Artificial Pancreas: Current Progress and Future Outlook in the Treatment of Type 1 Diabetes

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.

Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes

BACKGROUND

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

MATERIALS AND METHODS

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

RESULTS

The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

CONCLUSION

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment

Objective.This study aims to develop a comprehensive decoding framework to create a multivariate physiological model of vagus nerve transmission that reveals the complex interactions between the nervous and metabolic systems.Approach.Vagus nerve activity was recorded in female Sprague-Dawley rats using gold hook microwires implanted around the left cervical vagus nerve. The rats were divided into three experimental cohorts (intact nerve, ligation nerve for recording afferent activation, and ligation for recording efferent activation) and metabolic challenges were administered to change glucose levels while recording the nerve activity. The decoding methodology involved various techniques, including continuous wavelet transformation, extraction of breathing rate (BR), and correlation of neural metrics with physiological signals.Main results.Decrease in glucose level was consistently negatively correlated with an increase in the firing activity of the intact vagus nerve that was found to be conveyed by both afferent and efferent pathways, with the afferent response being more similar to the one on the intact nerve. A larger variability was observed in the sensory and motor responses to hyperglycaemia. A novel strategy to extract the BR over time based on inter-burst-interval is also presented. The vagus afferent was found to encode breathing information through amplitude and firing rate modulation. Modulations of the signal amplitude were also observed due to changes in heart rate in the intact and efferent recordings, highlighting the parasympathetic control of the heart.Significance.The analytical framework presented in this study provides an integrative understanding that considers the relationship between metabolic, cardiac, and breathing signals and contributes to the development of a multivariable physiological model for the transmission of vagus nerve signals. This work progresses toward the development of closed-loop neuro-metabolic therapeutic systems for diabetes.

A Systematic Review of Commercial Hybrid Closed-Loop Automated Insulin Delivery Systems

INTRODUCTION

Several different forms of automated insulin delivery systems (AID systems) have recently been developed and are now licensed for type 1 diabetes (T1D). We undertook a systematic review of reported trials and real-world studies for commercial hybrid closed-loop (HCL) systems.

METHODS

Pivotal, phase III and real-world studies using commercial HCL systems that are currently approved for use in type 1 diabetes were reviewed with a devised protocol using the Medline database.

RESULTS

Fifty-nine studies were included in the systematic review (19 for 670G; 8 for 780G; 11 for Control-IQ; 14 for CamAPS FX; 4 for Diabeloop; and 3 for Omnipod 5). Twenty were real-world studies, and 39 were trials or sub-analyses. Twenty-three studies, including 17 additional studies, related to psychosocial outcomes and were analysed separately.

CONCLUSIONS

These studies highlighted that HCL systems improve time In range (TIR) and arouse minimal concerns around severe hypoglycaemia. HCL systems are an effective and safe option for improving diabetes care. Real-world comparisons between systems and their effects on psychological outcomes require further study.

Experiences of Patients Adopting and Adapting to Closed-Loop Insulin Delivery Systems (CLIDS)

PURPOSE

The purpose of the study was to explore the perspectives and experiences of adults with type 1 diabetes (T1DM) who are currently using the closed-loop insulin delivery system (CLIDS).

METHODS

Eleven adults with T1DM who used closed-loop insulin pumps for at least 6 months participated in this qualitative descriptive study.

RESULTS

Four themes emerged from the rich descriptions: (1) striving for improvement, (2) missing a magic wand effect, (3) seeking support, and (4) barriers to adaptation. These themes represent both process-based and psychosocial implications for nursing practice and patient education.

CONCLUSIONS

To optimize CLIDS use and outcome, the antecedent conditions that contribute to patients' decision to adopt it must be understood. Then, interventions that focus on setting realistic expectations must be created. Patients need support as they incorporate CLIDS into their T1DM self-management. Training health care providers on the idiosyncrasies of adapting to CLIDS is critical. Patients must learn to relinquish control and trust the machine and manage the anxiety the system's intrusive alarms cause them so they can be better supported cognitively and psychosocially.

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

BACKGROUND

Controversy remains regarding whether closed-loop (CL) insulin delivery or insulin sensor-augmented pump (SAP) delivery is more efficient for clinical treatment. Therefore, we aimed to compare the efficacy and safety of CL insulin delivery systems versus insulin SAP delivery for adults with type 1 diabetes (T1D).

METHODS

Embase, Ovid MEDLINE, PubMed, ScienceDirect, Scopus, the Cochrane Library, and other databases were searched for related articles, and we analyzed the average blood glucose (BG), time in range (TIR), and adverse effects (AEs) as primary endpoints to evaluate efficacy and safety.

RESULTS

Of 1616 articles, 12 randomized-controlled trials (RCTs) were included in the final analysis. Regarding BG control efficacy, CL insulin delivery resulted better outcomes than SAP therapy with regard to the average BG value, which was detected and recorded by continuous glucose monitoring (mean difference [MD][mmol/L]:  - 0.25 95% confidence interval [CI]  - 0.42 to - 0.08, p = 0.003); TIR 3.9-10 mmol/L (MD [%]: 7.91 95% CI 4.45-11.37, p < 0.00001). Similar results were observed for the secondary outcomes including low blood glucose index (LBGI) (MD:  - 0.41 95% CI - 0.55 to - 0.26, p < 0.00001), high blood glucose index (HBGI) (MD:  - 2.56 95% CI - 3.38 to - 1.74, p < 0.00001), and standard deviation (SD) of glucose variability (MD [mmol/L]: -0.25 95% CI - 0.44 to - 0.06, p = 0.01). Furthermore, SAP therapy was associated with more adverse effects (risk ratio: 0.20 95% CI 0.07-0.52, p = 0.001) than CL insulin delivery, and one of the most common adverse effects was hypoglycemia.

CONCLUSIONS

CL insulin delivery appears to be a better treatment method than SAP therapy for adults with T1D because of its increased BG control efficacy and decreased number of hypoglycemic events.

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (-4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

A century past the discovery of insulin: global progress and challenges for type 1 diabetes among children and adolescents in low-income and middle-income countries

Type 1 diabetes is on the rise globally; however, the burden of mortality remains disproportionate in low-income and middle-income countries (LMICs). As 2021 marks 100 years since the discovery of insulin, we revisit progress, global burden of type 1 diabetes trends, and understanding of the pathogenesis and management practices related to the disease. Despite much progress, inequities in access and availability of insulin formulations persist and are reflected in differences in survival and morbidity patterns related to the disease. Some of these inequities have also been exacerbated by health-system challenges during the COVID-19 pandemic. There is a clear opportunity to improve access to insulin and related essential technologies for improved management of type 1 diabetes in LMICs, especially as a part of universal health coverage. These improvements will require concerted action and investments in human resources, community engagement, and education for the timely diagnosis and management of type 1 diabetes, as well as adequate health-care financing. Further research in LMICs, especially those in Africa, is needed to improve our understanding of the burden, risk factors, and implementation strategies for managing type 1 diabetes.

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.

A Multitask Learning Approach to Personalised Blood Glucose Prediction

Blood glucose prediction algorithms are key tools in the development of decision support systems and closed-loop insulin delivery systems for blood glucose control in diabetes. Deep learning models have provided leading results among machine learning algorithms to date in glucose prediction. However these models typically require large amounts of data to obtain best personalised glucose prediction results. Multitask learning facilitates an approach for leveraging data from multiple subjects while still learning accurate personalised models. In this work we present results comparing the effectiveness of multitask learning over sequential transfer learning, and learning only on subject-specific data with neural networks and support vector regression. The multitask learning approach shows consistent leading performance in predictive metrics at both short-term and long-term prediction horizons. We obtain a predictive accuracy (RMSE) of 18.8 2.3, 25.3 2.9, 31.8 3.9, 41.2 4.5, 47.2 4.6 mg/dL at 30, 45, 60, 90, and 120 min prediction horizons respectively, with at least 93\% clinically acceptable predictions using the Clarke Error Grid (EGA) at each prediction horizon. We also identify relevant prior information such as glycaemic variability that can be incorporated to improve predictive performance at long-term prediction horizons. Furthermore, we demonstrate consistent performance - 5% change in both RMSE and EGA (Zone A) - in rare cases of adverse glycaemic events with 1-6 weeks of training data. In conclusion, a multitask approach can allow for deploying personalised models even with significantly less subject-specific data without compromising performance.

Efficacy of advanced hybrid closed loop systems for the management of type 1 diabetes in children

Over the last years significant advances have been achieved in the development of technologies for diabetes management. Continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), predictive low glucose management (PLGM), hybrid closed loop (HCL) and advanced hybrid closed loop (AHCL) systems allow better diabetes management, thus reducing the burden of the disease and the risk of chronic complications. This review summarizes the main characteristics of the currently available HCL and AHCL systems and their primary effects in children and adolescents with type 1 diabetes (T1D). The findings of trials assessing the glucose control (time in range, HbA1c values, hypoglycemic events), the health-related quality of life and the existing limits of the use of these technologies are reported. The most recent data clearly confirm the ability of the HCL and AHCL insulin delivery systems to safely achieve a significant improvement of glucose control and quality of life in the pediatric population with T1D. Further studies are underway to overcame current barriers and future improvements in the usability of these technologies are awaited to facilitate their use in the routine clinical practice. The HCL and AHCL algorithms are the key features of today's insulin delivery systems that mark a crucial step towards fully automated closed loop systems.

Study of blood glucose and insulin infusion rate in real-time in diabetic rats using an artificial pancreas system

Artificial pancreas system (APS) is an emerging new treatment for type 1 diabetes mellitus. The aim of this study was to develop a rat APS as a research tool and demonstrate its application. We established a rat APS using Medtronic Minimed Pump 722, Medtronic Enlite sensor, and the open artificial pancreas system as a controller. We tested different dilutions of Humalog (100 units/ml) in saline ranged from 1:3 to 1:20 and determined that 1:7 dilution works well for rats with ~500g bodyweight. Blood glucose levels (BGL) of diabetic rats fed with chow diet (58% carbohydrate) whose BGL was managed by the closed-loop APS for the total duration of 207h were in euglycemic range (70-180 mg/dl) for 94.5% of the time with 2.1% and 3.4% for hyperglycemia (>180mg/dl) and hypoglycemia (<70 mg/dl), respectively. Diabetic rats fed with Sucrose pellets (94.8% carbohydrate) for the experimental duration of 175h were in euglycemic range for 61% of the time with 35% and 4% for hyperglycemia and hypoglycemia, respectively. Heathy rats fed with chow diet showed almost a straight line of BGL ~ 95 mg/dl (average 94.8 mg/dl) during the entire experimental period (281h), which was minimally altered by food intake. In the healthy rats, feeding sucrose pellets caused greater range of BGL in high and low levels but still within euglycemic range (99.9%). Next, to study how healthy and diabetic rats handle supra-physiological concentrations of glucose, we intraperitoneally injected various amounts of 50% dextrose (2, 3, 4g/kg) and monitored BGL. Duration of hyperglycemia after injection of 50% dextrose at all three different concentrations was significantly greater for healthy rats than diabetic rats, suggesting that insulin infusion by APS was superior in reducing BGL as compared to natural insulin released from pancreatic β-cells. Ex vivo studies showed that islets isolated from diabetic rats were almost completely devoid of pancreatic β-cells but with intact α-cells as expected. Lipid droplet deposition in the liver of diabetic rats was significantly lower with higher levels of triacylglyceride in the blood as compared to those of healthy rats, suggesting lipid metabolism was altered in diabetic rats. However, glycogen storage in the liver determined by Periodic acid-Schiff staining was not altered in diabetic rats as compared to healthy rats. A rat APS may be used as a powerful tool not only to study alterations of glucose and insulin homeostasis in real-time caused by diet, exercise, hormones, or antidiabetic agents, but also to test mathematical and engineering models of blood glucose prediction or new algorithms for closed-loop APS.

Evaluation of the Accuracy of Current Tubeless Pumps for Continuous Subcutaneous Insulin Infusion

Background: Recently two new tubeless pumps for insulin therapy were introduced. They were tested for accuracy and occlusion detection and compared with the established patch pump Omnipod^® (OP). Methods: Using a modified setup for tubeless pumps based on IEC 60601-2-24, the basal rate and bolus delivery of the Accu-Chek^® Solo micropump system (ACS) and the A6 TouchCare^® System (A6) were measured with a microgravimetric method. Bolus sizes of 0.2, 1, and 10 U, and basal rates of 0.1 and 1 U/h were evaluated in nine repetitions. For each parameter, mean deviation and number of individual boluses or 1-h basal rate windows within ±15% from target were calculated. In addition, occlusion detection time at basal rates of 0.1 and 1 U/h was determined. Results: Mean deviation of boluses of different volumes in the pumps ranged from -3.3% to +4.0% and 40%-100% of individual boluses were within ±15% of the target. During basal rate delivery, 48% to 98% of 1-h windows were within ±15% of the target with a mean deviation between -5.3% and +6.5%. In general, considerable differences between pump models were observed and deviations decreased with increasing doses. In most parameters, ACS was more accurate, and A6 less accurate, than OP. Mean occlusion detection time ranged from ∼3 to 7.5 h at 1 U/h and was >24 h or absent at 0.1 U/h. Conclusions: In this evaluation, significant differences between the tested tubeless pump models were observed that became most evident when regarding delivery errors over short time and small volumes.

Therapeutic Advances in Diabetes, Autoimmune, and Neurological Diseases

Since 2015, 170 small molecules, 60 antibody-based entities, 12 peptides, and 15 gene- or cell-therapies have been approved by FDA for diverse disease indications. Recent advancement in medicine is facilitated by identification of new targets and mechanisms of actions, advancement in discovery and development platforms, and the emergence of novel technologies. Early disease detection, precision intervention, and personalized treatments have revolutionized patient care in the last decade. In this review, we provide a comprehensive overview of current and emerging therapeutic modalities developed in the recent years. We focus on nine diseases in three major therapeutics areas, diabetes, autoimmune, and neurological disorders. The pathogenesis of each disease at physiological and molecular levels is discussed and recently approved drugs as well as drugs in the clinic are presented.

Decreased serum fibroblast growth factor 19 level is a risk factor for type 1 diabetes

Background

Increasing evidence suggests that fibroblast growth factor 19 (FGF19) is a regulator of glucose metabolism and may provide a new therapeutic target for type 1 diabetes (T1D). However, the clinical relevance of FGF19 in T1D remains unclear. In this study, we examined the relationship between the serum FGF19 concentration and T1D.

Methods

This study included 81 newly diagnosed T1D patients and 80 sex- and age-matched healthy controls. The correlation between the FGF19 concentration and clinical characteristics of T1D patients and healthy controls was investigated. Logistic regression analysis was performed to determine whether levels of FGF19 were independently associated with T1D.

Results

The fasting serum FGF19 levels in the T1D group were significantly lower than those in the control group [159.9 (100.0–272.7) vs. 205.0 (126.9–307.9) pg/mL, P=0.008]. In all subjects, serum FGF19 levels were negatively correlated with fasting blood glucose (FBG) (r=−0.192, P=0.015). In the control group, serum FGF19 levels were positively correlated with total cholesterol (TC) (r=0.338, P=0.002) and low-density lipoprotein cholesterol (LDL-c) (r=0.300, P=0.007). In addition to sex and body mass index (BMI), FGF19 was an independent impact factor for T1D [odds ratio (OR) =0.541, P=0.023; adjusted for sex, age, BMI, presence of hypertension, and presence of dyslipidemia].

Conclusions

Low serum FGF19 level is associated with T1D, which could serve as a risk factor for T1D.

Diabetic neuropathy and neuropathic pain: a (con)fusion of pathogenic mechanisms?

Neuropathy is a common complication of long-term diabetes that impairs quality of life by producing pain, sensory loss and limb amputation. The presence of neuropathy in both insulin-deficient (type 1) and insulin resistant (type 2) diabetes along with the slowing of progression of neuropathy by improved glycemic control in type 1 diabetes has caused the majority of preclinical and clinical investigations to focus on hyperglycemia as the initiating pathogenic lesion. Studies in animal models of diabetes have identified multiple plausible mechanisms of glucotoxicity to the nervous system including post-translational modification of proteins by glucose and increased glucose metabolism by aldose reductase, glycolysis and other catabolic pathways. However, it is becoming increasingly apparent that factors not necessarily downstream of hyperglycemia can also contribute to the incidence, progression and severity of neuropathy and neuropathic pain. For example, peripheral nerve contains insulin receptors that transduce the neurotrophic and neurosupportive properties of insulin, independent of systemic glucose regulation, while the detection of neuropathy and neuropathic pain in patients with metabolic syndrome and failure of improved glycemic control to protect against neuropathy in cohorts of type 2 diabetic patients has placed a focus on the pathogenic role of dyslipidemia. This review provides an overview of current understanding of potential initiating lesions for diabetic neuropathy and the multiple downstream mechanisms identified in cell and animal models of diabetes that may contribute to the pathogenesis of diabetic neuropathy and neuropathic pain.

Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes

OBJECTIVE

To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise.

RESULTS

Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates.

CONCLUSIONS

The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.Register Clinicaltrials.gov: NCT03577158.

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1-5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient's data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes

BACKGROUND AND OBJECTIVES

Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D).

METHODS

This closed-loop (CL) system was tested in 29 T1D patients at one site in a 4 h inpatient open-label study. Participants used an L-MPC CL system for 6 days after 2-day system identification using open-loop (OL) insulin system. During the CL period, the L-MPC system was started from 8:00 am to noon each day. At 9:00 am, each participant consumed 50 g of carbohydrates with no prandial insulin bolus. At 9:30 am on CL-Day 4 or CL-Day 6, participants rode bicycles for 20 minutes or drank 50 ml of beer, in a random order.

RESULTS

As the primary outcome, TIR on CL-Day 3 was 65.2±23.3%, which was 9.8 points higher (95% CI 1.8 to 17.8; P = 0.019) than that on CL-Day 1. The time of glucose >10 mmol/L was decreased by 11.0% (95% CI -18.7 to 3.3; P = 0.007), and mean glucose level was decreased by 1.1 mmol/L (95% CI -1.1 to 0.5; P = 0.000). The total daily insulin dosage showed no significant difference (-0.1U, 95% CI -1.34 to 1.32; P = 0.982). Compared with OL-Day1 with a postprandial bolus, the TIR was increased by 13.7 points (95% CI 1.4 to 26.0; P = 0.030), the time of glucose >10 mmol/L and the mean glucose level were also decreased. Compared with the exercise day (CL-Day E, 62.0 ± 23.3%; P = 0.347) or alcohol day (CL-Day A, 64.0 ± 23.6%; P = 0.756), there was no statistically significant difference in terms of TIR, time of glucose >10 mmol/L and mean glucose level. No severe hypoglycemic events occurred and hypoglycemic episodes were not increased by using closed-loop insulin system.

CONCLUSION

The L-MPC CL insulin system achieved good glycemic control challenged by an unannounced meal.

Closed-loop bioelectronic medicine for diabetes management

Modulation of the nervous system by delivering electrical or pharmaceutical agents has contributed to the development of novel treatments to serious health disorders. Recent advances in multidisciplinary research has enabled the emergence of a new powerful therapeutic approach called bioelectronic medicine. Bioelectronic medicine exploits the fact that every organ in our bodies is neurally innervated and thus electrical interfacing with peripheral nerves can be a potential pathway for diagnosing or treating diseases such as diabetes. In this context, a plethora of studies have confirmed the important role of the nervous system in maintaining a tight regulation of glucose homeostasis. This has initiated new research exploring the opportunities of bioelectronic medicine for improving glucose control in people with diabetes, including regulation of gastric emptying, insulin sensitivity, and secretion of pancreatic hormones. Moreover, the development of novel closed-loop strategies aims to provide effective, specific and safe interfacing with the nervous system, and thereby targeting the organ of interest. This is especially valuable in the context of chronic diseases such as diabetes, where closed-loop bioelectronic medicine promises to provide real-time, autonomous and patient-specific therapies. In this article, we present an overview of the state-of-the-art for closed-loop neuromodulation systems in relation to diabetes and discuss future related opportunities for management of this chronic disease.