GlucoFM: Foundation model for continuous glucose

What happened

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.

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: Finally, CGM-aware augmentations introduce baseline drift, compression-like drops, sparser sampling, and short disconnections, exposing the model to variation and missingness encountered in real CGM recordings.

We evaluated GlucoFM across four cohorts (CGMacros, Stanford, Hall and ShanghaiT2DM) and seven clinical prediction tasks, alongside a separate assessment of two-hour postprandial glycemic response prediction.

Specifically, we asked whether its frozen representations are informative for individual 24-hour windows from unseen participants; whether they provide useful historical context for predicting postprandial glucose trajectories; whether combining multiple days improves subject-level prediction; how well the representations transfer to new cohorts; and how effectively they adapt when labeled data are limited. First, we used subject-disjoint window-level linear probing.

We froze each model’s encoder, trained a linear classifier on individual 24-hour representations, and ensured that no participant appeared in both the training and test folds. This tests whether a single-day representation is phenotype-informative for unseen participants while retaining day-to-day variability. GlucoFM achieved the strongest task-averaged PR-AUC among the evaluated methods. 5% relative to that baseline.

Why it matters

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.

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. 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.

What to watch

GlucoFM achieved the highest PR-AUC in all diabetes-risk and beta-cell-dysfunction evaluations and in three of four insulin-resistance evaluations. To test GlucoFM on a dynamic prediction task, we used information available before each logged meal to predict the complete two-hour glucose-change trajectory relative to the meal-start value. We evaluated 874 paired meal events from 34 participants using subject-disjoint cross-validation, with Dexcom and Libre (two CGM devices) modeled separately under identical splits.

We progressively combined each frozen model representation with one hour of pre-meal CGM, meal nutrition — including energy, carbohydrate, fat, protein, and dietary fiber — and participant-level information such as fasting glucose, BMI, and diabetes status.