1
|
Heinemann L, Thomas A. Continuous Glucose Monitoring and Bolus Calculators: A Good Combination. J Diabetes Sci Technol 2024; 18:761-763. [PMID: 37804180 DOI: 10.1177/19322968231204371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Affiliation(s)
- Lutz Heinemann
- Science-Consulting in Diabetes GmbH, Düsseldorf, Germany
| | | |
Collapse
|
2
|
den Brok EJ, Svensson CH, Panagiotou M, van Greevenbroek MMJ, Mertens PR, Vazeou A, Mitrakou A, Makrilakis K, Franssen GHLM, van Kuijk S, Proennecke S, Mougiakakou S, Pedersen-Bjergaard U, de Galan BE. The effect of bolus advisors on glycaemic parameters in adults with diabetes on intensive insulin therapy: A systematic review with meta-analysis. Diabetes Obes Metab 2024; 26:1950-1961. [PMID: 38504142 DOI: 10.1111/dom.15521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 03/21/2024]
Abstract
AIM To conduct a systematic review with meta-analysis to provide a comprehensive synthesis of randomized controlled trials (RCTs) and prospective cohort studies investigating the effects of currently available bolus advisors on glycaemic parameters in adults with diabetes. MATERIALS AND METHODS An electronic search of PubMed, Embase, CINAHL, Cochrane Library and ClinicalTrials.gov was conducted in December 2022. The risk of bias was assessed using the revised Cochrane Risk of Bias tool. (Standardized) mean difference (MD) was selected to determine the difference in continuous outcomes between the groups. A random-effects model meta-analysis and meta-regression were performed. This systematic review was registered on PROSPERO (CRD42022374588). RESULTS A total of 18 RCTs involving 1645 adults (50% females) with a median glycated haemoglobin (HbA1c) concentration of 8.45% (7.95%-9.30%) were included. The majority of participants had type 1 diabetes (N = 1510, 92%) and were on multiple daily injections (N = 1173, 71%). Twelve of the 18 trials had low risk of bias. The meta-analysis of 10 studies with available data on HbA1c showed that the use of a bolus advisor modestly reduced HbA1c compared to standard treatment (MD -011%, 95% confidence interval -0.22 to -0.01; I2 = 0%). This effect was accompanied by small improvements in low blood glucose index and treatment satisfaction, but not with reductions in hypoglycaemic events or changes in other secondary outcomes. CONCLUSION Use of a bolus advisor is associated with slightly better glucose control and treatment satisfaction in people with diabetes on intensive insulin treatment. Future studies should investigate whether personalizing bolus advisors using artificial intelligence technology can enhance these effects.
Collapse
Affiliation(s)
- Elisabeth J den Brok
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Cecilie H Svensson
- Department of Endocrinology and Nephrology, Nordsjællands Hospital, Hillerød, Denmark
| | - Maria Panagiotou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | | | - Peter R Mertens
- Department of Kidney and Hypertension Diseases, Diabetology and Endocrinology, Otto-Von-Guericke-Univeristat Magdeburg, Magdeburg, Germany
| | | | - Asimina Mitrakou
- Diabetes Center, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Gregor H L M Franssen
- University Library, Department Education, Content & Support, Maastricht University, Maastricht, The Netherlands
| | - Sander van Kuijk
- Clinical epidemiology & Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Ulrik Pedersen-Bjergaard
- Department of Endocrinology and Nephrology, Nordsjællands Hospital, Hillerød, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Lausanne, Denmark
| | - Bastiaan E de Galan
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| |
Collapse
|
3
|
Moscoso-Vasquez M, Fabris C, Breton MD. Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems. J Diabetes Sci Technol 2023; 17:1470-1481. [PMID: 37864340 PMCID: PMC10658700 DOI: 10.1177/19322968231206798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
BACKGROUND Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system. METHOD The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range. RESULTS Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state. CONCLUSIONS The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.
Collapse
Affiliation(s)
| | - Chiara Fabris
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| | - Marc D. Breton
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
| |
Collapse
|
4
|
Schiavon M, Galderisi A, Basu A, Kudva YC, Cengiz E, Dalla Man C. A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes. Diabetes Technol Ther 2023; 25:270-278. [PMID: 36648253 PMCID: PMC10066780 DOI: 10.1089/dia.2022.0397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background: Estimation of insulin sensitivity (SI) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for SI estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (SISP) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of SISP performing well in daily life unrestrained conditions. Methods: The new SISP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model SI (SIMM). CGM and CSII data were used for SISP calculation, which was then validated against the gold standard SIMM. Results: SISP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. SISP and SIMM were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: SISP is suitable for estimating SI in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.
Collapse
Affiliation(s)
- Michele Schiavon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Alfonso Galderisi
- Department of Woman and Child's Health, University of Padova, Padova, Italy
- Department of Pediatrics, Yale University, New Haven, Connecticut, USA
| | - Ananda Basu
- Division of Endocrinology, University of Virginia, Charlottesville, Virginia, USA
| | - Yogish C. Kudva
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Internal Medicine, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Eda Cengiz
- Pediatric Diabetes Program, University of California San Francisco (UCSF) School of Medicine, San Francisco, California, USA
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| |
Collapse
|
5
|
Diaz C JL, Fabris C, Breton MD, Cengiz E. Insulin Replacement Across the Menstrual Cycle in Women with Type 1 Diabetes: An In Silico Assessment of the Need for Ad Hoc Technology. Diabetes Technol Ther 2022; 24:832-841. [PMID: 35714349 DOI: 10.1089/dia.2022.0154] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Background: Women with type 1 diabetes (T1D) of fertile age may experience fluctuations in insulin needs across the menstrual cycle. When present, these fluctuations complicate glucose management and oftentimes worsen glycemic control. In this work, an in silico analysis was conducted to assess whether current technology is sufficient to handle changes in insulin needs due to the menstrual cycle in women with T1D. Methods: Euglycemic clamp studies were performed in 16 women with T1D in the follicular phase (FP) and luteal phase (LP) of the menstrual cycle. Interphase insulin sensitivity (IS) variability observed in the data was modeled and introduced in the University of Virginia/Padova T1D Simulator. Open-loop and closed-loop insulin delivery was tested in two in silico studies, without (nominal study) and with (informed study) a priori knowledge on cycle-related IS variability informing insulin therapy. Glycemic metrics were computed on the obtained glucose traces. Results: In the pool of studied women, the glucose infusion rate area under the curve significantly decreased from FP to LP (P = 0.0107), indicating an average decrease of IS in LP. When introduced in the simulator, this pattern led to increased time spent >180 and >250 mg/dL during LP versus FP in the nominal studies, irrespective of the insulin delivery strategy. In the informed studies, glycemic metrics stabilized across the cycle. Conclusion: This work suggests that current insulin delivery technology may benefit from informing the dosing algorithm with knowledge on menstrual cycle related IS changes. Clinical validation of these results is warranted. ClinicalTrials.gov identifier: NCT02693938.
Collapse
Affiliation(s)
- Jenny L Diaz C
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Chiara Fabris
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Eda Cengiz
- Division of Pediatric Endocrinology, University of California San Francisco, San Francisco, California, USA
| |
Collapse
|
6
|
Pinsker JE, Church MM, Brown SA, Voelmle MK, Bode BW, Narron B, Huyett LM, Lee JB, O'Connor J, Benjamin E, Dumais B, Ly TT. Clinical Evaluation of a Novel CGM-Informed Bolus Calculator with Automatic Glucose Trend Adjustment. Diabetes Technol Ther 2022; 24:18-25. [PMID: 34491825 PMCID: PMC8783627 DOI: 10.1089/dia.2021.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Background: Expert opinion guidelines and limited data from clinical trials recommend adjustment to bolus insulin doses based on continuous glucose monitor (CGM) trend data, yet minimal evidence exists to support this approach. We performed a clinical evaluation of a novel CGM-informed bolus calculator (CIBC) with automatic insulin bolus dose adjustment based on CGM trend used with sensor-augmented pump therapy. Materials and Methods: In this multicenter, outpatient study, participants 6-70 years of age with type 1 diabetes (T1D) used the Omnipod® 5 System in Manual Mode, first for 7 days without a connected CGM (standard bolus calculator, SBC, phase 1) and then for 7 days with a connected CGM using the CIBC (CIBC phase 2). The integrated bolus calculator used stored pump settings plus user-estimated meal size and/or either a manually entered capillary glucose value (SBC phase) or an imported current CGM value and trend (CIBC phase) to recommend a bolus amount. The CIBC automatically increased or decreased the suggested bolus amount based on the CGM trend. Results: Twenty-five participants, (mean ± standard deviation) 27 ± 15 years of age, with T1D duration 12 ± 9 years and A1C 7.0% ± 0.9% completed the study. There were significantly fewer sensor readings <70 mg/dL 4 h postbolus with the CIBC compared to the SBC (2.1% ± 2.0% vs. 2.8 ± 2.7, P = 0.03), while percent of sensor readings >180 and 70-180 mg/dL remained the same. There was no difference in insulin use or number of boluses given between the two phases. Conclusion: The CIBC was safe when used with the Omnipod 5 System in Manual Mode, with fewer hypoglycemic readings in the postbolus period compared to the SBC. This trial was registered at ClinicalTrials.gov (NCT04320069).
Collapse
Affiliation(s)
- Jordan E. Pinsker
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Sue A. Brown
- Division of Endocrinology, Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Mary K. Voelmle
- Division of Endocrinology, Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Bruce W. Bode
- Atlanta Diabetes Associates, Atlanta, Georgia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Brooke Narron
- Atlanta Diabetes Associates, Atlanta, Georgia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Lauren M. Huyett
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Joon Bok Lee
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Jason O'Connor
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Eric Benjamin
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Bonnie Dumais
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Trang T. Ly
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
- Address correspondence to: Trang T. Ly, MBBS, FRACP, PhD, Insulet Corporation, 100 Nagog Park, Acton, MA 01720, USA
| |
Collapse
|
7
|
Fabris C, Gautier T, Breton M. Automated Adaptation of Insulin Treatment in Type 1 Diabetes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5039-5042. [PMID: 34892339 DOI: 10.1109/embc46164.2021.9630191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.
Collapse
|
8
|
Nguyen TTP, Jacobs PG, Castle JR, Wilson LM, Kuehl K, Branigan D, Gabo V, Guillot F, Riddell MC, Haidar A, El Youssef J. Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes. Am J Physiol Endocrinol Metab 2021; 320:E425-E437. [PMID: 33356994 PMCID: PMC7988786 DOI: 10.1152/ajpendo.00534.2020] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Aerobic exercise in type 1 diabetes (T1D) causes rapid increase in glucose utilization due to muscle work during exercise, followed by increased insulin sensitivity after exercise. Better understanding of these changes is necessary for models of exercise in T1D. Twenty-six individuals with T1D underwent three sessions at three insulin rates (100%, 150%, 300% of basal). After 3-h run-in, participants performed 45 min aerobic exercise (moderate or intense). We determined area under the curve for endogenous glucose production (AUCEGP) and rate of glucose disappearance (AUCRd) over 45 min from exercise start. A novel application of linear regression of Rd across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUCRd increased 12.45 mmol/L (CI = 10.33-14.58, P < 0.001) and 13.13 mmol/L (CI = 11.01-15.26, P < 0.001) whereas AUCEGP increased 1.66 mmol/L (CI = 1.01-2.31, P < 0.001) and 3.46 mmol/L (CI = 2.81-4.11, P < 0.001) above baseline during moderate and intense exercise, respectively. AUCEGP increased during intense exercise by 2.14 mmol/L (CI = 0.91-3.37, P < 0.001) compared with moderate exercise. There was significant effect of insulin infusion rate on AUCRd equal to 0.06 mmol/L per % above basal rate (CI = 0.05-0.07, P < 0.001). Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period. To our knowledge, this method of isolating dynamic insulin- and non-insulin-mediated uptake has not been previously employed during exercise. These results will be useful in informing glucoregulatory models of T1D. The study has been registered at www.clinicaltrials.gov as NCT03090451.NEW & NOTEWORTHY Separating insulin and non-insulin glucose uptake dynamically during exercise in type 1 diabetes has not been done before. We use a multistep process, including a previously described linear regression method, over three insulin infusion sessions, to perform this separation and can graph these components before, during, and after exercise for the first time.
Collapse
Affiliation(s)
- Thanh-Tin P Nguyen
- School of Medicine, Oregon Health & Science University (OHSU), Portland, Oregon
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University (OHSU), Portland, Oregon
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Kerry Kuehl
- Department of Sports Medicine, Oregon Health & Science University (OHSU), Portland, Oregon
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Florian Guillot
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Michael C Riddell
- School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada
| | - Ahmad Haidar
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
| | - Joseph El Youssef
- Department of Biomedical Engineering, Oregon Health & Science University (OHSU), Portland, Oregon
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| |
Collapse
|
9
|
Deichmann J, Bachmann S, Burckhardt MA, Szinnai G, Kaltenbach HM. Simulation-Based Evaluation of Treatment Adjustment to Exercise in Type 1 Diabetes. Front Endocrinol (Lausanne) 2021; 12:723812. [PMID: 34489869 PMCID: PMC8417413 DOI: 10.3389/fendo.2021.723812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/26/2021] [Indexed: 01/26/2023] Open
Abstract
Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies in-silico to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our in-silico results in a clinical setting.
Collapse
Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- *Correspondence: Hans-Michael Kaltenbach,
| |
Collapse
|
10
|
Digitale Boluskalkulation nach der Real-Time-Insulinsensitivität. DIABETOL STOFFWECHS 2020. [DOI: 10.1055/a-1199-9222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
11
|
Tyler NS, Jacobs PG. Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3214. [PMID: 32517068 PMCID: PMC7308977 DOI: 10.3390/s20113214] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 12/16/2022]
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
Collapse
Affiliation(s)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| |
Collapse
|
12
|
Fabris C, Kovatchev B. The closed‐loop artificial pancreas in 2020. Artif Organs 2020; 44:671-679. [DOI: 10.1111/aor.13704] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Chiara Fabris
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
| | - Boris Kovatchev
- Center for Diabetes Technology University of Virginia Charlottesville VA USA
| |
Collapse
|