Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation

Science ◽  
2021 ◽  
Vol 373 (6554) ◽  
pp. 522-527
Author(s):  
Bruce A. Perkins ◽  
Jennifer L. Sherr ◽  
Chantal Mathieu

Despite innovations in insulin therapy since its discovery, most patients living with type 1 diabetes do not achieve sufficient glycemic control to prevent complications, and they experience hypoglycemia, weight gain, and major self-care burden. Promising pharmacological advances in insulin therapy include the refinement of extremely rapid insulin analogs, alternate insulin-delivery routes, liver-selective insulins, add-on drugs that enhance insulin effect, and glucose-responsive insulin molecules. The greatest future impact will come from combining these pharmacological solutions with existing automated insulin delivery methods that integrate insulin pumps and glucose sensors. These systems will use algorithms enhanced by machine learning, supplemented by technologies that include activity monitors and sensors for other key metabolites such as ketones. The future challenges facing clinicians and researchers will be those of access and broad clinical implementation.

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 136-OR
Author(s):  
MERYEM K. TALBO ◽  
VIRGINIE MESSIER ◽  
KATHERINE DESJARDINS ◽  
RÉMI RABASA-LHORET ◽  
ANNE-SOPHIE BRAZEAU ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5058 ◽  
Author(s):  
Taiyu Zhu ◽  
Kezhi Li ◽  
Lei Kuang ◽  
Pau Herrero ◽  
Pantelis Georgiou

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70–180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.


Medicine ◽  
2015 ◽  
Vol 94 (3) ◽  
pp. e421 ◽  
Author(s):  
Yin-Chun Chen ◽  
Yu-Yao Huang ◽  
Hung-Yuan Li ◽  
Shih-Wei Liu ◽  
Sheng-Hwu Hsieh ◽  
...  

2019 ◽  
Vol 24 (2) ◽  
pp. 99-106
Author(s):  
Michelle Condren ◽  
Samie Sabet ◽  
Laura J. Chalmers ◽  
Taylor Saley ◽  
Jenna Hopwood

Type 1 diabetes mellitus has witnessed significant progress in its management over the past several decades. This review highlights technologic advancements in type 1 diabetes management. Continuous glucose monitoring systems are now available at various functionality and cost levels, addressing diverse patient needs, including a recently US Food and Drug Administration (FDA)–approved implantable continuous glucose monitoring system (CGMS). Another dimension to these state-of-the-art technologies is CGMS and insulin pump integration. These integrations have allowed for CGMS-based adjustments to basal insulin delivery rates and suspension of insulin delivery when a low blood glucose event is predicted. This review also includes a brief discussion of upcoming technologies such as patch-based CGMS and insulin-glucagon dual-hormonal delivery.


2011 ◽  
Vol 9 (1) ◽  
pp. 13 ◽  
Author(s):  
R Brett McQueen ◽  
Samuel L Ellis ◽  
Jonathan D Campbell ◽  
Kavita V Nair ◽  
Patrick W Sullivan

2020 ◽  
Vol 11 ◽  
pp. 204201882090601
Author(s):  
Zavuga Zuberi ◽  
Elingarami Sauli ◽  
Liu Cun ◽  
Jing Deng ◽  
Wen-Jun Li ◽  
...  

Efforts directed toward restoring normal metabolic levels by mimicking the physiological insulin secretion, thereby ensuring safety, efficacy, minimal invasiveness and conveniences, are of great significance in the management of type 1 diabetes among children and adolescents. Regardless of the various technologies being discovered in addressing invasiveness and enhancing medication adherence in the management of type 1 diabetes, yet limited success had been observed among children and adolescents. The multiple daily subcutaneous insulin injections route using vial and syringe, and occasionally insulin pens, remain the most predictable route for insulin administration among children and adolescents. However, this route has been associated with compromised patient compliance, fear of injections and unacceptability, resulting in poor glycemic control, which promote the demand for alternative routes of insulin administration. Alternative routes for delivering insulin are being investigated in children and adolescents with type 1 diabetes; these include the hybrid closed-loop ‘artificial pancreas’ system, oral, inhalation, intranasal routes, and others. This review article explores the current advances in insulin-delivery methods that address the needs of children and adolescents in the treatment of type 1 diabetes.


2021 ◽  
Vol 1 (3) ◽  
Author(s):  
CADTH Health Technology Assessment Service

Blood glucose monitoring and insulin delivery are essential parts of the management of type 1 diabetes. Hybrid closed-loop insulin delivery (HCL) systems are a treatment option for people with type 1 diabetes and consist of an insulin pump, a continuous glucose monitor (CGM), and a computer program (algorithm) that allows the devices to communicate with each other and calculates insulin needs. CADTH conducted a Health Technology Assessment (HTA) of the use of HCL systems compared to other insulin delivery methods in people with type 1 diabetes to inform decisions regarding whether HCL systems have a place in the management of type 1 diabetes. HCL therapy generally improved the amount of time a person spent in target blood glucose ranges. Additionally, people who used HCL systems had improved average blood glucose levels (glycated hemoglobin [A1C]) over the preceding 2 or 3 months. However, the effectiveness or safety of HCL systems based on age, sex, race, glucose management, or other clinical features (e.g., those who are pregnant or planning pregnancy, or who have hypoglycemia unawareness or a history of severe hypoglycemia) is unknown. HCL systems were generally as safe as other insulin delivery methods. Additional studies with longer follow-up periods and more participants are needed to confirm the clinical effectiveness and safety of HCL systems. From a pan-Canadian, publicly funded health care system perspective, the cost of covering HCL systems for individuals with type 1 diabetes who are eligible for insulin pumps in their jurisdictions was estimated to be an additional $822,635,045 over 3 years compared with diabetes supplies that are currently covered. If HCL systems are covered for all individuals with type 1 diabetes, regardless of their current insulin-pump eligibility, the budget impact will be higher. HCL systems can help provide distance from demanding self-management and monitoring tasks for people living with type 1 diabetes; however, in order to do this, people using these systems must navigate complex relationships built on trust and collaboration. Given that type 1 diabetes self-management to date has required considerable attention to blood glucose numbers and technical tasks, developing these relationships of trust and collaboration will require a shift in understanding what it means to care for someone who has — or to self-manage — type 1 diabetes. It is not possible to conclude whether HCL systems will improve overall population health over the longer-term because the data for this are not available. It is also unclear which people with type 1 diabetes would benefit most from HCL systems. Eligibility criteria for the existing public insulin-pump program may be useful in making coverage decisions; trial periods may be considered to ensure HCL systems are working well for new users. Education and support are needed for people living with type 1 diabetes when they start to use HCL systems. Clinicians noted the need for interactions between diabetes educators and HCL system pump users. User-friendly devices and understandable reports are key to effective use. Eligibility for access through any publicly funded program for HCL systems should be based on evidence. The criteria for coverage should be consistent with broader public health goals and should not contribute to existing inequities in diabetes management.


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