A foundation model for continuous glucose monitoring data
Lutsker, Guy Sapir, Gal Shilo, Smadar Merino, Jordi Godneva, Anastasia Greenfield, Jerry R Samocha-Bonet, Dorit Dhir, Raja Gude, Francisco Mannor, Shie
Lutsker, Guy
Sapir, Gal
Shilo, Smadar
Merino, Jordi
Godneva, Anastasia
Greenfield, Jerry R
Samocha-Bonet, Dorit
Dhir, Raja
Gude, Francisco
Mannor, Shie
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Computational Biology
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Journal article
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English
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Abstract
Continuous glucose monitoring (CGM) generates detailed temporal profiles of glucose dynamics, but its full potential for achieving glucose homeostasis and predicting long-term outcomes remains underutilized. Here we present GluFormer, a generative foundation model for CGM data trained with self-supervised learning on more than 10 million glucose measurements from 10,812 adults mainly without diabetes1,2. Using autoregressive prediction, the model learned representations that transferred across 19 external cohorts (n = 6,044) spanning 5 countries, 8 CGM devices and diverse pathophysiological states, including prediabetes, type 1 and type 2 diabetes, gestational diabetes and obesity. These representations provided consistent improvements over baseline blood glucose and HbA1c levels and other CGM-derived measures for forecasting glycaemic parameters3,4. In individuals with prediabetes, GluFormer stratified those likely to experience clinically significant increases in HbA1c over a 2-year period, outperforming baseline HbA1c and common CGM metrics. In a cohort of 580 adults with short-term CGM and a median follow-up of 11 years5, GluFormer identified individuals at elevated risk of diabetes and cardiovascular mortality more effectively than HbA1c. Specifically, 66% of incident diabetes cases and 69% of cardiovascular deaths occurred in the top risk quartile, compared with 7% and 0%, respectively, in the bottom quartile. In clinical trials, baseline CGM representations improved outcome prediction. A multimodal extension of the model that integrates dietary data generated plausible glucose trajectories and predicted individual glycaemic responses to food. Together, these findings indicate that GluFormer provides a generalizable framework for encoding glycaemic patterns and may inform precision medicine approaches for metabolic health.
Citation
Lutsker, G., Sapir, G., Shilo, S., Merino, J., Godneva, A., Greenfield, J.R., Samocha-Bonet, D., Dhir, R., Gude, F., Mannor, S., Meirom, E., Xing, E.P., Chechik, G., Rossman, H., Segal, E. (2026). A foundation model for continuous glucose monitoring data. Nature, 1-9. https://doi.org/10.1038/s41586-025-09925-9
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Nature
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32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 52 Psychology, 3 Good Health and Well Being
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Springer Nature