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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. 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. 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. 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.

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.

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.

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 platform 3 to generate over 50,000 glucose observations to train a k -nearest neighbours 4 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. An automated decision support system is reported that provides weekly insulin dosage recommendations to users with T1D and that, when analysing previously collected patient data, exhibits high agreement with recommendations from physicians.

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.

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. 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. 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/m ; 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. 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. 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.

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.

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.

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. [PUBLICATION ABSTRACT]

With the discovery of insulin came a deeper understanding of therapeutic options for one of the most devastating chronic diseases of the modern era, diabetes mellitus. The use of insulin in the treatment of diabetes, especially in those with severe insulin deficiency (type 1 diabetes), with multiple injections or continuous subcutaneous infusion, has been largely successful, but the risk for short term and long term complications remains substantial. Insulin treatment decisions are based on the patient’s knowledge of meal size, exercise plans and the intermittent knowledge of blood glucose values. As such, these are open loop methods that require human input. The idea of closed loop control of diabetes treatment is quite different: automated control of a device that delivers insulin (and possibly glucagon or other medications) and is based on continuous or very frequent glucose measurements. Closed loop insulin control for type 1 diabetes is not new but is far from optimized. The goal of such a system is to avoid short-term complications (hypoglycemia) and long-term complications (diseases of the eyes, kidneys, nerves and cardiovascular system) by mimicking the normal insulin secretion pattern of the pancreatic beta cell. A control system for automated diabetes treatment consists of three major components, (1) a glucose sensing device that serves as the afferent limb of the system; (2) an automated control unit that uses algorithms which acquires sensor input and generates treatment outputs; and (3) a drug delivery device (primarily for delivery of insulin), which serves as the system’s efferent limb. There are several major issues that highlight the difficulty of interacting with the complex unknowns of the biological world. For example, development of accurate continuous glucose monitors is crucial; the state of the art in 2009 is that such devices sometimes experience drift and are intended only to supplement information received from standard intermittent blood glucose data. In addition, it is important to acknowledge that an “automated” closed loop pancreas cannot approach the complexity of the normal human endocrine pancreas, which takes continuous data from substrates, hormones, paracrine compounds and autonomic neural inputs, and in response, secretes four hormones. Another major issue is the substantial absorption/action delay of insulin given by the subcutaneous route. Because of this delay, some researchers have recently given a portion of the meal-related insulin in an open loop manner before the meal and found this hybrid approach to be superior to closed loop control. Proportional-Integral-Derivative (PID) systems adapted from the industrial sector utilize control algorithms that alter output based on proportional (difference between actual and target levels), derivative (rate of change) and integral (time-related summative) errors in glucose. These algorithms have proven to be very promising in limited clinical trials. Related algorithms include a “fading memory” system that combines the proportional-derivative components of a classic PID system with time-relating decay of input signals that allow greater emphasis on more recent glucose values, a characteristic noted in mammalian beta-cells. Model Predictive Control (MPC) systems are highly adaptive methods that utilize mathematical models based on observations of biological behavior patterns using system identification and are now undergoing testing in humans. The application of further mathematical models, such as fuzzy control and artificial neural networks, are also promising, but are largely clinically untested. In summary, the prospects for closed loop control of glycemia in persons with diabetes have improved considerably. Major limitations include the delayed absorption/action of subcutaneous insulin and the imperfect stability of currently-available continuous glucose sensors. The potential for improved glycemic control in persons with diabetes brings with it the potential for reduction in the frequency of acute and chronic complications of diabetes.