Personalized glucose prediction using in situ data only
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Nutrition |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1539118/full |
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| _version_ | 1849333847304437760 |
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| author | Rohan Singh Marouane Toumi Marcel Salathé |
| author_facet | Rohan Singh Marouane Toumi Marcel Salathé |
| author_sort | Rohan Singh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-65ae42c9b79141eea9284e970fb0ff6f |
| institution | Kabale University |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nutrition |
| spelling | doaj-art-65ae42c9b79141eea9284e970fb0ff6f2025-08-20T03:45:44ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-06-011210.3389/fnut.2025.15391181539118Personalized glucose prediction using in situ data onlyRohan SinghMarouane ToumiMarcel SalathéThe 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.https://www.frontiersin.org/articles/10.3389/fnut.2025.1539118/fullpersonalized nutritionreal-world datareal-world evidencedigital cohortgut microbiome |
| spellingShingle | Rohan Singh Marouane Toumi Marcel Salathé Personalized glucose prediction using in situ data only Frontiers in Nutrition personalized nutrition real-world data real-world evidence digital cohort gut microbiome |
| title | Personalized glucose prediction using in situ data only |
| title_full | Personalized glucose prediction using in situ data only |
| title_fullStr | Personalized glucose prediction using in situ data only |
| title_full_unstemmed | Personalized glucose prediction using in situ data only |
| title_short | Personalized glucose prediction using in situ data only |
| title_sort | personalized glucose prediction using in situ data only |
| topic | personalized nutrition real-world data real-world evidence digital cohort gut microbiome |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1539118/full |
| work_keys_str_mv | AT rohansingh personalizedglucosepredictionusinginsitudataonly AT marouanetoumi personalizedglucosepredictionusinginsitudataonly AT marcelsalathe personalizedglucosepredictionusinginsitudataonly |