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

Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control

INTRODUCTION

Diabetes mellitus type 1 is a chronic disease that implies mandatory external insulin delivery. The patients must monitor their blood glucose levels and administer appropriate insulin boluses to keep their blood glucose within the desired range. It requires a lot of time and endeavour, and many patients struggle with suboptimal glucose control despite all their efforts.

MATERIALS AND METHODS

This narrative review combines existing knowledge with new discoveries from animal experiments.

DISCUSSION

In the last decade, artificial pancreas (AP) devices have been developed to improve glucose control and relieve patients of the constant burden of managing their disease. However, a feasible and fully automated AP is yet to be developed. The main challenges preventing the development of a true, subcutaneous (SC) AP system are the slow dynamics of SC glucose sensing and particularly the delay in effect on glucose levels after SC insulin infusions. We have previously published studies on using the intraperitoneal space for an AP; however, we further propose a novel and potentially disruptive way to utilize the vasodilative properties of glucagon in SC AP systems.

CONCLUSION

This narrative review presents two lesser-explored viable solutions for AP systems and discusses the potential for improvement toward a fully automated system: A) using the intraperitoneal approach for more rapid insulin absorption, and B) besides using glucagon to treat and prevent hypoglycemia, also administering micro-boluses of glucagon to increase the local SC blood flow, thereby accelerating SC insulin absorption and SC glucose sensor site dynamics.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

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.

(Hybrid) Closed-Loop Systems: From Announced to Unannounced Exercise

Physical activity and exercise have many beneficial effects on general and type 1 diabetes (T1D) specific health and are recommended for individuals with T1D. Despite these health benefits, many people with T1D still avoid exercise since glycemic management during physical activity poses substantial glycemic and psychological challenges - which hold particularly true for unannounced exercise when using an AID system. Automated insulin delivery (AID) systems have demonstrated their efficacy in improving overall glycemia and in managing announced exercise in numerous studies. They are proven to increase time in range (70-180 mg/dL) and can especially counteract nocturnal hypoglycemia, even when evening exercise was performed. AID-systems consist of a pump administering insulin as well as a CGM sensor (plus transmitter), both communicating with a control algorithm integrated into a device (insulin pump, mobile phone/smart watch). Nevertheless, without manual pre-exercise adaptions, these systems still face a significant challenge around physical activity. Automatically adapting to the rapidly changing insulin requirements during unannounced exercise and physical activity is still the Achilles' heel of current AID systems. There is an urgent need for improving current AID-systems to safely and automatically maintain glucose management without causing derailments - so that going forward, exercise announcements will not be necessary in the future. Therefore, this narrative literature review aimed to discuss technological strategies to how current AID-systems can be improved in the future and become more proficient in overcoming the hurdle of unannounced exercise. For this purpose, the current state-of-the-art therapy recommendations for AID and exercise as well as novel research approaches are presented along with potential future solutions - in order to rectify their deficiencies in the endeavor to achieve fully automated AID-systems even around unannounced exercise.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest

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.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

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.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence

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.

Performance of a dual-hormone closed-loop system versus insulin-only closed-loop system in adolescents with type 1 diabetes. A single-blind, randomized, controlled, crossover trial

Objective

To assess the efficacy and safety of a dual-hormone (DH [insulin and glucagon]) closed-loop system compared to a single-hormone (SH [insulin only]) closed-loop system in adolescents with type 1 diabetes.

Methods

This was a 26-hour, two-period, randomized, crossover, inpatient study involving 11 adolescents with type 1 diabetes (nine males [82%], mean ± SD age 14.8 ± 1.4 years, diabetes duration 5.7 ± 2.3 years). Except for the treatment configuration of the DiaCon Artificial Pancreas: DH or SH, experimental visits were identical consisting of: an overnight stay (10:00 pm until 7:30 am), several meals/snacks, and a 45-minute bout of moderate intensity continuous exercise. The primary endpoint was percentage of time spent with sensor glucose values below range (TBR [<3.9 mmol/L]) during closed-loop control over the 26-h period (5:00 pm, day 1 to 7:00 pm, day 2).

Results

Overall, there were no differences between DH and SH for the following glycemic outcomes (median [IQR]): TBR 1.6 [0.0, 2.4] vs. 1.28 [0.16, 3.19]%, p=1.00; time in range (TIR [3.9-10.0 mmol/L]) 68.4 [48.7, 76.8] vs. 75.7 [69.8, 87.1]%, p=0.08; and time above range (TAR [>10.0 mmol/L]) 28.1 [18.1, 49.8] vs. 23.3 [12.3, 27.2]%, p=0.10. Mean ( ± SD) glucose was higher during DH than SH (8.7 ( ± 3.2) vs. 8.1 ( ± 3.0) mmol/L, p<0.001) but coefficient of variation was similar (34.8 ( ± 6.8) vs. 37.3 ( ± 8.6)%, p=0.20). The average amount of rescue carbohydrates was similar between DH and SH (6.8 ( ± 12.3) vs. 9.5 ( ± 15.4) grams/participant/visit, p=0.78). Overnight, TIR was higher, TAR was lower during the SH visit compared to DH. During and after exercise (4:30 pm until 7 pm) the SH configuration produced higher TIR, but similar TAR and TBR compared to the DH configuration.

Conclusions

DH and SH performed similarly in adolescents with type 1 diabetes during a 26-hour inpatient monitoring period involving several metabolic challenges including feeding and exercise. However, during the night and around exercise, the SH configuration outperformed DH.

Efficacy of intermittent short-term use of a real-time continuous glucose monitoring system in non-insulin-treated patients with type 2 diabetes: A randomized controlled trial

AIM

To evaluate the efficacy of intermittent short-term use of a real-time continuous glucose monitoring (RT-CGM) system in non-insulin-treated patients with type 2 diabetes (T2D) uncontrolled with oral antidiabetic drugs (OADs).

MATERIALS AND METHODS

In this multicentre, randomized prospective study, 61 participants were randomly assigned to treatment group 1 (one session of RT-CGM), treatment group 2 (two sessions of RT-CGM with a 3-month interval between sessions) and a control group. All participants used blinded continuous glucose monitoring for up to 6 days with education before randomization, and RT-CGM was additionally applied for 1 week in the intervention groups. The primary outcome was change in HbA1c at 6 months.

RESULTS

Among 61 participants, 48 subjects completed the study (baseline HbA1c 8.2% ± 0.5%). At 3 months, a significant HbA1c reduction was observed in treatment group 1 (adjusted difference = -0.60%, P = .044) and treatment group 2 (adjusted difference = -0.64%, P = .014) compared with the control group. However, at 6 months, only treatment group 2 achieved a significant HbA1c reduction (adjusted difference = -0.68%, P = .018). Especially in the treatment groups, patients performing self-monitoring of blood glucose (SMBG) at least 1.5 times/day showed a significant HbA1c improvement, at both 3 and 6 months, but those performing SMBG less than 1.5 times/day showed no significant improvement.

CONCLUSIONS

In non-insulin-treated patients with T2D uncontrolled with OADs, intermittent short-term use of RT-CGM was an effective method for glucose control, especially in those performing SMBG frequently.

Precision medicine in type 1 diabetes

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.

Comparison of Nocturnal Glucose After Exercise Among Dual-Hormone, Single-Hormone Algorithm-Assisted Insulin Delivery System and Usual Care in Adults and Adolescents Living with Type 1 Diabetes: A Pooled Analysis

Background: Available studies comparing the efficacy of dual-hormone (DH)-algorithm-assisted insulin delivery (AID), single-hormone (SH)-AID and usual care on postexercise overnight glucose in people with type 1 diabetes (T1D) have had different outcomes. By pooling data from all available studies, we aim to draw stronger conclusions. Methods: Data were pooled from two three-arm, open-label, randomized, controlled, crossover studies. Forty-one adults [median (Q1, Q3) age: 34.0 years (29.5, 51.0), mean HbA1c: 7.5% ± 1.0%] and 17 adolescents with T1D [age: 14.0 (13.0, 16.0), HbA1c: 7.8% ± 0.8%] underwent DH-AID, SH-AID, and usual care. Each intervention involved evening aerobic exercise (60-min). The primary outcome, time in range% (TIR%) overnight (00:00-06:00) postexercise based on continuous glucose monitoring, was compared among treatments using linear mixed effect model or generalized linear mixed model. Results: Among adults, mean TIR% was 94.0% ± 11.9%, 83.1% ± 20.5%, and 65.1% ± 37.0% during DH-AID, SH-AID, and usual care intervention, respectively (P < 0.05 for all between-group comparisons). DH-AID was superior to SH-AID and usual care, and SH-AID was superior to usual care regarding hypoglycemia and hyperglycemia prevention, but not glycemic variability. Among adolescents, DH-AID and SH-AID reduced dysglycemia, but not glycemic variability, better than usual care. Glycemic outcomes were similar between DH-AID and SH-AID. Conclusion: AID systems allow improved postexercise nocturnal glycemic management than usual care for both adults and adolescents. DH-AID was better than SH-AID among adults, but not adolescents.

Dual-hormone artificial pancreas for glucose control in type 1 diabetes: A meta-analysis

AIM

To evaluate the efficacy and safety of a dual-hormone artificial pancreas (DH) in type 1 diabetes.

MATERIAL AND METHODS

PubMed, Embase, the Cochrane Library and ClinicalTrials.gov were searched for studies published up to February 16, 2022. We included randomized controlled trials that compared DH with single-hormone artificial pancreas (SH), continuous subcutaneous insulin infusion (CSII) or sensor-augmented pumps (SAP), and predictive low glucose suspend systems (PLGS) in type 1 diabetes. The primary outcome was percent time in target (3.9-10 mmol/L [70-180 mg/dL]). Data were summarized as mean differences (MDs) or risk differences (RDs).

RESULTS

A total of 17 randomized crossover trials (438 participants) were included. There were nine trials of DH versus SH, 13 trials of DH versus SAP/CSII, and two trials of DH versus PLGS. For time in target, DH showed no significant difference in time in target compared with SH (MD 2.69%, 95% confidence interval [CI] -0.38 to 5.76) but resulted in 16.05% (95% CI 12.06 to 20.05) and 6.89% (95% CI 2.63 to 11.14) more time in target range compared with SAP/CSII and PLGS, respectively. DH slightly reduced time in hypoglycaemia (MD -1.20%, 95% CI -1.85 to -0.56) but increased the risk of gastrointestinal symptoms (RD 0.18, 95% CI 0.08 to 0.27) compared with SH.

CONCLUSIONS

The results of this study suggest that DH has a comparable effect on time in target compared with SH, but is associated with a longer time in target range compared with SAP/CSII and PLGS. The DH slightly reduced time in hypoglycaemia but may increase the risk of gastrointestinal symptoms compared with the SH. PROSPERO registration number: CRD42022314015.

Current Status and Emerging Options for Automated Insulin Delivery Systems

Combining technologies including rapid insulin analogs, insulin pumps, continuous glucose monitors, and control algorithms has allowed for the creation of automated insulin delivery (AID) systems. These systems have proven to be the most effective technology for optimizing metabolic control and could hold the key to broadly achieving goal-level glycemic control for people with type 1 diabetes. The use of AID has exploded in the past several years with several options available in the United States and even more in Europe. In this article, we review the largest studies involving these AID systems, and then examine future directions for AID with an emphasis on usability.

Quantifying the impact of physical activity on future glucose trends using machine learning

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.

Recent advances in closed-loop insulin delivery

Since the discovery of insulin 100 years ago, we have seen considerable advances across diabetes therapies. The more recent advent of glucose-responsive automated insulin delivery has started to revolutionise the management of type 1 diabetes in children and adults. Evolution of closed-loop insulin delivery from research to clinical practice has been rapid, and multiple systems are now commercially available. In this review, we summarise key evidence on currently available closed-loop systems and those in development. We comment on dual-hormone and do-it-yourself systems, as well as reviewing clinical evidence in special populations such as very young children, older adults and in pregnancy. We identify future directions for research and barriers to closed-loop adoption, including how these might be addressed to ensure equitable access to this novel therapy.

Empfehlungen zur Diabetes-Behandlung mit automatischen Insulin-Dosierungssystemen

Das Prinzip der automatischen Insulindosierung, kurz „AID“ genannt, zeigt in Zulassungsstudien und Real-World-Erfahrungen ausgezeichnete Behandlungsergebnisse. Beim AID wird eine Insulinpumpe mit einem System zur kontinuierlichen Glukosemessung zusammengeschaltet, während ein Rechenprogramm, der sogenannte Algorithmus, die Steuerung der Insulingabe nach Bedarf übernimmt. Idealerweise wäre das System ein geschlossener Kreis, bei dem die Menschen mit Diabetes keine Eingabe mehr machen müssten. Jedoch sind bei den heute verfügbaren Systemen verschiedene Grundeinstellungen und Eingaben erforderlich (insbesondere von Kohlenhydratmengen der Mahlzeiten oder körperlicher Aktivität), die sich von den bisherigen Empfehlungen der sensorunterstützten Pumpentherapie in einzelnen Aspekten unterscheiden. So werden die traditionellen Konzepte von „Basal“ und „Bolus“ mit AID weniger nützlich, da der Algorithmus beide Arten der Insulinabgabe verwendet, um die Glukosewerte dem eingestellten Zielwert zu nähern. Daher sollte bei diesen Systemen statt der Erfassung von „Basal“ und „Bolus“, zwischen einer „nutzerinitiierten“ und einer „automatischen“ Insulindosis unterschieden werden. Gemeinsame Therapieprinzipien der verschiedenen AID-Systeme umfassen die passgenaue Einstellung des Kohlenhydratverhältnisses, die Bedeutung des Timings der vom Anwender initiierten Insulinbolusgaben vor der Mahlzeit, den korrekten Umgang mit einem verzögerten oder versäumten Mahlzeitenbolus, neue Prinzipien im Umgang mit Sport oder Alkoholgenuss sowie den rechtzeitigen Umstieg von AID zu manuellem Modus bei Auftreten erhöhter Ketonwerte. Das Team vom Diabetes-Zentrum AUF DER BULT in Hannover hat aus eigenen Studienerfahrungen und der zugrunde liegenden internationalen Literatur praktische Empfehlungen zur Anwendung und Schulung der gegenwärtig und demnächst in Deutschland kommerziell erhältlichen Systeme zusammengestellt. Für den Erfolg der AID-Behandlung scheint das richtige Erwartungsmanagement sowohl beim Behandlungsteam und als auch beim Anwender von großer Bedeutung zu sein.

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.

The Evolution of Insulin Administration in Type 1 Diabetes

Insulin has been utilized in the treatment of type 1 diabetes (T1D) for 100 years. While there is still no cure for T1D, insulin administration has undergone a remarkable evolution which has contributed to improvements in quality of life and life expectancy in individuals with T1D. The advent of faster-acting and longer-acting insulins allowed for the implementation of insulin regimens more closely resembling normal insulin physiology. These improvements afforded better glycemic control, which is crucial for limiting microvascular complications and improving T1D outcomes. Suspension of insulin delivery in response to actual and forecasted hypoglycemia has improved quality of life and mitigated hypoglycemia without compromising glycemic control. Advances in continuous glucose monitoring (CGM) and insulin pumps, efforts to model glucose and insulin kinetics, and the application of control theory to T1D have made the automation of insulin delivery a reality. This review will summarize the past, present, and future of insulin administration in T1D.

Strategically Playing with Fire: SGLT Inhibitors as Possible Adjunct to Closed-Loop Insulin Therapy

As closed-loop insulin therapies emerge into clinical practice and evolve in medical research for type 1 diabetes (T1D) treatment, the limitations in these therapies become more evident. These gaps include unachieved target levels of glycated hemoglobin in some patients, postprandial hyperglycemia, the ongoing need for carbohydrate counting, and the lack of non-glycemic benefits (such as prevention of metabolic syndrome and complications). Multiple adjunct therapies have been examined to improve closed-loop systems, yet none have become a staple. Sodium-glucose-linked cotransporter inhibitors (SGLTi's) have been extensively researched in T1D, with average reductions in placebo-adjusted HbA1c by 0.39%, and total daily dose by approximately 10%. Unfortunately, many trials revealed an increased risk of diabetic ketoacidosis, as high as 5 times the relative risk compared to placebo. This narrative review discusses the proven benefits and risks of SGLTi in patients with T1D with routine therapy, what has been studied thus far in closed-loop therapy in combination with SGLTi, the potential benefits of SGLTi use to closed-loop systems, and what is required going forward to improve the benefit to risk ratio in these insulin systems.

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

Tissue engineering and 3D printing of bioartificial pancreas for regenerative medicine in diabetes

Diabetes is a severe chronic disease worldwide. In various types of diabetes, the pancreatic beta cells fail to secrete sufficient insulin, at some point, to regulate blood glucose levels. Therefore, the replacement of dysfunctional pancreas, islets of Langerhans, or even the insulin-secreting beta cells facilitates physiological regulation of blood glucose levels. However, the current lack of sufficient donor human islets for cell replacement therapy precludes a routine and absolute cure for most of the existing diabetes cases globally. It is envisioned that tissue engineering of a bioartificial pancreas will revolutionize regenerative medicine and the treatment of diabetes. In this review, we discuss the anatomy and physiology of the pancreas, and identify the clinical considerations for engineering a bioartificial pancreas. Subsequently, we dissect the bioengineering problem based on the design of the device, the biomaterial used, and the cells involved. Last but not least, we highlight current tissue engineering challenges and explore potential directions for future work.

Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

Over the last few years, technological advances have led to tremendous improvement in the management of type 1 diabetes (T1D). Artificial pancreas systems have been shown to improve glucose control compared with conventional insulin pump therapy. However, clinically significant hypoglycemic and hyperglycemic episodes still occur with the artificial pancreas. Postprandial glucose excursions and exercise-induced hypoglycemia represent major hurdles in improving glucose control and glucose variability in many patients with T1D. In this regard, dual-hormone artificial pancreas systems delivering other hormones in addition to insulin (glucagon or amylin) may better reproduce the physiology of the endocrine pancreas and have been suggested as an alternative tool to overcome these limitations in clinical practice. In addition, novel ultra-rapid-acting insulin analogs with a more physiological time-action profile are currently under investigation for use in artificial pancreas devices, aiming to address the unmet need for further improvements in postprandial glucose control. This review article aims to discuss the current progress and future outlook in the development of novel ultra-rapid insulin analogs and dual-hormone closed-loop systems, which offer the next steps to fully closing the loop in the artificial pancreas.

The current schemes of insulin therapy: Pro and contra

Insulin regimens have been evolving for a century. The schemes used for type 1 (T1D) and type 2 (T2D) diabetes differ due to differences in pathophysiology but share important features. Insulin is required for both types of diabetes when other means of controlling glucose are insufficient. For T1D this requires multiple daily injections or continuous subcutaneous infusion assisted by CGM, whereas in early T2D basal insulin together with oral agents or GLP-1RA is usually effective. In both cases current schemes typically maintain HbA1c levels between 7 and 8%, a range that limits but does not eliminate the long-term complications of diabetes, but do not restore glycemic control to a fully protective level. Inability to control postprandial hyperglycemia without problematic weight gain and hypoglycemia is a leading obstacle in both T1D and long-duration T2D. A greater share of prandial dosing decisions will have to be provided by smart electronic systems. Further changes in the structure or formulation of insulin are of uncertain potential, but schemes including delivery of amylin, GLP-1, and glucagon show promise. More reliable access to insulins, delivery devices, and capable medical advisors will be needed to optimize replacement of this essential hormone.

Diabetes education in pediatrics: How to survive diabetes

Diabetes mellitus is the most common abnormal carbohydrate metabolism disorder affecting millions of people worldwide. It is characterized by hyperglycemia as a result of ß-cell destruction or dysfunction by both genetic and environmental factors. Over time chronic hyperglycemia leads to microvascular (i.e., retinopathy, nephropathy and neuropathy) and macrovascular (i.e., ischemic heart disease, peripheral vascular disease, and cerebrovascular disease) complications of diabetes. Diabetes complication trials showed the importance of achieving near-normal glycemic control to prevent and/or reduce diabetes-related morbidity and mortality. There is a staggering rate of increased incidence of diabetes in youth, raising concerns for future generations' health, quality of life and its enormous economic burden. Despite advancements in the technology, diabetes management remains cumbersome. Training individuals with diabetes to gain life-long survival skills requires a comprehensive and ongoing diabetes education by a multidisciplinary team. Diabetes education and training start at the time of diagnosis of diabetes and should be continuous throughout the course of disease. The goal is to empower the individuals and families to gain diabetes self-management skills. Diabetes education must be individualized depending on the individual's age, education, family dynamics, and support. In this article, we review the history of diabetes, etiopathogenesis and clinical presentation of both type 1 and type 2 diabetes in children as well as adolescents. We then focus on diabetes management with education methods and materials.

Best Achievements in Clinical Medicine in Diabetes and Dyslipidemia in 2020

Over the last two decades, our understanding of diabetes and treatment strategies have evolved tremendously, from scientific, mechanistic, and human perspectives. The categories of anti-diabetic medications expanded from a few to numerous, enabling clinicians to personalize diabetes care and treatment. Thanks to rapid growth in the field of science and medical engineering, newer treatment options are coming to the market with various advantages and disadvantages to be aware of. Therefore, clinicians should rapidly adopt new trends based on guidelines and data from many clinical trials in the field of diabetes. In the treatment of dyslipidemia, trends and guidelines are changing every year, and novel therapies are being developed. In this review, we would like to summarize the major achievements in clinical medicine in 2020 in the field of diabetes mellitus and dyslipidemia.

Time in range centered diabetes care

Optimal glycemic control remains challenging and elusive for many people with diabetes. With the comprehensive clinical evidence on safety and efficiency in large populations, and with broader reimbursement, the adoption of continuous glucose monitoring (CGM) is rapidly increasing. Standardized visual reporting and interpretation of CGM data and clear and understandable clinical targets will help professionals and individuals with diabetes use diabetes technology more efficiently, and finally improve long-term outcomes with less everyday disease burden. For the majority of people with type 1 or type 2 diabetes, time in range (between 70 and 180 mg/dL, or 3.9 and 10 mmol/L) target of more than 70% is recommended, with each incremental increase of 5% towards this target being clinically meaningful. At the same time, the goal is to minimize glycemic excursions: a recommended target for a time below range (< 70 mg/dL or < 3.9 mmol/L) is less than 4%, and time above range (> 180 mg/dL or 10 mmol/L) less than 25%, with less stringent goals for older individuals or those at increased risk. These targets should be individualized: the personal use of CGM with the standardized data presentation provides all necessary means to accurately tailor diabetes management to the needs of each individual with diabetes.