Cited by 154Diabetes Management and ResearchDiabetes Treatment and ManagementDiabetes, Cardiovascular Risks, and Lipoproteins

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Glucose Variability: A Review of Clinical Applications and Research Developments

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Rodbard, David·2018·Diabetes Technology & Therapeutics

DOI 10.1089/dia.2018.0092

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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.

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Authors: Rodbard, D.
Affiliations: Biomedical Informatics Consultants LLC
Corresponding author: Rodbard, David
ORCID: Rodbard, David
Venue: Diabetes Technology & Therapeutics
Publisher: SAGE Publications
Published: 2018-06
Volume: 20
Issue: 2_suppl
Pages: S25-S215
ISSN: 1520-9156 (ISSN-L)
1557-8593
DOI: 10.1089/dia.2018.0092
OpenAlex: W2809144128
Cited by: 154
References: 61
Citations by year: 2026: 9 · 2025: 14 · 2024: 21 · 2023: 25 · 2022: 24 · 2021: 29 · 2020: 12 · 2019: 14 · 2018: 4
FWCI: 11.82
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Diabetes Technology & Therapeutics (publishedVersion, cc-by)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords: glycemic variability, coefficient of variation, standard deviation, hypoglycemia risk, type 1 diabetes, type 2 diabetes, prediabetes, multiscale entropy, Fourier analysis, artificial pancreas, continuous glucose monitoring, glycemic control, postprandial glucose excursions, reference population
MeSH terms: Blood Glucose, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Insulin, Research, Blood Glucose Self-Monitoring

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Glucose Variability: A Review of Clinical Applications and Research Developments

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