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  3. Integrating The Glycemia Risk Index Into Clinical Practice And Research: A Consensus Report.
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  3. Integrating The Glycemia Risk Index Into Clinical Practice And Research: A Consensus Report.

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Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report.

Guillermo E Umpierrez1, Viral N Shah2, Veronica Brady3,4

  • 1Emory University School of Medicine, Atlanta, GA, USA.

Journal of Diabetes Science and Technology
|March 7, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The new Glycemia Risk Index (GRI) offers a single metric to assess glucose control quality from continuous glucose monitoring (CGM) data. Experts recommend its integration into CGM platforms for improved diabetes management and research.

Keywords:
compositecontinuous glucose monitoringdata platformsdata visualizationglycemia risk indextime in range

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Area of Science:

  • Endocrinology
  • Biomedical Informatics
  • Data Science

Background:

  • Continuous glucose monitoring (CGM) provides extensive data on glucose levels.
  • Existing metrics like the Ambulatory Glucose Profile (AGP) offer insights but lack a single composite score.
  • There is a need for a standardized metric to evaluate overall glycemic control quality from CGM data.

Purpose of the Study:

  • To discuss the utility of the Glycemia Risk Index (GRI) for clinical care, research, and population health management.
  • To present the mathematical derivation and application of the GRI metric.
  • To introduce the GRI Calculator smartphone application and its role in assessing CGM data.

Main Methods:

  • A panel of experts convened to discuss the GRI, a composite metric derived from seven components of the AGP.
  • The mathematical basis of the GRI was presented.
  • The GRI Calculator app was demonstrated for calculating GRI and visualizing CGM tracings.

    • Main Results:

    • The GRI provides a single, percentile-based score (0-100, lower is better) reflecting glycemia quality over 14 days.
    • The GRI demonstrated utility in specific cases, including patients with diabetes and cancer on glucose-affecting medications.
    • The GRI serves as a benchmark for AI models assessing glycemic quality and is integrated into the iCoDE-2 project.

    Conclusions:

    • The expert panel unanimously recommended incorporating the GRI into CGM data platforms for insulin users.
    • Further research is needed to explore the GRI's value for non-insulin users.
    • The GRI enhances the interpretation of CGM data, aiding clinical care, research, and AI model development.