Severe Hypoglycemia and Impaired Awareness of Hypoglycemia Persist in People With Type 1 Diabetes Despite Use of Diabetes Technology: Results From a Cross-Sectional Survey.

OBJECTIVE: To determine how diabetes technologies, including continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems, impact glycemic metrics, prevalence of severe hypoglycemic events (SHEs), and impaired awareness of hypoglycemia (IAH) in people with type 1 diabetes in a real-world setting within the U.S. RESEARCH DESIGN AND METHODS: In this retrospective, observational study with cross-sectional elements, participants aged ≥18 years were enrolled from the T1D Exchange Registry/online community. Participants completed a one-time online survey describing glycemic metrics, SHEs, and IAH. The primary objective was to determine the proportions of participants who reported achieving glycemic targets (assessed according to self-reported hemoglobin A1c) and had SHEs and/or IAH. We performed additional subgroup analyses focusing on the impact of CGM and insulin delivery modality. RESULTS: A total of 2,074 individuals with type 1 diabetes were enrolled (mean ± SD age 43.0 ± 15.6 years and duration of type 1 diabetes 26.3 ± 15.3 years). The majority of participants (91.7%) were using CGM, with one-half (50.8%) incorporating AID. Despite high use of diabetes technologies, only 57.7% reported achieving glycemic targets (hemoglobin A1c <7%). SHEs and IAH still occurred, with ∼20% of respondents experiencing at least one SHE within the prior 12 months and 30.7% (95% CI 28.7, 32.7) reporting IAH, regardless of CGM or AID use. CONCLUSIONS: Despite use of advanced diabetes technologies, a high proportion of people with type 1 diabetes do not achieve glycemic targets and continue to experience SHEs and IAH, suggesting an ongoing need for improved treatment strategies.

Navigating Two Roads to Glucose Normalization in Diabetes: Automated Insulin Delivery Devices and Cell Therapy.

Incredible strides have been made since the discovery of insulin almost 100 years ago. Insulin formulations have improved dramatically, glucose levels can be measured continuously, and recently first-generation biomechanical "artificial pancreas" systems have been approved by regulators around the globe. However, still only a small fraction of patients with diabetes achieve glycemic goals. Replacement of insulin-producing cells via transplantation shows significant promise, but is limited in application due to supply constraints (cadaver-based) and the need for chronic immunosuppression. Over the past decade, significant progress has been made to address these barriers to widespread implementation of a cell therapy. Can glucose levels in people with diabetes be normalized with artificial pancreas systems or via cell replacement approaches? Here we review the road ahead, including the challenges and opportunities of both approaches.

Open Source Closed-Loop Insulin Delivery Systems: A Clash of Cultures or Merging of Diverse Approaches?

Biomedical outcomes for people with diabetes remain suboptimal for many. Psychosocial care in diabetes does not fare any better. "Artificial pancreas" (also known as "closed-loop" and "automated insulin delivery") systems present a promising therapeutic option for people with diabetes (PWD)-simultaneously improving glycemic outcomes, reducing the burden of self-management, and improving health-related quality of life. In recent years there has emerged a growing movement of PWD innovators rallying behind the mantra #WeAreNotWaiting, developing "do-it-yourself artificial pancreas systems (DIY APS)." Self-reported results by DIY APS users show improved metabolic outcomes such as impressive stability of glucose profiles, significant reduction of A1c, and more time within their glycemic target range. However, the benefits remain unclear for the broader population of PWD beyond these highly engaged, highly tech-savvy users willing and able to engage in the demands of building and maintaining their DIY APS. We discuss the challenges faced by key stakeholder groups in terms of potential collaboration and open debate of these challenges.

The Hypoglycaemia-Hyperglycaemia Minimizer System in the Management of Type 1 Diabetes.

Living with type 1 diabetes (T1D) presents many challenges in terms of daily living. Insulin users need to frequently monitor their blood glucose levels and take multiple injections per day and/or multiple boluses through an insulin infusion pump, with the consequences of failing to match the insulin dose to the body's needs resulting in hypoglycaemia and hyperglycaemia. The former can result in seizures, coma and even death; the latter can have both acute and long-term health implications. Many patients with T1D also fail to meet their treatment goals. In order to reduce the burdens of self-administering insulin, and improve efficacy and safety, there is a need to at least partially remove the patient from the loop via a closed-loop 'artificial pancreas' system. The Hypoglycaemia-Hyperglycaemia Minimizer (HHM) System, comprising a continuous, subcutaneous insulin infusion pump, continuous glucose monitor (CGM) and closed-loop insulin dosing algorithm, is able to predict changes in blood glucose and adjust insulin delivery accordingly to help keep the patient at normal glucose levels. Early clinical data indicate that this system is feasible, effective and safe, and has the potential to dramatically improve the therapeutic outcomes and quality of life for people with T1D.

Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor.

BACKGROUND: The Predictive Hypoglycemia Minimizer System ("Hypo Minimizer"), consisting of a zone model predictive controller (the "controller") and a safety supervision module (the "safety module"), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor," a pivotal variable in the system, governs the speed and magnitude of the controller's insulin dosing characteristics in response to changes in CGM levels. METHODS: Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results. RESULTS: As aggressiveness increased from "conservative" to "medium" to "aggressive," the controller recommended less insulin (-3.3% vs -14.4% vs -19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent <70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values. CONCLUSION: The Hypo Minimizer's controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics.

Effect of algorithm aggressiveness on the performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System.

The Hypoglycemia-Hyperglycemia Minimizer (HHM) System aims to mitigate glucose excursions by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor" is a key parameter in the HHM System algorithm, affecting how readily the system adjusts insulin infusion in response to changing CGM levels. Twenty adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 26 hours. This analysis focused on the effect of the aggressiveness factor on the insulin dosing characteristics of the algorithm and, to a lesser extent, on the glucose control results observed. As the aggressiveness factor increased from conservative to medium to aggressive: the maximum observed insulin dose delivered by the algorithm­which is designed to give doses that are corrective in nature every 5 minutes­increased (1.00 vs 1.15 vs 2.20 U, respectively); tendency to adhere to the subject's nominal basal dose decreased (61.9% vs 56.6% vs 53.4%); and readiness to decrease insulin below basal also increased (18.4% vs 19.4% vs 25.2%). Glucose analyses by both CGM and Yellow Springs Instruments (YSI) indicated that the aggressive setting of the algorithm resulted in the least time spent at levels >180 mg/dL, and the most time spent between 70-180 mg/dL. There was no severe hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia for any of the aggressiveness values investigated. These analyses underscore the importance of investigating the sensitivity of the HHM System to its key parameters, such as the aggressiveness factor, to guide future development decisions.

Closed-Loop Control Performance of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System in a Feasibility Study.

BACKGROUND: This feasibility study investigated the insulin-delivery characteristics of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System-an automated insulin delivery device-in participants with type 1 diabetes. METHODS: Thirteen adults with type 1 diabetes were enrolled in this nonrandomized, uncontrolled, clinical-research-center-based feasibility study. The HHM System comprised a continuous subcutaneous insulin infusion pump, a continuous glucose monitor (CGM), and a model predictive control algorithm with a safety module, run on a laptop platform. Closed-loop control lasted approximately 20 hours, including an overnight period and two meals. RESULTS: When attempting to minimize glucose excursions outside of a prespecified target zone, the predictive HHM System decreased insulin infusion rates below the participants' preset basal rates in advance of below-zone excursions (CGM < 90 mg/dl), and delivered 80.4% less insulin than basal during those excursions. Similarly, the HHM System increased infusion rates above basal during above-zone excursions (CGM > 140 mg/dl), delivering 39.9% more insulin than basal during those excursions. Based on YSI, participants spent a mean ± standard deviation (SD) of 0.2 ± 0.5% of the closed-loop control time at glucose levels < 70 mg/dl, including 0.3 ± 0.9% for the overnight period. The mean ± SD glucose based on YSI for all participants was 164.5 ± 23.5 mg/dl. There were nine instances of algorithm-recommended supplemental carbohydrate administrations, and there was no severe hypoglycemia or diabetic ketoacidosis. CONCLUSIONS: Results of this study indicate that the current HHM System is a feasible foundation for development of a closed-loop insulin delivery device.

Effects of everyday life events on glucose, insulin, and glucagon dynamics in continuous subcutaneous insulin infusion-treated type 1 diabetes: collection of clinical data for glucose modeling.

BACKGROUND: In the development of glucose control algorithms, mathematical models of glucose metabolism are useful for conducting simulation studies and making real-time predictions upon which control calculations can be based. To obtain type 1 diabetes (T1D) data for the modeling of glucose metabolism, we designed and conducted a clinical study. METHODS: Patients with insulin pump-treated T1D were recruited to perform everyday life events on two separate days. During the study, patients wore their insulin pumps and, in addition, a continuous glucose monitor and an activity monitor to estimate energy expenditure. The sequence of everyday life events was predetermined and included carbohydrate intake, insulin boluses, and bouts of exercise; the events were introduced, temporally separated, in different orders and in different quantities. Throughout the study day, 10-min plasma glucose measurements were taken, and samples for plasma insulin and glucagon analyses were obtained every 10 min for the first 30 min after an event and subsequently every 30 min. RESULTS: We included 12 patients with T1D (75% female, 34.3±9.1 years old [mean±SD], hemoglobin A1c 6.7±0.4%). During the 24 study days we collected information-rich, high-quality data during fast and slow changes in plasma glucose following carbohydrate intake, exercise, and insulin boluses. CONCLUSIONS: This study has generated T1D data suitable for glucose modeling, which will be used in the development of glucose control strategies. Furthermore, the study has given new physiologic insight into the metabolic effects of carbohydrate intake, insulin boluses, and exercise in continuous subcutaneous insulin infusion-treated patients with T1D.

Automatic Detection of Stress States in Type 1 Diabetes Subjects in Ambulatory Conditions.

Two levels of control are crucial to the robustness of an artificial ß-cell, a medical device that would automatically regulate blood glucose levels in patients with type 1 diabetes. A low-level component would attempt to regulate blood glucose continuously, while a supervisory-level, or monitoring, component would detect underlying changes in the subject's glucose-insulin dynamics and take corrective actions accordingly. These underlying changes, or "faults," can include changes in insulin sensitivity, sensor problems, and insulin delivery problems, to name a few. A multivariate statistical monitoring technique, principal component analysis (PCA), has been applied to both simulated and experimental type 1 diabetes data. The objective of this study was to determine if PCA could be used to distinguish between normal patient data, and data for abnormal conditions that included a variety of "faults." The PCA results showed a high degree of accuracy; for data from nine type 1 diabetes subjects in ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Thus, the proposed monitoring technique shows considerable promise for incorporation into an artificial ß-cell.

Quest for the artificial pancreas: combining technology with treatment.

The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.

Experimental evaluation of a recursive model identification technique for type 1 diabetes.

BACKGROUND: A model-based controller for an artificial beta cell requires an accurate model of the glucose-insulin dynamics in type 1 diabetes subjects. To ensure the robustness of the controller for changing conditions (e.g., changes in insulin sensitivity due to illnesses, changes in exercise habits, or changes in stress levels), the model should be able to adapt to the new conditions by means of a recursive parameter estimation technique. Such an adaptive strategy will ensure that the most accurate model is used for the current conditions, and thus the most accurate model predictions are used in model-based control calculations. METHODS: In a retrospective analysis, empirical dynamic autoregressive exogenous input (ARX) models were identified from glucose-insulin data for nine type 1 diabetes subjects in ambulatory conditions. Data sets consisted of continuous (5-minute) glucose concentration measurements obtained from a continuous glucose monitor, basal insulin infusion rates and times and amounts of insulin boluses obtained from the subjects' insulin pumps, and subject-reported estimates of the times and carbohydrate content of meals. Two identification techniques were investigated: nonrecursive, or batch methods, and recursive methods. Batch models were identified from a set of training data, whereas recursively identified models were updated at each sampling instant. Both types of models were used to make predictions of new test data. For the purpose of comparison, model predictions were compared to zero-order hold (ZOH) predictions, which were made by simply holding the current glucose value constant for p steps into the future, where p is the prediction horizon. Thus, the ZOH predictions are model free and provide a base case for the prediction metrics used to quantify the accuracy of the model predictions. In theory, recursive identification techniques are needed only when there are changing conditions in the subject that require model adaptation. Thus, the identification and validation techniques were performed with both "normal" data and data collected during conditions of reduced insulin sensitivity. The latter were achieved by having the subjects self-administer a medication, prednisone, for 3 consecutive days. The recursive models were allowed to adapt to this condition of reduced insulin sensitivity, while the batch models were only identified from normal data. RESULTS: Data from nine type 1 diabetes subjects in ambulatory conditions were analyzed; six of these subjects also participated in the prednisone portion of the study. For normal test data, the batch ARX models produced 30-, 45-, and 60-minute-ahead predictions that had average root mean square error (RMSE) values of 26, 34, and 40 mg/dl, respectively. For test data characterized by reduced insulin sensitivity, the batch ARX models produced 30-, 60-, and 90-minute-ahead predictions with average RMSE values of 27, 46, and 59 mg/dl, respectively; the recursive ARX models demonstrated similar performance with corresponding values of 27, 45, and 61 mg/dl, respectively. The identified ARX models (batch and recursive) produced more accurate predictions than the model-free ZOH predictions, but only marginally. For test data characterized by reduced insulin sensitivity, RMSE values for the predictions of the batch ARX models were 9, 5, and 5% more accurate than the ZOH predictions for prediction horizons of 30, 60, and 90 minutes, respectively. In terms of RMSE values, the 30-, 60-, and 90-minute predictions of the recursive models were more accurate than the ZOH predictions, by 10, 5, and 2%, respectively. CONCLUSION: In this experimental study, the recursively identified ARX models resulted in predictions of test data that were similar, but not superior, to the batch models. Even for the test data characteristic of reduced insulin sensitivity, the batch and recursive models demonstrated similar prediction accuracy. The predictions of the identified ARX models were only marginally more accurate than the model-free ZOH predictions. Given the simplicity of the ARX models and the computational ease with which they are identified, however, even modest improvements may justify the use of these models in a model-based controller for an artificial beta cell.

Use of continuous glucose monitoring to estimate insulin requirements in patients with type 1 diabetes mellitus during a short course of prednisone.

BACKGROUND: Insulin requirements to maintain normoglycemia during glucocorticoid therapy and stress are often difficult to estimate. To simulate insulin resistance during stress, adults with type 1 diabetes mellitus (T1DM) were given a three-day course of prednisone. METHODS: Ten patients (7 women, 3 men) using continuous subcutaneous insulin infusion pumps wore the Medtronic Minimed CGMS (Northridge, CA) device. Mean (standard deviation) age was 43.1 (14.9) years, body mass index 23.9 (4.7) kg/m(2), hemoglobin A1c 6.8% (1.2%), and duration of diabetes 18.7 (10.8) years. Each patient wore the CGMS for one baseline day (day 1), followed by three days of self-administered prednisone (60 mg/dl; days 2-4), and one post-prednisone day (day 5). RESULTS: Analysis using Wilcoxon signed rank test (values are median [25th percentile, 75th percentile]) indicated a significant difference between day 1 and the mean of days on prednisone (days 2-4) for average glucose level (110.0 [81.0, 158.0] mg/dl vs 149.2 [137.7, 168.0] mg/dl; p = .022), area under the glucose curve and above the upper limit of 180 mg/dl per day (0.5 [0, 8.0] mg/dl.d vs 14.0 [7.7, 24.7] mg/dl.d; p = .002), and total daily insulin dose (TDI) , (0.5 [0.4, 0.6] U/kg.d vs 0.9 [0.8, 1.0] U/kg.d; p = .002). In addition, the TDI was significantly different for day 1 vs day 5 (0.5 [0.4, 0.6] U/kg.d vs 0.6 [0.5, 0.8] U/kg.d; p = .002). Basal rates and insulin boluses were increased by an average of 69% (range: 30-100%) six hours after the first prednisone dose and returned to baseline amounts on the evening of day 4. CONCLUSIONS: For adults with T1DM, insulin requirements during prednisone induced insulin resistance may need to be increased by 70% or more to normalize blood glucose levels.

Practical issues in the identification of empirical models from simulated type 1 diabetes data.

BACKGROUND: A model-based controller for an artificial beta-cell automatically regulates blood glucose levels based on available glucose measurements, insulin infusion and meal information, and model predictions of future glucose trends. Thus, the identification of simple, accurate models plays an important role in the development of an artificial beta-cell. METHODS: Glucose data simulated from a nonlinear physiological model of type 1 diabetes are used to identify linear dynamic models of two types: autoregressive exogenous input (ARX) and output-error (OE) models. The model inputs are meal carbohydrates and exogenous insulin, which in practice are often administered simultaneously and in the same ratio, i.e., the insulin-to-carbohydrate ratio. The effect of modeling these inputs as impulses versus time-smoothed profiles ("transformed inputs") is explored in depth. The models are evaluated based on their ability to describe the data from which they were identified (i.e., calibration data) as well as independent data (i.e., validation data). RESULTS: In general, the best models described their calibration data more accurately using transformed inputs (R(Cal) (2) = 71% for the ARX models and R (Cal) (2) = 78% for the OE models) than using impulse inputs (R (Cal) (2) = 14% for the ARX models and R (Cal) (2) = 70% for the OE models). The only model/input combination that resulted in consistently accurate validation fits was the ARX models using transformed inputs (39%

Calculating the insulin to carbohydrate ratio using the hyperinsulinaemic-euglycaemic clamp-a novel use for a proven technique.

BACKGROUND: In patients with type 1 diabetes, three main variables need to be assessed to optimize meal-related insulin boluses: pre-meal blood glucose (BG), insulin to carbohydrate ratio (I : C), and basal insulin. We are presenting data for a novel use of the hyperinsulinaemic-euglycaemic clamp (HEC) in patients with type 1 diabetes that minimizes the impact of these variables and can be used to determine the I : C. METHODS: Ten subjects (six men and four women) using continuous subcutaneous insulin infusion (CSII) pumps were recruited for this study [24-65 years; BMI 27.1 +/- 4.9 kg/m(2); A1C 7.2 +/- 1.4% (mean +/- SD)]. The HEC used a primed continuous intravenous insulin infusion of 40 mU/m(2)/min and a variable infusion of 20% glucose to maintain BG at 90 mg/dL. After subjects were in steady state (SS) for 50 min, a standardized meal (40% of total calories/day - 30% carbohydrate, 30% protein, 40% fat) was consumed. Subjects gave the insulin bolus with their CSII pump. No changes were made in the glucose infusion rate. RESULTS: Mean BG at SS was 85.7 +/- 10.4 mg/dL. Peak BG was 115.0 +/- 12.7 mg/dL at 68.5 +/- 8.8 min after the meal. Mean I : C was 1 : 9.3 +/- 1.7 (range 1 : 7-1 : 12). Insulin sensitivity varied from 1.9 to 9.1 mg/kg/min. CONCLUSIONS: The HEC can be used to reduce confounding factors and to determine the I : C. As a first estimate of the I : C in patients with type 1 diabetes, it is recommended to start with a ratio of 1 : 9.3 and to measure post-prandial BG at 70 min.