GLYCEMIC VARIABILITY INDICATORS BY CONTINUOUS GLUCOSE MONITORING AND HYPOGLYCEMIA RISK IN CHILDREN AND YOUNG ADULTS WITH TYPE 1 DIABETES

Abstract

Background: Hypoglycemia is the main barrier to optimizing insulin treatment in people with type 1 diabetes, different risk factors have been studied and one of the mechanisms involved is glycemic variability. Aims: To assess hypoglycemia risk showed by the glycemic variability metrics: coefficient of variation (CV), continuous overlapping net glycemic action (CONGA), low blood glucose index (LBGI) and lability index (LI) in a group of children, adolescents, and young adults with type 1 diabetes. Methods: A group of 31 subjects with type 1 diabetes under 25 years were evaluated, data from professional continuous glucose monitoring records were studied, glycemic variability metrics, including CV, CONGA24, LBGI, and LI, were calculated. Correlation with percentage time of hypoglycemia under 54mg% was assessed. Multiple linear regression models were generated, univariate and multivariate analysis was also performed, area under curve of glycemic variability metrics was obtain from ROC curves analysis, the optimal cutoff points were calculated. Results: The average age was 14.5 years with a range of 5 to 24 years. The mean duration of diabetes was 6.6 ± 3.7 years, and the glycated hemoglobin mean value was 8.2% ± 2.1%. The average percentage of time for hypoglycemia alert was 4.23% (0.2 to 13%), while for clinically significant hypoglycemia was 4.55% (0 to 17.1%). LBGI with R= 0.913 and CV with R= 0.735 (p < 0.0001) expressed the highest degree of correlation with percentage of time in hypoglycemia, furthermore, after multivariate analysis, they showed the highest predictive load. CV expressed an AUC of 0.97, while LBGI was 0.95, both statistically significant (p<0.0005). The cut-off point for CV of 38% had sensitivity of 93% and specificity of 74% in detection of time in hypoglycemia under 54mg/dl, and the cut-off point for LBGI of 5.4 expressed sensitivity of 87% and specificity of 94%. Conclusions: Glycemic variability metrics studied outperformed the clinical variables as indicators of risk of hypoglycemia, and those with the greatest predictive power of hypoglycemia risk were LBGI and CV above 38%.