{"id":696880,"date":"2026-01-15T04:31:12","date_gmt":"2026-01-15T04:31:12","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/696880\/"},"modified":"2026-01-15T04:31:12","modified_gmt":"2026-01-15T04:31:12","slug":"a-foundation-model-for-continuous-glucose-monitoring-data","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/696880\/","title":{"rendered":"A foundation model for continuous glucose monitoring data"},"content":{"rendered":"
  • \n

    Shilo, S. et al. 10 K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36<\/b>, 1187\u20131194 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reicher, L. et al. Deep phenotyping of health\u2013disease continuum in the Human Phenotype Project. Nat. Med. 31<\/b>, 3191\u20133203 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 977\u2013986 (1993).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 643\u2013648 (1999).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gude, F. et al. Glycemic variability and its association with demographics and lifestyles in a general adult population. J. Diabetes Sci. Technol. 11<\/b>, 780\u2013790 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Saab, K. et al. Capabilities of Gemini models in medicine. Preprint at https:\/\/doi.org\/10.48550\/arxiv.2404.18416<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622<\/b>, 156\u2013163 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2024).<\/p>\n<\/li>\n

  • \n

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

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Thapa, R. et al. SleepFM: multi-modal representation learning for sleep across brain activity, ECG and respiratory signals. Proc. Mach. Learn. Res. 235<\/b>, 48019\u201348037 (2024).<\/p>\n<\/li>\n

  • \n

    Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30<\/b>, 850\u2013862 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Krishnan, R., Rajpurkar, P. & Topol, E. J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 6<\/b>, 1346\u20131352 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 203\u2013234 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rawshani, A. et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N. Engl. J. Med. 376<\/b>, 1407\u20131418 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Moser, E. G., Crew, L. B. & Garg, S. K. Role of continuous glucose monitoring in diabetes management. Av. Diabetol. 26<\/b>, 73\u201378 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 11<\/b>, 42\u201357 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kieu, A., King, J., Govender, R. D. & \u00d6stlundh, L. The benefits of utilizing continuous glucose monitoring of diabetes mellitus in primary care: a systematic review. J. Diabetes Sci. Technol. 17<\/b>, 762\u2013774 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

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

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zahedani, A. D. et al. Digital health application integrating wearable data and behavioral patterns improves metabolic health. npj Digit. Med. 6<\/b>, 216 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Shilo, S. et al. Continuous glucose monitoring and intrapersonal variability in fasting glucose. Nat. Med. 30<\/b>, 1424\u20131431 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    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<\/a> (2018).<\/p>\n<\/li>\n

  • \n

    Bergenstal, R. M. et al. Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care 41<\/b>, 2275\u20132280 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Broll, S. et al. Interpreting blood GLUcose data with R package iglu. PLoS ONE 16<\/b>, e0248560 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rodbard, D. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol. Ther. 11<\/b>, 551\u2013565 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, J. et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat. Med. 31<\/b>, 609\u2013617 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35<\/b>, 758\u2013769 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, i6 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    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<\/b>, e037859 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rein, M. S. et al. BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy\u2014a protocol for a randomised clinical trial. BMJ Open 12<\/b>, e062498 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44<\/b>, 1980\u20131991 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 623\u2013634 (1999).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32<\/b>, 1327\u20131334 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Cersosimo, E., Solis-Herrera, C., Trautmann, M. E., Malloy, J. & Triplitt, C. L. Assessment of pancreatic \u03b2-cell function: review of methods and clinical applications. Curr. Diabetes Rev. 10<\/b>, 2\u201342 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, E401\u2013E406 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ansari, A. F. et al. Chronos: learning the language of time series. Transact. Mach. Learn. Res. https:\/\/openreview.net\/forum?id=gerNCVqqtR<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    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<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    van den Oord, A. et al. WaveNet: a generative model for raw audio. In Proc. 9th ISCA Speech Synthesis Workshop 125 (2016).<\/p>\n<\/li>\n

  • \n

    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\u20131756 (2016).<\/p>\n<\/li>\n

  • \n

    Danne, T. et al. International consensus on use of continuous glucose monitoring. Diabetes Care 40<\/b>, 1631\u20131640 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Cefalu, W. T. et al. A global initiative to deliver precision health in diabetes. Nat. Med. 30<\/b>, 1819\u20131822 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes 69<\/b>, 2086\u20132093 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Vaswani, A. et al. Attention is all you need. In Adv. Neural Information Processing Systems (eds Guyon, I. et al.) 30<\/b>, 5998\u20136008 (2017).<\/p>\n<\/li>\n

  • \n

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

  • \n

    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 Daum\u00e9, H. D. III & Singh, A.) 119<\/b>, 1597\u20131607 (2020).<\/p>\n<\/li>\n

  • \n

    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\u20139738 (2020).<\/p>\n<\/li>\n

  • \n

    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<\/b>, 8748\u20138763 (2021).<\/p>\n<\/li>\n

  • \n

    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\u20134186 (2019).<\/p>\n<\/li>\n

  • \n

    Yuan, H. et al. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. npj Digit. Med. 7<\/b>, 86 (2024).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Shilo, S. et al. 10 K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36, 1187\u20131194…\n","protected":false},"author":2,"featured_media":696881,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[105,3965,3690,3966,4434,210065,70,4460,2752,16,15],"class_list":{"0":"post-696880","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-health","8":"tag-health","9":"tag-humanities-and-social-sciences","10":"tag-machine-learning","11":"tag-multidisciplinary","12":"tag-nutrition","13":"tag-predictive-medicine","14":"tag-science","15":"tag-type-1-diabetes","16":"tag-type-2-diabetes","17":"tag-uk","18":"tag-united-kingdom"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/115897242924142740","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/696880","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=696880"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/696880\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/696881"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=696880"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=696880"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=696880"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}