Glycemic Variability among Older Adults with Type 2 DiabetesChin Voon Tong*, Nurain Mohd Noor, Masni Mohamad, Shalena Nesaratnam and Zanariah Hussein
Endocrine Unit, Department of Medicine, Putrajaya Hospital, Malaysia
- Corresponding Author:
- Chin Voon Tong
Endocrine Unit, Department of Medicine, Putrajaya Hospital
Pusat Pentadbiran Kerajaan Persekutuan Presinct 7, 62250 Putrajaya, Malaysia
E-mail: [email protected]
Received Date: February 16, 2016 Accepted Date: February 26, 2016 Published Date: February 29, 2016
Citation: Tong CV, Noor NM, Mohamad M, Nesaratnam S, Hussein Z (2016) Glycemic Variability among Older Adults with Type 2 Diabetes. J Diabetes Metab 7:652. doi:10.4172/2155-6156.1000652
Copyright: © 2016 Tong CV, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objective: The aim of this study was to evaluate Glycemic Variability (GV) among older adults with type 2 diabetes in a tertiary center (Putrajaya Hospital) using the Continuous Glucose Monitoring System (CGMS) and to compare the GV between patients with optimal versus suboptimal glycemic control. Research designs and methods: A total of 138 patients (69 with HbA1c<7% (53 mmol/mol) and another 69 with HbA1c ≥ 7% (53 mmol/mol) with type 2 diabetes age 65 and above were included in this study. All subjects underwent baseline clinical evaluation followed by monitoring using CGMS for six days. Data from CGMS was extracted to calculate GV using the Easy GV software available at www.easygv.co.uk. Results: The patients with HbA1c ≥ 7% (53 mmol/l) had significantly longer duration of diabetes, higher use of insulin, more micro-vascular complications, higher systolic blood pressure, higher fasting blood glucose, total cholesterol and triglyceride levels. The Mean Amplitude Glycemic Excursions (MAGE), Continuous Overlapping Net Glycemic Action (CONGA, Standard Deviation (SD), M-value, Average Daily Risk Ratio (ADDR), Lability Index (LI) , High Blood Glucose Index (HBGI), Mean of Daily Difference (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE) and Mean Absolute Glucose (MAG) were significantly higher in the group with HbA1c ≥ 7% (53 mmol/mol). The Low Blood Glucose Index (LBGI) [2.14(IQR 3.4) versus 2.11(2.6)] which represents risks of hypoglycemia was the only parameter which was not significantly different between both groups. Conclusions: We present the glycemic variability parameters for older adults with type 2 diabetes. Among this population, the risk of hypoglycemia is similar between those with optimal HbA1c versus their counterparts. This underscores the importance of looking out for hypoglycemia in every older individual with type 2 diabetes.