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Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring
Open accessAbstract
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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“The mean glucose of 154±26.8 mg/dL corresponded with an HbA1c of approximately 7%.14”
From Clinical utility of the twiist Automated Insulin Delivery System: first study with twiist (2026) · cites this paper as [14]
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“Using the standardized report the clinician can also address glucose variability (e.g., %CV metric) (75) or use glucose management indicator (GMI) metric (43) to discuss the possible discrepancies noted in glucose exposure derived from CGM data versus the individual's laboratory-measured A1C (40; 76).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [75]
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“For example, if HbA1c is lower than a concurrent GMI measure, the HbA1c can be interpreted to underestimate average blood glucose by the difference in measurements, allowing adjustment of HbA1c targets accordingly. 230,231”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [230]
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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-citationTranslating 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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Self-citationBeck, 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
Self-citationBeck, 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
Self-citation4. Lifestyle Management: Standards of Medical Care in Diabetes—2018
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dc18-s004 · Cited by 563
Heinemann, L., Freckmann, G., Ehrmann, D., Faber-Heinemann, G., Guerra, S., Waldenmaier, D., & Hermanns, N. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30297-6 · Cited by 522 · cited 1×
“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-citationThe 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
Self-citationAleppo, 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
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
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Items 201–300 of 615
Angellotti, E., Muppavarapu, S., Siegel, R. D., & Pittas, A. G. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0405 · Cited by 18
“9 The GMI indicates the HbA1c value that would be expected based on mean glucose concentrations measured in a large number of persons with diabetes.”
InfluentialFirst real-world experience with bigfoot unity: a 6-month retrospective analysis
Baliga, B. S., Tillman, J. B., Olson, B., Vaughan, S., Sheikh, F. N., & Malone, J. K. (2023) · Clinical Diabetes · doi:10.2337/cd22-0126 · Cited by 15
“Prespeci fi ed outcomes to analyze were glucose management indicator (GMI) calculated as described by Bergenstal et al. (12), percent time in range (TIR; 70 – 180 mg/dL), percent times below range ( < 70, 54 – 69, and < 54 mg/dL), percent times above range ( > 180, 181 – 250, and > 250 mg/dL), sensor data availability…”
BackgroundResultRizzi, A., Tartaglione, L., Paoli, L. L., Leo, M. L., Popolla, V., Viti, L., Barberio, A., Leo, M. A. S. D., Pontecorvi, A., & Pitocco, D. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15766 · Cited by 16
“1,13,35 In addition to these outcomes, the overall percentage of time that participants spent using the CGM sensor and the percentage of time spent using the sleep function of Control IQ were assessed.”
Lepore, G., Borella, N. D., Castagna, G., Ippolito, S., Bonfadini, S., Corsi, A. M., Scaranna, C., Dodesini, A. R., Bellante, R., & Trevisan, R. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0261 · Cited by 22
Barua, S., Sabharwal, A., GLANTZ, N. M., Conneely, C., LAREZ, A. J., Bevier, W. C., & Kerr, D. E. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101241 · Cited by 18
Tanenbaum, M. L., Ngo, J., Hanes, S. J., Basina, M., Buckingham, B. A., Hessler, D., Maahs, D. M., Mulvaney, S. A., & Hood, K. K. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0198 · Cited by 17
Diabetes mellitus in dialysis and renal transplantation
Ben-David, E., Hull, R., & Banerjee, D. (2021) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/20420188211048663 · Cited by 19
Fellinger, P., Rodewald, K., Ferch, M., Itariu, B., Kautzky-Willer, A., & Winhofer, Y. (2022) · Biosensors · doi:10.3390/bios12050288 · Cited by 16
“This furthermore supports previously published results that a sampling duration of ten to fourteen days of CGM data is sufficient to estimate glycaemic control appropriately [3].”
BackgroundMethodResultInfluentialManaging discordance between HbA 1c and glucose management indicator
Lenters‐Westra, E., Fokkert, M. J., Kilpatrick, E. S., Schleicher, E. D., Pilla, S. J., English, E., & van Dijk, P. R. (2025) · Diabetic Medicine · doi:10.1111/dme.70023 · Cited by 21
“13,17 However, it is often unclear which equations are used by different CGM devices to estimate HbA 1c .”
InfluentialRacial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c
Karter, A. J., Parker, M. M., Moffet, H. H., & Gilliam, L. K. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0153 · Cited by 21
“The Glucose Management Indicator (GMI), calculated from CGM readings, serves as a surrogate for A1C 45,46 and may be an unbiased estimate of glycemia with respect to race/ethnicity because GMI is based on an individual’s mean glucose alone.”
Rachmiel, M., Lebenthal, Y., Mazor‐Aronovitch, K., Brener, A., Levek, N., Jacobi‐Polishook, T., Ari, T. B., Abiri, S., Landau, Z., & Pinhas‐Hamiel, O. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0081 · Cited by 17
Chico, A., Navas de Solís, S., Lainez, M., Rius, F., & Cuesta, M. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0449 · Cited by 14
CASTORINO, K. N., Durnwald, C. P., Ehrenberg, S., Ehrhardt, N. M., Isaacs, D., Levy, C. J., Valent, A. M., & Continuous Glucose Monitoring for Gestational Diabetes Management Working Group (2024) · Journal of Women s Health · doi:10.1089/jwh.2023.0864 · Cited by 14
Using continuous glucose monitoring data in daily clinical practice
Martens, T. W., Simonson, G. D., & Bergenstal, R. M. (2024) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.91a.23090 · Cited by 17
“26 It is based purely on glycemia over the period refl ected on the AGP Report and can vary from the 3-month time-averaged hemoglobin A1c due to short-term clinical impacts (eg, change in diet, use of steroids, or short-term stress).”
Self-citationMunda, A., Kovacic, C., & Barlovič, D. P. (2024) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2024.108795 · Cited by 15
Isaksson, S. S., Imberg, H., Hirsch, I. B., Schwarcz, E., Hellman, J., Wijkman, M. O., Bolinder, J., Nyström, T., Holmer, H., Hallström, S., et al. (2024) · Diabetologia · doi:10.1007/s00125-024-06151-2 · Cited by 19
“The treatment target for most adults with diabetes is an HbA 1c value or an MG-derived estimated HbA 1c glucose management indicator (GMI) [9] of 53 mmol/mol ( 7.0”
InfluentialIsaksson, S. S., Ólafsdóttir, A. F., Ivarsson, S., Imberg, H., Toft, E., Hallström, S., Rosenqvist, U., Ekström, M., & Lind, M. (2023) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100799 · Cited by 13
“%) reduction in HbA1c 29 which has been associated with a reduced risk of retinopathy.”
BackgroundInfluentialThe ambulatory glucose profile and its interpretation
Lin, R., Brown, F., & Ekinci, E. I. (2022) · The Medical Journal of Australia · doi:10.5694/mja2.51666 · Cited by 15
How use of continuous glucose monitoring can address therapeutic inertia in primary care
Martens, T. W., & Parkin, C. G. (2022) · Postgraduate Medicine · doi:10.1080/00325481.2022.2080419 · Cited by 16
“The Glucose Statistics component (A) provides assessments of the average glucose, glucose variability (%CV), and estimated glucose control using the glucose management indicator (GMI), which aligns with HbA1c levels [79].”
MethodAl-Harbi, M. Y., Albunyan, A., Nahari, A., Kao, K., Brandner, L., Jammal, M. E., & Dunn, T. C. (2022) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-022-00831-y · Cited by 17
“Abbreviations CV: Coefficient of variation; eA1c: Estimated A1c; GMI: Glucose Management Index; HbA1c: Glycated hemoglobin; SD: Standard deviation; TIR: Time in range.”
MethodInfluentialXu, Y., Hirota, Y., Ajjan, R., Yamamoto, A., Matsuoka, A., Ogawa, W., & Dunn, T. C. (2021) · Diabetes and Vascular Disease Research · doi:10.1177/14791641211013734 · Cited by 15
Ismail, H. M., Spall, M., Evans‐Molina, C., & DiMeglio, L. A. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01373-4 · Cited by 17
“CGM data will be reviewed to evaluate total time in hypoglycemia, euglycemia, hyperglycemia, mean amplitude of glycemic excursions, the coefficient of variation of glucose levels, and time CGM was in use [36–38].”
MethodUrakami, T., Yoshida, K., Kuwabara, R., Mine, Y., Aoki, M., Suzuki, J., & Morioka, I. (2020) · Endocrine Journal · doi:10.1507/endocrj.ej20-0193 · Cited by 22
“Thus, the eA1c level will likely become an essential indicator for glycemic control as the GMI in the near future [17, 25].”
BackgroundOriot, P., & Hermans, M. P. (2018) · Acta Clinica Belgica · doi:10.1080/17843286.2018.1561780 · Cited by 16
“when eA1C is used for clinical decision purposes in the absence of a contemporary measure of HbA1c [20].”
MethodBattelino, T., Edelman, S. V., Nishimura, R., & Bergenstal, R. M. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0180 · Cited by 13
“…significantly different between Gla-100/IDet and IDeg in the other trials; only one of the six trials found a significant reduction in mean anytime TBR between treatment with Gla-100 or IDet at baseline and after 12 weeks of treatment with IDeg (mean [SD]: 14.7% [14.9] vs. 9.6% [11.5]; P = 0.045).”
InfluentialSelf-citationZhong, H., Zhang, K., Lin, L., Yan, Y., Shen, L., Chen, H., Liang, X., Chen, J., Miao, Z., Zheng, J., et al. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02409-6 · Cited by 15
“GMI was a linear transformation of the mean glucose value to estimate HbA1C and was calculated using the following formula: 3.31 + 0.02329* (mean glucose in mg/ dL) [13].”
MethodInfluentialNandam, N., Thung, S., Venkatesh, K. K., Gabbe, S. G., Ma, J., Peng, J., Dungan, K. M., & Buschur, E. (2024) · Cureus · doi:10.7759/cureus.52369 · Cited by 10
“If CGM downloads were unavailable, average sensor glucose from pump downloads was converted to an estimated A1C to use in lieu of the GMI [20].”
MethodMaking 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
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
von Sengbusch, S., Schneidewind, J., Bokelmann, J., Scheffler, N., Bertram, B., Frielitz, F., Hiort, O., & Lange, K. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.110135 · Cited by 18
“S. von Sengbusch et al. Diabetes Research and Clinical Practice 193 (2022) 110135 by both parents had lower GMI (p = 0.001).”
BackgroundInfluentialBomholt, T., Feldt‐Rasmussen, B., Butt, R., Borg, R., Sarwary, M. H., Elung‐Jensen, T., Almdal, T., Knop, F. K., Nørgaard, K., Ranjan, A. G., et al. (2021) · The Nephron journals/Nephron journals · doi:10.1159/000519493 · Cited by 15
Gildner, T. E., Eick, G. N., Schneider, A. L., Madimenos, F. C., & Snodgrass, J. J. (2021) · American Journal of Human Biology · doi:10.1002/ajhb.23689 · Cited by 15
“Still, despite these well publicized cases, the allure of having one device capable of rapidly performing multiple tests from a small amount of biological sample continues to grow in the healthcare field (Bergenstal et al., 2018; Chen et al., 2017; Paknikar et al., 2016; Waltz, 2017).”
BackgroundImproved Individual and Population-Level HbA1c Estimation Using CGM Data and Patient Characteristics
Grossman, J., Ward, A., Crandell, J. L., Prahalad, P., Maahs, D., & Scheinker, D. (2021) · Journal of diabetes and its complications · doi:10.1016/j.jdiacomp.2021.107950 · Cited by 13
Homhuan, W., Poomthavorn, P., Paksi, W., Khlairit, P., Nongnuch, A., & Pirojsakul, K. (2020) · Pediatric Nephrology · doi:10.1007/s00467-020-04720-2 · Cited by 12
Kim, J. Y., Kim, J. H., & Kim, J. H. (2025) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2024.0160 · Cited by 14
“Because mean glucose and either CV or SD values were available, the GMI was calculated as 3.31+0.02392×mean glucose (mg/dL) [20].”
InfluentialLiang, Y., Liang, J., Jiang, W., Wang, W., Yang, X., Liu, Y., Jin, X., Guo, Q., Xu, Y., Lü, B., et al. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01515-5 · Cited by 11
Ku, C. W., Zheng, R. T., Tan, H.-Y., Lim, J., Chen, L.-W., Cheung, Y.-B., Godfrey, K., Chan, J., Yap, F., Lek, N., et al. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01508-4 · Cited by 12
Lin, B., Liu, Z.-G., Chen, D.-R., Yang, Y., Yang, D., Yan, J., Zeng, L., Yang, X., & Xu, W. (2024) · World Journal of Diabetes · doi:10.4239/wjd.v15.i10.2058 · Cited by 11
Real-Life Use of Automated Insulin Delivery in Individuals With Type 2 Diabetes
Fabris, C., & Kovatchev, B. P. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241274786 · Cited by 11
Suyoto, P. S. T., de Rijk, M. G., de Vries, J. H. M., & Feskens, E. J. M. (2023) · Journal of Nutrition · doi:10.1016/j.tjnut.2023.11.025 · Cited by 14
Ullah, I., Hassan, M., Khan, K. M., Sajid, M., Umar, M., Hassan, S., Ullah, A., El‐Serehy, H. A., Charifi, W., & Yasmin, H. (2022) · IUBMB Life · doi:10.1002/iub.2699 · Cited by 16
“To verify these findings, the two compounds were evaluated at these concentrations in glucose consumption studies.(8) 3.”
BackgroundMethodLi, L., Sun, J., Ruan, L.-M., & Song, Q.-F. (2021) · Journal of Clinical Endocrinology and Metabolism · doi:10.1210/clinem/dgab356 · Cited by 12
Horowitz, M. E., Kaye, W., Pepper, G., Reynolds, K., Patel, S., Knudson, K. C., Kale, G., Gutierrez, M., Cotto, L., & Horowitz, B. S. (2021) · Diabetes Research and Clinical Practice · doi:10.1210/jendso/bvab048.930 · Cited by 12
Saadane, I., Ali, T., El-Laboudi, A. H., & Lessan, N. G. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108592 · Cited by 14
Significance of the CGM metric of time in range in children and adolescents with type 1 diabetes
Urakami, T. (2022) · Endocrine Journal · doi:10.1507/endocrj.ej22-0257 · Cited by 11
Pemberton, J. S., Andrews, R. C., Barnard-Kelly, K. D., Battelino, T., Danne, T., Heinemann, L., Kar, P., Lumb, A. N., Maahs, D. M., Mader, J. K., et al. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70460 · Cited by 14
Self-citationShah, V. N., Xu, Y., Dabiri, Y., Mohanadas, H. P., Cheng, A., & Dunn, T. C. (2025) · Diabetologia · doi:10.1007/s00125-025-06599-w · Cited by 12
“Among these metrics, sensor mean glucose (AG), time in range (TIR: 3.9–10 mmol/l) and the glucose management indicator (GMI), an estimation of HbA 1c based on linear regression of mean glucose, have gained prominence [11, 12].”
InfluentialCichosz, S. L., Kronborg, T., Hangaard, S., Vestergaard, P., & Jensen, M. H. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2025.0102 · Cited by 8
Fisker, S., Christensen, M., Bach, E., Bibby, B. M., & Hansen, K. W. (2025) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70043 · Cited by 13
Puig-Jové, C., Viñals, C., Conget, I., Quirós, C., Vinagre, I., Berrocal, B., Blanco-Carrasco, A.-J., Granados, M., Mesa, A., Serés-Noriega, T., et al. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02637-4 · Cited by 11
Simonson, G. D., Criego, A. B., Battelino, T., Carlson, A. L., Choudhary, P., Franc, S., Gershenoff, D. C., Grunberger, G., Hirsch, I. B., Isaacs, D. M., et al. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0107 · Cited by 11
Self-citationKesavadev, J., Basanth, A., Krishnan, G., Shankar, A., Sanal, G., & Jothydev, S. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01427-z · Cited by 16
“This is in line with previous publications and provides a level of consistency in the analysis [20].”
ResultZaharieva, D. P., Addala, A., Prahalad, P., Leverenz, B., Arrizon‐Ruiz, N., Ding, V. Y., Desai, M., Karger, A. B., & Maahs, D. M. (2022) · Diabetology · doi:10.3390/diabetology3030037 · Cited by 12
“Therefore, as reported by Beck and colleagues [5], there is an increased need for alternate methods to assess HbA1c, including academic laboratories’ fingerstick capillary blood collection kits suitable for home use (HbA1c home kit) and CGM-derived GMI [6].”
BackgroundVerma, R., Bhardwaj, S., Lathia, T. B., Kalra, S., RANADIVE, R. M., Tanna, S., Padsalge, M. V., Juneja, A., Samundra, K., Thakkar, P. B., et al. (2022) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-022-01111-1 · Cited by 9
“GMI was used as an estimate of glycated hemoglobin level, as described by Bergenstal et al. 2018 [27].”
MethodSchierbauer, J., Günther, S., Haupt, S., Zimmer, R. T., Zunner, B., Zimmermann, P., Wachsmuth, N., Eckstein, M., Aberer, F., Sourij, H., et al. (2022) · Italian National Conference on Sensors · doi:10.3390/s22093104 · Cited by 11
“The accordance between the GMI and the laboratory assessed HbA1c levels have been shown to be valid, detailing the one or the other might be used together with additional sensor glucose parameters [3].”
BackgroundFadini, G. P. (2022) · Diabetes Care · doi:10.2337/dci21-0069 · Cited by 17
Real-World Outcomes of Glucose Sensor Use in Type 1 Diabetes—Findings from a Large UK Centre
Lee, K.-H., Gunasinghe, S., Chapman, A., Findlow, L. A., Hyland, J., Ohol, S., Urwin, A., Rutter, M. K., Schofield, J. D., Thabit, H., et al. (2021) · Biosensors · doi:10.3390/bios11110457 · Cited by 15
“Participants were assigned an English index of multiple deprivation rank (IMD) [12] based upon their postcode [13].”
MethodSaito, K., Okada, Y., Torimoto, K., Takamatsu, Y., & Tanaka, Y. (2021) · Sleep And Breathing · doi:10.1007/s11325-021-02442-9 · Cited by 11
Oscillatory pattern of glycemic control in patients with diabetes mellitus
Vásquez-Muñoz, M., Arce‐Álvarez, A., von Igel, M., Véliz, C. V., Gonzalo Ruiz-Esquide, S., Ramírez‐Campillo, R., Álvarez, C., Ramírez‐Vélez, R., Crespo, F., Izquierdo, M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84822-5 · Cited by 11
Rodbard, D., & Garg, S. K. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0030 · Cited by 13
“…(%Coefficient of Variation [%CV]), 52 estimates of Hemoglobin A1c (HbA1c) calculated from glucose data such as the glucose management indicator (GMI) 53 or alternative approaches with better accuracy, 54–57 percentages of time in several ranges (time in range [%TIR]), time below range (%TBR, levels…”
Standardized Hybrid Closed-Loop System Reporting
Shah, V. N., & Garg, S. K. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0622 · Cited by 14
“75,76 It is accepted in the glucose reporting standards, especially during the COVID-19 pandemic, when A1c measurements may not be possible during a virtual/telehealth encounter.”
Alhodaib, H. I., Antza, C., Chandan, J. S., Hanif, W., Sankaranarayanan, S., Paul, S., Sutcliffe, P. A., & Nirantharakumar, K. (2020) · JMIR Diabetes · doi:10.2196/19650 · Cited by 12
Glycemic Status Assessment by the Latest Glucose Monitoring Technologies
Malandrucco, I., Russo, B., Picconi, F., Menduni, M., & Frontoni, S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218243 · Cited by 17
“The GMI with CGM metrics provides information for a more personalized diabetes management [6].”
BackgroundGalindo, R. J., Moazzami, B., Tuttle, K. R., Bergenstal, R. M., Peng, L., & Umpierrez, G. E. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0145 · Cited by 13
“9 GMI was estimated using the original linear regression model, 10,11 based on sensor glucose.”
InfluentialSelf-citationYoo, J. H., Yang, S. H., Jin, S., & Kim, J. H. (2024) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2023.0083 · Cited by 11
“The published GMI equation was used to calculate the GMI from the CGM-derived MG [21].”
Malglycemia in the critical care setting. Part II: Relative and absolute hypoglycemia
Roberts, G., Krinsley, J. S., Preiser, J., Quinn, S. J., Rule, P. R., Brownlee, M., Umpierrez, G. E., & Hirsch, I. B. (2023) · Journal of Critical Care · doi:10.1016/j.jcrc.2023.154429 · Cited by 13
Azcoitia, P., Rodríguez-Castellano, R., Saavedra, P., Alberiche-Ruano, M. P., Marrero, D., Wägner, A. M., Ojeda, A., & Boronat, M. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231191544 · Cited by 11
“This led a group of experts to rename estimated-HbA1c as “glucose management indicator” (GMI), 8 on the assumption that its value should not necessarily be matched with the laboratory HbA1c.”
Joshi, K., Harris, M. A., Cotterill, A. M., Wentworth, J. M., Couper, J., Haynes, A., Davis, E. A., Lomax, K. E., & Huynh, T. (2023) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2023-0234 · Cited by 13
“The GMI allows for evaluation of dysglycaemia over periods shorter than 3months and therefore provides information about more recent metabolic deterioration.”
MethodInfluentialBiester, T., Grimsmann, J. M., Heidtmann, B., Rami-Merhar, B. M., Ermer, U., Wolf, J., Freff, M., Karges, B., Agena, D., Bramlage, P., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0373 · Cited by 12
“36 The newly developed method by Bergenstal and Beck, 37 now called ‘‘Glucose Management Index’’ (GMI) led to values closer to measured HbA1c values from blood samples.”
Corathers, S. D., & DeSalvo, D. J. (2020) · Diabetes Spectrum · doi:10.2337/ds19-0017 · Cited by 13
Bertachi, A. H., Biagi, L., Beneyto, A., & Vehı́, J. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/1414597 · Cited by 14
“7%, which gives the approximate A1C levels based on CGM measurements [36].”
BackgroundTweden, K. S., Deiss, D., Rastogi, R., Addaguduru, S., & KAUFMAN, F. R. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0342 · Cited by 10
Saadane, I., Ashraf, T., Ali, T., & Lessan, N. G. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.05.020 · Cited by 9
Pluchino, K. M., Wu, Y., Silk, A. D., Yi, J.-S., & Lias, C. (2018) · Diabetes Care · doi:10.2337/dc18-2366 · Cited by 10
Bergenstal, R. M., Martens, T. W., & Beck, R. W. (2025) · JAMA · doi:10.1001/jama.2025.19197 · Cited by 10
Self-citationAl Hayek, A. A., & Al Dawish, M. A. (2023) · Advances in Therapy · doi:10.1007/s12325-023-02508-y · Cited by 9
Navigating the discordance: a comprehensive review of HbA1c-glycemia mismatch in clinical practice
Sureshkumar, P., Kumar, S. S., Anusree, E., Cheriyan, J., & Masood, A. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113216 · Cited by 11
How to compare algorithms for automated insulin delivery using different sensors?
Hansen, K. W. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16234 · Cited by 11
Roberts, G. W., Krinsley, J. S., Preiser, J., Quinn, S. J., Rule, P. R., Brownlee, M., Umpierrez, G. E., & Hirsch, I. B. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154537 · Cited by 10
Randazzese, S. F., Bombaci, B., Costantino, S., Giorgianni, Y., Lombardo, F., & Salzano, G. (2024) · Children · doi:10.3390/children11020210 · Cited by 10
“Formerly referred to as estimated A1C (eA1C), this metric is derived through a formula established from a regression line, plotted with mean blood glucose concentration points on the x axis and contemporaneously measured HbA1c values on the y axis [21].”
MethodLandau, Z., Lebenthal, Y., Mazor‐Aronovitch, K., Brener, A., Levek, N., Jacobi‐Polishook, T., Ari, T. B., Abiri, S., Haim, A., Nir, J., et al. (2024) · Endocrine · doi:10.1007/s12020-024-03683-w · Cited by 10
“Other parameters collected included the coef fi cient of variation (CV), mean glucose levels (mg/dL) and glucose management indicator (GMI) [10].”
Lu, Y., Zhang, Q., Wang, X., Jiang, Y., & Xue, Y. (2023) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2023.108613 · Cited by 7
Molfetta, S. D., Rossi, A., Assaloni, R., Cherubini, V., Consoli, A., Bartolo, P. D., Guardasole, V., Laurenzi, A., Lombardo, F., Maffeis, C., et al. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109867 · Cited by 13
Fatouhi, D. E., Héritier, H., Allémann, C., Malisoux, L., Laouali, N., Riveline, J., Salathé, M., & Fagherazzi, G. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0294 · Cited by 10
“02392 · mean daily glucose in mg/dL).(31,32) Three measures of glycemic variability were explored within this study: within day SD and within day CV of daily glucose values, and the approximate area under the sensor glucose curve over 24 h.”
MethodAbdulhussein, F. S., Chesser, H., Boscardin, W. J., Gitelman, S. E., & Wong, J. C. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0131 · Cited by 11
Estimated HbA1c and glucose management indicator (GMI): are they the same?
Leelarathna, L., Thabit, H., Hovorka, R., & Evans, M. (2020) · Diabetic Medicine · doi:10.1111/dme.14423 · Cited by 9
Monnier, L., Colette, C., & Owens, D. R. (2020) · Diabetes & Metabolism · doi:10.1016/j.diabet.2020.06.004 · Cited by 12
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
Li, Z., Natarajan, K., Zhang, W.-Z., Zhou, M., Lee, S. A., Zhang, Y.-W., Xu, M. A., Esmaeilpour, Z., Salim, F., Malhotra, M., et al. (2026) · arXiv.org · doi:10.48550/arxiv.2605.30865 · Cited by 8
“We compare GlucoFM with a same-window 7-day threshold base-line based on a glucose management indicator (GMI)-equivalent value [6] for diabetes classification.”
Bergenstal, R., Xu, Y., Dunn, T. C., Choudhary, P., & Ajjan, R. (2026) · Diabetologia · doi:10.1007/s00125-026-06739-w · Cited by 8
“The glucose management indicator (GMI) [1], derived from continuous glucose monitoring (CGM) data, is intended to represent the expected laboratory-measured HbA 1c .”
Self-citationCichosz, S., Hangaard, S., Kronborg, T., Vestergaard, P., & Jensen, M. H. (2025) · medRxiv · doi:10.1101/2025.01.01.25319870 · Cited by 8
“• Glucose Management Indicator (GMI) [29]: The GMI provides an estimate of average glucose levels by applying a formula that correlates with hemoglobin A1C values.”
Morris-Murray, M., & Frazzitta, M. A. (2024) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2024.30.10-b.s30 · Cited by 5
den Braber, N., Braem, C. I. R., Vollenbroek-Hutten, M. M. R., Hermens, H. J., Urgert, T., Yavuz, U. Ş., Veltink, P. H., & Laverman, G. D. (2024) · Interactive Journal of Medical Research · doi:10.2196/50849 · Cited by 9
“…mg/dL or 3.0 mmol/L; %), TAR (glucose >180 mg/dL or 10.0 mmol/L; %), TAR level 2 (TAR2; glucose >250.2 mg/dL or 13.9 mmol/L; %), SD glucose (mmol/L), CV (%), GMI (mmol/mol), low blood glucose index (LBGI), high blood glucose index (HBGI), and the risk index (RI; Multimedia Appendix 1) [8,10,25-27].”
InfluentialPemberton, J. S., Collins, L., Drummond, L., Dias, R. P., Krone, R. E., Kershaw, M., & Uday, S. (2024) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2024-004045 · Cited by 8
“33 Hence, caution should be exercised when interpreting improvements based solely on GMI in an ethnically diverse population.”
BackgroundPangrace, M., Dolan, S., Grace, T., Greene, E., Long, E., McClelland, S., Moore, J., Morgan, D. E., Mullins, H., & Wescott, S. (2024) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2024.30.1-a.s1 · Cited by 9
Song, Y., Zhai, X., Bai, Y., Liu, C., & Zhang, L. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1218602 · Cited by 11
Kovatchev, B. P., & Lobo, B. J. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0117 · Cited by 10
“13 The latest addition to the family of glucose control metrics was the Glycemia Risk Index (GRI), which was based on the collective opinions of a number of physicians and attempted to balance the risks for hypoglycemia and hyperglycemia in a single metric.”
Remission in Ketosis-Prone Diabetes
Kikani, N. J., & Balasubramanyam, A. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2022.06.005 · Cited by 11
Glennie, J. L., Berard, L., & Levrat‐Guillen, F. (2022) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor.s346736 · Cited by 11
“Other accepted metrics that have emerged from CGM data are glucose variability, expressed as the coefficient of variation (CV) of glucose,(29) and the glucose management indicator (GMI), which is a measure of short-term glucose exposure that allows treatment adjustment to be fine-tuned when used in conjunction with a…”
MethodEldor, R., Roitman, E., Merzon, E., Toledano, Y., Alves, C., & Tsur, A. (2022) · Endocrine Practice · doi:10.1016/j.eprac.2022.02.004 · Cited by 9
Dağdelen, S., Deyneli, O., Dınççağ, N., ILKOVA, H. M., SİVA, Z. O., Yetkin, İ., & Yilmaz, T. M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.663222 · Cited by 8
Metadata
- 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
- 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
- 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
- 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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