Overnight closed-loop insulin delivery with model predictive control: assessment of hypoglycemia and hyperglycemia risk using simulation studies

Novel Methods to Understand the Temporal Nature and Accuracy of Delivery for Insulin Infusion Pumps

BACKGROUND

A wide suite of methods are available to evaluate delivery accuracy of insulin pumps. However, these methods do not capture any temporal information, which may be critical for design of artificial pancreas (AP) systems. We propose a novel video microscopy method to understand the delivery accuracy and temporal nature for a new durable pump under development (IFP), and a commercially available pump (Medtronic 722G, M722G).

METHODS

The cannula tip of an infusion set is inserted into a graduated pipette placed under a digital microscope. A video of the delivery is captured to track the fluid meniscus, to measure volumetric delivery rate and accuracy. This was done for a programmed value of 0.5 and 1 U. A similar procedure was adopted to track linear motion of the piston rod, which actuates the reservoir plunger, for a programmed value of 10 U.

RESULTS

It was observed that the commercially available pump delivers insulin in pulses of 0.05 U every two seconds. The mean absolute volumetric delivery error (MAE) for both pumps was found to be within the values reported previously. More importantly, it was found that a significant fraction of the programmed value is delivered, after completion of the planned bolus duration (IFP: 14.31% vs M722G: 9.38% for 1 U delivery).

CONCLUSIONS

The methods presented in this article help understand the delivery dynamics of liquid drug delivery devices. Our results indicate that a significant fraction of insulin delivery happens after the planned bolus duration, which might be important consideration for design of AP systems.

Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

BACKGROUND

The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes.

METHODS

We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month).

RESULTS

The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (-1.34%, 95% CI [-1.87, -0.81] p < 0.00001) over a long period of use (> 1 month).

CONCLUSIONS

Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).

Challenge of diabetes mellitus and researchers’ contributions to its control

The aim of this review study was to assess the past significant events on diabetes mellitus, transformations that took place over the years in the medical records of treatment, countries involved, and the researchers who brought about the revolutions. This study used the content analysis to report the existence of diabetes mellitus and the treatments provided by researchers to control it. The focus was mainly on three main types of diabetes (type 1, type 2, and type 3 diabetes). Ethical consideration has also helped to boost diabetic studies globally. The research has a history path from pharmaceuticals of organic-based drugs to metal-based drugs with their nanoparticles in addition to the impacts of nanomedicine, biosensors, and telemedicine. Ongoing and future studies in alternative medicine such as vanadium nanoparticles (metal nanoparticles) are promising.

A revised Sorensen model: Simulating glycemic and insulinemic response to oral and intra-venous glucose load

In 1978, Thomas J. Sorensen defended a thesis in chemical engineering at the University of California, Berkeley, where he proposed an extensive model of glucose-insulin control, model which was thereafter widely employed for virtual patient simulation. The original model, and even more so its subsequent implementations by other Authors, presented however a few imprecisions in reporting the correct model equations and parameter values. The goal of the present work is to revise the original Sorensen's model, to clearly summarize its defining equations, to supplement it with a missing gastrio-intestinal glucose absorption and to make an implementation of the revised model available on-line to the scientific community.

Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study

BACKGROUND

Tight control of blood glucose concentration in people with type 1 diabetes predisposes to hypoglycaemia. We aimed to investigate whether day-and-night hybrid closed-loop insulin delivery can improve glucose control while alleviating the risk of hypoglycaemia in adults with HbA1c below 7·5% (58 mmol/mol).

METHODS

In this open-label, randomised, crossover study, we recruited adults (aged ≥18 years) with type 1 diabetes and HbA1c below 7·5% from Addenbrooke's Hospital (Cambridge, UK) and Medical University of Graz (Graz, Austria). After a 2-4 week run-in period, participants were randomly assigned (1:1), using web-based randomly permuted blocks of four, to receive insulin via the day-and-night hybrid closed-loop system or usual pump therapy for 4 weeks, followed by a 2-4 week washout period and then the other intervention for 4 weeks. Treatment interventions were unsupervised and done under free-living conditions. During the closed-loop period, a model-predictive control algorithm directed insulin delivery, and prandial insulin delivery was calculated with a standard bolus wizard. The primary outcome was the proportion of time when sensor glucose concentration was in target range (3·9-10·0 mmol/L) over the 4 week study period. Analyses were by intention to treat. This study is registered with ClinicalTrials.gov, number NCT02727231, and is completed.

FINDINGS

Between March 21 and June 24, 2016, we recruited 31 participants, of whom 29 were randomised. One participant withdrew during the first closed-loop period because of dissatisfaction with study devices and glucose control. The proportion of time when sensor glucose concentration was in target range was 10·5 percentage points higher (95% CI 7·6-13·4; p<0·0001) during closed-loop delivery compared with usual pump therapy (65·6% [SD 8·1] when participants used usual pump therapy vs 76·2% [6·4] when they used closed-loop). Compared with usual pump therapy, closed-loop delivery also reduced the proportion of time spent in hypoglycaemia: the proportion of time with glucose concentration below 3·5 mmol/L was reduced by 65% (53-74, p<0·0001) and below 2·8 mmol/L by 76% (59-86, p<0·0001). No episodes of serious hypoglycaemia or other serious adverse events occurred.

INTERPRETATION

Use of day-and-night hybrid closed-loop insulin delivery under unsupervised, free-living conditions for 4 weeks in adults with type 1 diabetes and HbA1c below 7·5% is safe and well tolerated, improves glucose control, and reduces hypoglycaemia burden. Larger and longer studies are warranted.

FUNDING

Swiss National Science Foundation (P1BEP3_165297), JDRF, UK National Institute for Health Research Cambridge Biomedical Research Centre, and Wellcome Strategic Award (100574/Z/12/Z).

Home Use of Day-and-Night Hybrid Closed-Loop Insulin Delivery in Suboptimally Controlled Adolescents With Type 1 Diabetes: A 3-Week, Free-Living, Randomized Crossover Trial

OBJECTIVE

This study evaluated the feasibility, safety, and efficacy of day-and-night hybrid closed-loop insulin delivery in adolescents with type 1 diabetes under free-living conditions.

RESEARCH DESIGN AND METHODS

In an open-label randomized crossover study, 12 suboptimally controlled adolescents on insulin pump therapy (mean ± SD age 14.6 ± 3.1 years; HbA_1c 69 ± 8 mmol/mol [8.5 ± 0.7%]; duration of diabetes 7.8 ± 3.5 years) underwent two 21-day periods in which hybrid closed-loop insulin delivery was compared with sensor-augmented insulin pump therapy in random order. During the closed-loop intervention, a model predictive algorithm automatically directed insulin delivery between meals and overnight. Participants used a bolus calculator to administer prandial boluses.

RESULTS

The proportion of time that sensor glucose was in the target range (3.9-10 mmol/L; primary end point) was increased during the closed-loop intervention compared with sensor-augmented insulin pump therapy by 18.8 ± 9.8 percentage points (mean ± SD; P < 0.001), the mean sensor glucose level was reduced by 1.8 ± 1.3 mmol/L (P = 0.001), and the time spent above target was reduced by 19.3 ± 11.3 percentage points (P < 0.001). The time spent with sensor glucose levels below 3.9 mmol/L was low and comparable between interventions (median difference 0.4 [interquartile range -2.2 to 1.3] percentage points; P = 0.33). Improved glucose control during closed-loop was associated with increased variability of basal insulin delivery (P < 0.001) and an increase in the total daily insulin dose (53.5 [39.5-72.1] vs. 51.5 [37.6-64.3] units/day; P = 0.006). Participants expressed positive attitudes and experience with the closed-loop system.

CONCLUSIONS

Free-living home use of day-and-night closed-loop in suboptimally controlled adolescents with type 1 diabetes is safe, feasible, and improves glucose control without increasing the risk of hypoglycemia. Larger and longer studies are warranted.

Adjuvant Liraglutide and Insulin Versus Insulin Monotherapy in the Closed-Loop System in Type 1 Diabetes: A Randomized Open-Labeled Crossover Design Trial

BACKGROUND

The closed-loop (CL) system delivers insulin in a glucose-responsive manner and optimal postprandial glycemic control is difficult to achieve with the algorithm and insulin available. We hypothesized that adjunctive therapy with liraglutide, a once-daily glucagon-like peptide-1 agonist, would be more effective in normalizing postprandial hyperglycemia versus insulin monotherapy in the CL system, in patients with type 1 diabetes.

METHODS

This was a randomized, controlled, open-label, crossover design trial comparing insulin monotherapy versus adjuvant subcutaneous liraglutide 1.2 mg and insulin, using the CL system in 15 patients. Blood glucose (BG), insulin, and glucagon concentrations were analyzed.

RESULTS

The liraglutide arm was associated with overall decreased mean BG levels (P = .0002). The average BG levels from 8:00 pm (day 1) to 9:00 pm (day 2) were lower in the liraglutide arm (144.6 ± 36.31 vs 159.7 ± 50.88 mg/dl respectively; P = .0002). Two-hour postbreakfast and lunch BG profiles were better in the liraglutide arm (P < .05) and the insulin and glucagon assay values were lower (P < .0001). Postprandially, the area under the curve (AUC) for 2-hour postbreakfast and lunch BG levels were significant (P = .01, P = .03) and the AUC for glucagon, postbreakfast (P < .0001) and lunch (P < .05), was also significant. The incidence of hypoglycemia did not differ between arms (P = .83, Fisher's exact test). Overall, adjunct liraglutide therapy plus CL was well tolerated even with expected side effects.

CONCLUSION

This is a proof-of-concept study showing liraglutide can be a potential adjunctive therapy in addition to CL with insulin to reduce postprandial hyperglycemia in type 1 diabetes.

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.

Day-and-Night Hybrid Closed-Loop Insulin Delivery in Adolescents With Type 1 Diabetes: A Free-Living, Randomized Clinical Trial

OBJECTIVE

To evaluate feasibility, safety, and efficacy of day-and-night hybrid closed-loop insulin delivery in adolescents with type 1 diabetes under free-living conditions without remote monitoring or supervision.

RESEARCH DESIGN AND METHODS

In an open-label, randomized, free-living, crossover study design, 12 adolescents receiving insulin pump therapy (mean [±SD] age 15.4 ± 2.6 years; HbA1c 8.3 ± 0.9%; duration of diabetes 8.2 ± 3.4 years) underwent two 7-day periods of sensor-augmented insulin pump therapy or hybrid closed-loop insulin delivery without supervision or remote monitoring. During the closed-loop insulin delivery, a model predictive algorithm automatically directed insulin delivery between meals and overnight; prandial boluses were administered by participants using a bolus calculator.

RESULTS

The proportion of time when the sensor glucose level was in the target range (3.9-10 mmol/L) was increased during closed-loop insulin delivery compared with sensor-augmented pump therapy (72 vs. 53%, P < 0.001; primary end point), the mean glucose concentration was lowered (8.7 vs. 10.1 mmol/L, P = 0.028), and the time spent above the target level was reduced (P = 0.005) without changing the total daily insulin amount (P = 0.55). The time spent in the hypoglycemic range was low and comparable between interventions.

CONCLUSIONS

Unsupervised day-and-night hybrid closed-loop insulin delivery at home is feasible and safe in young people with type 1 diabetes. Compared with sensor-augmented insulin pump therapy, closed-loop insulin delivery may improve glucose control without increasing the risk of hypoglycemia in adolescents with suboptimally controlled type 1 diabetes.

A Review of Safety and Design Requirements of the Artificial Pancreas

As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components—type 1 diabetes, artificial pancreas, and safety or design—and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.

Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes

The management of postprandial glucose excursions in type 1 diabetes has a major impact on overall glycaemic control. In this work, we propose and evaluate various mechanistic models to characterize the disposal of meal-attributable glucose. Sixteen young volunteers with type 1 diabetes were subject to a variable-target clamp which replicated glucose profiles observed after a high-glycaemic-load ([Formula: see text]) or a low-glycaemic-load ([Formula: see text]) evening meal. [6,6-[Formula: see text]] and [U-[Formula: see text];1,2,3,4,5,6,6-[Formula: see text]] glucose tracers were infused to, respectively, mimic: (a) the expected post-meal suppression of endogenous glucose production and (b) the appearance of glucose due to a standard meal. Six compartmental models (all a priori identifiable) were proposed to investigate the remote effect of circulating plasma insulin on the disposal of those glucose tracers from the non-accessible compartments, representing e.g. interstitium. An iterative population-based parameter fitting was employed. Models were evaluated attending to physiological plausibility, posterior identifiability of their parameter estimates, accuracy-via weighted fitting residuals-and information criteria (i.e. parsimony). The most plausible model, best representing our experimental data, comprised: (1) a remote effect x of insulin active above a threshold [Formula: see text] = 1.74 (0.81-2.50) [Formula: see text] min[Formula: see text] [median (inter-quartile range)], with parameter [Formula: see text] having a satisfactory support: coefficient of variation CV = 42.33 (31.34-65.34) %, and (2) steady-state conditions at the onset of the experiment ([Formula: see text]) for the compartment representing the remote effect, but not for the masses of the tracer that mimicked endogenous glucose production. Consequently, our mechanistic model suggests non-homogeneous changes in the disposal rates for meal-attributable glucose in relation to plasma insulin. The model can be applied to the in silico simulation of meals for the optimization of postprandial insulin infusion regimes in type 1 diabetes.

Accuracy of Continuous Glucose Monitoring During Three Closed-Loop Home Studies Under Free-Living Conditions

OBJECTIVES

Closed-loop (CL) systems modulate insulin delivery based on glucose levels measured by a continuous glucose monitor (CGM). Accuracy of the CGM affects CL performance and safety. We evaluated the accuracy of the Freestyle Navigator(®) II CGM (Abbott Diabetes Care, Alameda, CA) during three unsupervised, randomized, open-label, crossover home CL studies.

MATERIALS AND METHODS

Paired CGM and capillary glucose values (10,597 pairs) were collected from 57 participants with type 1 diabetes (41 adults [mean±SD age, 39±12 years; mean±SD hemoglobin A1c, 7.9±0.8%] recruited at five centers and 16 adolescents [mean±SD age, 15.6±3.6 years; mean±SD hemoglobin A1c, 8.1±0.8%] recruited at two centers). Numerical accuracy was assessed by absolute relative difference (ARD) and International Organization for Standardization (ISO) 15197:2013 15/15% limits, and clinical accuracy was assessed by Clarke error grid analysis.

RESULTS

Total duration of sensor use was 2,002 days (48,052 h). Overall sensor accuracy for the capillary glucose range (1.1-27.8 mmol/L) showed mean±SD and median (interquartile range) ARD of 14.2±15.5% and 10.0% (4.5%, 18.4%), respectively. Lowest mean ARD was observed in the hyperglycemic range (9.8±8.8%). Over 95% of pairs were in combined Clarke error grid Zones A and B (A, 80.1%, B, 16.2%). Overall, 70.0% of the sensor readings satisfied ISO criteria. Mean ARD was consistent (12.3%; 95% of the values fall within ±3.7%) and not different between participants (P=0.06) within the euglycemic and hyperglycemic range, when CL is actively modulating insulin delivery.

CONCLUSIONS

Consistent accuracy of the CGM within the euglycemic-hyperglycemic range using the Freestyle Navigator II was observed and supports its use in home CL studies. Our results may contribute toward establishing normative CGM performance criteria for unsupervised home use of CL.

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals

BACKGROUND

Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement.

MATERIALS AND METHODS

A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern.

RESULTS

On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively.

CONCLUSIONS

The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.

Assessing the effectiveness of 3 months day and night home closed-loop insulin delivery in adults with suboptimally controlled type 1 diabetes: a randomised crossover study protocol

INTRODUCTION

Despite therapeutic advances, many people with type 1 diabetes are still unable to achieve optimal glycaemic control, limited by the occurrence of hypoglycaemia. The objective of the present study is to determine the effectiveness of day and night home closed-loop over the medium term compared with sensor-augmented pump therapy in adults with type 1 diabetes and suboptimal glycaemic control.

METHODS AND ANALYSIS

The study will adopt an open label, three-centre, multinational, randomised, two-period crossover study design comparing automated closed-loop glucose control with sensor augmented insulin pump therapy. The study will aim for 30 completed participants. Eligible participants will be adults (≥18 years) with type 1 diabetes treated with insulin pump therapy and suboptimal glycaemic control (glycated haemoglobin (HbA1c)≥7.5% (58 mmol/mmol) and ≤10% (86 mmol/mmol)). Following a 4-week optimisation period, participants will undergo a 3-month use of automated closed-loop insulin delivery and sensor-augmented pump therapy, with a 4-6 week washout period in between. The order of the interventions will be random. All analysis will be conducted on an intention to treat basis. The primary outcome is the time spent in the target glucose range from 3.9 to 10.0 mmol/L based on continuous glucose monitoring levels during the 3 months free living phase. Secondary outcomes include HbA1c changes; mean glucose and time spent above and below target glucose levels. Further, participants will be invited at baseline, midpoint and study end to participate in semistructured interviews and complete questionnaires to explore usability and acceptance of the technology, impact on quality of life and fear of hypoglycaemia.

ETHICS AND DISSEMINATION

Ethical approval has been obtained at all sites. Before screening, all participants will be provided with oral and written information about the trial. The study will be disseminated by peer-review publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT01961622 (ClinicalTrials.gov).

Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study

BACKGROUND

Closed-loop insulin delivery is a promising option to improve glycaemic control and reduce the risk of hypoglycaemia. We aimed to assess whether overnight home use of automated closed-loop insulin delivery would improve glucose control.

METHODS

We did this open-label, multicentre, randomised controlled, crossover study between Dec 1, 2012, and Dec 23, 2014, recruiting patients from three centres in the UK. Patients aged 18 years or older with type 1 diabetes were randomly assigned to receive 4 weeks of overnight closed-loop insulin delivery (using a model-predictive control algorithm to direct insulin delivery), then 4 weeks of insulin pump therapy (in which participants used real-time display of continuous glucose monitoring independent of their pumps as control), or vice versa. Allocation to initial treatment group was by computer-generated permuted block randomisation. Each treatment period was separated by a 3-4 week washout period. The primary outcome was time spent in the target glucose range of 3·9-8·0 mmol/L between 0000 h and 0700 h. Analyses were by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01440140.

FINDINGS

We randomly assigned 25 participants to initial treatment in either the closed-loop group or the control group, patients were later crossed over into the other group; one patient from the closed-loop group withdrew consent after randomisation, and data for 24 patients were analysed. Closed loop was used over a median of 8·3 h (IQR 6·0-9·6) on 555 (86%) of 644 nights. The proportion of time when overnight glucose was in target range was significantly higher during the closed-loop period compared to during the control period (mean difference between groups 13·5%, 95% CI 7·3-19·7; p=0·0002). We noted no severe hypoglycaemic episodes during the control period compared with two episodes during the closed-loop period; these episodes were not related to closed-loop algorithm instructions.

INTERPRETATION

Unsupervised overnight closed-loop insulin delivery at home is feasible and could improve glucose control in adults with type 1 diabetes.

FUNDING

Diabetes UK.

Role of glucagon-like peptide-1 analogue versus amylin as an adjuvant therapy in type 1 diabetes in a closed loop setting with ePID algorithm

Postprandial hyperglycemia due to paradoxical hyperglucagonemia is a major challenge of diabetes treatment despite the use of the artificial pancreas. We postulated that adjunctive therapy with pramlintide or exenatide would attenuate hyperglycemia in the postprandial phase through glucagon suppression, thereby optimizing the functioning of the closed-loop (CL) system. Subjects with type 1 diabetes (T1DM) on insulin pump therapy were recruited to participate in a 27-hour hospitalized admission on 3 occasions (2-4 weeks apart) and placed on the insulin delivery via CL system in random order to receive (1) insulin alone (control), (2) exenatide 2.5 µg + insulin, (3) pramlintide 30 µg + insulin. Medications were given prior to lunch and dinner, which was a standardized meal of 60 grams of carbohydrates. Insulin delivery was as per the ePID algorithm via the Medtronic CL system and continuous subcutaneous glucose monitoring via Medtronic Sof-sensors. Ten subjects age 23 ± 1 years with a HbA1c of 7.29 ± 0.3% (56 ± 1 mmol/mol) and duration of T1DM 10.6 ± 2.0 years participated in the 3-part study. Exenatide was found to be significantly better in attenuating postprandial hyperglycemia as compared to insulin monotherapy (P < .03) and pramlintide (P > .05). Glucagon suppression was statistically significant with exenatide (P < .03) as compared to pramlintide. Insulin requirements were lower with adjunctive therapy, but statistically insignificant. Insulin monotherapy results in postprandial hyperglycemia in T1DM in the CL setting and adjunctive therapy with exenatide reduces postprandial hyperglycemia effectively and should be considered as adjunctive therapy in T1DM.

Day and night home closed-loop insulin delivery in adults with type 1 diabetes: three-center randomized crossover study

OBJECTIVE

To evaluate the feasibility of day and night closed-loop insulin delivery in adults with type 1 diabetes under free-living conditions.

RESEARCH DESIGN AND METHODS

Seventeen adults with type 1 diabetes on insulin pump therapy (means ± SD age 34 ± 9 years, HbA1c 7.6 ± 0.8%, and duration of diabetes 19 ± 9 years) participated in an open-label multinational three-center crossover study. In a random order, participants underwent two 8-day periods (first day at the clinical research facility followed by 7 days at home) of sensor-augmented insulin pump therapy (SAP) or automated closed-loop insulin delivery. The primary end point was the time when sensor glucose was in target range between 3.9 and 10.0 mmol/L during the 7-day home phase.

RESULTS

During the home phase, the percentage of time when glucose was in target range was significantly higher during closed-loop compared with SAP (median 75% [interquartile range 61-79] vs. 62% [53-70], P = 0.005). Mean glucose (8.1 vs. 8.8 mmol/L, P = 0.027) and time spent above target (P = 0.013) were lower during closed loop, while time spent below target was comparable (P = 0.339). Increased time in target was observed during both daytime (P = 0.017) and nighttime (P = 0.013).

CONCLUSIONS

Compared with SAP, 1 week of closed-loop insulin delivery at home reduces mean glucose and increases time in target without increasing the risk of hypoglycemia in adults with relatively well-controlled type 1 diabetes.

Overnight closed-loop insulin delivery in young people with type 1 diabetes: a free-living, randomized clinical trial

OBJECTIVE

To evaluate feasibility, safety, and efficacy of overnight closed-loop insulin delivery in free-living youth with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Overnight closed loop was evaluated at home by 16 pump-treated adolescents with type 1 diabetes aged 12-18 years. Over a 3-week period, overnight insulin delivery was directed by a closed-loop system, and on another 3-week period sensor-augmented therapy was applied. The order of interventions was random. The primary end point was time when adjusted sensor glucose was between 3.9 and 8.0 mmol/L from 2300 to 0700 h.

RESULTS

Closed loop was constantly applied over at least 4 h on 269 nights (80%); sensor data were collected over at least 4 h on 282 control nights (84%). Closed loop increased time spent with glucose in target by a median 15% (interquartile range -9 to 43; P < 0.001). Mean overnight glucose was reduced by a mean 14 (SD 58) mg/dL (P < 0.001). Time when glucose was <70 mg/dL was low in both groups, but nights with glucose <63 mg/dL for at least 20 min were less frequent during closed loop (10 vs. 17%; P = 0.01). Despite lower total daily insulin doses by a median 2.3 (interquartile range -4.7 to 9.3) units (P = 0.009), overall 24-h glucose was reduced by a mean 9 (SD 41) mg/dL (P = 0.006) during closed loop.

CONCLUSIONS

Unsupervised home use of overnight closed loop in adolescents with type 1 diabetes is safe and feasible. Glucose control was improved during the day and night with fewer episodes of nocturnal hypoglycemia.

Continuous glucose monitoring: current use and future directions

Continuous glucose monitoring (CGM) is an emerging technology that provides a continuous measure of interstitial glucose levels. In addition to providing a more complete pattern of glucose excursions, CGMs utilize real-time alarms for thresholds and predictions of hypo- and hyperglycemia, as well as rate of change alarms for rapid glycemic excursions. CGM users have been able to improve glycemic control without increasing their risk of hypoglycemia. Sensor accuracy, reliability, and wearability are important challenges to CGM success and are critical to the development of an artificial pancreas (or closed-loop system).

Closed-loop basal insulin delivery over 36 hours in adolescents with type 1 diabetes: randomized clinical trial

OBJECTIVE

We evaluated the safety and efficacy of closed-loop basal insulin delivery during sleep and after regular meals and unannounced periods of exercise.

RESEARCH DESIGN AND METHODS

Twelve adolescents with type 1 diabetes (five males; mean age 15.0 [SD 1.4] years; HbA1c 7.9 [0.7]%; BMI 21.4 [2.6] kg/m(2)) were studied at a clinical research facility on two occasions and received, in random order, either closed-loop basal insulin delivery or conventional pump therapy for 36 h. During closed-loop insulin delivery, pump basal rates were adjusted every 15 min according to a model predictive control algorithm informed by subcutaneous sensor glucose levels. During control visits, subjects' standard infusion rates were applied. Prandial insulin boluses were given before main meals (50-80 g carbohydrates) but not before snacks (15-30 g carbohydrates). Subjects undertook moderate-intensity exercise, not announced to the algorithm, on a stationary bicycle at a 140 bpm heart rate in the morning (40 min) and afternoon (20 min). Primary outcome was time when plasma glucose was in the target range (71-180 mg/dL).

RESULTS

Closed-loop basal insulin delivery increased percentage time when glucose was in the target range (median 84% [interquartile range 78-88%] vs. 49% [26-79%], P = 0.02) and reduced mean plasma glucose levels (128 [19] vs. 165 [55] mg/dL, P = 0.02). Plasma glucose levels were in the target range 100% of the time on 17 of 24 nights during closed-loop insulin delivery. Hypoglycemia occurred on 10 occasions during control visits and 9 occasions during closed-loop delivery (5 episodes were exercise related, and 4 occurred within 2.5 h of prandial bolus).

CONCLUSIONS

Day-and-night closed-loop basal insulin delivery can improve glucose control in adolescents. However, unannounced moderate-intensity exercise and excessive prandial boluses pose challenges to hypoglycemia-free closed-loop basal insulin delivery.

Evaluating the accuracy and large inaccuracy of two continuous glucose monitoring systems

OBJECTIVE

This study evaluated the accuracy and large inaccuracy of the Freestyle Navigator (FSN) (Abbott Diabetes Care, Alameda, CA) and Dexcom SEVEN PLUS (DSP) (Dexcom, Inc., San Diego, CA) continuous glucose monitoring (CGM) systems during closed-loop studies.

RESEARCH DESIGN AND METHODS

Paired CGM and plasma glucose values (7,182 data pairs) were collected, every 15-60 min, from 32 adults (36.2±9.3 years) and 20 adolescents (15.3±1.5 years) with type 1 diabetes who participated in closed-loop studies. Levels 1, 2, and 3 of large sensor error with increasing severity were defined according to absolute relative deviation greater than or equal to ±40%, ±50%, and ±60% at a reference glucose level of ≥6 mmol/L or absolute deviation greater than or equal to ±2.4 mmol/L,±3.0 mmol/L, and ±3.6 mmol/L at a reference glucose level of <6 mmol/L.

RESULTS

Median absolute relative deviation was 9.9% for FSN and 12.6% for DSP. Proportions of data points in Zones A and B of Clarke error grid analysis were similar (96.4% for FSN vs. 97.8% for DSP). Large sensor over-reading, which increases risk of insulin over-delivery and hypoglycemia, occurred two- to threefold more frequently with DSP than FSN (once every 2.5, 4.6, and 10.7 days of FSN use vs. 1.2, 2.0, and 3.7 days of DSP use for Level 1-3 errors, respectively). At levels 2 and 3, large sensor errors lasting 1 h or longer were absent with FSN but persisted with DSP.

CONCLUSIONS

FSN and DSP differ substantially in the frequency and duration of large inaccuracy despite only modest differences in conventional measures of numerical and clinical accuracy. Further evaluations are required to confirm that FSN is more suitable for integration into closed-loop delivery systems.

Challenges and Recent Progress in the Development of a Closed-loop Artificial Pancreas

Pursuit of a closed-loop artificial pancreas that automatically controls the blood glucose of individuals with type 1 diabetes has intensified during the past six years. Here we discuss the recent progress and challenges in the major steps towards a closed-loop system. Continuous insulin infusion pumps have been widely available for over two decades, but "smart pump" technology has made the devices easier to use and more powerful. Continuous glucose monitoring (CGM) technology has improved and the devices are more widely available. A number of approaches are currently under study for fully closed-loop systems; most manipulate only insulin, while others manipulate insulin and glucagon. Algorithms include on-off (for prevention of overnight hypoglycemia), proportional-integral-derivative (PID), model predictive control (MPC) and fuzzy logic based learning control. Meals cause a major "disturbance" to blood glucose, and we discuss techniques that our group has developed to predict when a meal is likely to be consumed and its effect. We further examine both physiology and device-related challenges, including insulin infusion set failure and sensor signal attenuation. Finally, we discuss the next steps required to make a closed-loop artificial pancreas a commercial reality.

Design of the health monitoring system for the artificial pancreas: low glucose prediction module

BACKGROUND

The purpose of this study was to design and evaluate a safety system for the artificial pancreas device system (APDS). Safe operation of the APDS is a critical task, where the safety system is engaged only as needed to ensure reliable operation without positive feedback to the controller.

METHODS

The Health Monitoring System (HMS) was designed as a modular system to ensure the safety of the APDS and the user. It was designed using a large set of ambulatory data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS alerting. The HMS was validated in-clinic with a real-life challenge of a subject who overdosed insulin prior to admission.

RESULTS

The HMS was evaluated for clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes were detected with 2.9 false alarms per day. During in silico evaluation, the HMS reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested in-clinic, the subject overdosed ~3 U of insulin prior to her arrival to a closed-loop session (against protocol). The controller reduced insulin delivery, and the HMS gave four alerts that were successfully received via clinical software and text and multimedia messages. Even with insulin reduction and CHO supplements, hypoglycemia was unavoidable but manageable due to the HMS, confirming that a safety system to detect adverse events is an essential part of the APDS.

CONCLUSIONS

The ability of the HMS to be an effective alert system that provides a safety layer to the APDS controller has been demonstrated in a clinical setting.

Closed-loop insulin delivery in type 1 diabetes

Advances in diabetes technology have led to significant improvements in the quality of life and care received by individuals with diabetes. Despite this, achieving tight glycemic control through intensive insulin therapy and modern insulin regimens is challenging because of the barrier of hypoglycemia, the most feared complication of insulin therapy as reported by patients, caregivers, and physicians. This article outlines the individual components of the closed-loop system together with the existing clinical evidence. The artificial pancreas prototypes currently used in clinical studies are reviewed as well as obstacles and limitations facing the technology.

Closed-loop insulin delivery for treatment of type 1 diabetes

Type 1 diabetes is one of the most common endocrine problems in childhood and adolescence, and remains a serious chronic disorder with increased morbidity and mortality, and reduced quality of life. Technological innovations positively affect the management of type 1 diabetes. Closed-loop insulin delivery (artificial pancreas) is a recent medical innovation, aiming to reduce the risk of hypoglycemia while achieving tight control of glucose. Characterized by real-time glucose-responsive insulin administration, closed-loop systems combine glucose-sensing and insulin-delivery components. In the most viable and researched configuration, a disposable sensor measures interstitial glucose levels, which are fed into a control algorithm controlling delivery of a rapid-acting insulin analog into the subcutaneous tissue by an insulin pump. Research progress builds on an increasing use of insulin pumps and availability of glucose monitors. We review the current status of insulin delivery, focusing on clinical evaluations of closed-loop systems. Future goals are outlined, and benefits and limitations of closed-loop therapy contrasted. The clinical utility of these systems is constrained by inaccuracies in glucose sensing, inter- and intra-patient variability, and delays due to absorption of insulin from the subcutaneous tissue, all of which are being gradually addressed.

Closed-loop in children with type 1 diabetes: specific challenges

OBJECTIVE

To review challenges and opportunities related to closed-loop glucose control in children with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

During closed-loop glucose control, insulin infusion rates on a subcutaneous insulin pump are adjusted by a control algorithm according to subcutaneous glucose sensor readings. A literature review is performed and personal experience of work with closed-loop systems at the University of Cambridge, UK, is presented.

RESULTS

The main challenges in the management of T1D in children are identified together with a summary of current therapeutics options. Review of the literature recognises hypoglycaemia as a limiting factor for the attainment of optimal glycaemic control, primarily in children. Additional specific confounding issues include unpredictable eating and exercise patterns especially in the youngest age group. Closed-loop systems might be particularly helpful, but have to consider increased insulin sensitivity, lower insulin doses, and human factors such as size and usability of closed-loop components.

CONCLUSIONS

Closed-loop systems may represent an alternative treatment option to achieve target glucose levels whilst reducing the risk of hypoglycaemia in children with T1D.

In silico evaluation of glucose control protocols for critically ill patients

This letter presents an in silico evaluation method of glucose control protocols for critically ill patients with hyperglycemia. Although various glucose control protocols were introduced and investigated in clinical trials, development and validation of a novel glucose control protocol for critically ill patients require too much time and resources in clinical evaluation. We employed a virtual patient model of the critically ill patient with hyperglycemia and evaluated the clinically investigated glucose control protocols in a computational environment. The three-day simulation results presented the time profiles of glucose and insulin concentrations, the amount of enteral feed and intravenous bolus of glucose, and the intravenous insulin infusion rate. The hyperglycemia and hypoglycemia index, blood glucose concentrations, insulin doses, intravenous glucose infusion rates, and glucose feed rates were compared between different protocols. It is shown that a similar hypoglycemia incidence exists in simulation and clinical results. We concluded that this in silico simulation method using a virtual patient model could be useful for predicting hypoglycemic incidence of novel glucose control protocols for critically ill patients, prior to clinical trials.

Automated overnight closed-loop glucose control in young children with type 1 diabetes

BACKGROUND

We evaluated the effectiveness of automated overnight closed-loop (AOCL) insulin delivery and the influence of timing of initiation on glucose control overnight in young children with type 1 diabetes (T1D).

METHODS

Eight children with T1D (four boys, four girls) (mean ± SD: 9.4 ± 2.7 years old; body mass index, 18.3 ± 2.3 kg/m(2); duration of diabetes, 3.9 ± 2.5 years; total daily insulin dose, 0.7 ± 0.1 U/kg/day; glycosylated hemoglobin, 7.9 ± 0.9%) were studied in a clinical research facility on two separate occasions. Subjects had a meal at 18:00 (77 ± 8 g of carbohydrate [CHO]) and snack at 21:00 (21 ± 6 g of CHO), both accompanied by a prandial insulin bolus. In random order, AOCL was started at 18:00 or 21:00 h and ran until 08:00 h the next day. Subcutaneous continuous glucose monitoring data were fed automatically into the model predictive control algorithm. Calculated subcutaneous insulin infusion rates were sent wirelessly to an insulin pump. Plasma glucose was measured to assess closed-loop performance.

RESULTS

No rescue CHOs were administered. Time spent with plasma glucose in the target range from 3.9 to 8.0 mmol/L was 50.7% (29.0%, 72.2%), and it did not differ on the two occasions: median (interquartile range), 42% (18%, 64%) versus 58% (32%, 79%) (P = 0.161). Time when plasma glucose was above 8.0 mmol/L (42% [25%, 82%] vs. 29% [14%, 64%], P = 0.093), time below 3.9 mmol/L (0% [0%, 11%] vs. 8% [0%, 17%], P = 0.500), low blood glucose index (0.1 [0.0, 2.5] vs. 1.7 [0.4, 3.3], P = 0.380), plasma glucose at the start of AOCL (12.5 ± 2.7 vs. 11.6 ± 4.2 mmol/L, P = 0.562), and mean overnight plasma glucose (8.3 ± 2.1 vs. 7.5 ± 2.2 mmol/L, P = 0.246) were also similar.

CONCLUSIONS

AOCL is feasible in young children with T1D. Comparable results were obtained when closed-loop was initiated at 18:00 or 21:00 h.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.

Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day) not used during ANN training. For BGL predictions of up to 1 hour aRMSE5 dayof (±SD)0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, aRMSE5  dayof (±SD)0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

Stabilized glucagon formulation for bihormonal pump use

BACKGROUND

A promising approach to treat diabetes is the development of an automated bihormonal pump administering glucagon and insulin. A physically and chemically stable glucagon formulation does not currently exist. Our goal is to develop a glucagon formulation that is stable as a clear ungelled solution, free of fibrils at a pH of 7 for at least 7 days at 37 °C.

METHODS

Experimental glucagon formulations were studied for stability at 25 and 37 °C. Chemical degradation was quantified by reverse phase ultra-performance liquid chromatography. Physical changes were studied using light obscuration and visual observations.

RESULTS

Glucagon content of Biodel glucagon and Lilly glucagon at pH 2 and pH 4, as measured by high-performance liquid chromatography at 25 °C, was 100% at 7 days compared to 87% and <7%, respectively. Light obscuration measurements indicated Lilly glucagon at pH 4 formed an opaque gel, while Biodel glucagon formulation remained a clear solution beyond 50 days at 37 °C. Visual observations confirmed these results.

CONCLUSIONS

Biodel glucagon is a stabilized formulation at physiological pH and remains chemically and physically stable beyond 7 days at 37 °C, suggesting its utility for use in a bihormonal pump.

Paracrinology of islets and the paracrinopathy of diabetes

New results have brought to light the importance of the regulation of glucagon by β-cells in the development of diabetes. In this perspective, we examine the normal paracrinology of α- and β-cells in nondiabetic pancreatic islets. We propose a Sherringtonian model of coordinated reciprocal secretory responses of these juxtaposed cells that secrete glucagon and insulin, hormones with opposing actions on the liver. As insulin is a powerful inhibitor of glucagon, we propose that within-islet inhibition of α-cells by β-cells creates an insulin-to-glucagon ratio that maintains glycemic stability even in extremes of glucose influx or efflux. By contrast, in type 1 diabetes mellitus, α-cells lack constant action of high insulin levels from juxtaposed β-cells. Replacement with exogenous insulin does not approach paracrine levels of secreted insulin except with high doses that "overinsulinize" the peripheral insulin targets, thereby promoting glycemic volatility. Based on the stable normoglycemia of mice with type 1 diabetes during suppression of glucagon with leptin, we conclude that, in the absence of paracrine regulation of α-cells, tonic inhibition of α-cells improves the dysregulated glucose homeostasis. These results have considerable medical implications, as they suggest new approaches to normalize the extreme volatility of glycemia in diabetic patients.

Impact of blood glucose self-monitoring errors on glucose variability, risk for hypoglycemia, and average glucose control in type 1 diabetes: an in silico study

BACKGROUND

Clinical trials assessing the impact of errors in self-monitoring of blood glucose (SMBG) on the quality of glycemic control in diabetes are inherently difficult to execute. Consequently, the objectives of this study were to employ realistic computer simulation based on a validated model of the human metabolic system and to provide potentially valuable information about the relationships among SMBG errors, risk for hypoglycemia, glucose variability, and long-term glycemic control.

METHODS

Sixteen thousand computer simulation trials were conducted using 100 simulated adults with type 1 diabetes. Each simulated subject was used in four simulation experiments aiming to assess the impact of SMBG errors on detection of hypoglycemia (experiment 1), risk for hypoglycemia (experiment 2), glucose variability (experiment 3), and long-term average glucose control, i.e., estimated hemoglobin A1c (HbA1c)(experiment 4). Each experiment was repeated 10 times at each of four increasing levels of SMBG errors: 5, 10, 15, and 20% deviation from the true blood glucose value.

RESULTS

When the permitted SMBG error increased from 0 to 5-10% to 15-20%-the current level allowed by International Organization for Standardization 15197-(1) the probability for missing blood glucose readings of 60 mg/dl increased from 0 to 0-1% to 3.5-10%; (2) the incidence of hypoglycemia, defined as reference blood glucose <or=70 mg/dl, changed from 0 to 0-0% to 0.1-5.5%; (3) glucose variability increased as well, as indicated by control variability grid analysis; and (4) the incidence of hypoglycemia increased from 15.0 to 15.2-18.8% to 22-25.6%. When compensating for this increase, glycemic control deteriorated with HbA1c increasing gradually from 7.00 to 7.01-7.12% to 7.26-7.40%.

CONCLUSIONS

A number of parameters of glycemic control deteriorated substantially with the increase of permitted SMBG errors, as revealed by a series of computer simulations (e.g., in silico) experiments. A threshold effect apparent between 10 and 15% permitted SMBG error for most parameters, except for HbA1c, which appeared to be increasing relatively linearly with increasing SMBG error above 10%.

Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial

BACKGROUND

Closed-loop systems link continuous glucose measurements to insulin delivery. We aimed to establish whether closed-loop insulin delivery could control overnight blood glucose in young people.

METHODS

We undertook three randomised crossover studies in 19 patients aged 5-18 years with type 1 diabetes of duration 6.4 years (SD 4.0). We compared standard continuous subcutaneous insulin infusion and closed-loop delivery (n=13; APCam01); closed-loop delivery after rapidly and slowly absorbed meals (n=7; APCam02); and closed-loop delivery and standard treatment after exercise (n=10; APCam03). Allocation was by computer-generated random code. Participants were masked to plasma and sensor glucose. In APCam01, investigators were masked to plasma glucose. During closed-loop nights, glucose measurements were fed every 15 min into a control algorithm calculating rate of insulin infusion, and a nurse adjusted the insulin pump. During control nights, patients' standard pump settings were applied. Primary outcomes were time for which plasma glucose concentration was 3.91-8.00 mmol/L or 3.90 mmol/L or lower. Analysis was per protocol. This trial is registered, number ISRCTN18155883.

FINDINGS

17 patients were studied for 33 closed-loop and 21 continuous infusion nights. Primary outcomes did not differ significantly between treatment groups in APCam01 (12 analysed; target range, median 52% [IQR 43-83] closed loop vs 39% [15-51] standard treatment, p=0.06;

INTERPRETATION

Closed-loop systems could reduce risk of nocturnal hypoglycaemia in children and adolescents with type 1 diabetes.

FUNDING

Juvenile Diabetes Research Foundation; European Foundation for Study of Diabetes; Medical Research Council Centre for Obesity and Related Metabolic Diseases; National Institute for Health Research Cambridge Biomedical Research Centre.

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Key Words Collapse

MESH Headings

• Adolescent
• Algorithms
• Biosensing Techniques
• Blood Glucose/metabolism
• Child
• Child, Preschool
• Cross-Over Studies
• Diabetes Mellitus, Type 1/blood
• Diabetes Mellitus, Type 1/drug therapy
• Female
• Humans
• Hypoglycemic Agents/administration & dosage
• Infusions, Subcutaneous
• Insulin/administration & dosage
• Insulin/blood
• Insulin Infusion Systems
• Male
• Treatment Outcome

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Grants

• G0600717 Medical Research Council

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Affiliation(s)

Roman Hovorka Department of Paediatrics, University of Cambridge, Cambridge, UK.
Janet M Allen
Daniela Elleri
Ludovic J Chassin
Julie Harris
Dongyuan Xing
Craig Kollman
Tomas Hovorka
Anne Mette F Larsen
Marianna Nodale
Alessandra De Palma
Malgorzata E Wilinska
Carlo L Acerini
David B Dunger

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Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes

BACKGROUND

Closed-loop insulin delivery systems linking subcutaneous insulin infusion to real-time continuous glucose monitoring need to be evaluated in humans, but progress can be accelerated with the use of in silico testing. We present a simulation environment designed to support the development and testing of closed-loop insulin delivery systems in type 1 diabetes mellitus (T1DM).

METHODS

The principal components of the simulation environment include a mathematical model of glucose regulation representing a virtual population with T1DM, the glucose measurement model, and the insulin delivery model. The simulation environment is highly flexible. The user can specify an experimental protocol, define a population of virtual subjects, choose glucose measurement and insulin delivery models, and specify outcome measures. The environment provides graphical as well as numerical outputs to enable a comprehensive analysis of in silico study results. The simulation environment is validated by comparing its predictions against a clinical study evaluating overnight closed-loop insulin delivery in young people with T1DM using a model predictive controller.

RESULTS

The simulation model of glucose regulation is described, and population values of 18 synthetic subjects are provided. The validation study demonstrated that the simulation environment was able to reproduce the population results of the clinical study conducted in young people with T1DM.

CONCLUSIONS

Closed-loop trials in humans should be preceded and concurrently guided by highly efficient and resource-saving computer-based simulations. We demonstrate validity of population-based predictions obtained with our simulation environment.

Modeling the error of continuous glucose monitoring sensor data: critical aspects discussed through simulation studies

BACKGROUND

Knowing the statistical properties of continuous glucose monitoring (CGM) sensor errors can be important in several practical applications, e.g., in both open- and closed-loop control algorithms. Unfortunately, modeling the accuracy of CGM sensors is very difficult for both experimental and methodological reasons. It has been suggested that the time series of CGM sensor errors can be described as realization of the output of an autoregressive (AR) model of first order driven by a white noise process. The AR model was identified exploiting several reference blood glucose (BG) samples (collected frequently in parallel to the CGM signal), a procedure to recalibrate CGM data, and a linear time-invariant model of blood-to-interstitium glucose (BG-to-IG) kinetics. By resorting to simulation, this work shows that some assumptions made in the Breton and Kovatchev modeling approach may significantly affect the estimated sensor error and its statistical properties.

METHOD

Three simulation studies were performed. The first simulation was devoted to assessing the influence of CGM data recalibration, whereas the second and third simulations examined the role of the BG-to-IG kinetic model. Analysis was performed by comparing the "original" (synthetically generated) time series of sensor errors vs its "reconstructed" version in both time and frequency domains.

RESULTS

Even small errors either in CGM data recalibration or in the description of BG-to-IG dynamics can severely affect the possibility of correctly reconstructing the statistical properties of sensor error. In particular, even if CGM sensor error is a white noise process, a spurious correlation among its samples originates from suboptimal recalibration or from imperfect knowledge of the BG-to-IG kinetics.

CONCLUSIONS

Modeling the statistical properties of CGM sensor errors from data collected in vivo is difficult because it requires perfect calibration and perfect knowledge of BG-to-IG dynamics. Results suggest that correct characterization of CGM sensor error is still an open issue and requires further development upon the pioneering contribution of Breton and Kovatchev.

Progress in development of an artificial pancreas

This issue of Journal of Diabetes Science and Technology contains a collection of 12 original articles describing the latest advances in the development of algorithms for controlling insulin delivery in an artificial pancreas. Algorithms presented in this issue are affected by numerous quantifiable factors, including insulin pharmaco-kinetics, timing of meal carbohydrate appearance, meal size, amount of exercise, presence of stress, day-to-day variations in insulin sensitivity, insulin time-activity profiles, accuracy of glucose monitor calibration, metabolic profiles of both adults and neonates, and risks of hypoglycemia/hyperglycemia. These articles present theoretical advances in insulin delivery algorithms from modeled in silico patients, as well as clinical data from actual patients who have used closed loop systems. The novel approaches described in these articles are expected to bring us much closer to realization of a commercially available closed loop system for controlling glucose levels in patients with diabetes.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.