Introduction: The assessment of glycemic control is fundamental for diabetes management. However, traditional measures have limitations, including susceptibility to non-glycemic factors. To address these limitations, there is a growing need for personalized metrics of glycemic control that take into account individual variability and provide a more comprehensive assessment of glycemic response. Objective: To develop the Individualized Glycemic Index (IGI) as a new marker of glycemic control. Methods: A simulated dataset representing individuals with varied glycemic profiles, including fasting glucose levels, glycemic variability measures, glycemic response to foods, HbA1c, fructosamine, and other relevant factors, was created. An algorithm was implemented in the Python language using designated libraries. We evaluated: the algorithm's performance using simulated data with known glycemic control outcomes; the algorithm's ability to accurately predict glycemic control based on the provided data; the algorithm's performance with glycemic control analyses. Results: The IGI algorithm uses a comprehensive set of input data to provide a personalized assessment of glycemic control. A program in Python language was developed to calculate the IGI, with a comprehensive metric for evaluating glycemic control. The structured algorithm incorporated the most relevant factors to create a program taking into account each patient's individuality. Conclusion: The IGI provides a more comprehensive and personalized assessment of glycemic control, which may improve diabetes management and outcomes, becoming a promising marker of glycemic control that surpasses the limitations of traditional measures. Keywords: Glycemic control, diabetes mellitus, glycemic control marker.

The authors have declared no competing interest.

This study did not receive any funding

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