New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy
Condition: Type 1 Diabetes
MeSH terms: Diabetes Mellitus, Type 1
Study type: Interventional
Phase: Phase 3

Summary: 
      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: 
      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:

        1. "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).

        2. "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).
    

Intervention name(s): iBolus; tBolus (traditional bolus)
Intervention type(s): Other; Other
Intervention description(s): Insulin bolus calculated from data obtained through CGM; Insulin bolus dose calculated using the standard procedure based on the insulin-to-carbohydrate ratio
Allocation: Randomized
Intervention model: Crossover Assignment
Masking: Double (Participant, Investigator)
Primary purpose: Treatment
Keywords: T1DM; CSII; CGM; postprandial control; glycemic variability; Prandial insulin dosing in Type 1 diabetes treated with continuous subcutaneous insulin infusion