
Mozaffarian, D., Blanck, H. M., Garfield, K. M., Wassung, A. & Petersen, R. A Food is Medicine approach to achieve nutrition security and improve health. Nat. Med. 28, 2238–2240 (2022).
Google Scholar
Lichtenstein, A. H. et al. 2021 Dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association. Circulation 144, e472–e487 (2021).
Google Scholar
The Diabetes and Nutrition Study Group (DNSG) of the European Association for the Study of Diabetes (EASD). Evidence-based European recommendations for the dietary management of diabetes. Diabetologia 66, 965–985 (2023).
Google Scholar
Dietary Guidelines for Americans, 2020–2025 9th edn. (U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2020).
Hassapidou, M. et al. European Association for the Study of Obesity position statement on medical nutrition therapy for the management of overweight and obesity in adults developed in collaboration with the European Federation of the Associations of Dietitians. Obes. Facts 16, 11–28 (2023).
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).
Google Scholar
Rodgers, G. P. & Collins, F. S. Precision nutrition—the answer to ‘what to eat to stay healthy’. JAMA 324, 735–736 (2020).
Google Scholar
Franks, P. W. et al. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol. 11, 822–835 (2023).
Google Scholar
Roberts, M. C., Holt, K. E., Del Fiol, G., Baccarelli, A. A. & Allen, C. G. Precision public health in the era of genomics and big data. Nat. Med. 30, 1865–1873 (2024).
Google Scholar
Wang, D. D. & Hu, F. B. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol. 6, 416–426 (2018).
Google Scholar
Piernas, C. & Merino, J. Interwoven challenges of covid-19, poor diet, and cardiometabolic health. BMJ 383, e076810 (2023).
Google Scholar
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).
Google Scholar
Juniusdottir, R. et al. Composition of school meals in Sweden, Finland, and Iceland: official guidelines and comparison with practice and availability. J. Sch. Health 88, 744–753 (2018).
Google Scholar
Volkert, D. et al. ESPEN practical guideline: clinical nutrition and hydration in geriatrics. Clin. Nutr. 41, 958–989 (2022).
Google Scholar
Mosher, A. L. et al. Dietary guidelines for Americans: implications for primary care providers. Am. J. Lifestyle Med. 10, 23–35 (2014).
Google Scholar
Hernandez, T. L. & Brand-Miller, J. C. Nutrition therapy in gestational diabetes mellitus: time to move forward. Diabetes Care 41, 1343–1345 (2018).
Google Scholar
Sparks, J. R., Ghildayal, N., Hivert, M.-F. & Redman, L. M. Lifestyle interventions in pregnancy targeting GDM prevention: looking ahead to precision medicine. Diabetologia 65, 1814–1824 (2022).
Google Scholar
GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).
Google Scholar
Liu, J., Rehm, C. D., Onopa, J. & Mozaffarian, D. Trends in diet quality among youth in the United States, 1999–2016. JAMA 323, 1161–1174 (2020).
Google Scholar
Agurs-Collins, T. et al. Perspective: Nutrition Health Disparities Framework: a model to advance health equity. Adv. Nutr. 15, 100194 (2024).
Google Scholar
White, M. et al. What role should the commercial food system play in promoting health through better diet? BMJ 368, m545 (2020).
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).
Google Scholar
Eisenberg, D. M. et al. Proposed nutrition competencies for medical students and physician trainees: a consensus statement. JAMA Netw. Open 7, e2435425 (2024).
Google Scholar
Franks, P. W. & McCarthy, M. I. Exposing the exposures responsible for type 2 diabetes and obesity. Science 354, 69–73 (2016).
Google Scholar
Tsereteli, N. et al. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions. Diabetologia 65, 356–365 (2022).
Google Scholar
Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964–973 (2020).
Google Scholar
Dunton, G. F. Sustaining health-protective behaviors such as physical activity and healthy eating. JAMA 320, 639–640 (2018).
Google Scholar
Fumagalli, M. et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science 349, 1343–1347 (2015).
Google Scholar
Wang, T. et al. Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: gene–diet interaction analysis in two prospective cohort studies. BMJ 360, j5644 (2018).
Google Scholar
Qi, Q. et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 367, 1387–1396 (2012).
Google Scholar
Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).
Google Scholar
Merino, J. et al. Interaction between type 2 diabetes prevention strategies and genetic determinants of coronary artery disease on cardiometabolic risk factors. Diabetes 69, 112–120 (2020).
Google Scholar
Merino, J. et al. Polygenic scores, diet quality, and type 2 diabetes risk: an observational study among 35,759 adults from 3 US cohorts. PLoS Med. 19, e1003972 (2022).
Google Scholar
Merino, J. et al. Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis. BMJ 366, l4292 (2019).
Google Scholar
Franks, P. W. & Merino, J. Gene–lifestyle interplay in type 2 diabetes. Curr. Opin. Genet. Dev. 50, 35–40 (2018).
Google Scholar
Valles-Colomer, M. et al. Cardiometabolic health, diet and the gut microbiome: a meta-omics perspective. Nat. Med. 29, 551–561 (2023).
Google Scholar
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
Google Scholar
Wang, D. D. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat. Med. 27, 333–343 (2021).
Google Scholar
Meslier, V. et al. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake. Gut 69, 1258–1268 (2020).
Google Scholar
Shalon, D. et al. Profiling the human intestinal environment under physiological conditions. Nature 617, 581–591 (2023).
Google Scholar
Folz, J. et al. Human metabolome variation along the upper intestinal tract. Nat. Metab. 5, 777–788 (2023).
Google Scholar
Tan, H.-E. et al. The gut–brain axis mediates sugar preference. Nature 580, 511–516 (2020).
Google Scholar
van der Klaauw, A. A. & Farooqi, I. S. The hunger genes: pathways to obesity. Cell 161, 119–132 (2015).
Google Scholar
DiFeliceantonio, A. G. et al. Supra-additive effects of combining fat and carbohydrate on food reward. Cell Metab. 28, 33–44 (2018).
Google Scholar
van Der Klaauw, A. A. et al. Divergent effects of central melanocortin signalling on fat and sucrose preference in humans. Nat. Commun. 7, 13055 (2016).
Google Scholar
Merino, J. et al. Genetic predisposition to macronutrient preference and workplace food choices. Mol. Psychiatry 28, 2606–2611 (2023).
Google Scholar
Lowell, B. B. New neuroscience of homeostasis and drives for food, water, and salt. N. Engl. J. Med. 380, 459–471 (2019).
Google Scholar
Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).
Google Scholar
Lei, Y. et al. Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus. Cell Metab. 36, 438–453 (2024).
Google Scholar
Tadross, J. A. et al. Human HYPOMAP: a comprehensive spatio-cellular map of the human hypothalamus. Preprint at bioRxiv https://doi.org/10.1101/2023.09.15.557967 (2023).
Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).
Google Scholar
Merino, J. Precision nutrition in diabetes: when population-based dietary advice gets personal. Diabetologia 65, 1839–1848 (2022).
Google Scholar
Trouwborst, I. et al. Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: a precision nutrition trial. Cell Metab. 35, 71–83 (2023).
Google Scholar
Nair, A. T. N. et al. Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nat. Med. 28, 982–988 (2022).
Google Scholar
Florez, J. C. Advancing precision medicine in type 2 diabetes. Lancet Diabetes Endocrinol. 12, 87–88 (2024).
Google Scholar
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
Google Scholar
Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44, 1980–1991 (2021).
Google Scholar
Bermingham, K. M. et al. Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial. Nat. Med. 30, 1888–1897 (2024).
Google Scholar
Guess, N. Big data and personalized nutrition: the key evidence gaps. Nat. Metab. 6, 1420–1422 (2024).
Google Scholar
Bucher, A., Blazek, E. S. & Symons, C. T. How are machine learning and artificial intelligence used in digital behavior change interventions? A scoping review. Mayo Clin. Proc. Digit. Health 2, 375–404 (2024).
Google Scholar
Poslusna, K., Ruprich, J., de Vries, J. H. M., Jakubikova, M. & van’t Veer, P. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br. J. Nutr. https://doi.org/10.1017/S0007114509990602 (2009).
Google Scholar
Mendez, M. A. Invited commentary: Dietary misreporting as a potential source of bias in diet–disease associations: future directions in nutritional epidemiology research. Am. J. Epidemiol. 181, 234–236 (2015).
Google Scholar
Cuparencu, C. et al. Towards nutrition with precision: unlocking biomarkers as dietary assessment tools. Nat. Metab. 6, 1438–1453 (2024).
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).
Google Scholar
Eriksen, R. et al. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: an IMI DIRECT study. EBioMedicine 58, 102932 (2020).
Google Scholar
Li, J. et al. The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur. Heart J. 41, 2645–2656 (2020).
Google Scholar
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).
Google Scholar
Landberg, R. et al. Dietary biomarkers—an update on their validity and applicability in epidemiological studies. Nutr. Rev. 82, 1260–1280 (2024).
Google Scholar
van Dam, R. M., Hu, F. B. & Willett, W. C. Coffee, caffeine, and health. N. Engl. J. Med. 383, 369–378 (2020).
Google Scholar
Lutsker, G. et al. From glucose patterns to health outcomes: a generalizable foundation model for continuous glucose monitor data analysis. Preprint at https://doi.org/10.48550/arXiv.2408.11876 (2024).
Sorkin, B. C., Kuszak, A. J., Williamson, J. S., Hopp, D. C. & Betz, J. M. The challenge of reproducibility and accuracy in nutrition research: resources and pitfalls. Adv. Nutr. 7, 383–389 (2016).
Google Scholar
Spector, T. D. & Gardner, C. D. Challenges and opportunities for better nutrition science—an essay by Tim Spector and Christopher Gardner. BMJ 369, m2470 (2020).
Google Scholar
Siri-Tarino, P. W., Sun, Q., Hu, F. B. & Krauss, R. M. Saturated fat, carbohydrate, and cardiovascular disease. Am. J. Clin. Nutr. 91, 502–509 (2010).
Google Scholar
Ludwig, D. S., Ebbeling, C. B. & Heymsfield, S. B. Improving the quality of dietary research. JAMA 322, 1549–1550 (2019).
Google Scholar
Trajanoska, K. et al. From target discovery to clinical drug development with human genetics. Nature 620, 737–745 (2023).
Google Scholar
Gao, S. et al. Empowering biomedical discovery with AI agents. Cell 187, 6125–6151 (2024).
Google Scholar
Corbin, L. J. et al. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat. Commun. 9, 711 (2018).
Google Scholar
Franks, P. W. & Timpson, N. J. Genotype-based recall studies in complex cardiometabolic traits. Circ. Genom. Precis. Med. 11, e001947 (2018).
Google Scholar
Yeo, G. S. H. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20, 111–112 (1998).
Google Scholar
van der Klaauw, A. et al. Role of melanocortin signalling in the preference for dietary macronutrients in human beings. Lancet 385, S12 (2015).
Google Scholar
Johnson-Mann, C. N. et al. A systematic review on participant diversity in clinical trials—have we made progress for the management of obesity and its metabolic sequelae in diet, drug, and surgical trials. J. Racial Ethn. Health Disparities 10, 3140–3149 (2023).
Google Scholar
Singh, B. et al. A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. NPJ Digit. Med. 7, 179 (2024).
Google Scholar
Iribarren, S. J., Cato, K., Falzon, L. & Stone, P. W. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS ONE 12, e0170581 (2017).
Google Scholar
Fakih El Khoury, C. F. et al. The effects of dietary mobile apps on nutritional outcomes in adults with chronic diseases: a systematic review and meta-analysis. J. Acad. Nutr. Diet. 119, 626–651 (2019).
Google Scholar
Mateo, G. F., Granado-Font, E., Ferré-Grau, C. & Montaña-Carreras, X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J. Med. Internet Res. 17, e253 (2015).
Google Scholar
Sharma, Y., Saha, A. & Goldsack, J. C. Defining the dimensions of diversity to promote inclusion in the digital era of health care: a lexicon. JMIR Public Health Surveill. 10, e51980 (2024).
Google Scholar
Alexandrou, C. et al. User experiences of an app-based mHealth intervention (MINISTOP 2.0) integrated in Swedish primary child healthcare among Swedish-, Somali- and Arabic-speaking parents and child healthcare nurses: a qualitative study. Digit. Health 9, 20552076231203630 (2023).
Google Scholar
Alexandrou, C. et al. Effectiveness of a smartphone app (MINISTOP 2.0) integrated in primary child health care to promote healthy diet and physical activity behaviors and prevent obesity in preschool-aged children: randomized controlled trial. Int. J. Behav. Nutr. Phys. Act. 20, 22 (2023).
Google Scholar
Gilbert, S. et al. Indigenous women and their nutrition during pregnancy (the Mums and Bubs Deadly Diets Project): protocol for a co-designed mHealth resource development study. JMIR Res. Protoc. 12, e45983 (2023).
Google Scholar
Commodore-Mensah, Y. et al. Design and rationale of the cardiometabolic health program linked with community health workers and mobile health telemonitoring to reduce health disparities (LINKED-HEARTS) program. Am. Heart J. 275, 9–20 (2024).
Google Scholar
Levy, D. E. et al. Design of ChooseWell 365: randomized controlled trial of an automated, personalized worksite intervention to promote healthy food choices and prevent weight gain. Contemp. Clin. Trials 75, 78–86 (2018).
Google Scholar
Thorndike, A. N., Gelsomin, E. D., McCurley, J. L. & Levy, D. E. Calories purchased by hospital employees after implementation of a cafeteria traffic light-labeling and choice architecture program. JAMA Netw. Open 2, e196789 (2019).
Google Scholar
Mattes, R. D. et al. Valuing the diversity of research methods to advance nutrition science. Adv. Nutr. 13, 1324–1393 (2022).
Google Scholar
Ziolkovska, A. & Sina, C. Personalized nutrition as the catalyst for building food-resilient cities. Nat. Food 5, 267–269 (2024).
Google Scholar
Bedsaul-Fryer, J. R. et al. Precision nutrition opportunities to help mitigate nutrition and health challenges in low- and middle-income countries: an expert opinion survey. Nutrients 15, 3247 (2023).
Google Scholar
Ben-Yacov, O. et al. Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes. Gut 72, 1486–1496 (2023).
Google Scholar
Bermingham, K. M. et al. Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study. Eur. J. Nutr. 63, 121–133 (2024).
Google Scholar
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