Open-loop Therapy Research Articles

Glycaemic patterns during breastfeeding with postpartum use of closed-loop insulin delivery in women with type 1 diabetes.

This study aimed to describe the relationship between breastfeeding episodes and maternal glucose levels, and to assess whether this differs with closed-loop vs open-loop (sensor-augmented pump) insulin therapy. Infant-feeding diaries were collected at 6 weeks, 12 weeks and 24 weeks postpartum in a trial of postpartum closed-loop use in 18 women with type 1 diabetes. Continuous glucose monitoring (CGM) data were used to identify maternal glucose patterns within the 3 h of breastfeeding episodes. Generalised mixed models adjusted for breastfeeding episodes in the same woman, repeat breastfeeding episodes, carbohydrate intake, infant age at time of feeding and early pregnancy HbA1c. This was a secondary analysis of data collected during a randomised trial (ClinicalTrials.gov registration no. NCT04420728). CGM glucose remained above 3.9 mmol/l in the 3 h post-breastfeeding for 93% (397/427) of breastfeeding episodes. There was an overall decrease in glucose at nighttime within 3 h of breastfeeding (1.1 mmol l-1 h-1 decrease on average; p=0.009). A decrease in nighttime glucose was observed with open-loop therapy (1.2±0.5 mmol/l) but was blunted with closed-loop therapy (0.4±0.3 mmol/l; p<0.01, open-loop vs closed-loop). There is a small decrease in glucose after nighttime breastfeeding that usually does not result in maternal hypoglycaemia; this appears to be blunted with the use of closed-loop therapy.

Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One.

Accurate blood glucose (BG) prediction are key in next-generation tools for type 1 diabetes (T1D) management, such as improved decision support systems and advanced closed-loop control. Glucose prediction algorithms commonly rely on black-box models. Large physiological models, successfully adopted for simulation, were little explored for glucose prediction, mostly because their parameters are hard to individualize. In this work, we develop a BG prediction algorithm based on a personalized physiological model inspired by the UVA/Padova T1D Simulator. Then we compare white-box and advanced black-box personalized prediction techniques. A personalized nonlinear physiological model is identified from patient data through a Bayesian approach based on Markov Chain Monte Carlo technique. The individualized model was integrated within a particle filter (PF) to predict future BG concentrations. The black-box methodologies considered are non-parametric models estimated via gaussian regression (NP), three deep learning methods: long-short-term-memory (LSTM), gated recurrent unit (GRU), temporal convolutional networks (TCN), and a recursive autoregressive with exogenous input model (rARX). BG forecasting performances are assessed for several prediction horizons (PH) on 12 individuals with T1D, monitored in free-living conditions under open-loop therapy for 10 weeks. NP models provide the most effective BG predictions by achieving a root mean square error (RMSE), RMSE = 18.99 mg/dL, RMSE = 25.72 mg/dL and RMSE = 31.60 mg/dL, significantly outperforming: LSTM, GRU (for PH = 30 minutes), TCN, rARX, and the proposed physiological model for PH=30, 45 and 60 minutes. Black-box strategies remain preferable for glucose prediction even when compared to a white-box model with sound physiological structure and individualized parameters.

Site-Specifically Launched Microneedles for the Combined Treatment of Psoriasis-Diabetic Comorbidity.

Psoriasis and diabetes are both common comorbidities for each other, where inflammation and insulin resistance act in a vicious cycle, driving the progression of disease through the activation of the NF-κB signaling pathway. Therefore, disrupting the linkage between inflammation and insulin resistance by inhibiting the NF-κB pathway presents a promising therapeutic strategy for addressing psoriasis-diabetic comorbidity. Herein, an open-loop therapy was developed by integrating microneedle-mediated short- and long-range missiles to target psoriasis and diabetes, respectively. The short-range missile (curcumin nanoparticle) could be stationed in the psoriatic skin for topical and prolonged antipsoriasis therapy, while the long-range missile (metformin) is capable of penetrating transdermal barriers to induce a systemic hypoglycemic effect. More attractively, the short- and long-range missiles could join hands to inhibit the NF-κB signaling pathway and diminish inflammation, effectively disrupting the crosstalk between inflammation and insulin resistance. Pharmacodynamic studies showed that this microneedle-mediated combination, possessing dual anti-inflammatory and antihyperglycemic properties, proves to be highly efficacious in alleviating typical symptoms and inflammatory response in both nondiabetic and diabetic mice with imiquimod (IMQ)-induced psoriasis models. Hence, the microneedle-mediated open-loop therapy shows great potential in the management of psoriasis-diabetes comorbidity.

Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections

Background and objectives: Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections.Methods: The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm.Results: The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70–180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach.Conclusions: Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetes.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems

Background and ObjectivesHybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. MethodsThe add-on module consists of an internal-model controller that generates a “virtual” control action to compensate for disturbances. This “virtual” action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. ResultsFor the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). ConclusionsThe proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.

Opportunities and challenges in closed-loop systems in type 1 diabetes

Commercial automated insulin delivery therapy can improve glucose outcomes by increasing glucose time-in-range (3·9–10·0 mmol/L; 70–180 mg/dL) and reducing hypoglycaemia (<3·9 mmol/L; <70 mg/dL) in people with type 1 diabetes. Commercial closed-loop systems have shown modest improvement in percentage time-in-range, with an increase of 5 percentage points for MiniMed 670G 1 Bergenstal RM Garg S Weinzimer SA et al. Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes. JAMA. 2016; 316: 1407-1408 Crossref PubMed Scopus (377) Google Scholar and of 10 percentage points for Control-IQ 2 Brown SA Kovatchev BP Raghinaru D et al. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. N Engl J Med. 2019; 381: 1707-1717 Crossref PubMed Scopus (266) Google Scholar compared with open-loop therapy. New developments with closed-loop systems are likely to enable further improvements in glucose outcomes while reducing user burden for individuals able to access closed-loop systems. These developments are focused on addressing deficiencies with current closed-loop systems by, for example, reducing or eliminating the requirement of prandial insulin dosing, overcoming the slow kinetics of subcutaneous insulin compared with endogenous insulin production, and improving the handling of insulin before, during, and after exercise. These advances will ideally lead to improved performance and reduced patient burden, and enable more widespread usage.

Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial.

In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD (R2>0.95), with slopes of , for ∆TIR, , for ∆TAR, and , for ∆TBR. The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes.

To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates. The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.Register Clinicaltrials.gov: NCT03577158.

Parametric Identification of Sorensen model for glucose-insulin-carbohydrates dynamics using evolutive algorithms

Diabetes mellitus (DM) is a disease affecting millions of people worldwide, and its medical care brings an economic wear to patients and public health systems. Many efforts have been made to deal with DM, one of them is the full-automation of insulin delivery. This idea consists in design a closed-loop control system to maintain blood glucose levels (BGL) within normal ranges. Dynamic models of glucose-insulin-carbohydrates play an important role in synthesis of control algorithms, but also in other aspects of DM care, such as testing glucose sensors, or as support systems for health care decisions. Therefore, there are several mathematical models reproducing glycemic dynamics of DM, most of them validated with nominal parameters of standardized patients. Nevertheless, individual patient-oriented models could open the possibility of having closed-loop personalized therapies. This problem can be addressed through the information provided by open-loop therapy based on continuous glucose monitoring and subcutaneous insulin infusion. This paper considers the problem of identifying particular parameters of a compartmental model of glucose-insulin dynamics in DM; the goal is fitting the model response to historical data of a diabetic patient collected during a time period of her/his daily life. At this time, Sorensen model is one of the most complete compartmental models representing the complex dynamics of the glucose-insulin metabolism. This is a system of 19 ordinary differential equations (ODEs), thus the identification of its parameters is a non-easy task. In this contribution, parameter identification was performed via three evolutionary algorithms: differential evolution, ant colony optimization and particle swarm optimization. The obtained results show that evolutionary algorithms are powerful tools to solve problems of parametric identification. Also, a comparative analysis of the three algorithms was realized throw a wilcoxon sign-rank test, in which colony optimization had the better performance. The model obtained with the estimated parameters could be used to in type 1 diabetes mellitus (T1DM) care, such as in the design of full-automation of insulin infusion.

Adjustment of Sensitive Parameters of a Mathematical Model of Glucose Metabolism Using an Evolutionary Algorithm

Currently, closed-loop therapy is a promising option for individuals with type 1 diabetes mellitus. It requires a glucose metabolism model and a control algorithm to reach a suitable automatic insulin delivery. Many mathematical models have been proposed considering the glucose-insulin dynamics and nominal parameters of a standard patient. Nevertheless, the open-loop therapy based on continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) provides sufficient data to seek patient-oriented models instead of the nominal ones. In consequence, patient-oriented models could open the possibility of having personalized closed-loop therapies. In this work, a methodology for automatic adjustment of sensitive parameters of a physiological mathematical model of glucose metabolism is presented. The methodology is based on an evolutionary algorithm and it is exemplified with a physiological model. This methodology provides a criterion to select the best model which can reproduce the glucose-insulin dynamics of a diabetic patient according to the data provided by the CGM and CSII systems.

Time Lag of Glucose From Intravascular to Interstitial Compartment in Humans

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open- and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3–6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5–6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closed-loop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes.

Feasibility of a Portable Bihormonal Closed-Loop System to Control Glucose Excursions at Home Under Free-Living Conditions for 48 Hours

This study assessed the feasibility of a portable bihormonal closed-loop system at home. Sixteen pump-treated patients with type 1 diabetes received 48 h of closed-loop therapy with a telemonitored insulin- and glucagon-delivering closed-loop system and 48 h of patient-managed open-loop therapy. Owing to technical problems in five cases, only 11 patients could be analyzed. Whereas median (interquartile range) glucose levels were not significantly different during Day 1 of open-loop control (OL1) from closed-loop control (CL1) (8.27 [0.83] mmol/L vs. 8.84 [1.47] mmol/L; P=0.206), they were significantly lower during Day 2 of closed-loop control (CL2) versus open-loop control (OL2) (7.70 [2.29] mmol/L vs. 8.84 [0.87] mmol/L; P=0.027). Time spent in euglycemia (3.9-10 mmol/L) was comparable with 67.2% (38.5%) in OL1 versus 79.2% (16.9%) in CL1 (P=0.189) and 66.0% (29.8%) in OL2 versus 76.5% (23.9%) in CL2 (P=0.162). Time spent in hypoglycemia (<3.9 mmol/L) was comparable on Day 1 of control (OL1, 0.68% [8.68%]; CL1, 2.08% [7.61%]; P=0.593) but significantly higher during Day 2 of control (OL2, 0.00% [11.07%]; CL2, 2.8% [9.8%]; P=0.0172) (P=0.017). Bihormonal closed-loop control is feasible at home, with comparable time in euglycemia to open-loop control and significantly lower median glucose levels on Day 2 of control at the expense of more time in hypoglycemia, albeit still at a very low percentage of time.

Closed-Loop Insulin Therapy Improves Glycemic Control in Children Aged &amp;lt;7 Years

OBJECTIVETo assess the possibility of improving nocturnal glycemic control as well as meal glycemic response using closed-loop therapy in children aged <7 years.RESEARCH DESIGN AND METHODSThis was a randomized controlled crossover trial comparing closed-loop with standard open-loop insulin pump therapy performed in an inpatient clinical research center. Ten subjects aged <7 years with type 1 diabetes for >6 months treated with insulin pump therapy were studied. Closed-loop therapy and standard open-loop therapy were compared from 10:00 p.m. to 12:00 p.m. on 2 consecutive days. The primary outcome was plasma glucose time in range (110–200 mg/dL) during the night (10:00 p.m.–8:00 a.m.). Secondary outcomes included peak postprandial glucose levels, incidence of hypoglycemia, degree of hyperglycemia, and prelunch glucose levels.RESULTSA trend toward a higher mean nocturnal time within target range was noted for closed- versus open-loop therapy, although not reaching statistical significance (5.3 vs. 3.2 h, P = 0.12). There was no difference in peak postprandial glucose or number of episodes of hypoglycemia. There was significant improvement in time spent >300 mg/dL overnight with closed-loop therapy (0.18 vs. 1.3 h, P = 0.035) and the total area under the curve of glucose >200 mg/dL (P = 0.049). Closed-loop therapy returned prelunch blood glucose closer to target (189 vs. 273 mg/dL on open loop, P = 0.009).CONCLUSIONSClosed-loop insulin delivery decreases the severity of overnight hyperglycemia without increasing the incidence of hypoglycemia. The therapy is better able to reestablish target glucose levels in advance of a subsequent meal. Younger children with type 1 diabetes may reap significant benefits from closed-loop therapy.

5thAnnual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond, May 3–5, 2012, Dublin, Ireland

International experts in the fields of diabetes, diabetes technology, endocrinology, and pediatrics gathered for the 5th Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and Beyond. The aim of this meeting was to continue setting up a global network of experts in this field and provide an international platform for exchange of ideas to improve life for people with diabetes. In line with previous editions, the 2012 meeting comprised a comprehensive scientific program, parallel interactive workshops, pros and cons debates, and a keynote. All these discussions were intended to help identifying gaps and areas where further scientific work and clinical studies are needed.

Journal of Diabetes NEWS

Journal of Diabetes NEWS

Model Predictive Control of Type 1 Diabetes added to Conventional Therapy

AbstractRecent technological advancements in subcutaneous glucose monitoring and subcutaneous insulin delivery systems have stimulated the development of closed-loop systems for maintaining normoglycaemia in Type I diabetes. The main obstacle to satisfactory closed-loop control is the presence of disturbances (e.g. meals and physical activity), delays, pump limitation, sensor noise and inter-variability. A Model Predictive Control (MPC) strategy has been applied in a clinical study at the University of Virginia, Padova and Montpellier covering night and breakfast on 20 patients. Night regulation was very satisfactory, while breakfast compensation was slightly worse than with a conventional therapy. This was due to the trade-off in the tuning of the cost function between prompt meal reaction and attenuation of measurement noise. In order to overcome this limitation, in this paper an MPC algorithm added to a conventional therapy is described. In this way all the knowledge incorporated in the (personalized) open-loop therapy is used as a feedforward term while the MPC controller is a feedback correction that guarantees robustness with respect to model uncertainty and incomplete meal knowledge. Finally a procedure for tuning the regulator aggressiveness as a function of few clinical parameters (carbo-ratio and basal insulin delivery) is described.

Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results

In 2008-2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N=300 synthetic "subjects" with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3-4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p<.01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p=.03). In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.

Automatic Bolus and Adaptive Basal Algorithm for the Artificial Pancreatic β-Cell

The current basal and bolus insulin pump therapy is dependent on user intervention; because of its open-loop nature, the therapy does not accommodate insulin variability and unmeasured meal disturbances. To conquer these challenges, an automatic bolus and adaptive basal (ABAB) therapy is proposed to regulate glucose levels for people with type 1 diabetes mellitus. The basal insulin profile is adjusted by the proposed algorithm every 30 min based on interstitial glucose level and its rate of change. An automated bolus is suggested by the system if a meal is detected or a hyperglycemia event occurs. A conservative insulin bolus is administered, the size of which is determined based on glucose prediction and the subject-specific correction factor. One hour later, the algorithm checks whether another bolus is needed. To prevent overdelivery, insulin-on-board is used as a safety constraint. The ABAB therapy was compared with the optimal open-loop therapy and missed-bolus scenario on 100 adult subjects from the Food and Drug Administration-accepted University of Virginia/Padova Metabolic Simulator. The ABAB therapy presented superior performance according to the control-variability grid analysis. In addition, the ABAB therapy shows excellent robustness to insulin sensitivity rise: the hypoglycemia percentage was only 3.3% even when insulin sensitivity was increased by 20%. Independent of user intervention, the ABAB therapy is a good candidate for the first generation of an artificial pancreas. The proposed therapy shows excellent robustness to insulin dosing mismatches.

Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine

In order to stave off deleterious complications of the disease, the ultimate task for people with diabetes is to maintain their blood glucose in euglycemic range. Despite technological advancements, conventional open-loop therapy often results in prolonged hyperglycemia and episodic hypoglycemia, in addition to necessitating carbohydrate counting, frequent glucose monitoring, and drug administration. The logical conclusion in the evolution of exogenous insulin therapy is to develop an automated closed-loop control system. Eleven closed-loop control experiments were conducted in four anesthetized diabetic pigs, with carbohydrate loads simulated by intravenous glucose administration through ear-vein catheters. Type 1 diabetes-like pathology was induced using intravenous administration of cytotoxin streptozotocin. The augmented model-predictive control algorithm accounts for the accumulation of subcutaneous insulin, which is critical in avoiding excessive insulin dosing. Control results consistently showed successful blood-glucose regulation to euglycemic range within 80-120 minutes after intravenous glucose loads, with no incidence of hypoglycemia. This is consistent with a negative oral glucose tolerance test for diabetes and is the optimal postprandial regulation that can be achieved with subcutaneous insulin administration. Results also demonstrated the potency of subcutaneous glucagon in staving off episodic hypoglycemia and revealed efficacy of the control algorithm in coping with a twofold variation in subject weights, while simultaneously overlooking erratic blood-glucose fluctuations. Using an automated adaptive glucose-control system, we show successful blood-glucose regulation in vivo and establish, definitively, the plausibility and practicality of closed-loop blood-glucose control using subcutaneous insulin and glucagon infusion in type 1 diabetes. The control system strikes an intricate balance between tight blood-glucose control and optimal drug consumption, while simultaneously maintaining emphasis on simplicity and reliability.

LETTERS/COMMENTARY

At the “Second International Workshop on Seizure Prediction” held recently in Washington, DC, under the co-sponsorship of NIH/NINDS and the AES, the importance of prospectively comparing the therapeutic efficacy of “closed-” vs. “open-loop” brain electrical stimulation was identified as an important research aim, and the meaning of these terms was briefly debated. Therapies that utilize any type of feedback, including patient seizure diaries, are “closed-loop”. The term “open-loop” was adopted for description of clinical trials with devices lacking automated means for (a) detecting seizures, quantifying their severity, and logging these data; and (b) triggering delivery of therapy based on seizure detections. As currently used, the term “open loop” may create confusion since the therapies to which it is applied rely on patient diary-feedback. The following clarification may be useful: Closed-loop therapy: Any therapy that uses a feedback loop. The feedback may be real-time or delayed (off-line); for both these options, the feedback may be automated or manual, objective or subjective, and may be used to trigger therapy delivery or to adjust parameters or dose. Open-loop therapy: Any therapy that operates without any feedback loop. Recent trials that use automated seizure detection to trigger electrical stimulation directly to the brain (Osorio et al., 2005; Bergey et al., 2006) are examples of automated real-time closed-loop. One of these trials (Osorio et al., 2005) provides real-time automated therapy delivery and objective quantitative assessment of response, while the other (Bergey et al., 2006) provides only automated therapy (as assessment is based on seizure diaries). Vagus nerve stimulation with the neurocybernetic prosthesis (Cyberonics, Houston, TX, U.S.A.), exemplifies two closed-loop modalities: (1) real-time manual closed loop, when the magnet is used to activate the generator in response to an “aura”; and (2) delayed closed loop, when device parameter changes are based upon subjective feedback in the form of seizure diaries. With these definitions in place, the incorrectly stated and irrelevant/futile aim of comparing “open-loop” vs. “closed-loop” therapies should be re-framed. With respect to brain stimulation studies, we offer the following formulation: The aim is to compare efficacy (and adverse events) of therapies that use either continuous, periodic, or random brain electrical stimulation to that of therapies based on contingent stimulation (i.e., in response to) seizure detections. This would be a worthwhile endeavor that should be pursued using objective and quantifiable assessment means such as seizure detection algorithms, instead of patient seizure diaries; diaries are inherently and grossly inaccurate (Blum et al., 1996), provide information only about seizures with clinical manifestations (a small fraction of all recorded seizures) (Osorio et al., 2002), and do not quantify intensity, duration, and extent of spread.