A foundation model for continuous glucose monitoring data

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 diabetes^1,2. 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 parameters^3,4. 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.