Yizhou Zou1,2,3,4, Wanli Wang5, Dongmei Zheng6,7,8,9, Xu Hou10,11,12,13. 1. Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China. 2. Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. 3. Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China. 4. Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China. 5. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China. 6. Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China. zhengdongmei1971@163.com. 7. Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. zhengdongmei1971@163.com. 8. Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China. zhengdongmei1971@163.com. 9. Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China. zhengdongmei1971@163.com. 10. Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jing 5 Road, Jinan, 250021, China. 15153285655@163.com. 11. Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. 15153285655@163.com. 12. Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China. 15153285655@163.com. 13. Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China. 15153285655@163.com.
Abstract
BACKGROUND: There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established. METHODS: In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30). RESULTS: Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels. CONCLUSIONS: In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.
BACKGROUND: There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established. METHODS: In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30). RESULTS: Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels. CONCLUSIONS: In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabeticpatients undergoing CGM treatment.
Authors: Simona Frontoni; Paolo Di Bartolo; Angelo Avogaro; Emanuele Bosi; Giuseppe Paolisso; Antonio Ceriello Journal: Diabetes Res Clin Pract Date: 2013-09-25 Impact factor: 5.602
Authors: David B Sacks; Mark Arnold; George L Bakris; David E Bruns; Andrea Rita Horvath; M Sue Kirkman; Ake Lernmark; Boyd E Metzger; David M Nathan Journal: Diabetes Care Date: 2011-06 Impact factor: 19.112
Authors: Assam El-Osta; Daniella Brasacchio; Dachun Yao; Alessandro Pocai; Peter L Jones; Robert G Roeder; Mark E Cooper; Michael Brownlee Journal: J Exp Med Date: 2008-09-22 Impact factor: 14.307
Authors: Thomas Danne; Revital Nimri; Tadej Battelino; Richard M Bergenstal; Kelly L Close; J Hans DeVries; Satish Garg; Lutz Heinemann; Irl Hirsch; Stephanie A Amiel; Roy Beck; Emanuele Bosi; Bruce Buckingham; Claudio Cobelli; Eyal Dassau; Francis J Doyle; Simon Heller; Roman Hovorka; Weiping Jia; Tim Jones; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; David Maahs; Helen R Murphy; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; Banshi Saboo; Mauro Scharf; William V Tamborlane; Stuart A Weinzimer; Moshe Phillip Journal: Diabetes Care Date: 2017-12 Impact factor: 19.112
Authors: Catherine Gorst; Chun Shing Kwok; Saadia Aslam; Iain Buchan; Evangelos Kontopantelis; Phyo K Myint; Grant Heatlie; Yoon Loke; Martin K Rutter; Mamas A Mamas Journal: Diabetes Care Date: 2015-12 Impact factor: 19.112