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Marcello Ciaccio

Marcello Ciaccio

University of Palermo, Italy

Title: Clinical use of glycated albumin as a non-traditional measure of glycemic status

Biography

Biography: Marcello Ciaccio

Abstract

Background: Glycated Albumin (GA) has been suggested as an additional or alternative biomarker that can circumvent some
of the limitation of HbA1c. The much shorter half-lives of albumin compared to hemoglobin makes it more responsive to
changes in glycemic status. Moreover, GA shows a stronger correlation with continuous glucose measurement over one to two
days than HbA1c, so it may reflect glycemic variability and glucose excursions more accurately. Although GA represents a
promising biomarker for the evaluation of glycemic status in both experimental and clinical settings, its introduction in clinical
practice requires further validation in relation to basic interpretative criteria and diagnostic accuracy.
Objectives: The objectives are to define reference limit of GA with a direct approach and to evaluate diagnostic accuracy of GA
in predicting diabetes in asymptomatic subjects at risk of suffering from diabetes.
Methods: One thousand thirty-four consecutive blood donors were recruited for reference range definition. Asymptomatic
subjects at risk for diabetes were recruited for diagnostic accuracy study. GA was measured by an enzymatic-colorimetric method.
Results: The calculated GA URL in blood donors was 14.5% (95% CI: 14.3–14.7). Among subjects at risk of diabetes, GA
median levels were 13.2% (IQR: 12.2–14.4). Eighteen subjects (5.4%) were classified as diabetics based on their HbA1c. GA was
significantly correlated with HbA1c (r=0.31; P<0.0001). According to ROC curve analysis, GA identified subjects with diabetes
with a sensitivity of 72.2% (95% CI: 46.5–90.3) and a specificity of 71.8% (95% CI: 66.5–76.7) (AUC: 0.80; 95% CI: 0.75–0.84;
P<0.0001) at the cut-off of 14%.
Conclusion: The knowledge of GA distribution in Healthy subjects is essential to promote its introduction in both research and
clinical practice. GA can also be considered a useful biomarker of glycemic status that can predict diabetes with high accuracy.