From the Journals

AI Model Finds Hidden Risk Signals in CGM Data

GluFormer accurately identified patients likely to experience clinically meaningful rises in HbA1c over a 2-year period.

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  • 1

    GluFormer trained on over 10 million CGM measurements.

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    Developed using self-supervised learning techniques.

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    Outperformed traditional metrics like HbA1c.

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    Accurate in identifying prediabetes risks.

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    Forecasted diabetes and cardiovascular mortality effectively.

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    Involves 19 independent cohorts across various countries.

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    Integrates dietary data for personalized glucose trajectory predictions.

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    Revolutionizes glucose data analysis in clinical settings.

Original Source(s)

AI Model Finds Hidden Risk Signals in CGM Data

GluFormer accurately identified patients likely to experience clinically meaningful rises in HbA1c over a 2-year period.

  • by Alun Evans

  • March 5, 2026

  • 3 min

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