Personalized glucose prediction using in situ data only Article Swipe
Rohan Bir Singh
,
Marouane Toumi
,
Marcel Salathé
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnut.2025.1539118
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnut.2025.1539118
The worldwide rise in blood glucose levels is a major health concern, as various metabolic diseases become increasingly common. Diet, a modifiable health behaviour, is a primary target for the preventive management of glucose levels. Recent studies have shown that blood glucose responses after meals (post-prandial glucose responses, PPGR) can vary greatly among individuals, even with identical food consumption, and demonstrated accurate PPGR prediction using various features like microbiome data and blood parameters. Our study addresses whether accurate PPGR prediction can be achieved with a limited and easily obtainable set of data collected in real-world, everyday settings. Here, we show that a machine learning algorithm with such real-world data (RWD) collected from a digital cohort with over 1,000 participants can achieve high accuracy in PPGR prediction. Interestingly, we find that the best PPGR prediction model only required glycemic and temporally resolved diet data. This ability to predict PPGR accurately without the need for biological lab analysis offers a path toward highly scalable personalized nutrition and glucose management strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnut.2025.1539118
- https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1539118/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411161245
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4411161245Canonical identifier for this work in OpenAlex
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https://doi.org/10.3389/fnut.2025.1539118Digital Object Identifier
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Personalized glucose prediction using in situ data onlyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-09Full publication date if available
- Authors
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Rohan Bir Singh, Marouane Toumi, Marcel SalathéList of authors in order
- Landing page
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https://doi.org/10.3389/fnut.2025.1539118Publisher landing page
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https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1539118/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1539118/pdfDirect OA link when available
- Concepts
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Computer science, Machine learning, Glycemic, Artificial intelligence, Data set, Diabetes management, Predictive modelling, Scalability, Data mining, Diabetes mellitus, Medicine, Type 2 diabetes, Endocrinology, DatabaseTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-06-09 |
| publication_year | 2025 |
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