Analyzing Basal Insulin Dosing as Perturbations in Compositional Glycemic Profiles for Time in Range in Type 1...

Area of Science:

• Endocrinology
• Computational Biology
• Data Science

Background:

• Basal insulin dosing is critical for Type 1 Diabetes (T1D) management with multiple daily injections.
• Adjusting basal insulin remains a complex challenge for clinicians, impacting glycemic control.

Purpose of the Study:

• To introduce a novel decision support system using Compositional Data Analysis (CoDa) to model basal insulin perturbations.
• To assess the impact of basal insulin adjustments on glycemic control, including time in range (TIR) and glycemic distribution.
• To develop a predictive model for personalized T1D treatment strategies.

Main Methods:

• In silico simulations using a virtual cohort of 49 adults over 180 days.
• Application of Compositional Data Analysis (CoDa) to model basal insulin adjustments (±5%, ±10%, ±15%).
• Multivariate linear regression to predict glycemic outcomes and correlation analysis with a glycemic risk index.

Main Results:

• The CoDa framework successfully quantified the impact of basal insulin perturbations on TIR, with high R² values (up to 0.85).
• A strong negative correlation (r = -0.9141) was found between a CoDa-derived log-ratio coordinate and a glycemic risk index.
• A visualization panel was developed integrating arithmetic mean TIR, geometric mean TIR, and glycemic profile distributions.

Conclusions:

• CoDa analysis provides a robust framework for understanding basal insulin's effect on glycemic control in T1D.
• The developed decision support system aids physicians in optimizing basal insulin titration, reducing empirical adjustments.
• This approach facilitates personalized treatment strategies and improves overall T1D management.