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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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Cites
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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 4,237
“Glucose management indicator (GMI) (75)”
BackgroundInfluentialSelf-citationKDIGO 2020 Clinical practice guideline for diabetes management in chronic kidney disease
de Boer, I. H., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.06.019 · Cited by 1,399
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Self-citationThe relationships between time in range, hyperglycemia metrics, and HbA1c
Beck, R. W., Bergenstal, R. M., Cheng, P., Kollman, C., Carlson, A. L., Johnson, M. L., & Rodbard, D. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818822496 · Cited by 479
Self-citationConsensus report: definition and interpretation of remission in type 2 diabetes
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“In some circumstances, an eA1C or GMI <6.5% can be considered an equivalent criterion.”
InfluentialSelf-citationSamson, S. L., Vellanki, P., Blonde, L., Christofides, E. A., Galindo, R. J., Hirsch, I. B., Isaacs, S. D., Izuora, K. E., Low Wang, C. C., Twining, C. L., et al. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.02.001 · Cited by 375
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Self-citationde Boer, I. H., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.06.024 · Cited by 278
“CGM may be useful to index hemoglobin A1c values for patients in whom hemoglobin A1c is not concordant with directly measured blood glucose levels or clinical symptoms.(11,12) In addition, CGM and self-monitoring of blood glucose can be used for short-term titration of treatments, prevention of hypoglycemia, and impro…”
MethodCollyns, O. J., Meier, R. A., Betts, Z. L., Chan, D. S. H., Frampton, C., Frewen, C. M., Hewapathirana, N. M., Jones, S. D., Roy, A., Grosman, B., et al. (2021) · Diabetes Care · doi:10.2337/dc20-2250 · Cited by 276
“This mean SG during AHCL equates to a glucose management indicator (16) of 6.”
BackgroundAdvances in technology for management of type 1 diabetes
Beck, R. W., Bergenstal, R. M., Laffel, L. M., & Pickup, J. C. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31142-0 · Cited by 242
Self-citationConsensus report: definition and interpretation of remission in type 2 diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P., et al. (2021) · Diabetologia · doi:10.1007/s00125-021-05542-z · Cited by 122
Self-citationReal-World Performance of the MiniMed™ 780G System: First Report of Outcomes from 4120 Users
Silva, J. D., Lepore, G., Battelino, T., Arrieta, A., Castañeda, J., Grossman, B., Shin, J., & Cohen, O. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0203 · Cited by 229
“Users with ‡10 days of sensor glucose (SG) data after AHCL was enabled for the first time (post-AHCL) were included in the analysis, based on previous publications using a similar duration of time to estimate or determine CGM-derived metrics.(13,14) All data available were included, whether the system was in AHCL cont…”
MethodA View Beyond HbA1c: Role of Continuous Glucose Monitoring
Chehrehgosha, H., Khamseh, M. E., Malek, M., Hosseinpanah, F., & Ismail‐Beigi, F. (2019) · Diabetes Therapy · doi:10.1007/s13300-019-0619-1 · Cited by 257
Glycemic Monitoring and Management in Advanced Chronic Kidney Disease
Galindo, R. J., Beck, R. W., Scioscia, M. F., Umpierrez, G. E., & Tuttle, K. R. (2020) · Endocrine Reviews · doi:10.1210/endrev/bnaa017 · Cited by 211
Self-citation7. Diabetes Technology: Standards of Medical Care in Diabetes—2019
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s007 · Cited by 179
“This provided a new way to use CGM data to estimate A1C (63).”
MethodPositioning time in range in diabetes management
Advani, A. (2020) · Diabetologia · doi:10.1007/s00125-019-05027-0 · Cited by 170
“The data analysis tools offer patients and healthcare professionals a wide range of metrics of glucose control quality, including number of days worn, percentage of time CGM is active, mean glucose, glucose management indicator (GMI; formerly estimated HbA1c [16]), glycaemic variability (%CV) and TIR [17].”
MethodChamberlain, J. J., Doyle-Delgado, K., Peterson, L., & SKOLNIK, N. (2019) · Annals of Internal Medicine · doi:10.7326/m19-1638 · Cited by 55
“Recently, estimated HbA1c was renamed the glucose management indicator (24).”
BackgroundMayeda, L., Katz, R., Ahmad, I., Bansal, N., Batacchi, Z., Hirsch, I. B., Robinson, N., Trence, D. L., Zelnick, L., & de Boer, I. H. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000991 · Cited by 162
Lum, J. W., Bailey, R. J., Barnes-Lomen, V., Naranjo, D., Hood, K. K., Lal, R. A., Arbiter, B., Brown, A. S., DeSalvo, D. J., Pettus, J., et al. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0535 · Cited by 150
“To be included in the cohort for analysis, participants had to (1) not have started Loop, or used it for <7 days at the time of enrollment; (2) provided at least 50 records of Loop basal insulin data (represents 4–8 h of Loop use) or at least 1 Loop device issue report after starting Loop; and (3) provided at least 33…”
MethodSelf-citationArrieta, A., Battelino, T., Scaramuzza, A. E., Silva, J. D., Castañeda, J., Cordero, T. L., Shin, J., & Cohen, O. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14714 · Cited by 163
“While there is a clinically important difference between HbA1c and the GMI,(35) the latter provides a validated correlation between CGM metrics and managed glycaemia over time.(36) As GMI has become a CGM-derived standard for glycaemic control, it was used as a surrogate for HbA1c for determining treatment goal achiev…”
MethodNavaneethan, S. D., Zoungas, S., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Liew, A., Michos, E. D., Olowu, W. A., Sadusky, T., et al. (2020) · Annals of Internal Medicine · doi:10.7326/m20-5938 · Cited by 171
“Continuous glucose monitoring or selfmonitoring of blood glucose may be particularly useful among patients in whom HbA1c is not concordant with directly measured blood glucose levels or clinical symptoms (12).”
BackgroundManaging New-Onset Type 1 Diabetes During the COVID-19 Pandemic: Challenges and Opportunities
Garg, S. K., Rodbard, D., Hirsch, I. B., & Forlenza, G. P. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0161 · Cited by 156
“Hence, the TIR (and, nearly equivalently, the TAR) can be used as the basis for clinical day-to-day management.(27,28,50,51)”
Continuous glucose monitoring targets in type 1 diabetes pregnancy: every 5% time in range matters
Murphy, H. R. (2019) · Diabetologia · doi:10.1007/s00125-019-4904-3 · Cited by 137
Consensus report: Definition and interpretation of remission in type 2 diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P. R., et al. (2021) · Diabetic Medicine · doi:10.1111/dme.14669 · Cited by 31
Self-citationContinuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology
Galindo, R. J., & Aleppo, G. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108502 · Cited by 144
“[77] Glucose variability is presented as a percentage coefficient of variation (%CV), with the recommended goal of <36%.”
BackgroundSelf-citationTime in range: a new parameter to evaluate blood glucose control in patients with diabetes
Gabbay, M. A. L., Rodacki, M., Calliari, L. E., Vianna, A. G. D., Krakauer, M., Pinto, M. S., Reis, J. S., Puñales, M. K., Miranda, L. G., Ramalho, A. C., et al. (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00529-z · Cited by 143
Encapsulated stem cell–derived β cells exert glucose control in patients with type 1 diabetes
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“The glucose management indicator (GMI), a secondary endpoint, expresses mean glycemia as a CGM substitute for HbA1c values that are measured only at 3-month intervals 25,26 .”
Beck, R. W., Kanapka, L., Breton, M. D., Brown, S. A., Wadwa, R., Buckingham, B. A., Kollman, C., & Kovatchev, B. P. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0558 · Cited by 102
Self-citationEngineering digital biomarkers of interstitial glucose from noninvasive smartwatches
Bent, B., Cho, P. J., Henriquez, M., Wittmann, A., Thacker, C., Feinglos, M. N., Crowley, M. J., & Dunn, J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00465-w · Cited by 101
Interpreting blood GLUcose data with R package iglu
Broll, S., Urbanek, J. K., Buchanan, D., Chun, E., Muschelli, J., Punjabi, N. M., & Gaynanova, I. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248560 · Cited by 123
“Our R package iglu calculates all CGM metrics summarized in [2] in addition to several others [6, 12, 19], a full list of currently implemented metrics is summarized in Table 1.”
MethodPeralta, F. G., Choudhary, P., Cosson, E., Irace, C., Rami-Merhar, B. M., & Seibold, A. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14638 · Cited by 120
“glucose monitoring (CGM) during the last decade has enabled us to track glycaemic control directly, including the metrics of estimated A1c (eA1c) and subsequently the glucose management indicator (GMI).(5) This has also led to a widespread assumption that eA1c or GMI are intended to approximate HbA1c as closely as pos…”
BackgroundMethodInfluentialSherr, J. L., Buckingham, B. A., Forlenza, G. P., Galderisi, A., Ekhlaspour, L., Wadwa, R. P., Carria, L., Hsu, L., Berget, C., Peyser, T. A., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0286 · Cited by 92
“While GMI is generally suggested to be calculated from at least 10 days of sensor use, 27 a recent study indicates that 5 days of sensor data can provide an estimation of mean glucose concentration that is reasonably well-correlated with that calculated from a 3-month period of use.”
Consensus Report: Definition and Interpretation of Remission in Type 2 Diabetes
Riddle, M. C., Cefalu, W. T., Evans, P. H., Gerstein, H. C., Nauck, M. A., Oh, W. K., Rothberg, A. E., le Roux, C. W., Rubino, F., Schauer, P., et al. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab585 · Cited by 92
“In some circumstances, an eA1C or GMI <6.5% can be considered an equivalent criterion.”
InfluentialSelf-citationRaj, R., Mishra, R., Jha, N., Joshi, V. R., Correa, R. R., & Kern, P. A. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002573 · Cited by 110
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Self-citationPotential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap
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Electrospun nanofiber-based glucose sensors for glucose detection
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“are young adults (Bergenstal et al., 2018); 2) Type 2, with a low rate of insulin production or the body is without insulin produced by the pancreas, accounts for 90% of people with”
BackgroundWhole-Grain Processing and Glycemic Control in Type 2 Diabetes: A Randomized Crossover Trial
Åberg, S., Mann, J. I., Neumann, S., Ross, A. B., & Reynolds, A. N. (2020) · Diabetes Care · doi:10.2337/dc20-0263 · Cited by 107
Prahalad, P., Ding, V. Y., Zaharieva, D. P., Addala, A., Johari, R., Scheinker, D., Desai, M., Hood, K., & Maahs, D. M. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab859 · Cited by 82
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“The formula was derived using regression analysis of laboratory-measured HbA1c and CGM-measured mean glucose based on population from three clinical trials using specific sensor types (Dexcom G4 and G5; Dexcom, San Diego, CA, USA) in non-Hispanic whites [15-19].”
MethodFlash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain
Peralta, F. G., Dunn, T. C., Landuyt, K., Xu, Y., & Merino-Torres, J. F. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001052 · Cited by 101
HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis
Perlman, J. E., Gooley, T. A., MCNULTY, B. K., Meyers, J., & Hirsch, I. B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0501 · Cited by 101
Dalmazi, G. D., Maltoni, G., Bongiorno, C., Tucci, L., Natale, V. D., Moscatiello, S., Laffi, G., Pession, A., Zucchini, S., & Pagotto, U. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001664 · Cited by 87
cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data
Vigers, T., Chan, C. L., Snell-Bergeon, J., Bjornstad, P., Zeitler, P., Forlenza, G. P., & Pyle, L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216851 · Cited by 78
“The American Diabetes Association (ADA) recently proposed a set of key metrics for reporting CGM data [8], all of which are calculated by our code, in addition to the glucose management indicator (GMI) [3], time in range [2], and other variables proposed by Hernandez et al. [4].”
BackgroundThe trials and tribulations of determining HbA1c targets for diabetes mellitus
Klein, K. R., & Buse, J. B. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00425-6 · Cited by 91
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Evolution of Diabetes Technology
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Real-World Use of a New Hybrid Closed Loop Improves Glycemic Control in Youth with Type 1 Diabetes
Messer, L. H., Berget, C., Pyle, L. L., Vigers, T., Cobry, E. C., Driscoll, K. A., & Forlenza, G. P. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0165 · Cited by 76
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Standardization process of continuous glucose monitoring: Traceability and performance
Freckmann, G., Nichols, J. H., Hinzmann, R., Klonoff, D. C., Ju, Y., Diem, P., Makris, K., & Slingerland, R. J. (2021) · Clinica Chimica Acta · doi:10.1016/j.cca.2020.12.025 · Cited by 62
Basics and use of continuous glucose monitoring (CGM) in diabetes therapy
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“The so-called GMI, however, calculates the estimated A1c based on CGM-derived glucose values [38].”
BackgroundExpanding the Role of Continuous Glucose Monitoring in Modern Diabetes Care Beyond Type 1 Disease
Klupa, T., Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01431-3 · Cited by 89
“Therefore, it has been proposed that the glucose management indicator (GMI) [170], a CGM-derived marker, can be used to evaluate overall glucose exposure for people in whom HbA1c may be an unreliable [171, 172].”
BackgroundReal-world evidence of automated insulin delivery system use
Considine, E. G., & Sherr, J. L. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0442 · Cited by 59
Fabris, C., Heinemann, L., Beck, R., Cobelli, C., & Kovatchev, B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0236 · Cited by 58
“In 2018, Bergenstal and coworkers introduced a new metric for converting CGMderived mean BG to HbA1c units, which was named ‘‘glucose management indicator’’ (GMI).(27,28) A decision was made to abandon the term ‘‘estimated A1c’’ (eA1c) to avoid confusion if eA1c and laboratory HbA1c did not agree.”
BackgroundSelf-citationDigital Technology for Diabetes
Hughes, M. S., Addala, A., & Buckingham, B. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2215899 · Cited by 59
Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance
Abuin, P., Rivadeneira, P. S., Ferramosca, A., & González, A. H. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.06.009 · Cited by 57
Wu, Z.-K., Luo, S.-H., Zheng, X.-Y., Bi, Y., Xu, W., Yan, J.-H., Yang, D.-Z., & Weng, J. (2020) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018820950146 · Cited by 57
Associations between HbA1c and continuous glucose monitoring‐derived glycaemic variables
Hirsch, I. B., Welsh, J. B., Calhoun, P. M., Puhr, S., Walker, T. C., & Price, D. A. (2019) · Diabetic Medicine · doi:10.1111/dme.14065 · Cited by 63
Reference Guide for Integrating Continuous Glucose Monitoring Into Clinical Practice
Kruger, D. F., Edelman, S. V., Hinnen, D. A., & Parkin, C. G. (2019) · The Diabetes Educator · doi:10.1177/0145721718818066 · Cited by 55
Zelnick, L. R., Batacchi, Z. O., Ahmad, I., Dighe, A., Little, R. R., Trence, D. L., Hirsch, I. B., & de Boer, I. H. (2020) · Diabetes Care · doi:10.2337/dc20-0915 · Cited by 55
Aleppo, G., & Webb, K. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818813581 · Cited by 67
“Glucose average or glucose exposure to have a sense of the glucose management indicator (GMI).(60) 4.”
Self-citationTime in range centered diabetes care
Dovč, K., & Battelino, T. (2021) · Clinical Pediatric Endocrinology · doi:10.1297/cpe.30.1 · Cited by 66
“The consensus suggested easy to understand TIR targets, along with TBR and TAR targets for routine management of type 1 and type 2 diabetes (Table 1).”
MethodInfluentialAsgharzadeh, A., Patel, M., Connock, M. J., Damery, S. L., Ghosh, I., Jordan, M., Freeman, K., Brown, A., Court, R. A., Baldwin, S., et al. (2024) · Health Technology Assessment · doi:10.3310/jypl3536 · Cited by 32
Self-citationStandardizing the haemoglobin glycation index
Hempe, J. M., Yang, S., Liu, S., & Hsia, D. S. (2021) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.299 · Cited by 55
Type 1 diabetes and COVID-19: The “lockdown effect”
Aragona, M., Rodia, C., Bertolotto, A., Campi, F., Coppelli, A., Giannarelli, R., Bianchi, C., Dardano, A., & Prato, S. D. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108468 · Cited by 50
“To the purpose of this analysis, we analyzed Average Glucose, Glucose Management Indicator (GMI), Glucose Variability (calculated as the coefficient of variation, CV), Time In Range (TIR, 70–180 mg/dl), Time Above Range (TAR, >180 mg/dl) and Time Below Range (TBR, <70 mg/ dl) [9,10] across 4 lockdown periods: 14 days…”
BackgroundMethodPrahalad, P., Zaharieva, D. P., Addala, A., New, C., Scheinker, D., Desai, M., Hood, K. K., & Maahs, D. M. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00360 · Cited by 57
Kovatchev, B. P., Singh, H., Mueller, L., & Gonder-Frederick, L. A. (2022) · Diabetes Care · doi:10.2337/dc22-1217 · Cited by 49
“Note that the GMI is a unit-less linear function of average glucose, presented sometimes as a percentage because of its clinical analogy with HbA1c (24).”
BackgroundMethodInfluentialLu, J., Ma, X., Zhang, L., Mo, Y., Lu, W., Zhu, W., Bao, Y., Jia, W., & Zhou, J. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108032 · Cited by 49
Tauschmann, M., Cardona‐Hernandez, R., DeSalvo, D. J., Hood, K. K., Лаптев, Д. Н., Olinder, A. L., Wheeler, B. J., & Smart, C. E. (2025) · Hormone Research in Paediatrics · doi:10.1159/000543156 · Cited by 69
“…70 and 180 mg/dL or 3.9 and 10 mmol/L), glucose management indicator (GMI) (estimated HbA1c value derived from CGM data over a de fi ned duration) [26], or most recently “ time in tight range ” [27] (the time spent within the 70 – 140 mg/ dL or 3.9 – 7.8 mmol/L range), have emerged as valuable…”
InfluentialHengist, A., Davies, R. G., Walhin, J., Buniam, J., Merrell, L. H., Rogers, L. M., Bradshaw, L., Moreno‐Cabañas, A., Rogers, P. J., Brunstrom, J. M., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101667 · Cited by 53
Real‐world performance of the MiniMed™ 670G system in Europe
Silva, J. D., Bosi, E., JENDLE, J. H., Arrieta, A., Castañeda, J., Grosman, B., Cordero, T. L., Shin, J., & Cohen, O. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14424 · Cited by 62
“International and national guidelines have, accordingly, been updated to the near-physiological recommended goals of HbA1c less than 53 mmol/mol ( 7.0%), indicating a preference for lower HbA1c levels such as 48 mmol/mol (6.5%), provided it can be achieved safely.1,2 Additionally, professional societies have endorsed…”
BackgroundInfluentialZelnick, L. R., Batacchi, Z., Ahmad, I., Dighe, A. L., Little, R., Trence, D., Hirsch, I., & de Boer, I. D. (2020) · Diabetes Care · doi:10.2337/figshare.12674651.v1 · Cited by 47
Information and consensus document for the detection and management of chronic kidney disease
Maset, R. G., Bover, J., Segura, J., Diezhandino, M. G., del Hoyo, J. C., Escalada, J., Fácila, L., Gamarra, J., García‐Donaire, J. A., Matarin, L. G., et al. (2022) · Nefrología (English Edition) · doi:10.1016/j.nefroe.2022.07.003 · Cited by 61
Monitoring of Pediatric Type 1 Diabetes
Marks, B. E., & Wolfsdorf, J. I. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00128 · Cited by 55
Ahmadi, A., Khoshfetrat, S. M., Kabiri, S., Dorraji, P., Larijani, B., & Omidfar, K. (2021) · Microchimica Acta · doi:10.1007/s00604-021-04959-y · Cited by 42
“Therefore, the HbA1c determination in blood sample is important for healthcare providers to make a diagnosis and monitor long-term progress of diabetes without the influence of short-term fluctuation of blood glucose [6].”
BackgroundTime-in-range for monitoring glucose control: Is it time for a change?
Bellido, V., Pinés-Corrales, P. J., Villar‐Taibo, R., & Ampudia‐Blasco, F. J. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108917 · Cited by 49
Hypoglycemia in People with Type 2 Diabetes and CKD
Ahmad, I., Zelnick, L. R., Batacchi, Z. O., Robinson, N., Dighe, A. L., Manski-Nankervis, J.-A. E., Furler, J., O'Neal, D. N., Little, R. R., Trence, D., et al. (2019) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.11650918 · Cited by 49
“180 mg/dl), coefficient of variation (SD divided by mean of all CGM glucose concentrations, a preferred metric of glycemic variability), SD and interquartile range of all CGM glucose concentrations (alternative variability metrics), and the glucose management indicator (GMI) (a measure of mean blood glucose calculated…”
BackgroundA foundation model for continuous glucose monitoring data
Lutsker, G., Sapir, G., Shilo, S., Merino, J., Godneva, A., Greenfield, J. R., Samocha‐Bonet, D., Dhir, R., Gude, F., Mannor, S., et al. (2026) · Nature · doi:10.1038/s41586-025-09925-9 · Cited by 64
Implications of the Hemoglobin Glycation Index on the Diagnosis of Prediabetes and Diabetes
Hsia, D. S., Rasouli, N., Pittas, A. G., Lary, C. W., Peters, A. L., Lewis, M. R., Kashyap, S. R., Johnson, K., LeBlanc, E. S., Phillips, L. S., et al. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa029 · Cited by 44
Continuous glucose monitoring in cystic fibrosis – A practical guide
Chan, C. L., Ode, K. L., Granados, A., Moheet, A. A., Moran, A., & Hameed, S. A. (2019) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2019.08.025 · Cited by 50
Wright, E. E., Morgan, K., Fu, D. K., Wilkins, N., & Guffey, W. J. (2020) · Clinical Diabetes · doi:10.2337/cd20-0042 · Cited by 40
Danne, T. P. A., Joubert, M., Hartvig, N. V., Kaas, A., Knudsen, N. N., & Mader, J. K. (2024) · Diabetes Care · doi:10.2337/dc23-2176 · Cited by 42
Chesser, H., Srinivasan, S., Puckett, C., Gitelman, S. E., & Wong, J. C. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221139873 · Cited by 40
Real-World Data from the First U.S. Commercial Users of an Implantable Continuous Glucose Sensor
Sanchez, P., Ghosh-Dastidar, S., Tweden, K. S., & Kaufman, F. R. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0234 · Cited by 40
“The patient metrics included mean SG, SD, coefficient of variation (CV), and glucose measurement index (GMI; a mathematical estimate of A1c published by Bergenstal et al. 12 ).”
InfluentialPrahalad, P., Scheinker, D., Desai, M., Ding, V. Y., Bishop, F. K., Lee, M. Y., Ferstad, J. O., Zaharieva, D. P., Addala, A., Johari, R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02975-y · Cited by 45
“The GMI, an established estimator of HbA1c, was computed at 2-week intervals by applying the formula developed by Bergenstal et al. 54 to CGM glucose readings, averaged across a lookback window of up to 90 days.”
Rodbard, D. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14906 · Cited by 45
“Use of Mean Glucose avoids the uncertainties in the conversion of Mean Glucose into GMI introduced by choice of a reference dataset and the large scatter of the data around the regression line.(3,4,19,20) The increasing stability and”
BackgroundMethodInfluentialGonzalez, A., Deng, Y., Lane, A. N., Benkeser, D. C., Cui, X., Staimez, L. R., Ford, C. N., Khan, F. N., WEBSTER, S. C. M., Leong, A., et al. (2019) · Diabetic Medicine · doi:10.1111/dme.14181 · Cited by 49
Kovatchev, B. P., Lobo, B. J., Fabris, C., Ganji, M., Fathi, A. E., Breton, M. D., Kanapka, L. G., Kollman, C., Battelino, T., & Beck, R. W. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0404 · Cited by 34
Self-citationKong, A. P. S., Lim, S. K., Yoo, S.-H., Ji, L., Chen, L., Bao, Y., Yeoh, E. C. K., Chan, S.-P., Wang, C., Mohan, V., et al. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110718 · Cited by 49
Friedman, J. G., Cardona, Z., Szmuilowicz, E. D., & Aleppo, G. (2023) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s374663 · Cited by 43
Self-citationKeshet, A., & Segal, E. (2024) · Nature Communications · doi:10.1038/s41467-024-53832-y · Cited by 44
“Measures of hyperglycemia — % above 180, HBGI 31 and Hyper index 32 — showed the lowest numbers of signi fi cant associations with bacterial gene families and pathways. eA1C 33 and GMI 34 , both transformations of the mean-glucosemeasuredfromCGM,showedsimilarassociationpatterns,and were found to be signi fi cantly assoc…”
BackgroundMontaser, E., Fabris, C., & Kovatchev, B. P. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0104 · Cited by 44
“For example, GMI is a linear function of MG, 23 implying a correlation of 1.0, and TIR = 100 - TAR - TBR, by definition.”
InfluentialNew and Emerging Technologies in Type 1 Diabetes
Sherwood, J. S., Russell, S. J., & Putman, M. S. (2020) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2020.07.006 · Cited by 46
Dunn, T. C., Xu, Y., Bergenstal, R. M., Ogawa, W., & Ajjan, R. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0146 · Cited by 43
“There have been numerous empirical relationships proposed between glucose and HbA1c, both using fasting glucose and average glucose by CGM, 6,11,27,50–52 and the underlying variation is large enough that it is almost certain to find statistically significant differences in the regression terms for each study due to unco…”
InfluentialSelf-citationValenzano, M., Cibrario Bertolotti, I., Valenzano, A., & Grassi, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001045 · Cited by 34
“Patients were first given instructions about the use of the prescribed devices and the procedures to calibrate their sensors and upload CGM data (in the case they were requested to do so themselves, in particular with rtCGM sensors) during a preliminary visit and a subsequent 2-week training, and…”
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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