GlucoFM: Foundation model for continuous glucose monitoring

Summary

August 26, 2026 Ahmed A. Metwally, Staff Research Scientist, and Zechen Li, Student Researcher, Google Research GlucoFM is a lightweight, self-supervised CGM foundation model that models slower glucose trends and short-term deviations in separate streams, producing transferable representations and setting new performance standards across diverse metabolic prediction tasks, including diabetes risk assessment, insulin resistance, beta-cell dysfunction, and post-prandial glycemic response. Consumer wearables use motion and physiological sensors to estimate activity and sleep, but these signals provide only an indirect view of glucose regulation. Continuous glucose monitors (CGM) complement these measurements by tracking interstitial glucose every few minutes through a small sensor inserted under the skin, capturing fasting, overnight, and post-meal patterns. Yet making sense of these traces remains challenging, especially when high-quality clinical labels that help interpret them are sparse and costly to obtain. Many existing CGM foundation models — including CGMformer, GluFormer, and CGM-JEPA — process glucose through a single representation stream rather than explicitly separating slow baseline and transient event dynamics. But CGM is not an undifferentiated data stream: it contains relatively slow baseline patterns punctuated by short-term deviations that may reflect meals, activity, or sensor artifacts. What if we could leverage daily CGM data to estimate things like diabetes risk, insulin resistance, and beta-cell dysfunction using limited labeled data? That's why we built GlucoFM, a self-supervised foundation model with a dual-stream design that separates slower glycemic trends from short-term deviations while preserving time-of-day and missingness. Latent-prediction objectives then learn their daily context and temporal evolution. We evaluated GlucoFM across four diverse cohorts on seven clinical prediction tasks — diabetes risk, insulin resistance, beta-cell dysfunction, hyperlipidemia, hypoglycemia, obesity, and glucotype — comprising 14 cohort–task evaluations. Across these evaluations, GlucoFM’s PR-AUC was 5.8 percentage points higher on average than that of the best-performing GluFormer variant evaluated, with both pre-trained on the same corpus. On PR-AUC, GlucoFM led all diabetes-risk and beta-cell-dysfunction evaluations and three of four insuli…