Literature DB >> 25685275

Utility of different glycemic control metrics for optimizing management of diabetes.

Klaus-Dieter Kohnert1, Peter Heinke1, Lutz Vogt1, Eckhard Salzsieder1.   

Abstract

The benchmark for assessing quality of long-term glycemic control and adjustment of therapy is currently glycated hemoglobin (HbA1c). Despite its importance as an indicator for the development of diabetic complications, recent studies have revealed that this metric has some limitations; it conveys a rather complex message, which has to be taken into consideration for diabetes screening and treatment. On the basis of recent clinical trials, the relationship between HbA1c and cardiovascular outcomes in long-standing diabetes has been called into question. It becomes obvious that other surrogate and biomarkers are needed to better predict cardiovascular diabetes complications and assess efficiency of therapy. Glycated albumin, fructosamin, and 1,5-anhydroglucitol have received growing interest as alternative markers of glycemic control. In addition to measures of hyperglycemia, advanced glucose monitoring methods became available. An indispensible adjunct to HbA1c in routine diabetes care is self-monitoring of blood glucose. This monitoring method is now widely used, as it provides immediate feedback to patients on short-term changes, involving fasting, preprandial, and postprandial glucose levels. Beyond the traditional metrics, glycemic variability has been identified as a predictor of hypoglycemia, and it might also be implicated in the pathogenesis of vascular diabetes complications. Assessment of glycemic variability is thus important, but exact quantification requires frequently sampled glucose measurements. In order to optimize diabetes treatment, there is a need for both key metrics of glycemic control on a day-to-day basis and for more advanced, user-friendly monitoring methods. In addition to traditional discontinuous glucose testing, continuous glucose sensing has become a useful tool to reveal insufficient glycemic management. This new technology is particularly effective in patients with complicated diabetes and provides the opportunity to characterize glucose dynamics. Several continuous glucose monitoring (CGM) systems, which have shown usefulness in clinical practice, are presently on the market. They can broadly be divided into systems providing retrospective or real-time information on glucose patterns. The widespread clinical application of CGM is still hampered by the lack of generally accepted measures for assessment of glucose profiles and standardized reporting of glucose data. In this article, we will discuss advantages and limitations of various metrics for glycemic control as well as possibilities for evaluation of glucose data with the special focus on glycemic variability and application of CGM to improve individual diabetes management.

Entities:  

Keywords:  Continuous glucose monitoring; Diabetes mellitus; Glucose dynamics; Glycemic variability; Hemoglobin A1c; Markers of glycemic control; Postprandial glucose; Risk of hyperglycemia and hypoglycemia; Standardization

Year:  2015        PMID: 25685275      PMCID: PMC4317309          DOI: 10.4239/wjd.v6.i1.17

Source DB:  PubMed          Journal:  World J Diabetes        ISSN: 1948-9358


  100 in total

1.  Glycated albumin is a better glycemic indicator than glycated hemoglobin values in hemodialysis patients with diabetes: effect of anemia and erythropoietin injection.

Authors:  Masaaki Inaba; Senji Okuno; Yasuro Kumeda; Shinsuke Yamada; Yasuo Imanishi; Tsutomu Tabata; Mikio Okamura; Shigeki Okada; Tomoyuki Yamakawa; Eiji Ishimura; Yoshiki Nishizawa
Journal:  J Am Soc Nephrol       Date:  2007-01-31       Impact factor: 10.121

2.  The use of a computer program to calculate the mean amplitude of glycemic excursions.

Authors:  Gert Fritzsche; Klaus-Dieter Kohnert; Peter Heinke; Lutz Vogt; Eckhard Salzsieder
Journal:  Diabetes Technol Ther       Date:  2011-02-03       Impact factor: 6.118

3.  Serum fructosamine versus glycosylated hemoglobin as an index of glycemic control, hospitalization, and infection in diabetic hemodialysis patients.

Authors:  Neal Mittman; Brinda Desiraju; Irfan Fazil; Hiteshkumar Kapupara; Jyoti Chattopadhyay; Chinu M Jani; Morrell M Avram
Journal:  Kidney Int Suppl       Date:  2010-08       Impact factor: 10.545

4.  Evaluation of a new measure of blood glucose variability in diabetes.

Authors:  Boris P Kovatchev; Erik Otto; Daniel Cox; Linda Gonder-Frederick; William Clarke
Journal:  Diabetes Care       Date:  2006-11       Impact factor: 19.112

5.  Chronic hyperglycemia but not glucose variability determines HbA1c levels in well-controlled patients with type 2 diabetes.

Authors:  Klaus-Dieter Kohnert; Petra Augstein; Peter Heinke; Eckhard Zander; Karolina Peterson; Ernst-Joachim Freyse; Eckhard Salzsieder
Journal:  Diabetes Res Clin Pract       Date:  2007-02-28       Impact factor: 5.602

6.  Diabetes and biomarkers.

Authors:  Erica J Caveney; Oren J Cohen
Journal:  J Diabetes Sci Technol       Date:  2011-01-01

7.  Detrended fluctuation analysis is considered to be useful as a new indicator for short-term glucose complexity.

Authors:  Naomune Yamamoto; Yutaka Kubo; Kaya Ishizawa; Gwang Kim; Tatsumi Moriya; Toshikazu Yamanouchi; Kuniaki Otsuka
Journal:  Diabetes Technol Ther       Date:  2010-10       Impact factor: 6.118

8.  Glycated albumin is a better indicator for glucose excursion than glycated hemoglobin in type 1 and type 2 diabetes.

Authors:  Kazutomi Yoshiuchi; Munehide Matsuhisa; Naoto Katakami; Yoshihisa Nakatani; Kenya Sakamoto; Takaaki Matsuoka; Yutaka Umayahara; Keisuke Kosugi; Hideaki Kaneto; Yoshimitsu Yamasaki; Masatsugu Hori
Journal:  Endocr J       Date:  2008-04-30       Impact factor: 2.349

9.  Relationship of glycated albumin to blood glucose and HbA1c values and to retinopathy, nephropathy, and cardiovascular outcomes in the DCCT/EDIC study.

Authors:  David M Nathan; Paula McGee; Michael W Steffes; John M Lachin
Journal:  Diabetes       Date:  2013-08-29       Impact factor: 9.461

10.  Glycated hemoglobin measurement and prediction of cardiovascular disease.

Authors:  Emanuele Di Angelantonio; Pei Gao; Hassan Khan; Adam S Butterworth; David Wormser; Stephen Kaptoge; Sreenivasa Rao Kondapally Seshasai; Alex Thompson; Nadeem Sarwar; Peter Willeit; Paul M Ridker; Elizabeth L M Barr; Kay-Tee Khaw; Bruce M Psaty; Hermann Brenner; Beverley Balkau; Jacqueline M Dekker; Debbie A Lawlor; Makoto Daimon; Johann Willeit; Inger Njølstad; Aulikki Nissinen; Eric J Brunner; Lewis H Kuller; Jackie F Price; Johan Sundström; Matthew W Knuiman; Edith J M Feskens; W M M Verschuren; Nicholas Wald; Stephan J L Bakker; Peter H Whincup; Ian Ford; Uri Goldbourt; Agustín Gómez-de-la-Cámara; John Gallacher; Leon A Simons; Annika Rosengren; Susan E Sutherland; Cecilia Björkelund; Dan G Blazer; Sylvia Wassertheil-Smoller; Altan Onat; Alejandro Marín Ibañez; Edoardo Casiglia; J Wouter Jukema; Lara M Simpson; Simona Giampaoli; Børge G Nordestgaard; Randi Selmer; Patrik Wennberg; Jussi Kauhanen; Jukka T Salonen; Rachel Dankner; Elizabeth Barrett-Connor; Maryam Kavousi; Vilmundur Gudnason; Denis Evans; Robert B Wallace; Mary Cushman; Ralph B D'Agostino; Jason G Umans; Yutaka Kiyohara; Hidaeki Nakagawa; Shinichi Sato; Richard F Gillum; Aaron R Folsom; Yvonne T van der Schouw; Karel G Moons; Simon J Griffin; Naveed Sattar; Nicholas J Wareham; Elizabeth Selvin; Simon G Thompson; John Danesh
Journal:  JAMA       Date:  2014-03-26       Impact factor: 56.272

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  32 in total

1.  Comment on 'Exercise training decreases pancreatic fat content and improves beta cell function regardless of baseline glucose tolerance: a randomised controlled trial'.

Authors:  Payam Amini; Sevda Moharamzadeh
Journal:  Diabetologia       Date:  2018-11-06       Impact factor: 10.122

2.  The chicken or the egg? Does glycaemic control predict cognitive function or the other way around?

Authors:  Ithamar Ganmore; Michal Schnaider Beeri
Journal:  Diabetologia       Date:  2018-07-12       Impact factor: 10.122

Review 3.  Impact of HbA1c Testing at Point of Care on Diabetes Management.

Authors:  Oliver Schnell; J Benjamin Crocker; Jianping Weng
Journal:  J Diabetes Sci Technol       Date:  2016-11-27

4.  Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population.

Authors:  Gregory P Forlenza; Laura L Pyle; David M Maahs; Timothy C Dunn
Journal:  Pediatr Diabetes       Date:  2016-11-23       Impact factor: 4.866

5.  Investigation of glucose fluctuations by approaches of multi-scale analysis.

Authors:  Yunyun Lai; Zhengbo Zhang; Peiyao Li; Xiaoli Liu; YiXin Liu; Yi Xin; Weijun Gu
Journal:  Med Biol Eng Comput       Date:  2017-08-21       Impact factor: 2.602

6.  Variation in Diabetes Management: A National Assessment of Primary Care Providers.

Authors:  John W Peabody; Enrico de Belen; Jeffrey R Dahlen; Maria Czarina Acelajado; Mary T Tran; David R Paculdo
Journal:  J Diabetes Sci Technol       Date:  2019-07-07

7.  Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus.

Authors:  Channabasappa Shivaprasad; Yalamanchi Aiswarya; Shah Kejal; Atluri Sridevi; Biswas Anupam; Barure Ramdas; Kolla Gautham; Premchander Aarudhra
Journal:  J Diabetes Sci Technol       Date:  2019-07-07

8.  The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring.

Authors:  Pamela R Kushner; Davida F Kruger
Journal:  Clin Diabetes       Date:  2020-10

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.  Relationship between vitamin D and glycemic control in patients with type 2 diabetes mellitus.

Authors:  Serdar Olt
Journal:  Int J Clin Exp Med       Date:  2015-10-15
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