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Optimal dietary patterns for prevention of chronic disease

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

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An Author Correction to this article was published on 07 March 2024

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Abstract

Multiple dietary patterns have been associated with different diseases; however, their comparability to improve overall health has yet to be determined. Here, in 205,776 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 = 0.68–0.84). Participants with low insulinemic (HR = 0.68, 95% confidence interval (CI) = 0.67, 0.70), low inflammatory (HR = 0.70, 95% CI = 0.69, 0.72) or diabetes risk-reducing (HR = 0.77, 95% CI = 0.75, 0.79) 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 = 205,776 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 = 205,776 participants).
Fig. 4: MV-adjusted associations between cumulative average dietary patterns (comparing the 90th with the 10th percentile) and major chronic disease in subgroups.

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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.

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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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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 WCRF/AICR (P < 0.0001), rEDIH (P = 0.001), and rEDIP (P < 0.0001) (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 = 205,776 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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