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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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

Article 
PubMed 

Google Scholar
 

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

Article 
PubMed 

Google Scholar
 

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
 

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

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
 

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
 

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
 

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
 

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

Article 
PubMed 
PubMed Central 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
PubMed 

Google Scholar
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
CAS 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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).

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
 

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

Article 

Google Scholar
 

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).

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


Google Scholar
 

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
 

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

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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).

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
 

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
 

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).

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
 

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
 

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


Google Scholar
 

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


Google Scholar
 

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

Article 

Google Scholar
 

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

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).

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
 

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
 

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
 

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
 

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
 

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
 

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).

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

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
 

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
 

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
 

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

Article 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
PubMed 

Google Scholar
 

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

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
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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

Article 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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

Article 
PubMed 
PubMed Central 

Google Scholar
 

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
 

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

Article 
CAS 
PubMed 

Google Scholar
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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
 

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