Searchable abstracts of presentations at key conferences in endocrinology
Endocrine Abstracts (2012) 29 P504

ICEECE2012 Poster Presentations Diabetes (248 abstracts)

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

Samsung Medical Center, Seoul, Republic of Korea.

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
Mean glucose0.600**0.676**0.636**−0.493**
**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.

Volume 29

15th International & 14th European Congress of Endocrinology

European Society of Endocrinology 

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