Development of a glycated hemoglobin prediction model using continuous glucose monitoring metrics in pediatric type 1 diabetes mellitus: insights into average glucose and recent glycemic trends.

PURPOSE: Given the limitations of glycated hemoglobin (HbA1c), continuous glucose monitoring (CGM) metrics have been proposed as complementary indicators of glycemic control. This study evaluated the association between CGM metrics and HbA1c and developed HbA1c prediction models in Korean pediatric patients with type 1 diabetes mellitus (T1DM).

METHODS

We retrospectively analyzed CGM data from 85 patients aged 2-18 years using real-time CGM systems (G6 or G7). CGM records over 12 weeks were segmented into 5 intervals (0-2, 0-4, 4-8, 8-12, and 0-12 weeks) prior to HbA1c measurement. Metrics included time-in-range (TIR), time-above-range (TAR), time-below-range, time-in-tight-range, coefficient of variation, and average glucose. HbA1c prediction models were constructed using ridge regression and validated in a separate test dataset.

RESULTS

TIR consistently showed the strongest negative association with HbA1c, while TAR and average glucose showed the strongest positive associations. Among all intervals, 0- to 4-week CGM data demonstrated the strongest relationship with HbA1c (all P<0.05). Average glucose achieved the best explanatory power among all metrics (R²=0.83, Akaike Information Criterion=84.34), and prediction models incorporating average glucose and TAR yielded the lowest mean squared error (0.15) and highest R² (0.83), with robust results in the test dataset.

CONCLUSION

Short-term CGM metrics, particularly average glucose during the 0-4 weeks preceding HbA1c testing, are strong predictors of HbA1c. These findings support the clinical utility of recent CGM data in optimizing individualized glycemic management in pediatric patients with T1DM.