Beyond HbA1c: using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision‐making
Abstract
Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user-friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research-related goals and clinical guidance for individuals. Traditionally, HbA 1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA 1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo- and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review pr…
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These data facilitate a dynamic, iterative optimization of insulin dosing, bolus timing, and fractionation, moving away from purely empirical adjustments.
“These data facilitate a dynamic, iterative optimization of insulin dosing, bolus timing, and fractionation, moving away from purely empirical adjustments [132, 143, 145, 146].”
From Diabetic gastroparesis: pathophysiology and impact on insulin timing choices (2026) · cites this paper as [143]
Cites
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Self-citationValidation of Time in Range as an Outcome Measure for Diabetes Clinical Trials
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Grunberger, G., Sherr, J., Allende, M., Blevins, T., Bode, B., Handelsman, Y., Hellman, R., Lajara, R., Roberts, V. L., Rodbard, D., et al. (2021) · Endocrine Practice · doi:10.1016/j.eprac.2021.04.008 · Cited by 288 · see citing works
Diabetic gastroparesis: pathophysiology and impact on insulin timing choices
Galeano, F., Tumminia, A., Oteri, V., Le Moli, R., Piticchio, T., Scilletta, S., Di Marco, M., Di Pino, A., & Frasca, F. (2026) · Endocrine · doi:10.1007/s12020-026-04693-6
“These data facilitate a dynamic, iterative optimization of insulin dosing, bolus timing, and fractionation, moving away from purely empirical adjustments [132, 143, 145, 146].”
- Authors
- Brown, S. A., Basu, A., & Kovatchev, B. P.
- Affiliations
- University of Virginia (US)
- Corresponding author
- Brown, S. A.
- Venue
- Diabetic Medicine
- Publisher
- Wiley
- Published
- 2019-04-05
- Volume
- 36
- Issue
- 6
- Pages
- 679-687
- ISSN
- 0742-3071 (ISSN-L)
- 1464-5491
- OpenAlex
- W2920819944
- Cited by
- 27
- References
- 7
- Citations by year
- 2026: 5 · 2024: 1 · 2023: 1 · 2022: 8 · 2021: 6 · 2020: 5 · 2019: 1
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK085516)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- continuous glucose monitoring, glycemic variability, HbA1c limitations, time in range, hypoglycemia risk, ambulatory glucose profile, diabetes management, type 1 diabetes, type 2 diabetes, clinical decision-making
- MeSH terms
- Clinical Decision-Making, Blood Glucose, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Glycated Hemoglobin, Humans, Blood Glucose Self-Monitoring, Drug Monitoring, Diabetes Complications, Quality Improvement
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