Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes

Jared G Friedman1, Kasey Coyne1, Grazia Aleppo1

  • 1Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States.

Endocrine Connections
|April 18, 2023
PubMed

Insights

Hemoglobin A1c (HbA1c) provides average glucose but lacks trend data. Continuous glucose monitoring (CGM) offers detailed insights into glucose fluctuations and alerts for hypo- and hyperglycemia, improving diabetes management.

Area of Science:

  • Endocrinology
  • Metabolic Diseases
  • Medical Technology

Background:

  • Hemoglobin A1c (HbA1c) is a standard for diabetes mellitus (DM) management, indicating average glycemia and predicting complications.
  • HbA1c has limitations due to non-glycemic influences and lack of glucose trend or hypo/hyperglycemia episode data.
  • Conventional blood glucose monitoring (BGM) provides only momentary readings, insufficient for trend analysis or detecting episodic glycemic extremes.

Purpose of the Study:

  • To explore the benefits and limitations of continuous glucose monitoring (CGM) in diabetes management.
  • To discuss the clinical application and integration of CGM data in patient care.
  • To examine the role of CGM in advanced diabetes technologies and its correlation with glycemic control metrics.

Main Methods:

  • Review of existing literature on CGM technology and its clinical utility.
  • Analysis of CGM data in comparison to HbA1c and BGM.
  • Exploration of the correlation between CGM metrics (e.g., time in range) and DM complications.

Main Results:

  • CGM provides crucial glucose trend information and detects undetected hypo- and hyperglycemia episodes.
  • CGM adoption is increasing due to improved accuracy, usability, and demonstrated clinical benefits.
  • Percent time in range, a CGM metric, correlates with HbA1c and is a validated indicator of glycemic control and complication risk.

Conclusions:

  • CGM offers actionable insights beyond HbA1c, enabling more targeted diabetes therapy.
  • CGM data reveal glucose patterns crucial for managing diabetes effectively.
  • The integration of CGM is vital for advancing diabetes care and utilizing emerging diabetes technologies.

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