Glycoalbumin is the best indicator for glycemic variability assessed by continuous glucose monitoring

S. Suh; J. Joung; S. Park; Y. Cho; S. Jin; S. Kim; H. Kim; J. Bae; S. Kim; J. Chung; Y. Min; M. Lee; M. Lee; K. Kim; J. Kim

Aims: We investigated the interrelationship between HbA1c, fructosamine, glycoalbumin (GA), 1,5-anhydroglucitol (1,5-AG) with glycemic variability data from continuous glucose monitoring system (CGMS).

Methods: Seventy diabetic patients (mean age of 54 years including seven type 1 diabetes mellitus subjects) were enrolled, and all wore a CGMS for 72 hours. For the parameters of glucose excursion, mean glucose, standard deviation (SD) of glucose, the area under the curve for glucose levels >180 mg/dl (AUC-180), and continuous overlapping net glycemic action calculated (CONGA), mean of the daily differences (MODD) and mean post meal maximum glucose (MPMG) were calculated from the CGMS data.

Results: Spearman’s correlation coefficients were significant between all glycemic markers and various parameters of glucose excursion. Among them, GA displayed highest correlation (r between 0.581–0.684) with all the parameters (i.e. AUC-180, SD, MAGE, MODD, CONGA-1, CONGA-24, MPMG) except for mean glucose. Mean glucose was best correlated with fructosamine (r=0.676, P< 0.01). In patients with HbA1c < 7.5% (n=36), 1,5-AG and GA showed better correlation with glycemic parameters for variability than HbA1c and fructosamine. In patients with HbA1c ≧7.5% (n=34), GA showed a statistically significant correlation with just AUC-180 and MODD among glycemic variability parameters. Furthermore, GA was significantly correlated to only SD among 3 independent parameters (mean glucose, SD, AUC-180) in multiple regression analysis but the other glycemic markers were significantly correlated with mean glucose.

Conclusion: Our results suggest that GA was the most representative maker for glycemic variability. 1–5 AG was also able to reflect glucose excursions only in well-controlled patients.

Spearman’s correlation coefficient between glycemic markers and CGMS data

Table 1 Spearman’s correlation coefficient between glycemic markers and CGMS data
N=70	HbAlc	Fructosamine	Glycoalbumin	1,5-AG
Mean glucose	0.600**	0.676**	0.636**	−0.493**
AUC-180	0.608**	0.628**	0.630**	−0.550**
SD	0.549**	0.567**	0.646**	−0.559**
MAGE	0.505**	0.526**	0.608**	−0.540**
MODD	0.525**	0.594**	0.684**	−0.501**
CONGA-1	0.463**	0.570**	0.581**	−0.455**
CONGA-24	0.451**	0.553**	0.583**	−0.473**
MPMG	0.540**	0.602**	0.656**	−0.565**
*P<0.01, P<0.05 (two-tailed).

Declaration of interest: The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Funding: This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

Endocrine Abstracts

P504