Jingyi Lu1, Xiaojing Ma1, Lei Zhang1, Yifei Mo1, Wei Lu1, Wei Zhu1, Yuqian Bao1, Weiping Jia1, Jian Zhou2. 1. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China. 2. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China. Electronic address: zhoujian@sjtu.edu.cn.
Abstract
AIMS: Although there is a linear relationship between time in range (TIR) and hemoglobin A1c (HbA1c), a great variability of calculated TIR values for a given HbA1c, and vice versa, has been reported. Whether glycemic variability accounts for part of this variability remains to be investigated. METHODS: The data of continuous glucose monitoring (CGM) from 2559 patients with type 2 diabetes was analyzed. Glycemic variability was assessed by glucose coefficient of variation (CV), and estimated HbA1C (eHbA1c) was calculated from mean sensor glucose. RESULTS: A strong correlation between TIR and eHbA1c (r = -0.908) was observed. The slopes of regression lines fitted to TIR values as a function of eHbA1c differed significantly for individuals with varying degrees of CV, especially when patients were stratified as stable (CV < 36%) or unstable (CV ≥ 36%) glucose levels. For patients in the high- or low-range of eHbA1c, there was a high variability of TIR values according to CV. CONCLUSIONS: Glycemic variability significantly mediates the relationship between TIR and eHbA1c, and should be taken into consideration when setting an individualized target of TIR.
AIMS: Although there is a linear relationship between time in range (TIR) and hemoglobin A1c (HbA1c), a great variability of calculated TIR values for a given HbA1c, and vice versa, has been reported. Whether glycemic variability accounts for part of this variability remains to be investigated. METHODS: The data of continuous glucose monitoring (CGM) from 2559 patients with type 2 diabetes was analyzed. Glycemic variability was assessed by glucose coefficient of variation (CV), and estimated HbA1C (eHbA1c) was calculated from mean sensor glucose. RESULTS: A strong correlation between TIR and eHbA1c (r = -0.908) was observed. The slopes of regression lines fitted to TIR values as a function of eHbA1c differed significantly for individuals with varying degrees of CV, especially when patients were stratified as stable (CV < 36%) or unstable (CV ≥ 36%) glucose levels. For patients in the high- or low-range of eHbA1c, there was a high variability of TIR values according to CV. CONCLUSIONS: Glycemic variability significantly mediates the relationship between TIR and eHbA1c, and should be taken into consideration when setting an individualized target of TIR.
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