Sensor-Augmented Insulin Pumps and Hypoglycemia Prevention in Type 1 Diabetes

Advancements in Insulin Pumps: A Comprehensive Exploration of Insulin Pump Systems, Technologies, and Future Directions

Insulin pumps have transformed the way diabetes is managed by providing a more accurate and individualized method of delivering insulin, in contrast to conventional injection routines. This research explores the progression of insulin pumps, following their advancement from initial ideas to advanced contemporary systems. The report proceeds to categorize insulin pumps according to their delivery systems, specifically differentiating between conventional, patch, and implantable pumps. Every category is thoroughly examined, emphasizing its unique characteristics and capabilities. A comparative examination of commercially available pumps is provided to enhance informed decision making. This section provides a thorough analysis of important specifications among various brands and models. Considered factors include basal rate and bolus dosage capabilities, reservoir size, user interface, and compatibility with other diabetes care tools, such as continuous glucose monitoring (CGM) devices and so on. This review seeks to empower healthcare professionals and patients with the essential information to improve diabetes treatment via individualized pump therapy options. It provides a complete assessment of the development, categorization, and full specification comparisons of insulin pumps.

Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21, 5273

The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones.

Nomogram for Predicting Hypoglycemia in Type 2 Diabetes Mellitus Patients Treated with Insulin Pump During Enteral Nutrition

Purpose

To develop a prediction model for hypoglycemia in type 2 diabetes mellitus (T2DM) patients treated with an insulin pump during enteral nutrition.

Methods

This retrospective study included T2DM patients treated with an insulin pump during enteral nutrition at the First Affiliated Hospital of Jinan University, Guangzhou Red Cross Hospital, Foshan First People's Hospital, and Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2016 and December 2023. The patients were randomized 3:1 to the training and validation sets. The risk factors for hypoglycemia were analyzed. A prediction model was developed.

Results

This study included 122 patients, and 57 patients had at least one hypoglycemic event during their hospitalization (46.72%). The multivariable logistic regression analysis showed that the time to reach the glycemic targets (odds ratio (OR)=1.408, 95% confidence interval (CI)=1.084-1.825, P=0.006), average glycemia (OR=0.387, 95% CI=0.233-0.643, P=0.010), coronary heart disease (OR=0.089, 95% CI=0.016-0.497, P<0.001), and the administration of hormone therapy (OR=6.807, 95% CI=1.128-41.081, P=0.037) were independently associated with hypoglycemia. A nomogram was built. The receiver operating characteristics analysis showed that the area under the curve of the model was 0.872 (95% CI=0.0.803-0.940) for the training set and 0.839 (95% CI=0.688-0.991) in the validation set.

Conclusion

A nomogram was successfully built to predict hypoglycemia in T2DM patients treated with an insulin pump during enteral nutrition, based on the time to reach the glycemic targets, average glycemia, coronary heart disease, and the administration of hormone therapy.

The development and evolution of insulin pumps: from early beginnings to future prospects

Diabetes technology has proliferated extensively over the past few decades with vast ameliorations in glucose monitoring and in insulin delivery systems. From a treatment based on daily insulin injections, we have moved to increasingly advanced technologies. Despite such advancements which have allowed better glycemic control, decreased diabetes-related complications, and improved the quality of life among diabetic patients, it has left many individuals unsatisfied with the current rate of commercial artificial pancreas development, stemming the need for further research into novel technologies. Accordingly, the Juvenile Diabetes Research Foundation has marked three generations for the development of an artificial pancreas comprising historical landmarks and future prospects which aim to produce an advanced technological system that attempts to mimic the endogenous pancreas, eliminating the need for user input. This review presents a synopsis of the development and evolution of insulin pumps, starting with the earliest technologies available such as continuous subcutaneous insulin infusion and continuous glucose monitoring as separate components, to currently available integrated advanced closed-loop hybrid systems and possible future technologies. The aim of the review is to provide insight of the advantages and limitations of past and currently available insulin pumps with the hope of driving research into novel technologies that attempt to mimic endogenous pancreatic function as closely as possible.

Insulin therapy among diabetic patients in rural communities of Sub-Saharan Africa: a perspective review

In this perspective review, we describe a brief background on the status quo of diabetes mellitus-related therapies and glycemic control among patients in rural communities in sub-Saharan Africa. The article discusses insulin therapy as well as the difficulties in obtaining insulin and oral hypoglycemic medications for diabetic patients living in sub-Saharan Africa. We wrap up our discussion with suggestions on solutions and opportunities for future research to tackle this health challenge in these impoverished communities. We conducted a literature search from PubMed and Google Scholar up until August 2023. Key words were used to generate search terms used to retrieve the required information. All types of literature with pertinent information on the current topic were included in the study. Diabetes mellitus is on the rise in sub-Saharan Africa. Several studies have reported poor glycemic control, low screening rates for diabetes mellitus, cigarette smoking, high alcohol consumption, prescription of antidiabetic therapy, and associated costs as contributors to the uptake of antidiabetic treatment. Although there is paucity of data on the extent of insulin therapy uptake and its possible modifiable contributors among the diabetic patients in the region, the anticipated increase in the number of people with diabetes on the continent makes it critical for global leaders to address the research gaps in insulin therapy among rural communities of sub-Saharan Africa, thus reducing the burden of diabetes in these populations.

Approach to the Hypoglycemic Patient

Hypoglycemia is commonly encountered in the emergency department. Patients can present with a myriad of symptoms and its presentation can mimic other more serious diagnoses. Despite the relative ease of its management, clinicians often miss the diagnosis or mismanage it even when discovered. Glucose is an important energy source for the brain and failing to recognize hypoglycemia or mismanaging it can lead to permanent neurologic disability or death. Although it is important to replenish glucose in a rapid fashion, it is equally important to discover and manage the underlying etiology to prevent further episodes of hypoglycemia.

Current and future therapies to treat impaired awareness of hypoglycemia

In order to achieve optimal glycemic control, intensive insulin regimes are needed for individuals with Type 1 Diabetes (T1D) and insulin-dependent Type 2 Diabetes (T2D). Unfortunately, intensive glycemic control often results in insulin-induced hypoglycemia. Moreover, recurrent episodes of hypoglycemia result in both the loss of the characteristic warning symptoms associated with hypoglycemia and an attenuated counterregulatory hormone responses. The blunting of warning symptoms is known as impaired awareness of hypoglycemia (IAH). Together, IAH and the loss of the hormonal response is termed hypoglycemia associated autonomic failure (HAAF). IAH is prevalent in up to 25% in people with T1D and up to 10% in people with T2D. IAH and HAAF increase the risk of severe hypoglycemia 6-fold and 25-fold, respectively. To reduce this risk for severe hypoglycemia, multiple different therapeutic approaches are being explored that could improve awareness of hypoglycemia. Current therapies to improve awareness of hypoglycemia include patient education and psychoeducation, the use of novel glycemic control technology, pancreas/islet transplantation, and drug therapy. This review examines both existing therapies and potential therapies that are in pre-clinical testing. Novel treatments that improve awareness of hypoglycemia, via improving the counterregulatory hormone responses or improving hypoglycemic symptom recognition, would also shed light on the possible neurological mechanisms that lead to the development of IAH. To reduce the risk of severe hypoglycemia in people with diabetes, elucidating the mechanism behind IAH, as well as developing targeted therapies is currently an unmet need for those that suffer from IAH.

Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes

Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.

Understanding temporal changes and seasonal variations in glycemic trends using wearable data

Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year's Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.

Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

An Update on the Molecular and Cellular Basis of Pharmacotherapy in Type 2 Diabetes Mellitus

Diabetes mellitus (DM) is a chronic illness with an increasing global prevalence. More than 537 million cases of diabetes were reported worldwide in 2021, and the number is steadily increasing. The worldwide number of people suffering from DM is projected to reach 783 million in 2045. In 2021 alone, more than USD 966 billion was spent on the management of DM. Reduced physical activity due to urbanization is believed to be the major cause of the increase in the incidence of the disease, as it is associated with higher rates of obesity. Diabetes poses a risk for chronic complications such as nephropathy, angiopathy, neuropathy and retinopathy. Hence, the successful management of blood glucose is the cornerstone of DM therapy. The effective management of the hyperglycemia associated with type 2 diabetes includes physical exercise, diet and therapeutic interventions (insulin, biguanides, second generation sulfonylureas, glucagon-like peptide 1 agonists, dipeptidyl-peptidase 4 inhibitors, thiazolidinediones, amylin mimetics, meglitinides, α-glucosidase inhibitors, sodium-glucose cotransporter-2 inhibitors and bile acid sequestrants). The optimal and timely treatment of DM improves the quality of life and reduces the severe burden of the disease for patients. Genetic testing, examining the roles of different genes involved in the pathogenesis of DM, may also help to achieve optimal DM management in the future by reducing the incidence of DM and by enhancing the use of individualized treatment regimens.

The efficacy of automated insulin delivery systems in children and adolescents with Type 1 Diabetes Mellitus: a systematic review and meta-analysis of randomized controlled trials

AIMS

Insulin administration is the treatment of choice for people with type 1 diabetes mellitus (T1D). Technological advances have led to the development of automated insulin delivery (AID) systems, aiming to optimize the quality of life of patients with T1D. We present a systematic review and meta-analysis of the current literature about the efficacy of AID systems in children and adolescents with T1D.

METHODS

We conducted a systematic literature search for randomized controlled trials (RCTs) until August 8^th, 2022, investigating the efficacy of AID systems in the management of patients <21 years of age with T1D. A priori subgroup and sensitivity analyses based on different settings (free-living settings, type of AID system, parallel group or crossover design) were also conducted.

RESULTS

In total, 26 RCTs reporting a total of 915 children and adolescents with T1D were included in the meta-analysis. AID systems revealed statistically significant differences in the main outcomes, such as the proportion of time in the target glucose range (3.9-10 mmol/L) (p<0.00001), in hypoglycemia (<3.9 mmol/L) (p=0.003) and mean proportion of HbA1C (p=0.0007) compared to control group.

CONCLUSIONS

According to the present meta-analysis, AID systems are superior to insulin pump therapy, sensor-augmented pumps and multiple daily insulin injections. Most of the included studies have a high risk of bias because of allocation, blinding of patients and blinding of assessment. Our sensitivity analyses showed that patients <21 years of age with T1D can use AID systems, after proper education, following their daily activities. Further RCTs examining the effect of AID systems on nocturnal hypoglycemia, under free-living settings and studies examining the effect of dual-hormone AID systems are pending.

Management of Individuals With Diabetes at High Risk for Hypoglycemia: An Endocrine Society Clinical Practice Guideline

CONTEXT

Hypoglycemia in people with diabetes is common, especially in those taking medications such as insulin and sulfonylureas (SU) that place them at higher risk. Hypoglycemia is associated with distress in those with diabetes and their families, medication nonadherence, and disruption of life and work, and it leads to costly emergency department visits and hospitalizations, morbidity, and mortality.

OBJECTIVE

To review and update the diabetes-specific parts of the 2009 Evaluation and Management of Adult Hypoglycemic Disorders: Endocrine Society Clinical Practice Guideline and to address developing issues surrounding hypoglycemia in both adults and children living with diabetes. The overriding objectives are to reduce and prevent hypoglycemia.

METHODS

A multidisciplinary panel of clinician experts, together with a patient representative, and methodologists with expertise in evidence synthesis and guideline development, identified and prioritized 10 clinical questions related to hypoglycemia in people living with diabetes. Systematic reviews were conducted to address all the questions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make recommendations.

RESULTS

The panel agreed on 10 questions specific to hypoglycemia risk and prevention in people with diabetes for which 10 recommendations were made. The guideline includes conditional recommendations for use of real-time continuous glucose monitoring (CGM) and algorithm-driven insulin pumps in people with type 1 diabetes (T1D), use of CGM for outpatients with type 2 diabetes at high risk for hypoglycemia, use of long-acting and rapid-acting insulin analogs, and initiation of and continuation of CGM for select inpatient populations at high risk for hypoglycemia. Strong recommendations were made for structured diabetes education programs for those at high risk for hypoglycemia, use of glucagon preparations that do not require reconstitution vs those that do for managing severe outpatient hypoglycemia for adults and children, use of real-time CGM for individuals with T1D receiving multiple daily injections, and the use of inpatient glycemic management programs leveraging electronic health record data to reduce the risk of hypoglycemia.

CONCLUSION

The recommendations are based on the consideration of critical outcomes as well as implementation factors such as feasibility and values and preferences of people with diabetes. These recommendations can be used to inform clinical practice and health care system improvement for this important complication for people living with diabetes.

Long-term efficacy of sensor-augmented pump therapy (Minimed 640G system) combined with a telemedicine follow-up in patients with type 1 diabetes: A real life study

Objective

Evaluate the efficacy of a new modality of insulin therapy associating both the sensor-augmented pump therapy with predictive low-glucose management (SAP-PLGM) and a telemedicine follow-up in patients with Type 1 diabetes (T1D) in a real-life setting.

Methods

T1D adults under Minimed 640G system with a telemedicine follow-up for glucose management were included in a retrospective study. The primary endpoint was HbA1c while continuous glucose monitoring parameters (CGM) and treatment compliance were the secondary endpoints. These parameters were analyzed according to the therapeutic indication, HbA1c ≥ 8 % (Group A) or severe hypoglycemic events (Group B) and in patients switched to SAP-PLGM therapy.

Results

62 patients were analyzed with a 28 ± 12 months of follow-up. In Group A, HbA1c decreased from 8.3 ± 0.4 % to 7.7 ± 0.7 % (p < 0.05) and to 7.9 ± 0.3 % (p < 0.05) after 2 and 3 years, respectively. In patients switched to SAP-PLGM therapy, HbA1c decreased from 7.7 ± 0.7 % to 7.2 ± 0.8 % (p < 0.05) at 2 years. After 6 months, the time-below-range (<70 mg/dL) decreased from 2.1 % [0.6-4] to 1.1 % [0.3-2.6] (p < 0.05). Severe hypoglycemic events decreased from 1.62 to 0.5 events/patient/year in Group B (p < 0.05). At 3 years, treatment compliance was 92 % [70-97] in the total population.

Conclusions

Long-term real-life treatment with the SAP-PLGM therapy combined with telemedicine was associated with improved glycemic control in T1D, along with high treatment compliance.

A computational framework for discovering digital biomarkers of glycemic control

Digital biomarkers can radically transform the standard of care for chronic conditions that are complex to manage. In this work, we propose a scalable computational framework for discovering digital biomarkers of glycemic control. As a feasibility study, we leveraged over 79,000 days of digital data to define objective features, model the impact of each feature, classify glycemic control, and identify the most impactful digital biomarkers. Our research shows that glycemic control varies by age group, and was worse in the youngest population of subjects between the ages of 2–14. In addition, digital biomarkers like prior-day time above range and prior-day time in range, as well as total daily bolus and total daily basal were most predictive of impending glycemic control. With a combination of the top-ranked digital biomarkers, we achieved an average F1 score of 82.4% and 89.7% for classifying next-day glycemic control across two unique datasets.

Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes

Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.

Enhancing Choices Regarding the Administration of Insulin Among Patients With Diabetes Requiring Insulin Across Countries and Implications for Future Care

There are a number of ongoing developments to improve the care of patients with diabetes across countries given its growing burden. Recent developments include new oral medicines to reduce cardiovascular events and death. They also include new modes to improve insulin administration to enhance adherence and subsequent patient management thereby reducing hypoglycaemia and improving long-term outcomes. In the case of insulins, this includes long-acting insulin analogues as well as continuous glucose monitoring (CGM) systems and continuous subcutaneous insulin infusion systems, combined with sensor-augmented pump therapy and potentially hybrid closed-loops. The benefits of such systems have been endorsed by endocrine societies and governments in patients with Type 1 diabetes whose HbA1c levels are not currently being optimised. However, there are concerns with the low use of such systems across higher-income countries, exacerbated by their higher costs, despite studies suggesting their cost-effectiveness ratios are within accepted limits. This is inconsistent in higher-income countries when compared with reimbursement and funding decisions for new high-priced medicines for cancer and orphan diseases, with often limited benefits, given the burden of multiple daily insulin injections coupled with the need for constant monitoring. This situation is different among patients and governments in low- and low-middle income countries struggling to fund standard insulins and the routine monitoring of HbA1c levels. The first priority in these countries is to address these priority issues before funding more expensive forms of insulin and associated devices. Greater patient involvement in treatment decisions, transparency in decision making, and evidence-based investment decisions should help to address such concerns in the future.

Glycaemic control and novel technology management strategies in pregestational diabetes mellitus

INTRODUCTION

Pregestational diabetes (PGDM) is an increasingly common and complex condition that infers risk to both mother and infant. To prevent serious morbidity, strict glycaemic control is essential. The aim of this review is to review the glucose sensing and insulin delivering technologies currently available for women with PGDM.

METHODS

We reviewed online databases for articles relating to technology use in pregnancy using a combination of keywords and MeSH headings. Relevant articles are included below.

RESULTS

A number of technological advancements have improved care and outcomes for women with PGDM. Real time continuous glucose monitoring (rtCGM) offers clear advantages in terms of infants size and neonatal intensive care unit admissions; and further benefits are seen when combined with continuous subcutaneous insulin delivery (insulin pump) and algorithms which continuously adjust insulin levels to glucose targets (hybrid closed loop). Other advancements including flash or intermittent scanning CGM (isCGM) and stand-alone insulin pumps do not confer as many advantages for women and their infants, however they are increasingly used outside of pregnancy and many women enter pregnancy already using these devices.

DISCUSSION

This article offers a discussion of the most commonly used technologies in pregnancy and evaluates their current and future roles.

Hybrid Closed-Loop Systems for the Treatment of Type 1 Diabetes: A Collaborative, Expert Group Position Statement for Clinical Use in Central and Eastern Europe

In both pediatric and adult populations with type 1 diabetes (T1D), technologies such as continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), or sensor-augmented pumps (SAP) can consistently improve glycemic control [measured as glycated hemoglobin (HbA1c) and time in range (TIR)] while reducing the risk of hypoglycemia. Use of technologies can thereby improve quality of life and reduce the burden of diabetes management compared with self-injection of multiple daily insulin doses (MDI). Novel hybrid closed-loop (HCL) systems represent the latest treatment modality for T1D, combining modern glucose sensors and insulin pumps with a linked control algorithm to offer automated insulin delivery in response to blood glucose levels and trends. HCL systems have been associated with increased TIR, improved HbA1c, and fewer hypoglycemic events compared with CSII, SAP, and MDI, thereby potentially improving quality of life for people with diabetes (PwD) while reducing the costs of treating short- and long-term diabetes-related complications. However, many barriers to their use and regional inequalities remain in Central and Eastern Europe (CEE). Published data suggest that access to diabetes technologies is hindered by lack of funding, underdeveloped health technology assessment (HTA) bodies and guidelines, unfamiliarity with novel therapies, and inadequacies in healthcare system capacities. To optimize the use of diabetes technologies in CEE, an international meeting comprising experts in the field of diabetes was held to map the current regional access, to present the current national reimbursement guidelines, and to recommend solutions to overcome uptake barriers. Recommendations included regional and national development of HTA bodies, efficient allocation of resources, and structured education programs for healthcare professionals and PwD. The responsibility of the healthcare community to ensure that all individuals with T1D gain access to modern technologies in a timely and economically responsible manner, thereby improving health outcomes, was emphasized, particularly for interventions that are cost-effective.

Nocturnal hypoglycemia in type 1 and type 2 diabetes: an update on prevalence, prevention, pathophysiology and patient awareness

INTRODUCTION

Despite considerable progress in diabetes treatment, prevalence of nocturnal hypoglycemia in type 1 diabetes mellitus (T1DM) and advanced insulin treated type 2 diabetes mellitus (T2DM) remains high.

AREAS COVERED

The present manuscript describes the prevalence of night-time hypoglycemia as reported in observational and randomized controlled trials. Factors that affect the risk of hypoglycemia are highlighted. The authors also describe impaired awareness of hypoglycemia and available preventive methods.

EXPERT OPINION

Prevention of nocturnal hypoglycemia includes behavioral, dietary and pharmacologic interventions. The most recent development with the lowest rate of hypoglycemia is sensor-augmented pumps with predictive low glucose suspend technology. These pumps combine continuous subcutaneous insulin infusion with continuous glucose monitoring and use various algorithms to predict and stop hypoglycemia before it develops.

The Cost-Effectiveness of an Advanced Hybrid Closed-Loop System in People with Type 1 Diabetes: a Health Economic Analysis in Sweden

INTRODUCTION

Swedish National Diabetes Registry data show a correlation of improved glycemic control in people with type 1 diabetes (T1D) with increased use of diabetes technologies over the past 25 years. However, novel technologies are often associated with a high initial outlay. The aim of the present study was to evaluate the long-term cost-effectiveness of the advanced hybrid closed-loop (AHCL) MiniMed 780G system versus intermittently scanned continuous glucose monitoring (isCGM) plus self-injection of multiple daily insulin (MDI) or continuous subcutaneous insulin infusion (CSII) in people with T1D in Sweden.

METHODS

Outcomes were projected over patients' lifetimes using the IQVIA CORE Diabetes Model (v9.0). Clinical data, including changes in glycated hemoglobin (HbA1c) and hypoglycemia rates, were sourced from observational studies and a randomized crossover trial. Modeled patients were assumed to receive the treatments for their lifetimes, with HbA1c kept constant following the application of treatment effects. Costs were accounted from a societal perspective and expressed in Swedish krona (SEK). Utilities and days off work estimates were taken from published sources.

RESULTS

The MiniMed 780G system was associated with an improvement in life expectancy of 0.16 years and an improvement in quality-adjusted life expectancy of 1.95 quality-adjusted life years (QALYs) versus isCGM plus MDI or CSII. These clinical benefits were due to a reduced incidence and a delayed time to onset of diabetes-related complications. Combined costs were estimated to be SEK 727,408 (EUR 72,741) higher with MiniMed 780G, with treatment costs partially offset by direct cost savings from the avoidance of diabetes-related complications and indirect cost savings from the avoidance of lost workplace productivity. The MiniMed 780G system was associated with an incremental cost-effectiveness ratio of SEK 373,700 per QALY gained.

CONCLUSIONS

Based on a willingness-to-pay threshold of SEK 500,000 per QALY gained, the MiniMed 780G system was projected to be cost-effective versus isCGM plus MDI or CSII for the treatment of T1D in Sweden.

Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization

Background

The increasing prevalence of diabetes mellitus and associated morbidity worldwide justifies the need to create new approaches and strategies for diabetes therapy. Therefore, the ongoing digitalization offers novel opportunities in this field.

Objective

The aim of this study is to provide an updated overview of available technologies, possibilities, and novel insights into diabetes therapy 4.0.

Methods

A scoping review was carried out, and a literature search was performed using electronic databases (MEDLINE [PubMed], Cochrane Library, Embase, CINAHL, and Web of Science). The results were categorized according to the type of technology presented.

Results

Different types of technology (eg, glucose monitoring systems, insulin pens, insulin pumps, closed-loop systems, mobile health apps, telemedicine, and electronic medical records) may help to improve diabetes treatment. These improvements primarily affect glycemic control. However, they may also help in increasing the autonomy and quality of life of people who are diagnosed with diabetes mellitus.

Conclusions

Diabetes technologies have developed rapidly over the last few years and offer novel insights into diabetes therapy and a chance to improve and individualize diabetes treatment. Challenges that need to be addressed in the following years relate to data security, interoperability, and the development of standards.

The Evolution of Hemoglobin A1c Targets for Youth With Type 1 Diabetes: Rationale and Supporting Evidence

The American Diabetes Association 2020 Standards of Medical Care in Diabetes (Standards of Care) recommends a hemoglobin A1c (A1C) of <7% (53 mmol/mol) for many children with type 1 diabetes (T1D), with an emphasis on target personalization. A higher A1C target of <7.5% may be more suitable for youth who cannot articulate symptoms of hypoglycemia or have hypoglycemia unawareness and for those who do not have access to analog insulins or advanced diabetes technologies or who cannot monitor blood glucose regularly. Even less stringent A1C targets (e.g., <8%) may be warranted for children with a history of severe hypoglycemia, severe morbidities, or short life expectancy. During the "honeymoon" period and in situations where lower mean glycemia is achievable without excessive hypoglycemia or reduced quality of life, an A1C <6.5% may be safe and effective. Here, we provide a historical perspective of A1C targets in pediatrics and highlight evidence demonstrating detrimental effects of hyperglycemia in children and adolescents, including increased likelihood of brain structure and neurocognitive abnormalities, microvascular and macrovascular complications, long-term effects, and increased mortality. We also review data supporting a decrease over time in overall severe hypoglycemia risk for youth with T1D, partly associated with the use of newer insulins and devices, and weakened association between lower A1C and severe hypoglycemia risk. We present common barriers to achieving glycemic targets in pediatric diabetes and discuss some strategies to address them. We aim to raise awareness within the community on Standards of Care updates that impact this crucial goal in pediatric diabetes management.

A Systematic Review of Collective Evidences Investigating the Effect of Diabetes Monitoring Systems and Their Application in Health Care

INTRODUCTION

Diabetes monitoring systems (DMS) are a possible approach for regular control of glucose levels in patients with Type 1 or 2 diabetes in order to improve therapeutic outcomes or to identify and modify inappropriate patient behaviors in a timely manner. Despite the significant number of studies observing the DMS, no collective evidence is available about the effect of all devices.

GOAL

To review and consolidate evidences from multiple systematic reviews on the diabetes monitoring systems and the outcomes achieved.

MATERIALS AND METHODS

Internet-based search in PubMed, EMBASE, and Cochrane was performed to identify all studies relevant to the research question. The data regarding type of intervention, type of diabetes mellitus, type of study, change in clinical parameter(s), or another relevant outcome were extracted and summarized.

RESULTS

Thirty-three out of 1,495 initially identified studies, involving more than 44,100 patients with Type 1, Type 2, or gestational diabetes for real-time or retrospective Continuous Glucose Monitoring (CGMS), Sensor Augmented Pump Therapy (SAPT), Self-monitoring Blood Glucose (SMBG), Continuous subcutaneous insulin infusion (CSII), Flash Glucose Monitoring (FGM), Closed-loop systems and telemonitoring, were included. Most of the studies observed small nominal effectiveness of DMS. In total 11 systematic reviews and 15 meta-analyses, with most focusing on patients with Type 1 diabetes (10 and 6, respectively), reported a reduction in glycated hemoglobin (HbA1c) levels from 0.17 to 0.70% after use of DMS.

CONCLUSION

Current systematic review of already published systematic reviews and meta-analyses suggests that no statistically significant difference exists between the values of HbA1c as a result of application of any type of DMS. The changes in HbA1c values, number and frequency of hypoglycemic episodes, and time in glucose range are the most valuable for assessing the appropriateness and effectiveness of DMS. Future more comprehensive studies assessing the effectiveness, cost-effectiveness, and comparative effectiveness of DMS are needed to stratify them for the most suitable diabetes patients' subgroups.

Psychological Well-Being of Parents of Very Young Children With Type 1 Diabetes - Baseline Assessment

BACKGROUND

Type 1 diabetes in young children is a heavy parental burden. As part of pilot phase of the KIDSAP01 study, we conducted a baseline assessment in parents to study the association between hypoglycemia fear, parental well-being and child behavior.

METHODS

All parents were invited to fill in baseline questionnaires: hypoglycemia fear survey (HFS), WHO-5, Epworth Sleepiness Scale and Strength and Difficulties Questionnaire (SDQ).

RESULTS

24 children (median age: 5-year, range 1-7 years, 63% male, mean diabetes duration: 3 ± 1.7 years) participated. 23/24 parents filled out the questionnaires. We found a higher score for the hypoglycemia fear behavior 33.9 ± 5.6 compared to hypoglycemia worry 34.6 ± 12.2. Median WHO-5 score was 16 (8 - 22) with poor well-being in two parents. Median daytime sleepiness score was high in five parents (>10). For six children a high total behavioral difficulty score (>16) was reported. Pro social behavior score was lower than normal in six children (<6). Parental well-being was negatively associated with HFS total (r = - 0.50, p <.05) and subscale scores (r = - 0.44, p <.05 for HFS-Worry and HFS-Behavior), child behavior (r = - 0.45, p = .05) and positively with child age and diabetes duration (r = 0.58, p <.01, r = 0.6, p <.01). HFS, parental well-being nor daytime sleepiness are associated with the HbA1c.

CONCLUSION

Regular screening of parental well-being, hypoglycemia fear and child behavior should be part of routine care to target early intervention.

Health Care Provider Knowledge and Perceptions of FDA-Approved and Do-It-Yourself Automated Insulin Delivery

BACKGROUND

Automated insulin delivery (AID) technology may reduce variability in blood glucose, resulting in lower risk for hypoglycemia and associated complications, and by extension improve quality of life. While clinical trials, research, and patient experience have consistently demonstrated the value of AID, this technology is still inaccessible to many patients. Patient-driven innovation has resulted in alternative do-it-yourself (DIY) solutions to available off-the-shelf AID devices.

METHOD

This two-phase cross-sectional observational study addressed health care provider (HCP) perceptions of AID as well as the perceived need for, development of, and evaluation of an AID fact sheet comparing the most commonly used Federal Drug Administration approved AID and DIY AID devices.

RESULTS

Negative attitudes toward the use of DIY AID were low. The majority of HCPs saw their lack of knowledge about how DIY AID work to be the greatest barrier to answering patient questions about what is available (74.4%). Additionally, the majority of HCPs (64.5%) indicated they were either "likely" or "very likely" to use the fact sheet when answering patient questions about AID options.

CONCLUSION

Increased awareness and utilization of AID technology offer hope to further reduce the burden of diabetes, but there is a need to bridge the knowledge gap about DIY AID. A fact sheet provides a way to facilitate discussions of this emerging technology between HCPs and patients. Next steps could investigate additional ways to put needed information in the hands of HCPs.

Glucagon for hypoglycaemia treatment in type 1 diabetes

To achieve strict glycaemic control and avoid chronic diabetes complications, individuals with type 1 diabetes (T1D) are recommended to follow an intensive insulin regimen. However, the risk and fear of hypoglycaemia often prevent individuals from achieving the treatment goals. Apart from early insulin suspension in insulin pump users, carbohydrate ingestion is the only option for preventing and treating non-severe hypoglycaemic events. These rescue treatments may give extra calories and cause overweight. As an alternative, the use of low-dose glucagon to counter hypoglycaemia has been proposed as a tool to raise glucose concentrations without adding extra calories. Previously, the commercially available glucagon formulations required reconstitution from powder to a solution before being injected subcutaneously or intramuscularly-making it practical only for treating severe hypoglycaemia. Several companies have developed more stable formulations that do not require the time-consuming reconstitution process before use. As well as treating severe hypoglycaemia, non-severe and impending hypoglycaemia can also be treated with lower doses of glucagon. Once available, low-dose glucagon can be either delivered manually, as an injection, or automatically, by an infusion pump. This review focuses on the role and perspectives of using glucagon to treat and prevent hypoglycaemia in T1D.

Evolution of Insulin Delivery Devices: From Syringes, Pens, and Pumps to DIY Artificial Pancreas

The year 2021 will mark 100 years since the discovery of insulin. Insulin, the first medication to be discovered for diabetes, is still the safest and most potent glucose-lowering therapy. The major challenge of insulin despite its efficacy has been the occurrence of hypoglycemia, which has resulted in sub-optimal dosages being prescribed in the vast majority of patients. Popular devices used for insulin administration are syringes, pens, and pumps. An artificial pancreas (AP) with a closed-loop delivery system with > 95% time in range is believed to soon become a reality. The development of closed-loop delivery systems has gained momentum with recent advances in continuous glucose monitoring (CGM) and computer algorithms. This review discusses the evolution of syringes, disposable, durable pens and connected pens, needles, tethered and patch insulin pumps, bionic pancreas, alternate controller-enabled infusion (ACE) pumps, and do-it-yourself artificial pancreas systems (DIY-APS).

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime

Factory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10-14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay.

A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia

To evaluate the effectiveness of predictive low glucose suspend (PLGS) systems within sensor-augmented insulin infusion pumps at preventing nocturnal hypoglycemia in patients with type 1 diabetes (DM1), we performed a systematic review and meta-analysis of randomized crossover trials. Pubmed and Google Scholar were searched for randomized crossover trials, published between January 2013 and July 2018, in nonpregnant outpatients with DM1, which compared event rates during PLGS overnight periods and non-PLGS overnight periods. The primary outcome was the proportion of overnight periods with one or more hypoglycemic measurement. When available, individual patient data were used to assess the effect of clustering measurements within patients. Four studies (272 patients, 10,735 patient-nights: 5422 PLGS and 5313 non-PLGS) were included in the meta-analysis. Two studies reported patient-level data that permitted assessment of the effect of clustering measurements within patients. The effect on the risk difference was minimal. The proportion of overnight periods with one or more episodes of hypoglycemia was 19.6% for the PLGS periods and 27.8% for the non-PLGS periods. Based on the pooled estimate, PLGS overnight periods were associated with an 8.8% lower risk of hypoglycemia (risk difference -0.088; 95% CI -0.119 to -0.056, I² = 67.4%, τ² = 0.0006, 4 studies). PLGS systems can reduce nocturnal hypoglycemic events in patients with DM1.

A Clinical Overview of Insulin Pump Therapy for the Management of Diabetes: Past, Present, and Future of Intensive Therapy

IN BRIEF Insulin pump therapy is advancing rapidly. This article summarizes the variety of insulin pump technologies available to date and discusses important clinical considerations for each type of technology.

The Predictive Low Glucose Management System in Prevention of Clinically Significant Hypoglycemia in Type 1 Diabetes. A Preliminary Study Identifying the Most Common Events Leading Up to Hypoglycemia During Insulin Pump Therapy

BACKGROUND/AIM

Prevention of hypoglycemia remains a major challenge in diabetic management, despite the introduction of modern insulin pumps in daily clinical practice. The Low Glucose Suspend (LGS) and the newer Predictive Low Glucose Management (PLGM) systems incorporated in the Medtronic insulin pumps have shown promising results in prevention of hypoglycemia. Our aim was to evaluate the effect of the 2 systems relative to the frequency of clinically significant hypoglycemia in Type 1 diabetes (T1DM). In addition, we investigated the events preceding clinically significant hypoglycemia episodes.

METHODS

A cross-sectional study was conducted in 30 T1DM patients using the MiniMed 640G vs. 30 using the MiniMed Veo sensor-augmented insulin pump. All data was recorded during patients' normal daily activity and living conditions. The patients were matched for age and duration of diabetes.

RESULTS

PLGM use was associated with lower incidence of clinically significant hypoglycemia (1.9±1.4 vs. 3.6±1.9 episodes per week), along with reduced exposure to hypoglycemia. The data indicated that both pump systems are effective in preventing severe hypoglycemic episodes. In both groups the most common events preceding hypoglycemic episodes included adjustment of hyperglycemia, basal rate increase and miscalculation of carbohydrates.

CONCLUSIONS

The results indicated that the use of the Minimed 640G pump system can help reduce the frequency of clinically significant hypoglycemia. Management of hyperglycemia must be addressed in diabetes education programs in order to encourage proper adjustment of high blood glucose levels. Future studies would be useful in exploring the details of the events preceding hypoglycemia episodes in insulin pump users.

Preserved glucose response to low-dose glucagon after exercise in insulin-pump-treated individuals with type 1 diabetes: a randomised crossover study

AIMS/HYPOTHESIS

This study aimed to compare the increase in plasma glucose after a subcutaneous injection of 200 μg glucagon given after 45 min of cycling with resting (study 1) and to investigate the effects of glucagon when injected before compared with after 45 min of cycling (study 2). We hypothesised that: (1) the glucose response to glucagon would be similar after cycling and resting; and (2) giving glucagon before the activity would prevent the exercise-induced fall in blood glucose during exercise and for 2 h afterwards.

METHODS

Fourteen insulin-pump-treated individuals with type 1 diabetes completed three visits in a randomised, placebo-controlled, participant-blinded crossover study. They were allocated by sealed envelopes. Baseline values were (mean and range): HbA_1c 54 mmol/mol (43-65 mmol/mol) or 7.1% (6.1-8.1%); age 45 years (23-66 years); BMI 26 kg/m² (21-30 kg/m²); and diabetes duration 26 years (8-51 years). At each visit, participants consumed a standardised breakfast 2 h prior to 45 min of cycling or resting. A subcutaneous injection of 200 μg glucagon was given before or after cycling or after resting. The glucose response to glucagon was compared after cycling vs resting (study 1) and before vs after cycling (study 2).

RESULTS

The glucose response to glucagon was higher after cycling compared with after resting (mean ± SD incremental peak: 2.6 ± 1.7 vs 1.8 ± 2.0 mmol/l, p = 0.02). As expected, plasma glucose decreased during cycling (-3.1 ± 2.8 mmol/l) but less so when glucagon was given before cycling (-0.9 ± 2.8 mmol/l, p = 0.002). The number of individuals reaching glucose values ≤3.9 mmol/l was the same on the 3 days.

CONCLUSIONS/INTERPRETATION

Moderate cycling for 45 min did not impair the glucose response to glucagon compared with the glucose response after resting. The glucose fall during cycling was diminished by a pre-exercise injection of 200 μg glucagon; however, no significant difference was seen in the number of events of hypoglycaemia.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02882737 FUNDING: The study was funded by the Danish Diabetes Academy founded by Novo Nordisk foundation and by an unrestricted grant from Zealand Pharma.

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

For individuals affected by Type 1 diabetes (T1D), a chronic disease in which the pancreas does not produce any insulin, maintaining the blood glucose (BG) concentration as much as possible within the safety range (70–180 mg/dl) allows avoiding short- and long-term complications. The tuning of exogenous insulin infusion can be difficult, especially because of the inter- and intra-day variability of physiological and behavioral factors. Continuous glucose monitoring (CGM) sensors, which monitor glucose concentration in the subcutaneous tissue almost continuously, allowed improving the detection of critical hypo- and hyper-glycemic episodes. Moreover, their integration with insulin pumps for continuous subcutaneous insulin infusion allowed developing algorithms that automatically tune insulin dosing based on CGM measurements in order to mitigate the incidence of critical episodes. In this work, we aim at reviewing the literature on methods for CGM-based automatic attenuation or suspension of basal insulin with a focus on algorithms, their implementation in commercial devices and clinical evidence of their effectiveness and safety.