Modeling the Nonlinear Time Dynamics of Multidimensional Hormonal Systems

A novel measure of glucose homeostasis (or loss thereof) comprising the joint dynamics of glucose, insulin, glucagon, and cortisol

Quantification of disturbances in glucose-insulin homeostasis has been the cornerstone of appraising insulin resistance and detecting early-stage diabetes. Metabolic homeostasis arises from feedback and feed-forward interactions among (at least) all four of glucose, insulin, glucagon, and cortisol. Quantifying such tetrapartite interactions in the fasting (endogenously regulated) state overnight could elucidate very early regulatory disruption. In the present study, healthy subjects without diabetes (ND; n = 20) and patients with Type 2 diabetes (T2D; n = 21) were investigated by repeated overnight blood sampling of all four of glucose, insulin, glucagon, and cortisol concentrations. To obviate confounding by hormone-specific disappearance rates, analyses were performed at the level of production (glucose) or secretion (insulin, glucagon, and cortisol) rates estimated by regularized deconvolution analysis. Then, a novel method for quantifying the loss of homeostasis among glucose, insulin, and glucagon (and, when available, cortisol) secretion patterns was developed. Potential early stage prediabetic candidates were identified. The new methodology avoids many of the difficulties encountered in the conventional estimation of insulin-glucose sensitivity or resistance, while incorporating the dynamics of the key coregulators under fasting conditions.

Pulsatility of Hypothalamo-Pituitary Hormones: A Challenge in Quantification

Neuroendocrine systems control many of the most fundamental physiological processes, e.g., reproduction, growth, adaptations to stress, and metabolism. Each such system involves the hypothalamus, the pituitary, and a specific target gland or organ. In the quantification of the interactions among these components, biostatistical modeling has played an important role. In the present article, five key challenges to an understanding of the interactions of these systems are illustrated and discussed critically.