Assessment of hypoglycaemia awareness using continuous glucose monitoring

AIMS: To investigate the possibility of assessing hypoglycaemia awareness in patients with Type 1 diabetes using continuous glucose monitoring. METHODS: Twenty patients with Type 1 diabetes were investigated. Ten patients with Type 1 diabetes and strongly impaired hypoglycaemia awareness were compared with 10 patients with intact hypoglycaemia awareness regarding quality of hypoglycaemia perception (number of undetected hypoglycaemic episodes per 24 h, glucose level < 3.3 mmol/l). Hypoglycaemia detection was assessed using the event function of the Continuous Glucose Monitoring System (CGMS; Medtronic MiniMed, Northridge, CA, USA). Patients were instructed to enter an event upon suspecting being hypoglycaemic. RESULTS: Satisfactory CGMS performance could be achieved [mean r = 0.893 between calibration measurements and CGMS data, mean absolute difference (MAD) = 20.6%], although artefacts were observable and had to be controlled. Hypoglycaemia unaware patients showed a significantly higher total number of hypoglycaemic episodes (P < 0.05), number of undetected hypoglycaemic episodes (P < 0.01), and mean glucose levels (P < 0.05). Even in aware patients, undetected hypoglycaemia was observable. No significant differences regarding occurrence of nocturnal hypoglycaemia were observable. CONCLUSIONS: The possibility of direct assessment of hypoglycaemia awareness using continuous glucose monitoring was demonstrated. Its application in clinical practice could be of use for assessing hypoglycaemia perception and evaluating the impact of treatment changes on hypoglycaemia awareness

BIOMICS Project : Biological and Mathematical Basis of Interaction Computing

The main idea that led us to propose this project is inspired by the observation that cell metabolic/regulatory systems are able to self-organise and/or construct order dynamically, through random interactions between their components and based on a wide range of possible inputs. We think it is possible for this behaviour to be reproduced in a controllable way through interacting finite-state automata. This will lead to a radically new model of “bottom-up computation” with equal applicability to computer science and systems biology that we call Interaction Computing (IC).Peer reviewe