Yu-Ming Hu1,2, Li-Hua Zhao3, Xiu-Lin Zhang4, Hong-Li Cai5, Hai-Yan Huang3, Feng Xu3, Tong Chen4, Xue-Qin Wang3, Ai-Song Guo2, Jian-An Li6, Jian-Bin Su7. 1. Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, 210029, Nanjing, China. 2. Department of Rehabilitation, The Affiliated Hospital of Nantong University, No. 20 Xishi Road, 226001, Nantong, China. 3. Department of Endocrinology, The Second Affiliated Hospital of Nantong University, No. 6 North Hai-er-xiang Road, 226001, Nantong, China. 4. Department of Clinical Laboratory, The Second Affiliated Hospital of Nantong University, No. 6 North Hai-er-xiang Road, 226001, Nantong, China. 5. Department of Geriatrics, The Second Affiliated Hospital of Nantong University, No. 6 North Hai-er-xiang Road, 226001, Nantong, China. 6. Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, 210029, Nanjing, China. lijianan@carm.org.cn. 7. Department of Endocrinology, The Second Affiliated Hospital of Nantong University, No. 6 North Hai-er-xiang Road, 226001, Nantong, China. sujbzjx@163.com.
Abstract
PURPOSE: Diabetic peripheral neuropathy (DPN), a common microvascular complication of diabetes, is linked to glycaemic derangements. Glycaemic variability, as a pattern of glycaemic derangements, is a key risk factor for diabetic complications. We investigated the association of glycaemic variability with DPN in a large-scale sample of type 2 diabetic patients. METHODS: In this cross-sectional study, we enrolled 982 type 2 diabetic patients who were screened for DPN and monitored by a continuous glucose monitoring (CGM) system between February 2011 and January 2017. Multiple glycaemic variability parameters, including the mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), standard deviation of glucose (SD), and 24-h mean glucose (24-h MG), were calculated from glucose profiles obtained from CGM. Other possible risks for DPN were also examined. RESULTS: Of the recruited type 2 diabetic patients, 20.1% (n = 197) presented with DPN, and these patients also had a higher MAGE, MODD, SD, and 24-h MG than patients without DPN (p < 0.001). Using univariate and multiple logistic regression analyses, MAGE and conventional risks including diabetic duration, HOMA-IR, and hemoglobin A1c (HbA1c) were found to be independent contributors to DPN, and the corresponding odds ratios (95% confidence interval) were 4.57 (3.48-6.01), 1.10 (1.03-1.17), 1.24 (1.09-1.41), and 1.33 (1.15-1.53), respectively. Receiver operating characteristic analysis indicated that the optimal MAGE cutoff value for predicting DPN was 4.60 mmol/L; the corresponding sensitivity was 64.47%, and the specificity was 75.54%. CONCLUSIONS: In addition to conventional risks including diabetic duration, HOMA-IR and HbA1c, increased glycaemic variability assessed by MAGE is a significant independent contributor to DPN in type 2 diabetic patients.
PURPOSE:Diabetic peripheral neuropathy (DPN), a common microvascular complication of diabetes, is linked to glycaemic derangements. Glycaemic variability, as a pattern of glycaemic derangements, is a key risk factor for diabetic complications. We investigated the association of glycaemic variability with DPN in a large-scale sample of type 2 diabeticpatients. METHODS: In this cross-sectional study, we enrolled 982 type 2 diabeticpatients who were screened for DPN and monitored by a continuous glucose monitoring (CGM) system between February 2011 and January 2017. Multiple glycaemic variability parameters, including the mean amplitude of glycaemic excursions (MAGE), mean of daily differences (MODD), standard deviation of glucose (SD), and 24-h mean glucose (24-h MG), were calculated from glucose profiles obtained from CGM. Other possible risks for DPN were also examined. RESULTS: Of the recruited type 2 diabeticpatients, 20.1% (n = 197) presented with DPN, and these patients also had a higher MAGE, MODD, SD, and 24-h MG than patients without DPN (p < 0.001). Using univariate and multiple logistic regression analyses, MAGE and conventional risks including diabetic duration, HOMA-IR, and hemoglobin A1c (HbA1c) were found to be independent contributors to DPN, and the corresponding odds ratios (95% confidence interval) were 4.57 (3.48-6.01), 1.10 (1.03-1.17), 1.24 (1.09-1.41), and 1.33 (1.15-1.53), respectively. Receiver operating characteristic analysis indicated that the optimal MAGE cutoff value for predicting DPN was 4.60 mmol/L; the corresponding sensitivity was 64.47%, and the specificity was 75.54%. CONCLUSIONS: In addition to conventional risks including diabetic duration, HOMA-IR and HbA1c, increased glycaemic variability assessed by MAGE is a significant independent contributor to DPN in type 2 diabeticpatients.
Authors: Christian Herder; Imke Schamarek; Bettina Nowotny; Maren Carstensen-Kirberg; Klaus Straßburger; Peter Nowotny; Julia M Kannenberg; Alexander Strom; Sonja Püttgen; Karsten Müssig; Julia Szendroedi; Michael Roden; Dan Ziegler Journal: Heart Date: 2016-08-01 Impact factor: 5.994
Authors: Laura Mayeda; Ronit Katz; Iram Ahmad; Nisha Bansal; Zona Batacchi; Irl B Hirsch; Nicole Robinson; Dace L Trence; Leila Zelnick; Ian H de Boer Journal: BMJ Open Diabetes Res Care Date: 2020-01