Franks, P. W. et al. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol. 11, 822–835 (2023).
Guasch-Ferré, M. et al. Precision nutrition for cardiometabolic diseases. Nat. Med. 31, 1444–1453 (2025).
Bjørnsbo, K. S. et al. Protocol for the combined cardiometabolic deep phenotyping and registry-based 20-year follow-up study of the Inter99 cohort. BMJ Open 14, e078501 (2014).
Shen, X. et al. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat. Biomed. Eng. 8, 11–29 (2024).
Sankar, P. L. & Parker, L. S. The Precision Medicine Initiative’s All of Us Research Program: an agenda for research on its ethical, legal and social issues. Genet. Med. 19, 743–750 (2017).
Willett, W. et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).
Li, Y. et al. Reducing climate change impacts from the global food system through diet shifts. Nat. Clim. Change 14, 943–953 (2024).
Springmann, M., Clark, M. A., Rayner, M., Scarborough, P. & Webb, P. The global and regional costs of healthy and sustainable dietary patterns: a modelling study. Lancet Planet. Health 5, e797–e807 (2021).
He, P., Feng, K., Baiocchi, G., Sun, L. & Hubacek, K. Shifts towards healthy diets in the US can reduce environmental impacts but would be unaffordable for poorer minorities. Nat. Food 2, 664–672 (2021).
Henn, K., Goddyn, H., Olsen, S. B. & Bredie, W. L. P. Identifying behavioral and attitudinal barriers and drivers to promote consumption of pulses: a quantitative survey across five European countries. Food Qual. Preference 98, 104455 (2022).
Grummon, A. H., Lee, C. J. Y., Robinson, T. N., Rimm, E. B. & Rose, D. Simple dietary substitutions can reduce carbon footprints and improve dietary quality across diverse segments of the US population. Nat. Food 4, 966–977 (2023).
Tuninetti, M., Ridolfi, L. & Laio, F. Compliance with EAT–Lancet dietary guidelines would reduce global water footprint but increase it for 40% of the world population. Nat. Food 3, 143–151 (2022).
Bunge, A. C., Mazac, R., Clark, M., Wood, A. & Gordon, L. Sustainability benefits of transitioning from current diets to plant-based alternatives or whole-food diets in Sweden. Nat. Commun. 15, 951 (2024).
Ravelli, M. N. & Schoeller, D. A. Traditional self-reported dietary instruments are prone to inaccuracies and new approaches are needed. Front. Nutr. 7, 90 (2020).
Kipnis, V. et al. Structure of dietary measurement error: results of the OPEN biomarker study. Am. J. Epidemiol. 158, 14–21 (2003).
Willett, W. Nutritional Epidemiology (Oxford Univ. Press, 2012).
Palaniappan, U., Cue, R., Payette, H. & Gray-Donald, K. Implications of day-to-day variability on measurements of usual food and nutrient intakes. J. Nutr. 133, 232–235 (2003).
Bingham, S. A. et al. in Manual on Methodology for Food Consumption Studies (eds Cameron, M. E. & van Staveren, W. A.) 53–106 (Oxford Univ. Press, 1988).
Eldridge, A. L. et al. Evaluation of new technology-based tools for dietary intake assessment—an ILSI Europe Dietary Intake and Exposure Task Force evaluation. Nutrients 11, 55 (2018).
Blanton, C. A., Moshfegh, A. J., Baer, D. J. & Kretsch, M. J. The USDA Automated Multiple-Pass Method accurately estimates group total energy and nutrient intake. J. Nutr. 136, 2594–2599 (2006).
Subar, A. F. et al. Addressing current criticism regarding the value of self-report dietary data. J. Nutr. 145, 2639–2645 (2015).
Forster, H., Walsh, M. C., Gibney, M. J., Brennan, L. & Gibney, E. R. Personalised nutrition: the role of new dietary assessment methods. Proc. Nutr. Soc. 75, 96–105 (2016).
Kipnis, V. et al. Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr. 5, 915–923 (2002).
Thompson, F. E. & Subar, A. F. in Nutrition in the Prevention and Treatment of Disease 4th edn (eds Coulston, A. M. et al.) 5–48 (Academic Press, 2017).
Young, L. R. & Nestle, M. Portion sizes in dietary assessment: issues and policy implications. Nutr. Rev. 53, 149–158 (1995).
Amoutzopoulos, B. et al. Portion size estimation in dietary assessment: a systematic review of existing tools, their strengths and limitations. Nutr. Rev. 78, 885–900 (2020).
Faulkner, G. P. et al. An evaluation of portion size estimation aids: precision, ease of use and likelihood of future use. Public Health Nutr. 19, 2377–2387 (2016).
Lucassen, D. A., Willemsen, R. F., Geelen, A., Brouwer-Brolsma, E. M. & Feskens, E. J. M. The accuracy of portion size estimation using food images and textual descriptions of portion sizes: an evaluation study. J. Hum. Nutr. Diet. 34, 945–952 (2021).
Vuckovic, N., Ritenbaugh, C., Taren, D. L. & Tobar, M. A qualitative study of participants’ experiences with dietary assessment. J. Am. Diet. Assoc. 100, 1023–1028 (2000).
Brinkley, S. et al. The state of food composition databases: data attributes and FAIR data harmonization in the era of digital innovation. Front. Nutr. 12, 1552367 (2025).
Li, Z., Forester, S., Jennings-Dobbs, E. & Heber, D. Perspective: a comprehensive evaluation of data quality in nutrient databases. Adv. Nutr. 14, 379–391 (2023).
Pennington, J. A. T. et al. Food composition data: the foundation of dietetic practice and research. J. Am. Diet. Assoc. 107, 2105–2113 (2007).
Neuhouser, M. L. et al. Novel application of nutritional biomarkers from a controlled feeding study and an observational study to characterization of dietary patterns in postmenopausal women. Am. J. Epidemiol. 190, 2461–2473 (2021).
Neuhouser, M. L. et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative. Am. J. Epidemiol. 167, 1247–1259 (2008).
Jenab, M., Slimani, N., Bictash, M., Ferrari, P. & Bingham, S. A. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum. Genet. 125, 507–525 (2009).
Bingham, S. A. Biomarkers in nutritional epidemiology. Public Health Nutr. 5, 821–827 (2002).
Shiffman, S., Stone, A. A. & Hufford, M. R. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008).
Lucassen, D. A., Brouwer-Brolsma, E. M., Slotegraaf, A. I., Kok, E. & Feskens, E. J. DIetary ASSessment (DIASS) Study: design of an evaluation study to assess validity, usability and perceived burden of an innovative dietary assessment methodology. Nutrients 14, 1156 (2022).
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).
Lucassen, D. A., Brouwer-Brolsma, E. M., Boshuizen, H. C., Balvers, M. & Feskes, E. J. Evaluation of the smartphone-based dietary assessment tool “Traqq” for assessing habitual dietary intake by random 2-H recalls in adults: comparison with a Food Frequency Questionnaire and blood concentration biomarkers. J. Nutr. 155, 634–642 (2024).
Vu, T., Lin, F., Alshurafa, N. & Xu, W. Wearable food intake monitoring technologies: a comprehensive review. Computers 6, 4 (2017).
Fontana, J., Farooq, M. & Sazonov, E. in Wearable Sensors (ed. Sazonov, E.). 541–574 (Academic Press, 2020).
McClung, H. L. et al. Dietary intake and physical activity assessment: current tools, techniques and technologies for use in adult populations. Am. J. Prevent. Med. 55, e93–e104 (2018).
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).
Bedri, A., Li, D., Khurana, R., Bhuwalka, K. & Goel, M. FitByte: automatic diet monitoring in unconstrained situations using multimodal sensing on eyeglasses. In Proc. 2020 CHI Conference on Human Factors in Computing Systems 1–12 (ACM, 2020).
Lo, F. P. W. et al. Dietary assessment with multimodal ChatGPT: a systematic analysis. IEEE J. Biomed. Health Inform. 28, 7577–7587 (2024).
Zhang, S., Callaghan, V. & Che, Y. Image-based methods for dietary assessment: a survey. J. Food Meas. Charact. 18, 727–743 (2024).
Jia, W. et al. Automatic food detection in egocentric images using artificial intelligence technology. Public Health Nutr. 22, 1168–1179 (2019).
Marín-Méndez, J.-J. et al. Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food. Curr. Res. Food Sci. 9, 100799 (2024).
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).
Gao, Q. et al. A scheme for a flexible classification of dietary and health biomarkers. Genes Nutr. 12, 34 (2017).
Scalbert, A. et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014).
Cuparencu, C. et al. Towards nutrition with precision: unlocking biomarkers as dietary assessment tools. Nat. Metab. 6, 1438–1453 (2024).
Dragsted, L. O. et al. Validation of biomarkers of food intake-critical assessment of candidate biomarkers. Genes Nutr. 13, 14 (2018).
Brouwer-Brolsma, E. M. et al. Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance. Proc. Nutr. Soc. 76, 619–627 (2017).
Playdon, M. C. et al. Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake. Am. J. Clin. Nutr. 119, 511–526 (2024).
Eichelmann, F. et al. Lipidome changes due to improved dietary fat quality inform cardiometabolic risk reduction and precision nutrition. Nat. Med. 30, 2867–2877 (2024).
Aristizabal-Henao, J. J., Biltoft-Jensen, A. P., Christensen, T. & Stark, K. D. Lipidomic and fatty acid biomarkers in whole blood can predict the dietary intake of eicosapentaenoic and docosahexaenoic acids in a Danish population. J. Nutr. 154, 2108–2119 (2024).
Bagheri, M. et al. A lipid-related metabolomic pattern of diet quality. Am. J. Clin. Nutr. 112, 1613–1630 (2020).
McKeown, N. M. et al. Comparison of plasma alkylresorcinols (AR) and urinary AR metabolites as biomarkers of compliance in a short-term, whole-grain intervention study. Eur. J. Nutr. 55, 1235–1244 (2016).
Andersen, M. B. S. et al. Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. J. Proteome Res. 13, 1405–1418 (2014).
Unión-Caballero, A. et al. Metabolome biomarkers linking dietary fibre intake with cardiometabolic effects: results from the Danish Diet, Cancer and Health-Next Generations MAX study. Food Function 15, 1643–1654 (2024).
Marklund, M. et al. A dietary biomarker approach captures compliance and cardiometabolic effects of a healthy Nordic diet in individuals with metabolic syndrome. J. Nutr. 144, 1642–1649 (2014).
Wilson, T. et al. Spot and cumulative urine samples are suitable replacements for 24-hour urine collections for objective measures of dietary exposure in adults using metabolite biomarkers. J. Nutr. 149, 1692–1700 (2019).
Lloyd, A. J. et al. Developing community-based urine sampling methods to deploy biomarker technology for the assessment of dietary exposure. Public Health Nutr. 23, 3081–3092 (2020).
Xi, M. et al. Combined urinary biomarkers to assess coffee intake using untargeted metabolomics: discovery in three pilot human intervention studies and validation in cross-sectional studies. J. Agric. Food Chem. 69, 7230–7242 (2021).
Vázquez-Manjarrez, N. et al. Discovery and validation of banana intake biomarkers using untargeted metabolomics in human intervention and cross-sectional studies. J. Nutr. 149, 1701–1713 (2019).
Cuparencu, C. et al. The anserine to carnosine ratio: an excellent discriminator between white and red meats consumed by free-living overweight participants of the PREVIEW study. Eur. J. Nutr. 60, 179–192 (2021).
Landberg, R. et al. Dose response of whole-grain biomarkers: alkylresorcinols in human plasma and their metabolites in urine in relation to intake. Am. J. Clin. Nutr. 89, 290–296 (2009).
Yin, X. et al. Estimation of chicken intake by adults using metabolomics-derived markers. J. Nutr. 147, 1850–1857 (2017).
Gibbons, H. et al. Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example. Mol. Nutr. Food Res. https://doi.org/10.1002/mnfr.201700037 (2017).
Hu, Y. et al. Calibration of citrus intake assessed by food frequency questionnaires using urinary proline betaine in an observational study setting. Am. J. Clin. Nutr. 120, 178–186 (2024).
Maixner, F. et al. The Iceman’s last meal consisted of fat, wild meat and cereals. Curr. Biol. 28, 2348–2355 (2018).
Garnick, S., Barboza, P. S. & Walker, J. W. Assessment of animal-based methods used for estimating and monitoring rangeland herbivore diet composition. Rangeland Ecol. Manag. 71, 449–457 (2018).
Wibowo, M. C. et al. Reconstruction of ancient microbial genomes from the human gut. Nature 594, 234–239 (2021).
Søe, M. J. et al. Ancient DNA from latrines in Northern Europe and the Middle East (500 BC–1700 AD) reveals past parasites and diet. PLoS ONE 13, e0195481 (2018).
Maixner, F. et al. Hallstatt miners consumed blue cheese and beer during the Iron Age and retained a non-Westernized gut microbiome until the Baroque period. Curr. Biol. 31, 5149–5162.e6 (2021).
Carlino, N. et al. Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Cell 187, 5775–5795.e15 (2024).
Shinn, L. M. et al. Fecal metagenomics to identify biomarkers of food intake in healthy adults: findings from randomized, controlled, nutrition trials. J. Nutr. 154, 271–283 (2024).
Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environ. DNA 2, 391–406 (2020).
Thuo, D. et al. Food from faeces: evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS ONE 14, e0225805 (2019).
Zeng, J. et al. Food DNA sequencing reveals associations between dietary perturbations and patient outcomes in hematopoietic stem cell transplant. Transplant. Cell. Ther. 30, S132 (2024).
Petrone, B. L. et al. Diversity of plant DNA in stool is linked to dietary quality, age, and household income. Proc. Natl Acad. Sci. USA 120, e2304441120 (2023).
Petrone, B. L. et al. A pilot study of metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans. Food Function 16, 282–296 (2025).
Diener, C. et al. Metagenomic estimation of dietary intake from human stool. Nat. Metab. 7, 617–630 (2025).
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).
Dragsted, L. O., Roager, H. M. & Cuparencu, C. Querying stool for dietary information. Nat. Metab. 7, 450–451 (2025).
Jacobs, D. R. & Temple, N. J. in Nutritional Health: Strategies for Disease Prevention (eds Temple, N. J. et al.) 287–296 (Springer, 2023).
Jacobs, D. R., Gross, M. D. & Tapsell, L. C. Food synergy: an operational concept for understanding nutrition. Am. J. Clin. Nutr. 89, 1543S–1548S (2009).
Gürdeniz, G. et al. Analysis of the SYSDIET Healthy Nordic Diet randomized trial based on metabolic profiling reveal beneficial effects on glucose metabolism and blood lipids. Clin. Nutr. 41, 441–451 (2022).
D’Angelo, S. et al. Combining biomarker and food intake data: calibration equations for citrus intake. Am. J. Clin. Nutr. 110, 977–983 (2019).
Hua, H. et al. A wipe-based stool collection and preservation kit for microbiome community profiling. Front. Immunol. 13, 889702 (2022).
Ahmed, S. et al. Foodomics: a data-driven approach to revolutionize nutrition and sustainable diets. Front. Nutr. 9, 874312 (2022).
Chakraborty, H. et al. The Dietary Biomarkers Development Consortium: an initiative for discovery and validation of dietary biomarkers for precision nutrition. Curr. Dev. Nutr. 9, 107435 (2025).
Bobokhidze, E. et al. Standardised and Objective Dietary Intake Assessment Tool (SODIAT): protocol of a dual-site dietary intervention study to integrate dietary assessment methods. F1000Res. 13, 1144 (2024).