A physiologically based mathematical model describing estradiol and progesterone actions on glucose-insulin dynamics throughout the menstrual cycle

Abstract

Our knowledge about women's health from a systemic physiological perspective, particularly regarding the interactions and multiple feedback loops between the ovulatory-menstrual cycle (OMC) and the rest of the female body, is limited. Mathematical models describing such interactions are scattered, and quantitative studies are often contradictory in the current literature. The impact of the OMC on glucose metabolism in healthy women has not been mathematically modeled so far, and the interactions between those two systems have barely been taken into consideration when addressing altered glucose-insulin dynamics in, e.g., Type 1 Diabetes (T1D), Type 2 (T2D), and gestational diabetes. This constitutes a knowledge gap not only for women with diabetes, but also for comprehending glucose homeostasis in the non-diabetic female population. The mathematical model presented here includes the impact of the OMC on glucose-insulin dynamics described by a minimal model. The phases of the OMC are represented by the dynamics of estradiol (E2) and progesterone (P4), which serve as input to the glucose-insulin model. A physiologically based expression depending on E2 and P4 is introduced to modulate the parameter known as insulin sensitivity, which modulates the glucose (G) state. The dependency on both steroid hormones (E2 and P4) is also introduced in the other two states of the system describing glucose metabolism as follows: 1) in the secretion rate of insulin from β−cells, which affects the insulin (I) state, and 2) in the β-cell mass preservation, impacting the β state. The results are in agreement with physiological descriptions and available data. They demonstrate an essential role of P4 along with E2 effects in glucose homeostasis, i.e., insulin sensitivity, insulin secretion and β-cell maintenance. This work paves the way for future studies in which the hormonal dynamics could be coupled with more detailed metabolic models including, e.g., hepatic glucose metabolism, and it calls for better experimental data to continue deepening our comprehension of these interactions.