New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy

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.

Conditions

• Type 1 Diabetes

Interventions

OTHER

iBolus

Insulin bolus calculated from data obtained through CGM

OTHER

tBolus (traditional bolus)

Insulin bolus dose calculated using the standard procedure based on the insulin-to-carbohydrate ratio

Sponsors & Collaborators

• European Union

  collaborator OTHER

• Ministerio de Ciencia e Innovación, Spain

  collaborator OTHER_GOV

• Fundación para la Investigación del Hospital Clínico de Valencia

  lead OTHER

Principal Investigators

• Francisco Javier Ampudia-Blasco, MD, PhD · Fundación INCLIVA, Hospital Clínico Universitario de Valencia

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

CROSSOVER

Eligibility

Min Age

18 Years

Max Age

60 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2010-02-28

Primary Completion

2011-06-30

Completion

2011-06-30

Countries

• Spain

Study Locations