Results 271 to 280 of about 32,524 (285)
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Glycemic Variability: Looking Beyond the A1C
Diabetes Spectrum, 2012In Brief Hyperglycemia in diabetes is known to be associated with both micro- and macrovascular complications. However, multiple studies have raised the question of whether variation in glucose levels, in addition to average glucose, might be a risk factor for these complications.
Dace L. Trence, Tracy S. Tylee
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Glycemic variability and diabetes retinopathy: A missing link
Journal of Diabetes and its Complications, 2015Daily glucose variability, such as fasting plasma glucose fluctuation or postprandial hyperglycemia, has been proposed as contributors to diabetes-related macrovascular complications. However, its impacts on microvascular complications, such as diabetes retinopathy remain controversial.
Cherng-Ru Hsu +2 more
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The impact of glycemic variability on diabetic peripheral neuropathy
Endocrine, 2016Mean glucose values alone cannot explain the patterns of morbidity and mortality due to dysglycemia in diabetes. Development of continuous glucose monitoring systems has improved the analysis and interpretation of glycemic variability. The roles of glycemic components other than constant hyperglycemia in diabetic complications must be investigated ...
Heung Yong Jin +2 more
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Long-term Glycemic Variability: A Variable Glycemic Metric Entangled With Glycated Hemoglobin
American Journal of Kidney Diseases, 2023Mengyao, Tang, Sahir, Kalim
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Diabetic Kidney Disease: Is There a Role for Glycemic Variability?
Current Diabetes Reports, 2018Diabetes is the leading cause of kidney disease globally. Diabetic kidney disease (DKD) is a heterogeneous disorder manifested as albuminuria and/or decreasing GFR. Hyperglycemic burden is the major contributor to the development of DKD. In this article, we review the evidence for the contribution of glycemic variability and the pitfalls associated ...
Irl B. Hirsch, Savitha Subramanian
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Is glycemic variability important to assessing antidiabetes therapies? [PDF]
Current Diabetes Reports, 2006 Traditionally, statistical estimation of glycemic variability includes computing standard deviation of glucose readings or related statistics (eg, M value, mean amplitude of glucose excursions, and so forth). We advocate an alternative approach using risk measures of variability, which have substantial clinical and numerical advantages.
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HFPEF AND GLYCEMIC VARIABILITY: GHIDD TRIAL
European Heart Journal SupplementsAbstract Background Recently the guidelines of European Association of Preventive Cardiology reported that the prevalence of diabetes mellitus in patients with diastolic heart failure (HFpEF) is around 31%.
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Continuous Glucose Monitoring and Glycemic Variability
2018Glycemic variability refers to the unstable state of glucose levels fluctuating from its peaks to valleys. With a deep understanding of glycemic variability, more and more evidence shows that glycemic variability contributes to the development of chronic complications of diabetes mellitus. Glycemic variability has become an important aspect of glycemic
J. Zhou, W. Jia
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Measures of Glycemic Variability and Links with Psychological Functioning
Current Diabetes Reports, 2010The goal of this article is to review the recent literature on measures of glycemic variability, links between glycemic variability and psychological functioning, and methods for examining these links. A number of commonly used measures of glycemic variability are reviewed and compared, including recently proposed methods.
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Diabetes Technology & Therapeutics, 2009
There are a large number of measures of glycemic variability, including standard deviation (SD), percentage coefficient of variation (%CV), interquartile range (IQR), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), and continuous overlapping net glycemic action over an n-hour period (CONGA(n)).
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There are a large number of measures of glycemic variability, including standard deviation (SD), percentage coefficient of variation (%CV), interquartile range (IQR), mean amplitude of glucose excursion (MAGE), mean of daily differences (MODD), and continuous overlapping net glycemic action over an n-hour period (CONGA(n)).
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