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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This unreviewed computational study reports that time-series foundation models (e.g. Chronos-Bolt) require CGM-specific fine-tuning to outperform task-specific baselines, and that multimodal dietary context modestly improves forecasting, particularly in the postprandial period. The findings are based on retrospective analysis of public datasets and a new multimodal dataset (CGMacros) but lack peer review, clinical outcome validation, and prospective deployment evidence.
Retrospective empirical comparison across eight public CGM datasets with multimodal context evaluation. Type 1 diabetes, Type 2 diabetes, and non-diabetes populations from eight public CGM datasets; multimodal study population from CGMacros (detailed cohort characteristics not provided in abstract).. Intervention: Fine-tuned foundation models (Chronos-Bolt) and multimodal dietary context fusion framework. Compared with: Zero-shot foundation models, task-specific baselines (Elastic Net, PatchTST), and CGM-only forecasting.
Zero-shot foundation models did not consistently outperform strong task-specific baselines (Elastic Net, PatchTST) across datasets Fine-tuned Chronos-Bolt reduced RMSE by 6.5%-18.4% in T1D cohort and 8.6%-18.2% in non-diabetes/T2D cohort Multimodal dietary context reduced overall RMSE by approximately 3% and postprandial RMSE by approximately 15% relative to CGM-only baseline
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If validated prospectively, these findings suggest that foundation models could improve CGM forecasting when fine-tuned on diabetes-specific data, and that dietary context integrates clinically meaningful information for meal-response prediction. However, the study is preprint and does not demonstrate real-world clinical benefit or safety in deployed settings.
Preprint evaluation of foundation models for CGM forecasting using eight public datasets with empirical comparisons, but lacks peer review, clinical validation, and prospective real-world deployment evidence needed for practice guidance.
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If validated prospectively, these findings suggest that foundation models could improve CGM forecasting when fine-tuned on diabetes-specific data, and that dietary context integrates clinically meaningful information for meal-response prediction. However, the study is preprint and does not demonstrate real-world clinical benefit or safety in deployed settings.
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Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes management. Although time-series foundation models have shown strong general forecasting ability, their effectiveness for CGM prediction and the added value of multimodal dietary context remain unclear. We conduct a comprehensive empirical study using eight public CGM datasets spanning Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol across multiple context lengths and prediction horizons, zero-shot foundation models did not consistently outperform strong task-specific baselines such as Elastic Net and PatchTST. In contrast, lightweight fine-tuning substantially improved forecasting performance. For example, fine-tuned Chronos-Bolt reduced RMSE by 6.5%-18.4% in the T1D cohort and by 8.6%-18.2% in the non-diabetes/T2D cohort, with comparable improvements in both in-distribution and out-of-distribution test settings. We further evaluate multimodal dietary context using CGMacros, which provides temporally aligned CGM signals, food images, and macronutrient records. A residual-based fusion framework reduced overall RMSE by approximately 3% and postprandial RMSE by approximately 15% relative to the CGM-only baseline. Moreover, Chronos-based CGM representations were more strongly correlated with observed postprandial glucose increments than representations from LSTM and CatBoost, even after those models incorporated additional dietary modalities, suggesting that pretrained temporal representations better preserve meal-induced excursion patterns. These findings show that foundation models require CGM-specific adaptation for reliable forecasting and that dietary context provides clinically meaningful signals beyond CGM alone, especially during postprandial periods.
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