Therapeutic advances in endocrinology and metabolism · 2020
Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus.
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Liu Hongxia, Yang Daizhi, Deng Hongrong, Xu Wen, Lv Jing, Zhou Yongwen, Luo Sihui, Zheng Xueying, Liang Hua, Yao Bin, Qiu Liling, Wang Funeng, Liu Fang, Yan Jinhua, Weng Jianping
Аннотация
AIMS: Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c).
METHODS: Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).
RESULTS: A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.
CONCLUSIONS: This study validated the GMI formula using the iPro™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.
METHODS: Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).
RESULTS: A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.
CONCLUSIONS: This study validated the GMI formula using the iPro™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.