The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A View Beyond HbA1c: Role of Continuous Glucose Monitoring
Open accessAbstract
Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium-glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.
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.
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
Inzucchi, S. E., Bergenstal, R. M., Buse, J. B., Diamant, M., Ferrannini, E., Nauck, M., Peters, A. L., Tsapas, A., Wender, R., & Matthews, D. R. (2015) · Diabetes Care · doi:10.2337/dc14-2441 · Cited by 3,066
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
Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients
Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K. (2016) · Biomarker Insights · doi:10.4137/bmi.s38440 · Cited by 1,381
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
Lind, M., Polonsky, W., Hirsch, I. B., Heise, T., Bolinder, J., Dahlqvist, S., Schwarz, E., Ólafsdóttir, A. F., Frid, A., Wedel, H., et al. (2017) · JAMA · doi:10.1001/jama.2016.19976 · Cited by 705
Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring
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., et al. (2018) · Diabetes Care · doi:10.2337/dc18-1581 · Cited by 684
Goldstein, D. E., Little, R. R., Lorenz, R. A., Malone, J. I., Nathan, D., Peterson, C. M., & Sacks, D. B. (2004) · Diabetes Care · doi:10.2337/diacare.27.7.1761 · Cited by 625
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
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
Polonsky, W. H., Fisher, L., Schikman, C. H., Hinnen, D. A., Parkin, C. G., Jelsovsky, Z., Petersen, B., Schweitzer, M., & Wagner, R. S. (2011) · Diabetes Care · doi:10.2337/dc10-1732 · Cited by 429
Cited by
Didyuk, O., Econom, N., Guardia, A., Livingston, K., & Klueh, U. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819899394 · Cited by 175
A Review of Research Progress on Glycemic Variability and Gestational Diabetes
Yu, W., Wu, N., Li, L., OuYang, H., Qian, M., & Shen, H. (2020) · Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy · doi:10.2147/dmso.s261486 · Cited by 44
The History, Evolution and Future of Continuous Glucose Monitoring (CGM)
Bender, C., Vestergaard, P., & Cichosz, S. L. (2025) · Diabetology · doi:10.3390/diabetology6030017 · Cited by 43
Wright, E. E., Morgan, K., Fu, D. K., Wilkins, N., & Guffey, W. J. (2020) · Clinical Diabetes · doi:10.2337/cd20-0042 · Cited by 40
Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care
Simonson, G. D., Bergenstal, R. M., Johnson, M. L., Davidson, J. L., & Martens, T. W. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296821998724 · Cited by 37
Valenzano, M., Cibrario Bertolotti, I., Valenzano, A., & Grassi, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001045 · Cited by 34
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
Metadata
- Authors
- Chehregosha, H., Khamseh, M. E., Malek, M., Hosseinpanah, F., & Ismail-Beigi, F.
- Venue
- Diabetes Therapy
- Published
- 2019-04-29
- Volume
- 10
- Issue
- 3
- Pages
- 853-863
- ISSN
- 1869-6953 (ISSN-L)
- 1869-6961
- OpenAlex
- W2943456521
- Cited by
- 235
- References
- 14
- FWCI
- 16.00
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
