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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Cites
Multiscale Entropy Analysis of Complex Physiologic Time Series
Costa, M., Goldberger, A. L., & Peng, C.-K. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.068102 · Cited by 3,207
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069
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?
Siegelaar, S. E., Holleman, F., Hoekstra, J. B. L., & DeVries, J. H. (2010) · Endocrine Reviews · doi:10.1210/er.2009-0021 · Cited by 409
Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes
Monnier, L., Colette, C., Wojtusciszyn, A., Dejager, S., Renard, E., Molinari, N., & Owens, D. R. (2017) · Diabetes Care · doi:10.2337/dc16-1769 · Cited by 390
Rodbard, D. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0132 · Cited by 324
Self-citationBergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307
Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes
Kovatchev, B. P., & Cobelli, C. (2016) · Diabetes Care · doi:10.2337/dc15-2035 · Cited by 276
Symmetrization of the Blood Glucose Measurement Scale and Its Applications
Kovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (1997) · Diabetes Care · doi:10.2337/diacare.20.11.1655 · Cited by 258
Statistical Tools to Analyze Continuous Glucose Monitor Data
Clarke, W. L., & Kovatchev, B. P. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0138 · Cited by 248
Flagged first, then by citations
Research works printed but not linked: 4
- Cameron FJ, , Baghurst PA, , Rodbard D: Assessing glycemic variation: why, when and how?. Pediatr Endocrinol Rev, 2010; 7 Suppl 3:432–444.
- Strange P, , Shi L, , Miller MF: Glucose Energy Spectrum as a function of dysglycemia progression. Abstract/Poster 268. European Association for the Study of Diabetes, Annual Meeting, Lisbon, Portugal September 2017. Diabetologia, 60(Suppl 1):S126.
- Gómez AM Muñoz OM Marin A et al.: Different indexes of glycemic variability as identifiers of patients with risk of hypoglycemia in type 2 diabetes mellitus. J Diabetes Sci Technol 2018; 1–9. http://journals.sagepub.com/doi/pdf/10.1177/1932296818758105. · DOI
- Rodbard D: Potential role of computers in clinical investigation and management of diabetes mellitus. Diabetes Care, 1988; 11 Suppl 1:54–61.
Cited by
Sorted by citations
Items 101–154 of 154
Matabuena, M., Sartini, J., & Gude, F. (2026) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02748-2 · Cited by 11
Zhang, S., Zhang, C., Chen, J., Deng, F., Wu, Z., Zhu, D., Chen, F., Duan, Y., Zhao, Y., & Hou, K. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1171399 · Cited by 4
Mathematical and informational tools for classifying blood glucose signals - a pilot study
Amadio, A., Rey, A., Legnani, W. E., Blesa, M. G., Bonini, C., & Otero, D. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129071 · Cited by 4
Glucose monitoring in diabetes: from clinical studies to real‐world practice
Sagar, R. C., Abbas, A., & Ajjan, R. (2019) · Practical Diabetes · doi:10.1002/pdi.2215 · Cited by 4
Syafaah, L., Basuki, S., Sumadi, F. D. S., Faruq, A., & Purnomo, M. H. (2020) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v9i6.2387 · Cited by 4
Beyond A1C: A Practical Approach to Interpreting and Optimizing Continuous Glucose Data in Youth
Al-Gadi, I., Menon, S., Lyons, S. K., & DeSalvo, D. J. (2021) · Diabetes Spectrum · doi:10.2337/ds20-0095 · Cited by 4
Orexin cells efficiently decode blood glucose dynamics to drive adaptive behaviorPreprint
Viskaitis, P., Tesmer, A. L., Karnani, M., Arnold, M., Donegan, D., Bracey, E. F., Grujic, N., Peleg‐Raibstein, D., & Burdakov, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.14.488310 · Cited by 4
Sugimoto, H., Hironaka, K., Nakamura, T., Yamada, T., Miura, H., Otowa‐Suematsu, N., Fujii, M., Hirota, Y., Sakaguchi, K., Ogawa, W., et al. (2023) · medRxiv · doi:10.1101/2023.09.18.23295711 · Cited by 4
Landau, J., Tiram, Y., & Ilan, Y. (2025) · Biomedicines · doi:10.3390/biomedicines13112645 · Cited by 4
Chen, L., Sun, J., Kong, S., Tan, Q., Liu, X., Cheng, Y., Yang, F., Yin, X., Zhang, C., Liu, J., et al. (2024) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2024.111706 · Cited by 4
Li, X., Wang, L., Liang, X., Ling, Y., Tang, Y., Meng, J., Cheng, H., Lyu, J., & Wang, Y. (2025) · Geriatrics and gerontology international/Geriatrics & gerontology international · doi:10.1111/ggi.70200 · Cited by 3
Zheng, F., Jalbert, M., Forbes, F., Bonnet, S., Wojtusciszyn, A., Lablanche, S., & Benhamou, P. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0250 · Cited by 3
A novel classification of glucose profile in pregnancy based on continuous glucose monitoring data
Inayama, Y., Yamanoi, K., Shitanaka, S., Ogura, J., Ohara, T., Sakai, M., Suzuki, H., Kishimoto, I., Tsunenari, T., & Suginami, K. (2021) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.14677 · Cited by 3
Gan, Y., Chen, M., Kong, L., Wu, J., Pu, Y. D., Wang, X., Zhou, J., Fan, X., Xiong, Z., & Qi, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1216897 · Cited by 4
Chernikova, N. A., Kamynina, L. L., & As, A. (2019) · Meditsinskiy sovet = Medical Council · doi:10.21518/2079-701x-2019-4-38-43 · Cited by 3
Johnson, J., Franklin, V. L., Shepherd, A. J., Chau, G., Keen, K., Lennon, S., Leveridge, M., Maclean, K., Nicol, J., Phillipson, V., et al. (2025) · Pediatric Diabetes · doi:10.1155/pedi/5545726 · Cited by 2
Richardson, R. R. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251390822 · Cited by 2
Ju, J.-W., Lee, J., Joo, S., Kim, J. E., Lee, S., Cho, Y. J., Jeon, Y., & Nam, K. (2024) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2024.05.008 · Cited by 2
Zhou, L., Sun, Q., Wang, Y., Zhou, J., & Zhao, X. (2024) · Endocrine · doi:10.1007/s12020-024-04047-0 · Cited by 2
Fatulla, P., Imberg, H., Isaksson, S. S., Hirsch, I. B., Mårtensson, J., Liljebäck, H., Heise, T., & Lind, M. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0540 · Cited by 2
Monnier, L., & Colette, C. (2022) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2021.12.006 · Cited by 2
Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments
Satuluri, V. K. R. R., & Ponnusamy, V. (2024) · Diagnostics · doi:10.3390/diagnostics14040436 · Cited by 2
Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus
Gao, X., Li, H., Yu, Y., Huai, X., Feng, B., & Song, J. (2023) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s410761 · Cited by 1
Tan, J. C., Khalil, M. A. M., Avari, P., & Leelarathna, L. (2025) · Frontiers in Nephrology · doi:10.3389/fneph.2025.1630597 · Cited by 1
Lee, C., & Lee, S. (2026) · Exercise Science · doi:10.15857/ksep.2025.00542 · Cited by 1
Beyond the Coefficient of Variation: Reframing Glycemic Variability as Dispersion and Volatility
Richardson, R. R. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261441490 · Cited by 1
Maiorino, M. I., Gesualdo, C., Angelino, S., Rossi, S., Martino, N. D., Iodice, C. M., Longo, M., Scappaticcio, L., Bellastella, G., Simonelli, F., et al. (2026) · Acta Diabetologica · doi:10.1007/s00592-026-02711-7 · Cited by 1
Valero, P., Costa, S. M. B., Rudge, M. V. C., Silva, K., van der Beek, E. M., & Sobrevía, L. (2026) · Handbook of experimental pharmacology · doi:10.1007/164_2026_823 · Cited by 1
Wen, S., Li, H., & Tao, R. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02866-3 · Cited by 1
Dawnbringer, J., Hill, H., Lundgren, M., Catrina, S., Caballero-Corbalán, J., Cederblad, L., Carlsson, P., & Espes, D. (2024) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2024-004350 · Cited by 1
Sebastián‐Valles, F., Martín, J. A. A., Alfonso, J. M., Jiménez‐Díaz, J., Alday, I. H., Navas-Moreno, V., Joya, T. A., García, M., Gómez, G. L. R., Hierro, J. G., et al. (2024) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70020 · Cited by 1
The indicators of glucose variability
Nam, K., & Lee, S. (2021) · Journal of Anesthesia · doi:10.1007/s00540-021-02945-5 · Cited by 1
Lal, A., Mishra, A. K., & Trivedi, N. (2019) · Endocrine Practice · doi:10.4158/1934-2403-25.8.859 · Cited by 1
Klimontov, V. V., Koroleva, E. A., & Fazullina, O. N. (2018) · International Multiconference on Bioinformatics of Genome Regulation and Structure / Systems Biology (BGRS/SB) · doi:10.1109/csgb.2018.8544728
Glycemic Variation and Cancer Occurrence—A New Paradigm
Sharma, R. (2019) · The Cancer Journal · doi:10.1097/ppo.0000000000000388
Capturing glucose variability in patients with diabetes
Laight, D. W. (2020) · Prescriber · doi:10.1002/psb.1864
Ruan, Y., Zhong, J., Chen, R., Zhang, Z., Liu, D., Sun, J. M., & Chen, H. (2021) · Research Square · doi:10.21203/rs.3.rs-144260/v1
Moshenets, K. I., & Перцева, Н. О. (2021) · INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) · doi:10.22141/2224-0721.17.7.2021.244970
Zhang, C., Angulo, J., Sotil, K., Acho-Carranza, E., Manrique, H. A., Vélez, M., & Leey, J. (2022) · Boletín de la Sociedad Peruana de Medicina Interna/Revista de la Sociedad Peruana de Medicina Interna · doi:10.36393/spmi.v35i2.666 · Cited by 2
Time in range: new parameter to evaluate blood glucose control
Flekač, M. (2022) · Vnitřní lékařství · doi:10.36290/vnl.2022.066
USE OF SGLT2 INHIBITORS IN TYPE 1 DIABETES: EXPERIENCE FROM A PORTUGUESE TERTIARY CENTER
Lavrador, M., Araújo, B., Melo, M., Barros, L., & Paiva, I. (2023) · Revista Portuguesa de Endocrinologia Diabetes e Metabolismo · doi:10.26497/ao220037
Pérez-Gualdrón, C. E., Ordoñez, J. S. F., Bernal, N. B., Pacheco, A. F. P., Chaparro, N. J. M., & Cadena, J. D. C. (2023) · Revista Colombiana de Endocrinología Diabetes & Metabolismo · doi:10.53853/encr.10.2.793
Urbanschitz, T., Huber, L., Tichy, A., Burgener, I. A., & Zeugswetter, F. K. (2024) · Research in Veterinary Science · doi:10.1016/j.rvsc.2024.105156
Rusanov, A. N., & Родионова, Т. И. (2024) · Saratov Journal of Medical Scientific Research · doi:10.15275/ssmj2002171
Rusanov, A. N., & Родионова, Т. И. (2024) · Saratov Medical Journal · doi:10.15275/sarmj.2024.0203
Lin, C.-C., Li, C.-I., Liu, C.-S., Lin, C.-H., Yang, S.-Y., & Li, T.-C. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-05182-w
A Physics-Informed Glucose-Insulin Neural Network Model for Glucose Prediction
Wang, W., Pei, R., Li, D., Liu, S., Geng, Y., & Wang, S. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010140
Wang, J., Wang, L.-L., Yang, Y.-M., Tan, H.-Q., & Zhu, J. (2024) · Figshare · doi:10.6084/m9.figshare.25770422
Morar-Stan, A., Dobrotă, L., Radutu, A., & Domnariu, C. D. (2025) · medRxiv · doi:10.64898/2025.12.19.25342708
Jiao, Y., & Ma, J. (2026) · British Journal of Hospital Medicine · doi:10.31083/bjhm54062
Hutchinson, K. A., Calancie, O. G., Wang, P., Awad, S., Campbell, R. J., & Bal, M. (2026) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2026.06.008
Cao, S., Wang, H., Zhu, Y., Shan, C., & Chen, W. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1901259
Flabe, A. H., Zabeen, B., Amirat, S., & Luo, J. (2026) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70347
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
