128-LB: Accessing Glucose Fluctuation—The Role of FPG, PPG, and HbA1c

Abstract

Background: Time in range (TIR) has been widely used clinically to investigate the glucose fluctuation in diabetes management. Studies have been done in exploring the relationship between TIR and HbA1c to estimate TIR without multi-invasive measurements. Beyond the univariate correlation model, this study incorporated fasting plasma glucose (FPG), postprandial glucose (PPG) obtained from glucose tolerance test, which can to some extent reflect glucose fluctuations, and revealed the advantage and the potential of the multi-variate linear or no-linear model to estimate TIR. Method: This study involved 399 Chinese T2DM patients. TIR were calculated from continuous glucose monitoring (CGM) data with a coverage of at least 70%. FPG, PPG were measured by oral or mixed-meal glucose tolerance test. Univariate, multivariate linear regression and non-linear random forest (RF) regression were used in regression analysis. Model performance was assessed using MAE, RMSE and MSE. Results: Regression analysis between glucose tolerance test data and CGM suggests that FPG were strongly linked to trough glucose while PPG were strongly correlated with peak glucose. Regression analysis revealed a stronger correlation between TIR and a triad of FPG, PPG, HbA1c than single HbA1c variable. And the performance of RF model is superior (correlation = 0.79, MAE = 14.77), among multivariate linear (0.73, 16.67) and univariate linear (0.62, 21.29) model. Conclusion: Our study shows that FPG and PPG are closely associated to trough and peak glucose in CGM data, as they provide a more complete picture of glucose fluctuations compared to HbA1c alone. The TIR estimation model that based on FPG, PPG, and HbA1c outperformed univariate model. However, the performance of RF based non-linear model suggests a more complex relationship between those parameters, indicating personalized models for different populations is needed in TIR estimating without long-term invasive glucose monitoring. Disclosure J. Ma: None. X. Su: None. L. Feng: Employee; Hua Medicine. B. Ding: None. R. Yan: None. R. Sun: None. Y. Zhang: None. Y. Duan: None. Funding Nanjing Municipal Health Commission (ZKX22038)