Literature DB >> 26207054

Glycemic Variability and Diabetes Complications: Does It Matter? Of Course It Does!

Irl B Hirsch1.   

Abstract

There is no argument that improving mean levels of glycemic control as judged by assays for glycated hemoglobin (HbA(1c)) reduces the risks of microvascular complications and cardiovascular disease events in patients with type 1 and type 2 diabetes. However, observations in some trials have suggested that targeting HbA(1c) to suggested targets may not always result in improved outcomes for people with long-standing type 2 diabetes. The reasons why the glycemic control strategies that primarily use HbA(1c) in these studies did not have predicted outcomes are not clear. Thus, controversy remains as to whether there are glycemic metrics beyond HbA(1c) that can be defined as effective measures that can be used in addition to HbA(1c) to help in assessing the risk of an individual developing diabetes complications. In this regard, the concept of "glycemic variability" (GV) is one metric that has attracted a lot of attention. GV can be simply defined as the degree to which a patient's blood glucose level fluctuates between high (peaks) and low (nadir) levels. The best and most precise way to assess GV is also one that is still debated. Thus, while there is universal agreement that HbA(1c) is the current gold standard for the primary clinical target, there is no consensus as to whether other proposed glycemic metrics hold promise to provide additional clinical data or whether there should be additional targets beyond HbA(1c). Therefore, given the current controversy, we provide a Point-Counterpoint debate on this issue. In the point narrative below, Dr. Hirsch provides his argument that fluctuations in blood glucose as assessed by GV metrics are deleterious and control of GV should be a primary treatment target. In the following counterpoint narrative, Dr. Bergenstal argues that there are better markers to assess the risk of diabetes than GV and provides his consideration of other concepts.
© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

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Year:  2015        PMID: 26207054     DOI: 10.2337/dc14-2898

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  79 in total

Review 1.  Near normal HbA1c with stable glucose homeostasis: the ultimate target/aim of diabetes therapy.

Authors:  L Monnier; C Colette; S Dejager; D R Owens
Journal:  Rev Endocr Metab Disord       Date:  2016-03       Impact factor: 6.514

2.  Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

Authors:  Enrico Longato; Giada Acciaroli; Andrea Facchinetti; Alberto Maran; Giovanni Sparacino
Journal:  J Diabetes Sci Technol       Date:  2019-03-31

3.  Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.

Authors:  Giada Acciaroli; Giovanni Sparacino; Liisa Hakaste; Andrea Facchinetti; Giorgio Maria Di Nunzio; Alessandro Palombit; Tiinamaija Tuomi; Rafael Gabriel; Jaime Aranda; Saturio Vega; Claudio Cobelli
Journal:  J Diabetes Sci Technol       Date:  2017-06-01

Review 4.  Positioning time in range in diabetes management.

Authors:  Andrew Advani
Journal:  Diabetologia       Date:  2019-11-07       Impact factor: 10.122

5.  Alternate glycemic markers reflect glycemic variability in continuous glucose monitoring in youth with prediabetes and type 2 diabetes.

Authors:  Christine L Chan; Laura Pyle; Megan M Kelsey; Lindsey Newnes; Amy Baumgartner; Philip S Zeitler; Kristen J Nadeau
Journal:  Pediatr Diabetes       Date:  2016-11-22       Impact factor: 4.866

6.  Exploring residual risk for diabetes and microvascular disease in the Diabetes Prevention Program Outcomes Study (DPPOS).

Authors:  L Perreault; Q Pan; V R Aroda; E Barrett-Connor; D Dabelea; S Dagogo-Jack; R F Hamman; S E Kahn; K J Mather; W C Knowler
Journal:  Diabet Med       Date:  2017-09-19       Impact factor: 4.359

7.  A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control.

Authors:  Michelle Nguyen; Julia Han; Elias K Spanakis; Boris P Kovatchev; David C Klonoff
Journal:  Diabetes Technol Ther       Date:  2020-03-04       Impact factor: 6.118

8.  Mean Levels and Variability in Affect, Diabetes Self-Care Behaviors, and Continuously Monitored Glucose: A Daily Study of Latinos With Type 2 Diabetes.

Authors:  Julie Wagner; Stephen Armeli; Howard Tennen; Angela Bermudez-Millan; Howard Wolpert; Rafael Pérez-Escamilla
Journal:  Psychosom Med       Date:  2017-09       Impact factor: 4.312

Review 9.  Glucose variability, HbA1c and microvascular complications.

Authors:  Jan Škrha; Jan Šoupal; Jan Škrha; Martin Prázný
Journal:  Rev Endocr Metab Disord       Date:  2016-03       Impact factor: 6.514

10.  Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

Authors:  Boris Kovatchev; Claudio Cobelli
Journal:  Diabetes Care       Date:  2016-04       Impact factor: 19.112

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