Literature DB >> 29916742

Glucose Variability: A Review of Clinical Applications and Research Developments.

David Rodbard1.   

Abstract

Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

Entities:  

Keywords:  Ambulatory glucose profile; Continuous glucose monitoring; Glycemic variability; Hyperglycemia; Hypoglycemia; Time series analysis

Mesh:

Substances:

Year:  2018        PMID: 29916742     DOI: 10.1089/dia.2018.0092

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  21 in total

1.  Glycaemic variation is a predictor of all-cause mortality in the Veteran Affairs Diabetes Trial.

Authors:  Jin J Zhou; Juraj Koska; Gideon Bahn; Peter Reaven
Journal:  Diab Vasc Dis Res       Date:  2019-03       Impact factor: 3.291

2.  Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems.

Authors:  Arsalan Shahid; Dana M Lewis
Journal:  Nutrients       Date:  2022-05-02       Impact factor: 6.706

Review 3.  Review of methods for detecting glycemic disorders.

Authors:  Michael Bergman; Muhammad Abdul-Ghani; Ralph A DeFronzo; Melania Manco; Giorgio Sesti; Teresa Vanessa Fiorentino; Antonio Ceriello; Mary Rhee; Lawrence S Phillips; Stephanie Chung; Celeste Cravalho; Ram Jagannathan; Louis Monnier; Claude Colette; David Owens; Cristina Bianchi; Stefano Del Prato; Mariana P Monteiro; João Sérgio Neves; Jose Luiz Medina; Maria Paula Macedo; Rogério Tavares Ribeiro; João Filipe Raposo; Brenda Dorcely; Nouran Ibrahim; Martin Buysschaert
Journal:  Diabetes Res Clin Pract       Date:  2020-06-01       Impact factor: 5.602

4.  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
Journal:  J Diabetes Sci Technol       Date:  2021-05-18

Review 5.  Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.

Authors:  Tadej Battelino; Thomas Danne; Richard M Bergenstal; Stephanie A Amiel; Roy Beck; Torben Biester; Emanuele Bosi; Bruce A Buckingham; William T Cefalu; Kelly L Close; Claudio Cobelli; Eyal Dassau; J Hans DeVries; Kim C Donaghue; Klemen Dovc; Francis J Doyle; Satish Garg; George Grunberger; Simon Heller; Lutz Heinemann; Irl B Hirsch; Roman Hovorka; Weiping Jia; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; Brian Levine; Alexander Mayorov; Chantal Mathieu; Helen R Murphy; Revital Nimri; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; David Rodbard; Banshi Saboo; Desmond Schatz; Keaton Stoner; Tatsuiko Urakami; Stuart A Weinzimer; Moshe Phillip
Journal:  Diabetes Care       Date:  2019-06-08       Impact factor: 19.112

6.  Beyond A1C: A Practical Approach to Interpreting and Optimizing Continuous Glucose Data in Youth.

Authors:  Iman Al-Gadi; Sruthi Menon; Sarah K Lyons; Daniel J DeSalvo
Journal:  Diabetes Spectr       Date:  2021-05-25

7.  Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches.

Authors:  Brinnae Bent; Peter J Cho; Maria Henriquez; April Wittmann; Connie Thacker; Mark Feinglos; Matthew J Crowley; Jessilyn P Dunn
Journal:  NPJ Digit Med       Date:  2021-06-02

8.  Association of Body Fat Percentage with Time in Range Generated by Continuous Glucose Monitoring during Continuous Subcutaneous Insulin Infusion Therapy in Type 2 Diabetes.

Authors:  Yuting Ruan; Jiana Zhong; Rongping Chen; Zhen Zhang; Dixing Liu; Jia Sun; Hong Chen
Journal:  J Diabetes Res       Date:  2021-05-28       Impact factor: 4.011

9.  Greater daily glucose variability and lower time in range assessed with continuous glucose monitoring are associated with greater aortic stiffness: The Maastricht Study.

Authors:  Yuri D Foreman; William P T M van Doorn; Nicolaas C Schaper; Marleen M J van Greevenbroek; Carla J H van der Kallen; Ronald M A Henry; Annemarie Koster; Simone J P M Eussen; Anke Wesselius; Koen D Reesink; Miranda T Schram; Pieter C Dagnelie; Abraham A Kroon; Martijn C G J Brouwers; Coen D A Stehouwer
Journal:  Diabetologia       Date:  2021-05-15       Impact factor: 10.122

10.  Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes.

Authors:  Rachel Brandt; Minsun Park; Kristen Wroblewski; Lauretta Quinn; Esra Tasali; Ali Cinar
Journal:  Diabetologia       Date:  2021-06-17       Impact factor: 10.460

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