Closed-loop Insulin Delivery Research Articles

Fully closed-loop insulin delivery—are we nearly there yet?

Fully closed-loop insulin delivery—are we nearly there yet?

Efficacy and safety of closed-loop insulin delivery versus sensor-augmented pump in the treatment of adults with type 1 diabetes: a systematic review and meta-analysis of randomized-controlled trials

Controversy remains regarding whether closed-loop (CL) insulin delivery or insulin sensor-augmented pump (SAP) delivery is more efficient for clinical treatment. Therefore, we aimed to compare the efficacy and safety of CL insulin delivery systems versus insulin SAP delivery for adults with type 1 diabetes (T1D). Embase, Ovid MEDLINE, PubMed, ScienceDirect, Scopus, the Cochrane Library, and other databases were searched for related articles, and we analyzed the average blood glucose (BG), time in range (TIR), and adverse effects (AEs) as primary endpoints to evaluate efficacy and safety. Of 1616 articles, 12 randomized-controlled trials (RCTs) were included in the final analysis. Regarding BG control efficacy, CL insulin delivery resulted better outcomes than SAP therapy with regard to the average BG value, which was detected and recorded by continuous glucose monitoring (mean difference [MD][mmol/L]: -0.25 95% confidence interval [CI] -0.42 to-0.08, p = 0.003); TIR 3.9-10mmol/L (MD [%]: 7.91 95% CI 4.45-11.37, p < 0.00001). Similar results were observed for the secondary outcomes including low blood glucose index (LBGI) (MD: -0.41 95% CI-0.55 to-0.26, p < 0.00001), high blood glucose index (HBGI) (MD: -2.56 95% CI-3.38 to-1.74, p < 0.00001), and standard deviation (SD) of glucose variability (MD [mmol/L]: -0.25 95% CI-0.44 to-0.06, p = 0.01). Furthermore, SAP therapy was associated with more adverse effects (risk ratio: 0.20 95% CI 0.07-0.52, p = 0.001) than CL insulin delivery, and one of the most common adverse effects was hypoglycemia. CL insulin delivery appears to be a better treatment method than SAP therapy for adults with T1D because of its increased BG control efficacy and decreased number of hypoglycemic events.

Parents’ Experiences of Using a Hybrid Closed-Loop System to Care for a Very Young Child With Type 1 Diabetes: Qualitative Study

Background: We explored parents’ experiences of using a hybrid closed-loop system when caring for a very young child with type 1 diabetes. Our objectives were to understand the impact of using this technology on users’ quality of life and inform its use in routine clinical care. Methods: Interviews were conducted with parents of 30 children (aged 1-7 years) who participated in a randomised controlled trial comparing closed-loop insulin delivery with sensor-augmented pump therapy. Findings: Parents reported major clinical benefits to using the closed-loop system, including more time being spent in range. Parents welcomed opportunities to collaborate with the system (using Ease-off and Boost functions) when their own knowledge could enhance its efficacy. Parents also reported a lessened need for health professional input after the initial transition onto the system had been made. Parents described wide-ranging quality-of-life benefits to using a system that automatically regulated blood glucose. Parents reported sleeping better, worrying less about their child and feeling more confident entrusting their child’s care to others, and how these individuals were more willing to assume caregiving responsibilities. Parents also described how their child felt more normal and experienced better sleep, mood and concentration, and lessened distress. Siblings also benefitted from parents’ time and attention being less focused on diabetes management. While parents highlighted multiple benefits to being able to administer insulin using a smartphone, difficulties were experienced stowing this device on the child’s body. Interpretation: Using a hybrid closed-loop system in very young children can have potentially life-changing consequences and may result in a lessened demand for health professionals’ input. Systems may need to be customised for use in very young children. Funding Information: The European Commission within the Horizon 2020 Framework Programme under the grant agreement number 731560 funded this work. Declaration of Interests: RH reports having received speaker honoraria from Eli Lilly and Novo Nordisk, serving on advisory panels for Eli Lilly and Novo Nordisk, receiving licence fees from BBraun and Medtronic. RH reports patent patents, patent applications, shareholding and directorship at CamDiab. FC has attended Advisory Boards and obtained speaking fees for Abbott, Medtronic, Lilly, and NovoNordisk. EFR reports having received speaker honoraria from Eli Lilly and Novo Nordisk, and serving on advisory boards for Eli Lilly and Sanofi. SH declares having received speakers honoraria from Eli Lilly, Sanofi and Pfizer. TMK has received speaking honoraria from Eli Lilly and MerckSerono, and consulted Sanofi for a transition brochure. BRM has received speaker honoraria from Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi and Menarini, and has been on the advisory boards of Roche Diabetes Care and Abbott Diabetes Care. The Authors JL, DR, RIH, CKB, JF, JMA, AT, US and BK declare that there is no conflict of interest. Ethics Approval Statement: Participants were consented into the interview study when they were consented into the trial. Approval was received from relevant Ethics Committees in participating sites/countries and relevant national regulatory authorities.

Adjunctive Therapies to Optimize Closed-loop Glucose Control.

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

Adolescents' Experiences of Using a Smartphone Application Hosting a Closed-loop Algorithm to Manage Type 1 Diabetes in Everyday Life: Qualitative Study.

Background:Closed-loop technology may help address health disparities experienced by adolescents, who are more likely to have suboptimal glycemic control than other age groups and, because of their age, find diabetes self-management particularly challenging. The CamAPS FX closed-loop has sought to address accessibility and usability issues reported by users of previous prototype systems. It comprises small components and a smartphone app used to: announce meal-time boluses, adjust (“boost” or “ease-off”) closed-loop insulin delivery, customize alarms, and review/share data. We explored how using the CamAPS FX platform influences adolescents’ self-management practices and everyday lives.Methods:Eighteen adolescents were interviewed after having ≥6 months experience using the closed-loop platform. Data were analyzed thematically.Results:Participants reported feeling less burdened and shackled by diabetes because closed-loop components were easier to carry/wear, finger-pricks were not required, the smartphone app provided a discreet and less stigmatizing way of managing diabetes in public, and they were able to customize alarms. Participants also reported checking and reviewing data more regularly, because they did so when using the smartphone for other reasons. Some reported challenges in school settings where use of personal phones was restricted. Participants highlighted how self-management practices were improved because they could easily review glucose data and adjust closed-loop insulin delivery using the “boost” and “ease-off” functions. Some described how using the system resulted in them forgetting about diabetes and neglecting certain tasks.Conclusions:A closed-loop system with small components and control algorithm on a smartphone app can enhance usability and acceptability for adolescents and may help address the health-related disparities experienced by this age group. However, challenges can arise from using a medical app on a device which doubles as a smartphone.Trial registration:Closed Loop From Onset in Type 1 Diabetes (CLOuD); NCT02871089; https://clinicaltrials.gov/ct2/show/NCT02871089

Artificial intelligence in diabetology

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

Искусственный интеллект в диабетологии

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes

Objective: Controlling blood glucose in the euglycemic range is the main goal of developing the closed-loop insulin delivery system for type 1 diabetes patients. The closed-loop system delivers the amount of insulin dose determined by glucose predictions through the use of computational algorithms. A computationally efficient and accurate model that can capture the physiological nonlinear dynamics is critical for developing an efficient closed-loop system. Methods: Four data-driven models are compared, including different neural network architectures, a reservoir computing model, and a novel linear regression approach. Model predictions are evaluated over continuous 30 and 60 min time horizons using real-world data from wearable sensor measurements, a continuous glucose monitor, and self-reported events through mobile applications. The four data-driven models are trained on 12 data contributors for around 32 days, 8 days of data are used for validation, with an additional 10 days of data for out-of-sample testing. Model performance was evaluated by the root mean squared error and the mean absolute error. Results: A neural network model using an encoder-decoder architecture has the most stable performance and is able to recover missing dynamics in short time intervals. Regression models performed better at long-time prediction horizons (i.e., 60 min) and with lower computational costs. Significance: The performance of several distinct models was tested for individual-level data from a type 1 diabetes data set. These results may enable a feasible solution with low computational cost for the time-dependent adjustment of artificial pancreas for diabetes patients.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Hybrid Closed-Loop Insulin Delivery Systems for People With Type 1 Diabetes

&#x0D; The CADTH Health Technology Expert Review Panel (HTERP) suggests that hybrid closed-loop insulin delivery (HCL) systems hold promise for the care of people with type 1 diabetes. HTERP considers that, at present, there are insufficient long-term data on clinically relevant and patient-important outcomes to recommend how extensive the role of HCL systems should be in care.&#x0D; HTERP recommends the collection of robust and comparative data for consideration of future reassessments that compare HCL systems to existing insulin delivery and glucose monitoring methods in terms of glycated hemoglobin (hemoglobin A1C); time-in-range; time above and below range; glycemic variability; quality of life; patient, parent or caregiver, and health care provider satisfaction; diabetes-related complications; discontinuation rates; and health system impact. Robust data are collected in well-designed comparative studies that are, among other considerations, of sufficient duration to ensure a clinically meaningful outcome assessment.&#x0D;

691-P: Closed-Loop Glycemic Control with the Insulin-Only Bionic Pancreas for the Management of Type 2 Diabetes

Closed-loop insulin delivery has been shown to improve glycemia in patients with type 1 diabetes (T1D) but fewer studies have applied these technologies to the management of type 2 diabetes (T2D). We conducted a random-order crossover feasibility study in 5 adults with T2D who used basal and prandial insulin, comparing automated glycemic control with the insulin-only bionic pancreas (BP) to usual care (UC) in the home-use setting. Each study period was one week long. Non-insulin diabetes medications besides metformin were held during the BP arm only. The BP utilized the same control algorithms previously tested in T1D. The system was initialized with participants’ body mass only; no information on insulin requirement or regimen was provided to the BP. Participants were encouraged to enter qualitative meal announcements (“typical”, “small”, or “ large” amounts of carbohydrate) for main meals but not for snacks. The BP used a target glucose of 100 mg/dl (110 mg/dl is the minimum target previously used by the insulin-only configuration in T1D studies). There was a nominal improvement in mean continuous glucose monitor (CGM) glucose in the BP compared to UC arms (137±8 vs. 153±21 mg/dl) with similar % time Disclosure J. Sherwood: None. C. A. Balliro: Consultant; Self; Beta Bionics, Inc., Novo Nordisk. M. Hillard: None. R. Selagamsetty: Employee; Self; Beta Bionics, Inc. F. El-khatib: Employee; Self; Beta Bionics, Inc., Stock/Shareholder; Self; Beta Bionics, Inc. E. Damiano: Other Relationship; Self; Beta Bionics, Inc., Stock/Shareholder; Spouse/Partner; Beta Bionics, Inc. S. J. Russell: Advisory Panel; Self; Companion Medical, ConvaTec Inc., Consultant; Self; Beta Bionics, Inc., Novo Nordisk, Other Relationship; Self; Beta Bionics, Inc., Research Support; Self; Beta Bionics, Inc., Novo Nordisk, Zealand Pharma A/S. Funding The Leona M. and Harry B. Helmsley Charitable Trust

147-OR: Use of Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World Experience

Background: Closed-loop control insulin delivery systems (CLC) have been available to adults with type 1 diabetes (T1D) for many years. However, for older adults with T1D, Tandem Control IQ is the first CLC available through Medicare. We evaluated the use of CLC in older adults with T1D in a real-world scenario. Methods: We retrospectively analyzed electronic health record (EHR) data for older adults (&amp;gt;65) with T1D started on CLC in a single tertiary center between January and December 2020. Continuous glucose monitoring (CGM) data from the 2 weeks prior (baseline) and 2 weeks post CLC system (post-CLC) initiation were analyzed. We evaluated glycemic measures, including coefficient of variation (CV) for glucose variability and glucose management indicator (GMI) for overall glycemic control. Demographic, clinical and diabetes technology data were gathered from EHR. Results: We present interim data from 25 older adults (Mean age 69±4 yrs, duration of diabetes 43±13 yrs) who started using CLC over the 12 month period. Overall, 23/25 were prior pump users, and 23/25 were prior CGM users. Only 2/25 discontinued CLC use. Comparison of CGM metrics measured at baseline vs. post-CLC showed an increase in Time in Range (TIR) from 63±13% to 77±9% (p&amp;lt;0.001) and a reduction in time &amp;gt;180 mg/dL from 30±12% to 18±9% (p=0.004). While there was a trend in decrease of time &amp;lt;70 mg/dL from baseline to post-CLC, this change was not statistically significant (2.8±2.4% vs. 2.2±2.5% (p=0.5)). CV improved from 0.33±0.04% to 0.30±0.04% (p=0.04) and GMI decreased from 7.0±0.5% to 6.7±0.4% (p=0.06). CLC use averaged 92% of the time over 2 weeks. Conclusion: In a small cohort of older adults with T1D using CLC in a real-world scenario, the majority of the cohort was able to use the system successfully, resulting in a higher TIR and improved glycemic variability, as seen by a decrease in CV over a 3 month period. Thus, CLC has the potential to improve glycemic outcomes in this elderly population. Disclosure E. Toschi: Consultant; Self; Medtronic. C. Slyne: None. A. Atakov-castillo: None. M. Munshi: Consultant; Self; Sanofi. Funding National Institutes of Health (1DP3DK112214-01)

695-P: Comparison of Inpatient Glycemic Control in Hybrid Closed-Loop vs. Sensor-Augmented Insulin Delivery Systems

The hybrid closed-loop (HCL) insulin delivery system is at the forefront of continuous subcutaneous insulin infusion technology. However, to our knowledge, no studies to date have compared inpatient glycemic control between the HCL and non-HCL delivery systems of the HCL insulin pumps. We aim to compare the efficacy and safety of glycemic control between these two insulin delivery systems in the hospital setting. We are performing a prospective study on patients using a HCL insulin pump who are admitted to a non-critical care floor. We included patients who were ≥18 years of age, diagnosed with type 1 or type 2 diabetes, on a HCL insulin pump for more than 4 weeks, and admitted for ≥2 days. The primary outcome is the comparison of percentage of point of care blood glucose readings between 70 and 180 mg/dL in the HCL system versus the non-HCL system. Secondary outcomes include percentage of glucose readings >180 mg/dL and Here, we report preliminary findings of 9 patients using the non-HCL system and 4 patients using the HCL system (Table 1). In the HCL group, >70% of the time was spent in the target blood glucose range of 70-180 mg/dL, 180 mg/dL, and Disclosure Y. Tsushima: None. A. Iqbal: None. J. F. Bena: None. M. Lansang: Consultant; Self; Sanofi. Research Support; Self; Dexcom, Inc.

524-P: Diabetes Device Use Linked with Lower Family Conflict in Teens with Type 1 Diabetes

Introduction: Use of hybrid closed-loop insulin delivery systems have been associated with better glycemic control and lower hemoglobin A1c (HbA1c) values, and better glycemic control is linked with less diabetes-specific family conflict among adolescents with type 1 diabetes (T1D). Little is known, however, about how the use of closed-loop systems relates to diabetes family conflict. Methods: Participants included 60 teens ages 13-17 who had been diagnosed with T1D for ≥1 year and who reported at least moderate diabetes distress (M age= 16.15±1.4, 68% white, non-Hispanic 68% female), and their parents (78% white, non-Hispanic, 78% Married, 83% female). As part of baseline data collection of an ongoing two-site trial of a behavioral intervention, parents and teens each reported on diabetes device use and diabetes-related family conflict. HbA1c values were obtained from teens’ medical records or mail-in kits. Nonparametric (Mann-Whitney) tests were conducted to examine differences between parent and teen-reported diabetes family conflict and HbA1c in relation to use of closed-loops systems. Results: Of the participants, 12 teens (20%) were using a closed-loop system. The use of closed-loop systems was associated with lower levels of parent-reported diabetes-related family conflict (z= -2.372, p=0.018). Use of closed-loop systems was also associated with lower HbA1c values (z= -2.865, p=.004). However, teens’ own report of family conflict was not significantly related to use of the closed-loop systems (z= -1.331, .183). Conclusion: Results suggest that the use of closed-loop systems is associated with both lower HbA1c and lower parent perceptions of diabetes-related family conflict. While future studies are needed to replicate these findings in larger samples and over time, our findings further support that closed-loop systems may reduce the burden of diabetes - particularly for parents - and improve both psychosocial and glycemic outcomes in families of youth with T1D. Disclosure H. R. Moore: None. S. S. Jaser: None. R. Streisand: None. H. Inverso: None. T. Morrow: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK121316)

Day-to-day variability of insulin requirements in the inpatient setting: Observations during fully closed-loop insulin delivery.

The aim of this study was to characterize the variability of exogenous insulin requirements during fully closed-loop insulin delivery in hospitalized patients with type 2 diabetes or new-onset hyperglycaemia, and to determine patient-related characteristics associated with higher variability of insulin requirements. We retrospectively analysed data from two fully closed-loop inpatient studies involving adults with type 2 diabetes or new-onset hyperglycaemia requiring insulin therapy. The coefficient of variation quantified day-to-day variability of exogenous insulin requirements during up to 15 days using fully automated closed-loop insulin delivery. Data from 535 days in 67 participants were analysed. The coefficient of variation of day-to-day exogenous insulin requirements was 30% ± 16%, and was higher between nights than between any daytime period (56% ± 29% overnight [11:00 pm to 4:59 am] compared with 41% ± 21% in the morning [5:00 am to 10:59 am], 39% ± 15% in the afternoon [11:00 am to 4:59 pm] and 45% ± 19% during the evening [5:00 pm to 10:59 pm]; all P < 0.01). There is high day-to-day variability of exogenous insulin requirements in inpatients, particularly overnight, and diabetes management approaches should account for this variability.

Time in Range in Pregnancy: Is There a Role?

Continuous glucose monitoring (CGM) is being used with increasing frequency as an adjunct to self-monitoring of blood glucose in pregnancy, and novel targets based on CGM data are becoming standardized. This adoption of CGM is the result of its improving accuracy, patient preference, and evolving data demonstrating associations of novel targets such as time in range (TIR) with pregnancy and neonatal outcomes. A greater understanding of the relationship of various CGM metrics to outcomes in pregnancy complicated by diabetes is needed. It is clear that TIR parameters need to be uniquely lower for pregnant women than for nonpregnant individuals. CGM technology is also an integral part of hybrid closed-loop insulin delivery systems. These insulin delivery systems will be a significant advance in the management of diabetes during pregnancy if they can achieve the pre- and postprandial targets required for pregnancy and optimize TIR.

Benefits and Challenges of Current Closed-Loop Technologies in Children and Young People With Type 1 Diabetes.

Recent advances in diabetes technology have led to the development of closed-loop insulin delivery systems for the management of type 1 diabetes. Several such systems are now commercially available for children and young people. While all available systems have been shown to improve glycaemic control and quality of life in this population, qualitative data also highlights the challenges in using closed-loop systems, which vary among different pediatric age-groups. Very young children require systems that are able to cope with low insulin doses and significant glycaemic variability due to their high insulin sensitivity and unpredictable eating and exercise patterns. Adolescents' compliance is often related to size and number of devices, usability of the systems, need for calibrations, and their ability to interact with the system. Given the speed of innovations, understanding the capabilities and key similarities and differences of current systems can be challenging for healthcare professionals, caregivers and young people with type 1 diabetes alike. The aim of this review is to summarize the key evidence on currently available closed-loop systems for children and young people with type 1 diabetes, as well as commenting on user experience, where real-world data are available. We present findings on a system-basis, as well as identifying specific challenges in different pediatric age-groups and commenting on how current systems might address these. Finally, we identify areas for future research with regards to closed-loop technology tailored for pediatric use and how these might inform reimbursement and alleviate disease burden.

Erythrocyte-Membrane-Enveloped Biomineralized Metal–Organic Framework Nanoparticles Enable Intravenous Glucose-Responsive Insulin Delivery

A "closed-loop" insulin delivery system that can mimic the dynamic and glucose-responsive insulin secretion as islet β-cells is desirable for the therapy of type 1 and advanced type 2 diabetes mellitus (T1DM and T2DM). Herein, we introduced a kind of "core-shell"-structured glucose-responsive nanoplatform to achieve intravenous "smart" insulin delivery. A finely controlled one-pot biomimetic mineralization method was utilized to coencapsulate insulin, glucose oxidase (GOx), and catalase (CAT) into the ZIF-8 nanoparticles (NPs) to construct the "inner core", where an efficient enzyme cascade system (GOx/CAT group) served as an optimized glucose-responsive module that could rapidly catalyze glucose to yield gluconic acid to lower the local pH and effectively consume the harmful byproduct hydrogen peroxide (H2O2), inducing the collapse of pH-sensitive ZIF-8 NPs to release insulin. The erythrocyte membrane, a sort of natural biological derived lipid bilayer membrane which has intrinsic biocompatibility, was enveloped onto the surface of the "inner core" as the "outer shell" to protect them from elimination by the immune system, thus making the NPs intravenously injectable and could stably maintain a long-term existence in blood circulation. The in vitro and in vivo results indicate that our well-designed nanoplatform possesses an excellent glucose-responsive property and can maintain the blood glucose levels of the streptozocin (STZ)-induced type 1 diabetic mice at the normoglycemic state for up to 24 h after being intravenously administrated, confirming an intravenous insulin delivery strategy to overcome the deficits of conventional daily multiple subcutaneous insulin administration and offering a potential candidate for long-term T1DM treatment.

Hybrid Closed-Loop Insulin Delivery Systems for People With Type 1 Diabetes

&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D; 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.&#x0D;

Modelling glucose dynamics during moderate exercise in individuals with type 1 diabetes.

The artificial pancreas is a closed-loop insulin delivery system that automatically regulates glucose levels in individuals with type 1 diabetes. In-silico testing using simulation environments accelerates the development of better artificial pancreas systems. Simulation environments need an accurate model that captures glucose dynamics during exercise to simulate real-life scenarios. We proposed six variations of the Bergman Minimal Model to capture the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. We estimated the parameters of each model with clinical data using a Bayesian approach and Markov chain Monte Carlo methods. The data consisted of measurements of plasma glucose, plasma insulin, and oxygen consumption collected from a study of 17 adults with type 1 diabetes undergoing aerobic exercise sessions. We compared the models based on the physiological plausibility of their parameters estimates and the deviance information criterion. The best model features (i) an increase in glucose effectiveness proportional to exercise intensity, and (ii) an increase in insulin action proportional to exercise intensity and duration. We validated the selected model by reproducing results from two previous clinical studies. The selected model accurately simulates the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. This work offers an important tool to develop strategies for exercise management with the artificial pancreas.