Parameters of glycemic variability in continuous glucose monitoring as predictors of diabetes: A prospective evaluation in a non-diabetic general population

Abstract

Objectives: To prospectively examine the ability of some glycemic variability metrics from continuous glucose monitoring (CGM) to predict the development of diabetes in a non-diabetic population. Methods: A total of 497 non-diabetic patients from the AEGIS study were included. Participants used a CGM system (iPro2®) over a six-day period. The following parameters were analyzed: standard deviation (SD), coefficient of variation (CV) and mean amplitude of glucose excursion (MAGE). Six-years follow-up was performed. ROC curves were constructed to determine the predictive value of glycemic variability metrics. Sensitivity and specificity were calculated. Results: Of the 497 participants, 16 women (4.9%) and 9 men (5.2%) developed diabetes. Initial HbA1c and fasting glucose levels were significantly higher in the participants who ultimately developed diabetes. Glycemic variability metrics were also significantly higher in these subjects (SD: 18 vs. 13mg/dL; CV: 17 vs. 14%; MAGE: 36 vs. 27mg/dL; p<0.001 in all cases). SD showed the highest AUC (0.81), with a sensitivity of 80% and a specificity of 72% for a cut-off of 14.9mg/dL. AUCs were higher in men for all metrics. Conclusions: The metrics obtained by MCG, especially SD, are effective predictors of progression to type 2 diabetes in a non-diabetic population. These findings suggest that glycemic variability is useful for the early identification of subjects at a higher risk of developing diabetes.