Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).

Physical Activity, Dietary Patterns, and Glycemic Management in Active Individuals with Type 1 Diabetes: An Online Survey

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy

OBJECTIVE

Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data.

METHODS

Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed.

RESULTS

The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm.

CONCLUSION

Our algorithm has the potential to improve glycemic control in people with T1D using MDI.

SIGNIFICANCE

This work is a step forward towards a decision support system that improves their quality of life.

Safety and Efficacy of Initializing the Control-IQ Artificial Pancreas System Based on Total Daily Insulin in Adolescents with Type 1 Diabetes

Objective: To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin ("MyTDI") in adolescents with type 1 diabetes under usual activity and during periods of increased exercise. Research Design and Methods: Adolescents with type 1 diabetes 12-18 years of age used Control-IQ for 5 days at home using their usual parameters. Upon arrival at a 60-h ski camp, participants were randomized to either continue Control-IQ using their home settings or to reinitialize Control-IQ with MyTDI parameters. Control-IQ use continued for 5 days following camp. The effect of MyTDI on continuous glucose monitoring outcomes were analyzed using repeated measures analysis of variance (ANOVA): baseline, camp, and at home. Results: Twenty participants were enrolled and completed the study; two participants were excluded from the analysis due to absence from ski camp (1) and illness (1). Time in range was similar between both groups at home and camp. A tendency to higher time <70 mg/dL in the MyTDI group was present but only during camp (median 3.8% vs. 1.4%, P = 0.057). MyTDI users with bolus/TDI ratios >40% tended to show greater time in the euglycemic range improvements between baseline and home than users with ratios <40% (+16.3% vs. -9.0%, P = 0.012). All participants maintained an average of 95% time in closed loop (84.1%-100%). Conclusions: MyTDI is a safe, effective, and easy way to determine insulin parameters for use in the Control-IQ artificial pancreas. Future modifications to account for the influence of carbohydrate intake on MyTDI calculations might further improve time in range.

Young-onset diabetes, nutritional therapy and novel insulin delivery systems: a report from the 21st Hong Kong Diabetes and Cardiovascular Risk Factors - East Meets West Symposium

The prevalence and incidence of young-onset diabetes are increasing in many parts of the world, with the most rapid increase occurring in Asia, where one in five people with diabetes are diagnosed below the age of 40 years. Accumulation of glycaemic burden from an early age significantly increases the lifetime risks of developing complications from diabetes. Despite impending health threats, young people fare worse in the control of blood glucose and other metabolic risk factors. Challenges in the management of young-onset diabetes are compounded by heterogeneity of the underlying causes, pathophysiology and clinical phenotypes in this group. Effective characterization of people with diabetes has implications in steering the choice of glucose-lowering drugs, which, in turn, determines the clinical outcome. Medical nutritional therapy is key to effective management of people with diabetes but dietary adherence is often suboptimal among younger individuals. A recently published consensus report on nutritional therapy addresses dietary management in people with prediabetes as well as diabetes, and summarizes clinical evidence regarding macronutrient and micronutrient composition as well as eating patterns in people with diabetes. For people with type 1 diabetes, automated insulin delivery systems have rapidly evolved since the concept was first introduced at the National Institute of Health and the Juvenile Diabetes Research Foundation in 2005. The subsequent development of a type 1 diabetes simulator, developed using detailed human physiology data on carbohydrate metabolism replaced the need for pre-clinical animal studies and facilitated the seamless progression to artificial pancreas human clinical trials.

The Use of a Smart Bolus Calculator Informed by Real-time Insulin Sensitivity Assessments Reduces Postprandial Hypoglycemia Following an Aerobic Exercise Session in Individuals With Type 1 Diabetes

OBJECTIVE

Insulin dosing in type 1 diabetes (T1D) is oftentimes complicated by fluctuating insulin requirements driven by metabolic and psychobehavioral factors impacting individuals' insulin sensitivity (IS). In this context, smart bolus calculators that automatically tailor prandial insulin dosing to the metabolic state of a person can improve glucose management in T1D.

RESEARCH DESIGN AND METHODS

Fifteen adults with T1D using continuous glucose monitors (CGMs) and insulin pumps completed two 24-h admissions in a hotel setting. During the admissions, participants engaged in an early afternoon 45-min aerobic exercise session, after which they received a standardized dinner meal. The dinner bolus was computed using a standard bolus calculator or smart bolus calculator informed by real-time IS estimates. Glucose control was assessed in the 4 h following dinner using CGMs and was compared between the two admissions.

RESULTS

The IS-informed bolus calculator allowed for a reduction in postprandial hypoglycemia as quantified by the low blood glucose index (2.02 vs. 3.31, P = 0.006) and percent time <70 mg/dL (8.48% vs. 15.18%, P = 0.049), without increasing hyperglycemia (high blood glucose index: 3.13 vs. 2.09, P = 0.075; percent time >180 mg/dL: 13.24% vs. 10.42%, P = 0.5; percent time >250 mg/dL: 2.08% vs. 1.19%, P = 0.317). In addition, the number of hypoglycemia rescue treatments was reduced from 12 to 7 with the use of the system.

CONCLUSIONS

The study shows that the proposed IS-informed bolus calculator is safe and feasible in adults with T1D, appropriately reducing postprandial hypoglycemia following an exercise-induced IS increase.

A Randomized, Placebo-Controlled Double-Blind Trial of a Closed-Loop Glucagon System for Postbariatric Hypoglycemia

BACKGROUND

Postbariatric hypoglycemia (PBH) can threaten safety and reduce quality of life. Current therapies are incompletely effective.

METHODS

Patients with PBH were enrolled in a double-blind, placebo-controlled, crossover trial to evaluate a closed-loop glucose-responsive automated glucagon delivery system designed to reduce severe hypoglycemia. A hypoglycemia detection and mitigation algorithm was embedded in the artificial pancreas system connected to a continuous glucose monitor (CGM, Dexcom) driving a patch infusion pump (Insulet) filled with liquid investigational glucagon (Xeris) or placebo (vehicle). Sensor/plasma glucose responses to mixed meal were assessed during 2 study visits. The system delivered up to 2 doses of study drug (300/150 μg glucagon or equal-volume vehicle) if triggered by the algorithm. Rescue dextrose was given for plasma glucose <55 mg/dL or neuroglycopenia.

RESULTS

Twelve participants (11 females/1 male, age 52 ± 2, 8 ± 1 years postsurgery, mean ± SEM) completed all visits. Predictive hypoglycemia alerts prompted automated drug delivery postmeal, when sensor glucose was 114 ± 7 vs 121 ± 5 mg/dL (P = .39). Seven participants required rescue glucose after vehicle but not glucagon (P = .008). Five participants had severe hypoglycemia (<55 mg/dL) after vehicle but not glucagon (P = .03). Nadir plasma glucose was higher with glucagon vs vehicle (67 ± 3 vs 59 ± 2 mg/dL, P = .004). Plasma glucagon rose after glucagon delivery (1231 ± 187 vs 16 ± 1 pg/mL at 30 minutes, P = .001). No rebound hyperglycemia occurred. Transient infusion site discomfort was reported with both glucagon (n = 11/12) and vehicle (n = 10/12). No other adverse events were observed.

CONCLUSION

A CGM-guided closed-loop rescue system can detect imminent hypoglycemia and deliver glucagon, reducing severe hypoglycemia in PBH.

CLINICAL TRIALS REGISTRATION

NCT03255629.

The Efficacy of Basal Rate and Carbohydrate Ratio Learning Algorithm for Closed-Loop Insulin Delivery (Artificial Pancreas) in Youth with Type 1 Diabetes in a Diabetes Camp

Background: Optimizing programmed basal rates and carbohydrate ratios may improve the performance of the artificial pancreas. We tested, in a diabetes camp, the efficacy of a learning algorithm that updates daily basal rates and carbohydrate ratios in the artificial pancreas. Materials and Methods: We conducted a randomized crossover trial in campers and counselors aged 8-21 years with type 1 diabetes on pump therapy. Participants underwent 2 days of artificial pancreas alone and 6 days of artificial pancreas with learning. During the artificial pancreas with learning, programmed basal rates and carbohydrate ratios were updated daily based on the learning algorithm's recommendations. All algorithm recommendations were reviewed for safety by camp physicians. The primary outcome was the time in target range (3.9-10 mmol/L) of the last 2 days of each intervention. Results: Thirty-four campers (age 13.9 ± 3.9, hemoglobin A1c 8.3% ± 0.2%) were included. Ninety-six percent of algorithm recommendations were approved by the camp physicians. Participants were in closed-loop mode 74% of the time. There was no difference between interventions in time in target (55%-55%; P = 0.71) nor in hypoglycemia events (0.8-0.9 events per day; P = 0.63). This was despite changes in programmed basal rate ranging from -21% to +117%, and changes in breakfast, lunch, and supper carbohydrate ratios from -17% to +40%, -36% to +37%, and -35% to +63%, respectively. Morever, postprandial hyperglycemia and hypoglycemia did not decrease in participants whose carbohydrate ratios were decreased (more insulin boluses) and increased (less insulin boluses), respectively. Conclusions: In camp settings, despite adjustments to programmed basal rates and carbohydrate ratios, the learning algorithm did not change glycemia, which may point toward limited effect of these adjustments in environments with large day-to-day variability in insulin needs. Longer randomized studies in real-world settings are required to further assess the efficacy of automatic adjustments of programmed basal rates and carbohydrate ratios.

Performance of Omnipod Personalized Model Predictive Control Algorithm with Moderate Intensity Exercise in Adults with Type 1 Diabetes

Background: The objective of this study was to assess the safety and performance of the Omnipod^® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18-65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Results: Twelve subjects participated in the study, with (mean ± standard deviation) age 36.5 ± 14.4 years, diabetes duration 21.7 ± 15.7 years, HbA1c 7.6% ± 1.1%, and total daily dose 0.60 ± 0.22 U/kg. Outcomes for the 54-h HCL period were mean glucose: 136 ± 14 mg/dL, percentage time <70 mg/dL: 1.4% ± 1.3%, 70-180 mg/dL: 85.1% ± 9.3%, and ≥250 mg/dL: 1.8% ± 2.4%. In the 12-h period after exercise start, percentage time <70 mg/dL was 1.4% ± 2.7% with the raised glucose set point and 1.6% ± 3.0% with reduced basal rate. The percentage time <70 mg/dL overnight was 0% ± 0% on both study nights. Conclusions: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to variable glucose set points and with temporarily raised glucose set point or reduced basal rate 90 min in advance of moderate intensity exercise in adults with T1D.

Design and Clinical Evaluation of the Interoperable Artificial Pancreas System (iAPS) Smartphone App: Interoperable Components with Modular Design for Progressive Artificial Pancreas Research and Development

BACKGROUND

There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone.

METHODS

After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions.

RESULTS

Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70-180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention.

CONCLUSIONS

The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.

Multivariate learning framework for long-term adaptation in the artificial pancreas

The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

Adjusting Insulin Delivery to Activity (AIDA) clinical trial: Effects of activity-based insulin profiles on glucose control in children with type 1 diabetes

BACKGROUND

Increased daytime activity in children with type I diabetes mellitus (T1DM) is associated with increased risk of hypoglycemia.

OBJECTIVE

To determine whether an automated weekly review of accelerometer, continuous glucose monitoring (CGM), and insulin pump data, could be used to identify children with increased risk of nighttime hypoglycemia and preemptively adjust the nighttime basal insulin profile according to daytime activity.

RESEARCH AND DESIGN METHODS

Clinical trial of children with T1DM on insulin pump and CGM therapy. Subjects at risk of nighttime hypoglycemia were identified from regression analysis of daytime step count vs nighttime nadir glucose. If the regression slope was significantly different from zero (P < 0.05) subjects were managed with different algorithm derived nighttime basal insulin profiles following high and low activity days.

RESULTS

Twenty children (median age: 12; range: 7-17 years) were enrolled. Regression slopes were significant in 10 children. In these children, baseline nighttime nadir glucose level was lower following high activity days (120 [110-139] vs 152 [130-162] mg/dL, P = 0.004). Use of activity-based nighttime basal profiles produced similar nighttime nadir glucose levels following high and low activity days (136 [123-175] vs 140 [108-180] mg/dL, P = 0.73) with fewer nighttime interventions to correct hypoglycemia (0 [0-0.16] vs 0.15 [0.13-0.22] per night, P = 0.008).

CONCLUSION

Children with lower nighttime glucose levels following high daytime activity can be identified using step count data obtained from readily available accelerometers and the nighttime glucose control improved using different activity-based basal profiles.

Analyzing the Potential of Advanced Insulin Dosing Strategies in Patients With Type 2 Diabetes: Results From a Hybrid In Silico Study

BACKGROUND

The ongoing improvement of continuous glucose monitoring (CGM) sensors and of insulin pumps are paving the way for a fast implementation of artificial pancreas (AP) for type 1 diabetes (T1D) patients. The case for type 2 diabetes (T2D) patients is less obvious since usually some residual beta cell function allows for simpler therapy approaches, and even multiple daily injections (MDI) therapy is not very widespread. However, the number of insulin dependent T2D patients is vastly increasing and therefore a need for understanding chances and challenges of an automated insulin therapy arises. Based on this background, this article analyzes conditions under which the use of more advanced therapeutic approaches, particularly AP, could bring a substantial improvement and should be considered as a viable therapy option.

METHOD

Data of 14 insulin-treated T2D patients on MDI wearing a CGM device and deviation analysis methods were used to estimate the expected improvements in the clinical outcome by using self-monitoring of blood glucose (SMBG) with advanced carbohydrate counting, a full AP or intermediate approaches, either CGM measurements with MDI therapy or SMBG with insulin pump. HbA1C and time in range (70-140 mg/dl, 70-180 mg/dl, respectively) were used as a performance measure. Outcome measures beyond glycemic control (eg, compliance, patient acceptance) have not been analyzed in this study.

RESULTS

AP has the potential to improve the condition of many poorly controlled insulin-treated T2D patients. However, as the interpatient variability is much higher than in T1D, a prescreening is recommended to select suitable patients.

CONCLUSIONS

Clinical criteria need to be developed for inclusion/exclusion of T2D patients for AP related therapies.

Advances in Closed-Loop Insulin Delivery Systems in Patients with Type 1 Diabetes

To provide a current review of closed-loop insulin delivery or artificial pancreas (AP) as therapy for people with type 1 diabetes mellitus (T1D) RECENT FINDINGS: The Medtronic Minimed 670G AP system has been in use in clinical practice since March 2017. Currently, Medtronic is conducting a large randomized clinical trial to evaluate its efficacy further in T1D. Simultaneously, the NIH has funded four research consortia to accelerate progress to approval of other AP and decision support systems. Several research groups are currently developing next-generation AP systems, with a number of companies moving toward releasing closed-loop systems in the future. AP systems are also being tested in select populations such as hypoglycemia-unaware T1D and pregnant T1D. AP research is rapidly advancing. The clinical range of AP will be expanded in the next decade.

Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties

OBJECTIVE

Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC.

METHODS

A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study.

RESULTS

For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; ) and percentage time in [70, 180] mg/dL ([Formula: see text] vs. [Formula: see text]; ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study.

CONCLUSION

The proposed method leads to improved glucose control without increasing hypoglycemia risks.

SIGNIFICANCE

The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.

Technology in the management of type 1 diabetes mellitus - current status and future prospects

Type 1 diabetes mellitus (T1DM) represents 5-10% of diabetes cases worldwide. The incidence of T1DM is increasing, and there is no immediate prospect of a cure. As such, lifelong management is required, the burden of which is being eased by novel treatment modalities, particularly from the field of diabetes technologies. Continuous glucose monitoring has become the standard of care and includes factory-calibrated subcutaneous glucose monitoring and long-term implantable glucose sensing. In addition, considerable progress has been made in technology-enabled glucose-responsive insulin delivery. The first hybrid insulin-only closed-loop system has been commercialized, and other closed-loop systems are under development, including dual-hormone glucose control systems. This Review focuses on well-established diabetes technologies, including glucose sensing, pen-based insulin delivery, data management and data analytics. We also cover insulin pump therapy, threshold-based suspend, predictive low-glucose suspend and single-hormone and dual-hormone closed-loop systems. Clinical practice recommendations for insulin pump therapy and continuous glucose monitoring are presented, and ongoing research and future prospects are highlighted. We conclude that the management of T1DM is improved by diabetes technology for the benefit of the majority of people with T1DM, their caregivers and guardians and health-care professionals treating patients with T1DM.

Evaluation of an Artificial Pancreas with Enhanced Model Predictive Control and a Glucose Prediction Trust Index with Unannounced Exercise

BACKGROUND

We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning.

MATERIALS AND METHODS

After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29-57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70-180, <70, >180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions.

RESULTS

Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70-180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%), <70 mg/dL (1.5% ± 1.9% vs. 7.8% ± 6.0%), and number of hypoglycemic events (0.6 ± 0.6 vs. 6.3 ± 3.4), all showed statistically significant improvement during AP use compared with the SAP run-in (P < 0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (P < 0.005).

CONCLUSIONS

In this population of well-controlled patients, we conclude that eMPC with trust index AP achieved nearly 90% time in the target glucose range. Additional studies will further validate these results.

Body Mass Index Effect on Differing Responses to Psychological Stress in Blood Glucose Dynamics in Patients With Type 1 Diabetes

OBJECTIVE

The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes.

METHODS

Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m² (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based "effectiveness index" (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes.

RESULTS

Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG.

CONCLUSION

Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.

Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors' previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.

The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

BACKGROUND

A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described.

METHOD

Intraday variability of insulin sensitivity (S_I) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily S_I. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added.

RESULTS

One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data.

CONCLUSIONS

The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia

BACKGROUND

Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon.

METHODS

We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation. Meal tolerance testing was performed in seven participants with PBH and history of neuroglycopenia. Glucagon was administered when hypoglycemia was predicted. Primary outcome measures included the safety and feasibility of this system to predict and prevent severe hypoglycemia. Secondary outcomes included hypoglycemia prediction by the prediction algorithm, minimization of time below hypoglycemia threshold using glucagon, and prevention of rebound hyperglycemia.

RESULTS

The hypoglycemia prediction algorithm alerted for impending hypoglycemia in the postmeal state, prompting delivery of glucagon (150 μg). After observations of initial incomplete efficacy to prevent hypoglycemia in the first two participants, system modifications were implemented: addition of PBH-specific detection algorithm, increased glucagon dose (300 μg), and a second glucagon dose if needed. These modifications, together with rescue carbohydrates provided to some participants, contributed to progressive improvements in glucose time above the hypoglycemia threshold (75 mg/dL).

CONCLUSIONS

Preliminary results indicate that our event-based automatic monitoring algorithm successfully predicted likely hypoglycemia. Minidose glucagon therapy was well tolerated, without prolonged or severe hypoglycemia, and without rebound hyperglycemia.

Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy

BACKGROUND AND AIM

The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the premeal insulin bolus. Usually, it is estimated by the physician based on patient diary, but modern diabetes technologies, such as subcutaneous glucose sensing (continuous glucose monitoring, CGM) and insulin delivery (continuous subcutaneous insulin infusion, CSII) systems, can provide important information for its optimization. In this study, a method for CR optimization based on CGM and CSII data is presented and its efficacy and robustness tested in several in silico scenarios, with the primary aim of increasing protection against hypoglycemia.

METHODS

The method is based on a validated index of insulin sensitivity calculated from sensor and pump data (S_I^SP), area under CGM and CSII curves. The efficacy and robustness of the method are tested in silico using the University of Virginia/Padova T1D simulator, in several suboptimal therapy scenarios: with nominal CR variation, over/underestimation of meal size or suboptimal basal insulin infusion. Simulated CGM and CSII data were used to calculate the optimal CR. The same scenarios were then repeated using the estimated CR and glycemic control was compared.

RESULTS

The optimized CR was efficacious in protecting against hypoglycemic events caused by suboptimal therapy. The method was also robust to possible error in carbohydrate count and suboptimal basal insulin infusion.

CONCLUSIONS

A novel method for CR optimization in T1D, implementable in daily life using CGM and CSII data, is proposed. The method can be used both in open- and closed-loop insulin therapy.

Extremum Seeking Control for Personalized Zone Adaptation in Model Predictive Control for Type 1 Diabetes

Zone model predictive control has proven to be an effective closed-loop method to regulate blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal model-free optimization scheme for adapting the zone for T1D patients individually. The adaptation is based on a clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI), which is calculated from glucose measurements by a continuous glucose monitor. The scheme's objective is to minimize rrGPI by simultaneously modulating a controller's blood glucose target zone's upper bound and lower bound. The adaptation mechanism is based on extremum seeking control, in which the zone boundaries are driven by gradient estimation obtained by continuously sinusoidally modulating and demodulating the rrGPI readings. To improve the adaptation method's robustness against uncertainties, a decaying feedback gain and a vanishing dither signal are employed. in-silico trials suggested that the personalized optimized zone can be reached within a week of adaptation. Both for announced and unannounced meals, the proposed method outperforms the fixed zone [80, 140] mg/dL, which has been employed in the authors' clinical trials. It is also shown that the developed method has strong robustness against real-life uncertainties.

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia

OBJECTIVE

Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks.

RESEARCH DESIGN AND METHODS

Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A_1c (HbA_1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials.

RESULTS

Twenty-nine patients completed the trial. HbA_1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events.

CONCLUSIONS

Use of our novel adaptive AP yielded significant reductions in HbA_1c and hypoglycemia.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial

OBJECTIVE

As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively.

RESEARCH DESIGN AND METHODS

A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA_1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms.

RESULTS

AP improved percent time 70-140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70-180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes.

CONCLUSIONS

Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.

A Review of the Current Challenges Associated with the Development of an Artificial Pancreas by a Double Subcutaneous Approach

INTRODUCTION

Patients with diabetes type 1 (DM1) struggle daily to achieve good glucose control. The last decade has seen a rush of research groups working towards an artificial pancreas (AP) through the application of a double subcutaneous approach, i.e., subcutaneous (SC) continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion. Few have focused on the fundamental limitations of this approach, especially regarding outcome measures beyond time in range.

METHODS

Based on insulin physiology, the limitations of CGM, SC insulin absorption, meal challenge, and physical activity in DM1 patients, we discuss the limitations of the double SC approach. Finally, we discuss safety measures and the achievements reported in some recent AP studies that have utilized the double SC approach.

RESULTS

Most studies show that a double SC AP increases the time in range compared to a sensor-augmented insulin pump and shortens the time in hypoglycemia. Despite these achievements, the proportion of time spent in hyperglycemia is still roughly 20-40%, and hypoglycemia is still present 1-4% of the time. The main factors limiting further progress are the latency of SC CGM (at least 5-10 min) and the slow pharmacokinetics of SC-delivered fast-acting insulin. The maximum blood insulin level is reached after 45 min and the maximum glucose-lowering effect is observed after 1.5-2 h, while the glucose-lowering effect lasts for at least 5 h.

CONCLUSIONS

Although using a double SC AP leads to significant improvements in glucose control, the SC approach has severe limitations that hamper further progress towards a robust AP.

Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control

BACKGROUND

The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise.

MATERIALS AND METHODS

Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced.

RESULTS

Ten adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred.

CONCLUSIONS

The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.

Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems

OBJECTIVE

The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose.

METHODS

Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator.

RESULTS

The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70-180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70-180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC.

CONCLUSION

A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations.

SIGNIFICANCE

Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

BACKGROUND

Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors.

METHOD

Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure.

RESULTS

Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered.

CONCLUSIONS

The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

Feasibility of Long-Term Closed-Loop Control: A Multicenter 6-Month Trial of 24/7 Automated Insulin Delivery

BACKGROUND

In the past few years, the artificial pancreas-the commonly accepted term for closed-loop control (CLC) of blood glucose in diabetes-has become a hot topic in research and technology development. In the summer of 2014, we initiated a 6-month trial evaluating the safety of 24/7 CLC during free-living conditions.

RESEARCH DESIGN AND METHODS

Following an initial 1-month Phase 1, 14 individuals (10 males/4 females) with type 1 diabetes at three clinical centers in the United States and one in Italy continued with a 5-month Phase 2, which included 24/7 CLC using the wireless portable Diabetes Assistant (DiAs) developed at the University of Virginia Center for Diabetes Technology. Median subject characteristics were age 45 years, duration of diabetes 27 years, total daily insulin 0.53 U/kg/day, and baseline HbA1c 7.2% (55 mmol/mol).

RESULTS

Compared with the baseline observation period, the frequency of hypoglycemia below 3.9 mmol/L during the last 3 months of CLC was lower: 4.1% versus 1.3%, P < 0.001. This was accompanied by a downward trend in HbA1c from 7.2% (55 mmol/mol) to 7.0% (53 mmol/mol) at 6 months. HbA1c improvement was correlated with system use (Spearman r = 0.55). The user experience was favorable with identified benefit particularly at night and overall trust in the system. There were no serious adverse events, severe hypoglycemia, or diabetic ketoacidosis.

CONCLUSION

We conclude that CLC technology has matured and is safe for prolonged use in patients' natural environment. Based on these promising results, a large randomized trial is warranted to assess long-term CLC efficacy and safety.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. AUTOMATICA : THE JOURNAL OF IFAC, THE INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL 2016;71:237-246. [PMID: 27695131 PMCID: PMC5040369 DOI: 10.1016/j.automatica.2016.04.015]

A novel Model Predictive Control (MPC) law for an Artificial Pancreas (AP) to automatically deliver insulin to people with type 1 diabetes is proposed. The MPC law is an enhancement of the authors' zone-MPC approach that has successfully been trialled in-clinic, and targets the safe outpatient deployment of an AP. The MPC law controls blood-glucose levels to a diurnally time-dependent zone, and enforces diurnal, hard input constraints. The main algorithmic novelty is the use of asymmetric input costs in the MPC problem's objective function. This improves safety by facilitating the independent design of the controller's responses to hyperglycemia and hypoglycemia. The proposed controller performs predictive pump-suspension in the face of impending hypoglycemia, and subsequent predictive pump-resumption, based only on clinical needs and feedback. The proposed MPC strategy's benefits are demonstrated by in-silico studies as well as highlights from a US Food and Drug Administration approved clinical trial in which 32 subjects each completed two 25 hour closed-loop sessions employing the proposed MPC law.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

OBJECTIVE

To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.

RESEARCH DESIGN AND METHODS

After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL.

RESULTS

Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.

CONCLUSIONS

This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

Techniques for Exercise Preparation and Management in Adults with Type 1 Diabetes

OBJECTIVES

People with type 1 diabetes are at risk for early- and late-onset hypoglycemia following exercise. Reducing this risk may be possible with strategic modifications in carbohydrate intake and insulin use. We examined the exercise preparations and management techniques used by individuals with type 1 diabetes before and after physical activity and sought to determine whether use of differing diabetes technologies affects these health-related behaviours.

METHODS

We studied 502 adults from the Type 1 Diabetes Exchange's online patient community, Glu, who had completed an online survey focused on diabetes self-management and exercise.

RESULTS

Many respondents reported increasing carbohydrate intake before (79%) and after (66%) exercise as well as decreasing their meal boluses before (53%) and after (46%) exercise. Most reported adhering to a target glucose level before starting exercise (77%). Despite these accommodations, the majority reported low blood glucose (BG) levels after exercise (70%). The majority of users of both insulin pump therapy (CSII) and continuous glucose monitoring (CGM) (Combined) reported reducing basal insulin around exercise (55%), with fewer participants adjusting basal insulin when using other devices (SMBG only = 20%; CGM = 34%; CSII = 42%; p<0.001). However, CSII and Combined users reported that exercise makes their BG levels harder to control (p<0.05) and makes them feel less able to predict their BG levels while exercising (p<0.001); they show agreement that fear of low BG levels keeps them from exercising (p<0.01).

CONCLUSIONS

These findings highlight the need for exercise-management strategies tailored to individuals' overall diabetes management, for despite making exercise-specific adjustments for care, many people with type 1 diabetes still report significant difficulties with BG control when it comes to exercise.

Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes: What Systems Are in Development?

BACKGROUND

Closed-loop artificial pancreas device (APD) systems are externally worn medical devices that are being developed to enable people with type 1 diabetes to regulate their blood glucose levels in a more automated way. The innovative concept of this emerging technology is that hands-free, continuous, glycemic control can be achieved by using digital communication technology and advanced computer algorithms.

METHODS

A horizon scanning review of this field was conducted using online sources of intelligence to identify systems in development. The systems were classified into subtypes according to their level of automation, the hormonal and glycemic control approaches used, and their research setting.

RESULTS

Eighteen closed-loop APD systems were identified. All were being tested in clinical trials prior to potential commercialization. Six were being studied in the home setting, 5 in outpatient settings, and 7 in inpatient settings. It is estimated that 2 systems may become commercially available in the EU by the end of 2016, 1 during 2017, and 2 more in 2018.

CONCLUSIONS

There are around 18 closed-loop APD systems progressing through early stages of clinical development. Only a few of these are currently in phase 3 trials and in settings that replicate real life.

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND

The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings.

METHODS

A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses.

RESULTS

Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices.

CONCLUSIONS

These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

Progress in understanding type 1 diabetes through its genetic overlap with other autoimmune diseases

Type 1 diabetes mellitus (T1DM) is the most common autoimmune disease in pediatrics with a prevalence of roughly 1 in 500 children in the USA. Genome-wide association studies have identified more than 50 variants associated with increased risk for type 1 diabetes. Comparison of these variants with those identified in other autoimmune diseases reveals three important findings: (1) there is a high degree of overlap in implicated variants in diseases with similar pathophysiology, (2) in diseases with differing pathophysiology the same variants are often implicated in opposite roles, (3) in diseases with differing pathophysiology that have many non-overlapping or oppositely implicated variants there are still several variants which are overlapping or shared. Thus, the genetic overlap between T1DM and other autoimmune diseases forms the basis for our understanding of druggable targets in type 1 diabetes.