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Advances and opportunities in measuring dietary intake: from omics to AI

Nature Metabolism (2026)Cite this article

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Abstract

Accurate measurement of dietary intake remains a cornerstone challenge in optimizing the efficacy of nutritional interventions in human disease. Traditional self-reporting methods, although scalable and widely used, are prone to major bias and measurement error, thereby limiting their precision and clinical utility. In this Review, we highlight recent advances in technology-assisted food intake measurement, including image-based logging, wearable sensors and artificial intelligence (AI)-based dietary estimation, which may reduce reliance on recall and improve intake estimation. We review the emergence of non-invasive biological methodologies, such as metagenome-informed metaproteomics, in accurately enabling objective measurement of food intake and nutrient digestion and absorption in molecular resolution. We explore the possible interactions and effects of the gut microbiome in modulating such person-specific digestive and absorptive patterns and discuss challenges and prospects in the convergence of omics-based, measurement-based and AI-based dietary assessment tools into precision nutrition, in fulfilling its immense potential towards optimization of patient care.

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Fig. 1: Overview of dietary intake estimation and nutrient absorption measurement tools.
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Fig. 2: Schematic illustration of the IPHOMED platform.
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Fig. 3: Microbiome effects on nutrient fate and absorption.
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Fig. 4: Current, emerging and future perspectives of dietary intake and absorption.
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References

  1. Afshin, A. et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 393, 1958–1972 (2019).

    Article  Google Scholar 

  2. Lee, C. D., Hardin, C. C., Longo, D. L. & Ingelfinger, J. R. Nutrition in medicine—a new review article series. N. Engl. J. Med. 390, 1324–1325 (2024).

    Article  Google Scholar 

  3. Veit, M., van Asten, R., Olie, A. & Prinz, P. The role of dietary sugars, overweight, and obesity in type 2 diabetes mellitus: a narrative review. Eur. J. Clin. Nutr. 76, 1497–1501 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kopp, W. How Western diet and lifestyle drive the pandemic of obesity and civilization diseases. Diabetes Metab. Syndr. Obes. 12, 2221–2236 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bendor, C. D., Bardugo, A., Pinhas-Hamiel, O., Afek, A. & Twig, G. Cardiovascular morbidity, diabetes and cancer risk among children and adolescents with severe obesity. Cardiovasc. Diabetol. 19, 79 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Abdeen, S. K., Mastandrea, I., Stinchcombe, N., Puschhof, J. & Elinav, E. Diet-microbiome interactions in cancer. Cancer Cell 43, 680–707 (2025).

    Article  CAS  PubMed  Google Scholar 

  7. Appel, L. J. et al. Dietary approaches to prevent and treat. Hypertension 47, 296–308 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Muñoz-Torres, A., García-Fontana, B. & Muñoz-Torres, M. Nutrients and dietary patterns related to osteoporosis. Nutrients 12, 1986 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pugliese, M. T., Lifshitz, F., Grad, G., Fort, P. & Marks-Katz, M. Fear of obesity. N. Engl. J. Med. 309, 513–518 (1983).

    Article  CAS  PubMed  Google Scholar 

  10. Brauer, M. et al. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403, 2162–2203 (2024).

    Article  Google Scholar 

  11. Heymsfield, S. B. & Shapses, S. A. Guidance on energy and macronutrients across the life span. N. Engl. J. Med. 390, 1299–1310 (2024).

    Article  CAS  PubMed  Google Scholar 

  12. Yannakoulia, M. & Scarmeas, N. Diets. N. Engl. J. Med. 390, 2098–2106 (2024).

    Article  CAS  PubMed  Google Scholar 

  13. Kumar, A., Sharma, E., Marley, A., Samaan, M. A. & Brookes, M. J. Iron deficiency anaemia: pathophysiology, assessment, practical management. BMJ Open Gastroenterol 9, e000759 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chen, R. Y. et al. Duodenal microbiota in stunted undernourished children with enteropathy. N. Engl. J. Med. 383, 321–333 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Owino, V. et al. Environmental enteric dysfunction and growth failure/stunting in global child health. Pediatrics 138, e20160641 (2016).

    Article  PubMed  Google Scholar 

  16. Sanz, Y. et al. The gut microbiome connects nutrition and human health. Nat. Rev. Gastroenterol. Hepatol. 22, 534–555 (2025).

    Article  PubMed  Google Scholar 

  17. Cade, J. E., Burley, V. J., Warm, D. L., Thompson, R. L. & Margetts, B. M. Food-frequency questionnaires: a review of their design, validation and utilisation. Nutr. Res. Rev. 17, 5–22 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Baranowski, T. in Nutritional Epidemiology (ed. Willett, W.) Ch. 4, 49–69 (Oxford Univ. Press, 2012).

  19. Chung, J. et al. A glasses-type wearable device for monitoring the patterns of food intake and facial activity. Sci. Rep. 7, 41690 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lo, F. P. W. et al. AI-enabled wearable cameras for assisting dietary assessment in African populations. NPJ Digit. Med. 7, 356 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Maruvada, P. et al. Perspective: dietary biomarkers of intake and exposure—exploration with omics approaches. Adv. Nutr. 11, 200–215 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Valdés-Mas, R. et al. Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease. Cell 188, 1062–1083.e36 (2025).

    Article  PubMed  Google Scholar 

  23. Diener, C. et al. Metagenomic estimation of dietary intake from human stool. Nat. Metab. 7, 617–630 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Superdock, D. K., Petrone, B. L., Kirtley, M. C. & David, L. A. From stool to sequence: decoding the human diet with FoodSeq. mSystems 10, e0015825 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Reese, A. T. et al. Using DNA metabarcoding to evaluate the plant component of human diets: a proof of concept. mSystems 4, e00458–19 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sonnenburg, J. L. & Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Hadadi, N., Berweiler, V., Wang, H. & Trajkovski, M. Intestinal microbiota as a route for micronutrient bioavailability. Curr. Opin. Endocr. Metab. Res. 20, 100285 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shim, J.-S., Oh, K. & Kim, H. C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 36, e2014009 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kroke, A. et al. Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. Am. J. Clin. Nutr. 70, 439–447 (1999).

    Article  CAS  PubMed  Google Scholar 

  31. Kirkpatrick, S. I. et al. Using short-term dietary intake data to address research questions related to usual dietary intake among populations and subpopulations: assumptions, statistical techniques, and considerations. J. Acad. Nutr. Diet. 122, 1246–1262 (2022).

    Article  PubMed  Google Scholar 

  32. Gersovitz, M., Madden, J. P. & Smiciklas-Wright, H. Validity of the 24-hr. dietary recall and seven-day record for group comparisons. J. Am. Diet. Assoc. 73, 48–55 (1978).

    Article  CAS  PubMed  Google Scholar 

  33. Lafay, L. et al. Determinants and nature of dietary underreporting in a free-living population: the Fleurbaix Laventie Ville Santé (FLVS) study. Int. J. Obes. 21, 567–573 (1997).

    Article  CAS  Google Scholar 

  34. Lissner, L. et al. OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI. Int. J. Obes. 31, 956–961 (2007).

    Article  CAS  Google Scholar 

  35. Bingham, S. A. et al. Validation of weighed records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biological markers. Br. J. Nutr. 73, 531–550 (1995).

    Article  CAS  PubMed  Google Scholar 

  36. Johnson, R., Goran, M. & Poehlman, E. Correlates of over- and underreporting of energy intake in healthy older men and women. Am. J. Clin. Nutr. 59, 1286–1290 (1994).

    Article  CAS  PubMed  Google Scholar 

  37. Taren, D. et al. The association of energy intake bias with psychological scores of women. Eur. J. Clin. Nutr. 53, 570–578 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Kipnis, V. Structure of dietary measurement error: results of the OPEN biomarker study. Am. J. Epidemiol. 158, 14–21 (2003).

    Article  PubMed  Google Scholar 

  39. Scrimshaw, N. INFOODS: the international network of food data systems. Am. J. Clin. Nutr. 65, 1190S–1193S (1997).

    Article  CAS  PubMed  Google Scholar 

  40. Deharveng, G., Charrondière, U., Slimani, N., Southgate, D. & Riboli, E. Comparison of nutrients in the food composition tables available in the nine European countries participating in EPIC. Eur. J. Clin. Nutr. 53, 60–79 (1999).

    Article  CAS  PubMed  Google Scholar 

  41. Hakala, P., Knuts, L.-R., Vuorinen, A., Hammar, N. & Becker, W. Comparison of nutrient intake data calculated on the basis of two different databases. Results and experiences from a Swedish–Finnish study. Eur. J. Clin. Nutr. 57, 1035–1044 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Some, J. et al. Omissions, intrusions, and use of standard recipes in computer assisted and pen and paper 24-hour dietary recalls compared with weighed food record: results from Burkina Faso. Curr. Dev. Nutr. 5, 883 (2021).

    Article  PubMed Central  Google Scholar 

  43. Murphy, S. P., Weinberg-Andersson, S. W., Neumann, C., Mulligan, K. & Calloway, D. H. Development of research nutrient data bases: an example using foods consumed in rural Kenya. J. Food Comp. Anal. 4, 2–17 (1991).

    Article  Google Scholar 

  44. Zhang, L., Boshuizen, H. & Ocké, M. How does a simplified recipe collection procedure in dietary assessment tools affect the food group and nutrient intake distributions of the population. Br. J. Nutr. 124, 189–198 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Rico-Campà, A. et al. Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ 365, l1949 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Buso, M. E. et al. Relative validity of habitual sugar and low/no-calorie sweetener consumption assessed by food frequency questionnaire, multiple 24-h dietary recalls and urinary biomarkers: an observational study within the SWEET project. Am. J. Clin. Nutr. 119, 546–559 (2024).

    Article  CAS  PubMed  Google Scholar 

  47. Chazelas, E. et al. Exposure to food additive mixtures in 106,000 French adults from the NutriNet-Santé cohort. Sci. Rep. 11, 19680 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Trakman, G. L. et al. Development and validation of surveys to estimate food additive intake. Nutrients 12, 812 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Khorasaniha, R. et al. Diversity of fibers in common foods: key to advancing dietary research. Food Hydrocoll 139, 108495 (2023).

    Article  CAS  Google Scholar 

  50. Okada, E. et al. National nutrition surveys applying dietary records or 24-h dietary recalls with questionnaires: a scoping review. Nutrients 15, 4739 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kennedy, E. T., Ohls, J., Carlson, S. & Fleming, K. The Healthy Eating Index. J. Am. Diet. Assoc. 95, 1103–1108 (1995).

    Article  CAS  PubMed  Google Scholar 

  52. Cavicchia, P. P. et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J. Nutr. 139, 2365–2372 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fung, T. T. et al. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. Am. J. Clin. Nutr. 82, 163–173 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Yue, Y. et al. Reproducibility and validity of diet quality scores derived from food-frequency questionnaires. Am. J. Clin. Nutr. 115, 843–853 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Racine, A. et al. Dietary patterns and risk of inflammatory bowel disease in Europe. Inflamm. Bowel Dis. 22, 345–354 (2016).

    Article  PubMed  Google Scholar 

  56. Riboli, E. et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 5, 1113–1124 (2002).

    Article  CAS  PubMed  Google Scholar 

  57. Cotillard, A. et al. A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project. Am. J. Clin. Nutr. 115, 432–443 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kirkpatrick, S. I. et al. Performance of the automated self-administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am. J. Clin. Nutr. 100, 233–240 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lucassen, D. A., Brouwer-Brolsma, E. M., van de Wiel, A. M., Siebelink, E. & Feskens, E. J. M. Iterative development of an innovative smartphone-based dietary assessment tool: Traqq. J. Vis. Exp. 169, e62032 (2021).

    Google Scholar 

  60. Lucassen, D. A. et al. Validation of the smartphone-based dietary assessment tool “Traqq” for assessing actual dietary intake by repeated 2-h recalls in adults: comparison with 24-h recalls and urinary biomarkers. Am. J. Clin. Nutr. 117, 1278–1287 (2023).

    Article  CAS  PubMed  Google Scholar 

  61. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964–973 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Chikwetu, L., Daily, S., Mortazavi, B. J. & Dunn, J. Automated diet capture using voice alerts and speech recognition on smartphones: pilot usability and acceptability study. JMIR Form. Res. 7, e46659 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ji, Y., Plourde, H., Bouzo, V., Kilgour, R. D. & Cohen, T. R. Validity and usability of a smartphone image-based dietary assessment app compared to 3-day food diaries in assessing dietary intake among Canadian adults: randomized controlled trial. JMIR Mhealth Uhealth 8, e16953 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Griffiths, C., Harnack, L. & Pereira, M. A. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public Health Nutr. 21, 1495–1502 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K. & Allman-Farinelli, M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 57, 208–216 (2019).

    Article  PubMed  Google Scholar 

  67. Cohen, Y., Valdés-Mas, R. & Elinav, E. The role of artificial intelligence in deciphering diet–disease relationships: case studies. Annu. Rev. Nutr. 43, 225–250 (2023).

    Article  CAS  PubMed  Google Scholar 

  68. Ho, D. K. N. et al. Validity of image-based dietary assessment methods: a systematic review and meta-analysis. Clin. Nutr. 39, 2945–2959 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. O’Loughlin, G. et al. Using a wearable camera to increase the accuracy of dietary analysis. Am. J. Prev. Med. 44, 297–301 (2013).

    Article  PubMed  Google Scholar 

  70. Scott, J. L., Vijayakumar, A., Woodside, J. V. & Neville, C. E. Feasibility of wearable camera use to improve the accuracy of dietary assessment among adults. J. Nutr. Sci. 11, e85 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Wang, W. et al. A review on vision-based analysis for automatic dietary assessment. Trends Food Sci. Technol. 122, 223–237 (2022).

    Article  CAS  Google Scholar 

  72. Zheng, J., Wang, J., Shen, J. & An, R. Artificial intelligence applications to measure food and nutrient intakes: scoping review. J. Med. Int. Res. 26, e54557 (2024).

    Google Scholar 

  73. Cofre, S., Sanchez, C., Quezada-Figueroa, G. & López-Cortés, X. A. Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review. Br. J. Nutr. 133, 1241–1253 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Tahir, G. A. & Loo, C. K. A comprehensive survey of image-based food recognition and volume estimation methods for dietary assessment. Healthcare 9, 1676 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Subhi, M. A., Ali, S. H. & Mohammed, M. A. Vision-based approaches for automatic food recognition and dietary assessment: a survey. IEEE Access 7, 35370–35381 (2019).

    Article  Google Scholar 

  76. Romero-Tapiador, S. et al. Are vision-language models ready for dietary assessment? Exploring the next frontier in AI-powered food image recognition. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 430–439 (IEEE, 2025).

  77. Adjuik, T. A., Boi-Dsane, N. A. A. & Kehinde, B. A. Enhancing dietary analysis: using machine learning for food caloric and health risk assessment. J. Food Sci. 89, 8006–8021 (2024).

    Article  CAS  PubMed  Google Scholar 

  78. Yin, S. et al. A survey on multimodal large language models. Natl Sci. Rev. 11, nwae40 (2024).

    Article  Google Scholar 

  79. Mohbat, F. & Zaki, M. J. LLaVA-Chef: a multi-modal generative model for food recipes. In Proc. 33rd ACM International Conference on Information and Knowledge Management 1711–1721 (ACM, 2024).

  80. Yin, Y. et al. FoodLMM: a versatile food assistant using large multi-modal model. IEEE 27, 6949–6961 (2025).

    Google Scholar 

  81. Lo, F. P. W. et al. Dietary assessment with multimodal ChatGPT: a systematic analysis. IEEE J. Biomed. Health Inform. 28, 7577–7587 (2024).

    Article  PubMed  Google Scholar 

  82. Zuppinger, C. et al. Performance of the digital dietary assessment tool MyFoodRepo. Nutrients 14, 635 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Singh, R. et al. Temporal nutrition analysis associates dietary regularity and quality with gut microbiome diversity: insights from the Food & You digital cohort. Nat. Commun. 16, 8635 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Moyen, A. et al. Relative validation of an artificial intelligence–enhanced, image-assisted mobile app for dietary assessment in adults: randomized crossover study. J. Med. Internet Res. 24, e40449 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Folson, G. K. et al. Validation of mobile artificial intelligence technology–assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana. J. Nutr. 153, 2328–2338 (2023).

    Article  CAS  PubMed  Google Scholar 

  86. Tagi, M. et al. A food intake estimation system using an artificial intelligence–based model for estimating leftover hospital liquid food in clinical environments: development and validation study. JMIR Form. Res. 8, e55218 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Shonkoff, E. et al. AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review. Ann. Med. 55, 2273497 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Konstantakopoulos, F. S., Georga, E. I. & Fotiadis, D. I. A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms. Sci. Rep. 13, 21040 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Huang, Y. et al. Automatic recognition of food ingestion environment from the AIM-2 wearable sensor. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 3685–3694 (IEEE, 2024).

  90. Theodore Armand, T. P., Nfor, K. A., Kim, J. I. & Kim, H. C. Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients 16, 1073 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Mezgec, S. & Seljak, B. K. NutriNet: a deep learning food and drink image recognition system for dietary assessment. Nutrients 9, 657 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Thompson, F. E. & Subar, A. F. Dietary assessment methodology. in Nutrition in the Prevention and Treatment of Disease. Chapter 1 5–48. Editors: Ann M. Coulston, Carol J. Boushey, Mario Ferruzzi, Linda Delahanty https://doi.org/10.1016/B978-0-12-802928-2.00001-1 (Elsevier, 2017).

  93. Kassem, H. et al. Investigation and assessment of AI’s role in nutrition—an updated narrative review of the evidence. Nutrients 17, 190 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Bell, B. M. et al. Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review. NPJ Digit. Med. 3, 38 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Min, W., Jiang, S., Liu, L., Rui, Y. & Jain, R. A survey on food computing. ACM Comput. Serv. 52, 92 (2019).

    Google Scholar 

  96. Min, W. et al. Large scale visual food recognition. IEEE 45, 9932–9949 (2023).

    Google Scholar 

  97. Yao, Y. et al. Exploiting web images for dataset construction: a domain robust approach. IEEE Trans. Multimedia 19, 1771–1784 (2017).

    Article  Google Scholar 

  98. Bossard, L., G. M. & G. L. V. in Lecture Notes in Computer Science (eds. Fleet, D. et al.) Vol. 8694 (Springer, 2014).

  99. Sun, M. et al. eButton: a wearable computer for health monitoring and personal assistance. In Proc. 51st Annual Design Automation Conference 1–6 (ACM, 2014).

  100. Gemming, L. et al. Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall. Br. J. Nutr. 113, 284–291 (2015).

    Article  CAS  PubMed  Google Scholar 

  101. Skinner, A., Toumpakari, Z., Stone, C. & Johnson, L. Future directions for integrative objective assessment of eating using wearable sensing technology. Front. Nutr. 7, 80 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Wang, L. et al. Enhancing nutrition care through real-time, sensor-based capture of eating occasions: a scoping review. Front. Nutr. 9, 852984 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Dong, Y., Hoover, A., Scisco, J. & Muth, E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl. Psychophysiol. Biofeedback 37, 205–215 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Doulah, A. et al. Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error. Int. J. Obes. 46, 2050–2057 (2022).

    Article  Google Scholar 

  105. Wang, C. et al. Detection of eating gestures in older persons using IMU sensors with multistage temporal convolutional network. IEEE Sens. J. 24, 35231–35244 (2024).

    Article  Google Scholar 

  106. Farha, Y. A. & Gall, J. MS-TCN: multi-stage temporal convolutional network for action segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2019).

  107. de Gooijer, F. J. et al. Assessing snacking and drinking behavior in real-life settings: validation of the SnackBox technology. Food Qual. Prefer. 112, 105002 (2023).

    Article  Google Scholar 

  108. Kok, E., Chauhan, A., Tufano, M., Feskens, E. & Camps, G. The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings. Front. Nutr. 11, 1520674 (2024).

    Article  PubMed  Google Scholar 

  109. Doulah, A., Ghosh, T., Hossain, D., Imtiaz, M. H. & Sazonov, E. ‘Automatic ingestion monitor Version 2’ - a novel wearable device for automatic food intake detection and passive capture of food images. IEEE J. Biomed. Health Inform. 25, 568–576 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Ghosh, T. et al. Integrated image and sensor-based food intake detection in free-living. Sci. Rep. 14, 1665 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Jiang, Z. et al. DietGlance: dietary monitoring and personalized analysis at a glance with knowledge-empowered AI assistant. ACM Trans. Comput. Healthcare https://doi.org/10.1145/3797883 (2026).

    Article  Google Scholar 

  112. Fernandes, G. J. et al. HabitSense: a privacy-aware, AI-enhanced multimodal wearable platform for mHealth applications. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8, 101 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Kumar, R. D., Julie, E. G., Robinson, Y. H., Vimal, S. & Seo, S. Recognition of food type and calorie estimation using neural network. J. Supercomputing 77, 8172–8193 (2021).

    Article  Google Scholar 

  114. Siemon, M. S. N., Shihavuddin, A. S. M. & Ravn-Haren, G. Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation. Sci. Rep. 11, 813 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Liang, Y. & Li, J. Deep learning-based food calorie estimation method in dietary assessment. Preprint at http://arxiv.org/abs/1706.04062 (2017).

  116. Hossain, D., Thomas, J. G., McCrory, M. A., Higgins, J. & Sazonov, E. A systematic review of sensor-based methods for measurement of eating behavior. Sensors 25, 2966 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Höchsmann, C. & Martin, C. K. Review of the validity and feasibility of image-assisted methods for dietary assessment. Int. J. Obes. 44, 2358–2371 (2020).

    Article  Google Scholar 

  118. Garcia-Perez, I. et al. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endocrinol 5, 184–195 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  119. John, R. S. The history and theory of the doubly labeled water technique. Am. J. Clin. Nutr. 68, 932S–938S (1998).

    Article  Google Scholar 

  120. Schoeller, D. A. & Hnilicka, J. M. Reliability of the doubly labeled water method for the measurement of total daily energy expenditure in free-living subjects. J. Nutr. 126, 348S–354S (1996).

    CAS  PubMed  Google Scholar 

  121. Bingham, S. & Cummings, J. Urine nitrogen as an independent validatory measure of dietary intake: a study of nitrogen balance in individuals consuming their normal diet. Am. J. Clin. Nutr. 42, 1276–1289 (1985).

    Article  CAS  PubMed  Google Scholar 

  122. Freedman, L. S. et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am. J. Epidemiol. 180, 172–188 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Zamora-Ros, R. et al. Urinary excretions of 34 dietary polyphenols and their associations with lifestyle factors in the EPIC cohort study. Sci. Rep. 6, 26905 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Edmands, W. M. et al. Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries. Am. J. Clin. Nutr. 102, 905–913 (2015).

    Article  CAS  PubMed  Google Scholar 

  125. Söderholm, P. P., Koskela, A. H., Lundin, J. E., Tikkanen, M. J. & Adlercreutz, H. C. Plasma pharmacokinetics of alkylresorcinol metabolites: new candidate biomarkers for whole-grain rye and wheat intake. Am. J. Clin. Nutr. 90, 1167–1171 (2009).

    Article  PubMed  Google Scholar 

  126. Cheung, W. et al. A metabolomic study of biomarkers of meat and fish intake. Am. J. Clin. Nutr. 105, 600–608 (2017).

    Article  CAS  PubMed  Google Scholar 

  127. Andersen, L., Solvoll, K. & Drevon, C. Very-long-chain n–3 fatty acids as biomarkers for intake of fish and n−3 fatty acid concentrates. Am. J. Clin. Nutr. 64, 305–311 (1996).

    Article  CAS  PubMed  Google Scholar 

  128. Münger, L. H. et al. Identification of urinary food intake biomarkers for milk, cheese, and soy-based drink by untargeted GC-MS and NMR in healthy humans. J. Proteome Res. 16, 3321–3335 (2017).

    Article  PubMed  Google Scholar 

  129. Wolk, A., Vessby, B., Ljung, H. & Barrefors, P. Evaluation of a biological marker of dairy fat intake. Am. J. Clin. Nutr. 68, 291–295 (1998).

    Article  CAS  PubMed  Google Scholar 

  130. Rothwell, J. A. et al. Biomarkers of intake for coffee, tea, and sweetened beverages. Genes Nutr. 13, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Dragsted, L. O. et al. Validation of biomarkers of food intake—critical assessment of candidate biomarkers. Genes Nutr. 13, 14 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Landberg, R. et al. Dietary biomarkers—an update on their validity and applicability in epidemiological studies. Nutr. Rev. 82, 1260–1280 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Cuparencu, C. et al. Towards nutrition with precision: unlocking biomarkers as dietary assessment tools. Nat. Metab. 6, 1438–1453 (2024).

    Article  PubMed  Google Scholar 

  134. Guasch-Ferré, M., Bhupathiraju, S. N. & Hu, F. B. Use of metabolomics in improving assessment of dietary intake. Clin. Chem. 64, 82–98 (2018).

    Article  PubMed  Google Scholar 

  135. Playdon, M. C. et al. Identifying biomarkers of dietary patterns by using metabolomics. Am. J. Clin. Nutr. 105, 450–465 (2017).

    Article  CAS  PubMed  Google Scholar 

  136. Shen, X. et al. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat. Biomed. Eng. 8, 11–29 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K. & Dorrestein, P. C. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20, 143–160 (2022).

    Article  CAS  PubMed  Google Scholar 

  138. Petrone, B. L. et al. A pilot study of metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans. Food Funct 16, 282–296 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Du, S. et al. Plasma protein biomarkers of healthy dietary patterns: results from the Atherosclerosis Risk in Communities Study and the Framingham Heart Study. J. Nutr. 153, 34–46 (2023).

    Article  CAS  PubMed  Google Scholar 

  140. Zhu, K. et al. Proteomic signatures of healthy dietary patterns are associated with lower risks of major chronic diseases and mortality. Nat. Food 6, 47–57 (2024).

    Article  PubMed  Google Scholar 

  141. Melse-Boonstra, A. Bioavailability of Micronutrients from nutrient-dense whole foods: zooming in on dairy, vegetables, and fruits. Front. Nutr. 7, 101 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Kussmann, M. & Fay, L. B. Nutrigenomics and personalized nutrition: science and concept. Per. Med. 5, 447–455 (2008).

    Article  PubMed  Google Scholar 

  143. Geissler, C. & Powers, H. J. The Physiology of Nutrient Digestion and Absorption (Oxford Univ. Press, 2017).

  144. Pinto-Sanchez, M. I., Blom, J.-J., Gibson, P. R. & Armstrong, D. Nutrition assessment and management in celiac disease. Gastroenterology 167, 116–131.e1 (2024).

    CAS  PubMed  Google Scholar 

  145. Hurrell, R. & Egli, I. Iron bioavailability and dietary reference values. Am. J. Clin. Nutr. 91, 1461S–1467S (2010).

    Article  CAS  PubMed  Google Scholar 

  146. Tan, Y. & McClements, D. J. Improving the bioavailability of oil-soluble vitamins by optimizing food matrix effects: a review. Food Chem 348, 129148 (2021).

    Article  CAS  PubMed  Google Scholar 

  147. Reboul, E. Absorption of vitamin A and carotenoids by the enterocyte: focus on transport proteins. Nutrients 5, 3563–3581 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Sandström, B. Micronutrient interactions: effects on absorption and bioavailability. Br. J. Nutr. 85, S181–S185 (2001).

    Article  PubMed  Google Scholar 

  149. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    Article  PubMed  Google Scholar 

  150. Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).

    Article  CAS  PubMed  Google Scholar 

  151. Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990).

    Article  CAS  PubMed  Google Scholar 

  152. Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716–724 (2018).

    Article  CAS  PubMed  Google Scholar 

  153. Zeng, X. et al. Gut bacterial nutrient preferences quantified in vivo. Cell 185, 3441–3456 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Magnúsdóttir, S., Ravcheev, D., de Crécy-Lagard, V. & Thiele, I. Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front. Genet. 6, 148 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Metges, C. C. et al. Incorporation of urea and ammonia nitrogen into ileal and fecal microbial proteins and plasma free amino acids in normal men and ileostomates. Am. J. Clin. Nutr. 70, 1046–1058 (1999).

    Article  CAS  PubMed  Google Scholar 

  156. Ilyas, A. et al. Implications of trimethylamine N-oxide (TMAO) and betaine in human health: beyond being osmoprotective compounds. Front. Mol. Biosci. 9, 964624 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Zhou, Y. et al. The gut microbiota derived metabolite trimethylamine N-oxide: Its important role in cancer and other diseases. Biomed. Pharmacother. 177, 117031 (2024).

    Article  CAS  PubMed  Google Scholar 

  158. Fluhr, L. et al. Gut microbiota modulates weight gain in mice after discontinued smoke exposure. Nature 600, 713–719 (2021).

    Article  CAS  PubMed  Google Scholar 

  159. Sonnenburg, E. D. & Sonnenburg, J. L. Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab 20, 779–786 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Corbin, K. D. et al. Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial. Nat. Commun. 14, 3161 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Cohen, Y. & Borenstein, E. The microbiome’s fiber degradation profile and its relationship with the host diet. BMC Biol 20, 266 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Moraïs, S. et al. Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans. Science 383, eadj9223 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  163. Mazidi, M. et al. Meal-induced inflammation: postprandial insights from the Personalised REsponses to DIetary Composition Trial (PREDICT) study in 1000 participants. Am. J. Clin. Nutr. 114, 1028–1038 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510 (2016).

    Article  CAS  PubMed  Google Scholar 

  165. Suez, J. et al. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. Cell 185, 3307–3328 (2022).

    Article  CAS  PubMed  Google Scholar 

  166. Abrams, S. A. Using stable isotopes to assess mineral absorption and utilization by children. Am. J. Clin. Nutr. 70, 955–964 (1999).

    Article  CAS  PubMed  Google Scholar 

  167. Miller, J. W. & Green, R. Assessing vitamin B-12 absorption and bioavailability: read the label. Am. J. Clin. Nutr. 112, 1420–1421 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  168. Borgstrom, B., Dahlqvist, A. & Lundh, G. On the site of absorption of fat from the human small intestine. Gut 3, 315–317 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Mottaleb, A. et al. The Lundh test in the diagnosis of pancreatic disease: a review of five years’ experience. Gut 14, 835–841 (1973).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Van de Kamer, J. H., Ten Bokkel Huinink, H. & Weyers, H. A. Rapid method for the determination of fat in feces. J. Biol. Chem. 177, 347–355 (1949).

    Article  PubMed  Google Scholar 

  171. Wang, X., He, Y., Gao, Q., Yang, D. & Liang, J. Approaches to evaluate nutrition of minerals in food. Food Sci. Hum. Wellness 10, 141–148 (2021).

    Article  CAS  Google Scholar 

  172. Braga, B. C. et al. Feasibility of using an artificial intelligence-based telephone application for dietary assessment and nudging to improve the quality of food choices of female adolescents in Vietnam: evidence from a randomized pilot study. Curr. Dev. Nutr. 8, 102063 (2024).

    Article  PubMed  Google Scholar 

  173. Mehta, S. et al. Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings. Nat. Commun. 16, 7673 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the members of the Elinav laboratory, Weizmann Institute of Science and Microbiome & Cancer Division, DKFZ, for insightful discussions. Y.C. is supported by the Clore Scholarship Foundation. E.E. is supported by the Leona M. and Harry B. Helmsley Charitable Trust, Bill and Melinda Gates Foundation, European Research Council, Israel Science Foundation, Israel Ministry of Science and Technology, Israel Ministry of Health, Helmholtz Foundation, European Crohn’s and Colitis Organization, Kenneth Rainin Foundation, Rising Tide Foundation, Lupus Research Alliance, Jose Carreras Foundation, Human Frontiers Science Program, Deutsch-Israelische Projektkooperation, IDSA Foundation, European Union THRIVE consortium (HORIZON-MISS-2023-CANCER) and the NUTRIOME consortium (HORIZON MSCA 2022 Doctoral Network, GA no. 101119497). E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial Chair; a Kimmel researcher; a CIFAR fellow; and a partner, Novo Nordisk Foundation Microbiome Health Initiative (MHI).

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  1. These authors contributed equally: Yotam Cohen, Tallulah Jansen, Serena Onwuka.

Authors and Affiliations

  1. Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel

    Yotam Cohen, Tallulah Jansen & Eran Elinav

  2. Microbiome & Cancer Division, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Serena Onwuka & Eran Elinav

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  1. Yotam Cohen
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  2. Tallulah Jansen
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Y.C., T.J. and S.O. performed the literature search, data integration and drafting of the manuscript, and have equally contributed to this work. E.E. conceived the review, supervised the work and wrote the manuscript.

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Correspondence to Eran Elinav.

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E.E. is an advisor to Purposebio and Zoe in topics unrelated to this work. The remaining authors declare no competing interests.

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Cohen, Y., Jansen, T., Onwuka, S. et al. Advances and opportunities in measuring dietary intake: from omics to AI. Nat Metab (2026). https://doi.org/10.1038/s42255-026-01494-z

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