The application of simple metrics in the assessment of glycaemic variability

医学 血糖性 糖尿病 期限(时间) 内科学 风险因素 变异系数 连续血糖监测 2型糖尿病 心脏病学 内分泌学 统计 数学 物理 量子力学
作者
Louis Monnier,C Colette,David R. Owens
出处
期刊:Diabetes & Metabolism [Elsevier BV]
卷期号:44 (4): 313-319 被引量:101
标识
DOI:10.1016/j.diabet.2018.02.008
摘要

The assessment of glycaemic variability (GV) remains a subject of debate with many indices proposed to represent either short- (acute glucose fluctuations) or long-term GV (variations of HbA1c). For the assessment of short-term within-day GV, the coefficient of variation for glucose (%CV) defined as the standard deviation adjusted on the 24-h mean glucose concentration is easy to perform and with a threshold of 36%, recently adopted by the international consensus on use of continuous glucose monitoring, separating stable from labile glycaemic states. More complex metrics such as the Low Blood Glucose Index (LBGI) or High Blood Glucose Index (HBGI) allow the risk of hypo or hyperglycaemic episodes, respectively to be assessed although in clinical practice its application is limited due to the need for more complex computation. This also applies to other indices of short-term intraday GV including the mean amplitude of glycemic excursions (MAGE), Shlichtkrull's M-value and CONGA. GV is important clinically as exaggerated glucose fluctuations are associated with an enhanced risk of adverse cardiovascular outcomes due primarily to hypoglycaemia. In contrast, there is at present no compelling evidence that elevated short-term GV is an independent risk factor of microvascular complications of diabetes. Concerning long-term GV there are numerous studies supporting its association with an enhanced risk of cardiovascular events. However, this association raises the question as to whether the impact of long-term variability is not simply the consequence of repeated exposure to short-term GV or ambient chronic hyperglycaemia. The renewed emphasis on glucose monitoring with the introduction of continuous glucose monitoring technologies can benefit from the introduction and application of simple metrics for describing GV along with supporting recommendations.
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