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Researchers have reportedly developed a mathematical model that can predict the blood glucose levels of individuals with type 1 diabetes with more than 90 percent accuracy in half an hour. 

"Many people with type 1 diabetes use continuous glucose monitors, which examine the fluid underneath the skin," said Peter Molenaar, Distinguished Professor of Human Development and Family Studies and of psychology in the press release. "But the glucose levels under the skin trail blood glucose levels from anywhere between 8 and 15 minutes. This is especially problematic during sleep. Patients may become hypoglycemic well before the glucose monitor alarm tells them they are hypoglycemic, and that could lead to death."

Molenaar added that an individual's blood glucose levels fluctuate in response to his/her insulin dose, meal intake physical activity and emotional state. 

"In the past decade, much progress has been made in the development of a mechanical 'artificial pancreas,' which would be a wearable or implantable automated insulin-delivery system consisting of a continuous glucose monitor, an insulin pump and a control algorithm closing the loop between glucose sensing and insulin delivery," he said. "But creating an artificial pancreas that delivers the right amount of insulin at the right times has been a challenge because it is difficult to create a control algorithm that can handle the variability among individuals. Our new model is able to capture this variability. It predicts the blood glucose levels of individuals based on insulin dose and meal intake."

The time-varying model estimates the results by the extended Kalman filtering technique. The model also accounted for time-varying changes in the glucose kinetics due to insulin and meal intake.

"We learned that the dynamic dependencies of blood glucose on insulin dose and meal intake vary substantially in time within each patient and between patients," said Qian Wang, professor of mechanical engineering in the press release.

The development of the study will appear this week in the Journal of Diabetes Science and Technology.