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Turner LV, Marak MC, Gal RL, Calhoun P, Li Z, Jacobs PG, Clements MA, Martin CK, Doyle FJ, Patton SR, Castle JR, Gillingham MB, Beck RW, Rickels MR, Riddell MC. Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort. Diabetologia 2024; 67:1009-1022. [PMID: 38502241 DOI: 10.1007/s00125-024-06127-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/16/2024] [Indexed: 03/21/2024]
Abstract
AIMS/HYPOTHESIS Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).
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Affiliation(s)
- Lauren V Turner
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | | | - Robin L Gal
- Jaeb Center for Health Research, Tampa, FL, USA
| | | | - Zoey Li
- Jaeb Center for Health Research, Tampa, FL, USA
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | | | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | | | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Melanie B Gillingham
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Roy W Beck
- Jaeb Center for Health Research, Tampa, FL, USA
| | - Michael R Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael C Riddell
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada.
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Castle JR, Beck SE. The Importance of Trial Design in Evaluating the Performance of Continuous Glucose Monitoring Systems: Details Matter. J Diabetes Sci Technol 2024; 18:741-742. [PMID: 38390844 DOI: 10.1177/19322968241234683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
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Gillingham MB, Marak MC, Riddell MC, Calhoun P, Gal RL, Patton SR, Jacobs PG, Castle JR, Clements MA, Doyle FJ, Rickels MR, Martin CK. The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes. Curr Dev Nutr 2024; 8:102146. [PMID: 38638557 PMCID: PMC11024491 DOI: 10.1016/j.cdnut.2024.102146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/20/2024] Open
Abstract
Background The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.
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Affiliation(s)
- Melanie B Gillingham
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, United States
| | | | - Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, FL, United States
| | - Robin L Gal
- Jaeb Center for Health Research, Tampa, FL, United States
| | - Susana R Patton
- Center for Healthcare Delivery Science, Nemours Children’s Health, Jacksonville, FL, United States
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, United States
| | - Jessica R Castle
- School of Medicine, Division of Endocrinology, Diabetes and Clinical Nutrition, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, United States
| | - Mark A Clements
- Department of Pediatrics, Endocrine/Diabetes Clinical Research, Children’s Mercy Hospital, Kansas City, MO, United States
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
| | - Michael R Rickels
- Division of Endocrinology, Diabetes & Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, United States
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Li Z, Calhoun P, Rickels MR, Gal RL, Beck RW, Jacobs PG, Clements MA, Patton SR, Castle JR, Martin CK, Gillingham MB, Doyle FJ, Riddell MC. Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative. J Diabetes Sci Technol 2024:19322968241234687. [PMID: 38456512 DOI: 10.1177/19322968241234687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
AIMS To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.
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Affiliation(s)
- Zoey Li
- JAEB Center for Health Research, Tampa, FL, USA
| | | | - Michael R Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin L Gal
- JAEB Center for Health Research, Tampa, FL, USA
| | - Roy W Beck
- JAEB Center for Health Research, Tampa, FL, USA
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | | | | | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA
| | - Melanie B Gillingham
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Young G, Dodier R, Youssef JE, Castle JR, Wilson L, Riddell MC, Jacobs PG. Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models. J Diabetes Sci Technol 2024; 18:324-334. [PMID: 38390855 DOI: 10.1177/19322968231223217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
BACKGROUND Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise. METHODS We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention). RESULTS exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.). CONCLUSIONS The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.
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Affiliation(s)
- Gavin Young
- School of Medicine, Oregon Health & Science University, Portland, OR, USA
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Robert Dodier
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Leah Wilson
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Michael C Riddell
- School of Kinesiology & Health Science and The Muscle Health Research Centre, York University, Toronto, ON, Canada
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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Mosquera-Lopez C, Roquemen-Echeverri V, Tyler NS, Patton SR, Clements MA, Martin CK, Riddell MC, Gal RL, Gillingham M, Wilson LM, Castle JR, Jacobs PG. Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections. J Am Med Inform Assoc 2023; 31:109-118. [PMID: 37812784 PMCID: PMC10746320 DOI: 10.1093/jamia/ocad196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
OBJECTIVE Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. MATERIALS AND METHODS We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. RESULTS The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. DISCUSSION Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. CONCLUSION A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Valentina Roquemen-Echeverri
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
| | - Susana R Patton
- Center for Healthcare Delivery Science, Nemours Children’s Health, Jacksonville, FL 32207, United States
| | - Mark A Clements
- Children’s Mercy Hospital, Kansas City, MO 64111, United States
- Glooko Inc., Palo Alto, CA 94301, United States
| | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA 70808, United States
| | - Michael C Riddell
- Muscle Health Research Centre, York University, Toronto, ON M3J1P3, Canada
| | - Robin L Gal
- Jaeb Center for Health Research, Tampa, FL 33647, United States
| | - Melanie Gillingham
- Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, United States
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Klonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, Kerr D, Ahn D, Peters AL, Umpierrez GE, Seley JJ, Xu NY, Nguyen KT, Simonson G, Agus MSD, Al-Sofiani ME, Armaiz-Pena G, Bailey TS, Basu A, Battelino T, Bekele SY, Benhamou PY, Bequette BW, Blevins T, Breton MD, Castle JR, Chase JG, Chen KY, Choudhary P, Clements MA, Close KL, Cook CB, Danne T, Doyle FJ, Drincic A, Dungan KM, Edelman SV, Ejskjaer N, Espinoza JC, Fleming GA, Forlenza GP, Freckmann G, Galindo RJ, Gomez AM, Gutow HA, Heinemann L, Hirsch IB, Hoang TD, Hovorka R, Jendle JH, Ji L, Joshi SR, Joubert M, Koliwad SK, Lal RA, Lansang MC, Lee WA(A, Leelarathna L, Leiter LA, Lind M, Litchman ML, Mader JK, Mahoney KM, Mankovsky B, Masharani U, Mathioudakis NN, Mayorov A, Messler J, Miller JD, Mohan V, Nichols JH, Nørgaard K, O’Neal DN, Pasquel FJ, Philis-Tsimikas A, Pieber T, Phillip M, Polonsky WH, Pop-Busui R, Rayman G, Rhee EJ, Russell SJ, Shah VN, Sherr JL, Sode K, Spanakis EK, Wake DJ, Waki K, Wallia A, Weinberg ME, Wolpert H, Wright EE, Zilbermint M, Kovatchev B. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol 2023; 17:1226-1242. [PMID: 35348391 PMCID: PMC10563532 DOI: 10.1177/19322968221085273] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
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Affiliation(s)
- David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - Jing Wang
- Florida State University College of Nursing, Tallahassee, FL, USA
| | - David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, MD, USA
| | - Michael A. Kohn
- University of California, San Francisco, San Francisco, CA, USA
| | - Chengdong Li
- Florida State University College of Nursing, Tallahassee, FL, USA
| | | | - David Kerr
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - David Ahn
- Hoag Memorial Hospital Presbyterian, Newport Beach, CA, USA
| | | | | | | | - Nicole Y. Xu
- Diabetes Technology Society, Burlingame, CA, USA
| | | | | | | | | | | | | | - Ananda Basu
- University of Virginia, Charlottesville, VA, USA
| | - Tadej Battelino
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | | | | | | | | | | | | | - Kong Y. Chen
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | | | | | | | | | - Thomas Danne
- Diabetes Center Auf der Bult, Hannover Medical School, Hannover, Germany
| | | | | | | | | | - Niels Ejskjaer
- Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | | | | | | | | | | | | | | | | | - Thanh D. Hoang
- Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | | | - Linong Ji
- Peking University People’s Hospital, Peking University Diabetes Center, Beijing, China
| | | | | | | | | | - M. Cecilia Lansang
- Cleveland Clinic, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Wei-An (Andy) Lee
- LAC + USC Medical Center, Los Angeles County Department of Health Service, Los Angeles, CA, USA
| | - Lalantha Leelarathna
- Manchester University NHS Foundation Trust and The University of Manchester, Manchester, UK
| | - Lawrence A. Leiter
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital and University of Toronto, Toronto, ON, Canada
| | - Marcus Lind
- University of Gothenburg, Gothenburg, Sweden
| | | | | | | | | | - Umesh Masharani
- University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | | | - Viswanathan Mohan
- Dr. Mohan’s Diabetes Specialities Centre, Chennai, India
- Madras Diabetes Research Foundation, Chennai, India
| | | | | | | | | | | | | | - Moshe Phillip
- Institute for Endocrinology and Diabetes, Schneider Children’s Medical Center of Israel and Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | | | | | - Gerry Rayman
- Ipswich Hospital, East Suffolk and North Essex Foundation Trust and University of East Anglia, Ipswich, UK
| | - Eun-Jung Rhee
- Kangbuk Samsung Hospital, Sungkyunkwan University, Seoul, Korea
| | - Steven J. Russell
- Massachusetts General Hospital Diabetes Research Center, Boston, MA, USA
| | - Viral N. Shah
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO, USA
| | | | - Koji Sode
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- North Carolina State University, Raleigh, NC, USA
| | | | | | - Kayo Waki
- The University of Tokyo, Tokyo, Japan
| | | | | | | | | | - Mihail Zilbermint
- Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Community Physicians, Bethesda, MD, USA
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Young GM, Jacobs PG, Tyler NS, Nguyen TTP, Castle JR, Wilson LM, Branigan D, Gabo V, Guillot FH, Riddell MC, El Youssef J. Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes. Am J Physiol Endocrinol Metab 2023; 325:E192-E206. [PMID: 37436961 PMCID: PMC10511169 DOI: 10.1152/ajpendo.00298.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/05/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (Rd) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65-1.43, P < 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001-0.006, P = 0.003). The AUC for Rd rose by 1.26 mM during RE (95% CI: 0.41-2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03-0.04, P < 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in Rd. Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia.NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.
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Affiliation(s)
- Gavin M Young
- Artificial Intelligence for Medical Systems (AIMS) Laboratory, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Laboratory, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States
| | - Nichole S Tyler
- School of Medicine, Oregon Health & Science University, Portland, Oregon, United States
| | - Thanh-Tin P Nguyen
- School of Medicine, Oregon Health & Science University, Portland, Oregon, United States
| | - Jessica R Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, United States
| | - Leah M Wilson
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, United States
| | - Deborah Branigan
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, United States
| | - Virginia Gabo
- School of Medicine, Oregon Health & Science University, Portland, Oregon, United States
| | - Florian H Guillot
- School of Medicine, Oregon Health & Science University, Portland, Oregon, United States
| | - Michael C Riddell
- School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada
| | - Joseph El Youssef
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, United States
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10
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Bergford S, Riddell MC, Jacobs PG, Li Z, Gal RL, Clements MA, Doyle FJ, Martin CK, Patton SR, Castle JR, Gillingham MB, Beck RW, Rickels MR, Calhoun P. The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest. Diabetes Technol Ther 2023; 25:602-611. [PMID: 37294539 PMCID: PMC10623079 DOI: 10.1089/dia.2023.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.
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Affiliation(s)
| | | | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA
| | - Zoey Li
- JAEB Center for Health Research, Tampa, Florida, USA
| | - Robin L. Gal
- JAEB Center for Health Research, Tampa, Florida, USA
| | | | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | | | - Jessica R. Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Melanie B. Gillingham
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA
| | - Roy W. Beck
- JAEB Center for Health Research, Tampa, Florida, USA
| | - Michael R. Rickels
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Peter Calhoun
- JAEB Center for Health Research, Tampa, Florida, USA
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11
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Jacobs PG, Resalat N, Hilts W, Young GM, Leitschuh J, Pinsonault J, El Youssef J, Branigan D, Gabo V, Eom J, Ramsey K, Dodier R, Mosquera-Lopez C, Wilson LM, Castle JR. Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial. Lancet Digit Health 2023; 5:e607-e617. [PMID: 37543512 PMCID: PMC10557965 DOI: 10.1016/s2589-7500(23)00112-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/21/2023] [Accepted: 06/06/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data. METHODS Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403. FINDINGS Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred. INTERPRETATION AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia. FUNDING JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.
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Affiliation(s)
- Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA.
| | - Navid Resalat
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Gavin M Young
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Joseph Leitschuh
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Joseph Pinsonault
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
| | - Jae Eom
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
| | - Katrina Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics and Design Program, Oregon Health and Science University, Portland, OR, USA
| | - Robert Dodier
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR, USA
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR, USA
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12
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Riddell MC, Li Z, Gal RL, Calhoun P, Jacobs PG, Clements MA, Martin CK, Doyle III FJ, Patton SR, Castle JR, Gillingham MB, Beck RW, Rickels MR. Examining the Acute Glycemic Effects of Different Types of Structured Exercise Sessions in Type 1 Diabetes in a Real-World Setting: The Type 1 Diabetes and Exercise Initiative (T1DEXI). Diabetes Care 2023; 46:704-713. [PMID: 36795053 PMCID: PMC10090894 DOI: 10.2337/dc22-1721] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/27/2022] [Indexed: 02/10/2023]
Abstract
OBJECTIVE Maintenance of glycemic control during and after exercise remains a major challenge for individuals with type 1 diabetes. Glycemic responses to exercise may differ by exercise type (aerobic, interval, or resistance), and the effect of activity type on glycemic control after exercise remains unclear. RESEARCH DESIGN AND METHODS The Type 1 Diabetes Exercise Initiative (T1DEXI) was a real-world study of at-home exercise. Adult participants were randomly assigned to complete six structured aerobic, interval, or resistance exercise sessions over 4 weeks. Participants self-reported study and nonstudy exercise, food intake, and insulin dosing (multiple daily injection [MDI] users) using a custom smart phone application and provided pump (pump users), heart rate, and continuous glucose monitoring data. RESULTS A total of 497 adults with type 1 diabetes (mean age ± SD 37 ± 14 years; mean HbA1c ± SD 6.6 ± 0.8% [49 ± 8.7 mmol/mol]) assigned to structured aerobic (n = 162), interval (n = 165), or resistance (n = 170) exercise were analyzed. The mean (± SD) change in glucose during assigned exercise was -18 ± 39, -14 ± 32, and -9 ± 36 mg/dL for aerobic, interval, and resistance, respectively (P < 0.001), with similar results for closed-loop, standard pump, and MDI users. Time in range 70-180 mg/dL (3.9-10.0 mmol/L) was higher during the 24 h after study exercise when compared with days without exercise (mean ± SD 76 ± 20% vs. 70 ± 23%; P < 0.001). CONCLUSIONS Adults with type 1 diabetes experienced the largest drop in glucose level with aerobic exercise, followed by interval and resistance exercise, regardless of insulin delivery modality. Even in adults with well-controlled type 1 diabetes, days with structured exercise sessions contributed to clinically meaningful improvement in glucose time in range but may have slightly increased time below range.
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Affiliation(s)
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, FL
| | | | | | - Peter G. Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | | | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | - Francis J. Doyle III
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | | | - Jessica R. Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR
| | - Melanie B. Gillingham
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR
| | | | - Michael R. Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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13
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Mosquera-Lopez C, Wilson LM, El Youssef J, Hilts W, Leitschuh J, Branigan D, Gabo V, Eom JH, Castle JR, Jacobs PG. Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence. NPJ Digit Med 2023; 6:39. [PMID: 36914699 PMCID: PMC10011368 DOI: 10.1038/s41746-023-00783-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70-180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph Leitschuh
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jae H Eom
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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14
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Phillip M, Nimri R, Bergenstal RM, Barnard-Kelly K, Danne T, Hovorka R, Kovatchev BP, Messer LH, Parkin CG, Ambler-Osborn L, Amiel SA, Bally L, Beck RW, Biester S, Biester T, Blanchette JE, Bosi E, Boughton CK, Breton MD, Brown SA, Buckingham BA, Cai A, Carlson AL, Castle JR, Choudhary P, Close KL, Cobelli C, Criego AB, Davis E, de Beaufort C, de Bock MI, DeSalvo DJ, DeVries JH, Dovc K, Doyle FJ, Ekhlaspour L, Shvalb NF, Forlenza GP, Gallen G, Garg SK, Gershenoff DC, Gonder-Frederick LA, Haidar A, Hartnell S, Heinemann L, Heller S, Hirsch IB, Hood KK, Isaacs D, Klonoff DC, Kordonouri O, Kowalski A, Laffel L, Lawton J, Lal RA, Leelarathna L, Maahs DM, Murphy HR, Nørgaard K, O’Neal D, Oser S, Oser T, Renard E, Riddell MC, Rodbard D, Russell SJ, Schatz DA, Shah VN, Sherr JL, Simonson GD, Wadwa RP, Ward C, Weinzimer SA, Wilmot EG, Battelino T. Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice. Endocr Rev 2023; 44:254-280. [PMID: 36066457 PMCID: PMC9985411 DOI: 10.1210/endrev/bnac022] [Citation(s) in RCA: 79] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/22/2022] [Indexed: 02/06/2023]
Abstract
The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.
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Affiliation(s)
- Moshe Phillip
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
- Sacker Faculty of Medicine, Tel-Aviv University, 39040 Tel-Aviv, Israel
| | - Revital Nimri
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
- Sacker Faculty of Medicine, Tel-Aviv University, 39040 Tel-Aviv, Israel
| | - Richard M Bergenstal
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | | | - Thomas Danne
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Boris P Kovatchev
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Laurel H Messer
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | | | | | - Lia Bally
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Roy W Beck
- Jaeb Center for Health Research Foundation, Inc., Tampa, FL 33647, USA
| | - Sarah Biester
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Torben Biester
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | - Julia E Blanchette
- College of Nursing, University of Utah, Salt Lake City, UT 84112, USA
- Center for Diabetes and Obesity, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
| | - Emanuele Bosi
- Diabetes Research Institute, IRCCS San Raffaele Hospital and San Raffaele Vita Salute University, Milan, Italy
| | - Charlotte K Boughton
- Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, UK
| | - Marc D Breton
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Sue A Brown
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Division of Endocrinology, University of Virginia, Charlottesville, VA 22903, USA
| | - Bruce A Buckingham
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA 94304, USA
| | - Albert Cai
- The diaTribe Foundation/Close Concerns, San Diego, CA 94117, USA
| | - Anders L Carlson
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Pratik Choudhary
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kelly L Close
- The diaTribe Foundation/Close Concerns, San Diego, CA 94117, USA
| | - Claudio Cobelli
- Department of Woman and Child’s Health, University of Padova, Padova, Italy
| | - Amy B Criego
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Elizabeth Davis
- Telethon Kids Institute, University of Western Australia, Perth Children’s Hospital, Perth, Australia
| | - Carine de Beaufort
- Diabetes & Endocrine Care Clinique Pédiatrique DECCP/Centre Hospitalier Luxembourg, and Faculty of Sciences, Technology and Medicine, University of Luxembourg, Esch sur Alzette, GD Luxembourg/Department of Paediatrics, UZ-VUB, Brussels, Belgium
| | - Martin I de Bock
- Department of Paediatrics, University of Otago, Christchurch, New Zealand
| | - Daniel J DeSalvo
- Division of Pediatric Diabetes and Endocrinology, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX 77598, USA
| | - J Hans DeVries
- Amsterdam UMC, University of Amsterdam, Internal Medicine, Amsterdam, The Netherlands
| | - Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children’s Hospital, Ljubljana, Slovenia, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Laya Ekhlaspour
- Lucile Packard Children’s Hospital—Pediatric Endocrinology, Stanford University School of Medicine, Palo Alto, CA 94304, USA
| | - Naama Fisch Shvalb
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, 49202 Petah Tikva, Israel
| | - Gregory P Forlenza
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Satish K Garg
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Dana C Gershenoff
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - Linda A Gonder-Frederick
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Ahmad Haidar
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Sara Hartnell
- Wolfson Diabetes and Endocrine Clinic, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - Simon Heller
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Irl B Hirsch
- Department of Medicine, University of Washington Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Korey K Hood
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Diana Isaacs
- Cleveland Clinic, Endocrinology and Metabolism Institute, Cleveland, OH 44106, USA
| | - David C Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA 94010, USA
| | - Olga Kordonouri
- AUF DER BULT, Diabetes-Center for Children and Adolescents, Endocrinology and General Paediatrics, Hannover, Germany
| | | | - Lori Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Julia Lawton
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Rayhan A Lal
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lalantha Leelarathna
- Manchester University Hospitals NHS Foundation Trust/University of Manchester, Manchester, UK
| | - David M Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA 94304, USA
| | - Helen R Murphy
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Kirsten Nørgaard
- Steno Diabetes Center Copenhagen and Department of Clinical Medicine, University of Copenhagen, Gentofte, Denmark
| | - David O’Neal
- Department of Medicine and Department of Endocrinology, St Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Sean Oser
- Department of Family Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Tamara Oser
- Department of Family Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, and Institute of Functional Genomics, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Michael C Riddell
- School of Kinesiology & Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - David Rodbard
- Biomedical Informatics Consultants LLC, Potomac, MD, USA
| | - Steven J Russell
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Desmond A Schatz
- Department of Pediatrics, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL 02114, USA
| | - Viral N Shah
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jennifer L Sherr
- Department of Pediatrics, Yale University School of Medicine, Pediatric Endocrinology, New Haven, CT 06511, USA
| | - Gregg D Simonson
- International Diabetes Center, HealthPartners Institute, Minneapolis, MN 55416, USA
| | - R Paul Wadwa
- Barbara Davis Center for Diabetes, University of Colorado Denver—Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Candice Ward
- Institute of Metabolic Science, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Stuart A Weinzimer
- Department of Pediatrics, Yale University School of Medicine, Pediatric Endocrinology, New Haven, CT 06511, USA
| | - Emma G Wilmot
- Department of Diabetes & Endocrinology, University Hospitals of Derby and Burton NHS Trust, Derby, UK
- Division of Medical Sciences and Graduate Entry Medicine, University of Nottingham, Nottingham, England, UK
| | - Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, UMC - University Children’s Hospital, Ljubljana, Slovenia, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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15
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Kahn SE, Anderson CAM, Buse JB, Selvin E, Angell SY, Aroda VR, Castle JR, Cheng AYY, Danne T, Echouffo-Tcheugui JB, Florez JC, Gadgil MD, Gastaldelli A, Green JB, Jastreboff AM, Kanaya AM, Kandula NR, Kovesdy CP, Laiteerapong N, Nadeau KJ, Pop-Busui R, Powe CE, Rebholz CM, Rickels MR, Sattar N, Shaw JE, Sims EK, Utzschneider KM, Vella A, Zhang C. "The Times They Are A-Changin'" at Diabetes Care. Diabetes Care 2023; 46:3-5. [PMID: 36548704 DOI: 10.2337/dci22-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Cheryl A M Anderson
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA
| | - John B Buse
- Division of Endocrinology, Department of Medicine, University of North Carolina, Chapel Hill, NC
| | - Elizabeth Selvin
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Sonia Y Angell
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Vanita R Aroda
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jessica R Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR
| | - Alice Y Y Cheng
- Division of Endocrinology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thomas Danne
- Diabetes Center and Clinical Research, Children's Hospital Auf der Bult, Hannover Medical School, Hannover, Germany
| | - Justin B Echouffo-Tcheugui
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, MD
| | - Jose C Florez
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Meghana D Gadgil
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Amalia Gastaldelli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Jennifer B Green
- Division of Endocrinology and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Ania M Jastreboff
- Division of Endocrinology and Metabolism, Department of Internal Medicine, and Division of Pediatric Endocrinology, Department of Pediatrics, Yale University School of Medicine, New Haven, CT
| | - Alka M Kanaya
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Namratha R Kandula
- Division of General Internal Medicine, Department of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Csaba P Kovesdy
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN
| | - Neda Laiteerapong
- Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, IL
| | - Kristen J Nadeau
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO
| | - Rodica Pop-Busui
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Camille E Powe
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA
| | - Casey M Rebholz
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Michael R Rickels
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, Scotland, U.K
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Emily K Sims
- Center for Diabetes and Metabolic Diseases, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
| | - Kristina M Utzschneider
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Adrian Vella
- Division of Diabetes, Endocrinology and Metabolism, Mayo Clinic, Rochester, MN
| | - Cuilin Zhang
- Global Center for Asian Women's Health and Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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16
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Castle JR, Wilson LM, Tyler NS, Espinoza AZ, Mosquera-Lopez CM, Kushner T, Young GM, Pinsonault J, Dodier RH, Hilts WW, Oganessian SM, Branigan DL, Gabo VB, Eom JH, Ramsey K, Youssef JE, Cafazzo JA, Winters-Stone K, Jacobs PG. Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections. Diabetes Technol Ther 2022; 24:892-897. [PMID: 35920839 PMCID: PMC9700374 DOI: 10.1089/dia.2022.0252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645.
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Affiliation(s)
- Jessica R. Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Leah M. Wilson
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Nichole S. Tyler
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Alejandro Z. Espinoza
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Clara M. Mosquera-Lopez
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Taisa Kushner
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Gavin M. Young
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph Pinsonault
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Robert H. Dodier
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Wade W. Hilts
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Sos M. Oganessian
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Deborah L. Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Virginia B. Gabo
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jae H. Eom
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Katrina Ramsey
- Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph El Youssef
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph A. Cafazzo
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, Canada
- Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Kerri Winters-Stone
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter G. Jacobs
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
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17
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Tyler NS, Mosquera-Lopez C, Young GM, El Youssef J, Castle JR, Jacobs PG. Quantifying the impact of physical activity on future glucose trends using machine learning. iScience 2022; 25:103888. [PMID: 35252806 PMCID: PMC8889374 DOI: 10.1016/j.isci.2022.103888] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/19/2021] [Accepted: 02/04/2022] [Indexed: 01/21/2023] Open
Abstract
Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.
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Affiliation(s)
- Nichole S. Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA,Corresponding author
| | - Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
| | - Gavin M. Young
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology Oregon Health & Science University Portland, OR 97239, USA
| | - Jessica R. Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology Oregon Health & Science University Portland, OR 97239, USA
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering Oregon Health & Science University Portland, OR 97232, USA
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18
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Wilson LM, Jacobs PG, Riddell MC, Zaharieva DP, Castle JR. Opportunities and challenges in closed-loop systems in type 1 diabetes. Lancet Diabetes Endocrinol 2022; 10:6-8. [PMID: 34762835 PMCID: PMC9255645 DOI: 10.1016/s2213-8587(21)00289-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 12/24/2022]
Affiliation(s)
- Leah M Wilson
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Michael C Riddell
- Muscle Health Research Centre, School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | | | - Jessica R Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA
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19
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Riddell MC, Li Z, Beck RW, Gal RL, Jacobs PG, Castle JR, Gillingham MB, Clements M, Patton SR, Dassau E, Doyle III FJ, Martin CK, Calhoun P, Rickels MR. More Time in Glucose Range During Exercise Days than Sedentary Days in Adults Living with Type 1 Diabetes. Diabetes Technol Ther 2021; 23:376-383. [PMID: 33259257 PMCID: PMC8080930 DOI: 10.1089/dia.2020.0495] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Objective: This study analysis was designed to examine the 24-h effects of exercise on glycemic control as measured by continuous glucose monitoring (CGM). Methods: Individuals with type 1 diabetes (ages: 15-68 years; hemoglobin A1c: 7.5% ± 1.5% [mean ± standard deviation (SD)]) were randomly assigned to complete twice-weekly aerobic, high-intensity interval, or resistance-based exercise sessions in addition to their personal exercise sessions for a period of 4 weeks. Exercise was tracked with wearables and glucose concentrations assessed using CGM. An exercise day was defined as a 24-h period after the end of exercise, while a sedentary day was defined as any 24-h period with no recorded exercise ≥10 min long. Sedentary days start at least 24 h after the end of exercise. Results: Mean glucose was lower (150 ± 45 vs. 166 ± 49 mg/dL, P = 0.01), % time in range [70-180 mg/dL] higher (62% ± 23% vs. 56% ± 25%, P = 0.03), % time >180 mg/dL lower (28% ± 23% vs. 37% ± 26%, P = 0.01), and % time <70 mg/dL higher (9.3% ± 11.0% vs. 7.1% ± 9.1%, P = 0.04) on exercise days compared with sedentary days. Glucose variability and % time <54 mg/dL did not differ significantly between exercise and sedentary days. No significant differences in glucose control by exercise type were observed. Conclusion: Participants had lower 24-h mean glucose levels and a greater time in range on exercise days compared with sedentary days, with mode of exercise affecting glycemia similarly. In summary, this study offers data supporting frequency of exercise as a method of facilitating glucose control but does not suggest an effect for mode of exercise.
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Affiliation(s)
- Michael C. Riddell
- School of Kinesiology and Health Science, York University, Toronto, Canada
- Address correspondence to, Michael C. Riddell, PhD, School of Kinesiology and Health Science, York University, 4700 Keele Street, Toronto M3J1P3, Ontario, Canada
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Robin L. Gal
- Jaeb Center for Health Research, Tampa, Florida, USA
| | | | | | | | - Mark Clements
- Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Susana R. Patton
- Center for Healthcare Delivery Science, Nemours Children's Specialty Clinic, Jacksonville, Florida, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Francis J. Doyle III
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Michael R. Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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20
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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: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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21
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Gillingham MB, Li Z, Beck RW, Calhoun P, Castle JR, Clements M, Dassau E, Doyle FJ, Gal RL, Jacobs P, Patton SR, Rickels MR, Riddell M, Martin CK. Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App. Diabetes Technol Ther 2021; 23:85-94. [PMID: 32833544 PMCID: PMC7868577 DOI: 10.1089/dia.2020.0357] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.
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Affiliation(s)
- Melanie B. Gillingham
- Oregon Health and Sciences University, Portland, Oregon, USA
- Address correspondence to: Melanie B. Gillingham, PhD, RD, Department of Molecular and Medical Genetics, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mailcode L103, Portland, OR 97239, USA
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, Florida, USA
| | | | - Mark Clements
- Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Robin L. Gal
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Jacobs
- Oregon Health and Sciences University, Portland, Oregon, USA
| | | | - Michael R. Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
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22
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Abstract
Insulin therapy has advanced remarkably over the past few decades. Ultra-rapid-acting and ultra-long-acting insulin analogs are now commercially available. Many additional insulin formulations are in development. This review outlines recent advances in insulin therapy and novel therapies in development.
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Affiliation(s)
- Leah M. Wilson
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jessica R. Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
- Address correspondence to: Jessica R. Castle, MD, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, L607, Portland, OR 97239-3098, USA
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23
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Mosquera-Lopez C, Dodier R, Tyler NS, Wilson LM, El Youssef J, Castle JR, Jacobs PG. Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis. Diabetes Technol Ther 2020; 22:801-811. [PMID: 32297795 PMCID: PMC7698985 DOI: 10.1089/dia.2019.0458] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia. Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico. Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3-99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75-0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9-10 mmol/L). Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
- Clara Mosquera-Lopez, PhD, Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR 97239, USA
| | - Robert Dodier
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Nichole S. Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Leah M. Wilson
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Joseph El Youssef
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Jessica R. Castle
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA
- Address correspondence to: Peter G. Jacobs, PhD, Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR 97239, USA
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Wilson LM, Jacobs PG, Ramsey KL, Resalat N, Reddy R, Branigan D, Leitschuh J, Gabo V, Guillot F, Senf B, El Youssef J, Steineck IIK, Tyler NS, Castle JR. Dual-Hormone Closed-Loop System Using a Liquid Stable Glucagon Formulation Versus Insulin-Only Closed-Loop System Compared With a Predictive Low Glucose Suspend System: An Open-Label, Outpatient, Single-Center, Crossover, Randomized Controlled Trial. Diabetes Care 2020; 43:2721-2729. [PMID: 32907828 DOI: 10.2337/dc19-2267] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 08/16/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia (<70 mg/dL) from the start of in-clinic aerobic exercise (45 min at 60% VO2max) to 4 h after. RESULTS DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0-4.2], SH 8.3% [0.0-12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study. CONCLUSIONS The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.
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Affiliation(s)
- Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Katrina L Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics and Design Program, Oregon Health & Science University & Portland State University School of Public Health, Portland, OR
| | - Navid Resalat
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Ravi Reddy
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Joseph Leitschuh
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Florian Guillot
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Brian Senf
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR.,Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | | | - Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
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Abstract
BACKGROUND Decision support smartphone applications integrated with continuous glucose monitors may improve glycemic control in type 1 diabetes (T1D). We conducted a survey to understand trends and needs of potential users to inform the design of decision support technology. METHODS A 70-question survey was distributed October 2017 through May 2018 to adults aged 18-80 with T1D from a specialty clinic and T1D Exchange online health community (myglu.org). The survey responses were used to evaluate potential features of a diabetes decision support tool by Likert scale and open responses. RESULTS There were 1542 responses (mean age 46.1 years [SD 15.2], mean duration of diabetes 26.5 years [SD 15.8]). The majority (84.2%) have never used an app to manage diabetes; however, a large majority (77.8%) expressed interest in using a decision support app. The ability to predict and avoid hypoglycemia was the most important feature identified by a majority of the respondents, with 91% of respondents indicating the highest level of interest in these features. The task that respondents find most difficult was management of glucose during exercise (only 47% of participants were confident in glucose management during exercise). The respondents also highly desired features that help manage glucose during exercise (85% of respondents were interested). The responses identified integration and interoperability with peripheral devices/apps and customization of alerts as important. Responses from participants were generally consistent across stratified categories. CONCLUSIONS These results provide valuable insight into patient needs in decision support applications for management of T1D.
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Affiliation(s)
- Leah M. Wilson
- Department of Medicine, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
- Leah M. Wilson, MD, Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, L607, Portland, OR 97239-3098, USA.
| | - Nichole Tyler
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Peter G. Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Virginia Gabo
- Department of Medicine, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Brian Senf
- Department of Medicine, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Ravi Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R. Castle
- Department of Medicine, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
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Guillot FH, Jacobs PG, Wilson LM, Youssef JE, Gabo VB, Branigan DL, Tyler NS, Ramsey K, Riddell MC, Castle JR. Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes. Biosensors (Basel) 2020; 10:bios10100138. [PMID: 33003524 PMCID: PMC7600074 DOI: 10.3390/bios10100138] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/23/2020] [Accepted: 09/25/2020] [Indexed: 12/11/2022]
Abstract
The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6–9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: −6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: −8.3 to −2.9%, HIIT MARD: 12.1 to 16.8% and MRD: −14.3 to −9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.
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Affiliation(s)
- Florian H. Guillot
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
- Correspondence:
| | - Leah M. Wilson
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
| | - Joseph El Youssef
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Virginia B. Gabo
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
| | - Deborah L. Branigan
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
| | - Nichole S. Tyler
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Katrina Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics & Design Program, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Michael C. Riddell
- Muscle Health Research Centre, School of Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, Canada;
| | - Jessica R. Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA; (F.H.G.); (L.M.W.); (J.E.Y.); (V.B.G.); (D.L.B.); (J.R.C.)
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Affiliation(s)
- Jessica R. Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
- Jessica R. Castle, MD, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, L607, Portland, OR 97239-3098, USA.
| | - Lolis Rocha
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
| | - Andrew Ahmann
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR, USA
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Tyler NS, Mosquera-Lopez CM, Wilson LM, Dodier RH, Branigan DL, Gabo VB, Guillot FH, Hilts WW, El Youssef J, Castle JR, Jacobs PG. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab 2020; 2:612-619. [PMID: 32694787 PMCID: PMC7384292 DOI: 10.1038/s42255-020-0212-y] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 04/23/2020] [Indexed: 11/08/2022]
Abstract
Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl-1) and hyperglycaemia (>180 mg dl-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform3 to generate over 50,000 glucose observations to train a k-nearest neighbours4 decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)5. In silico3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.
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Affiliation(s)
- Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
| | - Clara M Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Robert H Dodier
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Deborah L Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia B Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Florian H Guillot
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade W Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
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Abstract
Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.
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Affiliation(s)
- Melanie Jackson
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - Jessica R. Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
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Abstract
Background: People with diabetes-related ulcers may benefit from hyperbaric oxygen (HBO2) therapy and from continuous glucose monitors (CGM). Although blood glucose (BG) meters based on glucose oxidase (GO) report erroneously low values at high pO2, BG meters based on glucose dehydrogenase (GD) do not. We therefore examined the performance of a GO-based CGM system in comparison to GO-based and GD-based BG systems in normobaric air (NBAir), hyperbaric air (HBAir), and HBO2 environments. Materials and Methods: Twenty-six volunteers without diabetes mellitus (DM) wore Dexcom G6 CGM systems and provided periodic blood samples before, during, and after a standard HBO2 treatment consisting of three 30-min intervals of HBO2 separated by two 5-min intervals of HBAir. Accuracy of the CGM and GO-based BG meter were assessed by comparisons with the GD-based values. Results: The mean absolute relative difference for the CGM system was 15.96% and for the GO-based meter was 8.52%. Compared to NBAir, HBO2 exposure resulted in significantly higher CGM values (+3.76 mg/dL, P < 0.001) and significantly lower GO-based meter values (-10.38 mg/dL, P < 0.001). Pre-HBO2 and post-HBO2 values obtained in NBAir were also significantly different when measured by CGM (+4.13 mg/dL, P = 0.015) or the GO-based meter (-9.04 mg/dL, P < 0.001). Conclusions: In volunteers without DM, HBO2 exposure results in statistically significant differences in glucose measurements obtained with GO-based devices, but not a GD-based device. Standard HBO2 treatment results in statistically significant effects on glucose concentrations. These differences are of unlikely clinical significance.
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Affiliation(s)
- Enoch Huang
- Legacy Emanuel Medical Center, Harold Schnitzer Diabetes Health Center, Portland, Oregon
- Address correspondence to: Enoch Huang, MD, Legacy Emanuel Medical Center, 3001 N. Gantenbein Avenue, Portland, OR 97227
| | - Shaban Demirel
- Legacy Research Institute, Harold Schnitzer Diabetes Health Center, Portland, Oregon
| | - Chanelle Bliss
- Legacy Emanuel Medical Center, Harold Schnitzer Diabetes Health Center, Portland, Oregon
| | - Davut Savaser
- Legacy Emanuel Medical Center, Harold Schnitzer Diabetes Health Center, Portland, Oregon
| | - Jessica R. Castle
- Oregon Health and Science University, Harold Schnitzer Diabetes Health Center, Portland, Oregon
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Abstract
Treatment of type 1 diabetes with exogenous insulin often results in unpredictable daily glucose variability and hypoglycemia, which can be dangerous. Automated insulin delivery systems can improve glucose control while reducing burden for people with diabetes. One approach to improve treatment outcomes is to incorporate the counter-regulatory hormone glucagon into the automated delivery system to help prevent the hypoglycemia that can be induced by the slow pharmacodynamics of insulin action. This article explores the advantages and disadvantages of incorporating glucagon into dual-hormone automated hormone delivery systems.
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Affiliation(s)
- Leah M Wilson
- Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, Harold Schnitzer Diabetes Health Center, 3181 Southwest Sam Jackson Park Road, L607, Portland, OR 97239-3098, USA.
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Mail Code: CH13B, 3303 Southwest Bond Avenue, Portland, OR 97239, USA
| | - Jessica R Castle
- Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health & Science University, Harold Schnitzer Diabetes Health Center, 3181 Southwest Sam Jackson Park Road, L607, Portland, OR 97239-3098, USA
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Resalat N, Hilts W, Youssef JE, Tyler N, Castle JR, Jacobs PG. Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs. J Diabetes Sci Technol 2019; 13:1044-1053. [PMID: 31595784 PMCID: PMC6835177 DOI: 10.1177/1932296819881467] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals' different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions. METHODS A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient's insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control. RESULTS Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%. CONCLUSION Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.
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Affiliation(s)
- Navid Resalat
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Wade Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Artificial Intelligence for Medical Systems (AIMS) Lab, 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
| | - Nichole Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, 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
| | - Peter G. Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, 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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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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Affiliation(s)
- Jessica R Castle
- 1 Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon
| | - David Rodbard
- 2 Biomedical Informatics Consultants LLC, Potomac, Maryland
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Affiliation(s)
- Jessica R Castle
- 1 Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
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Reddy RK, Pooni R, Zaharieva DP, Senf B, El Youssef J, Dassau E, Doyle Iii FJ, Clements MA, Rickels MR, Patton SR, Castle JR, Riddell MC, Jacobs PG. Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study. JMIR Mhealth Uhealth 2018; 6:e10338. [PMID: 30530451 PMCID: PMC6305876 DOI: 10.2196/10338] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 06/17/2018] [Accepted: 09/05/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. OBJECTIVE The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. METHODS We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. RESULTS Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of -3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of -4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: -11.4% [SD 35.7]; Garmin: -14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: -1.7% [SD 11.5]; Garmin: -0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: -19.3% [SD 28.9], Garmin: -1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. CONCLUSIONS Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.
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Affiliation(s)
- Ravi Kondama Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, United States
| | - Rubin Pooni
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | - Dessi P Zaharieva
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | - Brian Senf
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, United States
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, United States
| | - Eyal Dassau
- Harvard John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
| | - Francis J Doyle Iii
- Harvard John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
| | - Mark A Clements
- Children's Mercy Kansas City, Kansas City, MO, United States
| | - Michael R Rickels
- Institute for Diabetes, Obesity & Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Susana R Patton
- Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS, United States
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, United States
| | - Michael C Riddell
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, United States
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Castle JR. Is Mini-Dose Glucagon the Answer to Preventing Exercise-Related Dysglycemia? Diabetes Care 2018; 41:1842-1843. [PMID: 30135198 DOI: 10.2337/dci18-0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Jessica R Castle
- Division of Endocrinology, Diabetes & Clinical Nutrition, Department of Medicine, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR
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Reddy R, Wittenberg A, Castle JR, El Youssef J, Winters-Stone K, Gillingham M, Jacobs PG. Effect of Aerobic and Resistance Exercise on Glycemic Control in Adults With Type 1 Diabetes. Can J Diabetes 2018; 43:406-414.e1. [PMID: 30414785 DOI: 10.1016/j.jcjd.2018.08.193] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/02/2018] [Accepted: 08/13/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVES Physical exercise is recommended for individuals with type 1 diabetes, yet the effects of exercise on glycemic control are not well established. We evaluated the impact of different modes of exercise on glycemic control in people with type 1 diabetes. METHODS In a 3-week randomized crossover trial, 10 adults with type 1 diabetes (4 men and 6 women, aged 33±6 years; duration of diabetes, 18±10 years; glycated hemoglobin level, 7.4%±1%) were assigned to 3 weeks of intervention: aerobic exercise (treadmill at 60% of maximum volume of oxygen utilization), resistance training (8 to 12 repetitions of 5 upper and lower body exercises at 60% to 80% of 1 repetition maximum) or no exercise (control). During each exercise week, participants completed 2 monitored 45 min exercise sessions. For each week of the study, we analyzed participants' insulin pump data, sensor glucose data and meal intake using a custom smart-phone application. The primary outcome was the percentage of time in range (glucose >3.9 mmol/L and ≤10 mmol/L) for the 24 h after each bout of exercise or rest during the control week. The study was registered on ClinicalTrials.gov (NCT:02687893). RESULTS Aerobic exercise caused a mean glucose reduction during exercise of 3.94±2.67 mmol/L, whereas the reduction during resistance training was 1.33±1.78 mmol/L (p=0.007). The mean percentage time in range for the 24 h after resistance training was significantly greater than that during the control period (70% vs. 56%, p=0.013) but not after aerobic exercise (60%). CONCLUSIONS The results indicate that when various confounders are considered, resistance training could improve glycemic control in this population.
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Affiliation(s)
- Ravi Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | - Amanda Wittenberg
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA
| | - Jessica R Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph El Youssef
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Kerri Winters-Stone
- School of Nursing, Human Performance Laboratory, Oregon Health and Science University, Portland, Oregon, USA
| | - Melanie Gillingham
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
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Castle JR, El Youssef J, Wilson LM, Reddy R, Resalat N, Branigan D, Ramsey K, Leitschuh J, Rajhbeharrysingh U, Senf B, Sugerman SM, Gabo V, Jacobs PG. Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors. Diabetes Care 2018; 41:1471-1477. [PMID: 29752345 PMCID: PMC6014543 DOI: 10.2337/dc18-0228] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/13/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy. RESEARCH DESIGN AND METHODS Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia (<70 mg/dL) and in a target range (70-180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal. RESULTS The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44). CONCLUSIONS The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.
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Affiliation(s)
- Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR.,Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Ravi Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Navid Resalat
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Deborah Branigan
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Katrina Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics & Design Program, Oregon Health & Science University, Portland, OR
| | - Joseph Leitschuh
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Uma Rajhbeharrysingh
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Brian Senf
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Samuel M Sugerman
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Virginia Gabo
- Harold Schnitzer Diabetes Health Center, Division of Endocrinology, Oregon Health & Science University, Portland, OR
| | - Peter G Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
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Abstract
Glycemic control is the mainstay of preventing diabetes complications at the expense of increased risk of hypoglycemia. Severe hypoglycemia negatively impacts the quality of life of patients with type 1 diabetes and can lead to morbidity and mortality. Currently available glucagon emergency kits are effective at treating hypoglycemia when correctly used, however use is complicated especially by untrained persons. Better formulations and devices for glucagon treatment of hypoglycemia are needed, specifically stable liquid glucagon. Out of the scope of this review, other potential uses of stable liquid glucagon include congenital hyperinsulinism, post-bariatric surgery hypoglycemia, and insulinoma induced hypoglycemia. In the 35 years since Food and Drug Administration (FDA) approval of the first liquid stable human recombinant insulin, we continue to wait for the glucagon counterpart. For mild hypoglycemia, a commercially available liquid stable glucagon would enable more widespread implementation of mini-dose glucagon use as well as glucagon in dual hormone closed-loop systems. This review focuses on the current and upcoming pharmaceutical uses of glucagon in the treatment of type 1 diabetes with an outlook on stable liquid glucagon preparations that will hopefully be available for use in patients in the near future.
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Affiliation(s)
- Leah M. Wilson
- Division of Endocrinology, Diabetes and
Clinical Nutrition, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R. Castle
- Division of Endocrinology, Diabetes and
Clinical Nutrition, Oregon Health & Science University, Portland, OR, USA
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Castle JR. Advances in Subcutaneous Glucose Sensing. Diabetes Technol Ther 2017; 19:441-442. [PMID: 28767273 PMCID: PMC5568179 DOI: 10.1089/dia.2017.0233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Jessica R Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University , Portland, Oregon
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Abstract
Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.
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Affiliation(s)
- Jessica R. Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon
| | - J. Hans DeVries
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
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Ward WK, Heinrich G, Breen M, Benware S, Vollum N, Morris K, Knutsen C, Kowalski JD, Campbell S, Biehler J, Vreeke MS, Vanderwerf SM, Castle JR, Cargill RS. An Amperometric Glucose Sensor Integrated into an Insulin Delivery Cannula: In Vitro and In Vivo Evaluation. Diabetes Technol Ther 2017; 19:226-236. [PMID: 28221814 PMCID: PMC5399735 DOI: 10.1089/dia.2016.0407] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Labeling prohibits delivery of insulin at the site of subcutaneous continuous glucose monitoring (CGM). Integration of the sensing and insulin delivery functions into a single device would likely increase the usage of CGM in persons with type 1 diabetes. METHODS To understand the nature of such interference, we measured glucose at the site of bolus insulin delivery in swine using a flexible electrode strip that was laminated to the outer wall of an insulin delivery cannula. In terms of sensing design, we compared H2O2-measuring sensors biased at 600 mV with redox mediator-type sensors biased at 175 mV. RESULTS In H2O2-measuring sensors, but not in sensors with redox-mediated chemistry, a spurious rise in current was seen after insulin lis-pro boluses. This prolonged artifact was accompanied by electrode poisoning. In redox-mediated sensors, the patterns of sensor signals acquired during delivery of saline and without any liquid delivery were similar to those acquired during insulin delivery. CONCLUSION Considering in vitro and in vivo findings together, it became clear that the mechanism of interference is the oxidation, at high bias potentials, of phenolic preservatives present in insulin formulations. This effect can be avoided by the use of redox mediator chemistry using a low bias potential.
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Affiliation(s)
| | | | - Matthew Breen
- Pacific Diabetes Technologies, Inc., Portland, Oregon
| | | | - Nicole Vollum
- Pacific Diabetes Technologies, Inc., Portland, Oregon
| | | | - Chad Knutsen
- Pacific Diabetes Technologies, Inc., Portland, Oregon
| | | | | | - Jerry Biehler
- Pacific Diabetes Technologies, Inc., Portland, Oregon
| | | | | | - Jessica R. Castle
- Division of Endocrinology, Diabetes and Nutrition, Oregon Health and Science University, Portland, Oregon
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Affiliation(s)
- Jessica R Castle
- Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University , Portland, Oregon
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Jacobs PG, El Youssef J, Reddy R, Resalat N, Branigan D, Condon J, Preiser N, Ramsey K, Jones M, Edwards C, Kuehl K, Leitschuh J, Rajhbeharrysingh U, Castle JR. Randomized trial of a dual-hormone artificial pancreas with dosing adjustment during exercise compared with no adjustment and sensor-augmented pump therapy. Diabetes Obes Metab 2016; 18:1110-1119. [PMID: 27333970 PMCID: PMC5056819 DOI: 10.1111/dom.12707] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 06/08/2016] [Accepted: 06/12/2016] [Indexed: 11/30/2022]
Abstract
AIMS To test whether adjusting insulin and glucagon in response to exercise within a dual-hormone artificial pancreas (AP) reduces exercise-related hypoglycaemia. MATERIALS AND METHODS In random order, 21 adults with type 1 diabetes (T1D) underwent three 22-hour experimental sessions: AP with exercise dosing adjustment (APX); AP with no exercise dosing adjustment (APN); and sensor-augmented pump (SAP) therapy. After an overnight stay and 2 hours after breakfast, participants exercised for 45 minutes at 60% of their maximum heart rate, with no snack given before exercise. During APX, insulin was decreased and glucagon was increased at exercise onset, while during SAP therapy, subjects could adjust dosing before exercise. The two primary outcomes were percentage of time spent in hypoglycaemia (<3.9 mmol/L) and percentage of time spent in euglycaemia (3.9-10 mmol/L) from the start of exercise to the end of the study. RESULTS The mean (95% confidence interval) times spent in hypoglycaemia (<3.9 mmol/L) after the start of exercise were 0.3% (-0.1, 0.7) for APX, 3.1% (0.8, 5.3) for APN, and 0.8% (0.1, 1.4) for SAP therapy. There was an absolute difference of 2.8% less time spent in hypoglycaemia for APX versus APN (p = .001) and 0.5% less time spent in hypoglycaemia for APX versus SAP therapy (p = .16). Mean time spent in euglycaemia was similar across the different sessions. CONCLUSIONS Adjusting insulin and glucagon delivery at exercise onset within a dual-hormone AP significantly reduces hypoglycaemia compared with no adjustment and performs similarly to SAP therapy when insulin is adjusted before exercise.
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Affiliation(s)
- P G Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, Portland.
| | - J El Youssef
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland
| | - R Reddy
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - N Resalat
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - D Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland
| | - J Condon
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - N Preiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - K Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics and Design Program, Oregon Health and Science University, Portland
| | - M Jones
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland
| | - C Edwards
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland
| | - K Kuehl
- Department of Medicine, Division of Health Promotion and Sports Medicine, Human Performance Laboratory, Oregon Health and Science University, Portland
| | - J Leitschuh
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - U Rajhbeharrysingh
- Department of Biomedical Engineering, Oregon Health and Science University, Portland
| | - J R Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland
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Castle JR, Youssef JE, Branigan D, Newswanger B, Strange P, Cummins M, Shi L, Prestrelski S. Comparative Pharmacokinetic/Pharmacodynamic Study of Liquid Stable Glucagon Versus Lyophilized Glucagon in Type 1 Diabetes Subjects. J Diabetes Sci Technol 2016; 10:1101-7. [PMID: 27325390 PMCID: PMC5032962 DOI: 10.1177/1932296816653141] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND There is currently no stable liquid form of glucagon commercially available. The aim of this study is to assess the speed of absorption and onset of action of G-Pump™ glucagon at 3 doses as compared to GlucaGen®, all delivered subcutaneously via an OmniPod®. METHODS Nineteen adult subjects with type 1 diabetes participated in this Phase 2, randomized, double-blind, cross-over, pharmacokinetic/pharmacodynamic study. Subjects were given 0.3, 1.2, and 2.0 µg/kg each of G-Pump glucagon and GlucaGen via an OmniPod. RESULTS G-Pump glucagon effectively increased blood glucose levels in a dose-dependent fashion with a glucose Cmax of 183, 200, and 210 mg/dL at doses of 0.3, 1.2, and 2.0 µg/kg, respectively (P = ns vs GlucaGen). Mean increases in blood glucose from baseline were 29.2, 52.9, and 77.7 mg/dL for G-Pump doses of 0.3, 1.2, and 2.0 µg/kg, respectively. There were no statistically significant differences between treatments in the glucose T50%-early or glucagon T50%-early with one exception. The glucagon T50%-early was greater following G-Pump treatment at the 2.0 μg/kg dose (13.9 ± 4.7 min) compared with GlucaGen treatment at the 2.0 μg/kg dose (11.0 ± 3.1 min, P = .018). There was more pain and erythema at the infusion site with G-Pump as compared to GlucaGen. No serious adverse events were reported, and no unexpected safety issues were observed. CONCLUSIONS G-Pump glucagon is a novel, stable glucagon formulation with similar PK/PD properties as GlucaGen, but was associated with more pain and infusion site reactions as the dose increased, as compared to GlucaGen.
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Affiliation(s)
- 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 Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR, USA
| | - Deborah Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR, USA
| | | | - Poul Strange
- Xeris Pharmaceuticals, Inc, Austin, TX, USA Integrated Medical Development, Princeton Junction, NJ, USA
| | | | - Leon Shi
- Integrated Medical Development, Princeton Junction, NJ, USA
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Abstract
While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care.
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Affiliation(s)
- Jessica R Castle
- Department of Medicine, 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
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Emami A, Youssef JE, Rabasa-Lhoret R, Pineau J, Castle JR, Haidar A. Modeling Glucagon Action in Patients With Type 1 Diabetes. IEEE J Biomed Health Inform 2016; 21:1163-1171. [PMID: 27448377 DOI: 10.1109/jbhi.2016.2593630] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The dual-hormone artificial pancreas is an emerging technology to treat type 1 diabetes (T1D). It consists of a glucose sensor, infusion pumps, and a dosing algorithm that directs hormonal delivery. Preclinical optimization of dosing algorithms using computer simulations has the potential to accelerate the pace of development for this technology. However, current simulation environments consider glucose regulation models that either do not include glucagon action submodels or include submodels that were proposed without comparison to other candidate models. We consider here nine candidate models of glucagon action featuring a number of possible characteristics: insulin-independent glucagon action, insulin/glucagon ratio effect on hepatic glucose production, insulin-dependent suppression of glucagon action, and the effect of rate of change of glucagon. To assess the models, we use measurements of plasma insulin, plasma glucagon, and endogenous glucose production collected from experiments involving eight subjects with T1D who receive four subcutaneous glucagon boluses. We estimate each model's parameters using a Bayesian approach, and the models are contrasted based on the deviance information criterion. The model achieving the best fit features insulin-dependent suppression of glucagon action and incorporates effects of both glucagon levels and its rate of change.
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Maahs DM, Buckingham BA, Castle JR, Cinar A, Damiano ER, Dassau E, DeVries JH, Doyle FJ, Griffen SC, Haidar A, Heinemann L, Hovorka R, Jones TW, Kollman C, Kovatchev B, Levy BL, Nimri R, O'Neal DN, Philip M, Renard E, Russell SJ, Weinzimer SA, Zisser H, Lum JW. Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report. Diabetes Care 2016; 39:1175-9. [PMID: 27330126 PMCID: PMC4915553 DOI: 10.2337/dc15-2716] [Citation(s) in RCA: 143] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.
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Affiliation(s)
- David M Maahs
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Bruce A Buckingham
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatric Endocrinology, Stanford University, Stanford, CA
| | - Jessica R Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL
| | - Edward R Damiano
- Department of Biomedical Engineering, Boston University, Boston, MA
| | - Eyal Dassau
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - J Hans DeVries
- Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Francis J Doyle
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | | | - Ahmad Haidar
- Biomedical Engineering Department, McGill University, Montreal, Quebec, Canada Division of Endocrinology, McGill University, Montreal, Quebec, Canada
| | | | - Roman Hovorka
- Wellcome Trust-Medical Research Clinical Institute of Metabolic Science, University of Cambridge, Cambridge, U.K
| | - Timothy W Jones
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | | | | | - Brian L Levy
- Johnson & Johnson Diabetes Care Companies, Wayne, PA
| | - Revital Nimri
- Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel
| | - David N O'Neal
- Department of Medicine, University of Melbourne, St. Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Moshe Philip
- Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel
| | - Eric Renard
- Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, INSERM Clinical Investigation Centre 1411, Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France
| | - Steven J Russell
- Department of Biomedical Engineering, Boston University, Boston, MA Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Howard Zisser
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA
| | - John W Lum
- Jaeb Center for Health Research, Tampa, FL
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Castle JR, El Youssef J, Bakhtiani PA, Cai Y, Stobbe JM, Branigan D, Ramsey K, Jacobs P, Reddy R, Woods M, Ward WK. Effect of Repeated Glucagon Doses on Hepatic Glycogen in Type 1 Diabetes: Implications for a Bihormonal Closed-Loop System. Diabetes Care 2015; 38:2115-9. [PMID: 26341131 PMCID: PMC4613914 DOI: 10.2337/dc15-0754] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 08/10/2015] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To evaluate subjects with type 1 diabetes for hepatic glycogen depletion after repeated doses of glucagon, simulating delivery in a bihormonal closed-loop system. RESEARCH DESIGN AND METHODS Eleven adult subjects with type 1 diabetes participated. Subjects underwent estimation of hepatic glycogen using (13)C MRS. MRS was performed at the following four time points: fasting and after a meal at baseline, and fasting and after a meal after eight doses of subcutaneously administered glucagon at a dose of 2 µg/kg, for a total mean dose of 1,126 µg over 16 h. The primary and secondary end points were, respectively, estimated hepatic glycogen by MRS and incremental area under the glucose curve for a 90-min interval after glucagon administration. RESULTS In the eight subjects with complete data sets, estimated glycogen stores were similar at baseline and after repeated glucagon doses. In the fasting state, glycogen averaged 21 ± 3 g/L before glucagon administration and 25 ± 4 g/L after glucagon administration (mean ± SEM) (P = NS). In the fed state, glycogen averaged 40 ± 2 g/L before glucagon administration and 34 ± 4 g/L after glucagon administration (P = NS). With the use of an insulin action model, the rise in glucose after the last dose of glucagon was comparable to the rise after the first dose, as measured by the 90-min incremental area under the glucose curve. CONCLUSIONS In adult subjects with well-controlled type 1 diabetes (mean A1C 7.2%), glycogen stores and the hyperglycemic response to glucagon administration are maintained even after receiving multiple doses of glucagon. This finding supports the safety of repeated glucagon delivery in the setting of a bihormonal closed-loop system.
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Affiliation(s)
- Jessica R Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR
| | - Joseph El Youssef
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR
| | - Parkash A Bakhtiani
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR
| | - Yu Cai
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR
| | - Jade M Stobbe
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR
| | - Deborah Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR
| | - Katrina Ramsey
- Oregon Clinical and Translational Research Institute Biostatistics & Design Program, Oregon Health & Science University, Portland, OR
| | - Peter Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Ravi Reddy
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR
| | - Mark Woods
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR Portland State University, Portland, OR
| | - W Kenneth Ward
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center Oregon Health & Science University, Portland, OR
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