New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy

Study Overview
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Brief Summary
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Achieving near-normoglycemia has been established as the main objective for most patients with type 1 diabetes (T1DM). However, insulin dosing is an empirical process and its success is highly dependent on the patients' and physicians' skills, either with multiple daily injections (MDI) or with continuous subcutaneous insulin infusion (CSII, the gold standard of insulin treatment). Postprandial glucose control is one of the most challenging issues in the everyday diabetes care. Indeed, postprandial glucose excursions are the major contributors to plasma glucose (PG) variability of subjects with (T1DM) and the poor reproducibility of postprandial glucose response is burdensome for both patients and healthcare professionals. During the past 10-15 years, there has been an exponentially increasing intrusion of technology into diabetes care with the expectation of making life easier for patients with diabetes. Some tools have been developed to aid patients in the prandial bolus decision-making process, i.e. bolus advisors, which have been implemented in insulin pumps and more recently in the newest generations of glucometers. Currently, the availability of continuous glucose monitoring (CGM) has opened new scenarios for improving glycemic control and increasing understanding of post-prandial glycemic response in patients with diabetes. Results from clinical studies suggest that sensor-augmented pumps (SAP)may be effective in improving metabolic control, especially when included as part of structured educational programs resulting in patients' empowerment. Similarly, preliminary results from pilot studies indicate that automated glycemic control, especially during nighttime,based on information from CGM is feasible. However, automatic management of meal bolus is currently one of the main challenges found in clinical validations of the few existing prototypes of an artificial pancreas. Indeed, fully closed-loop systems where information about meals size and timing is not given to the system have shown poor performance, with postprandial glucose higher and post meal nadir glucose lower than desired. This has promoted other less-ambitious approaches, where prandial insulin is administered following meal announcement (semi closed-loop). However, despite the use of meal announcement, currently used algorithms for glucose control (the so-called PID and MPC), show results that are not yet satisfactory due to the risk of producing hypoglycemia. One of the limitations of the current open-loop (bolus advisors) and closed-loop control strategies is that glycemic variability is not taken into account. As an example, settings of CSII consider inter-individual variation of the parameters (insulin/carbohydrates ratio, correction dose, etc.) but disregard the day-to-day intra-individual variability of postprandial glucose response. Availability of massive amount of information from CGM, together with mathematic tools, may allow for the characterization of the individual variability and the development of strategies to cope with the uncertainty of the glycemic response to a meal. In this project, a rigorous clinical testing of a CGM-based, user-independent algorithm for prandial insulin administration will be carried out in type 1 diabetic patients treated with insulin CSII. First of all, an individual patient's model characterizing a 5-hour postprandial period will be obtained from a 6-day CGM period. The model will account for a 20% uncertainty in insulin sensitivity and 10% variability in the estimation of the ingested carbohydrates. Based on this model (derived from CGM), a mealtime insulin dose will be calculated (referred as iBolus). Then, the same subjects will undergo standardized meal test studies comparing the administration of a traditional bolus (tBolus, based on insulin to CHO ratio, correction factor, etc.) with the CGM-based prandial insulin delivery (iBolus). Significant advances in postprandial control are expected. Should its efficiency be demonstrated clinically, the method could be incorporated in advanced sensor augmented pumps as well as feedforward action in closed-loop control algorithms for the artificial pancreas, in future work.

Detailed Description
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Over the last 30 years, even with the development of new glucose monitoring techniques and the availability of new insulin preparations with more physiological profiles, SC continuous administration systems were still not able to be universal, efficient and safe systems able to achieve a near-normalization of glucose levels in diabetic patients. Indeed, in developed countries, only one third of diabetic patients meet criteria for good metabolic control, i.e. glycosylated haemoglobin < 7%. During the past 10-15 years, there has been an exponentially increasing intrusion of technology into diabetes care with the expectation of improving metabolic control and making life easier for patients with diabetes. In the last years, some tools have been developed to aid patients in the prandial bolus decision-making process as the bolus advisors, which are implemented in insulin pumps and more recently in newest generations of glucometers. Currently, the availability of continuous glucose monitoring (CGM) has opened two scenarios: Open-loop control strategies. In the short/mid term CGM may help in the implementation of more effective strategies of insulin treatment, especially in CSII treated patients, with the development of smarter pumps (sensor augmented pumps which use the information from the CGM to tune insulin infusion). Closed-loop control strategies. In the long term, CGM may allow for automated glucose control (the so-called artificial pancreas). The artificial pancreas would represent the ideal solution for the attainment of the therapeutic goals needed to prevent chronic complications of diabetes. Indeed, in the last two decades, technological progresses have fuelled research on closed-loop glucose control systems aiming for effective treatment of diabetic subjects. Preliminary studies using off-the-shelf insulin pumps and continuous glucose monitoring (CGM) sensors have suggested that in research settings, closed-loop systems that automatically dispense insulin can achieve better glucose control than open-loop systems in which people have to take dosing decisions. Such promising results prompted the Juvenile Diabetes Research Foundation (JDRF) to push the research forward by launching its Artificial Pancreas Project in 2006. Also, the US Food and Drug Administration (FDA) designated the artificial pancreas as a priority within its Critical Path Initiative. However, due to its complexity, only a few prototypes so far have been developed and tested in controlled clinical settings. Among problems related to glycemic closed-loop control, management of postprandial glycaemic excursions is a key issue in the future artificial pancreas. Indeed, meal-induced perturbations on glucose control is one of the major problems to counteract and the main challenge found in current clinical validations of the few existing prototypes of closed-loop glycemic control systems. The first significant clinical result regarding fully automated closed loop in the fasting condition comes from Medtronic Inc. who demonstrated the feasibility of a fully automated closed loop system in 10 adults with type 1 diabetes mellitus, using an external pump (CSII), a sensor for continuous subcutaneous glucose monitoring (CGM), and a control algorithm called ePID. This algorithm consists of a classical Proportional-Integral-Derivative controller plus insulin on-board feedback. Since then, several initial clinical trials of closed-loop control have been made to prove the feasibility of other control algorithms like Model Predictive Control (MPC). MPC has obtained positive results in type 1 diabetic patients and also in Intensive Care Units. Different approaches have been suggested to deal with meal disturbances in these controllers. Fully closed-loop systems where information about meals size and timing is not given to the system have shown poor performance, with postprandial glucose higher and post-meal nadir glucose lower than desired. This has promoted other less-ambitious approaches, where meals are announced to the system generating a feed-forward action like for instance a prandial insulin bolus (semi closed-loop). Hybrid approaches have also been proposed, where only a percentage of the prandial bolus is applied ('priming bolus') and the rest is left to the closed-loop controller. Clinical studies have demonstrated the efficacy of these solutions to reduce postprandial excursions during closed-loop control versus fully closed-loop systems, showing that first generations of an artificial pancreas will require announcement of meals and priming insulin boluses. However, despite the use of meal announcement, the main challenge of control algorithms is still the avoidance of overcorrection. An aggressive-enough tuning for a low post-prandial glucose peak may cause an accumulation of insulin producing a late hypoglycemia. This imposes the consideration of constraints on residual insulin activity (insulin-on-board) both in PID and MPC-based systems. However, despite the inclusion of constraints, clinical results during a meal of PID and MPC are not yet satisfactory. Interval techniques have shown to be particularly suitable to deal with constraints under uncertainty, leading to more robust solutions and potentially reducing the risk of hypoglycaemia while maintaining good performance. These techniques were first introduced by Bondia et al in 2009, who proposed a set-inversion-based algorithm for calculation of meal-related insulin. This algorithm computed the feasible set of insulin profiles to fulfill the given constraints on postprandial glycemia, according to a patient's prediction model. In particular, physiological constraints were applied using postmeal guidelines from the International Diabetes Federation aiming at no hypoglycemia and two-hour glucose below 140 mg/dL, in a 5-hour time horizon. A refined algorithm was presented by Revert et al in 2009, allowing for the determination of the optimal insulin administration mode (standard, square, dual-wave or temporal basal decrement/iBolus). In this work, an in silico validation using the FDA-accepted UVA simulator for the testing of control algorithms was performed. Results of this study demonstrated the effectiveness of this strategy, including the challenge of meals with high carbohydrate content. To date, priming prandial boluses in the context of semi-automated glucose control are computed based on the patient's insulin-to-carbohydrate ratio, as currently done in 'standard' CSII therapy. In this latter, bolus insulin is infused over the patient's basal insulin rate, usually following one of three available choices: 1) simple bolus (all of the insulin dose is administered as a bolus, i.e. like with a pen or syringe); 2) dual wave bolus (a percentage of the insulin dose is administered as a bolus, being the remaining insulin being infused as a square wave during a pre-specified time interval following the meal); 3) square wave bolus (all the insulin dose is administered as a square wave). However, the above mentioned study by Revert et al. has demonstrated 'in silico' (i.e. by means of an FDA-accepted computer simulator), that a coordinate action of basal and bolus insulin is required to maintain blood glucose in a physiological range, in the postprandial state. In particular, a bolus greater than the standard one, paralleled by a temporary reduction of the basal insulin infusion rate (referred as iBolus, which may be considered as a generalization of the superbolus concept introduced by Walsh et al. is needed, especially for meals with higher carbohydrate content. This study was planned to validate this new methodology for prandial insulin administration, and it is expected to confirm the hypothesis that set-inversion techniques may be applied to SAP-CSII therapy. Of note, this strategy would represent the first attempt of developing a non-heuristic tool for mealtime insulin dosing. It could be implemented not only in closed-loop strategies of glycemic control but also in open-loop strategies as an advanced bolus advisor in newest generations of insulin pumps. Primary objective: In type 1 DM subjects treated with CSII, assessment and clinical validation of a new algorithm for optimization of postprandial glucose control, the iBolus (CGM-based prandial insulin administration) in comparison with a standard bolus (tBolus).

Official Title
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New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy: Feasibility of Insulin Dosing Based on Information From Continuous Glucose Monitoring

Conditions
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Type 1 Diabetes

Intervention / Treatment
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* Other: iBolus
* Other: tBolus (traditional bolus)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Aged between 18 and 60 years Under CSII treatment for at least six months before Visit 1 Body mass index of between 18 and 35 kg/m2 HbA1c 6.0-8.5% at Visit 1 Normal laboratory values, ECG, and vital signs unless the investigator considered an abnormality to be clinically irrelevant Women postmenopausal or using contraception judged by the investigator to be adequate (e.g., oral contraceptives, intra-uterine device or surgical treatment), with a negative urine pregnancy tests Exclusion Criteria: Pregnancy and lactation History of hypersensitivity to the study medications or to drugs with similar chemical structures Hypoglycemia unawareness Progressive fatal diseases History of drug or alcohol abuse History of positive HIV or hepatitis B or C test Impaired hepatic function, as shown by, but not limited to, SGPT or SGOT of more than twice the upper limit of the normal range at visit 1 Impaired renal function, as shown by, but not limited to, serum creatinine > 1.5 mg/dL at visit 1 Clinically relevant microvascular, cardiovascular, hepatic, neurologic, endocrine or other major systemic diseases other than T1DM which could hinder implementation of the clinical study protocol or interpretation of the study results Pre-planned surgery during the study Blood donation of more than 500 ml during the past three months for men, or during the past six months for women Mental condition rendering the subject unable to understand the nature, scope and possible consequences of the study Subject unlikely to comply with clinical study protocol, e.g., uncooperative attitude, inability to return for follow-up visits, or poor likelihood of completing the study Receipt of an experimental drug or use of an experimental device during the past 30 days.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 60 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Crossover Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: tBolus (traditional bolus)<br>Traditional mealtime insulin bolus based on the individual insulin-to-CHO ratio | Other: tBolus (traditional bolus)<br>* Insulin bolus dose calculated using the standard procedure based on the insulin-to-carbohydrate ratio<br>|
| Experimental: iBolus (CGM-based insulin administration)<br>This is a CGM-based algorithm for prandial insulin administration. An individual patient's model characterizing a 5-hour postprandial period (0-5h PP) is obtained from a 6-day CGM period. A model with interval parameters accounting for patient's variability is calculated considering 20% uncertainty in insulin sensitivity and 10% in carbohydrates (CHO) estimation. Based on this model, constraints on plasma glucose are posed and a set-inversion problem lead to a set of solutions (the iBolus) that contains a bolus insulin dose, a specific mealtime basal insulin dose and the time for restoration of basal to baseline values. | Other: iBolus<br>* Insulin bolus calculated from data obtained through CGM<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The Area Under the Curve (AUC) of Plasma Glucose (PG) Concentrations During the 5-hour Postprandial Period (AUC-PG0-5 h). | AUC-PG0-5 h (5-hour postprandial glucose following the mixed meal test) is a measure of the overall glucose-lowering efficacy of the insulin bolus. The lower the AUC-PG0-5 h without hypoglycemia, the greater the effectiveness of the prandial insulin administration to control the meal related glucose excursion. Plasma glucose (PG) for calculation of AUC-PG was measured every 15 minutes following the insulin administration and during the whole 5-hour postprandial period (300 minutes). | The whole experiment, i.e. 5 hours | 
| The Area Under the Curve (AUC) of the Glucose Infusion Rate (GIR) During the 5-hour Postprandial Period (AUC-GIR0-5h). | The amount of glucose infused during the 5-hour postprandial period (AUC-GIR0-5h) is a measure of the hypoglycemic exposure associated with the modality of prandial insulin administration. Indeed, glucose will be infused only when patients are under a predefined blood glucose values (80 mg/dl) with a descending trend. Glucose infusion rate (GIR) for calculation of AUC-GIR was measured every minute following the insulin administration and during the whole 5-hour postprandial period (300 minutes). | The whole experiment, i.e. 5 hours. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The Area Under the Curve (AUC) of Plasma Glucose (PG) Above the Threshold of 140 mg/dl (AUC-PG>140). | The AUC-PG>140 during the 5-hour period following the meal test represents the hyperglycemic risk related to the modality of prandial insulin administration. Plasma glucose (PG) for calculation of AUC-PG>140 was measured every 15 minutes following the insulin administration and during the whole 5-hour postprandial period (300 minutes). | The whole experiment, i.e. the 5-hour postprandial period | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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T1DM, CSII, CGM, postprandial control, glycemic variability, Prandial insulin dosing in Type 1 diabetes treated with continuous subcutaneous insulin infusion