Vadim V Klimontov1, Natalia E Myakina2. 1. Professor, Laboratory of Endocrinology, Scientific Institute of Clinical and Experimental Lymphology, Timakov Str. 2, Novosibirsk 630060, Russian Federation. Electronic address: klimontov@mail.ru. 2. MD, Laboratory of Endocrinology, Scientific Institute of Clinical and Experimental Lymphology, Novosibirsk, Russian Federation.
Abstract
AIM: O determine the applicability of glucose variability (GV) indices derived from continuous glucose monitoring (CGM) data for prediction of nocturnal hypoglycemia (NH) in elderly patients with type 2 diabetes treated with insulin. METHODS: We observed 83 insulin-treated in-patients, 65-80 years of age. Blinded CGM data for 176 nights were analyzed. Daytime (06:00-22:59) mean glucose, Standard Deviation (SD), 2-h Continuous Overlapping Net Glycemic Action (CONGA2) and Mean Absolute Glucose (MAG), pre-midnight (23:00-23:59) mean glucose, SD and MAG, 24-h Mean Amplitude of Glucose Excursions (MAGE), were estimated. Pre-midnight glucose trends were estimated as the absolute difference between glucose values at 23:00 and 23:59 (deltaG). Episode of interstitial glucose ≤70mg/dL observed from 0:00 to 5:59 was considered as NH. RESULTS: NH was present in 68 out of 176 24-h recordings (39%). Lower daytime mean glucose and CONGA2, and higher MAG values were found in patients with NH as compared to those without (p=0.0002, p=0.0001 and p=0.02, respectively). Pre-midnight mean glucose was lower, while pre-midnight deltaG was higher in patients with NH (p<0.0001 and p=0.02). Antecedent daytime hypoglycemia increased the risk of NH (p<0.0001). In logistic regression analysis, the combination of daytime MAG and pre-midnight mean glucose was the most reliable predictor of subsequent NH (accuracy 75.6%, p=0.0004). CONCLUSION: The analysis of CGM-derived GV parameters could improve prediction of NH in elderly patients with type 2 diabetes treated with insulin.
AIM: O determine the applicability of glucose variability (GV) indices derived from continuous glucose monitoring (CGM) data for prediction of nocturnal hypoglycemia (NH) in elderly patients with type 2 diabetes treated with insulin. METHODS: We observed 83 insulin-treated in-patients, 65-80 years of age. Blinded CGM data for 176 nights were analyzed. Daytime (06:00-22:59) mean glucose, Standard Deviation (SD), 2-h Continuous Overlapping Net Glycemic Action (CONGA2) and Mean Absolute Glucose (MAG), pre-midnight (23:00-23:59) mean glucose, SD and MAG, 24-h Mean Amplitude of Glucose Excursions (MAGE), were estimated. Pre-midnight glucose trends were estimated as the absolute difference between glucose values at 23:00 and 23:59 (deltaG). Episode of interstitial glucose ≤70mg/dL observed from 0:00 to 5:59 was considered as NH. RESULTS: NH was present in 68 out of 176 24-h recordings (39%). Lower daytime mean glucose and CONGA2, and higher MAG values were found in patients with NH as compared to those without (p=0.0002, p=0.0001 and p=0.02, respectively). Pre-midnight mean glucose was lower, while pre-midnight deltaG was higher in patients with NH (p<0.0001 and p=0.02). Antecedent daytime hypoglycemia increased the risk of NH (p<0.0001). In logistic regression analysis, the combination of daytime MAG and pre-midnight mean glucose was the most reliable predictor of subsequent NH (accuracy 75.6%, p=0.0004). CONCLUSION: The analysis of CGM-derived GV parameters could improve prediction of NH in elderly patients with type 2 diabetes treated with insulin.
Authors: Alexandra K Lee; Clare J Lee; Elbert S Huang; A Richey Sharrett; Josef Coresh; Elizabeth Selvin Journal: Diabetes Care Date: 2017-09-19 Impact factor: 19.112