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Optimal dietary patterns for prevention of chronic disease

Nature Medicine volume 29, pages 719–728 (2023)Cite this article

Subjects

Abstract

Multiple dietary patterns have been associated with different diseases; however, their comparability to improve overall health has yet to be determined. Here, in 205,852 healthcare professionals from three US cohorts followed for up to 32 years, we prospectively assessed two mechanism-based diets and six diets based on dietary recommendations in relation to major chronic disease, defined as a composite outcome of incident major cardiovascular disease (CVD), type 2 diabetes and cancer. We demonstrated that adherence to a healthy diet was generally associated with a lower risk of major chronic disease (hazard ratio (HR) comparing the 90th with the 10th percentile of dietary pattern scores = 0.58–0.80). Participants with low insulinemic (HR = 0.58, 95% confidence interval (CI) = 0.57, 0.60), low inflammatory (HR = 0.61, 95% CI = 0.60, 0.63) or diabetes risk-reducing (HR = 0.70, 95% CI = 0.69, 0.72) diet had the largest risk reduction for incident major CVD, type 2 diabetes and cancer as a composite and individually. Similar findings were observed across gender and diverse ethnic groups. Our results suggest that dietary patterns associated with markers of hyperinsulinemia and inflammation and diabetes development may inform on future dietary guidelines for chronic disease prevention.

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Fig. 1: Detailed components of dietary patterns.
Fig. 2: MV-adjusted associations of cumulative average dietary patterns (comparing the 90th with the 10th percentile) with major chronic disease and secondary outcomes in the pooled data of three cohorts (n = 205,852 participants).
Fig. 3: Baseline Spearman’s correlations between energy-adjusted cumulative average dietary patterns and food groups in the pooled data of three cohorts (n = 205,852 participants).
Fig. 4: MV-adjusted associations between cumulative average dietary patterns (comparing the 90th with the 10th percentile) and major chronic disease in subgroups.

Data availability

As a result of participant confidentiality and privacy concerns, data are available upon written request. According to standard controlled access procedure, applications to use the NHS, NHS II and HPFS resources will be reviewed by our External Collaborators Committee for scientific aims, evaluation of the fit of the data for the proposed methodology, and verification that the proposed use meets the guidelines of the Ethics and Governance Framework and of the consent that was provided by the participants. Investigators wishing to use the NHS, NHS II and HPFS data are asked to submit a brief description of the proposed project. Further information including the procedures to obtain and access data from the NHS, NHS II and HPFS is described at https://www.nurseshealthstudy.org/researchers (contact email: nhsaccess@channing.harvard.edu) and https://sites.sph.harvard.edu/hpfs/for-collaborators.

Code availability

The analysis programs are publicly available through https://github.com/pwangepi/DPs-and-chronic-disease.

References

  1. Buttorff, C., Ruder, T. & Bauman, M. Multiple Chronic Conditions in the United States (RAND Corporation, 2017).

  2. Murphy, S. L., Kochanek, K. D., Xu, J. & Arias, E. Mortality in the United States, 2020. NCHS Data Brief No. 427 (National Center for Health Statistics, 2021).

  3. Rao, M., Afshin, A., Singh, G. & Mozaffarian, D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open 3, e004277 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 393, 1958–1972 (2019).

  5. Dietary Guidelines Advisory Committee. Scientific Report of the 2020 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Agriculture and the Secretary of Health and Human Services (US Department of Agriculture, Agricultural Research Service, 2020).

  6. Dietary Guidelines for Americans, 2020–2025 (US Department of Agriculture and US Department of Health and Human Services, 2020).

  7. Tabung, F. K. et al. Development and validation of an empirical dietary inflammatory index. J. Nutr. 146, 1560–1570 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tabung, F. K. et al. Development and validation of empirical indices to assess the insulinaemic potential of diet and lifestyle. Br. J. Nutr. 116, 1787–1798 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jin, Q. et al. Insulinemic and inflammatory dietary patterns show enhanced predictive potential for type 2 diabetes risk in postmenopausal women. Diabetes Care 44, 707–714 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lee, D. H. et al. Dietary inflammatory and insulinemic potential and risk of type 2 diabetes: results from three prospective U.S. cohort studies. Diabetes Care 43, 2675–2683 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wan, Y. et al. Dietary insulinemic potential and risk of total and cause-specific mortality in the Nurses’ Health Study and the Health Professionals Follow-up Study. Diabetes Care 45, 451–459 (2022).

    Article  CAS  PubMed  Google Scholar 

  12. Li, J. et al. Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S. J. Am. Coll. Cardiol. 76, 2181–2193 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shi, N. et al. Proinflammatory and hyperinsulinemic dietary patterns are associated with specific profiles of biomarkers predictive of chronic inflammation, glucose-insulin dysregulation, and dyslipidemia in postmenopausal women. Front Nutr. 8, 690428 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Rhee, J. J., Mattei, J., Hughes, M. D., Hu, F. B. & Willett, W. C. Dietary diabetes risk reduction score, race and ethnicity, and risk of type 2 diabetes in women. Diabetes Care 38, 596–603 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, C. R. et al. Type 2 diabetes-prevention diet and all-cause and cause-specific mortality: a prospective study. Am. J. Epidemiol. 191, 472–486 (2022).

    Article  PubMed  Google Scholar 

  16. Morze, J., Danielewicz, A., Hoffmann, G. & Schwingshackl, L. Diet quality as assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: a second update of a aystematic review and meta-analysis of cohort studies. J. Acad. Nutr. Diet. 120, 1998–2031.e1915 (2020).

    Article  PubMed  Google Scholar 

  17. Sofi, F., Cesari, F., Abbate, R., Gensini, G. F. & Casini, A. Adherence to Mediterranean diet and health status: meta-analysis. Br. Med. J. 337, a1344 (2008).

    Article  Google Scholar 

  18. Bloomfield, H. E. et al. Effects on health outcomes of a Mediterranean diet qith no restriction on fat intake: a systematic review and meta-analysis. Ann. Intern. Med. 165, 491–500 (2016).

    Article  PubMed  Google Scholar 

  19. Bahrami, A. et al. Adherence to the Mediterranean diet and the risk of lung cancer: a systematic review and dose-response meta-analysis of observational studies. Nutr. Rev. 80, 1118–1128 (2022).

    Article  PubMed  Google Scholar 

  20. Gan, Z. H., Cheong, H. C., Tu, Y. K. & Kuo, P. H. Association between plant-based dietary patterns and risk of cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. Nutrients 13, 3952 (2021).

  21. Baden, M. Y. et al. Changes in plant-based diet quality and total and cause-specific mortality. Circulation 140, 979–991 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen, Z. et al. Changes in plant-based diet indices and subsequent risk of type 2 diabetes in women and men: three U.S. prospective cohorts. Diabetes Care 44, 663–671 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Shams-White, M. M. et al. Operationalizing the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Cancer Prevention Recommendations: a standardized scoring system. Nutrients 11, 1572 (2019).

  24. Kaluza, J., Harris, H. R., Hakansson, N. & Wolk, A. Adherence to the WCRF/AICR 2018 recommendations for cancer prevention and risk of cancer: prospective cohort studies of men and women. Br. J. Cancer 122, 1562–1570 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shams-White, M. M., Brockton, N. T., Mitrou, P., Kahle, L. L. & Reedy, J. The 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) score and all-cause, cancer, and cardiovascular disease mortality risk: a longitudinal analysis in the NIH-AARP diet and health study. Curr. Dev. Nutr. 6, nzac096 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tabung, F. K. et al. Associations between adherence to the World Cancer Research Fund/American Institute for Cancer Research cancer prevention recommendations and biomarkers of inflammation, hormonal, and insulin response. Int J. Cancer 140, 764–776 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. A review of human carcinogens. Part E: personal habits and indoor combustions. IARC Monogr. Eval. Carcinog. Risks Hum. 100, 373–472 (2012).

    Google Scholar 

  28. 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).

    Article  PubMed  Google Scholar 

  29. Knott, C., Bell, S. & Britton, A. Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes Care 38, 1804–1812 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Xi, B. et al. Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in U.S. adults. J. Am. Coll. Cardiol. 70, 913–922 (2017).

    Article  PubMed  Google Scholar 

  31. Grosso, G., Godos, J., Galvano, F. & Giovannucci, E. L. Coffee, caffeine, and health outcomes: an umbrella review. Annu Rev. Nutr. 37, 131–156 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Zhao, B. et al. Relationship between chocolate consumption and overall and cause-specific mortality, systematic review and updated meta-analysis. Eur. J. Epidemiol. 37, 321–333 (2022).

    Article  CAS  PubMed  Google Scholar 

  33. Hooper, L. et al. Effects of chocolate, cocoa, and flavan-3-ols on cardiovascular health: a systematic review and meta-analysis of randomized trials. Am. J. Clin. Nutr. 95, 740–751 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Tabung, F. K., Satija, A., Fung, T. T., Clinton, S. K. & Giovannucci, E. L. Long-term change in both dietary insulinemic and inflammatory potential is associated with weight gain in adult women and men. J. Nutr. 149, 804–815 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Salvini, S. et al. Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption. Int. J. Epidemiol. 18, 858–867 (1989).

    Article  CAS  PubMed  Google Scholar 

  36. Yuan, C. et al. Validity of a dietary questionnaire assessed by comparison with multiple weighed dietary records or 24-hour recalls. Am. J. Epidemiol. 185, 570–584 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Feskanich, D. et al. Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire. J. Am. Diet. Assoc. 93, 790–796 (1993).

    Article  CAS  PubMed  Google Scholar 

  38. Hu, F. B. et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am. J. Clin. Nutr. 69, 243–249 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Al-Shaar, L. et al. Reproducibility and validity of a semiquantitative food frequency questionnaire in men assessed by multiple methods. Am. J. Epidemiol. 190, 1122–1132 (2021).

    Article  PubMed  Google Scholar 

  40. Yue, Y. et al. Reproducibility and validity of diet quality scores derived from food-frequency questionnaires. Am. J. Clin. Nutr. 115, 843–853 (2022).

    Article  PubMed  Google Scholar 

  41. Chiuve, S. E. et al. Alternative dietary indices both strongly predict risk of chronic disease. J. Nutr. 142, 1009–1018 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fung, T. T. et al. Diet quality is associated with the risk of estrogen receptor-negative breast cancer in postmenopausal women. J. Nutr. 136, 466–472 (2006).

    Article  CAS  PubMed  Google Scholar 

  43. Satija, A. et al. Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med. 13, e1002039 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Fung, T. T. et al. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch. Intern. Med. 168, 713–720 (2008).

    Article  PubMed  Google Scholar 

  45. Wang, T. et al. Diabetes risk reduction diet and survival after breast cancer diagnosis. Cancer Res. 81, 4155–4162 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Iyer, H. S. et al. Impact of neighborhood socioeconomic status, income segregation, and greenness on blood biomarkers of inflammation. Environ. Int. 162, 107164 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lauby-Secretan, B. et al. Body fatness and cancer—viewpoint of the IARC Working Group. N. Engl. J. Med. 375, 794–798 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. World Health Assembly, 19. Report of the International Conference for the Eighth Revision of the International Classification of Diseases (World Health Organization, 1966); https://apps.who.int/iris/handle/10665/137300

  49. Colditz, G. A. et al. Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women. Am. J. Epidemiol. 123, 894–900 (1986).

    Article  CAS  PubMed  Google Scholar 

  50. Stampfer, M. J. et al. Test of the National Death Index. Am. J. Epidemiol. 119, 837–839 (1984).

    Article  CAS  PubMed  Google Scholar 

  51. Rich-Edwards, J. W., Corsano, K. A. & Stampfer, M. J. Test of the National Death Index and Equifax Nationwide Death Search. Am. J. Epidemiol. 140, 1016–1019 (1994).

    Article  CAS  PubMed  Google Scholar 

  52. Walker, A. E., Robins, M. & Weinfeld, F. D. The National Survey of Stroke. Clinical findings. Stroke 12, I13–I44 (1981).

    CAS  PubMed  Google Scholar 

  53. Mendis, S. et al. World Health Organization definition of myocardial infarction: 2008–09 revision. Int J. Epidemiol. 40, 139–146 (2011).

    Article  PubMed  Google Scholar 

  54. American Diabetes Association. Standards of medical care in diabetes—2010. Diabetes Care 33, S11–S61 (2010).

    Article  PubMed Central  Google Scholar 

  55. National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28, 1039–1057 (1979).

    Article  Google Scholar 

  56. Manson, J. E. et al. Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet 338, 774–778 (1991).

    Article  CAS  PubMed  Google Scholar 

  57. Hu, F. B. et al. Physical activity and television watching in relation to risk for type 2 diabetes mellitus in men. Arch. Intern. Med. 161, 1542–1548 (2001).

    Article  CAS  PubMed  Google Scholar 

  58. Mozaffarian, D., Hao, T., Rimm, E. B., Willett, W. C. & Hu, F. B. Changes in diet and lifestyle and long-term weight gain in women and men. N. Engl. J. Med. 364, 2392–2404 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Willett, W. & Stampfer, M. J. Total energy intake: implications for epidemiologic analyses. Am. J. Epidemiol. 124, 17–27 (1986).

    Article  CAS  PubMed  Google Scholar 

  60. Durrleman, S. & Simon, R. Flexible regression models with cubic splines. Stat. Med. 8, 551–561 (1989).

    Article  CAS  PubMed  Google Scholar 

  61. Lee, J. E. et al. Folate intake and risk of colorectal cancer and adenoma: modification by time. Am. J. Clin. Nutr. 93, 817–825 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge the contribution to the present study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries and/or the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program. Central registries may also be supported by state agencies, universities and cancer centers. Participating central cancer registries include the following: Alabama, Alaska, Arizona, Arkansas, California, Delaware, Colorado, Connecticut, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia and Wyoming. This work was supported by grants from the National Institutes of Health (nos. UM1 CA186107 to A.H.E., P01 CA087969 to A.H.E. and E.L.G., R01 HL034594, R01 HL088521 and U01 CA176726 to W.C.W. and A.H.E., U01 HL145386 and U01 CA167552 to W.C.W., R01 HL035464 to E.B.R., U01 CA261961 to M.S. and K99 CA207736 to F.K.T.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Fred K. Tabung, Edward L. Giovannucci.

Authors and Affiliations

  1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Peilu Wang, Mingyang Song, A. Heather Eliassen, Molin Wang, Eric B. Rimm, Frank B. Hu, Walter C. Willett & Edward L. Giovannucci

  2. Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China

    Peilu Wang

  3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Mingyang Song, A. Heather Eliassen, Teresa T. Fung, Eric B. Rimm, Frank B. Hu, Walter C. Willett, Fred K. Tabung & Edward L. Giovannucci

  4. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mingyang Song

  5. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Mingyang Song

  6. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    A. Heather Eliassen, Molin Wang, Eric B. Rimm & Frank B. Hu

  7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Molin Wang

  8. Department of Nutrition, Simmons University, Boston, MA, USA

    Teresa T. Fung

  9. Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA

    Steven K. Clinton & Fred K. Tabung

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Contributions

P.W., F.K.T. and E.L.G. conceived and designed the study. M.S., A.H.E., E.B.R., W.C.W., F.K.T. and E.L.G. acquired the data and obtained funding. P.W. conducted statistical analysis and wrote the first draft of the paper. M.S. provided technical review. All authors interpreted the results and revised the paper. F.K.T. and E.L.G. supervised the study. All authors approved the final paper as submitted.

Corresponding author

Correspondence to Peilu Wang.

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Nature Medicine thanks Yan-Bo Zhang, Keren Papier and Bernard Srour for their contributions to the peer review of this paper. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Flowchart of participants included in the main analysis.

BMI, body mass index; FFQ, food frequency questionnaires; Nurses’ Health Study (NHS); Health Professionals Follow-up Study (HPFS).

Extended Data Fig. 2 Baseline Spearman correlations between energy-adjusted cumulative average dietary patterns in (a) all cohorts, (b) the Health Professionals Follow-up Study, (c) the Nurses’ Health Study, and (d) Nurses’ Health Study II.

P values based on the two-sided tests were <0.0001 for all correlations (not adjusted for multiple comparisons). AHEI-2010, Alternative Healthy Eating Index-2010; AMED, Alternate Mediterranean Diet score; DASH, Dietary Approaches to Stop Hypertension score; DRRD, Diabetes Risk Reduction Diet; hPDI, Healthful plant-based diet index; rEDIH, reversed Empirical dietary index for hyperinsulinemia; rEDIP, reversed Empirical dietary inflammation pattern; WCRF/AICR, World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) dietary score.

Extended Data Fig. 3 Multivariable-adjusted spline analysis of dietary pattern scores with risk of major chronic disease.

P values for nonlinearity based on the two-sided tests were statistically significant for AMED (P = 0.03), WCRF/AICR (P = 0.007), and rEDIP (P = 0.008) (not adjusted for multiple comparisons). The hazard ratios (black line) and the 95% confidence intervals (grey bands) are shown. The models were adjusted for the same list of covariates as in Table 2. AHEI-2010, Alternative Healthy Eating Index-2010; AMED, Alternate Mediterranean Diet score; DASH, Dietary Approaches to Stop Hypertension score; DRRD, Diabetes Risk Reduction Diet; hPDI, Healthful plant-based diet index; HR, Hazard ratio; rEDIH, reversed Empirical dietary index for hyperinsulinemia; rEDIP, reversed Empirical dietary inflammation pattern; WCRF/AICR, World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) dietary score.

Extended Data Fig. 4 Multivariable-adjusted associations between cumulative average dietary patterns (comparing the 90th to 10th percentile) and major chronic disease and major components in the pooled data of three cohorts (n = 205,852 participants) with different lags.

Analyses details and corresponding estimates are provided in Extended Data Table 3. The hazard ratios are indicated by the circles and the 95% confidence intervals are reflected by the error bars. AHEI-2010, Alternative Healthy Eating Index-2010; AMED, Alternate Mediterranean Diet score; DASH, Dietary Approaches to Stop Hypertension score; DRRD, Diabetes Risk Reduction Diet; hPDI, Healthful plant-based diet index; rEDIH, reversed Empirical dietary index for hyperinsulinemia; rEDIP, reversed Empirical dietary inflammation pattern; WCRF/AICR, World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) dietary score.

Extended Data Table 1 Associations between cumulative average dietary patterns (comparing the 90th with the 10th percentile) and secondary outcomes in the pooled data
Full size table
Extended Data Table 2 Multivariable-adjusted associations of the cumulative average dietary patterns (comparing the 90th with the 10th percentile) with major chronic disease in subgroups
Full size table
Extended Data Table 3 Association between cumulative average dietary patterns (comparing the 90th with the 10th percentile) and major chronic disease and major components in the pooled data with different latency periods
Full size table
Extended Data Table 4 Association between cumulative average dietary patterns without alcohol component (comparing the 90th with the 10th percentile) and outcomes in the pooled data
Full size table
Extended Data Table 5 Association between cumulative average dietary patterns without coffee component (comparing the 90th with the 10th percentile) and outcomes in the pooled data
Full size table
Extended Data Table 6 Associations between cumulative average dietary patterns and risk of major chronic disease using the same reference group
Full size table

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Wang, P., Song, M., Eliassen, A.H. et al. Optimal dietary patterns for prevention of chronic disease. Nat Med 29, 719–728 (2023). https://doi.org/10.1038/s41591-023-02235-5

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