GlucoFM：用于持续血糖监测的基础模型

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 insulin-resistance evaluations. We also evaluated GlucoFM on postprandial glycemic response (PPGR) forecasting. Under matched inputs and evaluation protocols, GlucoFM achieved the lowest mean absolute error (MAE), averaged across two CGM devices ( Dexcom and Libre ). Moreover, GlucoFM achieved the best overall cross-dataset transfer performance and demonstrated strong few-shot adaptation, even when data from a new cohort or labeled subjects are extremely limited. Overview of GlucoFM, a lightweight dual-stream foundation model for continuous glucose monitoring. We pre-trained GlucoFM on 109,066 hours of unlabeled CGM data from Wear-CGM [8e73d8] and four published datasets, totaling 477 participant/session records. CGM recordings can contain gaps, different sampling intervals, and sensor artifacts. GlucoFM aligns each recording to a 24-hour, five-minute grid and retains an observation mask, keeping measured and unobserved positions distinct. Its dual-stream encoder separates a lower-frequency state component, representing slower glycemic trends, from a residual event component capturing short-term deviations that may arise from physiology, behavior, or sensing artifacts. Rather than reconstructing exact raw glucose readings, which can be affected by measurement noise and sensor artifacts, GlucoFM uses latent predictive pre-training with two complementary tasks: Contextual prediction: We mask (i.e., hide) parts of a daily glucose sequence and ask the model to predict their latent representations from the surrounding context. By predicting in latent space, the model captures broad