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Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring

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Bergenstal, Richard M., Beck, Roy W., Close, Kelly L., Grunberger, George, Sacks, David B., Kowalski, Aaron, Brown, Adam S., Heinemann, Lutz, Aleppo, Grazia, Ryan, Donna B., Riddlesworth, Tonya D., Cefalu, William T.·2018·Diabetes Care

DOI 10.2337/dc18-1581PMC PMC6196826

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Abstract

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

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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 · cited 1×
“An international consensus group recently included the eA1C in its list of core CGMmetrics recommended for inclusion in all standard CGM reports (2).”
BackgroundSelf-citation
Translating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812
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Beck, R. W., Riddlesworth, T., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S., Kollman, C., Kruger, D., McGill, J. B., Polonsky, W., et al. (2017) · JAMA · doi:10.1001/jama.2016.19975 · Cited by 1,185
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Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections: A Randomized Trial
Beck, R. W., Riddlesworth, T. D., Ruedy, K., Ahmann, A., Haller, S., Kruger, D., McGill, J. B., Polonsky, W., Price, D., Aronoff, S., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2855 · Cited by 635
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4. Lifestyle Management: Standards of Medical Care in Diabetes—2018
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dc18-s004 · Cited by 563
Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): a multicentre, randomised controlled trial
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“To validate this formula, CGM data from the HypoDE study (10) (141 individuals with T1D for whom 4 weeks of data were collected with the Dexcom G5 sensor prior to A1C measurement) were analyzed to compute the regression equation to estimate A1C from the CGMmeasured mean glucose concentration.”
MethodSelf-citation
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
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REPLACE-BG: a randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes
Aleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2482 · Cited by 329
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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
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“18”
Response to Comment on Bergenstal et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018;41:2275–2280
Bergenstal, R., Beck, R., Close, K. L., Grunberger, G., Sacks, D., Kowalski, A. J., Brown, A. S., Heinemann, L., Aleppo, G., Ryan, D. B., et al. (2018) · Diabetes Care · doi:10.2337/dci18-0061 · Cited by 8
“(2) is appropriate for inclusion in CGM”
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“The AGP provides summary statistics for user-specified date ranges including the average glucose value, its coefficient of variation, and the Glucose Management Indicator (GMI), which is derived from the average glucose and intended to align closely with patients’ HbA1c values [22].”
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“Both post-hoc outcomes were calculated using previously published equations 54,55 .”
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“= 3.31 + 0.02392 × mean sensor glucose value (mg/dL) [66].”
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“5,10,11,14,21,22 Importantly, Tozzo et al 23 found that the mean glucose derived from 90 days of CGM metrics was more accurate than that of the HbA 1c with data obtained from G5 or G6.”
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Should We Bury HbA1c?
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“GMI was found to be useful in the clinical management of diabetes; however, GMI and HbA 1c may not always align precisely due to individual factors and glucose fluctuations [6].”
Background
Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring
Christakis, N. J., Gioe, M., Gómez, R., Felipe, D. L., Soros, A., McCarter, R., & Chalew, S. A. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231199113 · Cited by 8
“22 In this study, GMI underestimated actual observed HbA1c for B patients leading to higher HGI GMI for B compared to W patients which is in agreement with the analyses of MBG CGM and TIR on HbA1c.”
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Lameijer, A., Bakker, J. J., Kao, K., Xu, Y., Gans, R. O. B., Bilo, H. J. G., Dunn, T. C., & van Dijk, P. R. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110735 · Cited by 11
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“23 In contrast, in the T2D African American cohort, 56% of estimated HbA1c values differed by ‡ 0.5%, with the tendency being for the estimated HbA1c to be lower than the measured HbA1c.”
InfluentialSelf-citation
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“18,19 Our results indicate a similar association between mean glucose, TIR, and A1C during daytime versus mean glucose, TIR, and A1C during nighttime in adults with T1D.”
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“A 24-h mean glucose concentration of 7.19 mmol/L (129 mg/dL) corresponds to an estimated HbA1c levels of 6.4% (46.4mmol/mol) 9 and, therefore, it can be considered that a %CV 30.3% is a relevant target for reducing the risk of hypoglycemia to a low level.”
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“For each child, we computed the following CGM metrics: (1) mean glucose concentration, (2) standard deviation (SD) of glucose concentrations, (3) percentage of time in the target range (70-180 mg/dL), (4) percentage of time in the hyperglycemic range (>180 and >250 mg/dL), (5) percentage of time in the hypoglycemic ra…”
Method
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“45 Previous studies have demonstrated the value of these AGP metrics, 46 but were rarely reported in the articles included.”
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“They are used for optimizing therapy in patients with diabetes [4].”
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“18,19 The rates of meeting clinic targets and the rates of having no-show to scheduled appointments were used as markers for engagement with standard care.”
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“7 Now, CGM is becoming part of standard care for people living with T1DM.”
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“…TBR level1 (TBR1) was defined as the time spent with glucose levels between 54–70 mg/dL. TBR level2 (TBR2) was defined as the time spent with glucose levels below 54 mg/ dL. GMI was calculated using the average glucose value obtained from CGM data (GMI = 3.31 + 0.02392 × average glucose) [8].”
Influential
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“= 3.31 + 0.02392 × (mean glucose in mg/dL), where mean glucose stands for the average sensor glucose concentration over the selected time period [18].”
BackgroundInfluential
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Self-citation
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“40 Importantly, it is reported using the same Diabetes Control and Complications Trial or International Federation of Clinical Chemistry units as HbA1c (% or mmol/mol).”
Method
The Ile191Val Variant of the TAS1R2 Subunit of Sweet Taste Receptors Is Associated With Reduced HbA1c in a Human Cohort With Variable Levels of Glucose Homeostasis
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“resents glucose levels predominantly during the last 2–3 months [19].”
Background
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Glucose alarms approach with flash glucose monitoring system: a narrative review of clinical benefits
Krakauer, M., Alves, S. T. D. F., Rodacki, M., Gabbay, M. A. L., Vianna, A. G. D., Scharf, M., Lamounier, R. N., Franco, D. R., Araujo, L. R., & Calliari, L. E. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01797-3 · Cited by 4
“…(TIR—time spend with glucose values between 70 and 180 mg/dL) [10], time above range (TAR—time spend with glucose values higher than 180 mg/dL) and time below range (TBR—time spend with glucose values lower than 70 mg/dL), coefficient of variation [11], and glucose monitoring index (GMI) [12].”
BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches
James, S., Armstrong, M., O'Kane, A., Emerson, H., & Abdallah, Z. (2025) · arXiv.org · doi:10.48550/arxiv.2507.17757 · Cited by 5
“For the blood glucose readings, the 2019 standardised CGM metrics for clinical care were calculated [16, 17, 18], which are shown in Table 7.”
Hemoglobin glycation index among adults with type 1 diabetes: Association with double diabetes features
Ji, X.-L., Yin, M., Deng, C., Fan, L., Xie, Y.-T., Huang, F.-S., Chen, Y., & Li, X. (2025) · World Journal of Diabetes · doi:10.4239/wjd.v16.i4.100917 · Cited by 5
Optimal Periodic Impulsive Strategies in Glycemic Control
Borri, A., Cacace, F., Gaetano, A. D., Pompa, M., & Panunzi, S. (2024) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2024.3398288 · Cited by 5
The Relation of Diabetes Complications to a New Interpretation of Glycaemic Variability from Continuous Glucose Monitoring in People with Type 1 Diabetes
Heald, A. H., Stedman, M., Warner-Levy, J. J., Belston, L., Paisley, A. N., Patel, R., White, A., Jude, E. B., Gibson, M., Habte‐Asres, H. H., et al. (2024) · Diabetes Therapy · doi:10.1007/s13300-024-01648-w · Cited by 5
The Interplay between Dietary Habits and Glycemic Control in Type 1 Diabetes: A Comprehensive Prospective FGM Study
Centner, M. G., Kenjeric, D. C., Schönberger, E., Centner, H., Rimac, D. S., Steiner, K., Marušić, R., Bakula, M., Vitković, D. F., Mihaljević, I., et al. (2024) · Life · doi:10.3390/life14091153 · Cited by 6
“…range (TAR, glucose values > 10.1 mmol/L), percentage of time below target range (TBR, glucose values 3.9 mmol/L), scanning frequency, percentage of captured FGM data, and time spent in hypoglycemia) were analyzed using reports generated from the FSL FGM system (Abbott, Chicago, IL, USA) [27].”
MethodInfluential
Distribution-based sub-population selection (DSPS): A method for in-silico reproduction of clinical trials outcomes
Ganji, M., Fathi, A. E., Fabris, C., Lv, D., Kovatchev, B. P., & Breton, M. D. (2024) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2025.109714 · Cited by 5
How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions
Thomas, A., Haak, T., Tombek, A., Kulzer, B., Ehrmann, D., Kordonouri, O., Kröger, J., Schubert-Olesen, O., Kolassa, R., Siegmund, T., et al. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241267768 · Cited by 5
Self-citation
Continuous glucose monitoring profile in COVID-19 patients with and without diabetes receiving methylprednisolone
He, X., Duan, G., Lu, J., Wang, Y., Cai, J., Tong, Y., Wu, W., Ma, X., Qi-ming, F., & Zhou, J. (2024) · Endocrine · doi:10.1007/s12020-024-03841-0 · Cited by 5
“= 3.31 + 0.02392 × 18 × mean sensor glucose (mmol/L) [18].”
Influential
Evaluation of Association between Time in Range, a Continuous Glucose Monitoring Metric, and Cardiac Autonomic Neuropathy in Type 2 Diabetes Patients
Karthikeyan, A., Ramakrishna, M. P., Swamy, N. A., & Latha, A. T. (2024) · Annals of African Medicine · doi:10.4103/aam.aam_117_23 · Cited by 5
“[4] The association between TIR and various diabetes‑related outcomes has been extensively studied.”
Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes
Xi, W., Yang, H., Yang, M., Tao, W., Chen, J., Zhao, S., Yin, M., Zhou, X., Yang, Y., & Li, Y. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15350 · Cited by 5
Glucose management indicator – potential factors affecting differences in comparison with HbA1c and clinical significance of this phenomenon
Wybrańczyk, Ł., Brudzińska, A., Jarosz-Chobot, P., & Deja, G. (2023) · Pediatric Endocrinology Diabetes and Metabolism · doi:10.5114/pedm.2023.130028 · Cited by 5
“That is why glucose management indicator (GMI) has been established as a new term for eHbA1c, and this wording should be used in the current medical nomenclature [5].”
BackgroundMethodInfluential
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
Toward Personalized Hemoglobin A1c Estimation for Type 2 Diabetes
Kim, N., Lee, D. Y., Seo, W., Kim, N. H., & Park, S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3215004 · Cited by 10
“However, discordance between HbA1c-based long-term and BG-based short-term management leads to confusion in patients and caregivers, resulting in low compliance to the management [6].”
BackgroundMethodInfluential
Evaluation of continuous glucose monitoring‐derived person‐specific HbA1c in the presence and absence of complications in type 1 diabetes
Xu, Y., Oriot, P., Dunn, T. C., Hermans, M. P., Ram, Y., Cheng, A., & Ajjan, R. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14824 · Cited by 6
“5% of laboratory HbA1c results compared with 25% and 54% of subjects for GMI and estimated HbA1c (eHbA1c), respectively.(12,13,18) In a study evaluating 51 Japanese adults with T1D, cHbA1c had 92.”
Result
Discordance in the reduction rate between glycated albumin and glycated hemoglobin levels in type 2 diabetes patients receiving SGLT2 inhibitors
Shimizu, N., Ogawa, A., Hayashi, A., & Shichiri, M. (2022) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2022.108225 · Cited by 4
Anthropometric Measurements and Laboratory Investigations in Children and Youth With Type 1 Diabetes Before and During the COVID-19 Pandemic
Silva, C., Zhang, Q., Bone, J., & Amed, S. (2022) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2022.04.003 · Cited by 5
“Moreover, whereas A1C could be estimated based on continuous glucose monitoring data (29), there may be significant discordance in a real-world setting, reinforcing the value of laboratory-measured A1C (50).”
Detecting dysglycaemia in compensated liver cirrhosis: Comparison of oral glucose tolerance test and glycated haemoglobin, with continuous glucose monitoring
Barron, C. V., Heenan, H., Thompson, H., Chan, H., Ngu, J., & Lunt, H. (2021) · Diabetic Medicine · doi:10.1111/dme.14778 · Cited by 5
Development of application to automatically calculate mean amplitude of glycaemic excursions using intermittently scanned continuous glucose monitoring data
Kitae, A., Kimura, T., Munekawa, C., Hosomi, Y., Osaka, T., Kitagawa, N., Ushigome, E., Yamazaki, M., Hamaguchi, M., & Fukui, M. (2021) · Diabetes, obesity and metabolism · doi:10.1111/dom.14457 · Cited by 5
Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes
Bosoni, P., Calcaterra, V., Tibollo, V., Malovini, A. L., Zuccotti, G., Mameli, C., Sacchi, L., Bellazzi, R., & Larizza, C. (2021) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0725 · Cited by 8
“Re-sults presented in Table 3 are consistent even with other ﬁ ndings reported in the literature [12, 23, 24, 9].”
Influential
Relationship between interstitial glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics; a pilot study
Tokutsu, A., Okada, Y., Torimoto, K., & Tanaka, Y. (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00577-5 · Cited by 5
“IQR is in the 25–75th percentile range on the AGP, and is an excellent visual indicator of interstitial glucose variability [13, 14]; although there is no reported relationship between IQR and standardized CGM metrics.”
MethodInfluential
The Changing Landscape of Glycemic Targets: Focus on Continuous Glucose Monitoring
Kushner, P., & Kruger, D. (2020) · Clinical Diabetes · doi:10.2337/figshare.12245486.v1 · Cited by 5
Closed-loop insulin delivery systems for patients with diabetes.
Garg, S., & Shah, V. N. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30007-x · Cited by 5
Clinical Impact of Continuous Glucose Monitoring in Noninsulin Treated Type 2 Diabetes: A Review
Aleppo, G., Carlson, A. L., McGill, J. B., Alshannaq, H. A., Galindo, R. J., Kruger, D. F., Levy, C. J., Matuoka, J. Y., Ilham, S., Umpierrez, G. E., et al. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251414980 · Cited by 5
Self-citation
Prediction of gestational diabetes mellitus using continuous glucose monitoring metrics
Chen, L., Ku, C. W., Zheng, R. T., Chan, J. K. Y., Yap, F. K. P., & Loy, S. L. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.113044 · Cited by 4
Glycemic variability and iatrogenic hypoglycemia: how to resolve
Monnier, L., Colette, C., Renard, É., Benhamou, P., Aouinti, S., Molinari, N., & Owens, D. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112360 · Cited by 3
Artificial pancreas: the past and the future
Kitagawa, H., Munekage, M., Seo, S., & Hanazaki, K. (2025) · Journal of Artificial Organs · doi:10.1007/s10047-025-01510-1 · Cited by 7
“In addition, the estimated HbA1c value calculated from the mean glucose value obtained via CGM serves as a glucose management indicator (GMI), providing a shorter-term measure of blood glucose control than HbA1c [11].”
The Role of a Continuous Glucose Monitoring-Derived Glycation Ratio in the Development of Microvascular Complications
Bovee, L. B., Gooley, T., Perlman, J. E., & Hirsch, I. B. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0580 · Cited by 4
Novel indicator of microvascular complications in patients with type 2 diabetes mellitus and shortened erythrocyte lifespan: a multicenter cross-sectional analysis
Wu, Y., Zhang, B., Ma, X., Yu, P., Zhou, S., & Wang, X. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01591-1 · Cited by 5
Use of a decision support tool and quick start onboarding tool in individuals with type 1 diabetes using advanced automated insulin delivery: a single-arm multi-phase intervention study
Sehgal, S., de Bock, M. I., Grosman, B., Williman, J. A., Kurtz, N., Guzman, V., Benedetti, A., Roy, A., Turksoy, K., Juarez, M., et al. (2024) · BMC Endocrine Disorders · doi:10.1186/s12902-024-01709-y · Cited by 4
“…< 25%, (> 13.9mmol/250mg/dl) 5% b (GMI)Glucose Management Indicator, a measure converting mean sensor glucose into an estimated Hba1c Value [15] c p value comparing phase 1 to phase 2 d number of exits from OL for different reasons per week expressed as average and standard deviation TIR…”
Method
Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review
Peralta, F. G., Gea, I. L., Duque, N., Artime, E., & Santos, M. R. (2024) · Advances in Therapy · doi:10.1007/s12325-024-02943-5 · Cited by 6
“The glucose management indicator (GMI) is a new glucometric that can be used to calculate an estimated HbA1c [22, 23].”
Method
Potential Benefits of Continuous Glucose Monitoring for Predicting Vascular Outcomes in Type 2 Diabetes: A Rapid Review of Primary Research
Jadav, R. K., Yee, K. C., Turner, M. R., & Mortazavi, R. (2024) · Healthcare · doi:10.3390/healthcare12151542 · Cited by 5
“It is an estimated HbA1c based on the average glucose levels measured by CGM [3] Needs glucose values for a consecutive period of 10–14 days for calculation [31].”
Method
Impact of nocturnal hypoxia on glycaemic control, appetite, gut microbiota and inflammation in adults with type 2 diabetes mellitus: A single‐blind cross‐over trial
Shepherd, A. I., James, T. J., Gould, A. A. M., Mayes, H. S., Neal, R., Shute, J. K., Tipton, M., Massey, H. C., SAYNOR, Z. L., Perissiou, M., et al. (2024) · The Journal of Physiology · doi:10.1113/jp285322 · Cited by 6
“Plasma [glucose] (10 day grand mean) and AUC, time in range, estimated HbA1 c and glucose management indicator (GMI) (Bergenstal et al., 2018) were calculated.”
Influential
Glucose values from the same continuous glucose monitoring sensor significantly differ among readers with different generations of algorithm
Babaya, N., Noso, S., Hiromine, Y., Taketomo, Y., Niwano, F., Yoshida, S., Yasutake, S., Kawabata, Y., Maeda, N., & Ikegami, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-55124-3 · Cited by 4
Low Blood Glucose Index Associated with Cardiovascular Events in Diabetic Hemodialysis Patients
He, R., Xuan, Y., Zhu, L., Pang, S., Qin, L., Tian, J., & Yuan, J. (2023) · Blood Purification · doi:10.1159/000531964 · Cited by 5
“GMI is calculated from a formula derived from the regression line computed from a plot of mean glucose concentration points on the x-axis and contemporaneously measured HbA1c values on the y-axis [15].”
Method
A pregnancy‐specific Glucose management indicator derived from continuous glucose monitoring in pregnant women with type 1 diabetes
Ling, P., Yang, D., Wang, C., Zheng, X., Luo, S., Yang, X., Deng, H., Xu, W., Yan, J., & Weng, J. (2023) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3689 · Cited by 5
“1 A1C ‐ Derived Average Glucose (ADAG) study has explored the relationship between HbA1c and mean blood glucose (MBG) derived from CGM among individuals with diabetes, converting the MBG into an estimated A1C (eA1c) 4–9 which also named GMI recommended by the Food and Drug Administration.”
MethodResultInfluential
Characteristics of people with optimally-managed type 1 diabetes
Stoet, G., & Holt, R. I. G. (2023) · Diabetes Epidemiology and Management · doi:10.1016/j.deman.2023.100153 · Cited by 4
“The GMI is calculated from the average sensor glucose over the preceding 14 days and provides an approximation of a laboratory-measured HbA 1c [5] although it may be higher or lower than the actual HbA 1c for some individuals [6].”
Type 1 diabetes iron‐deficiency anaemia case report and the clinical relevance of red blood cell lifespan‐adjusted glycated haemoglobin
Hirota, Y., Xu, Y., Yamamoto, A., Matsuoka, A., Dunn, T., & Ogawa, W. (2022) · Diabetes, obesity and metabolism · doi:10.1111/dom.14860 · Cited by 4
Effects of structured Ramadan Nutrition Plan on glycemic control and variability using continuous glucose monitoring in individuals with type 2 diabetes: A pilot study.
Hasbullah, F. Y., Yusof, B. M. M., Zukiman, W. Z. H. H. W., Zaid, Z. A., Omar, N., Liu, R. X. Y., Marczewska, A., & Hamdy, O. (2022) · Diabetes & metabolic syndrome · doi:10.1016/j.dsx.2022.102617 · Cited by 4
Determination of Time in Range Associated With HbA1c ≤7% in a Prospective Cohort of Patients With Type 1 Diabetes Using Continuous Glucose Monitoring for Three Months
Gómez, A. M., Diana Cristina Henao, C., Rebolledo, M., Jaramillo, P., Múñoz, Ó., Laura María Niño, G., & Yepes, C. A. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221108424 · Cited by 5
Expertenaustausch zum Einsatz von kontinuierlichem Glukosemonitoring (CGM) im Diabetesmanagement: Eine aktuelle Bestandsaufnahme und Blick in die Zukunft
Thomas, A., Haak, T., Tombek, A., Kulzer, B., Ehrmann, D., Kordonouri, O., Kroeger, J., Schubert-Olesen, O., Kolassa, R., Siegmund, T., et al. (2022) · Diabetologie und Stoffwechsel · doi:10.1055/a-1849-2137 · Cited by 4
“[9] Bergenstal RM, Beck RW, Close KL et al.”
BackgroundInfluentialSelf-citation
Glycemic variability and indices of glycemic control among pregnant women with type 1 diabetes (T1D) based on the use of continuous glucose monitoring share technology
Sklar, J., Pyle, L. L., Snell‐Bergeon, J. K., Garcetti, R., Joshee, P., Demmitt, J. K., & Polsky, S. (2021) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2021.2008895 · Cited by 4
“ously published regression formula [17].”
Special Considerations in the Management and Education of Older Persons With Diabetes
Mobley-Bukstein, W., Reece, S. W., & Hebdon, K. (2021) · ADCES in Practice · doi:10.1177/2633559x211015972
Pumps that predict and manage low blood glucose are superior to pumps with stand-alone CGM for reducing hypoglycaemia in type 1 diabetes patients in a real-world setting
Telliam, C., & Thivolet, C. H. (2020) · Diabetes & Metabolism · doi:10.1016/j.diabet.2020.05.010 · Cited by 5
New technical approach to the diabetes therapy
Dovč, K., & Battelino, T. (2020) · Minerva Pediatrics · doi:10.23736/s0026-4946.20.05865-x · Cited by 3
National Consensus in the use of the Flash Glucose Monitoring System in Portugal
Cardoso, H., Carvalho, D., Pape, E., Carrilho, F., Raposo, J. F., Melo, M., Carvalho, E., & Duarte, R. (2019) · Revista Portuguesa de Endocrinologia Diabetes e Metabolismo · doi:10.26497/na190022 · Cited by 3
Glycemic Variability Bridges Time in Range and Time in Tight Range: A Unified Equation for Both Type 1 and Type 2 Diabetes Based on Large‐Scale Continuous Glucose Monitoring Data
Yao, Y., Hu, Z., Mo, Y.-F., Han, M., Cao, L., Cai, J.-H., Ma, X.-J., Lu, J.-Y., Wu, X.-B., & Zhou, J. (2026) · Diabetes, obesity and metabolism · doi:10.1111/dom.70702 · Cited by 3
“= 3.31 + 0.02392 × MG (mg/dL) [17].”
Influential
Continuous glucose monitoring and risks of acute and chronic diabetes-related complications and mortality in adults with type 1 diabetes: a nationwide cohort study
Kim, J. Y., Kim, J. H., & Kim, J. H. (2026) · Diabetologia · doi:10.1007/s00125-026-06709-2 · Cited by 3
“= 3.31 + 0.02392 × mean glucose (mg/dl) [34].”
Influential
Narrowing the A1c gap: Personalized modeling of HbA1c– continuous glucose monitor discordance in type 1 diabetes
Cichosz, S. L., Thomsen, C. H. N., Klonoff, D. C., Hirsch, I. B., & Jensen, M. H. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001229 · Cited by 4
“The analysis aimed to assess the association between the Glucose Management Indicator (GMI) [5] and laboratory-measured HbA1c, using paired GMI–HbA1c values derived from the combined data sources (Cohorts A and B).”
Differences Between Glycemia Estimates from Hemoglobin A1c and Continuous Glucose Monitoring and Their Association with Complete Blood Counts
Tozzo, V., Nathan, D. M., Krause-Steinrauf, H. J., Lachin, J. M., Larkin, M., Mow, C., Butera, N. M., Cohen, R. M., & Higgins, J. M. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251395036 · Cited by 2
“12 When HbA1c is translated to average glucose levels (eAG A1c ) using regression equations, as provided by the A1c-Derived Average Glucose study, 4,13 the eAG A1c may differ from eAG CGM , causing confusion for patients and providers.”

Authors: Bergenstal, R. M., Beck, R. W., Close, K. L., Grunberger, G., Sacks, D. B., Kowalski, A., Brown, A. S., Heinemann, L., Aleppo, G., Ryan, D. B., Riddlesworth, T. D., & Cefalu, W. T.
Affiliations: Park Nicollet Health Services (US)
Jaeb Center for Health Research (US)
The diaTribe Foundation, San Francisco, CA
Grunberger Diabetes Institute (US)
National Institutes of Health (US)
Breakthrough T1D (US)
Close Concerns (United States) (US)
Science Consulting in Diabetes, Neuss, Germany
Northwestern University (US)
Sacred Heart Hospital (US)
American Diabetes Association (US)
Corresponding author: Bergenstal, Richard M.
ORCID: Bergenstal, Richard M.
Beck, Roy W.
Close, Kelly L.
Grunberger, George
Sacks, David B.
Kowalski, Aaron
Brown, Adam S.
Heinemann, Lutz
Aleppo, Grazia
Riddlesworth, Tonya D.
Cefalu, William T.
Venue: Diabetes Care
Publisher: American Diabetes Association
Published: 2018-09-17
Volume: 41
Issue: 11
Pages: 2275-2280
ISSN: 0149-5992 (ISSN-L)
1935-5548
DOI: 10.2337/dc18-1581
PMC: PMC6196826
OpenAlex: W2891178783
Cited by: 684
References: 18
Citations by year: 2026: 79 · 2025: 95 · 2024: 114 · 2023: 84 · 2022: 95 · 2021: 101 · 2020: 75 · 2019: 34 · 2018: 6
FWCI: 43.18
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.diabetesjournals.org/content/license (vor, 35 d)
Locations: Diabetes Care (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Sanofi
National Institute of Diabetes and Digestive and Kidney Diseases (UC4DK108611)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords: glucose management indicator, continuous glucose monitoring, HbA1c, mean blood glucose, nomenclatural changes, diabetes management, diabetes education
MeSH terms: Blood Glucose, Diabetes Mellitus, Health Education, Health Status Indicators, Glycated Hemoglobin, Humans, Terminology as Topic, Statistics as Topic, Blood Glucose Self-Monitoring

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Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring

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