Literature DB >> 28484994

Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes.

K-D Kohnert1, P Heinke2, L Vogt3, P Augstein2,4, A Thomas5, E Salzsieder2.   

Abstract

AIMS: The dynamical structure of glucose fluctuation has largely been disregarded in the contemporary management of diabetes.
METHODS: In a retrospective study of patients with diabetes, we evaluated the relationship between glucose dynamics, antihyperglycemic therapy, glucose variability, and glucose exposure, while taking into account potential determinants of the complexity index. We used multiscale entropy (MSE) analysis of continuous glucose monitoring data from 131 subjects with type 1 (n = 18), type 2 diabetes (n = 102), and 11 nondiabetic control subjects. We compared the MSE complexity index derived from the glucose time series among the treatment groups, after adjusting for sex, age, diabetes duration, body mass index, and carbohydrate intake.
RESULTS: In type 2 diabetic patients who were on a diet or insulin regimen with/without oral agents, the MSE index was significantly lower than in nondiabetic subjects but was lowest in the type 1 diabetes group (p < 0.001). The decline in the MSE complexity across the treatment groups correlated with increasing glucose variability and glucose exposure. Statistically, significant correlations existed between higher MSE complexity indices and better glycemic control. In multivariate regression analysis, the antidiabetic therapy was the most powerful predictor of the MSE (β = -0.940 ± 0.242, R 2 = 0.306, p < 0.001), whereas the potential confounders failed to contribute.
CONCLUSIONS: The loss of dynamical complexity in glucose homeostasis correlates more closely with therapy modalities and glucose variability than with clinical measures of glycemia. Thus, targeting the glucoregulatory system by adequate therapeutic interventions may protect against progressive worsening of diabetes control.

Entities:  

Keywords:  Antihyperglycemic therapy; Continuous glucose monitoring; Diabetes control; Glucose dynamics; Multiscale entropy analysis

Mesh:

Substances:

Year:  2017        PMID: 28484994     DOI: 10.1007/s40618-017-0682-2

Source DB:  PubMed          Journal:  J Endocrinol Invest        ISSN: 0391-4097            Impact factor:   4.256


  21 in total

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Journal:  Phys Rev Lett       Date:  2002-07-19       Impact factor: 9.161

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.  Glycemic variability: measurement and utility in clinical medicine and research--one viewpoint.

Authors:  David Rodbard
Journal:  Diabetes Technol Ther       Date:  2011-08-04       Impact factor: 6.118

4.  Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles.

Authors:  N A Khovanova; I A Khovanov; L Sbano; F Griffiths; T A Holt
Journal:  Comput Methods Programs Biomed       Date:  2012-12-17       Impact factor: 5.428

Review 5.  Utility of different glycemic control metrics for optimizing management of diabetes.

Authors:  Klaus-Dieter Kohnert; Peter Heinke; Lutz Vogt; Eckhard Salzsieder
Journal:  World J Diabetes       Date:  2015-02-15

6.  Dynamical glucometry: use of multiscale entropy analysis in diabetes.

Authors:  Madalena D Costa; Teresa Henriques; Medha N Munshi; Alissa R Segal; Ary L Goldberger
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Review 7.  Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition: A Review.

Authors:  Felicity Thomas; Matthew Signal; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2015-06-30

8.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application.

Authors:  Boris P Kovatchev; William L Clarke; Marc Breton; Kenneth Brayman; Anthony McCall
Journal:  Diabetes Technol Ther       Date:  2005-12       Impact factor: 6.118

9.  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

10.  Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data.

Authors:  Jin-Long Chen; Pin-Fan Chen; Hung-Ming Wang
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2014-07-15       Impact factor: 3.619

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  6 in total

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3.  Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes.

Authors:  Petra Augstein; Peter Heinke; Lutz Vogt; Klaus-Dieter Kohnert; Eckhard Salzsieder
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4.  Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients.

Authors:  Klaus-Dieter Kohnert; Peter Heinke; Lutz Vogt; Petra Augstein; Eckhard Salzsieder
Journal:  Front Physiol       Date:  2018-09-06       Impact factor: 4.566

5.  Decreased complexity of glucose dynamics in diabetes in rhesus monkeys.

Authors:  Richard Raubertas; Jeremy Beech; Wendy Watson; Steven Fox; Scott Tiesma; David B Gilberto; Ashleigh Bone; Patricia A Rebbeck; Liza T Gantert; Stacey Conarello; Walter Knapp; Tasha Gray; Larry Handt; Cai Li
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6.  An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals.

Authors:  Xinzheng Dong; Chang Chen; Qingshan Geng; Zhixin Cao; Xiaoyan Chen; Jinxiang Lin; Yu Jin; Zhaozhi Zhang; Yan Shi; Xiaohua Douglas Zhang
Journal:  Entropy (Basel)       Date:  2019-03-12       Impact factor: 2.524

  6 in total

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