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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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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-citation
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“9 The value of GMI as an accurate reflection of HbA1c has been questioned.”
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“4,18,24 9, 25 In contrast, The young age of our participants (4 – 16 years) might be relevant to our ﬁ ndings, since MG levels for a given A1c are known to be higher in youth compared with adults.”
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“25 Others, such as Vigersky et al., opted for HbA1c as a predictor in their studies.”
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“16 The relationship between GMI and HbA1c and Hba1c and average glucose was described using Spearman ’ s correlation coef ﬁ cient.”
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Wild, D., Kerr, M., Figueiredo, C., & Partridge, H. (2023) · Practical Diabetes · doi:10.1002/pdi.2445 · Cited by 2
Comparison of Point-of-Care and Laboratory Glycated Hemoglobin A1c and Its Relationship to Time-in-Range and Glucose Variability: A Real-World Study
Hayek, A. A., Alzahrani, W. M., Sobki, S. H., Alsaeed, A. H., & Dawish, M. A. A. (2023) · Cureus · doi:10.7759/cureus.33416 · Cited by 1
“Debate exists about the independent impact of glycemic variability on DM-related complications that go beyond average glucose or HbA1c [18-21].”
Background
What Nephrologists Should Know about the Use of Continuous Glucose Monitoring in Type 2 Diabetes Mellitus Patients on Chronic Hemodialysis
Lamine, F., Pruijm, M. T., Bahon, V., & Zanchi, A. (2022) · Kidney and Dialysis · doi:10.3390/kidneydial2030042 · Cited by 2
Predicting Glycated Hemoglobin Through Continuous Glucose Monitoring in Real-Life Conditions: Improved Estimation Methods
Valenzano, M., Bertolotti, I. C., Grassi, G., Broglio, F., & Valenzano, A. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221081556 · Cited by 2
Time in range is a tool for assessing the quality of glycemic control in diabetes
Суплотова, Л. А., Сергеевна, С. А., Романова, Н. В., & Shestakova, M. V. (2021) · Diabetes Mellitus · doi:10.14341/dm12703 · Cited by 9
Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists
Cowart, K., & Zgibor, J. C. (2021) · Journal of Pharmacy Practice · doi:10.1177/08971900211000273 · Cited by 4
“The AGP provides the average glucose over the sensor wear period along with a metric called the glucose management indicator (GMI), which is used to estimate HbA1c from the average glucose.(56) The GMI can be used to explore reasons for the discordant results, if applicable.”
Method
Monitoreo continuo de glucosa: indicaciones, interpretación de datos y toma de decisiones terapéuticas
Litwak, L., Carreño, N., Carnero, R., Daín, A., Grosembacher, L., Musso, C., Proietti, A., Rista, L., Rovira, G., Rubín, G., et al. (2020) · Revista de la Sociedad Argentina de Diabetes · doi:10.47196/diab.v54i3.455 · Cited by 2
Interprétation rétrospective des données du FreeStyle Libre en téléconsultation chez des patients ayant un diabète de type 1. Leçons tirées de la période COVID-19
Picard, S. (2020) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2020.06.021 · Cited by 1
“été changé et qu'on parle maintenant d'index de gestion de la glycémie, ou GMI (pour Glucose Management Indicator) [9].”
BackgroundInfluential
Glycaemic Monitoring in Diabetic Kidney Disease – Is HbA1c Reliable?
Isa, S. B., Hami, R. B., JA, A. N. J. N., Noordin, S. S., & Ismail, T. S. T. (2021) · Journal of Health Science and Medical Research · doi:10.31584/jhsmr.2021831 · Cited by 1
“02392 × [mean glucose in milligram per decilitre].(53) Another CGM metric of clinically importance, is time in range.”
Background
Observed Glycemic and Psychosocial Benefits in the Prospective Bigfoot Unity Real World Study: A 6-Month Analysis
Tillman, J. B., Beck, R. W., Polonsky, W. H., Calhoun, P., Mouse, T., Bailey, R., Jacoby, J., Nandan, R., Sheikh, F. N., & Malone, J. K. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae778 · Cited by 1
Self-citation
Glycemic assessment when hemoglobin A1c is unreliable: a clinical-laboratory framework for kidney disease, anemia, and hemoglobinopathies
Wu, T., Guan, Z., & Cai, W. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1898713 · Cited by 1
“When the disagreement is large enough to in ﬂ uence care, a lower-risk interpretation examines both sides of the comparison before therapy is changed (6, 11).”
Use of continuous glucose monitoring (CGM) in diabetes management in Africa: opportunities, challenges, and future prospects
Olamoyegun, M., Olamoyegun, O. D., Akinlade, A. T., Saliu, O. O., & Adeyemi, T. A. (2026) · Cardiovascular Diabetology – Endocrinology Reports · doi:10.1186/s40842-026-00310-7
“These metrics facilitate individualized diabetes management by identifying specific glucose patterns requiring intervention, such as nocturnal hypoglycaemia, post-prandial hyperglycaemia, or excessive glycaemic variability [20].”
Comparative Analysis of Capillary HbA1c and CGM-Derived GMI Estimates: A Real-World Data from a Single Center
Dagan, A., Yackobovitch‐Gavan, M., Phillip, M., & Nimri, R. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261459274 · Cited by 1
“15 Although designed to complement or replace laboratory HbA1c, accumulating evidence demonstrates frequent and sometimes substantial discrepancies between the two.”
Abnormal Glycaemic Fluctuations Associated With Early Microvascular Changes in the Retina and Choroid of Nondiabetic Adults
Jiang, Y., Gu, L., MA, Y., Qu, Y., Wan, H., Lv, K., Zhu, Q., Zhou, H., Sun, J., Lu, F., et al. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70973 · Cited by 1
“In nondiabetic populations, previous studies have also shown that there can be significant inconsistencies between GMI and laboratory HbA1c [43, 44].”
Alogliptin-Pioglitazone and Insulin Regimen for Inpatient Glycemic Control: A Randomized Continuous Glucose Monitoring Pilot Trial
Camuto, P., Guadagnini, D., Assalin, H. B., Magro, D. O., Santos, A., & Saad, M. J. A. (2026) · AACE Endocrinology and Diabetes · doi:10.1016/j.aed.2026.06.010 · Cited by 1
Virtual Weight Management and Continuous Glucose Monitoring in Patients With Type 2 Diabetes: A Randomized Controlled Trial
Katzmarzyk, P. T., DREWS, K., Ard, J. D., McLaughlin, T., Harris, M. N., Lee, A., Foster, G. D., Lee, H. J., Dunn, T. C., Martin, C. K., et al. (2026) · Obesity · doi:10.1002/oby.70204 · Cited by 1
Agreement Between HbA1C and Continuous Glucose Monitoring–Derived Glycemic Metrics in Adults With Cystic Fibrosis Related Diabetes
Ahmed, A., Batth, J., Wang, Q., Vigers, T., Ode, K. L., Chan, C. L., & Moheet, A. A. (2026) · Endocrine Practice · doi:10.1016/j.eprac.2026.04.007 · Cited by 1
Quality-Score (Q-Score) Can Be More Sensitive than %Time in Range and Several Other CGM Metrics in Detecting Responses to Therapeutic Interventions
Rodbard, D., Augstein, P., Heinke, P., Salzsieder, E., Thomas, A., & Reindel, J. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261423504 · Cited by 1
“…%time in range 70 – 180 mg/dL (3.9 – 10 mmol/L); GMI, Glycemic Management Indicator calculated from mean sensor glucose using a linear relationship 9 ; GRI, Glycemia Risk Index 21 ; MODD, Mean of absolute Daily Differences (mg/dL or mmol/L) 17 ; MSG, mean sensor glucose; Range: average daily…”
Influential
Clinical Value of HbA1c/updatedGMI Ratio in Identifying Early Retinopathy Risk in Type 1 Diabetes.
Lurquin, F., Petit, E., Oriot, P., Ahn, S., & Hermans, M. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261426305 · Cited by 1
Unveiling glycaemic variability patterns linked to the triglyceride‐glucose index in type 2 diabetes: Insights from continuous glucose monitoring
Wu, Y., Feng, K., Fu, Y., Zhuo, Q., Zheng, M., Zhang, H., Lin, G., Liang, X.-Y., Zhou, X.-H., Ji, L., et al. (2026) · Diabetes, obesity and metabolism · doi:10.1111/dom.70558 · Cited by 1
“16 Common time-domain metrics including time in range (TIR), time below range (TBR), time above range (TAR), coefficient of variation (CV), standard deviation (SD), and Mean amplitude of glucose excursions (MAGE) describe overall glucose control and fluctuation magnitude.”
Navigating the dual burden of type 1 diabetes and mental illness: A phenomenological study
Cooper, Z., Davis, G. M., Pasquel, F., & Johnson, L. (2026) · Diabetic Medicine · doi:10.1111/dme.70200 · Cited by 1
The Evolving Landscape of Continuous Glucose Monitoring Metrics in Type 1 Diabetes: Narrative Literature Review
Williams, E., Rand, C., Ayers, A. T., Ho, C., DuBord, A. Y., Klonoff, D. C., & Espinoza, J. (2026) · JMIR Diabetes · doi:10.2196/92455 · Cited by 4
“AUC e [22,42,43] Indicator of glycemic excursions in a specified period Reflects duration of CGM usage for data reliability. eA1c [31] Tracking of average glycemia and the estimation of HbA 1c eA1c(t)=0.9512 * eA1c (t-1 d) +0.0488 * f (SMBG) Fills the gap between infrequent laboratory HbA 1c tests.”
Influential
The Relationship between Hemoglobin A1c and the Glucose Management Indicator and Glucose Metrics in Children and Adolescents with Type 1 Diabetes Mellitus Using Automated Insulin Delivery Systems
Arslan, E., Balkı, H. G., Kızılay, D., Demir, G., Özen, S., Darcan, S., & Gökşen, D. (2026) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.galenos.2025.2025-10-11 · Cited by 1
“HbA1c cannot capture short-term glucose fluctuations or provide information about glycemic variability, hypoglycemic episodes, or postprandial excursions (3).”
Insulin Pump Guide for Health Care Professionals: Settings, Interpretation, and Adjustment
Lansang, M. C., Malloy, K., Knapp, S., Veeramachaneni, R., Rahhal, M.-N., Arce, K. M., Mucci, A., Zhou, K., & Isaacs, D. M. (2026) · Endocrine Practice · doi:10.1016/j.eprac.2025.12.025 · Cited by 1
Safety and Glycemic Outcomes Among Youth With New‐Onset Type 1 Diabetes Using a Tubeless Automated Insulin Delivery System
DeSalvo, D. J., Linde, N. M., Sickler, J. K., Villegas, C., & Cescon, M. (2026) · Journal of Diabetes Research · doi:10.1155/jdr/2896084 · Cited by 1
“GMI was used in this study as a calculated estimate of laboratory-measured HbA1C [6] since HbA1c was not routinely available at the key study time points.”
Automated Insulin Delivery Beyond Glycemic Outcomes: Endpoints and Evidence
Jones, H. C., Trawley, S. L., Jenkins, A. J., MacIsaac, R. J., Kong, Y. W., Morrison, D., & O'Neal, D. N. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251390831 · Cited by 1
“32 36 While there have been limited studies on associations between CGM use and progression to diabetic nephropathy, increased TIR has been show to signi ﬁ cantly reduce albuminuria.”
Tubeless Insulin Pump Combined with a Cloud-Based Data Management Platform: Real-World Evidence from 14,765 French People with Diabetes
Jeandidier, N., Bagel, A., Bonin, A., Guigand, G., & Guerci, B. (2025) · Diabetes Therapy · doi:10.1007/s13300-025-01814-8 · Cited by 1
Challenges and Limitations of HbA1c as a Surrogate Marker in Assessing Glycemic Control: A Critical Review
Salman, I., Gharab, K., Ghannawi, L., & Abed, B. A. (2025) · Dijlah Journal of Medical Sciences P-ISSN:3078-3178, E-ISSN:3078-8625 · doi:10.65204/djms-c-l-hsm · Cited by 1
“Therefore, using HbA1c alone without taking into account data from continuous glucose monitoring (CGM) may understate the risk of complications from diabetes, especially in individuals with type1 diabetes or type2 diabetes receiving insulin [10].”
Minimum Continuous Glucose Monitor Data Required to Assess Glycemic Control in Youth with Type 1 Diabetes
Gera, S., Rearson, A., Gallop, R., & Marks, B. E. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2025.0173 · Cited by 1
Glycated albumin and fructosamine do not improve accuracy of glycaemic control assessment in patients with conditions reported to affect HbA 1c reliability
Niwaha, A. J., Balungi, P., McDonald, T. J., Hattersley, A. T., Shields, B. M., Nyirenda, M., & Jones, A. G. (2025) · Diabetic Medicine · doi:10.1111/dme.70011 · Cited by 5
“15,16 We compared the performance of HbA 1c , GA and fructosamine to identify participants with CGM glucose values ≥ 8 mmol/L using receiver operating characteristic (ROC) curve analysis, and assessed the sensitivity, specificity and positive/negative predictive values of these tests using the…”
Musculoskeletal disorders in type 1 diabetes: Clinical phenotyping and associations with quality of life and glucose control - The French SFDT1 cohort study
Topalian, N., Picard, S., Riveline, J., Canha, D., Julla, J., Lablanche, S., Salle, L., Sonnet, E., Berot, A., Gouet, D., et al. (2025) · Diabetes & Metabolism · doi:10.1016/j.diabet.2025.101647 · Cited by 1
Associations of >1-h compared with 1-h meal timing variability (eating jetlag) with plasma glycemic parameters and continuous glucose monitoring measures among pregnant females: a prospective cohort study
Chen, Y.-E., Ku, C. W., Chong, M. F., Yap, F. K. P., Chan, J. K. Y., Loy, S. L., & Chen, L. (2025) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2025.04.026 · Cited by 5
The Usefulness of the Glucose Management Indicator in Evaluating the Quality of Glycemic Control in Patients with Type 1 Diabetes Using Continuous Glucose Monitoring Sensors: A Cross-Sectional, Multicenter Study
Lazăr, S., Potre, O., Ioniță, I., Reurean‐Pintilei, D., Timar, R. Z., Herascu, A., Avram, V. F., & Timar, B. (2025) · Biosensors · doi:10.3390/bios15030190 · Cited by 2
“It is essential to set individualized, realistic and safe goals in such instances/cases, since an excessive focus on constantly achieving the ideal GMI value may increase pressure for the patient, potentially exacerbating the emotional stress [17,46].”
The Use of Freestyle Libre Glucose Monitoring System and Diabetes Treatment Progression in Type 2 Diabetes Mellitus: A Retrospective Cohort Study in Saudi Arabia
Aljohani, R. A., Altaib, H. N., & Kofi, M. A. (2025) · Cureus · doi:10.7759/cureus.79705
“However, the development of continuous glucose monitoring (CGM) systems has revolutionized diabetes care [4].”
Quantification of the relation between continuous glucose monitoring observation period and the estimation error in assessing long-term glucose regulation
Zoet, S., Urgert, T., Veldhuis, A., van Beijnum, B.-J. F., & Laverman, G. D. (2025) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2024-004768 · Cited by 1
CLINICAL SIGNIFICANCE OF FLASH MONITORING GLYCEMIA INDICATORS IN CHILDREN WITH TYPE 1 DIABETES MELLITUS
Алимова, И. Л., & Demyanenko, A. N. (2024) · Russian Medicine · doi:10.17816/medjrf629450 · Cited by 1
Glycemic Control Assessed by Intermittently Scanned Glucose Monitoring in Type 1 Diabetes during the COVID-19 Pandemic in Austria
Secco, K., Baumann, P., Pöttler, T., Aberer, F., Cigler, M., Elsayed, H., Harer, C., Weitgasser, R., Schütz-Fuhrmann, I., & Mader, J. (2024) · Italian National Conference on Sensors · doi:10.3390/s24144514 · Cited by 1
“…variation ( CV gluc ) for an individual i in phase p is the standard deviation ( SD gluc ) of glucose measurements ( G 1 ... j ) divided by Mean gluc : where The glucose management indicator ( GMI ) for an individual i in phase p is calculated by the formula given by Bergenstal et al. [27].”
Method
Time in range prediction using the experimental mobile application in type 1 diabetes
Русанов, А. Н., & Родионова, Т. И. (2024) · Diabetes Mellitus · doi:10.14341/dm13111 · Cited by 2
The Glucose Management Indicator: Time to Change Course? Diabetes Care 2024;47:906–914
Bergenstal, R. M., & Beck, R. W. (2024) · Diabetes Care · doi:10.2337/dc24-0317 · Cited by 3
Self-citation
Impact of Hypoglycemia on Glucose Variability over Time for Individuals with Open-Source Automated Insulin Delivery Systems
Shahid, A., & Lewis, D. M. (2024) · Diabetology · doi:10.3390/diabetology5050038 · Cited by 1
Prognostic Value of Hyperglycemia and Insulin Resistance Among Patients with Confirmed COVID-19 Infections at Admission to the Alexandria Fever Hospital, Egypt
Alsewy, F. Z., Megallaa, M., Imbaby, S., Zidan, H. M., Kassab, H., & Badrah, M. (2024) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2024.0066 · Cited by 1
Intermittently Scanned Continuous Glucose Monitoring Performance in Patients With Liver Cirrhosis
Rigon, F., Ronsoni, M. F., Hohl, A., Vianna, A. G. D., van de Sande‐Lee, S., & Schiavon, L. D. L. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241232686 · Cited by 3
The Discrepancy Between Hemoglobin A1c and Glucose Management Indicators in 26 Patients Treated With Continuous Glucose Monitoring in an Internal Medicine Residency Clinic
Manov, A. E., Holt, N., Dini, E., Rivera, R., Donepudi, A., Haddadin, R. W., Mefferd, K., & Qadir, I. (2024) · Cureus · doi:10.7759/cureus.56768 · Cited by 1
“This study looked at 528 individuals without other comorbidities from four clinical trials [2].”
BackgroundMethodInfluential
Diabetes Care and Mental Health During the COVID-19 Pandemic: Perspectives of Adolescents with Diabetes, Parents, and Providers
Leite, R. O., Brodar, K. E., Saab, P. G., Marchetti, D., Jaramillo, M., Sanchez, J. E., Davis, E., Delamater, A. M., & Greca, A. M. L. (2024) · Journal of Clinical Psychology in Medical Settings · doi:10.1007/s10880-023-09995-9 · Cited by 1
“Paired samples t -tests assessed for differences in HbA1c and psychosocial functioning between the two timepoints, from before the pandemic began to the year after the pandemic started.”
Influential
Updating Our Understanding of Device Use in Adolescents: Demographic Factors and Preferences in a Group of Adolescents With Type 1 Diabetes
Garretson, S., Parmar, A. M., Streisand, R. M., Jaser, S. S., & Datye, K. A. (2024) · The Science of Diabetes Self-Management and Care · doi:10.1177/26350106231220023 · Cited by 2
Primary care diabetes assessment when HbA1c and other measures of glycemia disagree
Friedman, J. G., Smith, E. P., Awasty, S. S., Behan, M., Genco, M. T., Hempel, H. C., Jafri, S., Jandarov, R., Nagaraj, T., Franco, R. S., et al. (2024) · Primary care diabetes · doi:10.1016/j.pcd.2023.12.005 · Cited by 1
Evaluation of HbA1c from CGM traces in an Indian population
Majumdar, S., Kalamkar, S. D., Dudhgaonkar, S., Shelgikar, K. M., Ghaskadbi, S., & Goel, P. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1264072 · Cited by 2
“Bergenstal et al. (15) renamed the estimated HbA1c as the glucose management indicator (GMI), a metric for glycemic control and management.”
Background
Risk of hypoglycemia in type 1 diabetes management: An in-silico sensitivity analysis to assess and rank the quantitative impact of different behavioral factors
Roversi, C., Camerlingo, N., Vettoretti, M., Facchinetti, A., Choudhary, P., Sparacino, G., & Favero, S. D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107943 · Cited by 1
Asociación entre el puntaje del cuestionario de Clarke y las métricas aportadas por el monitoreo continuo de glucosa en pacientes con diabetes y alto riesgo de hipoglucemia
Henao, D. C., Medina, A. M. G., García-Cajiao, A., Robledo, S., Mejía, G., Padilla, S. N. C., Kerguelen, A., Rubio, C., Rondón, M., & Múñoz, Ó. (2023) · Revista Colombiana de Endocrinología Diabetes & Metabolismo · doi:10.53853/encr.10.3.796
“La mediana de TIR 75 % [RIC 63-92], TBR 3 % [1-7], TAR 15 % [3-27].”
BackgroundInfluential
Examining a Remote Group-Based Type 2 Diabetes Self-Management Education Program in the COVID-19 Era Using the ORBIT Model: Small 6-Week Feasibility Study
Hiemstra, M. S., Reichert, S. M., & Mitchell, M. (2023) · JMIR Formative Research · doi:10.2196/46418 · Cited by 2
“To examine the potential impact of the program on behavioral and clinical risk factors, device-assessed (4) weekly mean daily step count and (5) estimated glycated hemoglobin (HbA 1c ; the average glucose level from the FreeStyle Libre readings for 14 or more days [17]) were collected.”
Background

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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