Publications (2)0 Total impact
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ABSTRACT: In the post-Diabetes Control and Complications Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications (EDIC) era of type 1 diabetes mellitus (T1DM) care, glycosylated hemoglobin (A1C) has enjoyed primacy as the clinical outcome variable (1). Metabolic control as defined by A1C, however, only defines approximately 25% of the risk of subsequent microvascular pathology (2) and, hence, other glycemic outcome variables are also being canvassed as being of potential significance. Transcription-regulating actions of glucose and the phenomenon of "metabolic memory" have recently become recognized (3,4). Simultaneously, ambulant continuous glucose monitoring (CGM) technologies have become available. The convergence of these factors has increased the interest in the impacts of fluctuations in glycemia, otherwise known as glycemic variation (GV). Initially, this interest was focused upon the effects of post-prandial glycemic excursions (5), but more recently, associations of GV and oxidative stress, microvascular pathology (6), and GV prediction associated with closedloop insulin delivery (7) have evolved. Notwithstanding this emerging interest in GV, there still remains a lack of consensus as to the importance of GV, in what circumstances it can be measured, and what GV metrics are best suited for various purposes. The aim of this review is to discuss these 3 key areas: Why measure GV? When can GV be meaningfully assessed?; How to measure to GV?.Pediatric endocrinology reviews: PER 08/2010; 7 Suppl 3:432-44.
Article: The minimum frequency of glucose measurements from which glycemic variation can be consistently assessed.[show abstract] [hide abstract]
ABSTRACT: While there has been much debate about the clinical importance of glycemic variation (GV), little attention has been directed to the properties of data sets from which it is measured. The purpose of this study is to assess the minimum frequency of glucose measurements from which GV can be consistently and meaningfully measured. Forty-eight 72 h continuous glucose monitoring traces from children with type 1 diabetes were assessed. Measures of GV included standard deviation (SD), mean amplitude of glycemic excursion (MAGE), and continuous overlapping net glycemic action (CONGA1-4). Measures of GV calculated using 5 min sampling were designated as the 100% or "best estimate" value. Calculations were then repeated for each patient using glucose values spaced at increasing intervals. For each of the specified sampling frequencies, the ratio (%) of the between-subject SD based on the reduced subset of data to the estimate of the SD based on the full 5 min sampling data set was calculated. As the interval between observations increased, so did the variability of the estimators of GV. Standard deviation exhibited the least systematic change at all measurement intervals, and MAGE exhibited the greatest systematic change. In patients with type 1 diabetes, GV as measured by SD or CONGA4, becomes unreliable if observations are more than 2-4 h apart, and estimates of MAGE become unreliable if glucose measurements are more than 1 h apart. MAGE is more unstable and prone to random measurement error than either SD or CONGA. The frequency of glycemic measurements is thus pivotal when selecting a parameter for measurement of GV.Journal of diabetes science and technology 01/2010; 4(6):1382-5.