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Article ; Online: New Developments in Glucagon Treatment for Hypoglycemia.

Story, LesleAnn Hayward / Wilson, Leah M

2022 Volume 82, Issue 11, Page(s) 1179–1191

Abstract: Glucagon is essential for endogenous glucose regulation along with the paired hormone, insulin. Unlike insulin, pharmaceutical use of glucagon has been limited due to the unstable nature of the peptide. Glucagon has the potential to address hypoglycemia ... ...

Abstract	Glucagon is essential for endogenous glucose regulation along with the paired hormone, insulin. Unlike insulin, pharmaceutical use of glucagon has been limited due to the unstable nature of the peptide. Glucagon has the potential to address hypoglycemia as a major limiting factor in the treatment of diabetes, which remains very common in the type 1 and type 2 diabetes. Recent developments are poised to change this paradigm and expand the use of glucagon for people with diabetes. Glucagon emergency kits have major limitations for their use in treating severe hypoglycemia. A complicated reconstitution and injection process often results in incomplete or aborted administration. New preparations include intranasal glucagon with an easy-to-use and needle-free nasal applicator as well as two stable liquid formulations in pre-filled injection devices. These may ease the burden of severe hypoglycemia treatment. The liquid preparations may also have a role in the treatment of non-severe hypoglycemia. Despite potential benefits of expanded use of glucagon, undesirable side effects (nausea, vomiting), cost, and complexity of adding another medication may limit real-world use. Additionally, more long-term safety and outcome data are needed before widespread, frequent use of glucagon is recommended by providers.
MeSH term(s)	Blood Glucose ; Diabetes Mellitus, Type 1/drug therapy ; Diabetes Mellitus, Type 2/drug therapy ; Glucagon/therapeutic use ; Humans ; Hypoglycemia/drug therapy ; Insulin/therapeutic use
Chemical Substances	Blood Glucose ; Insulin ; Glucagon (9007-92-5)
Language	English
Publishing date	2022-08-06
Publishing country	New Zealand
Document type	Journal Article ; Review
ZDB-ID	120316-2
ISSN	1179-1950 ; 0012-6667
ISSN (online)	1179-1950
ISSN	0012-6667
DOI	10.1007/s40265-022-01754-8
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Zs.A 762: Show issues

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Article ; Online: Recent Advances in Insulin Therapy.

Wilson, Leah M / Castle, Jessica R

Diabetes technology & therapeutics

2020 Volume 22, Issue 12, Page(s) 929–936

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 ... ...

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.
MeSH term(s)	Humans ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Insulin, Long-Acting ; Insulin, Regular, Human
Chemical Substances	Hypoglycemic Agents ; Insulin ; Insulin, Long-Acting ; Insulin, Regular, Human
Language	English
Publishing date	2020-05-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	1452816-2
ISSN	1557-8593 ; 1520-9156
ISSN (online)	1557-8593
ISSN	1520-9156
DOI	10.1089/dia.2020.0065
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Zs.A 5269: Show issues

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Article ; Online: Stable Liquid Glucagon: Beyond Emergency Hypoglycemia Rescue.

Wilson, Leah M / Castle, Jessica R

Journal of diabetes science and technology

2018 Volume 12, Issue 4, Page(s) 847–853

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. ... ...

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.
MeSH term(s)	Diabetes Mellitus, Type 1 ; Glucagon/administration & dosage ; Glucagon/chemistry ; Humans ; Hypoglycemia/drug therapy
Chemical Substances	Glucagon (9007-92-5)
Language	English
Publishing date	2018-02-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ISSN	1932-2968
ISSN (online)	1932-2968
DOI	10.1177/1932296818757795
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Article ; Online: Role of Glucagon in Automated Insulin Delivery.

Wilson, Leah M / Jacobs, Peter G / Castle, Jessica R

Endocrinology and metabolism clinics of North America

2019 Volume 49, Issue 1, Page(s) 179–202

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 ... ...

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.
MeSH term(s)	Automation/instrumentation ; Diabetes Mellitus, Type 1/blood ; Diabetes Mellitus, Type 1/drug therapy ; Drug Therapy, Combination/instrumentation ; Drug Therapy, Combination/methods ; Glucagon/administration & dosage ; Glucagon/physiology ; Glycemic Control/instrumentation ; Glycemic Control/methods ; Humans ; Hypoglycemia/chemically induced ; Hypoglycemia/prevention & control ; Insulin/administration & dosage ; Insulin Infusion Systems ; Pancreas, Artificial
Chemical Substances	Insulin ; Glucagon (9007-92-5)
Language	English
Publishing date	2019-12-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	92116-6
ISSN	1558-4410 ; 0889-8529
ISSN (online)	1558-4410
ISSN	0889-8529
DOI	10.1016/j.ecl.2019.10.008
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Article ; Online: Opportunities and challenges in closed-loop systems in type 1 diabetes.

Wilson, Leah M / Jacobs, Peter G / Riddell, Michael C / Zaharieva, Dessi P / Castle, Jessica R

The lancet. Diabetes & endocrinology

2021 Volume 10, Issue 1, Page(s) 6–8

MeSH term(s)	Diabetes Mellitus, Type 1/drug therapy ; Humans ; Insulin Infusion Systems
Language	English
Publishing date	2021-11-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2213-8595
ISSN (online)	2213-8595
DOI	10.1016/S2213-8587(21)00289-8
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Article ; Online: 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.

Mosquera-Lopez, Clara / Roquemen-Echeverri, Valentina / Tyler, Nichole S / Patton, Susana R / Clements, Mark A / Martin, Corby K / Riddell, Michael C / Gal, Robin L / Gillingham, Melanie / Wilson, Leah M / Castle, Jessica R / Jacobs, Peter G

Journal of the American Medical Informatics Association : JAMIA

2023 Volume 31, Issue 1, Page(s) 109–118

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 ... ...

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.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 1/complications ; Diabetes Mellitus, Type 1/drug therapy ; Snacks ; Blood Glucose ; Blood Glucose Self-Monitoring ; Uncertainty ; Hypoglycemia/prevention & control ; Hypoglycemic Agents/therapeutic use ; Insulin
Chemical Substances	Blood Glucose ; Hypoglycemic Agents ; Insulin
Language	English
Publishing date	2023-10-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocad196
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Zs.A 4128: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes.

Young, Gavin M / Jacobs, Peter G / Tyler, Nichole S / Nguyen, Thanh-Tin P / Castle, Jessica R / Wilson, Leah M / Branigan, Deborah / Gabo, Virginia / Guillot, Florian H / Riddell, Michael C / El Youssef, Joseph

American journal of physiology. Endocrinology and metabolism

2023 Volume 325, Issue 3, Page(s) E192–E206

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 ... ...

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 (R
MeSH term(s)	Humans ; Glucose ; Insulin ; Blood Glucose ; Diabetes Mellitus, Type 1 ; Resistance Training ; Hypoglycemia ; Exercise ; Lactic Acid
Chemical Substances	Glucose (IY9XDZ35W2) ; Insulin ; Blood Glucose ; Lactic Acid (33X04XA5AT)
Language	English
Publishing date	2023-07-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	603841-4
ISSN	1522-1555 ; 0193-1849
ISSN (online)	1522-1555
ISSN	0193-1849
DOI	10.1152/ajpendo.00298.2022
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Article ; Online: Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.

Mosquera-Lopez, Clara / Wilson, Leah M / El Youssef, Joseph / Hilts, Wade / Leitschuh, Joseph / Branigan, Deborah / Gabo, Virginia / Eom, Jae H / Castle, Jessica R / Jacobs, Peter G

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 39

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 ... ...

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.
Language	English
Publishing date	2023-03-13
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00783-1
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Article: Reduced psychological distress in familial chylomicronemia syndrome after patient support group intervention.

Wilson, Leah M / Cross, Russell R / Duell, P Barton

Journal of clinical lipidology

2017 Volume 12, Issue 1, Page(s) 240–242

Abstract: Familial chylomicronemia syndrome (FCS) is a rare genetic disorder that is associated with severe hypertriglyceridemia and complications that often include recurrent pancreatitis beginning in childhood. Patients with FCS frequently struggle to maintain ... ...

Abstract	Familial chylomicronemia syndrome (FCS) is a rare genetic disorder that is associated with severe hypertriglyceridemia and complications that often include recurrent pancreatitis beginning in childhood. Patients with FCS frequently struggle to maintain normality in their lives as a consequence of the necessity to severely restrict their intake of dietary fat coupled with the constant threat of recurrent pancreatitis. Patients typically face a high level of psychological stress and anxiety in association with reduced measures of quality of life. Routine medical care for affected patients usually does not adequately address the day-to-day struggles that accompany a diagnosis of FCS, resulting in ongoing suffering for many patients. We describe herein the highly beneficial effects of a support group interaction for a patient with FCS.
MeSH term(s)	Adaptation, Psychological ; Anxiety/etiology ; Diabetes Mellitus, Type 2/drug therapy ; Diabetes Mellitus, Type 2/pathology ; Glucagon-Like Peptide 1/analogs & derivatives ; Glucagon-Like Peptide 1/therapeutic use ; Humans ; Hyperlipoproteinemia Type I/complications ; Hyperlipoproteinemia Type I/diagnosis ; Hyperlipoproteinemia Type I/genetics ; Hypoglycemic Agents/therapeutic use ; Male ; Middle Aged ; Self-Help Groups ; Sleep Apnea, Obstructive/etiology
Chemical Substances	Hypoglycemic Agents ; rGLP-1 protein (5E7U48495E) ; Glucagon-Like Peptide 1 (89750-14-1)
Language	English
Publishing date	2017-11-13
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	2365061-8
ISSN	1876-4789 ; 1933-2874
ISSN (online)	1876-4789
ISSN	1933-2874
DOI	10.1016/j.jacl.2017.11.002
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Article ; Online: Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial.

Jacobs, Peter G / Resalat, Navid / Hilts, Wade / Young, Gavin M / Leitschuh, Joseph / Pinsonault, Joseph / El Youssef, Joseph / Branigan, Deborah / Gabo, Virginia / Eom, Jae / Ramsey, Katrina / Dodier, Robert / Mosquera-Lopez, Clara / Wilson, Leah M / Castle, Jessica R

The Lancet. Digital health

2023 Volume 5, Issue 9, Page(s) e607–e617

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- ... ...

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.
MeSH term(s)	Female ; Humans ; Activities of Daily Living ; Artificial Intelligence ; Cross-Over Studies ; Diabetes Mellitus, Type 1/drug therapy ; Glucose/therapeutic use ; Health Expenditures ; Hypoglycemia ; Hypoglycemic Agents/therapeutic use ; Insulin ; United States ; Wearable Electronic Devices ; Male
Chemical Substances	Glucose (IY9XDZ35W2) ; Hypoglycemic Agents ; Insulin
Language	English
Publishing date	2023-08-03
Publishing country	England
Document type	Randomized Controlled Trial ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2589-7500
ISSN (online)	2589-7500
DOI	10.1016/S2589-7500(23)00112-7
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