Data availability

The data used in this paper are part of the HPP and are accessible to researchers from universities and other research institutions (https://humanphenotypeproject.org/data-access). Interested bona fide researchers should contact info@pheno.ai to obtain instructions for accessing the data. Deidentified participant data from the AEGIS study will be made available upon publication through the Runa Digital Repository (runa.sergas.gal). Access will require a signed data access agreement, and proposals should be directed to F.G.

Code availability

Implementation of GluFormer is available at GitHub (https://github.com/Guylu/GluFormer).

References

Shilo, S. et al. 10 K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36, 1187–1194 (2021).

Article PubMed Google Scholar 1.

Reicher, L. et al. Deep phenotyping of health–disease continuum in the Human Phenotype Project. Nat. Med. 31, 3191–3203 (2025).

Article PubMed Google Scholar 1.

Nathan, D. M. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329, 977–986 (1993).

Article PubMed Google Scholar 1.

King, P., Peacock, I. & Donnelly, R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. Br. J. Clin. Pharmacol. 48, 643–648 (1999).

Article PubMed PubMed Central Google Scholar 1.

Gude, F. et al. Glycemic variability and its association with demographics and lifestyles in a general adult population. J. Diabetes Sci. Technol. 11, 780–790 (2017).

Article PubMed Google Scholar 1.

Saab, K. et al. Capabilities of Gemini models in medicine. Preprint at https://doi.org/10.48550/arxiv.2404.18416 (2024). 1.

Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).

Article ADS PubMed PubMed Central Google Scholar 1.

Lutsker, G., Rossman, H., Godiva, N. & Segal, E. COMPRER: a multimodal multi-objective pretraining framework for enhanced medical image representation. Preprint at https://doi.org/10.48550/arxiv.2403.09672 (2024). 1.

Yuan, H. et al. Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. npj Digit. Med. 7, 91 (2024).

Article PubMed PubMed Central Google Scholar 1.

Thapa, R. et al. SleepFM: multi-modal representation learning for sleep across brain activity, ECG and respiratory signals. Proc. Mach. Learn. Res. 235, 48019–48037 (2024). 1.

Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024).

Article PubMed PubMed Central Google Scholar 1.

Krishnan, R., Rajpurkar, P. & Topol, E. J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 6, 1346–1352 (2022).

Article PubMed Google Scholar 1.

GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 402, 203–234 (2023).

Article Google Scholar 1.

Rawshani, A. et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N. Engl. J. Med. 376, 1407–1418 (2017).

Article PubMed Google Scholar 1.

Moser, E. G., Crew, L. B. & Garg, S. K. Role of continuous glucose monitoring in diabetes management. Av. Diabetol. 26, 73–78 (2010).

Article Google Scholar 1.

Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 11, 42–57 (2023).

Article PubMed Google Scholar 1.

Kieu, A., King, J., Govender, R. D. & Östlundh, L. The benefits of utilizing continuous glucose monitoring of diabetes mellitus in primary care: a systematic review. J. Diabetes Sci. Technol. 17, 762–774 (2023).

Article PubMed Google Scholar 1.

Holzer, R., Bloch, W. & Brinkmann, C. Continuous glucose monitoring in healthy adults-possible applications in health care, wellness, and sports. Sensors 22, 2030 (2022).

Article ADS PubMed PubMed Central Google Scholar 1.

Zahedani, A. D. et al. Digital health application integrating wearable data and behavioral patterns improves metabolic health. npj Digit. Med. 6, 216 (2023).

Article PubMed PubMed Central Google Scholar 1.

Shilo, S. et al. Continuous glucose monitoring and intrapersonal variability in fasting glucose. Nat. Med. 30, 1424–1431 (2024).

Article PubMed Google Scholar 1.

U.S. Food & Drug Administration. FDA clears first over-the-counter continuous glucose monitor. FDA https://www.fda.gov/news-events/press-announcements/fda-clears-first-over-counter-continuous-glucose-monitor (2024). 1.

McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arxiv.1802.03426 (2018). 1.

Bergenstal, R. M. et al. Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care 41, 2275–2280 (2018).

Article PubMed PubMed Central Google Scholar 1.

Broll, S. et al. Interpreting blood GLUcose data with R package iglu. PLoS ONE 16, e0248560 (2021).

Article PubMed PubMed Central Google Scholar 1.

Rodbard, D. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol. Ther. 11, 551–565 (2009).

Article PubMed Google Scholar 1.

Wang, J. et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat. Med. 31, 609–617 (2025).

Article PubMed Google Scholar 1.

Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35, 758–769 (2023).

Article PubMed Google Scholar 1.

Vickers, A. J., Van Calster, B. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352, i6 (2016).

Article PubMed PubMed Central Google Scholar 1.

Van Calster, B. et al. Performance evaluation of predictive AI models to support medical decisions: overview and guidance. Preprint at https://doi.org/10.48550/arxiv.2412.10288 (2024). 1.

Htet, T. D. et al. Rationale and design of a randomised controlled trial testing the effect of personalised diet in individuals with pre-diabetes or type 2 diabetes mellitus treated with metformin. BMJ Open 10, e037859 (2020).

Article PubMed PubMed Central Google Scholar 1.

Rein, M. S. et al. BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy—a protocol for a randomised clinical trial. BMJ Open 12, e062498 (2022).

Article PubMed PubMed Central Google Scholar 1.

Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44, 1980–1991 (2021).

Article PubMed Google Scholar 1.

The Diabetes Prevention Program Research Group. The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care 22, 623–634 (1999).

Article Google Scholar 1.

International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32, 1327–1334 (2009).

Article Google Scholar 1.

Cersosimo, E., Solis-Herrera, C., Trautmann, M. E., Malloy, J. & Triplitt, C. L. Assessment of pancreatic β-cell function: review of methods and clinical applications. Curr. Diabetes Rev. 10, 2–42 (2014).

Article PubMed PubMed Central Google Scholar 1.

Abdul-Ghani, M. A. et al. The relationship between fasting hyperglycemia and insulin secretion in subjects with normal or impaired glucose tolerance. Am. J. Physiol. Endocrinol. Metab. 295, E401–E406 (2008).

Article PubMed PubMed Central Google Scholar 1.

Ansari, A. F. et al. Chronos: learning the language of time series. Transact. Mach. Learn. Res. https://openreview.net/forum?id=gerNCVqqtR (2024). 1.

Rabanser, S., Januschowski, T., Flunkert, V., Salinas, D. & Gasthaus, J. The effectiveness of discretization in forecasting: an empirical study on neural time series models. Preprint at https://doi.org/10.48550/arxiv.2005.10111 (2020). 1.

van den Oord, A. et al. WaveNet: a generative model for raw audio. In Proc. 9th ISCA Speech Synthesis Workshop 125 (2016). 1.

van den Oord, A., Kalchbrenner, N. & Kavukcuoglu, K. Pixel recurrent neural networks. In Proc. 33rd International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1747–1756 (2016). 1.

Danne, T. et al. International consensus on use of continuous glucose monitoring. Diabetes Care 40, 1631–1640 (2017).

Article PubMed PubMed Central Google Scholar 1.

Cefalu, W. T. et al. A global initiative to deliver precision health in diabetes. Nat. Med. 30, 1819–1822 (2024).

Article PubMed PubMed Central Google Scholar 1.

Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes 69, 2086–2093 (2020).

Article PubMed Google Scholar 1.

Xiong, Z. et al. How generalizable are foundation models when applied to different demographic groups and settings? NEJM AI https://doi.org/10.1056/AIcs2400497 (2024). 1.

Vaswani, A. et al. Attention is all you need. In Adv. Neural Information Processing Systems (eds Guyon, I. et al.) 30, 5998–6008 (2017). 1.

Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In Proc. 7th International Conference on Learning Representations https://openreview.net/forum?id=Bkg6RiCqY7 (2019). 1.

Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning (eds Daumé, H. D. III & Singh, A.) 119, 1597–1607 (2020). 1.

He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (2020). 1.

Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) 139, 8748–8763 (2021). 1.

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds. Burstein, J. et al.) Vol. 1 (Long and Short Papers), 4171–4186 (2019). 1.

Yuan, H. et al. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. npj Digit. Med. 7, 86 (2024).

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Acknowledgements

We thank all members of the Segal Laboratory, the Pheno.AI data science and NVIDIA Tel Aviv Research groups, and E. Barkan and A. Shocher for discussions. J.M. was supported by Novo Nordisk Foundation grant NNF23SA0084103, an EFSD/Novo Nordisk Foundation Future Leaders Award (no. 0094134) and the European Union (HORIZON-EIC-2023-PATHFINDERCHALLENGES-01-101161509). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Innovation Council and SMEs Executive Agency (EISMEA). Neither the European Union nor the granting authority can be held responsible for them.

Author information

Authors and Affiliations

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

Guy Lutsker, Gal Sapir, Smadar Shilo, Anastasia Godneva & Eran Segal 1.

Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

Guy Lutsker, Smadar Shilo & Anastasia Godneva 1.

NVIDIA, Tel Aviv, Israel

Guy Lutsker, Shie Mannor, Eli Meirom & Gal Chechik 1.

Pheno.AI, Tel-Aviv, Israel

Gal Sapir & Hagai Rossman 1.

Faculty of Medical and Health Sciences, Tel Aviv University, Tel-Aviv, Israel

Smadar Shilo 1.

The Jesse and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children’s Medical Center of Israel, Petah Tikva, Israel

Smadar Shilo 1.

Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark

Jordi Merino 1.

Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA

Jordi Merino 1.

Clinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Sydney, New South Wales, Australia

Jerry R. Greenfield & Dorit Samocha-Bonet 1.

St Vincent’s Clinical Campus, School of Clinical Medicine, University of NSW, Sydney, New South Wales, Australia

Jerry R. Greenfield & Dorit Samocha-Bonet 1.

Department of Endocrinology and Diabetes, St Vincent’s Hospital, Sydney, New South Wales, Australia

Jerry R. Greenfield 1.

Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland

Raja Dhir 1.

Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain

Francisco Gude 1.

Concepción Arenal Primary Care Center, Santiago de Compostela, Spain

Francisco Gude 1.

Carnegie Mellon University Pittsburgh, Pittsburgh, PA, USA

Eric P. Xing 1.

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

Eric P. Xing, Hagai Rossman & Eran Segal

Authors

1. Guy Lutsker
2. Gal Sapir
3. Smadar Shilo
4. Jordi Merino
5. Anastasia Godneva
6. Jerry R. Greenfield
7. Dorit Samocha-Bonet
8. Raja Dhir
9. Francisco Gude
10. Shie Mannor
11. Eli Meirom
12. Eric P. Xing
13. Gal Chechik
14. Hagai Rossman
15. Eran Segal

Contributions

G.L. conceived the project, designed and conducted all analyses, interpreted the results and wrote the manuscript. G.S. developed protocols, interpreted the results and wrote the manuscript. A.G. designed pipelines and created preprocessing scripts. S.S. interpreted the results and wrote the manuscript. J.R.G. and D.S.-B. acquired the PREDICT cohort data. R.D. interpreted the results and wrote the manuscript. F.G. wrote the manuscript. J.M. interpreted the results and wrote the manuscript. S.M. guided computational analyses. E.M. guided computational analyses and managed code-running infrastructure. E.P.X. interpreted the results and wrote the manuscript. G.C. interpreted the results and wrote the manuscript, and directed the project. H.R. conceived and directed the project and analyses, designed the analyses, interpreted the results and wrote the manuscript. E.S. conceived and supervised the project and analyses, designed the analyses, interpreted the results and wrote the manuscript.

Corresponding authors

Correspondence to Hagai Rossman or Eran Segal.

Ethics declarations

Competing interests

G.S. and H.R. are employees in Pheno.AI, a biomedical data science company from Tel-Aviv, Israel. E.S. is a paid consultant of Pheno.AI. G.L.’s work was done during an internship at NVIDIA Research. The other authors declare no competing interests.

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Lutsker, G., Sapir, G., Shilo, S. et al. A foundation model for continuous glucose monitoring data. Nature (2026). https://doi.org/10.1038/s41586-025-09925-9

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Received: 20 August 2024

Accepted: 17 November 2025

Published: 14 January 2026

Version of record: 14 January 2026

DOI: https://doi.org/10.1038/s41586-025-09925-9