101
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Palisaitis E, El Fathi A, Von Oettingen JE, Krishnamoorthy P, Kearney R, Jacobs P, Rutkowski J, Legault L, Haidar A. The Efficacy of Basal Rate and Carbohydrate Ratio Learning Algorithm for Closed-Loop Insulin Delivery (Artificial Pancreas) in Youth with Type 1 Diabetes in a Diabetes Camp. Diabetes Technol Ther 2020; 22:185-194. [PMID: 31596127 DOI: 10.1089/dia.2019.0270] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background: Optimizing programmed basal rates and carbohydrate ratios may improve the performance of the artificial pancreas. We tested, in a diabetes camp, the efficacy of a learning algorithm that updates daily basal rates and carbohydrate ratios in the artificial pancreas. Materials and Methods: We conducted a randomized crossover trial in campers and counselors aged 8-21 years with type 1 diabetes on pump therapy. Participants underwent 2 days of artificial pancreas alone and 6 days of artificial pancreas with learning. During the artificial pancreas with learning, programmed basal rates and carbohydrate ratios were updated daily based on the learning algorithm's recommendations. All algorithm recommendations were reviewed for safety by camp physicians. The primary outcome was the time in target range (3.9-10 mmol/L) of the last 2 days of each intervention. Results: Thirty-four campers (age 13.9 ± 3.9, hemoglobin A1c 8.3% ± 0.2%) were included. Ninety-six percent of algorithm recommendations were approved by the camp physicians. Participants were in closed-loop mode 74% of the time. There was no difference between interventions in time in target (55%-55%; P = 0.71) nor in hypoglycemia events (0.8-0.9 events per day; P = 0.63). This was despite changes in programmed basal rate ranging from -21% to +117%, and changes in breakfast, lunch, and supper carbohydrate ratios from -17% to +40%, -36% to +37%, and -35% to +63%, respectively. Morever, postprandial hyperglycemia and hypoglycemia did not decrease in participants whose carbohydrate ratios were decreased (more insulin boluses) and increased (less insulin boluses), respectively. Conclusions: In camp settings, despite adjustments to programmed basal rates and carbohydrate ratios, the learning algorithm did not change glycemia, which may point toward limited effect of these adjustments in environments with large day-to-day variability in insulin needs. Longer randomized studies in real-world settings are required to further assess the efficacy of automatic adjustments of programmed basal rates and carbohydrate ratios.
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Affiliation(s)
- Emilie Palisaitis
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Anas El Fathi
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Julia E Von Oettingen
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
| | - Preetha Krishnamoorthy
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
| | - Robert Kearney
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Peter Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon
| | - Joanna Rutkowski
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
| | - Laurent Legault
- Department of Pediatric Endocrinology, McGill University Health Centre, Montreal Children's Hospital, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
| | - Ahmad Haidar
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- The Research Institute of McGill University Health Centre, Montreal, Canada
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Abstract
The advent of insulin pump therapy marked an important milestone in diabetes treatment in the past few decades and has become the tipping point for the development of automated insulin delivery systems (AID). Standalone insulin pump systems have evolved over the course of years and have been replaced by modern high-technology insulin pumps with continuous glucose monitor interface allowing real-time insulin dose adjustment to optimize treatment. This review summarizes evidence from AID studies conducted in children with type 1 diabetes and discusses the outlook for future generation AID systems from a pediatric treatment perspective.
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Affiliation(s)
- Eda Cengiz
- Yale School of Medicine, 333 Cedar Street, PO Box 208064, New Haven, CT 06520, USA; Bahçeşehir Üniversitesi, Istanbul, Turkey.
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103
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Maahs DM, Shalitin S. Diabetes Technology and Therapy in the Pediatric Age Group. Diabetes Technol Ther 2020; 22:S89-S108. [PMID: 32069148 DOI: 10.1089/dia.2020.2507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- David M Maahs
- Department of Pediatrics, Division of Endocrinology and Diabetes, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA
- Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA
| | - Shlomit Shalitin
- Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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104
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Rama Chandran S, A Vigersky R, Thomas A, Lim LL, Ratnasingam J, Tan A, S L Gardner D. Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels. Diabetes Technol Ther 2020; 22:103-111. [PMID: 31502876 DOI: 10.1089/dia.2019.0277] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Complex changes of glycemia that occur in diabetes are not fully captured by any single measure. The Comprehensive Glucose Pentagon (CGP) measures multiple aspects of glycemia to generate the prognostic glycemic risk (PGR), which constitutes the relative risk of hypoglycemia combined with long-term complications. We compare the components of CGP and PGR across type 1 and type 2 diabetes. Methods: Participants: n = 60 type 1 and n = 100 type 2 who underwent continuous glucose monitoring (CGM). Mean glucose, coefficient of variation (%CV), intensity of hypoglycemia (INThypo), intensity of hyperglycemia (INThyper), time out-of-range (TOR <3.9 and >10 mmol/L), and PGR were calculated. PGR (median, interquartile ranges [IQR]) for diabetes types, and HbA1c classes were compared. Results: While HbA1c was lower in type 1 (type 1 vs. type 2: 8.0 ± 1.6 vs. 8.6 ± 1.7, P = 0.02), CGM-derived mean glucoses were similar across both groups (P > 0.05). TOR, %CV, INThypo, and INThyper were all higher in type 1 [type 1 vs. type 2: 665 (500, 863) vs. 535 (284, 823) min/day; 39% (33, 46) vs. 29% (24, 34); 905 (205, 2951) vs. 18 (0, 349) mg/dL × min2; 42,906 (23,482, 82,120) vs. 30,166 (10,276, 57,183) mg/dL × min2, respectively, all P < 0.05]. Across each HbA1c class, the PGR remained consistently and significantly higher in type 1. While mean glucose remained the same across HbA1c classes, %CV, TOR, INThyper, and INThypo were significantly higher for type 1. Even within the same HbA1c class, the variation (IQR) of each parameter in type 1 was wider. The PGR increased across diabetes groups; type 2 on orals versus type 2 on insulin versus type 1 (PGR: 1.6 vs. 2.2 vs. 2.9, respectively, P < 0.05). Conclusion: Composite indices such as the CGP capture significant differences in glycemia independent of HbA1c and mean glucose. The use of such indices must be explored in both the clinical and research settings.
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Affiliation(s)
| | | | | | - Lee Ling Lim
- Division of Endocrinology, Department of Internal Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jeyakantha Ratnasingam
- Division of Endocrinology, Department of Internal Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Daphne S L Gardner
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
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105
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Gabbay MAL, Rodacki M, Calliari LE, Vianna AGD, Krakauer M, Pinto MS, Reis JS, Puñales M, Miranda LG, Ramalho AC, Franco DR, Pedrosa HPC. Time in range: a new parameter to evaluate blood glucose control in patients with diabetes. Diabetol Metab Syndr 2020; 12:22. [PMID: 32190124 PMCID: PMC7076978 DOI: 10.1186/s13098-020-00529-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 03/07/2020] [Indexed: 01/17/2023] Open
Abstract
The International Consensus in Time in Range (TIR) was recently released and defined the concept of the time spent in the target range between 70 and 180 mg/dL while reducing time in hypoglycemia, for patients using Continuous Glucose Monitoring (CGM). TIR was validated as an outcome measures for clinical Trials complementing other components of glycemic control like Blood glucose and HbA1c. The challenge is to implement this practice more widely in countries with a limited health public and private budget as it occurs in Brazil. Could CGM be used intermittently? Could self-monitoring blood glucose obtained at different times of the day, with the amount of data high enough be used? More studies should be done, especially cost-effective studies to help understand the possibility of having sensors and include TIR evaluation in clinical practice nationwide.
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Affiliation(s)
| | - Melanie Rodacki
- Nutrology and Diabetes Section, Internal Medicine Department Federal University of Rio de Janeiro–UFRJ, Rio de Janeiro, Brazil
| | - Luis Eduardo Calliari
- Pediatric Endocrinology Unit, Pediatric Department, Santa Casa de São Paulo School of Medical Sciences, São Paulo, Brazil
| | - Andre Gustavo Daher Vianna
- Curitiba Diabetes Center, Department of Endocrine Diseases, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | | | - Mauro Scharf Pinto
- Curitiba Diabetes Center, Department of Endocrine Diseases, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | | | - Marcia Puñales
- Institute of Child with Diabetes, Conceição Children Hospital, Conceição Hospitalar Group, Porto Alegre, Brazil
| | - Leonardo Garcia Miranda
- Unit of Endocrinology and Research Center, Regional Hospital of Taguatinga, Secretariat of Health of the Federal District, Brasilia, Brazil
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106
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Prahalad P, Zaharieva DP, Addala A, New C, Scheinker D, Desai M, Hood KK, Maahs DM. Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control-The 4T Study. Front Endocrinol (Lausanne) 2020; 11:360. [PMID: 32733375 PMCID: PMC7363838 DOI: 10.3389/fendo.2020.00360] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 05/07/2020] [Indexed: 12/12/2022] Open
Abstract
Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach-teamwork, targets, technology, and tight control-to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.
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Affiliation(s)
- Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- *Correspondence: Priya Prahalad
| | - Dessi P. Zaharieva
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Ananta Addala
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Christin New
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - David Scheinker
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Manisha Desai
- Quantitative Sciences Unit, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
| | - Korey K. Hood
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
| | - David M. Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford, CA, United States
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107
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108
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Urakami T. Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes? Front Endocrinol (Lausanne) 2020; 11:609. [PMID: 33042005 PMCID: PMC7523511 DOI: 10.3389/fendo.2020.00609] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022] Open
Abstract
Severe hypoglycemia is defined as a condition with serious cognitive dysfunction, such as a convulsion and coma, requiring external help from other persons. This condition is still lethal and is reported to be the cause of death in 4-10% in children and adolescents with type 1 diabetes. The incidence of severe hypoglycemia in the pediatric population was previously reported as high as more than 50-100 patient-years; however, there was a decline in the frequency of severe hypoglycemia during the past decades, and relationship with glycemic control became weaker than previously reported. A lot of studies have shown the neurological sequelae with severe hypoglycemia as cognitive dysfunction and abnormalities in brain structure. This serious condition also provides negative psychosocial outcomes and undesirable compensatory behaviors. Various possible factors, such as younger age, recurrent hypoglycemia, nocturnal hypoglycemia, and impaired awareness of hypoglycemia, are possible risk factors for developing severe hypoglycemia. A low HbA1c level is not a predictable value for severe hypoglycemia. Prevention of severe hypoglycemia remains one of the most critical issues in the management of pediatric patients with type 1 diabetes. Advanced technologies, such as continuous glucose monitoring (CGM), intermittently scanned CGM, and sensor-augmented pump therapy with low-glucose suspend system, potentially minimize the occurrence of severe hypoglycemia without worsening overall glycemic control. Hybrid closed-loop system must be the most promising tool for achieving optimal glycemic control with preventing the occurrence of severe hypoglycemia in pediatric patients with type 1 diabetes.
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109
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Lal RA, Ekhlaspour L, Hood K, Buckingham B. Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes. Endocr Rev 2019; 40:1521-1546. [PMID: 31276160 PMCID: PMC6821212 DOI: 10.1210/er.2018-00174] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 02/28/2019] [Indexed: 01/20/2023]
Abstract
Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.
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Affiliation(s)
- Rayhan A Lal
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.,Division of Endocrinology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Laya Ekhlaspour
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Korey Hood
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California.,Department of Psychiatry, Stanford University School of Medicine, Stanford, California
| | - Bruce Buckingham
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
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110
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Wilmot EG, Choudhary P, Leelarathna L, Baxter M. Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care. Diabetes Obes Metab 2019; 21:2599-2608. [PMID: 31364268 PMCID: PMC6899456 DOI: 10.1111/dom.13842] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/22/2019] [Accepted: 07/25/2019] [Indexed: 12/23/2022]
Abstract
Type 1 diabetes mellitus (T1DM) remains one of the most challenging long-term conditions to manage. Despite robust evidence to demonstrate that near normoglycaemia minimizes, but does not completely eliminate, the risk of complications, its achievement has proved almost impossible in a real-world setting. HbA1c to date has been used as the gold standard marker of glucose control and has been shown to reflect directly the risk of diabetes complications. However, it has been recognized that HbA1c is a crude marker of glucose control. Continuous glucose monitoring (CGM) provides the ability to measure and observe inter- and intraday glycaemic variability (GV), a more meaningful measure of glycaemic control, more relevant to daily living for those with T1DM. This paper reviews the relationship between GV and hypoglycaemia, and micro- and macrovascular complications. It also explores the impact on GV of CGM, insulin pumps, closed-loop technologies, and newer insulins and adjunctive therapies. Looking to the future, there is an argument that GV should become a key determinant of therapeutic success. Further studies are required to investigate the pathological and psychological benefits of reducing GV.
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Affiliation(s)
- Emma G Wilmot
- Diabetes Department, Royal Derby Hospital, University Hospitals of Derby and Burton NHSFT, Derby, Derbyshire, UK
- Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, UK
| | | | - Lalantha Leelarathna
- Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Diabetes, Endocrinology and Gastroenterology, University of Manchester, Manchester, UK
| | - Mike Baxter
- Department Medical Affairs, Sanofi, Guildford, UK
- Department of Diabetes and Endocrinology, University of Swansea, Swansea, South Wales, UK
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111
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Seo W, Lee YB, Lee S, Jin SM, Park SM. A machine-learning approach to predict postprandial hypoglycemia. BMC Med Inform Decis Mak 2019; 19:210. [PMID: 31694629 PMCID: PMC6833234 DOI: 10.1186/s12911-019-0943-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 10/21/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. RESULTS In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. CONCLUSION In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.
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Affiliation(s)
- Wonju Seo
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - You-Bin Lee
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea
| | - Seunghyun Lee
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea
| | - Sang-Man Jin
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea.
| | - Sung-Min Park
- Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea.
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112
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Hajizadeh I, Hobbs N, Samadi S, Sevil M, Rashid M, Brandt R, Askari MR, Maloney Z, Cinar A. Controlling the AP Controller: Controller Performance Assessment and Modification. J Diabetes Sci Technol 2019; 13:1091-1104. [PMID: 31561714 PMCID: PMC6835190 DOI: 10.1177/1932296819877217] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time. METHODS A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects. RESULTS Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events. CONCLUSIONS The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
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113
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Grando MA, Bayuk M, Karway G, Corrette K, Groat D, Cook CB, Thompson B. Patient Perception and Satisfaction With Insulin Pump System: Pilot User Experience Survey. J Diabetes Sci Technol 2019; 13:1142-1148. [PMID: 31055947 PMCID: PMC6835185 DOI: 10.1177/1932296819843146] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The goal of this study was to assess patient perspectives and satisfaction with the MiniMed 670G insulin pump. Those participants who used the pump as part of a hybrid closed loop were also asked to provide their views on the automatic feature (auto mode). METHODS Adults with type 1 diabetes mellitus using the Medtronic™ 670G pump were asked about their experience with the device using a semi-structured survey developed by the research team. Responses were quantified to identify emergent themes. RESULTS Seventeen participants used the pump as part of a hybrid closed loop system, while four participants used the pump in combination with a nonintegrated continuous glucose monitoring system. Overall, participants indicated a high level of satisfaction with the pump (14/21) mostly because of improvements in blood glucose (BG) control (15/21). Least liked features were physical design and structure (6/21), frequency of user input (5/21), alert frequency (4/21), and difficulty of use (3/21). Those using the hybrid closed loop were satisfied with the auto mode feature (11/17), mostly because of improvements in BG control (9/17). The least liked features of the auto mode technology were that blood glucose levels remained elevated (5/17) and the frequency of alerts (4/17). CONCLUSION Participants indicated a high level of satisfaction with the pump and its auto mode featured mostly because of improvements in BG control. They also pointed out some key aspects of the device that are of potential clinical or commercial relevance. Additional research is needed to further evaluate users' perspectives on this new device.
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Affiliation(s)
- Maria Adela Grando
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, USA
- Maria Adela Grando, PhD, Biomedical Informatics, College of Health Solutions, Arizona State University, 13212 E Shea Blvd, Scottsdale, AZ 85259, USA.
| | - Mike Bayuk
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, USA
| | - George Karway
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, USA
| | - Krystal Corrette
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, USA
| | - Danielle Groat
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, USA
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Curtiss B. Cook
- Department of Endocrinology, Arizona Mayo Clinic, Scottsdale, AZ, USA
| | - Bithika Thompson
- Department of Endocrinology, Arizona Mayo Clinic, Scottsdale, AZ, USA
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van Huijgevoort NCM, Del Chiaro M, Wolfgang CL, van Hooft JE, Besselink MG. Diagnosis and management of pancreatic cystic neoplasms: current evidence and guidelines. Nat Rev Gastroenterol Hepatol 2019; 16:676-689. [PMID: 31527862 DOI: 10.1038/s41575-019-0195-x] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/31/2019] [Indexed: 12/11/2022]
Abstract
Pancreatic cystic neoplasms (PCN) are a heterogeneous group of pancreatic cysts that include intraductal papillary mucinous neoplasms, mucinous cystic neoplasms, serous cystic neoplasms and other rare cystic lesions, all with different biological behaviours and variable risk of progression to malignancy. As more pancreatic cysts are incidentally discovered on routine cross-sectional imaging, optimal surveillance for patients with PCN is becoming an increasingly common clinical problem, highlighting the need to balance cancer prevention with the risk of (surgical) overtreatment. This Review summarizes the latest developments in the diagnosis and management of PCN, including the quality of available evidence. Also discussed are the most important differences between the PCN guidelines from the American Gastroenterological Association, the International Association of Pancreatology and the European Study Group on Cystic Tumours of the Pancreas, including diagnostic and follow-up strategies and indications for surgery. Finally, new developments in the management of patients with PCN are addressed.
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Affiliation(s)
- Nadine C M van Huijgevoort
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Marco Del Chiaro
- Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Christopher L Wolfgang
- Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Marc G Besselink
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.
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Ekhlaspour L, Nally LM, El-Khatib FH, Ly TT, Clinton P, Frank E, Tanenbaum ML, Hanes SJ, Selagamsetty RR, Hood K, Damiano ER, Buckingham BA. Feasibility Studies of an Insulin-Only Bionic Pancreas in a Home-Use Setting. J Diabetes Sci Technol 2019; 13:1001-1007. [PMID: 31470740 PMCID: PMC6835195 DOI: 10.1177/1932296819872225] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND We tested the safety and performance of the "insulin-only" configuration of the bionic pancreas (BP) closed-loop blood-glucose control system in a home-use setting to assess glycemic outcomes using different static and dynamic glucose set-points. METHOD This is an open-label non-randomized study with three consecutive intervention periods. Participants had consecutive weeks of usual care followed by the insulin-only BP with (1) an individualized static set-point of 115 or 130 mg/dL and (2) a dynamic set-point that automatically varied within 110 to 130 mg/dL, depending on hypoglycemic risk. Human factors (HF) testing was conducted using validated surveys. The last five days of each study arm were used for data analysis. RESULTS Thirteen participants were enrolled with a mean age of 28 years, mean A1c of 7.2%, and mean daily insulin dose of 0.6 U/kg (0.4-1.0 U/kg). The usual care arm had an average glucose of 145 ± 20 mg/dL, which increased in the static set-point arm (159 ± 8 mg/dL, P = .004) but not in the dynamic set-point arm (154 ± 10 mg/dL, P = ns). There was no significant difference in time spent in range (70-180 mg/dL) among the three study arms. There was less time <70 mg/dL with both the static (1.8% ± 1.4%, P = .009) and dynamic set-point (2.7±1.5, P = .051) arms compared to the usual-care arm (5.5% ± 4.2%). HF testing demonstrated preliminary user satisfaction and no increased risk of diabetes burden or distress. CONCLUSIONS The insulin-only configuration of the BP using either static or dynamic set-points and initialized only with body weight performed similarly to other published insulin-only systems.
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Affiliation(s)
- Laya Ekhlaspour
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Laya Ekhlaspour, MD, Pediatric Endocrinology and Diabetes, Lucille Packard Children’s Hospital at Stanford, 780 Welch Road, Stanford, CA 94305, USA.
| | - Laura M. Nally
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Firas H. El-Khatib
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Trang T. Ly
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Paula Clinton
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Eliana Frank
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Molly L. Tanenbaum
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sarah J. Hanes
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | | | - Korey Hood
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Edward R. Damiano
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Bruce A. Buckingham
- Division of Pediatric Endocrinology and Diabetes, Stanford University School of Medicine, Palo Alto, CA, USA
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116
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Beck RW, Bergenstal RM, Laffel LM, Pickup JC. Advances in technology for management of type 1 diabetes. Lancet 2019; 394:1265-1273. [PMID: 31533908 DOI: 10.1016/s0140-6736(19)31142-0] [Citation(s) in RCA: 132] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/26/2019] [Accepted: 05/01/2019] [Indexed: 01/07/2023]
Abstract
Technological advances have had a major effect on the management of type 1 diabetes. In addition to blood glucose meters, devices used by people with type 1 diabetes include insulin pumps, continuous glucose monitors, and, most recently, systems that combine both a pump and a monitor for algorithm-driven automation of insulin delivery. In the next 5 years, as many advances are expected in technology for the management of diabetes as there have been in the past 5 years, with improvements in continuous glucose monitoring and more available choices of systems that automate insulin delivery. Expansion of the use of technology will be needed beyond endocrinology practices to primary-care settings and broader populations of patients. Tools to support decision making will also need to be developed to help patients and health-care providers to use the output of these devices to optimise diabetes management.
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Affiliation(s)
- Roy W Beck
- Jaeb Center for Health Research, Tampa, FL, USA.
| | - Richard M Bergenstal
- International Diabetes Center, Park Nicollet and Health Partners, Minneapolis, MN, USA
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - John C Pickup
- King's College London, Faculty of Life Sciences and Medicine, Guy's Hospital, London, UK
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117
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Mathematical modeling of the glucagon challenge test. J Pharmacokinet Pharmacodyn 2019; 46:553-564. [PMID: 31571122 PMCID: PMC6868112 DOI: 10.1007/s10928-019-09655-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 09/16/2019] [Indexed: 01/12/2023]
Abstract
A model for the homeostasis of glucose through the regulating hormones glucagon and insulin is described. It contains a subsystem that models the internalization of the glucagon receptor. Internalization is a mechanism in cell signaling, through which G-protein coupled receptors are taken from the surface of the cell to the endosome. The model is used to interpret data from a glucagon challenge test in which subjects have been under treatment with a novel glucagon receptor anti-sense drug which is aimed at reducing the number of receptors in the liver. It is shown how the receptor internalization results in tolerance of the blood glucose concentration to glucagon-induced hyperglycemia. We quantify the reduction of the number of receptors using the model and the data before and after treatment.
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118
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Scholten L, Latenstein AEJ, van Eijck C, Erdmann J, van der Harst E, Mieog JSD, Molenaar IQ, van Santvoort HC, DeVries JH, Besselink MG. Outcome and long-term quality of life after total pancreatectomy (PANORAMA): a nationwide cohort study. Surgery 2019; 166:1017-1026. [PMID: 31500907 DOI: 10.1016/j.surg.2019.07.025] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/14/2019] [Accepted: 07/21/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND The threshold to perform total pancreatectomy is rather high, predominantly because of concerns for long-term consequences of brittle diabetes on patients' quality of life. Contemporary data on postoperative outcomes, diabetes management, and long-term quality of life after total pancreatectomy from large nationwide series are, however, lacking. METHODS We performed a nationwide, retrospective cohort study among adults who underwent total pancreatectomy in 17 Dutch centers (2006-2016). Morbidity and mortality were analyzed, and long-term quality of life was assessed cross-sectionally using the following generic and disease-specific questionnaires: the 5-level version European quality of life 5-dimension and the European Organization for Research and Treatment in Cancer Quality of Life Questionnaire Cancer. Several questionnaires specifically addressing diabetic quality of life included the Problem Areas in Diabetes Scale 20, the Diabetes Treatment Satisfaction Questionnaire-status version, and the Hypoglycemia Fear Survey-II. Results were compared with the general population and patients with type 1 diabetes. RESULTS Overall, 148 patients after total pancreatectomy were included. The annual nationwide volume of total pancreatectomy increased from 5 in 2006 to 32 in 2015 (P < .05). The 30-day and 90-day mortality were 5% and 8%, respectively. The major complication rate was 32%. Quality of life questionnaires were completed by 60 patients (85%, median follow-up of 36 months). Participants reported lower global (73 vs 78, P = .03) and daily health status (0.83 vs 0.87, P < .01) compared to the general population. Quality of life did not differ based on time after total pancreatectomy (<3, 3-5, or >5 years). In general, patients were satisfied with their diabetes therapy and experienced similar diabetes-related distress as patients with type 1 diabetes. CONCLUSION This nationwide study found increased use of total pancreatectomy with a relatively high 90-day mortality. Long-term quality of life was lower compared to the general population, although differences were small. Diabetes-related distress and treatment satisfaction were similar to patients with type 1 diabetes.
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Affiliation(s)
- Lianne Scholten
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Anouk E J Latenstein
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Casper van Eijck
- Department of Surgery, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Joris Erdmann
- Department of Surgery, University Medical Center Groningen, Groningen, the Netherlands
| | | | - J Sven D Mieog
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - I Quintus Molenaar
- Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hjalmar C van Santvoort
- Department of Surgery, Regional Academic Cancer Center Utrecht, St Antonius Hospital Nieuwegein, Utrecht, the Netherlands
| | - J Hans DeVries
- Department of Endocrinology, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Marc G Besselink
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, the Netherlands.
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119
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Ekhlaspour L, Forlenza GP, Chernavvsky D, Maahs DM, Wadwa RP, Deboer MD, Messer LH, Town M, Pinnata J, Kruse G, Kovatchev BP, Buckingham BA, Breton MD. Closed loop control in adolescents and children during winter sports: Use of the Tandem Control-IQ AP system. Pediatr Diabetes 2019; 20:759-768. [PMID: 31099946 PMCID: PMC6679803 DOI: 10.1111/pedi.12867] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/19/2019] [Accepted: 04/24/2019] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and prolonged exercise in adolescents and children. We present the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control. METHODS In a randomized controlled trial, 24 adolescents (ages 13-18 years) and 24 school-aged children (6-12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor-augmented pump (RM-SAP), and the experimental group used the t: slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff. RESULTS The Control-IQ system improved percent time within range (70-180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events. CONCLUSIONS The use of the Control-IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM-SAP in pediatric patients with T1D during prolonged intensive winter sport activities.
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Affiliation(s)
- Laya Ekhlaspour
- Department of Pediatrics, Stanford University, Palo Alto, California
| | - Gregory P. Forlenza
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado
| | - Daniel Chernavvsky
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | - David M. Maahs
- Department of Pediatrics, Stanford University, Palo Alto, California,Stanford Diabetes Research Center, Stanford, California
| | - R. Paul Wadwa
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado
| | - Mark D. Deboer
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | - Laurel H. Messer
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado
| | - Marissa Town
- Department of Pediatrics, Stanford University, Palo Alto, California
| | - Jennifer Pinnata
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | | | - Boris P. Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | - Bruce A. Buckingham
- Department of Pediatrics, Stanford University, Palo Alto, California,Stanford Diabetes Research Center, Stanford, California
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
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120
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Bowers DT, Song W, Wang LH, Ma M. Engineering the vasculature for islet transplantation. Acta Biomater 2019; 95:131-151. [PMID: 31128322 PMCID: PMC6824722 DOI: 10.1016/j.actbio.2019.05.051] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 04/13/2019] [Accepted: 05/20/2019] [Indexed: 12/17/2022]
Abstract
The microvasculature in the pancreatic islet is highly specialized for glucose sensing and insulin secretion. Although pancreatic islet transplantation is a potentially life-changing treatment for patients with insulin-dependent diabetes, a lack of blood perfusion reduces viability and function of newly transplanted tissues. Functional vasculature around an implant is not only necessary for the supply of oxygen and nutrients but also required for rapid insulin release kinetics and removal of metabolic waste. Inadequate vascularization is particularly a challenge in islet encapsulation. Selectively permeable membranes increase the barrier to diffusion and often elicit a foreign body reaction including a fibrotic capsule that is not well vascularized. Therefore, approaches that aid in the rapid formation of a mature and robust vasculature in close proximity to the transplanted cells are crucial for successful islet transplantation or other cellular therapies. In this paper, we review various strategies to engineer vasculature for islet transplantation. We consider properties of materials (both synthetic and naturally derived), prevascularization, local release of proangiogenic factors, and co-transplantation of vascular cells that have all been harnessed to increase vasculature. We then discuss the various other challenges in engineering mature, long-term functional and clinically viable vasculature as well as some emerging technologies developed to address them. The benefits of physiological glucose control for patients and the healthcare system demand vigorous pursuit of solutions to cell transplant challenges. STATEMENT OF SIGNIFICANCE: Insulin-dependent diabetes affects more than 1.25 million people in the United States alone. Pancreatic islets secrete insulin and other endocrine hormones that control glucose to normal levels. During preparation for transplantation, the specialized islet blood vessel supply is lost. Furthermore, in the case of cell encapsulation, cells are protected within a device, further limiting delivery of nutrients and absorption of hormones. To overcome these issues, this review considers methods to rapidly vascularize sites and implants through material properties, pre-vascularization, delivery of growth factors, or co-transplantation of vessel supporting cells. Other challenges and emerging technologies are also discussed. Proper vascular growth is a significant component of successful islet transplantation, a treatment that can provide life-changing benefits to patients.
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Affiliation(s)
- Daniel T Bowers
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Wei Song
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Long-Hai Wang
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Minglin Ma
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA.
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Reddy R, Resalat N, Wilson LM, Castle JR, El Youssef J, Jacobs PG. Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes. J Diabetes Sci Technol 2019; 13:919-927. [PMID: 30650997 PMCID: PMC6955453 DOI: 10.1177/1932296818823792] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
BACKGROUND Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. METHODS We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. RESULTS Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. CONCLUSIONS Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.
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Affiliation(s)
- Ravi Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Navid Resalat
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Leah M. Wilson
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon, Health & Science University, Portland, OR, USA
| | - Jessica R. Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon, Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon, Health & Science University, Portland, OR, USA
| | - Peter G. Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Peter G. Jacobs, PhD, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Ave, Mailstop: 13B, Portland, OR 97239, USA.
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Sherwood JS, Jafri RZ, Balliro CA, Zheng H, El-Khatib FH, Damiano ER, Russell SJ, Putman MS. Automated glycemic control with the bionic pancreas in cystic fibrosis-related diabetes: A pilot study. J Cyst Fibros 2019; 19:159-161. [PMID: 31420176 DOI: 10.1016/j.jcf.2019.08.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 07/26/2019] [Accepted: 08/04/2019] [Indexed: 11/16/2022]
Abstract
Cystic fibrosis-related diabetes (CFRD) is the most common extrapulmonary manifestation of cystic fibrosis. The current standard of care for CFRD involves treatment with insulin, typically via multiple daily injections. We conducted a small pilot study comparing usual care with automated glycemic control using the bihormonal (insulin and glucagon) and insulin-only configurations of the bionic pancreas. Both configurations of the bionic pancreas achieved good glycemic control, with mean glucose levels <150 mg/dl and minimal hypoglycemia. Subjects reported improved treatment satisfaction and reduced burden of diabetes management with the bionic pancreas. Further investigation of automated glycemic control in the treatment of CFRD is warranted.
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Affiliation(s)
- Jordan S Sherwood
- Diabetes Research Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Rabab Z Jafri
- Diabetes Research Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Courtney A Balliro
- Diabetes Research Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Hui Zheng
- Biostatics Center, Massachusetts General Hospital, Boston, MA, United States of America
| | | | | | - Steven J Russell
- Diabetes Research Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Melissa S Putman
- Diabetes Research Unit, Massachusetts General Hospital, Boston, MA, United States of America; Department of Endocrinology, Boston Children's Hospital, Boston, MA, United States of America.
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123
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Abstract
IN BRIEF Automated insulin delivery (AID; also known as artificial pancreas) has improved the regulation of blood glucose concentrations, reduced the frequency of hyperglycemic and hypoglycemic episodes, and improved the quality of life of people with diabetes and their families. Three different types of algorithms-proportional-integral-derivative control, model predictive control, and fuzzy-logic knowledge-based systems-have been used in AID control systems. This article will highlight the foundations of these algorithms and discuss their strengths and limitations. Multivariable artificial pancreas and dual-hormone (insulin and glucagon) systems will be introduced.
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Affiliation(s)
- Ali Cinar
- Departments of Chemical and Biological Engineering and Biomedical Engineering, Engineering Center for Diabetes Research and Education, Illinois Institute of Technology, Chicago, IL
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124
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Haidar A. Insulin-and-Glucagon Artificial Pancreas Versus Insulin-Alone Artificial Pancreas: A Short Review. Diabetes Spectr 2019; 32:215-221. [PMID: 31462876 PMCID: PMC6695257 DOI: 10.2337/ds18-0097] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
IN BRIEF The advantage of the insulin-and-glucagon artificial pancreas is based on the rapid effect of subcutaneous glucagon delivery in preventing hypoglycemia compared to suspension of insulin delivery. In short-term studies, the dual-hormone artificial pancreas reduced daytime hypoglycemia, especially during exercise, compared to the insulin-alone artificial pancreas, but the insulin-alone system seemed sufficient in eliminating nocturnal hypoglycemia. The comparative benefits of the single- and dual-hormone systems for improving A1C and preventing severe hypoglycemia remain unknown.
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Affiliation(s)
- Ahmad Haidar
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
- Division of Endocrinology and Metabolism, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
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125
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Beato-Víbora PI, Arroyo-Díez FJ. New uses and formulations of glucagon for hypoglycaemia. Drugs Context 2019; 8:212599. [PMID: 31402931 PMCID: PMC6675539 DOI: 10.7573/dic.212599] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/23/2019] [Accepted: 06/26/2019] [Indexed: 12/11/2022] Open
Abstract
Hypoglycaemia is the more frequent complication of insulin therapy and the main barrier to tight glycaemic control. Injectable glucagon and oral intake of carbohydrates are the recommended treatments for severe and non-severe hypoglycaemia episodes, respectively. Nasal glucagon is currently being developed as a ready-to-use device, to simplify severe hypoglycaemia rescue. Stable forms of liquid glucagon could open the field for different approaches for mild to moderate hypoglycaemia treatment, such as mini-doses of glucagon or continuous subcutaneous glucagon infusion as a part of dual-hormone closed-loop systems. Pharmaceutical companies are developing stable forms of native glucagon or glucagon analogues for that purpose.
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Affiliation(s)
- Pilar I Beato-Víbora
- Department of Endocrinology and Nutrition, Department of Paediatrics, Badajoz University Hospital, Badajoz, Spain
| | - Francisco J Arroyo-Díez
- Department of Endocrinology and Nutrition, Department of Paediatrics, Badajoz University Hospital, Badajoz, Spain
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126
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Kovatchev B. A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control. Trends Endocrinol Metab 2019; 30:432-444. [PMID: 31151733 DOI: 10.1016/j.tem.2019.04.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 04/14/2019] [Accepted: 04/25/2019] [Indexed: 12/24/2022]
Abstract
Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.
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Affiliation(s)
- Boris Kovatchev
- University of Virginia School of Medicine, UVA Center for Diabetes Technology, Ivy Translational Research Building, 560 Ray C. Hunt Drive, Charlottesville, VA 22903-2981, USA.
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127
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Lefkovits Y, Stewart Z, Murphy H. Using the Novel Approach of an Artificial Pancreas to Manage Type 1 Diabetes Mellitus in Pregnancy. EUROPEAN MEDICAL JOURNAL 2019. [DOI: 10.33590/emj/10312967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Recent National Institute for Health and Care Excellence (NICE) guidelines suggest that insulin pump therapy should be used in pregnant women with Type 1 diabetes mellitus (T1DM) who do not achieve optimal glycaemic control with multiple daily injection (MDI) therapy. Furthermore, a landmark trial has confirmed that prospective continuous glucose monitoring (CGM) may be beneficial for women using both MDI and insulin pumps during pregnancy, with positive effects on neonatal outcomes. More recently, overnight use of an artificial pancreas (AP) with a model-predictive control algorithm has been shown to improve the amount of time women spend within the overnight glucose target range (3.5–7.8 mmol/L) during pregnancy. However, preliminary studies where the AP is used day and night have shown a high degree of interindividual variability in response to the intervention, and further randomised trials are needed to understand which women are suitable candidates for CGM, insulin pump, and AP technology. It is understood that improvements in maternal glycaemic control can minimise the risk of adverse neonatal outcomes. Given the substantial improvements in glycaemic control with AP use outside of pregnancy, the recent advances in AP technology provide hope that AP systems will improve the effectiveness of continuous subcutaneous insulin infusion and CGM during pregnancy. Further research is needed to evaluate whether AP can optimise glucose control and neonatal outcomes in T1DM pregnancy. This paper will discuss emerging technologies available for the management of T1DM in pregnancy.
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Affiliation(s)
- Yael Lefkovits
- Monash University, Melbourne, Australia; University of Cambridge, Cambridge, UK
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128
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Abstract
Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia 22908
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129
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VandenBerg MA, Webber MJ. Biologically Inspired and Chemically Derived Methods for Glucose-Responsive Insulin Therapy. Adv Healthc Mater 2019; 8:e1801466. [PMID: 30605265 DOI: 10.1002/adhm.201801466] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/11/2018] [Indexed: 12/13/2022]
Abstract
The controlled delivery of therapeutics in a manner responsive to physiological indicators has promise in realizing new therapeutic approaches to combat disease. This approach is especially relevant in the context of diabetes. Natural fluctuations in blood glucose seen in the healthy state, complete with peaks and troughs, are poorly regulated as a result of detrimental production or ineffective signaling of the insulin hormone. While several manifestations of diabetes are treated with regularly administered exogenous insulin, the present standard of care results in suboptimal glycemic management that poorly recreates natural hormone control, leading to long-term instability and a significantly increased risk for secondary health complications. New synthetic technologies that make insulin available only when needed, and at the exact dose required, have been explored under the broad vision of realizing a "fully synthetic pancreas." Yet, many challenges remain to realizing a technology that is appropriately responsive, safe, and well integrated into a manageable routine. Herein, many of the approaches explored thus far to sense physiological blood glucose and elicit response through the release of therapeutic insulin are summarized. The approaches point to a new, autonomous approach to managing diabetes with biomimetic therapy.
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Affiliation(s)
- Michael A. VandenBerg
- Department of Chemical & Biomolecular EngineeringUniversity of Notre Dame 205 McCourtney Hall Notre Dame IN 46556 USA
| | - Matthew J. Webber
- Department of Chemical & Biomolecular EngineeringUniversity of Notre Dame 205 McCourtney Hall Notre Dame IN 46556 USA
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130
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Stimple SD, Kalyoncu S, Desai AA, Mogensen JE, Spang LT, Asgreen DJ, Staby A, Tessier PM. Sensitive detection of glucagon aggregation using amyloid fibril‐specific antibodies. Biotechnol Bioeng 2019; 116:1868-1877. [DOI: 10.1002/bit.26994] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 04/03/2019] [Accepted: 04/11/2019] [Indexed: 02/05/2023]
Affiliation(s)
- Samuel D. Stimple
- Department of Pharmaceutical Sciences, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
- Department of Chemical Engineering, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
| | - Sibel Kalyoncu
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary StudiesRensselaer Polytechnic InstituteTroy NY
| | - Alec A. Desai
- Department of Chemical Engineering, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
| | | | - Lotte T. Spang
- New Product Introduction, Product SupplyNovo Nordisk A/SCopenhagen Denmark
| | - Désirée J. Asgreen
- New Product Introduction, Product SupplyNovo Nordisk A/SCopenhagen Denmark
| | - Arne Staby
- CMC Development, R&DNovo Nordisk A/SCopenhagen Denmark
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
- Department of Chemical Engineering, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
- Department of Biomedical Engineering, Biointerfaces InstituteUniversity of MichiganAnn Arbor MI
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131
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Hajizadeh I, Rashid M, Cinar A. Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems. JOURNAL OF PROCESS CONTROL 2019; 77:97-113. [PMID: 31814659 PMCID: PMC6897508 DOI: 10.1016/j.jprocont.2019.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.
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Affiliation(s)
- Iman Hajizadeh
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | - Mudassir Rashid
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | - Ali Cinar
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
- Dept of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616
- Correspondence concerning this article should be addressed to A. Cinar at
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Weissberg‐Benchell J, Shapiro JB, Hood K, Laffel LM, Naranjo D, Miller K, Barnard K. Assessing patient-reported outcomes for automated insulin delivery systems: the psychometric properties of the INSPIRE measures. Diabet Med 2019; 36:644-652. [PMID: 30761592 PMCID: PMC6593869 DOI: 10.1111/dme.13930] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/12/2019] [Indexed: 12/17/2022]
Abstract
AIM Participants in clinical trials assessing automated insulin delivery systems report perceived benefits and burdens that reflect their experiences and may predict their likelihood of uptake and continued use of this novel technology. Despite the importance of understanding their perspectives, there are no available validated and reliable measures assessing the psychosocial aspects of automated insulin delivery systems. The present study assesses the initial psychometric properties of the INSPIRE measures, which were developed for youth and adults with Type 1 diabetes, as well as parents and partners. METHODS Data from 292 youth, 159 adults, 150 parents of youth and 149 partners of individuals recruited from the Type 1 Diabetes Exchange Registry were analysed. Participants completed INSPIRE questionnaires and measures of quality of life, fear of hypoglycaemia, diabetes distress, glucose monitoring satisfaction. Exploratory factor analysis assessed factor structures. Associations between INSPIRE scores and other measures, HbA1c , and technology use assessed concurrent and discriminant validity. RESULTS Youth, adult, parent and partner measures assess positive expectancies of automated insulin delivery systems. Measures range from 17 to 22 items and are reliable (α = 0.95-0.97). Youth, adult and parent measures are unidimensional; the partner measure has a two-factor structure (perceptions of impact on partners versus the person with diabetes). Measures showed concurrent and discriminant validity. CONCLUSIONS INSPIRE measures assessing the positive expectancies of automated insulin delivery systems for youth, adults, parents and partners have meaningful factor structures and are internally consistent. The developmentally sensitive INSPIRE measures offer added value as clinical trials test newer systems, systems become commercially available and clinicians initiate using these systems.
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Affiliation(s)
- J. Weissberg‐Benchell
- Department of Psychiatry and Behavioral SciencesAnn and Robert H., Lurie Children's Hospital of ChicagoNorthwestern UniversityFeinberg School of MedicineChicagoIL
| | | | - K. Hood
- Departments of PediatricsPsychiatry& Behavioral Sciences, Stanford University School of MedicineStanfordCA
| | - L. M. Laffel
- Joslin Diabetes CenterHarvard Medical SchoolBostonMA
| | - D. Naranjo
- Departments of PediatricsPsychiatry& Behavioral Sciences, Stanford University School of MedicineStanfordCA
| | - K. Miller
- Jaeb Center for Health ResearchTampaFloridaUSA
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Biester T, Nir J, Remus K, Farfel A, Muller I, Biester S, Atlas E, Dovc K, Bratina N, Kordonouri O, Battelino T, Philip M, Danne T, Nimri R. DREAM5: An open-label, randomized, cross-over study to evaluate the safety and efficacy of day and night closed-loop control by comparing the MD-Logic automated insulin delivery system to sensor augmented pump therapy in patients with type 1 diabetes at home. Diabetes Obes Metab 2019; 21:822-828. [PMID: 30478937 DOI: 10.1111/dom.13585] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 11/02/2018] [Accepted: 11/13/2018] [Indexed: 12/22/2022]
Abstract
AIMS Previous DREAM studies demonstrated the safety and efficacy of the CE marked MD-Logic closed-loop system (DreaMed GlucoSitter) in different settings for overnight glycaemic control. The present study aimed to evaluate the system for day and night use for 60 hours during the weekend at home compared to sensor-augmented pump (SAP) therapy in participants with type 1 diabetes. METHODS This was a prospective, multicentre, crossover, controlled study (clinicaltrials.gov NCT01238406). All participants were connected in randomized order for one weekend to SAP therapy or the MD-Logic System. In the intervention arm only, the amount of carbohydrate was entered into the bolus calculator; the rest of insulin delivery was automated and wireless via a tablet computer. The primary endpoint was percentage of glucose values between 70 and 180 mg/dL. RESULTS The ITT population comprised 48 (19 males, 29 females) adolescents and adults experienced in sensor use: (median, [IQR]): age, 16.1years [13.2-18.5]; diabetes duration, 9.4 years [5.0-12.7]; pump use, 5.4 years [3.1-9.4]; HbA1c, 7.6% [7.0-8.1]. A significant increase in the percentage of time within target range (70-180 mg/dL) (66.6% vs 59.9%, P = 0.002) was observed with the closed-loop system vs control weekends with unchanged percentage of time below 70 mg/dL (2.3% vs 1.5%, P = 0.369). Mean weekend glucose level per participant was significantly lower (153 [142-175] vs 164 [150-186] mg/dL, P = 0.003). No safety signals were observed. CONCLUSIONS The MD-Logic system was safe and associated with better glycaemic control than SAP therapy for day and night use. The absence of remote monitoring did not lead to safety signals in adapting basal rates nor in administration of automated bolus corrections.
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Affiliation(s)
- Torben Biester
- Children's Hospital "Auf der Bult," Diabetes-Center for Children and Adolescents, Hannover, Germany
| | - Judith Nir
- Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikvah, Israel
| | - Kerstin Remus
- Children's Hospital "Auf der Bult," Diabetes-Center for Children and Adolescents, Hannover, Germany
| | - Alon Farfel
- Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikvah, Israel
| | - Ido Muller
- DreaMed Diabetes Ltd, Petah Tikvah, Israel
| | - Sarah Biester
- Children's Hospital "Auf der Bult," Diabetes-Center for Children and Adolescents, Hannover, Germany
| | - Eran Atlas
- DreaMed Diabetes Ltd, Petah Tikvah, Israel
| | - Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, UMC Ljubljana, Ljubljana, Slovenia
| | - Nataša Bratina
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, UMC Ljubljana, Ljubljana, Slovenia
| | - Olga Kordonouri
- Children's Hospital "Auf der Bult," Diabetes-Center for Children and Adolescents, Hannover, Germany
| | - Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, UMC Ljubljana, Ljubljana, Slovenia
| | - Moshe Philip
- Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikvah, Israel
| | - Thomas Danne
- Children's Hospital "Auf der Bult," Diabetes-Center for Children and Adolescents, Hannover, Germany
| | - Revital Nimri
- Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikvah, Israel
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Steineck IIK, Ranjan A, Schmidt S, Clausen TR, Holst JJ, Nørgaard K. Preserved glucose response to low-dose glucagon after exercise in insulin-pump-treated individuals with type 1 diabetes: a randomised crossover study. Diabetologia 2019; 62:582-592. [PMID: 30643924 DOI: 10.1007/s00125-018-4807-8] [Citation(s) in RCA: 15] [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: 08/20/2018] [Accepted: 12/06/2018] [Indexed: 12/15/2022]
Abstract
AIMS/HYPOTHESIS This study aimed to compare the increase in plasma glucose after a subcutaneous injection of 200 μg glucagon given after 45 min of cycling with resting (study 1) and to investigate the effects of glucagon when injected before compared with after 45 min of cycling (study 2). We hypothesised that: (1) the glucose response to glucagon would be similar after cycling and resting; and (2) giving glucagon before the activity would prevent the exercise-induced fall in blood glucose during exercise and for 2 h afterwards. METHODS Fourteen insulin-pump-treated individuals with type 1 diabetes completed three visits in a randomised, placebo-controlled, participant-blinded crossover study. They were allocated by sealed envelopes. Baseline values were (mean and range): HbA1c 54 mmol/mol (43-65 mmol/mol) or 7.1% (6.1-8.1%); age 45 years (23-66 years); BMI 26 kg/m2 (21-30 kg/m2); and diabetes duration 26 years (8-51 years). At each visit, participants consumed a standardised breakfast 2 h prior to 45 min of cycling or resting. A subcutaneous injection of 200 μg glucagon was given before or after cycling or after resting. The glucose response to glucagon was compared after cycling vs resting (study 1) and before vs after cycling (study 2). RESULTS The glucose response to glucagon was higher after cycling compared with after resting (mean ± SD incremental peak: 2.6 ± 1.7 vs 1.8 ± 2.0 mmol/l, p = 0.02). As expected, plasma glucose decreased during cycling (-3.1 ± 2.8 mmol/l) but less so when glucagon was given before cycling (-0.9 ± 2.8 mmol/l, p = 0.002). The number of individuals reaching glucose values ≤3.9 mmol/l was the same on the 3 days. CONCLUSIONS/INTERPRETATION Moderate cycling for 45 min did not impair the glucose response to glucagon compared with the glucose response after resting. The glucose fall during cycling was diminished by a pre-exercise injection of 200 μg glucagon; however, no significant difference was seen in the number of events of hypoglycaemia. TRIAL REGISTRATION Clinicaltrials.gov NCT02882737 FUNDING: The study was funded by the Danish Diabetes Academy founded by Novo Nordisk foundation and by an unrestricted grant from Zealand Pharma.
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Affiliation(s)
- Isabelle I K Steineck
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650, Hvidovre, Denmark.
- Danish Diabetes Academy, Odense, Denmark.
| | - Ajenthen Ranjan
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650, Hvidovre, Denmark
- Danish Diabetes Academy, Odense, Denmark
- Department of Pediatrics, Copenhagen University Hospital, Herlev, Denmark
| | - Signe Schmidt
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650, Hvidovre, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | | | - Jens J Holst
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kirsten Nørgaard
- Department of Endocrinology, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650, Hvidovre, Denmark
- Steno Diabetes Center, Copenhagen, Denmark
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135
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Messori M, Toffanin C, Del Favero S, De Nicolao G, Cobelli C, Magni L. Model individualization for artificial pancreas. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 171:133-140. [PMID: 27424482 DOI: 10.1016/j.cmpb.2016.06.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 05/13/2016] [Accepted: 06/28/2016] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient. METHODS The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error. RESULTS Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model. CONCLUSIONS The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.
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Affiliation(s)
- Mirko Messori
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
| | - Chiara Toffanin
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giuseppe De Nicolao
- Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lalo Magni
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
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Shirin A, Della Rossa F, Klickstein I, Russell J, Sorrentino F. Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon. PLoS One 2019; 14:e0213665. [PMID: 30893335 PMCID: PMC6426249 DOI: 10.1371/journal.pone.0213665] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.
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Affiliation(s)
- Afroza Shirin
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
- * E-mail:
| | - Fabio Della Rossa
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Isaac Klickstein
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - John Russell
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
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Lv J, Wu G, Liu Y, Li C, Huang F, Zhang Y, Liu J, An Y, Ma R, Shi L. Injectable dual glucose-responsive hydrogel-micelle composite for mimicking physiological basal and prandial insulin delivery. Sci China Chem 2019. [DOI: 10.1007/s11426-018-9419-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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138
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Hövelmann U, Olsen MB, Mouritzen U, Lamers D, Kronshage B, Heise T. Low doses of dasiglucagon consistently increase plasma glucose levels from hypoglycaemia and euglycaemia in people with type 1 diabetes mellitus. Diabetes Obes Metab 2019; 21:601-610. [PMID: 30350477 PMCID: PMC6587565 DOI: 10.1111/dom.13562] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 10/07/2018] [Accepted: 10/16/2018] [Indexed: 02/06/2023]
Abstract
AIM To characterize the pharmacokinetic and pharmacodynamic properties of dasiglucagon, a novel, stable and liquid formulated glucagon analogue, during hypoglycaemic and euglycaemic conditions in adult patients with type 1 diabetes mellitus. RESEARCH DESIGN AND METHODS In this randomized double-blind trial, 17 patients received four single subcutaneous doses (0.03, 0.08, 0.2 and 0.6 mg) of dasiglucagon (4 mg/mL formulation) under euglycaemic (plasma glucose [PG] 5.6 mmol/L [100 mg/dL]) or hypoglycaemic (PG 3.1-3.7 mmol/L [56-66 mg/dL]) conditions. For comparison, three doses (0.03, 0.08 and 0.2 mg) of a commercial glucagon formulation (Eli Lilly) were investigated at euglycaemia. RESULTS Dasiglucagon led to a dose-dependent and rapid increase in PG levels across all doses tested (mean increases 30 minutes post-dosing of 2.2 to 4.4 mmol/L [39-80 mg/dL] from euglycaemia and 1.3 to 5.2 mmol/L [24-94 mg/dL] from hypoglycaemia), which was higher than the rises elicited by similar doses of commercial glucagon (1.7-3.9 mmol/L [30-71 mg/dL]). The median time (range) to an increase in PG of >1.1 mmol/L (20 mg/dL) was <20 (18-19.5) minutes with 0.03 mg dasiglucagon and, with higher doses, the median times ranged from 9 to 15 minutes (commercial glucagon 13-14 minutes). In hypoglycaemia, 0.03 and 0.08 mg dasiglucagon re-established normoglycaemia (PG ≥3.9 mmol/L [70 mg/dL]) within median times of 14 and 10 minutes, respectively. Nausea and vomiting occurred more frequently with dasiglucagon than with commercial glucagon at identical doses which might be attributable to dasiglucagon's higher potency. CONCLUSION Dasiglucagon rapidly increased PG at doses of 0.03 to 0.6 mg in a dose-dependent manner and, therefore, is a good candidate for use in dual-hormone artificial pancreas systems.
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Condren M, Sabet S, Chalmers LJ, Saley T, Hopwood J. Technology for Augmenting Type 1 Diabetes Mellitus Management. J Pediatr Pharmacol Ther 2019; 24:99-106. [DOI: 10.5863/1551-6776-24.2.99] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Type 1 diabetes mellitus has witnessed significant progress in its management over the past several decades. This review highlights technologic advancements in type 1 diabetes management. Continuous glucose monitoring systems are now available at various functionality and cost levels, addressing diverse patient needs, including a recently US Food and Drug Administration (FDA)–approved implantable continuous glucose monitoring system (CGMS). Another dimension to these state-of-the-art technologies is CGMS and insulin pump integration. These integrations have allowed for CGMS-based adjustments to basal insulin delivery rates and suspension of insulin delivery when a low blood glucose event is predicted. This review also includes a brief discussion of upcoming technologies such as patch-based CGMS and insulin-glucagon dual-hormonal delivery.
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140
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Castle JR, Elander M. Long-Term Safety and Tolerability of Dasiglucagon, a Stable-in-Solution Glucagon Analogue. Diabetes Technol Ther 2019; 21:94-96. [PMID: 30707621 DOI: 10.1089/dia.2018.0363] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Jessica R Castle
- 1 Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
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Cherubini V, Gesuita R, Skrami E, Rabbone I, Bonfanti R, Arnaldi C, D'Annunzio G, Frongia A, Lombardo F, Piccinno E, Schiaffini R, Toni S, Tumini S, Tinti D, Cipriano P, Minuto N, Lenzi L, Ferrito L, Ventrici C, Ortolani F, Cohen O, Scaramuzza A. Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes. Pediatr Diabetes 2019; 20:107-112. [PMID: 30378759 DOI: 10.1111/pedi.12792] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 07/24/2018] [Accepted: 10/18/2018] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVES To assess the optimal setting of the predictive low glucose management (PLGM) algorithm for preventing exercise-induced hypoglycemia in adolescents with type 1 diabetes. METHODS Thirty-four adolescents, 15 to 20 years, wearing PLGM system, were followed during 3 days exercise during a diabetes camp. PLGM threshold was set at 70 mg/dL between 8 am and 10 pm and 90 mg/dL during 10 pm and 8 am Adolescents were divided into group A and B, with PLGM threshold at 90 and 70 mg/dL, respectively, during exercise. Time spent in hypoglycemia and AUC for time slots 8 am to 1 pm, 1 to 4 pm, 4 to 11 pm, 11 pm to 3 am, 3 to 8 am, in 3 days were compared between groups by Wilcoxon rank sum test. RESULTS We analyzed 31 patients (median age 15.0 years, 58.1% males, median diabetes duration 7.0 years, hemoglobin A1c [HbA1c] 7.1%). No significant difference has been observed in time spent in hypoglycemia between groups using threshold 70 or 90. Time spent in target was similar in both groups, as well as time spent in hypo or hyperglycemia. The trends of blood glucose over the 3 days in the 2 groups over-lapped without significant differences. CONCLUSIONS A PLGM threshold of 90 mg/dL during the night was associated with reduced time in hypoglycemia in adolescents doing frequent physical exercise, while maintaining 65.1% time in range during the day. However, a threshold of 70 mg/dL seems to be safe in the duration of the physical exercise. PLGM system in adolescents with type 1 diabetes was effective to prevent hypoglycemia during and after exercise, irrespective of the PLGM thresholds used.
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Affiliation(s)
- Valentino Cherubini
- Division of Pediatric and Adolescent Diabetes, Department of Women's and Children's Health, AOU Salesi Hospital, Ancona, Italy
| | - Rosaria Gesuita
- Centre of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Edlira Skrami
- Centre of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Ivana Rabbone
- Department of Pediatrics, University of Turin, Turin, Italy
| | - Riccardo Bonfanti
- Pediatric Diabetes and Diabetes Research Institute, San Raffaele Hospital, Milan, Italy
| | | | - Giuseppe D'Annunzio
- Department of Pediatrics, Istituto "G. Gaslini" Children's Hospital, Genoa, Italy
| | | | | | - Elvira Piccinno
- Metabolic Diseases and Diabetology, Pediatric Hospital Giovanni XXIII, Bari, Italy
| | | | - Sonia Toni
- Juvenile Diabetes Center, Anna Meyer Children's Hospital, Florence, Italy
| | - Stefano Tumini
- Center of Pediatric Diabetology, University of Chieti, Chieti, Italy
| | - Davide Tinti
- Department of Pediatrics, University of Turin, Turin, Italy
| | - Paola Cipriano
- Center of Pediatric Diabetology, University of Chieti, Chieti, Italy
| | - Nicola Minuto
- Institute of Endocrinology, Sheba Medical Center, Ramat Gan, Israel
| | - Lorenzo Lenzi
- Juvenile Diabetes Center, Anna Meyer Children's Hospital, Florence, Italy
| | - Lucia Ferrito
- Division of Pediatric and Adolescent Diabetes, Department of Women's and Children's Health, AOU Salesi Hospital, Ancona, Italy
| | | | - Federica Ortolani
- Metabolic Diseases and Diabetology, Pediatric Hospital Giovanni XXIII, Bari, Italy
| | - Ohad Cohen
- Institute of Endocrinology, Sheba Medical Center, Ramat Gan, Israel.,Medtronic, Tolochenaz, Switzerland
| | - Andrea Scaramuzza
- Division of Pediatrics, ASST Cremona, "Ospedale Maggiore di Cremona", Cremona, Italy
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142
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Anderson SM, Dassau E, Raghinaru D, Lum J, Brown SA, Pinsker JE, Church MM, Levy C, Lam D, Kudva YC, Buckingham B, Forlenza GP, Wadwa RP, Laffel L, Doyle FJ, DeVries JH, Renard E, Cobelli C, Boscari F, Del Favero S, Kovatchev BP. The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability. Diabetes Technol Ther 2019; 21:73-80. [PMID: 30649925 PMCID: PMC6354594 DOI: 10.1089/dia.2018.0308] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller. METHODS Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use. RESULTS The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL). CONCLUSIONS CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.
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Affiliation(s)
- Stacey M. Anderson
- Center for Diabetes Technology, Department of Medicine, University of Virginia
- Address correspondence to: Stacey M. Anderson, MD, Center for Diabetes Technology, Department of Medicine, University of Virginia, PO Box 400888, Charlottesville, VA 22903
| | - Eyal Dassau
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
- Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | | | - John Lum
- Jaeb Center for Health Research, Tampa, Florida
| | - Sue A. Brown
- Center for Diabetes Technology, Department of Medicine, University of Virginia
| | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Carol Levy
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - David Lam
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Bruce Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Gregory P. Forlenza
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, Colorado
| | - R. Paul Wadwa
- Barbara Davis Center for Diabetes, University of Colorado, Anschutz Medical Campus, Aurora, Colorado
| | - Lori Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | - Francis J. Doyle
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - J. Hans DeVries
- Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France
- INSERM 1411 Clinical Investigation Center, Institute of Functional Genomics, UMR CNRS 5203/INSERM U1191, University of Montpellier, Montpellier, France
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Simone Del Favero
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Boris P. Kovatchev
- Center for Diabetes Technology, Department of Medicine, University of Virginia
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143
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Karageorgiou V, Papaioannou TG, Bellos I, Alexandraki K, Tentolouris N, Stefanadis C, Chrousos GP, Tousoulis D. Effectiveness of artificial pancreas in the non-adult population: A systematic review and network meta-analysis. Metabolism 2019; 90:20-30. [PMID: 30321535 DOI: 10.1016/j.metabol.2018.10.002] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/20/2018] [Accepted: 10/09/2018] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Artificial pancreas is a technology that minimizes user input by bridging continuous glucose monitoring and insulin pump treatment, and has proven safety in the adult population. The purpose of this systematic review and meta-analysis is to evaluate the efficacy of closed-loop (CL) systems in the glycemic control of non-adult type 1 diabetes patients in both a pairwise and network meta-analysis (NMA) context and investigate various parameters potentially affecting the outcome. METHODS Literature was systematically searched using the MEDLINE (1966-2018), Scopus (2004-2018), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2018), Clinicaltrials.gov (2008-2018) and Google Scholar (2004-2018) databases. Studies comparing the glycemic control in CL (either single- or dual-hormone) with continuous subcutaneous insulin infusion (CSII) in people with diabetes (PWD) aged <18 years old were deemed eligible. The primary outcome analysis was conducted with regard to time spent in the target glycemic range. All outcomes were evaluated in NMA in order to investigate potential between-algorithm differences. Pairwise meta-analysis and meta-regression were performed using the RevMan 5.3 and Open Meta-Analyst software. For NMA, the package pcnetmetain R 3.5.1 was used. RESULTS The meta-analysis was based on 25 studies with a total of 504 PWD. The CL group was associated with significantly higher percentage of time spent in the target glycemic range (Mean (SD): 67.59% (SD: 8.07%) in the target range and OL PWD spending 55.77% (SD: 11.73%), MD: -11.97%, 95% CI [-18.40, -5.54%]) and with lower percentages of time in hyperglycemia (MD: 3.01%, 95% CI [1.68, 4.34%]) and hypoglycemia (MD: 0.67%, 95% CI [0.21, 1.13%]. Mean glucose was also decreased in the CL group (MD: 0.75 mmol/L, 95% CI [0.18-1.33]). The NMA arm of the study showed that the bihormonal modality was superior to other algorithms and standard treatment in lowering mean glucose and increasing time spent in the target range. The DiAs platform was superior to PID in controlling hypoglycemia and mean glucose. Time in target range and mean glucose were unaffected by the confounding factors tested. CONCLUSIONS The findings of this meta-analysis suggest that artificial pancreas systems are superior to the standard sensor-augmented pump treatment of type 1 diabetes mellitus in non-adult PWD. Between-algorithm differences are also addressed, implying a superiority of the bihormonal treatment modality. Future large-scale studies are needed in the field to verify these outcomes and to determine the optimal algorithm to be used in the clinical setting.
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Affiliation(s)
- Vasilios Karageorgiou
- First Department of Cardiology, Biomedical Engineering Unit, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Theodoros G Papaioannou
- First Department of Cardiology, Biomedical Engineering Unit, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
| | - Ioannis Bellos
- First Department of Cardiology, Biomedical Engineering Unit, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Krystallenia Alexandraki
- Clinic of Endocrine Oncology, Section of Endocrinology, Department of Pathophysiology, Laiko Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos Tentolouris
- First Department of Propaedeutic Internal Medicine, Laiko General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | | | - George P Chrousos
- First Department of Pediatrics, Aghia Sophia Children's Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios Tousoulis
- First Department of Cardiology, Biomedical Engineering Unit, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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144
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Payne FW, Ledden B, Lamps G. Capabilities of Next-Generation Patch Pump: Improved Precision, Instant Occlusion Detection, and Dual-Hormone Therapy. J Diabetes Sci Technol 2019; 13:49-54. [PMID: 29792066 PMCID: PMC6313296 DOI: 10.1177/1932296818776028] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Insulin pumps allow patients to attain better blood glucose control with more lifestyle flexibility. Their size and cost, however, limit their usefulness. Current CSII pumps are bulky, intrusive, and expensive. SFC Fluidics is addressing these problems by developing a new type of wearable patch pump based on the patented electro-chemiosmotic (ECO) microfluidic pumping technology. This nonmechanical pumping technology allows accurate and precise delivery of very small amounts of insulin and/or other drugs, including concentrated insulin. The pump engine is small and can be made inexpensively from injection molded parts, allowing its use in a disposable or semidisposable pod format. In addition, a single ECO pump engine can be used to deliver two drugs through independent pathways. Other features of SFC Fluidics' pod include latching safety valves that prevent accidental overdosing of insulin due to pressure changes and an instantaneous occlusion sensor that can immediately detect delivery failure at the first missed dose. These features allow for the development of a series of patch pumps that will offer users the benefit of CSII therapy in a more discreet and reliable patch pump form.
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Affiliation(s)
| | | | - Greg Lamps
- SFC Fluidics, Inc, Fayetteville, AR, USA
- Greg Lamps, MS, MBA, SFC Fluidics, Inc, 534 W Research Center Blvd, Ste 260, Fayetteville, AR 72701, USA.
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145
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A steady decline in pancreas transplantation rates. Pancreatology 2019; 19:31-38. [PMID: 30448085 DOI: 10.1016/j.pan.2018.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 09/27/2018] [Accepted: 11/09/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND/OBJECTIVES After years of growth in many pancreas transplant programs, UNOS has reported declining transplant numbers in the USA. This precipitating trend urges for an evaluation of the transplant numbers and scientific productivity in the Eurotransplant region and the UK. METHODS We performed a trend analysis of pancreas transplantation rates, between 1997 and 2016, adjusting for changes in population size, and an analysis of scientific publications in this field. We used information from the UNOS, Eurotransplant, and UK transplant registry and bibliometric information from the Web of Science database. RESULTS Between 2004 and 2016 there was an average annual decline in pancreas transplantation rates per million inhabitants of 3.3% in the USA and 2.5% in the Eurotransplant region. In the UK, transplant numbers showed an average annual decline of 1.0% from 2009 to 2016. Publications in Q1 journals showed an annual change of -2.1% and +20.1%, before 2004, and a change of -3.8% and -5.5%, between 2004 and 2016, for USA and Eurotransplant publications, respectively. CONCLUSIONS Adjusting pancreas transplantation rates for changes in population size showed a clear decline in transplant numbers in both the USA and Eurotransplant region, with first signs of decline in the UK. Following this trend, the number of scientific publications in this field have declined worldwide.
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146
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Vigersky RA. Going beyond HbA1c to understand the benefits of advanced diabetes therapies. J Diabetes 2019; 11:23-31. [PMID: 30151979 DOI: 10.1111/1753-0407.12846] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 08/14/2018] [Accepted: 08/21/2018] [Indexed: 11/27/2022] Open
Abstract
The gold standard for monitoring overall glycemia is HbA1c. However, HbA1c has several important limitations, giving more weight to the prior 2 to 3 months rather than short-term glycemic control. In addition, the level of the HbA1c does not reflect the important interpersonal differences in its relationship with mean glucose, and HbA1c is affected by many common clinical conditions (anemia, uremia) that can interfere with the accuracy of its measurement in the laboratory. The development and refinement of continuous glucose monitoring (CGM), a glucose- and patient-centric technology, over the past two decades have permitted the creation of new single and composite metrics, such as the percentage of time in range and the glucose pentagon, respectively, which provide clinically relevant insights into short-term glycemic control. In addition, CGM creates new outcome metrics for clinical management and investigational studies (percentage of time in hypoglycemia, percentage of time in target range) that can accurately and meaningfully report the effects of an intervention, whether that is a drug, a device, or a psychosocial program, and CGM provides the key input to drive algorithm-based insulin delivery. Finally, CGM linked with artificial intelligence permits real-time feedback to patients about modifiable patterns of glycemic excursions.
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Affiliation(s)
- Robert A Vigersky
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
- Medtronic Diabetes, Northridge, California
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147
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Revised convexity, normality and stability properties of the dynamical feedback fuzzy state space model (FFSSM) of insulin–glucose regulatory system in humans. Soft comput 2018. [DOI: 10.1007/s00500-018-03682-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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148
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Hajizadeh I, Rashid M, Cinar A. Considering Plasma Insulin Concentrations in Adaptive Model Predictive Control for Artificial Pancreas Systems. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4452-4455. [PMID: 30441339 DOI: 10.1109/embc.2018.8513296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Adaptive and personalized model predictive control (MPC) algorithms that explicitly consider insulin dosing constraints are necessary to prevent overdose-induced hypoglycemia in type 1 diabetes. A personalized plasma insulin concentration (PIC) estimator is integrated with adaptive models to characterize the temporal dynamics of PIC and blood glucose concentration (BGC). The dynamic profile trajectories of the PIC and BGC are used to adapt the control problems and constraints on-line to accurately reflect the concurrent metabolic state of the individuals and improve glucose regulation. The adaptive MPC algorithm, explicitly considering the PIC in the insulin dose computations, is demonstrated to effectively control BGC without any manual user input on meal information while avoiding excessive insulin administration that can cause hypoglycemia. Simulation results demonstrate the ability of the proposed approach with an average 71.14 % of time spent in the target range (BGC ϵ [70, 180]mg/dL).
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149
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Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018; 4:14. [PMID: 32232090 PMCID: PMC7098217 DOI: 10.1186/s42234-018-0015-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/08/2018] [Indexed: 12/28/2022] Open
Abstract
The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.
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Affiliation(s)
- Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, P.O. Box 400888, Charlottesville, VA 22908 USA
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150
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Kim T, Holleman CL, Nason S, Arble DM, Ottaway N, Chabenne J, Loyd C, Kim JA, Sandoval D, Drucker DJ, DiMarchi R, Perez-Tilve D, Habegger KM. Hepatic Glucagon Receptor Signaling Enhances Insulin-Stimulated Glucose Disposal in Rodents. Diabetes 2018; 67:2157-2166. [PMID: 30150304 PMCID: PMC6198333 DOI: 10.2337/db18-0068] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 08/10/2018] [Indexed: 12/12/2022]
Abstract
Glucagon receptor (GCGR) agonists cause hyperglycemia but also weight loss. However, GCG-like peptide 1 receptor (GLP1R)/GCGR mixed agonists do not exhibit the diabetogenic effects often attributed to GCGR activity. Thus, we sought to investigate the effect of glucagon agonism on insulin action and glucose homeostasis. Acute GCGR agonism induced immediate hyperglycemia, followed by improved glucose tolerance and enhanced glucose-stimulated insulin secretion. Moreover, acute GCGR agonism improved insulin tolerance in a dose-dependent manner in both lean and obese mice. Improved insulin tolerance was independent of GLP1R, FGF21, and hepatic glycogenolysis. Moreover, we observed increased glucose infusion rate, disposal, uptake, and suppressed endogenous glucose production during euglycemic clamps. Mice treated with insulin and GCGR agonist had enhanced phosphorylation of hepatic AKT at Ser473; this effect was reproduced in isolated mouse primary hepatocytes and resulted in increased AKT kinase activity. These data reveal that GCGR agonism enhances glucose tolerance, in part, by augmenting insulin action, with implications for the use of GCGR agonism in therapeutic strategies for diabetes.
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Affiliation(s)
- Teayoun Kim
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Cassie L Holleman
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Shelly Nason
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Deanna M Arble
- Department of Biological Sciences, Marquette University, Milwaukee, WI
| | - Nickki Ottaway
- Metabolic Diseases Institute and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Cincinnati, Cincinnati, OH
| | | | - Christine Loyd
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Jeong-A Kim
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | | | - Daniel J Drucker
- Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Richard DiMarchi
- Novo Nordisk Research Center, Indianapolis, IN
- Department of Chemistry, Indiana University, Bloomington, IN
| | - Diego Perez-Tilve
- Metabolic Diseases Institute and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Cincinnati, Cincinnati, OH
| | - Kirk M Habegger
- Comprehensive Diabetes Center and Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
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