Towards nutrition with precision: unlocking biomarkers as dietary assessment tools
Open accessAbstract
Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.
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Different assessment methods to measure biomarkers via different kits with varying inter- and intra-assay coefficients of variability influence the overall accuracy of the reported effect estimates.
“Different assessment methods to measure these biomarkers via different kits with varying inter- and intra-assay coefficients of variability further influence the overall accuracy of the reported effect estimates [176,177].”
From Medical Nutrition Therapy for Women with Gestational Diabetes: Current Practice and Future Perspectives (2025) · cites this paper as [176]
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Self-citationBiomarkers of food intake for Allium vegetables
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Self-citationBiomarkers of intake for tropical fruits
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Self-citationDetecting Beer Intake by Unique Metabolite Patterns
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Self-citationBiomarkers of moderate alcohol intake and alcoholic beverages: a systematic literature review
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Self-citationSources of Variability in Metabolite Measurements from Urinary Samples
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Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake
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Estimation of Chicken Intake by Adults Using Metabolomics-Derived Markers
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Metabolomic‐Based Approach to Identify Biomarkers of Apple Intake
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Combining biomarker and food intake data: calibration equations for citrus intake
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The Potential of Multi-Biomarker Panels in Nutrition Research: Total Fruit Intake as an Example
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Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease
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Personal diet–microbiota interactions and weight loss
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Self-citationRelative validity of a web-based food frequency questionnaire for Danish adolescents
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Self-citationvan der Haar, S., Hoevenaars, F. P. M., van den Brink, W. J., van den Broek, T., Timmer, M., Boorsma, A., & Doets, E. L. (2021) · JMIR Formative Research · doi:10.2196/25043 · Cited by 18 · see citing works
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Forecasting Chronic Diseases Using Data Fusion
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Self-citationQuifer‐Rada, P., Chiva‐Blanch, G., Jáuregui, O., Estruch, R., & Lamuela-Raventós, R. M. (2017) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201600980 · Cited by 16 · see citing works
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Self-citationCuparencu, C., Rinnan, Å., Silvestre, M. P., Poppitt, S. D., Raben, A., & Dragsted, L. O. (2020) · European Journal of Nutrition · doi:10.1007/s00394-020-02230-3 · Cited by 14 · see citing works
Self-citationZhou, X., Ulaszewska, M., Cuparencu, C., Gobba, C. D., Vázquez-Manjarrez, N., Gürdeniz, G., Chen, J., Mattivi, F., & Dragsted, L. O. (2020) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.0c01136 · Cited by 14 · see citing works
Self-citationShibutami, E., Ishii, R., Harada, S., Kurihara, A., Kuwabara, K., Kato, S., Iida, M., Akiyama, M. O., Sugiyama, D., Hirayama, A., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250864 · Cited by 13 · see citing works
Dried Urine Swabs as a Tool for Monitoring Metabolite Excretion
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Self-citationBeckmann, M., Wilson, T., Lloyd, A. J., Torres, D. P. M., Góios, A., Willis, N. D., Lyons, L., Phillips, H. C., Mathers, J. C., & Draper, J. (2020) · Frontiers in Nutrition · doi:10.3389/fnut.2020.602515 · Cited by 11 · see citing works
The metabolic nature of individuality
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Self-citationSerum and Urine Metabolites in Healthy Men after Consumption of Acidified Milk and Yogurt
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Identification of urinary biomarkers of food intake for onion by untargeted LC-MS metabolomicsPreprint
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Precision nutrition for cardiometabolic diseases
Guasch‐Ferré, M., Wittenbecher, C., Palmnäs, M. S. A., Ben-Yacov, O., Blaak, E. E., Dahm, C. C., Fall, T., Heitmann, B. L., Licht, T. R., Löf, M., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03669-9 · Cited by 53 · see citing works
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MarkerDB 2.0: a comprehensive molecular biomarker database for 2025
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Precision or Personalized Nutrition: A Bibliometric Analysis
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Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence
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Guidance and Position of RINN22 regarding Precision Nutrition and Nutriomics
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Biomarkers of food intake: current status and future opportunities
Brennan, L. (2025) · Proceedings of The Nutrition Society · doi:10.1017/s0029665125000084 · Cited by 7 · see citing works
Glenn, A. J., Tessier, A., Kavanagh, M. E., Morgan, G. A., Clish, C. B., Salas‐Salvadó, J., Malik, V., Hanley, A. J., Bazinet, R. P., Comelli, E. M., et al. (2025) · European Journal of Clinical Nutrition · doi:10.1038/s41430-025-01625-x · Cited by 6 · see citing works
Precision nutrition for the prevention and management of inflammatory bowel disease
Chen, J., Dan, L., Wellens, J., Jairath, V., Yanai, H. A., Khalili, H., Danese, S., Peyrin‐Biroulet, L., Wang, X., Li, X., et al. (2026) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-026-01213-9 · Cited by 6 · see citing works
Muli, S., Blumenthal, A., Conzen, C., Benz, M. E., Alexy, U., Schmid, M. C., Keski‐Rahkonen, P., Floegel, A., & Nöthlings, U. (2024) · Journal of Nutrition · doi:10.1016/j.tjnut.2024.09.023 · Cited by 7 · see citing works
Clarke, E. D., Duncan, M. J., Burrows, T. L., Gómez‐Martín, M., Brain, K., Stanford, J., Ferguson, J. J. A., Wood, L., & Collins, C. E. (2025) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.70075 · Cited by 5 · see citing works
Integration of modern technologies to advance dietary assessment
Cuparencu, C., Diener, C., Wilson, T., Gibbons, S. M., & Lucassen, D. A. (2026) · Nature Food · doi:10.1038/s43016-025-01290-0 · Cited by 5 · see citing works
Self-citationAdvances and opportunities in measuring dietary intake: from omics to AI
Cohen, Y., Jansen, T., Onwuka, S., & Elinav, E. (2026) · Nature Metabolism · doi:10.1038/s42255-026-01494-z · Cited by 5 · see citing works
Maternal Adherence to Healthy Dietary Patterns During Pregnancy and Gestational Weight Gain
Lim, S. X., Wadhawan, S., DeVilbiss, E. A., Clayton, P., Wagner, K. A., Gleason, J. L., Chen, Z., Zhang, C., Grantz, K. L., & Grewal, J. (2025) · Nutrients · doi:10.3390/nu17162707 · Cited by 5 · see citing works
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Kisi, S. B., Petersen, C. F., Andersen, R. S., Rasmussen, S. I. I., Parlesak, A., Christensen, S. H., Petersen, H. L., Geiker, N. R. W., Hitz, M. F., & Tetens, I. (2025) · Journal of Nutritional Science · doi:10.1017/jns.2025.10010 · Cited by 4 · see citing works
The overlooked impact of background diet and adherence in nutrition trials
Ottaviani, J. I., Schroeter, H., Bier, D. M., Erdman, J. W., Sesso, H. D., Manson, J. E., & Kuhnle, G. (2025) · Food & Function · doi:10.1039/d5fo01134e · Cited by 4 · see citing works
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Johnson, J. J., Ghosh, S., Shaw, P. A., Neuhouser, M. L., Lampe, J. W., Tinker, L. F., Prentice, R. L., Tasevska, N., Freedman, L. S., Boyer, B. B., et al. (2025) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2025.09.049 · Cited by 4 · see citing works
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Discovery of urinary biomarkers of kiwifruit intake in a randomized intervention study
Xiao, Z., Shang, W., Li, P., Wang, N., Li, T., Liu, Y., Chen, Y., Wang, Y., Ma, H., Wang, X., et al. (2025) · Nutrition Journal · doi:10.1186/s12937-025-01230-5 · Cited by 3 · see citing works
Polyphenol Intake from Herbs and Spices
Blanton, C. A. (2025) · Nutrients · doi:10.3390/nu17152445 · Cited by 3 · see citing works
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Self-citationRisk-benefit assessment in food Systems: Towards personalized nutrition and sustainable diets
Li, Z., Ye, R., Yang, M., Chen, G., & Chen, C. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105039 · Cited by 2 · see citing works
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Fluxomics in precision nutrition: integrating dynamic metabolic profiling with multiomics
Lanuza, F., Viscardi, S., Wishart, D. S., & Andrés‐Lacueva, C. (2026) · Nutrition · doi:10.1016/j.nut.2026.113301 · Cited by 2 · see citing works
Advancing personalised and precision nutrition
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Self-citationDevelopment and User Experience Evaluation of an Experience Sampling-Based Dietary Assessment Method
Verbeke, J., & Matthys, C. (2024) · Current Developments in Nutrition · doi:10.1016/j.cdnut.2024.104479 · Cited by 3 · see citing works
Food Is Medicine: Diet Assessment Tools in Adult Inflammatory Bowel Disease Research
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Ferguson, J. J. A., Clarke, E. D., Stanford, J., Gómez‐Martín, M., Jakstas, T., Collins, C. E., Beck, K. L., Collins, C. E., Cuparencu, C., Souza, D. P. D., et al. (2025) · Advances in Nutrition · doi:10.1016/j.advnut.2025.100420 · Cited by 2 · see citing works
Self-citationQuerying stool for dietary information
Dragsted, L. O., Roager, H. M., & Cuparencu, C. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01219-8 · Cited by 1 · see citing works
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- Authors
- Cuparencu, C., Bulmuş-Tüccar, T., Stanstrup, J., La Barbera, G., Roager, H. M., & Dragsted, L. O.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Cuparencu, Cătălina
- Venue
- Nature Metabolism
- Published
- 2024-07-02
- Volume
- 6
- Issue
- 8
- Pages
- 1438-1453
- ISSN
- 2522-5812 (ISSN-L)
- OpenAlex
- W4400248433
- Cited by
- 77
- References
- 123
- Citations by year
- 2026: 31 · 2025: 37 · 2024: 9
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Nature Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (acceptedVersion, other-oa)
- Funders
- International Postdoctoral Research Fellowship Programme 2219 from The Scientific and Technological Research Council of Türkiye - TÜBİTAK
- Novo Nordisk Fonden (NNF19OC0056246)
- Carlsbergfondet (CF15-0574)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- dietary assessment, personalized nutrition, urinary biomarkers, blood biomarkers, dietary misreporting, biomarker development
- MeSH terms
- Diet, Eating, Feeding Behavior, Humans, Nutritional Status, Biomarkers, Nutrition Assessment, Precision Medicine
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