# MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes

arXiv: https://arxiv.org/abs/2606.18640 · HTML: https://mlsanity.com/paper/2606.18640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [UniTS: A Unified Multi-Task Time Series Model](https://mlsanity.com/paper/2403.00131/) (2024)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions](https://mlsanity.com/paper/2304.06506/) (2023)
- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [A Large Sensor Foundation Model Pretrained on Continuous Glucose Monitor Data for Diabetes Management](https://mlsanity.com/paper/2412.09727/) (2024)
- [AZT1D: A Real-World Dataset for Type 1 Diabetes](https://mlsanity.com/paper/2506.14789/) (2025)
- [MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management](https://mlsanity.com/paper/2601.11505/) (2026)
- [BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches](https://mlsanity.com/paper/2507.17757/) (2025)
- [Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions](https://mlsanity.com/paper/2507.14077/) (2025)

## Abstract (arXiv)

Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has developed numerous algorithms and models for forecasting. However, it is well-recognized that the lack of standardized model performance evaluation benchmarks makes fair comparison difficult and hinders further innovation, and thus benchmark standardization is in urgent need. Furthermore, many published glucose forecasting algorithms are limited to CGM data alone, ignoring other multimodal signals such as insulin dosing and carbohydrate intake. Here, we introduce MetaboNet-Bench, a benchmark for multimodal glucose forecasting for patients with type 1 diabetes that provides an extensible open-source evaluation framework for comparison of glucose forecasting algorithms that leverage glucose, insulin, and carbohydrate data. We then demonstrate its utility by benchmarking several recently published glucose forecasting models and a custom multimodal time-series model, representing different model architectures. The results show that the benefit of adding data modalities is conditioned on the complexity of the model and that incorporating more clinical metrics helps identify meaningful gaps to fill for future research.