Cited by 684Diabetes Management and ResearchDiabetes Management and EducationDiabetes Treatment and Management

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 access

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

Open this paper in CoChat — free account

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.

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

Crossref · 10.2337/dc18-1581

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry
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-citation
Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial
Beck, R. W., Riddlesworth, T., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S., Kollman, C., Kruger, D., McGill, J. B., Polonsky, W., et al. (2017) · JAMA · doi:10.1001/jama.2016.19975 · Cited by 1,185
Self-citation
Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections: A Randomized Trial
Beck, R. W., Riddlesworth, T. D., Ruedy, K., Ahmann, A., Haller, S., Kruger, D., McGill, J. B., Polonsky, W., Price, D., Aronoff, S., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2855 · Cited by 635
Self-citation
4. Lifestyle Management: Standards of Medical Care in Diabetes—2018
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dc18-s004 · Cited by 563
Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): a multicentre, randomised controlled trial
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
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citation
REPLACE-BG: a randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes
Aleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2482 · Cited by 329
Self-citation
Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the Ambulatory Glucose Profile (AGP)
Bergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307
Self-citation

Flagged first, then by citations

Show all 18 references

2 of the 18 references couldn’t be matched to a known paper.

Sorted by citations

Items 101–200 of 615

Comparing the effects of time-restricted eating on glycaemic control in people with type 2 diabetes with standard dietetic practice: A randomised controlled trial
Parr, E. B., Radford, B. E., Hall, R. C., Steventon-Lorenzen, N., Flint, S. A., Siviour, Z., Plessas, C., Halson, S. L., Brennan, L., Kouw, I. W. K., et al. (2024) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2024.111893 · Cited by 43
“…glucose concentration between 0000 – 0600 h), nocturnal glucose concentration (i.e. average between 0000 – 0600 h), time in range (TIR; 3.9 – 10.0 mmol/L), time in tight range (TITR; 3.9 – 7.8 mmol/L), time above range (TAR; > 10 mmol/L), and glucose managment indicator (GMI) [29] were calculated.”
Influential
Characterising frailty, metrics of continuous glucose monitoring, and mortality hazards in older adults with type 2 diabetes on insulin therapy (HARE): a prospective, observational cohort study
Fung, E., Lui, L.-T., Huang, L., Cheng, K. F., Lau, G. H. W., Chung, Y. T., Ahmadabadi, B. N., Xie, S., Lee, J. S. W., Hui, E., et al. (2021) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00251-8 · Cited by 36
What's Wrong with This Picture? A Critical Review of Current Centers for Medicare & Medicaid Services Coverage Criteria for Continuous Glucose Monitoring
Galindo, R. J., Parkin, C. G., Aleppo, G., Carlson, A. L., Kruger, D. F., Levy, C. J., Umpierrez, G. E., & McGill, J. B. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0107 · Cited by 38
Self-citation
Continuous Glucose Monitoring With Low-Carbohydrate Diet Coaching in Adults With Prediabetes: Mixed Methods Pilot Study
Yost, O., DeJonckheere, M. J., Stonebraker, S., Ling, G., Buis, L. R., Pop‐Busui, R., Kim, N., Mizokami‐Stout, K., & Richardson, C. J. (2020) · JMIR Diabetes · doi:10.2196/21551 · Cited by 43
Diabetes Technology Use in Adults with Type 1 and Type 2 Diabetes
Kravarusic, J., & Aleppo, G. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.006 · Cited by 46
Self-citation
Digital Health Interventions for Diabetes: Everything to Gain and Nothing to Lose
Kerr, D. E., King, F., & Klonoff, D. C. (2019) · Diabetes Spectrum · doi:10.2337/ds18-0085 · Cited by 39
“Glucose-based outcomes such as ambulatory glucose profiles (which show a patient’s daily glucose and insulin patterns, including time in the target range and a graphic representation of excursions out of that range) and glucose management indicators using CGM data are likely to have more impact for clinicians and peop…”
Background
Smart Pen Exposes Missed Basal Insulin Injections and Reveals the Impact on Glycemic Control in Adults With Type 1 Diabetes
Ekberg, N. R., Hartvig, N. V., Kaas, A., Møller, J. B., & Adolfsson, P. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221104142 · Cited by 36
Deciphering the intricacies of immune system dysfunction and its impact on diabetes mellitus: Revisiting the communication strategies to manage diabetes mellitus
Vaibhav, V., Nishad, S., Dongare, D. B., Tripathi, A. C. P., Tripathi, T., & Tripathi, P. (2024) · Health Sciences Review · doi:10.1016/j.hsr.2024.100201 · Cited by 24
The Association of Time in Range and Diabetic Complications: The Evidence Is Strong
Beck, R. W. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0141 · Cited by 37
“8 It is presumed that the discordance is often due to red blood cell lifespan or other nonglycemic factors.”
Self-citation
Clinical application guidelines for blood glucose monitoring in China (2022 edition)
Bao, Y., Zhu, D., & Chinese Diabetes Society (2022) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3581 · Cited by 39
“From a clinical perspective, the potential discordance between GMI and HbA1c could help inform diabetes management.(74) For instance, a greater difference between HbA1c and GMI was associated with higher risk of hypoglycaemia in a non‐White population.”
Background
Glycaemic control during the lockdown for COVID-19 in adults with type 1 diabetes: A meta-analysis of observational studies
Garofolo, M., Aragona, M., Rodia, C., Falcetta, P., Bertolotto, A., Campi, F., Prato, S. D., & Penno, G. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.109066 · Cited by 33
“Estimated HbA1c (eA1c) and glucose management indicator (GMI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .”
Influential
Ambulatory Glucose Profile (AGP) Report in Daily Care of Patients with Diabetes: Practical Tips and Recommendations
Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Klupa, T., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2022) · Diabetes Therapy · doi:10.1007/s13300-022-01229-9 · Cited by 35
“02392 9 mean glucose concentration (mg/dL) [14]”
Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept
Bent, B., Cho, P. J., Wittmann, A., Thacker, C., Muppidi, S., Snyder, M. P., Crowley, M. J., Feinglos, M. N., & Dunn, J. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-002027 · Cited by 43
Glycemic Outcomes During Real-World Hybrid Closed-Loop System Use by Individuals With Type 1 Diabetes in the United States
Arunachalum, S., Velado, K., Vigersky, R. A., & Cordero, T. L. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221088608 · Cited by 35
Efficacy of telemedicine for persons with type 1 diabetes during Covid19 lockdown
Boscari, F., Ferretto, S., Uliana, A., Avogaro, A., & Bruttomesso, D. (2021) · Nutrition & Diabetes · doi:10.1038/s41387-020-00147-8 · Cited by 31
Low carbohydrate high fat ketogenic diets on the exercise crossover point and glucose homeostasis
Noakes, T. D., Prins, P., Volek, J. S., D’Agostino, D. P., & Koutnik, A. P. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1150265 · Cited by 39
“Importantly, these glycemic phenotypes were captured utilizing 24-hour, minute-by-minute continuous glucose monitoring as it tracks long-term to HbA1c (Bergenstal et al., 2018; Hirsch et al., 2019; Valenzano et al., 2021), short term continuous glucose monitoring readings (10–14 days) are good estimates of 3-month con…”
BackgroundInfluential
Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes
Shah, V. N., Vigers, T., Pyle, L. L., Calhoun, P. M., & Bergenstal, R. M. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0544 · Cited by 34
Self-citation
Low and high carbohydrate isocaloric diets on performance, fat oxidation, glucose and cardiometabolic health in middle age males
Prins, P., Noakes, T. D., Buga, A., D’Agostino, D. P., Volek, J. S., Buxton, J. D., Heckman, K., Jones, D. W., Tobias, N. E., Grose, H. M., et al. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1084021 · Cited by 44
Intralymphatic GAD-Alum (Diamyd®) Improves Glycemic Control in Type 1 Diabetes With HLA DR3-DQ2
Nowak, C., Lind, M., Šumník, Z., Pelikánová, T., Nattero‐Chávez, L., Lundberg, E., Rica, I., Martínez‐Brocca, M. A., de Adana, M. R., Wahlberg, J., et al. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac343 · Cited by 33
“02392 × [mean glucose in mg/dL], and expressed as % DCCT units, similar to HbA1c) (22).”
Patients with highly unstable type 1 diabetes eligible for islet transplantation can be managed with a closed‐loop insulin delivery system: A series of N‐of‐1 randomized controlled trials
Benhamou, P., Lablanche, S., Vambergue, A., Doron, M., Franc, S., & Charpentier, G. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14214 · Cited by 39
Impact of a combined continuous glucose monitoring-digital health solution on glucose metrics and self-management behavior for adults with type 2 diabetes: real-world, observational study
Kumbara, A. B., Iyer, A. K., Green, C. R., Jepson, L. H., Leone, K., Layne, J. E., & Shomali, M. (2023) · JMIR Diabetes · doi:10.2196/47638 · Cited by 29
Flash glucose monitoring in the Netherlands: increased monitoring frequency is associated with improvement of glycemic parameters
Lameijer, A., Lommerde, N., Dunn, T. C., Fokkert, M. J., Edens, M. A., Kao, K., Xu, Y., Gans, R. O. B., Bilo, H. J. G., & van Dijk, P. R. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108897 · Cited by 27
Continuous Glucose Monitor, Insulin Pump, and Automated Insulin Delivery Therapies for Type 1 Diabetes: An Update on Potential for Cardiovascular Benefits
PAULEY, M. E., Tommerdahl, K. L., Snell‐Bergeon, J. K., & Forlenza, G. P. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01799-x · Cited by 39
The automated pancreas: A review of technologies and clinical practice
Biester, T., Tauschmann, M., Chobot, A. P., Kordonouri, O., Danne, T., Kapellen, T. M., & Dovč, K. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14576 · Cited by 32
“A glucose management index can be derived from the accumulated data, which is close to the measured HbA1c value.(9) F IGURE 7 Open-source automated insulin delivery (AID) system in use, with continuously high values, in which presets were changed several times (blue letters) without success; no ketone measurement or c…”
Method
Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges
Freckmann, G., Pleus, S., Schauer, S., Link, M., Jendrike, N. R. M., Waldenmaier, D., Haug, C., & Stuhr, A. (2021) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/a-1347-2550 · Cited by 30
Continuous Glucose Monitoring–Derived Data Report—Simply a Better Management Tool
Battelino, T., & Bergenstal, R. M. (2020) · Diabetes Care · doi:10.2337/dci20-0032 · Cited by 27
Self-citation
Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data
Piersanti, A., Giurato, F., Göbl, C. S., Burattini, L., Tura, A., & Morettini, M. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0237 · Cited by 31
“…percentage of time in target range (%TIR), percentage of time in hyperglycemic ranges (%TAR level 1 and 2), the glucose management indicator (GMI 41 ), reports on data sufﬁciency (percentage of expected CGM readings), area under the glycemic curve (AUC) divided by the time in hours, risk of…”
Use of Glucose-Lowering Agents in Diabetes and CKD
Alicic, R. Z., Neumiller, J. J., Galindo, R. J., & Tuttle, K. R. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.09.018 · Cited by 35
“” Glucose management indicator is a metric to estimate HbA1c, as derived from CGM values, that can be reported to patients as an alternative to HbA1c.(71) For most patients, target time in range (time-in-target range 70 180 mg/dl or 3.”
BackgroundInfluential
Leveraging advances in diabetes technologies in primary care: a narrative review
Bode, B. W., King, A., Russell-Jones, D. L., & Billings, L. K. (2021) · Annals of Medicine · doi:10.1080/07853890.2021.1931427 · Cited by 41
American Association of Clinical Endocrinology Consensus Statement: Algorithm for Management of Adults With Type 2 Diabetes – 2026 Update
Samson, S. L., Vellanki, P., Blonde, L., Hirsch, I. B., Hoang, T. D., Isaacs, S. D., Izuora, K. E., Wang, C. C. L., Ooi, C. P., Padilla, B. I., et al. (2026) · Endocrine Practice · doi:10.1016/j.eprac.2026.01.006 · Cited by 29
Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics
Pleus, S., Kamecke, U., Waldenmaier, D., Link, M., Zschornack, E., Jendrike, N. R. M., Haug, C., & Freckmann, G. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820931825 · Cited by 34
“1106 Journal of Diabetes Science and Technology 15(5)”
BackgroundInfluential
HbA1c
Carlson, A. L., Criego, A. B., Martens, T. W., & Bergenstal, R. M. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.010 · Cited by 33
Self-citation
Future of Time-in-Range Goals in the Era of Advanced Hybrid Closed-Loop Automated Insulin Delivery Systems
Aktürk, H. K., Battelino, T., Castañeda, J., Arrieta, A., van den Heuvel, T. R. A., & Cohen, O. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0432 · Cited by 26
“18,24 TIR was also suggested as a primary endpoint in diabetes clinical trials by the International Consensus.”
Glucose Management Indicator (GMI): Insights and Validation Using Guardian 3 and Navigator 2 Sensor Data
Leelarathna, L., Beck, R. W., Bergenstal, R. M., Thabit, H., & Hovorka, R. (2019) · Diabetes Care · doi:10.2337/dc18-2479 · Cited by 28
“The glucose management indicator (GMI) is an updated approach for estimating HbA1c from continuous glucose monitoring (CGM) data (1).”
BackgroundMethodInfluentialSelf-citation
Beyond HbA1c: using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision‐making
Brown, S. A., Basu, A., & Kovatchev, B. P. (2019) · Diabetic Medicine · doi:10.1111/dme.13944 · Cited by 27
Perioperative Care of Patients Using Wearable Diabetes Devices
Cruz, P., McKee, A. M., Chiang, H.-H., McGill, J. B., Hirsch, I. B., Ringenberg, K. J., & Wildes, T. S. (2024) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000007115 · Cited by 32
Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease
Oriot, P., Viry, C., Vandelaer, A., Grigioni, S., Roy, M., Philips, J., & Prévost, G. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221092050 · Cited by 26
“10 Some studies reported discordances between lab HbA 1 c and GMI, but data are still limited in individuals with diabetes and CKD. 10,11 The purpose of this study is to compare the relationship between HbA 1 c measured in the laboratory and GMI calculated from 14 and 90 days CGM data recorded in a…”
MethodInfluential
Not Control but Conquest: Strategies for the Remission of Type 2 Diabetes Mellitus
Kim, J., & Kwon, H. (2022) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0377 · Cited by 31
“Since continuous glucose monitoring (CGM) has been recently applied, it is recommended to use the estimated HbA1c level (eA1c) or a glucose management indicator on CGM [22].”
Method
Blood Glucose Monitoring in Adults and Children with Diabetes: Update 2021
CHENG, A. Y. Y., Feig, D. S., Ho, J., Siemens, R. L., BAJAJ, H. S., Gilbert, J. D., Houlden, R. L., Kim, J., MacKay, D., Rabi, D. M., et al. (2021) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2021.07.003 · Cited by 36
Practical guidance for using the FreeStyle Libre flash continuous glucose monitoring in primary care
Unger, J., Kushner, P. R., & Anderson, J. E. (2020) · Postgraduate Medicine · doi:10.1080/00325481.2020.1744393 · Cited by 27
“The report also presents the average glucose and the glucose management indicator (GMI) statistic, which closely aligns with HbA1c levels [48].”
Background
Chinese clinical guidelines for continuous glucose monitoring (2018 edition)
Bao, Y., Chen, L., Chen, L., Dou, J., Gao, Z., Gao, L., Guo, L., Guo, X., Ji, L., Ji, Q., et al. (2019) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3152 · Cited by 23
“Many patients and clinicians find the GMI to be a helpful educational tool in understanding the CGM‐ generated glucose profiles and will facilitate optimal diabetes management and the adjustment of anti‐diabetic therapy.(113)”
Background
Candidate Selection for Hybrid Closed Loop Systems
Forlenza, G. P., Breton, M. D., & Kovatchev, B. P. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0217 · Cited by 20
Remote glucose monitoring is feasible for patients and providers using a commercially available population health platform
Crossen, S. S., Romero, C. C., Lewis, C., & Glaser, N. S. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1063290 · Cited by 22
Continuous glucose sensor accuracy: beyond the headline metric
Oliver, N. S., Reddy, M., & Leelarathna, L. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00245-6 · Cited by 20
An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems
Ahmad, S., Beneyto, A., Zhu, T., Contreras, I., Georgiou, P., & Vehí, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-62912-4 · Cited by 25
“The GMI, an approximation of the A1C level based on the average BG from CGM 50 , was similar in all cases.”
Method
Continuous Glucose Monitoring in Dogs and Cats
Baldo, F. D., & Fracassi, F. (2023) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2023.01.008 · Cited by 20
AGATA: A Toolbox for Automated Glucose Data Analysis
Cappon, G., Sparacino, G., & Facchinetti, A. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221147570 · Cited by 22
Long‐term effectiveness of advanced hybrid closed loop in children and adolescents with type 1 diabetes
Piccini, B., Pessina, B., Casalini, E., Lenzi, L., & Toni, S. (2022) · Pediatric Diabetes · doi:10.1111/pedi.13440 · Cited by 22
Thrice daily consumption of a novel, premeal shot containing a low dose of whey protein increases time in euglycemia during 7 days of free-living in individuals with type 2 diabetes
Smith, K., Taylor, G., Brunsgaard, L. H., Walker, M., Davies, K. A. B., Stevenson, E., & West, D. J. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2022-002820 · Cited by 23
Continuous Glucose Monitoring Integration for Remote Diabetes Management: Virtual Diabetes Care with Case Studies
Carlson, A. L., Martens, T. W., Johnson, L., & Criego, A. B. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0241 · Cited by 28
“18 The panel also endorsed use of the Ambulatory Glucose Proﬁle (AGP) as a template for presenting CGM data in a standardized report.”
Relación entre hemoglobina glucosilada, tiempo en rango y variabilidad glucémica en una cohorte de pacientes pediátricos y adultos con diabetes tipo 1 con monitorización flash de glucosa
Díaz-Soto, G., Bahillo-Curieses, P., Jimenez, R., de la Marca, M. D. L. O. N., Hoyos, E. G., Torres, B., Gómez, J. J. L., & de Luis, D. A. (2021) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2020.09.008 · Cited by 13
Glycaemic control in the paediatric and young adult population with type 1 diabetes following a single telehealth visit - what have we learned from the COVID-19 lockdown?
Rachmiel, M., Lebenthal, Y., Mazor‐Aronovitch, K., Brener, A., Levek, N., Levran, N., Chorna, E., Dekel, M., Barash, G., Landau, Z., et al. (2021) · Acta Diabetologica · doi:10.1007/s00592-021-01673-2 · Cited by 26
“ADHD Attention-deficit hyperactivity disorder CGM Continuous glucose monitoring GMI Glycaemic metabolic index TIR Time in range”
Influential
Pediatric Endocrinology in the Time of COVID-19: Considerations for the Rapid Implementation of Telemedicine and Management of Pediatric Endocrine Conditions
Regelmann, M. O., Conroy, R., Gourgari, E., Gupta, A., Guttmann‐Bauman, I., Heksch, R. A., Kamboj, M. K., Krishnan, S., Lahoti, A., & Matlock, K. A. (2020) · Hormone Research in Paediatrics · doi:10.1159/000513060 · Cited by 26
Critical Reappraisal of the Time-in-Range: Alternative or Useful Addition to Glycated Hemoglobin?
Heinemann, L., Freckmann, G., Müller‐Wieland, D., & Kellerer, M. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819883885 · Cited by 25
“We will not discuss any different approaches to calculate an HbA1c value from the measured (blood) glucose values (estimated HbA1c or eHbA1c); as per the FDA, the term “Glucose Management Indicator” is now used for this purpose.(1,2) Likewise, we will not discuss the “estimated average glucose (eAG)”, where—contrary t…”
MethodSelf-citation
Glucose Management Indicator Based on Sensor Data and Laboratory HbA1c in People With Type 1 Diabetes From the DPV Database: Differences by Sensor Type
Grimsmann, J. M., von Sengbusch, S., Freff, M., Ermer, U., Placzek, K., Danne, T., Hammer, E., & Holl, R. (2020) · Diabetes Care · doi:10.2337/dc20-0259 · Cited by 22
“The formula was developed with real-time CGM (rtCGM) sensors (1).”
BackgroundMethodInfluential
Impact of professional continuous glucose monitoring by clinical pharmacists in an ambulatory care setting
Dril, E. V., & Schumacher, C. (2019) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.1088 · Cited by 25
Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes
Friedman, J. G., Coyne, K., Aleppo, G., & Szmuilowicz, E. D. (2023) · Endocrine Connections · doi:10.1530/ec-23-0085 · Cited by 22
Self-citation
Time in range—A new gold standard in type 2 diabetes research?
Goshrani, A., Lin, R., O'Neal, D. N., & Ekinci, E. I. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16279 · Cited by 25
Diabetes technology in people with diabetes and advanced chronic kidney disease
Galindo, R. J., Soliman, D., Cherñavvsky, D. R., & Rhee, C. M. (2024) · Diabetologia · doi:10.1007/s00125-024-06244-y · Cited by 21
Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy
Tozzo, V., Genco, M. T., Omololu, S. O., Mow, C., Patel, H. R., Patel, C. H., Ho, S. N., Lam, E., Abdulsater, B., Patel, N., et al. (2024) · Diabetes Care · doi:10.2337/dc23-1177 · Cited by 28
Acute and adaptive cardiovascular and metabolic effects of passive heat therapy or high‐intensity interval training in patients with severe lower‐limb osteoarthritis
Roxburgh, B. H., Campbell, H. A., Cotter, J. D., Reymann, U., Williams, M. J. A., Gwynne‐Jones, D., & Thomas, K. N. (2023) · Physiological Reports · doi:10.14814/phy2.15699 · Cited by 22
“Glucose management indicator is a novel measure for estimating A1c from CGM devices (Bergenstal et al., 2018); this was calculated as 12.71 + 4.70587 × [mean glucose in mmol L−1].”
MethodInfluential
Interindividual variability in average glucose‐glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice
Xu, Y., Bergenstal, R. M., Dunn, T. C., Ram, Y., & Ajjan, R. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14763 · Cited by 23
“Results: The mean age (range) was 30 years (8-72) with 94 younger than 19 years,”
BackgroundSelf-citation
Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems
Shahid, A., & Lewis, D. (2022) · Nutrients · doi:10.3390/nu14091906 · Cited by 21
“Glycemic management indicator GMI Indicates the expected mean hemoglobin A1C using mean glucose of individuals with diabetes [36].”
Method
Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes
Piona, C., Marigliano, M., Mozzillo, E., Candia, F. D., Zanfardino, A., Iafusco, D., Maltoni, G., Zucchini, S., Piccinno, E., & Maffeis, C. (2021) · Pediatric Diabetes · doi:10.1111/pedi.13299 · Cited by 21
“This metric is calculated from the mean glucose value using an equation derived from HbA1c and CGM data of four clinical trials.(2) These trials included subjects using Dexcom G4 and G5 sensors with a wide range of age and affected by both type 1 and type 2 diabetes.”
Method
Practical aspects of diabetes technology use: Continuous glucose monitors, insulin pumps, and automated insulin delivery systems
Marks, B. E., Williams, K. M., Sherwood, J. S., & Putman, M. S. (2021) · Journal of Clinical & Translational Endocrinology · doi:10.1016/j.jcte.2021.100282 · Cited by 30
“3 which provides an estimate of the A1c when ≥ 10 days of glucose data are available [28,29].”
Background
Current status of continuous glucose monitoring among Korean children and adolescents with type 1 diabetes mellitus
Kim, J. H. (2020) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2040038.019 · Cited by 22
Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c
Kahkoska, A. R., Adair, L. S., Aiello, A. E., Burger, K. S., Buse, J. B., Crandell, J., Maahs, D. M., Nguyen, C. T., Kosorok, M. R., & Mayer‐Davis, E. J. (2019) · Pediatric Diabetes · doi:10.1111/pedi.12856 · Cited by 20
“Recently, CGM data have also been used to provide validated metrics such as time in range(5,6) or average glucose exposure over a shorter period of time, also referred to as the glucose management indicator.(7) CGM as a source of patient data thus offers the opportunity to understand patterns of glycemia that are not…”
Method
A systematic literature review and meta‐analysis of real‐world evidence on commercially available automated insulin delivery systems in people with type 1 diabetes
Pöhlmann, J., Smith‐Palmer, J., Serné, E. H., Tuomaala, A., JENDLE, J. H., Choudhary, P., Bosi, E., Mohan, V., van den Heuvel, T. R. A., Cohen, O., et al. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70161 · Cited by 21
“37,50 In addition, the percentage of users achieving glycaemic targets such as TIR >70% or HbA1c 7% was an eligible outcome.”
Is It Time to Replace Time-in-Range with Time-in-Tight-Range? Maybe Not
Beck, R. W. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0602 · Cited by 20
Self-citation
Assessment of the Glucose Management Indicator Using Different Sampling Durations
Bailey, R. J., Calhoun, P. M., Bergenstal, R. M., & Beck, R. W. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0284 · Cited by 22
Self-citation
The Emerging Role of Continuous Glucose Monitoring in the Management of Diabetic Peripheral Neuropathy: A Narrative Review
Gouveri, E., & Παπάνας, Ν. (2022) · Diabetes Therapy · doi:10.1007/s13300-022-01257-5 · Cited by 22
“The use of CGM systems has been associated with increased TIR and reduced incidence of severe episodes of hypoglycaemia [16].”
Background
Utilizing continuous glucose monitoring in primary care practice: What the numbers mean
Edelman, S. V., Cavaiola, T. S., BOEDER, S. C., & Pettus, J. (2020) · Primary care diabetes · doi:10.1016/j.pcd.2020.10.013 · Cited by 25
Long‐term glycemic control and glucose variability assessed with continuous glucose monitoring in a pediatric population with type 1 diabetes: Determination of optimal sampling duration
Piona, C. A., Marigliano, M., Mozzillo, E., Franzese, A., Zanfardino, A., Iafusco, D., Maltoni, G., Zucchini, S., Delvecchio, M., & Maffeis, C. (2020) · Pediatric Diabetes · doi:10.1111/pedi.13115 · Cited by 21
Improvements in glycemic outcomes in 4738 children, adolescents, and adults with type 1 diabetes initiating a tubeless insulin management system
Aleppo, G., DeSalvo, D. J., Lauand, F., Huyett, L. M., Chang, A., Vienneau, T., & Ly, T. T. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01366-9 · Cited by 19
Self-citation
“Hyperglycemia aversiveness”: investigating an overlooked problem among adults with type 1 diabetes
Polonsky, W. H., Fortmann, A. L., Price, D., & Fisher, L. (2021) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2021.107925 · Cited by 19
Enhancing the Impact of Individualized Nutrition Therapy with Real-Time Continuous Glucose Monitoring Feedback in Overweight and Obese Individuals with Prediabetes
Basiri, R., & Cheskin, L. J. (2024) · Nutrients · doi:10.3390/nu16234005 · Cited by 22
“A significant reduction in GMI was observed in the treatment group but not in the control group, which potentially suggests a decrease in HbA1c and future diabetes-related complications [32,53].”
Older Adults Benefit From Virtual Support for Continuous Glucose Monitor Use But Require Longer Visits
Weinstock, R. S., Raghinaru, D., Gal, R. L., Bergenstal, R. M., Bradshaw, A., Cushman, T., Kollman, C., Kruger, D. F., Johnson, M. L., MCARTHUR, T. S., et al. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241294250 · Cited by 18
Self-citation
Multi-Horizon Glucose Prediction Across Populations With Deep Domain Generalization
Zhu, T., Afentakis, I., Li, K., Armiger, R., Hill, N. E., Oliver, N. S., & Georgiou, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3428921 · Cited by 21
“…CGM metrics that have been recognized by international consensus for their applications in clinical diabetes care [7], which include glucose management indicator (GMI) [57], time below range (TBR), time in range (TIR), time above range (TAR), blood glucose index, and coefﬁcient of variation (CV).”
Comparison of glucose time in range and area under curve in range in relation to risk of diabetic retinopathy in type 2 diabetes patients
Wang, Y., Lu, J., Shen, Y., Ni, J., Zhang, L., Lu, W., Zhu, W., Bao, Y., & Zhou, J. (2022) · Journal of Diabetes Investigation · doi:10.1111/jdi.13811 · Cited by 23
Continuous Glucose Monitoring Metrics in the Assessment of Glycemia in Moderate-to-Advanced CKD in Diabetes
Ling, J., Ng, J. K., Lau, E. S. H., Ronald, C. W., Kong, A. P. S., Luk, A. O. Y., Kwok, J. S.-S., Szeto, C., Chan, J. C. N., & Chow, E. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.03.029 · Cited by 18
“uACR (mg/mmol) 100 (28-305) 26 (5-105) 163 (41-271) 353 (125-423) 0.”
A New Analysis Tool for Continuous Glucose Monitor Data
OLAWSKY, E. A., Zhang, Y., Eberly, L. E., Helgeson, E. S., & Chow, L. S. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211028909 · Cited by 19
Daytime Glycemic Variability and Frailty in Older Patients with Diabetes: a Pilot Study Using Continuous Glucose Monitoring
Chung, S. M., Lee, Y. H., Kim, C. O., Lee, J. Y., Jin, S., Yoo, S.-H., Moon, J. S., & Kim, K. J. (2021) · Journal of Korean Medical Science · doi:10.3346/jkms.2021.36.e190 · Cited by 23
Impact of COVID-19 Lockdown on the Metabolic Control Parameters in Patients with Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wafa, I. A., Pratama, N. R., Sofia, N. F., Anastasia, E. S., Konstantin, T., Wijaya, M. A., Wiyono, M. R., Djuari, L., & Novida, H. (2022) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2021.0125 · Cited by 24
Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy
Rodbard, D. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0115 · Cited by 21
Factors Related to Improvement of Glycemic Control Among Adults with Type 1 Diabetes During Lockdown Due to COVID-19
Domínguez, Ó. M., de Villar, N. G. P., Barquiel, B., Hillman, N., Gaspar, R., Arévalo-Gómez, M., & Herranz, L. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0550 · Cited by 20
The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study
Kriventsov, S., Lindsey, A., & Hayeri, A. (2020) · JMIR Diabetes · doi:10.2196/18660 · Cited by 22
A systematic review on clinical implication of continuous glucose monitoring in diabetes management
Gillani, S. W., Azhar, A., Mohiuddin, G., & Majeed, R. A. (2020) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_7_20 · Cited by 29
Association of G6PD variants with hemoglobin A1c and impact on diabetes diagnosis in East Asian individuals
Leong, A., Lim, V., Wang, C., Chai, J. F., Dorajoo, R., Heng, C., van Dam, R. M., Koh, W., Yuan, J., Jonas, J. B., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001091 · Cited by 19
“31 32 Nevertheless, continuous glucose monitoring is not routinely used to detect pre-diabetes or diabetes, and so comparing the diagnostic classification by HbA1c to FG is more aligned with current screening practices.”
Impacts of glycemic variability on the relationship between glucose management indicator from iPro ™ 2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus
Liu, H., Yang, D., Deng, H., Xu, W., Lv, J., Zhou, Y., Luo, S., Zheng, X., Liang, H., Yao, B., et al. (2020) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018820931664 · Cited by 22
Reducing Diabetes Burden in Medtronic's Automated Insulin Delivery Systems
Forlenza, G. P., Dai, Z., Niu, F., & Shin, J. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0459 · Cited by 20
MiniMed 780G™ advanced hybrid closed-loop system performance in Egyptian patients with type 1 diabetes across different age groups: evidence from real-world users
Elbarbary, N. S., & Ismail, E. A. R. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01184-w · Cited by 20
“As described in several publications, only patients with ≥ 14 days of SG data were used to determine CGM-derived metrics [6, 23, 24].”
Method
Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study
Parise, M., Tartaglione, L., Cutruzzolà, A., Maiorino, M. I., Esposito, K., Pitocco, D., Gnasso, A., & Irace, C. (2021) · Journal of Medical Internet Research · doi:10.2196/24552 · Cited by 25
“The coefficient of variation (CV)—an estimate of glycemic variability—and the GMI—an estimate of glycated hemoglobin levels—were determined using the following formulae if not automatically generated by the software analyzing glucose data [12]:”
Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation
Bergenstal, R. M. (2023) · Diabetes Spectrum · doi:10.2337/dsi23-0005 · Cited by 19
Self-citation
Patient Perspectives on the Benefits and Challenges of Diabetes and Digital Technology
Edelman, S. V., Cheatham, W. W., Norton, A., & Close, K. L. (2023) · Clinical Diabetes · doi:10.2337/cd23-0003 · Cited by 18
“GMI is an estimate of A1C based on the average of glucose data collected by the device sensor during the report period and may differ from the laboratory-measured A1C (24,38).”
MethodSelf-citation
Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes
Fang, M., Wang, D., Rooney, M. R., Echouffo-Tcheugui, J. B., Coresh, J., Aurora, R. N., Punjabi, N. M., & Selvin, E. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvac210 · Cited by 22
Carbohydrate Intake and Closed-Loop Insulin Delivery System during Two Subsequent Pregnancies in Type 1 Diabetes
Munda, A., Kovacic, C., & Barlovič, D. P. (2022) · Metabolites · doi:10.3390/metabo12111137 · Cited by 17
Continuous Glucose Profiles in Healthy People With Fixed Meal Times and Under Everyday Life Conditions
Freckmann, G., Schauer, S., Beltzer, A., Waldenmaier, D., Buck, S., Baumstark, A., Jendrike, N. R. M., Link, M., Zschornack, E., Haug, C., et al. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221113341 · Cited by 23
Relationship Between Time-in-Range, HbA1c, and the Glucose Management Indicator in Pregnancies Complicated by Type 1 Diabetes
Shah, V. N., Snell‐Bergeon, J. K., Demmitt, J. K., Joshee, P., Garcetti, R., Pyle, L. L., & Polsky, S. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0093 · Cited by 25
“12,13 The GMI was developed and validated from CGM data of randomized controlled clinical trials that excluded pregnant women with diabetes.”
Real-Time Continuous Glucose Monitoring During the Coronavirus Disease 2019 Pandemic and Its Impact on Time in Range
van der Linden, J. H., Welsh, J., Hirsch, I., & Garg, S. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0649 · Cited by 17
“The glucose management indicator (GMI) is a linear function of mean glucose values as previously described.(14) The dependent t-test for paired samples was used for prepandemic and intrapandemic TIR comparisons; the simple linear Pearson correlation coefficient was used to test the relationship between regional diseas…”
Method
Personal Glycation Factors and Calculated Hemoglobin A1c for Diabetes Management: Real-World Data from the Diabetes Prospective Follow-up (DPV) Registry
Xu, Y., Grimsmann, J. M., Karges, B., Hofer, S. E., Danne, T., Holl, R. W., Ajjan, R., & Dunn, T. C. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0553 · Cited by 19
“The most widely adopted methods are estimated HbA1c (eHbA1c) 3 and glucose management indicator (GMI), 4 which assume a universal linear correlation between glucose and HbA1c.”

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

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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks