{"id":549228,"date":"2026-03-18T22:14:12","date_gmt":"2026-03-18T22:14:12","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/549228\/"},"modified":"2026-03-18T22:14:12","modified_gmt":"2026-03-18T22:14:12","slug":"advances-and-opportunities-in-measuring-dietary-intake-from-omics-to-ai","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/549228\/","title":{"rendered":"Advances and opportunities in measuring dietary intake: from omics to AI"},"content":{"rendered":"

Afshin, A. et al. Health effects of dietary risks in 195 countries, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 393, 1958\u20131972 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lee, C. D., Hardin, C. C., Longo, D. L. & Ingelfinger, J. R. Nutrition in medicine\u2014a new review article series. N. Engl. J. Med. 390, 1324\u20131325 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131501 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kopp, W. How Western diet and lifestyle drive the pandemic of obesity and civilization diseases. Diabetes Metab. Syndr. Obes. 12, 2221\u20132236 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Abdeen, S. K., Mastandrea, I., Stinchcombe, N., Puschhof, J. & Elinav, E. Diet-microbiome interactions in cancer. Cancer Cell 43, 680\u2013707 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Appel, L. J. et al. Dietary approaches to prevent and treat. Hypertension 47, 296\u2013308 (2006).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mu\u00f1oz-Torres, A., Garc\u00eda-Fontana, B. & Mu\u00f1oz-Torres, M. Nutrients and dietary patterns related to osteoporosis. Nutrients 12, 1986 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pugliese, M. T., Lifshitz, F., Grad, G., Fort, P. & Marks-Katz, M. Fear of obesity. N. Engl. J. Med. 309, 513\u2013518 (1983).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Brauer, M. et al. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990\u20132021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403, 2162\u20132203 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Heymsfield, S. B. & Shapses, S. A. Guidance on energy and macronutrients across the life span. N. Engl. J. Med. 390, 1299\u20131310 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yannakoulia, M. & Scarmeas, N. Diets. N. Engl. J. Med. 390, 2098\u20132106 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, R. Y. et al. Duodenal microbiota in stunted undernourished children with enteropathy. N. Engl. J. Med. 383, 321\u2013333 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sanz, Y. et al. The gut microbiome connects nutrition and human health. Nat. Rev. Gastroenterol. Hepatol. 22, 534\u2013555 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201322 (2004).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Baranowski, T. in Nutritional Epidemiology (ed. Willett, W.) Ch. 4, 49\u201369 (Oxford Univ. Press, 2012).<\/p>\n

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

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Maruvada, P. et al. Perspective: dietary biomarkers of intake and exposure\u2014exploration with omics approaches. Adv. Nutr. 11, 200\u2013215 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vald\u00e9s-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\u20131083.e36 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Diener, C. et al. Metagenomic estimation of dietary intake from human stool. Nat. Metab. 7, 617\u2013630 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reese, A. T. et al. Using DNA metabarcoding to evaluate the plant component of human diets: a proof of concept. mSystems 4, e00458\u201319 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sonnenburg, J. L. & B\u00e4ckhed, F. Diet\u2013microbiota interactions as moderators of human metabolism. Nature 535, 56\u201364 (2016).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & B\u00e4ckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332\u20131345 (2016).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shim, J.-S., Oh, K. & Kim, H. C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 36, e2014009 (2014).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013447 (1999).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131262 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201355 (1978).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lafay, L. et al. Determinants and nature of dietary underreporting in a free-living population: the Fleurbaix Laventie Ville Sant\u00e9 (FLVS) study. Int. J. Obes. 21, 567\u2013573 (1997).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lissner, L. et al. OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI. Int. J. Obes. 31, 956\u2013961 (2007).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bingham, S. A. et al. Validation of weighed records and other methods of dietary assessment using the 24\u2009h urine nitrogen technique and other biological markers. Br. J. Nutr. 73, 531\u2013550 (1995).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131290 (1994).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Taren, D. et al. The association of energy intake bias with psychological scores of women. Eur. J. Clin. Nutr. 53, 570\u2013578 (1999).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kipnis, V. Structure of dietary measurement error: results of the OPEN biomarker study. Am. J. Epidemiol. 158, 14\u201321 (2003).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Scrimshaw, N. INFOODS: the international network of food data systems. Am. J. Clin. Nutr. 65, 1190S\u20131193S (1997).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Deharveng, G., Charrondi\u00e8re, 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\u201379 (1999).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013Finnish study. Eur. J. Clin. Nutr. 57, 1035\u20131044 (2003).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201317 (1991).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, L., Boshuizen, H. & Ock\u00e9, 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\u2013198 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rico-Camp\u00e0, A. et al. Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ 365, l1949 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013559 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chazelas, E. et al. Exposure to food additive mixtures in 106,000 French adults from the NutriNet-Sant\u00e9 cohort. Sci. Rep. 11, 19680 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Trakman, G. L. et al. Development and validation of surveys to estimate food additive intake. Nutrients 12, 812 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kennedy, E. T., Ohls, J., Carlson, S. & Fleming, K. The Healthy Eating Index. J. Am. Diet. Assoc. 95, 1103\u20131108 (1995).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cavicchia, P. P. et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J. Nutr. 139, 2365\u20132372 (2009).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fung, T. T. et al. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. Am. J. Clin. Nutr. 82, 163\u2013173 (2005).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yue, Y. et al. Reproducibility and validity of diet quality scores derived from food-frequency questionnaires. Am. J. Clin. Nutr. 115, 843\u2013853 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Racine, A. et al. Dietary patterns and risk of inflammatory bowel disease in Europe. Inflamm. Bowel Dis. 22, 345\u2013354 (2016).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Riboli, E. et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 5, 1113\u20131124 (2002).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013443 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013240 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lucassen, D. A. et al. Validation of the smartphone-based dietary assessment tool \u201cTraqq\u201d 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\u20131287 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079\u20131094 (2015).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964\u2013973 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131502 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013216 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cohen, Y., Vald\u00e9s-Mas, R. & Elinav, E. The role of artificial intelligence in deciphering diet\u2013disease relationships: case studies. Annu. Rev. Nutr. 43, 225\u2013250 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ho, D. K. N. et al. Validity of image-based dietary assessment methods: a systematic review and meta-analysis. Clin. Nutr. 39, 2945\u20132959 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

O\u2019Loughlin, G. et al. Using a wearable camera to increase the accuracy of dietary analysis. Am. J. Prev. Med. 44, 297\u2013301 (2013).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, W. et al. A review on vision-based analysis for automatic dietary assessment. Trends Food Sci. Technol. 122, 223\u2013237 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cofre, S., Sanchez, C., Quezada-Figueroa, G. & L\u00f3pez-Cort\u00e9s, X. A. Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review. Br. J. Nutr. 133, 1241\u20131253 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201335381 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013439 (IEEE, 2025).<\/p>\n

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\u20138021 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yin, S. et al. A survey on multimodal large language models. Natl Sci. Rev. 11, nwae40 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131721 (ACM, 2024).<\/p>\n

Yin, Y. et al. FoodLMM: a versatile food assistant using large multi-modal model. IEEE 27, 6949\u20136961 (2025).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lo, F. P. W. et al. Dietary assessment with multimodal ChatGPT: a systematic analysis. IEEE J. Biomed. Health Inform. 28, 7577\u20137587 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zuppinger, C. et al. Performance of the digital dietary assessment tool MyFoodRepo. Nutrients 14, 635 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Moyen, A. et al. Relative validation of an artificial intelligence\u2013enhanced, image-assisted mobile app for dietary assessment in adults: randomized crossover study. J. Med. Internet Res. 24, e40449 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Folson, G. K. et al. Validation of mobile artificial intelligence technology\u2013assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana. J. Nutr. 153, 2328\u20132338 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tagi, M. et al. A food intake estimation system using an artificial intelligence\u2013based model for estimating leftover hospital liquid food in clinical environments: development and validation study. JMIR Form. Res. 8, e55218 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20133694 (IEEE, 2024).<\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thompson, F. E. & Subar, A. F. Dietary assessment methodology. in Nutrition in the Prevention and Treatment of Disease. Chapter 1 5\u201348. Editors: Ann M. Coulston, Carol J. Boushey, Mario Ferruzzi, Linda Delahanty https:\/\/doi.org\/10.1016\/B978-0-12-802928-2.00001-1<\/a> (Elsevier, 2017).<\/p>\n

Kassem, H. et al. Investigation and assessment of AI\u2019s role in nutrition\u2014an updated narrative review of the evidence. Nutrients 17, 190 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Min, W. et al. Large scale visual food recognition. IEEE 45, 9932\u20139949 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yao, Y. et al. Exploiting web images for dataset construction: a domain robust approach. IEEE Trans. Multimedia 19, 1771\u20131784 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Sun, M. et al. eButton: a wearable computer for health monitoring and personal assistance. In Proc. 51st Annual Design Automation Conference 1\u20136 (ACM, 2014).<\/p>\n

Gemming, L. et al. Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24\u2009h recall. Br. J. Nutr. 113, 284\u2013291 (2015).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013215 (2012).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20132057 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, C. et al. Detection of eating gestures in older persons using IMU sensors with multistage temporal convolutional network. IEEE Sens. J. 24, 35231\u201335244 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Doulah, A., Ghosh, T., Hossain, D., Imtiaz, M. H. & Sazonov, E. \u2018Automatic ingestion monitor Version 2\u2019 – a novel wearable device for automatic food intake detection and passive capture of food images. IEEE J. Biomed. Health Inform. 25, 568\u2013576 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ghosh, T. et al. Integrated image and sensor-based food intake detection in free-living. Sci. Rep. 14, 1665 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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<\/a> (2026).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20138193 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

H\u00f6chsmann, C. & Martin, C. K. Review of the validity and feasibility of image-assisted methods for dietary assessment. Int. J. Obes. 44, 2358\u20132371 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013195 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

John, R. S. The history and theory of the doubly labeled water technique. Am. J. Clin. Nutr. 68, 932S\u2013938S (1998).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013354S (1996).<\/p>\n

CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131289 (1985).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013188 (2014).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013913 (2015).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

S\u00f6derholm, 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\u20131171 (2009).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cheung, W. et al. A metabolomic study of biomarkers of meat and fish intake. Am. J. Clin. Nutr. 105, 600\u2013608 (2017).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Andersen, L., Solvoll, K. & Drevon, C. Very-long-chain n\u20133 fatty acids as biomarkers for intake of fish and n\u22123 fatty acid concentrates. Am. J. Clin. Nutr. 64, 305\u2013311 (1996).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

M\u00fcnger, 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\u20133335 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wolk, A., Vessby, B., Ljung, H. & Barrefors, P. Evaluation of a biological marker of dairy fat intake. Am. J. Clin. Nutr. 68, 291\u2013295 (1998).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rothwell, J. A. et al. Biomarkers of intake for coffee, tea, and sweetened beverages. Genes Nutr. 13, 15 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dragsted, L. O. et al. Validation of biomarkers of food intake\u2014critical assessment of candidate biomarkers. Genes Nutr. 13, 14 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Landberg, R. et al. Dietary biomarkers\u2014an update on their validity and applicability in epidemiological studies. Nutr. Rev. 82, 1260\u20131280 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cuparencu, C. et al. Towards nutrition with precision: unlocking biomarkers as dietary assessment tools. Nat. Metab. 6, 1438\u20131453 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guasch-Ferr\u00e9, M., Bhupathiraju, S. N. & Hu, F. B. Use of metabolomics in improving assessment of dietary intake. Clin. Chem. 64, 82\u201398 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Playdon, M. C. et al. Identifying biomarkers of dietary patterns by using metabolomics. Am. J. Clin. Nutr. 105, 450\u2013465 (2017).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shen, X. et al. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat. Biomed. Eng. 8, 11\u201329 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013160 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013296 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201346 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u201357 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kussmann, M. & Fay, L. B. Nutrigenomics and personalized nutrition: science and concept. Per. Med. 5, 447\u2013455 (2008).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Geissler, C. & Powers, H. J. The Physiology of Nutrient Digestion and Absorption (Oxford Univ. Press, 2017).<\/p>\n

Pinto-Sanchez, M. I., Blom, J.-J., Gibson, P. R. & Armstrong, D. Nutrition assessment and management in celiac disease. Gastroenterology 167, 116\u2013131.e1 (2024).<\/p>\n

CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hurrell, R. & Egli, I. Iron bioavailability and dietary reference values. Am. J. Clin. Nutr. 91, 1461S\u20131467S (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reboul, E. Absorption of vitamin A and carotenoids by the enterocyte: focus on transport proteins. Nutrients 5, 3563\u20133581 (2013).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sandstr\u00f6m, B. Micronutrient interactions: effects on absorption and bioavailability. Br. J. Nutr. 85, S181\u2013S185 (2001).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027\u20131031 (2006).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Makki, K., Deehan, E. C., Walter, J. & B\u00e4ckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705\u2013715 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567\u2013590 (1990).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716\u2013724 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zeng, X. et al. Gut bacterial nutrient preferences quantified in vivo. Cell 185, 3441\u20133456 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Magn\u00fasd\u00f3ttir, S., Ravcheev, D., de Cr\u00e9cy-Lagard, V. & Thiele, I. Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front. Genet. 6, 148 (2015).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131058 (1999).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fluhr, L. et al. Gut microbiota modulates weight gain in mice after discontinued smoke exposure. Nature 600, 713\u2013719 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013786 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cohen, Y. & Borenstein, E. The microbiome\u2019s fiber degradation profile and its relationship with the host diet. BMC Biol 20, 266 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mora\u00efs, S. et al. Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans. Science 383, eadj9223 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u20131038 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495\u20131510 (2016).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Suez, J. et al. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. Cell 185, 3307\u20133328 (2022).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Abrams, S. A. Using stable isotopes to assess mineral absorption and utilization by children. Am. J. Clin. Nutr. 70, 955\u2013964 (1999).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Miller, J. W. & Green, R. Assessing vitamin B-12 absorption and bioavailability: read the label. Am. J. Clin. Nutr. 112, 1420\u20131421 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Borgstrom, B., Dahlqvist, A. & Lundh, G. On the site of absorption of fat from the human small intestine. Gut 3, 315\u2013317 (1962).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mottaleb, A. et al. The Lundh test in the diagnosis of pancreatic disease: a review of five years\u2019 experience. Gut 14, 835\u2013841 (1973).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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\u2013355 (1949).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, X., He, Y., Gao, Q., Yang, D. & Liang, J. Approaches to evaluate nutrition of minerals in food. Food Sci. Hum. Wellness 10, 141\u2013148 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

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).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n","protected":false},"excerpt":{"rendered":"Afshin, A. et al. Health effects of dietary risks in 195 countries, 1990\u20132017: a systematic analysis for the…\n","protected":false},"author":2,"featured_media":549229,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[64,63,44639,1325,9865,4389,125947,532,105],"class_list":["post-549228","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-au","tag-australia","tag-biomarkers","tag-general","tag-life-sciences","tag-metabolism","tag-microbiota","tag-nutrition","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/549228","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=549228"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/549228\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/549229"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=549228"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=549228"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=549228"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}