Additional Signals

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  Project Lead
  Professor David O'Neal

  DTRG-t1research@unimelb.edu.au

Project Details

Full project Title: Improving the daily lives of people with type 1 diabetes by meeting the challenges of glucose control through the development of a next-generation closed-loop system

An Artificial Pancreas or Closed loop (CL) insulin delivery systems are a recent medical innovation aiming to reduce the risk of hypoglycemia while achieving tight control of glucose in people with type 1 diabetes. CL systems are characterized by real-time glucose-responsive insulin administration by combining both real-time glucose-sensing and insulin-delivery components.

However, current CL systems are challenged by everyday situations where insulin requirements change rapidly and often unpredictably such as with meals, acute illness, and exercise. Exercise in particular poses unique challenges to people with type 1 diabetes.  The effects depend upon the intensity of the exercise:  mild- to moderate-intensity exercise generally decreases glucose levels; in contrast, high-intensity and resistance exercise may increase glucose levels.

Current-generation   CL systems   measure   a single   parameter, glucose, to determine insulin   delivery. A potential novel strategy to improve metabolic control during and after exercise with a CL system is to measure and integrate novel biochemical or kinetic sensor inputs into the CL algorithm, such as heart rate, lactate, ketones or exercise measurement by accelerometer. It is likely that no single additional sensor input could address all the limitations of current CL systems when challenged with exercise. Therefore, the aim of this study is to profile and analyze novel biochemical and kinetic exercise data to develop a model for exercise in type 1 diabetes incorporating these additional inputs into a CL algorithm and model its in silico performance in comparison with a current  generation system  to  determine if  the  additional inputs  confer  metabolic advantages during exercise.

We propose a next generation CL system measuring multiple inputs in addition to sensor glucose levels which are incorporated into a model for exercise in type 1 diabetes to better maintain glucose in target range during and after exercise.  As an added advantage these additional inputs, such as lactate and ketones, could provide early signals for adverse events such as insulin delivery line failure and acute illness.

Researchers

Professor David O’Neal, Principal  Investigator
Catriona Sims, Project Portfolio Manager
Dr Melissa Lee, Clinical Physician (PhD)
Dr  Barbora Paldus, Clinical Physician (PhD)
Varuni Obeyesekere, Scientist
Dr Dale Morrison, Clinical Research Co-ordinator, Exercise Physiologist'
Emma Netzer, Credentialed Diabetes Educator
Hannah Jones, Credentialed Diabetes Educator

Collaborators

Assoc Prof Andre La Gerche, Baker Heart and Diabetes Institute
Dr Neal Cohen, Baker Heart and Diabetes Institute
Prof Elliot Botvinick, University of California, Irvine
Prof Peter Colman, Royal Melbourne Hospital
Prof Bruce King, John Hunter Hospital
Dr Carmel Smart, John Hunter Hospital
Prof Graham Goodwin, University of Newcastle
Dr Adrian Medioli, University of Newcastle
Prof Michael Riddell, University of York
Dr Karri Venn, University of York
Dr Alex Abitbol, University of York

Funding

Juvenile Diabetes Research Foundation
Helmsley Charitable Trust