulouse, University of Toulouse, Toulouse, France
| | - Reinhard W Holl
- Institute of Epidemiology and Medical Biometry, Central Institute of Biomedical Engineering (ZIBMT), University of Ulm, Ulm, Germany.,German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - John R Petrie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K
| | - Anne L Peters
- Keck School of Medicine of the University of Southern California, Los Angeles, CA
| | - Mark Evans
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
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17
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Kovatchev BP, Singh H, Mueller L, Gonder-Frederick LA. Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis. Diabetes Care 2022; 45:2636-2643. [PMID: 36126177 PMCID: PMC9862393 DOI: 10.2337/dc22-1217] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/22/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use. RESEARCH DESIGN AND METHODS We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.4% female, age (median/quartiles/range) 39/19-58/1-92 years, mean ± SD glucose management indicator (GMI) 7.5 ± 0.8. Primary outcome was time in target range (TIR) (70-180 mg/dL). Secondary outcomes included CGM-based glycemic control metrics and frequency of user-initiated boluses. RESULTS Compared with PLGS, AID increased TIR on average from 58.4 to 70.5%. GMI and percent time above and below target range improved as well: from 7.5 to 7.1, 39.9 to 28.1%, and 1.66 to 1.46%, respectively; all P values <0.0001. Stratification of outcomes by age and baseline GMI revealed clinically significant differences. Glycemic improvements were most pronounced in those <18 years old (TIR improvement 14.0 percentage points) and those with baseline GMI >8.0 (TIR improvement 13.2 percentage points). User-initiated correction boluses decreased from 2.7 to 1.8 per day, while user-initiated meal boluses remained stable at 3.6 to 3.8 per day. CONCLUSIONS Observed in real life of >19,000 individuals with type 1 diabetes, transitions from PLGS to AID resulted in improvement of all glycemic parameters, equivalent to improvements observed in randomized clinical trials, and reduced user-initiated boluses. However, glycemic and behavioral changes with AID use may differ greatly across different demographic and clinical groups.
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Affiliation(s)
- Boris P Kovatchev
- University of Virginia Center for Diabetes Technology, Charlottesville, VA
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18
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van Veldhuisen CL, Latenstein AEJ, Blauw H, Vlaskamp LB, Klaassen M, Lips DJ, Bonsing BA, van der Harst E, Stommel MWJ, Bruno MJ, van Santvoort HC, van Eijck CHJ, van Dieren S, Busch OR, Besselink MG, DeVries JH. Bihormonal Artificial Pancreas With Closed-Loop Glucose Control vs Current Diabetes Care After Total Pancreatectomy: A Randomized Clinical Trial. JAMA Surg 2022; 157:950-957. [PMID: 36069928 PMCID: PMC9453632 DOI: 10.1001/jamasurg.2022.3702] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/11/2022] [Indexed: 12/26/2022]
Abstract
Importance Glucose control in patients after total pancreatectomy is problematic because of the complete absence of α- and β-cells, leading to impaired quality of life. A novel, bihormonal artificial pancreas (BIHAP), using both insulin and glucagon, may improve glucose control, but studies in this setting are lacking. Objective To assess the efficacy and safety of the BIHAP in patients after total pancreatectomy. Design, Setting, and Participants This randomized crossover clinical trial compared the fully closed-loop BIHAP with current diabetes care (ie, insulin pump or pen therapy) in 12 adult outpatients after total pancreatectomy. Patients were recruited between August 21 and November 16, 2020. This first-in-patient study began with a feasibility phase in 2 patients. Subsequently, 12 patients were randomly assigned to 7-day treatment with the BIHAP (preceded by a 5-day training period) followed by 7-day treatment with current diabetes care, or the same treatments in reverse order. Statistical analysis was by Wilcoxon signed rank and Mann-Whitney U tests, with significance set at a 2-sided P < .05. Main Outcomes and Measures The primary outcome was the percentage of time spent in euglycemia (70-180 mg/dL [3.9-10 mmol/L]) as assessed by continuous glucose monitoring. Results In total, 12 patients (7 men and 3 women; median [IQR] age, 62.5 [43.1-74.0] years) were randomly assigned, of whom 3 did not complete the BIHAP phase and 1 was replaced. The time spent in euglycemia was significantly higher during treatment with the BIHAP (median, 78.30%; IQR, 71.05%-82.61%) than current diabetes care (median, 57.38%; IQR, 52.38%-81.35%; P = .03). In addition, the time spent in hypoglycemia (<70 mg/dL [3.9 mmol/L]) was lower with the BIHAP (median, 0.00% [IQR, 0.00%-0.07%] vs 1.61% [IQR, 0.80%-3.81%]; P = .004). No serious adverse events occurred. Conclusions and Relevance Patients using the BIHAP after total pancreatectomy experienced an increased percentage of time in euglycemia and a reduced percentage of time in hypoglycemia compared with current diabetes care, without apparent safety risks. Larger randomized trials, including longer periods of treatment and an assessment of quality of life, should confirm these findings. Trial Registration trialregister.nl Identifier: NL8871.
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Affiliation(s)
- Charlotte L. van Veldhuisen
- Amsterdam UMC, University of Amsterdam, Department of Surgery, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
- Department of Research and Development, St Antonius Hospital, Nieuwegein, the Netherlands
| | - Anouk E. J. Latenstein
- Amsterdam UMC, University of Amsterdam, Department of Surgery, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Helga Blauw
- Amsterdam UMC, Department of Internal Medicine, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
- Inreda Diabetic, Goor, the Netherlands
| | | | | | - Daan J. Lips
- Department of Surgery, Medical Spectrum Twente, Enschede, the Netherlands
| | - Bert A. Bonsing
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Marco J. Bruno
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Hjalmar C. van Santvoort
- Department of Research and Development, St Antonius Hospital, Nieuwegein, the Netherlands
- Department of Surgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Susan van Dieren
- Amsterdam UMC, University of Amsterdam, Department of Surgery, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Olivier R. Busch
- Amsterdam UMC, University of Amsterdam, Department of Surgery, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Marc G. Besselink
- Amsterdam UMC, University of Amsterdam, Department of Surgery, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - J. Hans DeVries
- Amsterdam UMC, Department of Internal Medicine, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, the Netherlands
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19
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Zeng B, Jia H, Gao L, Yang Q, Yu K, Sun F. Dual-hormone artificial pancreas for glucose control in type 1 diabetes: A meta-analysis. Diabetes Obes Metab 2022; 24:1967-1975. [PMID: 35638377 PMCID: PMC9542047 DOI: 10.1111/dom.14781] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/17/2022] [Accepted: 05/26/2022] [Indexed: 12/14/2022]
Abstract
AIM To evaluate the efficacy and safety of a dual-hormone artificial pancreas (DH) in type 1 diabetes. MATERIAL AND METHODS PubMed, Embase, the Cochrane Library and ClinicalTrials.gov were searched for studies published up to February 16, 2022. We included randomized controlled trials that compared DH with single-hormone artificial pancreas (SH), continuous subcutaneous insulin infusion (CSII) or sensor-augmented pumps (SAP), and predictive low glucose suspend systems (PLGS) in type 1 diabetes. The primary outcome was percent time in target (3.9-10 mmol/L [70-180 mg/dL]). Data were summarized as mean differences (MDs) or risk differences (RDs). RESULTS A total of 17 randomized crossover trials (438 participants) were included. There were nine trials of DH versus SH, 13 trials of DH versus SAP/CSII, and two trials of DH versus PLGS. For time in target, DH showed no significant difference in time in target compared with SH (MD 2.69%, 95% confidence interval [CI] -0.38 to 5.76) but resulted in 16.05% (95% CI 12.06 to 20.05) and 6.89% (95% CI 2.63 to 11.14) more time in target range compared with SAP/CSII and PLGS, respectively. DH slightly reduced time in hypoglycaemia (MD -1.20%, 95% CI -1.85 to -0.56) but increased the risk of gastrointestinal symptoms (RD 0.18, 95% CI 0.08 to 0.27) compared with SH. CONCLUSIONS The results of this study suggest that DH has a comparable effect on time in target compared with SH, but is associated with a longer time in target range compared with SAP/CSII and PLGS. The DH slightly reduced time in hypoglycaemia but may increase the risk of gastrointestinal symptoms compared with the SH. PROSPERO registration number: CRD42022314015.
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Affiliation(s)
- Baoqi Zeng
- Department of Science and EducationPeking University Binhai HospitalTianjinChina
| | - Hao Jia
- Drug Clinical Trial InstitutionPeking University Binhai HospitalTianjinChina
| | - Le Gao
- Department of Pharmacology and PharmacyThe University of Hong KongHong KongChina
| | - Qingqing Yang
- Department of Epidemiology and Biostatistics, School of Public HealthPeking University Health Science CentreBeijingChina
| | - Kai Yu
- Department of Science and EducationPeking University Binhai HospitalTianjinChina
| | - Feng Sun
- Department of Epidemiology and Biostatistics, School of Public HealthPeking University Health Science CentreBeijingChina
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20
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Abstract
The technology needed to "close the loop," that is, a system for continuous glucose monitoring and a pump that infuses insulin, are only 2 of the 3 components needed for each system for automated insulin delivery (AID), the other is a "translation" of the glucose information into the appropriate amount of insulin to be applied at a given point in time to keep glucose levels in the body in the target range. It might look straightforward to calculate the required insulin dose and control the pump to apply this immediately; however, once a given amount of insulin is in the body, it will be absorbed and become metabolically active. To avoid lowering glucose levels toward too low levels, the algorithms used to calculate the insulin dose have to take a number of other factors into account. This is needed to make sure that AID systems are not only efficient but also safe, that is, not only Time-in-Range should be maximal, also Time-below-Range should be minimal. The review characterizes the different types of AID algorithms that were developed in the last decades. Using a structured approach, the different algorithms are classified. A systematic evaluation of the performance of the different algorithms is missing, not only during the clinical development of AID systems, but also in daily practice. However, it might very well be that other factors determine which AID algorithms will be used in practice.
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Affiliation(s)
- Andreas Thomas
- Working Group Diabetes & Technology of the German Diabetes Association, Germany
- Andreas Thomas, PhD, Working Group Diabetes & Technology of the German Diabetes Association, An der Elbaue 12, Pirna, 01796, Germany.
| | - Lutz Heinemann
- Working Group Diabetes & Technology of the German Diabetes Association, Germany
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21
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Elbalshy M, Haszard J, Smith H, Kuroko S, Galland B, Oliver N, Shah V, de Bock MI, Wheeler BJ. Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta-analysis of randomised controlled trials. Diabet Med 2022; 39:e14854. [PMID: 35441743 PMCID: PMC9542260 DOI: 10.1111/dme.14854] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/16/2022] [Accepted: 04/12/2022] [Indexed: 12/17/2022]
Abstract
AIMS We aimed to conduct a systematic review and meta-analysis of randomised controlled clinical trials (RCTs) assessing separately and together the effect of the three distinct categories of continuous glucose monitoring (CGM) systems (adjunctive, non-adjunctive and intermittently-scanned CGM [isCGM]), compared with traditional capillary glucose monitoring, on HbA1c and CGM metrics. METHODS PubMed, Web of Science, Scopus and Cochrane Central register of clinical trials were searched. Inclusion criteria were as follows: randomised controlled trials; participants with type 1 diabetes of any age and insulin regimen; investigating CGM and isCGM compared with traditional capillary glucose monitoring; and reporting glycaemic outcomes of HbA1c and/or time-in-range (TIR). Glycaemic outcomes were extracted post-intervention and expressed as mean differences and 95%CIs between treatment and comparator groups. Results were pooled using a random-effects meta-analysis. Risk of bias was assessed using the Cochrane Rob2 tool. RESULTS This systematic review was conducted between January and April 2021; it included 22 RCTs (15 adjunctive, 5 non-adjunctive, and 2 isCGM)). The overall analysis of the pooled three categories showed a statistically significant absolute improvement in HbA1c percentage points (mean difference (95% CI): -0.22% [-0.31 to -0.14], I2 = 79%) for intervention compared with comparator and was strongest for adjunctive CGM (-0.26% [-0.36, -0.16]). Overall TIR (absolute change) increased by 5.4% (3.5 to 7.2), I2 = 71% for CGM intervention compared with comparator and was strongest with non-adjunctive CGM (6.0% [2.3, 9.7]). CONCLUSIONS For individuals with T1D, use of CGM was beneficial for impacting glycaemic outcomes including HbA1c, TIR and time-below-range (TBR). Glycaemic improvement appeared greater for TIR for newer non-adjunctive CGM technology.
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Affiliation(s)
- Mona Elbalshy
- Department of Women’s and Children’s HealthDunedin School of MedicineUniversity of OtagoDunedinNew Zealand
| | - Jillian Haszard
- Division of SciencesUniversity of Otago, New ZealandDunedinNew Zealand
| | - Hazel Smith
- Department of Women’s and Children’s HealthDunedin School of MedicineUniversity of OtagoDunedinNew Zealand
| | - Sarahmarie Kuroko
- Department of Women’s and Children’s HealthDunedin School of MedicineUniversity of OtagoDunedinNew Zealand
| | - Barbara Galland
- Department of Women’s and Children’s HealthDunedin School of MedicineUniversity of OtagoDunedinNew Zealand
| | - Nick Oliver
- Department of Metabolism, Digestion and ReproductionFaculty of MedicineImperial CollegeLondonUK
| | - Viral Shah
- Barbara Davis Center for DiabetesUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | | | - Benjamin J. Wheeler
- Department of Women’s and Children’s HealthDunedin School of MedicineUniversity of OtagoDunedinNew Zealand
- Paediatric EndocrinologySouthern District Health BoardDunedinNew Zealand
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22
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Garelli F, Fushimi E, Rosales N, Arambarri D, Mendoza L, Serafini MC, Moscoso-Vásquez M, Stasi M, Duette P, García-Arabehety J, Giunta JN, De Battista H, Sánchez-Peña R, Grosembacher L. First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America. J Diabetes Sci Technol 2022:19322968221096162. [PMID: 35549733 DOI: 10.1177/19322968221096162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.
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Affiliation(s)
- Fabricio Garelli
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Emilia Fushimi
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Nicolás Rosales
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Delfina Arambarri
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - Leandro Mendoza
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - María Cecilia Serafini
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires, Buenos Aires, Argentina
| | - Marcela Moscoso-Vásquez
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | | | | | | | | | - Hernán De Battista
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
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23
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Paldus B, Morrison D, Lee M, Zaharieva DP, Riddell MC, O'Neal DN. Strengths and Challenges of Closed-Loop Insulin Delivery During Exercise in People With Type 1 Diabetes: Potential Future Directions. J Diabetes Sci Technol 2022:19322968221088327. [PMID: 35466723 DOI: 10.1177/19322968221088327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Exercise has many physical and psychological benefits and is recommended for people with type 1 diabetes; however, there are many barriers to exercise, including glycemic instability and fear of hypoglycemia. Closed-loop (CL) systems have shown benefit in the overall glycemic management of type 1 diabetes, including improving HbA1c levels and reducing the incidence of nocturnal hypoglycemia; however, these systems are challenged by the rapidly changing insulin needs with exercise. This commentary focuses on the principles, strengths, and challenges of CL in the management of exercise, and discusses potential approaches, including the use of additional physiological signals, to address their shortcomings in the pursuit of fully automated CL systems.
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Affiliation(s)
- Barbora Paldus
- Department of Medicine, The University of Melbourne, Victoria, Australia
- Department of Endocrinology & Diabetes, St. Vincent's Hospital Melbourne, Victoria, Australia
| | - Dale Morrison
- Department of Medicine, The University of Melbourne, Victoria, Australia
| | - Melissa Lee
- Department of Medicine, The University of Melbourne, Victoria, Australia
- Department of Endocrinology & Diabetes, St. Vincent's Hospital Melbourne, Victoria, Australia
| | - Dessi P Zaharieva
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, CA, USA
| | - Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, ON, Canada
| | - David N O'Neal
- Department of Medicine, The University of Melbourne, Victoria, Australia
- Department of Endocrinology & Diabetes, St. Vincent's Hospital Melbourne, Victoria, Australia
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24
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Lascaris B, Thorne AM, Lisman T, Nijsten MWN, Porte RJ, de Meijer VE. Long-term normothermic machine preservation of human livers: what is needed to succeed? Am J Physiol Gastrointest Liver Physiol 2022; 322:G183-G200. [PMID: 34756122 DOI: 10.1152/ajpgi.00257.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Although short-term machine perfusion (≤24 h) allows for resuscitation and viability assessment of high-risk donor livers, the donor organ shortage might be further remedied by long-term perfusion machines. Extended preservation of injured donor livers may allow reconditioning, repairing, and regeneration. This review summarizes the necessary requirements and challenges for long-term liver machine preservation, which requires integrating multiple core physiological functions to mimic the physiological environment inside the body. A pump simulates the heart in the perfusion system, including automatically controlled adjustment of flow and pressure settings. Oxygenation and ventilation are required to account for the absence of the lungs combined with continuous blood gas analysis. To avoid pressure necrosis and achieve heterogenic tissue perfusion during preservation, diaphragm movement should be simulated. An artificial kidney is required to remove waste products and control the perfusion solution's composition. The perfusate requires an oxygen carrier, but will also be challenged by coagulation and activation of the immune system. The role of the pancreas can be mimicked through closed-loop control of glucose concentrations by automatic injection of insulin or glucagon. Nutrients and bile salts, generally transported from the intestine to the liver, have to be supplemented when preserving livers long term. Especially for long-term perfusion, the container should allow maintenance of sterility. In summary, the main challenge to develop a long-term perfusion machine is to maintain the liver's homeostasis in a sterile, carefully controlled environment. Long-term machine preservation of human livers may allow organ regeneration and repair, thereby ultimately solving the shortage of donor livers.
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Affiliation(s)
- Bianca Lascaris
- Section of Hepatopancreatobiliary Surgery & Liver Transplantation, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adam M Thorne
- Section of Hepatopancreatobiliary Surgery & Liver Transplantation, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ton Lisman
- Surgical Research Laboratory, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Maarten W N Nijsten
- Department of Critical Care, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert J Porte
- Section of Hepatopancreatobiliary Surgery & Liver Transplantation, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Vincent E de Meijer
- Section of Hepatopancreatobiliary Surgery & Liver Transplantation, Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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25
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Corbett JP, Garcia-Tirado J, Colmegna P, Diaz Castaneda JL, Breton MD. Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System. J Diabetes Sci Technol 2022; 16:52-60. [PMID: 34861786 PMCID: PMC8875044 DOI: 10.1177/19322968211059159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. METHODS A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. RESULTS Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%). CONCLUSIONS The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.
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Affiliation(s)
- John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
- John P. Corbett, PhD, Center for Diabetes Technology, University of Virginia, 560 Ray C. Hunt Drive, Charlottesville, VA 22903, USA.
| | - Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | | | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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26
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Biester T, Tauschmann M, Chobot A, Kordonouri O, Danne T, Kapellen T, Dovc K. The automated pancreas: A review of technologies and clinical practice. Diabetes Obes Metab 2022; 24 Suppl 1:43-57. [PMID: 34658126 DOI: 10.1111/dom.14576] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/07/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022]
Abstract
Insulin pumps and glucose sensors are effective in improving diabetes therapy and reducing acute complications. The combination of both devices using an algorithm-driven interoperable controller makes automated insulin delivery (AID) systems possible. Many AID systems have been tested in clinical trials and have proven safety and effectiveness. However, currently, none of these systems are available for routine use in children younger than 6 years in Europe. For continued use, both users and prescribers must have sound knowledge of the features of the individual AID systems. Presently, all systems require various user interactions (e.g. meal announcements) because fully automated systems are not yet developed. Open-source systems are non-regulated variants to circumvent existing regulatory conditions. There are risks here for both users and prescribers. To evaluate AID therapy, the metric data of the glucose sensors, 'time in target range' and 'glucose management index', are novel recognized and suitable parameters allowing a consultation based on real glucose and insulin pump download data from the daily life of people with diabetes. Read out via cloud-based software or automatic download of such individual treatment data provides the ideal technical basis for shared decision-making through telemedicine, which must be further evaluated for general use.
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Affiliation(s)
- Torben Biester
- AUF DER BULT, Diabetes Center for Children and Adolescents, Hannover, Germany
| | - Martin Tauschmann
- Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Agata Chobot
- Department of Pediatrics, Institute of Medical Sciences, University of Opole, Opole, Poland
| | - Olga Kordonouri
- AUF DER BULT, Diabetes Center for Children and Adolescents, Hannover, Germany
| | - Thomas Danne
- AUF DER BULT, Diabetes Center for Children and Adolescents, Hannover, Germany
| | - Thomas Kapellen
- Department of Pediatrics, MEDIAN Clinic for Children 'Am Nicolausholz' Bad Kösen, Naumburg, Germany
| | - Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children's Hospital, Ljubljana, Slovenia and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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27
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Paez-Mayorga J, Lukin I, Emerich D, de Vos P, Orive G, Grattoni A. Emerging strategies for beta cell transplantation to treat diabetes. Trends Pharmacol Sci 2021; 43:221-233. [PMID: 34887129 DOI: 10.1016/j.tips.2021.11.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 02/08/2023]
Abstract
Beta cell replacement has emerged as an attractive therapeutic alternative to traditional exogenous insulin administration for management of type 1 diabetes (T1D). Beta cells deliver insulin dynamically based on individual glycometabolic requirements, providing glycemic control while significantly reducing patient burden. Although transplantation into the portal circulation is clinically available, poor engraftment, low cell survival, and immune rejection have sparked investigation of alternative strategies for beta cell transplantation. In this review, we focus on current micro- and macroencapsulation technologies for beta cell transplantation and evaluate their advantages and challenges. Specifically, we comment on recent methods to ameliorate graft hypoxia including enhanced vascularization, reduction of pericapsular fibrotic overgrowth (PFO), and oxygen supplementation. We also discuss emerging beta cell-sourcing strategies to overcome donor shortage and provide insight into potential approaches to address outstanding challenges in the field.
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Affiliation(s)
- Jesus Paez-Mayorga
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Izeia Lukin
- NanoBioCel Research Group, School of Pharmacy, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain; Bioaraba, NanoBioCel Research Group, Vitoria-Gasteiz, Spain
| | | | - Paul de Vos
- Immunoendocrinology, Department of Pathology and Medical biology, University Medical Center Groningen, University of Groningen, The Netherlands
| | - Gorka Orive
- NanoBioCel Research Group, School of Pharmacy, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain; Bioaraba, NanoBioCel Research Group, Vitoria-Gasteiz, Spain; University Institute for Regenerative Medicine and Oral Implantology - UIRMI (UPV/EHU-Fundación Eduardo Anitua), Vitoria-Gasteiz, Spain; Singapore Eye Research Institute, The Academia, 20 College Road, Discovery Tower, Singapore.
| | - Alessandro Grattoni
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030, USA; Department of Surgery, Houston Methodist Hospital, Houston, TX 77030, USA; Department of Radiation Oncology, Houston Methodist Hospital, Houston, TX 77030, USA.
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28
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Abstract
Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology and
Diabetes, University of San Francisco, CA, USA
| | - Laya Ekhlaspour
- Division of Pediatric Endocrinology and
Diabetes, Stanford University, Palo Alto, CA, USA
| | - Eda Cengiz
- Division of Pediatric Endocrinology and
Diabetes, Yale University, New Haven, NJ, USA
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29
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Moon SJ, Jung I, Park CY. Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence. Diabetes Metab J 2021; 45:813-839. [PMID: 34847641 PMCID: PMC8640161 DOI: 10.4093/dmj.2021.0177] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/24/2021] [Indexed: 12/19/2022] Open
Abstract
Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.
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Affiliation(s)
- Sun Joon Moon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Inha Jung
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Cheol-Young Park
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
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30
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Abstract
In this review, we bring our personal experiences to showcase insulin from its breakthrough discovery as a life-saving drug 100 years ago to its uncovering as the autoantigen and potential cause of type 1 diabetes and eventually as an opportunity to prevent autoimmune diabetes. The work covers the birth of insulin to treat patients, which is now 100 years ago, the development of human insulin, insulin analogues, devices, and the way into automated insulin delivery, the realization that insulin is the primary autoimmune target of type 1 diabetes in children, novel approaches of immunotherapy using insulin for immune tolerance induction, the possible limitations of insulin immunotherapy, and an outlook how modern vaccines could remove the need for another 100 years of insulin therapy.
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31
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Lau HH, Gan SU, Lickert H, Shapiro AMJ, Lee KO, Teo AKK. Charting the next century of insulin replacement with cell and gene therapies. MED 2021; 2:1138-1162. [DOI: 10.1016/j.medj.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/24/2021] [Accepted: 09/07/2021] [Indexed: 10/20/2022]
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Glycaemic variabilities: Key questions in pursuit of clarity. DIABETES & METABOLISM 2021; 47:101283. [PMID: 34547451 DOI: 10.1016/j.diabet.2021.101283] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 09/05/2021] [Indexed: 12/12/2022]
Abstract
After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.
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Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System. SENSORS 2021; 21:s21175954. [PMID: 34502845 PMCID: PMC8434663 DOI: 10.3390/s21175954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/18/2021] [Accepted: 08/27/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements. METHODS seven healthy participants took part in an experiment where sequences of postures and movements were performed to train and assess a computationally sparing algorithm. RESULTS Using accelerometers, one on the hip and two on the abdomen, user-specific models achieved classification accuracies of 86.5% using only the hip sensor and 87.3% when including the abdomen sensors. With additional accelerometers on the sternum and upper leg for identification, 90.0% of the classified postures and movements were correct. CONCLUSIONS The current hardware configuration of the AP™ poses no limitation to the identification of postures and movements. If future research shows that identification can still be done accurately in a daily life setting, this algorithm may be an improvement for the AP™ to sense physical activity.
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Garcia-Tirado J, Diaz JL, Esquivel-Zuniga R, Koravi CLK, Corbett JP, Dawson M, Wakeman C, Barnett CL, Oliveri MC, Myers H, Krauthause K, Breton MD, DeBoer MD. Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal. Diabetes Care 2021; 44:dc210932. [PMID: 34400480 DOI: 10.2337/dc21-0932] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/16/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement. RESEARCH DESIGN AND METHODS Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics. RESULTS Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01). CONCLUSIONS A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Jenny L Diaz
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | | | - John P Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Martha Dawson
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Christian Wakeman
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Mary C Oliveri
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Helen Myers
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | | | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Mark D DeBoer
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Pediatrics, University of Virginia, Charlottesville, VA
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van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, van der Kallen CJH, Wesselius A, Schram MT, Henry RMA, Dagnelie PC, de Galan BE, Bekers O, Stehouwer CDA, Meex SJR, Brouwers MCGJ. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLoS One 2021; 16:e0253125. [PMID: 34166426 PMCID: PMC8224858 DOI: 10.1371/journal.pone.0253125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/31/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. METHODS We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). RESULTS Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). CONCLUSIONS Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.
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Affiliation(s)
- William P. T. M. van Doorn
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Yuri D. Foreman
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolaas C. Schaper
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Hans H. C. M. Savelberg
- Department of Human Biology and Movement Science, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Annemarie Koster
- CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
- Department of Social Medicine, Maastricht University, Maastricht, The Netherlands
| | - Carla J. H. van der Kallen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Anke Wesselius
- Department of Complex Genetics and Epidemiology, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Miranda T. Schram
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ronald M. A. Henry
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Pieter C. Dagnelie
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bastiaan E. de Galan
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Otto Bekers
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Steven J. R. Meex
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Clinical Chemistry, Central Diagnostic Laboratory, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Martijn C. G. J. Brouwers
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Abstract
Insulinpumpen und Glukosesensoren können laut Registerdaten die Diabetestherapie verbessern sowie die Rate akuter Komplikationen reduzieren. In der pädiatrischen Diabetologie ist daher die Nutzung mindestens eines dieser technischen Geräte Standard. Deren Kombination macht Systeme zur automatischen Insulinabgabe („automated insulin delivery“ [AID]) möglich. Viele AID-Systeme wurden in klinischen Studien getestet und erwiesen sich als sicher und effektiv. Die Versorgungsituation in Deutschland jedoch lässt derzeit nur ein System als Verordnung bei Versicherten der gesetzlichen Krankenversicherungen zu, und Kinder unter 7 Jahren können damit derzeit nicht versorgt werden. Gründe hierfür sind gesetzliche Hürden und die mangelnde Zertifizierung durch die Hersteller. Die CE-Zertifikate können zudem zu Problemen bei der Insulinverordnung führen. Open-Source-Systeme sind nicht geprüfte Varianten, um bestehende regulatorische Verhältnisse zu umgehen. Deren Anwendung geht mit Risiken sowohl für Nutzer als auch Verordner einher. Für ihren dauerhaften Einsatz müssen sowohl Anwender als auch Behandler über fundierte Kenntnisse der Eigenschaften der einzelnen AID-Systeme verfügen. Zur Evaluation der AID-Therapie sind die metrischen Daten der Glukosesensoren, die „time in range“ und der Glukosemanagementindex die anerkannten und geeigneten Parameter, da sie eine Beratung auf Basis der reellen Werte aus dem Alltag der Menschen mit Diabetes zulassen. Da alle Glukosesensoren über Cloud-basierte Software ausgelesen werden oder die Daten direkt automatisch übermitteln, ist hiermit die ideale technische Grundlage für eine telemedizinische Betreuung geschaffen, die noch der Ausgestaltung bedarf.
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Lal RA, Maikawa CL, Lewis D, Baker SW, Smith AAA, Roth GA, Gale EC, Stapleton LM, Mann JL, Yu AC, Correa S, Grosskopf AK, Liong CS, Meis CM, Chan D, Garner JP, Maahs DM, Buckingham BA, Appel EA. Full closed loop open-source algorithm performance comparison in pigs with diabetes. Clin Transl Med 2021; 11:e387. [PMID: 33931977 PMCID: PMC8087942 DOI: 10.1002/ctm2.387] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/24/2021] [Accepted: 03/30/2021] [Indexed: 12/20/2022] Open
Abstract
Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.
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Affiliation(s)
- Rayhan A Lal
- Division of Endocrinology, Department of Medicine, Stanford University, Stanford, California, USA.,Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Caitlin L Maikawa
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Sam W Baker
- Department of Comparative Medicine, Stanford University, Stanford, California, USA
| | - Anton A A Smith
- Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
| | - Gillie A Roth
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Emily C Gale
- Department of Biochemistry, Stanford University, Stanford, California, USA
| | - Lyndsay M Stapleton
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Joseph L Mann
- Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
| | - Anthony C Yu
- Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
| | - Santiago Correa
- Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
| | - Abigail K Grosskopf
- Department of Chemical Engineering, Stanford University, Stanford, California, USA
| | - Celine S Liong
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Catherine M Meis
- Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
| | - Doreen Chan
- Department of Chemistry, Stanford University, Stanford, California, USA
| | - Joseph P Garner
- Department of Comparative Medicine, Stanford University, Stanford, California, USA.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA
| | - David M Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Bruce A Buckingham
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Eric A Appel
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, California, USA.,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Materials Science & Engineering, Stanford University, Stanford, California, USA
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