Personalized metabolic profile estimations using oral glucose tolerance tests

Area of Science:

• Physiology
• Endocrinology
• Mathematical Modeling

Background:

• Oral glucose tolerance tests (OGTTs) are standard for diagnosing diabetes mellitus (DM).
• OGTT data offers insights into glucose and insulin dynamics across various organs.
• Dynamic modeling can extract comprehensive physiological information from OGTTs.

Purpose of the Study:

• To develop a dynamic model for analyzing glucose and insulin interactions using OGTT data.
• To reveal gender-specific and diabetes-related differences in glucose-insulin regulation.
• To identify dysfunctional organs in glucose and insulin regulation for personalized medicine.

Main Methods:

• A five-compartment insulin and two-compartment glucose dynamic model was created.
• Anthropometric data informed cardiac output estimations.
• Simplex and Levenberg-Marquardt algorithms fitted data from 42 healthy and 8 DM subjects.

Main Results:

• Significant gender differences were observed in glucose absorption, pancreatic sensitivity, insulin production, and endogenous glucose production.
• Diabetes mellitus (DM) subjects showed pancreatic and liver dysfunctions.
• Quantitative, time-dependent differences in insulin resistance, secretion, and glucose disposal were identified in DM patients.

Conclusions:

• The study quantitatively demonstrates glucose-insulin dynamics and organ function during OGTTs.
• The model successfully identified specific organ dysfunctions related to glucose and insulin regulation.
• This approach enables personalized and targeted interventions for metabolic health issues.