Glucose Variability: A Review of Clinical Applications and Research Developments

Rodbard, David

doi:10.1089/dia.2018.0092

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

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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Clinical utilization of CGM data has remained suboptimal.
“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]
Time constraints, reimbursement issues, clinician inexperience in data interpretation, and lack of standardization software for visualization of CGM data have played a role in suboptimal CGM utilization.
“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]

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 · see citing works
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 · see citing works
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 436 · see citing works
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 · see citing works
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 · see citing works
Interpretation of Continuous Glucose Monitoring Data: Glycemic Variability and Quality of Glycemic Control
Rodbard, D. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0132 · Cited by 314 · see citing works
Self-citation
Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the Ambulatory Glucose Profile (AGP)
Bergenstal, 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 · see citing works
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Kovatchev, B. P., & Cobelli, C. (2016) · Diabetes Care · doi:10.2337/dc15-2035 · Cited by 260 · see citing works
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 · see citing works
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 · see citing works
Glucose Variability: Where It Is Important and How to Measure It
DeVries, J. H. (2013) · Diabetes · doi:10.2337/db12-1610 · Cited by 236 · see citing works
New and Improved Methods to Characterize Glycemic Variability Using Continuous Glucose Monitoring
Rodbard, D. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0015 · Cited by 230 · see citing works
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Glucose Variability
Service, F. J. (2013) · Diabetes · doi:10.2337/db12-1396 · Cited by 222 · see citing works
Minimizing Hypoglycemia in Diabetes
International Hypoglycaemia Study Group (2015) · Diabetes Care · doi:10.2337/dc15-0279 · Cited by 213 · see citing works
Outcome measures for artificial pancreas clinical trials: a consensus report
Maahs, D. M., Buckingham, B. A., Castle, J. R., Cinar, A., Damiano, E. R., Dassau, E., DeVries, J. H., Doyle, F. J., Griffen, S. C., Haidar, A., et al. (2016) · Diabetes Care · doi:10.2337/dc15-2716 · Cited by 211 · see citing works
Continuous glucose monitoring: a consensus conference of the American Association of Clinical Endocrinologists and American College of Endocrinology
Fonseca, V. A., Grunberger, G., Anhalt, H., Bailey, T. S., Blevins, T., Garg, S. K., Handelsman, Y., Hirsch, I. B., Orzeck, E. A., Roberts, V. L., et al. (2016) · Endocrine Practice · doi:10.4158/ep161392.cs · Cited by 195 · see citing works
Characterizing Glucose Exposure for Individuals with Normal Glucose Tolerance Using Continuous Glucose Monitoring and Ambulatory Glucose Profile Analysis
Mazze, R. S., Strock, E. S., Wesley, D., Borgman, S. K., Morgan, B., Bergenstal, R. M., & Cuddihy, R. M. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0293 · Cited by 183 · see citing works
Ambulatory Glucose Profile: Representation of Verified Self-Monitored Blood Glucose Data
Mazze, R. S., Lucido, D., Langer, O., Hartmann, K., & Rodbard, D. (1987) · Diabetes Care · doi:10.2337/diacare.10.1.111 · Cited by 144 · see citing works
Self-citation
Long-term Instability of Fasting Plasma Glucose, a Novel Predictor of Cardiovascular Mortality in Elderly Patients With Non–Insulin-Dependent Diabetes Mellitus
Muggeo, M., Verlato, G., Bonora, E., Zoppini, G., Corbellini, M., & de Marco, R. (1997) · Circulation · doi:10.1161/01.cir.96.6.1750 · Cited by 142 · see citing works
Measurements of Glucose Control
Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987) · Diabetes Care · doi:10.2337/diacare.10.2.225 · Cited by 140 · see citing works
A method for assessing quality of control from glucose profiles
Hill, N. R., Hindmarsh, P. C., Stevens, R., STRATTON, I. M., Lévy, J., & Matthews, D. R. (2007) · Diabetic Medicine · doi:10.1111/j.1464-5491.2007.02119.x · Cited by 134 · see citing works
Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring
Xing, D., Kollman, C., Beck, R. W., Tamborlane, W. V., Laffel, L., Buckingham, B. A., Wilson, D. M., Weinzimer, S., Fiallo-Scharer, R., & Ruedy for the Juvenile Diabetes Research Foundation Continuo, K. J. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2010.0156 · Cited by 131 · see citing works
Clinical Interpretation of Indices of Quality of Glycemic Control and Glycemic Variability
Rodbard, D. (2011) · Postgraduate Medicine · doi:10.3810/pgm.2011.07.2310 · Cited by 127 · see citing works
Self-citation
American Association Of Clinical Endocrinologists And American College Of Endocrinology 2016 Outpatient Glucose Monitoring Consensus Statement
Bailey, T. S., Grunberger, G., Bode, B. W., Handelsman, Y., Hirsch, I. B., Jovanovič, L., Roberts, V. L., Rodbard, D., Tamborlane, W. V., & Walsh, J. S. A. (2016) · Endocrine Practice · doi:10.4158/ep151124.cs · Cited by 126 · see citing works
Self-citation
Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted from glycemic variability data
Qu, Y., Jacober, S. J., Zhang, Q., Wolka, L. L., & DeVries, J. H. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0099 · Cited by 126 · see citing works
Beyond HbA1c: comparing glycemic variability and glycemic indices in predicting hypoglycemia in type 1 and type 2 diabetes
Rama Chandran, S., Tay, W. L., Lye, W. K., Lim, L. L., Ratnasingam, J., Tan, A. T. B., & S.L. Gardner, D. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0388 · Cited by 114 · see citing works
Calculating the Mean Amplitude of Glycemic Excursion from Continuous Glucose Monitoring Data: An Automated Algorithm
Baghurst, P. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2010.0090 · Cited by 110 · see citing works
Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data
Peyser, T. A., Balo, A. K., Buckingham, B. A., Hirsch, I. B., & Garcia, A. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0187 · Cited by 90 · see citing works
Hypo- and Hyperglycemia in Relation to the Mean, Standard Deviation, Coefficient of Variation, and Nature of the Glucose Distribution
Rodbard, D. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0062 · Cited by 88 · see citing works
Self-citation
A Structured Self-Monitoring of Blood Glucose Approach in Type 2 Diabetes Encourages More Frequent, Intensive, and Effective Physician Interventions: Results from the STeP Study
Polonsky, W. H., Fisher, L., Schikman, C. H., Hinnen, D. A., Parkin, C. G., Jelsovsky, Z., Axel-Schweitzer, M., Petersen, B., & Wagner, R. S. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0073 · Cited by 85 · see citing works
Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring
El-Laboudi, A. H., Godsland, I. F., Johnston, D. G., & Oliver, N. S. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0146 · Cited by 83 · see citing works
Improved Postprandial Glycemic Control with Faster-Acting Insulin Aspart in Patients with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion
Bode, B. W., Johnson, J. A., Hyveled, L., Tamer, S. C., & Demissie, M. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0350 · Cited by 72 · see citing works
Characterizing Blood Glucose Variability Using New Metrics with Continuous Glucose Monitoring Data
Marling, C. R., Shubrook, J. H., Vernier, S. J., Wiley, M. T., & Schwartz, F. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500408 · Cited by 71 · see citing works
Personal Computer Programs to Assist with Self-Monitoring of Blood Glucose and Self-Adjustment of Insulin Dosage
Pernick, N. L., & Rodbard, D. (1986) · Diabetes Care · doi:10.2337/diacare.9.1.61 · Cited by 65 · see citing works
Self-citation
Dynamical glucometry: Use of multiscale entropy analysis in diabetes
Costa, M. D., Henriques, T. S., Munshi, M. N., Segal, A. R., & Goldberger, A. L. (2014) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4894537 · Cited by 64 · see citing works
Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with “Risk Indices”
Rodbard, D. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0416 · Cited by 58 · see citing works
Self-citation
Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
Augstein, P., Heinke, P., Vogt, L., Vogt, R., Rackow, C., Kohnert, K., & Salzsieder, E. (2015) · BMC Endocrine Disorders · doi:10.1186/s12902-015-0019-0 · Cited by 51 · see citing works
Glucose Variability Indices in Type 1 Diabetes: Parsimonious Set of Indices Revealed by Sparse Principal Component Analysis
Fabris, C., Facchinetti, A., Sparacino, G., Zanon, M., Guerra, S., Maran, A., & Cobelli, C. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0252 · Cited by 48 · see citing works
Display of Glucose Distributions by Date, Time of Day, and Day of Week: New and Improved Methods
Rodbard, D. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300619 · Cited by 47 · see citing works
Self-citation
Evaluation of the Mean Absolute Glucose Change as a Measure of Glycemic Variability Using Continuous Glucose Monitoring Data
Kohnert, K., Heinke, P., Fritzsche, G., Vogt, L., Augstein, P., & Salzsieder, E. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0303 · Cited by 47 · see citing works
The Minimum Frequency of Glucose Measurements from Which Glycemic Variation Can Be Consistently Assessed
Baghurst, P., Rodbard, D., & Cameron, F. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400612 · Cited by 46 · see citing works
Self-citation
A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas
Hirsch, I. B., Balo, A. K., Sayer, K., Garcia, A., Buckingham, B. A., & Peyser, T. A. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0080 · Cited by 38 · see citing works
Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis
Fabris, C., Facchinetti, A., Fico, G., Sambo, F., Arredondo, M. T., & Cobelli, C. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815596173 · Cited by 37 · see citing works
Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data
Chen, J., Chen, P.-F., & Wang, H. (2014) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00108.2014 · Cited by 33 · see citing works
Use of Fourier Models for Analysis and Interpretation of Continuous Glucose Monitoring Glucose Profiles
Miller, M., & Strange, P. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100506 · Cited by 29 · see citing works
A Consensus Perceived Glycemic Variability Metric
Marling, C. R., Struble, N., Bunescu, R., Shubrook, J. H., & Schwartz, F. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700409 · Cited by 28 · see citing works
Effects of Sitagliptin on 24-H Glycemic Changes in Japanese Patients with Type 2 Diabetes Assessed Using Continuous Glucose Monitoring
Mori, Y., Taniguchi, Y., Matsuura, K., Sezaki, K., Yokoyama, J., & Utsunomiya, K. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0025 · Cited by 27 · see citing works
A Semilogarithmic Scale for Glucose Provides a Balanced View of Hyperglycemia and Hypoglycemia
Rodbard, D. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300620 · Cited by 26 · see citing works
Self-citation
Diurnal Glucose Patterns of Exenatide Once Weekly: A 1-Year Study Using Continuous Glucose Monitoring with Ambulatory Glucose Profile Analysis
Mazze, R., Strock, E. S., Morgan, B., Wesley, D., Bergenstal, R. M., & Cuddihy, R. M. (2009) · Endocrine Practice · doi:10.4158/ep09046.orr · Cited by 25 · see citing works
Exploring the Frequency Domain of Continuous Glucose Monitoring Signals to Improve Characterization of Glucose Variability and of Diabetic Profiles
Fico, G., Hernández, L., Cancela, J., Isabel, M., Facchinetti, A., Fabris, C., Gabriel, R., Cobelli, C., & Arredondo, M. T. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816685717 · Cited by 22 · see citing works
Poincaré Plot Quantification for Assessing Glucose Variability from Continuous Glucose Monitoring Systems and a New Risk Marker for Hypoglycemia: Application to Type 1 Diabetes Patients Switching to Continuous Subcutaneous Insulin Infusion
Crenier, L. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0241 · Cited by 22 · see citing works
Yet Another Glucose Variability Index: Time for a Paradigm Change?
Cobelli, C., & Facchinetti, A. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0397 · Cited by 20 · see citing works
Poor Agreement of Computerized Calculators for Mean Amplitude of Glycemic Excursions
Sechterberger, M. K., Luijf, Y. M., & DeVries, J. H. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0138 · Cited by 19 · see citing works
Continuous Intraperitoneal Insulin Infusion Versus Subcutaneous Insulin Therapy in the Treatment of Type 1 Diabetes: Effects on Glycemic Variability
van Dijk, P. R., Groenier, K. H., DeVries, J. H., Gans, R. O. B., Kleefstra, N., Bilo, H. J. G., & Logtenberg, S. J. J. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0001 · Cited by 19 · see citing works
Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats
Zhang, X. D., Pechter, D., Yang, L. M., Ping, X., Yao, Z., Zhang, R., Shen, X., Li, N. X., Connick, J. H., Nawrocki, A. R., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182810 · Cited by 18 · see citing works
“Glucose-at-a-Glance”
Henriques, T. S., Munshi, M. N., Segal, A. R., Costa, M. D., & Goldberger, A. L. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814524095 · Cited by 16 · see citing works
Standardization Versus Customization of Glucose Reporting
Rodbard, D. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0116 · Cited by 15 · see citing works
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New Approaches to Display of Self-Monitoring of Blood Glucose Data
Rodbard, D. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300515 · Cited by 13 · see citing works
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Fonseca, V. A., & Grunberger, G. (2017) · Endocrine Practice · doi:10.4158/1934-2403-23.5.629 · Cited by 12 · see citing works
Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data
Garcia, A., Balo, A. K., Buckingham, B. A., Hirsch, I. B., & Peyser, T. A. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0188 · Cited by 7 · see citing works
Indices for Assessment of the Quality of Glycemic Control and Glucose Dynamics from Continuous Glucose Monitoring
Kohnert, K. (2017) · International Journal of Diabetes and Clinical Research · doi:10.23937/2377-3634/1410071 · Cited by 4 · see citing works

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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 4,032 · see citing works
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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 310 · see citing works
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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 202 · see citing works
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Ribet, F., Stemme, G., & Roxhed, N. (2018) · Biomedical Microdevices · doi:10.1007/s10544-018-0349-6 · Cited by 182 · see citing works
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Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., et al. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3331297 · Cited by 113 · see citing works
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Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?
Mo, Y., Lu, J., & Zhou, J. (2023) · Journal of Diabetes Investigation · doi:10.1111/jdi.14112 · Cited by 48 · see citing works
Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes
Brandt, R., Park, M., Wroblewski, K., Quinn, L. T., Tasali, E., & Çınar, A. (2021) · Diabetologia · doi:10.1007/s00125-021-05500-9 · Cited by 46 · see citing works
A retrospective in‐depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management
Peeks, F., Hoogeveen, I. J., Feldbrugge, R. L., Burghard, R., de Boer, F., Fokkert‐Wilts, M. J., van der Klauw, M. M., Oosterveer, M. H., & Derks, T. G. J. (2021) · Journal of Inherited Metabolic Disease · doi:10.1002/jimd.12383 · Cited by 42 · see citing works
Glycemic variability: Importance, relationship with physical activity, and the influence of exercise
Sparks, J. R., Kishman, E. E., Sarzynski, M. A., Davis, J. M., Grandjean, P. W., Durstine, J. L., & Wang, X. (2021) · Sports Medicine and Health Science · doi:10.1016/j.smhs.2021.09.004 · Cited by 43 · see citing works
Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia
Gómez, A. M., Henao, D. C., Madero, A. I., Taboada, L. B., Cruz, V., Gómez, M. A. R., Rondón, M., Múñoz, Ó., Maira, G., & Vargas, F. M. L. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0075 · Cited by 41 · see citing works
Key indices of glycaemic variability for application in diabetes clinical practice
Monnier, L., Bonnet, F., Colette, C., Renard, É., & Owens, D. R. (2023) · Diabetes & Metabolism · doi:10.1016/j.diabet.2023.101488 · Cited by 34 · see citing works
Glycemic Variability As a Prognostic Factor for Mortality in Patients With Critical Illness: A Systematic Review and Meta-Analysis
Hryciw, B. N., Ghossein, J., Rochwerg, B. N., Meggison, H., Fernando, S. M., Kyeremanteng, K., Tran, A., & SEELY, A. J. E. (2024) · Critical Care Explorations · doi:10.1097/cce.0000000000001025 · Cited by 30 · see citing works
Enhancing the Long‐Term Stability of a Polymer Dot Glucose Transducer by Using an Enzymatic Cascade Reaction System
Sun, K., Ding, Z., Zhang, J., Chen, H., Qin, Y., Xu, S., Wu, C., Yu, J., & Chiu, D. T. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.202001019 · Cited by 30 · see citing works
Impact of anxiety, depression and disease-related distress on long-term glycaemic variability among subjects with Type 1 diabetes mellitus
Déniz-García, A., Díaz-Artiles, A., Saavedra, P., Alvarado-Martel, D., Wägner, A. M., & Boronat, M. (2022) · BMC Endocrine Disorders · doi:10.1186/s12902-022-01013-7 · Cited by 30 · see citing works
Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes
Berikov, V., Андреевна, К. О., Semenova, J. F., & Klimontov, V. V. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081262 · Cited by 30 · see citing works
Orexin neurons track temporal features of blood glucose in behaving mice
Viskaitis, P., Tesmer, A. L., Liu, Z., Karnani, M., Arnold, M., Donegan, D., Bracey, E. F., Grujic, N., Patriarchi, T., Peleg‐Raibstein, D., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01648-w · Cited by 29 · see citing works
Effect of cinnamon spice on continuously monitored glycemic response in adults with prediabetes: a 4-week randomized controlled crossover trial
Zelicha, H., Yang, J., Henning, S. M., Huang, J., Lee, R., Thames, G., Livingston, E. H., Heber, D., & Li, Z. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.01.008 · Cited by 27 · see citing works
Glycemic variability assessed by continuous glucose monitoring in hospitalized patients with community-acquired pneumonia
Olsen, M. T., Dungu, A. M., Klarskov, C. K., Jensen, A. K., Lindegaard, B., & Kristensen, P. L. (2022) · BMC Pulmonary Medicine · doi:10.1186/s12890-022-01874-7 · Cited by 27 · see citing works
Glycaemic variation is a predictor of all-cause mortality in the Veteran Affairs Diabetes Trial
Zhou, J. J., Koška, J., Bahn, G., & Reaven, P. D. (2019) · Diabetes and Vascular Disease Research · doi:10.1177/1479164119827598 · Cited by 27 · see citing works
Is the Consensual Threshold for Defining High Glucose Variability Implementable in Clinical Practice?
Julla, J., Jacquemier, P., Fagherazzi, G., Vidal-Trécan, T., Juddoo, V., Jaziri, A., Mersel, H., Venteclef, N., Roussel, R., Massin, P., et al. (2021) · Diabetes Care · doi:10.2337/dc20-1847 · Cited by 26 · see citing works
Glucose variability in subjects with normal glucose tolerance: Relations with body composition, insulin secretion and sensitivity
Klimontov, V. V., & Semenova, J. F. (2022) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2022.102387 · Cited by 26 · see citing works
Glucose Variability and Risk of Hepatocellular Carcinoma in Patients with Diabetes: A Nationwide Population-Based Study
Yoo, J., Cho, E. J., Han, K., Heo, S. S., Kim, B., Shin, D. W., & Yu, S. J. (2021) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-1654 · Cited by 25 · see citing works
Association of specialized enteral nutrition with glycemic control and clinical outcomes in critically ill patients: A meta-analysis of randomized controlled trials
Eckert, I. D. C., Kumbier, M. C. C., Silva, F. M., Franzosi, O. S., & de Almeida, J. C. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2021.04.030 · Cited by 25 · see citing works
Critical Reappraisal of the Time-in-Range: Alternative or Useful Addition to Glycated Hemoglobin?
Heinemann, L., Freckmann, G., Müller‐Wieland, D., & Kellerer, M. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819883885 · Cited by 25 · see citing works
Comparison of the effects of gemigliptin and dapagliflozin on glycaemic variability in type 2 diabetes: A randomized, open‐label, active‐controlled, 12‐week study (STABLE II study)
Kwak, S. H., Hwang, Y., Won, J. C., Bae, J. C., Kim, H. J., Suh, S., Lee, E. Y., Lee, S., Kim, S., & Kim, J. H. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13882 · Cited by 24 · see citing works
Association of glycemic variability assessed by continuous glucose monitoring with subclinical diabetic polyneuropathy in type 2 diabetes patients
Pan, J., Yan, X., Li, F., Zhang, Y., Jiang, L., & Wang, C. (2021) · Journal of Diabetes Investigation · doi:10.1111/jdi.13652 · Cited by 22 · see citing works
Current landscape and comprehensive management of glycemic variability in diabetic retinopathy
Chen, B., Shen, C., & Sun, B. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05516-w · Cited by 21 · see citing works
Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy
Rodbard, D. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0115 · Cited by 21 · see citing works
Self-citation
Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems
Shahid, A., & Lewis, D. (2022) · Nutrients · doi:10.3390/nu14091906 · Cited by 21 · see citing works
Long‐term glycemic control and glucose variability assessed with continuous glucose monitoring in a pediatric population with type 1 diabetes: Determination of optimal sampling duration
Piona, C. A., Marigliano, M., Mozzillo, E., Franzese, A., Zanfardino, A., Iafusco, D., Maltoni, G., Zucchini, S., Delvecchio, M., & Maffeis, C. (2020) · Pediatric Diabetes · doi:10.1111/pedi.13115 · Cited by 21 · see citing works
How to Measure Glycemic Variability? A Literature Review
Lazăr, S., Ioniță, I., Reurean‐Pintilei, D., & Timar, B. (2023) · Medicina · doi:10.3390/medicina60010061 · Cited by 22 · see citing works
Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation
Li, C., Ma, X., Lu, J., Tao, R., Yu, X., Mo, Y., Lu, W., Bao, Y., Zhou, J., & Jia, W. (2022) · Frontiers of Medicine · doi:10.1007/s11684-022-0955-9 · Cited by 20 · see citing works
The Ambulatory Glucose Profile: Opportunities for Enhancement
Rodbard, D. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0524 · Cited by 20 · see citing works
Self-citation
Impact of acute glycemic variability on short-term outcomes in patients with ST-segment elevation myocardial infarction: a multicenter population-based study
Wang, J., Wang, L., Yang, Y., Tan, H., & Zhu, J. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02250-x · Cited by 19 · see citing works
Low‐carbohydrate, high‐fat enteral formulas for managing glycemic control in patients who are critically ill: A review of the evidence
Burslem, R., Radler, D. R., Parker, A., & Zelig, R. S. (2021) · Nutrition in Clinical Practice · doi:10.1002/ncp.10652 · Cited by 18 · see citing works
The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system
Castañeda, J., Arrieta, A., van den Heuvel, T. R. A., & Cohen, O. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15139 · Cited by 18 · see citing works
Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations
Matabuena, M., Félix, P., Meijide-Garcia, C., & Gudé, F. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106905 · Cited by 17 · see citing works
Cardiovascular autonomic neuropathy is associated with increased glycemic variability driven by hyperglycemia rather than hypoglycemia in patients with diabetes
Gad, H., Elgassim, E., Mohammed, I., Alhaddad, A. Y., Aly, H., Cabibihan, J., Al-Ali, A. K., Sadasivuni, K. K., Petropoulos, I. N., Ponirakis, G., et al. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110670 · Cited by 16 · see citing works
Admission Hyperglycemia Predicts Long-Term Mortality in Critically Ill Patients With Subarachnoid Hemorrhage: A Retrospective Analysis of the MIMIC-III Database
Liu, D., Tang, Y., & Zhang, Q. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.678998 · Cited by 17 · see citing works
Continuous glucose monitoring of steroid-induced hyperglycemia in patients with dermatologic diseases
Kleinhans, M., Albrecht, L. J., Benson, S., Fuhrer, D., Dissemond, J., & Tan, S. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221147937 · Cited by 19 · see citing works
Glycaemic variabilities: Key questions in pursuit of clarity
Monnier, L., Owens, D. R., Colette, C., & Bonnet, F. (2021) · Diabetes & Metabolism · doi:10.1016/j.diabet.2021.101283 · Cited by 16 · see citing works
Continuous Glucose Monitoring in Patients Following Simultaneous Pancreas–Kidney Transplantation: Time in Range and Glucose Variability
Дмитриев, И. В., Severina, A. S., Журавель, Н. С., Yevloyeva, M. I., Salimkhanov, R. K., Павловна, Щ. С., Bezunov, E. A., Шамхалова, М. Ш., Semenova, J. F., Klimontov, V. V., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13091606 · Cited by 15 · see citing works
The Role of Glucagon in Glycemic Variability in Type 1 Diabetes: A Narrative Review
Guo, K., Tian, Q., Yang, L., & Zhou, Z. (2021) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s343514 · Cited by 17 · see citing works
Association Between the Variability of Glycated Hemoglobin and Retinopathy in Patients with Type 2 Diabetes Mellitus: A Meta-Analysis
Zhai, L., Lü, J., Cao, X., Zhang, J., Yin, Y., & Hu, T. (2022) · Hormone and Metabolic Research · doi:10.1055/a-1931-4400 · Cited by 14 · see citing works
Glycemic Variability and Prognosis of Patients with Intracerebral Hemorrhage: A Meta-Analysis
Xu, J., Wang, H., Li, M., & Lu, Y. (2023) · Hormone and Metabolic Research · doi:10.1055/a-2010-2345 · Cited by 14 · see citing works
Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels
CHANDRAN, S. R., Vigersky, R. A., Thomas, A., Lim, L., Ratnasingam, J., Tan, A. T. B., & Gardner, D. S.-L. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0277 · Cited by 14 · see citing works
Making sense of glucose sensors in end-stage kidney disease: A review
Williams, M. E., Steenkamp, D., & Wolpert, H. A. (2022) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2022.1025328 · Cited by 13 · see citing works
Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review
Olsen, M. T., Klarskov, C. K., Dungu, A. M., Hansen, K. B., Pedersen‐Bjergaard, U., & Kristensen, P. L. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231221803 · Cited by 13 · see citing works
Standardizing Reporting of Glucose and Insulin Data for Patients on Multiple Daily Injections Using Connected Insulin Pens and Continuous Glucose Monitoring
Rodbard, D., & Garg, S. K. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0030 · Cited by 13 · see citing works
Self-citation
Association of Body Fat Percentage with Time in Range Generated by Continuous Glucose Monitoring during Continuous Subcutaneous Insulin Infusion Therapy in Type 2 Diabetes
Ruan, Y., Zhong, J., Chen, R., Zhang, Z., Liu, D., Sun, J. M., & Chen, H. (2021) · Journal of Diabetes Research · doi:10.1155/2021/5551216 · Cited by 15 · see citing works
Glycaemia dynamics in gestational diabetes mellitus
Valero, P., Salas, R., Pardo, F., Cornejo, M., Fuentes, G., Vega, S., Grismaldo, A., Hillebrands, J., van der Beek, E. M., van Goor, H., et al. (2022) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2022.130134 · Cited by 12 · see citing works
Application of medium-term metrics for assessing glucose homoeostasis: Usefulness, strengths and weaknesses
Monnier, L., Colette, C., & Owens, D. R. (2020) · Diabetes & Metabolism · doi:10.1016/j.diabet.2020.06.004 · Cited by 12 · see citing works
Glucose Variability and Time in Range in Type 2 Diabetes Treated with U-500R by Pump or Injection: CGM Findings from the VIVID Study
Blevins, T., Lane, W. S., Rodbard, D., Sindelar, D. K., Fan, L., Ellinor, K., Ilag, L. L., Ly, T. T., & Johnson, J. L. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0030 · Cited by 12 · see citing works
Self-citation
Compensatory thalamocortical functional hyperconnectivity in type 2 Diabetes Mellitus
Wang, J., Zhou, S., Deng, D., Chen, M., Cai, H., Zhang, C., Liu, F., Luo, W., Zhu, J., & Yu, Y. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00710-0 · Cited by 11 · see citing works
Association of Blood Glucose Variability with Sepsis-Related Disseminated Intravascular Coagulation Morbidity and Mortality
Liu, D., Fan, Y., Zhuang, Y., Hu, P. C., Gao, C., & Chen, Y. (2022) · Journal of Inflammation Research · doi:10.2147/jir.s383053 · Cited by 11 · see citing works
Glycemic Variability and Its Association with Traditional Glycemic Control Biomarkers in Patients with Type 1 Diabetes: A Cross-Sectional, Multicenter Study
Lazăr, S., Reurean‐Pintilei, D., Ioniță, I., Avram, V. F., Herascu, A., & Timar, B. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14072434 · Cited by 13 · see citing works
Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders
Presseller, E., Patarinski, A. G. G., Zhang, F., Page, K., Srivastava, P., Manasse, S., & Juarascio, A. (2022) · International Journal of Eating Disorders · doi:10.1002/eat.23838 · Cited by 13 · see citing works
Association between Intensity Levels of Physical Activity and Glucose Variability among Children and Adolescents with Type 1 Diabetes
Jaggers, J. R., King, K. M., McKay, T. E., Dyess, R. J., Thrasher, B. J., & Wintergerst, K. A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021623 · Cited by 10 · see citing works
Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients
Kohnert, K., Heinke, P., Vogt, L., Augstein, P., & Salzsieder, E. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01257 · Cited by 10 · see citing works
The Association Between Glycemic Variability and Myocardial Infarction: A Review and Meta-Analysis of Prospective Studies and Randomized Trials
Alatawi, Z., & Mirghani, H. O. (2020) · Cureus · doi:10.7759/cureus.11556 · Cited by 10 · see citing works
Proposed Practical Guidelines to Improve Glycaemic Management by Reducing Glycaemic Variability in People with Type 1 Diabetes Mellitus
de Torres-Sánchez, A., Ampudia‐Blasco, F. J., Murillo, S., Bellido, V., Amor, A. J., & Mezquita‐Raya, P. (2025) · Diabetes Therapy · doi:10.1007/s13300-025-01703-0 · Cited by 9 · see citing works
Glycemic Variability Assessment in Newly Treated Exocrine Pancreatic Insufficiency With Type 1 Diabetes
Lewis, D. M., & Shahid, A. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221108414 · Cited by 9 · see citing works
Association of Long-Term HbA1c Variability with Anxiety and Depression in Patients with Type 2 Diabetes: A Cross-Sectional Retrospective Study
Shi, Q., Ding, J., Su, H., Du, Y., Pan, T., & Zhong, X. (2023) · Psychology Research and Behavior Management · doi:10.2147/prbm.s441058 · Cited by 9 · see citing works
Patient understanding of discharge instructions for home diabetes self-management and risk for hospital readmission and emergency department visits
Pinkhasova, D., Swami, J. B., Patel, N., Karslioglu-French, E., Hlasnik, D. S., Delisi, K. J., Donihi, A. C., Rubin, D. J., Siminerio, L. S., Wang, L., et al. (2021) · Endocrine Practice · doi:10.1016/j.eprac.2021.03.013 · Cited by 9 · see citing works
Influence of dipeptidyl peptidase-4 inhibitors on glycemic variability in patients with type 2 diabetes: A meta-analysis of randomized controlled trials
Chai, S., Zhang, R., Zhang, Y., Carr, R. D., Zheng, Y., Rajpathak, S. N., & Yu, M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.935039 · Cited by 9 · see citing works
Glycemic Variability in Type 1 Diabetes Mellitus Pregnancies—Novel Parameters in Predicting Large-for-Gestational-Age Neonates: A Prospective Cohort Study
Lekšić, G., Baretić, M., Gudelj, L., Radić, M., Miličić, I., Ivanišević, M., & Jurišić‐Eržen, D. (2022) · Biomedicines · doi:10.3390/biomedicines10092175 · Cited by 9 · see citing works
The relationship between hypoglycaemia and glucose variability in type 1 diabetes
Perlman, J. E., Gooley, T. A., Meyers, J., & Hirsch, I. B. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14617 · Cited by 8 · see citing works
Comparison of efficacy and safety of insulin degludec/liraglutide and insulin glargine U‐100/lixisenatide in individuals with type 2 diabetes mellitus using professional continuous glucose monitoring
Kawaguchi, Y., Hajika, Y., Rinka, M., Masumoto, K., Sawa, J., Hamazaki, K., & Kumeda, Y. (2024) · Journal of Diabetes Investigation · doi:10.1111/jdi.14151 · Cited by 11 · see citing works
Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes
Augstein, P., Heinke, P., Vogt, L., Kohnert, K., & Salzsieder, E. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211008871 · Cited by 8 · see citing works
Comparisons of efficacy and safety in insulin glargine and lixisenatide plus glulisine combination therapy with multiple daily injection therapy in Japanese patients with type 2 diabetes
Kawaguchi, Y., Miyamoto, S., Hajika, Y., Ashida, N., Masumoto, K., Sawa, J., Hamazaki, K., & Kumeda, Y. (2021) · Journal of Diabetes Investigation · doi:10.1111/jdi.13677 · Cited by 8 · see citing works
The state of variability: A vision for descriptors of glycaemia
Zhou, T., Knopp, J. L., & Chase, J. G. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.06.004 · Cited by 8 · see citing works
Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis
Kingsnorth, A. P., Whelan, M., Orme, M. W., Routen, A. C., Sherar, L. B., & Esliger, D. (2020) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2020-0386 · Cited by 8 · see citing works
Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices
Sugimoto, H., Hironaka, K., Nakamura, T., Yamada, T., Miura, H., Otowa‐Suematsu, N., Fujii, M., Hirota, Y., Sakaguchi, K., Ogawa, W., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-00819-5 · Cited by 10 · see citing works
Carbohydrate-Energy Replacement Following High-Intensity Interval Exercise Blunts Next-Day Glycemic Control in Untrained Women
Estafanos, S., Friesen, B., Govette, A., & Gillen, J. B. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.868511 · Cited by 7 · see citing works
Preferences for Risks and Benefits of Islet Cell Transplantation for Persons With Type 1 Diabetes With History of Episodes of Severe Hypoglycemia: A Discrete-Choice Experiment to Inform Regulatory Decisions
Wilson, L., Kwok, T., Ma, Y.-L., Wong, J. C., Ho, M., Ionova, Y., Mcgrath, M. T., Mueller, M., Gitelman, S., & Irony, T. (2022) · Transplantation · doi:10.1097/tp.0000000000004189 · Cited by 5 · see citing works
Establishment of blood glucose control model in diabetic mice
Rao, C.-H., Gao, J., Du, Z.-H., & Gao, C. (2022) · International Journal of Ophthalmology · doi:10.18240/ijo.2022.12.03 · Cited by 7 · see citing works
Association between time in range 70-180 mg/dl in early stage and severity with in patients acute pancreatitis
Meng, C., Zhang, J., Wang, Y., Ye, X., & Zhuang, S. (2023) · BMC Endocrine Disorders · doi:10.1186/s12902-023-01414-2 · Cited by 7 · see citing works
Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?
Richardson, R. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241298248 · Cited by 7 · see citing works
Use of ambulatory glucose monitoring and analysis of ambulatory glucose profile in clinical practice for diabetes management; a position statement of the Arab Society of Paediatric Endocrinology and diabetes
Deeb, A., Muammar, T., Alsaffar, H., Sedaghat, S., Hassani, N. A., Odeh, R., Alkhayyat, H., Sinani, A. A., Attia, N. A., Adhami, S., et al. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108671 · Cited by 7 · see citing works
Optimizing postprandial glucose prediction through integration of diet and exercise: Leveraging transfer learning with imbalanced patient data
Hotta, S., Kytö, M., Koivusalo, S. B., Heinonen, S. T., & Marttinen, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298506 · Cited by 7 · see citing works
Prognostic effects of glycaemic variability on diastolic heart failure and type 2 diabetes mellitus: insights and 1-year mortality machine learning prediction model
Yang, Z., Li, Y., Liu, Y., Zhong, Z., Ditchfield, C., Guo, T., Yang, M., & Chen, Y. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01534-2 · Cited by 6 · see citing works
Rational Design and Efficacy of Glucose‐Responsive Insulin Therapeutics and Insulin Delivery Systems by Computation Using Connected Human and Rodent Models
Yang, S., Yang, J., Gong, X., Weiss, M. A., & Strano, M. S. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202300587 · Cited by 6 · see citing works
Performance and patients’ satisfaction with the A7+TouchCare insulin patch pump system: A randomized controlled non-inferiority study
Amadou, C., Melki, V., Allain, J., Clavel, S., Gouet, D., Chaillous, L., Catargi, B., Schaeplynck-Belicard, P., Petit, C., Thivolet, C. H., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289684 · Cited by 5 · see citing works
Are postprandial glucose responses sufficiently person-specific to use in personalized dietary advice? Design of the RepEAT study: a fully controlled dietary intervention to determine the variation in glucose responses
Daanje, M., Siebelink, E., Vrieling, F., van den Belt, M., van der Haar, S., Gerdessen, J. C., Kersten, S., Esser, D., & Afman, L. A. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1281978 · Cited by 5 · see citing works
Continuous Glucose Monitoring in Hospitalized Patients With Type 2 Diabetes: A Step Forward in Inpatient Glycemic Control
Montenegro, A. M. R., Áñez, R., Pastor, A. G.-S., & Albarrán, O. G. (2025) · Endocrine Practice · doi:10.1016/j.eprac.2025.02.007 · Cited by 5 · see citing works
Long-Term Effects and Potential Impact of Early Nutrition with Breast Milk or Infant Formula on Glucose Homeostasis Control in Healthy Children at 6 Years Old: A Follow-Up from the COGNIS Study
Diéguez, E., Nieto-Ruíz, A., Sepúlveda-Valbuena, N., Herrmann, F., Agil, A., De-Castellar, R., Jiménez, J., Azaryah, H., García-Santos, J. A., García-Bermúdez, M., et al. (2023) · Nutrients · doi:10.3390/nu15040852 · Cited by 5 · see citing works

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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 · 2017: 2
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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