Review Article | Published:

Diet, nutrition, and cancer: past, present and future

Nature Reviews Clinical Oncology volume 13, pages 504515 (2016) | Download Citation


Despite the potentially important roles of diet and nutrition in cancer prevention, the evidence to support these roles is widely perceived by the public and health professionals as being inconsistent. In this Review, we present the issues and challenges in conducting and interpreting diet–cancer research, including those relating to the design of epidemiological studies, dietary data collection methods, and factors that affect the outcome of intervention trials. Approaches to improve effect estimates, such as the use of biomarkers to improve the accuracy of characterizing dietary exposures, are also discussed. Nutritional and dietary patterns are complex; therefore, the use of a reductionist approach to investigations, by focusing on specific nutrients, can produce misleading information. The effects of tumour heterogeneity and the failure to appreciate the nonlinear, U-shaped relationship between micronutrients and cancer in both observational studies and clinical trials are discussed. New technologies and investigational approaches are enabling the exploration of complex interactions between genetic, epigenetic, metabolic, and gut-microbial processes that will inform our knowledge of the diet–cancer relationship. Communicating the status of the evolving science in the context of the overall scientific evidence base, and evidence-based dietary recommendations for cancer prevention, should be emphasized in guidance for the public and for individual patients.

Key points

  • Substantial experimental evidence indicates the potential importance of dietary and nutritional factors in cancer prevention, but identifying relationships between diet and cancer in observational epidemiological studies and intervention trials has proved challenging

  • Study design issues, imprecise dietary assessments, and a lack of consideration of tumour heterogeneity generally attenuate relative-risk estimates in observational studies; dietary biomarkers and characterization of aetiological subtypes can help to better identify diet–cancer associations

  • Interventional findings are constrained by the timing and brevity of intervention, nonlinear diet–cancer relationships, issues relating to baseline nutritional status, and magnitudes of change in diet that are generally insufficient to affect cancer outcomes

  • Foods and eating patterns are complex, and assessment of dietary patterns, rather than the traditional reductionist approach focused on specific dietary factors, is a new and more-promising strategy for investigating relationships with cancer

  • New technologies and advances in genetics, epigenetics and metabolomics, and consideration of the influence of the microbiome, will expand our understanding of the role of dietary factors in cancer risk and disease progression

  • Effectively communicating the status of the evolving science, and evidence-based dietary recommendations for cancer prevention that are based on rigorous review processes should be emphasized in guidance for the public and individual patients

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  1. 1.

    & Global cancer patterns: causes and prevention. Lancet 383, 549–557 (2014).

  2. 2.

    , , & Cancer statistics, 2014. CA Cancer J. Clin. 64, 9–29 (2014).

  3. 3.

    et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2014).

  4. 4.

    American Cancer Society. Cancer prevention & early detection facts & figures 2012. (2012).

  5. 5.

    , & Efficacy and safety of human papilloma virus vaccine in cervical cancer prevention: systematic review and meta-analysis. Arch. Argent. Pediatr. 110, 483–489 (2012).

  6. 6.

    World Cancer Research Fund/American Institute for Cancer Research. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. (2007). This paper provides a comprehensive, evidence-based review of the scientific literature on food, nutrition, and physical activity in relation to cancer.

  7. 7.

    World Cancer Research Fund/American Institute for Cancer Research. Breast cancer 2010 report: food, nutrition, physical activity, and the prevention of breast cancer. (2010).

  8. 8.

    World Cancer Research Fund/American Institute for Cancer Research. Colorectal cancer 2011 report: food, nutrition, physical activity, and the prevention of colorectal cancer. (2011).

  9. 9.

    World Cancer Research Fund/American Institute for Cancer Research. Pancreatic cancer 2012 report: food, nutrition, physical activity, and the prevention of pancreatic cancer. (2012).

  10. 10.

    World Cancer Research Fund/American Institute for Cancer Research. Endometrial cancer 2013 report: food, nutrition, physical activity, and the prevention of endometrial cancer. (2013).

  11. 11.

    World Cancer Research Fund/American Institute for Cancer Research. Ovarian cancer 2014 report: food, nutrition, physical activity, and the prevention of ovarian cancer 2014. (2014).

  12. 12.

    et al. Effects of alpha-tocopherol and beta-carotene supplements on cancer incidence in the Alpha-Tocopherol Beta-Carotene Cancer Prevention Study. Am. J. Clin. Nutr. 62, 1427S–1430S (1995).

  13. 13.

    World Cancer Research Fund International. Our cancer prevention recommendations. (2016).

  14. 14.

    et al. American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity. CA Cancer J. Clin. 62, 30–67 (2012). Guidelines from the American Cancer Society that consider both individual behaviours, as well as the community context to support such behaviours, for cancer prevention.

  15. 15.

    et al. Evidence-based nutrition principles and recommendations for the treatment and prevention of diabetes and related complications. Diabetes Care 26 (Suppl. 1), S51–S61 (2003).

  16. 16.

    et al. Diabetes UK evidence-based nutrition guidelines for the prevention and management of diabetes. Diabet. Med. 28, 1282–1288 (2011).

  17. 17.

    et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 63, 2960–2984 (2014).

  18. 18.

    , , , & Insights and perspectives on dietary modifications to reduce the risk of cardiovascular disease. Adv. Nutr. 5, 553–555 (2014).

  19. 19.

    et al. Diet, nutrition and the prevention of cancer. Publ. Health Nutr. 7, 187–200 (2004).

  20. 20.

    AICR IARC & UICC. Explore a timeline of the history of cancer from BCE to 2011. The Cancer Atlas .

  21. 21.

    , & The burden of disease and the changing task of medicine. N. Engl. J. Med. 366, 2333–2338 (2012).

  22. 22.

    Galen on Food and Diet (Routledge, 2000).

  23. 23.

    Cancer and Diet (Williams and Wilkins, 1937).

  24. 24.

    & The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. J. Natl Cancer Inst. 66, 1191–1308 (1981).

  25. 25.

    Committee on Diet Nutrition and Cancer. Diet, Nutrition, and Cancer (National Academy Press,1982).

  26. 26.

    Nutrition Policy Board, U.S. Public Health Service. The Surgeon General's Report on Nutrition and Health (U.S. Public Health Service, 1988).

  27. 27.

    U.S. Department of Health, Education and Welfare. Smoking and health: report of the Advisory Committee to the Surgeon General of the Public Health Service. (U.S Public Health Service, 1964).

  28. 28.

    National Research Council (US) Committee on Diet and Health. Diet and Health: Implications for Reducing Chronic Disease Risk (National Academies Press, 1989).

  29. 29.

    World Cancer Research Fund/American Institute for Cancer Research. Food, Nutrition and the Prevention of Cancer: a Global Perspective. (American Institute for Cancer Research, Washington, DC, USA 1997).

  30. 30.

    American Institute for Cancer Research. Continuous Update Project findings & reports. (2015). Website showing scientific research reports (by tumour site) on diet, nutrition, physical activity and cancer that are updated on a rolling basis.

  31. 31.

    et al. Observational epidemiologic studies of nutrition and cancer: the next generation (with better observation). Cancer Epidemiol. Biomarkers Prev. 18, 1026–1032 (2009). This paper provides a discussion of the challenges inherent in collecting dietary data, with suggestions for moving forward, including a discussion of internet-based resources and statistical approaches to augment standard assessment tools and biomarkers.

  32. 32.

    Overview of the epidemiology methods and applications: strengths and limitations of observational study designs. Crit. Rev. Food Sci. Nutr. 50 (Suppl. 1), 10–12 (2010).

  33. 33.

    & Bias. J. Epidemiol. Commun. Health 58, 635–641 (2004).

  34. 34.

    Study design and hypothesis testing: issues in the evaluation of evidence from research in nutritional epidemiology. Am. J. Clin. Nutr. 69, 1315S–1321S (1999).

  35. 35.

    , , & Epidemiological and clinical studies of nutrition. Semin. Oncol. 37, 282–296 (2010). The paper provides a thoughtful discussion about the lack of concordance between observational studies and randomized trials involving nutrition, and possible reasons why.

  36. 36.

    et al. Evidence of a healthy volunteer effect in the prostate, lung, colorectal, and ovarian cancer screening trial. Am. J. Epidemiol. 165, 874–881 (2007).

  37. 37.

    et al. Influence of a diet very high in vegetables, fruit, and fiber and low in fat on prognosis following treatment for breast cancer: the Women's Healthy Eating and Living (WHEL) randomized trial. JAMA 298, 289–298 (2007).

  38. 38.

    , & Lessons learned from randomized clinical trials of micronutrient supplementation for cancer prevention. Annu. Rev. Nutr. 32, 369–390 (2012). In this paper, the authors review results of trials of micronutrient supplements for cancer prevention, concluding that nutrient supplements may have benefit in populations with suboptimal nutritional status but conversely may be harmful in populations with higher status, describing the so-called U-shaped curve.

  39. 39.

    et al. Evidence-based criteria in the nutritional context. Nutr. Rev. 68, 478–484 (2010).

  40. 40.

    , , & Calibration of self-reported dietary measures using biomarkers: an approach to enhancing nutritional epidemiology reliability. Curr. Atheroscler. Rep. 15, 353 (2013).

  41. 41.

    , , , & Need for technological innovation in dietary assessment. J. Am. Diet Assoc. 110, 48–51 (2010).

  42. 42.

    , , & Dealing with dietary measurement error in nutritional cohort studies. J. Natl Cancer Inst. 103, 1086–1092 (2011).

  43. 43.

    et al. Structure of dietary measurement error: results of the OPEN biomarker study. Am. J. Epidemiol. 158, 14–21; discussion 22–26 (2003).

  44. 44.

    et al. Bias in dietary-report instruments and its implications for nutritional epidemiology. Publ. Health Nutr. 5, 915–923 (2002).

  45. 45.

    & Assessment methods for research and practice, in Nutrition in the Prevention and Treatment of Disease (eds Couldston, A. et al.) (Elsevier Inc., 2013).

  46. 46.

    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, 439–447 (1999).

  47. 47.

    et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am. J. Epidemiol. 158, 1–13 (2003).

  48. 48.

    et al. OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI. Int. J. Obes. (Lond.) 31, 956–961 (2007).

  49. 49.

    et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women's Health Initiative. Am. J. Epidemiol. 167, 1247–1259 (2008).

  50. 50.

    et al. Statistical aspects of the use of biomarkers in nutritional epidemiology research. Stat. Biosci. 1, 112–123 (2009).

  51. 51.

    et al. Regression calibration in nutritional epidemiology: example of fat density and total energy in relationship to postmenopausal breast cancer. Am. J. Epidemiol. 178, 1663–1672 (2013).

  52. 52.

    et al. Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 295, 629–642 (2006).

  53. 53.

    et al. A randomized trial of dietary intervention for breast cancer prevention. Cancer Res. 71, 123–133 (2011).

  54. 54.

    et al. Measurement error corrected sodium and potassium intake estimation using 24-hour urinary excretion. Hypertension 63, 238–244 (2014).

  55. 55.

    et al. Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations. Am. J. Epidemiol. 174, 1238–1245 (2011).

  56. 56.

    et al. Evaluation and comparison of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers. Am. J. Epidemiol. 174, 591–603 (2011).

  57. 57.

    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, 172–188 (2014).

  58. 58.

    et al. Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am. J. Epidemiol. 175, 340–347 (2012).

  59. 59.

    et al. The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J. Acad. Nutr. Diet 112, 1134–1137 (2012).

  60. 60.

    et al. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int. J. Epidemiol. 41, 1187–1203 (2012).

  61. 61.

    & Comparison of an electronic food diary with a nonquantitative food frequency questionnaire in male and female smokers and nonsmokers. J. Am. Diet Assoc. 96, 283–285 (1996).

  62. 62.

    & Validation of a new computerized technique for quantitating individual dietary intake: the Nutrition Evaluation Scale System (NESSy) versus the weighed food record. Am. J. Clin. Nutr. 51, 477–484 (1990).

  63. 63.

    et al. Evaluation of new computerized method for recording 7-day food intake in IDDM patients. Diabetes Care 14, 602–604 (1991).

  64. 64.

    et al. Description of a food recording electronic device for use in dietary surveys. Hum. Nutr. Appl. Nutr. 40, 13–18 (1986).

  65. 65.

    et al. Dietary assessment in children using electronic methods: telephones and tape recorders. J. Am. Diet Assoc. 90, 412–416 (1990).

  66. 66.

    & Vitamins for chronic disease prevention in adults: scientific review. JAMA 287, 3116–3126 (2002).

  67. 67.

    et al. The efficacy and safety of multivitamin and mineral supplement use to prevent cancer and chronic disease in adults: a systematic review for a National Institutes of Health state-of-the-science conference. Ann. Intern. Med. 145, 372–385 (2006).

  68. 68.

    et al. Risk factors for lung cancer and for intervention effects in CARET, the Beta-Carotene and Retinol Efficacy Trial. J. Natl Cancer Inst. 88, 1550–1559 (1996).

  69. 69.

    et al. Incidence of cancer and mortality following α-tocopherol and β-carotene supplementation: a postintervention follow-up. JAMA 290, 476–485 (2003).

  70. 70.

    , , , & Vitamin and mineral supplements in the primary prevention of cardiovascular disease and cancer: an updated systematic evidence review for the U. S. Preventive Services Task Force. Ann. Intern. Med. 159, 824–834 (2013). Systematic review of the benefit and harms of vitamin and mineral supplements for both cancer and cardiovascular disease prevention; the authors conclude that no evidence of an effect of nutritional doses in individuals without known nutritional deficiencies is available.

  71. 71.

    Dietary factors associated with death-rates from certain neoplasms in man. Lancet 2, 332–333 (1966).

  72. 72.

    et al. Breast cancer: weighing the evidence for a promoting role of dietary fat. J. Natl Cancer Inst. 89, 766–775 (1997).

  73. 73.

    et al. Macronutrient composition influence on breast cancer risk in Hispanic and non-Hispanic white women: the 4-Corners Breast Cancer Study. Nutr. Cancer 63, 185–195 (2011).

  74. 74.

    & Commentary: fat and breast cancer: time to re-evaluate both methods and results? Int. J. Epidemiol. 35, 1022–1024 (2006).

  75. 75.

    et al. A comparison of two dietary instruments for evaluating the fat-breast cancer relationship. Int. J. Epidemiol. 35, 1011–1021 (2006).

  76. 76.

    et al. Dietary fat and breast cancer: comparison of results from food diaries and food-frequency questionnaires in the UK Dietary Cohort Consortium. Am. J. Clin. Nutr. 94, 1043–1052 (2011).

  77. 77.

    & Dietary fat reduction and breast cancer outcome: results from the Women's Intervention Nutrition Study (WINS). Am. J. Clin. Nutr. 86, S878–S881 (2007).

  78. 78.

    , , , & Dietary patterns and oesophageal squamous cell carcinoma: a systematic review and meta-analysis. Br. J. Cancer 110, 2785–2795 (2014).

  79. 79.

    , & Breast cancer and dietary patterns: a systematic review. Nutr. Rev. 72, 1–17 (2014).

  80. 80.

    , , , & Dietary patterns and breast cancer risk: a systematic review and meta-analysis. Am. J. Clin. Nutr. 91, 1294–1302 (2010).

  81. 81.

    et al. Dietary patterns and gastric cancer risk: a systematic review and meta-analysis. Ann. Oncol. 24, 1450–1458 (2013).

  82. 82.

    , & Dietary patterns and risk of colorectal cancer: a systematic review of cohort studies (2000-2011). Asian Pac. J. Cancer Prev. 13, 4713–4717 (2012).

  83. 83.

    et al. Associations between dietary patterns and head and neck cancer: the Carolina head and neck cancer epidemiology study. Am. J. Epidemiol. 175, 1225–1233 (2012).

  84. 84.

    et al. Nutrient-based dietary patterns and the risk of head and neck cancer: a pooled analysis in the International Head and Neck Cancer Epidemiology consortium. Ann. Oncol. 23, 1869–1880 (2012).

  85. 85.

    , , & Nutrients, food groups, dietary patterns, and risk of pancreatic cancer in postmenopausal women. Cancer Epidemiol. Biomarkers Prev. 20, 711–714 (2011).

  86. 86.

    et al. Index-based dietary patterns and risk of incident hepatocellular carcinoma and mortality from chronic liver disease in a prospective study. Hepatology 60, 588–597 (2014).

  87. 87.

    et al. Dietary patterns and pancreatic cancer risk in men and women. J. Natl Cancer Inst. 97, 518–524 (2005).

  88. 88.

    et al. Principal component analysis of dietary and lifestyle patterns in relation to risk of subtypes of esophageal and gastric cancer. Ann. Epidemiol. 21, 543–550 (2011).

  89. 89.

    et al. Vegetable-based dietary pattern and liver cancer risk: results from the Shanghai women's and men's health studies. Cancer Sci. 104, 1353–1361 (2013).

  90. 90.

    et al. Update of the Healthy Eating Index: HEI-2010. J. Acad. Nutr. Diet 113, 569–580 (2013).

  91. 91.

    et al. Comparing 3 dietary pattern methods — cluster analysis, factor analysis, and index analysis — with colorectal cancer risk: the NIH-AARP Diet and Health Study. Am. J. Epidemiol. 171, 479–487 (2010). In this study, three different approaches for dietary pattern analysis in relation to colorectal cancer were compared using the same dataset, with the findings demonstrating the similarities and differences in results obtained via the use of different methods.

  92. 92.

    et al. Comparing indices of diet quality with chronic disease mortality risk in postmenopausal women in the Women's Health Initiative Observational Study: evidence to inform national dietary guidance. Am. J. Epidemiol. 180, 616–625 (2014).

  93. 93.

    et al. The Healthy Eating Index 2005 and risk for pancreatic cancer in the NIH-AARP study. J. Natl Cancer Inst. 105, 1298–1305 (2013).

  94. 94.

    et al. Is concordance with World Cancer Research Fund/American Institute for Cancer Research guidelines for cancer prevention related to subsequent risk of cancer? Results from the EPIC study. Am. J. Clin. Nutr. 96, 150–163 (2012).

  95. 95.

    et al. Intake of fruits and vegetables and risk of cancer of the upper aero-digestive tract: the prospective EPIC-study. Cancer Causes Control 17, 957–969 (2006).

  96. 96.

    et al. Nonlinear reduction in risk for colorectal cancer by fruit and vegetable intake based on meta-analysis of prospective studies. Gastroenterology 141, 106–118 (2011).

  97. 97.

    , , , & How many etiological subtypes of breast cancer: two, three, four, or more? J. Natl Cancer Inst. 106, dju165 (2014).

  98. 98.

    , & The synergistic effects of alcohol and tobacco consumption on the risk of esophageal squamous cell carcinoma: a meta-analysis. Am. J. Gastroenterol. 109, 822–827 (2014).

  99. 99.

    et al. Alcohol intake and risk of oesophageal adenocarcinoma: a pooled analysis from the BEACON Consortium. Gut 60, 1029–1037 (2011).

  100. 100.

    et al. A prospective study of tobacco, alcohol, and the risk of esophageal and gastric cancer subtypes. Am. J. Epidemiol. 165, 1424–1433 (2007).

  101. 101.

    et al. Prevalence and risk factors for esophageal squamous cell cancer and precursor lesions in Anyang, China: a population-based endoscopic survey. Br. J. Cancer 103, 1085–1088 (2010).

  102. 102.

    , & Association between human papillomavirus (HPV) and oesophageal squamous cell carcinoma: a meta-analysis. Epidemiol. Infect. 142, 1119–1137 (2014).

  103. 103.

    et al. Body mass index in relation to oesophageal and oesophagogastric junction adenocarcinomas: a pooled analysis from the International BEACON Consortium. Int. J. Epidemiol. 41, 1706–1718 (2012).

  104. 104.

    et al. Dietary fiber and the risk of precancerous lesions and cancer of the esophagus: a systematic review and meta-analysis. Nutr. Rev. 71, 474–482 (2013).

  105. 105.

    , & Folate intake, MTHFR polymorphisms, and risk of esophageal, gastric, and pancreatic cancer: a meta-analysis. Gastroenterology 131, 1271–1283 (2006).

  106. 106.

    et al. Endometrial cancer risk factors by 2 main histologic subtypes: the NIH–AARP Diet and Health Study. Am. J. Epidemiol. 177, 142–151 (2013).

  107. 107.

    et al. Dietary phyto-oestrogens and the risk of ovarian and endometrial cancers: findings from two Australian case–control studies. Br. J. Nutr. 111, 1430–1440 (2014).

  108. 108.

    , , , & Rare breast cancer subtypes: histological, molecular, and clinical peculiarities. Oncologist 19, 805–813 (2014).

  109. 109.

    et al. Associations of breast cancer risk factors with tumor subtypes: a pooled analysis from the Breast Cancer Association Consortium studies. J. Natl Cancer Inst. 103, 250–263 (2011).

  110. 110.

    & Fruit and vegetable intake and breast cancer risk: a case for subtype-specific risk? J. Natl Cancer Inst. 105, 164–165 (2013).

  111. 111.

    et al. Fruit and vegetable intake and risk of breast cancer by hormone receptor status. J. Natl Cancer Inst. 105, 219–236 (2013).

  112. 112.

    et al. Plasma folate, related genetic variants, and colorectal cancer risk in EPIC. Cancer Epidemiol. Biomarkers Prev. 19, 1328–1340 (2010).

  113. 113.

    et al. Colorectal adenomas and the C677T MTHFR polymorphism: evidence for gene-environment interaction? Cancer Epidemiol. Biomarkers Prev. 8, 659–668 (1999).

  114. 114.

    et al. A candidate-pathway approach to identify gene-environment interactions: analyses of colon cancer risk and survival. J. Natl Cancer Inst. 107, djv160 (2015).

  115. 115.

    , & Unprocessed red and processed meats and risk of coronary artery disease and type 2 diabetes — an updated review of the evidence. Curr. Atheroscler. Rep. 14, 515–524 (2012).

  116. 116.

    et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014). The authors discuss opportunities and challenges in food metabolome research, as discussed in the First International Workshop on the Food Metabolome.

  117. 117.

    The exposome: from concept to utility. Int. J. Epidemiol. 41, 24–32 (2012).

  118. 118.

    et al. Metabolomics provide new insight on the metabolism of dietary phytochemicals in rats. J. Nutr. 138, 1282–1287 (2008).

  119. 119.

    et al. HMDB 3.0 — the Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

  120. 120.

    Metabolomics Society. Databases. (2016).

  121. 121.

    et al. Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. Am. J. Clin. Nutr. 101, 1000–1011 (2015).

  122. 122.

    , , , & Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiol. Biomarkers Prev. 22, 2182–2201 (2013).

  123. 123.

    et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol. Biomarkers Prev. 22, 631–640 (2013).

  124. 124.

    et al. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. J. Proteome Res. 12, 1645–1659 (2013).

  125. 125.

    et al. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am. J. Clin. Nutr. 100, 208–217 (2014).

  126. 126.

    & The interaction between epigenetics, nutrition and the development of cancer. Nutrients 7, 922–947 (2015).

  127. 127.

    & Transgenerational epigenetic effects. Annu. Rev. Genom. Hum. Genet. 9, 233–257 (2008).

  128. 128.

    et al. The role of nutrition on epigenetic modifications and their implications on health. Biochimie 94, 2242–2263 (2012).

  129. 129.

    , , & MicroRNAs in cancer management and their modulation by dietary agents. Biochem. Pharmacol. 83, 1591–1601 (2012).

  130. 130.

    & MicroRNA, nutrition, and cancer prevention. Adv. Nutr. 2, 472–485 (2011).

  131. 131.

    , & The emerging role of MicroRNAs in the regulation of gene expression by nutrients. J. Nutrigenet. Nutrigenom. 6, 16–31 (2013).

  132. 132.

    , & Dietary sulforaphane, a histone deacetylase inhibitor for cancer prevention. J. Nutr. 139, 2393–2396 (2009).

  133. 133.

    , , & MicroRNAs, diet, and cancer: new mechanistic insights on the epigenetic actions of phytochemicals. Mol. Carcinog. 51, 213–230 (2012).

  134. 134.

    et al. MicroRNA profiling of carcinogen-induced rat colon tumors and the influence of dietary spinach. Mol. Nutr. Food Res. 56, 1259–1269 (2012).

  135. 135.

    et al. Influence of quercetin-rich food intake on microRNA expression in lung cancer tissues. Cancer Epidemiol. Biomarkers Prev. 21, 2176–2184 (2012).

  136. 136.

    & Breast cancer and the importance of early life nutrition. Cancer Treat. Res. 159, 269–285 (2014).

  137. 137.

    & Nutrition, epigenetics, and developmental plasticity: implications for understanding human disease. Annu. Rev. Nutr. 30, 315–339 (2010).

  138. 138.

    Cancer and the microbiota. Science 348, 80–86 (2015).

  139. 139.

    et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

  140. 140.

    , & Gut microbes, diet, and cancer. Cancer Treat. Res. 159, 377–399 (2014). In this paper, the authors provide an overview of the role of the gut-microbial community in carcinogenesis, including in tissues outside of the gastrointestinal tract, with implications for diet and nutrition.

  141. 141.

    et al. The human microbiome project. Nature 449, 804–810 (2007).

  142. 142.

    et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

  143. 143.

    Biomarkers intersect with the exposome. Biomarkers 17, 483–489 (2012).

  144. 144.

    Complementing the genome with an 'exposome': the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev. 14, 1847–1850 (2005).

  145. 145.

    International Agency for Research on Cancer. EPIC study: cohort description. (2016).

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  1. Yale School of Public Health, Yale University, 60 College Street, PO Box 208034, New Haven, Connecticut 06520, USA.

    • Susan T. Mayne
    •  & Mary C. Playdon
  2. Center for Food Safety and Applied Nutrition, US Food and Drug Administration, 5100 Paint Branch Parkway, College Park, Maryland 20740, USA.

    • Susan T. Mayne
  3. School of Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, California 92093–0901 USA.

    • Cheryl L. Rock


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All authors contributed substantially to researching data for the article, discussion of content, writing the article, and reviewing and editing of manuscript before submission.

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Correspondence to Cheryl L. Rock.

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