Advances in artificial pancreas systems

Insulin detection in diabetes mellitus: challenges and new prospects

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Importance of timing optimization for closed-loop applications of vagus nerve stimulation

In recent decades, vagus nerve stimulation (VNS) therapy has become widely used for clinical applications including epilepsy, depression, and enhancing the effects of rehabilitation. However, several questions remain regarding optimization of this therapy to maximize clinical outcomes. Although stimulation parameters such as pulse width, amplitude, and frequency are well studied, the timing of stimulation delivery both acutely (with respect to disease events) and chronically (over the timeline of a disease's progression) has generally received less attention. Leveraging such information would provide a framework for the implementation of next generation closed-loop VNS therapies. In this mini-review, we summarize a number of VNS therapies and discuss (1) general timing considerations for these applications and (2) open questions that could lead to further therapy optimization.

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

A review on artificial pancreas and regenerative medicine used in the management of Type 1 diabetes mellitus

Diabetes mellitus is one of the fastest-growing lifestyle disorders in the world. While numerous regimes have been developed to manage diabetes, there continue to be high numbers of diabetes-related deaths worldwide. The review gives a brief introduction to the pathology and aetiology of the disorder, different solutions developed over time with their advantages and disadvantages, and highlights the technological components and challenges of the latest technologies: artificial pancreas and regenerative medicine. The study is restricted to a set of high-quality publications from the last decade.

A methodical survey of mathematical model-based control techniques based on open and closed loop control approach for diabetes management

Disturbance of blood sugar level is controlled through well-known biomechanical feedback loops: high levels of glucose in blood facilitate to release insulin from the pancreas which accelerates the absorption rate of cellular glucose. Low glucose levels encourage to release pancreatic glucagon which induces glycogen breakdown to glucose in the liver. These bio-control systems do not function properly in diabetic patients. Though the control of disease seems intuitively easy, in real life, due to many differences in structure by diet and fasting, exercise, medications, patient’s profile and other stressors, it is not that easy. The mathematical models of the glucose-insulin regulatory system follow the patient’s physiological conditions which make it difficult to identify and estimate all the model parameters. In this paper, we have given a systematic literature review on mathematical models of the diabetic patients, and various kinds of disease control techniques through the development of open and closed loop insulin deliver command system and optimization of exogenous insulin rate. It demonstrates the open and closed loop type model-based control strategies underlying the assumptions of the concerned models. The combination of mathematical model with control strategies such as genetic algorithm (GA), neural network (NN), sliding mode controller (SMC), model predictive controller (MPC), and fuzzy logic control (FLC) has been considered, which provides an overview of this area, highlighting the control profile over the diabetic model with promising clinical results, outlining key challenges, and identifying needs for the future research. Also, the significance of these control algorithms has been discussed in the presence of the noises, the controller’s robustness and various other disturbances. It provides substantial information on diabetes management through various control techniques.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms

BACKGROUND

User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available.

METHODS

The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator.

RESULTS

Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%).

CONCLUSIONS

Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation

OBJECTIVE

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models.

METHODS

To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation.

RESULTS

In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population.

CONCLUSION

The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data.

SIGNIFICANCE

The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

Self-reported insulin pump prescribing practices in pediatric type 1 diabetes

INTRODUCTION

Disadvantaged and minority youth with type 1 diabetes are less likely to be on insulin pump therapy compared to the majority population. Little is known about how pediatric endocrinology providers determine eligibility for insulin pump. We aimed to identify provider factors influencing the decision to initiate insulin pump therapy.

METHODS

We conducted a survey of Pediatric Endocrine Society members who prescribe insulin pump therapy to pediatric patients with type 1 diabetes. The survey collected information about prescriber characteristics, use and adherence to guidelines, eligibility criteria, and objective and subjective factors that influence insulin pump prescription.

RESULTS

The survey was completed by 192 individuals who met eligibility criteria (14.1% response rate). The majority of respondents were attending providers, and were white, non-Hispanic females. A minority of providers (22%) reported following written insulin pump guidelines, and many (70%) reported using personal guidelines to guide patient selection. Most providers had no objective eligibility criteria, aside from standard glucose monitoring. Providers identified patient lifestyle and increased risk of hypoglycemia, as well as patient and family factors such as motivation, realistic expectations of insulin pump use, ability to demonstrate carbohydrate counting, patient request, and ability to communicate as important in the decision to initiate insulin pump.

CONCLUSION

Pediatric endocrinology providers place significant importance on subjective factors and utilize few objective criteria in determining eligibility for insulin pump. In the setting of the known disparities in insulin pump use, providers should utilize objective, consistent criteria to determine which patients are safe to initiate insulin pump.

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

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

Modulating the foreign body response of implants for diabetes treatment

Diabetes Mellitus is a group of diseases characterized by high blood glucose levels due to patients' inability to produce sufficient insulin. Current interventions often require implants that can detect and correct high blood glucose levels with minimal patient intervention. However, these implantable technologies have not reached their full potential in vivo due to the foreign body response and subsequent development of fibrosis. Therefore, for long-term function of implants, modulating the initial immune response is crucial in preventing the activation and progression of the immune cascade. This review discusses the different molecular mechanisms and cellular interactions involved in the activation and progression of foreign body response (FBR) and fibrosis, specifically for implants used in diabetes. We also highlight the various strategies and techniques that have been used for immunomodulation and prevention of fibrosis. We investigate how these general strategies have been applied to implants used for the treatment of diabetes, offering insights on how these devices can be further modified to circumvent FBR and fibrosis.

Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques

Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

Nanomedicine for the treatment of diabetes-associated cardiovascular diseases and fibrosis

Cardiomyopathy and fibrosis are the main causes of heart failure in diabetes patients. For therapeutic purposes, a delivery system is required to enhance antidiabetic drug efficacy and specifically target profibrotic pathways in cardiomyocytes. Nanoparticles (NPs) have distinct advantages, including biocompatibility, bioavailability, targeting efficiency, and minimal toxicity, which make them ideal for antidiabetic treatment. In this review, we overview the latest information on the pathogenesis of cardiomyopathy and fibrosis in diabetes patients. We summarize how NP applications improve insulin and liraglutide efficacy and their sustained release upon oral administration. We provide a comprehensive review of the results of NP clinical trials in diabetes patients and of animal studies investigating the effects of NP-mediated anti-fibrotic treatments. Collectively, the application of advanced NP delivery systems in the treatment of cardiomyopathy and fibrosis in diabetes patients is a promising and innovative therapeutic strategy.

Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application

Artificial intelligence (AI)-based algorithms are rapidly entering the health care field and have the potential to improve patient care. Our article focuses on the use of autonomous AI algorithms (ie, algorithms that can make clinical decisions without human oversight) in diagnostic imaging. In this article, we have used the example of diabetic retinopathy screening to highlight some important aspects to be considered by developers, policymakers, and end users when bringing autonomous AI algorithms into clinical practice. We have divided these aspects into (1) following the principles of safety, efficacy, and equity in all phases of development and implementation of the algorithm; (2) regulatory processes involving medical records, medical liability, and patient privacy; (3) cost and billing; and (4) the role of health care providers.

Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review

The do-it-yourself artificial pancreas system (DIYAPS) is a patient-driven initiative with the potential to revolutionise diabetes management, automating insulin delivery with existing pumps and CGM combined with open-source algorithms. Given the considerable interest in this topic within the diabetes community, we have conducted a systematic review of DIYAPS efficacy, safety, and user experience. Following recognised procedures and reporting standards, we identified 10 eligible publications of 730 participants within the peer-reviewed literature. Overall, studies reported improvements in time in range, HbA1c (glycated haemoglobin), reduced hypoglycaemia, and improved quality of life with DIYAPS use. While results were positive, the identified studies were small, and the majority were observational and at high risk of bias. Further research including well-designed randomised trials comparing DIYAPS with appropriate comparators is recommended.

Management of diabetes and hyperglycaemia in the hospital

Hyperglycaemia in people with and without diabetes admitted to the hospital is associated with a substantial increase in morbidity, mortality, and health-care costs. Professional societies have recommended insulin therapy as the cornerstone of inpatient pharmacological management. Intravenous insulin therapy is the treatment of choice in the critical care setting. In non-intensive care settings, several insulin protocols have been proposed to manage patients with hyperglycaemia; however, meta-analyses comparing different treatment regimens have not clearly endorsed the benefits of any particular strategy. Clinical guidelines recommend stopping oral antidiabetes drugs during hospitalisation; however, in some countries continuation of oral antidiabetes drugs is commonplace in some patients with type 2 diabetes admitted to hospital, and findings from clinical trials have suggested that non-insulin drugs, alone or in combination with basal insulin, can be used to achieve appropriate glycaemic control in selected populations. Advances in diabetes technology are revolutionising day-to-day diabetes care and work is ongoing to implement these technologies (ie, continuous glucose monitoring, automated insulin delivery) for inpatient care. Additionally, transformations in care have occurred during the COVID-19 pandemic, including the use of remote inpatient diabetes management-research is needed to assess the effects of such adaptations.

Pancreas transplant versus islet transplant versus insulin pump therapy: in which patients and when?

PURPOSE OF REVIEW

The aim of the present review is to gather recent reports on the use of pancreas and islet transplantation and conventional insulin therapy for treating patients experiencing diabetes and its related complications. The present review directs attention to the current status, challenges and perspectives of these therapies and sheds light on potential future cellular therapies.

RECENT FINDINGS

The risks and benefits of diabetes treatment modalities continue to evolve, altering the risk versus benefit calculation for patients. As continuous subcutaneous insulin infusion and monitoring technologies demonstrate increasing effectiveness in achieving better diabetes control and reducing hypoglycemia frequency, so are pancreas and islet transplantation improving and becoming more effective and safer. Both beta-cell replacement therapies, however, are limited by a dependence on immunosuppression and a shortage of cadaver donors, restricting more widespread and safer deployment. Based on the effectiveness of clinical beta-cell replacement for lengthening lifespan and improving quality of life, scientists are aggressively investigating alternative cell sources, transplant platforms, and means of preventing immunological damage of transplanted cells to overcome these principle limitations.

SUMMARY

Essential goals of diabetes therapy are euglycemia, avoidance of hypoglycemia, and prevention or stabilization of end-organ damage. With these goals in mind, all therapeutic options should be considered.

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

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

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records

OBJECTIVE

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.

RESEARCH DESIGN AND METHODS

Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation.

RESULTS

We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia.

CONCLUSIONS

Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

Blood glucose regulation and control of insulin and glucagon infusion using single model predictive control for type 1 diabetes mellitus

This study elaborates on the design of artificial pancreas using model predictive control algorithm for a comprehensive physiological model such as the Sorensen model, which regulates the blood glucose and can have a longer control time in normal glycaemic region. The main objective of the proposed algorithm is to eliminate the risk of hyper and hypoglycaemia and have a precise infusion of hormones: insulin and glucagon. A single model predictive controller is developed to control the bihormones, insulin, and glucagon for such a development unmeasured disturbance is considered for a random time. The simulation result for the proposed algorithm performed good regulation lowering the hypoglycaemia risk and maintaining the glucose level within the normal glycaemic range. To validate the performance of the tracking of output and setpoint, average tracking error is used and 4.4 mg/dl results are obtained while compared with standard value (14.3 mg/dl).

Glucose-Responsive Insulin and Delivery Systems: Innovation and Translation

Type 1 and advanced type 2 diabetes treatment involves daily injections or continuous infusion of exogenous insulin aimed at regulating blood glucose levels in the normoglycemic range. However, current options for insulin therapy are limited by the risk of hypoglycemia and are associated with suboptimal glycemic control outcomes. Therefore, a range of glucose-responsive components that can undergo changes in conformation or show alterations in intermolecular binding capability in response to glucose stimulation has been studied for ultimate integration into closed-loop insulin delivery or "smart insulin" systems. Here, an overview of the evolution and recent progress in the development of molecular approaches for glucose-responsive insulin delivery systems, a rapidly growing subfield of precision medicine, is presented. Three central glucose-responsive moieties, including glucose oxidase, phenylboronic acid, and glucose-binding molecules are examined in detail. Future opportunities and challenges regarding translation are also discussed.

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

INTRODUCTION

Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups.

RESEARCH DESIGN AND METHODS

Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively.

RESULTS

The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01).

CONCLUSIONS

There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results

BACKGROUND

Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment.

METHODS

The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented.

RESULTS

Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL).

CONCLUSION

The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment.

Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.