The DOI matches its Crossref record; 16 of the 18 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Miller, K. M., Foster, N. C., Beck, R. W., Bergenstal, R. M., DuBose, S. N., DiMeglio, L. A., Maahs, D. M., & Tamborlane, W. V. (2015) · Diabetes Care · doi:10.2337/dc15-0078 · Cited by 1,254
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
Self-citation
Cited by
Sorted by citations
Items 501–600 of 615
Glycemic control metrics for in silico testing of artificial pancreas systems
Novák, K., Kovács, L., Drexler, D. A., & Eigner, G. (2022) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti-macro57952.2022.10029513 · Cited by 1
“previously measured HbA1c values of a diabetic population with the same average glucose level [23].”
BackgroundAktueller Stand der Insulinpumpentherapie in der Kinderdiabetologie
Berge, T. V. D., Reschke, F., & Biester, T. (2023) · Pädiatrie up2date · doi:10.1055/a-1981-4040
Variation of glucose time in range in type 1 diabetes
Hansen, K. W., & Bibby, B. M. (2022) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.379 · Cited by 2
Moreno-Fernández, J., García-Seco, J. A., Virlaboa-Cebrian, R., Seco, A. M., Muñoz‐Rodríguez, J. R., & Romero, F. J. G. (2022) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2021.12.011
Early Glycemic Control Assessment Based on Consensus CGM Metrics
Mohebbi, A., Bohm, A.-K., Tarp, J. M., Jensen, M. L., Bengtsson, H., & Mørup, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9631015 · Cited by 3
“To clarify this, consider GMI, which is a scaled linear combination of MG [20].”
BackgroundSeibold, A. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0674 · Cited by 1
“However, if the sensor devices had comparable calibrated accuracy and sensitivity profiles, GMI and HbA1c would be expected to change congruently in a given study population, 5 but that is clearly not the case here (Fig.”
Adjustment of Insulin Therapy: New Insights from Continuous Glucose Monitoring
Oh, T. J. (2020) · Journal of Korean Diabetes · doi:10.4093/jkd.2020.21.4.191 · Cited by 1
“In the CGM era, new treatment targets such as time in range should be considered in conjunction with HbA1c.”
InfluentialMercuri, V., D’Amico, T., Costa, D., Vito, C. D., D’Angelo, L., Schiaffini, R., & Gargiulo, P. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820949882 · Cited by 4
Chernikova, N. A., Kamynina, L. L., & As, A. (2019) · Meditsinskiy sovet = Medical Council · doi:10.21518/2079-701x-2019-4-38-43 · Cited by 3
R Functions for Analysis of Continuous Glucose Monitor Data
Vigers, T., Chan, C. L., Snell-Bergeon, J., Bjornstad, P., Zeitler, P., Forlenza, G. P., & Pyle, L. (2019) · bioRxiv · doi:10.1101/625137 · Cited by 1
Converting haemoglobin A1c and average glucose. Time to change?
Hansen, K. W. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.03.013 · Cited by 1
Clinical utility of the twiist Automated Insulin Delivery System: first study with twiist
Ruiz, A., Mcdonough, A., Snow, K., Bode, B., & Mitri, J. (2026) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2026-006307
Documento de información y consenso para la detección y manejo de la enfermedad renal crónica
Maset, R. G., Bover, J., Segura, J., Diezhandino, M. G., del Hoyo, J. C., Martín, J. E. S., Fácila, L., Gamarra, J., García‐Donaire, J. A., Matarin, L. G., et al. (2021) · Nefrología · doi:10.1016/j.nefro.2021.07.010 · Cited by 160
Chen, J., Zhang, X., Li, R., Ma, R., Piao, Y., Sang, Y., & Wang, X. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1991026
Tang, M., Nathan, D. M., Wexler, D. J., Putman, M. S., Higgins, J. M., Zheng, H., Rhee, E. P., de Boer, I. H., & Kalim, S. (2026) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2026.109439
Herranz-Antolín, S., Martínez‐Brocca, M. A., Cánovas‐Molina, G., Aragón‐Alonso, A., Brito-Sanfiel, M. Á., Martínez‐Montoro, J. I., Bellido, V., Azriel, S., López-García, M. C., Pérez, C. T., et al. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71385
Wang, M., & Lv, C. (2026) · Clinica Chimica Acta · doi:10.1016/j.cca.2026.122898
Holder, M., Ziegler, R., Kamrath, C., Kluge, A., Kummer, S., Lange, K., & Reschke, F. (2026) · Diabetologie und Stoffwechsel · doi:10.1055/a-2874-3708
“In allen behandelnden Kliniken soll ein schriftlicher Behand-lungsplan zur Behandlung diabetischer Ketoazidosen”
Continuous Glucose Monitoring Use in Gestational Diabetes
Carlson, A. L. (2026) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2026.08.018
The Role of Hemoglobin A1C in the Era of Continuous Glucose Monitoring
Perlman, J. E. (2026) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2026.09.002
Three-Year Real-World Performance of Bihormonal Fully Closed-Loop Therapy in Type 1 Diabetes
Jansen, T. J. P., den Drijver, C., Vlaskamp, L. B., & van Bon, A. C. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261492793
PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell UsPreprint
Sapir, G., Diament, A., Wolf, A., Stupp, D., Gelbard-Solodkin, D., Azouri, D., Etzion-Fuchs, A., Lutsker, G., Segal, E., & Rossman, H. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.06080
“For HbA1c, the raw glucose management indicator [30] used directly as the prediction provides a heuristic comparator (on this cohort it scores below the demographic floor), and each model’s closed-book output provides its prior.”
Soláns, J. R. G. (2026) · Farmacéuticos Comunitarios · doi:10.33620/fc.2173-9218.2026.30
“GMI is calculated from the mean glucose recorded by CGM and is expressed in units equivalent to HbA1c [10].”
Chen, Y.-E., Ku, C. W., Zheng, R. T., Chan, J. K. Y., Yap, F. K. P., Loy, S. L., & Chen, L. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71264
Oweidat, K., & Farmer, C. (2026) · Clinical Kidney Journal · doi:10.1093/ckj/sfag252
“In contrast to laboratory HbA1c, which reflects the glycation of haemoglobin over the roughly 8–12-week red-cell lifespan and can be distorted by anaemia or altered erythrocyte turnover, the GMI is derived directly from the interstitial glucose measured by the CGM sensor [ 3 ].”
Kontinuierliche Glukosemessung und automatisierte Insulindosierungssysteme im klinischen Alltag
Mathew, A., & Führer, D. (2026) · Die Innere Medizin · doi:10.1007/s00108-026-02166-6
Hanna, A. G. D. B., Rojas, M. A. N., Rivas, I. Á., Rendón, I. P., Cabrera, L. G., Jaramillo, P. G., Perdigón, N. A., Russi, A., & Regalado, L. G. C. (2026) · Revista Colombiana de Endocrinología Diabetes & Metabolismo · doi:10.53853/encr.13.3.997
Kublin, O., Filipowicz, D., & Stȩpień, M. (2026) · Archives of Medical Science · doi:10.5114/aoms/218227
“GMI is another new parameter [19].”
Wu, W., Wang, S., Gao, J., Song, S., Zhang, Y., Fu, Y., Zhang, M., Yuan, T., & Zhao, W. (2026) · Diabetic Medicine · doi:10.1111/dme.70437
Geerts, I., Beunen, K., Berger, L., Wilder, N. V., Ballaux, D., Vanhaverbeke, G., Taes, Y., Aers, X.-P., Nobels, F. R. E., Huffel, L. V., et al. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261477154
Huang, Q., Kang, J., Wang, R., Zhang, M., & Chen, Y. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71224
“Together, these metrics constitute a comprehensive framework for CGM-based assessment, capturing static glycemic control status [36], dynamic glucose fluctuation patterns [37, 38], cumulative exposure within euglycemic boundaries [39], and composite metabolic risk profiles.”
Gerber, A., Longo, M., Jano, K., & Fischetti, B. (2026) · AIDS Patients Care and STDs · doi:10.1177/10872914261473699
“10,11 Prior research has shown that at least 70% of CGM data coverage over 10 days reliably re fl ects usual glycemic patterns.”
Abhishek, A., Verma, R., & Guria, R. (2026) · International Journal of Medical and Biomedical Studies · doi:10.32553/10.32553/ijmbs.v10i3.3309
He, C., Zhao, H., Liu, H., Zhao, X., Zhang, M.-T., & Li, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1861971
Continuous Glucose Monitoring in Type 2 Diabetes and Beyond
Dower, J. A., Johansson, M., CAMP, A. W., Montori, V. M., & Lipska, K. J. (2026) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2026.2772 · Cited by 1
Chung, Y.-I., Ash, G. I., Jeon, S., Ramachandran, R., Stults-Kolehmainen, M. A., Spanakis, E., & Fucito, L. (2026) · Human Factors in Healthcare · doi:10.1016/j.hfh.2026.100154
“Unlike the traditional finger-prick tests that provide a single point-in-time measure, or the HbA1c levels (glycated hemoglobin) that reflect long-term glucose exposure (Cappon et al., 2019), CGM has the unique benefit of providing real-time, continuous glycemic patterns (Bergenstal et al., 2018).”
Balogh, D. M., Osgyán, K., Békeffy, M., Kempler, M., Balaton, G., Rizzo, M., Horváth, V., Kempler, P., & Menyhárt, A. (2026) · Medicina · doi:10.3390/medicina62081539
Sonoda, S., Okada, Y., Mita, T., Torimoto, K., Tanaka, K., Sato, F., Katakami, N., Yoshii, H., Nishida, K., Nakayamada, S., et al. (2026) · Journal of Diabetes Investigation · doi:10.1111/jdi.70403
“= 3.31 + 0.02392 9 (average glucose level [mg/dL]) 25 .”
InfluentialLondon, B., Hornung, L. N., Elder, D. A., & TELLEZ, S. E. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261465344
Cigaina, V., Saggioro, A., Fabris, P., & Bonavina, L. (2026) · Journal of Laparoendoscopic & Advanced Surgical Techniques · doi:10.1177/10926429261468981
Griffiths, C., Santos, R. B., Wernimont, S., Anderson, J., Kristan, M. M., Helgeson, E. S., Chow, L. S., & Bartal, M. F. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261461603
Davis, G. M., Forlenza, G. P., Gehr, B., Pemberton, J. S., Penfornis, A., Do, D., Huyett, L. M., & Ly, T. T. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113450 · Cited by 1
Lambadiari, V., Tentolouris, N., Vazeou, A., Christoforidis, A., Kanaka‐Gantenbein, C., Makrilakis, K., Didangelos, T., Juachon, M. J., Smaniotto, V., van den Heuvel, T. R. A., et al. (2026) · Diabetes Therapy · doi:10.1007/s13300-026-01883-3 · Cited by 1
“However, while HbA1c remains the primary measure used to guide glucose man‑ agement, the use of GMI is becoming increas‑ ingly common as adoption of CGM increases and may contribute to the personalization of diabetes management [38].”
Continuous Glucose Monitoring (CGMs) in the Management of Diabetes: Boon or Bane?
Joshi, H. (2026) · International journal of community pharmacy · doi:10.65301/ijcp.2026.8.2.1
Qian, T.-M., Guo, D., Zhang, M., Liu, Y.-H., & Jin, J. (2026) · Diabetic Medicine · doi:10.1111/dme.70404
“17 The primary efficacy end point is TIR; 3.9–10.0 mmol/L. Higher TIR is associated with preserved β - cell function, reduced microvascular complications, and lower oxidative stress, while improvements in TIR predict greater durability of remission in early T2DM.”
InfluentialNi, J., Zhang, C., Cai, J., Wang, C., Zhu, W., Wang, Y., Ma, X., Lu, J., Xia, T., & Zhou, J. (2026) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70192
“Researchers have also proposed the ratio of glucose management indicator (GMI) to GA, where GMI provides an estimate of HbA1c levels derived from MSG [11].”
InfluentialGlycation gap size, stability over time and correlates in people with Type 1 diabetes.
Begbie, T. N., Huang, M. L. H., Nanayakkara, N., Ma, R. C., Cohen, N. D., & Jenkins, A. J. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113378
Seo, J. W., Park, K. B., & Jun, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-55271-9
Glucose Monitoring and Control Testing in Patients with Type 1 or Type 2 diabetes
Schlüter, S., Deiss, D., Gehr, B., Lange, K., von Sengbusch, S., Thomas, A., Ziegler, R., Wiesner, T., & Freckmann, G. (2026) · Experimental and clinical endocrinology & diabetes · doi:10.1055/a-2766-3426
Thomsen, C. H. N., Cichosz, S. L., Kronborg, T., Hangaard, S., Vestergaard, P., Hirsch, I. B., Klonoff, D. C., & Jensen, M. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261441312
Schleicher, E., Müller-Wieland, D., Nauck, M., Merz, T., Heinemann, L., & Freckmann, G. (2026) · Diabetologie und Stoffwechsel · doi:10.1055/a-2896-0406
“In dieser Studie zeigte sich, dass für versen-dete Proben mit z.B. einem wahren HbA 1c -Zielwert von 6,5% (48 mmol/mol) von fast allen teilnehmenden Laboratorien (n>2000; 21 Länder) ein Ergebnis innerhalb einer Abweichung von absolut 0,8% HbA 1c (9mmol/mol) erreicht wurde [12, 20].”
InfluentialSelf-citationHussain, M. A., Anil Kumar, H., & Lingaiah, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-47067-8
Bang, M., Shin, S.-Y., Kim, J., Kang, M., Lee, D.-H., Kim, J., Kim, C., Son, J., Jeon, S., & Kang, B. (2026) · Journal of Korean Medicine · doi:10.13048/jkm.26006
Hayashi, A., Tsukushi, T., Shinohara, R., Sogabe, H., Tsuji, S., Shimizu, N., & Miyatsuka, T. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261426379
Sakane, N., Hirota, Y., Yamamoto, A., Miura, J., Takaike, H., Hoshina, S., Toyoda, M., Saito, N., Hosoda, K., Matsubara, M., et al. (2026) · Diabetology International · doi:10.1007/s13340-025-00872-4
“= 3.31 + 0.02392 × mean CGM glucose (mg/dL) [ 21].”
InfluentialLi, L., Taha, S. N. S., Nishinaka, Y., Tan, Y., Ohtsu, H., Sinyoung, L. E. E., & Kiyono, K. (2026) · Sensors · doi:10.3390/s26041118
“The glucose management indicator (GMI) has been proposed as an alternative CGM-based index that expresses mean glucose on an HbA1c-like scale using a CGM-specific regression [36].”
Le, J., Li, X.-X., & Qin, S.-L. (2026) · Digital Health · doi:10.1177/20552076261494727
Basheer, H. S., & Al-Sharqi, A. (2026) · F1000Research · doi:10.12688/f1000research.173871.2
Al-Mamari, A., Gharabi, S. A., Busaidy, M. A., & Futaisi, A. A. (2026) · Sultan Qaboos University Medical Journal · doi:10.18295/2075-0528.2952
“16 Another important metric, estimated (eA1C), refers to a measure that estimates laboratory A1C using a formula derived from glucose readings of a population or the mean glucose from CGM. 17 To date, no such analysis has been conducted in Oman.”
Clinical Approaches to Managing Glycemia with the iLet Dosing Decision Software
Hirsch, I. B., Isaacs, D. M., Mora, P., Oser, S. M., Shah, V. N., Sulik, B., Wohlfahrt, K. M., Cook, K. P., MacLeod, J., & Russell, S. J. (2026) · Diabetes Technology and Obesity Medicine · doi:10.1177/29986702261456943
Continuous glucose monitoring in patients with type 2 diabetes mellitus on oral hypoglycemic therapy
Кузина, И. А., Акрамовна, Э. Э., & Petunina, N. A. (2025) · Meditsinskiy sovet = Medical Council · doi:10.21518/ms2025-414
He, L., Chen, J., Chen, W., & Shen, X. (2025) · Journal of Laboratory Medicine · doi:10.1515/labmed-2025-0017
Moreno-Fernández, J., López, L. M., Maqueda, E., Quiroga, I., González, J., Marco, A., Herranz-Antolín, S., Martínez, A., Edith, D., González-Maroto, I., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-22666-z
“It serves as a valuable tool for assessing long-term glycaemic trends and guiding clinical decision-making, especially in individuals using advanced technologies such as AHCL systems 45 .”
Lee, M., Park, S., Park, S., Park, H., Lee, Y. R., Kim, M.-S., Nam, M., Lee, J., Seo, H., Lee, Y., et al. (2025) · Clinical Hypertension · doi:10.5646/ch.2025.31.e28 · Cited by 1
“%) will be analyzed [32,33].”
Lenters-Westra, E., Schouten, I., van Vlodrop, I. V., & English, E. (2025) · Clinical Chemistry and Laboratory Medicine · doi:10.1515/cclm-2025-1044
“GMI is an estimate of HbA 1c levels calculated using the CGM derived mean glucose values [3].”
Schierbauer, J., Sanfilippo, S., Wachsmuth, N. B., & Moser, O. (2025) · Diabetes, obesity and metabolism · doi:10.1111/dom.70007
“20 This also means that if HbA 1c and GMI differ from each other within the same individual, it might be at least in part due to fluctuations in Hbmass and thus HbA 1c , which would alter diagnostic or therapeutic decisions by considering absolute rather than percentage HbA 1c .”
Continuous Blood Glucose Monitoring: Potential Applications from Diabetes Management
Manaf, F. A., Amin, M. Q. A. M., Ahmad, A., Farizan, N. H., Sukri, N. M., & Maliki, A. B. H. M. (2025) · Malaysian Journal of Medicine and Health Sciences · doi:10.47836/mjmhs.21.4.41
Diagnostik, Therapie und Verlaufskontrolle des Diabetes mellitus im Kindes- und Jugendalter
Holder, M., Kamrath, C., Lange, K., Kummer, S., & Ziegler, R. (2026) · Die Diabetologie · doi:10.1007/s11428-026-01468-5
“In allen behandelnden Kliniken soll ein schriftlicher Behand-lungsplan Behand-lungsplan zur Behandlung diabetischer Ketoazidosen bei Kindern und Jugendlichen vorliegen ( ▶ Tab.2 ) [77, 78, 79].”
A clinical case of the use of a closed loop system in the treatment of type 1 diabetes mellitus
Teplova, A. S., Parkhomenko, A., & Petrosyan, K. (2025) · FOCUS.Endocrinology · doi:10.62751/2713-0177-2025-6-2-11
Temporal Characterization of Glycemia Risk Index Sequences through Random Walk Bayesian Clustering
Pescol, F., Bosoni, P., Ghilotti, S., Cata, P. D., Tibollo, V., Dagliati, A., Ferrazzi, F., Sacchi, L., & Bellazzi, R. (2025) · IEEE International Conference on Healthcare Informatics · doi:10.1109/ichi64645.2025.00037
“…use in clinical practice by diabetologists of ICS Maugeri and as part of the key metrics identified by the American Diabetes Association (ADA) [5], [20], [21]; J-Index was involved as a useful combination of mean and standard deviation [22]; LI and Conga to evaluate the amplitude of the…”
Plaitano, E. G., & Stanger, C. (2025) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2025.109083
II. Treatment Indicators and Utilization of CGM in Diabetes Management
Ishiguro, M., & Nishimura, R. (2025) · Nihon Naika Gakkai Zasshi · doi:10.2169/naika.114.616
Diagnosis, Therapy and Follow-Up of Type 1 Diabetes Mellitus in Children and Adolescents
Holder, M., Ziegler, R., Kamrath, C., Lange, K., & Kummer, S. (2025) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/a-2490-5096
“The GMI provides an estimate of the A1C value based on an average of the CGM glucose values that a person had over a period of several weeks [71].”
Bilbie-Lupchian, L., Oliván-Blázquez, B., González-Álvarez, B., Matovelle-Ochoa, P., Casado-Vicente, V., & Sánchez-Calavera, M. A. (2025) · Primary Health Care Research and Development · doi:10.1017/s1463423625000155
Alqutub, S. T., & Aljehani, F. (2025) · Digital Health · doi:10.1177/20552076251325987 · Cited by 1
“1,24 For most adults with DM, the desirable target GMI value is an HbA1c of 7%, which corresponds to a mean glucose level of approximately 154 mg/dL.”
Monnier, L., Colette, C., Renard, É., & Benhamou, P. (2025) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2025.03.010
Diagnostik, Therapie und Verlaufskontrolle des Diabetes mellitus Typ 1 im Kindes- und Jugendalter
Holder, M., Kamrath, C., Lange, K., Kummer, S., & Ziegler, R. (2025) · Die Diabetologie · doi:10.1007/s11428-024-01279-6
“Der GMI liefert eine Schätzung des A 1C -Wer- tes auf der Grundlage eines Durchschnitts der CGM-Glukosewerte, die eine Person über einen Zeitraum von mehreren Wo-chen hatte [70].”
Glukosemessung und -kontrolle bei Patienten mit Typ-1- oder Typ-2-Diabetes
Schlüter, S., Deiss, D., Gehr, B., Lange, K., von Sengbusch, S., Thomas, A., Ziegler, R., Wiesner, T., & Freckmann, G. (2025) · Die Diabetologie · doi:10.1007/s11428-024-01275-w · Cited by 1
“Die transkutanen CGM-Systeme haben eine Nutzungsdauer”
Naiki, Y., Itho, R., & Misaki, Y. (2025) · Pediatrics International · doi:10.1111/ped.70060
ALGaseem, N. A. N., Shiba, N. M. M., Alawad, S. A. S., Fulayhi, N. A. A., Hakami, S. A. H., Almutairi, E. A. M., Alshakarah, W. M. T., Alzahrani, M. R., Alruwaili, O. N. S., Alotaibi, A. B., et al. (2024) · Saudi Journal of Medicine and Public Health · doi:10.64483/202412568
“SMBG, though invasive, remains a cornerstone for many, informing dietary choices, physical activity, and insulin dosing (Bergenstal et al., 2018).”
Intermittently scanned continuous glucose monitoring (isCGM) in hospitalized elderly patients
Matvejeva, D., Nováková, M., Piťhová, P., Tománková, L., Kotvová, V., Polanecká, D., & Kvapil, M. (2024) · Vnitřní lékařství · doi:10.36290/vnl.2024.086
Momani, A. M., Halimi, A., Nazari, S. S. H., Al-Marzouqi, Z., Jarrahi, A., Al-Yateem, N., Rahman, S. A., & Al-Marzouqi, A. (2024) · Current Diabetes Reviews · doi:10.2174/0115733998327893240905071326
“After reviewing the literature, average glucose level was found as a common variable in both HbA1c levels and GMI [25; 26]; therefore, HbA1c and GMI were converted to average glucose (mg/dL) using these formulas: (HbA1c*35.6)-77.3 and (GMI-3.”
InfluentialMcCoy, R. G. (2024) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s491642 · Cited by 1
Norris, A. W., & Lang, J. B. (2024) · Diabetes Care · doi:10.2337/dc24-0653
CAPÍTULO 2: CONTROL METABÓLICO EN DIFERENTES ESTADIOS DE ENFERMEDAD RENAL CRÓNICA
Cicchitti, A., Arinovich, B., Morales, E., & Argerich, I. (2024) · Revista de la Sociedad Argentina de Diabetes · doi:10.47196/diab.v58i2sup.783
A Confront on Monitoring Pediatric Type 1 Diabetes
Sivakumar, T., Kataveni, S., Peter, V., & Yashaswini, T. (2024) · Journal of Pediatrics and Congenital Disorders · doi:10.47275/2379-6707-120
Kusuma, B. S., Xanthippi, T., J., H. A. E., Ajay, T., & Loredana, M. M. (2024) · International Journal of Diabetes and Clinical Research · doi:10.23937/2377-3634/1410183
“CGM metrics collected were % CGM use, TIR, Coefficient of Variance (CoV), and average BG. Glucose management indicator was calculated using the formula derived from adult T1D data: GMI (mmol/ mol) = 12.71 + 4.70587 × [mean glucose in mmol/L] [12].”
InfluentialHevesi, J. I. (2024) · Belügyi Szemle · doi:10.38146/bsz.2024.1.5 · Cited by 4
“Így egy be - teg GMI értékéből következtetni lehet a vénás HbA1c értékre ( Bergenstal et al., 2018).”
On Standardization and Evaluation of Continuous Glucose Monitoring Systems
Темиргалиевич, М. К., Прокопьев, М. В., & Ivanov, I. V. (2024) · Measurement Standards Reference Materials · doi:10.20915/2077-1177-2023-19-5-113-125 · Cited by 1
Chenoweth, D. J., Palmer, B. A., Norris, A. W., Tansey, M., & Pinnaro, C. T. (2024) · Pediatric Diabetes · doi:10.1155/2024/5218915
“…of variation (the standard deviation of glucose readings over the last 2 weeks divided by the average glucose), and glucose management indicator (average glucose level represented as a percentage according to a formula derived by regressing HbA1c values against mean sensor glucose levels [15]).”
Haemoglobin glycation index (HGI) as a marker of individual glycation in diabetes
Pehr, M., & Škrha, J. (2023) · Vnitřní lékařství · doi:10.36290/vnl.2023.097
Security, extensibility, and redundancy in the Metabolic Operating System
King, S. T. (2023) · arXiv.org · doi:10.48550/arxiv.2401.01357
“Figure 11 shows Bob’s GMI [7], which approximates A1C numbers based on CGM data.”
MethodResultShaikh, A., Patil, T., Saraogi, P., & Baluni, S. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr23829140111
Michaelis-Menten kinetic modeling of hemoglobin A1c status facilitates personalized glycemic control
Nagy, Z., Poór, V. S., Fülöp, N., Chauhan, D., Miseta, A., & Nagy, T. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117526
How can we reach the target of glucose control in type 1 diabetes?
Bloomgarden, Z. (2023) · Journal of Diabetes · doi:10.1111/1753-0407.13413
Harmonization of Continuous Glucose Monitoring (CGM)
Friedecky, B., & Kratochvíla, J. (2023) · Klinická biochemie a metabolismus · doi:10.61568/kbm.2023.002
A Combined Acute and Chronic Risk Assessment Rolling Window for Type 1 Diabetes
Munawar, F., Donovan, J. S., Kiely, E., & Mulrennan, K. (2022) · International Conference on Image Processing, Applications and Systems · doi:10.1109/ipas55744.2022.10052880
“Although HbA1c is one of the primary measures for glucose management and a valuable marker of the risk of developing diabetes-related complications, GMI combined with other CGM metrics provides a much more personalised diabetes management plan [11].”
BackgroundMethodGlucose Controlin Type 2 Diabetes: In Silico Study of Sub-Optimal Adherence to Insulin Therapy
VISENTIN, R., Cobelli, C., Sieber, J., & Man, C. D. (2022) · International Conference on Systems and Control (ICSC) · doi:10.1109/icsc57768.2022.9993828
Time in range: new parameter to evaluate blood glucose control
Flekač, M. (2022) · Vnitřní lékařství · doi:10.36290/vnl.2022.066
“GMI umožňuje více individualizovat cíle léčby (13).”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
