Professional Flash Continuous Glucose Monitoring with Ambulatory Glucose Profile Reporting to Supplement A1c: Rationale and Practical Implementation
Abstract
Objective Recent consensus statements strongly advocate downloading and interpreting continuous glucose data for diabetes management in patients with type 1 or 2 diabetes. Supplementing periodic glycated hemoglobin (A1C) testing with intermittent continuous glucose monitoring (CGM) using a standardized report form known as the ambulatory glucose profile (AGP) is an evolving standard of care. The rationale for this approach and its implementation with a recently approved novel monitoring technology are explored. Methods Search of the medical literature, professional guidelines, and real-world evidence guided this introduction of an integrative practice framework that uses AGP in conjunction with intermittent flash continuous glucose monitoring (FCGM) as a supplement to A1C testing. Results The combination of intermittent continuous glucose pattern analysis, standardized glucose metrics, and a readily interpretable data report has the potential to practically extend the recognized benefits of CGM to more patients and clarify the relationship between A1C and average glucose levels in individual cases. Conclusion Novel FCGM technologies portend greater use of continuous forms of glucose monitoring and wider adoption of AGP report analysis. Additional formal and empirical evidence is needed to more fully characterize best practice. Abbreviations A1C = glycated hemoglobin; AGP = ambulatory glucose profile; CGM = continuous glucose monitoring; FCGM = flash continuous glucose monitoring; IQR = interquartile range; SMBG = self-monitoring of blood glucose.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Retrospective analysis of CGM data, utilizing standardized data management tools such as the AGP, enables clinicians and people with diabetes to work collaboratively in identifying problem areas and then set achievable goals.
“Retrospective analysis of CGM data, utilizing standardized data management tools such as the AGP, enables clinicians and people with diabetes to work collaboratively in identifying problem areas and then set achievable goals (71-73).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [71]
Cites
Monnier, L., Lapinski, H., & Colette, C. (2003) · Diabetes Care · doi:10.2337/diacare.26.3.881 · Cited by 1,461 · see citing works
Ceriello, A., Esposito, K., Piconi, L., Ihnat, M. A., Thorpe, J. E., Testa, R., Boemi, M., & Giugliano, D. (2008) · Diabetes · doi:10.2337/db08-0063 · Cited by 1,310 · see citing works
Achievement of Goals in U.S. Diabetes Care, 1999–2010
Ali, M. K., Bullard, K. M., Saaddine, J. B., Cowie, C. C., Imperatore, G., & Gregg, E. W. (2013) · New England Journal of Medicine · doi:10.1056/nejmsa1213829 · Cited by 979 · see citing works
Bolinder, J., Antuna, R., Geelhoed-Duijvestijn, P., Kröger, J., & Weitgasser, R. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31535-5 · Cited by 893 · see citing works
Hypoglycaemia: the limiting factor in the glycaemic management of type I and type II diabetes.
Cryer, P. (2002) · Diabetologia · doi:10.1007/s00125-002-0822-9 · Cited by 794 · see citing works
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 · see citing works
Fonseca, V. A., Grunberger, G., Anhalt, H., Bailey, T. S., Blevins, T., Garg, S. K., Handelsman, Y., Hirsch, I. B., Orzeck, E. A., Roberts, V. L., et al. (2016) · Endocrine Practice · doi:10.4158/ep161392.cs · Cited by 195 · see citing works
Xu, F., Zhao, L.-H., Su, J.-B., Chen, T., Wang, X.-Q., Chen, J.-F., Wu, G., Jin, Y., & Wang, X.-H. (2014) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-6-139 · Cited by 159 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,032 · see citing works
Self-citationDiMeglio, L. A., Acerini, C. L., Codner, E., Craig, M. E., Hofer, S. E., Pillay, K., & Maahs, D. M. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12737 · Cited by 604 · see citing works
“The system has acceptable accuracy compared with capillary (75-78) measurements, which has led to approval of both versions in more than 30 countries (79).”
Using Continuous Glucose Monitoring in Clinical Practice
Miller, E. M. (2020) · Clinical Diabetes · doi:10.2337/cd20-0043 · Cited by 125 · see citing works
Continuous Glucose Monitoring: A Brief Review for Primary Care Practitioners
Ajjan, R., Slattery, D., & Wright, E. E. (2019) · Advances in Therapy · doi:10.1007/s12325-019-0870-x · Cited by 126 · see citing works
A machine learning-based on-demand sweat glucose reporting platform
Sankhala, D., Sardesai, A. U., Pali, M. L., Lin, K., Jagannath, B., Muthukumar, S., & Prasad, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-06434-x · Cited by 81 · see citing works
Role of Continuous Glucose Monitoring in Diabetes Treatment
Hirsch, I., Battelino, T., Peters, A., Chamberlain, J., Aleppo, G., & Bergenstal, R. (2018) · American Diabetes Association eBooks · doi:10.2337/db20181 · Cited by 73 · see citing works
Self-citationAleppo, G., & Webb, K. M. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818813581 · Cited by 54 · see citing works
Understanding Continuous Glucose Monitoring Data
Bergenstal, R. (2018) · Role of Continuous Glucose Monitoring in Diabetes Treatment · doi:10.2337/db20181-20 · Cited by 37 · see citing works
Clinical Approach to Flash Glucose Monitoring: An Expert Recommendation
Chico, A., Aguilera, E., Ampudia-Blasco, F., Bellido, V., Cardona-Hernández, R., Escalada, F. J., Fernández, D., Gómez-Peralta, F., de Villar, N. G. P., Gorgojo, J., et al. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819841911 · Cited by 27 · see citing works
Diabetic Kidney Disease: Is There a Role for Glycemic Variability?
Subramanian, S., & Hirsch, I. B. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-0979-3 · Cited by 31 · see citing works
Self-citationThe ambulatory glucose profile and its interpretation
Lin, R., Brown, F., & Ekinci, E. I. (2022) · The Medical Journal of Australia · doi:10.5694/mja2.51666 · Cited by 15 · see citing works
Use of continuous glucose monitoring trend arrows in the younger population with type 1 diabetes
Elbarbary, N., Moser, O., Yaarubi, S. A., Alsaffar, H., Shaikh, A. A., Ajjan, R., & Deeb, A. (2021) · Diabetes & vascular disease research · doi:10.1177/14791641211062155 · Cited by 14 · see citing works
Using Flash Continuous Glucose Monitoring in Primary Practice
Hirsch, I. B., & Wright, E. E. (2018) · Clinical Diabetes · doi:10.2337/cd18-0054 · Cited by 15 · see citing works
“It is ideal to review results and recommendations face-toface with patients, using the report as a decision aid to illustrate relationships among glucose data, medication, and other therapeutic or behavioral interventions (34,88).”
BackgroundSelf-citationHirsch, I. B., Nardacci, E., & Verderese, C. A. (2019) · Diabetes Spectrum · doi:10.2337/ds18-0059 · Cited by 12 · see citing works
Self-citationJain, A. B. (2021) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2021.03.011 · Cited by 8 · see citing works
Deeb, A., Muammar, T., Alsaffar, H., Sedaghat, S., Hassani, N. A., Odeh, R., Alkhayyat, H., Sinani, A. A., Attia, N. A., Adhami, S., et al. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108671 · Cited by 7 · see citing works
Maines, E., Pertile, R., Cauvin, V., Soffiati, M., & Franceschi, R. (2024) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2024.111720 · Cited by 2 · see citing works
Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments
Satuluri, V. K. R. R., & Ponnusamy, V. (2024) · Diagnostics · doi:10.3390/diagnostics14040436 · Cited by 2 · see citing works
“Researchers employing the AGP report have stated its limitations for interpretation and the need for other valuable metrics [4–7].”
BackgroundSherrill, C. H., Houpt, C. T., Dixon, E. M., & Richter, S. J. (2021) · Journal of the American College of Clinical Pharmacy · doi:10.1002/jac5.1427 · Cited by 2 · see citing works
- Authors
- Hirsch, I. B., & Verderese, C. A.
- Affiliations
- University of Washington (US)
- Corresponding author
- Hirsch, Irl B.
- ORCID
- Hirsch, Irl B.
- Venue
- Endocrine Practice
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 23
- Issue
- 11
- Pages
- 1333-1344
- ISSN
- 1530-891X (ISSN-L)
- OpenAlex
- W2748403127
- Cited by
- 22
- References
- 0
- Citations by year
- 2024: 2 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 6
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Endocrine Practice (publishedVersion)
- PubMed (publishedVersion)
- 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
- ambulatory glucose profile, HbA1c, type 1 diabetes, type 2 diabetes, diabetes management, average glucose, self-monitoring of blood glucose
- MeSH terms
- Blood Glucose, Glycated Hemoglobin, Humans, Blood Glucose Self-Monitoring, Health Care Costs, Monitoring, Ambulatory
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
