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ORIGINAL ARTICLE
Year : 2017  |  Volume : 21  |  Issue : 3  |  Page : 410-414

Glycosylation gap in patients with diabetes with chronic kidney disease and healthy participants: A comparative study


Faculty of Medicine, Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N. Medical College, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

Correspondence Address:
Jamal Ahmad
Faculty of Medicine, Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N. Medical College, Aligarh Muslim University, Aligarh - 202 002, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijem.IJEM_2_17

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Aim: The aim of this study it to determine the level of glycosylation gap in patients with type 2 diabetes and its relation with kidney dysfunction. Materials and Methods: In this study, 150 individuals were enrolled (aged 20–75 year) and divided into three groups. Group 1 included 50 nondiabetic individuals who served as control. Group 2 included 50 patients with type 2 diabetes without chronic kidney disease (CKD), and in Group 3, there were 50 patients with type 2 diabetes with CKD. Glycated hemoglobin (HbA1c) and fructosamine (FA) were measured in all groups to determine the glycosylation gap (GG), predicted HbA1c, and mean blood glucose (MBG). GG is defined as the difference between measured HbA1c and HbA1c predicted from FA based on the population regression of HbA1c on FA. The variables were compared by correlation analysis. Results: Serum creatinine level was significantly high in patients with CKD (1.93 ± 0.99) as compared to patients with diabetes and control (0.891 ± 0.16; 0.912 ± 0.1), respectively. The study demonstrated a significant elevation in serum FA, measured HbA1c and predicted HbA1c, MBG in patients with diabetes with CKD as compared with those of without CKD, and controls. GG was found in healthy control (0.51 ± 0.78), patients with type 2 diabetes without CKD (0.62 ± 0.45), and patients with diabetes with CKD (1.0 ± 0.91), respectively. Conclusion: It is concluded that GG may be a useful clinical research tool for evaluating pathological source of variation in diabetes complications such as kidney disease.


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