Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones

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Abstract
Diabetes mellitus is a widespread disease associated with an impaired hormonal regulation of normal blood glucose levels. Patients with insulin-dependent diabetes mellitus (IDDM) who practice conventional insulin therapy are at risk of developing hypoglycemia (low levels of blood glucose), which can lead to severe dysfunction of the central nervous system. In large retrospective studies, up to approximately 4% of deaths of patients with IDDM have been attributed to hypoglycemia (Cryer, Fisher, & Shamoon, 1994; Tunbridge, 1981; Deckert, Poulson, & Larsen, 1978). Thus, a better understanding of the complex hormonal interaction preventing hypoglycemia is crucial for treatment. Experimental data from a study on insulin-induced hypoglycemia in healthy subjects are used to demonstrate that feedforward neural networks are capable of predicting the time course of blood glucose levels from the complex interaction of glucose counterregulatory (glucose-raising) hormones and insulin. By simulating the deficiency of single hormonal factors in this regulatory network, we found that the predictive impact of glucagon, epinephrine, and growth hormone secretion, but not of cortisol and norepinephrine, were dominant in restoring normal levels of blood glucose following hypoglycemia.