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Going beyond HbA1c to understand the benefits of advanced diabetes therapies
1Uniformed Services University of the Health Sciences, Bethesda, Maryland.
Insights
Continuous glucose monitoring (CGM) offers superior insights into short-term glycemic control compared to HbA1c. CGM provides patient-centric data for improved diabetes management and intervention assessment.
Area of Science:
- Endocrinology
- Metabolic Diseases
- Biomedical Engineering
Background:
- Hemoglobin A1c (HbA1c) is the standard for monitoring overall glycemia but has limitations.
- HbA1c overemphasizes long-term glucose levels and is affected by clinical conditions.
- It does not capture interpersonal variability in glucose control.
Purpose of the Study:
- To highlight the limitations of HbA1c in glycemic monitoring.
- To introduce Continuous Glucose Monitoring (CGM) as an advanced technology.
- To discuss the novel metrics and applications enabled by CGM.
Main Methods:
- Review of HbA1c limitations in clinical practice.
- Discussion of Continuous Glucose Monitoring (CGM) technology development.
- Analysis of new metrics derived from CGM data (e.g., time in range, glucose pentagon).
Main Results:
- CGM provides patient-centric, real-time data on glycemic control.
- CGM enables new outcome metrics like time in hypoglycemia and time in target range.
- CGM data are crucial for algorithm-based insulin delivery and AI-driven feedback.
Conclusions:
- CGM surpasses HbA1c by offering detailed short-term glycemic insights.
- CGM facilitates accurate assessment of interventions and personalized diabetes care.
- CGM, especially with AI, empowers real-time patient feedback for managing glycemic excursions.
Abstract:
The gold standard for monitoring overall glycemia is HbA1c. However, HbA1c has several important limitations, giving more weight to the prior 2 to 3 months rather than short-term glycemic control. In addition, the level of the HbA1c does not reflect the important interpersonal differences in its relationship with mean glucose, and HbA1c is affected by many common clinical conditions (anemia, uremia) that can interfere with the accuracy of its measurement in the laboratory. The development and refinement of continuous glucose monitoring (CGM), a glucose- and patient-centric technology, over the past two decades have permitted the creation of new single and composite metrics, such as the percentage of time in range and the glucose pentagon, respectively, which provide clinically relevant insights into short-term glycemic control. In addition, CGM creates new outcome metrics for clinical management and investigational studies (percentage of time in hypoglycemia, percentage of time in target range) that can accurately and meaningfully report the effects of an intervention, whether that is a drug, a device, or a psychosocial program, and CGM provides the key input to drive algorithm-based insulin delivery. Finally, CGM linked with artificial intelligence permits real-time feedback to patients about modifiable patterns of glycemic excursions.
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