Use of glycated albumin for the identification of diabetes in subjects from northeast China

BACKGROUND: Metabolic memory is important for the diagnosis and treatment of diabetes in the early stage, and in maintaining blood glucose concentrations within the normal range. The clinical diagnosis of diabetes mellitus is currently made using fasting plasma glucose, 2 h-plasma glucose (2h-PG) during a 75 g oral glucose tolerance test, and hemoglobin A1c (HbA1c) level. However, the fasting plasma glucose test requires fasting, which is a barrier to screening, and reproducibility of the 2h-PG level is poor. HbA1c is affected by a shortened red blood cell lifespan. In patients with anemia and hemoglobinopathies, the measured HbA1c levels may be inaccurate. Compared with HbA1c, glycated albumin (GA) is characterized by more rapid and greater changes, and can be used to diagnose new-onset diabetes especially if urgent early treatment is required, for example in gestational diabetes. In this study, we provided cutoff values for GA and evaluated its utility as a screening and diagnostic tool for diabetes in a large high-risk group study. AIM: To evaluate the utility of GA in identifying subjects with diabetes in northeast China, and to assess the diagnostic accuracy of the proposed GA cutoff in the diagnosis of diabetes mellitus. METHODS: This cross-sectional study included 1935 subjects, with suspected diabetes or in high-risk groups, from 2014 to 2015 in the Second Affiliated Hospital of Harbin Medical University (Harbin, China). The use of GA to identify diabetes was investigated using the area under the receiver operating characteristic curve (AUC). The GA cutoffs were derived from different 2h-PG values with hemoglobin A1c cutoffs used as a calibration curve. RESULTS: The GA cutoff for the diagnosis of diabetes mellitus was 15.15% from the receiver operating characteristic (ROC) curve. ROC analysis demonstrated that GA was an efficient marker for detecting diabetes, with an AUC of 90.3%. CONCLUSION: Our study supports the use of GA as a biomarker for the diagnosis of diabetes.