Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models

BACKGROUND

Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise.

METHODS

We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention).

RESULTS

exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.).

CONCLUSIONS

The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.

Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes

Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (Rd) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65-1.43, P < 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001-0.006, P = 0.003). The AUC for Rd rose by 1.26 mM during RE (95% CI: 0.41-2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03-0.04, P < 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in Rd. Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia.NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

BACKGROUND

Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data.

METHODS

Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403.

FINDINGS

Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred.

INTERPRETATION

AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia.

FUNDING

JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence

We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70-180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.

Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections

Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645.

Quantifying the impact of physical activity on future glucose trends using machine learning

Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.

Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes

Aerobic exercise in type 1 diabetes (T1D) causes rapid increase in glucose utilization due to muscle work during exercise, followed by increased insulin sensitivity after exercise. Better understanding of these changes is necessary for models of exercise in T1D. Twenty-six individuals with T1D underwent three sessions at three insulin rates (100%, 150%, 300% of basal). After 3-h run-in, participants performed 45 min aerobic exercise (moderate or intense). We determined area under the curve for endogenous glucose production (AUC_EGP) and rate of glucose disappearance (AUC_Rd) over 45 min from exercise start. A novel application of linear regression of R_d across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUC_Rd increased 12.45 mmol/L (CI = 10.33-14.58, P < 0.001) and 13.13 mmol/L (CI = 11.01-15.26, P < 0.001) whereas AUC_EGP increased 1.66 mmol/L (CI = 1.01-2.31, P < 0.001) and 3.46 mmol/L (CI = 2.81-4.11, P < 0.001) above baseline during moderate and intense exercise, respectively. AUC_EGP increased during intense exercise by 2.14 mmol/L (CI = 0.91-3.37, P < 0.001) compared with moderate exercise. There was significant effect of insulin infusion rate on AUC_Rd equal to 0.06 mmol/L per % above basal rate (CI = 0.05-0.07, P < 0.001). Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period. To our knowledge, this method of isolating dynamic insulin- and non-insulin-mediated uptake has not been previously employed during exercise. These results will be useful in informing glucoregulatory models of T1D. The study has been registered at www.clinicaltrials.gov as NCT03090451.NEW & NOTEWORTHY Separating insulin and non-insulin glucose uptake dynamically during exercise in type 1 diabetes has not been done before. We use a multistep process, including a previously described linear regression method, over three insulin infusion sessions, to perform this separation and can graph these components before, during, and after exercise for the first time.

Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis

Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia. Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico. Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3-99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75-0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9-10 mmol/L). Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia.

Dual-Hormone Closed-Loop System Using a Liquid Stable Glucagon Formulation Versus Insulin-Only Closed-Loop System Compared With a Predictive Low Glucose Suspend System: An Open-Label, Outpatient, Single-Center, Crossover, Randomized Controlled Trial

OBJECTIVE

To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia (<70 mg/dL) from the start of in-clinic aerobic exercise (45 min at 60% VO_2max) to 4 h after.

RESULTS

DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0-4.2], SH 8.3% [0.0-12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study.

CONCLUSIONS

The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6–9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: −6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: −8.3 to −2.9%, HIIT MARD: 12.1 to 16.8% and MRD: −14.3 to −9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

An artificial intelligence decision support system for the management of type 1 diabetes

Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)^1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl^-1) and hyperglycaemia (>180 mg dl^-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform³ to generate over 50,000 glucose observations to train a k-nearest neighbours⁴ decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)⁵. In silico^3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.

Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs

BACKGROUND

People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals' different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions.

METHODS

A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient's insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control.

RESULTS

Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%.

CONCLUSION

Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.

Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes

BACKGROUND

Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise.

METHODS

We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D.

RESULTS

Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity.

CONCLUSIONS

Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.

A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model

Purpose

We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.

Methods

A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70–180 mg/dL), time in hypoglycemia (<70 mg/dL) and time in hyperglycemia (>180 mg/dL) for both the virtual patients and the actual patients.

Results

The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.

Conclusions

We have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.

Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study

BACKGROUND

Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise.

OBJECTIVE

The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise.

METHODS

We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m²; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias.

RESULTS

Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of -3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of -4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: -11.4% [SD 35.7]; Garmin: -14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: -1.7% [SD 11.5]; Garmin: -0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: -19.3% [SD 28.9], Garmin: -1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures.

CONCLUSIONS

Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

Effect of Aerobic and Resistance Exercise on Glycemic Control in Adults With Type 1 Diabetes

OBJECTIVES

Physical exercise is recommended for individuals with type 1 diabetes, yet the effects of exercise on glycemic control are not well established. We evaluated the impact of different modes of exercise on glycemic control in people with type 1 diabetes.

METHODS

In a 3-week randomized crossover trial, 10 adults with type 1 diabetes (4 men and 6 women, aged 33±6 years; duration of diabetes, 18±10 years; glycated hemoglobin level, 7.4%±1%) were assigned to 3 weeks of intervention: aerobic exercise (treadmill at 60% of maximum volume of oxygen utilization), resistance training (8 to 12 repetitions of 5 upper and lower body exercises at 60% to 80% of 1 repetition maximum) or no exercise (control). During each exercise week, participants completed 2 monitored 45 min exercise sessions. For each week of the study, we analyzed participants' insulin pump data, sensor glucose data and meal intake using a custom smart-phone application. The primary outcome was the percentage of time in range (glucose >3.9 mmol/L and ≤10 mmol/L) for the 24 h after each bout of exercise or rest during the control week. The study was registered on ClinicalTrials.gov (NCT:02687893).

RESULTS

Aerobic exercise caused a mean glucose reduction during exercise of 3.94±2.67 mmol/L, whereas the reduction during resistance training was 1.33±1.78 mmol/L (p=0.007). The mean percentage time in range for the 24 h after resistance training was significantly greater than that during the control period (70% vs. 56%, p=0.013) but not after aerobic exercise (60%).

CONCLUSIONS

The results indicate that when various confounders are considered, resistance training could improve glycemic control in this population.

Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors

OBJECTIVE

Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy.

RESEARCH DESIGN AND METHODS

Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia (<70 mg/dL) and in a target range (70-180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal.

RESULTS

The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44).

CONCLUSIONS

The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

The effect of exercise on sleep in adults with type 1 diabetes

The aim of this pilot study was to investigate the effect of exercise on sleep and nocturnal hypoglycaemia in adults with type 1 diabetes (T1D). In a 3-week crossover trial, 10 adults with T1D were randomized to perform aerobic, resistance or no exercise. During each exercise week, participants completed 2 separate 45-minutes exercise sessions at an academic medical center. Participants returned home and wore a continuous glucose monitor and a wrist-based activity monitor to estimate sleep duration. Participants on average lost 70 (±49) minutes of sleep (P = .0015) on nights following aerobic exercise and 27 (±78) minutes (P = .3) following resistance exercise relative to control nights. The odds ratio with confidence intervals of nocturnal hypoglycaemia occurring on nights following aerobic and resistance exercise was 5.4 (1.3, 27.2) and 7.0 (1.7, 37.3), respectively. Aerobic exercise can cause sleep loss in T1D possibly from increased hypoglycaemia.

Design of a dual-hormone model predictive control for artificial pancreas with exercise model

The Artificial Pancreas (AP) is a new technology for helping people with type 1 diabetes to better control their glucose levels through automated delivery of insulin and optionally glucagon in response to sensed glucose levels. In a dual hormone AP, insulin and glucagon are delivered automatically to the body based on glucose sensor measurements using a control algorithm that calculates the amount of hormones to be infused. A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon. In this paper, we propose a novel dual-hormone (DH) switching model predictive control and compare it with a single-hormone (SH) MPC. We extended both MPCs by integrating an exercise model and compared performance with and without the exercise model included. Results were obtained on a virtual patient population undergoing a simulated exercise event using a mathematical glucoregulatory model that includes exercise. Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022). Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p <; 0.001).

Comparative Pharmacokinetic/Pharmacodynamic Study of Liquid Stable Glucagon Versus Lyophilized Glucagon in Type 1 Diabetes Subjects

BACKGROUND

There is currently no stable liquid form of glucagon commercially available. The aim of this study is to assess the speed of absorption and onset of action of G-Pump™ glucagon at 3 doses as compared to GlucaGen®, all delivered subcutaneously via an OmniPod®.

METHODS

Nineteen adult subjects with type 1 diabetes participated in this Phase 2, randomized, double-blind, cross-over, pharmacokinetic/pharmacodynamic study. Subjects were given 0.3, 1.2, and 2.0 µg/kg each of G-Pump glucagon and GlucaGen via an OmniPod.

RESULTS

G-Pump glucagon effectively increased blood glucose levels in a dose-dependent fashion with a glucose Cmax of 183, 200, and 210 mg/dL at doses of 0.3, 1.2, and 2.0 µg/kg, respectively (P = ns vs GlucaGen). Mean increases in blood glucose from baseline were 29.2, 52.9, and 77.7 mg/dL for G-Pump doses of 0.3, 1.2, and 2.0 µg/kg, respectively. There were no statistically significant differences between treatments in the glucose T50%-early or glucagon T50%-early with one exception. The glucagon T50%-early was greater following G-Pump treatment at the 2.0 μg/kg dose (13.9 ± 4.7 min) compared with GlucaGen treatment at the 2.0 μg/kg dose (11.0 ± 3.1 min, P = .018). There was more pain and erythema at the infusion site with G-Pump as compared to GlucaGen. No serious adverse events were reported, and no unexpected safety issues were observed.

CONCLUSIONS

G-Pump glucagon is a novel, stable glucagon formulation with similar PK/PD properties as GlucaGen, but was associated with more pain and infusion site reactions as the dose increased, as compared to GlucaGen.

Modeling Glucagon Action in Patients With Type 1 Diabetes

The dual-hormone artificial pancreas is an emerging technology to treat type 1 diabetes (T1D). It consists of a glucose sensor, infusion pumps, and a dosing algorithm that directs hormonal delivery. Preclinical optimization of dosing algorithms using computer simulations has the potential to accelerate the pace of development for this technology. However, current simulation environments consider glucose regulation models that either do not include glucagon action submodels or include submodels that were proposed without comparison to other candidate models. We consider here nine candidate models of glucagon action featuring a number of possible characteristics: insulin-independent glucagon action, insulin/glucagon ratio effect on hepatic glucose production, insulin-dependent suppression of glucagon action, and the effect of rate of change of glucagon. To assess the models, we use measurements of plasma insulin, plasma glucagon, and endogenous glucose production collected from experiments involving eight subjects with T1D who receive four subcutaneous glucagon boluses. We estimate each model's parameters using a Bayesian approach, and the models are contrasted based on the deviance information criterion. The model achieving the best fit features insulin-dependent suppression of glucagon action and incorporates effects of both glucagon levels and its rate of change.

Effect of Repeated Glucagon Doses on Hepatic Glycogen in Type 1 Diabetes: Implications for a Bihormonal Closed-Loop System

OBJECTIVE

To evaluate subjects with type 1 diabetes for hepatic glycogen depletion after repeated doses of glucagon, simulating delivery in a bihormonal closed-loop system.

RESEARCH DESIGN AND METHODS

Eleven adult subjects with type 1 diabetes participated. Subjects underwent estimation of hepatic glycogen using (13)C MRS. MRS was performed at the following four time points: fasting and after a meal at baseline, and fasting and after a meal after eight doses of subcutaneously administered glucagon at a dose of 2 µg/kg, for a total mean dose of 1,126 µg over 16 h. The primary and secondary end points were, respectively, estimated hepatic glycogen by MRS and incremental area under the glucose curve for a 90-min interval after glucagon administration.

RESULTS

In the eight subjects with complete data sets, estimated glycogen stores were similar at baseline and after repeated glucagon doses. In the fasting state, glycogen averaged 21 ± 3 g/L before glucagon administration and 25 ± 4 g/L after glucagon administration (mean ± SEM) (P = NS). In the fed state, glycogen averaged 40 ± 2 g/L before glucagon administration and 34 ± 4 g/L after glucagon administration (P = NS). With the use of an insulin action model, the rise in glucose after the last dose of glucagon was comparable to the rise after the first dose, as measured by the 90-min incremental area under the glucose curve.

CONCLUSIONS

In adult subjects with well-controlled type 1 diabetes (mean A1C 7.2%), glycogen stores and the hyperglycemic response to glucagon administration are maintained even after receiving multiple doses of glucagon. This finding supports the safety of repeated glucagon delivery in the setting of a bihormonal closed-loop system.

Expanding the net: The re-evaluation of the multidimensional nomogram calculating the upper limit of normal PTH (maxPTH) in the setting of secondary hyperparathyroidism and the development of the MultIdimensional Predictive hyperparaTHyroid model (Mi-PTH)

BACKGROUND

The multidimensional nomogram calculating the upper limit of normal PTH (maxPTH) model identifies a personalized upper limit of normal parathyroid hormone (PTH) and successfully predicts classical primary hyperparathyroidism (PHP). We aimed to assess whether maxPTH can distinguish normocalcemic PHP (NCPHP) from secondary hyperparathyroidism (SHP), including subjects who underwent bariatric surgery (BrS).

METHODS

A total of 172 subjects with 359 complete datasets of serum calcium (Ca), 25-OH vitamin D, and intact PTH from Oregon were analyzed: 123 subjects (212 datasets) with PHP and 47 (143) with SHP, including 28 (100) with previous BrS. An improved prediction model, MultIdimensional evaluation for Primary hyperparaTHyroidism (Mi-PTH), was created with the same variables as maxPTH by the use of a combined cohort (995 subjects) including participants from previous studies.

RESULTS

In the Oregon cohort, maxPTH's sensitivity was 100% for classical PHP and 89% for NCPHP, but only 50% for normohormonal PHP (NHPHP) and 40% specific for SHP. In comparison, although sensitivity for NCPHP was similar (89%), Mi-PTH vastly improved SHP specificity (85%). In the combined cohort, Mi-PTH had better sensitivity of 98.5% (vs 95%) and specificity 97% (vs 85%).

CONCLUSION

MaxPTH was sensitive in detecting PHP; however, there was low specificity for SHP, especially in patients who underwent BrS. The creation of Mi-PTH provided improved performance measures but requires further prospective evaluation.

Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

In this article, we present several important contributions necessary for enabling an artificial endocrine pancreas (AP) system to better respond to exercise events. First, we show how exercise can be automatically detected using body-worn accelerometer and heart rate sensors. During a 22 hour overnight inpatient study, 13 subjects with type 1 diabetes wearing a Zephyr accelerometer and heart rate monitor underwent 45 minutes of mild aerobic treadmill exercise while controlling their glucose levels using sensor-augmented pump therapy. We used the accelerometer and heart rate as inputs into a validated regression model. Using this model, we were able to detect the exercise event with a sensitivity of 97.2% and a specificity of 99.5%. Second, from this same study, we show how patients' glucose declined during the exercise event and we present results from in silico modeling that demonstrate how including an exercise model in the glucoregulatory model improves the estimation of the drop in glucose during exercise. Last, we present an exercise dosing adjustment algorithm and describe parameter tuning and performance using an in silico glucoregulatory model during an exercise event.

A novel, stable, aqueous glucagon formulation using ferulic acid as an excipient

Commercial glucagon is unstable due to aggregation and degradation. In closed-loop studies, it must be reconstituted frequently. For use in a portable pump for 3 days, a more stable preparation is required. At alkaline pH, curcumin inhibited glucagon aggregation. However, curcumin is not sufficiently stable for long-term use. Here, we evaluated ferulic acid, a stable breakdown product of curcumin, for its ability to stabilize glucagon. Ferulic acid-formulated glucagon (FAFG), composed of ferulic acid, glucagon, L-methionine, polysorbate-80, and human serum albumin in glycine buffer at pH 9, was aged for 7 days at 37°C. Glucagon aggregation was assessed by transmission electron microscopy (TEM) and degradation by high-performance liquid chromatography (HPLC). A cell-based protein kinase A (PKA) assay was used to assess in vitro bioactivity. Pharmacodynamics (PD) of unaged FAFG, 7-day aged FAFG, and unaged synthetic glucagon was determined in octreotide-treated swine. No fibrils were observed in TEM images of fresh or aged FAFG. Aged FAFG was 94% intact based on HPLC analysis and there was no loss of bioactivity. In the PD swine analysis, the rise over baseline of glucose with unaged FAFG, aged FAFG, and synthetic native glucagon (unmodified human sequence) was similar. After 7 days of aging at 37°C, an alkaline ferulic acid formulation of glucagon exhibited significantly less aggregation and degradation than that seen with native glucagon and was bioactive in vitro and in vivo. Thus, this formulation may be stable for 3-7 days in a portable pump for bihormonal closed-loop treatment of T1D.

Quantification of the glycemic response to microdoses of subcutaneous glucagon at varying insulin levels

OBJECTIVE

Glucagon delivery in closed-loop control of type 1 diabetes is effective in minimizing hypoglycemia. However, high insulin concentration lowers the hyperglycemic effect of glucagon, and small doses of glucagon in this setting are ineffective. There are no studies clearly defining the relationship between insulin levels, subcutaneous glucagon, and blood glucose.

RESEARCH DESIGN AND METHODS

Using a euglycemic clamp technique in 11 subjects with type 1 diabetes, we examined endogenous glucose production (EGP) of glucagon (25, 75, 125, and 175 μg) at three insulin infusion rates (0.016, 0.032, and 0.05 units/kg/h) in a randomized, crossover study. Infused 6,6-dideuterated glucose was measured every 10 min, and EGP was determined using a validated glucoregulatory model. Area under the curve (AUC) for glucose production was the primary outcome, estimated over 60 min.

RESULTS

At low insulin levels, EGP rose proportionately with glucagon dose, from 5 ± 68 to 112 ± 152 mg/kg (P = 0.038 linear trend), whereas at high levels, there was no increase in glucose output (19 ± 53 to 26 ± 38 mg/kg, P = NS). Peak glucagon serum levels and AUC correlated well with dose (r2 = 0.63, P < 0.001), as did insulin levels with insulin infusion rates (r2 = 0.59, P < 0.001).

CONCLUSIONS

EGP increases steeply with glucagon doses between 25 and 175 μg at lower insulin infusion rates. However, high insulin infusion rates prevent these doses of glucagon from significantly increasing glucose output and may reduce glucagon effectiveness in preventing hypoglycemia when used in the artificial pancreas.

Biochemical stabilization of glucagon at alkaline pH

BACKGROUND

For patients with type 1 diabetes mellitus, a bihormonal artificial endocrine pancreas system utilizing glucagon and insulin has been found to stabilize glycemic control. However, commercially available formulations of glucagon cannot currently be used in such systems because of physical instability characterized by aggregation and chemical degradation. Storing glucagon at pH 10 blocks protein aggregation but results in chemical degradation. Reductions in pH minimize chemical degradation, but even small reductions increase protein aggregation. We hypothesized that common pharmaceutical excipients accompanied by a new excipient would inhibit glucagon aggregation at an alkaline pH.

METHODS AND RESULTS

As measured by tryptophan intrinsic fluorescence shift and optical density at 630 nm, protein aggregation was indeed minimized when glucagon was formulated with curcumin and albumin. This formulation also reduced chemical degradation, measured by liquid chromatography with mass spectrometry. Biological activity was retained after aging for 7 days in an in vitro cell-based bioassay and also in Yorkshire swine.

CONCLUSIONS

Based on these findings, a formulation of glucagon stabilized with curcumin, polysorbate-80, l-methionine, and albumin at alkaline pH in glycine buffer may be suitable for extended use in a portable pump in the setting of a bihormonal artificial endocrine pancreas.

Automated control of an adaptive bihormonal, dual-sensor artificial pancreas and evaluation during inpatient studies

Automated control of blood glucose in patients with type-1 diabetes has not yet been fully implemented. The aim of this study was to design and clinically evaluate a system that integrates a control algorithm with off-the-shelf subcutaneous sensors and pumps to automate the delivery of the hormones glucagon and insulin in response to continuous glucose sensor measurements. The automated component of the system runs an adaptive proportional derivative control algorithm which determines hormone delivery rates based on the sensed glucose measurements and the meal announcements by the patient. We provide details about the system design and the control algorithm, which incorporates both a fading memory proportional derivative controller (FMPD) and an adaptive system for estimating changing sensitivity to insulin based on a glucoregulatory model of insulin action. For an inpatient study carried out in eight subjects using Dexcom SEVEN PLUS sensors, prestudy HbA1c averaged 7.6, which translates to an estimated average glucose of 171 mg/dL. In contrast, during use of the automated system, after initial stabilization, glucose averaged 145 mg/dL and subjects were kept within the euglycemic range (between 70 and 180 mg/dL) for 73.1% of the time, indicating improved glycemic control. A further study on five additional subjects in which we used a newer and more reliable glucose sensor (Dexcom G4 PLATINUM) and made improvements to the insulin and glucagon pump communication system resulted in elimination of hypoglycemic events. For this G4 study, the system was able to maintain subjects' glucose levels within the near-euglycemic range for 71.6% of the study duration and the mean venous glucose level was 151 mg/dL.

Discomfort from an alkaline formulation delivered subcutaneously in humans: albumin at pH 7 versus pH 10

BACKGROUND AND OBJECTIVE

There is a paucity of data regarding tolerability of alkaline drugs administered subcutaneously. The aim of this study was to assess the tolerability of alkaline preparations of human albumin delivered subcutaneously to healthy humans.

METHODS

We compared the tolerability of neutral versus alkaline (pH 10) formulations of human albumin in ten volunteers. With an intent to minimize the time required to reach physiological pH after injection, the alkaline formulation was buffered with a low concentration of glycine (20 mmol/L). Each formulation was given at two rates: over 5 seconds and over 60 seconds. A six-point scale was used to assess discomfort.

RESULTS

For slow injections, there was a significant difference between pH 7.4 and pH 10 injections (0.4 ± 0.2 vs 1.1 ± 0.2, mean ± SEM; p = 0.025), though the degree of discomfort at pH 10 injections was only 'mild or slight'. For fast injections, the difference between neutral and alkaline formulations was of borderline significance. Inflammation and oedema, as judged by a physician, were very minimal for all injections, irrespective of pH.

CONCLUSION

For subcutaneous drug administration (especially when delivered slowly), there was more discomfort associated with alkaline versus neutral formulations of albumin, though the discomfort was mild. This study suggests that there is little discomfort and inflammation resulting from subcutaneous administration of protein drugs formulated with weak buffers at alkaline pH.

Development of a fully automated closed loop artificial pancreas control system with dual pump delivery of insulin and glucagon

Patients with diabetes have difficulty controlling their blood sugar and suffer from acute effects of hypoglycemia and long-term effects of hyperglycemia, which include disease of the eyes, kidneys and nerves/feet. In this paper, we describe a new system that is used to automatically control blood sugar in people with diabetes through the fully automated measurement of blood glucose levels and the delivery of insulin and glucagon via the subcutaneous route. When a patient's blood sugar goes too high, insulin is given to the patient to bring his/her blood sugar back to a normal level. To prevent a patient's blood sugar from going too low, the patient is given a hormone called glucagon which raises the patient's blood sugar. While other groups have described methods for automatically delivering insulin and glucagon, many of these systems still require human interaction to enter the venous blood sugar levels into the control system. This paper describes the development of a fully automated closed-loop dual sensor bi-hormonal artificial pancreas system that does not require human interaction. The system described in this paper is comprised of two sensors for measuring glucose, two pumps for independent delivery of insulin and glucagon, and a laptop computer running a custom software application that controls the sensor acquisition and insulin and glucagon delivery based on the glucose values recorded. Two control algorithms are designed into the software: (1) an algorithm that delivers insulin and glucagon according to their proportional and derivative errors and proportional and derivative gains and (2) an adaptive algorithm that adjusts the gain factors based on the patient's current insulin sensitivity as determined using a mathematical model. Results from this work may ultimately lead to development of a portable, easy to use, artificial pancreas device that can enable far better glycemic control in patients with diabetes.

The accuracy benefit of multiple amperometric glucose sensors in people with type 1 diabetes

OBJECTIVE

To improve glucose sensor accuracy in subjects with type 1 diabetes by using multiple sensors and to assess whether the benefit of redundancy is affected by intersensor distance.

RESEARCH DESIGN AND METHODS

Nineteen adults with type 1 diabetes wore four Dexcom SEVEN PLUS subcutaneous glucose sensors during two 9-h studies. One pair of sensors was worn on each side of the abdomen, with each sensor pair placed at a predetermined distance apart and 20 cm away from the opposite pair. Arterialized venous blood glucose levels were measured every 15 min, and sensor glucose values were recorded every 5 min. Sensors were calibrated once at the beginning of the study.

RESULTS

The use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P < 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P < 0.001). Using only two sensors also significantly improved very large errors and accuracy. Intersensor distance did not affect the function of sensor pairs.

CONCLUSIONS

Sensor accuracy is significantly improved with the use of multiple sensors compared with the use of a single sensor. The benefit of redundancy is present even when sensors are positioned very closely together (7 mm). These findings are relevant to the design of an artificial pancreas device.

A controlled study of the effectiveness of an adaptive closed-loop algorithm to minimize corticosteroid-induced stress hyperglycemia in type 1 diabetes

To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportional-derivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h. The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm. An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia.

Safe glycemic management during closed-loop treatment of type 1 diabetes: the role of glucagon, use of multiple sensors, and compensation for stress hyperglycemia

Patients with type 1 diabetes mellitus (T1DM) must make frequent decisions and lifestyle adjustments in order to manage their disorder. Automated treatment would reduce the need for these self-management decisions and reduce the risk for long-term complications. Investigators in the field of closed-loop glycemic control systems are now moving from inpatient to outpatient testing of such systems. As outpatient systems are developed, the element of safety increases in importance. One such concern is the risk for hypoglycemia, due in part to the delayed onset and prolonged action duration of currently available subcutaneous insulin preparations. We found that, as compared to an insulin-only closed-loop system, a system that also delivers glucagon when needed led to substantially less hypoglycemia. Though the capability of glucagon delivery would mandate the need for a second hormone chamber, glucagon in small doses is tolerated very well. People with T1DM often develop hyperglycemia from emotional stress or medical stress. Automated closed-loop systems should be able to detect such changes in insulin sensitivity and adapt insulin delivery accordingly. We recently verified the adaptability of a model-based closed-loop system in which the gain factors that govern a proportional-integral-derivative-like system are adjusted according to frequently measured insulin sensitivity. Automated systems can be tested by physical exercise to increase glucose uptake and insulin sensitivity or by administering corticosteroids to reduce insulin sensitivity. Another source of risk in closed-loop systems is suboptimal performance of amperometric glucose sensors. Inaccuracy can result from calibration error, biofouling, and current drift. We found that concurrent use of more than one sensor typically leads to better sensor accuracy than use of a single sensor. For example, using the average of two sensors substantially reduces the proportion of large sensor errors. The use of more than two allows the use of voting algorithms, which can temporarily exclude a sensor whose signal is outlying. Elements such as the use of glucagon to minimize hypoglycemia, adaptation to changes in insulin sensitivity, and sensor redundancy will likely increase safety during outpatient use of closed-loop glycemic control systems.

Continuous glucose monitoring in subjects with type 1 diabetes: improvement in accuracy by correcting for background current

BACKGROUND

A cause of suboptimal accuracy in amperometric glucose sensors is the presence of a background current (current produced in the absence of glucose) that is not accounted for. We hypothesized that a mathematical correction for the estimated background current of a commercially available sensor would lead to greater accuracy compared to a situation in which we assumed the background current to be zero. We also tested whether increasing the frequency of sensor calibration would improve sensor accuracy.

METHODS

This report includes analysis of 20 sensor datasets from seven human subjects with type 1 diabetes. Data were divided into a training set for algorithm development and a validation set on which the algorithm was tested. A range of potential background currents was tested.

RESULTS

Use of the background current correction of 4 nA led to a substantial improvement in accuracy (improvement of absolute relative difference or absolute difference of 3.5-5.5 units). An increase in calibration frequency led to a modest accuracy improvement, with an optimum at every 4 h.

CONCLUSIONS

Compared to no correction, a correction for the estimated background current of a commercially available glucose sensor led to greater accuracy and better detection of hypoglycemia and hyperglycemia. The accuracy-optimizing scheme presented here can be implemented in real time.

Factors influencing the effectiveness of glucagon for preventing hypoglycemia

BACKGROUND

Administration of small, intermittent doses of glucagon during closed-loop insulin delivery markedly reduces the frequency of hypoglycemia. However, in some cases, hypoglycemia occurs despite administration of glucagon in this setting.

METHODS

Fourteen adult subjects with type 1 diabetes participated in 22 closed-loop studies, duration 21.5±2.0 h. The majority of subjects completed two studies, one with insulin + glucagon, given subcutaneously by algorithm during impending hypoglycemia, and one with insulin+placebo. The more accurate of two subcutaneous glucose sensors was used as the controller input. To better understand reasons for success or failure of glucagon to prevent hypoglycemia, each response to a glucagon dose over 0.5 µg/kg was analyzed (n=19 episodes).

RESULTS

Hypoglycemia occurred in the hour after glucagon delivery in 37% of these episodes. In the failures, estimated insulin on board was significantly higher versus successes (5.8±0.5 versus 2.9±0.5 U, p<.001). Glucose at the time of glucagon delivery was significantly lower in failures versus successes (86±3 versus 95±3 mg/dl, p=.04). Sensor bias (glucose overestimation) was highly correlated with starting glucose (r=0.65, p=.002). Prior cumulative glucagon dose was not associated with success or failure.

CONCLUSION

Glucagon may fail to prevent hypoglycemia when insulin on board is high or when glucagon delivery is delayed due to overestimation of glucose by the sensor. Improvements in sensor accuracy and delivery of larger or earlier glucagon doses when insulin on board is high may further reduce the frequency of hypoglycemia.

In vitro and in vivo evaluation of native glucagon and glucagon analog (MAR-D28) during aging: lack of cytotoxicity and preservation of hyperglycemic effect

BACKGROUND

For automated prevention of hypoglycemia, there is a need for glucagon (or an analog) to be sufficiently stable so that it can be indwelled in a portable pump for at least 3 days. However, under some conditions, solutions of glucagon can form amyloid fibrils. Currently, the usage instructions for commercially available glucagon allow only for its immediate use.

METHODS

In NIH 3T3 fibroblasts, we tested amyloid formation and cytotoxicity of solutions of native glucagon and the glucagon analog MAR-D28 after aging under different conditions for 5 days. In addition, aged native glucagon was subjected to size-exclusion chromatography (SEC). We also studied whether subcutaneous aged Novo Nordisk GlucaGen® would have normal bioactivity in octreotide-treated, anesthetized, nondiabetic pigs.

RESULTS

We found no evidence of cytotoxicity from native glucagon or MAR-D28 (up to 2.5 mg/ml) at a pH of 10 in a glycine solvent. We found a mild cytotoxicity for both compounds in Tris buffer at pH 8.5. A high concentration of the commercial glucagon preparation (GlucaGen) caused marked cytotoxicity, but low pH and/or a high osmolarity probably accounted primarily for this effect. With SEC, the decline in monomeric glucagon over time was much lower when aged in glycine (pH 10) than when aged in Tris (pH 8.5) or in citrate (pH 3). Congo red staining for amyloid was very low with the glycine preparation (pH 10). In the pig studies, the hyperglycemic effect of commercially available glucagon was preserved despite aging conditions associated with marked amyloid formation.

CONCLUSIONS

Under certain conditions, aqueous solutions of glucagon and MAR-D28 are stable for at least 5 days and are thus very likely to be safe in mammals. Glycine buffer at a pH of 10 appears to be optimal for avoiding cytotoxicity and amyloid fibril formation.

Novel use of glucagon in a closed-loop system for prevention of hypoglycemia in type 1 diabetes

OBJECTIVE

To minimize hypoglycemia in subjects with type 1 diabetes by automated glucagon delivery in a closed-loop insulin delivery system.

RESEARCH DESIGN AND METHODS

Adult subjects with type 1 diabetes underwent one closed-loop study with insulin plus placebo and one study with insulin plus glucagon, given at times of impending hypoglycemia. Seven subjects received glucagon using high-gain parameters, and six subjects received glucagon in a more prolonged manner using low-gain parameters. Blood glucose levels were measured every 10 min and insulin and glucagon infusions were adjusted every 5 min. All subjects received a portion of their usual premeal insulin after meal announcement.

RESULTS

Automated glucagon plus insulin delivery, compared with placebo plus insulin, significantly reduced time spent in the hypoglycemic range (15 +/- 6 vs. 40 +/- 10 min/day, P = 0.04). Compared with placebo, high-gain glucagon delivery reduced the frequency of hypoglycemic events (1.0 +/- 0.6 vs. 2.1 +/- 0.6 events/day, P = 0.01) and the need for carbohydrate treatment (1.4 +/- 0.8 vs. 4.0 +/- 1.4 treatments/day, P = 0.01). Glucagon given with low-gain parameters did not significantly reduce hypoglycemic event frequency (P = NS) but did reduce frequency of carbohydrate treatment (P = 0.05).

CONCLUSIONS

During closed-loop treatment in subjects with type 1 diabetes, high-gain pulses of glucagon decreased the frequency of hypoglycemia. Larger and longer-term studies will be required to assess the effect of ongoing glucagon treatment on overall glycemic control.