The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Glucose Variability: A Review of Clinical Applications and Research Developments
Open accessAbstract
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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“Despite its demonstrated value, clinical utilization of CGM data has remained suboptimal.”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [74]
In the citing paper
“Although time constraints and reimbursement issues are clearly obstacles, clinician inexperience in data interpretation and lack of standardization software for visualization of CGM data have also played a role (74) .”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [74]
Citations
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Multiscale Entropy Analysis of Complex Physiologic Time Series
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Glucose variability is associated with intensive care unit mortality*
Hermanides, J., Vriesendorp, T. M., Bosman, R. J., Zandstra, D. F., Hoekstra, J. B. L., & DeVries, J. H. (2010) · Critical Care Medicine · doi:10.1097/ccm.0b013e3181cc4be9 · Cited by 446
Glucose Variability; Does It Matter?
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Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes
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Cited by
Sorted by citations
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,237
Self-citationGlucose‐Sensitive Hydrogel Optical Fibers Functionalized with Phenylboronic Acid
Yetisen, A. K., Jiang, N., Fallahi, A., Montelongo, Y., Ruiz‐Esparza, G. U., Tamayol, A., Zhang, Y. S., Mahmood, I., Yang, S., Kim, K. S., et al. (2017) · Advanced Materials · doi:10.1002/adma.201606380 · Cited by 311
Review of methods for detecting glycemic disorders
Bergman, M., Abdul‐Ghani, M. A., DeFronzo, R. A., Manco, M., Sesti, G., Fiorentino, T. V., Ceriello, A., Rhee, M. K., Phillips, L. S., Chung, S. T., et al. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108233 · Cited by 210
Ribet, F., Stemme, G., & Roxhed, N. (2018) · Biomedical Microdevices · doi:10.1007/s10544-018-0349-6 · Cited by 184
Glucose Variability: How Does It Work?
Klimontov, V. V., Saik, O., & Korbut, A. I. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22157783 · Cited by 133
Metadata
- 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
- 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
- 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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