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Annual Review of Nutrition

Volume 43, 2023

The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies

Abstract

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting–related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet–disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation.

Keyword(s): artificial intelligence, diet, disease, neural networks, personalized nutrition, precision nutrition
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/content/journals/10.1146/annurev-nutr-061121-090535
2023-08-21
2024-04-28
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Literature Cited

  1. 1.
    Adlung L, Cohen Y, Mor U, Elinav E. 2021. Machine learning in clinical decision making. Medicine 2:6642–65
     [Google Scholar]
  2. 2.
    Alexandrov LB, Ju YS, Haase K, Van Loo P, Martincorena I et al. 2016. Mutational signatures associated with tobacco smoking in human cancer. Science 354:6312618–22
     [Google Scholar]
  3. 3.
    Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW et al. 2020. The repertoire of mutational signatures in human cancer. Nature 578:779394–101
     [Google Scholar]
  4. 4.
    Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S et al. 2013. Signatures of mutational processes in human cancer. Nature 500:7463415–21
     [Google Scholar]
  5. 5.
    Aljada B, Zohni A, El-Matary W. 2021. The gluten-free diet for celiac disease and beyond. Nutrients 13:113993
     [Google Scholar]
  6. 6.
    Álvaro Sanz E, Garrido Siles M, Rey Fernández L, Villatoro Roldán R, Rueda Domínguez A, Abilés J 2019. Nutritional risk and malnutrition rates at diagnosis of cancer in patients treated in outpatient settings: early intervention protocol. Nutrition 57:148–53
     [Google Scholar]
  7. 7.
    Aon MA, Bernier M, Mitchell SJ, Di Germanio C, Mattison JA et al. 2020. Untangling determinants of enhanced health and lifespan through a multi-omics approach in mice. Cell Metab. 32:1100–16.e4
     [Google Scholar]
  8. 8.
    Apaolaza I, San José-Enériz E, Valcarcel LV, Agirre X, Prosper F, Planes FJ 2022. A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism. PLOS Comput. Biol. 18:3e1009395
     [Google Scholar]
  9. 9.
    Babajide O, Hissam T, Anna P, Anatoliy G, Astrup A et al. 2020. A machine learning approach to short-term body weight prediction in a dietary intervention program. Computational Science (ICCS 2020): Proceedings of the 20th International Conference Part IV 441–55. Berlin: Springer
     [Google Scholar]
  10. 10.
    Ben-Yacov O, Godneva A, Rein M, Shilo S, Kolobkov D et al. 2021. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44:91980–91
     [Google Scholar]
  11. 11.
    Berenblum I. 1976. Carcinogenesis as a biological problem. Food Cosmet. Toxicol. 14:153–54
     [Google Scholar]
  12. 12.
    Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M et al. 2020. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26:6964–73
     [Google Scholar]
  13. 13.
    Bischoff SC, Escher J, Hébuterne X, Kłęk S, Krznaric Z et al. 2020. ESPEN Practical Guideline: clinical nutrition in inflammatory bowel disease. Clin. Nutr. 39:3632–53
     [Google Scholar]
  14. 14.
    Black CJ, Staudacher HM, Ford AC. 2022. Efficacy of a low FODMAP diet in irritable bowel syndrome: systematic review and network meta-analysis. Gut 71:61117–26
     [Google Scholar]
  15. 15.
    Blüher M. 2019. Obesity: global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 15:288–98
     [Google Scholar]
  16. 16.
    Bodini G, Zanella C, Crespi M, Lo Pumo S, Demarzo MG et al. 2019. A randomized, 6-wk trial of a low FODMAP diet in patients with inflammatory bowel disease. Nutrition 67/68:110542
     [Google Scholar]
  17. 17.
    Bolla AM, Caretto A, Laurenzi A, Scavini M, Piemonti L. 2019. Low-carb and ketogenic diets in type 1 and type 2 diabetes. Nutrients 11:5962
     [Google Scholar]
  18. 18.
    Burgos R, Bretón I, Cereda E, Desport JC, Dziewas R et al. 2018. ESPEN guideline clinical nutrition in neurology. Clin. Nutr. 37:1354–96
     [Google Scholar]
  19. 19.
    Caffa I, Spagnolo V, Vernieri C, Valdemarin F, Becherini P et al. 2020. Author correction: Fasting-mimicking diet and hormone therapy induce breast cancer regression. Nature 588:7839e33
     [Google Scholar]
  20. 20.
    Cao Y, Oh J, Xue M, Huh WJ, Wang J et al. 2022. Commensal microbiota from patients with inflammatory bowel disease produce genotoxic metabolites. Science 378:6618eabm3233
     [Google Scholar]
  21. 21.
    Carreras-Torres R, Ibáñez-Sanz G, Obón-Santacana M, Duell EJ, Moreno V. 2020. Identifying environmental risk factors for inflammatory bowel diseases: a Mendelian randomization study. Sci. Rep. 10:19273
     [Google Scholar]
  22. 22.
    Castro-Barquero S, Ruiz-León AM, Sierra-Pérez M, Estruch R, Casas R. 2020. Dietary strategies for metabolic syndrome: a comprehensive review. Nutrients 12:102983
     [Google Scholar]
  23. 23.
    Cavaliere A, De Marchi E, Banterle A. 2018. Exploring the adherence to the Mediterranean diet and its relationship with individual lifestyle: the role of healthy behaviors, pro-environmental behaviors, income, and education. Nutrients 10:2141
     [Google Scholar]
  24. 24.
    Cena H, Calder PC. 2020. Defining a healthy diet: evidence for the role of contemporary dietary patterns in health and disease. Nutrients 12:2334
     [Google Scholar]
  25. 25.
    Chiavaroli L, Lee D, Ahmed A, Cheung A, Khan TA et al. 2021. Effect of low glycaemic index or load dietary patterns on glycaemic control and cardiometabolic risk factors in diabetes: systematic review and meta-analysis of randomised controlled trials. BMJ 374:n1651
     [Google Scholar]
  26. 26.
    Chiavaroli L, Viguiliouk E, Nishi SK, Blanco Mejia S, Rahelić D et al. 2019. DASH dietary pattern and cardiometabolic outcomes: an umbrella review of systematic reviews and meta-analyses. Nutrients 11:2338
     [Google Scholar]
  27. 27.
    Chlebowski RT, Aragaki AK, Anderson GL, Simon MS, Manson JE et al. 2018. Association of low-fat dietary pattern with breast cancer overall survival: a secondary analysis of the Women's Health Initiative randomized clinical trial. JAMA Oncol. 4:10e181212
     [Google Scholar]
  28. 28.
    Chlebowski RT, Aragaki AK, Anderson GL, Thomson CA, Manson JE et al. 2017. Low-fat dietary pattern and breast cancer mortality in the Women's Health Initiative randomized controlled trial. J. Clin. Oncol. 35:252919–26
     [Google Scholar]
  29. 29.
    Churuangsuk C, Kherouf M, Combet E, Lean M. 2018. Low-carbohydrate diets for overweight and obesity: a systematic review of the systematic reviews. Obes. Rev. 19:121700–18
     [Google Scholar]
  30. 30.
    Cifre M, Palou A, Oliver P. 2020. Impaired CPT1A gene expression response to retinoic acid treatment in human PBMC as predictor of metabolic risk. Nutrients 12:82269
     [Google Scholar]
  31. 31.
    Cohen CW, Fontaine KR, Arend RC, Alvarez RD, Leath CA III et al. 2018. A ketogenic diet reduces central obesity and serum insulin in women with ovarian or endometrial cancer. J. Nutr. 148:81253–60
     [Google Scholar]
  32. 32.
    Couzin-Frankel J. 2013. Breakthrough of the year 2013. Cancer immunotherapy. Science 342:61651432–33
     [Google Scholar]
  33. 33.
    Cresci GA, Bawden E. 2015. Gut microbiome: what we do and don't know. Nutr. Clin. Pract. 30:6734–46
     [Google Scholar]
  34. 34.
    Critch J, Day AS, Otley A, King-Moore C, Teitelbaum JE et al. 2012. Use of enteral nutrition for the control of intestinal inflammation in pediatric Crohn disease. J. Pediatr. Gastroenterol. Nutr. 54:2298–305
     [Google Scholar]
  35. 35.
    Cryan JF, O'Riordan KJ, Sandhu K, Peterson V, Dinan TG. 2020. The gut microbiome in neurological disorders. Lancet Neurol. 19:2179–94
     [Google Scholar]
  36. 36.
    D'Andrea Meira I, Romão TT, Pires do Prado HJ, Krüger LT, Pires MEP, da Conceição PO. 2019. Ketogenic diet and epilepsy: what we know so far. Front. Neurosci. 13:5
     [Google Scholar]
  37. 37.
    Dansinger ML, Gleason JA, Griffith JL, Selker HP, Schaefer EJ. 2005. Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. JAMA 293:143–53
     [Google Scholar]
  38. 38.
    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:7484559–63
     [Google Scholar]
  39. 39.
    de Boer A. 2021. Fifteen years of regulating nutrition and health claims in Europe: the past, the present and the future. Nutrients 13:51725
     [Google Scholar]
  40. 40.
    de Cabo R, Mattson MP. 2019. Effects of intermittent fasting on health, aging, and disease. N. Engl. J. Med. 381:2541–51
     [Google Scholar]
  41. 41.
    de Toro-Martín J, Arsenault BJ, Després J-P, Vohl M-C. 2017. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients 9:8913
     [Google Scholar]
  42. 42.
    Del Bosque-Plata L, Martínez-Martínez E, Espinoza-Camacho MÁ, Gragnoli C. 2021. The role of TCF7L2 in type 2 diabetes. Diabetes 70:61220–28
     [Google Scholar]
  43. 43.
    Delgado-Lista J, Alcala-Diaz JF, Torres-Peña JD, Quintana-Navarro GM, Fuentes F et al. 2022. Long-term secondary prevention of cardiovascular disease with a Mediterranean diet and a low-fat diet (CORDIOPREV): a randomised controlled trial. Lancet 399:103381876–85
     [Google Scholar]
  44. 44.
    Di Francesco A, Di Germanio C, Bernier M, de Cabo R. 2018. A time to fast. Science 362:6416770–75
     [Google Scholar]
  45. 45.
    Diederen K, Li JV, Donachie GE, de Meij TG, de Waart DR et al. 2020. Exclusive enteral nutrition mediates gut microbial and metabolic changes that are associated with remission in children with Crohn's disease. Sci. Rep. 10:18879
     [Google Scholar]
  46. 46.
    Dinu M, Abbate R, Gensini GF, Casini A, Sofi F. 2017. Vegetarian, vegan diets and multiple health outcomes: a systematic review with meta-analysis of observational studies. Crit. Rev. Food Sci. Nutr. 57:173640–49
     [Google Scholar]
  47. 47.
    Dinu M, Pagliai G, Angelino D, Rosi A, Dall'Asta M et al. 2020. Effects of popular diets on anthropometric and cardiometabolic parameters: an umbrella review of meta-analyses of randomized controlled trials. Adv. Nutr. 11:4815–33
     [Google Scholar]
  48. 48.
    Dizman N, Meza L, Bergerot P, Alcantara M, Dorff T et al. 2022. Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial. Nat. Med. 28:4704–12
     [Google Scholar]
  49. 49.
    Dmitrieva-Posocco O, Wong AC, Lundgren P, Golos AM, Descamps HC et al. 2022. β-Hydroxybutyrate suppresses colorectal cancer. Nature 605:7908160–65
     [Google Scholar]
  50. 50.
    Donaldson MS. 2004. Nutrition and cancer: a review of the evidence for an anti-cancer diet. Nutr. J. 3:19
     [Google Scholar]
  51. 51.
    Drabsch T, Holzapfel C. 2019. A scientific perspective of personalised gene-based dietary recommendations for weight management. Nutrients 11:3617
     [Google Scholar]
  52. 52.
    Durand R, Ouellette A, Houde VP, Guénard F, Varin TV et al. 2020. Animal and cellular studies demonstrate some of the beneficial impacts of herring milt hydrolysates on obesity-induced glucose intolerance and inflammation. Nutrients 12:113235
     [Google Scholar]
  53. 53.
    Ed. Nat. Med. 2022. Assessing the evidence of risk. Nat. Med. 28:101967
     [Google Scholar]
  54. 54.
    Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. 2020. Ultra-processed foods and health outcomes: a narrative review. Nutrients 12:71955
     [Google Scholar]
  55. 55.
    Esposito K, Kastorini C-M, Panagiotakos DB, Giugliano D. 2011. Mediterranean diet and weight loss: meta-analysis of randomized controlled trials. Metab. Syndr. Relat. Disord. 9:11–12
     [Google Scholar]
  56. 56.
    FAO (Food Agric. Organ.) 2023. Dietary guidelines and sustainability Guidel., FAO Rome: https://www.fao.org/nutrition/education/food-dietary-guidelines/background/sustainable-dietary-guidelines/en
  57. 57.
    Ferrara G, Kim J, Lin S, Hua J, Seto E. 2019. A focused review of smartphone diet-tracking apps: usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates. JMIR mHealth uHealth 7:5e9232
     [Google Scholar]
  58. 58.
    Ferrari C, Sorbi S. 2021. The complexity of Alzheimer's disease: an evolving puzzle. Physiol. Rev. 101:31047–81
     [Google Scholar]
  59. 59.
    Fitzpatrick JA, Melton SL, Yao CK, Gibson PR, Halmos EP. 2022. Dietary management of adults with IBD—the emerging role of dietary therapy. Nat. Rev. Gastroenterol. Hepatol. 19:10652–69
     [Google Scholar]
  60. 60.
    Fletcher J, Cooper SC, Ghosh S, Hewison M. 2019. The role of vitamin D in inflammatory bowel disease: mechanism to management. Nutrients 11:51019
     [Google Scholar]
  61. 61.
    Foster GD, Wyatt HR, Hill JO, McGuckin BG, Brill C et al. 2003. A randomized trial of a low-carbohydrate diet for obesity. N. Engl. J. Med. 348:212082–90
     [Google Scholar]
  62. 62.
    Fragoso YD, Stoney PN, McCaffery PJ. 2014. The evidence for a beneficial role of vitamin A in multiple sclerosis. CNS Drugs 28:4291–99
     [Google Scholar]
  63. 63.
    Garralda-Del-Villar M, Carlos-Chillerón S, Diaz-Gutierrez J, Ruiz-Canela M, Gea A et al. 2018. Healthy lifestyle and incidence of metabolic syndrome in the SUN cohort. Nutrients 11:165
     [Google Scholar]
  64. 64.
    Gazerani P, Fuglsang R, Pedersen JG, Sørensen J, Kjeldsen JL et al. 2019. A randomized, double-blinded, placebo-controlled, parallel trial of vitamin D3 supplementation in adult patients with migraine. Curr. Med. Res. Opin. 35:4715–23
     [Google Scholar]
  65. 65.
    GBD 2019 Risk Factors Collab 2020. 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 396:102581223–49
     [Google Scholar]
  66. 66.
    Gerasimidis K, Bertz M, Hanske L, Junick J, Biskou O et al. 2014. Decline in presumptively protective gut bacterial species and metabolites are paradoxically associated with disease improvement in pediatric Crohn's disease during enteral nutrition. Inflamm. Bowel Dis. 20:5861–71
     [Google Scholar]
  67. 67.
    Ghosh TS, Shanahan F, O'Toole PW 2022. Toward an improved definition of a healthy microbiome for healthy aging. Nat. Aging 2:1054–69
     [Google Scholar]
  68. 68.
    Gianfrancesco MA, Goldstein ND. 2021. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med. Res. Methodol. 21:234
     [Google Scholar]
  69. 69.
    Gill SK, Rossi M, Bajka B, Whelan K. 2021. Dietary fibre in gastrointestinal health and disease. Nat. Rev. Gastroenterol. Hepatol. 18:2101–16
     [Google Scholar]
  70. 70.
    Goldstein SP, Zhang F, Thomas JG, Butryn ML, Herbert JD, Forman EM. 2018. Application of machine learning to predict dietary lapses during weight loss. J. Diabetes Sci. Technol. 12:51045–52
     [Google Scholar]
  71. 71.
    Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC et al. 2018. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359:637197–103
     [Google Scholar]
  72. 72.
    Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH et al. 2005. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112:172735–52
     [Google Scholar]
  73. 73.
    Gupta D, Lis CG, Granick J, Grutsch JF, Vashi PG, Lammersfeld CA. 2006. Malnutrition was associated with poor quality of life in colorectal cancer: a retrospective analysis. J. Clin. Epidemiol. 59:7704–9
     [Google Scholar]
  74. 74.
    Gurjao C, Zhong R, Haruki K, Li YY, Spurr LF et al. 2021. Discovery and features of an alkylating signature in colorectal cancer. Cancer Discov. 11:102446–55
     [Google Scholar]
  75. 75.
    Hall KD, Kahan S. 2018. Maintenance of lost weight and long-term management of obesity. Med. Clin. N. Am. 102:1183–97
     [Google Scholar]
  76. 76.
    Hardy DS, Racette SB, Garvin JT, Gebrekristos HT, Mersha TB. 2021. Ancestry specific associations of a genetic risk score, dietary patterns and metabolic syndrome: a longitudinal ARIC study. BMC Med. Genom. 14:118
     [Google Scholar]
  77. 77.
    Hasic Telalovic J, Music A. 2020. Using data science for medical decision making case: role of gut microbiome in multiple sclerosis. BMC Med. Inform. Decis. Mak. 20:1262
     [Google Scholar]
  78. 78.
    Hatthachote P, Rangsin R, Mungthin M, Sakboonyarat B. 2019. Trends in the prevalence of obesity among young Thai men and associated factors: from 2009 to 2016. Mil. Med. Res. 6:113
     [Google Scholar]
  79. 79.
    Haug CJ, Drazen JM. 2023. Artificial intelligence and machine learning in clinical medicine, 2023. N. Engl. J. Med. 388:1201–8
     [Google Scholar]
  80. 80.
    He Y, Shi M, Wu X, Ma J, Ng KT-P et al. 2021. Mutational signature analysis reveals widespread contribution of pyrrolizidine alkaloid exposure to human liver cancer. Hepatology 74:1264–80
     [Google Scholar]
  81. 81.
    Hirai F, Takeda T, Takada Y, Kishi M, Beppu T et al. 2020. Efficacy of enteral nutrition in patients with Crohn's disease on maintenance anti-TNF-α antibody therapy: a meta-analysis. J. Gastroenterol. 55:2133–41
     [Google Scholar]
  82. 82.
    Hirode G, Wong RJ. 2020. Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA 323:242526–28
     [Google Scholar]
  83. 83.
    Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA et al. 2010. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363:711–23
     [Google Scholar]
  84. 84.
    Hollander AA, van Rooij J, Lentjes GW, Arbouw F, van Bree JB et al. 1995. The effect of grapefruit juice on cyclosporine and prednisone metabolism in transplant patients. Clin. Pharmacol. Ther. 57:3318–24
     [Google Scholar]
  85. 85.
    Holzinger A, Biemann C, Pattichis CS, Kell DB. 2017. What do we need to build explainable AI systems for the medical domain?. arXiv:1712.09923 [cs.AI]
  86. 86.
    Hooson J, Hutchinson J, Warthon-Medina M, Hancock N, Greathead K et al. 2020. A systematic review of reviews identifying UK validated dietary assessment tools for inclusion on an interactive guided website for researchers: www.nutritools.org. Crit. Rev. Food Sci. Nutr. 60:81265–89
     [Google Scholar]
  87. 87.
    Imamura F, Lichtenstein AH, Dallal GE, Meigs JB, Jacques PF. 2009. Generalizability of dietary patterns associated with incidence of type 2 diabetes mellitus. Am. J. Clin. Nutr. 90:41075–83
     [Google Scholar]
  88. 88.
    Iwasaki Y, Arisawa K, Katsuura-Kamano S, Uemura H, Tsukamoto M et al. 2019. Associations of nutrient patterns with the prevalence of metabolic syndrome: results from the baseline data of the Japan Multi-Institutional Collaborative Cohort Study. Nutrients 11:5990
     [Google Scholar]
  89. 89.
    Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG et al. 2014. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 129:25 Suppl. 2S102–38
     [Google Scholar]
  90. 90.
    Jia W, Whitehead RN, Griffiths L, Dawson C, Waring RH et al. 2010. Is the abundance of Faecalibacterium prausnitzii relevant to Crohn's disease?. FEMS Microbiol. Lett. 310:2138–44
     [Google Scholar]
  91. 91.
    Jimenez-Torres J, Alcalá-Diaz JF, Torres-Peña JD, Gutierrez-Mariscal FM, Leon-Acuña A et al. 2021. Mediterranean diet reduces atherosclerosis progression in coronary heart disease: an analysis of the CORDIOPREV randomized controlled trial. Stroke 52:113440–49
     [Google Scholar]
  92. 92.
    Jinnette R, Narita A, Manning B, McNaughton SA, Mathers JC, Livingstone KM. 2021. Does personalized nutrition advice improve dietary intake in healthy adults? A systematic review of randomized controlled trials. Adv. Nutr. 12:3657–69
     [Google Scholar]
  93. 93.
    Johnson KB, Wei W-Q, Weeraratne D, Frisse ME, Misulis K et al. 2021. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 14:186–93
     [Google Scholar]
  94. 94.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:7873583–89
     [Google Scholar]
  95. 95.
    Kanarek N, Keys HR, Cantor JR, Lewis CA, Chan SH et al. 2018. Histidine catabolism is a major determinant of methotrexate sensitivity. Nature 559:7715632–36
     [Google Scholar]
  96. 96.
    Kane S, Huo D, Aikens J, Hanauer S. 2003. Medication nonadherence and the outcomes of patients with quiescent ulcerative colitis. Am. J. Med. 114:139–43
     [Google Scholar]
  97. 97.
    Kane S, Shaya F. 2008. Medication non-adherence is associated with increased medical health care costs. Dig. Dis. Sci. 53:41020–24
     [Google Scholar]
  98. 98.
    Karagiozoglou-Lampoudi T, Daskalou E, Agakidis C, Savvidou A, Apostolou A, Vlahavas G. 2012. Personalized diet management can optimize compliance to a high-fiber, high-water diet in children with refractory functional constipation. J. Acad. Nutr. Diet. 112:5725–29
     [Google Scholar]
  99. 99.
    Karczewski KJ, Snyder MP. 2018. Integrative omics for health and disease. Nat. Rev. Genet. 19:5299–310
     [Google Scholar]
  100. 100.
    Kaur H, Singh Y, Singh S, Singh RB. 2021. Gut microbiome–mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis. Genome 64:4355–71
     [Google Scholar]
  101. 101.
    Kim J, Campbell AS, de Ávila BE-F, Wang J. 2019. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37:4389–406
     [Google Scholar]
  102. 102.
    Kirk D, Catal C, Tekinerdogan B. 2021. Precision nutrition: a systematic literature review. Comput. Biol. Med. 133:104365
     [Google Scholar]
  103. 103.
    Knight-Sepulveda K, Kais S, Santaolalla R, Abreu MT. 2015. Diet and inflammatory bowel disease. Gastroenterol. Hepatol. 11:8511–20
     [Google Scholar]
  104. 104.
    Kolodziejczyk AA, Zheng D, Elinav E. 2019. Diet–microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17:12742–53
     [Google Scholar]
  105. 105.
    Konijeti GG, Kim N, Lewis JD, Groven S, Chandrasekaran A et al. 2017. Efficacy of the autoimmune protocol diet for inflammatory bowel disease. Inflamm. Bowel Dis. 23:112054–60
     [Google Scholar]
  106. 106.
    Koropatkin NM, Cameron EA, Martens EC. 2012. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10:5323–35
     [Google Scholar]
  107. 107.
    Kotecha R, Takami A, Espinoza JL. 2016. Dietary phytochemicals and cancer chemoprevention: a review of the clinical evidence. Oncotarget 7:3252517–29
     [Google Scholar]
  108. 108.
    Kraszewski S, Szczurek W, Szymczak J, Reguła M, Neubauer K. 2021. Machine learning prediction model for inflammatory bowel disease based on laboratory markers. Working model in a discovery cohort study. J. Clin. Med. Res. 10:204745
     [Google Scholar]
  109. 109.
    Kraus WE, Bhapkar M, Huffman KM, Pieper CF, Das SK et al. 2019. 2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial. Lancet Diabetes Endocrinol. 7:9673–83
     [Google Scholar]
  110. 110.
    Kucab JE, Zou X, Morganella S, Joel M, Nanda AS et al. 2019. A compendium of mutational signatures of environmental agents. Cell 177:4821–36.e16
     [Google Scholar]
  111. 111.
    Labbé DP, Zadra G, Yang M, Reyes JM, Lin CY et al. 2019. High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program. Nat. Commun. 10:4358
     [Google Scholar]
  112. 112.
    Lampe JW. 2009. Interindividual differences in response to plant-based diets: implications for cancer risk. Am. J. Clin. Nutr. 89:5S1553–57
     [Google Scholar]
  113. 113.
    Larkin J, Chiarion-Sileni V, Gonzalez R, Grob J-J, Rutkowski P et al. 2019. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 381:161535–46
     [Google Scholar]
  114. 114.
    Lee D, Albenberg L, Compher C, Baldassano R, Piccoli D et al. 2015. Diet in the pathogenesis and treatment of inflammatory bowel diseases. Gastroenterology 148:61087–106
     [Google Scholar]
  115. 115.
    Leffler DA, Edwards George JB, Dennis M, Cook EF, Schuppan D, Kelly CP 2007. A prospective comparative study of five measures of gluten-free diet adherence in adults with coeliac disease. Aliment. Pharmacol. Ther. 26:91227–35
     [Google Scholar]
  116. 116.
    Levine A, Wine E, Assa A, Boneh RS, Shaoul R et al. 2019. Crohn's disease exclusion diet plus partial enteral nutrition induces sustained remission in a randomized controlled trial. Gastroenterology 157:2440–50.e8
     [Google Scholar]
  117. 117.
    Lewis JD, Sandler RS, Brotherton C, Brensinger C, Li H et al. 2021. A randomized trial comparing the specific carbohydrate diet to a Mediterranean diet in adults with Crohn's disease. Gastroenterology 161:3837–52.e9
     [Google Scholar]
  118. 118.
    Limketkai BN, Iheozor-Ejiofor Z, Gjuladin-Hellon T, Parian A, Matarese LE et al. 2019. Dietary interventions for induction and maintenance of remission in inflammatory bowel disease. Cochrane Database Syst. Rev. 2:2CD012839
     [Google Scholar]
  119. 119.
    Lin Z, Akin H, Rao R, Hie B, Zhu Z et al. 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379:66371123–30
     [Google Scholar]
  120. 120.
    Liu D, Huang Y, Huang C, Yang S, Wei X et al. 2022. Calorie restriction with or without time-restricted eating in weight loss. N. Engl. J. Med. 386:161495–504
     [Google Scholar]
  121. 121.
    Liu H, Yang Y, Wang Y, Tang H, Zhang F et al. 2018. Ketogenic diet for treatment of intractable epilepsy in adults: a meta-analysis of observational studies. Epilepsia Open 3:19–17
     [Google Scholar]
  122. 122.
    Liu S, Gao J, Zhu M, Liu K, Zhang H-L. 2020. Gut microbiota and dysbiosis in Alzheimer's disease: implications for pathogenesis and treatment. Mol. Neurobiol. 57:125026–43
     [Google Scholar]
  123. 123.
    Liu S, Stampfer MJ, Hu FB, Giovannucci E, Rimm E et al. 1999. Whole-grain consumption and risk of coronary heart disease: results from the Nurses’ Health Study. Am. J. Clin. Nutr. 70:3412–19
     [Google Scholar]
  124. 124.
    López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. 2013. The hallmarks of aging. Cell 153:61194–217
     [Google Scholar]
  125. 125.
    López-Otín C, Galluzzi L, Freije JMP, Madeo F, Kroemer G. 2016. Metabolic control of longevity. Cell 166:4802–21
     [Google Scholar]
  126. 126.
    Lundberg SM, Lee S-I. 2017. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS ’17)4768–77. New York: ACM
     [Google Scholar]
  127. 127.
    Ma C, Chen Q, Mitchell DC, Na M, Tucker KL, Gao X. 2022. Application of the deep learning algorithm in nutrition research—using serum pyridoxal 5′-phosphate as an example. Nutr. J. 21:138
     [Google Scholar]
  128. 128.
    Madireddy S, Madireddy S. 2022. Nutritional interventions for the prevention and treatment of neurological disorders such as anxiety, bipolar disorder, depression, epilepsy, multiple sclerosis, and schizophrenia. J. Neurosci. Neurol. Disord. 6:252–71
     [Google Scholar]
  129. 129.
    Martin CR, Osadchiy V, Kalani A, Mayer EA. 2018. The brain-gut-microbiome axis. Cell Mol. Gastroenterol. Hepatol. 6:2133–48
     [Google Scholar]
  130. 130.
    Martínez-González MA, Gea A, Ruiz-Canela M. 2019. The Mediterranean diet and cardiovascular health. Circ. Res. 124:5779–98
     [Google Scholar]
  131. 131.
    Martinez-Gonzalez MA, Martin-Calvo N 2016. Mediterranean diet and life expectancy; beyond olive oil, fruits, and vegetables. Curr. Opin. Clin. Nutr. Metab. Care 19:6401–7
     [Google Scholar]
  132. 132.
    Martínez-Lapiscina EH, Galbete C, Corella D, Toledo E, Buil-Cosiales P et al. 2014. Genotype patterns at CLU, CR1, PICALM and APOE, cognition and Mediterranean diet: the PREDIMED-NAVARRA trial. Genes Nutr 9:3393
     [Google Scholar]
  133. 133.
    Mathers CD, Murray CJL, Ezzati M, Gakidou E, Salomon JA, Stein C. 2003. Population health metrics: crucial inputs to the development of evidence for health policy. Popul. Health Metr. 1:16
     [Google Scholar]
  134. 134.
    Mazidi M, Pennathur S, Afshinnia F. 2017. Link of dietary patterns with metabolic syndrome: analysis of the National Health and Nutrition Examination Survey. Nutr. Diabetes 7:3e255
     [Google Scholar]
  135. 135.
    Mirmiran P, Bahadoran Z, Gaeini Z. 2021. Common limitations and challenges of dietary clinical trials for translation into clinical practices. Int. J. Endocrinol. Metab. 19:3e108170
     [Google Scholar]
  136. 136.
    Miyauchi E, Kim S-W, Suda W, Kawasumi M, Onawa S et al. 2020. Gut microorganisms act together to exacerbate inflammation in spinal cords. Nature 585:7823102–6
     [Google Scholar]
  137. 137.
    Mogre V, Abanga ZO, Tzelepis F, Johnson NA, Paul C. 2017. Adherence to and factors associated with self-care behaviours in type 2 diabetes patients in Ghana. BMC Endocr. Disord. 17:120
     [Google Scholar]
  138. 138.
    Moon J, Koh G. 2020. Clinical evidence and mechanisms of high-protein diet–induced weight loss. J. Obes. Metab. Syndr. 29:3166–73
     [Google Scholar]
  139. 139.
    Morais LH, Schreiber HL 4th, Mazmanian SK. 2021. The gut microbiota–brain axis in behaviour and brain disorders. Nat. Rev. Microbiol. 19:4241–55
     [Google Scholar]
  140. 140.
    Morze J, Danielewicz A, Przybyłowicz K, Zeng H, Hoffmann G, Schwingshackl L. 2021. An updated systematic review and meta-analysis on adherence to Mediterranean diet and risk of cancer. Eur. J. Nutr. 60:31561–86
     [Google Scholar]
  141. 141.
    Mousavi H, Karandish M, Jamshidnezhad A, Hadianfard AM. 2022. Determining the effective factors in predicting diet adherence using an intelligent model. Sci. Rep. 12:12340
     [Google Scholar]
  142. 142.
    Ndanuko RN, Tapsell LC, Charlton KE, Neale EP, Batterham MJ. 2016. Dietary patterns and blood pressure in adults: a systematic review and meta-analysis of randomized controlled trials. Adv. Nutr. 7:176–89
     [Google Scholar]
  143. 143.
    Nestle M. 2018. Perspective: challenges and controversial issues in the Dietary Guidelines for Americans, 1980–2015. Adv. Nutr. 9:2148–50
     [Google Scholar]
  144. 144.
    Ni C, Jia Q, Ding G, Wu X, Yang M. 2022. Low-glycemic index diets as an intervention in metabolic diseases: a systematic review and meta-analysis. Nutrients 14:2307
     [Google Scholar]
  145. 145.
    Nielsen RL, Helenius M, Garcia SL, Roager HM, Aytan-Aktug D et al. 2020. Data integration for prediction of weight loss in randomized controlled dietary trials. Sci. Rep. 10:20103
     [Google Scholar]
  146. 146.
    Norman K, Klaus S. 2020. Veganism, aging and longevity: new insight into old concepts. Curr. Opin. Clin. Nutr. Metab. Care 23:2145–50
     [Google Scholar]
  147. 147.
    Olson CA, Vuong HE, Yano JM, Liang QY, Nusbaum DJ, Hsiao EY. 2018. The gut microbiota mediates the anti-seizure effects of the ketogenic diet. Cell 173:71728–41.e13
     [Google Scholar]
  148. 148.
    Osté MCJ, Duan M-J, Gomes-Neto AW, Vinke PC, Carrero J-J et al. 2022. Ultra-processed foods and risk of all-cause mortality in renal transplant recipients. Am. J. Clin. Nutr. 115:61646–57
     [Google Scholar]
  149. 149.
    Paoli A, Mancin L, Giacona MC, Bianco A, Caprio M. 2020. Effects of a ketogenic diet in overweight women with polycystic ovary syndrome. J. Transl. Med. 18:1104
     [Google Scholar]
  150. 150.
    Patterson RE, Sears DD. 2017. Metabolic effects of intermittent fasting. Annu. Rev. Nutr. 37:371–93
     [Google Scholar]
  151. 151.
    Pérez-Martínez P, Mikhailidis DP, Athyros VG, Bullo M, Couture P et al. 2017. Lifestyle recommendations for the prevention and management of metabolic syndrome: an international panel recommendation. Nutr. Rev. 75:5307–26
     [Google Scholar]
  152. 152.
    Piovani D, Danese S, Peyrin-Biroulet L, Nikolopoulos GK, Lytras T, Bonovas S. 2019. Environmental risk factors for inflammatory bowel diseases: an umbrella review of meta-analyses. Gastroenterology 157:3647–59.e4
     [Google Scholar]
  153. 153.
    Pleguezuelos-Manzano C, Puschhof J, Rosendahl Huber A, van Hoeck A, Wood HM et al. 2020. Mutational signature in colorectal cancer caused by genotoxic pks+E. coli. Nature 580:7802269–73
     [Google Scholar]
  154. 154.
    Pomatto-Watson LCD, Bodogai M, Bosompra O, Kato J, Wong S et al. 2021. Daily caloric restriction limits tumor growth more effectively than caloric cycling regardless of dietary composition. Nat. Commun. 12:6201
     [Google Scholar]
  155. 155.
    Prescott MP, Burg X, Metcalfe JJ, Lipka AE, Herritt C, Cunningham-Sabo L. 2019. Healthy Planet, Healthy Youth: a food systems education and promotion intervention to improve adolescent diet quality and reduce food waste. Nutrients 11:81869
     [Google Scholar]
  156. 156.
    Prüss-Ustün A, Wolf J, Corvalán C, Neville T, Bos R, Neira M. 2017. Diseases due to unhealthy environments: an updated estimate of the global burden of disease attributable to environmental determinants of health. J. Public Health 39:3464–75
     [Google Scholar]
  157. 157.
    Quince C, Ijaz UZ, Loman N, Eren AM, Saulnier D et al. 2015. Extensive modulation of the fecal metagenome in children with Crohn's disease during exclusive enteral nutrition. Am. J. Gastroenterol. 110:121718–29
     [Google Scholar]
  158. 158.
    Rahman MM, Vadrev SM, Magana-Mora A, Levman J, Soufan O. 2022. A novel graph mining approach to predict and evaluate food-drug interactions. Sci. Rep. 12:1061
     [Google Scholar]
  159. 159.
    Redman LM, Smith SR, Burton JH, Martin CK, Il'yasova D, Ravussin E. 2018. Metabolic slowing and reduced oxidative damage with sustained caloric restriction support the rate of living and oxidative damage theories of aging. Cell Metab. 27:4805–15.e4
     [Google Scholar]
  160. 160.
    Rubini A, Bosco G, Lodi A, Cenci L, Parmagnani A et al. 2015. Effects of twenty days of the ketogenic diet on metabolic and respiratory parameters in healthy subjects. Lung 193:6939–45
     [Google Scholar]
  161. 161.
    Saklayen MG. 2018. The global epidemic of the metabolic syndrome. Curr. Hypertens. Rep. 20:212
     [Google Scholar]
  162. 162.
    Salgado M, Madureira J, Mendes AS, Torres A, Teixeira JP, Oliveira MD. 2020. Environmental determinants of population health in urban settings. A systematic review. BMC Public Health 20:1853
     [Google Scholar]
  163. 163.
    Samek W, Wiegand T, Müller K-R. 2017. Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv:1708.08296 [cs.AI]
  164. 164.
    Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J et al. 2022. Mendelian randomization. Nat. Rev. Methods Primers 2:6
     [Google Scholar]
  165. 165.
    Sanmarco LM, Chao C-C, Wang Y-C, Kenison JE, Li Z et al. 2022. Identification of environmental factors that promote intestinal inflammation. Nature 611:7937801–9
     [Google Scholar]
  166. 166.
    Schnorr SL, Candela M, Rampelli S, Centanni M, Consolandi C et al. 2014. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5:3654
     [Google Scholar]
  167. 167.
    Schroeder BO, Bäckhed F. 2016. Signals from the gut microbiota to distant organs in physiology and disease. Nat. Med. 22:101079–89
     [Google Scholar]
  168. 168.
    Simpson RC, Shanahan ER, Batten M, Reijers ILM, Read M et al. 2022. Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome. Nat. Med. 28:112344–52
     [Google Scholar]
  169. 169.
    Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R et al. 2013. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339:6119548–54
     [Google Scholar]
  170. 170.
    Spencer CN, McQuade JL, Gopalakrishnan V, McCulloch JA, Vetizou M et al. 2021. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374:65751632–40
     [Google Scholar]
  171. 171.
    Stanaway JD, Afshin A, Ashbaugh C, Bisignano C, Brauer M et al. 2022. Health effects associated with vegetable consumption: a Burden of Proof study. Nat. Med. 28:102066–74
     [Google Scholar]
  172. 172.
    Steffen LM, Jacobs DR, Stevens J, Shahar E, Carithers T, Folsom AR. 2003. Associations of whole-grain, refined-grain, and fruit and vegetable consumption with risks of all-cause mortality and incident coronary artery disease and ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) study. Am. J. Clin. Nutr. 78:3383–90
     [Google Scholar]
  173. 173.
    Subramanian S, Huq S, Yatsunenko T, Haque R, Mahfuz M et al. 2014. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510:7505417–21
     [Google Scholar]
  174. 174.
    Suez J, Cohen Y, Valdés-Mas R, Mor U, Dori-Bachash M et al. 2022. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. Cell 185:183307–28.e19
     [Google Scholar]
  175. 175.
    Szczubełek M, Pomorska K, Korólczyk-Kowalczyk M, Lewandowski K, Kaniewska M, Rydzewska G. 2021. Effectiveness of Crohn's disease exclusion diet for induction of remission in Crohn's disease adult patients. Nutrients 13:114112
     [Google Scholar]
  176. 176.
    Szczubełek M, Pomorska K, Korólczyk-Kowalczyk M, Lewandowski K, Kaniewska M, Rydzewska G. 2022. Effectiveness of Crohn's disease exclusion diet for induction of remission in Crohn's disease adult patients. Gastroenterology 162:3S24–25
     [Google Scholar]
  177. 177.
    Tay J, Luscombe-Marsh ND, Thompson CH, Noakes M, Buckley JD et al. 2015. Comparison of low- and high-carbohydrate diets for type 2 diabetes management: a randomized trial. Am. J. Clin. Nutr. 102:4780–90
     [Google Scholar]
  178. 178.
    Taylor SR, Falcone JN, Cantley LC, Goncalves MD. 2022. Developing dietary interventions as therapy for cancer. Nat. Rev. Cancer 22:8452–66
     [Google Scholar]
  179. 179.
    Thaiss CA, Itav S, Rothschild D, Meijer MT, Levy M et al. 2016. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540:7634544–51
     [Google Scholar]
  180. 180.
    Thaiss CA, Zeevi D, Levy M, Zilberman-Schapira G, Suez J et al. 2014. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159:3514–29
     [Google Scholar]
  181. 181.
    Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC et al. 2012. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366:262443–54
     [Google Scholar]
  182. 182.
    van Opbroek A, Ikram MA, Vernooij MW, de Bruijne M. 2015. Transfer learning improves supervised image segmentation across imaging protocols. IEEE Trans. Med. Imaging 34:51018–30
     [Google Scholar]
  183. 183.
    Vangay P, Johnson AJ, Ward TL, Al-Ghalith GA, Shields-Cutler RR et al. 2018. US immigration westernizes the human gut microbiome. Cell 175:4962–72.e10
     [Google Scholar]
  184. 184.
    Veselkov K, Gonzalez G, Aljifri S, Galea D, Mirnezami R et al. 2019. HyperFoods: machine intelligent mapping of cancer-beating molecules in foods. Sci. Rep. 9:9237
     [Google Scholar]
  185. 185.
    Vlassoff C. 2007. Gender differences in determinants and consequences of health and illness. J. Health Popul. Nutr. 25:147–61
     [Google Scholar]
  186. 186.
    Volkova NV, Meier B, González-Huici V, Bertolini S, Gonzalez S et al. 2020. Mutational signatures are jointly shaped by DNA damage and repair. Nat. Commun. 11:2169
     [Google Scholar]
  187. 187.
    Wang F, Zheng J, Cheng J, Zou H, Li M et al. 2022. Personalized nutrition: a review of genotype-based nutritional supplementation. Front. Nutr. 9:992986
     [Google Scholar]
  188. 188.
    Wheatley SD, Deakin TA, Arjomandkhah NC, Hollinrake PB, Reeves TE. 2021. Low carbohydrate dietary approaches for people with type 2 diabetes—a narrative review. Front. Nutr. 8:687658
     [Google Scholar]
  189. 189.
    Wilson B, Cox SR, Whelan K. 2021. Challenges of the low FODMAP diet for managing irritable bowel syndrome and approaches to their minimisation and mitigation. Proc. Nutr. Soc. 80:119–28
     [Google Scholar]
  190. 190.
    Wilson KA, Chamoli M, Hilsabeck TA, Pandey M, Bansal S et al. 2021. Evaluating the beneficial effects of dietary restrictions: a framework for precision nutrigeroscience. Cell Metab. 33:112142–73
     [Google Scholar]
  191. 191.
    Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S et al. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36:D901–6
     [Google Scholar]
  192. 192.
    Wolchok JD, Chiarion-Sileni V, Gonzalez R, Rutkowski P, Grob J-J et al. 2017. Overall survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 377:141345–56
     [Google Scholar]
  193. 193.
    World Health Organ 2003. Adherence to Long-Term Therapies: Evidence for Action Geneva: World Health Organ.
  194. 194.
    World Health Organ 2009. WHO Child Growth Standards. Growth Velocity Based on Weight, Length and Head Circumference: Methods and Development Geneva: World Health Organ.
  195. 195.
    Wu Q, Gao Z-J, Yu X, Wang P. 2022. Dietary regulation in health and disease. Signal Transduct. Target. Ther. 7:1252
     [Google Scholar]
  196. 196.
    Yang Y, Yuan Y, Zhang G, Wang H, Chen Y-C et al. 2022. Artificial intelligence–enabled detection and assessment of Parkinson's disease using nocturnal breathing signals. Nat. Med. 28:102207–15
     [Google Scholar]
  197. 197.
    Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG et al. 2012. Human gut microbiome viewed across age and geography. Nature 486:7402222–27
     [Google Scholar]
  198. 198.
    Yin L, Song C, Cui J, Lin X, Li N et al. 2021. A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data. Clin. Nutr. 40:84958–70
     [Google Scholar]
  199. 199.
    Yıldırım S, Nalbantoğlu ÖU, Bayraktar A, Ercan FB, Gündoğdu A et al. 2022. Stratification of the gut microbiota composition landscape across the Alzheimer's disease continuum in a Turkish cohort. mSystems 7:1e0000422
     [Google Scholar]
  200. 200.
    Young JJ, Bruno D, Pomara N. 2014. A review of the relationship between proinflammatory cytokines and major depressive disorder. J. Affect. Disord. 169:15–20
     [Google Scholar]
  201. 201.
    Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D et al. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163:51079–94
     [Google Scholar]
  202. 202.
    Zheng P, Afshin A, Biryukov S, Bisignano C, Brauer M et al. 2022. The Burden of Proof studies: assessing the evidence of risk. Nat. Med. 28:102038–44
     [Google Scholar]
  203. 203.
    Zmora N, Zeevi D, Korem T, Segal E, Elinav E. 2016. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host Microbe 19:112–20
     [Google Scholar]
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The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies
Annual Review of Nutrition 43, 225 (2023); https://doi.org/10.1146/annurev-nutr-061121-090535
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