A Mathematical Modeling Approach to Estimate Blood Glucose Behavior in Individuals with Prediabetes

Background: Glucose homeostasis is a crucial physiological process, and its disruption is closely linked to the onset of Type 2 Diabetes Mellitus (T2DM), a major global health issue. Objective: This study presents a novel mathematical model to describe glucose dynamics in both healthy individuals and those with prediabetic risk factors. Methods: We analyzed 311 days of continuous glucose monitoring data from 43 participants (14 healthy and 29 at risk, aged 25–55), using a Dual Extended Kalman Filter to estimate parameters and unmeasurable variables, while accounting for parametric variability. We applied the Levenberg–Marquardt algorithm to minimize estimation error. Results: Based on average parameter values and standardized inputs, 311 simulations were conducted, showing strong agreement with experimental data (r = 0.98, p < 0.01). Conclusions: The model provides an accurate representation of glucose regulation and serves as a valuable in-silico tool for advancing preventive strategies against T2DM, marking one of the first models specifically tailored to individuals with prediabetes.