Michaelis-Menten kinetic modeling of hemoglobin A(1c) status facilitates personalized glycemic control

Introduction: Discrepancy between measured HbA(1c) and HbA(1c) calculated from plasma glucose is associated with higher risk for diabetic complications. However, quantification of this difference is inaccurate due to the imperfect linear conversion models. We propose to introduce a mathematical formula that correlates with the observational data and supports individualized glycemic control.Methods: We analysed 175,437 simultaneous plasma glucose and HbA(1c) records stored in our laboratory database. Employing the Michaelis-Menten (MM) equation, we compared the calculated HbA(1c) levels to the measured HbA(1c) levels. Data from patients with multiple records were used to establish the patients' glycemic status and to assess the predictive power of our MM model.Results: HbA(1c) levels calculated with the MM equation closely matched the population's average HbA(1c) levels. The Michaelis constant (K-m) had a negative correlation with HbA(1c) (r(2) = 0.403). Using personalized K-m values in the MM equation, 85.1% of HbA(1c) predictions were within 20% error (ADAG calculation: 78.4%). MM prediction also performed better in predicting pathologic HbA(1c) levels (0.904 AUC vs. 0.849 AUC for ADAG).Conclusion: MM equation is an improvement over linear models and could be readily employed in routine diabetes management. K-m is a reliable and quantifiable marker to characterize variations in glucose tolerance.

Automated summary

This study proposes a mathematical formula, based on the Michaelis-Menten equation, that accurately predicts HbA(1c) levels. By using personalized Michaelis constant (K-m) values, the formula can predict HbA(1c) levels with a high degree of accuracy, making it a valuable tool for personalized diabetes management.