Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring

Area of Science:

• Endocrinology and Metabolism
• Biomedical Engineering
• Computational Biology

Background:

• Type 2 diabetes (T2D) management requires monitoring physiological parameters like insulin sensitivity and beta-cell function.
• Continuous glucose monitors (CGM) provide accessible physiological data, but their full utility in T2D management is still under investigation.
• Current methods for assessing these parameters can be invasive or resource-intensive.

Purpose of the Study:

• To demonstrate the feasibility of estimating key physiological parameters in T2D using raw daily CGM data.
• To develop and validate a model-based approach for inferring insulin sensitivity and beta-cell function from CGM.
• To compare these inferred parameters with established laboratory-derived measurements.

Main Methods:

• A peak extraction algorithm was employed to identify relevant features from daily CGM data.
• A model-based approach was implemented to infer physiological parameters from the extracted CGM data.
• Inferred parameter estimates were correlated with ground truth measurements obtained from oral glucose tolerance tests (OGTT).

Main Results:

• The study successfully demonstrated the feasibility of using raw CGM data for physiological parameter estimation.
• Inferred estimates of insulin sensitivity showed a significant correlation with OGTT-derived ground truth measurements.
• Inferred estimates of beta-cell function also demonstrated a strong correlation with OGTT-derived ground truth measurements.

Conclusions:

• Raw daily CGM data can be effectively utilized to estimate crucial physiological parameters relevant to T2D.
• The developed model-based approach provides a non-invasive and potentially more accessible method for assessing insulin sensitivity and beta-cell function.
• These findings support the expanded use of CGM data in clinical decision-making for Type 2 diabetes management.