Article Sidebar
Views pdf: 87
ASSESSMENT OF GLYCEMIC VARIABILITY USING CONTINUOUS GLUCOSE MONITORING IN PATIENTS WITH TÍP 2 DIABETES MELLITUS AT XANH PON HOSPITAL
Main Article Content
Abstract
Objective: To investigate glycemic variability using CGM devices to provide comprehensive data for individualized treatment. Methods: A cross-sectional descriptive study was conducted on 30 patients with type 2 diabetes at Xanh Pon General Hospital. All patients underwent 14-day continuous glucose monitoring using the Freestyle Libre device. Recorded CGM metrics included AG (average glucose), GMI (glucose management indicator), GV (glycemic variability), TIR (time in range), TAR (time above range), and TBR (time below range). Results: The average age was 71.25 ± 10.05 years; 83.3% had at least one chronic comorbidity. Only 30% of patients met AG and GMI targets, and 10% met all three targets: GMI, GV, and TIR. The mean TIR was 53.33 ± 20.46%, with 23.3% meeting the general target. Among patients aged ≥65, 85% had TIR > 50%. TAR and TBR were 40.82% and 1.53%, respectively. Conclusion: Most patients did not meet glucose control targets based on CGM metrics. CGM is a valuable tool in managing type 2 diabetes, especially in elderly patients with multiple comorbidities.
Article Details
Keywords
Type 2 diabetes, Continuous glucose monitoring (CGM), Glycemic variability
References
2. Dungan KM, Buse JB, Largay J, Kelly MM, Button EA, Kato S, et al. 1, 5-anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes Care. (2006);29(6):1214-9.
3. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. (2019);42(8):1593-603.
4. Beck RW, Bergenstal RM, Cheng P, Kollman C, Carlson AL, Johnson ML, et al. The Relationships Between Time in Range, Hyperglycemia Metrics, and HbA1c. Journal of Diabetes Science and Technology. (2019);13(4): 614-26.
5. Vigersky RA, McMahon C. The Relationship of Hemoglobin A1C to Time-in-Range in Patients with Diabetes. Diabetes Technol Ther. (2019);21(2):81-5.
6. Dillmann C, Amoura L, Fall Mostaine F, Coste A, Bounyar L, Kessler L. Feasibility of Real-Time Continuous Glucose Monitoring Telemetry System in an Inpatient Diabetes Unit: A Pilot Study. Journal of Diabetes Science and Technology. (2022);16(4):955-61.
7. Yapanis M, James S, Craig ME, O’Neal D, Ekinci EI. Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring. The Journal of Clinical Endocrinology & Metabolism. (2022); 107(6):e2221-e36.
8. Hu Y-m, Zhao L-h, Zhang X-l, Cai H-l, Huang H-y, Xu F, et al. Association of glycaemic variability evaluated by continuous glucose monitoring with diabetic peripheral neuropathy in type 2 diabetic patients. Endocrine. (2018); 60(2):292-300.
9. Spanakis EK, Levitt DL, Siddiqui T, Singh LG, Pinault L, Sorkin J, et al. The Effect of Continuous Glucose Monitoring in Preventing Inpatient Hypoglycemia in General Wards: The Glucose Telemetry System. Journal of Diabetes Science and Technology. 2018;12(1):20-5.
10. Singh LG, Levitt DL, Satyarengga M, Pinault L, Zhan M, Sorkin JD, et al. Continuous Glucose Monitoring in General Wards for Prevention of Hypoglycemia: Results From the Glucose Telemetry System Pilot Study. Journal of Diabetes Science and Technology. 2020;14(4):783-90.